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Heterogeneity and plasticity of malignant and non-malignant circulating analytes in breast carcinomas

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Heterogeneity and plasticity of malignant and non-malignant circulating analytes in breast carcinomas

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Content Heterogeneity and plasticity of malignant and non-malignant circulating analytes in breast carcinomas by Lisa Welter 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 (MOLECULAR BIOLOGY) December 2022 Copyright 2022 Lisa Welter ii Acknowledgements A PhD is a journey, not a destination. It is about what we experience and learn on the way, not just the final product. This thesis reflects not only my scientific accomplishments, but also serves as a reminder to the lessons and skills that I learned on the way. Here is to all the people who supported me throughout my journey and made me the person that I am today! First, I would like to thank my advisors, Dr. Peter Kuhn and Dr. James Hicks, for their supervision these past years. As researchers, you could not differ more in your scientific thinking and the way you approach problems and I believe this is one of the main reasons why I have learned tremendously from both of you. Many thanks also to my clinical collaborators Dr. Jorge Nieva (and committee member) and Dr. Elena Shagisultanova. I really appreciate you taking the time to teach me about clinical decision making and expanding my knowledge of medical oncology. You are both amazing teachers! To my collaborator Dr. Costanza Paoletti. Thank you for all the scientific discussions as well as your support and kind introduction during the symposium at AACR. Dr. Norman Arnheim, thank you for the funny conversations and thoughtful questions as my PhD thesis committee member. To Dr. Carmen Ruiz Velasco, my first mentor at CSI-Cancer, a fantastic colleague and friend. Thank you for teaching me the basics of the HD-SCA workflow and for all the open and passionate discussions we had. To Dr. Liya Xu and Dr. Anand Kolatkar. You have been amazing mentors, both scientifically and personally! Thank you for all your help and support on my journey, the countless scientific discussions and words of encouragement. Rafael Nevarez, thank you for teaching me to code and to understand databases and for all the patience you had with me throughout the years. Many thanks also to Dr. Ryon Graf, who has spent endless hours introducing me to big data analysis and book keeping through KNIME and making me a fellow KNIME enthusiast. To Elvia Nunez, Allison Welsh, Doug Bourleson and Paloma Saenz. Thank you for helping me navigate through the PhD program requirements and helping me with all the paperwork that goes with it. I am incredibly grateful for the numerous and wonderful friendships that I developed throughout my years at USC. Thank you to all the past and current CSI-Cancer members. You have all contributed to my scientific and personal journey and I am grateful for all your help and support. So many wonderful people at the Michaelson Center have come and gone, but a very iii particular shoutout goes out to Barbara and Jana. You two will always have a special place in my heart! Sonia, thank you for being such a warm and supportive lab mate and friend. A very special thanks goes out to Nikki. It has been a true pleasure working with you, co-mentoring undergraduate students and learning with you through our numerous discussions. You are the best desk mate, an incredible scientist and great friend! It has been an honor to be working with you in our little breast cancer team! I also owe a tremendous thanks to my undergraduate mentees Dillon, Arushi, Vera Hsu, Audrey, as well as summer high-school interns Kevin, Marianne and Alexis. Thank you for working with me through some tough problems, boring and repetitive experiments and for your wiliness to learn and adapt. It has been a pleasure working with you and am really proud of much all of you have grown! I am grateful to all the wonderful students that I got to work with through MBGSA, DGSA and GSG. But a special shoutout goes out to the amazing archery team members and coaches. You have provided my with such an amazing outlet to strengthen my focus and I just love the energy that you all bring wherever you go. You are incredible! To my wonderful peers of the 2015 MCB cohort. I couldn’t have asked for a more fun, supportive and quirky cohort to get through the first years together. Go us! We are not just halfway there anymore, we made it! To my loving partner, Yak, thank you for walking a large part of my PhD journey with me. You were always there to vent to after frustrating experiments or to celebrate minor milestones on the way. Thank you for always being there for me! To my cats (Mari and Luna) and my dog (Pazu). Thank you for keeping me on my toes and providing unconditional love and the sweetest distraction. A huge thanks to my family. To my grandparents who made education a priority in my life and supported my journey however they could. To my sister Sarah, who is the most encouraging, yet also most critical person I know in the best way possible. You have a way of challenging me and to keep me going when things get tough! Thank you! You are amazing! Lastly, to my parents, Christel and Helmut, who have been the most supportive and caring parents that I could wish for. You are always there to back me up on my crazy ideas and no matter where I am in the world, you have a gift of making me feel like you are right here with me. I wouldn’t be where I am today without your unwavering support! iv Table of Contents Acknowledgements ......................................................................................................................... ii List of Tables ............................................................................................................................... viii List of Figures ................................................................................................................................ ix Chapter 1: Introduction ................................................................................................................... 1 1.1. Breast Anatomy and Pathology .............................................................................. 1 1.1.1. Breast Anatomy ......................................................................................... 1 1.1.2. Breast Cancer Pathology ............................................................................ 2 1.1.3. Breast Tumor Staging ................................................................................ 3 1.1.4. Tumor Grade .............................................................................................. 3 1.2. Breast Cancer Subtypes .......................................................................................... 4 1.2.1. Clinical Breast Cancer Subtypes ................................................................ 4 1.2.2. Intrinsic Breast Cancer Subtypes ............................................................... 5 1.2.3. Other breast cancer subtypes ..................................................................... 6 1.2.4. Clinical gene expression assays ................................................................. 7 1.3. Tumor heterogeneity and tumor evolution ............................................................. 8 1.3.1. Spatial heterogeneity .................................................................................. 9 1.3.2. Temporal heterogeneity ............................................................................. 9 1.4. Liquid Biopsy ....................................................................................................... 10 1.4.1. Circulating Tumor Cells .......................................................................... 11 1.4.2. CTC Detection Platforms ......................................................................... 11 1.4.3. CTC Characterization and Clinical Outcomes ......................................... 13 1.4.4. Cell-free DNA .......................................................................................... 14 Chapter 2: Treatment response and tumor evolution: Lessons from an extended series of multi- analyte liquid biopsies in a metastatic breast cancer patient ......................................................... 16 2.1. Abstract ................................................................................................................. 17 2.2. Introduction ........................................................................................................... 18 2.3. Results ................................................................................................................... 19 2.3.1. Clinical History and Sample Collection .................................................. 19 2.3.2. ER expression is related to treatment response ........................................ 23 2.3.3. CTC enumeration is correlated with ctDNA fraction, CA 27.29 levels and disease state .................................................................................................... 23 2.3.4. CNA Analysis of CTCs and tissue biopsies traces tumor lineage ........... 24 2.3.5. CNA Analysis of cfDNA reveals genomic relationship with CTCs ........ 27 2.3.6. ESR1 mutation analysis in tissue samples, cfDNA and CTCs uncovers parallel endocrine resistance evolution ................................................................. 28 v 2.3.7. ESR1 mutations occur independent of CNAs, but are positively associated with ER protein expression ................................................................. 31 2.3.8. Co-occurrence of PIK3CA mutation and subclone 4 marks point of multi-treatment failure .......................................................................................... 31 2.3.9. Ploidy of CTCs gives insight into tumor evolution through genomic duplication ............................................................................................................. 31 2.4. Discussion ............................................................................................................. 32 2.5. Materials and Methods .......................................................................................... 37 2.5.1. Collection and processing of bloods samples .......................................... 37 2.5.2. Immunofluorescence Staining and CTC Enumeration ............................ 37 2.5.3. Isolation of Single Cells ........................................................................... 38 2.5.4. cfDNA Isolation and Illumina Whole Genome Library Construction ..... 39 2.5.5. Histological Evaluation of FFPE tissue samples ..................................... 39 2.5.6. DNA extraction of FFPE tissue and Illumina Library Construction ....... 40 2.5.7. Copy Number Alteration Analysis ........................................................... 40 2.5.8. Determination of ctDNA Fraction in Plasma ........................................... 41 2.5.9. ESR1 and PIK3CA Single-Cell SNV Analysis ....................................... 41 2.5.10. Single Nucleotide Variation Analysis of cfDNA ..................................... 41 2.5.11. Whole Exome Sequencing of FFPE tissue .............................................. 42 2.5.12. Fluorescence in situ hybridization of CTCs ............................................. 42 2.6. Additional Information ......................................................................................... 43 2.6.1. Data Availability ...................................................................................... 43 2.6.2. Ethics Statement ....................................................................................... 43 2.6.3. Acknowledgments .................................................................................... 43 2.6.4. Author Contributions ............................................................................... 44 2.6.5. Funding .................................................................................................... 44 2.6.6. Competing Interest Statement .................................................................. 45 2.7. Supplementary ...................................................................................................... 45 Chapter 3: Assessment of clinical parameters and liquid biopsy analytes at the start of combination treatment with tucatinib, letrozole and palbociclib predicts shorter progression free survival in patients with metastatic ER+/HER2+ breast cancer. ........................................... 54 3.1. Introduction ........................................................................................................... 55 3.2. Materials and Methods .......................................................................................... 59 3.2.1. Patient Enrollment Criteria and ER/HER2 Tissue Status Assessment .... 59 3.2.2. Blood Sample Collection and Rare Cell Detection for Liquid Biopsy Analysis................................................................................................................. 60 3.2.3. ctDNA analysis ........................................................................................ 61 3.2.4. Data analysis ............................................................................................ 61 3.3. Results ................................................................................................................... 61 3.3.1. Overview of patient characteristics and liquid biopsy positivity ............. 61 3.3.2. Number and type of prior therapies affect response to TLP combination treatment. ......................................................................................... 63 3.3.3. High ctDNA and CTC levels at C1D1 are associated with a shorter PFS…… ................................................................................................................ 64 vi 3.3.4. Combination of clinical and liquid biopsy variables increases confidence in stratifying patients into good vs poor responders. .......................... 67 3.3.5. Longitudinal analysis of ctDNA and CTC reveals various patterns of biomarker release. ................................................................................................. 68 3.3.6. Copy number alterations are enriched in regions containing cell cycle genes… ................................................................................................................. 69 3.4. Discussion ............................................................................................................. 71 3.5. Additional Information ......................................................................................... 75 3.5.1. Authors’ Contributions ............................................................................ 75 Chapter 4: A randomized trial of fulvestrant, everolimus and anastrozole in the front-line treatment of advanced hormone receptor-positive breast cancer, SWOG S1222 ......................... 76 4.1. Abstract ................................................................................................................. 77 4.2. Translational Relevance ........................................................................................ 78 4.3. Introduction ........................................................................................................... 79 4.4. Design and Methods ............................................................................................. 79 4.4.1. Clinical Eligibility and Trial Conduct. ..................................................... 79 4.4.2. Translational Studies. ............................................................................... 80 4.4.3. Statistical Analysis. .................................................................................. 82 4.5. Results ................................................................................................................... 83 4.5.1. Clinical Outcomes According to Treatment Assignment. ....................... 83 4.5.2. Liquid Biopsy Analyses. .......................................................................... 84 4.6. Discussion ............................................................................................................. 94 4.7. Additional Information ......................................................................................... 96 4.7.1. Authors’ Disclosures ................................................................................ 96 4.7.2. Authors’ Contributions ............................................................................ 98 4.7.3. Acknowledgments .................................................................................... 99 4.8. Supplementary ...................................................................................................... 99 Chapter 5: Cell State and Cell Type: Deconvoluting Circulating Rare Cell Populations in Liquid Biopsies by Multi-Omics ................................................................................................ 102 Keywords ........................................................................................................................ 102 5.1. Simple Summary ................................................................................................. 103 5.2. Abstract ............................................................................................................... 103 5.3. Introduction ......................................................................................................... 104 5.4. Materials and Methods ........................................................................................ 106 5.4.1. Patients and samples. ............................................................................. 106 5.4.2. Blood Sample Collection and Processing .............................................. 107 5.4.3. Immunofluorescent staining of patient slides ........................................ 108 5.4.4. Rare cell identification and characterization .......................................... 109 5.4.5. Single cell Next Generation Sequencing and Bioinformatic Analysis .. 109 5.4.6. Single cell targeted proteomics and data analysis .................................. 110 5.4.7. Data Analysis and Visualization ............................................................ 111 5.5. Results ................................................................................................................. 111 vii 5.5.1. Characterization of circulating rare over time of a metastatic prostate cancer patient. ..................................................................................................... 111 5.5.2. Inter-patient assessment of cell type and cell state in the liquid biopsy. 115 5.5.3. Targeted proteomics identifies distinct phenotypes ............................... 117 5.5.4. Morphometrics and multi-omics to separate cell types ......................... 119 5.6. Discussion ........................................................................................................... 122 5.7. Conclusions ......................................................................................................... 125 5.8. Additional Information ....................................................................................... 126 5.8.1. Institutional Review Board Statement ................................................... 126 5.8.2. Informed Consent Statement .................................................................. 126 5.8.3. Acknowledgments .................................................................................. 126 5.8.4. Conflicts of Interest ................................................................................ 126 5.9. Supplemental Figures .......................................................................................... 127 Chapter 6: Perspectives ............................................................................................................... 128 References ................................................................................................................................... 130 viii List of Tables Table 1.1 Overview of different molecular breast cancer subtypes ............................................... 8 Table 3.1 Patient Characteristics .................................................................................................. 62 Table 4.1 Patient characteristics by study arm ............................................................................. 84 Table 5.1 Overview of breast cancer patient subtypes, stage and study IRBs. .......................... 106 Table 5.2 Overview of prostate cancer patient stage and study IRBs ........................................ 107 Supplemental Tables Supplemental table 2.1 Sample overview of cfDNA Oncomine analysis .................................. 53 Supplemental table 2.2 Sample overview of WES tissue analysis. ............................................ 53 ix List of Figures Figure 1.1 Anatomy of the female breast ....................................................................................... 2 Figure 1.2 Overview of female breast cancer cases per subtype ................................................... 5 Figure 2.1 Schematic overview of the HD-SCA workflow for CTC characterization and cfDNA analysis ............................................................................................................................. 20 Figure 2.2 Treatment history and liquid biomarker evaluation ................................................... 22 Figure 2.3 Correlation between CTCs/ml, ctDNA fraction and cfDNA concentration ............... 23 Figure 2.4 Copy Number Alteration Analysis of CTCs, tissue biopsies and cfDNA .................. 25 Figure 2.5 SNV analysis of cfDNA, tissue biopsies and CTCs ................................................... 29 Figure 2.6 Assessment of ploidy of CTCs by fluorescent in situ hybridization (FISH) .............. 32 Figure 3.1 Schematic overview of ER and HER2 mediated signaling. ....................................... 57 Figure 3.2 Kaplan Meier Curves of pre-analytical variables ....................................................... 64 Figure 3.3 Overview of CTC and ctDNA levels of all patients at C1D1 .................................... 66 Figure 3.4 Multi-variable assessment of clinical and liquid biopsy and variables ...................... 68 Figure 3.5 Fluctuations of CTCs and ctDNA over time per patient ............................................ 69 Figure 3.6 Cell Cycle Gene Alterations in ctDNA samples at C1D1 .......................................... 70 Figure 4.1 PFS and OS by randomized treatment groups ............................................................ 85 Figure 4.2 CTC expression of ER in 2 patients with elevated CTC levels .................................. 85 Figure 4.3 Comparison of samples where data was available for both CellSearch and HD- SCA ............................................................................................................................................... 86 Figure 4.4 CTC and microenvironment cells of all draws ........................................................... 87 Figure 4.5 Assessment of CTCs, CECs, ctDNA fraction and concentration of cfDNA over time ............................................................................................................................................... 88 Figure 4.6 Concordance of CTC and ctDNA positivity across all HD-SCA draws with available CTC enumeration and ctDNA assessment from all three timepoints ............................ 89 Figure 4.7 CTC genomic and phenotypic analysis in 2 patients with elevated CTC levels. ....... 90 Figure 4.8 Overview of molecular characteristics of patient 3 .................................................... 91 Figure 4.9 Overview of molecular characteristics of patient 4 .................................................... 93 Figure 5.1 Schematic overview of HDSCA platform. ............................................................... 108 Figure 5.2 Longitudinal assessment of circulating rare cells in a patient with metastatic prostate cancer ............................................................................................................................ 114 Figure 5.3 Representative single cell whole genome copy number alterations of breast (P1-6) and prostate (P7-8) cancer patients ............................................................................................. 116 Figure 5.4 Multiplex proteomics of circulating rare cells in patients with metastatic breast or prostate cancer. A) Multi-plex proteomics of 4 patients with breast cancer .............................. 118 Figure 5.5 Morphometrics and multi-omics A) CNA profiles together with IF images of representative EPI ....................................................................................................................... 120 Figure 5.6 Rare cell enumeration of spiked endothelial cells and CECs in patients with myocardial infarction .................................................................................................................. 121 x Supplemental Figures Supplemental figure 2.1 Clinical imaging and pathological evaluation of the patient ............... 47 Supplemental figure 2.2 Overview of CNA profiles of the four genomically distinct subclones. ...................................................................................................................................... 48 Supplemental figure 2.3 Rare intermediate subclones give insight into the stepwise process of tumor evolution through chromosomal alterations. .................................................................. 49 Supplemental figure 2.4 CNA heat-maps of single circulating tumor cells isolated of draw 1 and draw 17 ................................................................................................................................... 50 Supplemental figure 2.5 Phenotypic analysis of CTCs of draw 16. ........................................... 51 Supplemental figure 2.6 Sanger sequencing analysis for ESR1 SNVs.. .................................... 52 Supplemental figure 4.1 Consort Diagram ................................................................................. 99 Supplemental figure 4.2 Assessment of CTCs, CECs, ctDNA fraction and concentration of cfDNA over time ......................................................................................................................... 100 Supplemental figure 4.3 Single CTC vs cfDNA copy number profile ..................................... 101 Supplemental figure 5.1 Gallery of CTCs, pEMT.CTCs, endothelial cells and endothelial clusters detected in a patient with metastatic prostate cancer.. ................................................... 127 1 1. Chapter 1: Introduction 1.1. Breast Anatomy and Pathology Both male and female humans have breasts and can develop breast cancer, yet lesions of the female breast are much more common than those of the male breast, whereas 1 in 8 woman will be diagnosed with breast cancer in their lifetime, yet only 1 in 100 of all breast cancer cases are male [1]. I will hence focus solely on the female breast, and carcinomas of the female breast as the most common type of breast cancer. 1.1.1. Breast Anatomy The female breast consists of 15-20 lobes arranged in a circular fashion, each of which comprises multiple smaller lobules [2]. Each of these lobules is made up of 30 to 50 alveoli (non- pregnant state) or acini (pregnancy/lactation). Lobes and lobules are connected by milk ducts, which carry the milk produced in the glands acini to the nipple. Each lobule consists of an intralobular segment of the terminal duct, ductules, acini and stromal fibrous tissue. A lobule and its terminal duct form the functional unit of the breast called the terminal ductolobular unit (TDLU) [3, 4]. The ductal-lobular system consists of two layers cells: inner (luminal) epithelial cells and outer (basal) myoepithelial cells. Luminal cells are secretory cells that line the apical surface of the normal breast ductal-lobular system. These cells are highly responsive to hormonal changes and typically express estrogen and progesterone receptors. Basal myoepithelial cells act as contractile muscle to move the milk produced by the luminal cells from the acini through the milk ducts towards the nipple. These cells are typically estrogen and progesterone receptor negative. 2 Ducts and lobules are resting in a basement membrane which is enveloped in stroma. The stroma makes up for approximately 85-90% of the breast and contains mainly fat and connective tissue. The breast tissue is encircled by fascia, a thin layer of connective tissue, which borders the pectoralis muscle and the skin. The majority breast cancers (~90%) originate from epithelial tissue and can form in both the ducts and the lobes, most commonly in the TDLUs, and fall hence into the category of carcinomas. Figure 1.1 Anatomy of the female breast. A) Cross-section of a female breast. B) Cross-section of a milk duct. 1.1.2. Breast Cancer Pathology Carcinomas of the breast can be classified according to invasiveness: those that have not penetrated the basement membrane (non-invasive) and those that have (invasive) [5]. Non- invasive carcinomas can either occur in the ducts or in the lobules and are called ductal carcinoma in situ (DCIS) and lobular carcinoma in situ (LCIS), respectively. Among the invasive carcinomas, invasive ductal carcinoma is by far the most common one. Other more rare forms of invasive carcinomas include: invasive lobular carcinoma, medullary carcinoma, colloid carcinoma and tubular carcinoma. Metastatic spread of breast cancers can occurs through both lymphatic and hematogenous channels. 3 1.1.3. Breast Tumor Staging Breast carcinomas are divided into five stages based on their size, spread to lymph nodes and other organs [5]. Stage 0 tumors, DCIS and LCIS, are contained within the basement membrane. At stage I, invasive carcinomas are £2cm in diameter and have no nodal involvement, but may have nodal metastasis < 0.02 cm. Carcinomas in situ with microinvasions also fall into this category. Stage II breast cancers are either invasive carcinomas that have reached £5cm with up to three involved axillary nodes or that are greater than 5cm with no nodal involvement. At stage III, invasive carcinomas are £5 cm in diameter with four or more involved axillary nodes. Alternatively, they may be >5cm with nodal involvement; have ³10 involved axillary nodes or present with involvement of the ipsilateral internal lymph nodes. Invasive carcinomas with skin involvement, chest wall fixation or clinical inflammatory carcinomas are also considered stage III. Tumors which have metastasized to distant sites, including distant lymph nodes, are considered stage IV. 1.1.4. Tumor Grade The most commonly used grading system for breast cancers is the Nottingham grading system, which combines scores on tubule formation, nuclear grade and mitotic rate. Tubule formation assesses the resemblance of the tumor tissue normal breast structures; nuclear grade evaluates the size and shape of the nucleus of the tumor cells and mitotic rate represents the number of dividing cells within a certain region [6]. Each category is scored on a 1 to 3 scale, where 1 most closely and 3 least closely resembles the normal cells and tissue. Combined, these three categories will yield a total score from 3 to 9. Tumors with a total score of 3-5 are considered “well differentiated”; scores between 6 and 7 are classified as “moderately differentiated” and those scoring 8-9 are categorized as “poorly differentiated”. Well-differentiated tumors who closely 4 resemble their normal tissue, have a significantly better prognosis compared to poorly differentiated ones. While moderately differentiated tumors initially have a better prognosis, their survival tumors after 20 years of follow up will resemble more closely that of poorly differentiated [5]. 1.2. Breast Cancer Subtypes Breast cancers, like most cancers, are highly heterogenous with regards to their genetic alterations, transcriptional activity as well as protein expression. Traditionally, breast cancers are characterized by their tumor stage, grade and hormone receptor/HER2 expression to guide treatment selection and predict outcome. Yet, various subtype classifications have been developed to enable greater individualization of treatments for patients. 1.2.1. Clinical Breast Cancer Subtypes In the United States it is estimated that there will be 287,850 new cases of female breast cancer in 2022, making it one of the most common female malignancies [7]. There are four main breast cancer subtypes as defined by pathologists, which are based on the expression of hormonal receptors (HR) and human epidermal growth factor receptor 2 (HER2). HR+ tumors, which are positive for the Estrogen Receptor (ER) and/or Progesterone Receptor (PR) as measured by immunohistochemistry (IHC). HER2 positivity is defined by either fluorescent in-situ hybridization (FISH) or IHC. Tumors are divided into four subgroups based on their HR and HER2 expression: HR+/HER2+, HR-/HER-, HR+/HER2+ and HR-/HER2+, with the majority of tumors being HR+/HER2- (figure 1.2A). Patients with HR+/HER- localized breast cancer have the best 5-year relative survival, while those with HR-/HER-, also known as triple negative breast cancer have the worst 5-year relative survival among those with localized breast cancer (figure 1.2B). 5 Yet, the stage of the cancer is a more powerful factor compared to the breast cancer subtype, highlighting the importance of early detection (figure 1.2B). Figure 1.2 Overview of female breast cancer cases per subtype. A) Percent of female breast cancer cases by cancer subtype according to the SEER 22 2015-2019 data. B) 5-Year relative survival percent of female breast cancer and C) their subtypes according to SEER 17 2012-2018 [7]. 1.2.2. Intrinsic Breast Cancer Subtypes In 2000, Perou et. al. performed global gene expression analysis of frozen tissue, initiating what would become the classification of breast tumors into intrinsic subtypes: luminal-like (A and B), HER2-enriched, basal-like and normal-like [8, 9]. Luminal A tumors are characterized by a high expression of Estrogen Receptor, GATA3, X-box binding protein trefoil factor 3, hepatocyte nuclear factor 3 alpha and LIV-1. In contrast, luminal B cancers have a lower expression of luminal-specific genes, protein tyrosine phosphatase type IVA member, tumor necrosis factor receptor-associated factor 3, RAD21, and BRCA1-associated protein 1 (BAP1) and lower expression of FGFR1, CXCR4, ATF-3, and vascular cell adhesion molecule 1 [9]. While most luminal A and B tumors were HER2 neutral, a subpopulation overexpressed HER2, with a higher prevalence in the luminal B group. Tumors in the HER2-enriched group were defined HER2 as A B Localized Regional Distant 0 25 50 75 100 % of 5-year survival 5-year survival SEER HR+/HER2- TNBC HR+/HER2+ HR-/HER2+ Subtype Localized Regional Distant HR+/HER2- 100% 90.1% 31.9% HR-/HER2- 91.3% 65.8% 12.0% HR+/HER2+ 98.8% 89.3% 46.0% HR-/HER2+ 97.3% 82.8% 38.8% C 6 well as a higher expression of MDR1, S100 calcium-binding protein P, fatty acid synthase, RAL- B, RAB6A, fibronectin 1, and syndecan 1 and lower expression of c-kit and c-myc [9]. Basal-like tumors express genes found in myoepithelial cells of the breast such as CK5/6 and CK17, proliferation related genes as well as laminin and fatty acid-binding protein 7 and are commonly associated with BRCA1 germline mutations [10, 11]. Gene expression of normal-like resembled those of basal epithelial cells and adipose cells, with low expression of genes characteristic of luminal epithelial cells. In 2011, the claudin-low group was added to the intrinsic subtypes, which resembles the basal-like group, but differs from it but the low expression of cell-cell junction proteins including E-cadherin and the presence of lymphocyte infiltration [12]. Luminal A tumors have the most favorable prognosis of all subtypes. Almost a decade later, the same group developed a 50 gene qPCR assay (PAM50) to identify these intrinsic biological subtypes using RNA from formalin-fixed, paraffin-embedded (FFPE) tissue [13]. Since then various clinical studies have tested the ability of PAM50 and other gene signatures to stratify patients and assess personalized risk. They found that intrinsic breast cancer subtypes are independent of standard clinicopathological variables and identify patient groups which will likely derive benefit from adjuvant chemotherapy [14, 15]. 1.2.3. Other breast cancer subtypes In addition to the clinical and intrinsic subtypes, Curtis et. al. has clustered breast tumors into ten specific clusters (IntClust) with distinct clinical outcomes through combined gene expression and DNA copy number profiling [16]. This work provides evidence how gene copy number alterations affect gene expression. Further, Lehmann et. al. established four triple negative molecular subtypes: basal-like 1, basal-like 2, mesenchymal and luminal androgen receptor [17]. Patients with these four subtypes 7 differed in their diagnosis age, grade, local and distant disease progression and histopathology. The group demonstrated that these subtypes differed significantly in their response to similar chemotherapy and that their subclassification could inform future clinical trials to better match TNBC patients to the most effective treatment. 1.2.4. Clinical gene expression assays Commercially available tests using gene expression analysis that separate early stage breast cancer patients into distinct molecular subtypes with prognostic significance include Oncotype DX® (Exact Sciences, Redwood City, CA) and MammaPrint (Agendia, Amsterdam, the Netherlands). Oncotype DX® assesses gene expression levels of 16 cancer related genes and five genes for expression normalization in FFPE tissues [18, 19]. The assay calculates a recurrence score (RS) on a scale from 0 to 100, which translates into three risk-groups: low (RS <18), intermediate (RS from 18 to <31) and high (RS ≥31). Oncotype DX® has been particularly useful in aiding decision making for administering adjuvant chemotherapy in patients with ER+, node-negative breast cancer, specifically by identifying patients who do not require chemotherapy. It has been endorsed by the American Society of Clinical Oncology (ASCO) [20] and the National Comprehensive Cancer Network (NCCN) [21]. Another gene expression driven assay used as a prognostic test for woman with node negative breast cancers is the MammaPrint® assay [22]. It is based on 70 genes and predicts distant relapse-free survival and is used to assess whether patients will benefit from chemotherapy [23]. While these assays have made a great impact on early stage risk evaluation and treatment guidance, no similar assay nor biomarker similarly guides treatment selection in the metastatic setting. 8 Table 1.1 Overview of different molecular breast cancer subtypes (adapted from J Tsang et. al. [24]) 1.3. Tumor heterogeneity and tumor evolution Tumor heterogeneity is one of the major challenges for cancer therapy and can be divided into intertumor and intratumor heterogeneity. Intertumor heterogeneity refers to variation between tumors of different patients (interpatient heterogeneity) or to different tumors within a single patient. Intratumor heterogeneity describes the variation of tumor cells within a single tumor. Intratumor heterogeneity poses the risk of sampling bias, as not all lesions can be biopsied and for large tumors pathologists need to select regions that will be submitted for histological analysis. There are two main models describing how tumor heterogeneity arises. The stochastic or clonal evolution model hypothesizes that tumors arise from one single mutated cell, which acquires additional mutations over time [25]. The cancer stem cell model states that tumors arise from a subset of tumor stem cells, which have regained embryonic properties [26]. These cells are able to self-renew and contain a subset that will differentiate into heterogenous tumor cells. While there 9 is evidence for both models, more research is needed to fully understand how tumors evolve. Growing evidence suggests a combination of the stem cell and stochastic model as most likely cause for tumor evolution [27]. 1.3.1. Spatial heterogeneity Spatial heterogeneity refers to molecular variation of the tumor either within the primary site or between the primary tumor and its metastatic lesions. Spatial heterogeneity creates a therapeutic challenge as selective pressure of anticancer treatments may lead to the expansion of drug resistant subclones [28]. Discordant expression of HR and HER2 between primary and metastatic breast tissue has been found to vary from 9–30% for ER, 15–45% for PR and 4–16% for HER2 and loss of HR expression as well as changes in HER2 expression has been linked to worse survival, yet results vary greatly between studies [29-33]. When comparing five gene expression panels (Oncotype Dx, MammaPrint, PAM50, EndoPredict, and Breast Cancer Index) of ER+ breast cancer patients, Gyanchandani et. al. found that prognostic risk may be under- or overestimated in patients with highly heterogenous tumors [34]. 1.3.2. Temporal heterogeneity Temporal heterogeneity, characterized by changes over time, can give insight into tumor evolution and drug resistance mechanisms. Understanding temporal heterogeneity is challenging because sampling bias makes it hard to differentiate from spatial heterogeneity. As an outcome of clonal selection, temporal heterogeneity is often overlooked when it comes to clinical decision making. One example is that tumors of breast cancer patients are typically only assessed for ER/PR/HER2 expression and pathological characteristics at time of diagnosis. Yet, it is well established that tumors will undergo changes in their molecular profiles, both, due to natural progression over time as well as treatment pressure [35, 36]. Importantly, these changes often 10 result in the selection of more aggressive, treatment resistant subclones. In order to provide the patient with the optimal treatment regimen it would be valuable to continually assess a patient’s tumor for changes in its molecular characteristics. However, continuous sampling of the primary breast tissue or metastatic sites is not desirable in the routine clinical setting, as tissue biopsies are invasive, painful and costly. Liquid biopsies may be preferable and have been shown to provide minimally invasive access to tumor derived analytes and provide hence an opportunity for real time monitoring of disease status, tumor evolution and treatment resistance. 1.4. Liquid Biopsy While tissue biopsies are still today’s gold standard for cancer characterization and staging, liquid biopsies have rapidly gained popularity as both diagnostic and monitoring tool to access tumor derived analytes over time, as they present a minimally invasive alternative to surgical biopsies of solid tissues. Typically, they describe a blood sample, but may also refer to a urine sample [37], bone marrow aspirate [38], aqueous humor [39], cerebrospinal fluid sample [40] or other bodily fluids [41]. The minimally invasive nature of liquid biopsy from peripheral blood allows for repeated sampling and provides therefore the opportunity for real time assessment of the tumor’s molecular characteristics. Since liquid biopsy analytes can be released from any part within a tumor or metastatic site, they have the potential to provide comprehensive insight into spatially resolved subclones. Liquid biopsies are therefore an attractive option as biomarker and companion diagnostic. Analytes that can be assessed in a liquid biopsy sample include circulating tumor cells (CTCs), cell-free DNA (cfDNA), cell-free RNA [42, 43] and exosomes/oncosomes [44]. The next chapters will focus mainly on CTCs and cfDNA detected in the blood of metastatic breast cancer (MBC) patients. 11 1.4.1. Circulating Tumor Cells Circulating tumor cells (CTCs) are shed into the vasculature from the primary tumor in the process of seeding metastasis. Once a secondary organ is colonized, they may be released from the primary tumor and metastatic sites. CTCs were the first biomarker to be identified in liquid biopsy cancer research back in 1869 by Thomas Ashworth [45]. While the exact mechanism of how CTCs disseminate is still unclear, we are starting to gain a better understanding of the numerous processes involved. During the metastatic process, a tumor cell must undergo changes in order to overcome anoikis, enhance motility, adapt to survive in circulation and eventually adjust to colonize other tissues [46]. Epithelial- mesenchymal transition (EMT), is one of these changes that enables tumor cells to go through the metastatic cascade. It is characterized by their decreased ability to adhere to other cells and the matrix, loss of apicobasal polarity and gain mesenchymal characteristics. In the process, cells downregulate epithelial proteins such as E-cadherin, EpCAM and cytokeratins (CKs) and upregulate mesenchymal markers such as N-Cadherin, vimentin and TWIST [47, 48] Most commonly, cells do not complete the full transition, but remain in a more adaptable hybrid state, expressing both epithelial and mesenchymal markers [48]. 1.4.2. CTC Detection Platforms CTCs are rare cells in the context of the vast number of immune cells found in the blood and hence require specialized detection platforms. Over the past decade, several CTC detection platforms have emerged who exploit known characteristics of tumor cells to find them. Many rely on features such as size [49], charge [50, 51], deformability [52], protein expression [53, 54] or a combination thereof to enrich for cells of interest. The most common commercially available and only FDA approved CTC detection platform is the CellSearchâ system, which relies on EpCAM to capture CTCs and uses CK, CD45 12 and a nuclear stain to verify the captured cells [55, 56]. Enumeration with CellSearch has linked high CTC counts to worse poor prognosis in a variety of cancer types including metastatic breast cancer [57, 58], prostate cancer [59], small cell lung cancer [60, 61], non-small cell lung cancer [62] and bladder cancer [63]. Yet, it’s downstream analysis capabilities are limited and it has been criticized for its dependence on EpCAM as inclusion marker, which may be downregulated in a subset of CTCs. A filter based isolation technique, isolation by size of tumor cells (ISET), has shown to provide higher efficiency than CellSearch, yet it requires large amount of blood and misses smaller CTCs [64, 65]. There have been numerous comparisons across CTC detection platforms, but the final choice of the platform is not only determined by its performance, but also by the desired downstream analysis. While some assays are capable of capturing viable CTCs, others require cell fixation and are hence not suitable for retrieving viable CTCs. Here we use the enrichment free High-Definition Single Cell Assay (HD-SCA) to isolate plasma for cfDNA analysis and detect circulating rare cells [66, 67]. In short, all nucleated cells are plated onto glass slides and circulating rare cells are assessed in the context of the surrounding immune cells. The basis for CTC detection for carcinomas uses an epithelial inclusion marker (pan-Cytokeratin), a leukocyte marker (CD45) as exclusion marker, and a nuclear stain (4′,6- diamidino-2-phenylindole(DAPI)). A fourth marker may be added for further rare cell characterization. While this assay still relies on choosing a-priori markers to visualize CTCs in the context of immune cells, it requires only 1ml of blood per test, keeping a large portion of the 8ml blood sample cryobanked for future analysis and has the flexibility to run various specialized rare cell detection assays per sample, therefore enhancing the number of markers and rare cell types that can be assessed per blood draw. In addition, HD-SCA incorporates cellular and nuclear morphology in its search algorithm, enhancing the likelihood of rare cell detection. In addition, it 13 is compatible with downstream targeted proteomic and genomic analysis, enabling thorough characterization of the detected rare cells [68]. 1.4.3. CTC Characterization and Clinical Outcomes Multiple studies have linked elevated CTC counts with poor prognosis in a variety of cancers including patients with metastatic breast, prostate, colorectal and lung cancer [58, 59, 62, 69-71]. Longitudinal assessment of CTCs and CTC clusters has been associated with improved prognostication of patients with metastatic breast cancer starting first-line systemic therapy [72] and was confirmed in a separate metastatic breast cancer cohort [73]. However, researchers found that while CTC enumeration can identify patients with poor prognosis, mere enumeration of CTCs is insufficient for guiding treatment and improving patient survival. Thus, research has shifted from enumeration to multi-omic characterization of CTCs, with the hope of finding biomarkers that provide insight into the molecular nature of the tumor that could guide treatment decisions. One example is understanding the change of HER2 positivity over time and its impact on a patient’s treatment response. Evaluation of HER2 status of CTCs in HER2 negative MBC patients from a phase II studying the effect of combined trastuzumab and vinorelbine treatment found that sixty-nine out of 331 patients (22%) had HER2 + CTCs [74]. While other groups have confirmed the detection of HER2 + CTCs in patients with HER2 - primary tumors, it remains uncertain whether these patients could benefit from HER2 targeted agents [75-81]. The advent of single cell genomic [68, 82, 83], epigenomic [84-86], transcriptomic [87, 88] and proteomic [89, 90] techniques has opened up a vast new toolset to interrogate the CTCs across patients and over time. Yet, multi-omic CTC analysis is still challenged by the low 14 abundance of CTCs, biases in CTC isolation techniques, CTC heterogeneity and low sample material. 1.4.4. Cell-free DNA In the past decade, analysis of cfDNA rapidly gained popularity due to its ease of extraction from plasma and its potential to monitor tumor evolution, treatment resistance and guide treatment selection. cfDNA are DNA fragments released by dying cells, which have been degraded to approximately 147bp (one wrap around the nucleosome) to 167bp (nucleosome + linker histone) length. They are present at low concentrations in healthy individuals and at varying concentrations in conditions such as myocardial infarction [91], stroke [92], autoimmune disorders [93], transplant [94, 95] and cancer patients. First clinical application of cfDNA was for the monitoring of fetal genetic abnormalities, which has become common practice in high-risk pregnancies [96, 97]. In oncology, cfDNA has been studied for a variety of structural and genomic alterations including single nucleotide variations (SNVs) [98] and copy number alterations (CNAs) [99], fragment sizes [100, 101], nucleosome footprints [102, 103] and jagged ends [104]. cfDNA has become a popular tool to trace tumor driver and therapy resistance mutations as well as disease burden in lung, colorectal, prostate and breast cancer, making it a powerful tool for disease monitoring [105-108]. Genomic analysis of cfDNA of breast cancer patients commonly detects SNVs in PIK3CA, ESR1, TP53, ERBB2, MYC, CCND1 and CCNE1 [109]. ESR1 mutations are of particular interest for HR + patients as they are rarely detected in untreated patients, yet have been found to present a major mechanism of resistance to endocrine therapy [110-113]. Findings of the BOLERO-2 and TRINITI-1 trials indicate that HR + patients might benefit from combined targeting of the mTOR pathway together with endocrine therapy [114]. In similar patients, 15 evidence from the PALOMA-3 cohort indicates that combination of CDK4/6 inhibitors with fulvestrant improved efficacy over fulvestrant alone [115]. Yet, ESR1 mutant patients have a worse outcome compared to ESR1 wildtype patients. While genomic profiling was able to associate ESR1 mutations with resistance to endocrine therapy, we have yet to uncover effective treatment combination for these patients. PIK3CA is another commonly acquired mutation, which has been associated with progression on or after aromatase inhibitor treatment. Patients with PIK3CA mutations have been found to benefit from combination treatment of alpelisib with fulvestrant [116]. Monitoring of ESR1, PIK3CA or other drug induced mutations that confer treatment resistance may aid guiding treatment selection. Overall, CTCs and cfDNA provide an opportunity to repeatedly assess genomic and proteomic characteristics of the tumor and may guide treatment selection for optimal cancer management. The next chapter will highlight multi-omic assessment of CTCs and cfDNA in an unique index case can trace tumor evolution and may explain therapy failure at multiple timepoints throughout her treatment. 16 2. Chapter 2: Treatment response and tumor evolution: Lessons from an extended series of multi-analyte liquid biopsies in a metastatic breast cancer patient This chapter is an unmodified manuscript published in Molecular Case Studies. Lisa Welter 1 , Liya Xu 1 , Dillon McKinley 1 , Angel E. Dago 2 , Rishvanth K. Prabakar 1 , Sara Restrepo-Vassalli 1 , Kevin Xu 1 , Mariam Rodriguez-Lee 1 , Anand Kolatkar 1 , Rafael Nevarez 1 , Carmen Ruiz 1 , Jorge Nieva 3 , Peter Kuhn 1,3,4 , James Hicks 1 . 1 Convergent Science Institute in Cancer, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA, USA 2 The Scripps Research Institute, La Jolla, CA, USA 3 Keck School of Medicine, University of Southern California, Los Angeles, USA 4 Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA Corresponding author information: James Hicks, 1002 W Childs Way, Los Angeles, CA 90089-3502, MCB 351, Phone: 213-821-3980, jameshic@usc.edu Short running title: Monitoring genomic evolution at single-cell level 17 2.1. Abstract Currently, clinical characterization of metastatic breast cancer is based on tissue samples taken at time of diagnosis. However, tissue biopsies are invasive and tumors are continuously evolving, which indicates the need for minimal invasive longitudinal assessment of the tumor. Blood-based liquid biopsies provide minimal invasive means for serial sampling over the course of treatment and the opportunity to adjust therapies based on molecular markers. Here, we aim to identify cellular changes that occur in breast cancer over the lifespan of an affected patient through single-cell proteomic and genomic analysis of longitudinally sampled solid and liquid biopsies. Three solid and 17 liquid biopsies from peripheral blood of an ER+/HER2- metastatic breast cancer patient collected over 4 years and 8 treatment regimens were analyzed for morphology, protein expression, copy number alterations and single nucleotide variations. Analysis of 563 single morphometrically similar circulating tumor cells (CTCs) and 13 cell-free DNA (cfDNA) samples along with biopsies of the primary and metastatic tumor revealed progressive genomic evolution away from the primary tumor profiles, along with changes in ER expression and the appearance of resistance mutations. Both the abundance and the genomic alterations of CTCs and cfDNA were highly correlated and consistent with genomic alterations in the tissue samples. We demonstrate that genomic evolution and acquisition of drug resistance can be detected in real time and at single-cell resolution through liquid biopsy analytes and highlight the utility of liquid biopsies to guide treatment decisions. 18 2.2. Introduction Treatment strategies for breast tumors are based on histological and molecular characteristics of tissue samples taken at time of diagnosis, and only repeated in the case of metastatic relapse. Current clinical practice faces the challenge of tracing tumor evolution across multiple lines of therapy as well as accounting for tumor heterogeneity and its effects on treatment response [117, 118]. In contrast, liquid biopsies offer the potential to assess the state of a cancer at multiple time points over the course of treatment with the opportunity to guide therapeutic decisions in real time regardless of tissue of origin. In this extensive case study, we explore the value and feasibility of longitudinal multi- analyte liquid biopsies as a minimally invasive tool to monitor tumor evolution under therapeutic pressure and acquired drug resistance in a hormone-positive metastatic breast cancer patient. Over the past two decades, CTCs and cfDNA have been intensely studied as a means to gain access to tumor-derived content through minimally invasive procedures in order to assess drug response, monitor tumor evolution and detect drug resistance [119-122]. cfDNA analysis has become a popular tool to test for acquired resistance mutations across various cancer types such as lung, prostate, colorectal and breast cancer and is heavily studied for the diagnosis, prognosis, and monitoring of cancers [108, 123-127]. While cfDNA can provide valuable insight for targeted drug selection, these tests lack information on protein expression as well as single-cell resolution to characterize genomic heterogeneity available from CTCs. The presence of CTCs and CTC clusters has been associated with reduced progression-free and overall survival, yet enumeration alone is insufficient in capturing both the heterogeneity of the disease as well as the mechanisms of drug resistance [57, 128, 129]. Hence, recent efforts have focused on the combined proteo- genomic characterization of CTCs to identify common alterations and to detect druggable targets, 19 with the first clinically actionable breakthrough in castrate resistant prostate cancer [130]. Today, it is well established that cfDNA and CTCs are easily accessible surrogates for solid biopsies that are representative of the primary and metastatic tissues [131] and while they have been largely studied in parallel, the trend has shifted to a more comprehensive liquid biopsy with the combined analysis of CTCs, cfDNA as well as other blood based analytes [132-136]. Yet there is a need to demonstrate the value of repeated multi-analyte liquid biopsy assessment for monitoring patients over the entire time course of the disease. This unique index case presented an opportunity to assess 17 sequential blood draws collected over 4 years and 8 treatment regimens. Through genomic and proteomic characterization of single CTCs and cfDNA, we observed the sequential acquisition of cancer driver and drug resistance mutations in light of the patient’s clinical status. Our findings not only shed light into the relationship between CTCs and cfDNA, but also highlight how both analytes can be leveraged for precision medicine. 2.3. Results 2.3.1. Clinical History and Sample Collection The patient was first diagnosed in April 2012 with metastatic breast cancer with metastasis to the lung, lymph nodes and bone. Histological assessment of her breast tissue biopsy found strong staining for ER with 100% positive nuclei, which indicates a strong dependence of the tumor on the ER signaling pathway. Cells were weakly positive for PR with 2-3% nuclei staining. No HER2/neu gene amplification was detected in neoplastic cell population. As a result, she was started on an aromatase inhibitor and contributed 17 sequential blood draws at clinical examinations over a four-year period [137]. Analysis of each blood sample included the enumeration and characterization of CTCs, quantification of cfDNA and circulating tumor DNA 20 (ctDNA) fraction, as well as genomic analysis of both CTCs and cfDNA with the HD-SCA workflow (Figure 2.1A). CTCs were identified as DAPI and pan-Cytokeratin positive, CD45 negative events and further classified for ER expression. Representative images of ER+/- CTCs are shown in figure 2.1B. The ctDNA fractions were approximated based on comparison of the segmented copy number alteration (CNA) amplitudes in cfDNA with those from pure single-cell DNA with a limit of sensitivity at approximately 5% tumor DNA (figure 2.1C). Figure 2.1 Schematic overview of the HD-SCA workflow for CTC characterization and cfDNA analysis. A) Blood samples were centrifuged and plasma was collected for cfDNA analysis. The remaining blood underwent erythrocyte lysis and nucleated cells were plated onto custom microscopy slides. To visualize CTCs among white blood cells, slides were stained with DAPI (DNA), CD45 (WBC marker), pan-Cytokeratin (epithelial marker) and ER (CTC characterization). Candidate cells were isolated by micromanipulation and underwent whole genome amplification. PCR products and extracted cfDNA were analyzed for CNAs and SNVs. B) Representative images of ER positive and negative CTCs. Nuclei are stained with DAPI (blue) and antibodies against CKs (red), ER (white) and CD45 (green). CK = pan-Cytokeratin, ER = Estrogen Receptor. C) ctDNA content was approximated based on the amplitudes of copy number alterations. Single cells are per definition 100% of tumor DNA, while cfDNA is a mix of non-mutated WBCs/healthy tissue and tumor DNA. The height of the ctDNA amplitudes marks the tumor content. ctDNA content was estimated based on the ratio of CNA amplitudes between cfDNA and single-cell profiles. 21 At the time of the first blood draw in October 2013 the patient exhibited increasing metastatic burden and was switched from 1 st line treatment (letrozole) to fulvestrant. During the fulvestrant treatment (draws 1-3), her tumor markers (CA 27.29) and CTC counts increased. However, at draw 3 the fraction of ER+ CTCs decreased dramatically. Because of increasing pain and rising tumor markers she was switched in January 2014 to a combination of an mTOR inhibitor (everolimus) and an aromatase inactivator (exemestane) (draws 4 and 5). Tumor markers increased during this treatment and the CTC counts increased drastically to >3000 CTC/ml. In August 2014, a needle biopsy of the liver confirmed a visceral crisis, extensively involved with metastatic adenocarcinoma and her therapy was changed to a two-agent chemotherapy (capecitabine and paclitaxel) (draws 6-11). Drastic decreases in both CA 27.29 levels and CTC counts were observed in response to this therapy, and analytes remained low while she received chemotherapy. In October 2015, due to adverse events associated with prolonged cytotoxic therapy, her treatment was changed back to ER deprivation therapy (fulvestrant together with palbociclib) until December 2016 (draws 12-16). Tumor markers and CTCs remained relatively low through that period until December (draw 16) when both CA 27.29 and CTC counts again rose dramatically. Her treatment was changed back to chemotherapy (carboplatin), which led to an initial decrease in tumor markers. However, after a change in chemotherapeutic agents (from carboplatin to capecitabine), her CA 27.29 level again rose dramatically as did the CTC count (draw 17). Final paclitaxel treatment following the capecitabine regimen was also ineffective and the patient succumbed to the disease shortly thereafter. The treatment history along with CTC counts and CA 27.29 levels at each examination are shown in Figure 2.2. Details from the clinical narrative are presented in Supplemental Data (S1). 22 Figure 2.2 Treatment history and liquid biomarker evaluation. A) CTC counts (blue bars) and serum marker CA 27.29 (grey dots) evaluation over time. The normalized proportion of ER+ (light blue) and ER- (dark blue) are denoted in the bars. B) Summary of draw dates, therapy at time of blood draw, CTC abundance and ER expression as well as cfDNA/ctDNA abundance. N/A = Not Available; N/D = Not Detectable 23 2.3.2. ER expression is related to treatment response Of the seventeen collected blood draws, twelve draws had more than 5 CTCs/ml blood and four draws exceeded 3000 CTCs/ml. Examination of ER protein expression of CTCs by immunofluorescence found that ER positivity fluctuated drastically across draws. Interestingly, ER positivity decreased drastically in response to first line fulvestrant treatment (draws 1-3), but did not decrease equally during second line of fulvestrant treatment in 2016 (draws 14-16). Overall, ER+ CTCs represented the majority of cells across draws. 2.3.3. CTC enumeration is correlated with ctDNA fraction, CA 27.29 levels and disease state Next, we assessed whether the levels of liquid biopsy analytes were correlated. We found positive correlations between CTCs/ml, cfDNA concentrations and ctDNA fractions (Figure 2.3). While we could not calculate the correlation between CA27.29 and liquid biopsy analytes as CA27.29 levels were not measured at the same draw date as liquid biopsy draws, it is evident that that their levels trend similarly. Increases in CA 27.29, CTCs/ml, ctDNA fractions and cfDNA concentration were all associated with disease progression. Figure 2.3 Correlation between CTCs/ml, ctDNA fraction and cfDNA concentration. A) Correlation between CTCs/ml and the concentration of cfDNA. Pearson correlation coefficient R² = 0.92 and two-tailed p-value <0.0001. B) Correlation between CTCs/ml and the ctDNA fraction. Pearson correlation coefficient R² = 0.77 and two-tailed p- value <0.0001. C) Correlation between the ctDNA fraction and cfDNA concentration. Pearson correlation coefficient R² = 0.83 and two-tailed p-value <0.0001. ctDNA fractions <5% were considered 0. 24 2.3.4. CNA Analysis of CTCs and tissue biopsies traces tumor lineage To examine the extent of genomic heterogeneity at time of enrollment, we tested single cells detected by the HD-SCA for CNAs. Candidate cells were isolated by micromanipulation for low-pass sequencing and CNA profiling. Genomic analysis of 73 single CTCs of the first time point revealed three closely related subclones apparently derived from a common ancestor and exhibiting a sequential increase in CNA complexity (Figure 2.4A). Truncal events in all subclones include segmental losses on chromosomes 1p, 6q (ESR1), 11p/q (ATM), 13q (BRCA2, FOXO1A, RB1), 14q,16q (CDH1, CDH11) plus a gain of 1q (ABL2, ELK4, MDM4) and focal deletions on 3p (FOXP1) and 21q (Figure S2.2). Additional CNAs in subclones 2 and 3 include losses on 8p and 18q, and amplifications on 11q (CCND1), from which we infer progressive evolution. To confirm the tumorous origin of these cells, bulk tissue biopsies of the primary breast as well as bone and liver metastases were assessed for CNAs (Figure 2.4B). Comparative analysis of single cells from draw 1 with bulk tissue biopsies from primary breast and metastatic sites revealed that tumors in primary breast and bone were dominated by subclone 1 cells, whereas the later liver metastasis was comprised of nearly 100% subclone 2 cells. 25 Figure 2.4 Copy Number Alteration Analysis of CTCs, tissue biopsies and cfDNA. A) Heat-map of 73 CTCs isolated from draw 1. CTCs cluster into three distinct subclones. Copy number gains are shown in red and losses in blue. B) Direct comparison of tissue biopsies with specific CTC subclones. C) Fluctuations of CTC subclone abundance across the 17 draws. Time-points with less than 10 evaluable CTCs were excluded from analysis (draw 7-12). D) Comparative Analysis of ctDNA and CTC abundance and their genomic relationship. Left, Representative CTCs of each subclone highlight genomic relationship across subclones. CTC CNA profiles of subclone 2 and 4 are superimposed with cfDNA CNA profile of draw 5 and 16, respectively. Middle, cfDNA CNA profiles from draw 4-17. Plasma of earlier draws was not available for analysis. Right, Percentage of ctDNA, most dominant subclone and CTCs/ml blood. 26 Expanding our single-cell analysis to the remaining blood draws, we found that virtually all analyzed CTCs clustered with one of the three subclones, regardless of the blood draw, up until draw 16. Few exceptions were rare intermediate subclones, which shed light on how the 3 subclones formed (Figure S2.3). 3.5 years after enrollment (draw 16) we discovered a new genomically related subclone 4, which had acquired additional chromosomal gains of chromosome 5, 6p, 8q, 16p and 20, containing genes such as MAP3K1, APC, MYC, ASXL1 and ZNF217 (Figure 2.4C/D, S2.2 + S2.4). Interestingly, CTCs across the four genomic subtypes were phenotypically similar when compared for nuclear and cellular area, eccentricity and perimeter (Figure S2.5). Analysis of 42 commonly altered genes implicated in breast cancer finds that CNAs present at time of enrollment span a wide variety of genes implicated in apoptosis, DNA repair, cytokinesis and G1/S-phase transition (Table 2.1). In contrast, CNAs gained with subclone 4 primarily affect cell cycle control. Additionally, subclones 1-3 were dominated by copy number losses, while subclone 4 acquired mainly copy number gains. While the abundance of the clones varied among draws, we found that subclone 2 was the most prominent clone in nine of the eleven draws with high CTC counts (Figure 2.4C/D). 27 Table. 1 List of gains and losses of 42 frequently altered breast cancer genes per genomic subclone. Copy number alteration analysis of representative CTCs of the four subclones and a WBC for 42 commonly altered genes in breast cancer. Gains and losses are shown for 4 representative CTCs of each subclone and 1 representative WBC of draw 16. 2.3.5. CNA Analysis of cfDNA reveals genomic relationship with CTCs Next, we assessed how the abundance and genomic content of cfDNA are related to CTCs and solid tissue biopsies. cfDNA was extracted from draws 4-17 (plasma was not available from draws 1-3) and sequenced for CNA profiling. We found not only that ctDNA fraction, cfDNA concentration and CTC abundance was positively correlated (Figure 2.3), but also that the CNA profiles of ctDNA represented the aggregate of CTC subclones (Figure 2.4D). In particular, ctDNA Gene Subclone 1 Subclone 2 Subclone 3 Subclone 4 WBC ARID1A Loss Loss Loss Loss None SF3B1 None None None None None CASP8 None None None None None SETD2 None None None None None BAP1 None None None None None PIK3CA None None None None None MAP3K13 None None None None None MAP3K1 None None None Gain None APC None None None Gain None ESR1 Loss Loss Loss None None ARID1B Loss Loss Loss None None MLL3 None None None None None ZNF703 None Loss Loss None None FGFR1 None Loss Loss None None MYC None None None Gain None CDKN2A None None None None None GATA3 None None None None None PTEN None None None None None CCND1 None None Gain None None CDKN1B None None None None None KRAS None None None None None ARID2 None None None None None MLL2 None None None None None SMARCD1 None None None None None MDM2 None None None None None TBX3 None None None None None BRCA2 Loss Loss Loss Loss None RB1 Loss Loss Loss Loss None AKT1 None None None None None CDH1 Loss Loss Loss Loss None TP53 None None None None None MAP2K4 None None None None None NCOR1 None None None None None NF1 None None None None None ERBB2 None None None None None BRCA1 None None None None None SMAD4 None None None None None STK11 None None None None None AKT2 None None None None None ASXL1 None None None Gain None ZNF217 None None None Gain None AR None None None None None 28 CNA profiles reflect the most abundant CTC subclone per time point. Figure 2.4D contains an overlay of draw 5 cfDNA CNAs with subclone 2 and draw 16 cfDNA CNAs with subclone 4 showing the correspondence of genomic alterations and the differences in amplitude. 2.3.6. ESR1 mutation analysis in tissue samples, cfDNA and CTCs uncovers parallel endocrine resistance evolution To explore the cause of hormone treatment failure, we used the ThermoFisher Oncomine™ breast cancer panel to test cfDNA for SNVs. Oncomine™ test results revealed an ESR1 Y537N mutation at draw 5, which was collected after multiple rounds of hormone therapy, followed by a second ESR1 mutation (D538G) in draw 6 (Figure 2.5A). Given the heterozygous deletion in chromosome 6q in the region containing the ESR1 gene, a single mutation in the ESR1 gene resulted in complete loss of wild type ER in affected cells. In the 9 th blood draw a TP53 mutation was detected for the first time, but remained at low abundance. Draw 16 is the first time we find not only a new subclone based on CNA profiling, but also a mutation in PIK3CA (E542K) as well as an additional ESR1 mutation (Y537S). Overall, we detected a low SNV burden across all blood draws, with a majority of known endocrine resistance inducing ESR1 mutations. 29 Figure 2.5 SNV analysis of cfDNA, tissue biopsies and CTCs. A) Overview of cfDNA mutation status by Oncomine™ Breast Cancer panel and comparison with single-cell Sanger sequencing. B) Summary of Whole Exome Sequencing results of tissue biopsies for mutations detected in the ctDNA. C) Representative examples of ESR1 Sanger sequencing traces of WT and mutant CTCs. D) Representative examples of PIK3CA Sanger sequencing traces of WT and mutant CTCs. E) ER expression per subclone across all CTC positive draws. F) Comparative analysis of CNA subclones and ESR1 genotype of single CTCs across all evaluated time-points. Numbers indicate number of cells scored. G) Comparative analysis of ER protein expression measured by immunofluorescence and ESR1 genotype of single CTCs across all evaluated time-points. Numbers indicate number of cells scored. N/A = Not available, ND = Not determined. WT Y537N Y537S D538G 0% 25% 50% 75% 100% ER Expression vs ESR1 Genotype % ER status ER Negative ER Nuclear 129 38 32 1 7 WT Y537N Y537S D538G 0% 25% 50% 75% 100% CNA Subclones vs ESR1 Genotype % Subclones per Genotype 26 12 65 8 15 6 1 3 4 Subclone 2 Subclone 1 Subclone 3 Subclone 4 Subclone 1 Subclone 2 Subclone 3 Subclone 4 0 50 100 150 ER Expression vs CNA Subclones Number of Scored Cells ER Negative ER Nuclear Blood Draw Gene AA Change VAF(%) cfDNA fraction (%) VAF per ctDNA fraction (%) Found in CTCs Number of Mut/WT Cells Position Mol Depth (from UMI) Variants (UMI) LOD (from read count) Total Read Count/ Locus 1 ESR1 Any N/A N/A N/A no 0/17 3 ESR1 Any N/A N/A N/A no 0/25 5 ESR1 Y537N 11.72 31.8 36.9 yes 1/23 chr6:152419921 239 28 0.65 780 6 ESR1 Y537N 3.41 9.5 68.2 yes 14/38 chr6:152419922 7587 259 0.05 36583 ESR1 D538G 0.26 5.2 no 0/38 chr6:152419926 7587 20 0.05 36589 7 ESR1 Y537N 0.24 14.1 1.7 N/A No CTCs chr6:152419922 3718 8 0.05 20577 8 ESR1 Y537N 0.13 <5 2.6 N/A No CTCs chr6:152419922 3733 5 0.05 25944 9 TP53 R248Q 0.10 <5 2.0 N/A No CTCs chr17:7577538 1936 2 0.1 13158 10 ESR1 Y537N 0.11 <5 2.2 N/A No CTCs chr6:152419922 1870 2 0.1 11142 11 No Mut found <5 0.0 N/A No CTCs 12 ESR1 Y537N 0.10 <5 2.0 N/A No CTCs chr6:152419922 2004 2 0.1 15644 TP53 R248Q 0.20 4.0 chr17:7577538 1471 3 0.15 14634 13 ESR1 Y537N 1.15 16.9 6.8 yes 3/2 chr6:152419922 693 8 0.25 2892 TP53 R248Q 0.29 1.7 ND ND chr17:7577538 698 2 0.25 3171 14 ESR1 Y537N 3.69 12.4 29.7 yes 2/27 chr6:152419921 2466 91 0.1 13912 15 ESR1 Y537N 0.66 10.0 13.2 yes 5/15 chr6:152419922 3788 25 0.05 20197 ESR1 D538G 0.10 2.0 yes 5/15 chr6:152419926 3789 4 0.05 20197 16 PIK3CA E542K 0.31 48.8 0.6 yes 1/15 chr3:178936082 3265 10 0.05 12680 ESR1 Y537N 0.30 0.6 yes 1/38 chr6:152419921 2994 9 0.1 10594 ESR1 Y537S 0.07 0.1 no 0/38 chr6:152419923 2994 2 0.1 10594 ESR1 D538G 7.32 15.0 yes 2/38 chr6:152419926 2992 219 0.1 10594 TP53 R248Q 0.05 0.1 ND ND chr17:7577538 3680 2 0.05 13885 17 PIK3CA E542K 0.11 14.3 0.7 no 0/5 chr3:178936082 7482 8 0.05 54295 ESR1 Y537N 2.75 19.3 yes 12/34 chr6:152419922 5163 142 0.05 25057 ESR1 Y537S 0.14 0.9 yes 1/34 chr6:152419923 5163 7 0.05 25057 ESR1 D538G 2.19 15.3 yes 1/34 chr6:152419926 5162 113 0.05 25057 TP53 R248Q 0.13 0.9 ND ND chr17:7577538 5544 7 0.05 37737 F G E C A Primary Breast Bone Metastasis Liver Metastasis ESR1: Y537N No No Yes ESR1: Y537S No No No ESR1: D538G No No No TP53: R248Q N/D No Yes PIK3CA: E542K No No No B ESR1 WT ESR1 Y537N ESR1 Y537S ESR1 D538G PIK3CA E542K PIK3CA WT D 30 To test whether the patient’s tumor harbored any of these SNVs at time of diagnosis, we performed whole-exome sequencing of the primary breast tissue and metastatic bone and liver biopsies and confirmed results by Sanger sequencing (Figure 2.5B + S2.6A). None of the mutations detected later in the plasma were found in the breast and bone biopsies, which were collected at time of diagnosis. However, we detected the same Y537N mutation in the liver biopsy, which was taken 1.5 months after draw 5. This indicates that the tumor likely initially developed by large chromosomal alterations which remained remarkably stable over many years, yet eventually evolved primarily through specific SNVs. To pinpoint when the first ESR1 mutation emerged and to deconvolute the mutational heterogeneity of single cells, we designed an assay for single-cell SNV analysis to examine CTCs for ESR1 mutations. Leukocytes isolated from the same blood draw were used as controls (Figure S2.6A+B). None of the CTCs and WBCs isolated from draws 1 and 3 harbored the expected mutation (Figure 2.5A). In contrast, CTCs, but not WBCs from draw 5 onwards exhibited the same Y537N mutation found at high variant allele fraction in the cfDNA and the liver metastasis. Also, in line with the cfDNA results, we detected Y537S and D538G mutations in CTCs of the later blood draws (draw 15-17). Representative ESR1 Sanger sequencing traces are shown in Figure 2.5C. Interestingly, mutations in amino acid 537 were mutually exclusive from those in amino acid 538, indicating parallel mechanisms of resistance. Out of the 19 instances of ESR1 mutations found in cfDNA with the Oncomine™ assay across the draws, only two were not detected at the single-cell level (Figure 2.5A). However, these two mutations occurred at either very low frequency and/or low ctDNA fraction and could have been missed due to the limited number of cells sequenced. In contrast, all mutations detected at the single-cell level were found in the cfDNA by the Oncomine™ assay. 31 2.3.7. ESR1 mutations occur independent of CNAs, but are positively associated with ER protein expression Next, we determined whether there was any association between ESR1 mutations, ER expression and CNA-defined subclones. Despite their genomic differences, cells from the four subclones identified by CNA analysis were not associated with a specific ER expression phenotype (Figure 2.5E), indicating that fluctuations in ER expression are independent of their gross chromosomal alterations. In addition, ESR1 mutations occurred independently of the subclones, signifying that ESR1 resistance has developed independently from the CNAs (Figure 2.5F). Interestingly, all ESR1 mutated cells expressed nuclear ER protein as visualized by immunofluorescence (Figure 2.5G). Thus, while cfDNA analysis by Oncomine™ provided higher apparent sensitivity, single-cell analysis was required to evaluate the distribution of the 537 and 538 mutations and associate mutations with ER protein expression. 2.3.8. Co-occurrence of PIK3CA mutation and subclone 4 marks point of multi-treatment failure Finally, we tested whether there was an association between the occurrence of subclone 4 cells and the detection of PIK3CA mutations at draw 16, specifically whether PIK3CA mutations were exclusive to cells of subclone 4. Single-cell SNV analysis of draw 16 found one CTC with the E542K mutation out of 16 sequenced CTCs (Figure 2.5D). Interestingly, the CNA profile of the E542K mutated cell exhibits the same gains and losses typical of subclone 4, which indicates that the PIK3CA point mutation may have occurred specifically in a subset of subclone 4 cells. 2.3.9. Ploidy of CTCs gives insight into tumor evolution through genomic duplication Based on the CNA profiles, we hypothesized that subclone 4 might have been created by duplication of subclone 2. Hence, to test the ploidy of CTCs, we performed fluorescent in situ 32 hybridization with a probe for the centromeric region of chromosome 6 (Figure 2.6A). As this region was affected by a heterozygous loss as found by CNA analysis in subclone 1-3 cells, one focus marks diploid cells, whereas two foci point toward a tetraploid CTC for these subclones. Analysis of CTCs of draw 14, which solely contains cells of clone 1-3, finds only diploid cells (Figure 2.6B). The first time cells with 2 foci were detected was with the appearance of subclone 4 at draw 16 (Figure 2.6B). Subclone 4 is copy number normal for chromosome 6, meaning 2 foci mark a diploid cell. The percentage of cells with 2 foci in draw 16 is comparable to the percentage of CTCs of subclone 4 (Figure 2.6B/C), indicating that subclone 4 might have been created by duplication of multiple chromosomes of clone 2 as described by Navin et al. followed by additional mis-assortment of arms [122]. Figure 2.6 Assessment of ploidy of CTCs by fluorescent in situ hybridization (FISH). A) Representative images of CTCs stained with DAPI (blue), pan-Cytokeratin-Alexa-555 (red), Chromosome 6 probe (white) and CD45-Cy5 (green, not shown). Composite includes DNA staining, pan-Cytokeratin expression and chromosome 6 probe. CTCs and surrounding WBCs were scored for number of chromosome 6 foci. B) Percentage of cells with 1 or 2 foci were calculated for each draw. C) Percentage of CNA subclones are shown for each draw. We find approximately the same number of clone 4 cells than CTCs with 2 foci. 2.4. Discussion Our goal in this study was to shed light on the biology of treatment responses through characterization of CTCs and cfDNA in an individual patient throughout the course of treatment and to demonstrate the feasibility and utility of multi-analyte liquid biopsies in larger, multi-patient studies. In this context, we highlighted how protein and genomic changes in liquid biopsy analytes 33 can provide valuable information about tumor evolution, treatment response and resistance mechanisms. In short, longitudinal blood sampling of a metastatic breast cancer patient enabled tracing of disease evolution robustly and less invasively than solid tumor biopsies. First, we demonstrated that CTC enumeration and ctDNA abundance was associated with treatment response and showed similar trends as CA 27.29 levels. Assessment of ER provided insight into drug-induced alterations of ER protein expression. In particular, ER+ CTCs decreased dramatically in the first round of ER-targeted treatment with fulvestrant (draws 1-3), a selective estrogen receptor degrader (SERD), indicating that fulvestrant, but no other drug specifically reduced ER+ CTCs. In contrast, the second round of fulvestrant treatment (draws 13-15) was less effective in eliminating ER+ cells, possibly due to acquired mutations in the ESR1 gene, which have been linked to reduced efficacy of fulvestrant binding [138-140]. Single-cell CNA analysis revealed the presence of three genomically related subclones, with a fourth subclone appearing 3.5 years after trial initiation. It is striking that these three subclones persisted throughout various treatments, varying only in relative fraction, indicating high genomic stability. Comparative analysis of CNA profiles of tissue biopsies and CTCs found only subclone 1 in the tissue taken at diagnosis, while subclone 2 cells dominated the liver metastasis detected 2.5 years later. Due to the time gap between the breast/bone biopsy and the first liquid biopsy it is unclear whether subclones 2 and 3 developed in the breast/bone in that interval, or were present at diagnosis, but below detection limit. Although the clonal origin is not clear, we can conclude that matching CNA profiles of CTCs to those from the cancerous tissue proves their direct relationship, as all CTC subclones detected by HD-SCA are either present in the biopsied tissue or evolutionally related to them. 34 Plasma DNA analysis revealed not only a positive correlation between the abundance of ctDNA and CTCs, but also the genomic relationship between the two. Specifically, we found that CNA profiles of ctDNA mimic the most abundant CTC subclone in each draw. While plasma- based assays detected the presence and genomic makeup of tumor-derived DNA, single-cell high- content resolution was required to deconvolute the subclones and their relationship with ER protein expression at the cellular level. In addition to tracing disease burden, therapy response and gross chromosomal alterations, we monitored the emergence of potential resistance mutations in CTCs and cfDNA using PCR- based sequencing of ESR1 and the targeted OncoMine™ Breast Hotspot panel, respectively. Given the strong ER staining and high positivity of ER in the breast biopsy and cells of the first blood draw, it is not surprising that various ESR1 mutations (Y537N, Y537S, D538G) [110, 113, 114, 141] were detected in the cfDNA, single CTCs and tissue from the liver biopsy after treatment with a SERD. These mutations have been associated by other groups with reduced fulvestrant efficiency and hormone therapy resistance by promoting ligand-independent activation [114, 138, 140, 142, 143]. Single-cell SNV analysis found mutations in amino acid 537 to be mutually exclusive from those in amino acid 538, indicating the co-evolution of multiple resistance pathways. Interestingly, all ESR1 mutated cells expressed nuclear ER, signifying ER pathway activation. This is an important observation, because it cautions that nuclear ER expression does not necessarily translate to clinically targetable ER. In addition to ESR1 mutations, a TP53 mutation was detected after chemotherapy treatment and a PIK3CA mutation was first found together with the appearance of subclone 4. While the one detected PIK3CA mutant cell exhibited chromosomal rearrangements characteristic of subclone 4, more data is needed to establish a direct relationship. Co-occurrence of TP53 and PIK3CA mutations has been reported as worse clinical 35 outcome when compared to TP53 or PIK3CA mutations alone [144]. Aside from the likely treatment-induced ESR1 mutations, and the late appearing TP53 and PIK3CA lesions, we did not detect additional point mutations indicating the cancer, particularly in its early development, may have been largely driven by copy number alterations. Pathway analysis of 42 commonly altered genes in breast cancer revealed that CNAs of the evolutionary first subclone affect a variety of genes associated with hallmarks of cancer as described by Weinberg et al. [145]. Through the altered dosage of affected genes and their products these cells gained the ability, (1) to evade apoptosis (ARID1A) [146], (2) accumulate mitotic errors (CDH1) [147, 148], (3) diminish their DNA repair (BRCA2) [149] and (4) sustain proliferative signaling (RB1) [150]. In contrast, genes altered in subclone 4 such as MYC and APC [151, 152] primarily impact checkpoints in the cell cycle. Together with the TP53, ESR1 and newly acquired PIK3CA point mutations, these alterations are consistent with increased cyclin D abundance, and eventually enhanced proliferation [153, 154]. In short, CNAs of subclone 1 impact a variety of mechanisms required for tumor development, while later CNAs of subclone 4 specifically promote cell cycle transition. This could explain why chemotherapy has resulted in both, biochemical response and clinical improvement from draws 6 and 11 in the absence of subclone 4. In contrast, in the presence of subclone 4, chemotherapy agents as well as the CDK4/6 inhibitor might not have been sufficient to overcome the additional proliferative stimuli. While ESR1 mutations could have resulted in decreased response to endocrine therapy, we propose that the combination of new CNAs and SNVs at draw 16 possibly resulted in multi-treatment failure with the patient passing away shortly after. Yet, the detection of the PIK3CA SNV could have indicated alternative treatment options with targeted inhibitors such as alpelisib (which was not available at the time). 36 In summary, these results demonstrate that a combined solid and liquid biopsy analysis across multiple time points and integrated analysis of bulk and single-cell analytes, can provide a high-resolution view of tumor evolution under treatment pressure. While FFPE tissue and cfDNA can provide insight into protein expression and genomic aberrations respectively, neither can compete with the ready access, nor the single-cell resolution required for tracing tumor evolution enabled by characterization of CTCs. cfDNA, however, can be leveraged for multi-gene SNV analysis, which is currently too labor intensive on a single-cell level, while tissue biopsies inform about the tumor’s histopathology. We believe that liquid biopsy analysis of both, CTCs and cfDNA, should become a complementary tool to current standard-of-care solid biopsies. This would allow for minimally invasive follow up of patients throughout their treatments with the added advantage of detecting acquired therapy resistance mechanisms and guiding treatment selection. In the future, integrating additional assays such as the assessment of ESR1 methylation status, single cell RNA sequencing (scRNA-seq), multiplex proteomics as well as characterizing the circulating tumor microenvironment could all provide an even deeper characterization of a patient’s tumor, its evolution and response to therapy [155-159]. In addition, recent studies have investigated the biological features of cfDNA, such as nucleosome positioning, fragment size analysis and copy number alteration analysis as a measure for disease outcome [101, 102, 160]. Each of these assays has been proven or shown great potential to be clinically relevant for either diagnosis, prognosis or assessing response to therapy [161]. Hence, it is essential to expand on and replicate our findings in a larger cohort of patients to substantiate the utility of longitudinal multi- analyte liquid biopsy in metastatic breast cancer and to incorporate additional analytes for a comprehensive liquid biopsy. 37 2.5. Materials and Methods 2.5.1. Collection and processing of bloods samples Patient peripheral blood samples were collected in Streck DNA tubes according to the approved protocol established by the Institutional Review Board of Billings Clinic. We note that this was a retrospective study and results were not used to influence treatment decisions. After collection, blood samples were shipped overnight to the Kuhn-Hicks laboratory, initially at The Scripps Research Institute (TSRI) and subsequently University of Southern California and were processed within 48 hours of the blood draw consistent with previously validated protocols [137]. Sample preparation was performed as previously described in [66, 162] with an additional plasma collection step. In brief, blood plasma was separated by centrifugation for 10 min at 2000 g. Collected plasma was centrifuged again for 10 min at 14000 g to remove WBC and platelets and frozen at -80 °C for further analysis. The extracted plasma volume was replenished with 1x PBS. Blood samples underwent erythrocyte lysis in ammonium chloride solution and the nucleated cells were re-suspended in PBS to create a monolayer of cells on proprietary cell-adhesion slides (Marienfeld). Slides were incubated at 37 °C for 40 min, treated with 2% BSA and stored at −80 °C for further morphological and genomic analysis. 2.5.2. Immunofluorescence Staining and CTC Enumeration For this study, we used a four-color version of the published HD-SCA workflow [137, 163] in which the estrogen receptor (ER) status was assessed in the cytokeratin (CK)-positive, CD45- negative CTC population. Briefly, the cells were labeled using mouse monoclonal CK19 (1∶100; Dako) and panCK (1∶100; Sigma) to identify CK-positive cells. ER-positive CTCs were identified using the SP1 clone (1:250, Thermo Fisher Scientific). Secondary antibodies conjugated with Alexa 555 Goat (1:500) and Invitrogen Alexa 488 Goat Anti-rabbit (1:1000) were used to visualize 38 the CK and ER primary antibodies, respectively. Alexa Fluor® 647 conjugated mouse anti-human CD45 (1:125, AbD Serotec) was used to identify the white blood cells. DNA was stained with DAPI. CTC enumeration was initialized using a high-throughput fluorescence microscope at 10x magnification. Presumptive CTCs were identified as DAPI/CK positive and CD45 negative. For draws with >3000 CTCs/ml, CTC abundance was approximated by counting a subset of cells and extrapolating to the full set. The expression and sub-cellular localization of the estrogen receptor was simultaneously measured and CTCs were evaluated by identifying the presence (ER+) or absence (ER-) of nuclear ER staining. To determine phenotypic differences of cells within a draw, cells were analyzed for cellular/nuclear shape and size at 10x and 40x magnification in R using EBImage and ggfortify. Data was visualized with R and GraphPad Prism. 2.5.3. Isolation of Single Cells The identified CTCs were subsequently relocated and imaged at 40x for detailed morphometric analysis. In order to retrieve cells for genomic analysis, an Eppendorf TransferMan® NK2 micromanipulator was used to capture the cell of interest in a micropipette and transfer it to a PCR tube containing 2 μl of lysis buffer (200 mM KOH; 50 mM DTT). The lysate was frozen and stored at -80 °C for genomic processing. Decontamination of micropipettes and microscope stage was performed 30 minutes prior to each cell capture procedure. The lysed cell mixture was thawed and underwent whole genome amplification (WGA) and sequencing library construction as previously reported [162, 164]. Briefly, WGA was done using the WGA4 Genomeplex Single Cell Whole Genome Amplification Kit (Sigma-Aldrich) followed by purification with the QIAquick PCR Purification Kit (Qiagen). DNA concentration was measured using the Qubit Fluorometer system (ThermoFisher Scientific) and fragment size distribution was measured with the Agilent 2100 Bioanalyzer (High-sensitivity DNA Kit, Agilent 39 Technologies). Single-cell Illumina sequencing libraries were created using the Illumina paired indexing system and pools of up to 96 cells were sequenced at the USC Dornsife Sequencing Core to generate ~500,000 mapped reads per sample (minimum 250,000). 2.5.4. cfDNA Isolation and Illumina Whole Genome Library Construction Plasma was thawed and cfDNA was extracted with the QIAamp Circulating Nucleic Acid Kit (Qiagen) according to the manufacturer’s instructions. DNA concentration was measured using Qubit Fluorometric Quantitation (Thermo Scientific). Illumina DNA sequencing libraries were constructed with the NEBNext Ultra II DNA Library Prep Kit (New England Biolabs) according to manufacturer’s instructions and barcoded with Multiplex Oligos for Illumina (New England Biolabs). The sample size distribution of both the extracted DNA and the sequencing library was measured with the Agilent 2100 Bioanalyzer (High-sensitivity DNA Assay and Kit, Agilent Technologies). Samples were sequenced at low depth to generate ~500,000 mapped reads per sample (minimum 250,000). 2.5.5. Histological Evaluation of FFPE tissue samples As part of the routine clinical workup, the breast needle core biopsy was assessed for the degree of staining as well as % of stained nuclei of the Estrogen Receptor (monoclonal mouse anti- human ER (alfa), clone 1D5 from Dako) and Progesterone Receptor (Monoclonal mouse anti- human PR (alfa), clone PgR 636 from Dako). In addition, HER2/neu gene copy was determined by FISH using the FDA approved PathVysion ® Test Kits from Vysis, Inc. Copies of the HER2/neu gene and chromosome 17 were determined by FISH and probes for HER2/neu gene locus and centromeric position of chromosome 17. 40 2.5.6. DNA extraction of FFPE tissue and Illumina Library Construction FFPE tissue samples of the primary breast as well as bone and liver metastasis were micro- dissected and DNA was exacted with the AllPrep DNA/RNA FFPE kit according to manufacturer’s instructions. Sequencing libraries were constructed with the NEBNext Ultra II DNA Library Prep Kit and barcoded with Multiplex Oligos for Illumina (New England Biolabs) according to manufacturer’s instructions. The sample size distribution of the sequencing library was measured with the Agilent 2100 Bioanalyzer (High-sensitivity DNA Assay and Kit, Agilent Technologies) and the concentration was measured with Qubit (Thermo Fisher Scientific). Samples were sequenced at USC Core facilities. 2.5.7. Copy Number Alteration Analysis Bioinformatic analysis for copy number profiling was performed as previously published [165]. Briefly, Illumina sequence reads were deconvoluted based on sample barcodes and PCR duplicates were removed. The binned ratios were normalized according to guanine-cytosine (GC) content of each bin and mapped to 20,000 bins averaging 125 kbp of uniquely mapping sequence across the human genome (hg19, Genome Reference Consortium GRCh37, UCSC Genome Browser database). Read count data was segmented using the CBS segmentation algorithm and copy number profiles were generated from segmented bin count data and presented as ratios to the genome-wide median [166]. The hierarchical clustering was performed in R using the heatmap.2 function in the ggplots package. Cells were clustered by Ward’s method with Manhattan distance by their median centered data. Cutoffs for gains and losses were 1.25 and 0.75 over the median, respectively [165]. 41 2.5.8. Determination of ctDNA Fraction in Plasma The ctDNA percent was estimated by diluting the single cell amplitudes until they matched those of the cfDNA. We first determined the dilution factor (dilution factor = (100 - percent) / percent), which in turn was used to determine the percent ctDNA (percent ctDNA = (single cell value + dilution factor) / (dilution factor + 1)). ctDNA fractions calculated with this method were comparable to those derived by ichorCNA [167]. 2.5.9. ESR1 and PIK3CA Single-Cell SNV Analysis Amplicons of single cells as well as extracted DNA from plasma and FFPE tissue blocks were tested for point mutations in the ligand-binding domain of ESR1. We used a single primer set (Fwd: TACAGTAACAAAGGCATGGAGCA, Rev: CGATGAAGTAGAGCCCGCAG) to amplify the region of interest and PCR products were sequenced using Sanger Sequencing. Additionally, a subset of cells was tested for PIK3CA mutations (Fwd: CCAGAGGGGAAAAATATGACA, Rev: AGCACTTACCTGTGACTCCA). Data was analyzed using QSVanalyzer (University of Leeds) [168] and KNIME [169]. Cells with a raw intensity ratio of >80 were called wild type and those with a raw intensity ratio <20 were called mutant. The remaining cells were excluded from the analysis. 2.5.10. Single Nucleotide Variation Analysis of cfDNA cfDNA of 13 blood draws was tested for multiple hotspot mutations using the Oncomine™ Breast Assay v2 (A35865, Thermo Fisher Scientific) in combination with the Ion Torrent S5 (Thermo Fisher Scientific). Where available, 20ng cfDNA was used as starting material. The assay examines 152 hotspots, 3 copy number genes and the full length of TP53 gene. Data was processed with the Oncomine™ Breast Liquid Biopsy w1.3 workflow on Ion Reporter. Details on total reads, mapped reads and coverage are summarized in Table S2.1. 42 2.5.11. Whole Exome Sequencing of FFPE tissue Whole exome sequencing libraries were constructed from the whole genome libraries of the three tissue biopsies with the Illumina TruSight Exome Library Preparation kit (TG-141-1001, Illumina Inc) and each sample was sequenced on Illumina NextSeq 550 and HiSeq 2500 using paired end 150 bp and 100bp sequencing modes, respectively. Alignment on the reference genome (hg19) was made with BWA MEM (v0.7.17) [170]. Aligned reads from both runs were merged for downstream analysis. PCR duplicates were removed using GATK (v4.1.8.1) [171]. Specific variants detected in cfDNA samples by the Oncomine™ Breast Assay v2 were visualized by Integrative Genomics Viewer (IGV) [172]. Variants were called if ³2 more reads were altered. Details on total reads, mapped reads and coverage are summarized in Table S2.2. 2.5.12. Fluorescence in situ hybridization of CTCs Slides were stained with DAPI, CK and CD45 antibody cocktails, scanned and imaged as described above. Once CTC candidates were identified, slides were washed in 2x SSC buffer and gradually dehydrated in ethanol. The hybridization solution with a probe against the centromeric region of chromosome 6 (Biocare Medical) was added onto the slides and the slides were sealed. Probe and cellular DNA were denatured at 70 °C for 3 minutes. Hybridization took place at 37 °C overnight in a humidity chamber for 16-24h. Slides were then washed in 0.4x SSC-0.1% Tween at 40 °C for 10 min and 2x SSC at RT for 5 minutes, covered with antifade mounting medium and imaged using fluorescence microscopy. 43 2.6. Additional Information 2.6.1. Data Availability All analyzed data during in this study is included in this manuscript and in its supplementary information files. Some of the data can be accessed through our website: http://pivot.usc.edu/. The sequencing data of the single cells, cfDNA and FFPE is available through the BloodPAC Data Commons. 2.6.2. Ethics Statement This study was approved by the Institutional Review Board of Billings Clinic (BC) and the University of Southern California (USC) was conducted in accordance with the Declaration of Helsinki and the International Conference on Harmonization (IHC) E6 GCP guidelines. Associated IRBs were 13.10 (BC), Pro0044182 (BC), UP-14-00330 (USC), and UP-14-00523 (USC). The patient provided written consent stating the approval of the use of her blood and tissue specimen as well as her medical history for research use and publication. 2.6.3. Acknowledgments First and foremost, we thank the patient, who contributed her blood and tissue specimens to research and without whom this study would not have been possible. We thank her family and caregivers for supporting her on her journey. Appreciations also to nurse Tauna Jeffrey and other team members at Billings Clinic for the blood sample collection and Xiomara Villasenor and the rest of our technical team of USC for processing and cryobanking the blood samples. Nickolas Matsumoto, Shoujie Chai, and Varsha Sundaresan provided support in the technical data analysis. 44 2.6.4. Author Contributions L.W., L.X., J.N., A.K., A.E.D., P.K., and J.B.H. provided oversight of the project and conceptualized ideas. M.R.-L. and L.W. enumerated CTCs. L.W. scored cells for ER positivity and performed phenotypic image analysis. L.X., A.E.D., D.M., and L.W. isolated single cells, performed WGA, and prepared NGS libraries. L.W. and D.M. performed single-cell ESR1 analysis. L.W. isolated cfDNA and performed cfDNA CNA analysis. L.X. performed SNV analysis of cfDNA as well as whole-exome sequencing on FFPE samples. S.R.-V. performed copy number alteration analysis of FFPE tissue. L.W. and K.X. tested cells for ploidy by FISH. R.N. and R.K.P. provided technical support and aided in the data analysis. M.R.-L. coordinated with clinical site, and C.R. supervised sample acquisition. J.N. recruited the patient into the study and provided clinical insights. L.W., L.X., J.N., P.K., and J.B.H. prepared the manuscript with edits from R.K.P., S.R.-V., D.M., C.R., and A.K. 2.6.5. Funding P.K. and J.B.H. are supported in part by grant awards ID numbers BCRF-17-087 and BCRF-18-089 and awards prior to 2016 from the Breast Cancer Research Foundation. P.K. was supported by the National Cancer Institute, National Institutes of Health, under Contract No. HHSN261200800001E. The content of this publication does not necessarily reflect the views of policies of the Department of Health and Human Services nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government. J.N. was supported in part by award number P30CA014089 from the National Cancer Institute. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health. L.W. was supported by the Alan Joseph Endowed Fellowship. 45 2.6.6. Competing Interest Statement A.K., J.N., and P.K. are shareholders in Epic Sciences, Inc. J.H. is on the Advisory Board of Epic Sciences, Inc. 2.7. Supplementary Figure S2.1 Clinical history and collection of blood draws A 54-year old Caucasian female presented in April 2012 to her primary care physician with malaise and thrombocytopenia. Magnetic resonance imaging (MRI) revealed a tumor in the right breast with minimal FDA uptake as well as metastases in the bone together with compression fracture of the spine. Needle biopsies of the right breast and the left pelvic bone confirmed stage IV invasive lobular carcinoma on the right breast (ER+, PR-, HER2 neu-) to bone with mediastinal/hilar nodes and bilateral pulmonary metastasis. To reduce the risk of bone fractures, she was administered the RANKL inhibitor denosumab (120 mg subcutaneous monthly) from April 2012 until February 2015. As anti-tumor treatment, she was started on the aromatase inhibitor letrozole (2.5 mg PO daily), but was moved to the ER antagonist fulvestrant (500 mg IM monthly) in October 2013 due to increased metastasis shown in the bone scans. Because of increasing pain and rising tumor markers (CA 27.29) she was switched in January 2014 to a combination of an mTOR inhibitor and an aromatase inactivator. Everolimus (10 mg PO) and exemestane (25 mg PO) were administered daily. Her everolimus dose was reduced to 5 mg PO daily in August 2014. At the same time, a needle biopsy of the liver confirmed a visceral crisis, extensively involved with metastatic adenocarcinoma, consistent with her prior diagnosis. At the end of August 2014, her therapy was changed to a two agent chemotherapy, including capecitabine (1250 mg/m2 BID x14 days) with paclitaxel (90mg/m2 IV day 1 and cycled every 21 days). After the first cycle, her dose was altered to capecitabine (800mg/m2 BID x 14 days) with paclitaxel 46 (175 mg/m2 IV day 1 and every 21 days x3 cycles). She responded well to cytotoxic therapy and continued on single agent capecitabine (1000 mg/m2 BID x14 days then off 7 days) from January 2015 until October 2015. To reduce adverse events, her capecitabine regimen was changed to 7 days on followed by 7 days off treatment. Due to other adverse effects, she was moved to hormone suppression therapy, fulvestrant (500 mg IM monthly with loading dose), in October 2015. Palbociclib was added to her regimens since the rise in serum markers in March 2015. She was diagnosed with a pulmonary embolism in the lower IVC and within bifurcation of the left lobular branch in June 2014 and August 2014 and has since been on anticoagulant therapy with fondaparinux (7.5 mg subcutaneous daily). In January 2015 her regimen was changed to capecitabine (1000mg/m2 twice daily) 7 days on and 7 days off with initial dosage every 14 days on with 7 days off until May 2015. Due to adverse effects with diarrhea she changed to 7 day cycling. From October 2015 through December 2016, she received fulvestrant (500mg IM monthly) with concurrent palbociclib (125 mg po daily 1 through 21, cycled every 28 days) due to worsening of adverse events on capecitabine. Palbocilib was added March 2016. Denosumab 120mg subcutaneous monthly was added in October 2016 and cycling was changed to every 42 days in May 2017. The hormonal treatment regimen was stopped due to progression and carboplatin (AUC 2 IV weekly) was given from January 2017 to April 2017. The treatment was stopped due to progressing cytopenias. Cancer progression was suspected based on diminution of bone marrow function and recurrence of liver function abnormalities. Subsequently, she received capecitabine (650mg po twice daily days 1-14, cycled every 21 days), but discontinued in August 2017 due to progressive cytopenias and steadily increasing CA27.29 level. A bone scan and CT of the chest, abdomen and pelvis with contrast in August 2017 revealed stable, extensive skeletal metastasis, slightly increased in size in her spleen. Her CA27.29 levels at this time was 674 and 47 her therapy was changed to systemic palliative anastrozole 1 mg po daily in conjunction with capecitabine. Bone marrow biopsies found metastatic carcinoma consistent with breast primary (ER+/PR-/HER-2-) with normal karyotype. In November 2017 systemic therapy with palliative paclitaxel (80 mg/m2 IV weekly) was added. Diminished functional status with worsening CT imaging and ascites, decreased liver function tests and elevated bilirubin on December 4 th , 2017 lead to the decision to enter hospice. The patient passed away on December 11 th 2017. Supplemental figure 2.1 Clinical imaging and pathological evaluation of the patient. A) Magnetic resonance and PET imaging of patient at time of diagnosis. Arrowheads mark tumor nodules and bone compression. B) H&E Stained sections of breast, bone and liver biopsies. 48 Figure S2.2.: CNA profiles of the four subclones with annotated genes. Supplemental figure 2.2 Overview of CNA profiles of the four genomically distinct subclones. Known tumor suppressor and oncogenes in regions unique to the evolutionary next subclone have been annotated. 49 Figure S2.3 Genomic evolution captured in single CTCs While the majority of the CTCs clustered precisely with one of the four subclones, we occasionally found cells exhibiting intermediate genotypes, which give insight into the acquisition of CNAs and clonal selection. Genomic instability as well as drug treatment then selected for the specific four subclones that we detect across time-points. Miss-segregation during mitosis is one of the most common mechanisms leading to gross chromosomal alterations. CNA profiles of single CTCs describe the evolution from subclone 1 through an intermediate subclone to a more stable subclone 2. The schematic on the right visualizes how miss-segregation during mitosis leads to copy number alterations. Supplemental figure 2.3 Rare intermediate subclones give insight into the stepwise process of tumor evolution through chromosomal alterations. A) CNA profiles of subclone 1, 2 and an intermediate clone. B) Schematic representation of chromosomal miss-segregations during mitosis that could have led to subclone 2. 50 Figure S2.4 Chromosomal alterations over time (at first vs last draw) Each blood draw containing CTCs was analyzed for copy number alterations. Here, we compare alterations found in the first draw with the last draw obtained from the patient, just before she passed away. Supplemental figure 2.4 CNA heat-maps of single circulating tumor cells isolated of draw 1 and draw 17. Over time and under treatment pressure, a well-organized tumor can acquire additional gains and losses, which might contribute to therapy resistance. 51 Figure S2.5 Phenotypic analysis of CTCs across genomic subclones Supplemental figure 2.5 Phenotypic analysis of CTCs of draw 16. A) Representative image analysis with EBImage. From left to right: nuclear masks, nuclear masks numbered, cell masks based on CK stain, and DAPI + CK fluorescent images overlaid with nuclear and cellular masks. B) Comparison of cellular and nuclear features of cells from draw 16 across the four distinct subclones at 40x magnification. C+D) PCA analysis of 442 cellular and nuclear parameters as well as fluorescent marker expressions of CTCs found in draw 16 at 10x resolution. Panel C) shows only cells with genomic information whereas D) also includes non-sequenced cells (subclone 0). D Subclone C Subclone Subclone 1 Subclone 2 Subclone 3 Subclone 4 0 50 100 150 Cell Area Area in µm Subclone 1 Subclone 2 Subclone 3 Subclone 4 0.0 0.2 0.4 0.6 0.8 Cell Eccentricity Eccentricity factor Subclone 1 Subclone 2 Subclone 3 Subclone 4 20 25 30 35 40 Cell Perimeter Perimeter in µm Subclone 1 Subclone 2 Subclone 3 Subclone 4 0 50 100 150 Nuclear Area Area in µm Subclone 1 Subclone 2 Subclone 3 Subclone 4 0.0 0.2 0.4 0.6 0.8 1.0 Nuclear Eccentricity Eccentricity factor Subclone 1 Subclone 2 Subclone 3 Subclone 4 0 10 20 30 40 Nuclear Perimeter Perimeter in µm 0 50 100 150 0 50 100 150 Cell vs Nuclear Area Nuclear Area Cell Area 0 10 20 30 40 20 25 30 35 40 Cell vs Nuclear Perimeter Nuclear Perimeter Cell Perimeter 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 Cell vs Nuclear Eccentricity Nuclear Eccentricity Cell Eccentricity B A 52 Figure S2.6 Sanger sequencing analysis of WBCs and tissue biopsies Supplemental figure 2.6 Sanger sequencing analysis for ESR1 SNVs. A) Representative examples of ESR1 Sanger sequencing traces of WBCs and tissue biopsies. B) ESR1 sequence analysis of WBCs. 53 Supplemental table 2.1 Sample overview of cfDNA Oncomine analysis Supplemental table 2.2 Sample overview of WES tissue analysis. Blood Draw Gene AA Change VAF(%) Position Total Read Count/ Locus Mol Depth (from UMI) Variants (UMI) LOD (from read count) Oncomine Gene Class Genotype Reference 5 ESR1 Y537N 11.72 chr6:152419921 780 239 28 0.65 Gain-of-function CT/CA CT 6 ESR1 Y537N 3.41 chr6:152419922 36583 7587 259 0.05 Gain-of-function T/A T ESR1 D538G 0.26 chr6:152419926 36589 7587 20 0.05 Gain-of-function A/G A 7 ESR1 Y537N 0.24 chr6:152419922 20577 3718 8 0.05 Gain-of-function T/A T 8 ESR1 Y537N 0.13 chr6:152419922 25944 3733 5 0.05 Gain-of-function T/A T 9 TP53 R248Q 0.10 chr17:7577538 13158 1936 2 0.1 Loss-of-function C/T C 10 ESR1 Y537N 0.11 chr6:152419922 11142 1870 2 0.1 Gain-of-function T/A T 11 No Mut found 12 ESR1 Y537N 0.10 chr6:152419922 15644 2004 2 0.1 Gain-of-function T/A T TP53 R248Q 0.20 chr17:7577538 14634 1471 3 0.15 Loss-of-function C/T C 13 ESR1 Y537N 1.15 chr6:152419922 2892 693 8 0.25 Gain-of-function T/A T TP53 R248Q 0.29 chr17:7577538 3171 698 2 0.25 Loss-of-function C/T C 14 ESR1 Y537N 3.69 chr6:152419921 13912 2466 91 0.1 Gain-of-function CT/CA CT 15 ESR1 Y537N 0.66 chr6:152419922 20197 3788 25 0.05 Gain-of-function T/A T ESR1 D538G 0.10 chr6:152419926 20197 3789 4 0.05 Gain-of-function A/G A 16 PIK3CA E542K 0.31 chr3:178936082 12680 3265 10 0.05 Gain-of-function G/A G ESR1 Y537N 0.30 chr6:152419921 10594 2994 9 0.1 Gain-of-function CT/CA CT ESR1 Y537S 0.07 chr6:152419923 10594 2994 2 0.1 Gain-of-function A/C A ESR1 D538G 7.32 chr6:152419926 10594 2992 219 0.1 Gain-of-function A/G A TP53 R248Q 0.05 chr17:7577538 13885 3680 2 0.05 Loss-of-function C/T C 17 PIK3CA E542K 0.11 chr3:178936082 54295 7482 8 0.05 Gain-of-function G/A G ESR1 Y537N 2.75 chr6:152419922 25057 5163 142 0.05 Gain-of-function CT/CA CT ESR1 Y537S 0.14 chr6:152419923 25057 5163 7 0.05 Gain-of-function A/C A ESR1 D538G 2.19 chr6:152419926 25057 5162 113 0.05 Gain-of-function A/G A TP53 R248Q 0.13 chr17:7577538 37737 5544 7 0.05 Loss-of-function C/T C Sample Reads Mapped alignments Supplementary alignments Duplicates ESR1: Y537N ESR1: Y537S ESR1: D538G TP53: R248Q PIK3CA: E542K Breast 140517500 150109688 9928043 53276081 11 11 12 3 6 Bone 267724390 278772038 11369851 88433068 18 18 18 21 8 Liver 240677098 260063156 19769043 80287697 12 12 13 10 5 On target reads 54 3. Chapter 3: Assessment of clinical parameters and liquid biopsy analytes at the start of combination treatment with tucatinib, letrozole and palbociclib predicts shorter progression free survival in patients with metastatic ER+/HER2+ breast cancer. The analyses here are based on preliminary data of a clinical trial led by Dr. Shagisultanova. Lisa Welter 1,2 , Nikki Higa 1 , Vera Hsu 1 , Elena Shagisultanova 3 , Peter Kuhn 1,4,5,6,7 , James Hicks 1 3 Division of Medical Oncology, University of Colorado School of Medicine, Aurora, CO 80045, USA. 4 Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089, USA. 5 Department of Aerospace and Mechanical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089, USA. 6 Catherine & Joseph Aresty Department of Urology, Institute of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA 7 Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA. 55 3.1. Introduction Breast cancer treatments are largely guided by the expression of hormone receptors (HR) and human epidermal growth factor receptor 2 (HER2). Triple positive tumors, tumors expressing both HR (Estrogen Receptor and Progesterone Receptor) and HER2, have been shown to be an aggressive breast cancer subtype and patients with this subtype experience increased resistance to targeted therapies [173]. Young patients (<45 years) are disproportionally affected by this subtype [174]. Various therapies are available to specifically target HR+ and HER2+ cancers. HR+ tumors are commonly treated with either a selective Estrogen Receptor (ER) modulator (tamoxifen), an ER degrader (fulvestrant), aromatase inhibitors (e.g. anastrazole, letrozole, exemestane) or mTOR inhibitors (everolimus). HER2+ tumors are treated with either monoclonal antibodies (trastuzumab, pertuzumab), antibody-drug conjugates such as ado-trastuzumab (T-DM1) or most recently tyrosine kinase inhibitors all of which are targeting HER2. Patients with HR+/HER2+ cancers are traditionally treated with a combination of ER and HER2 targeting drugs with or without addition of chemotherapy [175]. Despite the availability of multiple targeted therapies for patients with HR+/HER2+ disease, therapy resistance to dual ER and HER2-targeted therapies is common [176]. Studies in HR+/HER2+ breast cancer cell lines noted upregulation of ER signaling and ER transcriptional activity following treatment with HER2-targeted agents, highlighting the ER signaling as a key survival pathway to overcome HER2-blockade [177-179]. These findings have been confirmed in pre-clinical studies, where HR expression was associated with lower responsiveness to HER2 targeted agents [177, 180]. Similarly, HER2 overexpression has been shown to lead to decreased effectiveness of hormone therapy [181-183]. Combination of ER and HER2 targeted therapies has only yielded moderate benefit in progression free survival and no 56 benefit in overall survival in patients with HR+/HER2 metastatic breast cancer, despite promising results in in vivo and in vitro models [184, 185]. Currently, patients with HR+/HER2 tumors will receive a combination of HER2-targeted agents (trastuzumab +/- pertuzumab) together with chemotherapy (taxane) as frontline treatment based on the findings in the CLEOPATRA trial (PFS in pertuzumab arm was 18.7 vs 12.4 months in placebo arm)[186]. Alternatively, T-DM1 has been approved as a frontline therapy based on the results of the MARIANNA trial [187]. Median progression free survival (PFS) for these drug combinations was 13.7 months with trastuzumab plus taxane, 14.1 months with T-DM1, and 15.2 months with T-DM1 plus pertuzumab [187]. While combination of HER2 targeted agents with chemotherapy is highly effective, these regimens are associated with substantial side effects. Hence there is a need to determine drug combinations to extend PFS and OS in patients with HR+/HER2+ tumors that can reduce the side effects from chemotherapy. A promising avenue for patients with HR+/HER2+ metastatic breast cancer is targeting proteins involved in cell cycle progression such as cyclins and cyclin dependent kinases (CDKs). These proteins are not only pivotal in regulating cell growth, but are commonly mis-regulated in cancers [141]. 57 Figure 3.1 Schematic overview of ER and HER2 mediated signaling. ER and HER2 mediated signaling converges at the cyclin D1-CDK4/6 complex, which serves as a key component of the cell cycle machinery by regulating G1 to S phase transition through phosphorylation and inactivation of the retinoblastoma protein (RB) [188]. Interestingly, Cyclin D1 overexpression is common in HR+ breast cancer, making the Cyclin D1-CDK4/6 complex an ideal target to halt tumor cell growth [189]. In addition, the complex has been proposed as a driver of therapeutic resistance to drugs targeting not just ER pathways, but also HER2 [189, 190]. Given the importance of this complex in cancer cell proliferation and disease progression, it is not surprising that the approval of specific CDK4/6 inhibitors was a major breakthrough in the 58 treatment of breast cancer. Palbociclib was the first CDK4/6 inhibitor to reach the market in 2015 [191, 192], followed by ribociclib [193-195] and abemaciclib [196, 197] in 2017. Combination of endocrine therapy with CDK4/6 inhibitors had the same PFS as chemotherapy alone, yet combination therapy was better tolerated compared to the chemotherapy [198]. Currently the use of CDK4/6 inhibitors are approved for HR+/HER2- metastatic breast cancers only. Given the convergence of ER and HER2 signaling in the cyclin D1-CDK4/6 complex, we propose that triple inhibition of ER, HER2 and CDK4/6 would not only halt tumor growth through multistep targeting of the proliferation pathways, but also bypasses cyclin D1-CDK4/6 drug resistance. Preclinical studies by Shagisultanova et al. show that triple targeting of ER, HER2 and Cyclin D1-CDK4/6 through fulvestrant, tucatinib and palbociclib reduces viability in vitro and growth in vivo in HR+/HER2+ cell lines and cell line based xenograft models [174]. Taking these promising findings to the clinic, Dr. Shagisultanova initiated a phase Ib/II clinical trial (TLP) to assess the safety and efficacy of combination treatment targeting HER2 (tucatinib), ER (letrozole), and the cyclin D1-CDK4/6 complex (palbociclib) in patients with HR+/HER2+ metastatic breast cancer (https://clinicaltrials.gov/ct2/show/NCT03054363). Interim analysis of the trial showed tolerable and manageable safety profile as well as evidence of considerable anti-tumor activity [199]. To delineate clinical and molecular parameters associated with PFS and to track treatment induced changes in patients enrolled in the TLP trial, we studied liquid biopsy analytes, specifically circulating tumor cells (CTCs) and cell-free DNA (cfDNA), at three timepoints during the trial. Blood was collected prior to starting the first treatment cycle (C1D1), four weeks after the first dose (C2D1) and at the end of the trial (EOT). Blood based liquid biopsies provide an unique opportunity to minimally invasively access tumor derived analytes throughout a patient’s course of disease and enable longitudinal monitoring 59 of such analytes like CTCs and ctDNA. Over the past years, their main application has been disease monitoring as a companion diagnostic where they can provide guidance on real-time treatment decision making [130]. While their clinical potential has been acknowledged for decades, currently there are only two clinically approved circulating cell based diagnostic tests for precision oncology [200, 201] and one plasma-based tests utilizing circulating DNA as companion diagnostic [189, 202, 203]. Here we leverage the combined analysis of CTCs and ctDNA in conjunction with clinical parameters. We show that ctDNA levels have the potential to predict PFS before the start of the trial. Further, we report various clinical variables that affect the effectiveness of the combination treatment. In addition, longitudinal liquid biopsy analysis finds fluctuations in the analyte abundance and high genomic stability as evidenced by the lack of additional CNAs over time. 3.2. Materials and Methods 3.2.1. Patient Enrollment Criteria and ER/HER2 Tissue Status Assessment All patients were part of a phase Ib/II single arm clinical trial studying the effects of combination treatment with tucatinib, letrozole and plabociclib. Eligible patients were histologically confirmed with HR+/HER2+ locally advanced unresectable or metastatic breast cancer. ER and PR positivity was assessed by immunohistochemistry (IHC) according to the 2010 guidelines of the American Society of Clinical Oncology (ASCO)/College of American Pathologists (CAP). HER2 positivity was assessed by either standard of care fluorescence in situ hybridization (FISH) and/or 3+ IHC staining according to ASCO/CAP guidelines 2014. Patients should have an Eastern Cooperative Oncology Group (ECOG) performance status of 0-1 and a life expectancy of more than 6 months in the opinion of the investigator. Women were either post- menopausal or if pre-menopausal received ovarian function suppression. Data was cut off on 60 November 2nd 2021 for PFS calculations. At that time, four of the forty patients had no documented progression. Patients who came off the study due to physician-patient decision were censored. A comprehensive description of the trial’s inclusion/exclusion criteria can be found at https://www.cancer.gov/about-cancer/treatment/clinical-trials/search/v?id=NCI-2017-01776 or https://www.clinicaltrials.gov/ct2/show/NCT03054363. 3.2.2. Blood Sample Collection and Rare Cell Detection for Liquid Biopsy Analysis Whole blood from breast cancer patients before start of the clinical trial (C1D1), at the first day of the second treatment cycle (C2D1) and at end of trial (EOT). End of trial was defined as either disease progression, toxicity or a patient/physician decision to discontinue the drug regime. Only patients who either are still on trial or have come off trial due to progressive disease or toxicity are included in the survival analysis. Blood samples were shipped from five clinical sites across the US and blood was processed at USC, Los Angeles as described earlier [204]. In summary, whole blood was centrifugated to collect plasma for ctDNA analysis and the remaining blood underwent erythrocyte lysis. The remaining nucleated cells were plated onto custom cell adhesion glass slides (Marienfeld) and cryo-banked at -80C for future analysis. To assess the abundance of circulating tumor cells (CTCs) per blood draw, cryobanked microscopy slides were stained with antibodies against pan-Cytokeratin, Vimentin, CD31 and CD45 and nuclei were counterstained with 4′,6-diamidino-2-phenylindole (DAPI) as described previously [204]. CTCs were detected by a scanning microscope and verified by a trained analyst. Levels of CTCs per blood draw are described as CTCs per ml whole blood (CTCs/ml). 61 3.2.3. ctDNA analysis ctDNA analysis was performed as described earlier [67]. Briefly, plasma was isolated from whole blood prior to red blood cell lysis and stored at -80C. cfDNA was extracted using the QIAamp Circulating Nucleic Acid kit (Qiagen) according to manufacturer’s instructions. DNA concentration was measured by Qubit Fluorometric Quantitation (Thermo Scientific) and whole genome sequencing libraries were prepared with the NEBNext Ultra II DNA Library Prep Kit (New England Biolabs). Samples were barcoded with Multiplex Oligos for Illumina (New England Biolabs) and DNA library size distribution was measured with the Agilent 2100 Bioanalyzer (High-sensitivity DNA Assay and Kit, Agilent Technologies). Libraries were sequenced at the USC core facility and copy number profiles were generated as previously described [165, 166]. Percentage of tumor fraction was calculated using ichorCNA [167]. 3.2.4. Data analysis Data visualization and statistical analysis was performed with GraphPad Prism and Microsoft Excel. 3.3. Results 3.3.1. Overview of patient characteristics and liquid biopsy positivity Talk about enrollment criteria here. Overall, ER+/HER2+ breast cancer is more common in younger women. Patients have received and average of 2 lines of therapy in the metastatic setting with 45% receiving chemotherapy and 55% receiving targeted therapy prior to the trial. Approximately half of the patients presented with visceral and 38% with CNS metastases. 62 Table 3.1 Patient Characteristics All Patients (n=40) % AGE Median 52 Minimum 22 Maximum 82 INITIAL DIAGNOSIS De novo 20 50% Recurrent 20 50% VICERAL METASTASIS Visceral 22 55% Non-visceral 18 45% CNS METASTASIS CNS 15 37.5% Non-CNS 25 62.5% TTR (months) Median 8.2 Minimum 1.6 Maximum 32.6 LINES OF TX IN METASTATIC SETTING 0 1 2.5% 1 17 43.6% 2 10 45.5% 3 5 16.7% 4 5 10.6% 5 1 2.4% 6 0 0% 7 1 2.4% Prior Tx: chemo vs targeted Tx chemo 18 45% targeted therapy 22 55% Prior anti-hormonal Adjuvant yes 20 50% no 20 50% Prior anti-hormonal MTS yes 30 75% no 10 25% Prior HER2 inhibitors H, P 22 55% H, P, T-DM1 16 40% H, P, T-DM1, MARGETUXIMAB 1 2.5% H, P, T-DM1, DS8201A 1 2.5% 63 3.3.2. Number and type of prior therapies affect response to TLP combination treatment. Analysis of pre-analytical variables found that patients with >=2 lines of prior treatments had a significantly shorter PFS compared to those with 0-1 lines of prior therapies (figure 3.2A). In addition, patients who had received prior T-DM1 treatment had a shorter PFS than those who did not receive T-DM1 (figure 3.2B). Interestingly, patients with visceral metastases which typically indicate more aggressive disease and worse outcome, responded equally well to the drug combination than those with no visceral metastases (figure 3.2C). Similarly, the drug combination worked equally well in patients with vs without CNS metastasis (figure 3.2D). Patients with low percentage of ER positive cells in their tissue biopsies also had a shorter, yet due to the small sample size not significantly shorter PFS (figure 3.1E). No difference in PFS was observed between patients presenting with de novo as compared to recurrent disease (figure 3.2F). 64 Figure 3.2 Kaplan Meier Curves of pre-analytical variables. P-values were calculated with Log-rank (Mantel-Cox) test. 3.3.3. High ctDNA and CTC levels at C1D1 are associated with a shorter PFS. First we examined levels of ctDNA and CTCs/ml at C1D1, before the start of the treatment, in relation to the patients PFS (figure 3.3A/B). We then subsetted the patients PFS into short (<6 months), intermediate (³6 - <12 months) and long (³12 months) responders and asessed ctDNA and CTC levels in each sub-group (figure 3.3C/D). We note that overall ctDNA values were different but not significantely between groups (p = 0.0635). Inter-group comparisons found no difference between the <6 and 6-12 month group (p-value = 0.99), but slight differences between the <6 and ³12 month (p-value = 0.20) and 6-12 vs ³12 month group (p-value = 0.069). No significant difference was observed in the CTC analysis. A B C D E F 0 10 20 30 40 0 50 100 Survival proportions: Survival of 20220616 PFS Number of prior Tx 0-1 vs 2-7 Time in Months Probability of Survival 0-1 Prior Lines of Tx >= 2 Prior Lines of Tx 0.0027 p-value 0 10 20 30 40 0 50 100 Survival proportions: Survival of 20220616 PFS ER% <=15 vs >15 Time in Months Probability of Survival ER% >15% ER% <=15% 0.8331 TLP-65-007 was excluded as no ER status was available p-value 0 10 20 30 40 0 50 100 Survival proportions: Survival of 20220616 PFS visceral vs non-visceral mets Time in Months Probability of Survival Visceral Mets Non Visceral Mets p-value 0.1633 0 10 20 30 40 0 50 100 Survival proportions: Survival of 20220616 PFS de novo vs recurrent Time in Months Probability of Survival de novo recurrent 0.3363 p-value 0 10 20 30 40 0 50 100 Survival proportions: Survival of 20220616 T-DM1 pretreatment vs PFS Time in Months Probability of Survival No T-DM1 T-DM1 0.0240 p-value 0 10 20 30 40 0 50 100 Survival proportions: Survival of 20220617 PFS CNS vs no CNS met Time in Days Probability of Survival No CNS Met CNS Met p-value 0.5565 65 To further investigate the effect of ctDNA and CTC levels, we grouped patients by their biomarker levels. Detection limit of ctDNA with low pass WGS is approximately 10% and ctDNA threshold was hence set at 10%. Cutoff for CTCs/ml was set to ³ 1 CTC detected vs no CTC detected. Representative examples of high, intermediate and low ctDNA profiles are shown in figure 3.2E, where high and intermediate levels fall in the ³10% group. Low ctDNA profiles commonly have ichorCNA levels of <10%. Survival analysis finds that patients with <10% ctDNA have a longer PFS than those with ³10% ctDNA at C1D1 (figure 3.3F). In contrast, CTC levels did not affect PFS (figure 3.3G). 66 Figure 3.3 Overview of CTC and ctDNA levels of all patients at C1D1. Show a low, medium and high ctDNA profile. C/D) Only samples were included were patients were either still on study or had disease progression. Differences between groups were calculated with the Kruskal-Wallis test including Dunn’s multiple comparison test. F) Survival curves of patients with <10% ctDNA vs ³10% ctDNA at the start of the trial. P-values were calculated using the log- rank (Mantel-Cox) test. G) Survival curves of patients with >1 CTC detected vs no CTC detected. P-values were calculated using the log-rank (Mantel-Cox) test. High ctDNA % Intermediate ctDNA % Low ctDNA % Chromosome Ratio to Median A B C < 6 months >=6 - <12 months >= 12 months 0 5 10 10 20 30 40 50 CTCs/ml at Baseline CTCs <6, 6-12, >12 months Median plotted Only included samples where patients either were still on study or had disease progression. p-value 0.5989 < 6 months >=6 - <12 months >= 12 months 0.0 0.2 0.4 0.6 ichor fraction at Baseline ctDNA ichor <6, 6-12, >12 months Kruskal-Wallis test P value Exact or approximate P value? P value summary Do the medians vary signif. (P < 0.05)? Number of groups Kruskal-Wallis statistic 0.0397 Approximate * Yes 3 6.454 Median plotted Only included samples where patients had disease progression or are still on study. E % ctDNA CTCs/ml D F G 0 10 20 30 40 0 50 100 Survival proportions: Survival of 20220617 PFS ctDNA ichor Baseline 2 groups Time in Months Probability of Survival < 10% ctDNA >=10% ctDNA p-value 0.1862 0 10 20 30 40 0 50 100 Survival proportions: Survival of 20220616 PFS CTC detected at Baseline Time in Months Probability of Survival >= 1 CTC < 1 CTC 0.4408 p-value 0 10 20 30 40 0 50 100 Survival proportions: Survival of 20220617 PFS ctDNA ichor Baseline 2 groups Time in Months Probability of Survival < 10% ctDNA >=10% ctDNA p-value 0.1862 0 10 20 30 40 0 50 100 Survival proportions: Survival of 20220616 PFS CTC detected at Baseline Time in Months Probability of Survival >= 1 CTC < 1 CTC 0.4408 p-value 0 10 20 30 40 0 10 20 30 40 50 20220707 Days until progression vs CTCs Baseline Months until progression CTCs/ml Baseline 0 10 20 30 40 0.0 0.2 0.4 0.6 20220707 Days until progression vs ichor fraction Baseline Months until progression ichor Baseline 67 3.3.4. Combination of clinical and liquid biopsy variables increases confidence in stratifying patients into good vs poor responders. Next, we assessed whether combining biomarkers and clinical variables can stratify patients better into short and long PFS groups. We found that survival predictions improved when ctDNA and CTC information was combined (figure 3.4A). In addition, we found that patients with 0-1 lines of prior treatments who were biomarker negative at C1D1 had the longest PFS of all groups (figure 3.4B). When combining the percentage of ER positivity of the tissue biopsies with the liquid biopsy biomarker status, we found that patients with ER positivity of ³15% who were biomarker negative had the longest PFS, aside from one exceptional reponder in the ER% <15 and biomarker negative group (figure 3.4C). Lastely, we tested the effect of combining the number of prior lines of therapy in the metastatic setting to the percentage of ER positive staining in the liquid biopsies. Here, patients with ³ 15% tissue ER positivity and only 0-1 lines of prior therapies had the longest PFS, again aside from exceptional responder in the ER% <15 and 0-1 Tx group (figure 3.4D). Overall, while ctDNA%, tissue ER positivity and the number of lines of prior therapies can be informative on their own, combination of these can improve stratification of patients into good vs poor responders for certain combinations (figure 3.4A/B). 68 Figure 3.4 Multi-variable assessment of clinical and liquid biopsy and variables. A) Patients were divided into biomarker positive and biomarker negative groups. Biomarker positive was defined as having either ³10% ctDNA and/or ³1 CTC detected whereas biomarker negative patients had <10% ctDNA and no CTC detected. B) Survival analysis of patients stratified for the number of lines of therapy in the metastatic setting and biomarker positivity. C) Survival analysis of patients stratified for ER tissue expression and biomarker status. D) Survival analysis of patients stratified for ER tissue expression and the number of lines of therapy in the metastatic setting. 3.3.5. Longitudinal analysis of ctDNA and CTC reveals various patterns of biomarker release. Next, we expanded our analysis from the C1D1 draw to the follow up draws, which were collected at Cycle 2 Day 1 (C2D1), which was approximately four weeks after treatment initiation, and at the end of trial (EOT). We observed fluctuations of %ctDNA over time in the same patient (figure 3.5A/B). We note that the %ctDNA across patients was lowest at C2D1. C1D1 %ctDNA was comparable to %ctDNA detected at EOT. Figure 3.5C shows CN profiles of a patient at C1D1, C2D1 and EOT. To investigate whether patterns in ctDNA expression could be related to PFS, we grouped patients by their presence/absence of ctDNA at C1D1 and C2D1. PFS did not differ significantly between the groups (figure 3.5D). Next, we investigated changes in CTCs/ml over time (figure 3.5E/F). Interestingly, CTC levels fluctuated across timepoints too and most patient’s CTC patterns were similar to the ctDNA 0 10 20 30 40 0 50 100 Survival proportions: Survival of 20220629 PFS ctDNA or CTC detected Time in Months Probability of Survival Biomarker detected Biomarker not detected p-value 0.1059 0 10 20 30 40 0 50 100 Survival proportions: Survival of 20220629 Lines of Tx and biomarker vs PFS Time in Months Probability of Survival 0-1 lines + biomarker pos 0-1 lines + biomarker neg 2-7 lines + biomarker pos 2-7 lines + biomarker neg p-value 0.0231 0 10 20 30 40 0 50 100 Survival proportions: Survival of 20220629 ER Status and biomarker vs PFS Time in Months Probability of Survival ER <15% + biomarker pos ER <15% + biomarker neg ER >=15% + biomarker pos ER >=15% + biomarker neg p-value 0.3979 0 10 20 30 40 0 50 100 Survival proportions: Survival of 20220629 ER Status and # of lines of Tx vs PFS Time in Months Probability of Survival ER <15% + 0-1 lines Tx ER <15% + 2+ lines Tx ER >=15% + 0-1 lines Tx ER >=15% + 2+ lines Tx p-value: 0.0092 A B C D 69 patterns. Similarly to the ctDNA patterns, no significant difference in the PFS was observed when stratifying patients based on their C1D1 and C2D1 CTC levels (figure 3.5G). Figure 3.5 Fluctuations of CTCs and ctDNA over time per patient. A) Percent of ctDNA as calculated by ichor at C1D1, C2D1 and EOT per patient. B) ctDNA levels scored by CN amplitudes at C1D1, C2D1 and EOT. C) Representative whole genome copy number profile of a patient across three timepoints. D) PFS in months compared to ctDNA patterns. E) CTCs/ml at C1D1, C2D1 and EOT per patient. F) Overview of CTC levels per patient at C1D1, C2D1 and EOT. Grey = sample data is not available. G) PFS in months compared to CTC patterns. P= positive, as defined by low-high ctDNA amplitudes; N = negative, as defined by no visible ctDNA. NN = no ctDNA detected at C1D1 and C2D1. NP = ctDNA only detected at C2D1, but not at C1D1. PP = ctDNA CNAs visible at both C1D1 and C2D1. PN = ctDNA CNAs visible at C1D1, but not at C2D1. 3.3.6. Copy number alterations are enriched in regions containing cell cycle genes. Analysis of cfDNA samples with detectable ctDNA at C1D1 found a high number of copy number alterations in regions containing cell cycle genes (figure 3.6). Figure 3.6A/B shows CN profiles of two patients at C1D1. To understand whether prior treatments could have induced these alterations, we compared patients who received Herceptin and Perjeta to those who received additional T-DM1 treatment. We found that patients who received T-DM1 had more alterations in the cell cycle genes compared to those who only received Herceptin and Perjeta (figure 3.6C). Similarly, we assessed whether the number of the lines of therapy in the metastatic setting was associated with the number of CN alterations in the cell cycle genes. We found that patients who 0 10 20 30 40 PN PP NP NN PFS in Months CTC positivity Baseline vs C2D1 CTC positive/negative scored A D Ratio To Median Chromosome C B E G Baseline C2D1 EOT 0 10 20 30 40 50 20220707 All Patients CTCs over time CTCs/ml Baseline C2D1 EOT 0.0 0.2 0.4 0.6 0.8 20220707 All Patients ctDNA ichor over time ichor fraction % ctDNA F 0 10 20 30 40 PN PP NP NN PFS in Months ctDNA levels Baseline vs C2D1 ctDNA positive/negative scored Longitudinal CTCs/ml per Patient C1D1_CTCs C2D1_CTCs EOT_CTCs CTC Levels 0 5 10 15 20 Longitudinal CTCs/ml per Patient C1D1_CTCs C2D1_CTCs EOT_CTCs CTC Levels 0 5 10 15 20 C1D1 C2D1 EOT Longitudinal ctDNA% per Patient C1D1_1 C2D1_1 EOT_1 ctDNA Levels high medium low flat N/A Longitudinal ctDNA% per Patient C1D1_1 C2D1_1 EOT_1 ctDNA Levels high medium low flat N/A C1D1 C2D1 EOT C1D1 C2D1 EOT ctDNA Levels C1D1 C1D1 C2D1 C2D1 EOT EOT 70 received 2+ lines of therapies had more alterations in the cell cycle genes compared to those with 0-1 prior lines of therapy (figure 3.6D). Next, we assessed whether the number of cell cycle gene alterations (CCGA) was associated with PFS. First, we stratified patients with detectable ctDNA into three groups, “No CCGA”, “1 CCGA” and “2+ CCGA” (figure 3.6E). Yet, average PFS was not significantly different between the groups. Next, we divided patients into groups based on their altered genes, but did not find significant differences between the groups either. Finally, we tested whether gains in the ERBB2 gene would affect treatment efficacy and with it PFS. Patients with detectable ctDNA were grouped into two groups, “no ERBB2 gain” and “ERBB2 gain”. No difference in PFS was detected between the two groups. Figure 3.6 Cell Cycle Gene Alterations in ctDNA samples at C1D1. A/B) are representative examples of cfDNA samples with medium-high ctDNA and copy number alterations in regions containing cell cycle genes. C) Overview of CNAs in cell cycle genes and ERBB3 at C1D1 in patients pretreated with or without T-DM1. D) Overview of CNAs in cell cycle genes and ERBB3 at C1D1 stratified by their number of lines of therapy in the metastatic setting. E-G) Patients with visible CNAs were assessed for CN gains in cell cycle genes and ERBB3. E) PFS of patients with no, one or 2+ alterations in the cell cycle genes. Kruskal-Wallis test (p=0.83) F) PFS of patients with specific 0 5 10 15 20 No CCGAs 1 CCGA 2+ CCGAs PFS in Months 0 5 10 15 20 No CCGAs CCND1 only CCNE1 only CDK6 only Multiple CCGAs PFS in months 0 5 10 15 20 no ERBB2 gain ERBB2 gain PFS in months ERBB2 CCNE1 CCND1 ERBB2 CCND1 A B E F G C D CNAs, % (n) Gene (n) CCN1 (n=3) CDK2 + ERBB3 (n=1) CCNE1 (n=1) CCND1 (n=4) CCNE1 (n=1) CCND1 + CCNE1 (n=2) CCND1 + CDK6 + CDK2 + ERBB3 (n=1) CCND1 + CDK4 + CDK2 + ERBB3 (n=1) CDK6 (n=1) Clinical parameters 31% (7/22) Trastuzumab, Pertuzumab Prior HER2 inhibitors Trastuzumab, Pertuzumab, T-DM1 66% (12/18) CNAs, % (n) Gene (n) CCN1 (n=3) CCNE1 (n=1) CCND1 (n=4) CCNE1 (n=1) CCND1 + CCNE1 (n=2) CCND1 + CDK6 + CDK2 + ERBB3 (n=1) CCND1 + CDK4 + CDK2 + ERBB3 (n=1) CDK6 (n=1) CDK2 + ERBB3 (n=1) 59% (13/22) ≥2 Lines of therapy in metastatic setting Clinical parameters 0-1 33% (6/18) 71 alterations in the cell cycle genes. Kruskal-Wallis test (p=0.71) G) PFS of patients with vs without CN amplifications in ERBB2. Unpaired t test with Welch's correction (p=0.97). 3.4. Discussion Here we demonstrate the effects of prior therapies on progression free survival and test liquid biopsy analytes, in particular CTCs and cfDNA, as potential biomarker for therapy response. CNS and visceral metastasis are often related with worse PFS [205, 206]. Yet, patients with CNS or visceral metastasis responded equally well to TLP, making this drug combination particularly attractive to these patients (figure 3.2C/D), possibly due to tucatinib’s ability to crossing the blood brain barrier [207]. Similarly, patients with de novo disease had no difference in PFS compared to those with recurrent disease (figure 3.2F). While patients with low ER expression (<=15%) in their tissue samples did not have significantly lower PFS compared to those with high percent of ER expression, the number of patients in the ER low group is too small to make any final conclusion and the results might be heavily influenced by one exceptional responder in the ER<=15% group (figure 3.2E). Further research would need to be done to establish whether patients with low ER expression would derive the same benefit from TLP treatment compared to those with high tissue ER expression. In addition to assessing the effects of clinical parameters on PFS, we also tested whether CTC and/or ctDNA levels at the start of treatment could be used as prognostic factors for PFS. Prior studies have both linked ctDNA and CTC levels to PFS [67, 125, 208]. While we found no association with CTC levels with PFS (figure 3.3B/D/G), patients with high ctDNA at the start of TLP treatment appeared to have a shorter PFS, yet the difference was not significantly different (figure 3.3A/B/F). We hypothesize that a larger sample size might be required to make final conclusions on the biomarker analysis, given the small number of patients positive for CTCs and ctDNA. Combining patients with CTC and/or ctDNA positive blood draws at C1D1, might 72 improve the predictive value of liquid biopsy analytes, yet more data is needed to support that hypothesis. When testing the added value of combining clinical parameters with biomarker status or multiple clinical parameters, we noticed an improvement in their predictive power (figure 3.4B/C/D). Yet the strongest factors to stratify the patients were the number of lines of prior therapy in the metastatic setting and T-DM1 pretreatment, indicating that TLP is particularly effective in the early setting (figure 3.2A/B) and treatment history of patients should be taken into account to decide the next appropriate therapy. Copy number analysis of plasma samples at C1D1 showed amplifications of cyclin and CDK genes in 47.5% (19 out of 40) patients (figure 3.6C/D). Interestingly, patients who received trastuzumab and pertuzumab prior to TLP treatment had fewer alterations in cell cycle genes compared to those who were exposed to trastuzumab, pertuzumab and T-DM1. Similarly, patients with a high number of lines of prior therapy in a metastatic setting presented with an increased probability and complexity of CNAs in cell cycle genes (figure 3.6D). No additional CCGAs were gained during TLP treatment (data not shown), indicating alternative resistance mechanisms such as point mutations or changes in proteins expression. It is not surprising that the earliest and most common CCGA detected in our study was in CCND1, given that the cyclin D1-CDK4/6 complex has been proposed to play a key role in tumorigenesis in ER and HER2 driven tumors, where both ER and HER2 signaling increase transcription of CCND1 [188]. In addition, CCND1 has been shown to be a main driver in drug resistance against ER and HER2 targeted therapies [189, 190, 209]. The convergence of ER and HER2 signaling in Cyclin D1 as well as its role in tumorigenesis and treatment resistance make the cyclin D1-CDK4/6 complex an important target for patients with ER and/or HER2 positive tumors. Naturally, CDK4/6 inhibitors such as palbociclib, ribociclib and abemaciclib, have become an important tool to combat ER+ breast cancer and studies are currently underway to show the efficacy of these agents in 73 patients with HER2+ tumors [210, 211]. In addition to Cyclin D, Cyclin E also been reported as resistance mechanism against both ER and HER2 targeted therapies and is present in 35% of HER2+ breast tumors [212, 213]. Gene expression analysis of tissue samples collected in the PALOMA-3 trial found that palbociclib efficacy was lower in patients with high vs low cyclin E1 mRNA expression [214]. Yet, despite the presence of various copy number alterations in cell cycle genes, we found that TLP treatment is highly effective for both patients with and without CCGAs (figure 3.6E/F). Longitudinal analysis of CTCs and ctDNA finds different patterns of CTC and ctDNA positivity across blood draws. Most notably, both CTC and ctDNA levels are the lowest at C2D1, marking a time point in the treatment where TLP are at their peak efficiency, and highest at either C1D1 or EOT. This overall decrease in liquid biopsy analytes could be interpreted as the effective killing of tumor cells, halting of tumor cell growth, turnover of cells and in turn the effectiveness of the treatment. Yet, not all patients have decreased biomarker levels at C2D1. Interestingly, changes in CTC or ctDNA positivity between C1D1 and C2D1 were not associated with PFS (figure 3.6D/G). Yet, given the small sample size, this analysis should be confirmed in a larger cohort. In contrast to our findings, recent data presented at the San Antonio Breast Cancer Symposium (SABCS) of the BioItaLEE trial, which included 263 ER+/HER2- postmenopausal woman treated with first-line ribociclib and letrozole, showed that individuals with VAF clearance at day 15 had a significant 49% lower risk of progression than those without clearance (https://www.medwirenews.com/oncology/breast-cancer/bioitalee-ctdna-target-mutations- prognostic/19948538). The findings indicate that mutation status at day 15 was most informative of patient outcome. Li et. al. made similar observations in 44 early breast cancer patients who underwent neoadjuvant chemotherapy [215]. They found that positive C1D1 ctDNA, as determined by presence of point mutations and/or structural variants, was significantly associated with worse disease free survival and overall survival. In addition, ctDNA change after 2 cycles of 74 NAC was predictive of local tumor response. In a meta-analysis of 8 studies compromising 739 patients, Cullinane et. al. noted that patients with evaluated ctDNA level before and after treatment had a significantly shorter disease free survival, yet there was significant heterogeneity between the studies [216]. Interestingly, when dividing the samples by timing of ctDNA sample collection, samples collected prior to therapy showed that patients with elevated ctDNA has shorter disease free survival across the studies. While samples collected on treatment also indicated a shorter DFS, there was a large heterogeneity across studies indicating further research to be required on the meaning of the on treatment draws. Hence, further studies are warranted to show the clinical utility of ctDNA as biomarker, the timing of blood draws and the technology to determine the presence of ctDNA. This will greatly help not just understanding the mechanisms of metastasis, but may also aid with the implementation of liquid biopsy biomarker into the clinical setting. While we able to identify some clinical and molecular factors that could be used as prognostic factors, we have yet to understand the molecular mechanisms of treatment failure after TLP treatment. Limitations of our study are the relatively small cohort size as well as the limited size of biomarker (CTC/ctDNA) positive patients. In the future, point mutation analysis of cfDNA could give insight into resistance mutations in key driver genes. Alternatively, multiplex proteomics of CTCs or tissue samples collected before TLP treatment and at time of disease progression could reveal protein expression changes in ER, HER2 and other proteins of the ER/HER2 signaling axis that could be have resulted in disease progression. These findings should be confirmed in a larger cohort with similar prior treatments and cancer subtype. 75 3.5. Additional Information 3.5.1. Authors’ Contributions L. Welter: Data analysis, writing; N. Higa: Data analysis, review; V. Hsu: Data analysis; E. Shagosultanova: Resources, conceptualization, editing; P. Kuhn: Resources, conceptualization, editing; J. Hicks: Resources, conceptualization, editing 76 4. Chapter 4: A randomized trial of fulvestrant, everolimus and anastrozole in the front-line treatment of advanced hormone receptor-positive breast cancer, SWOG S1222 This chapter is an extended version of a manuscript published in Clinical Cancer Research. Halle CF Moore 1* , William E. Barlow 2 , George Somlo 3 , Julie R. Gralow 4 , Anne F. Schott 5 , Daniel F. Hayes 5 , Peter Kuhn 6 , James B. Hicks 6 , Lisa Welter 6 , Philip A. Dy 7 , Christina H. Yeon 8 , Alison K. Conlin 9 , Ernie Balcueva 10 , Danika L. Lew 2 , Debasish Tripathy 11 , Lajos Pusztai 12 , Gabriel N. Hortobagyi 11 1 Cleveland Clinic, Cleveland, OH 2 SWOG Statistics and Data Management Center, Seattle, WA 3 City of Hope Medical Center, Duarte, CA 4 University of Washington School of Medicine/Seattle Cancer Care Alliance, Seattle, WA 5 University of Michigan Rogel Cancer Center, Ann Arbor, MI; 6 University of Southern California, Los Angeles, CA 7 Crossroads Cancer Center (Cancer Care Specialists of Illinois), Heartland NCORP 8 City of Hope, South Pasadena Cancer Center, South Pasadena, CA 9 Pacific Cancer Research Consortium NCORP, Portland, OR 10 Michigan CRC NCORP, St. Mary’s of Michigan, Saginaw, MI 11 The University of Texas MD Anderson Cancer Center, Houston, TX 12 Yale University, New Haven, CT Corresponding Author: Halle C.F. Moore, Taussig Cancer Institute, Cleveland Clinic, 10201 Carnegie Ave., Cleveland, OH 44195. Phone: 216-445-4624; Fax: 216-636-9286; E-mail: mooreh1@ccf.org Running Title: Fulvestrant, everolimus and anastrozole for advanced breast cancer Keywords: Metastatic breast cancer, Endocrine therapy, Circulating tumor cells, Circulating tumor DNA, ER-positive breast cancer 77 4.1. Abstract Purpose: Metastatic hormone receptor-positive (HR-positive), HER2-negative breast cancer is an important cause of cancer mortality. Endocrine treatment with or without additional targeted therapies has been the mainstay of treatment. This trial was designed to evaluate the combination of fulvestrant plus everolimus versus fulvestrant, everolimus and anastrozole compared to fulvestrant alone in the first-line treatment of advanced HR-positive, HER2-negative breast cancer. Experimental Design: This randomized placebo-controlled trial included postmenopausal women with HR-positive, HER2-negative advanced breast cancer who had received no prior systemic therapy for metastatic disease. Participants were randomized to one of three treatment arms and the primary outcome was progression-free survival (PFS), comparing combinations of fulvestrant and everolimus with or without anastrozole to fulvestrant alone. Circulating tumor cells (CTC), as measured with two different methods, and circulating tumor DNA (ctDNA) were evaluated serially prior to treatment and the beginning of the second cycle of therapy. Results: Due in part to changes in clinical practice, the study was closed after accruing only 37 participants. There was no evidence that everolimus-containing combination treatment improved PFS or overall survival relative to fulvestrant alone. When modeled continuously, an association was observed of baseline CTC and ctDNA with poorer survival. Conclusion: Although power of the study was limited, the findings were unable to support the routine use of everolimus combination endocrine therapy in the first-line treatment of advanced hormone-sensitive breast cancer. Prognostic impact of baseline ctDNA and copy number variations in CTC was demonstrated. 78 4.2. Translational Relevance SWOG S1222 is a Phase III randomized clinical trial of fulvestrant, anastrozole and everolimus in the front-line treatment of advanced hormone receptor-positive breast cancer. The study evaluated the hypothesis that addition of everolimus with or without anastrozole would improve progression-free survival compared with fulvestrant alone. Translational studies of circulating tumor cells (CTC) were also conducted with measurement by two distinct methods. The expectation was that patients with high CTC and/or high circulating tumor DNA (ctDNA) might benefit from additional therapy while those with low CTC would not and that CTC phenotype, specifically relative expression of estrogen receptor, BCL2, HER2, and Ki67, would predict benefit from endocrine therapy. Due to early study termination, only the prognostic value of the CTC and ctDNA measures could be evaluated. In this limited sample, CTC measures had high concordance and analysis of ctDNA using genomic copy number was shown to indicate poor prognosis. 79 4.3. Introduction The mainstay of treatment for endocrine-sensitive metastatic breast cancer has been sequential use of endocrine therapies including selective estrogen receptor modulators, aromatase inhibitors and a selective estrogen receptor down regulator. Recent studies have demonstrated favorable results with endocrine agents used in combination [217] or with the addition of targeted therapies including the mammalian target of rapamycin (mTOR) inhibitor everolimus [218, 219] or inhibitors of cyclin dependent kinase 4 and 6 (CDK4/6) [194, 197, 220, 221]. At initiation of the current study, CDK4/6 inhibitors were not yet FDA-approved therapeutic options. The primary objective of this study was to test the progression-free survival (PFS) benefit of combining fulvestrant with everolimus versus combining fulvestrant with everolimus and anastrozole, each compared with fulvestrant alone in the treatment of postmenopausal women with hormone receptor-positive (HR-positive) metastatic breast cancer. Further objectives included additional comparisons of PFS, overall survival (OS), response rates, toxicities, adherence and feasibility. Translational studies were also planned to investigate molecular determinants of response to treatment and prognosis in components of liquid biopsies: specifically circulating tumor cells (CTC) and circulating tumor DNA (ctDNA). 4.4. Design and Methods 4.4.1. Clinical Eligibility and Trial Conduct. Eligible patients were postmenopausal women with histologically confirmed HR-positive and HER2-negative metastatic breast cancer for which no prior systemic treatment had been received in the metastatic or recurrent setting. Prior chemotherapy and endocrine therapy in the adjuvant or neoadjuvant setting were permitted as long as any aromatase inhibitor therapy was 80 completed more than 12 months prior to randomization. Those with prior treatment with fulvestrant or mTOR inhibitors were ineligible. Participants were required to have adequate cardiac, hepatic, renal and bone marrow function. Those with elevated cholesterol or triglycerides and those with bleeding diathesis or on long-term anti-coagulant therapy were excluded. Participants were randomized with equal allocation to three arms: fulvestrant plus placebo for both everolimus and anastrozole (Arm 1), fulvestrant plus everolimus with placebo for anastrozole (Arm 2), or fulvestrant plus everolimus and anastrozole (Arm 3). Fulvestrant dosing was 500 mg IM every 4 weeks with an additional 500 mg loading dose day 15 of cycle 1, everolimus was dosed at 10 mg PO daily and anastrozole dose was 1 mg PO daily. Treatment was continued until disease progression, unacceptable toxicity, treatment delay > 4 weeks, if a need for anti-retroviral therapy arose, withdrawal of consent, or study closure. The study was conducted in accordance with U.S. Common rule ethical guidelines with written informed consent obtained from all participants and approval of local institutional review boards. 4.4.2. Translational Studies. The identification and enumeration of circulating tumor cells (CTC) has proven to be a clinically useful method of assessing progression in metastatic breast cancer [57, 69]. As an integrated translational study, blood was collected separately into CellSave tubes which were sent to the University of Michigan for CellSearch® analyses and into Streck tubes which were sent to the USC Michelson CSI-Cancer for High-Definition Single Cell Analysis (HD-SCA) and cell free DNA (cfDNA) analyses at treatment cycle1 day 1 (baseline), cycle 2 day 1 (C2D1) and at progression. CTC enumeration and characterization were performed using the CellSearch ® CXC Kit and CellSearch ® system (Menarini Silicon Biosystems, Inc., Huntingdon Valley, PA) at baseline 81 and then follow-up time points only if elevated at baseline [222] as previously described [57, 222, 223]. CTC levels were enumerated as the average of the CTC levels in the four different aliquots of 7.5 ml whole blood (WB), each of which was used to determine each of the 4 respective CTC- biomarker expressions to calculate the CTC-Endocrine Therapy Index (CTC-ETI) for that blood draw. The CTC-ETI was calculated as described [222]. As per prior studies [57, 69, 223], ≥5 CTC/7.5 ml WB were considered elevated, and 0-4 CTC/7.5ml WB were designated as low. The HD-SCA method of CTC enumeration and characterization [67] was to be performed for all samples at all three time points (baseline, cycle 2 day 1 and progression). An average volume of 0.55 ml of blood was analyzed per assay and all cells including leukocytes were identified using immunofluorescent stains and enumerated. The staining assay for enumeration consisted of DAPI, pan-CK, Vimentin and CD45/CD31 (mixed in one fluorescent channel) [204]. CTCs were defined as DAPI+, CK+, CD45- and may or may not be coated with platelets (CD31). For ER assessment, an additional set of slides was stained with DAPI, CK, ER and CD45 [67]. Cells with high levels of cytokeratin staining were counted as CTCs and scored as a continuous variable ranging from 2.2 to 145.8 CTC/ml blood. ER positivity was scored on a scale of 0-3, where 0 = no ER expression and 3 = high ER expression. Vimentin was scored as positive/negative. Microenvironment cells were defined by their different morphology from white blood cells (larger/elongated shape). They are DAPI+, have varying levels of Vimentin and CD31 and may be low for Cytokeratin. Prior to CTC capture, plasma was prepared by centrifugation and archived at -80 o C. Cell free DNA was extracted using the QIAamp Kit (QIAGEN) and cfDNA concentration was measured using Qubit (Thermo Scientific) as previously published [67]. The concentration of extracted cfDNA per ml plasma will be referred to as ng/ml cfDNA. Low pass DNA sequencing and copy number profiling were performed as previously described on both cfDNA and isolated single cells [67]. CtDNA 82 tumor fractions were estimated using the ichorCNA statistic [167] and scored as a continuous variable. Multiplex proteomic analysis of individual CTC was performed using the Hyperion Imaging Mass Cytometer (Fluidigm) as published [224]. 4.4.3. Statistical Analysis. The primary outcome was PFS defined as time to progression or death due to any cause. The primary aim was to compare the two combination arms to Arm 1. Secondary outcomes included overall survival (OS) defined as time from registration to death from any cause, as well as CTCAE toxicity. Survival times were compared using log-rank tests for comparisons of treatment and Cox regression analysis for hazard ratio estimation and testing of treatments and biomarkers. Response rates were compared by chi-squared testing. Predictive testing of the role of liquid biopsy results on treatment and subsequent clinical outcomes were planned, using Cox regression, with a hypothesis that participants with high CTC might benefit from combination therapy while those with low CTC would not. 83 4.5. Results 4.5.1. Clinical Outcomes According to Treatment Assignment. The original planned sample size of SWOG1222 (NCT02137837) was 825, assuming PFS medians of 15, 21.5, and 25 months for the three arms, respectively. Accrual of 37 participants occurred between May 2014 and February 2015 (Supplementary Figure 4.1). FDA approval of CDK4/6 inhibitors in the first-line treatment of hormone receptor-positive metastatic breast cancer in February 2015 made the trial not viable. In October 2015, the study sponsor permanently closed the study, offering participants the option to continue their current active drug therapy after unblinding. All study follow-up concluded December 2019. Patient characteristics are shown in Table 4.1. One participant received no protocol treatment and is not evaluable for clinical benefit or adverse events. Among 36 evaluable patients, no grade 3 or higher toxicity was observed in the fulvestrant arm; one patient receiving fulvestrant plus everolimus experienced grade 4 toxicity (hypophosphatemia) and an additional 10 participants receiving fulvestrant and everolimus with or without anastrozole experienced grade 3 toxicities. PFS appeared similar for all arms (Figure 4.1A; log-rank p=0.88) with an overall median of 11.2 months. At the end of study follow-up at 5 years, three patients (one in each arm) were still receiving protocol assigned treatment and had not progressed. There was also no evidence of a difference in OS (Figure 4.1B; log-rank p=0.81) with an overall median of 42 months. Median follow-up time for those still alive was 56 months. Among those with measurable disease there were 2 responses in 9 patients on Arm 1 (22.2%), 6 in 10 patients on Arm 2 (60.0%), and 4 in 9 patients on Arm 3 (44.4%). Though suggestive of better response on combination therapy, these differences were not statistically different (p=0.25). 84 Table 4.1 Patient characteristics by study arm 4.5.2. Liquid Biopsy Analyses. Due to a regulatory issue which delayed the ability of the University of Michigan laboratory to accept specimens, only 13 patients had CTC evaluation by CellSearch® at baseline. Two (15.4%) patients had elevated CTC (as defined by ³1cell/ml) prior to treatment (Figure 4.2) and were tested again after one cycle. CTC levels declined dramatically for both patients. For one, assigned to fulvestrant and everolimus, CTC declined from a baseline level of 18 to first follow- up level of 4 CTC/7.5 ml WB. The second patient, assigned to fulvestrant only, had an average of 46 CTC at baseline which declined at first follow-up to 8 CTC/7.5ml WB. PFS did not differ between these two patients with elevated CTC at baseline compared to those without elevated CTC at baseline (log-rank p=0.47). Since only two patients evaluated by the CellSearch® assay had elevated CTC levels, CTC-ETI analysis was determined, but association with outcomes was not performed. 85 Figure 4.1 PFS and OS by randomized treatment groups. Data cutoff was December 31, 2019. Log-rank test compared all three treatment groups. A, PFS. B, OS. Figure 4.2 CTC expression of ER in 2 patients with elevated CTC levels. CTC enumeration and ER expression determined using CellSearch. See text for details. 86 Next, we compared the number of CTC positive draws as well as the CTC concentration for all samples where both CellSearch and HD-SCA data was available. For the 13 patients evaluated for CTCs by both methods, there was perfect concordance of the two assays: the same 2 patients had elevated CTCs by both assays and the remaining 11 did not (figure 4.3A). Limit of rare cell detection for the HDCSA depends on the number of slides assayed. In this scenario, HDSCA cutoff was approximately 0.33 cells/ml. In the future, limit of detection in the HD-SCA assay could be increased by analyzing more slides per patient. In addition to the enumeration, ER protein status of CTCs was available for both the CellSearch and the HD-SCA assay for one patient. Percentage of positive CTCs was comparable between the assays for both draws (figure 4.3B). Figure 4.3 Comparison of samples where data was available for both CellSearch and HD-SCA. A) Number of CTCs found by CellSearch and the HD-SCA. B) ER positivity of CTCs detected by CellSearch and the HD-SCA for one patient. P = Patient, B = Baseline, C = C2D1, P = Progression. Using the HD-SCA assay, 25 of 34 cases (74%) had measurable non-leukocyte cell counts, which are here referred by as microenvironment cells, and 7 of the 34 (21%) had CTCs with high cytokeratin expression (figure 4.4). CTC-ETI vs CTC Comparison 700221 Baseline 700221 C2D1 700212 Baseline 700212 C2D1 700092 Baseline 700102 Baseline 700124 Baseline 700142 Baseline 700149 Baseline 700168 Baseline 700073 Progression 700149 Progression 700168 Progression 0 5 10 15 20 cells/ml CTC-ETI CTC P2-B P2-C P1-B P1-C P6-B P7-B P8-B P9-B P10-B P11-B P12-P P13-P P15-P CTC-ETI vs HD-CTC Comparison % ER Positive Cells % ER Positive Cells 700221 Baseline 700221 C2D1 0 20 40 60 80 CTC-ETI CTC Patient 2 Baseline Patient 2 C2D1 A B CTC-ETI vs HD-CTC Comparison % ER Positive Cells % ER Positive Cells 700221 Baseline 700221 C2D1 0 20 40 60 80 CTC-ETI CTC 87 Figure 4.4 CTC and microenvironment cells of all draws. A) Enumeration of CTCs and rare circulating cells from the microenvironment across all timepoints. The presence of CTCs was not associated with poorer PFS (HR=1.40; 95% CI 0.59-3.32; p=0.45). However, if the count of high cytokeratin CTC at baseline is modeled as a continuous variable in the Cox regression, there is a significant decrease in PFS with each unit of CTC by HD- SCA (HR=1.02; 95% CI 1.00-1.04; p=0.043). For draw 2 after one cycle of treatment, 3 of 32 (9.4%) patients measured by the HD-SCA assay were positive for high cytokeratin CTC, including two of the patients elevated at baseline. For draw 3 at the time of progression 5 of 18 (27.8%) patients had positive CTCs. Baseline ctDNA (n=25), measured as tumor DNA fraction, was also modeled continuously, and was statistically associated with poorer PFS (HR=1.08;95% CI 1.02- 1.15; p=0.005), while total cfDNA as purified from the plasma was not (HR=1.04; 95% CI 0.99- 1.09; p=0.11). Longitudinal assessment of CTCs found that all but one patient with elevated CTCs at baseline had a reduction of CTCs/ml after 4 weeks on treatment (C2D1) and another patient relapsed before the 4 week mark (figure 4.5A, S4.2A). While only half (18/33) of the patients with Baseline draws had their progression draw collected, we note that four of the five patients with CTCs at progression had no CTCs at C2D1. Interestingly, microenvironment cells remained consisted across the three time points in most patients, with the exception of three patients who had large number of cells at baseline and only limited amounts detected at C2D1 and progression (figure 4.5B, S4.2B). ctDNA fluctuations resembled those of the CTCs (figure 4.5C, S4.2C). The 0 50 100 150 200 0 50 100 150 200 CTCs/ml CTCs Microenvironment Microenvironment cells/ml 88 cfDNA concentration in contrast appeared to rise across timepoints and was significantly higher at time of progression compared to baseline (figure 4.5D, S4.2D). Figure 4.5 Assessment of CTCs, CECs, ctDNA fraction and concentration of cfDNA over time. All draws are included. Longitudinal assessment of A) CTCs, B) Microenvironment cells, C) tumor fraction and D) cfDNA concentration per ml plasma. Differences were tested using a two-way ANOVA with Tukey’s correction for multiple comparison testing. p-value = 0.1234(ns), p-value = 0.0332(*), p-value = 0.0021(**), p-value = 0.0002(***). CTC enumeration and ctDNA analysis by the HD-SCA assay was available for 67 samples across all three timepoints. Draws were subsetted based on a high and a low threshold for ctDNA and CTC abundance to assess how commonly their presence or absence correlated (figure 4.6). Concordance was 82% for the low ctDNA/CTC threshold (figure 4.6A) and 93% for the high ctDNA/CTC threshold (figure 4.6B). Baseline C2D1 Progression 0 5 10 15 20 25 50 100 150 CTCs/ml Baseline C2D1 Progression 0 50 100 150 200 Cells/mls Baseline C2D1 Progression 0.0 0.2 0.4 0.6 0.8 ctDNA fraction Baseline C2D1 Progression 0 50 100 250 500 ng/ml cfDNA ns ✱✱ ns A B C D 89 Figure 4.6 Concordance of CTC and ctDNA positivity across all HD-SCA draws with available CTC enumeration and ctDNA assessment from all three timepoints. A) Low positivity threshold for ctDNA and CTCs/ml. B) High positivity threshold for ctDNA and CTCs/ml. Next, we took a deeper look into the two patients with the highest CTC counts at baseline, patient 3 and patient 4. Assessment of ER positivity in the baseline draws finds a high number of CTCs with strong ER expression in patient 3 (figure 4.7A/B). In contrast, CTCs of patient 4 were almost exclusively ER negative (figure 4.7A/B). Copy number analysis of cfDNA and CTCs finds significant differences in the altered regions between patient 3 and 4 (figure 4.7C/D). In all detectable cases, copy number profiles of ctDNA represented an aggregate of all clones detected on a single cell level (Figure 4.7C/D, S4.3). Yes No Yes 7 7 No 5 48 ctDNA (>= 10%) CTC/ml (>= 1) Yes No Yes 4 4 No 1 58 CTC/ml (>= 10) ctDNA (>= 20%) # of samples % Total 67 100 Concordant 55 82 Discordant 12 18 # of samples % Total 67 100 Concordant 62 93 Discordant 5 7 A B 90 Figure 4.7 CTC genomic and phenotypic analysis in 2 patients with elevated CTC levels. CTC enumeration and genomic and phenotypic characterization determined using HD-SCA. A) Galleys of CTC of blood collected from patients 3 and 4, showing cytokeratin, ER, DNA, and CD45 expression. B) ER expression in CTC in patients 3 and 4. C) Genomic analysis of CTC in patients 3 and D) 4. Interestingly, while both patients presented with high CTC counts throughout the trial, their treatment response varied significantly. Patient 3 was an exceptional responder with a PFS over 2 years. In contrast, patient 4 relapsed within 18 days of enrolling in the trial. To understand more about the molecular mechanisms that set them apart we performed further genomic and proteomic analysis. We noted that while the CTC counts of patient 3 kept rising from baseline to C2D1 and increased even further at time of progression, ER expression fluctuated greatly between draws (figure 4.8A/B). CTCs at baseline were predominantly ER positive, weakly positive at C2D1 and strongly positive at time of progression (figure 4.8B). Point mutation analysis of single CTCs at time of progression found an ESR1 mutation in 2 out of 25 sequenced cells (figure 4.8C). Copy number profiles at baseline and C2D1 represented a single clone (figure 4.8D). In contrast, CTCs B Cytokeratin ER DNA CD45 Composite A Patient 3 Patient 4 Patient 3 Patient 4 0 20 40 60 80 100 % ER Positive Cells 0 1+ 2+ 3+ Patient 4 Single Cell cfDNA Patient 3 Single Cell cfDNA C D Chromosome Chromosome Ratio to Median Ratio to Median 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 20 22 X Y 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 20 22 X Y 1.00 1.25 1.50 1.75 2.00 2.50 3.00 0.50 1.00 1.25 1.50 1.75 2.00 2.50 3.00 0.50 91 at time of progression gained additional chromosomal alterations (figure 4.8D/E/F/G). Interestingly, we also found CTCs with apoptotic copy number profiles as indicated by their complete loss of large random chromosomal regions exclusively at time of progression (figure 4.8H-J). Figure 4.8 Overview of molecular characteristics of patient 3. A) CTCs/ml and B) ER% positive CTCs at the three blood draws. ER positivity was visually scored on a scale from 0-3, where 0 = negative and 3 = high ER expressing. C) Single cell point mutation analysis of CTCs for ESR1 mutations. D) Copy number alteration analysis of CTCs at baseline (green), C2D1 (orange) and progression (purple). E-G) Single cell copy number profiles of different clones at time of progression. H-J) Single cell copy number profiles of apoptotic CTCs at time of progression. Patient 4 relapsed before C2D1 could be collected and has therefore only two blood draws available. While CTC levels decreased from baseline to progression, the percentage of Vimentin positive cells remained the same (figure 4.9A/B). Interestingly, there was an increase in cfDNA from baseline to progression, but no change in the percentage of ctDNA (figure 4.9C/D). Multiplex proteomics at baseline finds that CTCs of patient 4 are ER negative and HER2 negative (figure 4.9E). In addition, CTCs express the proliferation marker ki67 as well as a combination of Baseline C2D1 Progression 0 50 100 150 200 CTCs/ml Baseline C2D1 Progression 0 20 40 60 80 100 % ER Positive Cells 0 1+ 2+ 3+ Wildtype - Y537 Mutated - Y537S TAT TCT A B C D ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 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● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.2 0.5 1.0 2.0 9D58_6, clade 1 Chromosome Ratio to median 1 2 3 4 5 6 7 8 9 10 11 12 13 15 17 19 22 Y ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 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● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.2 0.5 1.0 2.0 9D58_41, clade 1 Chromosome Ratio to median 1 2 3 4 5 6 7 8 9 10 11 12 13 15 17 19 22 Y ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 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● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.2 0.5 1.0 2.0 5.0 9D58_16, clade 1 Chromosome Ratio to median 1 2 3 4 5 6 7 8 9 10 11 12 13 15 17 19 22 Y ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 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● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.2 0.5 1.0 2.0 5.0 9D58_29, clade 1 Chromosome Ratio to median 1 2 3 4 5 6 7 8 9 10 11 12 13 15 17 19 22 Y ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 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● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.2 0.5 1.0 2.0 5.0 9D58_40, clade 1 Chromosome Ratio to median 1 2 3 4 5 6 7 8 9 10 11 12 13 15 17 19 22 Y ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 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profiles at baseline and progression are identical with regards to the tumor fraction as well as the genomic alterations (figure 4.9F). Similarly, the CTC at baseline has the same genomic alteration as those detected at progression (figure 4.9G). 93 Figure 4.9 Overview of molecular characteristics of patient 4. A) CTCs/ml and B) Percent Vimentin positive CTCs at the two blood draws. C) cfDNA concentration and D) ctDNA fraction at baseline and progression. E) Galleries of a representative CTC at baseline showing expression of HER2, E-Cadherin, EpCAM, and TWIST in three epithelial cells. F) ctDNA copy number profiles at baseline and progression. G) Copy number profiles of CTCs at baseline (N=1) and progression (N=13). CK8/18 DNA CD45 HER2 ER CD3 E-Cadherin Ki67 ALDH EpCAM TWIST CD66 Baseline Progression 0 50 100 150 CTCs/ml Baseline Progression 0 50 100 150 ng/ml cfDNA Baseline Progression 0 20 40 60 80 100 % Vimentin Positive Cells Negative Positive Baseline Progression 0.0 0.2 0.4 0.6 0.8 ctDNA fraction 0.50 1.00 1.25 1.50 1.75 2.00 2.50 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 20 22 X Y Chromosome Ratio to median Profile 8279_cf1 8392_cf1 Type Cell−based Cell−free Baseline ctDNA Progression ctDNA A B C D E F G Draw 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 X Alterations 0.6 0.8 1 1.2 1.4 Draw Baseline Progression Draw 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 X Alterations 0.6 0.8 1 1.2 1.4 Draw Baseline Progression 94 4.6. Discussion Due to the evolving landscape of first-line therapy for metastatic hormone-sensitive breast cancer, the current study was unable to complete accrual or determine the impact of the addition of everolimus to fulvestrant alone or to combination endocrine therapy in this setting. The observed median PFS of 11.2 months in this study (S1222) compares favorably with the 5.6 month PFS with fulvestrant 500 mg in the CONFIRM study which enrolled patients who progressed within 12 months of adjuvant therapy or while on first line endocrine therapy [225], and unfavorably with the median time to progression of 23.4 months observed with first-line fulvestrant alone in the FIRST study [226], highlighting difficulties in cross-study comparisons. The addition of everolimus to endocrine therapy in the current study, S1222, was associated with increased toxicity. In spite of an impressive impact on PFS in previous studies, an OS benefit with everolimus in the treatment of metastatic breast cancer has yet to be established. The recent demonstration of improved OS with CDK4/6 inhibitors [227, 228] firmly establishes their inclusion early on in the treatment of metastatic HR-positive breast cancer. Everolimus remains an option in subsequent lines of therapy as suggested by the PreE0102 study, in which the PFS was 10.3 months versus 5.1 months with the addition of everolimus to fulvestrant following progression aromatase inhibitor therapy [219]. Furthermore, everolimus toxicity in the form of stomatitis may be reduced with the use of oral dexamethasone mouthwash [229], which was not mandated in S1222. The planned translational liquid biopsy studies were likewise severely limited by the low accrual and by a regulatory issue that prevented analysis of the entire population of participants. Nonetheless, enumeration of CTC by two different methods (CellSearch® and HD-SCA) were completely concordant (2 elevated, 11 not). No statistically significant difference in PFS was observed between the two patients with elevated CTC by both methods compared to the 11 with 95 non-elevated CTC at baseline. However, both of these patients experienced a “CTC response,” which has been associated with a better prognosis when compared to patients whose CTC remain elevated at the end of the first cycle of therapy [69, 223]. When modeled as a continuous variable, elevated levels of CTC by HD-SCA were associated with a worse prognosis, consistent with several prior studies that have demonstrated that the presence of CTC enumerated with CellSearch® prior to start of therapy is associated with a worse outcome in metastatic breast cancer [57, 222]. Findings of CTC-ETI analyses of two participants with elevated CTCs at baseline are of interest, suggesting that further genomic and proteomic analysis might be valuable in treatment selection and outcomes. For patient 1, who was assigned to fulvestrant and everolimus, all of the CTC detected by CellSearch® and by HD-SCA at baseline were negative for ER expression, which we hypothesized would predict for lack of benefit from endocrine therapy (Figure 4.2). However, she experienced a “CTC response”, raising the speculation that blocking the mTOR pathway may be successful even if the cancer has reverted to a hormone receptor negative phenotype. By contrast, in patient 2, approximately 60% of the CTC were ER-positive at baseline. She was assigned to fulvestrant alone and also had a CTC response (Figure 4.2). Similarly, two other patients with the highest level of CTC elevation as identified by the HD-SCA assay provided provocative findings (Figure 4.7-9). In a manner similar to patient 1, Patient 3 had a high percentage of ER positive CTCs as well as a copy number profile suggestive of a luminal subtype (Figure 4.7A/B/C, 4.8B/D) [230]. This patient was treated with fulvestrant, anastrozole, and everolimus, and had a remarkable PFS of over two years. By contrast, patient 4’s CTCs were almost entirely ER negative with a basal-like genomic subtype (Figure 4.7A/B/D, 4.9F/G) [230]. Further, proteomic analysis demonstrated that many of her CTC expressed HER2 96 and TWIST (Figure 4.9E). This patient, treated with fulvestrant and everolimus, progressed within 18 days from the time of entry onto the trial. Taken together, these data suggest an intriguing hypothesis that CTC-ER phenotype might help select patients who could be treated with endocrine therapy alone or who are better treated with combination endocrine and other pathway (such as mTOR or CDK4/6) inhibition. Of course, these speculations require substantial validation. Assessment of ctDNA showed promise as a prognostic marker for PFS, similar to previously published reports [231]. While the genomic and proteomic analyses are only exploratory given the small number of CTC positive patients, our findings indicate that while CTC enumeration alone can be of prognostic value, deeper characterization of CTC combined with ctDNA analysis may provide further insight into the mechanisms underlying treatment response. Future studies should lead to improved understanding of molecular determinants of response and progression which may help to select which patients are most likely to benefit from the various therapeutic options. 4.7. Additional Information 4.7.1. Authors’ Disclosures H.C.F. Moore reports grants from SWOG Cancer Research Network during the conduct of the study and other support from AstraZeneca, Roche/Genentech, Daiichi-Sankyo, and Sermonix outside the submitted work. W.E. Barlow reports grants from NCI and other support from AstraZeneca during the conduct of the study. J.R. Gralow reports other support from Roche/Genentech, AstraZeneca, Sandoz/Hexal AG, Puma, Novartis, SeaGen, Genomic Health/Exact Sciences, and Radius outside the submitted work. D.F. Hayes reports grants and non- financial support from Janssen Diagnostics and personal fees from Janssen Diagnostics during the conduct of the study. D.F. Hayes also reports non-financial support from inbiomotion; grants and 97 personal fees from cepheid; personal fees from Cellworks, BioVeca, EPIC Sciences, L-Nutra, OncoCyte, Turnstone Biologics, predictus BioSciences, and Tempus; and grants from Merrimack pharma, Eli Lilly, and AstraZeneca outside the submitted work; in addition, D.F. Hayes has a patent regarding circulating tumor cells, for which the rights were licensed to the manufacturer of CellSearch: first Janssen and then Menarini Silicon Biosystems, and D.F. Hayes received annual royalties through January 2021. P. Kuhn reports grants from Hope Foundation for Cancer Research and Breast Cancer Research Foundation during the conduct of the study and grants and personal fees from Epic Sciences outside the submitted work; in addition, P. Kuhn has a patent for Systems, methods and assays for outlier clustering unsupervised learning automated report (ocular) pending, licensed, and with royalties paid from Epic Sciences and a patent for Methods for detection of circulating tumor cells and methods of diagnosis of cancer in a mammalian subject pending, licensed, and with royalties paid from Epic Sciences. J.B. Hicks reports grants from Breast Cancer Research Foundation during the conduct of the study and personal fees from Epic Sciences outside the submitted work; in addition, J.B. Hicks has a patent for OCULAR Technologies asn Software for Rare Cell Identification and Classification licensed and with royalties paid from Epic Sciences, Inc. L. Welter reports grants from Breast Cancer Research Foundation, Hope Foundation for Cancer Research, and Alan Joseph Endowed Fellowship during the conduct of the study. A.K. Conlin reports personal fees from AstraZeneca and SeaGen outside the submitted work. D.L. Lew reports grants from AstraZeneca during the conduct of the study. D. Tripathy reports grants and personal fees from Novartis during the conduct of the study; personal fees from AstraZeneca, Gilead, GlaxoSmithKline, Exact Sciences, and OncoPep; and grants and personal fees from Pfizer outside the submitted work. L. Pusztai reports personal fees from AstraZeneca, Merck, Novartis, Bristol-Myers Squibb Genentech, Eisai, Pieris, 98 Immunomedics, Seattle Genetics, Clovis, Syndax, H3Bio, and Daiichi outside the submitted work. G.N. Hortobagyi reports grants and personal fees from Novartis during the conduct of the study and outside the submitted work. No disclosures were reported by the other authors. 4.7.2. Authors’ Contributions H.C.F. Moore: Conceptualization, data curation, writing–original draft, writing–review and editing. W.E. Barlow: Conceptualization, data curation, formal analysis, methodology, writing–original draft, writing–review and editing. G. Somlo: Conceptualization, resources, data curation, investigation, methodology, writing–review and editing. J.R. Gralow: Conceptualization, writing–review and editing. A.F. Schott: Conceptualization, resources, writing–review and editing. D.F. Hayes: Conceptualization, resources, investigation, methodology, writing–review and editing. P. Kuhn: Resources, data curation, investigation, methodology, writing–review and editing. J.B. Hicks: Resources, data curation, investigation, methodology, writing–review and editing. L. Welter: Resources, data curation, investigation, methodology, writing–review and editing. P.A. Dy: Resources, writing–review and editing. C.H. Yeon: Resources, writing–review and editing. A.K. Conlin: Resources, writing–review and editing. E. Balcueva: Resources, writing–review and editing. D.L. Lew: Conceptualization, data curation, formal analysis, writing–review and editing. D. Tripathy: Conceptualization, resources, writing–review and editing. L. Pusztai: Conceptualization, resources, writing–review and editing. G.N. Hortobagyi: Conceptualization, resources, supervision, writing–review and editing. 99 4.7.3. Acknowledgments SWOG Clinical Trials Partnerships (CTP) manages the non-federally funded components of SWOG Cancer Research Network under The Hope Foundation for Cancer Research. This study received support from AstraZeneca plc and from Novartis Pharmaceuticals. D.F. Hayes received research funding support from Janssen Diagnostics during the conduct of this trial. P. Kuhn and J.B. Hicks received research funding from the Breast Cancer Research Foundation. L. Welter was supported by the Alan Joseph Endowed Fellowship. ClinicalTrials.gov identifier: NCT021378737 4.8. Supplementary Supplemental figure 4.1 Consort Diagram 100 Supplemental figure 4.2 Assessment of CTCs, CECs, ctDNA fraction and concentration of cfDNA over time. Only patients who had data available from all three blood draws or who relapsed before C2D1 were included. Longitudinal assessment of A) CTCs, B) Microenvironment cells, C) tumor fraction and D) cfDNA concentration per ml plasma. Differences were tested using a two-way ANOVA with Tukey’s correction for multiple comparison testing. p-value = 0.1234(ns), p-value = 0.0332(*), p-value = 0.0021(**), p-value = 0.0002(***). Baseline C2D1 Progression 0 5 10 15 20 25 50 100 150 CTCs/ml Baseline C2D1 Progression 0 50 100 150 200 Cells/mls Baseline C2D1 Progression 0.0 0.2 0.4 0.6 0.8 ctDNA fraction Baseline C2D1 Progression 0 50 100 250 500 ng/ml cfDNA ns ✱✱ ns A B C D 101 Supplemental figure 4.3 Single CTC vs cfDNA copy number profile. 0.50 1.00 1.25 1.50 1.75 2.00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 20 22 X Y Chromosome Ratio to median Type Cell−based Cell−free Profile 8879_3 8879_cf1 0.50 1.00 1.25 1.50 1.75 2.00 2.50 3.00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 20 22 X Y Chromosome Ratio to median Type Cell−based Cell−free Profile 8392_11 8392_cf1 0.50 1.00 1.25 1.50 1.75 2.00 2.50 3.00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 20 22 X Y Chromosome Ratio to median Type Cell−based Cell−free Profile 9D58_57 9D58_cf1 0.50 1.00 1.25 1.50 1.75 2.00 2.50 3.00 4.00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 20 22 X Y Chromosome Ratio to median Type Cell−based Cell−free Profile 8717_7 8717_cf1 0.50 1.00 1.25 1.50 1.75 2.00 2.50 3.00 4.00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 20 22 X Y Chromosome Ratio to median Type Cell−based Cell−free Profile 8849_9 8958_cf1 Patient 1 Patient 2 Patient 3 Patient 4 Patient 16 Single Cell cfDNA Single Cell cfDNA Single Cell cfDNA Single Cell cfDNA Single Cell cfDNA 102 5. Chapter 5: Cell State and Cell Type: Deconvoluting Circulating Rare Cell Populations in Liquid Biopsies by Multi-Omics This chapter is a manuscript in progress. Lisa Welter 1,2 , Serena Zheng 1 , Sonia Maryam Setayesh 1,2 , Michael Morikado 1 , Arushi Agrawal 1 , Rafael Nevarez 1 , Anna Sandström Gerdtsson 1 , Amin Naghdloo 1 , Drahomír Kolenčík 1 , Milind Pore 1 , Nikki Higa 1 , Anand Kolatkar 1 , Jana-Aletta Thiele 1 , Amado Zurita 3 , Elena Shagisultanova 4 , Anthony Elias 5 , Jennifer Richer 6 , Kenneth Pienta 7 , James Hicks 1 , Carmen Ruiz Velasco 1 , Peter Kuhn 1,2,8,9,10,11 1 Convergent Science Institute in Cancer, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA. 2 Department of Biological Sciences, Dornsife College of Letters, Arts, and Sciences, University of Southern California, Los Angeles, CA 90089, USA 3 Department of Genitourinary Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX 77230, USA. 4 Division of Medical Oncology, University of Colorado School of Medicine, Aurora, CO 80045, USA. 5 School of Medicine, Division of Oncology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA. 6 Department of Pathology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA. 7 The James Buchanan Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA. 8 Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089, USA. 9 Department of Aerospace and Mechanical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089, USA. 10 Catherine & Joseph Aresty Department of Urology, Institute of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA 11 Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA. Keywords Liquid Biopsy; Circulating Tumor Cells; Circulating Endothelial Cells; Epithelial-Mesenchymal Transition; Breast Cancer; Prostate Cancer 103 5.1. Simple Summary Advanced liquid biopsy platforms have enhanced capabilities of identifying cancer related analytes enabling a more precise description of the state of the disease. Cell detection platforms are designed to find disease associated cells in the blood of cancer patients such as circulating tumor cells (CTCs) but were traditionally based on a priori defined biological or biophysical characteristics of tumor cells. Initial cell counting has demonstrated clinical significance and initial characterization of disease characteristics over the past two decades. Consistent with primary tumor biology, the initial data provided insights that tumor cells may undergo phenotypic changes including the downregulation of epithelial markers in order to become more motile and adaptable. Here we demonstrate how Next-Generation Sequencing together with targeted proteomics can delineate CTC types and states and distinguish them from other cells in the tumor microenvironment. Our research highlights the opportunity and need to quantify the biology of the disease using single cell approaches to delineate the cancer’s plasticity over space and time. 5.2. Abstract Bi-directional crosstalk between the tumor and the tumor microenvironment (TME) has been shown to in-crease the rate of tumor evolution and plays a key role in neoplastic progression, therapeutic resistance, and a patient’s overall survival. Here we set out to use liquid biopsy to study cancer and specific TME cells in circulation and their association with disease status. Pan-cytokeratin positive, CD45 negative circulating rare cells (CRCs) from 9 breast and 5 prostate cancer patients were characterized through morphometrics, single cell copy number analysis or targeted proteomics and scored for Vimentin expression. 104 Genomic and proteomic analysis of CRCs delineates cancer lineage cells from those originating in the TME. We show that we can detect epithelial circulating tumor cells, CTCs undergoing epithelial-to-mesenchymal transition and circulating endothelial cells (CECs) in patients with breast and prostate cancer within a universal rare event detection platform. We find high inter-patient and temporal intra-patient variability in the expression of tissue specific markers such as ER, HER2, AR, PSA and PSMA and EpCAM. Longitudinal analysis of an index patient finds that CTCs are present at time of disease progression, while CECs are predominately present at time of stable disease. Our study highlights the importance of multi-omic characterization of CRCs due to the overlap of epithelial and mesenchymal markers expressed by disease associated cells as well as the high intra-patient tumor heterogeneity. 5.3. Introduction Liquid biopsies provide a unique opportunity to assess tumor derived analytes in the circulatory system throughout a patient’s course of disease. While various studies have provided insight into the benefit of circulating tumor cell (CTC) enumeration as well as limited assessment of protein expression on and molecular characterization of CTCs [200, 232], increasing evidence underlines the need of going beyond the current enrichment, identification and characterization of CTCs to represent the wider spectrum of tumor derived analytes [132, 204, 233, 234]. Carcinomas are comprised of a heterogeneous collection of cells with distinct genomic, proteomic, and morphometric properties. They are surrounded by a multitude of non-cancerous cell types forming a diverse microenvironment which supports the tumor’s growth [235]. Emerging evidence highlights bi-directional crosstalk between tumor cells and their surrounding microenvironment and the latter’s ability to induce changes in the cells state such as Epithelial- 105 Mesenchymal-Transition (EMT) in tumor cells, stressing the importance of studying cancers in the context of their TME [236]. The primary challenge in liquid biopsy research is the rarity of the tumor related analyte in the context of an overwhelming contribution of the normal blood environment with often unknown or insufficiently characterized biology of the tumor analytes in the context of the blood microenvironment. Assessing CTCs in the blood from patients with carcinomas requires the use of a spectrum of markers as cells may temporarily change their cell state to accommodate for a different microenvironment e.g., EMT, vascular mimicry [237-240] and tumor dormancy [241, 242]. Given the plasticity of both tumor cells and those from the TME, it is essential to build assay platforms that allow for characterizing of disease associated cells for their cell type and cell state in the context of large numbers of normal cells. EMT, a well-known cell state change in tumor cells and one of the proposed mechanisms of generating CTCs, has been associated with resistance to chemotherapy and worse prognosis [243-246]. While EMT has been viewed initially as a binary process, recent research indicates that EMT is rather a gradual transformation from an epithelial to mesenchymal state, including a partial EMT (pEMT) state, where cancer cells exhibit both epithelial and mesenchymal markers [48, 247]. Especially cells in adult tissues undergoing EMT under pathological conditions rarely complete the entire EMT program, indicating that pEMT represents the norm rather than the exception [48]. Angiogenesis is one of the hallmarks of cancer, as growing tumors require supply of oxygen and nutrients to sustain tumor growth [248]. Because of that, the tumor endothelium has been an important target of anti-cancer therapies [249]. Circulating endothelial cells (CECs), while mainly studied in the context of vascular damage and dysfunction in cardiovascular diseases [250, 251], have been proposed as a biomarker in cancer patients [252-254]. As of today, CECs have 106 been specifically studied in patients with breast, colorectal and small-cell lung cancer (SCLC) receiving anti-angiogenic therapies and increases in CEC counts have been associated with prolonged progression free survival in patients with breast, colorectal and SCLC [255-258]. Yet their predictive value remains controversial [253, 254]. In addition, little is known on the significance and frequency of CECs in patients with carcinomas not treated with anti-angiogenic drugs. Here we show that we can detect and characterize rare cells that are consistent with epithelial CTCs (EPI.CTC), CTCs undergoing partial EMT (pEMT.CTC) and circulating endothelial cells (CECs) in a small cohort of breast and prostate cancer patients. In addition, longitudinal analysis of a patient with aggressive prostate cancer associates CTCs and CECs with different treatment responses throughout the disease. Together this research demonstrates the need for liquid biopsies in precision oncology to deconvolute and quantify multiple circulating rare cell types and cell states within one marker agnostic platform. 5.4. Materials and Methods 5.4.1. Patients and samples. Table 5.1 Overview of breast cancer patient subtypes, stage and study IRBs. Patient ID Cancer Type Stage Cancer Subtype IRB # BC 1 Breast Late ER+/HER2- UP-14-00592 BC 2 Breast Late Triple Negative UP-16-0070 BC 3 Breast Late Triple Negative UP-16-0070 BC 4 Breast Late ER+/HER2- UP-17-00882 BC 5 Breast Late Triple Negative UP-16-0070 BC 6 Breast Late Triple Negative UP-16-0070 BC 7 Breast Late ER+/HER2- UP-14-00182 BC 8 Breast Late ER+/HER2- UP-14-00523 BC 9 Breast Late ER+/HER2- UP-14-00523 107 Table 5.2 Overview of prostate cancer patient stage and study IRBs. 5.4.2. Blood Sample Collection and Processing Peripheral blood samples were collected in Streck cell-free DNA blood collection tubes and shipped to the central laboratory. Blood sample processing and slide preparation for detection have been previously described [66, 204]. In short, blood samples underwent erythrocyte lysis in isotonic ammonium chloride solution and nucleated cells were plated onto custom adhesive glass slides (Marienfeld) as a monolayer of approximately 3 x 10 6 cells. Slides were incubated for 40 min at 37°C, treated with 7% BSA, and stored in a biorepository at -80°C for later analysis. Figure 5.1 summarizes the High-definition Single Cell Analysis (HDSCA) blood processing platform. Patient ID Cancer Type Stage IRB # PC 1 Prostate Late UP-16-00691 PC 2 Prostate Late UP-16-00643 PC 3 Prostate Late UP-16-00691 PC 4 Prostate Late UP-16-00691 108 Figure 5.1 Schematic overview of HDSCA platform. 5.4.3. Immunofluorescent staining of patient slides Slides underwent fluorescent staining as described previously [66, 204]. In short, cells were incubated with an antibody mix consisting of mouse anti-human CD45:Alexa Fluor® 647 (clone: F10-89-4, MCA87A647, AbD Serotec, Raleigh, NC), a cocktail of mouse IgG1/Ig2a anti-human cytokeratins (CK) 1, 4, 5, 6, 8, 10, 13, 18, and 19 (clones: C-11, PCK-26, CY-90, KS-1A3, M20, A53-B/A2, C2562, Sigma, St. Louis, MO), mouse IgG1 anti-human CK 19 (clone: RCK108, 109 GA61561-2, Dako, Carpinteria, CA), and rabbit IgG anti-human vimentin (VIM) (clone: D21H3, 9854BC, Cell Signaling, Danvers, MA) for 2 hours. Slides were then incubated with Alexa Fluor® 555 goat anti-mouse IgG1 antibody (A21127, Invitrogen, Carlsbad, CA) and counter-stained with 4′,6-diamidino-2-phenylindole (DAPI; D1306, ThermoFisher) for 1 hour. Slides were finally mounted with a glycerol-based aqueous mounting media to enable future coverslip removal for downstream genomic and proteomic analyses without disrupting cell integrity. 5.4.4. Rare cell identification and characterization Slides were imaged with an automated high-throughput microscope with a 10x optical lens, and candidate cells were identified based on their marker expression (DAPI+/CK+/CD45-) and morphological features as previously described [66]. All identified candidate cells were presented to a trained analyst for verification. Cytokeratin positivity was generally defined as 6 standard deviations over the mean (SDOM) signal intensity relative to surrounding leukocytes (negative control for CK) as the initial candidate selection. Vimentin expression was then scored in CK+ candidate cells. Vimentin intensity denotes the image intensity within the cell mask. 5.4.5. Single cell Next Generation Sequencing and Bioinformatic Analysis Candidate cells were isolated for whole genome amplification as previously described [162, 164]. Briefly, single cells were isolated from the slide using a robotic fluid micromanipulation system (Eppendorf) and were deposited into individual PCR tubes for whole genome amplification. Single cells underwent whole-genome amplification (WGA) using the WGA4 Genomeplex Single Cell Whole-Genome Amplification Kit (Sigma-Aldrich) followed by purification with the QIAquick PCR Purification Kit (QIAGEN). DNA concentration was measured using the Qubit Fluorometer system (Thermo Fisher Scientific). Single cell libraries were constructed using the NEBNext® Ultra™ II FS DNA Library Prep Kit with NEBNext® 110 Multiplex Oligos (New England Biolabs) and sequenced at the USC Dornsife Sequencing Core to generate ∼500,000 mapped reads per sample (minimum 250,000). To create copy number alteration (CNA) profiles, samples underwent bioinformatic analysis as previously published [165]. In summary, reads were deconvoluted based on sample barcodes and PCR duplicates were removed. Next, binned ratios were normalized based on the guanine-cytosine (GC) content per bin and mapped to 5,000 bins across the human genome (hg19, Genome Reference Consortium GRCh37, UCSC Genome Browser database). The CBS algorithm was used to segment the read count data which was used to generate copy-number profiles [166]. Gains were defined as >1.25 and losses as 0.75 over the median. Heatmaps were generated using the in R using the heatmap.2 function in the ggplots package. Clonality was defined as two or more cells with shared CN breakpoints. Additionally, if only one altered cell was detected as in the case of BC 5, cells were considered cancerous and here referred to as clonal cells, if their CNAs conformed with those commonly found in the respective cancer type [141, 259]. 5.4.6. Single cell targeted proteomics and data analysis After rare cell detection as described above, slides were stained with MaxPar metal-labeled antibodies as previously described [89]. Briefly, slides were washed and re-stained with metal- conjugated antibodies as previously described [89]. A DNA intercalator and a membrane stain were used as counterstains. Slides were dried overnight prior to laser ablation with the Hyperion TM+ Imaging System using 0.4 mm x 0.4 mm regions of interest (ROI) centered around each cell of interest (COI). After laser ablation and ion counting, cells were segmented using ilastik's random forest classifier ([260] v1.3.3) (ilastik feature settings: 0.3, 0.7, and 1.0 sigma for color/intensity, edge, and texture) and ilastik’s probability masks were used in CellProfiler ([261] v2.2.0) to create single 111 cell masks for all samples. COIs identified by a trained analyst during the rare cell detection step were relocated based on the cell plating pattern and confirmed by their protein expression consistent with the prior IF data. Background ion counts, defined here as negative mask space, were subtracted from ion counts within masked areas. COI mask specific data was extracted together with data from ~150 WBCs per cancer type and data was normalized to generate z-scores. Regions of interest (ROIs) were visualized by histocat ++ [262]. 5.4.7. Data Analysis and Visualization Data were visualized with GraphPad Prism (version 8.0.0 for Windows, GraphPad Software, San Diego, California USA, www.graphpad.com) and RStudio (Integrated Development for R. RStudio, PBC, Boston, MA URL http://www.rstudio.com/). Illustrations were designed in Biorender (Created with BioRender.com) and Microsoft PowerPoint. Statistical analysis was performed with GraphPad Prism. 5.5. Results 5.5.1. Characterization of circulating rare over time of a metastatic prostate cancer patient. In 2014, Dago et al. described heterogenous CTC populations at four sequential time points during treatment of a metastatic castrate resistant prostate cancer patient (mCRPC) and found that at least two dominant cell populations were present at each timepoint. Three draws taken at the times of disease progression (draws 1, 2 and 4) were dominated by genomically altered cells that were amplified for androgen receptor (AR) and exhibited the genomic copy number pattern of the metastatic tissue [162]. Morphologically, they were AR+ by immunofluorescence and round. In contrast, at the time of stable disease (draw 3), the majority of CTCs were copy number neutral, 112 AR- and elongated [162]. This observation indicated that these cells were not part of the cancer lineage, but likely cells from the TME, yet the specific cell type remained unknown. Utilizing additional markers, molecular approaches and improved analytics, we were able to further investigate the nature of these cell populations. Cryobanked slides from each timepoint were analyzed using pan-CK, CD45, and Vimentin for initial rare event identification. Consistent with our published results, we identified two major subpopulations of CD45 - /Cytokeratin + rare cells. We noted high abundance (³90%) of the round, Cytokeratin bright cell population in the progression draws (draw 1, 2, 4) and low abundance (~25%) in the draw collected at stable disease (draw 3). Cytokeratin dim cells were predominantly present (~75%) at time of stable disease, but at low abundance (<10%) in draws taken at time of progression. These cells reflected the morphometric features of non-altered, elongated, AR- cells as found in Dago et al. (Figure 5.2A), while the Cytokeratin bright and round cells presented with a morphology similar to the AR + population. Single cell genomic analysis found two distinct genomic patterns, one with complex copy number alterations shared across all altered cells and one with virtually no copy number alterations (figure 5.2B/C). Clonally altered cells had on average a significantly higher CK expression (figure 5.2E) and the tendency to be rounder (figure 5.2G), compared to the non-altered cells, which corresponded with the findings from Dago et. al. The clonally altered cell population had complex copy number profiles consistent with the reported tumor profile. In addition, we found that the majority (19/22) of copy number neutral cells were Vimentin positive (Figure 5.2C). In contrast, although the majority of the clonal cells was Vimentin negative as expected for epithelial tumor cells, approximately 40% exhibited varying levels of vimentin staining, indicating EMT-like change toward a mesenchymal cell state defining those cells as pEMT.CTCs (figure 5.2C). 113 To further characterize the rare cell types and cell states across the blood draws of the index case, we performed targeted proteomics using the Hyperion TM+ imaging system. Through this we identified three major proteomic profiles (figure 5.2D). The first two subsets of cells were characterized by the expression of prostate cancer specific proteins, PSA and PSMA, as well as EpCAM and AR defining those as epithelial CTCs (EPI.CTCs), while the third subset was only positive for the endothelial protein CD31 marking them as circulating endothelial cells (CECs). Cells positive for CD31 by targeted proteomics were also positive for Vimentin in IF (figure 5.2E- G). These CECs were both the dominant population at time of stable disease (~75%) and only observable in very low quantities (£10%) at the time of progression. Their morphometry and protein expression suggests that they are circulating endothelial cells (CECs). Consistent with the CNV analysis, targeted proteomics confirmed about 40% of cells expressing prostate cancer markers were found to express Vimentin, supporting the observation that a substantial subpopulation of CTCs underwent pEMT. 114 Figure 5.2 Longitudinal assessment of circulating rare cells in a patient with metastatic prostate cancer. A) Representative cells organized by their Vimentin and Androgen Receptor expression. B) CNA profiles together with IF images of representative copy number altered Vimentin- EPI.CTCs, copy number altered Vimentin+ pEMT.CTCs and non-altered Vimentin+ CECs. C) Copy number alterations of rare cells across four blood draws. D) Multiplex protein expression of rare cells and CD45+ white blood cells across four blood draws. Cells with no Vimentin score available are color coded grey in the top heatmap annotation. E) Cytokeratin intensity measured as standard deviation over the mean (SDOM), F) Vimentin Intensity and G) Cellular Eccentricity of all cells with NGS or multiplex proteomic data. Cells were scored as either clonal or non-altered based on the CNV profiles or as CTC or CEC based on the results from targeted proteomics. Kruskal-Wallis test with Dunn’s correction for multiple comparisons was used to test for differences between each group. P-value annotations: 0.1234 (ns), 0.0332 (*), 0.0021 (**), 0.0002 (***), <0.00001 (****). H) Longitudinal assessments across four blood draws. Cells were grouped by EPI.CTC (clonal, Vim - ), pEMT.CTC (clonal, Vim + ), and CEC (genomically non-altered, morphologically consistent with endothelial cell). Tick marks on x-axis are set to 4-week intervals. Percentages of cell types might differ slightly between the total cells found per draw by the imaging microscope and those sequenced, as not all cells can be sequenced and the sequenced cell population is hence a subset of the total cells detected. 0 50 100 % Scored Cells Non-altered Vim+ Non-altered Vim- ◆ ◆ ◆ ○ pEMT.CTC EPI.CTC AD AD AD SD Time in Weeks CK8.18 EPCAM PSMA PSA AR CD31 CD45 Cell Type Draw Vimentin * * * * z−score 0 0.5 1 1.5 2 Cell Type CEC CTC WBC Draw 1 2 3 4 Vimentin Negative Positive Composite CD45 Vimentin CK DAPI Whole Genome Copy Number Profile Chromosome Ratio to Median CK/CD45/DAPI/AR Androgen Receptor Staining CK/CD45/DAPI/Vimentin Vimentin Staining Vimentin - Vimentin + AR + AR - Altered Non-altered CTCs CECs 0 10 20 30 40 CK SDOM ✱ ns ✱✱ ✱✱✱ ns ✱✱✱✱ Altered Non-altered CTCs CECs 0.0 0.1 0.2 0.3 0.4 Vimentin Intensity ✱✱✱✱ ns ✱✱ ✱✱✱✱ ns ✱✱ Altered Non-altered CTCs CECs 0.0 0.2 0.4 0.6 0.8 1.0 Cellular Eccentricity ns ns ✱✱✱ ✱ ns ✱✱✱✱ A B C D E F G H EPI.CTC pEMT.CTC Non-altered Vimentin+ 115 5.5.2. Inter-patient assessment of cell type and cell state in the liquid biopsy. To assess the generalizability of the observations made in the index patient we followed the above experimental process for seven additional cases with confirmed CK+/CD45- cells for which additional slides were available. A total of 321 cells from six breast cases and one additional prostate case were scored for Vimentin intensity, sequenced and scored for clonality [141, 162]. Three of the seven cases harbored both pEMT.CTCs and EPI.CTCs. Additionally, Four cases had exclusively clonal cells (figure 5.3A/B/D/G), two cases had a mix of clonal and non-altered cells (figure 5.3C+E) and one contained exclusively non-altered cells (figure 5.3F). 116 Figure 5.3 Representative single cell whole genome copy number alterations of breast (P1-6) and prostate (P7-8) cancer patients. A) Representative copy number alteration profiles and matching immunofluorescent images of rare cells from prostate and B) breast cancer patients. CNA profiles are displayed as ratio to median. Blue = DNA, Red = CK, White = Vimentin, Green = CD45 in composite images. C-G) Vimentin status as determined by immunofluorescence is annotated on top of heatmaps where Vimentin negative cells = grey, vimentin positive cells = black. Copy number gains are defined as >1.25 above median, losses as <0.75 below median and copy number neutral between 0.75 – 1.25. Copy number gains = red, copy number loss = blue, copy number neutral = white. N = Number of cells per heatmap. C-F) Single Cell CNV heatmaps of breast cancer patients. G) Single Cell CNV heatmaps of a prostate cancer patient. Vimentin Score 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 X Alterations 0.6 0.8 1 1.2 1.4 Vimentin Score 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 X Alterations 0.6 0.8 1 1.2 1.4 Vimentin Score 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 X Alterations 0.6 0.8 1 1.2 1.4 Vimentin Score 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 X Alterations 0.6 0.8 1 1.2 1.4 Vimentin Score 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 X Alterations 0.6 0.8 1 1.2 1.4 Vimentin Score 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 X Alterations 0.6 0.8 1 1.2 1.4 Vimentin Score 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 X Alterations 0.6 0.8 1 1.2 1.4 CK8.18 EPCAM PSMA PSA AR CD31 CD45 Cell Type Patient Vimentin * * * * * * * * z−score 0 0.5 1 1.5 2 Cell Type CTC Endothelial Cell WBC Patient PC 1 PC 2 PC 3 PC 4 Vimentin Negative Positive Vimentin Score 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 X Alterations 0.6 0.8 1 1.2 1.4 Copy Number Alterations Chromosome Chromosome A B C D E F G 117 5.5.3. Targeted proteomics identifies distinct phenotypes The inter-patient characterization of CK+ rare cells and CD45+ white blood cells was carried out similar to the index patient to further delineate specific cell types by targeted proteomics. We identified two major subgroups of rare cells, those expressing breast or prostate tissue markers such as the estrogen receptor (ER), human epidermal growth factor 2 (HER2), androgen receptor (AR), prostate specific antigen (PSA) or prostate-specific membrane antigen (PSMA), and those that expressed the endothelial marker CD31 (figure 5.4). Cells of both subgroups were negative for leukocyte specific markers such as CD45 and CD3 (figure 5.4A/B). Consistent with the index patient’s cells, they separate into epithelial and endothelial lineages. We note high heterogeneity of cancer specific markers both intra- and inter-patient. 118 Figure 5.4 Multiplex proteomics of circulating rare cells in patients with metastatic breast or prostate cancer. A) Multi-plex proteomics of 4 patients with breast cancer. B) Multiplex proteomics of 4 prostate cancer patients, including the index patient. grey = N/A CK8.18 EpCAM ER HER2 CD31 CD3 CD45 Cell Type Patient Vimentin z−score 0 0.5 1 1.5 2 Cell Type CEC CTC WBC Patient BC 2 BC 7 BC 8 BC 9 Vimentin Negative Positive CK8.18 EPCAM PSMA PSA AR CD31 CD45 Cell Type Patient Vimentin z−score 0 0.5 1 1.5 2 Cell Type CEC CTC WBC Patient PC 1 PC 2 PC 3 PC 4 Vimentin Negative Positive A B CK8.18 EpCAM ER HER2 CD31 CD3 CD45 Cell Type Patient Vimentin z−score 0 0.5 1 1.5 2 Cell Type CEC CTC WBC Patient BC 2 BC 7 BC 8 BC 9 Vimentin Negative Positive CK8.18 EPCAM PSMA PSA AR CD31 CD45 Cell Type Patient Vimentin z−score 0 0.5 1 1.5 2 Cell Type CEC CTC WBC Patient PC 1 PC 2 PC 3 PC 4 Vimentin Negative Positive 119 5.5.4. Morphometrics and multi-omics to separate cell types Next, we assessed the morphometric features of all cells identified by the IF assay that either underwent WGS or multiplex proteomics. In particular, we assessed differences in protein expression, cellular shape and size of these cells. Due to the limitation of the destructive nature in both the single cell genomics and the targeted proteomics approach, cells grouped by similarity in the IF assay were compared. Single cell genomics was used to assess their copy number profile and separate into altered vs. non-altered. Targeted proteomics was used to distinguish CTCs and CECs based on their protein expression as described above. Figure 5.5A shows representative examples of Vimentin negative and Vimentin positive clonal CTCs as well as Vimentin positive copy number neutral rare cells from breast cancer patients. Protein expression of a representative EPI.CTC, pEMT.CTC and CEC derived from a breast cancer patient are shown in figure 5.5B. Clonal cells showed a significantly higher cytokeratin expression as measured by the SDOM of the fluorescent intensity and were rounder compared to non-altered cells (figure 5.5C/D). Similarly, CTCs, as defined by multiplex proteomics, had a higher Cytokeratin expression and presented a rounder phenotype compared to CECs. While WGS and targeted proteomics are mutually exclusive as both assays are destructive, morphometrics and IF protein expression suggests that the clonal cells are the same cell type as the cells labeled as CTCs by multiplex proteomics. Similarly, morphometric parameters of the copy number neutral cells resemble those labeled CECs by targeted proteomics. 120 Figure 5.5 Morphometrics and multi-omics A) CNA profiles together with IF images of representative EPI.CTC, pEMT.CTC and Non-altered Vimentin+ cell from breast cancer patients. B) Protein expression of representative EPI.CTC, pEMT.CTC and CEC by IF and targeted proteomics from a breast cancer patients. C) CK positivity of rare cells of all breast and prostate cancer patients. D) Cellular Eccentricity of COIs of all breast and prostate cancer patients. Eccentricity is determined on a scale of 0-1 where 0 = circle and 1 = ellipse. Kruskal-Wallis test with Dunn’s correction for multiple comparisons was used to test for differences between each group. P-value annotations: 0.1234 (ns), 0.0332 (*), 0.0021 (**), 0.0002 (***), <0.00001 (****). To further support the hypothesized CEC cell type, we also compared the cells from these cancer patients to endothelial cell lines and CECs found in patients with myocardial infarction (MI) [20], and tested for their presence in normal blood donors (NBDs). We spiked HPAECs and HUVECs into the blood of NBDs at different concentrations to mimic the environment and test our detection and characterization approach. Representative images of spiked HPAECs and HUVECs are shown in figure 5.6A. Recovery of spiked endothelial cells was 98% for HPAECs and 72% for HUVECs (figure 5.6B). In addition, we assessed two non-spiked NBDs. Rare cell analysis found 3.06 and 0 cells/ml, respectively (figure 5.6B). Cytokeratin SDOMs of endothelial cell lines (HPAECs and HUVEC) and CECs found in MI patients were either comparable or lower than those of the non-altered cells found in cancer Composite CD45 Vimentin CK DAPI Whole Genome Copy Number Profile Altered Non-altered CTCs CECs 0 10 20 30 40 CK SDOM ✱✱✱✱ ✱✱✱✱ ns ✱ ns ✱✱✱✱ Altered Non-altered CTCs CECs 0.0 0.2 0.4 0.6 0.8 1.0 Eccentricity ✱✱✱ ns ✱✱✱✱ ✱✱ ns ✱✱✱✱ Altered Non-altered CTCs CECs 0.0 0.2 0.4 0.6 0.8 1.0 Vimentin Intensity ✱✱✱✱ ns ✱✱✱✱ ✱✱ ns ✱✱ Chromosome Ratio to Median A B C D E Targeted Proteomics CEC pEMT.CTC EPI.CTC EPI.CTC pEMT.CTC Non-altered Vimentin+ 121 patients (figure 5.6C). However, all endothelial groups (non-altered, HPAEC, HUVEC and MI- CECs) had significantly lower CK expression compared to the altered group. Vimentin intensity was highest in the endothelial cell lines and lowest in the altered cell population (figure 5.6D). Interestingly, CEC detected in MI patients had similar Vimentin intensities compared to those found in cancer patients (non-altered group). Figure 5.6 Rare cell enumeration of spiked endothelial cells and CECs in patients with myocardial infarction. A) Representative immunofluorescent images of spiked HPAEC and HUVEC cell line cells. B) Rare cell enumeration of spiked endothelial cell lines (HPAECs and HUVECs) as well as non-spiked NBD controls. HPAECs were spiked at approximately 430 cells/ml and had a recovery of 98%. HUVECs were spiked at approximately 100 cell/ml and had a recovery of 72%. An average of 3.06 and 0 cells/ml were found in two normal blood donors. C) CK SDOM of spiked HPAECs, HUVECs and CECs detected in MI patients. Kruskal-Wallis test with Dunn’s correction for multiple comparisons was used to test for differences between each group. P-value annotations: 0.1234 (ns), 0.0332 (*), 0.0021 (**), 0.0002 (***), <0.00001 (****). D) Vimentin intensity of spiked HPAECs, HUVECs and CECs detected in MI patients. Kruskal-Wallis test with Dunn’s correction for multiple comparisons was used to test for differences between each group. P-value annotations: 0.1234 (ns), 0.0332 (*), 0.0021 (**), 0.0002 (***), <0.00001 (****). Non-altered Altered HPAEC HUVEC CEC 0 10 20 30 40 CK SDOM ✱✱✱✱ ns ns ✱✱✱✱ ✱✱✱✱ ✱✱✱✱ ✱✱✱✱ ✱✱✱ ✱✱✱✱ ✱ Non-altered Altered HPAEC HUVEC CEC 0.0 0.5 1.0 Vimentin Intensity ✱✱✱✱ ✱✱✱✱ ✱✱✱✱ ns ✱✱✱✱ ✱✱✱✱ ✱ ns ✱✱✱✱ ✱✱✱✱ HPAEC HUVEC NBD 1 NBD 2 0 100 200 300 400 500 Cells/ml A B D C 122 5.6. Discussion Bi-directional crosstalk between the tumor and the TME has been shown to increase the rate of tumor evolution and plays a key role in neoplastic progression, therapeutic resistance, and a patient’s overall survival [25, 263-266]. In addition, intratumor heterogeneity of tumor cells as well as differences between the primary tumor and metastatic sites further increases the complexity of the disease and hence its treatment [267]. Given the heterogeneity and plasticity of cancers and their microenvironment, their adaptability to stressors such as therapeutic pressure, as well as natural evolution, it is of great advantage to assess the tumor not just as a single time point at diagnosis, but throughout a patient’s course of disease to understand cancer evolution and provide optimal treatment regimens. The data presented here demonstrates that the combination of epithelial, mesenchymal, endothelial and leukocyte markers enabled characterization of disease associated CRCs and leukocytes in patients with breast and prostate cancer. Genomic and proteomic analysis of CRCs delineates cancerous CRCs (CTCs) from those originating in the TME. We show that we can detect EPI.CTCs, pEMT.CTCs and CECs at varying frequencies in patients using a non-enrichment based detection platform. Using a combination of WGS, morphometrics and multiplex proteomics, we found that copy number neutral CRCs were morphologically distinct from clonal CTCs and had high protein expression of the endothelial marker CD31 indicating an endothelial cell lineage (figure 5.4/5). Vimentin expression was not only detected in copy number neutral cells, but also within the clonal population. Clonal Vimentin + CRCs had morphological similarity to Vimentin- clonal cells and exhibited identical breakpoints in their copy number profile, indicating that these cells represent a subgroup of CTCs that has undergone pEMT (figure 5.2C, 5.3A/B/G). Interestingly, both WGS 123 and multiplex proteomics revealed heterogeneity of CTCs. Notably, we detected varying levels of expression of cancer specific markers such as ER and HER2 in breast cancer or AR, PSA and PSMA in prostate cancer patients. In addition, we found that only approximately half of CTCs expressing cancer specific markers co-expressed EpCAM. This supports the cautionary statements by various groups on depending for EpCAM expression as required inclusion marker for CTCs [268-271]. Similar caution is warrant-ed when using Vimentin as sole marker for EMT, given that Vimentin positive cells have been found to be both associated with cells of the tumor microenvironment, immune cells as well as CTCs. Yet, when combined with additional markers or analytical tools such as genomics or morphometrics, both EpCAM and Vimentin can aid the classification and characterization of circulating rare cell types and states. In a longitudinal study assessing CRCs in a patient with metastatic prostate cancer, we found not only clonal CTCs whose genomic alterations were consistent with the primary tumor, but also a subset of CRCs exhibiting copy number neutral genotype [162]. While CTCs were more prevalent at times of disease progression (AD), we found high levels of CECs at time of therapy response (draw 3) (figure 5.2C/D/H). Although the reason for the increased presence of CECs at time of remission warrants further research, our findings highlight the importance and feasibility of investigating not only cancerous cells, but also those of the tumor microenvironment from the liquid biopsy. It is to be expected that a comprehensive characterization of CECs and other cells present in the tumor microenvironment in conjunction with CTCs will lead to improved means to measure therapeutic response. Endothelial cells, despite their mesodermal origin, have been reported to express certain Cytokeratins such as CK7, CK1, CK8 and CK18, which explains why they can be found with pan- CK based rare cell detection assays [272-276]. Here we show that both spiked endothelial cell 124 lines as well as CECs can be robustly detected when using CK and Vimentin as inclusion and CD45 as exclusion marker. Yet, to detect and enumerate CECs specifically, it will be most beneficial to use endothelial specific markers as well as dedicated CEC detection assays. Most commonly, CEC detection is performed by flowcytometry or a modified CTC detection platform, yet there are few dedicated assays to detect CECs [250, 254, 277, 278]. Given the differences and lack of standardization of CEC detection assays as well as differences in enrolled patient populations, more studies are required to fully understand the role of CECs in mCRC and their potential as predictive biomarker. The same holds true for characterization of pEMT.CTCs. Given the gradual and incomplete transition from epithelial to partial mesenchymal phenotype, multi-marker analysis by either multiplex proteomics or gene expression analysis is essential to correctly classify these CTCs. Yet, the combination of Cytokeratin, Vimentin and CD45 allows for differentiation of circulating rare cells from leukocytes as a first pass, while ensuring to not omit CTCs with downregulated EpCAM expression (figure 5.6A/B). Downstream morphometric, genomic and proteomic assessment can then classify CRCs based on their cell state and cell type. Through longitudinal liquid biopsy analysis, we have the opportunity to trace the emergence of new tumor subclones together with circulating cells from the microenvironment minimally invasive at a single cell level [67, 72, 99, 258]. Recent breakthrough in single cell gene expression analysis, methylation and multiplex proteomics of rare cells will play an essential role in the future to deeply characterize circulating rare cells in cancer patients [279-281]. This will not just be crucial for the understanding of molecular mechanisms of tumor evolution and disease progression, but also in the characterization of the TME. In addition to technological advances for deeper characterization of rare cells, integration of other liquid biopsy analytes such as circulating 125 tumor DNA (ctDNA), exosomes and platelets will provide a more complete picture of a patient's tumor state and response to treatments [44, 204, 282]. Together, we believe that a multi-omic and multi-analyte analysis of the tumor and its microenvironment will be the future of precision oncology. Combined with longitudinal sampling enabled by liquid biopsy technologies, we have now the opportunity to trace the emergence of new tumor subclones together with circulating cells from the non-hematopoietic microenvironment minimally invasive at a single cell level. 5.7. Conclusions Our study highlights the importance of multi-omic characterization of circulating rare cells and highlights the opportunity of detecting different cell types and cells states within one platform. While Cytokeratin and Vimentin expression alone cannot fully distinguish the cell types and cell states, additional parameters such as morphology, genomic and multiplex proteomic analysis aid to provide confidence in the classification of these cells. Enumeration of pEMT.CTCs could provide important insight into the aggressiveness of the disease and an opportunity for specific treatment selection. We propose that in the absence of CTCs, such as during disease remission, CECs could provide valuable information about the current disease state. In future studies, CECs might also give insight into metastatic mechanisms and disease progression through the longitudinal liquid biopsy analysis. While our results show the feasibility of detecting CTCs, pEMT.CTCs and CECs within one assay and associate them with disease states, more research is needed to fully elucidate the role of CECs in carcinomas. Lastly, although we highlight here specifically CECs, additional detection markers combined with morphometrics and multiplex proteomics have the potential to subclassify numerous more circulating TME subtypes and elucidating their importance in tumor evolution, drug response and seeding of metastasis. 126 5.8. Additional Information 5.8.1. Institutional Review Board Statement: The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board (or Ethics Committee) of the University of Southern California (USC) (UP-14-00330) and UP-14- 00523) and Billings Clinic (13.10) and (Pro0044182). 5.8.2. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. 5.8.3. Acknowledgments: First and foremost, we would like to thank the patients and their caretakers for participating in this study without whom this research would not have been possible. We also thank our blood processing team and IT team for their support. Special appreciation goes to our administrative team members Elvia Nunez and Allison Welsh for their support in funding applications and manuscript submissions. 5.8.4. Conflicts of Interest: P. Kuhn and J. Hicks hold ownership interest in, are consultants to and receive royalties from licensed technology from Epic Sciences. A. Kolatkar is a stakeholder in Epic Sciences. Epic Sciences and the University of Southern California, USC Michelson Center (P. Kuhn and J. Hicks), have signed a sponsored research agreement to advance next-generation liquid biopsy technology for precision oncology. C. Ruiz: none to report. L. Welter: none to report. S. Zheng: none to report. M.S. Setayesh: none to report. M. Morikado: none to report. A. Agrawal: none to report. R. Nevarez: none to report. A. Sandström Gerdtsson: none to report. A. Naghdloo: none to re-port. D. Kolenčík: none to report. M. Pore: none to report. N. Higa: none to report. J.A. Thiele: none to report. 127 5.9. Supplemental Figures: Supplemental figure 5.1 Gallery of CTCs, pEMT.CTCs, endothelial cells and endothelial clusters detected in a patient with metastatic prostate cancer. CNV profiles and corresponding immunofluorescent images of A) CTCs, B) pEMT.CTCs, C) endothelial cells and D) endothelial clusters. 128 6. Chapter 6: Perspectives Characterization of CTCs and cfDNA together with other liquid biopsy analytes offers non- invasive diagnostic and therapeutic information. Today, we are still scratching the surface on our understanding of the multi-omic landscape of cancer and how existing knowledge can lead to clinically relevant tests. Through the rapid technological advancement in genomic, epigenomic, transcriptomic and proteomic technologies and their compatibility with single cell resolution, we will be able to shed more light on tumor heterogeneity and therapeutic resistance mechanisms. Here we demonstrated the opportunities of longitudinal liquid biopsy assessment in patients with metastatic breast cancer. Copy number alteration analysis revealed tumor evolution, while point mutation analysis of ctDNA and CTCs found known acquired resistance mutations. Proteomic analysis of CTCs aided tracing the expression of clinically targetable proteins such as ER and HER2 and uncovered particularly aggressive features which likely contributed to the poor outcome of an index patient. Combined proteo-genomic analysis enabled classification of rare circulating cells into their cell state (epithelial, pEMT, mesenchymal) and type (CTC vs CEC). Tracing the evolution of tumors and tumor analytes through multi-omics enables not only discovery research, but also has the potential to be leveraged for precision medicine. We identified treatment relevant mutations (ESR1 and PIK3CA) as well as proteomic changes such as HER2 expression in CTCs from a HER2- tumor, that can guide treatment decisions. 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Abstract (if available)

Abstract Characterization of circulating tumor cells (CTCs) and cell-free DNA cfDNA together with other liquid biopsy analytes offers non-invasive diagnostic and therapeutic information. Here I demonstrate the opportunities of longitudinal liquid biopsy assessment in patients with metastatic breast cancer. Copy number alteration analysis revealed tumor evolution, while point mutation analysis of circulating tumor DNA and CTCs found known acquired resistance mutations. Proteomic analysis of CTCs aided tracing the expression of clinically targetable proteins such as Estrogen Receptor and human epidermal growth factor receptor 2 (HER2) and uncovered particularly aggressive features which likely contributed to the poor outcome. Combined proteo-genomic analysis enabled classification of rare circulating cells into their cell state (epithelial, partial epithelial-mesenchymal transition, mesenchymal) and type (CTC vs circulating endothelial cells). Tracing the evolution of tumors and tumor analytes through multi-omics facilitates not only discovery research, but also has the potential to be leveraged for precision medicine. I identified treatment relevant mutations (ESR1 and PIK3CA) as well as proteomic changes such as HER2 expression in CTCs from a HER2- tumor, that can guide treatment decisions. While more comprehensive and larger scale studies are needed to determine the relationship between liquid biopsy analyte characteristics and treatment response, we are in a promising new aera for precision oncology with vastly improving tools to tackle these questions.

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Creator Welter, Lisa (author)

Core Title Heterogeneity and plasticity of malignant and non-malignant circulating analytes in breast carcinomas

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Molecular Biology

Degree Conferral Date 2022-12

Publication Date 12/09/2022

Defense Date 08/24/2022

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

Tag breast carcinomas,cancer,cell-free DNA,circulating tumor cells,circulating-tumor DNA,copy number aberration,liquid biopsy,Metastasis,multi-omics,OAI-PMH Harvest,single cell,tumor evolution,tumor heterogeneity

Format theses (aat)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Kuhn, Peter (committee chair), Arnheim, Norman (committee member), Hicks, James (committee member), Nieva, Jorge (committee member)

Creator Email l.welter@gmx.de,lisawelt@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC112619732

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Legacy Identifier etd-WelterLisa-11351

Document Type Dissertation

Format theses (aat)

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