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Evaluating novel chemotherapeutic strategies in colorectal and gastric cancer: the role of histone deacetylase inhibitors and human epidermal receptor family inhibitors

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Evaluating novel chemotherapeutic strategies in colorectal and gastric cancer: the role of histone deacetylase inhibitors and human epidermal receptor family inhibitors

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Content EVALUATING NOVEL CHEMOTHERAPEUTIC STRATEGIES IN COLORECTAL AND GASTRIC CANCER: THE ROLE OF HISTONE DEACETYLASE INHIBITORS AND HUMAN EPIDERMAL RECEPTOR FAMILY INHIBITORS by Melissa J. LaBonte A Dissertation Presented to the FACULTY OF THE USC GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (SYSTEMS BIOLOGY AND DISEASE) August 2010 Copyright 2010 Melissa J. LaBonte ii Acknowledgements To my mentor: I would like to thank Dr. Robert D. Ladner of the USC/Norris Comprehensive Cancer Center, Department of Pathology, for his gracious and enthusiastic support of the work that has gone into my years as a graduate student culminating in this thesis. His passion for the translation of basic science through to clinical evaluation has been very educational and inspiring. To my committee: Special thanks to my Research Committee Members: Drs. Ladner, Lenz, Press and Stallcup for their feedback during my research project and for their encouragement to develop me into a well-rounded scientist. I am appreciative to have been included in the weekly lab meeting of Dr. Lenz and for all of his input over the years and the clinical implications of the work that I was involved in. To my department: To my department, Systems Biology and Disease, I thank Dr. Alicia McDonough, other faculty members, staff, and the students who have encouraged me, inspired me to ask questions, solve problems, think, reason, and learn. To my lab members: I owe much gratitude to Dr. Peter M. Wilson for the never-ending support, encouragement, countless hours of teaching and refining the scientist in me. His dedication and critical thinking are noteworthy and have inspired me. In addition, I would like to thank Dr. William Fazzone, Kathy Lui, Margaret Kornacki, and Judy Gonzalez for their friendship, dedication and countless hours of assistance while we worked along side of each other. To Drs. Bruce Prins and Jon Milhon: I am grateful to each of you for seeing the potential scientist in me at the early stages of my development in high school and college. Both of you have guided me throughout the decision process to pursue a career as a research scientist. In addition, I am blessed by the friendship that has remained and continues to develop. To my family and friends: I am grateful to my family and friends, both near and far, who have kept me grounded throughout this educational process and for their continued support in all areas of my life. Each one has played a significant role in this achievement and I am grateful for each and everyone of you. To my Savior: Last but not least, I owe this accomplishment most of all, to my Lord and Savior, Jesus Christ. You are the one that gave me the knowledge and desire to seek, study and understand the biology of disease, mechanisms of development and supported me the most in the quest to discover novel therapeutic approaches. May you use this knowledge to further your purposes and help those patients who have lost hope and are suffering. “I can do all things through Christ who strengthens me.” -Philippians 4:13 iii Table of Contents Acknowledgements ii List of Tables v List of Figures vi Abbreviations ix Abstract xii Chapter 1: Introduction 1.1 Cancer 1 1.2 Colorectal Cancer 1 1.3 Gastric Cancer 10 1.4 Human Epidermal Receptor Family 15 1.5 Histone Deacetylases 27 Chapter 2: Hypothesis 2.1 The Clinical Reality 34 2.2 Study Rationale 35 2.3 Overall Hypothesis 36 2.4 Study Overview 36 Chapter 3: Material and Methods 3.1 Compounds and Reagents 40 3.2 Tissue Culture 39 3.3 Cell Viability Assays 40 3.4 Fluorescent Activated Cell Sorting (FACS) 42 3.5 Detection of H2A.X Phosphorylation by Chemiluminescence 43 3.6 Western Blotting 44 3.7 Quantitative Real-time PCR (qPCR) 46 3.8 Liquid Chromatography and Mass Spectrometery (LC-MS) 48 3.9 Microarray 50 3.10 Mouse Xenograft 55 Chapter 4: Integration of lapatinib with the standard of care chemotherapy for colorectal and gastric cancers. 4.1 Abstract 56 4.2 Study Aim 57 4.3 Results 59 4.4 Discussion 78 4.5 Manuscripts, Abstracts and Presentations 83 4.6 Translational Impact 84 iv Chapter 5: DNA microarray profiling of genes differentially regulated by histone deacetylase inhibitors (HDACi) vorinostat and LBH589 in colorectal cancer cell models. 5.1 Abstract 87 5.2 Study Aim 88 5.3 Results 90 5.4 Discussion 112 5.5 Manuscripts and Abstracts 121 5.6 Translational Impact 121 Chapter 6: The novel therapeutic combination with synergistic antitumor activity in colorectal cancer: The dual EGFR/HER2 tyrosine kinase inhibitor L-4804 Lapatinib and the histone deacetylase inhibitor panobinostat (LBH589). 6.1 Abstract 124 6.2 Study Aim 126 6.3 Results 127 6.4 Discussion 147 6.5 Manuscripts and Abstracts 152 6.6 Translational Impact 153 Chapter 7: Overall Conclusions and Future Directions 7.1 Overall Conclusions 155 7.2 Future Directions 158 7.3 Personal Perspective 161 References 164 v List of Tables Table 1-1. Staging and 5-year survival of CRC. 4 Table 1-2. Staging and 5-year survival rates for gastric carcinoma. 10 Table 1-3. Representative list of current clinical trials investigating lapatinib. 24 Table 1-4. Representative clinical trials investigating vorinostat. 31 Table 1-5. Representative clinical trails investigating panobinostat (LBH589). 33 Table 3-1. Western antibodies. 45 Table 3-2. qPCR gene primer sequences. 49 Table 4-1. Sensitivity of CRC and gastric cancer cells to single agent lapatinib 63 and SN-38. Table 4-2. In vivo tumor delay (Td) of LoVo CRC cancer xenografts. 76 Table 4-3. In vivo mouse bodyweight on day 24 of LoVo CRC cancer xenografts 77 treated with lapatinib and CPT-11. Table 4-4. Resulting Manuscripts, Abstracts and Presentations from the study 84 of the combination of dual EGFR/HER2 TKI lapatinib and irinotecan in CRC and gastric cancer. Table 5-1. Differentially expressed genes (>2 fold) in response to HDACi in 98 HCT116 CRC cancer cells. Table 5-2. Differentially expressed genes (>2 fold) in response to HDACi in HT29 99 CRC cancer cells. Table 5-3. Summary of changes in gene expression for the core set of HDACi 100 regulated genes. Table 5-4. LBH589 and vorinostat inhibit cell proliferation in a panel of CRC 107 cell lines. Table 5-5. Comparison of fold-change of selected mRNA gene expression from 111 treatment in HCT116 and HT29 CRC cells treated with HDACi, LBH589, by microarray, cell line qPCR and xenograft samples. Table 5-6. Resulting Manuscripts and Abstracts from the study of the global 121 gene expression changes in CRC cell line models following HDACi, vorinostat and panobinostat (LBH589), treatment. Table 6-1. In vivo tumor delay of LoVo CRC cancer xenograft following 145 treatment with panobinostat (LBH589) and L-4804 Lapatinib (LAP). Table 6-2. Resulting Manuscripts and Abstracts from the study of novel 152 combination of HDACi, panobinostat and the dual EGFR/HER2 TKI, L-4804 Lapatinib, in CRC cell line models. vi List of Figures Figure 1-1. The adenoma-carcinoma molecular events that characterize the 2 transition from normal colon epithelium to adenocarcinoma. Figure 1-2. Advances in the treatment of stage IV CRC. 6 Figure 1-3. Schematic mechanism of action of DNA damaging agents 9 (A) 5-Fluorouracil (5-FU), (B) irinotecan (CPT-11) and (C) oxaliplatin (Pt). Figure 1-4. Overview of normal EGFR intracellular signaling and monoclonal 17 1ntibody-mediated effects in KRAS wild-type and mutant tumors. Figure 1-5. Chemotherapeutic targeting of the HER family. 19 Figure 1-6. Overview of molecules that mediate response to the HER-targeted 25 agents. Figure 4-1. Analysis of basal EGFR and HER2 protein expression in a panel of 60 CRC and gastric cancer cell lines. Figure 4-2. Growth inhibitory effects of lapatinib combined with standard of care 62 chemotherapeutic agents: (A) 5-FU, (B) oxaliplatin, (C) cisplatin and (D) SN-38. Figure 4-3. Growth inhibitory effects of lapatinib combined with SN-38 in CRC 66 cell lines. Figure 4-4. Growth inhibitory effects of lapatinib combined with SN-38 in gastric 68 cancer cell lines. Figure 4-5. Effect of lapatinib and SN-38 on colony formation in CRC and gastric 69 cancer cell lines. Figure 4-6. Effects of lapatinib and SN-38 on apoptosis in CRC and gastric 70 cancer cell lines. Figure 4-7. Effects of lapatinib and SN-38 on cell cycle distribution in CRC and 71 gastric cancer cell lines. Figure 4-8. Inhibition of PI3K or MEK in combination with SN-38 potentiates 72 growth inhibition in LoVo and MKN28 cancer cells and Lapatinib and SN-38 modulate downstream EGFR and HER2 signaling pathways in LoVo CRC cells. Figure 4-9. Effect of inhibition of drug efflux on growth inhibitory effects of SN-38 73 in LoVo CRC cells and lapatinib enhances intracellular SN-38 drug accumulation. Figure 4-10. Lapatinib enhances SN-38 induced DNA damage and caspase-8 74 activation in LoVo CRC cells. vii Figure 4-11. Anti-tumor activity of lapatinib in combination with CPT-11 in a LoVo 75 CRC xenograft model. Figure 5-1. In vitro characterization of HDACi, LBH589 and vorinostat, in 90 HCT116 and HT29 CRC cells. Figure 5-2. Cell cycle and apoptotic analysis of HDACi-treated CRC cells. 91 Figure 5-3. Hierarchical cluster analysis of HDACi-treated HCT116 and HT29 94 CRC cells. Figure 5-4. Venn analysis of differentially expressed genes in vorinostat and 95 LBH589-treated HCT116 and HT29 CRC cells. Figure 5-5. Top 12 canonical pathways that were significantly modulated by 102 HDACi as identified by Ingenuity® Pathway Analysis (IPA). Figure 5-6. qPCR validation of house-keeping and cell-line specific HDACi- 103 induced gene expression changes. Figure 5-7. qPCR time-dependent validation of core HDACi-induced gene 104 expression changes in HCT116 and HT29 CRC cells. Figure 5-8. qPCR time-dependent validation of core HDACi-repressed gene 105 expression changes in HCT116 and HT29 CRC cells. Figure 5-9. Evaluation of identified common gene signature of HDACi in a panel 108 of CRC cells. Figure 5-10. Evaluation of gene expression changes in HCT116 and HT29 CRC 110 xenografts following treatment with HDACi, LBH589. Figure 6-1. Growth inhibitory effects of single agent panobinostat and L-4804 128 Lapatinib (LAP) in CRC cell lines. Figure 6-2. Growth inhibitory effects of panobinostat combined with L-4804 129 Lapatinib in CRC cell lines. Figure 6-3. Effects of panobinostat and L-4804 Lapatinib on colony formation 130 in CRC cell lines. Figure 6-4. Effects of panobinostat and L-4804 Lapatinib on apoptosis in CRC 131 cell lines. Figure 6-5. Analysis of DNA damage and induction of apoptosis following 134 treatment with panobinostat and L-4804 Lapatinib in H630 and LoVo CRC cell lines. Figure 6-6. Effect panobinostat (LBH589) and L-4804 Lapatinib on hallmark 135 features of HDAC inhibition and HER pathway activation status. . Figure 6-7. Panobinostat modulates ERBB family transcription and HER protein 137 expression in CRC cell lines. viii Figure 6-8. Effect of panobinostat and HSP90 inhibitor on EGFR and HER2 138 protein expression in H630 and LoVo CRC cells. Figure 6-9. The combination of L-4804 Lapatinib and panobinostat modulates 141 ERBB1, ERBB2, CCND1, NFkB1, and IRAK1 mRNA transcription. Figure 6-10. Inhibition of PI3K or MEK in combination with panobinostat 143 potentiates growth inhibition in H630 and LoVo CRC cells Figure 6-11. Antitumor activity of panobinostat in combination with L-4804 145 Lapatinib in a LoVo CRC xenograft model. Figure 6-12. Proposed mechanism of action for the synergistic interaction 150 between the dual EGFR/HER2-TKI L-4804 Lapatinib (LAP) with HDACi, panobinostat in CRC cell line models. ix Abbreviations 17-AAG 17-(allylamino)-17-demethoxygeldanamycin 5-FU 5-Fluorouracil Ac-H3 Acetyl-histone 3 Ac-H4 Acetyl-histone 4 ACS American Cancer Society ADAM A disintegrin and metalloprotease ADCC Antibody dependent cellular cytotoxicity AJCC American joint committee on cancer AKT Protein kinase B ANOVA Analysis of variance APAF-1 Apoptotic protease activating factor 1 APC Adenomatous polyposis coli AR Amphiregulin ARRCD4 Arrestin domain containing 4 ATP Adenosine triphosphate AVEN Apoptosis, caspase activation inhibitor AURKB Aurora kinase B Bcl-2 B-cell/lymphoma 2 BCRP Breast cancer resistance protein BSC Best supportive care BTC Betacellulin CCND1 Cyclin D CDCA7 Cell division cycle associated 7 cDNA Complimentary DNA CE Carboxyl esterase CI Combination index CIN Chromosomal instability CPT-11 Irinotecan CRC Colorectal cancer CsA Cyclosporin A CTCL Cutaneous T-cell lymphoma CTGF Connective tissue growth factor DCC Deleted in colorectal cancer DEGs Differentially expressed genes DHRS2 Dehydrogenase/reductase member 2 DMSO Dimethylsulphoxide DNA Deoxyribose nucleic acid DNMT1 DNA methyltransferase 1 dNTPs Deoxyribonucleotide triphosphate DSB Double –stranded DNA breaks EGF Epidermal growth factor EGFR Epidermal growth factor receptor EPR Epiregulin FA Fraction affected FDA Food and Drug Administration FDR False discovery rate x FGF Fibroblast growth factor FOLFIRI 5-FU/LV plus irinotecan FOLFOX 5-FU/LV plus oxaliplatin FTC Fumitremorgin C GAPDH Glyceraldehyde triphosphate dehydrogenase H2Ax Histone H2A HAT Histone acetyl transferase HB-EGF Heparin-binding EGF-like growth factor HDAC Histone deacetylase HDACi Histone deacetylase inhibitor HER Human epidermal receptor HIST1H2BD Histone 1, H2bd HIST1H1C Histone 1, H1c HPMC (hydroxypropyl)methylcellulose HNSCC Head and neck squamous cell carcinoma HSP90 Heat shock protein 90 HSRRB Health science research resource bank IGF-1R Insulin-like growth factor 1 receptor IL-1 Interleukin-1 IL-8 Interleukin-8 IPA Ingenuity® pathway analysis IRAK1 Interleukin-1 receptor associated kinase 1 JAK Just another kinase kb Kilobase kDa Kilodalton KRAS v-Ki-rase2 kirsten rat sarcoma viral oncogene homolog LAP Lapatinib LBH589 Panobinostat LC-MS Liquid chromatography mass spectrometry LV Leucovorin MAPK Mitogen activated protein kinase mCRC Metastatic colorectal cancer MEK Mitogen-activated protein erk kinase mRNA Messenger ribose nucleic acid MSI Microsatellite instability MT1X Metallothionein 1X MT1G Metallothionein 1G mTOR Mammalian target of Rapamycin MTS 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2- (4-sulfophenyl)-2H-tetrazolium NFκB1 Nuclear factor kappa light polypeptide gene enhancer (p105) NM-23 Non-metastatic protein 23 NRG Neuregulin NSCLC Non-small cell lung cancer OS Overall survival p21 Cyclin-dependent kinase inhibitor 1A TP53 Tumor protein 53 PARP Poly(ADP-ribose) polymerase xi PCR Polymerase chain reaction PFS Progression-free survival P-gp P-glycoprotein PI3K Phosphatidylinositol-3-kinase PTEN Phosphatidylinositol 3,4,5-triphosphate 3-phosphate qPCR Quantitative real-time RT-PCR RAF Raf murine sarcoma viral oncogene homolog RGL1 Tal guanine nucleotide dissociation stimulator-like 1 RNA Ribonucleic acid RR Response Rate rRNA Ribosomal RNA RT-PCR Reverse transcriptase-polymerase chain reaction RTK Receptor tyrosine kinase SAHA Suberoylanilide hydroxamic acid SDS-PAGE Sodium dodecyl sulfate-polyacrylamide gel electrophoresis siRNA Small interfering RNA STAT Signal transducer and activator of transcription Td Tumor delay TGFα, β Transforming growth factor α, β THBS1 Thrombospondin 1 TK Thymidine kinase TP Thymidine phoshporylase TOPO 1 Topoisomerase 1 TS Thymidylate synthase (protein) TV Tumor volume Tween-20 Polyoxyethylenesorbitan monolaurate TYMS Thymidylate synthase (gene) UGT UDP-glucuronosyltransferase UNG Uracil-DNA glycosylase VEGF Vascular endothelial growth factor VOR Vorinostat xii Abstract Colorectal cancer (CRC) the third leading causes of cancer-related death worldwide with an estimated 639,000 deaths each year. In the United States, CRC is the second leading cause of cancer-related death, resulting in approximately 49,920 deaths in 2009. Despite significant advances in research and development in CRC and gastric cancer, the current response rate for 1st line treatment of mCRC remains ~50% and dramatically decreases for 2nd line therapy. In addition, the five-year survival rate for patients diagnosed with mCRC is approximately 10%. While, molecularly targeted therapies have improved treatment outcomes for patients with cancer, these benefits are modest and in only select patient populations. It is clear that the new chemotherapeutic options and novel drug combinations must be developed to provide benefit for the approximately half of patients that fail to response to current chemotherapeutic options that are available. We hypothesize that combining novel agents that target alternative tumor associated pathways will result in additive to synergistic interactions with standard of care chemotherapy, leading to new treatment options for those patients who fail to respond to current therapy. The following body of work focuses on the integration and evaluation of a dual EGFR/HER2 targeted TKI, lapatinib, and the novel HDACi, vorinostat and LBH589, into CRC cancer combination chemotherapies utilizing in vitro and in vivo models. The HER receptor family plays an important role in driving the growth and progression of many types of solid tumors. In this body of work, it is demonstrated that the dual EGFR/HER2 TKI, lapatinib, enhances the antitumor activity of two anticancer agents with markedly differing mechanisms of action. Lapatinib in combination with the DNA- damaging agent irinotecan or in combination with the HDACi LBH589 resulted in synergistic increases in tumor growth inhibition both in vitro and in vivo models. xiii The HDACi's are emerging as a highly useful class of anticancer agents that inhibit HDAC enzymes that are involved in the deacetylation of histone and non-histone cellular proteins. The pan-HDACi's vorinostat and panobinostat (LBH589), achieve potent inhibition of all HDAC enzymes implicated in cancer and have demonstrated potent antitumor activity in preclinical models and promising clinical efficacy in cancer patients. In this study, we focused on the effect of these HDACi on global transcription profiles in CRC models and evaluated the effect of panobinostat on the HER family members expression and signaling in CRC, as well as further evaluated the antitumor effects when utilized in combination with lapatinib. The results from these studies provide valuable information on promising new strategies and warrant further evaluation in the treatment of gastrointestinal cancers. Ongoing clinical investigations with these agents and others currently in development along with the identification of clinically relevant biomarkers that are both prognostic and predictive will provide further advances to improve outcomes in the treatment of gastrointestinal malignancies. 1 Chapter 1 Introduction 1.1 Cancer Cancer is an abnormal state in which a group of cells undergo uncontrolled proliferation and interfere with normal biological function. Tumorigenesis is a multi-step process that reflects environmental and genetic alterations that drive a progressive transformation of normal cells into malignant cells. Evaluation of a large number of studies led to the identification of six essential alterations that occur in cell physiology that collectively dictate malignant growth: Self- sufficiency in growth signals, insensitivity to growth inhibitory signals, evasion of programmed cell death (apoptosis), limitless replicatitve potential, sustained angiogenesis, and tissue invasion and metastasis (Hanahan and Weinberg 2000). We have come to understand that cancer is a multistep (genetic and epigenetic alterations), multipath (involvement of multiple functional pathways), and multifocal (clonal and multi- clonal) disease development (William, Heymach et al. 2009). A better understanding of the molecular basis of cancer growth, progression and metastasis has contributed to the development of rationally designed molecular targeted therapies that interfere within the signaling cascades involved in cell differentiation, proliferation, survival, apoptosis and angiogenesis. 1.2 Colorectal Cancer Colorectal cancer (CRC) the third leading causes of cancer-related death worldwide with an estimated 639,000 deaths each year. In the United States, CRC is the second 2 leading cause of cancer-related death, resulting in approximately 49,920 deaths in 2009 (ACS 2009; Jemal, Siegel et al. 2009). While the majority of colon cancer cases are diagnosed at late stage disease due to the presence of few non-specific symptoms in early stage, improvements in screening detection methods and treatment options have led to a steady decrease in CRC related deaths. Early detection by colonoscopy and surgical removal of polyps has been shown to be the best method for stopping the spread of disease. Typically symptoms of CRC that are detectable by the patient include rectal bleeding and lower abdominal pain, which are frequently indicative of advanced disease. Nearly 20% of CRC patients will present with metastatic disease (mCRC) at the time of diagnosis (Ng and Zhu 2008). The sequence of genetic and epigenetic alterations that characterize CRC development from normal colon epithelium to premalignant adenoma to invasive CRC is well documented (Vogelstein, Fearon et al. 1988; Kerr 2003; Walther, Johnstone et al. 2009). Although over simplified, Fig. 1-1 represents a model of the molecular events involved in CRC development and progression. One of the initial steps in the transition of Figure 1-1. The adenoma-carcinoma molecular events that characterize the transition from normal colon epithelium to adenocarcinoma. The majority of colorectal tumors show (A) chromosome instability (CIN) or (B) microsatellite instability (MSI). Adapted from (Vogelstein, Fearon et al. 1988; Walther, Johnstone et al. 2009). DNA hypomethylation WNT pathway APC mutation !-catenin MAPK Pathway KRAS mutation TGF-! Pathway Loss of 18q DCC mutation SMAD2 and 4 mutation TP53 Normal Epithelium Early adenoma Intermediate adenoma Late adenoma Cancer MMR Gene Mutations MLH1; MSH2 MSH6; PMS2 WNT pathway !-catenin MAPK Pathway KRAS mutation BRAF mutation CDC2 TGFBR2 BAX IGF2R DNA hypermethylation Loss of MLH1; MGMT A. Chromosomal Instability Pathway B. Microsatellite Instability Pathway 3 the normal colon epithelium to early adenoma is the loss of adenomatous polyposis coli (APC) gene, which encodes a protein involved in cell proliferation, adhesion and transcription. Loss of APC has been reported in 85% of all CRC (Thorstensen, Lind et al. 2005). Early carcinomas also acquire mutations in the small GTPase KRAS, which is found mutated in 50-60% of CRC patients (Andreyev, Norman et al. 1998), loss of chromosome 18q which has been associated with mutations in the SMAD family genes SMAD2 and 4 which are involved in the transforming growth factor-β (TGF-β) signaling pathway (Alhopuro, Alazzouzi et al. 2005; Popat and Houlston 2005), and mutations in the transcription factor, TP53, which has been demonstrated to be a late event and involved in increasing the resistance of cancer cells to apoptosis (Mao, Perez-Losada et al. 2004). In addition, two types of genomic instability are recognized as alternative mechanisms of CRC tumorigenesis: chromosomal instability (CIN) and microsatellite instability (MSI). CIN is defined as the presence of multiple structural or numerical chromosomal changes within tumor cells and has been detected in 65 – 70% of CRC’s (Rowan, Halford et al. 2005). MSI is only detected in ~15% of CRC and defined as a tumor having instability in at least two of five standard microsatellite markers (Boland, Thibodeau et al. 1998). Further, CIN and MSI can be used as prognostic markers, with CIN+ patient having a poorer prognosis with a hazard ratio (HR) for death = 1.45 and MSI+ patients having a better prognosis with a HR = 0.65 (Walther, Houlston et al. 2008). One of the most critical prognostic factors for CRC is stage of disease. Numerous schemes have been developed to stage CRC and have included parameters such as local extent, lymph node metastases and distant metastasis (Table 1-1). CRC is typically classified into 4 stages (or Dukes A-D) based on the guidelines set by the American Joint Committee on Cancer (AJCC) (Scott-Conner and Christie 1995; Greene, 4 Stewart et al. 2002). Stage I and II involves cancer that is confined within the wall of the colon, stage III is invasive cancer that has spread to the regional lymph nodes, and stage IV is invasive cancer that has metastasized to distant sites, most commonly the lung or liver. There is a decrease in positive prognosis with increased stage of disease, whereby patients that are diagnosed with stage I disease have a 5-year survival rate of 90%, while in stage IV, patients 5-year survival rate drops to 10% (O'Connell, Maggard et al. 2004). This data highlights the importance of screening and early detection of CRC, but also points out the critical urgency to identify improved treatment options for patients that fail to respond to current treatments. Table 1-1: Staging and 5-year survival of CRC. M0 = No distant metastases. M1 = distant metastases Data obtained from Scott-Conner and Christie 1995; Greene, Stewart et al. 2001; O’Connell, Maggard et. al. 2004. 1.2.1 Treatment of Colorectal Cancer. While treatment depends on stage of disease, the main treatments options for CRC include surgery, radiation therapy, chemotherapy and combinations of these. Stage Tumor 1 Regional Lymph Nodes 2 Distant Metastases 3 Duke’s Stage 5-year Survival 5 Stage 0 Tis N0 M0 Stage I T1 N0 M0 A 93.2% T2 N0 M0 T3 N0 M0 Stage IIa T3 N0 M0 B 84.7% Stage IIb T3 N0 M0 72.2% Stage IIIa Any T1 or T2 N1 M0 D 83.4% Stage IIIb Any T3 or T4 N1 M0 64.1% Stage IIIc Any T N2 M0 44.3% Stage IV Any T Any N M1 D 8.1% 5 1.2.1.1 Surgery. For patients that are diagnosed with stage I and II CRC, surgery is the primary treatment option and has a high curative rate. Stage III patients often undergo surgery in combination with adjuvant chemotherapy. Seventy-three percent of these patients are cured, but subsequent relapses after complete surgical resections do occur. The five- year survival rates following surgery for patients with early disease ranges between 70- 90% and for patients with stage III disease falls to 30-50% (Van Cutsem, Dicato et al. 2001; Van Cutsem, Verslype et al. 2002). 1.2.1.2 Radiation Therapy. Radiation therapy utilizes high-energy x-rays to locally inhibit cancer cell growth, resulting in tumor cell shrinkage and apoptosis. Although radiotherapy is not a common treatment for colon cancer, it is often used to treat patients with localized rectal malignancy. Reasons for this variation is due to anatomical location of the diseases, thereby enhancing the feasibility of delivery of radiation without severe dose-limiting toxicities. Radiation therapy may also be used in combination with chemotherapy to shrink the rectal tumor prior to surgery (Chau, Allen et al. 2003) or used post-surgery to destroy any remaining malignant cells and reduce the risk of recurrent disease (Gunderson, Haddock et al. 2003). In addition, radiotherapy may be used to effectively relieve distressing symptoms and side effects of rectal cancer, including localized pain and fatigue. 1.2.1.3 Chemotherapy. For patients that have stage I and II CRC and undergo surgical resection, chemotherapy is not recommended. However, for patients with stage III and IV CRC, surgery alone is 6 insufficient and is coupled with chemotherapy. For over 35 years, the foundation of chemotherapy for mCRC consisted of the fluoropyrimidine, 5-fluorouracil (5-FU). When administered as a single agent, patients treated with 5-FU have an overall response rate (RR) and median survival of 10% and 10 months respectively (Petrelli, Douglass et al. 1989; Poon, O'Connell et al. 1989). Introduction of the cofactor, leucovorin (5- formyltetrahydrofolate; LV) a methyl donor that enhances the cytotoxic effects of 5-FU, increased RR to 20% when compared to 5-FU treatment alone (Piedbois and Buyse 1993; Drake, Voeller et al. 1995). During the last 10 years, there has been a substantial change in RR, progression-free survival (PFS) and overall survival (OS) due to the Food and Drug Administration (FDA) approval of the cytotoxic agents irinotecan, oxaliplatin and the 5- FU pro-drug, capecitabine. The addition of irinotecan and oxaliplatin to fluoropyrimidine- Figure 1-2: Advances in the treatment of stage IV CRC. (A) FDA-approval of new anticancer agents for advanced CRC cancer. (B) Changes in median overall survival in relation to approval of new agents. Data obtained from Petrelli, Douglass et al. 1989; Poon, O’Connel et al 1989; Douillard 2000; Giacchetti, Perpoint et al. 2000; Saltz, Cox et al. 2000. 7 based regimens represented a true paradigm shift in CRC standard-of-care therapy where RR climbed to over 45% and OS to over 20 months (Fig. 1-2) (Douillard 2000; Giacchetti, Perpoint et al. 2000; Saltz, Cox et al. 2000). To date, optimized combination chemotherapy remains the most effective treatment for advanced CRC. A number of novel targeted and biologic agents have further improved effective treatment options for CRC. Bevacizumab, an inhibitor against vascular endothelial growth factor (VEGF), cetuximab and panitumumab, monoclonal antibodies to the epidermal growth factor receptor (EGFR) received FDA-approval for the treatment of mCRC (Douillard, Cunningham et al. 2000; Cunningham, Humblet et al. 2004; Hurwitz, Fehrenbacher et al. 2004; Giantonio, Catalano et al. 2007; Goldberg, Rothenberg et al. 2007; Saltz, Lenz et al. 2007; Sobrero, Fehrenbacher et al. 2007; Van Cutsem, Nowacki et al. 2007; Van Cutsem, Peeters et al. 2007; Hecht, Mitchell et al. 2008; Network 2008; Van Cutsem, Siena et al. 2008). With the introduction of these targeted agents, there has been increased efficacy and clinical outcome. Figure 1-2 shows the increase of median survival in patients with mCRC in relation to approval of new anticancer drugs and the extent of exposure of these drugs in mCRC. 5-Fluorouracil: Since its discovery, 5-FU has remained the foundation chemotherapeutic in the treatment in CRC (Heidelberger, Chaudhuri et al. 1957). When used as a single agent, 5-FU efficacy is limited in advanced CRC with a RR of 15-30% depending on the regimen administered (Wils, Sahmoud et al. 1998). 5-FU has multiple mechanisms of action of which the best characterized is the inhibition of the nucleotide metabolizing enzyme thymidylate synthase (TS). Inhibition of TS results in the inhibition of the de novo synthesis of thymidylate, an essential component of DNA synthesis and the incorporation of addition 5-FU metabolites into RNA and DNA respectively (Fig. 1- 3A) (Longley, Harkin et al. 2003). 5-FU is now routinely administered as the central 8 component of combination chemotherapeutic regimens that typically include irinotecan or oxaliplatin, and often include the addition of a biological agent. Irinotecan. Irinotecan (CPT-11, Camptosar ® ; Pfizer Pharmaceuticals, New York, NY) is a DNA damaging agent that inhibits topoisomerase I (TOPO I) resulting in single- and double-stranded DNA breaks, S-phase specific cell cycle arrest and induction of apoptosis. Within the cell, CPT-11 exerts little cytotoxicity until converted to the active metabolite, SN-38 (7-ethyl-10-hydroxy-camptothecan), by the enzyme carboxylesterase (CE) which is located in serum, liver, intestine, as well as other tissues (Ahmed, Vyas et al. 1999). SN-38 demonstrates 100 – 1000 fold more biological activity than CPT-11 and functions through stabilizing the covalent complex between TOPO 1 and cleaved DNA, inhibiting the DNA re-ligation of single-strand breaks, which results in irreversible DNA damage when the DNA replication fork encounters the stabilized SN-38/TOPO 1/DNA complex (Fig. 1-3B) (Hsiang, Wu et al. 1988). SN-38 is detoxified through the hepatic isoform 1A1 of the uridine diphosphate glucuronosyltransferase (UGT) enzyme to SN-38 glucoronide (SN-38G) which is excreted in bile and urine. Irinotecan was approved by the FDA in 2000 for administration with 5-FU for CRC patients, increasing RR to 35% and OS to 17.4 months (Douillard, Cunningham et al. 2000; Saltz, Cox et al. 2000; Vanhoefer, Harstrick et al. 2001). Oxaliplatin. Oxaliplatin (Eloxatin; Sanofi-Aventis Inc, New York, NY) is a third generation DNA damaging platinum analog that reacts with DNA to cause platinum-DNA (pt-DNA) cross-links blocking both DNA transcription and replication, resulting in a G 2 cell cycle arrest, extensive DNA damage and apoptosis (Fig. 1-3C) (Adjei, Argiris et al. 1999). Studies have indicated that oxaliplatin in combination with 5-FU significantly improves CRC patient’s RR with up to 53% compared with 15% with 5-FU alone, an increase in TTP and lengthens median survival (Giacchetti, Perpoint et al. 2000). 9 Combination chemotherapy. While 5-FU, oxaliplatin and irinotecan each exert different mechanisms of actions, these agents are broadly considered to be the major cytotoxic agents implemented in the treatment of CRC. Chemotherapeutic regimens that are intended to treat advanced or metastatic disease typically consist of distinct combinations of these agents. In the first-line treatment of advanced CRC, two highly active regimens are considered the standard-of-care. 5-FU in combination with oxaliplatin and the folate-cofactor LV, otherwise known as FOLFOX and 5-FU in Figure 1-3. Schematic mechanism of action of DNA damaging agents (A) 5-Fluorouracil (5-FU), (B) irinotecan (CPT-11) and (C) oxaliplatin (Pt). These agents result in RNA and DNA damage that cause cell cycle arrest, inhibition of DNA synthesis and repair and induction of apoptosis. Abbreviations: TP and TK – thymidine phosphorylase and kinase, respectively; TS – thymidylate synthase; FdUDP= 5’- fluoro-2’-deoxyuridine-5’-diphosphate; FdUMP= 5’- fluoro-2’-deoxyuridine-5’-monophosphate; FUDR= 2’-deoxy-5-fluoro- uridine; FUMP= 5’-fluorouridine-5’-monophosphate; FUTP= 5’-fluoro uridine -5’-triphosphate; G= glutamate residue; CE – carboxylesterase; UGT – uridine diphosphate glycorono-syltransferase; TOPO 1 = topoisomerase I. Figure generated using MS Powerpoint (2008). !"#$% &'%!"#()*+*)+,-.(%/!"#$0% 1'%2+.3*45-,3%/678"990% !"#$% 678"99% !"#$% #$:7% #$;7% #$87% #$;<% #=$:7% #=$;7% #=$87% ;>&% ;,?,@5% !"# !$# <>&% ;,?,@5% 678"99% 678"99% A>"BCD% A>"BC% %&!# ;5@+,=,E*3% 6'%FG,(.H(,E3% 8A% '(# 8*H*%2% 78% 78% 78% 78% 78% "78"%"78"% "78"%"78"% •!%65(("-I-(5%,++5J4% •!%23K.L.E*3%*M%;>&%JI34K5J.J%,3=%+5H,.+% •!%&H*H4*J.J% 10 combination with irinotecan and LV that is termed FOLFIRI. These two regimens demonstrate similar antitumor activity in the first-line treatment of advanced CRC with RR of 40-50% (Douillard, Cunningham et al. 2000; Giacchetti, Perpoint et al. 2000). More recently these combinations have also seen the inclusion of the targeted therapies including the anti-angiogenic agent bevacizumab and the anti-EGFR monoclonal antibodies cetuximab and panitumumab (Hurwitz, Fehrenbacher et al. 2004; Saltz, Meropol et al. 2004; Saltz, Easley et al. 2006). 1.3 Gastric Cancer Gastric cancer is the fourth most commonly diagnosed cancer worldwide and the second leading cause of cancer related deaths with an estimated 803,000 deaths each year. In Table 1-2. Staging and 5-year survival rates for gastric carcinoma. M0 = No distant metastases. M1 = distant metastases Data obtained from Adachi, Yasuda et al. 2000; Adachi, Mori et al. 1994; Kodera, Yamamura et al. 1998. Stage Tumor Regional Lymph Nodes Distant Metastases 5-year Survival* Stage 0 Tis N0 M0 > 90% Stage IA T1 N0 M0 71% Stage 1B T1 N1 M0 57% T2 N0 M0 Stage II T1 N2 M0 45% T2 N1 M0 33% T3 N0 M0 Stage IIIA T2 N2 M0 20% T3 N1 M0 T4 N0 M1 Stage IIIB T3 N2 M0 12% Stage IV T4 N1 or N2 M0 < 5% Any T N3 M0 Any T Any N M1 11 2007, it was estimated that there were approximately one million new cases of gastric cancer worldwide, with 70% arising in developing countries (Garcia M 2007). In addition, about 800,000 people per year will succumb to their disease. According to the American Cancer Society, in 2009, there was an expected 21,500 cases of gastric cancer in the United States with 10,620 succumbing to their disease (ACS 2009). Within the United States, the five-year survival rate for gastric cancer is 28%, which increases to 60-80% if detected at an early stage. However, the majority of gastric cancer cases are diagnosed at late stage due to the presence of few and non-specific symptoms in early stage. By the time symptoms manifest and diagnosis occurs, the cancer has frequently spread throughout the stomach and to other organs, typically the esophagus, lungs or liver. For these patients, systemic chemotherapy is the main treatment option and although a large number of chemotherapy regimens have been tested in randomized clinical trials, there is no internationally accepted standard-of-care, and uncertainty remains regarding the choice of the regimen. Approximately 90-95% of malignant tumors of the stomach are classified as adenocarcinomas. Adenocarcinomas arise from cells of the mucosal (innermost) lining of the stomach. Infection with Helicobacter pylori, atrophic gastritis, intestinal metaplasia, and dysplasia have been identified as important steps in its pathogenesis (Correa 1996). A recent study by Ooi et al identified three oncogenic pathways that were deregulated in >70% of the 300 gastric cancers evaluated: proliferation/stem cell, Wnt/β- catenin, and NFκB signaling (Ooi, Ivanova et al. 2009). Histologically, gastric adenocarcinoma can be divided into two types: intestinal and diffuse. These two types have been determined to arise from distinct oncogenic mechanisms. The intestinal adenocarcinoma are typically well-differentiated cells and contain several genetic alterations including: inactivation of APC and deletion in colorectal cancer (DCC) through 12 mutation or deletion, alterations in expression of non-metastatic protein 23 (NM-23) and B-cell CLL/lymphoma 2 (bcl-2) genes, and human epidermal receptor 2 (HER2) overexpression (Adachi, Yasuda et al. 2000). In contrast, the diffuse adenocarcinoma cells are poorly differentiated, discohesive, secrete mucus, and contain the following genetic alterations: loss of E-cadherin and β-catenin (Adachi, Yasuda et al. 2000). Although not the focus of this body of work, the less common malignancies of the stomach include lymphoma, gastrointestinal stromal tumor and carcinoid tumor. One of the most critical prognostic factors for gastric cancer is stage of disease. Two of the most important factors for predicting outcomes of patients with gastric cancer are the depth of wall invasion and status of lymph node metastasis (Adachi, Yasuda et al. 2000). Several staging systems have been develop for gastric cancer based on these parameters and are similar to those used in CRC from the AJCC and Dukes classification system (Adachi, Mori et al. 1994; Kodera, Yamamura et al. 1998; Ichikura, Tomimatsu et al. 1999). 1.3.1 Treatment of Gastric Cancer. The main treatments for gastric cancer include surgery, radiation therapy, chemotherapy and combinations of these. 1.3.1.1 Surgery. The only curative treatment for gastric cancer is surgery. Surgery may be used to remove the cancer and part or all of the stomach. Even with advanced disease, while surgery would not be curative, palliative surgery may be used to relieve or prevent symptoms including, bleeding or blockage of the stomach by tumor growth. 13 The types of surgery performed depend on the location and stage of disease. When detected at a very early stage, an endoscopic mucosal resection may be used to remove the cancer through an endoscope. However, when the cancer is located in the lower portion of the stomach, a subtotal gastrectomy may be used to remove a portion of the stomach and nearby lymph nodes. When the cancer is located in the upper portion of the stomach and/or spread throughout the stomach, a total gastrectomy is used to completely remove the stomach and nearby lymph nodes. Intestinal tissue is then used to make a replacement stomach to make room for small amounts of food. Even with a successful surgery, the five-year survival rate for stage I disease is 60–90%, for stage II disease is 30–50%, and for stage III disease is 10–25% (Bozzetti, Marubini et al. 1999; Macdonald, Smalley et al. 2001; Cunningham, Allum et al. 2006). 1.3.1.2 Radiation Therapy. External beam radiation is the type of radiation that is most commonly used to treat gastric cancer and works through the use of high-energy rays directed against cancer cells to stop their growth. While treatment schedules vary, typically a patient will receive 5 treatments per week over a specified period of weeks or months. Radiation therapy is commonly used in combination with surgery, chemotherapy, or both in order to remove any residual cancer cells that were not removed during surgery. When combined with chemotherapy, radiation therapy may enhance patient survival through increasing the time to tumor recurrence. 1.3.1.3 Chemotherapy. The use of chemotherapy to treat gastric cancer has no established standard of care. Because gastric cancer is often diagnosed at an advanced or metastatic stage and not 14 curable by surgical means, chemotherapy is the main treatment option. While chemotherapeutic combinations such as 5-FU plus cisplatin have demonstrated benefit for gastric cancer patients, only 20 – 30% of patients respond and have a median OS of 7 – 8 months (Sastre, Garcia-Saenz et al. 2006). The most commonly used chemotherapeutic agents used in gastric cancer include 5-FU, doxorubicin (Adriamycin), methotrexate, epirubicin, etoposide (VP-16), cisplatin, mitomycin C, irinotecan and S-1. These agents have been studied in various combinations, however, it remains unclear which drugs or combinations of drugs work best against gastric cancer. Current research has focused on evaluating the use of targeted therapeutic agents in gastric cancer. Among the agents being tested are trastuzumab (Herceptin) and lapatinib (Tykerb) which target HER2, bevacizumab (Avastin), which targets VEGF, cetuximab that targets EGFR, bortezomib (Velcade) which is a proteasome inhibitor, everolimus (RAD001) that targets mammalian target of rapamycin (mTOR), and the multi-kinase inhibitors sunitinib (Sutent) and sorafenib (Nexavar) (Sastre, Garcia-Saenz et al. 2006; Wagner, Grothe et al. 2006; Wagner, Schneider et al. 2006). In 2006, Wagner et al reported a systematic review and meta-analysis on chemotherapy in advanced gastric cancer to assess the efficacy and tolerability of chemotherapy in patients with advanced gastric cancer. This study compared the effect of combination chemotherapy to the best supportive care (BSC) and found that combination chemotherapy improved OS compared to BSC (HR 0.39, 95% CI 0.28-0.52, p <0.00001), which translated to a benefit in weighted mean average survival of approximately 6 months compared to 3 months with BSC (Wagner, Grothe et al. 2006). 15 1.4 Human Epidermal Receptor Family Within the cellular environment, cell surface receptors integrate a multitude of extracellular signals including environmental stresses, growth factors, neuropeptides and hormones thus regulating a large diversity of signaling pathways and cellular responses. Receptor tyrosine kinases (RTKs) are a family of transmembrane proteins that have intrinsic kinase activity that are important mediators in the intracellular signaling mechanisms that regulate cell growth and proliferation, differentiation, motility and survival (Van der Geer et al, 2004). RTKs function through catalyzing the transfer of the γ phosphate of adenosine triphosphate (ATP) to the hydoxyl groups on target proteins. Structurally, RTKs contain an extracellular ligand binding domain, a transmembrane domain, a cytoplasmic domain that contains conserved protein tyrosine kinase core that upon activation can be autophosphorylated or phosphorylated by other protein kinases, and a C-terminal regulatory domain. One sub-class of the RTK super-family are the human epidermal receptors (HER) that consists of four members: EGFR (HER1; ERBB1), HER2/neu (ERBB2), HER3 (ERBB3) and HER4 (ERBB4) (Olayioye, Neve et al. 2000; Yarden and Sliwkowski 2001). The HER family of receptors are expressed in normal epithelial, mesenchymal, and neuronal tissues and function to translate extracellular to intracellular signaling. While these signaling cascades are tightly regulated under homeostatic conditions in normal cells, in cancer cells, these pathways may be inappropriately activated resulting in increased tumorigenesis. One of the initial steps in activation of the HER signaling network is ligand binding. Ligands to the HER family include epidermal growth factor (EGF), transforming growth factor-α (TGF-α), heparin-binding EGF-like growth factor (HB-EGF), 16 amphiregulin (AR), betacellulin (BTC), epiregulin (EPR), and neuregulins (NRG1-4) (Olayioye, Neve et al. 2000; Yarden and Sliwkowski 2001; Carter, Kelly et al. 2009). While there are no ligands that bind to HER2, HER2 has been demonstrated to be the preferred dimerization partner for the other family members, resulting in the most potent activation with regards to downstream signaling activity (Kruser and Wheeler 2010). While HER3 has been shown to bind ligands, it does not have any intrinsic kinase activity and therefore must heterodimerize with other members of the HER family to mediate downstream signaling cascades (Sergina, Rausch et al. 2007; Lee-Hoeflich, Crocker et al. 2008). EGFR ligands are synthesized as transmembrane precursors that can be proteolytically cleaved by cell surface proteases and in particular members of the ADAM (a disintegrin and metalloprotease) family. The binding of ligands to the receptor(s) induces a conformational change that results in receptor homo- and heterodimerzation and induces the autophosphorylation of specific tyrosine residue on the intracellular domain which initiates downstream signaling cascades through the RAS-RAF-MEK- MAPK, PI3K-AKT-mTOR, and/or JAK-STAT oncogeneic pathways (Fig. 1-4A) (Olayioye, Neve et al. 2000; Yarden 2001; Kruser and Wheeler 2010). Signaling through these pathways influence many aspects of tumor cell biology including cell cycle regulation, cell migration, proliferation, survival, invasion, metastasis, and angiogenesis (Yarden and Sliwkowski 2001; Kruser and Wheeler 2010). A newer function of EGFR and HER2 that has recently been reported is their roles as transcriptional activators within the nucleus of the cell. EGFR has been detected within the nuclei of cancer cells, primary tumor specimens, and highly proliferative tissues. This nuclear EGFR localization has been correlated with poor clinical outcome in breast, oropharyngeal squamous cell carcinoma and ovarian cancer patients. In 17 addition, nuclear EGFR expression has been correlated with increased expression of cyclin D1 (CCND1), iNOS, aurora kinase A, and b-myb, all of which are involved in the G 1 -S cell cycle progression and proliferation (Lin, Makino et al. 2001; Lo and Hung 2006). Figure 1-4. Overview of normal EGFR intracellular signaling and monoclonal antibody- medicated effects in KRAS wild-type and mutant tumors. (A) The activating ligand EGF binds to EGFR, stimulating receptor homodimerization, tyrosine kinase phosphorylation and signal transduction through the parallel RAS-RAF-MEK-MAPK and PI3K/AKT axes ultimately leading to an oncogenic transcription profile. (B) In this example, cetuximab binds to the extracellular domain of EGFR, inhibiting ligand binding and receptor homodimerization and sequestering signal transduction. Alternatively, somatic mutations in KRAS can render the RAS-RAF-MEK- MAPK pathway constitutively active even in the presence of receptor target inhibition. Abbreviations: EGFR = epidermal growth factor receptor; EGF = epidermal growth factor; MAPK = mitogen-activated protein kinase; PI3K = phosphoinositide 3-kinase; mTOR = mammalian target of rapamycin; BRAF = v-raf murine sarcoma viral oncogene homolog B1; NFκB = nuclear factor kappa-light-chain-enhancer of activated B cells. Figure generated using MS Powerpoint (2008). 18 1.4.1 The Role of HER Family in Cancer. HER family signaling has been shown to impact many aspects of tumor biology. Dysregulation of HER receptors and downstream signaling cascades have been implicated in the development and progression of multiple tumor types including, breast, CRC, gastric, bladder, ovarian, non-small cell lung (NSCLC), pancreatic and prostate cancers. EGFR: EGFR modifications are a consequence of mutations or gene amplifications that induce protein expression, structural rearrangements and autocrine signaling loops. The expression of EGFR in tumors has been correlated with disease progression, poor survival, poor response to therapy and the development of resistance to cytotoxic agents (Brabender, Danenberg et al. 2001). EGFR overexpression has been reported in a variety of solid tumors, including CRC, gastric, NSCLC, breast, head and neck, esophageal, prostate, bladder, pancreatic, renal and ovarian cancers (Wells 1999; Nicholson, Gee et al. 2001). In addition, EGFR mutations resulting in a truncated protein such as Δ801EGFR (EGFRvIII) are frequently found in NSCLC, glioma, breast, ovarian and prostate cancer (Kuan, Wikstrand et al. 2001). In CRC, EGFR protein expression has been found to be higher in tumor cells when compared with normal surrounding mucosa in 65 – 75% of CRC cases (McKay, Murray et al. 2002; Antonacopoulou, Tsamandas et al. 2008). Furthermore, it has been reported that EGFR expression increases with stage of disease and is associated with resistance to chemotherapy. HER2: HER2 overexpression, most commonly by gene amplification, has been found in a subset of breast, bladder, endometrial, ovarian, esophageal, gastric, pancreatic, NSCLC, and prostate cancer (Salomon, Brandt et al. 1995). It has been reported that HER2 overexpression and/or gene amplification functions as an oncogene due to the high-level of HER2 in the cell membrane and therefore acquisition of 19 malignant properties (Mendelsohn and Baselga 2000). In breast cancer, HER2 overexpression and/or amplification has been detected in 10-34% of invasive breast cancer and has been correlated with clinical outcome, poor prognosis (Slamon, Godolphin et al. 1989). In addition, HER2 amplification was observed in up to 20% of gastric cancers (Garcia, Vizoso et al. 2003; Marx, Tharun et al. 2009). 1.4.2 Therapeutic Targeting: Monoclonal Antibodies (mAB) and TKI. As the different members of the HER family are aberrantly overexpressed and/or activated in a wide range of human cancers, these receptors constitute excellent candidates for selective anticancer therapies. Two primary approaches have been successfully utilized to target EGFR and/or HER2 and include: monoclonal antibodies (mAB) and small molecule ATP-mimetic tyrosine kinase inhibitors (TKIs) (Fig. 1-5). Figure 1-5. Chemotherapeutic targeting of the HER family. (A) Normal HER receptor signaling, where ligand binding results in activating of downstream signaling pathways resulting in cell proliferation, differentiation, cell motility, adhesion and angiogenesis. (B) HER targeted agents include monoclonal antibodies (mAB) that bind to the extracellular domain of EGFR or HER preventing receptor dimerization and activation or small-molecule tyrosine kinase inhibitors (TKIs) that compete with ATP for binding within the intracellular kinase activation domain inhibiting activation of downstream signaling cascades. Figure generated using MS Powerpoint (2008). !"#$% !% !% !"#$% &$'% (')*% +,-.% +,-.% (')*% .*&,% $*&/% 01$% 0&!$% 0&!$% &$'% .*&,% $*&/! 01$% 21*% *3435678% 21*% *3435678% &9%+78(:;%21*%/<=>:;<>=% .9%21*%':8=363?%&=3>6@% 1A,% 1A,% Oncogenic Transcription 1A,% !"#$ 21*% *3435678% 1A,% 1A,% 2 '$"% 20 1.4.2.1 Monoclonal antibodies (mAB). Monoclonal antibodies are inhibitors that recognize and bind to the extracellular receptor domain and function through blockade of both ligand binding and dimerization of HER receptors (Imai and Takaoka 2006). This subsequently inhibits receptor autophosphorylation and induces receptor internalization and degradation (Dassonville, Bozec et al. 2007). In addition, some antibodies function through immune system activation resulting in antibody-dependent cellular toxicity (ADCC) and activation of complement system. Cetuximb: Cetuximab (IMC-C225, Erbitux; Bristol - Myers Squibb; ImClone Systems) is a human / mouse chimeric IgG1 mAB that targets the extracellular ligand binding domain of EGFR (Baselga 2001). In February 2004, the FDA approved cetuximab for the treatment of EGFR-expressing mCRC patients who are refractory or intolerant to irinotecan-based regimens (Cunningham, Humblet et al. 2004; Saltz, Meropol et al. 2004; Goldberg 2005). In addition, in 2006, the FDA approved cetuximab for use with radiation in locally advanced or regionally advanced head and neck squamous cell carcinoma (HNSCC) (Astsaturov, Cohen et al. 2006). Binding of cetuximab prevents ligand binding thereby blocking receptor activation and has been shown to induce G 1 cell cycle arrest though increasing levels of p27 kip1 and inducing apoptosis mediated through caspase –8, –9 and –3 and/or increasing BAX and decreasing Bcl-2 levels (Huang, Bock et al. 1999; Perrotte, Matsumoto et al. 1999; Liu, Fang et al. 2000). Cetuximab has further been shown to decrease production of angiogenic factors associated with proliferation of microvessels such as VEGF, basic fibroblast growth factor (bFGF) and interleukin-8 (IL-8) (Perrotte, Matsumoto et al. 1999). An additional mechanism of action of cetuximab is its ability to induce ADCC (Bleeker, Lammerts van Bueren et al. 2004). 21 Cetuximab has demonstrated a 23% response rate in patients with irinotecan- refractory mCRC when used in combination with irinotecan and an 8-11% response rate when used as a single agent (Saltz, Pro Am Soc Clin Oncol, 2001; Saltz, L, J Clin Oncol 2004). Within mCRC, mutation in the proto-oncogene KRAS was found in 40% of patients has been associated with resistance to cetuximab. The KRAS wild-type protein is transiently activated during tightly regulated signal transduction events, however, mutations in hotspots in codons 12 and 13 result in a constitutively active GTP-bound protein which consequently renders the downstream pathway permanently ‘switched on’ irrespective and independent of the activation status of upstream receptors such as EGFR or HER2. In such an instance, the binding of an anti-HER mAB or TKI and the inhibition of ligand mediated receptor activation, including the RAF-MEK-MAPK mitogenic pathway, leads to unregulated proliferation, impaired differentiation and resistance to HER targeted therapy (Fig. 1-4B). Clinical studies have demonstrated that patients that possess activating KRAS mutations do not benefit from cetuximab or panitumumab therapy and the FDA and European Medicines Agency have recommended that EGFR mABs only be administered to patients that have KRAS wild- type CRC (Lievre, Bachet et al. 2006; van Krieken, Jung et al. 2008; Allegra, Jessup et al. 2009). Panitumumab: Panitumumab, (ABX-EGF; Vectibix; Amgen) is a fully humanized IgG2 mAB that targets the extracellular ligand binding domain of EGFR and was approved by the FDA in September 2006 for the treatment of patients with EGFR- expressing mCRC with disease progression on or following fluoropyrimidine-, oxaliplatin- and/or irinotecan- containing regimens (Saltz, L et al 2006). Panitumumab antitumor activity has been attributed to inhibition of EGFR signaling rather than ADCC. Similarly to cetuximab, patients that harbor KRAS mutations failed to respond to panitumumab. 22 Trastuzumab: Trastuzumab (Herceptin ® ; Genetech, San Francisco, CA) is a monoclonal antibody that bind to the extracellular domain of HER2 and thereby inhibits receptor homo- and hetero-dimerization resulting in inhibition of downstream signaling and induces ADCC (Clynes, Towers et al. 2000; Agus, Akita et al. 2002). Trastuzumab is FDA approved for the treatment of HER2-postive metastatic breast cancer patients and has demonstrated enhanced survival rates and is currently under clinical evaluation in patients with HER2-positive cancers, including gastric cancer (Slamon, Leyland-Jones et al. 2001; Roukos 2010). 1.4.2.2 Tyrosine Kinase Inhibitors (TKIs). TKIs are small molecule inhibitors that function through binding at tyrosine kinase domains on the intracellular domain of the receptors and are in directly competition with ATP, resulting in inhibition of autophosphorylation and blockade of downstream signaling and cellular proliferation (Imai and Takaoka 2006; Dassonville, Bozec et al. 2007). To date, three quinazoline-based anti-HER TKIs have been approved by the FDA for treatment in oncology and they are different in their reversibility and capacity to preferentially inhibit a single receptor or inhibit multiple HER family members. Erlotinib: Erlotinib (OSI-774, Tarceva; Roche; OSI Pharmaceuticals) is a quinazoline derivative TKI that reversibly targets EGFR by competing with ATP, thereby inhibiting EGFR phosphorylation and blocking subsequent signal transduction. Erlotinib is FDA approved for treatment of advanced-stage NSCLC in both second and third line settings, and in 2005, the FDA approved erlotinib for the treatment of locally advanced, unresectable or metastatic pancreatic cancer in combination with gemcitabine (Dowell, Minna et al. 2005). 23 Gefitinib: Gefitinib (ZD1839, Iressa; Astrazenca) is a small-molecule reversible TKI of EGFR with similar mechanism of action to erlotinib and is efficacious against EGFR-expressing NSCLC and HNSCC (Imai and Takaoka 2006) (Muhsin, Graham et al. 2003). Lapatinib: Lapatinib (GW572016, Tykerb; GlaxoSmithKline, Triangle Park, NC) Lapatinib is a dual TKI targeting EGFR and HER2 belonging to the quinazoline class of inhibitors (Moy, Kirkpatrick et al. 2007). It functions through preventing phosphorylation and signal transduction of EGFR and HER2 regulated pathways inducing a G 1 cell cycle arrest and apoptosis (Rusnak, Affleck et al. 2001; Rusnak, Lackey et al. 2001). Lapatinib is currently FDA approved for the treatment of HER2 over-expressing chemorefractory breast cancer patients in combination with the 5-FU pro-drug capecitabine (Xeloda; Roche, Nutley, NJ) and for the treatment of postmenopausal women with hormone receptor positive, HER2 overexpressing metastatic breast cancer in combination with letrozole (Femara, Novaritis Pharmaceuticals Corp.) (Geyer, Forster et al. 2006). Lapatinib is under extensive preclinical and clinical evaluation in a variety of diseases (Table 1-3). 24 1.4.3 Resistance to HER Targeted Therapy. One of the key issues that arise when administering HER-directed therapy is the Table 1-3. Representative list of current clinical trials investigating lapatinib. Breast Cancer NCT00073528 January 2010 receptor positive , HER2-overexpressing for whom hormonal therapy is indicated Breast Cancer NCT000073572 In combination with capecitabine for the treatment of patients with advanced March 2007 or metastatic breast cancer whose tumors overexpress HER2 and who have received prior therapy including an anthracycline, a taxane, and trastuzumab Clinical Trial Estimated Date Start Date of Completion HER2 Amplified Gastric Cancer NCT00486954 Lapatinib and paclitaxel July 2007 HER2 Amplified Gastric, Esophageal or Gastro-esophageal Junction Adenocarcinoma Primary Breast Cancer NCT00567554 Lapatinib, epirubicin, cyclophosphamid, docetaxel October 2007 December 2015 Hormone positive Late stage Breast Cancer NCT00390455 Lapatinib and fulvestrant September 2006 December 2008 HER2-positive Metastatic Breast Cancer NCT00281658 Lapatinib and paclitaxel January 2006 July 2010 Adjuvant HER2-positive Amplifed Breast Cancer NCT00490139 Lapatinib and trastuzumab May 2007 January 2013 Neoadjuvant HER2-positive Amplifed Breast Cancer NCT00553358 Lapatinib and trastuzumab January 2008 September 2020 HER2-positive Metastatic Breast Cancer NCT00667251 Lapatinib, trastuzumab, docetaxel, paclitaxel July 2008 July 2011 Inflammatory Breast Cancer NCT00558103 Lapatinib and pazopanib December 2007 June 2012 HER2-overexprressing Metastatic Breast Cancer NCT00272987 Lapatinib, paclitaxel and trastuzumab December 2005 September 2014 HER2-positive Breast Cancer NCT00777101 Lapatini, capecitabine, and neratinib November 2008 November 2013 Stage IV Bladder Cancer NCT00949455 Lapatinib Monotherapy November 2007 June 2011 Metatatic Colorectal Cancer NCT00574171 Lapatinib and Capecitabine September 2007 September 2009 Gastric Cancer NCT00526669 Lapatinib and Capecitabine March 2008 February 2011 Gastric Cancer NCT00103324 Lapatinib Monotherapy December 2004 Colorectal Cancer NCT00044343 Lapatinib, 5-FU and either irinotecan or oxaliplatin September 2002 SCC of the Head and Neck NCT01044433 Lapatinib and capecitabine October 2009 October 2011 Metastatic Breast Cancer NCT01068704 Lapatinib, letrozole, and BMS-690514 March 2010 September 2013 HER2-positive Breast Cancer NCT00585983 Lapatinib, capecitabine, and cixutumumab July 2008 August 2009 HER2-positive Breast Cancer NCT01044485 Lapatinib and docetaxel November 2008 December 2010 HER2-positive Breast Cancer NCT00912275 Lapatinib and vinorelbine May 2009 September 2011 Advanced Solid Tumors NCT00313599 Lapatinib and Paclitaxel February 2006 February 2013 HER2-Overexpressing Breast Cancer NCT00849329 Lapatinib and esomeprazole March 2009 January 2010 Colrectal Cancer NCT00536809 Lapatinib, capecitabine, and oxaliplatin September 2007 December 2008 Advanced Cancers NCT01087983 Lapatinib with either sirolimus or metformin March 2010 March 2020 Breast Cancer NCT00632489 Lapatinib, LBH589, and capecitabine May 2008 May 2010 HER2-positve Breast Cancer NCT00367471 Lapatinib, carboplatin, trastuzumab, paclitaxel December 2006 December 2008 Abbreviations: HER: Human epidermal receptor; SCC: Squamous cell carcinoma; 5-FU: 5-fluorouracil; LV:leucovorin; Cell Carcinoma; CHOP: cyclophosphamide+doxorubicin+vincristine+prednisone; 5-FU: 5-fluorouracil; LV:leucovorin; NSCLC: non-small cell lung cancer *Data reteived from www.clinicaltrials.gov on 3/23/2010. These clinical trials represent a subset of the 180 results and were selected based on the spectrum of disease(s) and chemotherpeutic agent(s) utilized for treatment. FDA Approved Disease Clinical Trial ID Treatment Approval Phase I In combination with letrozole for post-menopausal women with hormone NCT00680901 June 2008 Lapatinib Monotherapy Phase II/III Phase III Disease Clinical Trial ID Treatment Phase II Phase I/II 25 existence or subsequent development of resistant cancer cells during treatment. There is a substantial body of evidence implicating various factors in determining EGFR/HER2 mediated drug resistance and includes: activating mutations in downstream signaling molecules, increased production of HER ligands and activation of alternative signaling pathways (Bardelli and Siena 2010). Mutations in downstream signaling molecules: Within the HER signal Figure 1-6. Overview of molecules that mediate response to the HER-targeted agents. (A) Somatic mutations in BRAF (1) render the RAF-MEK-MAPK pathway constitutively active in the presence of receptor inhibition. (B) Somatic mutations in PI3KCA (2) renders the PI3K/AKT signaling pathway constitutively active in the presence of receptor inhibition; loss of PTEN (3), a negative regulator of PI3K can increase oncogenic signaling through PI3K/AKT. (C) Overexpression of activating ligands (4) can promote HER signaling and these tumors may respond better to anti- HER-targeted agents. Expression of alternative membrane-bound receptors such as insulin-like growth factor 1 receptor (IGF1R) (5), HER2 or HER3 (6) or cMET can circumvent the treatment efficacy of anti-HER therapy through ligand-driven autocrine signaling. Figure generated using MS Powerpoint (2008). !"#$ !%&#$ !%&#$ '(%)$ #(%*$! !"#$ !"#$%&'()* "+)($ "+)($ %,$$ "+)$ "+)$ "+)$ "("+$ -+)$ -+).($ '(%)$"#! /)0'$ %#1$ 213($ &-4#$ &1"/$ &1"/$ %#1$ /)0'$ 213($ &-4#$"#! &$ &$ 5!"1$ &-4#$ %#1$ 213($ #(%*$ !"#$ !%&#$ /)0'$ +* +* +* +* &$ &$ &$ &$ "+)(67"(8$ '(%)$ "+)$ 7+)$ Oncogenic Transcription Oncogenic Transcription Oncogenic Transcription "("+$ "+)$ ',$ 9,$ 1 2 3 4 5 6 26 transduction network, a number of genes are reported to be genetically altered in CRC with significant prevalence and as such may represent potential determinant of resistance to HER targeted therapy in addition to KRAS as previously mentioned. Mutations in v-raf murine sarcaoma viral oncogene homolog B1 (BRAF), phosphatase and tensin homolog (PTEN) and phosphoinositide 3-kinase (PIK3CA) have all been reported and result in constitutive activation of either the MAPK or PI3K/AKT pathways (Fig. 1-6A and B) (Jhawer, Goel et al. 2008; Laurent-Puig, Cayre et al. 2009; Ogino, Nosho et al. 2009). Increased production of HER ligands: The overexpression of activating EGFR ligands is likely to be a key determinant of sensitivity to anti-HER therapy via ligand- driven autocrine oncongenic HER signaling. The proposed model suggests that high expression of activating ligands may contribute addiction to HER signaling in KRAS wild- type CRC (Fig. 1-6C) (Khambata-Ford, Garrett et al. 2007; Jacobs, De Roock et al. 2009). Activation of alternative signaling pathways: The HER network is not the only receptor-signaling network that drives cellular proliferation, and thus, with blockade of EGFR or HER2, these alternate pathways compensate for the interruption in signaling and lead to intrinsic resistance to HER targeted agents. Expression of additional transmembrane signaling receptors may circumvent the EGFR and/or HER2 blockade and maintain oncogenic signaling crucial for tumor cell proliferation and survival. Candidates for such receptors include HER3, insulin-like growth factor 1 receptor (IGF- 1R) and cMET (Fig. 1-6C) (Rho, Choi et al. 2009; van der Veeken, Oliveira et al. 2009; Kruser and Wheeler 2010). 27 1.5 Histone Deacetylases Within the cellular environment, regulation of gene expression can occur post- transcriptionally through modification of histones and non-histone proteins by acetylation, phosphorylation, methylation, ubiquitination and sumoylation (Nightingale, O'Neill et al. 2006). Two distinct family of enzymes, histone acetyltransferases (HATs) and histone deacetylases (HDACs), work in concert by performing opposing functions to maintain a tightly regulated pattern of acetylation homeostasis (Carrozza, Utley et al. 2003; Mottet and Castronovo 2008). Local acetylation of histones by HATs leads to a more relaxed chromatin structure and is characterized by transcriptionally active regions (Bernstein, Meissner et al. 2007). Conversely, deacetylation of histone proteins by HDACs targets chromatin to adopt a more tightly bound structure leading to transcriptional inactivation (Mack 2006). HDACs are zinc-dependent hydrolases that can be classified into 4 different families (class I, IIa, IIb, and IV) that are involved in the remodeling of chromatin by deacetylation of specific lysine residues on histone tails and non-histone proteins (Dokmanovic, Clarke et al. 2007; Mehnert and Kelly 2007). The classification of HDACs were originally based on their homology to the known yeast HDACs, but have been further delineated over the years to also account for differences in localization, enzymatic activity, and intracellular targets (Bolden, Peart et al. 2006). The action of HDACs occurs through formation of large multi-protein complexes with co-activating, co- repressing and chromatin-remodeling proteins (Mehnert and Kelly 2007). It has further been demonstrated that the actions of HDACs and the resultant deacetylation of specific lysine residues is not limited to histone proteins, but occurs on non-histone proteins such as α-tubulin, HSP90, glucocorticoid receptors, DNA methyltransferase 1 (DNMT 1) and 28 multiple transcription factors including TP53, E2F, GATA1, STAT3, NFκB, Ku70, TFIIE and TFIIF (Johnstone and Licht 2003; Drummond, Noble et al. 2005; Boyault, Sadoul et al. 2007). 1.5.1 Histone Deacetylases and Cancer. The development and progression of tumorigenesis is characterized by increases in genetic abnormalities in the cell, including gene mutations, amplifications and/or deletions. Additional factors which can lead to the development of cancer include epigenetic alterations (Lund and van Lohuizen 2004). Epigenetic changes are characterized as changes that do not alter the DNA sequence, but rather result in changes in local chromatin structure and gene expression. During cancer development, normal cellular epigenetic mechanisms controlling gene expression can become significantly altered, and this global dysregulation results in the activation of oncogenes and/or silencing of tumor suppressor gene expression (Baylin and Ohm 2006). Dysregulation of the HDAC and HAT activity has been identified in several cancers including, leukemia, lymphoma, epithelial, gastric, prostate and CRC. In gastrointestinal cancers, a number of studies have identified HDAC1, HDAC2 and HDAC3 overexpression and it is proposed that the action of these enzymes may contribute to tumor-specific processes making these enzymes attractive targets for therapeutic intervention (Choi, Kwon et al. 2001; Song, Noh et al. 2005; Wilson, Byun et al. 2006). 1.5.2 Histone Deacetylase Inhibitors. Histone deacetylase inhibitors (HDACi) have recently emerged as potent and selective anticancer agents. These agents demonstrate pleiotropic anticancer activities through 29 the inhibition of HDACs, leading to changes in the acetylation status of both histone and non-histone proteins, resulting in modulation of 2-10% of genes, promotion of differentiation, inhibition of cell cycle progression, induction of apoptosis and suppression of tumor angiogenesis (Peart, Smyth et al. 2005; Munshi, Tanaka et al. 2006). These cellular effects of HDACi have been attributed to occur preferentially in cancer cells compared with normal cells. The classes of compounds identified as HDACi include: short-chain fatty acids (such as valproic acid), hydroxamic acids (such as TSA, vorinostat, LBH589), cyclic tetrapeptides (such as depsipeptide, FK-228), and benzamides (such as MS-275) (Table 2) (Marks, Richon et al. 2001). Mechanistically, HDACi have been shown to cause cell cycle arrest through induction of G 1 and G 2 /M phase, promotion of differentiation, and induction of apoptotic signaling cascades, mitotic failure, polyploidy, senescence, and increased generation of reactive oxygen species (Marks, Rifkind et al. 2001; Marks, Richon et al. 2001; Richon 2006; Dokmanovic, Clarke et al. 2007). Furthermore, HDACi can lead to transcriptional upregulation of certain genes and alterations in chromatin structure (Dokmanovic, Clarke et al. 2007; Mehnert and Kelly 2007). There are 12 different HDACi currently undergoing clinical trials as single agents or in combination with other agents for hematologic and solid tumors as listed in table 1 (Bhalla 2005; Bolden, Peart et al. 2006; Stadler, Margolin et al. 2006; Dokmanovic, Clarke et al. 2007; Garber 2007). Two hydroxamic-acid based HDACi's, vorinostat (SAHA, Merck), Panobinostat (LBH589, Novartis), will be the focus of this thesis and are pan-inhibitors of class I and II HDACs that have demonstrated potent toxicity in vitro against a variety of solid tumor cell lines including CRC and gastric cell lines (Marks and Breslow 2007; Marson, Mahadevan et al. 2007; Rasheed, Johnstone et al. 2007). 30 Vorinostat was the first HDACi approved by the FDA for the treatment of cutaneous T-cell Lymphoma (CTCL) and is currently under clinical investigation for mesothelioma, non-small cell lung cancer, and CRC (Table 1-4) (Marks and Breslow 2007). Panobinostat is also under clinical investigation in CTCL and a variety of solid tumors (Table 1-5) (Rasheed, Johnstone et al. 2007). Importantly, HDACi represent a highly selective, tumor specific, targeted class of agents demonstrating an approximately 10-fold increase in activity in tumor cells when compared with normal cells. 31 Table 1-4. Representative clinical trials investigating vorinostat. 32 Furthermore, results from pharmacokinetic studies in human subjects demonstrated the peak serum concentration achieved are within or supersede the concentrations required to inhibit cell growth and induce apoptosis in in vitro studies (Kelly, O'Connor et al. 2005; Giles, Fischer et al. 2006). Several studies to date have demonstrated that HDACi induce alterations in the expression of multiple drug targets and/or metabolic pathways that are critical molecular determinants for cancer therapeutics. Importantly combination treatment with additional agents targeting these modulated pathways has resulted in synergistic growth inhibitory effects on cancer cells in vitro and in vivo. It has been recently reported that HDACi synergize with 5-FU in vitro and in vivo in CRC cell line models through HDACi-induced downregulation of the 5-FU target enzyme TS, providing a mechanistic basis for the drug synergy (Tumber, Collins et al. 2007; Fazzone, Wilson et al. 2009; Fazzone, Wilson et al. 2009). The HDACi vorinostat is also reported to cause acetylation of and markedly reduce the chaperone activity of HSP90 in CTCL models resulting in a synergistic interaction with the HSP90 inhibitor bortezomib (Zhang, Wang et al. 2009). This combination was subsequently extended to CRC cell lines with similar synergistic anti- proliferative effects (Pitts, Morrow et al. 2009). In addition, the HDACi vorinostat was demonstrated to induce tumor cell-selective expression of the TRAIL death receptors 4 and 5 sensitizing breast cancer xenografts to the effects of a TRAIL-agonistic antibody (Frew, Lindemann et al. 2008), an observation which is currently being clinically evaluated in lymphoma patients. More recently, HDACi were also reported to enhance the apoptotic effects of EGFR inhibitors in lung cancer models (Edwards, Li et al. 2007; Zhang, Peyton et al. 2009) and clinical evaluation of this is ongoing. Therefore, the identification of novel genes modulated by HDACi in CRC cells may provide pathway- driven rationale for novel and urgently needed efficacious drug combinations. 33 Table 1-5. Representative clinical trails investigating panobinostat (LBH589). 34 Chapter 2 Hypothesis 2.1 The Clinical Reality The treatment options for patients with gastrointestinal cancers have increased considerably in recent years, with the development of numerous targeted therapies. However, the most important progress that has been made is furthering our understanding the molecular pathways that play important roles in the development and progression of cancer. These include the regulation of a number of key tumor processes including cell-cycle, apoptosis, angiogenesis, invasion and metastasis (Capdevila, Saura et al. 2007). The mainstay of chemotherapy in CRC remains infusional 5-FU in combination with oxaliplatin (FOLFOX) or irinotecan (FOLFIRI). Both regimens have demonstrated similar RR, PFS and OS in first-line treatment of advanced and metastatic disease. To date, optimized combination chemotherapy with anti-VEGF or anti-EGFR agents remains the most effective treatment for CRC. The importance of identifying more effective chemotherapies is very well established. The hope of replacing cytotoxic chemotherapy with the introduction of the targeted agents such as cetuximab, panitumumab and bevacizumab has not come to fruition. In fact, cytotoxic chemotherapy remains superior and is the mainstay treatment for patients with metastatic disease and in the adjuvant setting. However, the critical problem is that more than half of all patients who receive cytotoxic chemotherapy for advanced or metastatic disease will not respond to treatment. In addition, since 2004 no 35 new drugs have been approved for CRC. This gap in the drug discovery process coupled with the disappointing RR in CRC illustrates the critical need for the development of novel drugs with antitumor activity that when used alone or as part of existing chemotherapeutic regimens induce significant anticancer activity in patients with CRC. In addition, improvements are needed in identifying and validating prognostic and predictive molecular markers, diagnostic modalities, and optimized treatment strategies. If met, this goal will lead to a significant improvement in the treatment of CRC, and a way forward for additional effective therapies after two or three lines of therapy. 2.2 Study Rationale The success of recently developed chemotherapeutic agents can be attributed, in part, to their inclusion in rational combination strategies that pair agents that work by different mechanisms of action to maximize targeted cytotoxicity against the tumor cells, minimize systemic toxicity, circumvent the development of resistant cancer cell populations and lead to increased efficacy for a longer survival. The evaluation of novel anticancer agents in combination with standard-of-care chemotherapies in CRC has the potential to provide novel efficacious treatment strategies for patients who have progressed on standard regimens and for whom further therapeutic options are limited. The goal of novel combination strategies is to interfere with major pathways involved in CRC growth and survival and hold the promise of increasing the efficacy of cytotoxic and targeted therapies in patients with metastatic disease. 36 2.3 Overall Hypothesis Despite significant advances in research and development in CRC and gastric cancer, the current response rate for 1 st line treatment of mCRC remains ~50% and dramatically decreases for 2 nd line therapy. In addition, the five-year survival rate for patients diagnosed with mCRC is approximately 10%. While, molecularly targeted therapies have improved treatment outcomes for patients with cancer, these benefits are modest and in only select patient populations. It is clear that the new chemotherapeutic options and novel drug combinations must be developed to provide benefit for the approximately half of patients that fail to response to current chemotherapeutic options that are available. We hypothesize that combining novel agents that target alternative tumor associated pathways will result in additive to synergistic interactions with standard of care chemotherapy, leading to new treatment options for those patients who fail to respond to current therapy. 2.4 Study Overview The following body of work focuses on the integration and evaluation of a dual EGFR/HER2 targeted TKI, lapatinib, and the novel HDACi, vorinostat and LBH589, into CRC cancer combination chemotherapies utilizing in vitro and in vivo models. The HER receptor family plays an important role in driving the growth and progression of many types of solid tumors. In this body of work, it is demonstrated that the dual EGFR/HER2 TKI, lapatinib, enhances the antitumor activity of two anticancer agents with markedly differing mechanisms of action. Lapatinib in combination with the 37 DNA-damaging agent irinotecan or in combination with the HDACi LBH589 resulted in synergistic increases in tumor growth inhibition both in vitro and in vivo models. The HDACi’s are emerging as a highly useful class of anticancer agents that inhibit HDAC enzymes that are involved in the deacetylation of histone and non-histone cellular proteins. The pan-HDACi’s vorinostat and LBH589, achieve potent inhibition of all HDAC enzymes implicated in cancer and have demonstrated potent antitumor activity in preclinical models and promising clinical efficacy in cancer patients. In this study, we focused on the effect of these HDACi on global transcription profiles in CRC models and evaluated the effect of LBH589 on the HER family members expression and signaling in CRC, as well as further evaluated the antitumor effects when utilized in combination with lapatinib. The results from these studies provide valuable information on promising new strategies and warrant further evaluation in the treatment of gastrointestinal cancers. Ongoing clinical investigations with these agents and others currently in development along with the identification of clinically relevant biomarkers that are both prognostic and predictive will provide further advances to improve outcomes in the treatment of gastrointestinal malignancies. 38 Chapter 3 Material and Methods 3.1 Compounds and Reagents Lapatinib (N-[3-chloro-4-[(3-fluorophenyl)methoxy]phenyl]-6-[5-[(2-methylsulfonylethyl amino)methyl]-2-furyl]quinazolin-4-amine) was obtained with permission from GlaxoSmithKline and L-4804 Lapatinib was purchased from LC Laboratories (Woburn, MA). Panobinostat (LBH589) was provided by Novartis Pharmaceuticals (East Hanover, NJ). Vorinostat was provided by Merck and Co., Inc. (Whitehouse Station, NJ) and the National Cancer Institute (NCI, Bethesda, MD). The irinotecan active metabolite SN-38 (7-ethyl-10-hydroxycamptothecin) and oxaliplatin was supplied by Pfizer (New York, NY). Irinotecan hydrochloride (CPT-11), 5-fluorouracil (5-FU), cisplatin, cyclosporin (CsA), fumitremorgin C (FTC), epidermal growth factor (EGF), MK-571 and (hydroxypropyl)methylcellulose (HPMC) were purchased from Sigma-Aldrich (St Louis, MO). PI3K inhibitor, LY294002 were purchased from EMD Biosciences (Gibbstown, NJ). MEK inhibitor, U0126, and CellTiter 96 AQueous MTS reagent were purchased from Promega (Madison, WI). 17-(Allylamino)-17-demethoxygeldanamycin (17-AAG) and MG-132 were purchased from A.G. Scientific, Inc. (San Diego, CA). Halt protease and phosphatase inhibitor cocktail was purchased from Thermo Scientific (Rockford, IL). 39 3.2 Tissue Culture 3.2.1 Cell Lines. The human CRC cell lines DLD-1, HCT116, HT29, LoVo, RKO, SW480 and SW620 and the gastric cancer cell lines AGS and NCI-N87 were purchased from American Type Culture Connection (ATCC, Lockville, MD). The H630 CRC cell line was a generous gift of Dr. Patrick Johnston at Queen’s University (Belfast, UK). HCT116 and HT29 CRC cells were maintained in McCoy’s 5A. DLD-1, H630, LoVo, NCI-N87, and RKO were maintained in DMEM. AGS were maintained in Ham’s F12K media (Mediatech, Manassas, VA). SW480 and SW620 were maintained in Liebovitz media. MKN28 gastric cancer cells were purchased from Health Science Research Resources Bank (HSRRB, Osaka, Japan) and maintained in RPMI-1640. SNU-484 gastric cancer cells were purchased from the Korean Cell Line Bank (Seoul, Korea) and maintained in RPMI- 1640. 3.2.2 Preparation of Growth Medium. All cell line media was supplemented with 10% fetal bovine serum (Lonza, East Rutherford, NJ), 50 µg/ml penicillin/streptomycin and 1 mM sodium pyruvate (Invitrogen, Carlsbad, CA). 3.2.3 Routine Culture of cell lines. Cells were maintained in a humidified Hepa Class 100 Incubator (Thermo) at 37 o C and 5% CO 2 . Cell lines were maintained in T75 tissue culture flasks with appropriate growth medium and were passaged every 3-5 days. 40 3.2.4 Mycoplasma Screening. Cell lines utilized throughout these studies were routinely screened for mycoplasma using the MycoAlert ® Detection kit (Lonza). This kit detects the mycoplasma by measuring the levels of ATP in a sample both before and after the addition of the MycoAlert®Substrate. Briefly, 1 ml of culture media was removed from logarithmic growing cells from cell culture and spun at 1500 rpm (200 x g) for 5-minutes. A 100 µl of cleared supernatant was placed into a luminometer cuvette, and 100 µl of MycoAlert®Reagent was added to each sample. Following a 5-minute incubation, the luminescence was measured (Reading A). A 100 µl of MycoAlert®Substrate was added to each sample and incubated for 10 minutes. A second luminescence was measured (Reading B). If the calculated ratio (reading B/reading A) was >1 indicated mycoplasma contaminated cell culture and a ratio <1 indicated uninfected cell cultures. 3.3 Cell Viability Assays 3.3.1 MTS Growth Inhibitory Assay. Cell viability was analyzed by the MTS (3-(4,5-dimethylthiazol-2-yl)-5-(3- carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium) assay (Promega) and was performed according to the manufacturer’s instructions. Cells were seeded at 3 x 10 3 cells/well in a 96-well plate. Twenty-four hours post-seeding, cells were treated with increasing concentrations of drug either alone or in combination for a continuous 72 h drug exposure at 37 o C and 5% CO 2 . After 72 h, 10 µl of MTS reagent was added to each well and incubated for 2 – 4 h at 37 o C and 5% CO 2 . Growth inhibition was measured by reading the absorbance of each sample at absorbance of 490nm using an ELISA microplate reader (Molecular Devices) and the comparing A 590 of drug-treated cells to 41 that of untreated controls set at 100%. The IC 50 (72 h) value was calculated from the sigmoid dose-response curves using Prism 5.0 (GraphPad, San Diego, CA). Statistical significance of IC 50 (72 h) values between cell lines was evaluated by ANOVA using SAS 9.3.1 statistical software (Cary, NY). The combination effect was determined using the combination index (CI) analysis methods of Chou-Talalay (Chou and Talalay 1984) utilizing Calcusyn software (Biosoft, Ferguson, MO) which quantifies the degree of synergy between two agents that both induce a linear pharmacologic response. Fraction affected (FA) was calculated from the percent growth inhibition using the following equation: (100 - % growth inhibition) / 100. CI values were interpreted as follows: <1 = synergism; 1-1.2 = additive; and >1.2 = antagonism. 3.3.2 Colony Formation Assay. For the colony formation assay, cells were seeded at 300 – 500 cells/well in a 24 well plate. Twenty-four h after seeding, cells were treated with the indicated concentrations of drug either alone or in combination for 24 or 48 h at 37°C with 5% CO 2 . Following treatment, culture medium was replaced with drug-free medium and cells were allowed to grow for 14 – 21 d at 37°C with 5% CO 2 . Colonies were fixed with 100% methanol and stained with 1% crystal violet. Stained colonies with >50 cells were counted and drug- treated samples compared directly to untreated controls set at 100%. Histograms represent mean ± SEM and statistical significance was determined by two-way ANOVA in Prism 5.0 (Graphpad). 42 3.4 Fluorescent Activated Cell Sorting (FACS) 3.4.1 Cell Cycle/Sub-G 1 Analysis. For flow cytometry, cells were seeded in 6-well plates at 2.5 x 10 5 cells per well. Twenty- four hours post-seeding, duplicate wells were treated with the indicated concentration of drug either alone or in combination for 24 – 72 h. Cells were harvested in 2 ml of 1X PBS/0.5 mM EDTA and pelleted by centrifugation at 2400 RPM at 4 o C for 5 minutes, fixed with 100% ethanol, stained with propidium idodide and analyzed using a Coulter ® EPICS ® ELITE flow cytometer (Beckman Coulter, Fullerton, CA) equipped with a 15mW Argon laser (excitation beam 488nm). Viable cells were gated on a dot plot display of forward scatter versus side scatter to eliminate cell doublets. Cell cycle populations were quantified using histogram analysis software (Expo32, Beckman Coulter). Cells with DNA content <1 were considered apoptotic. Histograms and two-way ANOVA statistical analysis were performed with Prism 5.0 (Graphpad). 3.4.2 Detection of Caspase-8 Activation. Detection of caspase-8 was performed using the CaspaTag TM Caspase-8 In-Situ Assay Kit (Chemicon, Madison, WI) utilizing a non-toxic carboxyfluorescein-labeled fluoromethyl ketone peptide inhibitor (FAM-LETD-FMK), which binds covalently to active caspase-8 to produce a green fluorescence. Cells were seeded in 6-well plates and allowed to adhere for 24 h. Sub-confluent cells were then treated for 24 h with the indicated concentration of drug either alone or in combination, harvested, and CaspaTag TM assay performed according to the manufacture’s instructions. Cells were analyzed on a Coulter ® EPICS ® ELITE flow cytometer (Beckman Coulter). Cells were plotted on histogram with log FL-1 channel fluorescence (X-axis) versus number of cells 43 (Y-axis). In treated samples, events falling to the right of a vertical gate (positioned at the peak fluorescence of untreated controls log FL1 10 2 ) were considered caspase-8 positive and are expressed as a percentage of total events for the given sample. Histograms represent the mean ± SEM and statistical significance determined by two- way ANOVA (Prism 5.0, Graphpad). 3.5 Detection of H2A.X Phosphorylation by Chemiluminescence Detection of phosphorylation of H2A.X was performed using the H2A.X Phosphorylation Assay Kit, Chemiluminescence Detection (Roche), a cell based ELISA formatted for chemiluminescent detection. Cells were seeded at a density of 2.0 x 10 5 cells per/well in a 24 well plate. Twenty-four hours post seeding, cells were treated with indicated concentrations of drug alone or in combination for 6 and 18 h. Cells were then fixed, permealized, and detected for phosphorylation of H2A.X at serine 139 by sequentially incubating with the anti-phospho-H2A.X (Ser 139 ), clone JBW301 and an anti-mouse-HRP conjugate according to manufacturer’s instructions. The chemiluminescence HRP substrate LumiGLO™ was then added and the signal measured on a microplate luminometer. Histograms represent mean relative luminescence of treated to untreated cells ± SEM. Statistical significance was determined by two-way ANOVA (Prism 5.0; Graphpad). 44 3.6 Western Blotting 3.6.1 Protein Isolation and Quantification. Following treatment with indicated concentrations of drug either alone or in combination, cells were collected and cell lysates were prepared using radioimmunoprecipitation assay (RIPA) buffer (25 mM Tris-HCl pH 7.6, 150 mM NaCl, 1% NP-40, 1% sodium deoxycholate, 0.1% SDS) supplemented with Halt protease and phosphatase Inhibitor Cocktail according to the manufactures guidelines (Thermo). Cell lysates were centrifuged at 13,000 rpm for 15 min at 4 o C. The supernatant was removed and the protein concentrations were quantified according to the BCA Protein Assay Kit (Pierce) and the absorbance was measured at 590 nm using an ELISA microplate reader (Molecular Devices). 3.6.2 SDS-polyacrylamide Gel Electrophoresis (SDS-PAGE). Fifty to 75 µg of total protein was denatured by diluting extracts in a 2X Western loading dye containing 10% β-mercaptoethanol and heating samples at 95 o C for 5 min. Denatured proteins were resolved on a 10-15% SDS-polyacrylamide (SDS-PAGE) gel for 90 min at 100V. 3.6.3 Western Blotting. Following separation by SDS-PAGE, proteins were transferred to 0.75 µM nitrocellulose membrane (Invitrogen) for 90 – 120 min at 100V. Membranes were then blocked in 5% milk in 1X Tris buffered saline with 0.05% Tween-20 (TBST) for 30 minutes, followed by 45 an overnight incubation in primary antibody at appropriate final concentrations (Table 3- 1) overnight at 4 o C with gentle agitation. The membrane was then washed 3X for 5 min in TBST and then incubated in secondary antibody (goat-anti-mouse HRP or goat-anti- rabbit HRP; Santa Cruz Biotechnology) for 1 – 2 h at room temperature with gentle agitation. Blots were then washed again 3X for 5 min in TBST before being detected using the HyGlo luminescence detection reagent and developed by exposing to HyBlot film (Denville Scientific) and the Hope-Micromax film processor (Hope X-Ray). Pixel intensity of Western bands were quantified using Scion Image Software (Scion Corporation, Frederick, MD). Relative expression was determined by normalizing pixel intensity for specified protein bands versus pixel intensity for the respective β-actin or β-tubulin. Table 3-1. Western antibodies. A Primary Antibody Molecular Weight (kDa) Company Type Dilution !-Actin 42 Sigma Mouse-Monoclonal 1:2000 !-Tubulin 53 Sigma Mouse-Monoclonal 1:2000 Acetyl-H3 17 Millipore Rabbit 1:2000 Acetyl-H4 10 Millipore Rabbit 1:2000 AKT 60 Cell Signaling Technology Rabbit 1:1000 phospho-AKT (Ser473) 60 Cell Signaling Technology Rabbit 1:1000 Caspase-8 55 (Pro-caspase) EMD Biosciences Mouse 1:1500 28 (Cleaved Fragment) EGFR 170 Santa Cruz Biotechnology Rabbit 1:800 phospho-H2A.X Cell Signaling Technology Rabbit 1;1000 HER2 185 Cell Signaling Technology Rabbit 1:1000 p44/42 MAPK (Thr202/204) 44/42 Cell Signaling Technology Rabbit 1:1000 phospho-p44/42 MAPK 44/42 Cell Signaling Technology Rabbit 1:1000 p21 Waf1/Cip1 21 Cell Signaling Technology Mouse 1:2000 PARP 116 (Full Length) Cell Signaling Technology Rabbit 1:1000 89 (Cleaved Fragment) B Secondary Antibody Molecular Weight (kDa) Company Type Dilution Anti-Mouse HRP n/a Santa Cruz Biotechnology Ig-horseradish peroxidase linked 1:1000 Anti-Rabbit HRP n/a Santa Cruz Biotechnology Ig-horseradish peroxidase linked 1:1000 46 3.7 Quantitative Real-time PCR (qPCR) 3.7.1 RNA Isolation and Preparation. Following treatment with indicated concentrations of drug either alone or in combination for specified time points, media was aspirated and RNA was isolated from cells with I ml of TRIzol reagent (Invitrogen) according to the manufactures protocol. Briefly, 200 µl of chloroform was added, vortexed and then centrifuged to separate into their respective phases. Following centrifugation, the aqueous phase was transferred to a new tube and RNA was precipitated by adding isopropanol and incubating at room temperature for 10 min. RNA was then pelleted by centrifugation and liquid aspirated followed by washing with 70% ethanol and re-suspended in 25 – 50 µl nuclease free water. RNA was quantified and purity was determined spectrophotmetrically and then normalized to 100 ng / µl. 3.7.2 cDNA synthesis. RNA (0.5 µg) was reversed transcribed to cDNA using the Quanta Biosciences Reverse Transcription System according to the manufactures instructions. Briefly, cDNA was synthesized in a total volume of 20 µl composed of 4 µl of Quanta qScript™ cDNA SuperMix (5X), 11 µl of RNase/DNase free water and 5 µl of 100 ng / µl RNA. cDNA was synthesized using the following protocol: 25 o C for 5 min, 42 o C for 30 min, 85 o C for 5 min and held at 4 o C. cDNA was diluted 1:4 in RNase/DNase free water and 2 µl used as a template for subsequent qPCR. 47 3.7.3 Real-time quantitative polymerase chain reaction (qPCR). cDNA was analyzed using primed pairs of chemically synthesized 18 to 22-mer oligonucleotides designed using published DNA sequences and freely available primer designed software (Primer-BLAST, NCBI). Primer sequences for each PCR reaction are located in Table 3-2. All primers utilized displayed PCR efficiencies of >90%. Melt curve analysis of all samples was routinely performed to ascertain that only the expected products had been generated. A primer concentration of 0.3 µM was found to optimal for each primer set and this concentration was used in all qPCR analyses. qPCR was performed in triplicate using Quanta Biosciences PerfeC T a® SYBR® Green SuperMix, Low ROX™ according to the manufactures instructions and analyzed on an Applied Biosystems 7500 PCR Detection System (Applied Biosystems, Inc.). All reactions were performed in a final volume of 20 µl. The reaction conditions were as follows: activation at 94 o C for 10 min; 40 cycles of denaturation at 94 o C for 5 sec, annealing at 60 o C for 30 sec and extension at 72 o C for 45 sec. Target genes were normalized to GAPDH and quantified using the comparative C T method previously described (Livak and Schmittgen 2001). By calculating the C T value for the target gene and a reference molecule (GAPDH) in each sample, the relative quantity of each target could be determined using the formula: Δ C T = 2 – (GAPDH C T – target gene C T ) . A control reaction containing all essential components of the amplification reaction minus the cDNA template was included for each primer set in each analysis to facilitate the detection of contaminating DNA. Messenger RNA (mRNA) expression was presented as percentage of untreated control set at 100%. Histograms and statistical analyses (two-tailed unpaired Student’s t-test) were performed with Prism 5.0 (Graphpad). 48 3.8 Liquid Chromatography and Mass Spectrometry (LC-MS) 3.8.1 SN-38. Cells were treated with 5 or 10 nM SN-38 alone or in combination with 3 µM lapatinib for 6 h. Supernatants were extracted and spiked with 50 µl of 100 ng/ml CPT-11 as an internal standard and 50 µl of the solution quantified using a validated SN-38 assay from 0.1 to 100 ng/ml as previously described (Garcia, Pujari et al. 2005). SN-38 and CPT-11 were separated using a C18 50 x 1.1 mm I.D. Hypersil column (Thermo Finnegan, Waltham, MA) and eluted utilizing component A consisting of ammonium acetate (pH 3.5 with formic acid) and B was methanol. A gradient consisted of 5 to 70% methanol was used, where the retention for CPT-11 and SN-38 was 10.15 and 11.01-11.22 min, respectively. CPT-11 and SN-38 level were quantified using API3000 (Applied Biosystems, Foster City, CA) mass spectrometer. SN-38 standard curve validated from 0.01 to 100 ng/10 6 cells, where m/z to transition were monitored for SN-38 and CPT-11 at 393.1→349.5 and 587.2→167.3, respectively. 49 3.8.2 Panobinostat (LBH589). Cells were treated with 10 or 15 nM LBH589 alone or in combination with 3 µM lapatinib for 6 h. Cells were harvested in 1 ml of methanol and supernatants were centrifuged repeatedly at 13,000 RPM for 10 min at 4°C to ensure clarity. Supernatants were spiked with 50 µl of the HDACi vorinostat (500 ng/ml) as an internal standard and 10 µl of the solution quantified using a validated LBH589 and vorinostat detection assay from 0.1 to Table 3-2. qPCR gene primer sequences. Name Accession Number Strand Primer Sequence Size (bp) F CGCCGCTAGAGGTGAAATTC R TTGGCAAATGCTTTCGCTC F CCGGCCGGTGAAGGCATCAT R TCCAACACTGCCCGCACACA F GACCTAAAGTTCCCCGCTTC R GACAGATTGAAGGGCAGAGG F TGCTCACAGCAGTAAATGCC R TGCAAGGAAGGAGGCTAGAA F CCCGCACGATTTCATTGAAC R AGGGCGGATTGGAAATGAAC F CATGGAAACCTCGTCATCCT R TACAGCCTTCCCGAACTGAC F GTCCTTCCTGTGCTCTCCAG R AGACTCTGCCTCCAGATCCA F AGGACCAAGCAACATGGTCA R CCTTGCAGCTGTTTTCACCT F CACAAAAGTGAGTGTGCACCGGC R CAGGCTGGCATTGGTGGGCA F AACTCCAGCCACGCTCTGCG R AAGTGCCGGCAGGCAAAGCA F ATGGGGAAGGTGAAGGTCG R GGGTCATTGATGGCAACAATATC F ACACCGAAGAAAGCGAAGAA R AGCCTTAGCAGCACTTTTGG F AAGGCCGTCACCAAGTACAC R TTTCAGGCAGATGAGACTTCC F GCTGGCCCTGTACGAGGAT R ACACATCAGCTCTGAAATTCATCAC F CCCCAACTGCTCCTGTGCCG R GGGAGCAGGGCTGTCCCGA F GCAAATGCAAAGAGTGCAAA R ACAGCTGTCCTGGCATCAG F CACGAATGACAGAGGCGTGTA R TGGCGGATTAGCTCTTTTTCC F GCCGTCCCAAGGGACCGAATG R GCCGCCTCTGGGTACGCTTC F CACGCTGCAGGACAGCAT R GGCCGCCTCAGCTCATT F GGAGGAGTTGCTGTGGTTTATCAAG R AGGCTGTCCAAAAAGTCTCGGG F TTATGGTGAAACAGGGGAGA R AGTGGAACTGGCAGAGACTG 134 140 HIST1H2BD NM_138729.1 136 HIST1H1C NM_005319.3 116 ERBB2 ERBB3 NM_001982.2 UNG NM_003362.2 176 TYMS NM_001071.2 108 THBS1 NM_003246.2 69 NF!B1 NM_003998.2 130 RGL1 NM_015149.3 166 MT1X NM_005952.3 146 IRAK1 NM_0001569.3 129 MT1G NM_005950.1 202 DHRS2 NM_182908.4 169 AURKB NM_004217.2 165 CDCA7 NM_031942.4 CCND1 NM_053056.2 95 AVEN NM_020371.2 155 Gene 18s rRNA NR_003286.1 62 ARRDC4 NM_183376.2 ERBB1 NM_005228.3 217 GAPDH NM_002046.3 107 NM_001005862 152 371 50 100 ng/ml on board an Agilent 1100 series HPLC interfaced with an AB Sciex 3000 mass spectrometer. LBH589 and vorinostat were separated using a BDS Hypersil Column (50 x 2.1mm, 5µm particle size) (Thermo Finnegan, Waltham, MA). The mobile phase to separate LBH589 and vorinostat consisted of a gradient system using two components; 0.1% formic acid in H 2 O (A) and MeOH (B). The gradient of the mobile phase ran the following program: from 0-2 minutes 90% A, then from 2-6 minutes there was a gradient step taking it to 20% A, where from 6-8 minutes the mobile phase was held at 20% A, and finally at 8.1 min the system returned back to 90% A. LBH589 and vorinostat were quantified using multiple reaction monitoring to measure the ion pairs at 350.1→158.4, and 265→232 respectively. The assay was validated from 0.1 to 500 ng/ml, with a lower level of quantification at 0.1 ng/ml. 3.9 Microarray 3.9.1 Microarray Drug Treatments and RNA Isolation. HCT116 and HT29 CRC cells were seeded at 7x10 6 cells/10 cm plate and treated with either 50 nM LBH589 or 2 µM vorinostat for 24 h at 37 o C and 5% CO 2 . All treatments were conducted in triplicate and fresh medium was added to untreated control cells. Following the 24 h incubation, cells were harvested and RNA was isolated using the RNeasy® Mini Kit (Qiagen, Valencia, CA) according to the manufacturer’s protocol. RNA was subjected to lithium chloride precipitation to remove any possible genomic DNA contamination. The integrity of the RNA was analyzed by spectrophotometry and capillary electrophoresis. 51 3.9.2 Microarray Expression Profiling. Microarray expression profiling was performed at the USC/Norris Cancer Center Genomics Core Facility (Los Angeles, CA). The RNA was amplified into cRNA and biotinylated by in vitro transcription using the Illumina® TotalPrep RNA Amplification Kit (Ambion, Applied Biosystems, Foster City, CA) according to the manufacturer’s protocol. Biotinylated cRNAs were purified, fragmented, and subsequently hybridized to an Illumina Human-6 V2 BeadChip (Illumina, San Diego, CA). 3.9.3 Data Normalization and Statistical Analysis. Microarray statistical analysis was performed with the assistance of Asuragen Inc, (Austin, TX). The background subtraction, expression summary, normalization, and log base 2 transformation of gene signals were carried out using Quantile Normalization (Bolstad, Irizarry et al. 2003). For statistical analysis, one-way ANOVA was used for multiple group comparison across all samples in the experiment, followed by multiple testing correction to determine the false discovery rate (FD) Benjamini and Hochberg method (Benjamini and Yekutieli 2005). Pairwise comparisons consisting of a two- sample t-test was carried out for every gene. Genes with a FDR-adjusted p-value of < 0.05 were considered differentially expressed genes (DEGs). The resulting list of genes and associated p-values were graphically represented by hierarchical clustering, Venn analysis, principal components analysis (not shown), and volcano plots (not shown). 3.9.4 Ingenuity  Pathway Analysis (IPA). In the HCT116 and HT29 CRC cell lines, a total of 3043 and 2232 differentially expressed genes respectively that had FDR-adjusted (p < 0.05) were used for the pathway analysis. Gene reference accession numbers were imported into the Ingenuity® 52 Pathway Analysis (IPA) software (Ingenuity® Systems, www.ingenuity.com, Mountain View, CA). In the HCT116 and HT29 cell lines, 2289 and 1679 of these genes respectively were mapped to the Ingenuity database. Up- and down-regulated genes were both included as a defined parameter for the core analysis. Genes mapped to genetic networks, were then ranked by a score that defines the probability that a collection of genes equal to or greater than the number in a network can be achieved by chance alone. According to IPA, a score of 3 indicates that there is a 1/1000 chance that the focus genes are in a network due to random chance, and therefore, scores of >3 have a 99.9% confidence of not being generated by random chance alone. This score was used as the cut-off for identifying gene networks that were significantly affected by the HDACi, LBH589 and vorinostat. In a similar way, DEGs were mapped to canonical pathways and tested by the Fishers Exact Test p-value. Canonical pathways were represented as a histogram of pathway vs. –log(p-value). In addition, for canonical pathways a ratio value was calculated as the number of molecules in a given pathway that meet the cut criteria, divided by the total number of molecules that make up that pathway. 3.9.5 qPCR Validation of Microarray. The abundance of selected transcripts, which had been previously identified by microarray expression profiling at 24 h, was re-evaluated by qPCR at 6, 12 and 24 h. The total RNA was isolated from HCT116 and HT29 CRC cells as previously described above and RNA (0.5 µg) was reverse transcribed to cDNA using the Promega Reverse Transcription System according to manufacturers instructions and analyzed using an Applied Biosystems 7500 PCR Detection System (Applied Biosystems Inc.). All reactions were performed in triplicate in a final volume of 25 µl. All amplifications were 53 primed by pairs of chemically synthesized 18- to 24-mer oligonucleotides designed using freely available primer design software (Primer-BLAST, NCBI) to generate target amplicons of 100-200 bp (Table 3-2). Reaction conditions were as follows: Activation at 95 o C for 10 min and 40 cycles of denaturation at 95 o C for 15 sec, annealing at 55 o C for 35 sec, and extension at 72 o C for 45 sec. Melt curve analysis of all samples was routinely performed to ascertain that only the expected products had been generated. All primers utilized displayed PCR efficiencies of >90%. Target genes were normalized to GAPDH and quantified using the comparative C T method (Livak and Schmittgen 2001). Histograms and statistical analyses (two-tailed unpaired Student’s t-test) were performed with Prism 5.0 (GraphPad). 3.10 Mouse Xenograft Models Male BALB/c nu/nu mice (Charles Rivers Laboratories, Wilmington, MA) or C57Bl/6 BALB/c mice (Taconic Labs, Hudson, NY) aged 6 - 8 wks were housed in laminar flow cabinets under specific pathogen-free conditions in facilities approved by the Association for Assessment and Accreditation of Laboratory Animal Care in accordance with the current regulations and standards of the U.S. Department of Agriculture, U.S. Department of Health and Human Services, and NIH. 3.10.1 Tumor Establishment and Growth. HCT116, HT29, and LoVo CRC xenografts were established by subcutaneous injection of 4 x 10 6 cells in log-phase growth in 100 µl of sterile PBS into the left flank. Once palpable tumors were established and reached approximately 100mm 3 (day 0), animals 54 were randomized to treatment groups (n = 7 – 8 per group). All animals were inspected daily for assessment of overall condition and food and water intake. 3.10.2 Drug Administration. Lapatinib was administered by oral gavage at 30 mg/kg bi-daily (BID). CPT-11 was administered intravenously (i.v.) every four days (q4d) at either 5 or 10 mg/kg. LBH589 was administered at either 2.5 or 10 mg/kg by intraperitoneal (i.p.) injection once daily for 5 days consecutive every 7 days. 3.10.3 Determination of Tumor Volume. Two perpendicular diameters of tumors were measured every 2 days with a digital caliper by the same investigator. When the mean tumor volume of vehicle-treated mice reached 1000 mm 3 the experiment was terminated. Tumor volume (TV) was calculated according to the following formula: TV (mm 3 ) = (length (mm) x [width (mm)] 2 ) / 2, where width was the shortest measurement. 3.10.4 Calculation of Tumor Delay. The combined drug effect was assessed by determining the tumor delay (Td); defined as the time in days to reach a tumor volume that was five times greater than the initial volume at the start of treatment. The expected Td of the combined treatment was calculated according to the following formula: Expected Td = Mean vehicle + (mean drug A – mean vehicle) + (mean drug B – mean vehicle). The combined antitumor effect of drug A and drug B was determined by calculating the ratio of the observed Td divided by that of the expected Td. If the ratio observed: expected is >1, the combination is synergistic; <1 the combination is antagonist; and =1 the combination is additive. 55 3.10.5 Mouse Bodyweight. Animal bodyweight was measured every 2 days as an index of toxicity. Percentage of initial bodyweight at the conclusion of the study was calculated by the following equation: (bodyweight at conclusion of study / body weight at commencement) *100. 56 Chapter 4 Integration of lapatinib with the standard of care chemotherapy for colorectal and gastric cancers. 4.1 Abstract Background: Members of the HER family are frequently associated with aggressive disease and poor prognosis in multiple malignancies. Lapatinib is a dual tyrosine kinase inhibitor for targeting the epidermal growth factor receptor (EGFR) and HER2. This study evaluated the therapeutic potential of lapatinib, alone and in combination with SN-38, the active metabolite of irinotecan (CPT-11), in CRC and gastric cancer cell lines. Methods: A panel of CRC and gastric cancer cell lines were treated with lapatinib and SN-38 and growth inhibition, colony formation, cell cycle profile, and apoptosis were analyzed in response to treatment by MTS, Western blotting and flow cytometeric analysis. In addition, intracellular drug accumulation studies were conducted for SN-38 by LC-MS. Results from in vitro studies were analyzed in an in vivo CRC xenograft model. Results: Concentration-dependent antiproliferative effects of both lapatinib and SN-38 were observed in all CRC and gastric cancer cell lines tested, but varied significantly between individual cell lines (lapatinib range 0.08 – 11.7 µM; SN-38 range 3.6 – 256 nM). Lapatinib potently inhibited the growth of HER2 overexpressing gastric cancer cell line and demonstrated moderate activity in gastric and CRC cells with detectable HER2 expression. The combination of lapatinib and SN-38 interacted synergistically to inhibit cell proliferation in all CRC and gastric cancer cell lines tested. 57 Co-treatment with lapatinib and SN-38 also resulted in enhanced cell cycle arrest and the induction of apoptosis with subsequent cellular pharmacokinetic analysis demonstrating that lapatinib promoted the increased intracellular accumulation and retention of SN-38 when compared to SN-38 treatment alone. Finally, the combination of lapatinib and CPT-11 demonstrated synergistic antitumor efficacy in the LoVo CRC xenograft model with no apparent increase in toxicity compared to CPT-11 monotherapy. Conclusion: These results provide compelling preclinical rational indicating lapatinib to be a potentially efficacious chemotherapeutic combination partner for irinotecan in the treatment of gastrointestinal carcinomas. 4.2 Study Aim Dysregulation of EGFR and HER-2 pathways by overexpression or constitutive activation can promote tumor processes including angiogenesis and metastasis and is associated with poor prognosis in many human malignancies (Mendelsohn and Baselga 2000). Colorectal cancer is associated with overexpression of EGFR and HER2 was recently reported to be overexpressed in 11% of colorectal cancer patients (McKay, Murray et al. 2002; Antonacopoulou, Tsamandas et al. 2008). In addition, HER2 amplification is observed in up to 20% of gastric cancers (Garcia, Vizoso et al. 2003; Kavanagh, Chambers et al. 2009). Successful strategies targeting EGFR and HER2 include mAB (cetuximab, panitumumab and trastuzumab) and TKI (erlotinib, gefitinib and lapatinib). These agents function through blockade of the EGFR or HER2 signaling pathways resulting in proapoptotic, antiangiogenic and anti-invasive effects (Giusti, Shastri et al. 2008). Lapatinib (Tykerb; GlaxoSmithKline) is an orally administered, dual-specificity TKI 58 demonstrating high in vitro affinity for the TK domains of EGFR and HER2 (Wood, Truesdale et al. 2004; Rusnak, Alligood et al. 2007). Lapatinib is currently approved in combination with the 5-FU pro-drug capecitabine (Xeloda; Roche) for the treatment of HER2 overexpressing chemorefractory breast cancer patients and is in clinical investigation in solid tumors (Geyer, Forster et al. 2006). Previous in vitro studies have reported that lapatinib demonstrates antitumor activity in cancer cell lines with high expression of either EGFR or HER2 (Konecny, Pegram et al. 2006; Rusnak, Alligood et al. 2007; Wainberg, Anghel et al. 2010). Therefore, as both EGFR and HER2 expression are reported to play an important role in colorectal and gastric cancer progression, these diseases represent important malignancies in which to evaluate the therapeutic efficacy of lapatinib. Overexpression and/or amplification of HER2 is reported to be a predictive marker for lapatinib treatment in breast cancer (Konecny, Pegram et al. 2006), but the role of either EGFR or HER2 in determining sensitivity to lapatinib in CRC remains to be fully evaluated. Lapatinib was recently reported to induce dose-dependent growth inhibitory effects in two CRC cell lines; however, the ability of lapatinib to induce apoptosis differed markedly between cell lines (Giannopoulou, Antonacopoulou et al. 2009). Observations from the use of anti-EGFR monoclonal antibodies in CRC would suggest that lapatinib may have efficacy in EGFR and/or HER2-expressing KRAS wild type CRC and HER2-amplified gastric cancer. Preclinical and clinical data have consistently reported that HER-targeted therapies interact synergistically to inhibit the proliferation of tumor cells when combined with conventional chemotherapeutics including DNA damaging agents. It is proposed that lapatinib may enhance the cytotoxicity of conventional chemotherapy by suppression of prosurvival responses (including DNA repair). This provides rationale for the evaluation of lapatinib in 59 combination with other chemotherapeutic agents used to treat CRC and gastric cancer. In support of this concept, clinical trials already in progress include the LOGiC trial (Lapatinib Optimization Study in HER2 Positive Gastric Cancer, ClinicalTrials.gov Identifier: NCT00680901) which is designed to test the clinical benefit of combining lapatinib with capecitabine and oxaliplatin in HER2-positive gastric cancer patients and the phase II trial in CRC which is designed to test the benefit and safety profile of combining lapatinib and the oral 5-FU pro-drug capecitabine (ClinicalTrials.gov Identifier: NCT00574171). The purpose of this study was to evaluate the therapeutic potential of lapatinib both alone and in combination with standard-of-care chemotherapeutic agents: 5-FU, oxaliplatin, irinotecan active metabolite SN-38 and cisplatin in colorectal and gastric cancer cell lines in vitro and in vivo. 4.3 Results 4.3.1 Analysis of EGFR and HER2 protein expression in CRC and gastric cancer cell lines. Considering that lapatinib is a dual TKI of both EGFR and HER2, we sought to evaluate the expression of these receptor targets in our panel of CRC and gastric cancer cell lines. Western blot analysis was performed to determine the basal EGFR and HER2 protein expression levels in the 5 CRC: DLD-1, H630, HCT116, HT29, and LoVo and 4 gastric cancer cell lines: AGS, MKN28, NCI-N87 ad SNU-484. Four of the 5 CRC cell lines had significant EGFR expression with the exception of the H630 which had almost undetectable EGFR expression but did express HER2 at a significant level (Fig. 4-1). HER2 was clearly overexpressed in the NCI-N87 HER2 amplified gastric cancer cells 60 but notably these cells also had the highest expression of EGFR throughout the panel of cell lines. This correlates with the significant sensitivity of the NCI-N87 cells to lapatinib and is consistent with HER2 reported as a predictive marker for lapatinib sensitivity (Fig. 4-1C and D). Interestingly, the SNU-484 and AGS gastric cancer cells expressed significant levels of HER2 with no EGFR expression detected (Fig. 4-1). Despite similar EGFR and HER2 expression profiles in the SNU-484 and AGS cells, the SNU-484 cells were >10-fold more sensitive to lapatinib (Table 4-1). Of note, the MKN28 gastric cancer cells did not demonstrate any detectable HER2 expression but expressed EGFR at high levels which correlates with their moderate sensitivity to lapatinib with an IC 50 (72 h) of 5.5 µM (Fig. 4-1 and Table 4-1). Figure 4-1. Analysis of basal EGFR and HER2 protein expression in a panel of CRC and gastric cancer cell lines. Selected colon and gastric cancer cell lines were evaluated for their basal EGFR and HER2 protein expression levels by Western blot analysis. Cell lines were maintained as described in Chapter 3. Densitometric analysis of Western blot bands was performed using Scion Image and were subsequently normalized to β-actin to control for loading. Relative EGFR and HER2 protein expression are presented in histogram format. NCI-N87* denotes that 33% of the total protein was loaded for HER2 analysis compared with all other cell lines due to the high level of HER2 overexpression and to facilitate an appropriate Western exposure. (LaBonte, MJ) 61 4.3.2 IC 50 (72 h) Growth inhibitory effects of lapatinib in selected CRC and gastric cancer cell lines. To investigate the anti-proliferative activity of both lapatinib and SN-38, we utilized 5 CRC (HT29, HCT116, DLD-1, LoVo, and H630) and 4 gastric cancer cell lines (SNU- 484, NCI-N87, AGS, and MKN28). Each cell line was exposed to increasing concentrations of lapatinib for 72 h and growth inhibition measured by MTS assay. In all CRC cell lines tested, lapatinib demonstrated concentration-dependent growth inhibitory activity. The IC 50 (72 h) obtained for lapatinib in the CRC cell lines ranged from moderate sensitivity of 5.1 µM in the H630 cells to relative insensitivity of 10.8 µM in HCT116 cells (Table 4-1). Similarly, lapatinib demonstrated concentration-dependent growth inhibitory activity in all gastric cancer cell lines tested with IC 50 (72 h) values in the range of 0.08 to 11.7 µM. The HER2 amplified NCI-N87 cells were highly sensitive to lapatinib with an IC 50 (72 h) of 0.08 µM. Of note, the SNU-484 cells which express elevated HER2 protein (but are not HER2 amplified (Garcia, Pujari et al. 2005)) were also sensitive to lapatinib with an IC 50 (72 h) of 0.99 µM. 4.3.3 Evaluation of Lapatinib Combined with Standard-of-Care Chemotherapeutic Agents on LoVo CRC Cell Line Proliferation. The interaction between lapatinib and standard-of-care chemotherapeutic agents was evaluated by growth inhibition assay (MTS, Promega) in the LoVo CRC cells. LoVo cells were treated with increasing concentrations of lapatinib alone and in combination with 5- FU, oxaliplatin, cisplatin and irinotecan active metabolite, SN-38. Increasing concentrations of each respective single agent corresponded to a linear increase (R 2 > 0.9) of between 0.1 and 0.9 fraction affected (FA) and combinations of these 62 concentrations for 72 h continuous exposure. The median-effect analysis method (Chou and Talalay 1984) was utilized in the evaluation of the combination drug effect. The effects of simultaneous treatment with lapatinib and 5-FU resulted in additive to synergistic growth inhibition at 0.5 to 0.7 FA (Fig. 4-2A). The addition of lapatinib to the DNA damaging agents oxaliplatin or cisplatin had no measureable increased effect compared to single agent cell proliferation (Fig. 4-2B and C). Most striking, the addition of lapatinib to the irinotecan active metabolite SN-38, resulted in the synergistic growth inhibition at 0.5 FA and demonstrated synergistic CI values <1 over the majority of concentrations tested (Fig. 4-2D). This observation was further evaluated in a panel of CRC and gastric cancer cell line models. Figure 4-2. Growth inhibitory effects of lapatinib combined with standard of care chemotherapeutic agents: (A) 5-FU, (B) Oxaliplatin, (C) Cisplatin and (D) SN-38. LoVo CRC cancer cells were exposed to increasing concentrations of drug for 72 h and growth inhibition was determined by MTS assay. Data points represent mean ± SD percent growth inhibition of three independent experiments compared to untreated time- matched control set at 100%. The combined drug effect was analyzed using the Chou- Talalay combination index (CI) equation and presented as CI with fraction affected (FA) values for above combinations. CI values were interpreted as follows: <1, synergism; 1 – 1.2, additive and >1.2, antagonism. ((LaBonte, MJ; Lui, K) ! " # $ ! "# #! $# %!! &'( #)*+ ,-./- %&'& 0000%000000000000"000000000010000000000200000000003000000000000!40&'( 000!5#00000000%5#00000000010000000000300000000%#00000000000!40#)*+ 00!5!600000!5!"0000000!5"0000000!5$0000000!5$0000000000*' 000!570000000%5#00000000!530000000!510000000!5200000000000,8 90,-:;<-= ' ! " # $ ! "# #! $# %!! &'( >?)63 ,-./- %&'& 0000%0000000000"000000000010000000000200000000003000000000000!40&'( 00!5"000000000%0000000000100000000003000000000%#00000000000:40>?)63 00!5!1000000!5"000000!5#0000000!5200000000!5$000000000*' 00!5##00000!5#10000!51200000!51700000!5#6000000000,8 90,-:;<-= @ ! " # $ ! "# #! $# %!! &'( ABC=DE=C;D: ,-./- %&'& 0000%00000000000"00000000010000000000200000000003000000000000!40&'( 0!5!#000000!5"0000000!510000000!530000000%5#00000000000!40ABC=D 0000000000000!5!1000000!5%%00000!5""00000!51!000000000*' 00000000000000"5"00000000"5%0000000%570000000%5200000000000,8 90,-:;<-= F ! " # $ ! "# #! $# %!! &'( ,DGE=C;D: ,-./- %&'& 0000%0000000000"00000000001000000000020000000000300000000!40&'( 00!5%0000000!5#000000000"000000000010000000000300000000!40,DGE=C;D: 0!5!300000!5!7000000!560000000!5#0000000!52000000*' 00!5#0000000%5%000000%5"0000000!57300000000%00000000,8 90,-:;<-= , 63 4.3.4 IC 50 (72 h) growth inhibitory effects SN-38 in selected CRC and gastric cancer cell lines. To investigate the anti-proliferative activity of SN-38, we utilized 5 CRC (HT29, HCT116, DLD-1, LoVo, and H630) and 4 gastric cancer cell lines (SNU-484, NCI-N87, AGS, and MKN28). Each cell line was exposed to increasing concentrations of SN-38 for 72 h and growth inhibition measured by MTS assay. The IC 50 (72 h) values obtained for SN-38 for all the cancer cell lines demonstrated a 65-fold range in sensitivity from 3.6 nM in HCT116 cells to 260 nM in DLD-1 cells (Table 4-1). 4.3.5 Lapatinib combined with SN-38 synergistically inhibits CRC and gastric cancer cell proliferation. To further evaluate the interaction between lapatinib and SN-38 observed in the LoVo CRC cell line, selected CRC and gastric cancer cell lines were treated with increasing concentrations of the respective single agents corresponding to a linear increase (R 2 > Table 4-1. Sensitivity of CRC and gastric cancer cells to single agent lapatinib and SN-38. IC 50 (72 h) = Concentration of drug required to inhibit growth by 50% compared with vehicle treated controls and calculated in Prism 5.0 (Graphpad). Points are averages of 4 independent experiments ± SEM. (LaBonte, MJ and Lui, K) !" #$ %&'()* "+,,(-./+ -01(%23*(((((((((45678(%/3* !"!#$ %&'($&)*+++++++++++,-)($&*, .-*) /&$($&$$++++++++++$'&-($&'* .01$$-++++++++$)&2($&)/+++++++++*&-$($&$2 "343 $)&-($&),++++++++++2*&5($&$, .1,% $)&/($&)*++++++++++$*&%($&)% ++++++++++++ 678++++++++++++++$$&'($&)5++++++++++5'&%($&,% 9:;,2 /&/($&)*+++++++++++$/)($&$, ;0< 8;=#525 #;2' )&)2()&))$+++++++++,/- +)&%%()&$++++++++++++*'&/($&%++++ ($&*/ 9.::;+(<=( >?.@./ 03>3? 7@ABCDE 64 0.9) of between 0.1 and 0.9 fraction affected (FA) and combinations of these concentrations for 72 h continuous exposure and growth inhibition measured by MTS assay. The median-effect analysis method (Chou and Talalay 1984) was utilized in the evaluation of the combination drug effect. The effects of simultaneous treatment with lapatinib and SN-38 resulted in synergistic growth inhibition at 0.5 FA and demonstrated synergistic CI values <1 over the majority of concentrations tested in all CRC (Fig. 4-3A) and gastric (Fig. 4-4A) cancer cell lines examined. Pharmacokinetic analyses have previously demonstrated that a standard daily schedule of lapatinib resulted in a peak serum concentration of 4 µM (Burris, Hurwitz et al. 2005; Midgley, Kerr et al. 2007). Having demonstrated that increasing concentrations of both agents resulted in synergistic growth inhibition, we analyzed the effect of lapatinib within a clinically achievable concentration fixed at 3 µM combined with increasing concentrations of SN- 38 (Fig. 4-3B and 4-4B). The effects of simultaneous incubation of 3 µM lapatinib and increasing concentrations of SN-38 also resulted in synergistic growth inhibition at 0.5 FA and CI values <1 over the majority concentrations tested in all cell lines examined (Fig. 4-3B and 4-4B). 4.3.6 Effects on colony formation of lapatinib combined with SN-38 in selected CRC and gastric cancer cell lines. We performed a long-term clonogenicity assay to assess the capacity of the lapatinib/SN-38 combinations to cause irreversible growth arrest in one selected CRC and one gastric cancer cell line. LoVo and MKN28 cells were exposed to lapatinib, SN- 38 and selected combinations for 48 h followed by outgrowth in drug-free medium for 14 days and surviving colonies with >50 cells were counted. Combined drug analysis was performed using increasing concentrations of both agents and with increasing SN-38 65 concentrations combined with lapatinib at a fixed clinically relevant concentration of 3 µM. In both cell lines, increasing concentrations of both lapatinib and SN-38 treatment alone resulted in a dose-dependent suppression of colony formation (Fig. 4-5A and C). Importantly, lapatinib treatment alone at a clinically relevant dose of 3 µM suppressed colony formation by 52.7% in the LoVo cells and by 42.02% in the MKN28 cells (Fig. 4- 5A and C). The combination of 3 µM lapatinib and increasing concentrations of SN-38 resulted in synergistic suppression of colony formation at all combinations tested in the LoVo cells, while MKN28 cells demonstrated enhanced suppression of clonogenic cell survival at the combinations utilizing SN-38 at concentrations >1.5 nM (Fig. 4-5B and D). These data demonstrate that a clinically relevant exposure of lapatinib is effective in significantly suppressing colony formation both alone and in combination with SN-38 in a CRC and gastric cancer cell line model. 4.3.7 Effects of lapatinib and SN-38 on cell cycle arrest and apoptosis. We sought to determine the effects of combining lapatinib and SN-38 on cell cycle distribution and cell death in two selected CRC and two gastric cancer cells. Treatment of LoVo cells with 1 µM lapatinib for 48 h demonstrated no significant alteration in cell cycle distribution compared to untreated control cells, while treatment with 3 µM lapatinib in LoVo cells resulted in the detection of 12.1% cells in Sub-G 1 (Fig. 4-6A and 4-7A). Treatment with 5, 10 and 20 nM SN-38 was characterized by significant G 2 /M arrest and an increase in the number of cells with Sub-G 1 content to 4.3, 5.9 and 11.6% respectively (Fig. 4-6A and 4-7A). Of note, the combination of 1 µM lapatinib with 5 and 10 nM SN-38 resulted in an increase in Sub-G 1 cells to 33.1 and 28.4% respectively, indicative of a synergistic increase in cell death (Fig. 4-6A) with the remaining cell 66 Figure 4-3. Growth inhibitory effects of lapatinib combined with SN-38 in CRC cancer cell lines. Growth inhibitory was determined by MTS assay for 72h. DLD-1, H630, HT-29, and LoVo CRC cancer cell lines were exposed to (A) increasing concentrations of lapatinib (LAP) or (B) a fixed concentration of 3 µM LAP and increasing concentrations of SN-38 alone and in combination. Dashed line represents % growth inhibition of 3 µM LAP alone. Data points represent mean ± SD % growth inhibition of 3 independent experiments compared to untreated time matched control set at 100%. The combined drug effect was analyzed using the Chou-Talalay combination index (CI) equation and presented as CI with fraction affected (FA) values for above combinations. CI values were interpreted as follows: <1, synergism; 1 – 1.2, additive; >1.2, antagonism. (LaBonte, MJ and Lui, K) ! " # $ % & ' ( ) * "! ! #& &! 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(& "!! 456 +,-$) &*+, ./01/ !7"$222222!7&22222"222222"7&222222#2222222$22222%7&22222(7&2222"& µ32456 22222"2222222%2222222)2222222"#2222"'222222#%22222$'222222'!2222"#!2222832+,-$) 22!7"22222!7$2222!7%22222!7%2222!7&2222!7'22222!7'22222!7(222!7)2222295 22!7%22222!7%2222!7&22222!7'2222!7(2222!7)22222!7*2222"7"2222"7(22222.: ;22./8<=/> ! " # $ % & ' ( ) * "! ! #& &! (& "!! 456 +,-$) ./01/ 222!7&222222"22222222#2222222$2222222%2222222'2222222)222222"!222222#! µ32456 2222"!22222#!222222%!22222'!22222)!2222"#!222"'!222#!!2222%!!222222832+,-$) 222!7"22222!7#22222!7%2222!7&2222!7'2222!7(2222!7)2222!7)2222!7*222222295 222"7$22222"7#22222!7)2222!7(2222!7)2222!7(2222!7'2222!7'2222"7"2222222.: &'() ;2./8<=/> ! " # $ % & ' ( ) * "! ! #& &! (& "!! 456 +,-$) ./01/ 222"2222222#2222222%22222222'222222)22222"!222222"&22222#&22222&! µ32456 22!7#22222"2222222%22222222)22222"&22222$!222222)!222"!!222#&!22222222832+,-$) 2!7"2222!7#2222!7&22222!7'2222!7(2222!7(22222!7)2222!7)2222!7)222222295 2!7&2222!7(2222!7&22222!7&2222!7'2222!7)22222"7"2222"7"2222#7$2222222.: #$%$ ;2./8<=/> / 0 ! " # $ % & ' ( ) * "! "" ! #& &! (& "!! 456 +,-$) ./01/ -#-!. 22222#222222$222222&22222(7&2222"!222"#7&222"&2222#!2222#&2222&! µ32456 22'7$22"#7&2222#&2222&!22222(&222"!!222#&!22&!!22(&!22"!!!22222832+,-$) 22!7#2222!7$222!7$222!7&2222!7'2222!7(222!7)2222!7)222!7)2222!7)22222295 22!7%2222!7&222!7(222!7(2222!7'2222!7'222!7'2222!7)222"7!2222#7!222222.: ;2./8<=/> 67 populations demonstrating a significant increase in cells arresting in G 2 /M when compared to either single agent alone (Fig. 4-7). In H630 CRC cells, synergistic increases in apoptosis were also observed with all combinations tested. For example, treatment with 1 µM lapatinib and 10 nM SN-38 alone resulted in the detection of 2.5 and 6% of cells in Sub-G 1 . The combination of 1 µM lapatinib and 10 nM SN-38 resulted in an increase in apoptotic cells to 23% (Fig. 4-6B). In the MKN28 and AGS gastric cells, combination treatment resulted in a significant increase in cells arresting in the G 2 /M phase of the cell cycle in all combinations at 48 h (data not shown) but the onset of apoptosis was not detected, we therefore analyzed the Sub-G 1 component at 72 h to determine if the enhanced cell cycle arrest observed at 48 h would translate to an increase in cell death at 72 h. In AGS gastric cancer cells significant increases in apoptosis were also observed following treatment with the combination of lapatinib and SN-38. Specifically, treatment with 1 µM lapatinib and 10 nM SN-38 resulted in the detection of 4 and 8% of cells in Sub-G 1 . However, the combination of 1 µM lapatinib and 10 nM SN-38 resulted in the detection of 20% in Sub-G 1 (Fig. 4-6C). In MKN28 cells, 1 and 3 µM lapatinib resulted in no significant increase in cells in Sub-G 1 compared to untreated controls. However, the combination of 1 µM lapatinib with 5 and 10 nM SN-38 resulted in significant increases in cells with Sub-G 1 to 21 and 30% respectively with all remaining combinations demonstrating additive increases (Fig. 4-6D). These data would indicate that the combination of lapatinib and SN-38 is not only effective at synergistically inhibiting cell growth, but was also effective at inducing cell death. 68 Figure 4-4. Growth inhibitory effects of lapatinib combined with SN-38 in gastric cancer cell lines. Growth inhibitory was determined by MTS assay for 72h. AGS, MKN28, SNU-484 and NCI-N87 gastric cancer cell lines were exposed to (A) increasing concentrations of lapatinib (LAP) or (B) a fixed concentration of 3 µM LAP and increasing concentrations of SN-38 alone and in combination. Dashed line represents % growth inhibition of 3 µM LAP alone. Data points represent mean ± SD % growth inhibition of 3 independent experiments compared to untreated time matched control set at 100%. The combined drug effect was analyzed using the Chou-Talalay combination index (CI) equation and presented as CI with fraction affected (FA) values for above combinations. CI values were interpreted as follows: <1, synergism; 1 – 1.2, additive; >1.2, antagonism. (LaBonte, MJ and Lui, K) ! " ! " # $ % & ' ( ) * "! ! #& &! 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(& "!! +,- ./0$) 12342 '*+,-,'). 5555&55555"!55555#!5555$!5555%!5555&!5555(&5555"&!5555#&!555&!!55555865+,- 5555&55555"!55555"&5555#&5555&!555"!!555#&!55&!!55"!!!55#!!!5555865./0$) 555!7"555!7"555!7$555!7%555!7'5555!7(555!7)555!7*5555!7*5555"7!55555559, 555$7'555$7*555"7&555!7)555!7'5555!7%555!7%555!7$5555!7#5555!7$55555551: ;55128<=2> ! " # $ % & ' ( ) * "! "" ! #& &! (& "!! ./0$) 12342 '*+,-,'). 555&!5555&!55555&!5555&!5555&!5555&!55555&!55555&!5555&!55555&!5555555865+,- 5555&55555"!55555"&5555#&5555&!555"!!555#&!55&!!55"!!!55#!!!5555865./0$) 555!7$555!7$5555!7%555!7&555!7'555!7(5555!7)5555!7)555!7*5555!7*5555559, 555"7&555"7%5555!7*555!7)555!7'555!7%5555!7#5555!7$555!7$5555!7&5555551: -,-,-,-555555+,- ;55128<=2> ! " # $ % & ' ( ) * "! ! #& &! (& "!! ./0$) 12342 55555$55555555$5555555$5555555$5555555$5555555$5555555$5555555$55555555$ µ65+,- !7!!#55!7!!%55!7!"55!7!$55!7!)55!7"'5555&5555555"55555555# µ65./0$) 555!7"55555!7#55555!7$5555!7%5555!7&5555!7(5555!7(5555!7)5555!7)55555559, 555#7!55555"7#55555!7)5555!7'5555!7'5555!7%5555!7(5555!7(5555"7!55555551: -,-,-,-555555+,- %&'() ;5128<=2> ! " # $ % & ' ( ) * "! ! #& &! (& "!! ./0$) 12342 5555$5555555$5555555$5555555$5555555$5555555$5555555$5555555$55555555$ µ65+,- 55!7&555555#5555555&555555"!555555#!55555&!555"&!555&!!555"!!!555555865./0$) 55!7"5555!7#55555!7%5555!7&5555!7'5555!7(5555!7)5555!7*55555!7*55555559, 55!7'5555!7)55555!7(5555!7'5555!7'5555!7&5555!7&5555!7)55555!7*55555551: -,-,-,-5555555+,- !#$ ;5128<=2> !7!#& !7!& !7" !7#& !7& " $ "! "& ! #& &! (& "!! +,- ./0$) 12342 !7!$5555!7!&5555!7"5555!7#&5555!7&555555"555555555$555555"!5555555"& µ65+,- 555"5555555"7&555555$55555555&5555555"!555555"&5555555#&5555&!!555"!!!555865./0$) 5!7"55555!7#55555!7%55555!7&55555!7'55555!7'555555!7'55555!7)55555!7*55559, 5#7&55555"7#55555!7#55555!7"55555!7#55555!7$555555!7&55555!7&55555!7$55551: $'/-0)0 ;55128<=2> ! " # $ % & ' ( ) * "! ! #& &! (& "!! ./0$) 12342 $'/-0)0 -,-,-,-555555+,- 555555$55555555$5555555$5555555$5555555$5555555$5555555$55555555$5555555$ µ65+,- 555555"555555"7&555555$5555555&55555"!55555"&555555#&55555"#&555#&!55555555865./0$) 555!7&55555!7'55555!7'5555!7'5555!7'5555!7'55555!7'5555!7'5555!7(5555555559, 555!7*55555!7(55555!7&5555!7'5555!7'5555!7%55555!7&5555!7*5555!7*5555555551: ;5128<=2> 69 4.3.8 PI3K and MEK Inhibition Potentiate the Growth Inhibitory Effects of SN-38. Having identified that lapatinib in combination with SN-38 synergistically suppresses the proliferation of CRC and gastric cancer cells, we sought to evaluate the relative contribution of both the PI3K/AKT and MAPK signaling pathways to this interaction in the Figure 4-5. Effect of lapatinib and SN-38 on colony formation in CRC and gastric cancer cell lines. (A) LoVo CRC cancer cells were treated with increasing concentrations of lapatinib (LAP) and SN-38 alone and in combination. (B) LoVo cells were treated with 3 µM fixed concentration of LAP and increasing concentrations of SN-38 alone and in combination. Fixed bar represents % colony formation of 3 µM LAP alone. (C) MKN28 gastric cancer cells were treated with increasing concentrations of lapatinib (LAP) and SN-38 alone and in combination. (D) MKN28 cells were treated with 3 µM fixed concentration of LAP and increasing concentrations of SN-38 alone and in combination. Fixed bar represents % colony formation of 3 µM LAP alone. Data presented as histograms of the percentage of colony formation compared with untreated controls. Histogram bars represent the mean ± SEM from three independent experiments. The combined drug effect was analyzed using the Chou-Talalay combination index (CI) equation and presented as CI with fraction affected (FA) values for above combinations. CI values were interpreted as follows: <1, synergism; 1 – 1.2, additive; >1.2, antagonism. (LaBonte, MJ) ! "# #! $# %!! &'( )*+,- ./01/ 22222,2222!22222,222222222,2222!2222,2222222222,2222!22222,2222222222,2222!2222, µ32&'( 22222!2222%22222%222222222!2222"2222"2222222222!222242222242222222222!222%"22%"22222222532)*+,- 222222+2222+222!64-222222+2222+222!6-!2222222+2222+222!6742222222+2222+22!67$222222228' 222222+2222+222!67%222222+2222+222!64!2222222+2222+222!6",2222222+2222+22!6"$222222222.9 !"#" :2./5;</= ! "# #! $# %!! &'( )*+,- ./01/ 222222,2222!2222,2222222222,2222!2222,2222222222,2222!2222,222222222,2222!22222, µ32&'( 222222!22!6#22!6#22222222!22%6#22%6#22222222!2222,2222,222222222!2222#22222#2222222532)*+,- 222222+2222+222!6,"2222222+2222+222!6#!2222222+2222+22!6-!2222222+2222+222!67>222228' 222222+2222+222%6742222222+2222+222%6$>2222222+2222+22!6$42222222+2222+222!6,"22222.9 3?*"- :2./5;</= $ % & ' % " , > ! "# #! $# %!! &'( )*+,- ./01/ 2!6#2222!2222!6#2222222%22222!2222%222222222,22222!2222,2222222224222222!22224 µ32&'( 222!22222%22222%222222222!22222"2222"222222222!22222422224222222222!2222%"222%"2222222532)*+,- 22222+2222+222!6#%222222+2222+222!6$>2222222+2222+222!6742222222+2222+22!67$2222222228' 22222+2222+222!6"%222222+2222+222!6"!2222222+2222+222!6%,2222222+2222+22!6"!222222222.9 !"#" :2./5;</= ! "# #! $# %!! &'( )*+,- ./01/ 222222%22222!2222%222222222,22222!2222,222222222422222!2222422222222%!2222!222%! µ32&'( 222222!222!6#222!6#222222!222%6#22%6#2222222!22222,2222,222222222!22222#22222#2222222532)*+,- 222222+2222+222!6"!2222222+2222+222!6#$2222222+2222+22!6-42222222+2222+222!67>22222228' 222222+2222+222"6%42222222+2222+222%6,$2222222+2222+22!6--2222222+2222+222!6-"2222222.9 ()*+, :2./5;</= 70 LoVo CRC and MKN38 gastric cancer cell line models. We utilized a specific inhibitor of PI3K (LY294002) which is located immediately upstream of AKT and the specific mitogen-activated protein erk kinase (MEK) inhibitor (U0126) which is located immediately upstream of MAPK. In both LoVo and MKN28 cells, treatment with either the PI3K or MEK inhibitor alone resulted in dose-dependent increases in growth inhibition. Increasing concentrations of SN-38 combined with either the PI3K or MEK inhibitors resulted in CI values <1-1.2 at the majority of combinations tested in both cell lines and primarily in the FA range of 0.1 – 0.8, indicative of additive to synergistic increases in growth inhibition (Fig. 4-9A and B). These data indicate that suppression of Figure 4-6. Effects of lapatinib and SN-38 on apoptosis in CRC and gastric cancer cell lines. Cell cycle analysis was determined by propidium iodide staining and subsequent flow cytometry following treatment with lapatinib (LAP) and SN-38 alone and in combination. Cells were seeded as described in the ‘‘Material and methods’’. Duplicate wells were exposed to indicated concentrations of lapatinib (LAP) and SN-38 alone and in combination for 48 or 72 h. Percentage of cells in Sub-G 1 , G1, S and G 2 /M were determined by propidium iodide staining and subsequent flow cytometry. (a) Percentage of LoVo CRC cancer cells in Sub-G 1 at 48 h. (b) Percentage of H630 colon cancer cells in Sub-G 1 at 48 h. (c) Percentage of AGS gastric cancer cells in Sub-G 1 at 72 h. (d) Percentage of MKN28 gastric cancer cells in Sub-G 1 at 72 h. Bars represent mean ± SD from two independent experiments. Statistical significance for Sub-G 1 was determined by two-way ANOVA, *p < 0.05, **p < 0.01 and ***p < 0.001. (LaBonte, MJ and Wilson, PM) * * ** *** A B C ! " #! #" ! " #! #" $! $" !"#$ %&'( ')''''''''''''#'''''*'''''''''''')''''''')''''')''''''''''''#''''#''''''''''''*''''* µ+',-. ')''''''''''')'''''')'''''''''''''"''''#!''''$!'''''''''''"'''#!'''''''''''"'''#!'''''''''/+'01*& 2'034)5# ! # $ * % " 6 7 & 8 #! ## #$ #* #% #" ! " #! #" $! $" ')''''''''''''#'''''*'''''''''''')''''''')''''')''''''''''''#''''#''''''''''''*''''* µ+',-. ')''''''''''')'''''')'''''''''''''"''''#!''''$!'''''''''''"'''#!'''''''''''"'''#!'''''''''/+'01*& %&' 7$'( 2'034)5# *** ** ** *** *** ** ** * ! ! #! $! *! %! ')''''''''''''#'''''*'''''''''''')''''''')''''')''''''''''''#''''#''''''''''''*''''* µ+',-. ')''''''''''')'''''')'''''''''''''"''''#!''''$!'''''''''''"'''#!'''''''''''"'''#!'''''''''/+'01*& ()*) %&'( 2'034)5# D *** ** ** * ! ! #! $! *! %! ')''''''''''''#'''''*'''''''''''')''''''')''''')''''''''''''#''''#''''''''''''*''''* µ+',-. ')''''''''''')'''''')'''''''''''''"''''#!''''$!'''''''''''"'''#!'''''''''''"'''#!'''''''''/+'01*& 2'034)5# +,-./ 7$'( 71 AKT and MAPK signaling both potentiate the growth inhibitory effects of SN-38 and would suggest that lapatinib has the potential to exert synergistic growth inhibitory effects with SN-38 in part through the suppression of PI3K/AKT and MAPK signaling pathways in LoVo CRC and MKN28 gastric cancer cells. 4.3.9 Lapatinib enhances the intracellular accumulation of SN-38. Having observed significant growth inhibitory effects and apoptosis associated with combination treatment in cells with limited sensitivity to lapatinib alone, we sought to Figure 4-7. Effects of lapatinib and SN-38 on cell cycle distribution in CRC and gastric cancer cell lines. Cell cycle analysis was determined by propidium iodide (PI) staining and subsequent flow cytometry following treatment with lapatinib (LAP) and SN-38 alone and in combination. Cells were seeded as described in the “Methods”. Duplicate wells were exposed to indicated concentrations of lapatinib (LAP) and SN-38 alone and in combination for 48 or 72 h. Percentage of cells in Sub-G 1 , G 1 , S, and G 2 /M were determined by PI staining and subsequent flow cytometry. (A) Cell cycle distribution of LoVo colon cancer cells 48 h. (B) Cell cycle distribution of H630 colon cancer cells at 48 h. (C) Cell cycle distribution of AGS gastric cancer cells at 72 h. (D) Cell cycle distribution of MKN28 gastric cancer cells at 72 h. Bars represent mean ± SD from two independent experiments. (LaBonte, MJ and Wilson, PM) ! "# #! $# %!! &% ' &"() **********+*************%*******,***************+********+*******+***************%*******%***************,******, µ)*-./ **********+**************+*******+***************#******%!*****"!**************#******%!*************#*****%!********0)*'1,2 !"#" 3*45667 % " , 8 # 9 $ 2 : %! %% %" %, %8 ! "# #! $# %!! &% ' &"() **********+*************%*******,***************+********+*******+***************%*******%***************,******, µ)*-./ **********+**************+*******+***************#******%!*****"!**************#******%!*************#*****%!********0)*'1,2 $%&'( 3*45667 % " , 8 # 9 $ 2 : %! %% %" %, %8 ! "# #! $# %!! &% ' &"() )*+ **********+*************%*******,***************+********+*******+***************%*******%***************,******, µ)*-./ **********+**************+*******+***************#******%!*****"!**************#******%!*************#*****%!********0)*'1,2 3*45667 ! "# #! $# %!! &% ' &"() **********+*************%*******,***************+********+*******+***************%*******%***************,******, µ)*-./ **********+**************+*******+***************#******%!*****"!**************#******%!*************#*****%!********0)*'1,2 ,-./ 3*45667 ) 0 1 2 72 investigate potential EGFR/HER2-independent mechanisms that may be contributing to the observed drug interactions. Previous reports have demonstrated that 4- anilinoquinazoline-based EGFR inhibitors have demonstrated the ability to inhibit Figure 4-8. Inhibition of PI3K or MEK in combination with SN-38 potentiates growth inhibition in LoVo and MKN28 cancer cells. Growth inhibition analysis was determined by MTS assay for 72 h. LoVo colon and MKN28 gastric cancer cells were exposed to indicated concentrations of either (A) the PI3K inhibitor (LY294002) or (B) MEK inhibitor (U0126) alone or in combination with SN-38 for 72 h. Data points represent mean ± SD percent growth inhibition of three independent experiments compared to untreated time-matched controls set at 100%. The combined drug effect was analyzed using the Chou-Talalay combination index (CI) equation and presented as CI with fraction affected (FA) values for combinations. CI values were determined as follows: <1 = synergism; 1-1.2 = additive; and >1.2 = antagonism. (C) Lapatinib and SN-38 Modulate Downstream EGFR and HER2 Signaling Pathways in LoVo CRC Cancer Cells. LoVo CRC cancer cells were treated with 3 µM lapatinib (LAP) and 5 nM SN-38 alone and in combination for 6 and 12 h and probed by Western blot for phospho-MAPK (Thr 202 /Tyr 204 ) and phospho-Akt (Ser 473 ). β-Actin was measured as a loading control. (LaBonte, MJ) A B C ! " # $ % & ' ( ) * ! #& &! (& "!! +!"#' ,-.$) /0120 3333333#33333333%33333333'3333333"#333333"'333333#!333333$!333333&! µ43+!"#' 3!5!!%333!5!"333!5!$3333!5!)333!5"'333333&33333333"33333333# µ433,-.$) 3333!5!333333!5"33333!5#33333!5%333333!5&33333!5'33333!5(33333!5)333333367 3333$5*333333"5%33333!5)33333!5(333333!5)33333!5*33333"5!33333"5&3333333/8 !"#$% 93/0:;<0= ! " # $ % & ' ( ) * ! #& &! 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(& "!! +!"#' ,-.$) /0120 3333333333333333"3333333#3333333%3333333'333333"#33333"'33333#!33333$!333333&! µ43+!"#' 3333333333333!5#3333333"3333333%3333333)333333"&33333$!33333)!3333"!!3333#&!33333333:43,-.$) 333333333333!5!333333!5"3333!5%3333!5&33333!5(3333!5(3333!5(3333!5(3333!5)3333333367 333333333333#5!333333"5"3333!5'3333!5&33333!5&3333!5'3333!5*3333"5#3333"5)33333333/8 ./0/ 93/0:;<0= . $3333333333. $333333333. $333333333. $ µ4 >7@ . . &333333333&333333333. . &333333333& :4 ,-.$) +112 '$112 A%#B%%.47@C A7CD !.7E;F: ./0/ 73 members of the ABC transporter super-family, resulting in increased drug accumulation and an increased sensitivity to TOPO 1 inhibitors (Erlichman, Boerner et al. 2001; Yanase, Tsukahara et al. 2004; Nagashima, Soda et al. 2006). In addition, a recent report identified lapatinib as a potent inhibitor of both breast cancer resistance protein (BCRP) and p-glycoprotein (p-gp), two members of the transmembrane ABC transporters that have been reported to efflux a wide variety of chemotherapeutic agents and are frequently highly expressed in tumor cells (Polli, Humphreys et al. 2008). We Figure 4-9. Effect of inhibition of drug efflux on growth inhibitory effects of SN-38 in LoVo CRC cells. Growth inhibition analysis was determined by MTS assay. LoVo CRC cancer cells were exposed to (A) 2.5 µM P-gp inhibitor, cyclosporin A (CsA), or 5 µM BCRP inhibitor, fumitremorgin C (FTC), and increasing concentrations of SN-38 alone and in combination for 72 h. Data points represent mean ± SD percent growth inhibition of three independent experiments compared to untreated time-matched controls set at 100%. The combined drug effect was analyzed using the Chou-Talalay combination index (CI) equation and presented as CI with fraction affected (FA) values for combinations. CI values were determined as follows: <1 = synergism; 1-1.2 = additive; and >1.2 = antagonism. Lapatinib enhances intracellular SN-38 drug accumulation. (B) Intracellular SN-38 concentrations in LoVo CRC and MKN28 gastric cancer cells were calculated as fold-change ± SEM (n=3) of the combinations of 3 µM lapatinib (LAP) with 5 and 10 nM SN-38 compared to SN-38 treatments alone for 6 h. Statistical significance was determined by one-tailed Student’s t-test, *p=0.055, **p<0.01. (LaBonte, MJ and Louie, S) ! " # $ % &'''''''''''"'''''''''''''&''''''''''''$ µ(')*+ ,'''''''''',''''''''''''"!''''''''''"!'''''''-('./&$0 !"#" 1 11 2345''678-9: ! " # $ % , &'''''''''''"'''''''''''''&''''''''''''$ µ(')*+ ,'''''''''',''''''''''''"!''''''''''"!'''''''-('./&$0 $%&'( 11 111 2345''678-9: B A ! " # $ % , ; < 0 ! #, ,! <, "!! ./&$0 63=>3 !"#" )*)*)*)''''''2?6 ''''','''''''''',''''''''',''''''''','''''''''',''''''''','''''''''', µ('2?6 ''''!@#''''''''"'''''''''%'''''''''0'''''''''",'''''''$!''''''''0!''''''''''-('./&$0 ''''!@"'''''''!@"''''''!@$''''''!@%'''''''!@;''''''!@<'''''''!@<''''''''''2* ''''"@!'''''''"@"''''''!@0''''''!@;'''''''!@,''''''!@<'''''''"@!''''''''''6A B''63-CD34 ! " # $ % , ; < 0 ! #, ,! <, "!! ./&$0 63=>3 !"#" )*)*)*)'''''''6E* ''''#@,''''''#@,'''''''#@,''''''#@,'''''''#@,''''''#@,'''''''#@, µ('6E* ''''!@#''''''''"'''''''''%'''''''''0''''''''''",''''''$!''''''''0!''''''''''-('./&$0 ''''!@"'''''''!@"'''''!@$'''''''!@,'''''''!@;''''''!@;'''''''!@< ''''''''2* '''"@$'''''''"@%''''''!@0'''''''!@,'''''''!@,''''''!@;''''''"@!'''''''''''6A B'63-CD34 !"#" 74 sought to test the hypothesis that lapatinib may be promoting increased intracellular SN- 38 concentrations through inhibiting drug efflux. We directly analyzed the accumulation of intracellular SN-38 in LoVo CRC cells treated with SN-38 alone and in combination with the clinically relevant concentration of 3 µM lapatinib for 6 h. Using liquid chromatography mass spectrometry (LC-MS), we determined that co-incubation of LoVo CRC and MKN28 gastric cancer cells with lapatinib and SN-38 resulted in a significant increase in the accumulation of intracellular SN-38 (Fig. 4-9B). Specifically, in LoVo cells the selected combination of 3 µM lapatinib with 10 nM SN-38 resulted in a 2.2-fold Figure 4-10. Lapatinib enhances SN-38 induced DNA Damage in LoVo CRC cells. (A) Detection of DNA double-strand breaks was determined by analysis of γH2A.X at serine 139 using the H2A.X phosphorylation assay kit (Millipore). LoVo CRC cancer cells were exposed to indicated 3 µM lapatinib (LAP) and 5 nM SN-38 alone and in combination for 6 and 18 h and detection of γH2A.X at serine 139 was performed. Bars represent mean relative luminescence ± SE corrected for background from untreated controls. Statistical significance was determined by Two- tailed Student’s t-test. *p<0.05 and **p<0.01 for combinations when compared to SN-38-treated cells. Effects of lapatinib and SN-38 on caspase-8 activation. (B, left) Representative histogram of flow cytometric analysis from LoVo CRC cancer cells illustrating the shift in fluorescence with the selected combination of 3 µM LAP and 5nM SN-38 when compared to single agents, indicative of active caspase-8 at 24 h post-treatment. Events falling to the right of the vertical dashed gate set at log FL-1 10 2 were considered caspase-8 positive. (B, right) Histogram of the percentage of cells staining positive for active caspase-8. Bars represent mean ± SEM corrected for untreated control background fluorescence. Statistical significance was determined by two-tailed Student’s t- test. *p<0.05 and **p<0.01 for combinations when compared to SN-38-treated cells. (LaBonte, MJ and Wilson, PM) ! "# #! $# %!! &&%&&&&&&&'&&&&&&&&&&&&&&&&&&(&&&&&&&&(&&&&&&&&&&&&&&&&&%&&&&&&&%&&&&&&&&&&&&&&&&&&'&&&&&&&&' µ)&*+, &&&(&&&&&&&(&&&&&&&&&&&&&&&&&&#&&&&&&%!&&&&&&&&&&&&&&&&#&&&&&&%!&&&&&&&&&&&&&&&&&#&&&&&&&%!&&&&&-)&./('0 1&234356(0&,75898:6&26;;5 * ** * ** 26;;&/<=>6? 27=>7 #-)&./('0 'µ)&*+, 27-9?7; %! % %! "&&&&&&&&&&&&&&&&& %! ' *7@&A*(% 26;;&/<=>6? 27=>7 #-)&./('0 'µ)&*+, 27-9?7; %! % %! "&&&&&&&&&&&&&&&&& %! ' *7@&A*(% 27=>7 #-)&./('0 'µ)&*+, 27-9?7; %! % %! "&&&&&&&&&&&&&&&&& %! ' *7@&A*(% A B * B&C %0&C ! "!!! D!!! B!!! 0!!! %!!!! 'µ)&*+, #-)&./('0 27=>7 E6;398:6&*<=8-65F6-56 ** !"#" !"#" !"#" 75 increase in intracellular SN-38 when compared to SN-38 treatment alone after a 6 h drug incubation (Fig. 4-9B). Similarly in MKN28 cells, co-incubation with 3 µM lapatinib with 10 nM SN-38 resulted in a >3-fold increase in intracellular accumulation of SN-38 (Fig. 4-9B). These observations demonstrate that lapatinib can enhance the accumulation of SN-38. Taking into consideration the rapid nature of this observation, inhibition of transmembrane drug efflux is a plausible mechanism. 4.3.10 Lapatinib in combination with CPT-11 synergistically inhibit the growth of LoVo xenografts in nude mice. Having determined that the combination of lapatinib and SN-38 resulted in potent growth inhibition and cell death in multiple CRC and gastric cancer cells in vitro we extended our studies to examine the antitumor activity of these agents in a mouse xenograft model. Prior to this, we selected the LoVo CRC cells to further characterize the synergistic interaction between lapatinib and SN-38. Western blot analysis revealed that combination treatment potently suppressed the phosphorylation of p44/42 MAPK and pAKT (Fig. Figure 4-11. Antitumor activity of lapatinib in combination with CPT-11 in a LoVo CRC xenograft model. Male balb/c nu/nu mice (n=7 per group) bearing subcutaneous 100mm 3 tumors were administered either vehicle, 30 mg/kg BID Lapatinib, 5 mg/kg q4d CPT-11, 10 mg/kg q4d CPT-11, or the combination of 30 mg/kg BID LAP and 5 mg/kg q4d CPT-11. Tumor volume (TV) was measured every two days and is represented as mean ± SEM. (LaBonte, MJ and Manegold, PC) ! " # $ % & ' ( ) !* !! !" * "%* %** '%* !*** !"%* +,-./0, #*123425678 %12342598:;!! !*12342598:;!! #*123425678<%12342598:;!! 55555!55555$5555&55555(555!*555!"555!$555!&55!(555"*555""555"$ :=1>?5+>0=1,5@11 # A BCDE5>F5:?,CG1,HG 76 4-8C). Analysis of the DNA double strand break marker H2A.X revealed a significant increase in the detection of γH2A.X foci in cells treated with the combination at both 6 and 18 h, indicative of increases in both early- and late-stage DNA damage (Fig. 4-10A). Combination treatment also resulted in a significant increase in cells staining positive for caspase-8 when compared to cells treated with either single agent, indicative of the onset of apoptosis (Fig. 4-10B). These results were consistent with the growth inhibition and apoptosis analysis and LoVo cells were subsequently utilized to test the in vivo significance of this drug interaction. LoVo CRC xenografts were established as outlined in Chapter 3. Lapatinib administered at 30 mg/kg BID and CPT-11 at both 5 and 10 mg/kg q4d resulted in tumor growth inhibition when compared to vehicle-treated controls. However, co-administration of 30 mg/kg lapatinib and 5 mg/kg CPT-11 resulted in the greatest tumor growth inhibition compared to vehicle controls. At the end of the 24-day treatment period, lapatinib monotherapy resulted in a 17% reduction in mean tumor volume to 910 mm 3 compared to the vehicle control group with a mean tumor volume of 1098 mm 3 (Fig. 4-11). CPT-11 administered at 5 mg/kg resulted in a reduction in mean tumor volume of 22% to 856 mm 3 with CPT-11 at 10 mg/kg resulting in a reduction in mean tumor volume of 42% to 637 mm 3 compared to the vehicle-treated group. However, the combination of Table 4-2. In vivo tumor delay (Td) of LoVo CRC cancer xenografts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mg/kg CPT-11 and 30 mg/kg lapatinib resulted in a reduction in mean tumor volume of 49.6% to 553 mm 3 when compared to the vehicle-treated group (Fig. 4-11). The differences in mean tumor volume between lapatinib (30 mg/kg BID) and CPT-11 (5 mg/kg, q4d) monotherapy treatment groups and the combination treatment group were statistically significant (p < 0.05). The combination of 30 mg/kg BID lapatinib and 5 mg/kg CPT-11 q4d also resulted in a highly significant increase in tumor delay (Td) with a ratio of observed: expected of 1.65, indicative of a synergistic increase in antitumor activity with combination treatment (Table 4-2). Of note, the combination of 30 mg/kg BID lapatinib and 5 mg/kg CPT-11 q4d resulted in greater Td than 10 mg/kg CPT-11 q4d treatment alone. Importantly, despite demonstrating increased anti-tumor efficacy compared to monotherapy with both 5 and 10 mg/kg q4d CPT-11, combination treatment did not result in any statistically significant decrease in bodyweight compared to monotherapy with 5 mg/kg CPT-11 q4d (p=0.48) and was significantly less than was observed with 10 mg/kg CPT-11 q4d monotherapy (Table 4-3). This data strongly suggests that the interaction between lapatinib and the reduced dose of irinotecan (5 mg/kg) is capable of exerting antitumor effects beyond that of either single agent alone or the increased dose of 10 mg/kg irinotecan without any evident increase in toxicity. Table 4-3. In vivo mouse bodyweight on day 24 of LoVo CRC cancer xenografts treated with lapatinib and CPT-11. !"#$%&#'% ()(*+(,'-%-$. /*01(2#-34%( !"#$%&" '(()*+'),-- --./)(+').- --.()'+')(- --/0)'+().- --/.)(+')* --------- ,(12342-567 012342-879:'' '(12342-879:'' 8;1<$=>?$;= - 5 $ $ 5 8;1<$=>?$;=-@-,(12342-567-A-012342-879:'' 7"B%"=?>2"-<;CDE"$2#?->?-F?GCD-%;=%&GF$;=-%;1H>B"C-?;-I>D-( 78 4.3.11 Summary. Chapter 4 evaluated the combination of the dual EGFR/HER2 TKI, lapatinib, and the irinotecan active metabolite, SN-38, in CRC and gastric cancer cell line models. Results from this study demonstrated: • Varying EGFR and HER2 protein expression in a panel of CRC and gastric cancer cell lines. • Combination treatment with lapatinib and SN-38 resulted in a strong synergistic interaction with increased growth inhibition and increased apoptosis in all cell lines regardless of EGFR or HER2 protein expression. • Lapatinib as an ATP-mimetic, inhibited ABC-transporter-mediated drug efflux resulting in enhanced intracellular accumulation of SN-38. • The in vitro efficacy of lapatinib and irinotecan was confirmed in vivo in the LoVo CRC cancer xenograft model. 4.4 Discussion The HER-family of receptors have been shown to promote aggressive tumor processes such as increased proliferation, migration, angiogenesis and inhibition of apoptosis in a wide variety of human malignancies. There is a significant body of evidence, both in vitro and in vivo, indicating that HER-family-targeted inhibitors used alone or in combination with additional cytotoxic agents can result in potent antitumor activity. Lapatinib in combination with capecitabine demonstrated increased response rates in HER2- overexpressing late-stage breast cancer patients, providing an effective treatment option for patients with highly chemorefractory disease (Geyer, Forster et al. 2006). The 79 monoclonal antibody cetuximab has demonstrated antitumor efficacy both as a monotherapy and when combined with chemotherapy (Lenz, Van Cutsem et al. 2006; Van Cutsem, Nowacki et al. 2007). Importantly, cetuximab treatment partially re- sensitized irinotecan-refractory colorectal cancer patients indicating that EGFR may drive resistance to irinotecan-based chemotherapy in a subset of patients (Cunningham, Humblet et al. 2004). Previous studies have also reported that the EGFR TKI gefitinib demonstrates synergism in combination with TOPO 1 inhibitors and platinum agents (Stewart, Leggas et al. 2004; Braun, Stark et al. 2005; Van Schaeybroeck, Karaiskou- McCaul et al. 2005; Nagashima, Soda et al. 2006). The results of our present study clearly demonstrate that combining the dual EGFR/HER2 TKI lapatinib with the DNA damaging agent SN-38 interact synergistically to inhibit the proliferation of CRC and gastric cancer cell lines characterized by different expression levels of EGFR and HER2. Previous studies have shown that human tumor cell lines overexpressing EGFR and HER2 demonstrated increased sensitivity to growth inhibition by lapatinib than those cell lines expressing low levels of the two receptors (Rusnak, Alligood et al. 2007). This is supported by our observations where the HER2 amplified gastric cancer cells were highly sensitive to lapatinib with IC 50 (72 h) of less than 100 nM. However, the SNU-484 cells demonstrated sensitivity with an IC 50 (72 h) of <1 µM in the absence of HER2 amplification (Kim, Kim et al. 2008) and with no detectable EGFR expression. All remaining cell lines tested had IC 50 (72 h) >5 µM indicating that these cell lines may not be critically dependent on EGFR or HER2 signaling to drive proliferation. However, it is important to note that both the MKN28 gastric and LoVo CRC cells were significantly growth inhibited by approximately 30% (measured by MTS assay at 72 h, Fig. 4-3 and 4- 4) and 50-60% (measured by clonogenic assay, Fig. 4-5, at a clinically relevant dose of 3 µM indicating that EGFR and HER2 blockade does retard proliferation in these models 80 and suggesting that the level of dependence on these signaling pathways although significant, is not as critical as observed with HER2 amplified models. One of our key study aims was to evaluate the anti-proliferative effects of combining lapatinib with cytotoxic chemotherapy. We therefore report that lapatinib has therapeutic potential in combination with the DNA-damaging TOPO 1 inhibitor SN-38 in CRC and gastric cancer cell models. The combination synergistically suppressed tumor proliferation in all cell lines tested and potently suppressed clonogenic cell survival and induced apoptosis with additive or synergistic effects in the selected cancer cell lines. Importantly, these synergistic effects were also observed in cell lines that were relatively insensitive to lapatinib single agent treatment with IC 50 (72 h) in excess of 10 µM (AGS and LoVo). This would suggest that while suppression of EGFR and HER2-mediated signaling pathways with lapatinib alone has only modest anti-proliferative effects, combining lapatinib with a DNA damaging agent such as SN-38 can significantly potentiate the level of growth inhibition and apoptosis. This is not surprising as both the PI3K and MAPK pathways have been reported to influence response to chemotherapeutic agents and can mediate pro-survival and anti-apoptotic receptor TK signaling in response to cytotoxic insult (Osaki, Kase et al. 2004; Fang and Richardson 2005; Cicenas 2008). In support of our findings, previous reports determined that constitutive activation of MEK signaling resulted in decreased sensitivity to irinotecan (Horikawa, Otaka et al. 2007). We further analyzed the combination of SN-38 with inhibitors of the receptor TK effector molecules PI3K and MEK and report synergistic growth inhibition with all combinations. These data would indicate that specific targeting of MAPK and PI3K might have therapeutic efficacy with inhibitors of TOPO 1. In addition, both MEK and PI3K are downstream of KRAS and PTEN which are reported to negate the effects of EGFR-targeted therapy and the targeting of PI3K and/or MEK may provide 81 much needed therapeutic options in mutant KRAS colorectal cancer (Loupakis, Pollina et al. 2008; Allegra, Jessup et al. 2009). A number of PI3K and MEK inhibitors are currently undergoing Phase I and II clinical testing. Our observation that lapatinib could interact synergistically with SN-38 to inhibit the growth of cancer cell lines with low expression of EGFR or HER2 and low sensitivity to lapatinib treatment alone led us to investigate potential receptor-independent effects. We therefore report the novel observation that the addition of lapatinib at a clinically relevant dose resulted in a >2-fold increase in the accumulation of intracellular SN-38 at 6 h post-treatment in LoVo and MKN28 cells, a rapid event which is likely to precede any downstream events from inhibition of EGFR or HER2 signaling. The enhanced accumulation of SN-38 would explain the observed increases in cell cycle arrest and apoptosis observed with combination treatment. In support of this, additional quinazolinamine class EGFR inhibitors such as gefitinib and CI1033 have been reported to promote intracellular accumulation of chemotherapeutic agents including SN-38 through inhibiting drug efflux (Yanase, Tsukahara et al. 2004; Nakamura, Oka et al. 2005; Nagashima, Soda et al. 2006) (Erlichman, Boerner et al. 2001). Consistent with our hypothesis, lapatinib is an inhibitor and substrate for both BCRP and p-gp in engineered canine kidney cell line models (Polli, Humphreys et al. 2008). Our laboratory also confirmed that LoVo cells could be sensitized to the effects of SN-38 utilizing specific inhibitors of endogenous p-gp and BCRP (cyclosporine A and fumitremorgin C, Fig. 4-9), confirming that inhibition of transporter-mediated efflux is a plausible contributory mechanism to the enhanced growth inhibition observed with lapatinib and SN-38 combinations. Both p-gp and BCRP can effectively efflux a structurally diverse array of chemotherapeutics resulting in multidrug resistance, a phenomenon which can effectively limit therapeutic options. Lapatinib may therefore represent a targeted 82 strategy, independent of EGFR/HER2 status, in sensitizing tumor cells with inherent and acquired BCRP/P-gp-mediated resistance to cytotoxicity. EGFR inhibitors have also demonstrated the ability to inhibit aspects of DNA repair resulting in increased DNA damage (Friedmann, Caplin et al. 2004; Balin-Gauthier, Delord et al. 2008; Vigneron, Gamelin et al. 2008) and this may also play a role in the synergistic interaction observed with lapatinib and SN-38 combinations in our study and warrants further investigation. To further evaluate the therapeutic efficacy of lapatinib, we extended our studies to an in vivo mouse xenograft model. We utilized the SN-38 parent drug irinotecan (CPT- 11) that is currently employed in both first and second line treatment of a range of gastrointestinal malignancies. We report that the combination of lapatinib and irinotecan results in synergistic tumor growth inhibition against a CRC cell model in vivo. In addition to the tumor-specific suppression of EGFR/HER2 signaling and enhanced intracellular accumulation, co-administration of lapatinib and CPT-11 may result in an increase in the bioavailability of SN-38 and increase tumor drug exposure as has previously been reported with gefitinib and irinotecan combinations in vivo (Stewart, Leggas et al. 2004). Modulation of systemic efflux transporters by lapatinib resulting in alterations in the bioavailability and disposition of combination agents has the potential to result in subsequent increases in adverse events from chemotherapy. Irinotecan treatment is frequently associated with toxic side effects including diarrhea, myelosuppression and liver dysfunction. Co-administration of lapatinib and 5-FU/irinotecan did demonstrate increased toxicity in a Phase I clinical trial that was managed with moderate dose reduction. Despite the dose reduction, promising disease control rates were reported for the patients with colorectal cancer (Midgley, Kerr et al. 2007). Lapatinib is also being investigated in additional clinical trials with TOPO 1 inhibitors such as topotecan in ovarian carcinoma and has progressed to Phase II evaluation (Molina, Kaufmann et al. 83 2008). It is possible that co-administration with lapatinib may allow dose reduction of irinotecan and alleviate toxic side effects in patients with poor performance status without compromising clinical efficacy. In our mouse xenograft study, the co- administration of lapatinib and irinotecan did not result in any statistically significant difference in weight loss when compared to irinotecan monotherapy at 5 mg/kg and was less than that observed with 10 mg/kg irinotecan monotherapy despite demonstrating better antitumor efficacy suggesting that co-administration of lapatinib and irinotecan is an effective combination to enhance antitumor efficacy in a CRC model. In summary, we report for the first time a clear biological interaction between the dual-specificity TKI lapatinib and the TOPO 1 inhibitor SN-38 to inhibit the proliferation of a panel of heterogeneous CRC and gastric cancer cell lines. Furthermore, the results of our study indicate that lapatinib has the potential to exert additional mechanisms of action which are independent of EGFR and HER2 antagonism, resulting in enhanced intracellular SN-38 accumulation and promoting increased cell death. As novel and more specific inhibitors of these pathways emerge, and as our knowledge of both their mechanisms of action and of patient pharmacokinetic and pharmacogenetic interactions increase, it is possible that such combinations could be directed towards patients who are most likely to benefit, avoiding unnecessary toxicities and maximizing the clinical impact of such chemotherapy. 4.5 Manuscripts, Abstracts and Presentations The data presented in Chapter 4 supporting the rational combination of the dual EGFR/HER2 TKI with irinotecan in CRC and gastric cancer models resulted in a 84 publication in the International Journal of Cancer and several meeting abstracts (Table 4-4). 4.6 Translational Impact The data presented in this Chapter and published in LaBonte et al. Int J Cancer 2009; 125 (12): 295-69 have been utilized as key evidence supporting this combination for the treatment of gastric cancer and CRC cancer including KRAS mutant CRC cancer. The use of chemotherapy to treat gastric cancer has no established standard of care. While, chemotherapeutic combinations such as 5-FU plus cisplatin have demonstrated benefit, standardized second line chemotherapy options for gastric cancer patients do not exist. CRC patients with KRAS mutation who have failed to benefit from first and second line chemotherapy have no third line chemotherapy options. Because of this clinical reality, there is an urgent need to identify novel treatment options for both of these diseases. Therefore, in collaboration with Drs. Lenz and El-Khoueiry of the division of Medical Oncology at USC/Keck School of Medicine, a Phase I/II clinical trial was designed to determine the feasibility of combining the TOPO 1 inhibitor, irinotecan, with Table 4-4. Resulting Manuscripts, Abstracts and Presentations from the study of the combination of dual EGFR/HER2 TKI lapatinib and irinotecan in CRC and gastric cancer. Manuscripts LaBonte MJ, Manegold PC, Wilson PM, Fazzone W, Louie SG, Lenz HJ, Ladner RD. The dual EGFR/HER-2 tyrosine kinase inhibitor lapatinib sensitizes colon and gastric cancer cells to the irinotecan active metabolite SN-38. Int J Cancer. 2009; 125(12):2957-69. Abstracts LaBonte MJ, Wilson PM, Fazzone W, Lenz HJ and Ladner RD. Evaluation of the dual tyrosine kinase inhibitor lapatinib in combination with 5-fluorouracil and SN-38 in colon and gastric cell line models. Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO) 2008 Annual Meeting Proceedings 26: 2008 (May 20 suppl; abstr 14643). LaBonte MJ, Wilson PM, Fazzone W, Louie S, Lenz HJ and Ladner RD. The Dual Tyrosine Kinase Inhibitor Lapatinib Sensitizes Colon and Gastric Cells to the Irinotecan Active Metabolite SN-38. Norris Comprehensive Cancer Center, Grand Rounds Annual Poster Presentations, Los Angeles, California, USA, April 2008. Presentations LaBonte MJ and Ladner RD. The Dual Tyrosine Kinase Inhibitor Lapatinib Sensitizes Colon and Gastric Cancer Cells to the Irinotecan Active Metabolite SN-38. University of Southern California, Keck School of Medicine Annual Pathology Retreat Presentations. Dana Point, California, USA, January 23rd, 2009. 85 the dual EGFR/HER2 TKI, lapatinib, as a strategy to enhance the efficacy of irinotecan by increasing intratumoral concentrations of the active metabolite SN-38 in the clinic. 4.6.1 Study Endpoints. Our preliminary data demonstrates that lapatinib is synergistic with irinotecan in CRC and gastric cancer cell line and xenograft models. In cell line models, the addition of lapatinib to irinotecan therapy resulted in a two to three fold increase in intracellular SN- 38 levels compared to irinotecan treatment alone, leading to enhanced DNA damage and apoptosis. Based on our findings, we propose a multidisciplinary, translational study to combine lapatinib with irinotecan for the treatment of patients with advanced CRC and gastric cancers. The study is aimed to define the maximum tolerated dose of the combination as well as establish the proof-of-principal regarding the mechanism of synergy between lapatinib and irinotecan in patients. We hypothesize that the combination of irinotecan and lapatinib will improve response rates and survival outcomes in patients with advanced gastric cancer who failed first line therapy and in KRAS mutant CRC cancer patients. The pilot/feasibility study, endpoints are to: • Establish the maximum tolerated doses for this drug combination in a phase I clinical trial. • Determine the concentration of irinotecan metabolites in plasma, peripheral blood mononuclear cells (PBMCs) and tumor core biopsies from patients treated with irinotecan alone or in combination with lapatinib. 86 • Measure the mechanistic activity of lapatinib in terms of decreased phosphorylation of AKT (Ser 473 ) and p44/42 MAPK (Thr 202 /Tyr 204 ) in PBMCs and tumor core biopsies. The success of this study will form the preliminary data needed to expand this combination into a Phase II study in second-line gastric cancer, and a Phase II study in KRAS mutant CRC patients resulting in a novel drug combination strategy that has the potential to expand the therapeutic options in each of these diseases. 87 Chapter 5 DNA microarray profiling of genes differentially regulated by the histone deacetylase inhibitors (HDACi) vorinostat and LBH589 in colorectal cancer cell line models. 5.1 Abstract Background: Despite the significant progress made in CRC chemotherapy, advanced disease remains largely incurable and novel efficacious chemotherapies are urgently needed. Histone deacetylase inhibitors (HDACi) represent a novel class of agents that have demonstrated promising preclinical activity and are undergoing clinical evaluation in CRC. The goal of this study was to identify genes in CRC cells that are differentially regulated by two of the most clinically advanced hydroxamic acid HDACi, vorinostat and LBH589 to provide valuable information regarding their mechanism of action and generate rationale for novel drug combination partners. Methods: HCT116 and HT29 CRC cells were treated with LBH589 or vorinostat and growth inhibition, acetylation status and apoptosis were analyzed in response to treatment using MTS, Western blotting and flow cytometric analyses. In addition, gene expression was analyzed in response to HDACi treatment using the Illumina Human-6 V2 BeadChip array and Ingenuity® Pathway Analysis. Results from microarray analysis were validated by qPCR and further analyzed in a panel of 9 CRC cell lines in vitro and in HCT116 and HT29 xenograft models. Results: Treatment with either vorinostat or LBH589 rapidly induced histone acetylation, cell cycle arrest and inhibited the growth of both HCT116 and HT29 cells. Bioinformatic analysis of the microarray profiling revealed significant similarity in the 88 genes altered in expression following treatment with the two HDACi tested within each cell line. However, analysis of genes that were altered in expression in the HCT116 and HT29 cells revealed cell-line-specific responses to HDACi treatment. In addition a core cassette of 11 genes modulated by both vorinostat and LBH589 were identified in both cancer cell lines analyzed. These 11 genes were further validated in a panel of 8 CRC cell lines by quantitative PCR. In addition, HCT116 and HT29 xenograft models treated with LBH589 demonstrated that the selected genes were also modulated in vivo. Conclusion: This study identified HDACi-induced alterations in critical genes involved in nucleotide metabolism, angiogenesis, mitosis and cell survival which may represent potential intervention points for novel therapeutic combinations in CRC. This information will assist in the identification of novel pathways and targets that are modulated by HDACi, providing much needed information on HDACi mechanism of action and providing rationale for novel drug combination partners. We identified a core signature of 11 genes that were modulated by both vorinostat and LBH589 in a similar manner in both cell lines in vitro and in vivo. These core genes will assist in the development and validation of a common gene set which may represent a molecular signature of HDAC inhibition in CRC. 5.2 Study Aim Several studies to date have demonstrated that HDACi induce alterations in the expression of multiple drug targets and/or metabolic pathways that are critical molecular determinants for cancer therapeutics. Importantly combination treatment with additional agents targeting these modulated pathways has resulted in synergistic growth inhibitory effects on cancer cells in vitro and in vivo. It has been recently reported that HDACi 89 synergize with 5-FU in vitro and in vivo in CRC cell line models through HDACi-induced downregulation of the 5-FU target enzyme thymidylate synthase (TS), providing a mechanistic basis for the drug synergy (Tumber, Collins et al. 2007; Fazzone, Wilson et al. 2009). Importantly, the combination of the HDACi vorinostat with 5-FU is currently being evaluated in a series of phase I/II clinical trials ((Wilson, El-Khoueiry et al. 2010) and ClinicalTrials.gov identifier: NCT00942266). The HDACi vorinostat is also reported to acetylate and markedly reduce the chaperone activity of HSP90 in CTCL models resulting in a synergistic interaction with the HSP90 inhibitor bortezomib (Zhang, Wang et al. 2009). This combination was subsequently extended to CRC cell lines with similar synergistic anti-proliferative effects (Pitts, Morrow et al. 2009) and clinical evaluations with this combination are underway (ClinicalTrials.gov identifiers: NCT00731952 and NCT00798720). More recently, HDACi were also reported to enhance the apoptotic effects of EGFR inhibitors in lung cancer models (Edwards, Li et al. 2007; Zhang, Peyton et al. 2009) and clinical evaluation of this rational combination is ongoing (ClinicalTrials.gov identifier: NCT00503971). Therefore, the identification of novel genes modulated by HDACi in CRC cells may provide pathway-driven rationale for novel and urgently needed efficacious drug combinations. This study was designed to determine the effects of two of the most clinically advanced HDACi, vorinostat and LBH589 on the growth characteristics of two cytogenetically distinct CRC cell line models HCT116 and HT29. In addition, HDACi- induced alterations in global gene expression were analyzed using the Illumina Human-6 V2 BeachChip arrays and Ingenuity® Pathway Analysis. 90 5.3 Results 5.3.1 Vorinostat and LBH589 inhibit the growth of CRC cells. The HCT116 and HT29 cell lines were originally derived from human CRC adenocarcinomas, and were selected in this study based on marked differences in their cytogenetics. Specifically, these cell lines differ in a number of key genes that have been reported to determine response to chemotherapeutics including the presence of mutant Figure 5-1. In vitro characterization of HDACi, LBH589 and vorinostat, in HCT116 and HT29 CRC cancer cells. HCT116 and HT29 CRC cancer cells were exposed to increasing concentrations of either (A) LBH589 or (B) vorinostat (Vor) alone for 72 h and subsequent growth inhibition was measured by MTS assay (Promega). Values are presented as percent control, calculated from the growth inhibition induced by a given concentration of drug compared to the untreated control. Values are averages of 3 independent experiments ± SEM. The IC 50 (72 h) values were calculated from the sigmoidal dose-response curves in Prism 5.0 (GraphPad). (C-D) Western blot analysis of acetyl- Histone H3 and H4 in (C) HCT116 and (D) HT29 cells treated with 2 µM vorinostat (Vor) or 50 nM LBH589 for 0.5, 1, 2 and 4 h. β-actin was used to control for loading. (LaBonte, MJ) !"#$%!&'()*$+$,-. /$0",12"3 !&'()* /$0",12"3 !"#$%4"2$+$5-. '67*$890(:$;<=$5-> '06;;=$890(:$;<:=$5-> 4"2?,"@1A1 ! " B:<( :<: :<( ;<: ;<( 7<: : 7: C: =: ): ;:: '67*$890(:$;:$,-> '06;;=$890(:$C$,-> B:<( :<: :<( ;<: : 7: C: =: ): ;:: # !"#$%&' ($")* !"#$%&' ($")+ '06;;= 7$5-$4"2 (:$,-$!&' 7$5-$4"2 (:$,-$!&' : :<( ; 7 C :<( ; 7 C : :<( ; 7 C :<( ; 7 C 6?DE$8F> !"#$%&' ($")+ '67* !"#$%&' ($")* 7$5-$4"2 (:$,-$!&' 7$5-$4"2 (:$,-$!&' : :<( ; 7 C :<( ; 7 C : :<( ; 7 C :<( ; 7 C 6?DE$8F> $ 91 TP53 in HT29 cells and activating KRAS and β-catenin mutations in the HCT116 cells. In addition, HCT116 cells display a near-diploid karyotype while HT29 cells exhibit hyper triploidy. These cell lines were initially analyzed to determine the effects of vorinostat and LBH589 on cellular proliferation. Cells were exposed to increasing concentrations of each drug for 72 h and subsequently analyzed by MTS assay. The IC 50 (72 h) values for LBH589 in the HCT116 and HT29 CRC cells were in the low nanomolar range at 3.49 nM (95% CI 3.1 - 3.9 nM) and 9.8 nM (95% CI 8.7 - 10.9 µM) respectively (Fig. 5-1A). The IC 50 (72 h) values for vorinostat in the HCT116 and HT29 cells were in the low micromolar range at 1.06 µM (95% CI 0.94 - 1.1 µM) and 1.56 µM (95% CI 1.45 - 1.67 µM) respectively (Fig. 5-1B). The HCT116 cells demonstrated a >2-fold increase in sensitivity to LBH589 (p =0.0019) and a 1.5-fold increase in sensitivity to vorinostat (p = 0.027) over the HT29 cells. Figure 5-2. Cell cycle and apoptotic analysis of HDACi-treated CRC cancer cells. Flow cytometric analysis of (A) HCT116 and (B) HT29 cells treated with 2 µM vorinostat (Vor) or 50 nM LBH589. Histogram bars represent mean ± SEM. Western blot analysis of poly (ADP- ribose) polymerase (PARP) cleavage as a measure of the induction of apoptosis in (C) HCT116 and (D) HT29 cells treated with 1 and 2 µM vorinostat (Vor) or 25 and 50 nM LBH589 for 12 and 24 h. β-actin was used to control for loading. (LaBonte, MJ and Wilson, PM) ! " !"# $ % %& &' $ % ()*+", #)*-./ !"#$%&' 0120 3&*45# ! $$&*45# ! %6*45# ! $%*7 %6*7 ()*+", #)*-./ %& &' !"# $ % %& &' $ % ()*+", #)*-./ !"#$%&' 0120 3&*45# ! $$&*45# ! %6*45# ! $%*7 %6*7 ()*+", #)*-./ %& &' # /!8$$9 :;<"=$ =$ : =%>) /8%? /!8$$9 /8%? $ ' %& &' @& $'' -./&3? +",&#"A%#% !"#%,"B C*!DBBA ' %& &' @& $'' -./&3? +",&#"A%#% !"#%,"B C*!DBBA 92 5.3.2 HDACi treatment rapidly induces histone acetylation. Inhibition of HDACs results in disruption of cellular acetylation homeostasis and can induce hyper-acetylation of both histone and non-histone proteins. In order to examine this effect in our CRC cell line models, we treated cells with either 2 µM vorinostat or 50 nM LBH589 and analyzed the acetylation status of selected histone proteins. As histone acetylation is reported to be a rapid event following HDACi treatment we analyzed the expression of acetyl-H3 (Ac-H3) and acetyl-H4 (Ac-H4) from 0.5 to 4 h post-treatment. In HCT116 cells, treatment with 2 µM vorinostat resulted in significant Ac-H4 at 2 h post- treatment, however 50 nM LBH589 induced modest but detectable Ac-H4 as early as 0.5 and 1 h post-treatment which increased significantly at 2 and 4 h (Fig. 5-1C and 5-1D). Interestingly, Ac-H3 was detected as early as 0.5 h post-treatment with both HDACi and increased in a time-dependent manner. In HT29 cells, an increase in Ac-H4 was not detectable following treatment with both HDACi until 4 h post-treatment (Fig. 5-1D). In contrast, Ac-H3 was detected at low levels as early as 0.5 h post-treatment with levels remaining consistent until 4 h post-treatment where a marked increase in Ac-H3 was observed. These results demonstrate that HDACi treatment has detectable and measurable effects on histone acetylation in CRC cells within 30 minutes of drug treatment. 5.3.3 HDACi-induce cell cycle arrest and apoptosis. HDACi are reported to rapidly induce cell cycle arrest and induce tumor cell-selective apoptosis. To investigate this, flow cytometry was subsequently utilized to examine the effects of HDACi treatment on cell cycle distribution in HCT116 and HT29 CRC cells. Each cell line was treated with 50 nM LBH589 and 2 µM vorinostat (concentrations which were shown to induce similar patters of histone acetylation) for 24 h and DNA 93 content was subsequently analyzed by propidium iodide staining. The HCT116 CRC cells treated with either HDACi, LBH589 or vorinostat, displayed a significant G 2 /M arrest accompanied by a sharp reduction of cells in G 1 . Interestingly, cells with sub-diploid DNA content (Sub-G 1 ), indicative of cell death, increased from 2% in untreated controls to 30.2 and 34.4% following treatment with LBH589 and vorinostat respectively (Fig. 5-2A). In HT29 cells, treatment with vorinostat resulted in an accumulation of cells arresting in G 1 accompanied by a reduction of cells in both G 2 and S. Interestingly, LBH589 induced a G 2 arrest with a reduction of cells in G 1 and S phases (Fig. 5-2B). Despite displaying a similar IC 50 (72 h) value for vorinostat to that of the HCT116 cells, HT29 cells showed only a modest increase in cell death from 2% to 9.5% following treatment with vorinostat. Similarly, despite the concentration of LBH589 being in excess of the IC 50 (72 h) value for HT29 cells, cell death increased modestly from 2% to 14.4% (Fig. 5-2B). These data suggest that while both cell lines display similar sensitivity to the growth inhibitory effects of HDACi, the HT29 cells are significantly more resistant to the onset of HDACi induced apoptosis in this time-frame. To confirm these differential levels of HDACi-induced apoptosis, HCT116 and HT29 cells were analyzed for the cleavage of poly (ADP-ribose) polymerase (PARP) (a hallmark of apoptosis) from its native 115 kDa to the 85 kDa subunit by Western blot. Compared to vehicle-treated cells, HCT116 cells displayed strong dose-dependent cleavage of PARP at 24 h post treatment evidenced in particular by the strong immunoreactivity of the 85 kDa subunit when compared to the full length PARP (Fig. 5-2C). Twenty-four h post-treatment, PARP cleavage was detected at low levels in HT29 cells in a dose-dependent manner as evidenced by the appearance of the cleaved subunits (Fig. 5-2D). These results support the flow cytometric analysis whereby HCT116 are significantly more susceptible to rapid HDACi- induced apoptosis than the HT29 cells. 94 5.3.4 Microarray profiling in HDACi treated CRC cells. To identify the molecular events that occur in response to HDAC inhibition in CRC cells, we treated both HCT116 and HT29 CRC cells with the clinically relevant concentrations of 50 nM LBH589 or 2 µM vorinostat for 24 h, isolated mRNA and subsequently analyzed gene expression using the Illumina Human-6 V2 BeadChip array platform as outlined in Chapter 3. Genes with a FDR-adjusted p-value of <0.05 were considered differentially expressed genes (DEGs) relative to vehicle treated controls. The heat maps generated from the microarray analysis in HCT116 and HT29 cells treated with HDACi were subject to hierarchical clustering analysis. The heat map demonstrates that both vorinostat and LBH589 segregated independently from the vehicle-treated controls in both cell lines. However, the cluster tree generated also demonstrates that while vorinostat and LBH589 segregate from the vehicle- treated controls, they demonstrate very similar Figure 5-3. Hierarchical cluster analysis of HDACi- treated HCT116 and HT29 CRC cancer cells. Cells were treated with 2 µM vorinostat or 50 nM LBH589 for 24 h and gene expression was analyzed using the Illumina Human-6 V2 BeadChip array. Hierarchical cluster heat map and tree was generated from HDACi- induced changes in gene expression (1-way ANOVA, p < 0.05). (Asuragen; LaBonte, MJ) !"#$%"& '()*(+"% ,-(#*(./0 !"#$ !%"&&' !"#$%"& '()*(+"% ,-(#*(./0 1 / 2 3 4 5- 55 5' 56 57 5, 95 clustering patterns indicating that they induce similar transcriptional response within each cell line (Fig. 5-3). 5.3.5 Differentially expressed genes in response to HDACi treatment. To compare the effects of HDACi treatment relative to vehicle treated cells, Venn analysis was utilized. In HCT116 cells, a combined total of 3566 genes were modulated by HDACi treatment (both LBH589 or vorinostat) representing approximately 7% of the total gene set analyzed by the array. Within this set, 3100 DEGs were identified following vorinostat treatment of which 57 genes were uniquely modulated by vorinostat treatment as illustrated by the Venn diagram (Fig. 5-4A). Following treatment with LBH589, 3509 DEGs were identified of which 466 genes were uniquely modulated by LBH589 (Fig. 5-4A). This data demonstrates that in HCT116 cells, vorinostat and LBH589 exert similar effects on gene expression with 85% of all DEGs modulated in a consistent manner by both vorinostat and LBH589 treatment. In HT29 cells, 2645 genes were modulated in total by both Figure 5-4. Venn analysis of differentially expressed genes in vorinostat and LBH589-treated HCT116 and HT29 CRC cancer cells. HCT116 and HT29 cells were treated with either 2 µM vorinostat or 50 nM LBH589 for 24 h and gene expression analyzed on the Illumina Human-6 V2 Bead- Chip array. Genes with an FDR-adjusted p-value of < 0.05 were considered differentially expressed and subjected to Venn analysis. Venn analysis was first performed by analyzing cell-line-specific alterations in each individual cell line; (A) HCT116 cells treated with vorinostat or LBH589. (B) HT29 cells treated with vorinostat or LBH589. Subsequent Venn analysis demonstrates the drug-specific alterations induced by (C) vorinostat (Vor) and (D) LBH589 in both cell lines. Numbers within each circle represent the total number of genes modulated in that experimental condition, the numbers immediately below each Venn diagram indicate the total number of modulated genes by both experimental conditions in that Venn diagram. (LaBonte, MJ; Fazzone, W, and Wilson, PM) F i g u r e 4 . V e n n a n a l y s !"# $% &'(& )*+, )-*../ 01)$2, (// ++&+ 01)$2, .,% +./ ! " # $ !"#$%&&'()) !"#$%&&&*)+( )-*../ )*+, +$,( ,.$ .$.( 01)$2, !"#$%&&&(,*' !"# )-*../ )*+, ++(' 2/' .$22 !"#$%&&&+)-- !"# 96 HDACi representing approximately 5% of the total gene set analyzed by the array (Fig. 5-4B). Of this total, 2448 genes were modulated by vorinostat of which 216 of these DEGs were unique only to vorinostat treatment (Fig. 5-4B). Following treatment with LBH589, 2429 genes were modulated of which 197 were unique transcriptional responses to LBH589 not observed with vorinostat treatment (Fig. 5-4B). This indicates that there is also significant similarity in the transcriptional changes induced by vorinostat and LBH589 in HT29 cells with 75% of the total DEG set common transcriptional changes in response to either HDACi. Of the 3100 DEGs modulated by vorinostat treatment in HCT116 cells, 24 were upregulated and 17 were downregulated >2-fold. Of the 3509 genes modulated following treatment with LBH589 in HCT116 cells, 92 genes were upregulated and 150 were downregulated >2-fold. The top 15 up- and downregulated genes modulated >2-fold for both vorinostat and LBH589 treatment in HCT116 cells are displayed in Table 5-1. Similarly, in HT29 cells, the majority of DEGs were also modulated <2-fold when compared to vehicle-treated controls as was observed in the HCT116 cells. Of the 2448 DEGs modulated by vorinostat treatment in HT29 cells, 138 were up-regulated and 53 were down-regulated >2-fold. Of the 3509 genes modulated following treatment with LBH589 in HT29 cells, 163 genes were upregulated and 54 were downregulated >2-fold. The top 15 up- and downregulated genes for both vorinostat and LBH589 treatment in HT29 cells are displayed in Table 5-2. 5.3.6 Identification of biological pathways modulated by HDACi. We further analyzed the HDACi DEGs to explore the key biological pathways modulated by HDACi treatment. We performed pathway analysis using Ingenuity® Pathway Analysis (IPA) on the DEGs in both the HCT116 and HT29 cell lines, treated with 97 LBH589 and vorinostat. In the HCT116 cells, 2289 of the 3043 DEGs and 1679 of the 2232 DEGs in the HT29 cells mapped to defined genetic networks in IPA Knowledge Base. Five networks were found to be altered by HDACi in that they possessed significantly more of the identified DEGs present than would be expected by random chance. These networks included cell cycle; DNA replication, recombination and repair; apoptosis; gene expression and cell growth and proliferation. The mapped DEGs were subsequently analyzed for the top 12 canonical biological pathways that demonstrated 98 Table 5-1. Differentially expressed genes (>2 fold) in response to HDACi in HCT116 CRC cancer cells. Accession # Gene Symbol Gene Name Fold Change P-Value Induced NM_001901.2 CTGF Connective tissue growth factor 6.09 6.9E-04 NM_182908.3 DHRS2 Dehydrogenase/reductase member 2 4.78 1.7E-04 NM_003378.2 VGF VGF nerve growth factor inducible 4.74 1.0E-04 NM_183376.1 ARRDC4 Arrestin domain containing 4 4.15 6.9E-04 NM_173798.2 ZCCHC12 Zinc finger, CCHC domain containing 12 4.13 1.8E-05 NM_006865.2 LILRA3 Leukocyte immunoglobulin-like receptor, subfamily A, member 3 3.94 7.2E-05 NM_033184.2 KRTAP2-4 Keratin associated protein 2-4 3.61 7.2E-04 NM_000558.3 HBA1 Hemoglobin, alpha 1 3.60 3.2E-04 NM_016352.3 CPA4 Carboxypeptidase A4 3.47 2.7E-04 NM_001554.4 CYR61 Cysteine-rich, angiogenic inducer, 61 3.44 2.7E-05 NM_005319.3 HIST1H1C Histone 1, H1c 3.37 9.9E-05 NM_138720.1 HIST1H2BD Histone 1, H2bd 3.35 2.2E-04 NM_031476.1 CRISPLD2 Cysteine-rich secretory protein LCCL domain containing 2 3.26 8.4E-05 NM_139072.2 DNER Delta-notch-like EGF repeat-containing transmembrane 3.21 1.5E-03 NM_005061.2 RPL3L Ribosomal protein L3-like 3.08 2.5E-05 Repressed NM_013233.2 STK39 Serine threonine kinase 39 -2.91 8.2E-04 NM_004091.2 E2F2 E2F transcription factor 2 -2.93 2.4E-04 NM_003302.1 TRIP6 Thyroid hormone receptor interactor 6 -2.99 7.3E-04 NM_005733.2 KIF20A Kinesin family member 20A -3.00 2.2E-05 NM_005329.2 HAS3 Hyaluronan synthase 3 -3.02 4.1E-05 NM_145810.1 CDCA7 Cell division cycle associated 7 -3.06 2.4E-03 NM_005434.4 MALL Mal, T-cell differentiation protein-like -3.07 2.8E-04 NM_002129.2 HMGB2 High-mobility group box 2 -3.13 3.6E-03 NM_018649.2 H2AFY2 H2A histone family, member Y2 -3.28 8.7E-04 NM_001038.5 SCNN1A Sodium channel, nonvoltage-gated 1 -3.59 6.6E-04 NM_004217.2 AURKB Aurora kinase B -3.61 5.4E-04 NM_001237.3 CCNA2 Cyclin A2 -3.72 2.9E-05 NM_001071.1 TYMS Thymidylate synthase -3.88 1.1E-04 NM_181803.1 UBE2C Ubiquitin-conjugating enzyme E2C -3.95 2.6E-04 NM_001423.2 EMP1 Eepithelial membrane protein 1 -4.17 1.3E-05 Induced NM_001901.2 CTGF Connective tissue growth factor 4.76 2.0E-05 NM_003378.2 VGF VGF nerve growth factor inducible 4.35 1.8E-03 NM_182908.3 DHRS2 Dehydrogenase/reductase member 2 4.05 6.2E-04 NM_183376.1 ARRDC4 Arrestin domain containing 4 3.35 4.4E-04 NM_173798.2 ZCCHC12 Zinc finger, CCHC domain containing 12 3.27 1.7E-06 NM_016352.3 CPA4 Carboxypeptidase A4 2.91 1.0E-03 NM_017445.1 H2BFS H2B histone family, member S 2.86 2.8E-04 NM_138720.1 HIST1H2BD Histone 1, H2bd 2.85 1.6E-04 NM_033184.2 KRTAP2-4 Keratin associated protein 2-4 2.81 2.6E-03 NM_001554.4 CYR61 Cysteine-rich, angiogenic inducer, 61 2.73 1.3E-05 NM_139072.2 DNER Delta-notch-like EGF repeat-containing transmembrane 2.62 2.2E-03 NM_005319.3 HIST1H1C Histone 1, H1c 2.57 5.4E-04 NM_006865.2 LILRA3 Leukocyte immunoglobulin-like receptor, subfamily A, member 3 2.37 1.2E-03 NM_005952.2 MT1X Metallothionein 1X 2.37 1.2E-03 NM_080593.1 HIST1H2BK Histone 1, H2bk 2.36 9.2E-06 NM_005950.1 MT1G Metallothionein 1G 2.33 7.4E-03 Repressed NM_003998.2 NFKB1 Nuclear factor of kappa light polypeptide -2.04 6.9E-04 gene enhancer in B-cells 1 (p105) NM_018043.5 TMEM16A Transmembrane protein 16A -2.05 1.6E-03 NM_003302.1 TRIP6 Thyroid hormone receptor interactor 6 -2.08 6.6E-03 NM_145810.1 CDCA7 Cell division cycle associated 7 -2.08 2.4E-03 NM_004217.2 AURKB Aurora kinase B -2.08 1.7E-03 NM_001235.2 SERPINH1 Serpin peptidase inhibitor, clade H (heat shock protein 47), member 1 -2.11 7.4E-05 NM_001425.2 EMP3 Epithelial membrane protein 3 -2.11 1.0E-03 NM_005329.2 HAS3 Hyaluronan synthase 3 -2.11 2.4E-05 NM_001237.3 CCNA2 Cyclin A2 -2.15 1.5E-03 NM_002129.2 HMGB2 High-mobility group box 2 -2.17 1.3E-03 NM_005434.4 MALL Mal, T-cell differentiation protein-like -2.17 4.2E-04 NM_001423.2 EMP1 Epithelial membrane protein 1 -2.23 1.6E-04 NM_181803.1 UBE2C Ubiquitin-conjugating enzyme E2C -2.31 3.1E-04 NM_018649.2 H2AFY2 H2A histone family, member Y2 -2.51 2.2E-06 NM_001071.1 TYMS Thymidylate synthase -2.74 7.4E-06 Vorinostat HCT116 LBH589 99 Table 5-2. Differentially expressed genes (>2 fold) in response to HDACi in HT29 CRC cancer cells. Accession # Gene Symbol Gene Name Fold Change P-Value Induced NM_002305.2 LGALS1 Lectin, galactoside-binding soluble 1 5.22 3.0E-03 NM_003088.2 FSCN1 Fascin homolog 1, actin-bundling protein 5.06 5.6E-03 NM_006262.3 PRPH Peripherin 4.58 9.8E-04 NM_004223.3 UBE2L6 Ubiquitin-conjugating enzyme E2L 6 4.58 9.7E-04 NM_182908.3 DHRS2 Dehydrogenase/reductase member 2 4.37 5.5E-03 NM_006086.2 TUBB3 Tubulin, beta 3 4.06 1.4E-02 NM_002084.2 GPX3 Glutathione peroxidase 3 4.05 5.2E-03 NM_153247.1 SLC29A4 Solute carrier family 29, member 4 3.92 1.1E-03 NM_003283.3 TNNT1 Troponin T type 1 3.92 1.3E-03 NM_178012.3 TUBB2B Tubulin, beta 2B 3.92 3.4E-03 NM_001928.2 CFD Complement factor D (adipsin) 3.87 5.4E-03 NM_006117.2 PECI Peroxisomal D3,D2-enoyl-CoA isomerase 3.83 6.8E-03 NM_005319.3 HIST1H1C Histone 1, H1c 3.78 1.3E-03 NM_005952.2 MT1X Metallothionein 1X 3.66 5.9E-03 NM_017707.2 DDEFL1 Development and differentiation enhancing factor-like 1 3.60 6.6E-04 Repressed NM_206963.1 RARRES1 Retinoic acid receptor responder 1 -2.41 1.0E-02 NM_001031733.1 CALML4 Calmodulin-like 4 -2.42 1.1E-03 NM_007167.2 ZMYM6 Zinc finger, MYM-type 6 -2.43 8.8E-03 NM_002423.3 MMP7 Matrix metallopeptidase 7 -2.55 7.1E-03 NM_080911.1 UNG Uracil-DNA glycosylase -2.58 1.1E-02 NM_145810.1 CDCA7 Cell division cycle associated 7 -2.59 2.0E-03 NM_006169.2 NNMT Nicotinamide N-methyltransferase -2.61 4.4E-03 NM_020371.2 AVEN Apoptosis, caspase activation inhibitor -2.66 4.3E-04 NM_005375.2 MYB V-myb myeloblastosis viral oncogene homolog -2.72 1.2E-03 NM_052813.2 CARD9 Caspase recruitment domain family, member 9 -2.75 1.8E-02 NM_014312.3 VSIG2 V-set and immunoglobulin domain containing 2 -2.81 2.5E-03 NM_020384.2 CLDN2 Claudin 2 -3.03 2.5E-02 NM_020299.3 AKR1B10 Aldo-keto reductase family 1, member B10 -3.20 1.7E-02 NM_007193.3 ANXA10 Annexin A10 -3.69 4.1E-03 NM_001071.1 TYMS Thymidylate synthase -3.82 1.1E-03 Induced NM_002305.2 LGALS1 Lectin, galactoside-binding, soluble, 1 5.07 1.6E-03 NM_003088.2 FSCN1 Fascin homolog 1, actin-bundling protein 4.61 2.3E-04 NM_004223.3 UBE2L6 Ubiquitin-conjugating enzyme E2L 6 4.47 1.2E-02 NM_006262.3 PRPH Peripherin 4.39 5.9E-03 NM_182908.3 DHRS2 Dehydrogenase/reductase member 2 4.21 1.1E-04 NM_006086.2 TUBB3 Tubulin, beta 3 4.08 1.6E-03 NM_002084.2 GPX3 Glutathione peroxidase 3 3.98 2.0E-03 NM_017707.2 DDEFL1 Development and differentiation enhancing factor-like 1 3.74 7.9E-03 NM_001928.2 CFD Complement factor D 3.72 1.5E-02 NM_003078.3 SMARCD3 SWI/SNF related, matrix associated, actin dependent 3.67 2.0E-02 regulator of chromatin, subfamily d, member 3 NM_003283.3 TNNT1 Troponin T type 1 3.67 1.5E-03 NM_006117.2 PECI Peroxisomal D3,D2-enoyl-CoA isomerase 3.62 3.5E-03 NM_178012.3 TUBB2B Tubulin, beta 2B 3.59 7.1E-04 NM_015896.2 ZMYND10 Zinc finger, MYND-type containing 10 3.57 8.8E-03 NM_005319.3 HIST1H1C Histone 1, H1c 3.50 7.7E-03 Repressed NM_001031733.1 CALML4 Calmodulin-like 4 -2.43 5.0E-03 NM_005752.2 CLEC3A C-type lectin domain family 3, member A -2.45 7.5E-04 NM_005375.2 MYB V-myb myeloblastosis viral oncogene homolog -2.56 3.5E-03 NM_020371.2 AVEN Apoptosis, caspase activation inhibitor -2.60 7.8E-03 NM_014312.3 VSIG2 V-set and immunoglobulin domain containing 2 -2.62 3.5E-04 NM_005117.2 FGF19 Fibroblast growth factor 19 -2.64 9.3E-03 NM_007167.2 ZMYM6 Zinc finger, MYM-type 6 -2.67 1.6E-02 NM_004688.1 NMI N-myc (and STAT) interactor -2.69 3.9E-04 NM_052813.2 CARD9 Caspase recruitment domain family, member 9 -2.70 2.8E-04 NM_080911.1 UNG Uracil-DNA glycosylase -2.79 1.6E-02 NM_018689.1 KIAA1199 KIAA1199 -2.95 3.2E-04 NM_020384.2 CLDN2 Claudin 2 -3.06 2.3E-02 NM_020299.3 AKR1B10 Aldo-keto reductase family 1, member B10 -3.07 2.8E-05 NM_007193.3 ANXA10 Annexin A10 -3.49 1.3E-02 NM_001071.1 TYMS Thymidylate synthase -3.59 8.3E-03 Vorinostat LBH589 HT29 100 significance within each dataset. In HCT116 cells, 5 common pathways were modulated by both HDACi; coagulation system, pyrimidine metabolism, metabolism of xenobiotics, arachidonic acid metabolism and fatty acid metabolism (Fig. 5-5A and B). In HT29 cells, 7 common pathways were modulated by both HDACi; arginine and proline metabolism; urea cycle and metabolism of amino groups; arachidonic acid metabolism; fructose and mannose metabolism; pentose phosphotate pathway; nitrogen metabolism and bile acid biosynthesis (Fig. 5-5C and D). 5.3.7 Common gene signature of HDAC inhibition in CRC cells. One of the key objectives of this study was to identify a cassette of genes which were consistently regulated by both vorinostat and LBH589 in both cell lines examined. Such a cassette of consistently regulated genes may serve as molecular markers for HDACi treatment in CRC cells. From the Venn analysis, it is apparent that there are significant differences in how the HCT116 and HT29 cells respond to HDACi treatment. Specifically, when HCT116 and HT29 cells were treated with vorinostat, a combined total of 4688 DEGs (p- Table 5-3. Summary of changes in gene expression for the core set of HDACi regulated genes. Accession # Gene Symbol Gene Name Induced NM_182908.3 DHRS2 Dehydrogenase/reductase member 2 4.78 4.05 4.37 4.21 NM_183376.1 ARRDC4 Arrestin domain containing 4 4.15 3.35 2.21 2.14 NM_138720.1 HIST1H2BD Histone 1, H2bd 3.35 2.53 3.44 3.33 NM_005952.2 MT1X Metallothionein 1X 2.87 2.37 3.66 3.31 NM_005950.1 MT1G Metallothionein 1G 2.78 2.08 3.30 3.00 NM_015149.2 RGL1 Tal guanine nucleotide dissociation stimulator-like 1 2.52 1.56 3.12 3.03 Repressed NM_001071.1 TYMS Thymidylate synthase -3.88 -2.74 -3.82 -3.59 NM_145810.1 CDCA7 Cell division cycle associated 7 -3.06 -2.08 -2.59 -2.42 NM_080911.1 UNG Uracil-DNA glycosylase -2.54 -1.55 -2.58 -2.79 NM_003998.2 NFkB1 Nuclear factor of kappa light polypeptide -2.55 -2.04 -1.65 -1.8 gene enhancer in B-cells 1 (p105) NM_001025242.1 IRAK1 Interleukin-1 receptor-associated kinase 1 -1.97 -1.65 -2.15 -2.08 * Fold change (P<0.01) LBH589 VOR LBH589 VOR Fold Change* HCT116 HT29 Fold Change* 101 value < 0.05) were identified. However, of this combined total of 4688 DEGs, only 860 (18.3%) genes were transcriptional changes common to both cell lines (Fig. 5-4C and D). Similarly, in both cell lines a combined total of 5023 DEGs (p-value < 0.05) were identified following treatment with LBH589. However, of these 5023 DEGs, only 915 (18.2%) were transcriptional changes common to both cell lines (Fig. 5-4C and D). From this overlapping gene list, up- and downregulated genes in the HCT116 and HT29 cells were directly compared using a 1.5-fold cutoff. From this comparative list, we identified a panel of 11 genes, 6 genes that are significantly upregulated and 5 that are downregulated in a consistent manner in both cell lines by treatment with both HDACi (Table 5-3). 5.3.8 Verification of microarray results by quantitative real-time RT-PCR. In order to assess the robustness of the microarray analysis, quantitative real-time RT- PCR (qPCR) analysis was performed to validate a selected panel of 15 DEGs and 2 non-DEGs (Livak and Schmittgen 2001), using the primer sets given in Table 3-2. qPCR was performed on cDNA generated using RNA independently isolated from HCT116 and HT29 treated with either 2 µM vorinostat or 50 nM LBH589. Due to the pleiotropic effects on gene expression induced by HDACi, we first confirmed that our selected qPCR normalizing gene was not modulated by HDACi treatment in either cell line prior to DEG verification. We selected 2 house-keeping genes, 18s rRNA and GAPDH, whose expression was unchanged in the microarray analysis (non-DEGs) and confirmed using qPCR that these genes retained consistent expression during HDACi treatment (Fig. 5- 6A). GAPDH was subsequently used to normalize all qPCR data. To further validate and characterize the DEGs identified by the microarray analysis, we analyzed the time dependent change in expression of the selected DEGs at 6, 12 and 24 hours post- 102 Figure 5-5. Top 12 canonical pathways that were significantly modulated by HDACi as identified by Ingenuity® Pathway Analysis (IPA). HCT116 CRC cancer cells treated for 24 h with (A) 2 µM vorinostat (Vor) or (B) 50 nM LBH589 (LBH); HT29 CRC cancer cells treated for 24 h with (C) 2 µM vorinostat (Vor) or (D) 50 nM LBH589 (LBH). 2289 of the 3043 differentially expressed genes (DEGs) in the HCT116 and 1679 of the 2232 DEGs in the HT29 cancer cell lines mapped to defined genetic networks in IPA. Fisher's exact test was used to calculate a p-value determining the probability that the association between the genes in the dataset and the canonical pathway is explained by chance alone. A ratio of the number of genes from the dataset that map to the pathway divided by the total number of molecules in a given pathway that meet the cut criteria, divided by the total number of molecules that make up that pathway is displayed. (LaBonte, MJ) In order to assess the 103 Figure 5-6. qPCR validation of house-keeping and cell-line specific HDACi-induced gene expression changes. HCT116 and HT29 CRC cells were treated with 2 µM vorinostat or 50 nM LBH589 for 6, 12 and 24 h. Total RNA was extracted and qPCR analysis was performed as described in the 'materials and methods' using the primer sets given in Table 4. Histogram bars represent the mean ± SD for two independent RNA isolations analyzed in triplicate. (A) Verification of unaffected 18s and GAPDH expression with HDACi treatment. GAPDH was normalized to 18s and 18s was normalized to GAPDH. qPCR validation of the induction of (B) THBS-1, (C) AVEN (D) AURKB (E) HIST1H1C. All genes were normalized to GAPDH, * denotes a p-value< 0.05 for both HDACi treatment groups when compared to respective time-matched control. (LaBonte, MJ and Wilson, PM) 104 Figure 5-7. qPCR time-dependent validation of core HDACi-induced gene expression changes in HCT116 and HT29 CRC cells. HCT116 and HT29 cells were treated with 2 µM vorinostat (Vor) or 50 nM LBH589 for 6, 12 and 24 h. Total RNA was extracted, reverse transcribed and qPCR analysis was performed as described in the 'materials and methods' using the primer sets given in Table 3-2. Histogram bars represent the mean ± SD for two independent RNA isolations analyzed in triplicate. All genes were normalized to GAPDH, * denotes p-value < 0.05 for both HDACi treatment groups when compared to respective time-matched control. (LaBonte, MJ and Wilson, PM) 105 HDACi treatment when compared to vehicle-treated time-matched controls. The pattern of expression obtained for 14 of the 15 selected DEGs 24 h post-treatment showed consistent directional conformation (up- or downregulation) and cell-line specific Figure 5-8. qPCR time-dependent validation of core HDACi-repressed gene expression changes in HCT116 and HT29 CRC cells. HCT116 and HT29 CRC cells were treated with 2 µM vorinostat (Vor) or 50 nM LBH589 for 6, 12 and 24 h. Total RNA was extracted, reverse transcribed and qPCR analysis was performed as described in the 'materials and methods' using the primer sets given in Table 3-2. Histogram bars represent the mean ± SD for two independent RNA isolations analyzed in triplicate. All genes were normalized to GAPDH, *denotes p-value < 0.05 for both HDACi treatment groups when compared to respective time-matched control. (LaBonte, MJ and Wilson, PM) 106 modulation between the qPCR and microarray analyses. THBS-1, AVEN and AURKB demonstrated significant cell-line specific changes in expression at 24 h as observed in the microarray analyses (Fig. 5-6B-D). HIST1H1C was initially identified as consistently upregulated by the microarray analysis, but subsequent qPCR analysis indicated this gene to be consistently downregulated. In addition, these 11 genes also demonstrated time-dependent changes in expression at 6 and/or 12 h post HDACi treatment (Fig. 5-7 and 5-8). However, in several instances, the fold-changes obtained by qPCR were significantly higher for several genes than those obtained in the microarray analyses, particularly for the more heavily regulated genes as previously reported (Beckman, Lee et al. 2004). For example, DHRS2 was induced by ~5-fold in both cell lines following HDACi treatment in the microarray analysis. Subsequent qPCR analysis determined the fold-increase in DHRS2 transcripts to be in the order of 36-97-fold in HT29 cells and 226-445-fold in the HCT116 cells (Fig. 5-7). Similarly, thymidylate synthase (TYMS) was downregulated by HDACi in both cell lines 2.7 - 3.8 fold in the microarray analysis, whereas qPCR determined that HDACi treatment induced a >30-fold downregulation of TYMS 24 h post-treatment in both cell lines (Fig. 5-8). 5.3.9 Evaluation of identified common gene signature of HDACi in panel of CRC cells. To further validate the identified gene signature of response to the HDACi’s vorinostat and LBH589 in the HCT116 and HT29 cells, we extended the qPCR validation to a panel of cytogenetically distinct CRC cell lines that represent differences in MIN and CIN status and p53, KRAS, PI3K, PTEN and β-catenin mutation status. 107 The panel of CRC cell lines was first analyzed to determine the effects of vorinostat and LBH589 on cellular proliferation. Cells were exposed to increasing concentrations of each drug for 72 h and subsequently analyzed by MTS assay. The IC 50 (72 h) values for LBH589 in the panel of 9 CRC cells ranged from the low nanomolar range of 4.9 ± 0.2 nM in the LoVo cells to 42.0 ± 0.2 nM in the DLD-1 cells (Table 5-4). The IC 50 (72 h) values for vorinostat in the panel of 9 CRC cells ranged from low micromolar range of 1.2 ± 0.1 µM in the LoVo cells to 7.8 ±0.7 µM in the DLD-1 cells (Table 5-4). In order to confirm and validate the gene expression results from the microarray and in vitro analyses the extended panel of 9 CRC cell lines were treated for 24 hours with either 50 nM LBH589 or 2 µM vorinostat and qPCR performed on cDNA generated using RNA independently isolated from the cell lines. qPCR was performed as described in Chapter 3 utilizing the primer sets given in Table 3-2 and GAPDH was subsequently used to normalize all resulting qPCR data. The pattern of expression obtained for the six selected DEGs 24 h post-treatment showed consistent directional conformation (up- or downregulation) in all cell lines examined further confirming the microarray results and further confirming that these genes are Table 5-4. LBH589 and vorinostat inhibit cell proliferation in panel of CRC cell lines. Panel of colon cancer cells were exposed to increasing concentrations of either LBH589 or vorinostat alone for 72 h and subsequent growth inhibition was measured by MTS assay (Promega). Values are presented as percent control, calculated from the growth inhibition induced by a given concentration of drug compared to the untreated control. Values are averages of 3 independent experiments ± SEM. The IC 50 (72 h) values were calculated from the sigmoidal dose-response curves in Prism 5.0 (GraphPad). (LaBonte, MJ and Yan, L) 108 consistently significantly modulated following treatment with either HDACi, LBH589 or vorinostat in CRC cell line models. The fold-change in gene expression of the majority of genes analyzed were similar in magnitude within each cell line following treatment with A B Figure 5-9. Evaluation of identified common gene signature of HDACi in a panel of CRC cells. A panel of 9 CRC cells were treated with 2 µM vorinostat (VOR) or 50 nM LBH589 for 24 h. Total RNA was extracted, reverse transcribed and qPCR analysis performed using validated primer sets. Representative genes that were (A) upregulated and (B) downregulated from gene signature in pan-HDACI gene signature. Histogram bars represent the mean ± SD for two independent RNA isolations analyzed in triplicate. All genes were normalized to GAPDH. (LaBonte, MJ, Yan, L and Wilson, PM) 109 either vorinostat or LBH589 for 24 h. However, the fold-change in gene expression between cell lines demonstrated a wide range in the magnitude of up- or downregulation (Fig. 5-9). TYMS was consistently the most significantly downregulated gene by both the microarray and qPCR validation in both the HCT116 and HT29 cancer cells, and when analyzed in the panel of 9 CRC cell lines (Fig. 5-9A). The fold downregulation ranged from the lowest in the H630 (-4.9 ± 1.7 for LBH589; -3.5 ± 0.4 for vorinostat) to the highest in the HCT116 (-46.4 ± 2.8 for LBH589; -33.2 ± 7.6 for vorinostat). Similar results were observed for the other genes analyzed (NFκB1, CDCA7 and IRAK1). DHRS2 was the most significantly upregulated gene by both the microarray and qPCR validation in both the HCT116 and HT29 cancer cells, and when analyzed in the panel of 9 CRC cell lines, DHRS2 was upregulated in all cell lines analyzed following treatment with either LBH589 or vorinostat (Fig. 5-9B). The fold upregulation ranged from the lowest in the RKO (9.7 ± 2.7 for LBH589; 6.0 ± 1.3 for vorinostat) to the highest in the H630 (2253.6 ± 101.6 for LBH589; 37.8 ± 3.1 for vorinostat). Similar results were observed for the other genes analyzed (HIST1H2BD, ARRDC4 and MT1X). 5.3.10 Evaluation of gene expression changes in HCT116 and HT29 CRC xenografts following treatment with HDACi, LBH589. The two initial CRC cell lines, HCT116 and HT29, that were used to generate the DEG list in the microarray analysis were selected as candidate cell lines to establish xenografts in nude mice to validate the identified gene signature. 110 Following injection, tumor cell were allowed to proliferate and form palpable tumors of approximately, 100 mm 3 (day 0) and then mice were randomized to either vehicle control treatment group or 10 mg/kg/day LBH589 for 3 consecutive days. Eight hours post treatment on the third day, mice were euthanized and tumors were extracted and snap-frozen by liquid nitrogen. RNA was extracted from tumors and cDNA was synthesized as previously described in Chapter 3. qPCR was used to measure the expression of six of the genes that comprised part of the previously described HDACi gene signature. Fig. 5-10 shows the relative mRNA expression of genes analyzed from HCT116 and HT29 xenografts (n=8 per group) normalized to GAPDH and presented as fold change relative to vehicle treated control. All six genes analyzed were significantly induced or repressed following LBH589 compared with vehicle treated control (Fig. 5-10). Of the six genes analyzed, previous in vitro results demonstrated that three were Figure 5-10. Evaluation of gene expression changes in HCT116 and HT29 CRC xenografts following treatment with HDACi, LBH589. (A) HCT116 and (B) HT29 cells were injected subcutaneously into both flanks of C57Bl/6 BALB/c mice (Taconic Labs) for xenograft studies. When tumors reached 100mm 3 , mice were injected intraperitoneally (i.p.) with 100 mg/kg/day LBH589 once a day for 3 consecutive days. On day 3, mice were sacrificed 8 h post-treatment and tumors were excised. Total RNA was extracted, reverse transcribed and qPCR analysis performed using validated primer sets. Histogram bars represent the mean ± SD for eight independent RNA isolations analyzed in triplicate. All genes were normalized to GAPDH. Statistical significance was determined by paired Student’s t-test (Prism), * p<0.05; **p<0.001, *** p<0.0001. (LaBonte, MJ, Yan, L, and Wilson, PM) !"#$%"& '()*+ (,*-.(+/' 0))'!1 2 . + 3 1 4 5 .2 (-+6 (!-..4 777 777 777 777 77 77 89#9 :"&;<!=>#?9 @)9&>$AB9<C)D0<EFG%9HHA"#I !"#$%"& -JK* D:L/. !'!0M N1 N3 N+ N. 2 (-+6 (!-..4<<< 77 777 777 777 77 7 89#9 :"&;<!=>#?9 @)9&>$AB9<C)D0<EFG%9HHA"#I ! " 111 upregulated (DHRS2; HIST1H2BD; ARRDC4) and three were downregulated (TYMS; NFκB1; CDCA7) consistent with in vitro results. Following three consecutive days of LBH589 treatment in vivo, qPCR demonstrated fold-change values that were significantly less than in the previous in vitro qPCR analysis as previously discussed but of a similar magnitude to that of the microarray analysis. Importantly, all three methods of analysis demonstrated consistent directional confirmation in the HDACi-induced gene expression changes (Table 5-5). Table 5-5. Comparison of fold-change of selected mRNA gene expression from treatment in HCT116 and HT29 CRC cells treated with HDACi, LBH589, by microarray, cell line qPCR and xenograft samples. (LaBonte, MJ) A Microarray in vitro in vivo DHRS2 4.78 445.0 + 16 8.85 + 1.8 HIST1H2BD 4.15 20.9 + 1.1 2.42 + 1.2 ARRDC4 3.35 1476 + 364 5.46 + 1.9 TYMS -3.88 .-48.38 + 2.8 .-2.29 + 0.04 NFkB1 -2.55 .-25.8 + 7.3 .-1.87 + 0.09 CDCA7 -3.06 .-12.6 + 0.08 .-1.64 + 0.06 B Microarray in vitro in vivo DHRS2 4.37 97.4 + 7.2 2.37 + 1.4 HIST1H2BD 3.4 37.4 + 2.7 2.84 + 1.4 ARRDC4 2.21 1385 + 471 3.78 + 1.6 TYMS -3.82 .-33.76 + 1.1 .-3.03 + 0.03 NFkB1 -1.65 .-25.2 + 2.7 .-2.38 + 0.07 CDCA7 -2.59 .-3.67 + 0.43 .-1.35 + 0.09 * Fold change = mean + SD Fold Change* HCT116 HT29 Fold Change* 112 5.3.11 Summary. Chapter 5 evaluated the effects of HDACi’s vorinostat and panobinostat (LBH589) on changes in gene expression in CRC cell line models. Key results from this study: • Identification of a core signature of 11 genes that were modulated by both vorinostat and panobinostat (LBH589) in a similar manner in HCT116 and HT29 CRC cells. • The robustness of the microarray data was confirmed by qPCR. The consistency of the core 11 gene signature was independently validated by qPCR in 6 additional CRC cell lines and 2 in vivo CRC models. • Both vorinostat and panobinostat induced alterations in critical genes involved in nucleotide metabolism, angiogenesis, mitosis and cell survival. These genes may represent potential intervention points for novel therapeutic combinations with anti-angiogenic, anti-metabolic and anti-mitotic agents in CRC. • These core genes will assist in the development and validation of a common gene set that may represent a molecular signature of HDAC inhibition in CRC. 5.4 Discussion In an effort to characterize the response of CRC cells to HDACi treatment, we analyzed the gene expression profile of two CRC cell lines following treatment with two HDACi, which are currently in advanced stages of clinical development, vorinostat and LBH589. Both HDACi resulted in significant inhibition of CRC cell proliferation, an accumulation of acetylated histones and the onset of apoptotic cell death. However, LBH589 exerted 113 antiproliferative effects at significantly lower concentrations than vorinostat, consistent with previous reports utilizing these HDACi (George, Bali et al. 2005; Edwards, Li et al. 2007; Fazzone, Wilson et al. 2009). Specifically, the IC 50 (72 h) for LBH589 was in the single to low digit nanomolar range while vorinostat required concentrations in excess of 1 µM. In the extended panel of CRC cell lines, LBH589 IC 50 (72 h) concentrations ranged from 5 nM in the LoVo cells to 42 nM in the DLD-1 cells while sensitivity to vorinostat ranged from 1.2 to 7.8 µM. The concentrations at which HDACi induce their anti- proliferative effects is of importance particularly in the context of clinically achievable serum concentrations and the extrapolation of in vitro observations to the clinical settings. Pharmacokinetic data from clinical trials following a twice-daily dose of vorinostat determined that the half-life was in the range of 1 to 3.5 h and maximal serum concentrations did not peak over 2 µM and rapidly diminished (Kelly, Richon et al. 2003; Kelly, O'Connor et al. 2005; Fakih, Pendyala et al. 2009). Of note, the half-life of LBH589 was determined to be in the order of 10 to 14 h and serum concentrations of 400 to 700 nM are achievable at doses which are well tolerated (Giles, Fischer et al. 2006). Therefore, the concentration of LBH589 required to achieve 50% growth inhibition in the CRC cells presented in the study was well within clinically achievable peak concentrations, and remain relevant when considering the half-life of LBH589. However, the concentration of vorinostat required to achieve 50% growth inhibition in vitro in the CRC cells was within the upper limit of reported serum concentration ranges for 7 out of 9 CRC cell lines and when considering the relatively short half-life of vorinostat in the clinic, that results in peak serum concentrations rapidly declining, the duration of exposure to elevated concentrations of vorinostat is relatively transient. cDNA microarray analysis was then used to analyze the cellular response to treatment with vorinostat and LBH589. The cDNA microarray analysis demonstrated that in each cell 114 line that the gene expression profile was significantly altered after a 24 h exposure to either HDAC inhibitor, vorinostat (2 µM) or LBH589 (50 nM). Considering the mechanism of action of HDACi including histone acetylation-induced chromatin remodeling and the acetylation of non-histone proteins including transcription factors, it is intriguing that only 5-7% of genes in the CRC cell lines analyzed were modulated by HDACi treatment. However, these results are consistent with other microarray profiling experiments which reported as few as 2% and as high as 10% modulated by HDACi. These reports and the data presented herein would indicate that HDACi do not induce global gene expression changes and may instead target specific sets of genes. An important observation in this study was that vorinostat and LBH589 induced very similar transcriptional profiles within each cell line. As both of these agents are hydroxamate-acid class HDACi, this observation is somewhat expected. However, as mentioned previously, although as members of the same class, vorinostat and LBH589 have significantly different potency in vitro and display markedly different pharmacokinetic and dynamic properties in the clinic. Additional studies have identified very similar transcriptional changes produced by the two hydroxamic-acid based HDACi, TSA and vorinostat, while also demonstrating a different gene expression profile obtained with the benzamide class HDACi MS-275 (Glaser, Staver et al. 2003) indicating that the different specificities for HDAC families by the differing HDACi classes can directly impact the resulting transcriptional changes. The analysis of the data presented demonstrates that HDACi induce significant cell-line specific effects on genes involved in the regulation of a number of critical tumor processes including angiogenesis, mitosis, DNA replication, recombination and repair and apoptosis. More specifically, the potent anti-angiogenic matrix glycoprotein thrombospondin-1 (THBS1), was significantly upregulated 14-fold in HCT116 cells at 24 h (Table 5-1). HT29 cells however, showed no modulation until 24 h post-treatment 115 where only a modest increase of 2-fold was observed by qPCR. HDACi are reported to be potent inhibitors of tumor angiogenesis and induction of THBS1 has previously been reported following HDAC inhibition (Kang, Kim et al. 2008). Similarly, in HCT116 the most heavily upregulated gene following treatment with either vorinostat or LBH589 was connective tissue growth factor (CTGF; Table 5-1). CTGF is a multifunctional secreted matricellular protein associated with fibrotic disorders, angiogenic regulation, and possibly tumor development (Cicha and Goppelt-Struebe 2009). Human tumors overexpressing CTGF demonstrated reduced microvessel density indicative of potential antiangiogenic properties, and ovarian tumors overexpressing CTGF demonstrated enhanced tumor cell invasion (Barbolina, Adley et al. 2009). In HT29 cells, fibroblast growth factor 19 (FGF19) was significantly downregulated by both HDACi (Table 5-2). FGF19 binds to FGF receptor 4 and has been shown to mediate cell cycle progression, angiogenesis and promote tumor growth through the β-catenin pathway. Knockdown of FGF19 in CRC cells decreased tumor growth in vitro and in vivo (Pai, Dunlap et al. 2008). It is possible that the anti-angiogenic and antitumor action of HDACi are mediated, in part, through modulation of key angiogenic regulators such as these and would indicate that HDACi may potentiate the therapeutic efficacy when used in combination with inhibitors of tumor angiogenesis. In HT29 cells, microarray analysis identified that both HDACi induce a potent downregulation of the anti-apoptotic caspase inhibitor protein AVEN. qPCR confirmed that AVEN is significantly downregulated in HT29 cells by vorinostat and LBH589 >5-fold at 24 h and only modestly regulated in HCT116 cells <2-fold at 24 h. AVEN is reported to inhibit caspase activation through inhibition of APAF-1 self-association (Chau, Cheng et al. 2000). The downregulation of AVEN would suggest that HDACi-induced apoptosis in the HT29 cells may be regulated in part via the mitochondria, supporting the mechanism of oxidative stress injury as 116 previously reported (Portanova, Russo et al. 2008). We also observed significant cell- line-specific alterations in genes involved in mitosis. Aurora kinase B was identified as downregulated by both vorinostat and LBH589 in HCT116 cells. The Aurora kinase family are critical regulators of mitotic cell division having roles in centrosome function, mitotic spindle formation, chromosome segregation and cytokinesis (Carmena and Earnshaw 2003). Overexpression of Aurora kinases A and B have been linked to genetic instability and are frequently overexpressed in solid tumors such as colorectal cancer (Bischoff, Anderson et al. 1998; Katayama, Ota et al. 1999) and inhibition of aurora kinases has become an attractive therapeutic strategy with multiple inhibitors in clinical development. Of note, recent studies have reported that LBH589 induces the degradation of aurora kinase A and B in renal and non-small cell lung cancer cells resulting in G 2 /M arrest and apoptosis (Zhang, Rao et al. 2008; Cha, Chuang et al. 2009). Interestingly, we observed downregulation of aurora kinase B with HDACi treatment only in the HCT116 cells where a potent G 2 /M arrest and significant apoptosis was observed (Table 5-1, Fig. 5-2). Approximately 18% of the DEGs identified after HDACi treatment were modulated in a similar manner in both cell lines. This core set of genes encompass genes involved in cell cycle, nucleotide metabolism, nucleosome assembly and apoptosis. We identified a panel of 11 genes, 6 up and 5 downregulated by both HDACi in both cell lines. Previously, Glaser et al. identified a core set of 13 genes regulated by three HDACi’s in bladder and breast cancer carcinoma cell line models. Upon comparison, one up-regulated gene (histone H2B) and one downregulated gene (thymidylate synthase) are consistent between our core gene set and those reported by Glaser et al. (Glaser, Staver et al. 2003). One of the primary reasons for this is that out core gene set was defined solely from CRC cells which are physiologically distinct from 117 both bladder and breast cancers and may employ different mechanisms of gene expression regulation. An additional study analyzed the effects of HDACi in renal cancer cells and identified consistent directional modulation of short-chain alcohol dehydrogenase aldo-keto reductase and fibroblast growth factor gene families (Tavares, Nanus et al. 2008). Two genes within our core set of HDACi-modulated genes are directly involved in nucleotide metabolism and DNA repair. Downregulation of both thymidylate synthase (TYMS) and UNG was observed in both cell lines following treatment with either HDACi. Thymidylate synthase is essential for the de novo synthesis of thymidylate, an essential precursor required for DNA replication and repair. UNG is the gene encoding uracil-DNA glycosylase, a base excision repair protein involved in uracil excision from DNA. Both these enzymes are reported to mediate response to the anti-metabolite class of chemotherapeutic agents including inhibitors of TS such as 5-FU (Dusseau, Murray et al. 2001; Popat, Matakidou et al. 2004). A number of other studies have confirmed that downregulation of TS mRNA and protein is a common event in response to HDACi treatment (Glaser, Staver et al. 2003; Lee, Park et al. 2006; Fazzone, Wilson et al. 2009). We recently confirmed that downregulation of TS was a common event in an extended panel of CRC cell lines and was driven primarily through a transcriptional mechanism in response to HDAC inhibition. This interaction resulted in synergistic antiproliferative effects between HDACi and 5-FU in CRC cells (Fazzone, Wilson et al. 2009) supporting the concept that HDACi-mediated alterations in known drug targets may provide opportunity for new therapeutic combinations. Short-chain alcohol dehydrogenase family member 2 (DHRS2) was identified as the most heavily induced gene by HDACi in our core set of genes. DHRS2 was originally identified following its upregulation by treatment with butyrate and was later confirmed to be involved in the 118 differentiation of monocytes to dendritic cells (Donadel, Garzelli et al. 1991; Gabrielli, Donadel et al. 1995). HDACi treatment is reported to induce cellular differentiation and induction of pro-differentiation genes such as DHRS2 is a plausible mechanism (Carew, Giles et al. 2008). MT1X and MT1G were both heavily induced in both cell lines by HDACi treatment. These genes encode two highly inducible ubiquitous proteins belonging to a family of cysteine-rich metallothionein proteins. Metallothioneins can bind to both physiological and xenobiotic heavy metals (Thirumoorthy, Manisenthil Kumar et al. 2007). Previous studies have identified regulation of other metallothionein family members in response to HDACi (Glaser, Staver et al. 2003). MT1G is reported to be a tumor suppressor gene and is frequently epigenetically silenced in a number of human malignancies (Henrique, Jeronimo et al. 2005; Ferrario, Lavagni et al. 2008). Although the mechanism that results in the induction of metallothionein proteins is unknown, both the MT1X and MT1G genes map to chromosome 16q13 and it is likely that HDACi- mediated events in this region such as chromatin relaxation result in the increased transcription of both of these genes. NF-κB regulates the expression of a significant number of genes involved in immune response, angiogenesis, cell adhesion, proliferation, differentiation, and apoptosis (Tergaonkar 2006; Terragni, Graham et al. 2008). The NFκB1 gene encodes the predominant p50/p105 form and represents one of the core genes significantly downregulated by HDACi treatment in this study. As such, many different types of human tumors have dysregulated NFκB1, primarily via constitutive activation that mediates continued cell proliferation and averts the onset of apoptosis (Bernal-Mizrachi, Lovly et al. 2006). Downregulation of NFκB1 is a likely mechanism by which HDACi induce aspects of their apoptotic effects in CRC cells. We also identified the IL-1 receptor associated kinase (IRAK1) as consistently 119 downregulated by HDACi in our core set of genes. IRAK1 encodes the interleukin-1 receptor-associated kinase 1 which is reported to be partially responsible for IL1-induced upregulation of NFκB1 (Vig, Green et al. 1999) and was one of ~100 genes identified as consistently upregulated in a microarray meta-comparison of genes upregulated in solid tumors of epithelial origin (Pilarsky, Wenzig et al. 2004). The core set of genes identified in this study includes the histone family member HIST1H2BD which encodes the histone H2B protein and was >3-fold induced by HDACi treatment. HIST1H2BD has previously been reported to be induced by HDACi treatment (Glaser, Staver et al. 2003). While the mechanism of induction of this gene is unknown, it is located within the large histone gene cluster on chromosome 6p22-p21.3 and it is likely that the HDACi-induced alterations in this region, possibly through chromatin relaxation allowing transcriptional machinery access, results in this induction. This study analyzed the gene expression profiles of two of the most clinically advanced hydroxamate class HDACi, vorinostat and LBH589, in two CRC cell line models. We identified significant overlap in differentially expressed gene profiles for vorinostat and LBH589 within each cell line indicating similar mechanism of action for these HDACi. Interestingly, a strong cell-line dependence of gene expression changes induced by these HDACi was identified with only 18% commonality in HDACi-induced DEGs. Within this gene expression overlap, a core set of 6 up- and 5 downregulated genes that are regulated by both of HDACi in both cell lines was identified. Defining a core set of genes that represent markers of HDAC inhibition is an important first step in the identification and validation of clinical markers for evaluating HDACi target inhibition and efficacy. Currently, analysis of histone acetylation from tumor tissue and more frequently from isolated peripheral blood mononuclear cells is used as evidence of HDACi biological activity. However, histone acetylation following 120 HDACi treatment has been shown to be highly reversible and often inconsistent. A panel of HDACi-regulated genes may provide a more sensitive and reliable means to determining the efficacy of HDACi treatment in the clinic. Importantly, this study also demonstrates that the reliable analysis of the change in expression of these genes is achievable utilizing qPCR from extracted tumor tissue. The ability to consistently measure gene expression from tumor tissue is especially important considering the heterogeneity of the tumor architecture and the contribution of non-tumor cell material to the qPCR and can directly impact the clinical applicability of such an analysis. This study has also identified alterations in additional pathways that may enhance the therapeutic potential of both conventional and targeted therapeutics, including genes involved in angiogenesis, nucleotide metabolism and mitosis. As HDACi advance in clinical development, these agents are likely to be incorporated into combination treatment strategies with both conventional and novel chemotherapeutic agents. Therefore, the identification of pathways and drug targets modulated by HDAC inhibition could be critically important in elucidating their disease-specific mechanism of action and assisting in the identification of effective drug combination partners. This study identified HDACi-induced alterations in critical genes involved in nucleotide metabolism, angiogenesis, mitosis and cell survival that may represent potential intervention points for novel therapeutic combinations in CRC. This information will assist in the identification of novel pathways and targets that are modulated by HDACi, providing much-needed information on HDACi mechanism of action and providing rationale for novel drug combination partners. We identified a core signature of 11 genes that were modulated by both vorinostat and LBH589 in a similar manner in multiple CRC cell lines. These core genes will assist in the development and validation 121 of a common gene set that may represent a molecular signature of HDAC inhibition in CRC. 5.5 Manuscripts and Abstracts The data presented in Chapter 5 evaluating the effects of the HDACi’s vorinostat and panobinostat (LBH589) on changes in gene expression in CRC cell line models resulted in a publication in BMC Medical Genomics and several meeting abstracts (Table 5-6). 5.6 Translational Impact One of the most heavily downregulated genes in our microarray analysis in terms of overall reduction in mRNA expression was thymidylate synthase (TYMS). In addition, TYMS was consistently downregulated in the extended panel of 6 CRC cell lines analyzed. As TS protein is the primary target for the fluoropyrimidines and it’s overexpression represents a significant mechanism of resistance, it was postulated that HDACi may represent a novel strategy to reduce tumoral TS expression and thus sensitize resistant Table 5-6. Resulting Manuscripts and Abstracts from the study of the global gene expression changes in CRC cell line models following HDACi, vorinostat and panobinostat (LBH589), treatment. Manuscripts LaBonte MJ, Wilson PM, Fazzone W, Groshen S, Lenz HJ, Ladner, RD. DNA microarray profiling of genes differentially regulated by the histone deacetylase inhibitors vorinostat and LBH589 in colon cancer cell lines. BMC Medical Genomics. 2009; 30;2:67. Wilson PM, El-Khoueiry A, Iqbal S, Fazzone W, LaBonte MJ, Groshen S, Yang D, Danenberg KD, Cole S, Kornacki M, Ladner RD, Lenz HJ. A phase I/II trial of vorinostat in combination with 5-fluorouracil in patients with metastatic colorectal cancer who previously failed 5-FU-based chemotherapy. Cancer Chemother Pharmacol. 2010; 65(5):979-88. Abstracts LaBonte MJ, Wilson PM, Fazzone W, Yan LW, Groshen S, Lenz HJ, Ladner RD. Identification of a core cassette of 11 genes modulated by both vorinostat and LBH589 by microarray profiling in colon cancer cell lines with xenograft validation. AACR/JCA Joint Conference: Cancer Genomics, Epigenomics, and the Development of Novel Therapeutics. Waikoloa, HI, USA, February 2010. 122 tumors to the cytotoxic effects of 5-FU. This concept was further analyzed in our laboratory by Drs Peter M Wilson and William Fazzone and it was shown that HDACi and the fluoropyrimidines interact synergistically to inhibit the proliferation of CRC cells through enhanced TS inhibition as a result of this potent TS downregulation (Fazzone, Wilson et al. 2009). Additional groups using structurally similar HDACi’s reported similar findings (Tumber, Collins et al. 2007). A Phase I/II clinical trial was initiated at USC Norris to test the safety and feasibility of combining daily oral vorinostat with 5-FU in patients with metastatic colorectal cancer (mCRC) who had failed 5-FU-based chemotherapy and who had elevated intratumoral TS mRNA expression. The trial also prospectively investigated the ability of vorinostat to downregulate TS mRNA expression from pre- and post-treatment biopsies. The trial concluded in June 2009 having accrued 10 patients. The trial was terminated as a maximum tolerated dose could not be established in the heavily pre-treated patient populations and grade 3 toxicities including fatigue and thrombocytopenia were observed. Vorinostat did not induce significant downregulation of intratumoral TS and this was primarily attributed to the short half-life and rapid systemic clearance (30 - 90 mins). Although efficacy was not a primary endpoint and could not be formally analyzed due to patients withdrawing from treatment, notably, two patients experienced stable disease of 4 and 6 months in the absence of any dose-limiting toxicity, suggesting that this regimen does have some activity (Wilson, El-Khoueiry et al. 2010). Interestingly, Fakih et al. from the Roswell Park Cancer Institute have implemented a reduced twice-daily dose of vorinostat in combination with FOLFOX. Although Fakih reported that vorinostat serum concentrations did not peak over 2 µM and as such no TS downregulation was observed, 8 out of 17 evaluable patients with advanced colorectal cancer developed stable disease, again signaling that this is a viable therapeutic combination. 123 The continued pre-clinical and clinical data continue to support panobinostat as a more potent and selective HDACi with a significantly extended half-life (~14 h) and serum concentrations well in excess of its nanomolar activity in vitro, when compared to the pharmacokinetics of vorinostat. In light of the promising observations from HDACi and 5-FU combinations, USC/Norris are currently in the planning and initiation stages of a phase I/II clinical trial to evaluate the safety and tolerability of panobinostat in combination with 5-FU-based chemotherapy. 124 Chapter 6 A novel therapeutic combination with synergistic antitumor activity in colorectal cancer: The dual EGFR/HER2 tyrosine kinase inhibitor L-4804 Lapatinib and the histone deacetylase inhibitor panobinostat (LBH589). 6.1 Abstract Background: Colorectal cancer (CRC) is the third most commonly diagnosed cancer in the United States. Effective treatment is hindered by the high incidence of drug resistance and tumor recurrence, resulting in the need to identify and exploit novel therapeutic targets and drug combinations to improve clinical efficacy. The epidermal growth factor receptor (EGFR) has been implicated in CRC growth, progression and chemoresistance and inhibition of EGFR has therefore become an efficacious drug target. Lapatinib is a dual TKI targeting EGFR and HER2 and suppresses oncogenic signaling through the RAS-RAF-MEK-MAPK and PI3K/AKT pathways. Histone deacetylase inhibitors (HDACi) are a novel class of agents demonstrating promising activity against CRC cells through the hyper-acetylation of histone and non-histone proteins resulting in cell cycle arrest and apoptosis. HDACi are also reported to disrupt HSP90 function, inducing the degradation of EGFR-pathway client proteins (e.g. EGFR, HER2, BRAF). This study sought to evaluate the therapeutic potential of L-4804 Lapatinib in combination with the HDACi panobinostat (LBH589) in a panel of 6 CRC cell lines with varying EGFR/HER2 expression and KRAS mutational status. Methods: CRC cell lines with varying expression of EGFR and HER2 were exposed to panobinostat and L-4804 Lapatinib (LC Laboratories) and the effects on cell proliferation, cell survival, HER family mRNA and protein expression, cell cycle profile 125 and apoptosis were analyzed in response to treatment by MTS, qPCR, Western blotting and flow cytometeric analysis. Results from in vitro studies were subsequently analyzed in a CRC xenograft model. Results: Concentration-dependent antiproliferative effects of both panobinostat (LBH589) and L-4804 Lapatinib (LAP) were observed in vitro (LBH589 range 7.2-30 nM; LAP range 7.6-25.9 µM). The combination of panobinostat and LAP interacted synergistically to inhibit both CRC cell proliferation and colony formation in all cell lines tested with a combination index <1. Combination treatment resulted in decreased signaling through both the PI3K and MAPK pathways as determined by decreased levels of phosphorylation of AKT (Ser 473 ) and p44/42 MAPK (Thr 202 /Tyr 204 ). Co-treatment with panobinostat and LAP also resulted in the rapid induction of apoptosis as early as 18 h post-treatment. In addition, panobinostat treatment as a single agent resulted in a dose- dependent decrease in ERBB1 (EGFR) and ERBB2 (HER2) mRNA and protein expression. These in vitro observations were extended into a LoVo CRC xenograft model where combination treatment resulted in greater antitumor activity than either LAP or panobinostat alone, with no apparent increase in toxicity. Conclusions: These data demonstrate that panobinostat as a single agent results in modifications to the HER family mRNA and protein expression levels and when combined with a dual EGFR/HER2 targeted TKI such as L-4804 Lapatinib, results in synergistic antitumor efficacy in CRC cells. Together these data provide scientific rationale warranting further investigation of HDACi in combination with EGFR and HER2-targeted therapies for the treatment of gastrointestinal malignancies. 126 6.2 Study Aim Despite advances in chemotherapeutic options for the treatment of CRC, the high incidence of drug resistance and disease progression is a major stumbling block to effective disease control. This has created a critical need for newer and more effective treatment strategies in the treatment of CRC. Members of the HER family including EGFR and to a lesser extent HER2 play key roles in driving the oncogenic pathways important for CRC growth and proliferation, survival, angiogenesis, invasion and metastasis. Targeting EGFR has proven successful in CRC patients with the use of the monoclonal antibodies cetuximab and panitumumab. However, success with these agents is currently limited to a subset of CRC patients and the presence of intrinsic resistance or the subsequent development of resistance during EGFR-targeted therapy is a critical problem in the clinic. Identifying alternative therapeutic approaches that block additional resistance pathways and/or further disrupt EGFR-dependent tumor cell growth is timely and of clinical importance. The novel TKI, lapatinib demonstrates dual specificity for both EGFR and HER2 and is currently approved for metastatic breast cancer and is under clinical investigation in a variety of tumor types including CRC and gastric cancer. Importantly, a recent study demonstrated that panobinostat induced downregulation of EGFR in EGFR- expressing lung cancer cell lines and that combined treatment of panobinostat with the EGFR TKI erlotinib resulted in synergistic antiproliferative effects (Edwards, Li et al. 2007). As EGFR and HER2 are of key importance in promoting tumor cell proliferation and survival in CRC, we hypothesized that panobinostat would demonstrate antiproliferatve effects in a panel of CRC cells with varying levels of EGFR and HER2 127 expression. Furthermore, we proposed that the combined targeting of EGFR and HER2 with lapatinib in combination with panobinostat would demonstrate synergistic increases in CRC cell growth inhibition and apoptosis. The ability of lapatinib to inhibit cell surface- initiated EGFR and HER2 signaling combined with the HDACi-induced depletion of EGFR-signaling molecules through the disruption of HSP90 function may prove to be a novel and efficacious approach in the treatment of CRC. 6.3 Results 6.3.1 IC 50(72h) growth inhibitory effects of panobinostat and L-4804 Lapatinib, in selected CRC cell lines. Prior to evaluating L-4804 Lapatinib (LAP) in combination with panobinostat (LBH589) we first analyzed the antiproliferative activity of both agents in a panel of 6 CRC cell lines. DLD-1, H630, HCT116, HT29, LoVo, and RKO CRC cells were exposed to increasing concentrations of LBH589 and LAP for 72 h and growth inhibition was measured by MTS assay as described in Chapter 3. In all CRC cell lines tested, panobinostat demonstrated concentration-dependent growth inhibitory activity with IC 50 (72 h) values ranging between 4.15 to 26.0 nM (Fig. 6-1A). In addition, LAP also exerted concentration-dependent growth inhibitory activity with IC 50 (72 h) values ranging from 8.9 to 25.9 µM (Fig. 6-1B). Consistent with our previous observations, the IC 50 (72 h) values in this 72 h growth inhibition assay are representative of cells that range from moderately responsive to resistant to the growth inhibitory effects of LAP. 128 6.3.2 Panobinostat combined with the dual EGFR/HER2 TKI, L-4804 Lapatinib, synergistically inhibits CRC cell proliferation and colony formation. In Chapter 4, we previously determined the EGFR and HER2 protein expression levels in a panel of CRC cell lines and demonstrated that of the five cell lines analyzed (DLD-1, H630, HCT116, HT-29 and LoVo) four had significant EGFR expression with the exception of the H630 cell line which had low EGFR expression but expressed HER2 at a significant level compared to the other CRC cell lines (LaBonte, Manegold et al. 2009). We subsequently tested the hypothesis that combining the EGFR/HER2 TKI L-4804 Lapatinib (LAP) with the HDACi panobinostat would exert synergistic antiproliferative Figure 6-1. Growth inhibitory effects of single agent panobinostat and L-4804 Lapatinib (LAP) in CRC cell lines. Sensitivity of CRC cell lines to single agent (A) panobinostat LBH589 and (B) L-4804 Lapatinib (LAP) was measured by MTS assay following exposure of cells to indicated concentrations of each drug for 72 h. Data points represent a mean ± SD of three independent experiments and IC 50 (72h) values were calculated utilizing Graphpad (Prism 5.0) as described in the Chapter 3. (LaBonte, MJ) A B !"! !"# $"! $"# %"! ! %# #! &# $!! '(')$ $*"&+!+!"!, -.*! $$"*+!+!"%/ -01$$. %#"2+!+!",/+++ -1%2 $*"&+!+!"*# (343 +/"2++!+!"%! 567 +2"%++!+!",# !"##$%&'" (!$ )*$+,-$./ %0102&'&3 (38+!9+(:;:<=>=? @+03><A3B )!"# !"! !"# $"! $"# %"! %"# ! %# #! &# $!! '(')$ %."!+!+!"*&+ -.*!+ #"*,+!+!"!.+++ -01$$. ,"$#+!+!"#!++ -1%2+ .".%+!+!"$!++ (343+ 2"%/+!+!"!/+ 567 $!"#+!+!"%$+++ !"##$%&'" (!$ )*$+,-$./ %45)67 (38+>9+(C-#/2 @+03><A3B 129 effects in a panel of CRC cell lines. Selected CRC cell lines were treated with increasing concentrations of panobinostat and LAP alone and in combination for 72 h and growth Figure 6-2. Growth inhibitory effects of panobinostat combined with L-4804 Lapatinib in CRC cell lines. Sensitivity of CRC cell lines, DLD-1, H630, HCT116, HT29, LoVo and RKO, to the combination of panobinostat (LBH589) and L-4804 Lapatinib (LAP) was determined by MTS assay following exposure to indicated concentrations of each drug alone and in combination for 72 h. Data points represent mean ± SD percent growth inhibition of three independent experiments compared to untreated time-matched controls set at 100%. The combined drug effect was analyzed using the Chou-Talalay combination index (CI) equation and presented as CI with fraction affected (FA) values for above combinations. CI values were interpreted as follows: <1, synergism; 1 – 1.2, additive and >1.2, antagonism. (LaBonte, MJ) ! 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" # $ % & ' ( ) "% %) '% !)) *+, *-.%(/ 01231 444)7%4444444!44444444#444444444&44444444(4444444!)444444!%444444!(4444444!54*+, 444)7%4444444!44444444#444444444&44444444(4444444!)444444!%444444!(4444444654*-.%(/ 44)7)"444)7)&444)7!%4444)7#!44)7%)4444)7'!444)7'(444)7()44448+4 44)7&"444)7%#444)7%)4444)7'%44)7(#4444)7%'444)7%%444)7'%444409 -./ :4016;<1= " # $ & ( !) !" !& ) "% %) '% !)) *+, *-.%(/ 01231 4444"4444444#4444444$4444444&4444444(444444!)44444!"44444!&4444444444!54*+, 4444"4444444#4444444$4444444&4444444(444444!)44444!"44444!&4444444444654*-.%(/ 4)7)(44)7!$44)7"/44)7'#44)7(%44)7(%44)7(%44)7(%44444448+ 4!7!&44)7//44)7(#44)7"(44)7#!44)7##44)7$%44!7)!444444409 %*01 :4016;<1= 130 inhibition was subsequently measured by MTS assay as described in Chapter 3. Cells were treated with selected single agent concentrations that corresponded to a linear increase (R 2 >0.9) of between 0.1 and 0.9 in the fraction affected (FA) and combinations of these agents for 72 h continuous exposure. Growth inhibition was subsequently measured by MTS assay. The median-effect analysis method (Chou and Talalay 1984) was utilized to evaluate the combined drug effect. The effects of simultaneous treatment with panobinostat and LAP resulted in synergistic increases in growth inhibition at 0.5 FA Figure 6-3. Effects of panobinostat and L-4804 Lapatinib on colony formation in CRC cell lines. (A) DLD-1, (B) H630, (C) HCT116 and (D) LoVo CRC cells were treated with 3 µM L-4804 Lapatinib (LAP) and increasing concentrations of panobinostat (LBH589) alone and in combination. Data presented as histograms of the percentage of colony formation compared with untreated controls. Histogram bars represent the mean ± SEM from two independent experiments. Statistical significance was determined by two-way ANOVA (Graphpad, Prism 5.0); *p<0.05; **p<0.01; and ***p<0.001. (LaBonte, MJ) !"!#$ ! " # $ "% %$ &% !$$ '() '*+%,- ./01/ 2#222222$222222#2222222222#222222$222222#22222222222#222222$222222#22222!32'() 2$222222#222222#2222222222$2222224222222422222222222$22222!$2222!$2222532'*+%,- ! " # 6 % 4 & , - !$ !! $ "% %$ &% !$$ 222#22222$22222#22222222222#222222$22222#222222222222#22222$22222#2222!32'() 222$22222%22222%22222222222$22222!$2222!$2222222222$222"$2222"$222532'*+%,- 722./589/: '() '*+%,- ./01/ $222222222222222222#2222222222222222$22222222222222222#2222;32'() $222222222222222222$2222222222222222%22222222222222222%2222532'*+%,- %&'( 722./589/: ) * ! " # 6 % 4 & , - !$ !! $ "% %$ &% !$$ %+,$$& + '() '*+%,- ./01/ 722./589/: 22#22222$22222#222222222222#22222$22222#222222222222#22222$22222#22222!32'() 22$22222!22222!222222222222$22222#22222#222222222222$2222242222242222532'*+%,- "-.- ! ! " # 6 % 4 & , - !$ !! $ "% %$ &% !$$ 22#222$222#22222222#222$222#22222222#222$222#222!32'() 22$2$<%22$<%22222$222!222!22222222$222#222#222532'*+%,- 722./589/: '() '*+%,- ./01/ $222222222222222222#2222222222222222$22222222222222222#2222;32'() $222222222222222222$222222222222222242222222222222222242222532'*+%,- $222222222222222222#2222222222222222$22222222222222222#2222;32'() $222222222222222222$2222222222222222!22222222222222222!2222532'*+%,- $222222222222222222#2222222222222222$22222222222222222#2222;32'() $222222222222222222$222222222222222242222222222222222242222532'*+%,- === == = == = = == == === === == 131 and demonstrated synergistic CI values of <1 over the majority of concentrations tested in all CRC cell lines examined (Fig. 6-2). We performed a clonogenicity assay to assess the capacity of panobinostat and LAP combinations to cause irreversible growth arrest in 4 CRC cell lines (DLD-1, H630, HCT116, and LoVo). Combined drug analysis was performed using increasing concentrations of panobinostat at clinically relevant doses combined with LAP at a fixed clinically relevant dose of 3 µM. CRC cells were exposed to increasing concentrations of panobinostat, 3 µM LAP and selected combinations for 24 h followed by drug removal and outgrowth in drug-free medium. After 12-15 days, surviving colonies with >50 cells were fixed, stained and counted. In all cell lines evaluated, increasing doses of panobinostat alone resulted in a dose-dependent suppression of colony formation (Fig. Figure 6-4. Effects of panobinostat and L-4804 Lapatinib on apoptosis in CRC cell lines. Cell cycle analysis was determined by propidium iodide staining and subsequent flow cytometry following treatment with 3 µM L-4804 Lapatinib (LAP) and 10 and 15 nM panobinostat (LBH589) alone and in combination for 24 h. Percentage of cells in Sub-G 1 , G 1 , S and G 2 /M were determined by propidium iodide staining and subsequent flow cytometry. Percentage of (A) DLD-1, (B) H630, (C) HCT116 and (D) LoVo CRC cells in Sub-G 1 at 24 h. Bars represent mean ± SD from two independent experiments. Statistical significance for percent Sub-G 1 cells was determined by two-way ANOVA (Graphpad; Prism 5.0), *p<0.05; **p<0.01; ***p<0.001. (LaBonte, MJ and Wilson, PM) ! " # $ % & ' !' "' #' $' ' !' "' #' $' %' &' ()*+,-. !) /0112 ()*+,-. !) /0112 !"#" $!$%& ' !' "' #' $' %' '()&&* ()*+,-. !) /0112 ' !' "' #' $' %' &' '*+, ()*+,-. !) /0112 - ,.....+......,.....,.....+......+...!/.!-0 ,.....,.....&,...&1...&,....&1..2/.!3'145 ,.....+......,.....,.....+......+...!/.!-0 ,.....,.....&,...&1...&,....&1..2/.!3'145 ,.....+......,.....,.....+......+...!/.!-0 ,.....,.....&,...&1...&,....&1..2/.!3'145 ,.....+......,.....,.....+......+...!/.!-0 ,.....,.....&,...&1...&,....&1..2/.!3'145 333 333 333 33 33 33 33 3 3 $ ( 132 6-3). The addition of 3 µM LAP to increasing concentrations of panobinostat resulted in synergistic suppression of colony formation for all combinations tested in the DLD-1, H630, HCT116, and LoVo cells (Fig. 6-3). These data demonstrate that a clinically relevant combination of panobinostat and LAP is effective in suppressing colony formation in these CRC cell line models. 6.2.3 L-4804 Lapatinib enhances panobinostat-induced apoptosis in CRC cells. Having demonstrated that the combination of panobinostat and L-4804 Lapatinib (LAP) results in synergistic increases in growth inhibition and suppression of colony formation, we sought to determine if this combination was inducing apoptosis. DLD-1, H630, HCT116, and LoVo CRC cells were treated with the single agent concentrations used in the colony formation assay of 3 µM LAP, 10 and 15 nM panobinostat and combinations of LAP and panobinostat for 24 h and DNA content was analyzed by dual-laser flow cytometric analysis. Treatment with 3 µM LAP did not result in any significant alterations in the percentage of cells in Sub-G 1 compared with untreated control cells in any of the CRC cell lines (Fig. 6-4). Treatment with both 10 and 15 nM panobinostat was characterized by a G 1 cell cycle arrest in the DLD-1, H630 and LoVo CRC cell lines and a G 2 /M cell cycle arrest in the HCT116 CRC cells (data not shown). In addition, treatment with 10 and 15 nM panobinostat resulted in a dose-dependent increase in the percentage of cells in Sub-G 1 in 3 of the 4 cell lines with the DLD-1 cells demonstrating a marked resistance to panobinostat with only modest increases in the percentage of cells in Sub-G 1 (Fig. 6-4). Specifically, treatment with 10 and 15 nM panobinostat in DLD-1 cells resulted in the detection of 2.3 and 5.2% of cells in Sub-G 1 respectively (Fig. 6-4A). However, the combinations of 3 µM LAP with both 10 and 15 nM panobinostat had a dramatic effect in the DLD-1 cells resulting in a highly significant increase in Sub-G 1 cells 133 from 2.3 to 24.1% from combination treatment with 10 nM panobinostat and 5.2 to 30.1% for combination treatment with 15 nM panobinostat. Highly significant increases in apoptosis were also observed with combination treatment in the remaining 3 cell lines (Fig. 6-4). For example, in the H630, HCT116 and LoVo cells, the addition of 3 µM LAP to 15 nM panobinostat resulted in a synergistic increase in cells in Sub-G 1 from 28.6, 24.9 and 27.5% with panobinostat treatment alone to 52.1, 43.4 and 50.9% respectively. To confirm the mechanistic effect of the panobinostat and lapatinib combination on the induction of apoptosis, we examined the levels of DNA damage, and activation of apoptotic cascades following single agent and combination treatment. Utilizing Western blot analysis, we analyzed the induction of DNA damage using γH2A.X and measured the activation of apoptotic correlates including caspase-8 and PARP cleavage in two selected CRC models. H630 and LoVo CRC cells were treated with 3 µM LAP, 15 nM panobinostat, and the combination for 18 and 24 h. DNA damage was assessed by measuring DNA double-strand breaks (DSB) by Western blot for γH2A.X Ser 139 . LAP treatment resulted in no detectable increase in γH2A.X when compared to untreated controls whereas panobinostat treatment alone induced a detectable increase in γH2A.X at 24 h in both H630 and LoVo cells consistent with the induction of apoptosis noted with the flow cytometric analysis (Fig. 6-5). Interestingly, the combination of LAP and panobinostat induced γH2A.X to a significantly greater extent in both cell lines when compared to either single agent at 18 and 24 h. In a similar manner, LAP treatment alone in H630 and LoVo cells did not induce cleavage of caspase-8 or PARP (Fig. 6-5). However, 15 nM panobinostat induced detectable levels of PARP cleavage that was significantly increased with the addition of LAP at both 18 and 24 h in both cell lines. In H630 CRC cells, 15 nM panobinostat induced a modest increase in cleaved caspase-8 134 at 18 h. However, the combination of LAP and 15 nM panobinostat induced the cleavage of caspase-8 to a significantly greater extent. These data support our observations with the flow cytometric analysis indicating that the combination of lapatinib and panobinostat is effective at inhibiting CRC proliferation and rapidly inducing apoptotic cell death greater than either agent alone in the CRC cell lines examined. 6.3.4 Effect of panobinostat and L-4804 Lapatinib on hallmark features of HDAC inhibition. Having determined that the combination of L-4804 Lapatinib (LAP) and panobinostat demonstrates a significant drug interaction through the inhibition of CRC cell growth and the rapid induction of apoptosis, we sought to further analyze and elucidate the molecular mechanisms contributing to this observation. One of the hallmark features of treatment with HDACi’s is the hyperacetylation of histones H3 and H4, and induction of Figure 6-5. Analysis of DNA damage and induction of apoptosis following treatment with panobinostat and L-4804 Lapatinib in H630 and LoVo CRC cell lines. Western blot analysis of markers of DNA damage (γH2A.X Ser 139 ) and induction of apoptosis (caspase- 8 and PARP). (A) H630 and (B) LoVo CRC cells were treated with 15 nM panobinostat (LBH589) and 3 µM L-4804 Lapatinib (LAP) alone and in combination for 18 and 24 h. Prior to collecting cells for Western blotting, cells were EGF stimulated (25 ng/ml) for 20 minutes as described in Chapter 3. β-tubulin was used to control for loading. (LaBonte, MJ) ! !!"#$#%&' ()*( +()*(,-./,0123 4256257!. +4256257!.,689:68; "!<=)>?,-@7A,;9/3 B,,,,9,,,,B,,,,,9,,,,,,B,,,,9,,,,B,,,,,9,,,,,,#C,D)( B,,,,B,,,;E,,,;E,,,,,B,,,,B,,,;E,,,;E,,,,,'C,DF<E./ "#$% &'$% ()*+ , !!"#$#%&' ()*( +()*(,-./,0123 4256257!. +4256257!.,689:68; "!<=)>?,-@7A,;9/3 B,,,,9,,,,B,,,,,9,,,,,,B,,,,9,,,,B,,,,,9,,,,,,#C,D)( B,,,,B,,,;E,,,;E,,,,,B,,,,B,,,;E,,,;E,,,,,'C,DF<E./ "#$% &'$% -./. 135 the cell cycle cyclin-dependent kinase inhibitor 1A (p21). In order to determine if combination treatment showed any measurable differences in the level of hyperacetylation and induction of p21 when compared to panobinostat single agent treatment, we measured the acetylation status of H3 and H4 and the induction of p21 by Western blot (Fig. 6-6). Western blot analysis demonstrates that treatment with 15 nM panobinostat alone induces acetylation of H3 and H4 as well as increased protein expression of p21 (Fig. 6-6) in both H630 and LoVo CRC cell lines. In addition, in both cell lines, combination treatment with 15 nM panobinostat and 3 µM LAP resulted in a similar level of acetylation of H3 and H4 and induction of p21 suggesting that the enhanced anticancer effects observed with the addition of LAP to panobinostat treatment is not mediated through increases in histone acetylation or reflected by any differences in p21 induction. Figure 6-6. Effect panobinostat (LBH589) and L-4804 Lapatinib on hallmark features of HDAC inhibition and HER pathway activation status. Western blot analysis of markers of HDAC inhibition Acetyl-histone H3 and H4 and p21 and HER pathway activation state (A) H630 and (B) LoVo CRC cells were treated with 15 nM panobinostat (LBH589) and 3 µM L-4804 Lapatinib (LAP) alone and in combination for 18 and 24 h. Prior to collecting cells for Western blotting, cells were EGF stimulated (25 ng/ml) for 20 minutes as described in Chapter 3. β- tubulin was used to control for loading. (LaBonte, MJ) !"# $%&'()*+,-'./&0+1 $%&'()*+,-'./&0+2 !*3454),/ 0000600000000010000000000600000000010000000060000000100000000600000001000000000"708$9 000060000000006000000000#:0000000#:00000006000000060000000#:00000#:00000000/708;+:<= #<0> "20> !"#" ?@AB +?B" !"# $%&'()*+,-'./&0+1 $%&'()*+,-'./&0+2 !*3454),/ ?@AB +?B" 9>.-!>.*$C30DE&F02G1H 9>.-!>.*!22I2"*7$9C0 D3>F"6"I3(F"62H 0000600000000010000000000600000000010000000060000000100000000600000001000000000"708$9 000060000000006000000000#:0000000#:00000006000000060000000#:00000#:00000000/708;+:<= #<0> "20> $%&' $C3 !22I2"*7$9C0 $C3 9>.-!>.*$C30DE&F02G1H !22I2"*7$9C0 9>.-!>.*!22I2"*7$9C0 D3>F"6"I3(F"62H $ ; 136 6.3.5 HDAC inhibition modulates ERBB family gene and protein expression. Based on a previous report indicating that panobinostat was effective in downregulating EGFR protein in EGFR-mutant NSCLC cell line models, we sought to analyze the effect of panobinostat on both EGFR and HER2 gene and protein expression in CRC models. To evaluate if the panobinostat, influenced ERBB gene expression, we treated DLD-1, H630, HCT116 and LoVo CRC cells with increasing concentrations of panobinostat for 24 h and analyzed the mRNA expression of ERBB1 (EGFR) and ERBB2 (HER2) by qPCR. Treatment with 100 nM panobinostat resulted in a significant reduction of ERBB1 mRNA in all cell lines examined. Interestingly, the DLD-1 and LoVo cells demonstrated an initial induction of ERBB1 mRNA with the lower doses of LBH589 of 10 - 25 nM suggesting the presence of a feedback loop in the presence of HDACi treatment (Fig. 6- 7A). After panobinostat treatment, ERBB2 mRNA expression was significantly reduced 137 Figure 6-7. Panobinostat modulates ERBB family transcription and HER protein expression in CRC cell lines. DLD-1, H630, HCT116, and LoVo CRC cells were exposed to 10, 15, 25, 50, and 100 nM panobinostat (LBH589) for 24 h and cells were examined for the effects on (A) ERBB1 (EGFR) and ERBB2 (HER2) mRNA expression and (B) EGFR and HER2 protein expression. Densitometry analysis of Western blot bands was performed using Scion Image and were subsequently normalized to β-Tubulin to control for loading. Relative EGFR and HER2 protein expression are presented as fold-downregulation of target protein/tubulin expression relative to untreated time-matched control. (LaBonte, MJ) !"" #!""" #$"" %$"" $!"" #!!"" ! %$ $! &$ #!! #%$ '(")*+$,- . .. . . . ... ... ... . .. .. . !"" #!""" #$"" %$"" $!"" #!!"" ! %$ $! &$ #!! #%$ #$! #&$ %!! '(")*+$,- ... .. ... ... .. .. ... ... ... . .. . ... ... .. . /)/0# +12! )343 +56##1"" %7"8 9:;< !06=>=?@' +9<% !"##$ !"##% A""53'BC3?" D<E?FB@GE"H<IJ"9KLCEMM@3'N A""53'BC3?" D<E?FB@GE"H<IJ"9KLCEMM@3'N ! " #$#%& "!""""""""#!"""""""""#$"""""""%$""""""""$!"""""""#!!""""""'(")*+$,-""""" %7"8 '()* "!""""""""#!"""""""""#$"""""""%$""""""""$!"""""""#!!""""""" "!""""""""#!"""""""""#$"""""""%$""""""""$!"""""""#!!"""""""""'(")*+$,-"""""" "!""""""""#!"""""""""#$"""""""%$""""""""$!"""""""#!!""""""" %7"8 $+,+ %7"8 '-.&&( #O!"""""""!O$!"""""!O7,"""""!O72""""!O#%""""""!O#1 #O!"""""""!O&&"""""!O7-"""""!O%$""""!O%$""""""!O%# #O!"""""""!O#7"""""!O!,"""""!O#2"""""!O!,""""""!O!, #O!"""""""!O,&"""""!O%-""""!O%2""""!O!-""""""!O!7 #O!"""""""!O$-"""""!O%&"""""!O%2"""""!O!&"""""!O#! #O!""""""!O2!"""""!O%7""""!O#2"""""!O!$"""""!O!7 #O!"""""""!O-7"""""!O1$"""""!O$$"""""!O%$"""""!O2, #O!"""""""#O!#"""""!O,1"""""!O&&"""""!O7&"""""!O#, /)/0# +12! )343 +56##1"" 9:;< !06=>=?@' +9<% 138 in DLD-1, H630 and LoVo, however, in HCT116 cells there was no significant reduction in ERBB2 mRNA expression following treatment with 100 nM panobinostat (Fig. 6-7A). As mRNA expression does not always correlate with protein expression, we analyzed the effect of increasing concentrations of panobinostat on EGFR and HER2 protein expression in DLD-1, H630, HCT116 and LoVo CRC cancer cell lines. Panobinostat treatment for 24 h resulted in a dose-dependent decrease in both EGFR and HER2 protein expression in all cell lines examined at 24 h (Fig. 6-7B). Importantly, at the concentration of 15 nM panobinostat used for the apoptotic analysis, all cell lines demonstrated measurable downregulation of EGFR and HER2 protein expression. To determine if the dose- dependent downregulation of EGFR and HER2 protein is mediated in part by acetylation and subsequent dysfunction of HSP90, cells were treated with the combination of the HSP90 inhibitor 17-AAG alone and in combination with panobinostat. Treatment of LoVo and H630 CRC cells with panobinostat resulted in a dose-dependent downregulation of EGFR and HER2 protein expression as expected. Treatment with 17- AAG also resulted in significant downregulation of EGFR and HER2 Figure 6-8. Effect of panobinostat and HSP90 inhibitor on EGFR and HER2 protein expression in H630 and LoVo CRC cells. H630 and LoVo CRC cells were exposed to 15, 25 and 50 nM panobinostat (LBH589) alone and in combination with 1 µM of the HSP90 inhibitor, 17-AAG, for 24 h and cells were evaluated for the effects on EGFR and HER2 protein expression. Densitometry analysis of Western blot bands was performed using Scion Image and were subsequently normalized to β-Tubulin to control for loading. Relative EGFR and HER2 protein expression are presented as fold-downregulation of target protein/tubulin expression relative to untreated time-matched control. (LaBonte, MJ) !"#$ !%&'(')*+ ,!$- !"#$ !%&'(')*+ ,!$- ./........01........-1.......1/......../........01.......-1.......1/.........+2.34,156 ./........../........./........./.........0.........0.........0.........0.........."2.07%88" -9.: !"#$ ./........01........-1.......1/......../........01.......-1.......1/.........+2.34,156 ./........../........./........./.........0.........0.........0.........0.........."2.07%88" -9.: %&'& 0;/....../;</..../;=<..../;06..../;0<...../;/5..../;/=..../;/0 0;/...../;59..../;7=..../;9<..../;=<..../;-7.../;0=..../;/7 0;/...../;5/..../;9=..../;05..../;==...../;/<..../;/9..../;/9 0;/...../;<9..../;7=..../;90..../;=0...../;/6..../;-5..../;00 139 consistent with the reported role of HSP90 as a molecular chaperone for these client proteins. Interestingly, combined treatment with 17-AAG and panobinostat resulted in downregulation of EGFR and HER2 protein expression greater than that of either agent alone (Fig. 6-8). Collectively, these data would suggest that the panobinostat-mediated downregulation of EGFR and HER2 is mediated through distinct mechanisms that include disruption of HSP90 function, but additionally through transcriptional suppression of the EGFR and HER2 genes at higher concentrations. 6.3.6 The combination of panobinostat and L-4804 Lapatinib enhances inhibition of HER expression and downstream signaling pathways. Having already established that treatment with panobinostat in our CRC cell line models resulted in a decrease in ERBB1 and ERBB2 mRNA transcripts and dose-dependent decreases in EGFR and HER2 protein expression we sought to further analyze the effect of panobinostat (LBH589) and L-4804 Lapatinib (LAP) combination treatment on both ERBB1 and ERBB2 mRNA and EGFR and HER2 protein expression. H630 and LoVo CRC cells were treated with 3 µM LAP and 10 and 15 nM panobinostat alone and in combination for 24 h. mRNA expression of ERBB1 and ERBB2 were analyzed by qPCR along with the expression of three downstream HER-signaling molecules including the pro-survival transcription factor NFκB1 and the cell cycle regulatory gene CCND1 (cyclin D1). Treatment with lapatinib alone had no significant effect on the mRNA expression of ERBB1 and ERBB2. However, as previously observed ERBB1 and ERBB2 mRNA expression was significantly reduced in a dose-dependent manner by panobinostat in the H630 cells. In the LoVo cells, ERBB1 mRNA was increased and ERBB2 mRNA decreased by panobinostat treatment. Of note, combination treatment with LAP and panobinostat resulted in an increased downregulation of ERBB2 in both 140 the LoVo and H630 cells. In the H630 cells ERBB1 mRNA was downregulated to a greater extent than panobinostat treatment alone as a result of combination treatment. However, the induction of ERBB1 mRNA observed with panobinostat alone in the LoVo cells was abrogated with combination treatment and ERBB1 mRNA expression remained similar to controls (Fig. 6-9). Two key signaling molecules downstream of EGFR and HER2 were analyzed to determine if their expression was decreased to a greater extent by combination treatment to confirm the increased disruption of signaling. The cell cycle regulatory CCND1 gene was previously identified as one of the most consistently downregulated genes in response to EGFR/HER2 inhibition. In the same study, IRAK1 was also identified as a major transcriptional target of AKT signaling and significantly repressed in response to EGFR/HER2 inhibition (Hedge, Rusnak et al. Figure 6-9. The combination of L-4804 Lapatinib and panobinostat modulates ERBB1, ERBB2, CCND1, NFkB1, and IRAK1 mRNA transcription. H630 and LoVo CRC cells were treated with 3 µM L-4804 Lapatinib (LAP) and 10 and 15 nM panobinostat (LBH589) alone and in combination for 24 h. mRNA expression was measured for (A) ERBB1, (B) ERBB2, (C) CCND1, (D) NFκB1 and (E) IRAK1 and were normalized to GAPDH. Histograms represent percent control compared with untreated-time matched controls set at 100%. Bars represent the mean ± SD of two independent experiments. (LaBonte, MJ) !"##$ ! " ! " ! " ! " ! " ! " # "$ $# %$ !## !"$ !$# "## "$# #&&&&&&&&&&&&&&'&&&&&&&&&&&&&&#&&&&&&&&&&&&&&#&&&&&&&&&&&&&&'&&&&&&&&&&&&&&&'&&&&&&!(&)*+ #&&&&&&&&&&&&&&#&&&&&&&&&&&&&!#&&&&&&&&&&&!$&&&&&&&&&&&&!#&&&&&&&&&&&&&!$&&&&&&,(&)-.$/0 .1'# )232 4&52,6728 9:8;6<=:&>9?*&@AB7:CC<2, !"##% ! " ! " ! " ! " ! " ! " # "$ $# %$ !## !"$ .1'# )232 #&&&&&&&&&&&&&&'&&&&&&&&&&&&&&#&&&&&&&&&&&&&&#&&&&&&&&&&&&&&'&&&&&&&&&&&&&&&'&&&&&&!(&)*+ #&&&&&&&&&&&&&&#&&&&&&&&&&&&&!#&&&&&&&&&&&!$&&&&&&&&&&&&!#&&&&&&&&&&&&&!$&&&&&&,(&)-.$/0 4&52,6728 9:8;6<=:&>9?*&@AB7:CC<2, &'(#$ ! " ! " ! " ! " ! " ! " # "$ $# %$ !## !"$ .1'# )2!2 #&&&&&&&&&&&&&&'&&&&&&&&&&&&&&#&&&&&&&&&&&&&&#&&&&&&&&&&&&&&'&&&&&&&&&&&&&&&'&&&&&&!(&)*+ #&&&&&&&&&&&&&&#&&&&&&&&&&&&&!#&&&&&&&&&&&!$&&&&&&&&&&&&!#&&&&&&&&&&&&&!$&&&&&&,(&)-.$/0 4&52,6728 9:8;6<=:&>9?*&@AB7:CC<2, ))&*$ ! " ' D $ 1 # "$ $# %$ !## .1'# )232 #&&&&&&&&&&&&&&'&&&&&&&&&&&&&&#&&&&&&&&&&&&&&#&&&&&&&&&&&&&&'&&&&&&&&&&&&&&&'&&&&&&!(&)*+ #&&&&&&&&&&&&&&#&&&&&&&&&&&&&!#&&&&&&&&&&&!$&&&&&&&&&&&&!#&&&&&&&&&&&&&!$&&&&&&,(&)-.$/0 4&52,6728 9:8;6<=:&>9?*&@AB7:CC<2, ! " # $ !"#$% ! " ! " ! " ! " ! " ! " # "$ $# %$ !## !"$ .1'# )2!2 #&&&&&&&&&&&&&&'&&&&&&&&&&&&&&#&&&&&&&&&&&&&&#&&&&&&&&&&&&&&'&&&&&&&&&&&&&&&'&&&&&&!(&)*+ #&&&&&&&&&&&&&&#&&&&&&&&&&&&&!#&&&&&&&&&&&!$&&&&&&&&&&&&!#&&&&&&&&&&&&&!$&&&&&&,(&)-.$/0 4&52,6728 E9:8;6<=:&>9?*&@AB7:CC<2,F % 141 2007). The transcription factor NFκB1 is a key effector transcription factor downstream of AKT signaling and its expression is, in part, self-regulated. CCND1, NFκB1 and IRAK1 were all downregulated to a greater extent with combination treatment than either LAP or panobinostat treatment alone, suggesting that combination treatment is having a significantly great effect at suppressing the activity of these pathways than either agent alone. The protein expression of EGFR and HER2 and activation status of downstream signaling proteins, AKT and MAPK, was subsequently analyzed by Western blot analysis to investigate the effect of combination treatment on EGFR and HER2 signaling in the H630 and LoVo CRC cells treated with 3 µM LAP and 15 nM panobinostat alone and in combination for 18 and 24 h (Fig. 6-6). At 24 h post-treatment, both cell lines demonstrate downregulation of EGFR and HER2 with combination treatment to the same or greater extent than panobinostat treatment alone. Phospho-AKT (Ser 473 ) levels are significantly repressed in both cell lines with both panobinostat and combination treatment at 18 and 24 h. Analysis of phospho-p44/42-MAPK (Tyr 204 /Thr 202 ) shows an enhanced suppression with combination treatment in the H630 at both 18 and 24 h but only at 24 h post combination treatment in the LoVo cells. Interestingly, in the LoVo cells, panobinostat treatment appears to cause an increase in the levels of phospho-p44/42- MAPK (Tyr 204 /Thr 202 ) at 18 and 24 h that is sequestered by combination treatment with lapatinib. These data indicate that the combination of LAP and panobinostat downregulated EGFR and HER2 expression in a similar manner to panobinostat alone, but demonstrated enhanced suppression of phospho-p44/42-MAPK (Tyr 204 /Thr 202 ) in particular (Fig. 6-6). 142 6.3.7 PI3K and MEK Inhibition Potentiate the Growth Inhibitory Effects of panobinostat. Having identified that the combination of L-4804 Lapatinib (LAP) and panobinostat (LBH589) synergistically suppresses the proliferation and survival of CRC cells, we sought to evaluate the relative contribution of both the PI3K/AKT and MAPK signaling pathways to this interaction in the H630 and LoVo CRC cell line models. We utilized commercially available specific small molecule inhibitors of PI3K (LY294002), which is located immediately upstream of AKT and MEK (U0126), which is located immediately upstream of MAPK. H630 and LoVo CRC cells were exposed to increasing concentrations of LY294002, U0126 and panobinostat alone and in combination for 72 h Figure 6-10. Inhibition of PI3K or MEK in combination with panobinostat potentiates growth inhibition in H630 and LoVo CRC cells. Growth inhibition analysis was determined by MTS assay for 72 h. H630 and LoVo CRC cells were exposed to indicated concentrations of either (A) P13K inhibitor (LY294002) or (B) MEK inhibitor (U0126) alone or in combination with panobinostat (LBH589) for 72 h. Data points represent mean ± SEM percent growth inhibition of three independent experiments compared to untreated time-matched controls set at 100%. The combined drug effect was analyzed using the Chou-Talalay combination index (CI) equation and presented as CI with fraction affected (FA) values for the combinations. CI values were interpreted as follows: <1, synergism; 1- 1.2, additive; >1.2, antagonism. (LaBonte, MJ) ! " # $ % & ' ( ) ! "# #! $# %!! &'(#)* &+"*,!!" -./0. *+,+ 12-.345.6 !7#2222222%222222"7#222222#2222222%!222222"#22222#!2222%!!2222"#!222!82&+"*,!!" !7#222222%22222222922222222:22222222)2222222%!22222%#222222%)22222"#22222382&'(#)* ! " # $ % & ' ( ) ! "# #! $# %!! &'(#)* ;!%": -./0. *+,+ 12-.345.6 !7#2222222%222222"7#222222#2222222%!222222"#22222#!2222%!!2222"#!222!82;!%": !7#222222%22222222922222222:22222222)2222222%!22222%#222222%)22222"#22222382&'(#)* ! " # $ % & ' ( ) ! "# #! $# %!! &'(#)* ;!%": -./0. 2!7#222222%222222"7#2222222#222222%!222222"#22222#!2222%!!2222"#!222!82;!%": !7"#2222!7#22222%22222222922222222:22222222)222222%!2222222%#22222%)2222382&'(#)* -&#. 12-.345.6 ! " # $ % & ' ( ) ! "# #! $# %!! &'(#)* &+"*,!!" -./0. 2!7#222222%222222"7#2222222#222222%!222222"#22222#!2222%!!2222"#!222!82&+"*,!!" !7"#2222!7#22222%22222222922222222:22222222)222222%!2222222%#22222%)2222382&'(#)* -&#. 12-.345.6 / 0 143 and cell proliferation was measured by the MTS assay as described in Chapter 3. Increasing concentrations of panobinostat in combination with either the PI3K or MEK inhibitor resulted in synergistic increases in growth inhibition in both cell lines analyzed as indicated by CI values <1 at a FA of 0.5 (Fig. 6-10). These data would suggest that suppression of either AKT or MAPK signaling can contribute to the enhanced growth inhibitory effects associated with the combination of panobinostat and LAP through suppression of HER signaling pathways in CRC cells. 6.3.8 Effect of L-4804 Lapatinib on intracellular concentrations of HDACi, panobinostat. We have demonstrated that panobinostat and L-4804 Lapatinib (LAP) have potent growth inhibitory and anticancer activity when these agents are administered in combination. Of note, this combination appears to be highly active in CRC cell lines that display limited sensitivity to LAP treatment alone. We therefore sought to determine the potential contribution of any EGFR/HER2-independent mechanisms to the observed synergy between LAP and panobinostat. Although LAP belongs to the 4- anilinoquinzaoline class of TKIs and is considered a targeted agent with high affinity for EGFR and HER2, it has been also reported to potently inhibit two members of the transmembrane ABC-transporter super-family including p-gp and BCRP (Polli, Humphreys et al. 2008). Previously, we reported that LAP treatment inhibited the efflux of SN-38 from CRC and gastric cancer cells resulting in enhanced intracellular drug accumulation, DNA damage and apoptosis that occurred independently of EGFR and HER2 inhibition (LaBonte, Manegold et al. 2009). Therefore, we directly analyzed the intracellular levels of panobinostat in H630 and LoVo CRC cells treated with 15 nM panobinostat alone and in combination with clinically relevant concentrations of 3 µM 144 LAP for 6 h. Using LC-MS, we determined that co-incubation of H630 or LoVo CRC cells with LAP and panobinostat did not result in a significant increase in the accumulation of intracellular panobinostat (data not shown). This observation demonstrates that enhanced intracellular accumulation of panobinostat is not a contributing factor to the synergistic interaction observed between LAP and panobinostat and strongly indicates that the enhanced effects observed with the incorporation of LAP are mediated via suppression of EGFR and HER2 signaling. Figure 6-11. Antitumor activity of panobinostat in combination with L-4804 Lapatinib in a LoVo CRC xenograft model. Male balb/c nu/nu mice (n=6 per group) bearing subcutaneous 100 mm 3 tumors were administered either vehicle, 30 mg/kg BID L- 4804 Lapatinib (LAP), 2.5 mg/kg panobinostat (LBH589) for five consecutive days weekly or a combination of LAP and LBH589. (A) Tumor volume was measured every 2 days and represented as the mean ± SEM. (B) Percent of initial mouse body weight was calculated as percent bodyweight at the study conclusion compared to day 0. Combo = 30 mg/kg LAP + 2.5 mg/kg LBH589. Statistical significance was determined by two-way ANOVA (Graphpad, Prism 5.0). ***p<0.001. (LaBonte, MJ and Wilson, PM) ! ! " # $ %& %' %( &% &) ! &!! )!! #!! (!! %!!! %&!! %)!! *+,-./+ "!0123420567 &8'012342059:'($ ;<1=< >?@A0<B0CD+?E1+FE CG1<D0*</G1+0H11 " I " ! &' '! J' %!! %&' *+,-./+ "!012342 567 &8'012342 59:'($ ;<1=< CD+?E1+FE K0<B0LF-E-?/09<M@0N+-2,E OOO 145 6.3.9 Panobinostat in combination with L-4804 Lapatinib synergistically inhibits the growth of LoVo xenografts in nude mice. Having demonstrated that the combination of panobinostat and L-4804 Lapatinib (LAP) synergistically inhibited CRC cell growth, survival, ERBB1 (EGFR) and ERBB2 (HER2) mRNA and protein expression, and enhanced apoptosis in multiple cell lines, we extended our studies into CRC xenograft models to confirm our in vitro results. LoVo CRC xenografts were established as outlined in Chapter 3. Panobinostat was administered at 2.5 mg/kg by i.p. injection once daily (q.d.) for five consecutive days each week. LAP was administered at 30 mg/kg BID by oral gavage for the duration of the study. Co-administration of LAP in combination with panobinostat resulted in a statistically significant reduction in tumor growth inhibition when compared to vehicle-treated controls or single agent LAP and panobinostat. At the end of the 24-day treatment period, LAP monotherapy resulted in a 4.1% reduction in mean tumor volume to 1075.3 ± 163.3 mm 3 compared to the vehicle control group with a mean tumor volume of 1121.7 ± 288.9 mm 3 (Fig. 6-11A). Panobinostat administered at 2.5 mg/kg resulted in a reduction in mean tumor volume of 23.8% to 854.6 ± 275.9 mm 3 Table 6-1. In vivo tumor delay of LoVo CRC cancer xenograft following treatment with panobinostat (LBH589) and L-4804 Lapatinib (LAP). 1 Td, time in days to reach a tumor volume that was 5 times greater than the initial volume on day 0. 2 Expected Td = Mean control + (mean Lapatinib (LAP) – mean control) + (mean LBH589 – mean control). 3 Combination = 30 mg/kg LAP + 2.5 mg/kg LBH589. Treatment Tumor Delay (Td) 1 days Vehicle 12.7 ± 2.3 30 mg/kg LAP 16 ± 1.6 2.5 mg/kg LBH589 19.3 ± 5.2 Expected 2 Effect of combination 3 22.6 Observed Effect of combination 3 26 ± 4.7 Ratio of Observed: Expected Td = 1.15 146 when compared to the vehicle-treated group. However, the combination of 2.5 mg/kg panobinostat and 30 mg/kg LAP resulted in a reduction in mean tumor volume of 49.8% to 563.2 ± 111.6 mm 3 when compared to the vehicle-treated group (Fig. 6-11A). The differences in mean tumor volume between LAP and panobinostat monotherapy treatment groups and the combination treatment group were statistically significant (p=0.0013). The combination of LAP and panobinostat also resulted in a highly significant increase in tumor delay (Td) with a ratio of observed: expected of 1.15, indicative of a synergistic increase in antitumor activity with combination treatment (Table 6-1). Importantly, despite demonstrating increased antitumor efficacy compared to monotherapy with 2.5 mg/kg panobinostat, combination treatment did not result in any statistically significant difference in bodyweight compared to monotherapy with 2.5 mg/kg panobinostat (p=0.48) (Fig. 6-11B). This data strongly suggests that the interaction between LAP and panobinostat is capable of exerting antitumor effects in an in vivo model beyond that of either single agent alone without any evident increase in toxicity. 6.3.10 Summary. Chapter 6 evaluated the combination of the dual EGFR/HER2 TKI, L-4804 Lapatinib (LAP), and the HDACi, panobinostat (LBH589), in CRC cell line models. Key results from this study: • Combination treatment with panobinostat and LAP demonstrated a strong synergistic interaction resulting in decreased cell proliferation and increased apoptosis in all cell line models analyzed with varying EGFR or HER2 protein expression. 147 • Panobinostat induced a potent downregulation of ERBB gene transcription and protein expression at clinically achievable concentrations. • LAP as an inhibitor of ABC-drug transporters did not result in any change in intracellular accumulation or steady state concentrations of panobinostat. • The combination of LAP and panobinostat demonstrated a clear antitumor synergistic interaction in LoVo CRC xenograft confirming the in vitro observations. 6.4 Discussion Despite recent advances in mCRC chemotherapy with the approval of new therapeutic agents in the last 10 years, effective disease control remains hindered by the high incidence of drug resistance, treatment failure and subsequent patient mortality. As median OS for patients with mCRC has now surpassed 20 months, many patients fail all standard therapeutic options while still maintaining an excellent performance status and candidacy for continued therapy. This has resulted in a critical need to identify and exploit novel therapeutic strategies in patients who have failed standard of care chemotherapies. Panobinostat (formerly known as LBH589) is a cinnamic hydroxamic acid analog that has demonstrated activity in the hematologic malignancies and cutaneous lymphomas. However, studies investigating panobinostat in models of CRC are still relatively few (Dedes, Dedes et al. 2009; Fazzone, Wilson et al. 2009; LaBonte, Wilson et al. 2009). This present study therefore sought to evaluate the potential therapeutic effects of combining the HDACi panobinostat and the small molecule EGFR/HER2 TKI lapatinib in a panel of CRC cell lines. 148 EGFR is expressed in approximately 60% of CRC and is reported to play an important role in driving the growth and progression of CRC. More importantly, EGFR has been implicated in promoting resistance to a number of cytotoxic therapeutics in CRC and a wide range of other malignancies. As such, EGFR has become an efficacious therapeutic target in CRC with the approval of the EGFR monoclonal antibodies cetuximab and panitumumab. However, acquired or intrinsic resistance to these therapies is common and to date, only somatic mutation in the oncogene KRAS has been definitively linked to resistance to EGFR in EGFR-expressing CRC. Importantly, a recent report identified that HDACi could downregulate EGFR and other key pathway signaling proteins in EGFR-mutant NSCLC cell lines resulting in sensitization to the EGFR TKI erlotinib (Edwards, Atadja et al. 2007). Based on the evidence in NSCLC and the substantial evidence indicating that EGFR plays a key role in the survival and progression of CRC, we evaluated the efficacy of EGFR/HER2 inhibition in combination with panobinostat in CRC cell line models. 149 In our CRC cell line models, combining the dual EGFR/HER2 TKI, L-4804 Lapatinib (LAP) with panobinostat resulted in synergistic decreases in CRC cell proliferation, colony formation and rapidly induced apoptotic cell death. Mechanistic evaluation of this combination demonstrated that there was a significant enhancement of double-strand DNA damage (as measured by γH2A.X), increased cleavage of apoptotic effectors caspase-8 and PARP, decreased ERBB1 (EGFR) and ERBB2 (HER2) mRNA and protein expression and decreased phosphorylation of AKT (Ser 473 ) and p44/42 Figure 6-12. Proposed mechanism of action for the synergistic interaction between the dual EGFR/HER2-TKI lapatinib with HDACi, panobinostat in CRC cell line models. Combination treatment with lapatinib (LAP) and panobinostat (LBH589) in CRC cell line models resulted in (1) lapatinib-mediated suppression of HER-mediated signaling through decreased activation of PI3K-AKT and RAS-RAF-MEK-MAPK, (2) LBH589-mediated downregulation of HER family (ERBB1 and ERBB2) and downstream transcription factor (NFκB1 and IRAK1) and cell cycle gene (CCND1) mRNA expression and (3) HSP90 mediated degradation of EGFR and HER2 protein expression. Figure generated using MS Powerpoint (2008). 150 MAPK (Thr 202 /Thy 204 ). Importantly, we extended our analysis to test the combination of LAP and panobinostat in an in vivo LoVo CRC cell line xenograft model. The in vivo analysis demonstrated that LAP and panobinostat exerted synergistic antitumor activity, significantly delaying tumor growth and inducing a significant reduction in tumor size in the absence of any detectable adverse events. We previously reported that lapatinib promoted elevated intracellular concentrations of the TOPO 1 inhibitor SN-38 in LoVo CRC and MKN28 gastric cancer cell line models. This increased drug exposure resulted in enhanced growth inhibition and apoptosis that was independent of the inhibition of EGFR and HER2 in these models. Lapatinib has been reported to be an inhibitor of p-gp and BCRP at clinically relevant concentrations (Polli, Olson et al. 2009). Although previous reports indicated that panobinostat is not a substrate for p-gp or BCRP cellular efflux, we nonetheless investigated the possibility that perhaps lapatinib may be inhibiting p-gp, BCRP or additional members of the ABC transporter family resulting in enhanced intracellular panobinostat accumulation. Based on the synergistic interactions we observed in our CRC cells that expressed low levels of both receptors and were relatively insensitive to LAP treatment alone, it was feasible that an element of the synergy may be mediated through EGFR/HER2-independent mechanisms. After directly measuring the panobinostat intracellular concentration by LC-MS we determined that there was no significant difference between panobinostat alone or in combination with LAP. Therefore, the enhanced effects of the combination could not be attributed to an enhanced intracellular accumulation of panobinostat. Beyond inhibition of p-gp and BCRP, no other non-specific inhibitory activity has been reported to date for lapatinib. Furthermore, a recent report confirmed that lapatinib was the most highly specific clinically utilized kinase inhibitor in a screen of >50% of all kinases in the human kinome (Karraman, 151 Herrgard et al. 2008). Therefore, it is highly likely that the synergistic effects observed with panobinostat are mediated solely through inhibition of EGFR and/or HER2 signaling. One of the most interesting observations is that CRC cell lines with varied expression of EGFR and/or HER2 demonstrated a highly significant sensitization to panobinostat as a result of LAP treatment. This data would strongly suggest that inhibition of HER-mediated signaling overcomes a key resistance mechanism to HDACi treatment. Based on the modest growth inhibition and lack of apoptosis observed with LAP treatment alone at lower doses (3 µM), it seems plausible that cell survival and continued proliferation following HDACi treatment may be dependent on signaling from EGFR and or HER2. Therefore, concomitant treatment with LAP may reduce the cells ability to tolerate and survive the cytotoxic effects of panobinostat. This hypothesis is supported by observations in CRC where EGFR-targeted therapies have had their greatest impact and demonstrated the most promising efficacy in combination with other cytotoxic agents. Specifically, cetuximab was reported to re-sensitize irinotecan- refractory mCRC patients to irinotecan. In addition, cetuximab and panitumumab have both increased the clinical outcome in combination with the cytotoxic regimens FOLFOX and FOLFIRI. The clinical data strongly supports the role of EGFR in mediating resistance to cellular stress and cytotoxicity induced by chemotherapeutic agents. The mechanistic basis for the observed synergy between LAP and panobinostat combination appears to be multi-factorial (Fig. 6-12). We provide the first report indicating that panobinostat can downregulate EGFR and HER2 protein expression in a dose-dependent manner in a panel of CRC cell lines. In addition, we also have shown that panobinostat can downregulate ERBB1 and ERBB2 gene expression levels at a clinically achievable concentration. The transcriptional and translational disruption of 152 HER-expression induced by panobinostat combined with the inhibition of EGFR and HER2 activation and signaling by lapatinib results in enhanced growth inhibition and apoptotic cell death in in vitro CRC cell line models. We subsequently extended our analyses to a LoVo CRC xenograft model and confirmed our synergistic in vitro results with enhanced tumor growth delay and a highly significant overall reduction in mean tumor size observed the combination treatment. 6.5 Manuscripts and Abstracts The data presented in Chapter 6 supporting the rational combination of the dual EGFR/HER2 TKI, lapatinib, with the HDACi, panobinostat, resulted in the submission of a peer-reviewed manuscript to Cancer Research that is currently under review. In addition, several other meeting abstracts were presented (Table 6-2). Table 6-2. Resulting Manuscripts and Abstracts from the study of novel combination of HDACi, panobinostat and the dual EGFR/HER2 TKI, L-4804 Lapatinib, in CRC cell line models. Manuscripts LaBonte MJ, Wilson PM, Louie S, El-Khoueiry A, Lenz HJ and Ladner RD. A novel therapeutic combination with synergistic antitumor activity in colorectal cancer: The dual EGFR/HER2 tyrosine kinase inhibitor L-4808 Lapatinib and the histone deacetylase inhibitor panobinostat (LBH589). Cancer Research. 2010; Under Review. Abstracts LaBonte MJ, Wilson PM, Fazzone W, El-Khoueiry A, Lenz HJ and Ladner RD. A novel therapeutic combination with synergistic antitumor activity in colon cancer: The dual tyrosine kinase inhibitor L-4804 Lapatinib and the histone deacetylase inhibitor panobinostat (LBH589). American Association for Cancer Research (AACR) 101th Annual meeting, Washington DC, USA, April 2010. Abstract #7867. LaBonte MJ, Wilson PM, Fazzone W, Lenz HJ and Ladner RD. Effects of histone deacetylase inhibitors on epidermal growth factor receptor expression in colon cancer cell lines: Implications for combination chemotherapy. University of Southern California, Keck School of Medicine Annual Pathology Retreat Presentations. Oxnard, California, USA, 2008. LaBonte MJ, Wilson PM, Fazzone W, Lenz HJ and Ladner RD. Effects of histone deacetylase inhibitors on epidermal growth factor receptor expression in colon cancer cell lines: Implications for combination chemotherapy. American Association for Cancer Research (AACR) 98th Annual meeting, Los Angeles, California, USA, April 2007. Abstract #683. 153 6.6 Translational Impact The data presented in this Chapter have been utilized as key evidence supporting this combination for the treatment of CRC and gastric cancer. The use of chemotherapy to treat gastric cancer has no established standard of care. While, chemotherapeutic combinations such as 5-FU plus cisplatin have demonstrated benefit, standardized second line chemotherapy options for gastric cancer patients do not exist. CRC cancer patients who have failed to benefit from multiple lines of chemotherapy have limited options. Because of this clinical reality, there is an urgent need to identify novel treatment options for both of these diseases. Therefore, in collaboration with Drs. Lenz and El-Khoueiry of the division of Medical Oncology at USC/Keck School of Medicine, a phase I/II clinical trial is currently being designed to determine the feasibility of combining the HDACi, panobinostat, with the dual EGFR/HER2 TKI, lapatinib, as a strategy to enhance the antitumor efficacy of both panobinostat and lapatinib in solid tumors within the clinic. 6.6.1 Study Endpoints. Our preliminary data demonstrates that lapatinib is synergistic with panobinostat in CRC cell line and xenograft models. In cell line models, the addition of lapatinib to panobinostat therapy resulted in synergistic antitumor efficacy compared to panobinostat treatment alone, without increasing intracellular levels of panobinostat and leading to enhanced DNA damage and apoptosis. Based on our findings, we propose a multidisciplinary, translational study to combine lapatinib with panobinostat for the treatment of patients with advanced breast, CRC and gastric cancers. The study is aimed to define the maximum tolerated dose of 154 the combination as well as establish the proof-of-principal regarding the mechanism of synergy between lapatinib and panobinostat in patients. We hypothesize that the combination of irinotecan and panobinostat will improve response rates and survival outcomes in patients with advanced breast, CRC and gastric cancer who have previously failed multiple lines of chemotherapy. The pilot/feasibility study, endpoints are to: • Establish the maximum tolerated doses for this drug combination in a phase I clinical trial. • Determine the acetylation status of markers of panobinostat actions such as histones 3 and 4 in plasma, peripheral blood mononuclear cells (PBMCs) and tumor core biopsies from patients treated with panobinostat alone or in combination with lapatinib. • Measure the mechanistic activity of lapatinib in terms of decreased phosphorylation of AKT (Ser 473 ) and p44/42 MAPK (Thr 202 /Tyr 204 ) in PBMCs and tumor core biopsies. The success of this study will form the preliminary data needed to expand this combination into a phase II study in second-line gastric cancer, and a phase II study in metastatic CRC cancer patients resulting in a novel drug combination strategy that has the potential to expand the therapeutic options in each of these diseases. 155 Chapter 7 Overall Conclusions and Future Directions 7.1 Overall Conclusions Despite advances in chemotherapeutic options for the treatment of CRC, there remains a high incidence of drug resistance and disease progression, which remains a major stumbling block to effective disease control. Despite the approval of multiple new agents to treat CRC within the last 10 years including, capecitabine, irinotecan, oxaliplatin and the biologically targeted agents, advanced and metastatic disease is often incurable with a 5-year survival of less than 10% (Douillard, Cunningham et al. 2000; Giacchetti, Perpoint et al. 2000; Giusti, Shastri et al. 2008; Sanoff, Sargent et al. 2008). In addition, the median survival of patients with metastatic disease has surpassed 20 months and these patients often fail all standard therapeutic options whilst maintaining an adequate performance status for continued therapy. One of the objectives in cancer chemotherapy has been to improve both the quality of life and OS of patients through rationale development of novel agents and combinations of agents. In addition, a better understanding of the molecular biology of cancer has increased insights into important new pathways for chemotherapeutic intervention, identification of predictive and prognostic markers and resulted in the development of an era where personalized medicine may become a reality in the clinic. The work presented in this dissertation focused on two novel chemotherapeutic classes of anticancer agents: the small molecule TKI (lapatinib) and HDACi (vorinostat and LBH589) and further evaluated (1) their mechanism of action in CRC and gastric 156 cancer cells and (2) which agents when used in combination, resulted in a synergistic antitumor efficacy with standard of care agents as well as novel combinations. Results in Chapter 4 provide an understanding of the interaction between lapatinib and standard of care combination therapies for CRC and gastric cancer and provide preclinical rationale for the incorporation of lapatinib into drug combination approaches. The hypothesis was that the addition of lapatinib to standard of care chemotherapeutic agents 5-FU, oxaliplatin, cisplatin, and irinotecan active metabolite, SN-38, would result in enhanced antitumor efficacy in CRC or gastric cancer cell line models. In these studies, we evaluated the combination of lapatinib with 5-FU, oxaliplatin, cisplatin, and the irinotecan active metabolite, SN-38. Initial results indicated that there was a strong synergistic interaction between lapatinib and SN-38. Irinotecan is a TOPO 1 inhibitor that has demonstrated clinical activity in both CRC and gastric cancer when used as a single agent or in combination with other chemotherapeutic agents. The EGFR pathway has previously been demonstrated to activate DNA repair associated with TOPO 1 inhibition leading to reduced DNA damage and growth arrest. Clinical data also supports the rationale for combining EGFR-targeted therapies with irinotecan in colorectal cancer, best demonstrated by the significant activity reported when cetuximab was combined with irinotecan in irinotecan-refractory patients. Chapter 4 reports that lapatinib was synergistic with irinotecan in CRC and gastric cancer models through multiple mechanisms of action including: inhibition of cell proliferation, suppression of PI3K and MAPK signaling, enhanced formation of DNA double-strand breaks, activation of caspase-8 and induction of apoptosis in vitro. Further, we demonstrated that lapatinib has the potential to exert additional mechanisms of action that were independent of EGFR and HER2 antagonism, resulting in enhanced 157 intracellular SN-38 accumulation and promoting increased cell death. In vitro results were validated in a CRC xenograft model. In Chapter 5, the effect of HDACi’s vorinostat and LBH589 were evaluated in CRC models to compare their effects on cell growth, acetylation status, apoptosis as well as the alterations in global transcription profiles following treatment. Microarray analysis successfully identified a core signature of 11 genes which were modulated by both vorinostat and LBH589 in a similar manner in both HCT116 and HT29 CRC cell lines. The robustness of the microarray was confirmed by qPCR and the core signature was independently validated by qPCR in 9 CRC cell lines and 2 in vivo models. Vorinostat and LBH589 induced alterations in critical genes involved in nucleotide metabolism, angiogenesis, mitosis, and cell survival, which may represent potential intervention points for novel therapeutic combinations with anti-angiogenic, anti- metabolic, and anti-mitotic agents in CRC. These core genes will assist in the development and validation of a common gene set which may represent a molecular signature of HDAC inhibition in CRC. Given the evidence of single agent efficacy of HDACi in CRC cells, and the potential success of combining a broad spectrum drug with a targeted agent to enhance efficacy, we therefore evaluated the effect the combination of LBH589 and lapatinib in CRC cancer cell line models in vitro and in vivo. In addition, we investigated the effect of LBH589 on the HER pathway expression and downstream signaling cascades in vitro. Chapter 6 reports the results of this study and found that LBH589 alone was able to downregulate both the mRNA and protein of not only EGFR and HER2, but also HER3, the predominant dimerization partner for HER2, whose expression has been reported to be a mechanism of acquired resistance to HER2 targeted therapy. Furthermore, the combination of LBH589 with lapatinib resulted in synergistic growth inhibition, 158 suppression of PI3K and MAPK signaling, enhanced DNA damage, activation of caspase-8, PARP and increased levels of apoptosis. CRC xenografts treated with LBH589 and lapatinib resulted in a synergistic antitumor efficacy with no measureable increase in toxicity compared to either single agent. These studies demonstrate the role of novel chemotherapeutic agents to enhance efficacy of standard of care chemotherapeutic agents and further show that their combination(s) may provide a novel way in which to enhance patient outcome. As novel and more specific inhibitors of these pathways emerge, and as our knowledge of both their mechanisms of action and of patient pharmacokinetic and pharmacogenetic interactions increase, it is possible that such combinations could be directed towards patients who are most likely to benefit, avoiding unnecessary toxicities and maximizing the clinical impact of such chemotherapy. 7.2 Future directions Our results have demonstrated that simultaneously targeting both EGFR and HER2 in combination with a standard of care cytotoxic agent, irinotecan, or a novel HDACi, LBH589, has therapeutic potential in the treatment of gastrointestinal malignancies including CRC and gastric cancers. Results from the microarray analysis of two HDACi, vorinostat and LBH589, identified a panel of core HDACi-regulated genes that showed significant up- or downregulation by both agents in a panel of CRC cell lines and xenograft models. Defining a core set of genes that represent markers of HDAC inhibition is an important first step in the identification and validation of clinical markers for evaluating HDACi target inhibition, biological activity and treatment efficacy. The preliminary nature of 159 these genes is recognized and further validation is required. Specifically, analysis of the identified core set of genes for specificity in tumor versus normal cells is of crucial importance. The results from these studies will guide the integration of this signature into the clinic as a measure of HDACi efficacy. If these transcriptional changes are specific to tumor cells, then future clinical trials should validate them in tumor biopsies pre- and post treatment. However, if these transcriptional changes are not specific to tumor cells, but occur in normal cells, then this gene signature could be used to design a diagnostic test on accessible cell populations such as buccal mucosa to evaluate the biological activity of HDACi treatment in a patient-specific manner. Moreover, as acetylation induced by HDACi treatment is rapidly reversible, definition of a gene signature of HDAC inhibition may be a more favorable and robust method to determine target inhibition. Further experimentation is needed to elucidate the mechanism of HDACi-induced transcriptional downregulation of ERBB1 (EGFR) and ERBB2 (HER2) in CRC cells. This transcriptional downregulation of the HER family members may be due to histone modifications resulting in alteration of chromatin structure, acetylation status of transcription factors required for ERBB transcription and/or changes in proteins involved in ERBB mRNA stability. In addition, further analysis of specific HDACi isozymes involved in this interaction can be analyzed through the use of recently developed class or isozyme specific HDACi. The identification of the mechanism of this transcriptional downregulation will yield useful information for both the clinical development of HDACi, the novel combination of HDACi with dual EGFR/HER2 TKIs as well as identification of novel combination partners for HDACi with promising preclinical and clinical potential. The goal of the research presented in this thesis was to evaluate novel therapeutic approaches in the treatment of CRC through the use of existing efficacious therapeutic agents and the potential incorporation of novel agents that are currently in 160 clinical development both in CRC and other malignancies. The research presented within this thesis and within associated peer-reviewed publications has been used as the foundational rationale for the design of two clinical trials initiated in collaboration with Drs. Heinz-Josef Lenz and Anthony El-Khoueiry of the Division of Medical Oncology at the USC/Norris Comprehensive Cancer Center here in Los Angeles. The first trial was designed to determine the feasibility of combining the TOPO1 inhibitor irinotecan with the dual EGFR/HER2 inhibitor lapatinib as a strategy to enhance the efficacy of irinotecan through inhibition of EGFR signaling and through the enhancement of intratumoral concentrations of irinotecan. Our pre-clinical data demonstrated that lapatinib was synergistic with irinotecan in CRC and gastric cancer cell lines and in mouse xenograft models. Specifically, the addition of lapatinib to irinotecan therapy resulted in the inhibition of HER-mediated pro-survival signaling in addition to inducing significant increases in intracellular irinotecan levels compared to irinotecan treatment alone. The combined effect of these dual mechanisms was enhanced DNA damage and apoptosis in all cell line models evaluated. Based on the clear activity in the pre-clinical in vitro and in vivo studies, we hypothesize that the combination of irinotecan and lapatinib will demonstrate efficacy in patients with advanced colorectal and gastric cancers. In addition to investigating the efficacy of lapatinib in combination with currently approved therapeutics in CRC, this study sought to evaluate its potential in combination with a novel class of anticancer agents HDACi’s. HDACi’s have demonstrated significant activity in the hematological malignancies that are currently considered the primary target population for these agents. However, increasing evidence indicates that these agents have significant activity against solid tumors. The pre-clinical research presented herein clearly demonstrates that the combination of lapatinib with the HDACi LBH589 161 represents a highly synergistic drug combination in CRC cell line models. These results were strongly considered in the decision to design a clinical trial at USC Norris to determine the feasibility of this combination initially for the treatment of gastric cancers that overexpress HER2. The combination of lapatinib with irinotecan or LBH589 both represent novel treatment strategies that have the potential to have significant impact in CRC and gastric cancer patients that currently do not respond to standard of care therapy. Although these trials are early in development and may only initially provide information on safety and tolerability, such trials pave the way for further expansion of safe and promising combination strategies and importantly provide hope for patients and families who have exhausted all other remaining treatment options. 7.3 Personal Perspective It is important to recognize that the extrapolation of preclinical data to the clinical setting is often plagued with difficulties. Many successful novel chemotherapeutic combinations from in vitro and in vivo studies have demonstrated limited efficacy or unexpected toxicities when translated to the clinical setting. The primary reason is that response (or resistance) to any chemotherapy is multi-factorial and is dictated by both host and tumor heterogeneity and environmental factors which often times are difficult to predict using cell culture-based and even xenograft-based models. Such heterogeneity includes variations in numerous metabolic pathways including genetic polymorphisms and expression changes in drug targets, metabolizing enzymes, transporters and influential receptors that result in profound clinical consequences. Indeed, a significant portion of the literature that has evaluated the anticancer activity of novel compounds or attempted to characterize resistance mechanisms in cell culture models have utilized experimental 162 conditions that are greatly detached from any possible clinical scenario. While such proof-of-principle studies have oftentimes provided some insight into tumor biology, the observations rarely have any direct translational impact in the clinic. With this in mind, the research that is presented in this thesis was designed and executed, wherever possible, with careful consideration given to the concentrations of drugs used, the length of exposure and the interpretation of the data. Throughout my graduate research and having had the opportunity to work alongside translational researchers and clinicians, I have concluded that it is not difficult to kill tumor cells in cell culture models and even induce significant tumor regression in mouse xenograft models. However, the complexities of treating a patient with cytotoxic chemotherapy who has been battling metastatic colon cancer, while minimizing associated adverse events and attempting to provide that patient some measure of quality of life is something that can neither be predetermined nor recapitulated in the laboratory. One key area of cancer research which has the power to truly revolutionize how we approach chemotherapy and associated treatment decisions is the implementation of biomarkers. The once prevailing “one size fits all” approach to cancer chemotherapy is now becoming a relic of the past and the realization that treatment should be tailored on an individual case-specific basis is taking hold. In recent years, research efforts have focused on the identification of biomarkers with the power to predict both response to chemotherapy (measured through clinical parameters such as tumor response, time to tumor progression and adverse reactions) and prognostic markers to evaluate the aggressiveness of the disease and the likelihood of tumor recurrence. While the validation and integration of reliable and robust biomarkers into clinical practice for GI malignancies is still in its infancy, significant progress is being made. The future of cancer treatment will be on an ‘individualized’ basis, involving a simultaneous case- 163 specific analysis of clinical and pathological characteristics and analysis of a patient’s genetic and tumor biomarker profile. Such an approach holds promise of directing the most efficacious treatment to the patient, while minimizing the likelihood of any severe toxicities. 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Abstract (if available)

Abstract Colorectal cancer (CRC) the third leading causes of cancer-related death worldwide with an estimated 639,000 deaths each year. In the United States, CRC is the second leading cause of cancer-related death, resulting in approximately 49,920 deaths in 2009. Despite significant advances in research and development in CRC and gastric cancer, the current response rate for 1st line treatment of mCRC remains ~50% and dramatically decreases for 2nd line therapy. In addition, the five-year survival rate for patients diagnosed with mCRC is approximately 10%. While, molecularly targeted therapies have improved treatment outcomes for patients with cancer, these benefits are modest and in only select patient populations. It is clear that the new chemotherapeutic options and novel drug combinations must be developed to provide benefit for the approximately half of patients that fail to response to current chemotherapeutic options that are available. We hypothesize that combining novel agents that target alternative tumor associated pathways will result in additive to synergistic interactions with standard of care chemotherapy, leading to new treatment options for those patients who fail to respond to current therapy.

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Creator LaBonte, Melissa Janae (author)

Core Title Evaluating novel chemotherapeutic strategies in colorectal and gastric cancer: the role of histone deacetylase inhibitors and human epidermal receptor family inhibitors

School Keck School of Medicine

Degree Doctor of Philosophy

Degree Program Systems Biology

Publication Date 08/10/2010

Defense Date 04/27/2010

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag colorectal cancer,EGFR,gastic cancer,HER2,histone deacetylase inhibitors,irinotecan,KRAS,lapatinib,OAI-PMH Harvest,panobinostat,SN-38,vorinostat

Language English

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Advisor Ladner, Robert D. (committee chair), Lenz, Heinz-Josef (committee member), Press, Michael (committee member), Stallcup, Michael R. (committee member)

Creator Email labonte@usc.edu,mjlabonte@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-m3366

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