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Molecularly targeted micelle nanoparticles for cancer drug delivery and lymph node metastasis detection

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Molecularly targeted micelle nanoparticles for cancer drug delivery and lymph node metastasis detection

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Content MOLECULARLY TARGETED MICELLE NANOPARTICLES FOR CANCER DRUG DELIVERY AND LYMPH NODE METASTASIS DETECTION By NOAH TOM TRAC 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 (BIOMEDICAL ENGINEERING) DECEMBER 2023 Copyright 2023 Noah Tom Trac ii Acknowledgments I would like to first thank my mother Narinn. I know you have always regretted not being able to provide more for our family financially, but you have shown me what it means to sacrifice and put others above yourself, which I am certain is more valuable. This PhD is as much yours as it is mine. To my brother Nick, we have never been great at showing affection for each other, so I will keep this brief. You have always been the person I respect the most, not because of your intelligence or skill at tennis, because I am obviously better at both those things, but because of your diligence and strength of character. To my sister Itsy, your unconditional love and affection has meant the world to me. In one month, you changed me from not really wanting you (sorry!) to me searching “gourmet dog treats” and “cute shoes for dogs” on the Internet. I still need you to lose some weight, though! To my cousin Diane, you have always been my favorite cousin, which I think you already knew. Watching you overcome so much adversity to succeed in your career and at life inspires me to try and do the same. To all my other family, I know I never talk about it, but I absolutely love being part of this family and you all are a major reason for me wanting to dedicate my career to the medical sciences. iii To John Gillen, the only person from high school I bother to keep in touch with, you are legitimately one of the smartest guys I have ever met, and I know you are going to crush grad school. Best of luck! To my best friends at Brown, Sajal, James, Chris, Andrew, Caleb, and Oussama, I can’t believe how consistently we stayed up until 2 a.m. going to Jo’s, gaming, and watching anime together, but those four years were absolutely awesome. To my Japan friends (SNAACK), those 4 months in Japan are honestly the greatest experience of my life, and I still look back on those days fondly. Every time someone asks my why I consider it the best time of my life, the answer is always you. ➢ Scott, you are one of the funniest and most laid-back dudes I have ever met. One of my favorite memories is you rocking out to the Friends theme song while Cayli was throwing up all over Mt. Fuji in the background. Never change, Scott. ➢ Arvhic, you were my best friend from that trip. Special shout-out to you for bringing us all together! I never would have guessed that the most significant inflection point for my time in Japan would be you inviting me to a phallic festival the day after we met. You’re the best, and congrats on your engagement! ➢ Aida, I always considered you to be the mom of the group, helping to plan group activities and making sure we got home safely, which is why I was stunned when you started crying after we took a train to the wrong airport in Osaka and missed our flight. I still stand by what I said to you back then: That overnight bus back to Tokyo was waaaay more memorable than a boring plane ride! iv ➢ Cayli, I feel like unfortunate things that were completely out of your control just kept happening to you during that trip. I always laugh when I remember you bragging about being an experienced hiker immediately before getting altitude sickness during the Fuji trip. ➢ Kara, thanks for nothing. Ok, jokes aside, I remember my first impression of you was that you were totally unfriendly and someone I should avoid (I didn’t know you had a migraine until afterwards!). But you surprisingly ended up being one of the nicest and easiest-to-talk-to people I’ve ever met. Thanks for taking on the majority of the planning duties! To all the friends I made at USC, thank you so much for all the career advice and memories! To my post-doc mentor, Chris, thank you so much for taking me under your wing and teaching me what it means to be a scientist. Although we didn’t have the opportunity to hang out much, I have always held you in really high regard. To Nathan, you are easily the kindest and most down-toearth guy I have ever met. Thanks for always being there to listen to me complain about the struggles of the PhD. My only regret with you is that I never got to witness the legend of “Strawberry Daiquiri Nathan” for myself. One day. To Jeff, thanks for letting me tag along on all your foodie adventures throughout LA and just being an easy person to talk to and hang out with. Also, thanks for letting me join your fantasy NBA league, it was a reliable source of income every year for me. To Jonathan, I always had trouble gauging how close we were as friends, but your steady presence in lab, especially when we had to stay after-hours, was always very comforting to me. I admire and appreciate your constant willingness to help me (and everyone else, really) despite your busy schedule. To Deborah, out of all the friends I made in grad school, you definitely had the biggest impact on my PhD journey. Thanks for being there to really help me out and drag me out of my shell when I was really struggling to adjust to the PhD lifestyle, which is ironic since v I thought you were soooo mean when we first met. Having you sit behind me at lab was a lot of fun, and thanks for putting up with me constantly turning around and rambling about literally anything that popped into my head. And to all the current members of the Chung Lab, good luck finishing, and always feel free to reach out to me if you need anything! Thank you to my qualifying exam and defense committee, Drs. Gross, Shen, MacKay, and Finley, for your mentorship and guidance, which will no doubt serve me well as I move forward with my career in science. And lastly, to my advisor, Dr. Eun Ji Chung, thank you for truly mentoring me throughout this journey. Your commitment to your career and how you attack life in general is almost terrifying, but has no doubt been very inspiring not just to me, but to all members of the Chung Lab. Thank you for constantly pushing me to achieve more for myself; it means more to me than words can express. vi Table of Contents Acknowledgments.......................................................................................................................ii List of Tables ..............................................................................................................................x List of Figures ............................................................................................................................xi Abbreviations ........................................................................................................................... xiii Abstract................................................................................................................................... xvii Chapter 1. Introduction............................................................................................................... 1 1.1 Cancer Immunology..................................................................................................... 1 1.1.1 Cytotoxic T lymphocytes ....................................................................................... 2 1.1.2 Tumor-associated Macrophages........................................................................... 3 1.1.3 Regulatory T cells ................................................................................................. 6 1.1.4 Myeloid-derived Suppressor Cells......................................................................... 8 1.1.5 Current Immunotherapeutic Strategies.................................................................10 1.2 Peptides for Molecular Targeting of Immunosuppressive Cells ...................................12 1.2.1 Targeting TAMs ...................................................................................................14 1.2.2 Targeting Tregs ...................................................................................................16 1.2.3 Targeting MDSCs ................................................................................................18 1.3 Nanomedicine for Lymph Node-targeting....................................................................20 1.3.1 Introduction to the Lymphatic System ..................................................................20 1.3.2 Lymph Nodes as Targets for Drug Delivery .........................................................22 1.3.3 Physiological Barriers Impose Size Constraints for Lymph Node Targeting .........24 1.3.4 In Vivo Applications for Lymph Node Drug Delivery .............................................31 1.3.5 Hitchhiking Strategies for Lymph Node Drug Delivery..........................................34 1.4 Peptide Amphiphile Micelles as a Nanoplatform for Targeted Immunotherapy, Drug Delivery, and Metastatic Lymph Node Detection in Cancer ...................................................41 1.5 Objective and Aims.....................................................................................................42 1.5.1 Aim 1. Synthesize and Characterize Tumor-targeting and Immunotherapeutic Properties of CCR2-targeted PAMs. ..................................................................................43 1.5.2 Aim 2. Evaluate CCR2-targeted PAMs for Early MRI Detection of Lymph Node Metastasis. ........................................................................................................................43 1.5.3 Aim 3. Evaluate PAM Delivery of HIF2α siRNA to Renal Cell Carcinoma Cells....43 Chapter 2. CCR2-targeted Micelles for anti-cancer Peptide Delivery and Immune Stimulation................................................................................................................................44 2.1 Introduction, Objective, and Rationale .............................................................................44 2.2 Materials and Methods ....................................................................................................46 2.2.1 Materials ...................................................................................................................46 vii 2.2.2 Amphiphile Synthesis................................................................................................46 2.2.3 Self-assembly of Micelles..........................................................................................47 2.2.4 Transmission Electron Microscopy (TEM).................................................................48 2.2.5 Dynamic Light Scattering (DLS) and Zeta Potential Measurements ..........................48 2.2.6 Cell Culture ...............................................................................................................48 2.2.7 In vitro Micelle Binding ..............................................................................................49 2.2.8 MCP-1 and CCR2 mRNA Expression .......................................................................49 2.2.9 In Vitro CCR2 Protein Expression of Cell Lines.........................................................50 2.2.10 In Vitro Cytotoxicity of Micelles................................................................................50 2.2.11 In Vivo Efficacy .......................................................................................................51 2.2.12 Immunohistochemistry of Excised Tumors ..............................................................51 2.2.13 Flow Cytometric Analysis of Excised Tumors..........................................................52 2.2.14 In Vivo Biodistribution of Micelles ............................................................................52 2.2.15 Histology .................................................................................................................52 2.2.16 Serum Analysis of Liver and Kidney Function .........................................................53 2.2.17 Statistical Analysis ..................................................................................................53 2.3 Results and Discussion ...................................................................................................53 2.3.1 Synthesis and Characterization of KLAK-MCP-1 Micelles.........................................53 2.3.2 KLAK-MCP-1 Micelle Binding to Cancer Cells in Vitro...............................................55 2.3.3 KLAK-MCP-1 Micelles Modulate CCR2 and MCP-1 mRNA Expression....................56 2.3.4 In Vitro Cytotoxicity of KLAK-MCP-1 is Dependent on CCR2 Expression..................57 2.3.5 In Vivo Efficacy of KLAK-MCP-1 in a Subcutaneous B16F10 Melanoma Model........60 2.3.6 Immunohistochemical (IHC) Analysis of Tumor Tissue Sections...............................61 2.3.7 Flow Cytometric Analysis of Micelle-treated Tumors .................................................63 2.3.8 In Vivo Biodistribution of Micelles..............................................................................63 2.3.9 Biocompatibility of Micelles in Vivo............................................................................65 2.4 Conclusion.......................................................................................................................67 Chapter 3. MRI Detection of Lymph Node Metastasis through Molecular Targeting of CCR2 and Monocyte Hitchhiking...............................................................................................68 3.1 Introduction, Objective, and Rationale .............................................................................68 3.2 Materials and Methods ....................................................................................................71 3.2.1 Materials and Cells ...................................................................................................71 3.2.2 Synthesis of MCP1-Gd Micelles................................................................................71 3.2.3 Transmission Electron Microscopy (TEM).................................................................73 3.2.4 Dynamic Light Scattering (DLS)................................................................................73 viii 3.2.5 r1 Relaxivity of Micelles .............................................................................................73 3.2.6 Cell Culture ...............................................................................................................74 3.2.7 In Vitro Micelle Biocompatibility.................................................................................75 3.2.8 In Vitro Micelle Binding (Microtiter Plate Reader) ......................................................75 3.2.9 In Vitro Micelle Binding (Confocal Microscopy)..........................................................75 3.2.10 In Vitro Micelle Hitchhiking ......................................................................................76 3.2.11 In Vivo metastatic Lymph Node Mouse Model.........................................................77 3.2.12 In Vivo Lymph Node Recurrence Model..................................................................77 3.2.13 In Vivo Lymph Node Accumulation of MCP1-Gd.....................................................77 3.2.14 MRI Scans ..............................................................................................................78 3.2.15 Histology .................................................................................................................78 3.2.16 In Vivo Monocyte Depletion.....................................................................................79 3.2.17 Flow Cytometry .......................................................................................................79 3.2.18 In Vivo Immunogenicity ...........................................................................................80 3.2.19 Plasma Half-life.......................................................................................................80 3.2.20 Blood Chemistry Markers of Renal and Liver Health ...............................................81 3.2.21 Statistical Analysis ..................................................................................................81 3.3 Results and Discussion ...................................................................................................81 3.3.1 Synthesis and Characterization of MCP1-Gd ............................................................81 3.3.2 In Vitro MCP1-Gd Biocompatibility, Binding, and Hitchhiking ....................................82 3.3.3 In Vivo Targeting of MCP1-Gd in a Metastatic Lymph Node (MetLN) Model .............85 3.3.4 In Vivo Targeting of MCP1-Gd in a Metastatic LN Recurrence (rMLN) Model ...........88 3.3.5 Evaluation of MCP1-Gd Hitchhiking onto Monocytes in Vivo.....................................89 3.3.6 In Vivo Safety and Biocompatibility of MCP1-Gd.......................................................91 3.4 Conclusion.......................................................................................................................93 Chapter 4. CD70-targeted Micelles Enhance HIF2α siRNA Delivery and Inhibit Oncogenic Functions in Patient-derived Clear Cell Renal Carcinoma Cells ..............................95 4.1 Introduction, Objective, and Rationale .............................................................................95 4.2 Materials and Methods ....................................................................................................96 4.2.1 Materials and Cells ...................................................................................................96 4.2.2 Synthesis of HIF2α-CD27 Peptide Amphiphile Micelles ............................................97 4.2.3 Dynamic Light Scattering (DLS) and Zeta Potential ..................................................98 4.2.4 Transmission Electron Microscopy (TEM).................................................................99 4.2.5 Gel Electrophoresis Assay ........................................................................................99 4.2.6 Characterization of siRNA Release ...........................................................................99 ix 4.2.7 Isolation and Culture of Patient-derived ccRCC Cells..............................................100 4.2.8 In Vitro Micelle Binding to Patient-derived ccRCC Cells ..........................................100 4.2.9 IHC Staining and PAM Binding to ccRCC Patient Tissues ......................................101 4.2.10 In Vitro Transfection and mRNA Expression of Patient ccRCC Cells ....................101 4.2.11 MTS Assay ...........................................................................................................102 4.2.12 Glucose Uptake Assay..........................................................................................102 4.2.13 Collection of ccRCC-conditioned Cell Culture Medium and in Vitro Culture and Growth of Endothelial Cells..............................................................................................103 4.2.14 Wound Healing Assay...........................................................................................103 4.2.15 Statistical Analysis ................................................................................................104 4.3 Results and Discussion .................................................................................................104 4.3.1 Synthesis of and Characterization of HIF2α-CD27 PAMs........................................104 4.3.2 HIF2α and Downstream Genes are Upregulated in Patient-derived ccRCC Cells ...106 4.3.3 CD70-targeting Micelles Bind to Patient-derived ccRCC Cells in Vitro ....................108 4.3.4 HIF2α-CD27 PAM Treatment Reduces HIF2α mRNA Expression in Vitro...............110 4.3.5 HIF2α-CD27 PAMs Inhibit in Vitro ccRCC Glucose Transport and Proliferation by Reducing SLC2A1 and CCND1 Expression.....................................................................111 4.3.6 Anti-angiogenic Properties of HIF2α-CD27 PAMs...................................................113 4.3.7 HIF2α-CD27 PAM Treatment Reduces Patient-derived ccRCC Cell Migration and Wound Closure................................................................................................................114 4.4 Conclusion.....................................................................................................................115 Chapter 5. Contributions to Nanomedicine for Cardiovascular Disease ..................................117 5.1 Immunization using ApoB-100 Peptide-linked Nanoparticles Reduce Atherosclerosis...117 5.1.1 Introduction, Objective, and Rationale.....................................................................117 5.1.2 Materials and Methods............................................................................................119 5.1.3 Results and Discussion...........................................................................................123 5.1.4 Conclusion ..............................................................................................................128 5.2 Peptide Amphiphiles Micelles for ROCK2 siRNA Delivery to Th17 T Cells towards the Treatment of Hypertension: Preliminary Results..................................................................128 5.2.1 Introduction, Objective, and Rationale.....................................................................128 5.2.2 Materials and Methods............................................................................................129 5.2.3 Results and Discussion...........................................................................................133 5.2.4 Conclusion ..............................................................................................................136 Chapter 6. Conclusions and Future Work................................................................................138 References .............................................................................................................................140 x List of Tables Table 2.1. Characterization of micelles ....................................................................................55 Table 2-2. CCR2 expression in cancer cells ............................................................................58 Table 2-3. IC50 concentrations of micelle treatment to cancer cells ..........................................59 Table 2-4. Serum ALT, AST, BUN, and creatinine levels in micelle-treated mice.....................66 Table 4-1. ccRCC patient characteristics...............................................................................107 Table 4-2. ccRCC patients have higher HIF2α-related gene expression than HK-2 cells.......108 Table 4-3. mRNA expression of individual ccRCC samples, normalized to HK-2 cells...........108 xi List of Figures Figure 1-1. Cancer progression is mediated by intratumor immunosuppressive cells.............. 3 Figure 1-2. Mechanisms of TAM inhibition of CTL function...................................................... 6 Figure 1-3. Cancer cells promote MDSC formation by preventing myeloid cell maturation ...... 8 Figure 1-4. Peptide targeting of M2-like TAMs....................................................................... 14 Figure 1-5. Treg-targeted AH1 peptide vaccines inhibit CT-26 tumor growth......................... 17 Figure 1-6. Diagram of lymphatic vasculature........................................................................ 21 Figure 1-7. Overview of lymph node structure ....................................................................... 22 Figure 1-8. Physiological barriers to lymph node drug delivery.............................................. 24 Figure 1-9. Diagram of endothelial tight junctions.................................................................. 27 Figure 1-10. Nanoparticle transport from circulation into interstitium ..................................... 29 Figure 1-11. Charged fibers in the ECM inhibit nanoparticle transport................................... 31 Figure 1-12. Micelle-mediated epirubicin delivery to metastatic lymph nodes in vivo............. 33 Figure 1-13. In vivo accumulation of T cell-targeted nanoparticles in tumor-draining lymph nodes..................................................................................................................................... 37 Figure 2-1. KLAK-MCP-1 micelle characterization................................................................. 54 Figure 2-2. In vitro micelle binding to cancer cells ................................................................. 56 Figure 2-3. In vitro micelle effect on gene expression in cancer cells .................................... 57 Figure 2-4. In vitro micelle cytotoxicity to cancer cells ........................................................... 59 Figure 2-5. In vivo micelle efficacy to B16F10 tumor-bearing mice........................................ 61 Figure 2-6. In vivo micelle biodistribution in B16F10 tumor-bearing mice .............................. 64 Figure 2-7. In vivo biocompatibility of micelles....................................................................... 67 Figure 3-1. MCP1-Gd micelles accumulate in metastatic lymph nodes through cancer celltargeting and monocyte hitchhiking........................................................................................ 70 Figure 3-2. Synthesis and characterization of MCP1-Gd....................................................... 84 Figure 3-3. In vivo accumulation of MCP1-Gd in metastatic lymph nodes ............................. 87 Figure 3-4. In vivo accumulation of MCP1-Gd in recurrent lymph nodes ............................... 89 Figure 3-5. In vivo evaluation of monocyte role in MCP1-Gd targeting of lymph nodes ......... 91 Figure 3-6. In vivo immunogenicity and biocompatibility of MCP1-Gd ................................... 93 Figure 4-1. Synthesis and characterization of HIF2α-CD27 PAMs ...................................... 105 Figure 4-2. Baseline mRNA expression of patient-derived ccRCC cells .............................. 107 xii Figure 4-3. In vitro binding of HIF2α-CD27 PAMs to ccRCC patient cell cultures and tissue sections ..................................................................................................................... 109 Figure 4-4. In vitro mRNA expression of HIF2A and CD70 in ccRCC cells following HIF2α-CD27 PAM treatment ................................................................................................ 111 Figure 4-5. In vitro knockdown of CCND1 and SLC2A1 in ccRCC cells following HIF2αCD27 PAM treatment........................................................................................................... 112 Figure 4-6. In vitro knockdown of VEGFA in ccRCC cells following HIF2α-CD27 PAM treatment.............................................................................................................................. 114 Figure 4-7. In vitro knockdown of CXCR4 and CXCL12 in ccRCC cells following HIF2αCD27 PAM treatment........................................................................................................... 115 Figure 5-1. 1H-NMR analysis of diC16 lipid tails.................................................................... 120 Figure 5-2. MALDI-TOF-MS spectra of P210, MSA, and Cy7 amphiphiles .......................... 122 Figure 5-3. Characterization and in vitro dendritic cell uptake of P210-PAMs...................... 125 Figure 5-4. In vivo biodistribution and tissue retention of P210-PAMs and MSA-PAMs ....... 127 Figure 5-5. Synthesis and characterization of GP120-ROCK2 micelles............................... 134 Figure 5-6. In vitro T cell binding and biocompatibility of GP120-ROCK2 micelles .............. 135 Figure 5-7. In vitro siRNA delivery of GP120-ROCK2 micelles ............................................ 136 xiii Abbreviations Acm acetamidomethyl AE adverse event ALT alanine transaminase ANOVA analysis of variance APC antigen-presenting cell AST aspartate transaminase BSA bovine serum albumin BUN blood urea nitrogen CAR chimeric antigen receptor CCL chemokine (C-C motif) ligand CCR C-C chemokine receptor ccRCC clear cell renal cell carcinoma CD cluster of differentiation CLN control or non-metastatic lymph node CMC critical micelle concentration CSF-1R colony-stimulating factor 1 receptor CT computed tomography CTL cytotoxic T lymphocyte CTLA-4 cytotoxic T lymphocyte-associated protein 4 CXCL chemokine (C-X-C motif) ligand DAPI 4’,6-diamidino-2-phenylindole DC dendritic cell DIPEA N,N-Diisopropylethylamine DiR-BOA 1,1′-dioctadecyl-3,3,3′,3′-tetramethylindotricarbo-cyanine iodide bisoleate DLS dynamic light scattering DMF dimethylformamide DMSO dimethylsulfoxide DSPE 1,2-distearoyl-sn-glycero-3phosphoethanolamine DTPA Diethylenetriamine-N,N,N”,N”-tetra-tert-butyl acetate-N’-acetic acid ECG electrocardiogram ECM extracellular matrix EDC 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide EDTA ethylenediaminetetraacetic acid EPR enhanced permeability and retention FA flip angle FAS FS-7-associated surface antigen xiv FBS fetal bovine serum FDA food and drug administration FFPE formalin-fixed paraffin-embedded FITC fluorescein isothiocyanate FLASH flip angle fast low angle shot FOV field-of-view FoxP3 forkhead box P3 GAPDH Glyceraldehyde 3-phosphate dehydrogenase Gd gadolinium GM-CSF granulocyte-macrophage colony-stimulating factor GSH glutathione H&E hematoxylin & eosin HER2 human epidermal growth factor 2 HEV high endothelial venule HIF2α hypoxia-inducible transcription factor 2 alpha HPLC high-pressure liquid chromatography HRP horseradish peroxidase HUVEC human umbilical vein endothelial cell IC50 half-inhibitory concentration ICI immune checkpoint inhibitor ID injected dose IF immunofluorescence IFN-y interferon gamma IHC immunohistochemistry IL interleukin KLAK KLAKLAK LAG-3 lymphocyte activation gene-3 LFA-1 lymphocyte function-associated antigen 1 LLC Lewis lung carcinoma LN lymph node LYVE1 lymphatic vessel endothelial hyaluronan receptor 1 MALDI-TOF-MS matrix-assisted laser desorption ionization time-of-flight mass spectrometry MCP-1/MCP1 monocyte chemoattractant protein 1 M-CSF macrophage-colony stimulating factor MDSC myeloid-derived suppressor cell MEL melitten MEMS multi-echo multi-slice spine echo sequence MetLN metastatic lymph node mouse model MHC major histocompatibility complex MIP-1a macrophage inflammatory protein-1a xv MLN metastatic lymph node MMP-9 matrix metalloproteinase 9 MPS mononuclear phagocytic system MQ milliQ MRI magnetic resonance imaging MTS (3-(4,5-dimethylthiazol-2-yl)-5-(3carboxymethoxyphenyl)-2-(4- sulfophenyl)2H-tetrazolium) NA number of averages NHS mono-N-hydroxysuccinimide NIRF near-infrared fluorescence NK cell natural killer cell NMR nuclear magnetic resonance NOS nitric oxide synthase Nrp1 neuropilin-1 OCT optimum cutting temperature PAM peptide amphiphile micelle PBS phosphate buffered saline PCB poly(carboxybetaine) PD1 programmed cell death protein 1 PDL1 programmed death ligand 1 PEG polyethylene glycol PET positron emission tomography PFA paraformaldehyde PHDCA poly(cyanoacrylate-co-n-hexadecyl cyanoacrylate) PS penicillin-streptomycin rMLN recurrent lymph node metastasis mouse model ROI region of interest ROS reactive oxygen species RT room temperature SCS subcapsular sinus SDF-1 stromal-derived factor-1 SNR signal-to-noise ratio TAA tumor-associated antigen TAM tumor-associated macrophage TCR T cell receptor TE echo time TEM transmission electron microscopy TFA trifluoroacetic acid TGF transforming growth factor TIS triisopropylsilane TKI tyrosine kinase inhibitor xvi TME tumor microenvironment TNF tumor necrosis factor TR repetition time Treg regulatory T cell US ultrasonography USPIO ultrasmall superparamagnetic iron oxide VEGF vascular endothelial growth factor VHL von Hippel-Lindau xvii Abstract Cancer remains the second-leading cause of death in the United States. Despite the over $5 billion invested in cancer research every year, current cancer mortality rates are largely similar to those of a century ago, indicating that novel tools for the treatment and diagnosis of cancer are still needed. Clinical standards of surgical resection and radiotherapy work well for curing localized cancers, but as they are more considered local therapies, are ill-equipped for treating cancers that have metastasized. Systemic chemotherapies are capable of treating metastatic disease but are limited by severe toxicities and low therapeutic efficacy. Recent developments in targeted therapies, in particular immunotherapies, have been met with more positive results in the clinic. Although immunotherapies can be efficacious in some patients, the proportion of patients that actually respond is low, limiting its current potential as a cancer therapy, and also indicating a need for the discovery and validation of newer molecular targets for cancer therapy. In order to meet this need, we propose a nanomedicine approach for the molecular targeting of cancer for therapy and diagnostics. Specifically, we aim to investigate the efficacy of a peptide amphiphile micelle therapy targeted to CCR2, a cell-surface receptor that is known to stimulate cancer cell growth, metastasis, and immunosuppression. Then, we will evaluate the potential of CCR2-targeted nanoparticles as diagnostic tools for the MRI-guided detection of lymph node metastasis. Lastly, as we will demonstrate the versatility of our nanoparticle platform by engineering the micelles to deliver siRNA targeted to HIF2α, a transcription factor that upregulates the expression of genes associated with cancer cell growth, angiogenesis, and migration, and is over-expressed in >70% of patients with clear cell renal cell carcinoma. These studies will demonstrate the therapeutic and diagnostic potential of molecular targeted cancer nanomedicine. 1 Chapter 1. Introduction Cancer represents the second-leading cause of death in the United States, and incidence rates have steadily risen over the past forty years1, 2. Cancer is positioned to remain a major healthcare issue in this country moving forward, as the National Cancer Institute has estimated that 40% of Americans will receive a cancer diagnosis within their lifetimes. While surgical resection and radiotherapy remain the clinical standards in cancer therapies, they are considered local therapies and are ill-equipped for treating metastatic cancers, which have been identified as the cause of over 67% of cancer-related deaths3 . While systemic chemotherapies have been observed to be effective in treating metastatic cancers, their utility is hampered by their non-specific delivery, which leads to dose-limiting toxicities that diminishes treatment efficacy as well as patient quality of life4-6 . Newer targeted therapies, specifically immunotherapies, leverage a growing understanding of the relationship between cancer and immunology to target immune cells that support the propagation and progression of cancer. Before further discussing the efficacy of cancer immunotherapies, an overview of cancer immunology will be provided. 1.1 Cancer Immunology A significant hurdle in the treatment of cancer is attributed to the infiltration of immune-derived immunosuppressive cells, such as tumor-associated macrophages (TAMs), regulatory T lymphocytes (Tregs), and myeloid-derived suppressor cells (MDSCs) into the tumor microenvironment (TME). The recruitment of these cells into the TME has been linked to poor patient outcomes in a variety of cancers, such as skin, prostate, lung, and ovarian cancer7-10 . These cells support cancer progression by suppressing the body's tumoricidal immune response, namely CD8+ cytotoxic T lymphocytes (CTLs), and such immunosuppression is mediated largely 2 through the expression of immune checkpoint molecules, as well as the secretion of antiinflammatory cytokines11. For example, programmed death ligand 1 (PD-L1) and CD80/CD86 are expressed on the surfaces of TAMs and interact with the immune checkpoints programmed death protein 1 (PD-1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), respectively, present on the surfaces of activated CTLs and impairs their potency and proliferation12 . 1.1.1 Cytotoxic T lymphocytes CTLs are a major component of the adaptive immune system, along with B lymphocytes. In contrast to the natural killer (NK) cells, macrophages, and dendritic cells (DCs) that comprise the innate immune system, CTLs must first be primed and expanded against a specific antigen before exhibiting cytotoxicity. Tumor-associated antigens (TAAs) are short peptide sequences (~8–11 amino acids) scavenged from cancer cells by antigen-presenting cells (APCs) that then present these peptides on a complex of surface proteins, called the major histocompatibility complex (MHC)13. Antigen-loaded MHCs are used to activate naïve CTLs through the T cell receptor (TCR), priming them to react specifically to the presented antigen. The activation of CTLs occurs in a costimulatory manner, contingent on the simultaneous activation of the TCR by the MHC, as well as the activation of another surface protein, CD28, by CD80/CD86 also expressed by APCs. Following activation, CTLs undergo clonal expansion in the presence of IL-2 and exhibit cytotoxicity towards antigen-expressing cells. The cytotoxic effector functions of CTLs are mediated by the release of cytotoxins such as granzymes, perforin, and granulysin that induce pore formation and membrane lysis in target cells14. Additionally, CTLs can directly induce apoptosis through cell-cell contact via the expression of the FAS (FS-7-associated surface antigen) death ligand. Following activation, CTLs exhibit downregulated expression of CD28 and upregulated expression of immune checkpoints such as PD-1 and CTLA-4. Interaction of these checkpoints with their associated ligands (PD-L1 and CD80/CD86, respectively) expressed on 3 macrophages, DCs, Tregs, and cancer cells can halt the immune response by impairing effector function and proliferation of CTLs. As CTLs comprise the bulk of the adaptive immune response, re-stimulation of CTL-mediated tumor immunity has been the major focus of the cancer immunotherapy field. 1.1.2 Tumor-associated Macrophages Infiltrating immune cells such as M2-like TAMs, Tregs, and MDSCs adopt suppressive roles in cancer, inhibiting CTL-mediated tumor immunity (Figure 1-1) 15-17. The endogenous functions of M2 macrophages and Tregs are to halt the immune response once an infection has been dealt with, as well as to prevent autoimmunity. However, in the context of cancer, these cells are affiliated with disease progression by inhibiting the CTL-mediated immune response to the disease. MDSCs are a unique sub-population of cells observed only in pathological scenarios, including cancer. The origins, markers, and roles of TAMs, Tregs, and MDSCs in cancer progression will be further discussed. 4 Figure 1-1. Cancer progression is mediated by the infiltration of immunosuppressive cell types into the tumor that aid the developing tumor in avoiding eradication. Adapted and reprinted with permission from the National Cancer Institute. Macrophages are immune cells that phagocytose abnormal cells and foreign invaders, as well as present antigens and secrete immunomodulatory cytokines18. Monocyte-derived macrophages are recruited from the blood stream as undifferentiated monocytes in accordance to inflammatory cues, such as CCL2 (or monocyte chemoattractant protein-1, MCP-1), CCL3 (or macrophage inflammatory protein-1a, MIP-1a), CXCL12 (or stromal-derived factor-1, SDF-1), and CX3CL1 (or fractalkine)19-24 . Depending on the immunological landscape into which these monocytes extravasate, they may differentiate into the pro-inflammatory M1 phenotype or anti-inflammatory M2 phenotype macrophage. M1 macrophages are polarized by lipopolysaccharides and Th1 cytokines, such as interferon gamma (IFN-y) and granulocyte-macrophage colony-stimulating factor (GM-CSF)25. They are associated with the killing of pathogens and release of proinflammatory cytokines (TNF-α, CCL3, IL-6, IL-12), making them tumoricidal26-28. M2 macrophages, on the other hand, are activated by Th2 cytokines like IL-4 and IL-1329, 30 and associated with extracellular matrix remodeling, as well as anti-inflammatory and immunosuppressive cytokine secretion (IL-10, TGF-β) that serves to support tumor growth31. As M1 and M2 macrophages serve different functions in the immune regime, discrimination between the two can be done by evaluating metabolic output. For example, the disparity in macrophage function is elegantly demonstrated in the metabolism of the amino acid arginine. M1 macrophages metabolize arginine through nitric oxide synthase to produce nitric oxide, which diffuses through the membranes of target cells and mediates cytotoxicity32. On the other hand, M2 macrophages have been observed to utilize arginase to convert arginine into ornithine, an amino acid involved in the urea cycle that promotes cell proliferation and tissue repair by stimulating the generation of matrix proteins, such as collagen33, 34. TAMs typically fall under the M2 classification and play a major role in cancer progression, as they are linked to poor patient outcomes35. Studies have 5 shown that infiltration of TAMs into the tumor and surrounding microenvironment is mediated by chemical signaling from cancer cells, which recruit undifferentiated monocytes from the peripheral blood largely through the MCP-1 signaling axis36. Cancer-secreted IL-4 and IL-13 then promote the polarization of these infiltrating monocytes towards the M2 phenotype37 . TAMs have been observed to modulate the TME by actively inhibiting CTL responses (Figure 1-2). This is done primarily through the expression of inhibitory ligands on the cell surface, as well as the release of immunosuppressive cytokines that impair CTL expansion and function. TAMs also play a role in the recruitment of immunosuppressive Tregs, whose function and role in contributing to the immunosuppressive microenvironment will be discussed further in later sections. TAMs express many ligands that interact with immune checkpoints displayed on the surfaces of CTLs, resulting in CTL suppression. For example, TAMs express PD-L1, which binds to PD-1 receptors that populate the surfaces of activated CTLs, down-regulating their function and proliferation38, 39. TAMs also express the CD80 and CD86 ligands, which can be immunostimulatory or immunosuppressive, depending on which receptor they interact with40 . CD80/CD86 can interact with CD28 for co-stimulation of naive CTLs. However, these ligands have a much stronger affinity for the CTLA-4 on activated CTLs41. CTLA-4 is expressed at very low levels in naïve CTLs, but its expression becomes up-regulated upon activation/priming of these cells42, 43. Thus, CTLA-4 can act as a braking mechanism, allowing CD80-and CD86- expressing TAMs that have been attracted to the site by CTL effector activity, to shut down the immune response before it can eliminate the cancer44. This dynamic is often exploited to prematurely suppress CTL function before the disease is cleared, as in the case of cancer with the heavy infiltration of CD80+ /CD86+ TAMs. 6 Figure 1-2. TAMs inhibit effector function of T cells through the expression of inhibitor ligands such as PD-L1, B7 (CD80/CD86), and prostaglandins, secretion of T cell-suppressing IL-10 and TGF-β, and metabolic starvation through arginine depletion. Adapted and reprinted with permission from ref.37 . 1.1.3 Regulatory T cells Tregs comprise an important sub-type of CD4+ T cells that serve to maintain immunogenic self-tolerance and suppress adaptive immune responses after the clearance of foreign bodies45 . Disruption of the balance between CTLs and Tregs has been observed in many pathologies, including cancer. In the case of autoimmune diseases such as type I diabetes, multiple sclerosis, and rheumatoid arthritis, the failure of Tregs to suppress CTLs leads to autoimmunity46. On the opposite end of the spectrum, hyper-activity of Tregs renders the CTL population incapable of neutralizing pathogens or tumors. It is this upregulation in Treg activity that allows cancers to avoid eradication by the CTL response. Tregs originate as naïve CD4+ T cells that continue to mature in the thymus until TCR activation and forkhead box P3 (FoxP3) expression promotes a suppressive phenotype47. Tregs can be identified by their expression of CD4, CD25, CTLA-4, lymphocyte activation gene 3 (LAG3), neuropilin-1 (Nrp1), and FoxP3 48, 49. Tregs are able to suppress activated CTL function directly 7 through contact-dependent inhibition, as well as through the release of regulatory cytokines. Tregs are attracted to sites of inflammation and activated via IL-2, an inflammatory cytokine secreted by active CTLs50. Since IL-2 is also necessary for the activation and expansion of CTLs, the high expression of the IL-2 receptor, IL-2R, on Tregs allows them to rapidly deplete the surrounding microenvironment of IL-2, inhibiting CTL activation. Tregs may also inhibit effector function more directly through contact-dependent mechanisms. Nakamura et al. has shown that the expression of TGF-β1 on the surfaces of Tregs contributes to immunosuppression in a contact-dependent manner, although the biomolecular mechanisms governing this process are yet unknown51. Tregs also express LAG3, another immunosuppressive cell surface ligand with an affinity for MHC class II and CD4. Although LAG3 is an inhibitory receptor primarily associated with CTLs, recent studies have shown it to be vital to the immunosuppressive function of Tregs, although the cause for this remains to be elucidated52. This phenomenon in which LAG3 is associated with inhibition in one CTLs but associated with the activation of another (Tregs), has been observed in other cell-surface markers, such as PD-1 and CTLA-4. Immunosuppressive cytokines secreted by Tregs also play a role in down-regulating the immune response. These regulatory cells have been observed to release high levels of soluble TGF-β1 and IL-10, both of which have negative effects on CTL activity and proliferation. TGF-β1 acts by inhibiting the activation of the TCR complex necessary for CTL expansion53. IL-10 operates by inhibiting the phosphorylation of CD28, the surface protein complementary to the TCR in the co-stimulatory pathway of CTL activation54 . The induction of cancer is oftentimes accompanied by the establishment of an immunosuppressive environment spearheaded by the recruitment and activation of naïve T lymphocytes into Tregs. Similar to TAM recruitment, Tregs follow a chemotactic gradient established by cancer cells, based largely on the CCL22 chemokine55. Growth factors like TGFβ and IL-10 stimulate the rapid proliferation of Tregs in the TME56. TGF-β plays an additional role 8 by promoting the conversion of non-suppressive CD25− T lymphocytes into the suppressive CD4+ , CD25+ , FoxP3+ Treg phenotype. The trafficking of Tregs into a TME that stimulates their expansion results in a synergistic cycle in which cancer cells and Tregs promote the other's growth and proliferation, establishing a heavily immunosuppressive environment. 1.1.4 Myeloid-derived Suppressor Cells MDSCs are immunosuppressive myeloid cells found in TMEs that have been linked to poor patient prognoses57. In a healthy individual, MDSCs do not exist. These cells, unique to pathological conditions such as cancer, are derived from myeloid progenitor cells whose differentiation into mature myeloid lineages has been inhibited (Figure 1-3) 58. Normally, immature myeloid cells travel from the bone marrow to peripheral organs, where they quickly mature into macrophages, dendritic cells, or granulocytes. However, in cancer, the differentiation of these immature cells is inhibited by various signaling factors present in the tumor, such as GM-CSF, macrophage colony-stimulating factor (M-CSF), IL-6, IL-10, and TNF-α 59, 60. Furthermore, these factors also induce the activation of immature myeloid cells into an immunosuppressive phenotype. These immature, immunosuppressive myeloid cells are dubbed MDSCs. 9 Figure 1-3. Cancer cells inhibit the maturation of immature myeloid cells into dendritic cells, macrophages, and granulocytes, and instead promote their activation into MDSCs. Adapted and reprinted with permission from ref.58 . MDSCs do not have an MDSC-specific biomarker. Rather, they express biomarkers indicative of the myeloid lineage, such as CD11b and CD33, but lack markers of fully differentiated cells. Utilizing these criteria, MDSCs are generally characterized by the CD11b+CD14−CD33+ biomarker profile in humans and CD11b+Gr1+ profile in mice61. MDSCs are recruited into the TME via chemokine secretion. In particular, CCL2, CXCL8, and CXCL12 produced by cancer cells have been implicated in the majority of MDSC trafficking62 . Immunosuppression by MDSCs is mediated by reactive oxygen species (ROS) and cytokine production, as well as arginine depletion. In a study by Corzo et al., increased generation of ROS in MDSCs led to DNA damage and apoptosis in CTLs63. ROS also downregulates CTL activity by interfering with their ability to recognize antigens through the TCR. For example, superoxide (O2 − ) can react with nitric oxide (NO) to form peroxynitrite (ONOO− ), a species observed to inhibit CTL activity by inducing apoptosis and inhibiting phosphorylation pathways that govern the proper formation of the TCR64. Metabolic starvation of CTLs is another means through which MDSCs can inhibit the immune response. Arginine is an amino acid essential for protein synthesis in and expansion of CTLs65. MDSCs express nitric oxide synthase (NOS) and arginase, two major metabolizers of arginine, at high levels, severely lowering the amount of arginine available to CTLs, impairing their ability to proliferate and control cancer growth66 . In addition, MDSCs are capable recruiters of other immunosuppressive cells. Adoptive cell transfer studies from Huang et al. show that IL-10 and TGF-β by MDSCs are necessary for the induction of Tregs and their associated immunosuppression67. Additionally, Sinha et al. cocultured MDSCs with M1 macrophages and showed polarization toward a tumor-promoting M2 phenotype in a contact-dependent manner68. This observation was marked by a reduction in the 10 release of T cell-stimulatory IL-12 by the macrophages. Moreover, their studies revealed that depletion of MDSCs through the use of the chemotherapy drug gemcitabine restored IL-12 production and tumor immunity. As described above, immunosuppressive cells are not segregated cell populations individually contributing to cancer progression. Rather, they interact with each other and the surrounding environment in a synergistic and interdependent manner. There is much overlap in the biochemical signals, particularly chemokines, that they are reliant on, which makes these signaling molecules potential targets for immunotherapy. 1.1.5 Current Immunotherapeutic Strategies The field of immunotherapy has been developed in an effort to stimulate the CTLs that are suppressed in cancer. Although the mechanisms behind cancer-driven immunosuppression have only been discovered recently, the link between immune stimulation and therapy has been empirically observed as far back as the nineteenth century. The first study documenting this concept occurred in 1868 by Wilhelm Busch, in which he observed tumor regression following a bacterial infection mediated by Streptococcus pyogenes. Twenty-five years later, William Coley published a report corroborating the efficacy of bacterial infections in treating cancer in 10 different patient cases69. A bacterial vaccine including this strain was named “Coley's toxin” in homage of his work and was used for several decades as an anti-cancer remedy. The concept of immunotherapy was set aside in favor of small-molecule cell cycle inhibitors and radiotherapies until the 1990s when advances in immunology identified the crucial role of immune cells in controlling cancer growth70, 71 . The use of monoclonal antibodies as immune checkpoint inhibitors comprises most immunotherapies, with the first successful pre-clinical application reported in 1996 by Allison et 11 al. 72. Results from a clinical trial utilizing a CTLA-4-targeted monoclonal antibody (ipilimumab) was published in 2010, showing improved survival in patients with metastatic melanoma compared to the standard of care gp100 peptide vaccine (10.1 months vs. 6.4 months)73. These results led to ipilimumab gaining FDA-approval the following year under the trade name Yervoy for use in metastatic melanoma74. Not long after, the FDA also approved two PD-1 immune checkpoint inhibitors, pembrolizumab/lambrolizumab (Keytruda) and nivolumab (Opdivo), for melanoma, non-small cell lung cancer, and renal cell carcinoma75, 76. Additionally, an anti-PD-L1 monoclonal antibody, atezolizumab (Tecentriq), was approved in 2016 for use in bladder cancer, and then again in 2019 for small cell lung cancer and triple-negative breast cancer. Notably, the 2018 Nobel Prize in Physiology or Medicine was jointly awarded to Allison and Honjo, researchers who first demonstrated the efficacy of CTLA-4 and PD-1 immunotherapies77 . While immune checkpoint inhibitors function by preventing the premature shut-down of the immune response, other immunotherapies focus on assisting the priming of CTLs to mount a greater immune response. Peptide vaccines have been explored in both pre-clinical models and clinical trials78, 79. The purpose of peptide vaccines is to synthesize a peptide sequence identical to the TAAs presented on cancer cells and deliver it to CTLs to increase their activation and priming against cancer cells expressing these antigens. This concept can be extrapolated to engineer CTLs in vitro that express chimeric antigen receptors (CARs) that have antigen-binding and T cell-activating moieties (CAR T cells)80. CAR T cells are generated by adoptive cell transfer, in which autologous T lymphocytes are taken from the patient, engineered to express CARs, primed against a patient-specific antigen, expanded in vitro, and re-introduced into the patient81, 82 . Although both peptide vaccines and adoptive cell therapies have shown clinical efficacy, they are not without limitations. Both treatment options require the expression of specific TAAs by the cancer cells, but cancer cells can rapidly evolve to downregulate or even eliminate their 12 expression of TAAs83. Additionally, peptide vaccines are limited by their weak immunogenicity and instability in vivo, as they are prone to degradation by proteases84. Moreover, CAR T cell therapy is hindered by drawbacks inherent to the procedure of adoptive cell therapy, including a limited amount of autologous T cells derived from patients that is necessary for the procedure85 . Although these immunotherapies have shown clinical efficacy, their drawbacks have pushed researchers to investigate other alternatives. An alternative and promising immunotherapeutic approach is to target and deliver therapeutic agents such as peptides, monoclonal antibodies, and nucleic acid aptamers to immunosuppressive TAMs, Tregs, and MDSCs86-89. In particular, peptides are strong candidates for immunotherapy and have been used in a variety of studies targeting immunosuppressive cells, as they possess a number of attractive qualities, such as biocompatibility, cost-efficiency, and versatility as both targeting moieties and therapeutic agents90, 91. However, peptides are limited by their poor stability in vivo, as they are vulnerable to degradation by proteases present in the serum and tissues. Nanoparticle systems are often used to circumvent this issue, allowing the safe delivery of peptides to target cells. Furthermore, nanoparticles functionalized with peptides exhibiting specificity for immunosuppressive cells have been used to manipulate these small cell populations, even though they sit within a highly heterogeneous microenvironment. 1.2 Peptides for Molecular Targeting of Immunosuppressive Cells Peptides are strong candidates for immunotherapy, as they are capable of binding to and inducing responses from target cells/receptors on a highly specific level. Furthermore, recent advances in peptide chemistry have made their synthesis faster, cost-effective, and more convenient92. Additionally, peptides can be incorporated into nanoparticle systems, enhancing their stability in vivo and allowing for their inclusion in multimodal therapies23, 93-95 . For example, nanoparticles may be functionalized with peptides to facilitate interaction with the cell 13 membrane and mediate endocytosis96. Incorporation into nanoparticles also serves to enhance the therapeutic efficacy of peptides by concentrating them into compact nanoparticles97. This is generally done through the engraftment of peptide moieties onto a nanomaterial substrate through chemical conjugation. For example, a commonly used conjugation strategy is to create an amide linker between the peptide and nanoparticle substrate by using 1-ethyl-3-(3- dimethylaminopropyl)carbodiimide (EDC) to react the free amines found in the N-termini of peptides with nanoparticle substrates functionalized with carboxylic acid groups98-100 . As peptides also have free carboxylic acids displayed on their C-terminus, this same strategy can be leveraged to conjugate peptides to amine-terminated nanoparticles101. Another means of directly conjugating peptides to nanoparticle substrates is to terminate the peptide sequence with a sulfurrich cysteine group and react with a maleimide-functionalized nanoparticle102. High-affinity, noncovalent interactions may also be used to link peptides to nanoparticle substrates. For example, biotinylated peptides can be used to associate closely to nanoparticles displaying streptavidin moieties103. Moreover, electrostatic interactions can be tuned to achieve the desired interaction between peptide and nanoparticle as reported by Blank-Shim et al. who used a strongly positivelycharged arginine homo-peptide (isoelectric point of 11.15) to bind to negatively-charged magnetic nanoparticles104, 105 . Confirmation of nanoparticle functionalization can be obtained through characterization of nanoparticle properties via dynamic light scattering (DLS) and transmission electron microscopy (TEM) to examine nanoparticle size, morphology, and polydispersity before and after peptide conjugation106, 107 . Additionally, nanoparticle zeta potential is often characterized to examine differences in surface charge conferred by peptide functionalization108, 109. Shifts in nuclear magnetic resonance (NMR) and circular dichroism (CD) spectra are also used to confirm peptide conjugation, as well as examine peptide secondary structure110 . 14 1.2.1 Targeting TAMs Given the immunosuppressive roles of TAMs and their negative correlation to cancer prognoses, a variety of novel peptides have been developed to target not only the macrophages themselves, but also their monocyte precursors, as well as the biochemical pathways that facilitate their induction and pathological behavior. Cieslewicz et al. developed and validated a novel M2 macrophage-targeting peptide, M2pep (YEQDPWGVKWWY), through phage display111 . The targeting ability of M2pep was determined through injection of Alexa Fluor 660-tagged M2pep into the intraperitoneal cavity of mice. Harvesting of intraperitoneal cavity cells, as well as those from the spleen, show the ability of M2pep to target F4/80+ , CD301+ , CD11c+ M2 macrophages in a mixed population of cells including B cells, T cells, and neutrophils (Figure 1-4A and 1-4B). Moreover, M2pep exhibited increased binding compared to the non-targeting (scrambled peptide) control. Figure 1-4. Targeting of M2-like TAMs by M2pep and scM2pep in the peritoneal cavity (A) and spleen (B). Adapted and reprinted with permission from ref.111 . A number of other studies have utilized the same M2pep sequence for TAM targeting and observed positive results. For example, M2pep-functionalized poly (lactic-co-glycolic acid) PLGA nanoparticles loaded with CSF-1R (colony-stimulating factor 1 receptor) inhibitors were used to block proliferative and pro-survival signaling in TAMs in a B16F10 murine melanoma model. After 15 tumors became palpable (170 mm3 size), treatment was administered every two days for ten days, showing an approximately 50% decrease in tumor growth rate compared to free inhibitor112. In addition, Qian et al. linked the M2pep to apolipoprotein A1-mimetic α-helical peptide (α-peptide) to form α-M2pep113. The α-peptide moiety was included to target scavenger receptor B type 1 (SR-B1), a surface receptor highly expressed in TAMs. The addition of phospholipids induced self-assembly into lipid nanoparticles in which a hydrophobic core was surrounded by the two peptide moieties to facilitate interaction with TAMs. The resultant nanoparticle was then loaded with a cholesterol-modified anti–CSF–1R siRNA (siCD115) to interfere with pro-tumor CSF-1 signaling, forming M2NP-siCD115s. In order to evaluate M2 TAM-targeting, M2NPs (no siRNA) loaded with the near-infrared fluorescent dye DiR-BOA (1,1′-dioctadecyl-3,3,3′,3′- tetramethylindotricarbo-cyanine iodide bisoleate), as well as NPs incorporating a scrambled (nontargeting) peptide sequence, were incubated with M2 macrophages for 1 h, observing a 7.5-fold increase in M2 uptake of M2NPs compared to the scrambled control. Additionally, in vivo studies using the B16 murine melanoma model revealed an 87% decrease in tumor size of mice treated with M2NP-siCD115s every 2 days, compared to a saline-treated control. Phage display experiments performed by Scodeller et al. identified a short peptide sequence, CSPGAKVRC (dubbed “UNO”), capable of targeting the M2 macrophage-specific CD206 surface marker114. The targeting capability of UNO was validated through injection of fluorescently labeled UNO into 4T1 tumor-bearing mice. Mice were sacrificed, and their organs were harvested 2 h post-injection. Confocal microscopy of harvested tissues shows high colocalization of UNO and CD206, with 96% of UNO positive cells also being positive for CD206 staining. Additionally, Lee et al. identified melittin (MEL), a 26-amino acid peptide found in the venom of honeybees, as a CD206-targeting sequence115, 116. MEL is an amphipathic peptide that has been studied as an anti-cancer drug for its ability to induce apoptosis in cancer cells through mitochondrial pore formation, as well as inhibition of angiogenesis via the downregulation of VEGF expression117 . 16 The authors investigated the anti-tumor efficacy of both MEL alone and MEL fused with the cytotoxic (KLAKLAK)2 peptide (KLA) (MEL-KLA) by administering MEL or MEL-KLA to a Lewis lung carcinoma (LLC) mouse model. Treatment began 5 days after tumor inoculation and consisted of an injection every 3 days until the mice were sacrificed 12 days after inoculation. Results showed a significant decrease in tumor weight upon treatment of MEL-KLA compared to a PBS-treated control, as well as KLA and MEL monotherapies. To further investigate the M2- targeting ability of MEL, another study from this group showed an increase of M1/M2 macrophage ratio from 0.65 to 1.55 in an LLC model upon MEL treatment. The M1/M2 ratio is an increasingly used metric that has been reported to be positively correlated to prognosis in human cancers118 . The change in M1/M2 ratio was attributed to cytotoxicity of MEL to M2 macrophages, validated through the reduction of M2 macrophage marker CD206 in flow cytometry and qPCR. 1.2.2 Targeting Tregs The neuropilin-1 (Nrp1) receptor was first identified as a potential Treg marker in 2004 by Bruder et al.49. Originally identified as a co-receptor for VEGF, Nrp1 has now been identified as essential for Treg function. Its expression on these T cells has been correlated to FoxP3 expression, implicating its role as a mediator of the immunosuppressive phenotype119. Moreover, a recent study by Delgoffe et al. has identified its role in maintaining Treg stability, as destabilization of this population through the upregulation of Nrp1 is a common characteristic of cancers120. Unsurprisingly, preclinical studies of Nrp1 involving genetic knockout models and antibody blockades have resulted in decreased tumor growth in skin, lung, and prostate cancers121. The discovery of an Nrp1 peptide, LyP-1 (CGNKRTRGC), has led to the development of NP systems targeting Tregs specifically through Nrp1122, 123. LyP-1 is part of a family of peptides, denoted as C-terminal C-end Rule (CendR) motif peptides, which share tumor penetrative ability. In a study by Ou et al., a nanoparticle incorporating a PLGA core loaded with anti-CTLA-4, the 17 tyrosine kinase inhibitor imatinib (IMT), and LyP-1 as a targeting ligand, significantly reduced tumor growth in a B16 murine melanoma model compared to conventional anti-CTLA-4 immunotherapy. Mice were treated every 2 days for 15 days, starting 10 days post-tumor inoculation with either PBS, free IMT, free anti-CTLA-4, non-targeting IMT-loaded nanoparticles, targeting IMT-loaded nanoparticles, and targeted anti-CTLA-4- and IMT-loaded nanoparticles. The full nanoparticle incorporating LyP-1, IMT, and anti-CTLA-4 proved most effective, decreasing tumor volume by over 50% compared to free anti-CTLA-4, demonstrating the potential of peptide-based nanoparticles to augment the clinical standard121 . Another Treg-targeting nanoparticle was created by Pastor et al. through conjugation of a FoxP3-inhibiting peptide (P60) to a CD28-targeting aptamer (AptCD28-P60)124, 125. The conjugated molecule was shown to be capable of countering Treg-mediated immunosuppression of CTLs at a 200-fold lower concentration to unconjugated P60 (100 μM vs. 0.5 μM). Furthermore, treatment of CT-26 carcinoma models with AptCD28-P60 in conjunction with a tumor antigen peptide vaccine (AH1) was shown to eradicate tumors compared to saline control, peptide vaccine alone, peptide vaccine and unconjugated P60, and peptide vaccine and unconjugated P60 with unconjugated AptCD28, showing the necessity in conjugating P60 to AptCD28 in achieving synergistic therapy (Figure 1-5). Figure 1-5. Tumor volume of CT-26 carcinoma following treatment of saline, AH1 peptide vaccine, AH1 + unconjugated P60 FoxP3-inhibiting peptide, AH1 + unconjugated P60 + unconjugated CD28Apt, or 18 AH1 + conjugated CD28Apt-P60 show prevention of tumors upon vaccination of AH1 + CD28-Apt-P60. Injections were given at a dose of 125 pmol every 2 days for 10 days before tumor inoculation. Adapted and reprinted with permission from ref.125 . 1.2.3 Targeting MDSCs Given MDSCs’ status as an immature cell population, they have not been found to express an MDSC-specific biomarker that can be used for peptide binding. However, Qin et al. has used phage display to screen for MDSC-binding peptides126. The selected peptide (MEWSLEKGYTIK) was fused with the Fc region of murine IgG2b to create a peptide-antibody conjugate (H6 peptibody). Treatment with H6 peptibody led to the depletion of MDSCs from the spleen and in circulation in multiple murine lymphoma models (EL4, EG.7, A20), and exhibited antitumor efficacy in an EL4 model. In addition, Wang et al. investigated the effectiveness of chemokine blockade as a means of indirectly affecting MDSC populations127. They used a CCL2 agonist to inhibit CCL2 signaling, a major chemoattractant implicated in MDSC migration128, 129. This resulted in significantly decreased MDSC infiltration into the TME of a lung cancer model. Notably, it was observed that CCL2 blockade also improved the efficacy of anti-PDL1 treatment, likely via MDSC depletion. Advancements in understanding the interactions between immunosuppressive cells and cancer cells in establishing and maintaining an immunosuppressive microenvironment have been made in recent years. Furthermore, there have been strides in the application of this knowledge into peptide-based therapies as described herein. The application of peptides and nanomedicine targeting immunosuppressive cells for cancer immunotherapy, however, is only in its early stages. Consequently, clinical translation of these technologies for human use has been limited. This may be due to the lack of understanding regarding the peptide nanoparticle interactions with the biological milieu and the formation of a protein shell that alters the nanoparticle fate, efficacy, and toxicity130, 131. Another challenge includes scaling up peptide-functionalized nanoparticles, as well 19 as batch-to-batch variation, limiting commercialization potential. Moreover, before immunotherapeutic targeting of immunosuppressive cells can be translated to the clinic, a greater understanding of the body's immunologic network needs to be achieved. For instance, although the interactions of TAMs, Tregs, and MDSCs with the classical CTL have been well-studied, other immune cells, such as NK cells, B lymphocytes, and dendritic cells, all have unique and complex sets of functions and interactions that merit further study. Peptide-based nanoparticles are tools that offer researchers and clinicians a means of targeting specific molecular pathways and intercellular interactions that contribute to cancer progression, but it is crucial to expand our understanding of immunology to utilize these tools effectively. Furthering our knowledge with regards to the immunological network will allow researchers to not only better develop novel nanoparticle systems, but also better predict their efficacy in vivo. A major shortcoming in our knowledge of cancer immunotherapy lies in the identification of predictive biomarkers for clinical response to treatment. Although there have been cases in which immunotherapy has eradicated chemotherapy-resistant tumors, the reality is that only a minority of patients respond to treatment. The inconsistency in patient response can be attributed to the heterogeneity of cancer but also to the lack of predictive biomarkers12. Although some markers, such as immune checkpoints and immunomodulatory cytokines, have been identified, the discovery of other prognostic markers can serve to optimize patient selection, as well as further the understanding of the mechanisms behind immunotherapy132. Furthermore, the use of these biomarkers in combination can hold more accurate predictive power. Nanomedicine makes the use of combinatorial therapies more feasible, as nanoparticles can be engineered to include multiple biologically active moieties. Additionally, packaging of bio-active materials into nanoparticles ensures they are delivered to their desired targets, reducing off-target delivery and toxicity. Many of the studies highlighted here have demonstrated that combination therapies can improve the efficacy and safety of mono-therapeutic clinical standards. As scientific 20 research continues to fill in the knowledge gaps discussed above, peptide-based nanoparticles will continue to develop and comprise a growing class of nanomaterials capable of targeting immunosuppressive cell populations. 1.3 Nanomedicine for Lymph Node-targeting The lymph nodes are major sites of cancer metastasis and immune activity, and thus represent important clinical targets. Although not as well-studied compared to subcutaneous administration, intravenous drug delivery is advantageous for lymph node delivery, as it is commonly practiced in the clinic and has the potential to deliver therapeutics systemically to all lymph nodes. However, rapid clearance by the mononuclear phagocyte system, tight junctions of the blood vascular endothelium, and the collagenous matrix of the interstitium can limit the efficiency of lymph node drug delivery, which has prompted research into the design of nanoparticle-based drug delivery systems. Thus, it is imperative to understand these physiological barriers when designing a nanoparticle system for lymph node delivery. 1.3.1 Introduction to the Lymphatic System The lymphatic system is a network of organs, including the thymus, bone marrow, spleen, tonsils, and lymph nodes, interconnected by lymphatic vessels through which lymph flows. The primary functions of the lymphatic system are to maintain fluid homeostasis and regulate the adaptive immune response. Approximately 10% of blood plasma leaks out of the blood vessel capillaries and enters the interstitial space, with approximately 5-8 liters of plasma pushed into the interstitium each day133, 134. As shown in Figure 1-6, this fluid enters the lymphatic system through highly permeable lymphatic capillaries, which feed the fluid, now called lymph, into the larger lymphatic vessels. Since there is no central pump in the lymphatic system, fluid flow in 21 lymphatic vessels is generated by the contraction and relaxation of the surrounding tissues. Oneway valves in lymphatic vessels ensure that lymph flows unidirectionally from the lymphatic capillaries to the subclavian veins, where lymph mixes with venous blood and re-enters the cardiovascular system135. Hence, one major function of the lymphatics is to maintain fluid balance in the circulatory system136, 137 . Figure 1-6. Lymphatic vessels drain interstitial fluid secreted by blood vessels and tissues. Lymph flows unidirectionally toward the subclavian veins, where it mixes with venous blood and re-enters circulation. Another important function of the lymphatic system is immune surveillance138, 139. As lymph flows from the lymphatic capillaries toward the subclavian veins, it passes through several lymphoid organs called lymph nodes, which are densely populated with immune cells, such as macrophages, dendritic cells, B cells, and T cells140. As shown in Figure 1-7, lymph enters the lymph node through the afferent lymphatic vessels and flows through the node via the subcapsular sinus into the medullary sinus and out through the efferent lymphatic vessels141. The lymph node is home to several populations of resident immune cells, such as subcapsular sinus (SCS) macrophages and dendritic cells (DCs) that line the subcapsular sinus or the conduits 22 formed by the fibrous matrix inside the node. Their primary function is to sample incoming lymph for antigens to present to naïve B cells and T cells to initiate the adaptive immune response142-144 . Circulating B cells and T cells use an array of cell surface proteins such as CCR7 (C-C chemokine receptor 7), LFA-1 (lymphocyte function-associated antigen 1), and L-selectin to migrate into the lymph nodes through specialized blood vessels called high endothelial venules (HEVs) that innervate the lymph node, where they undergo clonal expansion in their respective zones following antigen exposure145 . Figure 1.7. Structure of the lymph node. Lymph enters via the afferent lymphatic vessels and exits via the efferent lymphatic vessels. Antigens from the lymph are sampled by node-resident immune cells such as lymphatic sinus associated dendritic cells (LS-DCs) and subcapsular sinus (SCS) macrophages and presented to B cells and T cells to generate an immune response. Antigen-naïve B cells and T cells enter the lymph node through specialized blood vessels called high endothelial venules (HEVs) and undergo expansion following antigen exposure in the B cell follicles and T cell zones, respectively. Reprinted with permission from ref. 141 . 1.3.2 Lymph Nodes as Targets for Drug Delivery Because there is a rich population of immune cells within the lymph nodes, especially the effector cells of adaptive immunity (B cells and T cells), these lymphoid tissues have been 23 fervently studied in the development of immunotherapeutic strategies146-152 . The lymph nodes also represent an important clinical target for chemotherapeutic drug delivery. In addition to being a primary site for lymphoid cancers such as lymphoma, the lymph nodes are the most common site of metastases for solid cancers, as an estimated 80% of cancer metastasis occurs through the lymphatics 153-156. Lymphatic vessels are prone to cancer cell invasion for several reasons. Firstly, lymphatic vessels are highly permeable especially in the initial lymphatics, which lack a complete basement membrane. Additionally, endothelial cells in the initial lymphatics are joined via discontinuous button-like junctions of vascular endothelial cadherins spaced 3 μm apart, which is in contrast to endothelial cells in downstream vessels that are joined via continuous zippers157-162 . Secondly, tumor cells secrete lymphangiogenic factors like VEGF-C and VEGF-D that stimulate the formation and dilation of lymphatic vessels near the tumor163. Thirdly, the lower rate of fluid flow in lymphatic vessels compared to blood vessels (0.4 dyne/cm2 vs. >30 dyne/cm2 ) results in less fluid shear stresses experienced by circulating cancer cells in lymphatic vessels, increasing their likelihood of survival164-169 . Early preclinical studies for lymph node drug delivery have focused on subcutaneous drug administration, as this route circumvents clearance by the mononuclear phagocyte system that patrols the blood vasculature170-172. Comparatively, intravenous drug delivery to the lymph nodes has been less explored. However, intravenous drug delivery to the lymph nodes holds great clinical potential, because it can deliver drugs systemically to all lymph nodes, while subcutaneous injection concentrates drugs to lymph nodes downstream of the injection site and provides a local effect173, 174. Despite the clinical significance of the lymph nodes as a therapeutic target, intravenous drug delivery to the lymph nodes has challenges, such as the monocyte phagocyte system (MPS), blood vessel endothelium, and extracellular matrix (ECM) of the interstitium171, 175, 176 . 24 1.3.3 Physiological Barriers Impose Size Constraints for Lymph Node Targeting Once encapsulated into a nanoparticle, the in vivo fate of therapeutic drugs is determined by the carrier’s interaction with plasma as well as the cellular and biological environment. Size and surface charge are two key physical parameters that influence a nanocarrier’s interaction with its environment as well as its biodistribution following intravenous injection174, 177-191. Notably, rational design of nanoparticle size and charge can be leveraged to bypass certain physiological barriers and instruct nanoparticle accumulation into specific tissues like the lymph nodes192-194. As shown in Figure 1-8, nanocarriers designed for lymph node delivery must (1) avoid clearance from the bloodstream by the MPS, (2) extravasate out of fenestrated blood vessels, and (3) traverse the extracellular matrix of the interstitium195. Hence, a deep understanding of each of these physiological barriers and the size and charge constraints they place on nanoparticle design are critical to develop successful nanocarrier systems to the lymph nodes. Figure 1-8. To reach the lymph nodes, nanoparticles must avoid clearance from the blood stream by the mononuclear phagocyte system, extravasate out of the blood vessel endothelium, and diffuse past the extracellular matrix in the interstitium. Adapted with permission from ref. 195 . Mononuclear Phagocyte System (MPS) Blood vessel Lymphatic vessel Interstitium Nanoparticle Lymph node 25 The first challenge faced by nanocarriers following intravenous administration is to resist clearance from the bloodstream mediated by macrophages and monocytes of the MPS, as well as by neutrophils, which have been shown to have phagocytic capacity and affect nanoparticle clearance in vivo186, 196, 197. Macrophages can be found in all major organs, such as the liver, kidneys, spleen, lungs, brain, and lymph nodes, while monocytes and neutrophils patrol the bloodstream198. The recognition of nanocarriers as foreign materials in the bloodstream is largely influenced by the adsorption of serum proteins called opsonins onto the particle surface, which are recognized by phagocytes through surface receptors186. This relationship between serum protein adsorption and subsequent phagocytosis and clearance has driven the investigation of antifouling polymer coatings like polyethylene glycol (PEG), as well as zwitterionic polymers like poly(carboxybetaine) (PCB), which have gained wider use due to potential PEG immunogenicity199-203. In addition, the physical properties of nanoparticles, such as size and surface charge have been reported as important determinants of in vivo stability and clearance. Nanoparticle size has been considered a major factor in phagocyte-mediated clearance that can be tuned to enhance nanoparticle circulation. Generally, nanoparticles that are <100 nm are associated with less protein adsorption and longer circulation half-lives177, 178. For example, Fang et al. incubated 80 nm, 171 nm, and 243 nm PEG-poly(cyanoacrylate-co-n-hexadecyl cyanoacrylate) (PEG-PHDCA) nanoparticles in 50% mouse serum found that protein adsorption increased with nanoparticle size (6%, 23%, and 34%, respectively), which was correlated to macrophage uptake, as the 80 nm particles were phagocytosed at a 40% lower rate than 243 nm particles when incubated with RAW 264.7 murine macrophages in vitro177. The same dependence of phagocytosis on nanoparticle size was also reported in a study by Bisso et al., who tested the uptake of 20 nm, 50 nm, 100 nm, and 200 nm polystyrene beads in human neutrophils in vitro204 . To study the effects of nanoparticle size on in vivo half-life, Choi et al. injected PEG-coated gold nanoparticles of different sizes, ranging from 5 nm to 98 nm intravenously into BALB/c mice via 26 the tail-vein while controlling particle concentration and surface charge and found that nanoparticle half-life decreased with size178. This trend was also found in studies by Perrault et al. who also found that increasing PEG weight prolonged the half-lives of PEGylated gold particles of similar core sizes205 . The surface properties of nanoparticles like charge are also important design criteria to consider for avoiding clearance from circulation by phagocytes. Because most serum proteins are negatively charged, nanoparticles with strong positive surface charge (+30 mV) are generally phagocytosed and eliminated quicker than uncharged or negatively charged particles179-181, 206, 207 . For example, Feng et al. incubated 10 nm iron oxide nanoparticles coated with polyethylenimine (PEI, +29 mV) or PEG (-1 mV) with RAW264.7 macrophages in vitro and observed that the positively charged particles were phagocytosed at 3-fold the rate of the neutral particle206. When injected intravenously into mice, PEI-covered iron oxide particles (225 nm) were cleared from the bloodstream within 10 min and had 78-fold lower AUC than negatively charged particles207 . However, other studies report that negatively charged nanoparticles can also be prone to phagocytosis and clearance, due to greater interaction with the positively charged scavenger receptors of phagocytic cells182-186, 191. For example, Metz et al. reported 21 nm negatively charged iron oxide nanoparticles were internalized by phagocytic monocytes at a higher rate than similarly sized uncharged iron oxide particles182. Interestingly, this study also reported that 62 nm negatively charged iron oxide particles were phagocytosed at 3-fold the rate of 150 nm uncharged iron oxide particles, despite being much smaller, which suggests that particle surface charge may be more influential on clearance than nanoparticle size. These physical properties govern the ability of nanoparticles to resist phagocyte-mediated clearance from the bloodstream, which is a critical consideration for lymph node targeting strategies, as longer-circulating particles have more opportunities to extravasate from the blood endothelium and accumulate in lymphatic tissues. 27 Blood Vessel Endothelium In addition to limiting phagocytic clearance, nanoparticles must extravasate out of the blood vasculature to reach the lymph nodes. As depicted in Figure 1-9, blood vessels are comprised of flat, quiescent endothelial cells joined laterally via tight junction proteins and attached to a basement membrane comprising collagen, fibronectin, laminin, and glycosaminoglycans208, 209 . The blood vasculature functions primarily as a barrier restricting the movement of fluid, proteins, blood cells, and nanoparticles between the intravascular and interstitial compartments209. Before extravasation can occur, however, nanoparticles must first marginate towards the blood vessel walls, for which mathematical modelling and microfluidic studies have revealed that nanoparticles with higher aspect ratios like ellipsoid or rod-shaped particles can have superior margination to the vessel wall compared to spherical nanoparticles210-212. Upon reaching the vessel wall, nanoparticles must extravasate out of the blood vessel through fenestrations in the endothelium or through transcellular transport. Figure 1-9. Junction proteins maintain tight cell-cell junctions between blood vessel endothelial cells. Adapted with permission from ref. 208 . 28 Fenestrations in the blood vessel endothelium can span 60 nm, which can be up to 800 nm in tumor vasculature, and these fenestrations are believed to be a major route of extravasation for nanoparticles smaller than 200 nm out of the blood vasculature174, 187-190. As such, nanoparticle extravasation out of the blood vessel endothelium into the interstitium has been reported to increase as nanoparticle size is decreased. For example, Vu et al. used human umbilical vein endothelial cells (HUVECs) as an in vitro model of blood vasculature and reported an over tenfold increase in the extravasation of 40 nm polystyrene nanoparticles out of the blood endothelium compared to that of 70 nm and 130 nm particles213. Kong et al. corroborated these results in vivo by using a mouse model of ovarian cancer to evaluate the extravasation of liposomes sized from 100 nm to 400 nm out of tumor blood vessels under hyperthermic conditions and reported that particle extravasation decreased with nanoparticle size214. However, none of the liposomes were observed to extravasate under physiological conditions, which highlights growing concerns with the potency of the enhanced permeability and retention (EPR) effect that many researchers have assumed lead to increased nanoparticle delivery to tumor tissue215-217. Indeed, a study by Smith et al. reported that 25 nm quantum dots were able to extravasate out of the vasculature in only 1 of 3 murine tumor models218. These studies indicate that further is research is necessary to characterize the complex interactions between nanoparticles and blood vasculature in vivo. In addition to extravasation through gaps between endothelial cells, or paracellular transport, recent studies have suggested nanoparticles can take advantage of transcytosis through endothelial cells as an additional means of transport out of the blood vasculature (Figure 1-10) 219, 220. Positively charged nanoparticles have been reported to initiate transcytosis through increased interaction and adsorption to the negatively charged cell membranes of endothelial cells221-223 . Zhang et al. studied the transcytosis of neutral and positively charged polystyrene nanoparticles (22, 48, and 100 nm) through an in vitro model of blood vessel endothelium. While no size effects were reported to affect transcytosis in this study, positively-charged particles entered the 29 endothelium at 100-fold the rate of neutral particles, and a similar result was reported by Gil et al. comparing negatively-charged (-12 mV) and positively-charged (+14 mV) cyclodextrin nanoparticles222, 223 . Figure 1-10. Nanoparticles can extravasate out of the blood vessel endothelium into the ECM-containing interstitium through cell-cell junctions (green arrow) or transcytosis (blue arrow). Extracellular Matrix (ECM) of the Interstitium Upon extravasation out of the blood vasculature, nanoparticles can encounter another barrier in the form of the ECM in the interstitium before reaching the lymph nodes. The ECM contains hundreds of different proteins including collagen, glycosaminoglycans, laminins, and fibronectin that are primarily produced by fibroblasts224. Collagen fibrils crosslink to form a mesh-like structure with pore sizes ranging from 20 nm up to 130 nm225, 226. Given the variable nature of ECM pore sizes, smaller nanoparticles are believed to have increased diffusivity past the ECM227-229. Wong et al. compared the diffusivity of 10 nm quantum dots with 100 nm quantum dot-coated silica nanoparticles in a cell-free collagen matrix227. The 10 nm particles had a diffusivity of 2.3 x 10-7 cm2 /s, while the 100 nm particles were unable to diffuse in the matrix. While this study only compared nanoparticles of two sizes, Goodman et al. evaluated the ability of 20, 40, 100, and 200 nm polystyrene nanoparticles to penetrate the ECM of a tumor spheroid culture and found 30 that diffusion past the ECM was inversely correlated with nanoparticle size, as 20 nm particles were most efficient in penetrating the ECM (57% of all particles), while 100 and 200 nm particles had <5% penetration228 . Along with positively charged collagen fibrils, the ECM also contains thin fibers of negatively charged glycosaminoglycans, namely hyaluronic acid, that effectively make the ECM a patchwork of positive and negative charges as shown in Figure 1-11230, 231. Le Goas et al. studied nanoparticle surface charge effects on the diffusion of 12-20 nm polymethacrylate-coated gold nanoparticles in Matrigel and found that negatively-charged nanoparticles (<-20 mV) exhibited greater diffusivity, while positively-charged particles (+10 mV) did not diffuse, a result also confirmed by Kim et al.232, 233. However, others have reported that neutral particles have increased diffusion through the ECM compared to both positively- and negatively charged nanoparticles231, 234. The observation of charged particles having hindered diffusion in ECM has been attributed to their interaction and binding with positively charged (collagen) and negativelycharged (glycosaminoglycans) matrix proteins in the ECM230, 231. Interestingly, modeling studies performed by Stylianopoulos et al. using 1-10 nm quantum dots concluded that charge effects diminish with decreasing nanoparticle size, suggesting that smaller nanoparticles, regardless of surface charge, may have superior diffusivity based on their decreased interaction with the proteins of the ECM230 . Once past the ECM and the other physiological barriers described in this section, nanoparticles can enter the lymphatic system via highly permeable lymphatic vessels and be carried by lymph into the lymph node to unload a therapeutic payload. 31 Figure 1-11. The ECM in the interstitium forms a mesh with positively and negatively charged protein fibers. Reprinted with permission from ref. 231 . 1.3.4 In Vivo Applications for Lymph Node Drug Delivery The nanoparticle size and charge constraints imposed by the mononuclear phagocyte system, blood vessel endothelium, and extracellular matrix of the interstitium suggest that small, uncharged nanoparticles may target the lymph nodes more efficiently following intravenous injection. Nanoparticle delivery to the lymph nodes has been used in numerous clinical and preclinical applications, such as the imaging and treatment of lymph node cancer metastases, as well as for vaccine delivery173, 235-238. Although the majority of nanoparticle formulations are sequestered in the liver and spleen following intravenous administration, leading to lymph node concentrations of <0.1% of the injected dose (ID), engineering the physical characteristics of nanoparticles such as size and charge have been reported to boost lymph node accumulation239- 241. For example, dextran-coated ultrasmall superparamagnetic iron oxide (USPIO) nanoparticles with hydrodynamic diameters of approximately 40 nm have been explored clinically for the identification of lymph node metastases in prostate cancer using MRI, with preclinical studies reporting up to 12% ID in the lymph nodes after intravenous administration in rats174, 242, 243. The USPIO particles function as contrast agents by entering the lymph nodes and retention by noderesident macrophages174. The efficacy of USPIO particles for lymph node metastases detection was evaluated in a clinical study involving 80 patients and 334 lymph nodes; USPIO-aided MRI 32 following intravenous injection improved the sensitivity of lymph node metastases detection from 35.4% to 90.5% compared to MRI alone 242 . In addition to cancer imaging, nanoparticles targeted to the lymph nodes have also been investigated for cancer therapy. Cabral et al. investigated the ability of uncharged polymeric micelles administered intravenously to accumulate in the metastatic lymph nodes of B16F10 tumor-bearing mice and deliver a platinum-based drug173. While mice treated intravenously with 8 mg/kg of free oxaliplatin had similar tumor growth to an untreated control, mice treated with 3 mg/kg of the drug loaded into 30 nm polymeric micelles had significantly smaller tumors and lymph node metastases after 9 days (3 total injections given on days 0, 2, and 4). Moreover, the lymph node targeting of the micelles was reported to be size-dependent, as 30 nm micelles were reported to have approximately 4 times better accumulation in lymph node metastases than 70 nm micelles. Another study by this group investigated the ability of 55 nm micelles to deliver the chemotherapy drug epirubicin to the metastatic lymph nodes of a luciferase-expressing murine breast cancer model 235. To limit off-target toxicity associated with drug release in healthy lymph nodes, the epirubicin was loaded into the micelles via pH-sensitive hydrazine linkers, to facilitate increased drug release in the acidic environment of the primary tumor and lymph node metastases. Eight hours after intravenous injection, the epirubicin concentration in the metastatic lymph nodes of micelle-treated mice were 10-fold higher than in the nodes of mice treated with free epirubicin, and the increased drug concentration resulted in significant reduction in the size of lymph node metastases (Figure 1-12). Moreover, the drug concentration in metastatic lymph nodes was almost 3-fold higher than the drug concentration in healthy nodes forty-eight hours after injection. 33 Figure 1-12. (A) Epirubicin-loaded micelles deliver drugs to metastatic lymph nodes. (B) Epirubicin micelle treatment inhibits the growth of lymph node metastases in a luciferase expressing murine breast cancer model. *p<0.05, **p<0.01. Adapted with permission from ref. 235 . Because the majority of solid cancers metastasize via the lymphatics, lymph node-targeted nanoparticles have also been investigated for their ability to inhibit the formation of lymph node metastases244. Liu et al. characterized the distribution of nanoparticle clusters 100 nm in size assembled from 5 nm dendrimers loaded with a cisplatin prodrug and tested the nanoparticle’s ability to inhibit lymph node metastases. Particles injected intravenously were found to colocalize with FITC-labeled lymphatic vessels, confirming their ability to enter the lymphatics following systemic injection. Moreover, nanoparticle treatment inhibited the formation of lymph node metastases in a metastatic 4T1 breast cancer model and improved the median survival of mice from 45 days to 56 days relative to a PBS control, validating the efficacy of lymph node-targeted nanoparticles for cancer therapy. In addition, nanoparticles have been used to deliver vaccines to the lymph nodes for immunization against cancer. Specifically, Baharom et al. explored intravenous delivery of 20-50 nm micelles loaded with MC38 tumor peptide neoantigens for anti-tumor vaccination of mice237 . Mice challenged with MC38 colon cancer cells 28 days after vaccination were observed to have significantly reduced tumor growth compared to unvaccinated mice. Interestingly, intravenous 34 vaccination of mice bearing established MC38 tumors was observed to control tumor growth, while subcutaneous vaccination was ineffective. These studies reported the success of small, uncharged nanoparticles for lymph node targeting, which coincides with the ideal nanoparticle physical properties described above. The rising number of studies regarding lymph node drug delivery for cancer therapy reflect the growing recognition of the lymphatic system’s role in mediating cancer metastasis and recurrence. 1.3.5 Hitchhiking Strategies for Lymph Node Drug Delivery Albumin-mediated Drug Delivery to the Lymph Nodes An alternative approach to drug delivery to the lymph nodes is to design a delivery system that can bind to endogenous molecules or immune cells that are regularly trafficked to the lymph nodes. Albumin is the most abundant protein in blood, interstitial fluid, and lymph166. Albumin is produced in the liver and released into the bloodstream as a globular protein with 4 x 15 nm dimensions, with an average half-life of 19 days245, 246. The main functions of albumin are to maintain the osmotic pressure of blood, and to transport other biomolecules, such as fatty acids, hormones, ions, and steroids247, 248. About 5% of the albumin in circulation is pushed out of the blood vasculature every hour into the interstitial space, and nearly 100% of this albumin is absorbed into the lymphatics before re-entering circulation166 . Given albumin’s excellent stability and half-life, as well as its frequent trafficking to the lymphatics, subcutaneous delivery systems leveraging albumin-hitchhiking for the delivery of vaccines and imaging molecules, such as the albumin-binding dye Evans Blue, to draining lymph nodes have been explored166, 249, 250. Albumin has also been investigated as a nanocarrier for anticancer therapies, due to its ability to passively accumulate in primary tumors via leaky tumor vasculature, as well as its active transport into tumor cells via the Cav-1 protein251, 252 . 35 Additionally, albumin hitchhiking has been adopted for intravenous delivery systems targeted to the lymph nodes in preclinical and clinical studies253-257. For example, Yu et al. synthesized combination nanoparticles by loading nanospheres comprised of crosslinked albumin with the chemotherapy drug gemcitabine and photodynamic therapy agent pheophorbide-a and evaluated therapeutic efficacy in the lymph node metastases of a murine BxPC-3 pancreatic cancer model257 . Twenty-four hours after intravenous administration, the combination nanoparticle was observed to have increased accumulation in metastatic lymph nodes compared to free pheophorbide-a. Furthermore, in vivo efficacy studies demonstrated reduced volume and weight of metastatic lymph nodes of mice treated with nanoparticle. Nab-paclitaxel (Abraxane) is a nanoformulation of the anti-cancer drug paclitaxel bound to albumin that is FDA approved for the treatment of metastatic breast cancer, non-small cell lung cancer, and pancreatic adenocarcinoma258, 259. Given its lymphatic targeting properties, it has also recently been examined in lymph node-resident cancers such as non-Hodgkin lymphoma253-256 . In two separate case reports of patients presenting diffuse large B-cell lymphoma, each achieving only partial responses after multiple lines of chemotherapy, the patients were prescribed combination therapies including nab-paclitaxel. The first patient received a weekly dose of nabpaclitaxel over 6 months after initial treatment with the chemotherapy drug azacitine, and PETCT scans revealed complete disease remission after the third month of treatment254. Follow-up scans performed 5 years after the end of treatment showed no evidence of disease recurrence. The second patient was prescribed a combination of nab-paclitaxel and liposomal doxorubicin as a fourth-line chemotherapy regimen and achieved complete disease remission after 4 cycles of treatment, with PET-CT scans showing no disease recurrence after 7 years253. Based on the success of these case reports, a phase II clinical trial involving 13 patients with relapsed or refractory diffuse large B cell lymphoma was performed using a combination therapy of rituximab, nab-paclitaxel, and liposomal doxorubicin255. Of the 13 patients in the study, 5 achieved complete 36 response, 6 achieved partial response, and 2 patients had stable or progressive disease. Although clinical studies reported therapeutic success in patients unresponsive to other chemotherapies, they were limited by small sample sizes. Thus, the use of nab-paclitaxel as a monotherapy for diffuse large B cell lymphoma warrants further study. Immune Cell-mediated Drug Delivery to the Lymph Nodes Another approach for delivering therapeutics to the lymph nodes is to hitch nanoparticles with immune cells that regularly traffic to the lymph nodes, such as T cells monocytes, and dendritic cells (DCs) which can enter the lymph node via the lymphatics, as well as through the blood vessels in the lymph node. Naïve T cells constantly travel between the blood circulation and lymphatics in search of antigen. Upon activation, T cells express chemokine receptors CCR5, CCR7, and CCR8 that facilitate their migration to the lymph nodes through the lymphatic vasculature and lymph node blood vasculature260-262. Huang et al. exploited the lymph node homing ability of T cells by covalently linking cultured luciferase+ T cells to lipid nanocapsules (NCs) loaded with the anticancer drug SN-38263. After intravenous injection into a mouse model of lymphoma, T cells conjugated with the NCs accumulated in the lymph nodes within 20 hours, reaching peak levels at 60 hours post-injection, and were retained until the end of the study (80 hours). Importantly, these kinetic properties were similar to unmodified T cells, confirming that conjugation to the NCs did not affect T cell trafficking. When drug concentrations in the tumorbearing lymph nodes were evaluated, T cell-mediated NC delivery resulted in increased drug concentration, over 60-fold greater than the concentration achieved by the injection of unbound NCs. The increased drug concentration associated with T cell-NC treatment translated to an approximately 10-fold reduction in tumor burden compared to unbound NC treatment. 37 Additionally, T cell hitchhiking can also be achieved in situ. For example, Schmid et al. evaluated the ability of intravenously administered T cell-targeted PEG-PLGA nanoparticles to accumulate in the tumor-draining lymph nodes of a mouse colorectal cancer model and deliver the immunomodulatory drug SD-208264. SD-208 functions by inhibiting the TGF-BR1 kinase in cancer cells, which promotes immunosuppression 265. T cell targeting was achieved by reacting maleimide-terminated nanoparticles with free thiol groups of PD1 or CD8 antibody fragments, as both PD1 and CD8 have been reported to be expressed in active T cells264, 266-268. 24 hours following intravenous injection, T cell-targeted nanoparticles were observed to accumulate in the tumor-draining lymph nodes, with approximately 25% of T cells in the node bound to nanoparticles, while nanoparticles functionalized with non-targeting isotype control antibody fragments were not observed in the lymph nodes (Figure 1-13A). The increased lymph node accumulation of the targeted nanoparticles led to delayed tumor growth in a subcutaneous MC38 colorectal cancer model. While treatment with non-targeted nanoparticles had no effect on tumor growth, mice treated with T cell-targeted nanoparticles delayed tumor growth and prolonged mouse survival by approximately 12 days (Figure 1-13B). Figure 1-13. (A) T cell-targeted nanoparticles accumulate in the tumor-draining lymph nodes and (B) improve mouse survival (B). ***p<0.001. Adapted with permission from ref. 264 . Like T cells, dendritic cells (DCs) are immune cells that migrate to the lymph nodes and are critical for mediating adaptive immunity. As the primary antigen-presenting cell of the immune 38 system, DCs are dispersed throughout the body and are typically found in higher concentrations in tissues exposed to the external environment, such as the skin, intestinal lining, and nasal passages269, 270. Although DCs have been reported to migrate to the lymph nodes to some degree during steady-state conditions, the majority of DC migration is reported in the context of postantigen exposure, after which DCs begin to express surface receptors like CCR7 that are used to follow chemokines such as CCL19 and CCL21 to the lymphatics, where DCs initiate adaptive immune responses via antigen presentation to naïve T cells and B cells271-273. Due to the role of DCs in adaptive immunity, a plethora of studies and reviews have been conducted with regards to targeting nanoparticles to DCs for immunotherapy and vaccination274-277 . Although many of the studies have used the subcutaneous or intradermal route for DCtargeting, intravenous injection of nanoparticles actively targeted to DCs have also shown success in immunotherapeutic and vaccination strategies. Sancho et al. utilized DC, NK lectin group receptor-1 (DNGR-1) for targeted antigen delivery to DCs for the generation of CD8 T cell responses against B16F10 melanoma tumors278. Specifically, anti-DNGR-1 antibodies conjugated to the immunogenic peptide SIINFEKL were injected intravenously into mice bearing B16F10-OVA tumors and were found to colocalize with DCs in the lymph nodes. This resulted in CD8 T cell responses that significantly mitigated tumor metastases in the lungs, confirming the potency of DC-targeting for immune stimulation. In addition, Stead et al. investigated DC-targeting in the context of generating immune tolerance to allogenic transplants in mice by using targeted silicon nanoparticles (160 nm) to deliver the immunosuppressive drug, rapamycin, to induce expansion and proliferation of regulatory T cells279. DC-targeting was achieved by functionalizing the particles with antibodies targeted to the DC marker CD11c. Mice received a total of 3 intravenous injections on days 0, 14, and 28 of the study and were euthanized after 40 days. When harvested spleens were evaluated for the presence of regulatory T cells, DC-targeted nanoparticle therapy showed a five-fold increase in the number of regulatory T cells compared to 39 mice treated with nanoparticles functionalized with isotype control antibodies. Collectively, these studies demonstrate the feasibility of leveraging DC-targeted nanoparticles for the delivery of antigens and small molecule drugs to the lymph node to induce an immunomodulatory effect. In addition to T cells and DCs, monocytes are immune cells that rapidly accumulate in the lymph nodes through blood vessels during and immediately following inflammation, with functions spanning antigen transport and presentation and differentiation into DCs, although they have also been reported to be present in steady-state conditions144, 280-285. The influx of monocytes during inflammatory states has been reported to be a result of the accumulation of lymph-borne chemokines such as monocyte chemoattractant protein-1 (MCP-1) in the lymph node, entering the lymph nodes via the afferent lymphatic vessels281-284. The increased trafficking of monocytes to the lymph nodes during inflammatory states such as cancer make monocyte targeting a useful means of drug delivery to the lymph nodes. Our group has explored this by synthesizing micelles functionalized with peptides derived from the CCR2-binding motif of the MCP-1 chemokine22, 24, 286-288. When injected intravenously into B16F10 tumor-bearing mice, micelles containing the CCR2-binding motif were observed to accumulate in the lymph nodes at twice the rate of a nontargeted micelle 3 hours post-injection, although further study is required to elucidated the mechanisms behind this286. The development of nanoparticle systems that can strongly associate with immune cells that migrate through the lymphatics offers a novel means of drug delivery to the lymph nodes. While early nanoparticle characterization and cancer efficacy studies focused on prolonging nanoparticle circulation in the blood vasculature and tumor accumulation, the prominent role of the lymphatic system, particularly the lymph nodes, in the progression of numerous pathologies, such as cancer, cardiovascular disease, autoimmunity, and viral infections, is now widely recognized138, 289, 290. As a result, improving drug delivery to the lymph nodes has become a heavily researched topic173, 235-237, 253-257, 263, 264, 286. As reviewed here, intravenous drug delivery to 40 the lymph nodes is challenged by numerous biological barriers, such as the MPS, blood vessel endothelium, and ECM, and overcoming these delivery barriers will be critical to their successful implementation in the clinic187-189, 225, 226, 291-293 . An area for further research is to design nanocarriers that incorporate ligands that actively target lymph node-specific biomarkers to increase nanoparticle retention in the lymph nodes. For example, targeting nanoparticles to the lymphatic vessel endothelial hyaluronan receptor 1 (LYVE1) that is expressed by all lymphatic endothelial cells may promote nanoparticle accumulation deep into the cortex and paracortex of lymph nodes, similar to the use of peptides containing the integrin-binding RGD motif for tumor targeting and penetration294-296. Another biomarker that can be used for lymph node targeting is cluster of differentiation 169 (CD169), which is expressed by macrophages lining the subcapsular sinus of lymph nodes, as well as macrophages in the bone marrow297, 298. Subcapsular sinus macrophages come into direct contact with lymph that flows into the node via the afferent lymphatic vessels and take up lymph-borne antigens that are presented to naïve B cells of the cortex297, 299. Thus, nanocarriers actively targeted to subcapsular sinus macrophages have the potential to improve antigen delivery for immunization purposes. Although CD169 ligands have been used to target liposomes to bone marrow macrophages, their use in targeting the CD169+ macrophages in the lymph nodes have yet to be explored300 . An alternative route for lymph node entry is through extravasation out of the blood vessels that innervate the lymph nodes, called high endothelial venules (HEVs). HEVs are specialized venules whose primary function is to transport lymphocytes, such as T cells, in and out of the lymph nodes by producing lymphocyte-attracting chemokines like CCL21301. Compared to the flat, endothelial cells of normal blood vessels, HEV endothelial cells are cuboidal in shape and are supported by a thicker basement membrane. While several nanoparticle studies have reported HEVs as a primary means of lymph node accumulation, the mechanisms underlying HEV- 41 mediated transport have not been well-characterized and require more rigorous study174, 242 . Although intravenous drug delivery to the lymph nodes is challenged by several physiological barriers, studies characterizing these barriers have paved the way to rational nanocarrier design that can improve therapeutic outcomes. However, the design rules reviewed here apply to intravenous administration, and likely differ from other administration routes; hence, the intended route of administration must be taken into consideration when designing nanomedicine strategies for in vivo use. 1.4 Peptide Amphiphile Micelles as a Nanoplatform for Targeted Immunotherapy, Drug Delivery, and Metastatic Lymph Node Detection in Cancer Peptide amphiphile micelles (PAMs) are small organic nanoparticles comprised of amphiphilic monomers, typically sized between 8 and 20 nm23. The amphiphiles themselves consist of a hydrophobic lipid tail conjugated to a hydrophilic polyethylene glycol (PEG) group and bioactive peptide. When suspended in aqueous solutions, peptide amphiphiles self-assemble due to hydrophobic forces between the lipid tails to form micellar nanoparticles, with the lipid tails forming a hydrophobic core, and the bioactive peptide groups decorating the micelle surface. Extensive research has been conducted with PAMs within the last decade, exploring their application in tissue targeting, imaging, and nucleic acid delivery within the contexts of diseases including atherosclerosis, autosomal dominant polycystic kidney disease, and cancer93, 302-305 . This nanoparticle platform offers several distinct advantages for molecular targeted therapies. First, the micelles are composed of biocompatible and biodegradable lipids and peptides. Second, the chemistry used to conjugate peptides to the hydrophobic lipid tails is simple, allowing the incorporation of multiple modalities, such as targeting, therapy, and imaging, into a single nanoparticle. Third, PAMs contain both hydrophobic and hydrophilic regions that make it compatible with a wide variety of drugs. Lastly, the small size of PAMs (8-20 nm) is desirable 42 within the context of cancer and lymph node targeting. Nanoparticle size has been reported to be inversely related to tumor penetration, which is an important factor in determining therapeutic efficacy of drug delivery systems306, 307. In addition, as previously discussed in 1.3, nanoparticle targeting of the lymph nodes is strictly constrained by physiological size barriers. Thus, PAMs are an attractive nanoplatform for the development of targeted cancer therapies and diagnostics. 1.5 Objective and Aims As described above, cancer remains a leading cause of death and financial burden in the U.S. Although the clinical success of immunotherapies has been promising and highlights the overall potential of molecular targeting as a therapeutic strategy, the percentage of patients that respond to treatment is limited, which indicates a need for novel molecular targets of cancer to be identified and investigated. Our work seeks to contribute to fulfilling this need by implementing a nanomedicine for the molecular targeting of cancer for therapy and diagnostics. Specifically, we plan to investigate the efficacy of a peptide amphiphile micelle therapy targeted to CCR2, a cellsurface receptor that is known to stimulate cancer cell growth, metastasis, and immunosuppression. Then, we will evaluate the potential of CCR2-targeted nanoparticles as diagnostic tools for the MRI-guided detection of lymph node metastasis. Lastly, as we will demonstrate the versatility of our nanoparticle platform by engineering the micelles to deliver siRNA targeted to HIF2α, a transcription factor that is upregulates the expression of genes associated with cancer cell growth, angiogenesis, and migration, and is over-expressed in >70% of patients with clear cell renal cell carcinoma. Towards this objective, we have devised the following aims: 43 1.5.1 Aim 1. Synthesize and Characterize Tumor-targeting and Immunotherapeutic Properties of CCR2-targeted PAMs. We will: a) Synthesize CCR2-targeted PAMs incorporating the cytotoxic peptide KLAK and characterize the physical properties of the resultant KLAK-MCP-1 PAMs, b) assess the in vitro binding and cytotoxicity of KLAK-MCP-1 PAMs to cancer cells, and c) evaluate the anti-cancer and immunomodulatory potential of KLAK-MCP-1 PAMs in vivo. 1.5.2 Aim 2. Evaluate CCR2-targeted PAMs for Early MRI Detection of Lymph Node Metastasis. We will: a) Synthesize CCR2-targeted PAMs incorporating a gadolinium chelate for MRI imaging and characterize in vitro binding of the resultant MCP1-Gd PAMs to CCR2-expressing cancer cells and monocytes, b) assess the in vivo metastatic lymph node targeting of MCP1-Gd PAMs, and c) evaluate the role of monocyte hitchhiking in the lymph node targeting of MCP1-Gd PAMs. 1.5.3 Aim 3. Evaluate PAM Delivery of HIF2α siRNA to Renal Cell Carcinoma Cells. We will: a) Synthesize and characterize HIF2α PAMs targeted to clear cell renal cell carcinoma (ccRCC) cells, b) evaluate in vitro gene silencing in patient-derived ccRCC cells following PAM treatment, and c) evaluate in vitro functional effects of HIF2α knockdown in ccRCC cells following PAM treatment. Impact: Broadly, our work will demonstrate the efficacy of molecular targeting of cancer biomarkers for applications in cancer drug delivery and diagnostics. 44 Chapter 2. CCR2-targeted Micelles for anti-cancer Peptide Delivery and Immune Stimulation 2.1 Introduction, Objective, and Rationale Signaling between the C-C chemokine receptor 2 (CCR2) with its ligand, monocyte chemoattractant protein-1 (MCP-1) has been correlated with disease progression and metastasis in a variety of cancers including prostate cancer and melanoma36, 308-311. Clinically, elevated serum levels of MCP-1 and intratumor CCR2 expression are associated with an unfavorable disease prognosis 312. Tumor cells exploit MCP-1-CCR2 signaling to stimulate tumor cell proliferation and survival via downregulation of apoptotic pathways313-318. In addition to sustaining pro-survival signaling cascades in tumor cells, intratumor MCP-1 secretion also drives the recruitment of circulating CCR2+ monocytes that differentiate into tumor-associated macrophages (TAMs)319, 320 . Clinical studies have linked high TAM infiltration to poor prognosis and cancer progression, as TAMs produce angiogenic cytokines, release enzymes that facilitate tissue remodeling and tumor cell migration, and establish an immunosuppressive tumor microenvironment through the production of anti-inflammatory cytokines and inhibition of cytotoxic T lymphocyte (CTL) activity38, 39, 321-324 . Given the multiple roles of MCP-1/CCR2 signaling in cancer, disruption of this signaling axis has been proposed as an anti-cancer therapy. However, clinical trials utilizing monoclonal antibodies against MCP-1 have not been successful325-327 . For example, in a phase II clinical trial using the anti-MCP-1 monoclonal antibody Carlumab in metastatic, castration-resistant prostate cancer patients, none of the 46 enrolled patients had an objective response to treatment. Although Carlumab was able to reduce free serum MCP-1 levels 2 hours post-administration, this reduction was only transient. Free serum MCP-1 levels quickly increased to 3- to 5-fold of baseline levels 45 48 hours post-administration and the absence of a durable effect of Carlumab on MCP-1 levels has been suggested to explain the lack of clinical effectiveness. In addition to MCP-1 blockade, another approach to disrupt the MCP-1/CCR2 axis is to target MCP-1’s binding partner, CCR2. Noel et al. administered the CCR2 antagonist PF-04136309 in conjunction with nab-paclitaxel/gemcitabine to pancreatic ductal adenocarcinoma patients328. In 21 treated patients, 23.8% responded to treatment with a reduction in tumor size compared to the baseline. These clinical studies indicate that CCR2-blockade may be an alternative strategy for disrupting MCP-1/CCR2 signaling. Notably, CCR2-targeting therapy also offers the ability to directly target multiple aberrant cells including tumor cells and monocytes, which can be further leveraged to deliver cytotoxic drugs for therapeutic effect in addition to mitigating MCP-1/CCR2 signaling. In an effort to target CCR2 for potential applications in cancer therapy, our group has previously developed a micelle comprised of a 23-residue peptide sequence derived from the binding motif (residues 13-35) of the MCP-1 chemokine (MCP-1 micelle) and reported its ability to bind to monocytes and induce toxicity against prostate cancer cells in vitro22, 24, 287. Incorporation of the CCR2-binding peptide (MCP-1 peptide) into a micelle can improve its secondary structure and in vivo stability by providing protection from serum peptidases24, 287 . To test CCR2-targeting and evaluate its anticancer properties and potential to modulate the tumor microenvironment in vivo, herein, we incorporate the apoptotic peptide, KLAKLAK (KLAK peptide) into the MCP-1 micelle to augment its anticancer efficacy (KLAK-MCP-1 micelle). Specifically, we demonstrate that the addition of KLAKLAK improves micelle toxicity to cancer cells both in vitro and in vivo. Additionally, KLAK-MCP-1 micelles bind and are cytotoxic to multiple cancer cell lines, as well as monocytes, in a CCR2-dependent manner in vitro and were observed to inhibit tumor growth in subcutaneous B16F10 melanoma models by decreasing TAM and increasing CTL infiltration into the tumor. 46 2.2 Materials and Methods 2.2.1 Materials MCP-1 [CYNFTNRKISVQRLASYRRITSSK] and scrambled MCP-1 [CYNSLVFRIRNSTQRKYRASIST] peptides were purchased from Ontores Biotechnologies (Zhejiang, China). 1,2-distearoyl-sn-glycero-3phosphoethanolamine-N-[maleimide-(polyethylene glycol)-2000] (DSPE-PEG2000-maleimide) and 1,2-distearoyl-sn-glycero3phosphoethanolamine-N-[amino(polyethylene glycol)-2000] (DSPE-PEG2000-amine) were purchased from Avanti Lipids (Alabaster, AL, USA). Cy7 mono-N-hydroxysuccinimide (NHS) ester was purchased from Lumiprobe (Hunt Valley, MD, USA). Culture media and reagents, including DMEM, Ham’s F-12K, RPMI-1640, Keratinocyte-SFM, fetal bovine serum (FBS), penicillinstreptomycin, and phosphate buffered saline (PBS) were purchased from Gibco, Waltham, MA, USA. 2.2.2 Amphiphile Synthesis KLAKLAK peptides were synthesized on an automated peptide synthesizer (PS3, Protein Technologies, Tucson, AZ, USA) with Fmoc-mediated solid phase peptide synthesis on a WangLys resin and cleaved using a 94:2.5:2.5:1 vol% trifluoroacetic acid:1,2- ethanedithiol:water:triisopropylsilane solution. All peptides were synthesized with an N-terminal cysteine residue to facilitate covalent conjugation to maleimide-terminated lipid tails. Following cleavage, peptides were precipitated and washed twice with ice-cold diethyl ether and dissolved in water and lyophilized. Crude peptides were resuspended in water and purified using reversephase, high-pressure liquid chromatography (RP-HPLC, Prominence, Shimadzu, Columbia, MD, 47 USA) on a Luna C18 column (Phenomenex, Torrance, CA, USA) at 55oC with 0.1% formic acid in water/acetonitrile mobile phases. The purity of the eluted peptides was characterized using matrix-assisted laser desorption/ionization time-of-flight mass spectroscopy (MALDI-TOF-MS, Bruker, MA, USA). The expected mass peak for the KLAKLAK peptide is 874 g/mol, while the expected mass peaks for the MCP-1 and scr-MCP-1 peptides are 2892 g/mol. Pure KLAKLAK, MCP-1, and scr-MCP-1 peptides were conjugated to a 10% molar excess of DSPE-PEG2000-maleimide through thioether linkage in water as previously described22, 24, 93, 287, 288, 304, 329. The reaction was performed under a pH of 7.2 at room temperature for 72 hours with constant agitation. Afterwards, peptide amphiphiles were purified and characterized through RPHPLC and MALDI-TOF-MS. The expected mass peak for the KLAKLAK peptide amphiphile is 3716 g/mol, and the expected mass peaks for the MCP-1 and scr-MCP-1 peptide amphiphiles are 5834 g/mol. Cy7 amphiphiles were synthesized by reacting DSPE-PEG2000-amine with a 3- fold molar excess of cy7 NHS ester in a 0.1M sodium bicarbonate buffer (pH 8.2) overnight. Cy7 amphiphiles were purified and characterized through RP-HPLC and MALDI-TOF-MS, with an expected mass peak of 3409 g/mol. 2.2.3 Self-assembly of Micelles Micelles were self-assembled through thin-film hydration. Amphiphiles were dissolved and sonicated in methanol before evaporation of the solution under a gentle nitrogen stream into a lipid film. The film was dried overnight under vacuum before hydration with water or PBS. The suspension was then sonicated, heated to 80oC for 30 minutes, and cooled to room temperature. KLAK-MCP-1 micelles were synthesized with KLAK and MCP-1 peptides at a 1:1 mole ratio. Fluorescent KLAK-MCP-1 micelles were synthesized by mixing KLAK, MCP-1, and cy7 amphiphiles at a 45:45:10 ratio287 . 48 2.2.4 Transmission Electron Microscopy (TEM) 7 L of 50 M KLAK-MCP-1 micelles suspended in MilliQ (MQ) water was placed on 400 mesh lacey carbon grids (Ted Pella, Redding, CA, USA) for 5 minutes, before excess liquid was wicked, and the samples were washed with MQ water. The sample was then stained with 2% uranyl acetate, washed once more with MQ water, and dried before imaging on a JEOL JEM 2100-F TEM (JEOL, Tokyo, Japan). 2.2.5 Dynamic Light Scattering (DLS) and Zeta Potential Measurements Micelle size and polydispersity was evaluated through DLS (Wyatt Technology Mobius system, Santa Barbara, CA, USA). 50 M micelles in water or PBS (pH 7.4) was adjusted to pH 5, 5.5, 6.0, 6.5, or 7.0 through the addition of 1N hydrochloric acid (n = 3). Zeta potential measurements were made in MQ water using the same Mobius system with a platinum dip probe (n = 3). 2.2.6 Cell Culture All cell lines (B16F10, PC3, 22Rv1, WEHI-274.1, NCI-H460, and RWPE-1) were purchased from ATCC (Old Town Manassas, VA, USA) and screened for mycoplasma. B16F10 cells were cultured in DMEM supplemented with 10% v/v FBS and 1% penicillin-streptomycin. PC3 cells were cultured in Ham’s F-12K supplemented with 10% v/v FBS and 1% v/v penicillin-streptomycin. 22Rv1 and NCI-H460 cells were cultured in RPMI-1640 supplemented with 10% v/v FBS and 1% v/v penicillin-streptomycin. WEHI-274.1 cells were cultured in DMEM supplemented with 10% v/v FBS, 1% penicillin-streptomycin, and 0.05 mM 1,2-mercaptoethanol. RWPE-1 cells were cultured in keratinocyte-SFM basal media supplemented with 50 g/mL bovine pituitary extract and 5 49 ng/mL human recombinant epidermal growth factor. All cells were cultured in a humidified chamber maintained at 37oC under 5% CO2 and media were renewed every 2-3 days. 2.2.7 In vitro Micelle Binding To evaluate micelle binding to cancer cells and monocytes, 11.25 M of cy7-labeled KLAKMCP-1 or KLAK-scr-MCP-1 micelles were incubated for 4 hours with B16F10, PC3, 22Rv1, NCIH460, or WEHI-274.1 cells that had been seeded onto glass cover slips (400,000 cells per slip), with or without a 1-hour pre-incubation with 250 M MCP-1 peptide. After three PBS washes, cells were fixed with 4% PFA and blocked with 1% BSA, 22.52 mg/mL glycine, and 0.1% Tween20 for 1 hour before incubation with anti-CCR2 primary antibodies for 1 hour (Abcam, Cambridge, UK, 1:100). Following three PBS washes, cells were incubated for 1 hour in the dark with a secondary antibody labelled with Alexa Fluor® 594 (Thermo Fisher Scientific, Waltham, MA, USA, 1:200) before counter-staining with DAPI (1 g/mL). Cover slips were mounted onto microscope slides using the VectaMount aqueous mounting medium (Vector Laboratories, Burlingame, CA, USA) and imaged with a Leica DMi8 fluorescence microscope (Leica, Wetzlar, Germany). 2.2.8 MCP-1 and CCR2 mRNA Expression B16F10 cells were seeded into 12-well plates at a density of 100,000 cells/well. Cells were incubated with PBS, KLAK-MCP-1 micelles, MCP-1 micelles, KLAK-scr-MCP-1 micelles, KLAK micelles, or scr-MCP-1 micelles at a concentration of 5 M for 4 hours before mRNA extraction using the RNeasy kit (Qiagen, Hilden, Germany) according to kit instructions. cDNA was generated using the RT2 First Strand kit from Qiagen. mRNA was generated using the RT2 SYBR Green qPCR Mastermix (Qiagen) before analysis using a CFX384 PCR detection system (Bio- 50 Rad, Hercules, CA, USA). Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) was used as a housekeeping gene to normalize gene expression data, and the 2ΔΔCq method was used to quantify mRNA expression. 2.2.9 In Vitro CCR2 Protein Expression of Cell Lines CCR2 protein expression in the B16F10, PC3, 22Rv1, NCI-H460, and RWPE-1 cell lines was evaluated through an ELISA (Biomatik, Wilmington, DE, USA). Cells were plated at a density of 80,000 cells/well (n = 4) in a 24-well plate and allowed to adhere overnight. Cells were lysed using RIPA buffer (Thermo Fisher Scientific, Waltham, MA, USA) supplemented with the HALT protease inhibitor cocktail (Thermo Fisher Scientific, Waltham, MA, USA). Lysed cells were centrifuged at 12,000g for 10 minutes at 4oC and the supernatants were processed according to the manufacturer’s protocol. CCR2 protein expression was quantified by measuring the sample absorbance at 450 nm using a Varioskan Lux microplate reader (Thermo Fisher Scientific, Waltham, MA, USA) and converting absorbance measurements to protein expression (pg/mL) using a CCR2 standard curve provided by the ELISA kit. 2.2.10 In Vitro Cytotoxicity of Micelles The in vitro cytotoxicity of micelles was evaluated with the (3-(4,5-dimethylthiazol-2-yl)-5-(3- carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium) (MTS) assay (Biovision, Milpitas, CA, USA). B16F10, PC3, 22Rv1, NCI-H460, WEHI-274.1, and RWPE-1 cells were plated at a density of 2000 cells/well in 96-well plates. 0.5-50 M of KLAK-MCP-1, KLAK-scr-MCP-1, KLAK, MCP-1, scr-MCP-1, and empty micelles were incubated with cells for 72 hours before addition of the MTS reagent and evaluation using a Varioskan Lux microplate reader at an absorbance wavelength of 51 490 nm. After subtracting the background absorbance of culture media + MTS reagent alone, cell viability data was normalized to PBS-treated cells, and IC50 values were calculated using GraphPad Prism 7 (GraphPad, San Diego, CA, USA). For each assay, n = 6 was used for each concentration. 2.2.11 In Vivo Efficacy The in vivo efficacy of KLAK-MCP-1, KLAK-scr-MCP-1, and MCP-1 micelles was evaluated in a subcutaneous B16F10 murine melanoma model in 5-week-old, male C57BL/6J mice (n = 11 to 18, Jackson Laboratory, Bar Harbor, ME). On day 1, mice were inoculated with 1 x 105 B16F10 cells suspended in 100 L of a 1:1 solution of culture medium:Matrigel (Corning, Corning, NY, USA) into the left flank. On days 2, 5, 8, and 11, mice were injected via the tail-vein with 100 L of PBS or 500 M MCP-1, KLAK-MCP-1, or KLAK-scr-MCP-1 micelles. On day 15, mice were sacrificed, and tumors and organs were harvested. Tumor volume was measured using digital calipers, according to: 0.5*(tumor length)*(tumor width)2 . 2.2.12 Immunohistochemistry of Excised Tumors Excised tumors were fixed in 10% formalin overnight at 4oC, embedded in paraffin, and sectioned (5 m) for immunohistochemical (IHC) analyses. Sectioned tissues were incubated with primary antibodies for CCR2 (Abcam, Cambridge, UK, 1:100), cleaved caspase-3 (Cell Signaling Technology, Danvers, MA, USA, 1:500), F4/80 (Thermo Fisher Scientific, Waltham, MA, USA, 1:100), PDL1 (Cell Signaling Technology, Danvers, MA, USA, 1:100), or CD31 (Cell Signaling Technology, Danvers, MA, USA, 1:100) before the use of a horseradish peroxidase (HRP) staining kit (Cell Signaling Technology, Danvers, MA, USA). Sections were counterstained with 52 hematoxylin and imaged using an EVOS M7000 fluorescent microscope (Invitrogen, Carlsbad, CA, USA). 2.2.13 Flow Cytometric Analysis of Excised Tumors Excised tumors were passed through a cell strainer to obtain single cell suspensions. After incubation in a red blood cell lysis buffer, suspensions were counted, stained with CD4 (CST, Danvers, MA, USA, 1:160), CD8 (CST, Danvers, MA, USA, 1:160), and CD45 (Thermo Fisher Scientific, Waltham, MA, USA, 1:160), fixed, and immunophenotyped using a MACSQuant flow cytometer (n = 8, Miltenyi Biotec, Bergisch Gladbach, Germany). Intratumor cytotoxic T lymphocyte (CTL) populations were immunophenotyped as CD4- /CD8+ /CD45+ . 2.2.14 In Vivo Biodistribution of Micelles Subcutaneous B16F10 melanoma mouse models were established as described in Section 2.11. To evaluate biodistribution of micelles, 14 days after tumor inoculation, mice were intravenously injected with 100 L of 500 M of cy7-labelled MCP-1, KLAK-MCP-1, or KLAK-scrMCP-1 micelles via tail-vein and sacrificed after 3 hours. Tissues (tumors, lymph nodes, brain, lungs, heart, intestines, spleen, liver, kidneys) were harvested and imaged ex vivo using an AMI HTX imaging system and analyzed using the Aura imaging software package (Spectral Instruments, Tucson, AZ, USA). 2.2.15 Histology 53 Harvested tissues were snap-frozen in blocks of optimum cutting temperature (OCT) compound (Sakura Finetek, Torrance, CA, USA), sectioned (CM3050 S Cryostat, Leica, Wetzlar, Germany), and 8 m sections were stained using hematoxylin and eosin (H&E) and imaged using a Leica DMi8 fluorescence microscope (Leica, Wetzlar, Germany). 2.2.16 Serum Analysis of Liver and Kidney Function Liver function was evaluated by using commercially available kits to assess the serum activity of alanine transaminase (ALT) and aspartate transaminase (AST, Sigma-Aldrich, St. Louis, MO, USA). Kidney function was evaluated by using commercially available kits to analyze serum samples for blood urea nitrogen (BUN, Bioo Scientific, Austin, TX, USA) and creatinine (Crystal Chem, Elk Grove Village, IL, USA). 2.2.17 Statistical Analysis Data are expressed as mean ± SD. Statistical analysis between two groups was performed using a Student’s t-test. Comparisons of three or means were performed with using analysis of variance (ANOVA) followed by post-hoc Tukey’s tests for multiple comparisons. A p-value ≤ 0.05 was considered to be statistically significant. 2.3 Results and Discussion 2.3.1 Synthesis and Characterization of KLAK-MCP-1 Micelles KLAK-MCP-1 micelles were constructed using KLAK and MCP-1 peptide amphiphiles at a 50:50 ratio using methods previously reported by our group22, 24, 287. Through TEM and DLS, 54 KLAK-MCP-1 micelles were found to be spherical with a hydrodynamic diameter of 11.9 ± 2.3 nm (Figure 2-1A, Table 2-1), which is within the reported range of 8-200 nm for favorable in vivo halflife330-332. To ensure KLAK-MCP-1 micelles are stable under acidic conditions that have been reported in tumor microenvironments, DLS measurements were taken in PBS buffers titrated from pH 5 to 7.4333-335. As shown in Figure 2-1B, KLAK-MCP-1 micelles maintained a size of 12-14 nm at 24 hours in various pH environments. As shown in Table 2-1, KLAK-MCP-1 micelles were near neutral and had a zeta potential of 7.2 ± 1.1 mV. Non-targeting KLAK-scr-MCP-1 micelles consisting of KLAK and scr-MCP-1 peptides, KLAK micelles, MCP-1 micelles, scr-MCP-1 micelles, and empty DSPE-PEG2000- methoxy micelles were also synthesized and characterized in Table 2-1. All synthesized nanoparticles were similar in size, ranging from 8 to 12 nm with low polydispersity (< 0.2). While empty micelles had a slightly negative zeta potential of -9.6 ± 3.5 mV due to the negatively charged phosphate on the DSPE-PEG2000 tail, all other micelles had neutral to positive zeta potentials ranging from 0.9 mV to 16.2 mV, due to the basic lysine and arginine residues within the KLAK and MCP-1 peptides336, 337 . Figure 2-1. KLAK-MCP-1 micelle characterization. TEM of KLAK-MCP-1 micelles (A). DLS measurements demonstrate KLAK-MCP-1 micelle stability after 24 hours in various pH found in tumor tissue (n = 3) (B). Scale bar = 50 nm. 4 5 6 7 8 0 5 10 15 20 Nanoparticle Stability pH Diameter [nm] A B 55 Table 2-1. Characterization of Micelles. 2.3.2 KLAK-MCP-1 Micelle Binding to Cancer Cells in Vitro To evaluate micelle binding to cancer cells in vitro, cy7-labelled KLAK-MCP-1 or KLAK-scrMCP-1 micelles were incubated with B16F10, PC3, 22Rv1, or NCI-H460 cancer cells at a noncytotoxic concentration of 11.25 M for 4 hours and examined via fluorescence microscopy. Immunostaining confirmed CCR2 expression in B16F10, PC3, and 22Rv1 cancer cells as shown in Figure 2-2. As such, KLAK-MCP-1 micelles bound to B16F10, PC3, and 22Rv1 cells to a greater extent than KLAK-scr-MCP-1 micelles (2.35-, 8.20-, and 42.42-fold binding, respectively), indicating the MCP-1 peptide facilitated enhanced binding to CCR2-expressing cancer cells. To evaluate the specificity of KLAK-MCP-1 micelle binding to CCR2, a competitive binding assay was performed by incubating cells with KLAK-MCP-1 micelles following a 1-hour pre-treatment with 250 M MCP-1 peptide. As shown in Figure 2-2, preincubation of cells with MCP-1 peptide reduced KLAK-MCP-1 micelle binding to B16F10, PC3, and 22Rv1 cancer cells compared to cells incubated with only KLAK-MCP-1 micelles, demonstrating the specificity of KLAK-MCP-1 micelle binding to CCR2. Conversely, KLAK-MCP-1 micelles were unable to bind to NCI-H460 cancer cells, which do not express CCR2, further confirming the dependence of CCR2 expression for KLAK-MCP-1 micelle binding (Figure 2-2D) 338 . Micelle (n = 3) Diameter [nm] PDI Zeta Potential [mV] KLAK-MCP-1 11.9 ± 2.3 0.14 ± 0.09 7.2 ± 1.1 KLAK-scr-MCP-1 11.7 ± 1.5 0.14 ± 0.05 9.5 ± 2.2 KLAK 11.3 ± 0.7 0.19 ± 0.01 0.9 ± 0.4 MCP-1 9.3 ± 2.1 0.10 ± 0.01 16.2 ± 0.8 Scr-MCP-1 9.2 ± 0.5 0.18 ± 0.04 13.5 ± 2.0 Empty 8.6 ± 1.2 0.07 ± 0.04 -9.6 ± 3.5 1 56 Figure 2-2. In vitro binding of micelles (red) to cancer cells. Fluorescence microscopy images show binding of B16F10 (A), PC3 (B), 22Rv1 (C), and NCI-H460 (D) cells after 4-hour treatment with KLAK-MCP-1 micelles (top), KLAK-scr-MCP-1 micelles (middle) or KLAK-MCP-1 micelles (11.25 M) after pre-incubation with 250 M MCP-1 peptides for 1 hour (bottom). Scale bar = 50 m. 2.3.3 KLAK-MCP-1 Micelles Modulate CCR2 and MCP-1 mRNA Expression To assess the effects of micelle treatment on CCR2 and MCP-1 mRNA expression, qRT-PCR was performed using B16F10 cells treated with non-toxic concentrations of KLAK-MCP-1, KLAKscr-MCP-1, KLAK, MCP-1, or scr-MCP-1 micelles. As shown in Figure 2-3A, both KLAK-MCP-1 micelle and MCP-1 micelle treatment reduced CCR2 mRNA expression by 58 ± 19% and 41 ± 20% of the PBS control, respectively (p < 0.005 and p < 0.05), demonstrating that micelle treatment can downregulate CCR2 expression in addition to antagonizing its function. Additionally, KLAK-scr-MCP-1 micelle and KLAK micelle treatment also reduced CCR2 mRNA expression by CCR2 PAMs Merge KLAK-MCP-1 KLAK-scr-MCP-1 MCP-1 + KLAK-MCP-1 KLAK-MCP-1 KLAK-scr-MCP-1 MCP-1 + KLAK-MCP-1 CCR2 PAMs Merge A B C D CCR2 PAMs Merge CCR2 PAMs Merge KLAK-MCP-1 KLAK-scr-MCP-1 MCP-1 + KLAK-MCP-1 KLAK-MCP-1 KLAK-scr-MCP-1 MCP-1 + KLAK-MCP-1 57 47 ± 19% (p < 0.01) and 42 ± 22% (NS), respectively, indicating that the KLAK peptide may also alter CCR2 expression. The downregulation of CCR2 expression resulting from micelle treatment at non-toxic concentrations demonstrates that KLAK-MCP-1 micelles may be capable of inducing therapeutic efficacy without directly exerting toxicity to cancer cells. Additional qRT-PCR analysis of MCP-1 mRNA expression was performed, although treatment with any micelles yielded no significant change compared to the PBS control (Figure 2-3B). Figure 2-3. Effect of micelle treatment on gene expression of B16F10 cells in vitro. CCR2 (A) and MCP-1 (B) mRNA expression in B16F10 cells treated with PBS or 5 µM micelles. *p<0.05, **p<0.01, ***p<0.005 compared to PBS. n = 4. 2.3.4 In Vitro Cytotoxicity of KLAK-MCP-1 is Dependent on CCR2 Expression An ELISA was used to quantify CCR2 expression in B16F10, PC3, and 22Rv1 cells and correlate their reported metastatic potential and CCR2 levels (Table 2-2) 36, 308-311. B16F10 and PC3 are highly aggressive cell lines that readily metastasize in vivo, while 22Rv1 has been characterized as non-metastatic339-341. As shown in Table 2-2, B16F10 had the highest CCR2 expression (408.6 pg/mL), followed by PC3 (266.4 pg/mL), while 22Rv1 had the lowest (190.7 pg/mL), agreeing with clinical reports of a correlation between high CCR2 expression and disease 0 50 100 150 MCP-1 0 50 100 150 CCR2 *** * ** A B 58 progression36, 308-312. The negative control, NCI-H460, which is a lung cancer cell line that is CCR2- , showed no detectable levels of CCR2 through ELISA. Table 2-2. CCR2 Expression of B16F10, PC3, 22Rv1, NCI-H460, and RWPE-1 Cells. *below assay detection limit. To determine if KLAK-MCP-1 micelle efficacy against cancer cells is dependent on the level of CCR2 expression, micelles were incubated with B16F10, PC3, 22Rv1, and NCI-H460 cells, and cytotoxicity was evaluated using an MTS cell proliferation assay. As shown in Figure 2-4 and Table 2-3, KLAK-MCP-1 micelle treatment was most effective against B16F10 (IC50 = 1.4 ± 0.3 M, p < 0.005 compared to PC3 or 22Rv1), which had the highest CCR2 expression, demonstrating that KLAK-MCP-1 efficacy increases with CCR2 expression. Additionally, PC3, which had higher CCR2 expression than 22Rv1 (266.4 ± 21.7 pg/mL vs. 190.7 ± 15.7 pg/mL, p<0.01), was observed to be more responsive to KLAK-MCP-1 micelle treatment, as shown in Table 2-3 (IC50 = 18.0 ± 1.4 M vs. 31.3 ± 11.0 M, p < 0.05). Since PC3 is often used as a model for late-stage, metastatic, androgen-independent prostate cancer, a type of cancer associated with a 5-year survival rate of just 29%, our results suggest that CCR2-targeted therapies may be an effective option for treating late-stage prostate cancers340, 342, 343 . Cell line B16F10 PC3 22Rv1 NCI-H460 RWPE-1 CCR2 expression (pg/mL) 408.6 ± 172.9 266.4 ± 21.7 190.7 ± 15.7 * * 59 Figure 2-4. In vitro cytotoxicity of micelles (0.5 – 50 M) against B16F10 (A), PC3 (B), 22Rv1 (C), and NCIH460 (D) cells upon incubation for 72 hours (n = 6). Table 2-3. IC50 Values of Micelles (n = 6). a p < 0.005 compared to KLAK-MCP-1 IC50 in PC3 and 22Rv1. b p < 0.0001 compared to KLAK-scr-MCP-1 and MCP-1 in B16F10. Additionally, KLAK-MCP-1 micelles were observed to be more cytotoxic than KLAK-scr-MCP1 micelle, KLAK micelle, MCP-1 micelle, scr-MCP-1 micelle, and empty micelle controls. For 1 10 100 0 20 40 60 80 100 120 B16F10 Concentration [uM] Viability (%) KLAK-MCP-1 KLAK-scr-MCP-1 MCP-1 Scr-MCP-1 Empty micelle KLAK 0 1 10 100 0 20 40 60 80 100 120 22Rv1 Concentration [uM] Viability (%) KLAK-MCP-1 KLAK-scr-MCP-1 MCP-1 Scr-MCP-1 Empty micelle KLAK 0 1 10 100 0 40 80 120 160 PC3 Concentration [uM] Viability (%) KLAK-MCP-1 KLAK-scr-MCP-1 MCP-1 Scr-MCP-1 Empty micelle KLAK 0 1 10 100 0 50 100 150 NCI-H460 Concentration [uM] Viability (%) KLAK-MCP-1 KLAK-scr-MCP-1 MCP-1 Scr-MCP-1 Empty micelle KLAK 0 A B C D 0 10 20 30 40 50 0 20 40 60 80 100 120 NCI-H460 Concentration [uM] V i a b i l i t y % ( ) KLAK-MCP-1 KLAK-scr-MCP-1 MCP-1 Scr-MCP-1 Empty micelle KLAK 0 10 20 30 40 50 0 20 40 60 80 100 120 NCI-H460 Concentration [uM] V i a b i l i t y ( % ) KLAK-MCP-1 KLAK-scr-MCP-1 MCP-1 Scr-MCP-1 Empty micelle KLAK 0 10 20 30 40 50 0 20 40 60 80 100 120 NCI-H460 Concentration [uM] V i a b i l i t y ( % ) KLAK-MCP-1 KLAK-scr-MCP-1 MCP-1 Scr-MCP-1 Empty micelle KLAK 0 10 20 30 40 50 0 20 40 60 80 100 120 NCI-H460 Concentration [uM] V i a b i l i t y ( % ) KLAK-MCP-1 KLAK-scr-MCP-1 MCP-1 Scr-MCP-1 Empty micelle KLAK 0 10 20 30 40 50 0 20 40 60 80 100 120 NCI-H460 Concentration [uM] V i a b i l i t y ( % ) KLAK-MCP-1 KLAK-scr-MCP-1 MCP-1 Scr-MCP-1 Empty micelle KLAK 0 10 20 30 40 50 0 20 40 60 80 100 120 NCI-H460 Concentration [uM] V i a b i l i t y ( % ) KLAK-MCP-1 KLAK-scr-MCP-1 MCP-1 Scr-MCP-1 Empty micelle KLAK IC50 [M] Micelle B16F10 P1C3 22Rv1 NCI-H460 RWPE-1 KLAK-MCP-1 1.4 ± 0.3a, b 18.0 ± 1.4 31.3 ± 11.0 >50 >50 KLAK-scr-MCP-1 17.8 ± 2.2 >50 >50 >50 >50 KLAK 7.0 ± 2.7 >50 >50 >50 >50 MCP-1 31.9 ± 7.0 47.5 ± 12.4 >50 >50 >50 Scr-MCP-1 >50 >50 >50 >50 >50 Empty >50 >50 >50 >50 >50 1 60 example, in B16F10, KLAK-MCP-1 micelles had significantly lower IC50 values compared to KLAK-scr-MCP-1 micelles (1.4 ± 0.3 M vs. 17.8 ± 2.2 M, p < 0.0001) and MCP-1 micelles (1.4 ± 0.3 M vs. 31.9 ± 7.0 M, p < 0.0001). The observed synergy between KLAK and MCP-1 may be explained through enhanced delivery of the KLAK peptide through interaction of the MCP-1 peptide with CCR2 present on the cancer cells, as the in vitro binding studies showed KLAKMCP-1 micelles as having 2.35x more binding compared to KLAK-scr-MCP-1 micelles (Figure 2- 2). CCR2 expression and micelle toxicity was also evaluated in the CCR2- NCI-H460 lung cancer cell line, as well as the RWPE-1 human prostate epithelial cell line. As shown in Table 2-2, neither NCI-H460 nor RWPE-1 cell lines had detectable levels of CCR2. No micelles showed any efficacy (IC50 > 50 M) against either cell line at the maximum tested concentration (50 M, Figure 2-4D). The lack of toxicity against these cell lines indicates CCR2 expression is necessary to achieve therapeutic efficacy. 2.3.5 In Vivo Efficacy of KLAK-MCP-1 in a Subcutaneous B16F10 Melanoma Model After testing the in vitro efficacy of KLAK-MCP-1 micelles, we then evaluated their ability to inhibit tumor growth in a subcutaneous B16F10 murine melanoma model in C57BL/6J mice. The B16F10 model was chosen as it is a well-studied, metastatic melanoma model that expresses CCR2 to facilitate KLAK-MCP-1 efficacy (Table 2-3). After 24 hours following tumor inoculation, mice were treated with PBS or MCP-1, KLAK-MCP-1, or KLAK-scr-MCP-1 micelles via tail-vein injection. Mice were euthanized two weeks after tumor inoculation and tumors were excised. As shown in Figure 2-5A, mice treated with KLAK-MCP-1 micelles were observed to have smaller tumor volumes compared to PBS-treated control mice (353 ± 181 mm3 vs. 538 ± 272 mm3 , p < 0.05). Additionally, mice treated with MCP-1 micelles showed a modest decrease (17%) in tumor 61 volume (447 ± 214 mm3 vs. 538 ± 272 mm3 , NS) compared to the PBS control. Similar to the in vitro toxicity studies, the combination of KLAK and MCP-1 proved to be more efficacious than MCP-1 treatment alone. Figure 2-5. In vivo efficacy of micelles (n = 11 to 18). Tumor volume measurements of mice treated with PBS, MCP-1, KLAK-MCP-1, or KLAK-scr-MCP-1 micelles (A). IHC staining of tumors treated with PBS, MCP-1, KLAK-MCP-1, or KLAK-scr-MCP-1 for (i) CCR2, (ii) cleaved caspase-3, (iii) F4/80 (iv) PDL1, or (v) CD31 (B). Quantification of infiltrating cytotoxic T lymphocytes (CTLs) (C). *p < 0.05, **p < 0.01. Scale bar = 100 m. 2.3.6 Immunohistochemical (IHC) Analysis of Tumor Tissue Sections In order to elucidate the mechanisms behind the observed tumor inhibition, we performed IHC analysis of tumor tissue sections (Figure 2-5B). We performed IHC staining of tumors for CCR2 to evaluate if KLAK-MCP-1 micelle treatment also decreased intratumor CCR2 expression in a similar manner. As shown in Figure 2-5B(i), KLAK-MCP-1 and MCP-1 micelle groups had less CCR2 expression than PBS control tumors, demonstrating the ability of KLAK-MCP-1 and MCP1 micelles to reduce intratumor CCR2 expression. Furthermore, we observed that smaller tumors PBS MCP-1 KLAK-MCP-1 KLAK-scr-MCP-1 0 400 800 1200 Tumor Volume (mm 3 ) * * PBS MCP-1 KLAK-MCP-1 KLAK-scr-MCP-1 0 5 10 15 % of immune infiltrate ** * A CD4- /CD8+ /CD45+ CTLs B (i) (iii) (iv) (v) C PBS MCP-1 KLAK-MCP-1 KLAK-scr-MCP-1 (ii) 62 (KLAK-MCP-1 and MCP-1 micelle groups) had less CCR2 expression, in agreement with clinical studies reporting the correlation between CCR2 expression and disease progression36, 308-312. As intratumor CCR2 expression and signaling has been reported to downregulate apoptotic signaling, we performed IHC staining for cleaved caspase-3 to assess intratumor apoptosis313-318. In agreement with these reports, tumors from KLAK-MCP-1 and MCP-1 micelle groups, which had reduced CCR2 expression relative to the PBS control, were observed to have increased cleaved caspase-3 expression, indicating higher levels of tumor apoptosis (Figure 2-5B(ii)). Since our in vitro mRNA expression studies also indicated that KLAK-MCP-1 micelle treatment may reduce cancer cell production of MCP-1, we performed IHC staining for the pan-macrophage marker F4/80 to evaluate the effect of micelle treatment on MCP-1 driven TAM infiltration into the tumor. As shown in Figure 2-5B(iii), we observed reduced staining in KLAK-MCP-1 and MCP-1 micelle groups compared to the PBS control. As TAM infiltration is known to mediate cancer progression through mechanisms such as immunosuppression and angiogenesis, we also evaluated expression of programmed death ligand 1 (PDL1), a major immune checkpoint molecule expressed by TAMs that contributes to an immunosuppressive microenvironment39, 344 , as well as for CD31, a protein expressed on the surface of endothelial cells that is indicative of angiogenesis116, 321. As shown in Figure 2-5B(iv), tumors from the KLAK-MCP-1 and MCP-1 micelle groups were observed to have less PDL1 staining than the PBS control, indicating a less immunosuppressive tumor microenvironment, which correlates with the reduction in F4/80 staining in these tumors. The decreased PDL1 expression may lead to a stronger anti-tumor cytotoxic T lymphocyte (CTL) response and hence reduced tumor growth. As shown in Figure 2- 5B(v), IHC staining for CD31 showed a similar trend to PDL1, in which KLAK-MCP-1 and MCP1 micelle groups exhibited reduced staining compared to the PBS control, indicating the presence of fewer blood vessels in these treatment groups to fuel tumor growth. IHC analysis of PDL1 and 63 CD31 reveal an attenuation in tumor-promoting immunosuppression and angiogenesis in tumors treated with KLAK-MCP-1 or MCP-1 micelles. 2.3.7 Flow Cytometric Analysis of Micelle-treated Tumors Since reduced IHC staining of PDL1 in KLAK-MCP-1 and MCP-1 micelle groups suggested an amelioration in immunosuppression, we hypothesized that micelle treatment may also induce a concomitant recovery in CTL activity, as numerous studies have reported greater CTL infiltration upon PDL1-depletion345-347. We analyzed tumor infiltration of CD4- /CD8+ /CD45+ CTLs through flow cytometry. As shown in Figure 2-5C, KLAK-MCP-1 micelle treatment was observed to increase CTL infiltration by approximately two-fold relative to the PBS control (6.5 ± 2.6% vs. 3.0 ± 0.9%, p < 0.05). Additionally, CTL infiltration in the KLAK-MCP-1 group was greater than that of the KLAK-scr-MCP-1 group (6.5 ± 2.6% vs. 3.6 ± 1.6%, p < 0.05), demonstrating the importance of the MCP-1 peptide in modulating the immune response. As CTLs are a major component of the anti-cancer immune response, the increased CTL activity observed in KLAK-MCP-1-treated tumors may have contributed to the tumor inhibition seen in this group. 2.3.8 In Vivo Biodistribution of Micelles To assess the biodistribution of micelles, cy7-labeled MCP-1, KLAK-MCP-1, and KLAK-scrMCP-1 micelles were intravenously injected in the subcutaneous B16F10 melanoma model two weeks after tumor inoculation and evaluated 3 hours post-injection (Figure 2-6). Although we hypothesized that interaction with the CCR2-expressing tumors would facilitate tumor accumulation of KLAK-MCP-1 micelles, ex vivo imaging showed minimal tumor retention (2.5 ± 0.1%). As shown in Figure 2-6, KLAK-MCP-1 micelles mostly accumulated in the liver (35.0 ± 64 1.0%) and kidneys (24.1 ± 0.7%), which is in agreement with previous micelle studies22, 93, 304 . MCP-1, KLAK-MCP-1, and KLAK-scr-MCP-1 micelles had similar biodistribution profiles, with the exception of lymph node accumulation (Figure 2-6A). MCP-1 micelles were found to have higher lymph node accumulation compared to the KLAK-scr-MCP-1 micelles (21.2 ± 4.6% vs. 10.6 ± 5.8, p < 0.05). KLAK-MCP-1 micelles also showed enhanced lymph node accumulation compared to KLAK-scr-MCP-1 (13.4 ± 2.3% vs. 10.6 ± 5.8%, NS). Increased lymph node accumulation and therapeutic efficacy despite low tumor retention may indicate that KLAK-MCP-1 micelles induced a therapeutic response by modulating the anticancer immune response, rather than directly exerting toxicity to the tumor, a phenomenon that has been observed in other cancer studies348- 350. For example, Korangath et al. demonstrated nanoferrite particles inhibited breast cancer growth in immunocompetent mice with low tumor retention. Furthermore, their studies showed an increase in CTL infiltration into the tumor, demonstrating the ability of nanoparticles to induce therapeutic efficacy by eliciting a systemic immune response. Figure 2-6. In vivo biodistribution of 500 M cy7-labeled micelles 3 hours after intravenous injection. (n = 3, *p < 0.05). MCP-1 KLAK-scr-MCP-1 KLAK-MCP-1 Tumor LN Brain Lung Heart Intest. Spleen Liver Kidney A B Tumor Lymph Nodes Brain Lungs Heart Intestines Spleen Liver Kidneys 0 11008 21008 31008 41008 51008 Mean radiant ef iciency ((p/s/cm^2/sr)/(uW/cm^2)) KLAK-MCP-1 KLAK-scr-MCP-1 MCP-1 * 65 Similarly, our flow cytometry and IHC studies demonstrated an ability of KLAK-MCP-1 micelles to induce tumor CTL infiltration (Figure 2-5C) and reduce tumor macrophage infiltration, supporting KLAK-MCP-1 micelles induce an immunomodulatory effect that can lead to therapeutic outcomes (Figure 2-5B(iii)). In addition, KLAK-MCP-1 micelles may bind and induce cytotoxicity to CCR2+ circulating monocytes, inhibiting their recruitment to the tumor immune microenvironment. In vitro, KLAK-MCP-1 micelles were found to bind and exert cytotoxicity against murine monocytes (WEHI-274.1) and had an IC50 value of 5.5 ± 0.3 M, which suggests monocytes are vulnerable to KLAK-MCP-1 micelle treatment. These preliminary results show that KLAK-MCP-1 micelle treatment is capable of affecting monocyte populations in vitro, although additional studies will need to be performed in the future to probe its effects on circulating monocytes in vivo. 2.3.9 Biocompatibility of Micelles in Vivo To assess any off-target toxicity upon KLAK-MCP-1 micelle treatment, biocompatibility was evaluated through histological analysis, as well as evaluation of serum chemistry markers pertinent to liver and kidney health. Upon H&E staining of the liver, kidneys, spleen, lungs, heart, intestines, and brain, no signs of morphological damage or differences in tissue morphology compared to the PBS control group were observed (Figure 2-7). 66 Figure 2-7. Evaluation of micelle biocompatibility through H&E staining of organs from mice treated with (i) PBS, (ii) MCP-1 micelles, (iii) KLAK-MCP-1 micelles, or (iv) KLAK-scr-MCP-1 micelles. Scale bar = 100 m. As micelle accumulation was highest in the liver and kidneys, liver function was evaluated by assessing serum activity of alanine aminotransferase (ALT) and aspartate aminotransferase (AST)351, 352 and kidney health assessed via serum levels of blood urea nitrogen (BUN) and creatinine353, 354. As shown in Table 2-4, no statistical differences were observed between treatment groups in any of the biomarkers, indicating no adverse effects upon micelle treatment. Table 2-4. Serum ALT, AST, BUN, and creatinine levels of mice treated with PBS, MCP-1, KLAK-MCP-1, or KLAK-scr-MCP-1 micelles (n = 4). Lung Spleen Liver Kidney (i) (ii) (iii) (iv) Brain Heart Intestine Treatment ALT (IU/L) AST (IU/L) BUN (mg/dL) Creatinine (mg/dL) PBS 10.6 ± 5.5 40.6 ± 18.1 16.7 ± 1.3 0.19 ± 0.07 MCP-1 15.3 ± 16.3 41.7 ± 4.7 16.7 ± 2.5 0.30 ± 0.09 KLAK-MCP-1 9.5 ± 2.8 50.4 ± 1.6 17.7 ± 2.1 0.42 ± 0.23 KLAK-scr-MCP-1 10.4 ± 8.5 54.0 ± 9.1 15.7 ± 2.3 0.28 ± 0.06 67 2.4 Conclusion In this study, we designed and synthesized KLAK-MCP-1 micelles to evaluate the potential of CCR2-targeted nanotherapies for the treatment of CCR2+ cancers which are clinically associated with unfavorable outcomes. Upon incubation with cancer cells in vitro, the KLAK-MCP-1 micelle was found to bind and induce cytotoxicity to multiple cancer cell lines in a CCR2-dependent manner. Administration of KLAK-MCP-1 micelles led to reduced tumor growth in a B16F10 melanoma model, and immunohistochemical (IHC) staining showed reduced TAM infiltration into the tumor, as well as reduced expression of markers of immunosuppression and angiogenesis within the tumor. Additionally, flow cytometry analysis of tumors showed KLAK-MCP-1 micelle treatment was able to increase the number of tumor-infiltrating cytotoxic T lymphocytes. Interestingly, biodistribution analyses showed micelle accumulation in the lymph nodes and low tumor retention. This study also corroborated recent reports of achieving therapeutic efficacy through systemic immune responses without the observation of significant nanoparticle accumulation within the tumor. Given the ability of KLAK-MCP-1 micelles to induce a systemic anticancer response, future studies will evaluate their therapeutic application in metastatic models of cancer. Additionally, the ability of the micelles to accumulate in the lymph nodes is an interesting result that merits further study. It is possible that the micelles and their peptide components are being processed by antigen presenting cells and used to generate an adaptive immune response. In summary, our studies demonstrate the application of CCR2-targeted micelles in inhibiting tumor growth by modulating tumor infiltration of immune cell populations. 68 Chapter 3. MRI Detection of Lymph Node Metastasis through Molecular Targeting of CCR2 and Monocyte Hitchhiking 3.1 Introduction, Objective, and Rationale Nearly 20 million patients are diagnosed with cancer each year globally355. Given that an estimated 90% of solid cancers metastasize through the lymph nodes (LNs), the accurate detection of LN metastasis is critical in cancer staging, treatment, and patient outcome356. For example, melanoma progression from regional LN to distant metastasis is associated with a 55% decrease in patient 5-year survival, demonstrating the importance of the detection and treatment of LN metastasis357. In addition, early detection of metastatic LN recurrence has been reported to increase patient survival from 14% to 36% by identifying patients that would benefit from adjuvant chemo- and immunotherapies358 . Despite the clear significance of metastatic LN detection in patient outcome, LN biopsies remain the clinical standard for detecting LN metastasis during initial cancer screening. However, because biopsies only evaluate regional LNs near the primary tumor, they are subject to falsenegative rates as high as 20%359. Moreover, LN biopsies are invasive and can lead to patient morbidities including infection, lymphedema, and thrombosis360, 361. In response to these shortcomings, non-invasive imaging tools have been explored for detecting LN metastasis but have yet to gain clinically adoption. Positron emission tomography/computed tomography (PET/CT) can combine anatomical and functional imaging but has low sensitivity for detecting LN metastasis (~11%) and requires the use of radioactive tracers and ionizing radiation362. Nearinfrared fluorescence (NIRF) imaging does not require radiation but is associated with poor tissue penetration (<2 cm), limiting its application to superficial imaging363. Ultrasonography (US) is a low-cost imaging modality that can detect larger LN metastases but is relatively insensitive for detecting micrometastases smaller than 1 cm (~7% sensitivity)364 . 69 MRI is another non-invasive imaging tool that produces three-dimensional anatomic images similar to PET/CT without the need for ionizing radiation, which is ideal for applications that require frequent imaging, such as screenings for LN metastasis or recurrence. However, MRI is limited by low sensitivity (~35%), as traditional gadolinium contrast agents cannot differentiate healthy from metastatic LNs due to nonspecific accumulation242. Instead, MRI scans are used to visualize LN size and shape to determine metastasis365. However, these physical characteristics are poor diagnostic factors for LNs with micrometastases (<1 cm), resulting in misdiagnosis rates as high as 60%366, 367 . In addition, the safety of clinical gadolinium contrast agents has been of concern, as they have been demonstrated to accumulate in the brain, bones, and liver368 . Given these limitations, nanomedicine approaches have the potential to develop contrast agents with superior sensitivity and safety over gadolinium contrast agents. For example, peptide amphiphile micelles (PAMs) are nanoparticles self-assembled from amphiphilic monomers comprised of a bioactive peptide headgroup and a hydrophobic lipid tail93. In addition to peptides, the headgroup can be engineered to incorporate other payloads, such as gadolinium (Gd) chelates to enable multimodal functionality including therapy, imaging, and molecular targeting302 . Furthermore, PAMs are synthesized using polyethylene glycol (PEG) as a linker, which mitigates nanoparticle clearance by the mononuclear phagocyte system (MPS)369. PAMs have also been demonstrated to be biocompatible and cleared within 7 days through renal filtration304. Given their multifunctionality, biocompatibility, and rapid clearance, PAMs represent an ideal nanoparticle carrier for targeted delivery of MRI contrast agents to metastatic LNs. To target metastatic LNs, we selected C-C chemokine receptor 2 (CCR2) as a molecular target, as CCR2 is expressed in most cancers and is further upregulated in metastatic LNs370 . This is due to the pro-tumoral functions of CCR2 signaling, including secretion of angiogenic factors, expression of proteases that promote metastasis, and suppression of adaptive immunity371 . In addition, metastatic cancer cells secrete monocyte chemotactic protein 1 (MCP1), 70 the natural ligand of CCR220. MCP1 binds to CCR2 expressed by inflammatory monocytes, resulting in chemotactic migration to the metastatic LN372. The inherent migration of monocytes in response to MCP1 has been reported as an effective strategy for targeted drug delivery373. For example, Kuang et al. developed liposomes that hitchhiked onto monocytes through CD14 binding, resulting in increased doxorubicin delivery to glioblastomas in vivo374. Hence, nanoparticle hitchhiking onto inflammatory monocytes through molecular targeting of CCR2 may provide an additional mechanism for the targeted delivery of MRI contrast agents to metastatic LNs. In this study, we synthesized PAMs functionalized with the CCR2-binding motif of MCP1 to target metastatic LNs and incorporated Gd to enable MRI detection (MCP1-Gd)24. We hypothesized that MCP1-Gd would detect LN metastasis earlier than clinical MRI through a combination of metastatic cancer cell targeting and monocyte hitchhiking (Figure 3-1). To test this hypothesis, we synthesized MCP1-Gd and confirmed in vitro biocompatibility, binding, and hitchhiking onto monocytes. When injected into in vivo models of LN metastasis and LN recurrence, MCP1-Gd targeted metastatic LNs and enabled MRI detection earlier than a clinical gadolinium contrast agent standard. Collectively, our studies demonstrate the potential of molecular CCR2 targeting for early detection of LN metastasis. 71 Figure 3-1. MCP1-Gd micelles accumulate in metastatic lymph nodes (MLNs) through CCR2-binding to cancer cells and monocyte hitchhiking. 3.2 Materials and Methods 3.2.1 Materials and Cells 1,2-distearoyl-sn-glycero-3phosphoethanolamine-N-[maleimide(polyethylene glycol)-2000] (DSPE-PEG2000-maleimide) and 1,2-distearoyl-sn-glycero-3phosphoethanolamine-N- [amino(polyethylene glycol)-2000] (DSPE-PEG2000-amine) were purchased from Avanti Lipids (Alabaster, AL, USA). Cy7 mono-N-hydroxysuccinimide (NHS) ester was purchased from Lumiprobe (Hunt Valley, MD, USA). DSPE-PEG2000-FITC was purchased from Creative PEGWorks (Durham, NC, USA). Diethylenetriamine-N,N,N”,N”-tetra-tert-butyl acetate-N’-acetic acid (DTPA-tetra(t-Bu ester)) was purchased from Macrocyclics (Plano, TX, USA). GdCl3 was purchased from Sigma Aldrich (St. Louis, MO, USA). Antibodies were purchased from Thermo Fisher (Waltham, MA, USA). Cell lines were purchased from the American Type Culture Collection (ATCC, Manassas, VA, USA). Cell culture reagents were purchased from Gibco (Waltham, MA, USA) and Sigma Aldrich. 3.2.2 Synthesis of MCP1-Gd Micelles MCP1 [CYNFTNRKISVQRLASYRRITSSK] and scrMCP1 [CYNSLVFRIRNSTQRKYRASIST] peptides were synthesized through Fmoc-mediated solid phase peptide synthesis and cleaved from the resin using a 94:2.5:2.5:1 vol% mixture of trifluoroacetic acid, water, ethanedithiol, and triisopropylsilane. Following ether precipitation and lyophilization, crude peptides were dissolved in milliQ (MQ) water and purified through high-performance liquid chromatography (HPLC, Prominence, Shimadzu, Columbia, MD, USA) on a Luna C18 reverse-phased column 72 (Phenomenex, Torrance, CA, USA) at 55 oC using water and acetonitrile supplemented with 0.1% formic acid as the mobile phases. The purity of eluted peptides was characterized using matrixassisted laser desorption/ionization time-of-flight mass spectroscopy (MALDI-TOF-MS, Bruker, MA, USA). Following purification, peptides were lyophilized, resuspended in MQ water, and mixed with a 10% molar excess of DSPE-PEG2000-maleimide. The pH of the mixture was then adjusted to 7.2 with 1M NaOH, incubated for 72 h at room temperature (RT) under constant agitation, purified through HPLC, and characterized via MALDI-TOF-MS. DSPE-PEG2000-Gd amphiphiles were synthesized by first combining DTPA-tetra(t-Bu ester) and DSPE-PEG2000-amine in peptide synthesis-grade dimethylformamide (DMF). To facilitate the formation of a peptide bond, 4x and 8x molar equivalents of the coupling reagent HCTU and N,N-Diisopropylethylamine (DIPEA), respectively, were added under constant stirring. Additional 1x and 2x molar equivalents of HCTU and DIPEA were added 12 h and 24 h following the start of the reaction. Crude DSPE-PEG2000-DTPA-tetra(t-Bu ester) was isolated through ether precipitation, lyophilized, and then purified through HPLC. Following HCl-mediated t-Bu ester deprotection, Gd3+ ions from GdCl3 were chelated to DSPE-PEG2000-DTPA-tetra in a 0.5 M sodium acetate (pH 5.5) solution for 1 h. Free Gd3+ ions were separated using a desalting column and successful synthesis of DSPE-PEG2000-Gd was confirmed via MALDI-TOF-MS. DSPE-PEG2000-Cy7 amphiphiles were synthesized by dissolving DSPE-PEG2000-amine in a 0.1 M sodium bicarbonate solution and mixing with a solution containing 3x molar equivalents of Cy7 NHS ester in peptide synthesis-grade DMF (reaction volume was 90% sodium bicarbonate and 10% DMF). The resultant mixture was protected from light, stirred overnight at RT, and then purified through HPLC before characterization via MALDI-TOF-MS. Micelles were self-assembled through thin-film hydration375. Briefly, MCP1, scrMCP1, DSPEPEG2000-Gd, DSPE-PEG2000-Cy7, and DSPE-PEG2000-FITC amphiphiles were dissolved in 73 methanol, mixed at desired molar ratios, sonicated, and placed under a gentle nitrogen stream to evaporate the methanol before hydration in MQ water, 1 mM NaCl, or PBS. 3.2.3 Transmission Electron Microscopy (TEM) 400-mesh carbon TEM grids (Ted Pella, Redding, CA, USA) were glow discharged before application of 5 µL of 30 µM micelles suspended in MQ water for 5 min. Samples were then wicked to remove excess liquid and washed with MQ water. Grids were stained twice with 2% uranyl acetate for 3 min before washing with MQ water. Grids were dried overnight and then imaged on a FEI Talos F200C microscope (Thermo Fisher). 3.2.4 Dynamic Light Scattering (DLS) Micelles were suspended at 25 µM in 1 mM NaCl, filtered, and then placed in a folded capillary zeta cell for size, zeta potential, and polydispersity measurements using a Zetasizer Ultra (Malvern Panalytical, Malvern, UK). N=3. 3.2.5 r1 Relaxivity of Micelles T1 relaxation times of MCP1-Gd micelles (0.2, 0.4, 0.6, 0.8, & 1.0 mM Gd) were evaluated using a 7T PET-MR system (MR Solutions Ltd., Guildford, UK) at the Zilkha Neurogenetic Institute (University of Southern California, Los Angeles, CA, USA). A 9-cm diameter radiofrequency body coil was used (bore size ≈24-mm, up to 600 mT m−1 maximum gradient). A T1-weighted variable flip angle fast low angle shot (FLASH) gradient echo sequence was used with the following imaging parameters on a 7T scanner to determine particle T1 relaxation times: echo time 74 (TE) = 5 ms, repetition time (TR) = 60 ms, flip angle (FA) array = (15°, 30°, 45°, 70°, and 80°), field of view (FOV) = 36 × 36 mm, slice thickness = 1 mm, image matrix = 192 × 192, number of averages (NA) = 4. A multi-echo multi-slice spine echo sequence (MEMS) was used to determine T2 relaxation times of the particle with the following parameters: ten echoes with 15 ms interval beginning with TE = 15, TR = 2500, FOV = 36 × 36 mm, slice thickness = 1 mm, matrix size = 256 × 256, NA = 1. Using ROCKETSHIP v.1.1 code in MATLAB (R2014b), T1 and T2 maps were generated through a pixel-by-pixel exponential fitting of signal intensities across the different FA and TE values, respectively. Regions of interest (ROIs) were manually drawn around each concentration of MCP1-Gd micelles that were measured using ImageJ. R1 maps were generated by taking the inverse of the T1. Using R1 values, r1 relaxivity was determined (R1/concentration of Gd) over Gd concentrations of 0.2, 0.4, 0.6, 0.8, and 1.0 mM. 3.2.6 Cell Culture SVEC4-10 cells were cultured in Dulbecco’s Modified Eagle Medium (DMEM) with 4.5 g/L glucose supplemented with 10% fetal bovine serum (FBS) and 1% penicillin and streptomycin (PS). Cells were maintained below 80% confluency and culture media was renewed every 2-3 days. B16F10-Luc2 cells were cultured in DMEM with 4.5 g/L glucose supplemented with 10% FBS, 1% PS, and 10 µg/mL blasticidin. Cells were maintained below 80% confluency and culture media was renewed every 2-3 days. NCI-H460 cells were cultured in Roswell Park Memorial Institute (RPMI) 1640 medium supplemented with 10% FBS and 1% PS. Cells were maintained below 80% confluency and culture media was renewed every 2-3 days. WEHI-274.1 cells were cultured in DMEM with 4.5 g/L glucose supplemented with 10% FBS, 0.05 mM 2-mercaptoethanol, and 1% PS. Cells were passaged every 2-3 days. All cells were cultured in a humidified incubator set to 37 °C and 5% CO2. 75 3.2.7 In Vitro Micelle Biocompatibility 4,000 SVEC4-10 or WEHI274.1 cells were seeded into the wells of a 96-well plate and cultured overnight before the addition of MCP1-Gd or scrMCP1-Gd micelles (1, 10, or 100 µM) or PBS. After 48 h of incubation, cell viability was measured using the MTS Cell Proliferation Assay Kit from BioVision (Milpitas, CA, USA). Viability was calculated as a percentage by normalizing sample absorbance measurements to measurements of a PBS-treated control. N=6. 3.2.8 In Vitro Micelle Binding (Microtiter Plate Reader) For adherent cells (B16F10-Luc2 and NCI-H460), 10,000 cells were seeded into black 96-well plates and cultured overnight. Cells were then incubated with 25 µM of Cy7-labeled MCP1-Gd or scrMCP1-Gd micelles for 1 h at 37 °C before 2 PBS washes and quantification of fluorescence (ex./em. 750/773). For non-adherent cells (WEHI-274.1), 10,000 cells were incubated with 25 µM of Cy7-labeled MCP1-Gd or scrMCP1-Gd micelles for 1 h at 37 °C in plastic microtubes. After 2 PBS washes, cells were resuspended in 100 µL PBS and transferred to black 96-well plates for fluorescence measurement. N=6. 3.2.9 In Vitro Micelle Binding (Confocal Microscopy) For adherent cells (B16F10-Luc2 and NCI-H460), 250,000 cells were seeded onto 22 mm x 22 mm glass coverslips in 6-well plates and allowed to adhere overnight at 37 °C. Then, cells were incubated with 25 µM of Cy7-labeled MCP1-Gd or scrMCP1-Gd for 1 h at °C before 2 PBST washes and fixation with 4% PFA for 10 min at RT. After subsequent PBST washes, cells were blocked with 5% NGS for at least 30 min and then incubated overnight at 4 °C with a primary 76 CCR2 antibody (Thermo Fisher PA5-23043, 1:50). Afterwards, cells were washed 2x with PBST and incubated with a secondary antibody conjugated with AF594 (Thermo Fisher A-11037, 1:500) for 1 h at RT. Following PBST washes, cells were counterstained with 2 µg/mL DAPI for 5 min at RT, mounted onto microscope slides using VectaMount, sealed with nail polish, and then imaged using a Zeiss LSM 880 confocal microscope (Zeiss, Oberkochen, Germany). For non-adherent cells (WEHI-274.1), 500,000 cells were incubated with 25 µM of Cy7-labeled MCP1-Gd or scrMCP1-Gd in plastic microtubes for 1 h at 37 °C. Cells were then washed with PBS, fixed using 4% PFA, and then spun onto 22 mm x 22 mm glass coverslips at 500g for 5 min before immunostaining and imaging as described above. 3.2.10 In Vitro Micelle Hitchhiking To develop an in vitro model of a lymphatic endothelial barrier, 24-well transwell membrane inserts with 5 µm pores were coated with 0.1% gelatin for 1 h at RT. After washing the membrane twice with PBS, 75,000 SVEC4-10 cells were seeded and cultured for 72 h to form a monolayer376 . Monolayer formation and barrier function was evaluated by placing FITC-dextran (MW 4,000) in the top chamber, incubating for 1 h at 37 °C, and then quantifying FITC fluorescence in the bottom chamber. Following monolayer formation, transwell systems were incubated at 37 °C for 4 h under the following 3 conditions: 1) MCP1-Gd or scrMCP1-Gd micelles in top chamber, 2) MCP1-Gd or scrMCP1-Gd micelles and monocytes in top chamber, 3) MCP1-Gd or scrMCP1-Gd micelles and monocytes in top chamber, and chemoattractant (FBS) in bottom chamber. A micelle concentration of 25 µM was used for the hitchhiking experiments. After incubation, media was sampled from the bottom chamber to evaluate micelle transport through Cy7 fluorescence and monocyte migration through dsDNA quantification using the Quant-iT PicoGreen assay (Thermo Fisher). N=3. 77 3.2.11 In Vivo metastatic Lymph Node Mouse Model To develop an in vivo metastatic lymph node model, 100 µL of a 1:1 mixture of Matrigel (Corning, Corning, NY, USA) and PBS containing 200,000 luciferase-expressing B16F10-Luc2 cells was injected into the flanks of C57BL/6 mice. After 2 weeks, mice were injected i.p. with 200 uL of a 15 mg/mL suspension of D-luciferin in sterile PBS and bioluminescence imaging was performed every 3-5 min to determine the kinetic profile of luciferase activity and optimal timepoint for dosage. Mice were injected with D-luciferin and euthanized 2 weeks after tumor inoculation and ipsilateral and contralateral inguinal lymph nodes were collected and bioluminescence imaging was performed to evaluate metastasis. In addition, lymph nodes were processed into single-cell suspensions and cultured for 2 weeks in culture media containing 10 µg/mL blasticidin to determine the presence of B16F10-Luc2 cells within the lymph nodes. 3.2.12 In Vivo Lymph Node Recurrence Model To simulate lymph node recurrence following surgical resection of a primary tumor, the mouse model described above was first developed, and then the primary tumor was surgically removed 12 d after tumor implantation using a linear incision. Wounds were sutured shut using nonadsorbable nylon sutures. Mice were then euthanized 7 d post-surgery to evaluate the presence of cancer in the LNs through bioluminescent imaging. 3.2.13 In Vivo Lymph Node Accumulation of MCP1-Gd Metastatic lymph node models or lymph node recurrence models were established as described above and then 100 µL of 2.2 mM Cy7-labeled MCP1-Gd were then injected via the 78 tail-vein. MRI scans were taken 3 h post-injection of micelles as described below. Mice were euthanized immediately following the MRI scans, and their lymph nodes and other tissues were collected for subsequent ex vivo imaging and histology. A clinically used formulation of DTPA-Gd, Magnevist, was used as a control contrast agent. N=3 or 6. 3.2.14 MRI Scans Mice were anesthetized with 2.5% isoflurane at a flow rate of 250 µL/min, before transfer onto a heated scanner bed with isoflurane levels between 1.5% and 2%. Temperature was monitored and maintained at 37 °C. Electrocardiogram (ECG) was monitored and used for gating by inserting two leads subcutaneously on the anterior side close to each axilla and inserting another lead subcutaneously on the lower right abdomen. A pneumatic pillow was placed underneath the mice for respiration monitoring and gating. The mice were positioned at the magnet bore isocenter using a motorized system. Temperature, respiration, and ECG were monitored using SAII equipment and accompanying PC-SAM software. A bird cage whole mouse body coil, with an axial field of view of 60 cm and 35 mm inner diameter, was used during ECG and respiration gated cardiac imaging. A gradient echo scan was used to obtain three orthogonal slices for positioning. A series of multi slice T1-weighted FSE (TE = 11 ms, TR = 1500 ms, FA = 90°, FOV = 32 × 32 mm, slice thickness = 0.5 mm, matrix size = 192 × 192, NA = 2) sequences were used to image the inguinal lymph nodes. ImageJ was used to measure T1 signal intensity and calculate signal-to-noise ratios (SNRs). 3.2.15 Histology 79 Following dissection, tissues (liver, kidneys, spleen, lungs, heart, small intestines, brain, inguinal lymph nodes) were embedded in optimal cutting temperature (OCT) compound (Sakura Finetek, Torrance, CA, USA) and snap frozen using isopentane and liquid nitrogen. Then, frozen tissues were cryosectioned into 10 µm slices using a CM3050 S Cryostat (Leica, Nussloch, Germany). Tissues sections were stained with hematoxylin and eosin or processed for immunohistochemical (IHC) staining and imaging. For IHC staining, slides were fixed in 10% neutral-buffered formalin, washed with TBST, blocked with 5% NGS, immunostained for CCR2 (1:50) and LYVE1 (1:50), counterstained with 2 µg/mL DAPI, and imaged using a DMi8 inverted fluorescence microscope (LEICA, Wetzlar, Germany). 3.2.16 In Vivo Monocyte Depletion The CCR2 antagonist RS102895 was dissolved in dimethyl sulfoxide (DMSO) at a concentration of 12.5 mg/mL and then diluted 20x in 0.9% saline. Mice were injected i.p. with 200 µL of the diluted RS102895 solution every 6 h for 18 h (4 total injections) to deplete monocytes377 . 24 h after the first RS102895 injection, 100 µL of 2.2 mM FITC- or Cy7-labeled MCP1-Gd or scrMCP1-Gd were injected via the tail-vein, and mice were euthanized 3 h post-injection. Tissues including the inguinal lymph nodes were collected for ex vivo optical imaging, flow cytometry, or immunofluorescent staining. Control mice were injected with PBS. 3.2.17 Flow Cytometry All flow cytometry experiments were run on a CytoFLEX cytometer (Beckman Coulter, Brea, CA, USA) and analyzed using FlowJo. The flow cytometry buffer used was sterile PBS supplemented with 0.1% bovine serum albumin (BSA) and 1 mM ethylenediaminetetraacetic acid 80 (EDTA). Tissue samples were prepared by rough-cutting the tissues with sterile scissors and gently pushing the resultant pieces through 70-µm cell strainers to yield single-cell suspensions that were then centrifuged at 500 g for 5 min at RT. After discarding the supernatant, the cell pellet was resuspending in 100 µL of flow cytometry buffer and immunostained for CD11b (Miltenyi 130- 113-238, San Diego, CA, 1:50) and CCR2 (Miltenyi 130-119-658, 1:50) 10 min in the dark at 4 °C. Samples were then washed once more with flow cytometry buffer, resuspended at 1 million cells/mL, and analyzed in the flow cytometer. 3.2.18 In Vivo Immunogenicity C57BL/6 mice were injected once per week with 2.2 mM Cy7-labeled MCP1-Gd intravenously through the tail-vein for 4 weeks (4 total injections). Blood samples were collected via tail-vein bleed prior to each injection using heparinized capillary tubes. Plasma was collected by spinning blood samples at 1,000 g for 10 min at 4 °C and collecting the supernatant. Plasma samples were then assayed for anti-PEG IgG and IgM via ELISA according to the manufacturer instructions. 3.2.19 Plasma Half-life Blood samples from naïve mice or mice injected weekly with 2.2 mM MCP1-Gd (3 prior doses) were collected via the tail-vein 5 min, 1 h, 3 h, 6 h, 12 h, and 24 h following injection with Cy7- labeled MCP1-Gd and then spun at 1,000 g for 10 min at 4 °C to obtain plasma. Plasma samples were then analyzed for Cy7 fluorescence using a microplate reader to determine MCP1-Gd halflife. 81 3.2.20 Blood Chemistry Markers of Renal and Liver Health Plasma samples from mice dosed weekly with MCP1-Gd as described above or with PBS were evaluated for hepatic and renal health markers using ALT, AST, BUN, and creatinine kits (Sigma Aldrich, St. Louis, MO) as directed by manufacturer instructions. 3.2.21 Statistical Analysis Data are expressed as mean ± SD. Statistical analysis between two groups was performed using a Student's t-test. Comparisons of three or means were performed through analysis of variance (ANOVA) followed by post-hoc Tukey's tests for multiple comparisons. A p-value <0.05 was considered to be statistically significant. 3.3 Results and Discussion 3.3.1 Synthesis and Characterization of MCP1-Gd Monocyte chemotactic protein 1 (MCP1) is a chemokine whose primary function is to bind to circulating monocytes through the C-C chemokine receptor 2 (CCR2) and induce monocyte migration towards sites of inflammation, such as metastatic lymph nodes (LNs)378. To enable specific binding, we synthesized a 23-residue peptide derived from the CCR2-binding motif of MCP1 (MCP1 peptide), as well as a scrambled peptide control (scrMCP1) using solid phase peptide synthesis23. Peptides were then purified through high-performance liquid chromatography (HPLC) and characterized through matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). After purification, peptides were conjugated to DSPE-PEG2000- maleimide through thioether linkage to form peptide amphiphiles. Gadolinium (Gd) and Cy7 82 amphiphiles were similarly synthesized, purified, and characterized following conjugation to DSPE-PEG2000-amine through peptide bonds379 . MCP1-Gd and control micelles were synthesized through thin-film hydration (Figure 3-2A). Transmission electron microscopy (TEM) indicated MCP1-Gd micelles were spherical with a mean diameter of 12.3 nm, which was corroborated by dynamic light scattering (DLS) (Figure 3-2B and 3-2C). To characterize the feasibility of MCP1-Gd as a diagnostic tool for MRI, we used a 7T PET-MR system to determine the r1 relaxivity of MCP1-Gd as 1.66 mM-1s -1 , which is comparable to the relaxivity of the clinical contrast agent Magnevist (Gd-DTPA) (3.20 mM-1s -1 ) (Figure 3-2D) 380 . 3.3.2 In Vitro MCP1-Gd Biocompatibility, Binding, and Hitchhiking To characterize the in vitro biocompatibility of MCP1-Gd, we incubated SVEC4-10 lymphatic endothelial cells or WEHI-274.1 monocytes with MCP1-Gd or scrMCP1-Gd for 48 h and observed no cytotoxicity (>90% viability) (Figure 3-2E). After confirming biocompatibility, we incubated CCR2-expressing B16F10-Luc2 melanoma cells and WEHI-274.1 monocytes with MCP1-Gd or scrMCP1-Gd in vitro to verify CCR2 binding and specificity. B16F10-Luc2 cells treated with MCP1-Gd had 50% more Cy7 fluorescence than cells treated with scrMCP1-Gd, indicating increased targeting of MCP1-Gd (Figure 3-2F, p<0.01). Similarly, MCP1-Gd had 7-fold more binding to WEHI-274.1 monocytes than scrMCP1-Gd (Figure 3-2G, p<0.001). As a control, we repeated the binding assay using NCI-H460 cells, a CCR2- lung cancer cell line, and observed no differences in binding, confirming that MCP1-Gd binding is dependent on CCR2 expression338 . Recent studies have reported that molecular targeting can enable nanoparticle hitchhiking onto monocytes, which facilitates transport across endothelial barriers and thus enhances delivery to target tissues381. To assess the effect of monocyte hitchhiking on MCP1-Gd transport, we developed an in vitro model of the lymphatic endothelium by seeding SVEC4-10 cells into a 83 transwell insert. After allowing the cells to form a monolayer, we evaluated MCP1-Gd transport across the endothelial barrier with or without migrating monocytes (Figure 3-2H). Incubating MCP1-Gd with migrating monocytes increased micelle transport 2-fold compared to micelle only controls (p<0.005). Importantly, there was no change in transport when micelles were incubated with non-migrating monocytes (no chemoattractant). In addition, no changes in micelle transport were observed when scrMCP1-Gd was incubated with monocytes, indicating that monocyte hitchhiking is facilitated by CCR2-targeting and improves MCP1-Gd transport across endothelial barriers in vitro, representing an additional mechanism for metastatic LN targeting. 84 85 Figure 3-2. Synthesis and characterization of MCP1-Gd micelles. A) Scheme depicting the self-assembly of MCP1-Gd micelles. B) TEM image confirming synthesis of MCP1-Gd micelles. Scale bar = 50 nm. C) Size (blue) and zeta potential (red) of MCP1-Gd micelles. N=3. D) Phantom scans of MCP1-Gd micelles ranging from 0.0 - 1.0 mM Gd indicate an r1 relaxivity of 1.66 mM-1s -1 . E) MCP1-Gd and scrMCP1-Gd are biocompatible with SVEC4-10 lymphatic endothelial cells and WEHI-274.1 monocytes after 48 h. N=6. In vitro micelle binding to F) B16F10-Luc2 and G) WEHI-274.1 cells after 1 h incubation demonstrate increased binding of MCP1-Gd. Data normalized to scrMCP1-Gd. Scale bar = 50 µm. **p<0.01. ****p<0.001. N=6. H) In vitro transwell assay evaluating the role of monocyte hitchhiking in MCP1-Gd transport across an endothelial barrier, indicating a 2-fold increase micelle transport when incubated with migrating monocytes. Data normalized to micelle only control. ***p<0.005. N=3. 3.3.3 In Vivo Targeting of MCP1-Gd in a Metastatic Lymph Node (MetLN) Model After confirming that monocyte hitchhiking improved MCP1-Gd transport across a lymphatic barrier in vitro, we investigated MCP1-Gd targeting of metastatic LNs in vivo. First, we developed a metastatic LN (MetLN) mouse model by implanting luciferase-expressing B16F10-Luc2 cells into the flanks of C57BL/6 mice. After 14 days, bioluminescence imaging confirmed the presence of metastasis in the ipsilateral inguinal LN (MLN), while no metastasis was detected in the contralateral inguinal LN (CLN, Figure 3-3A). After confirming the development of the in vivo MetLN model, we characterized CCR2 expression in the LNs of MetLN mice. Immunofluorescent staining of sectioned LNs indicated increased CCR2 expression in MLNs compared to CLNs (Figure 3-3B). In addition, flow cytometry indicated that MLNs contained more CCR2+ cells, confirming the feasibility of CCR2 as an in vivo biomarker for MLNs (Figure 3-3C, p<0.05). To further determine the viability of CCR2 as a molecular target for LN metastasis, we isolated B16F10-Luc2 cells from metastatic LNs and used PCR and flow cytometry to evaluate CCR2 expression (Figure 3-3D). In agreement with literature reports correlating CCR2 with cancer progression and metastasis, we observed increases in both mRNA and protein levels of CCR2 following LN metastasis (p<0.01)309, 322, 382. These characterization studies indicate that CCR2 is highly expressed in MLNs and thus an ideal biomarker for MLN targeting in vivo. After verifying increased CCR2 expression in MLNs, we injected MCP1-Gd into MetLN mice and performed MRI scans 3 h after injection to evaluate MLN detection (Figure 3-3E). T1- 86 weighted signal and SNR was 50% higher in MLNs compared to CLNs, confirming MLN detection through molecular targeting of CCR2 (Figure 3-3F, p<0.01). Furthermore, we observed no differences in signal nor SNR between LNs of mice administered with the clinical contrast agent Magnevist, demonstrating the advantages of MLN targeting for MRI. Ex vivo imaging of LNs corroborated the findings from the MRI scans, with the Cy7 signal in the MLNs approximately 2.5x greater than the CLNs following MCP1-Gd injection (Figure 3-3G, p<0.001). Lastly, we used ImageJ to characterize LN size, as LN enlargement is used clinically to identify LN metastasis via MRI383, 384. As shown in Figure 3-3H, MLN size was not altered relative to CLNs (1.31 mm vs. 1.20 mm, p=0.358). Moreover, none of the LNs measured were larger than 2 mm, the reported upper limit for normal LN size in C57BL/6 mice, reflecting literature reports indicating the poor diagnostic power of LN size in MRI and highlighting the need for improved diagnostic tools for identifying LN metastasis385, 386. These studies demonstrate that through molecular targeting of MLNs, MCP1-Gd enables MRI detection of LN metastasis earlier than clinical contrast agents. 87 Figure 3-3. In vivo lymph node accumulation of MCP1-Gd in a metastatic lymph node (MetLN) model. A) Lymph node metastasis is confirmed through bioluminescence imaging of the ipsilateral inguinal lymph node 14 days after implantation of B16F10-Luc2 cells into the flanks of C57BL/6 mice. B) Immunofluorescent staining of sectioned LNs indicates greater CCR2 expression (green) in metastatic LNs (MLNs) than control LNs (CLNs). Sections were counterstained with DAPI (blue). Scale bar = 100 µm. C) Flow cytometric analysis indicates greater CCR2 expression in MLNs than CLNs. *p<0.05. N=4. D) Evaluation of CCR2 mRNA and cell surface expression through PCR (left) and flow cytometry (right) 88 indicate an increase in CCR2 following LN metastasis. **p<0.01. ****p<0.001. N=6 for PCR and N=4 for flow cytometry. E) Representative T1-weighted MRI scans of MetLN mice 3 h post-injection of MCP1-Gd or Magnevist. Purple and red boxes denote MLNs and CLNs, respectively. F) Normalized MRI signal (top) and SNR (bottom) in MLNs and CLNs of MetLN mice i.v. injected with MCP1-Gd or Magnevist, indicating MLN targeting of MCP1-Gd. **p<0.01. ***p<0.005. N=6. G) Normalized radiance measurements of LNs from MetLN mice indicate greater MCP1-Gd specificity for MLNs. ****p<0.001. N=6. H) Quantification of short-axis diameters of LNs from MRI scans of mice containing LN metastases indicates no difference between MLNs and CLNs, confirming that LN size is a poor indicator of LN metastasis. N=12. 3.3.4 In Vivo Targeting of MCP1-Gd in a Metastatic LN Recurrence (rMLN) Model The early detection of melanoma recurrence identifies patients for adjuvant chemo- and immunotherapies, which has been reported to more than double patient survival rates387. Since the LNs are the most common site of recurrence in melanoma patients, we next tested the potential of MCP1-Gd as a follow-up diagnostic tool for evaluating recurrent LN metastasis following surgical resection of a primary tumor388, 389. We developed an in vivo model of metastatic LN recurrence (rMLN) by implanting B16F10-Luc2 cells into the flanks of C57BL/6 mice and resecting the tumors after 12 days. The presence of rMLNs was confirmed through bioluminescence imaging 7 days following surgery. Then, as shown in Figure 3-4A, we injected MCP1-Gd into rMLN mice and performed MRI scans, which indicated 30% higher signal intensity and SNR in rMLNs relative to CLNs, confirming detection of metastasis (Figures 3-4B & 3-4C, p<0.05). Ex vivo fluorescence imaging also demonstrated greater MCP1-Gd signal in the rMLNs, approximately 2-fold that of the CLNs (Figure 3-4D, p<0.05). In addition, confocal microscopy images indicated that MCP1-Gd was distributed throughout the rMLN, while micelles in the CLN were limited to the outer capsule (Figure 3-4E). This difference in distribution could be due to increased monocyte hitchhiking to the rMLNs, as monocytes enter the LNs via high endothelial venules (HEVs) located within the inner cortex285. Collectively, these studies demonstrate the potential of MCP1-Gd as a follow-up diagnostic tool for the identification of metastatic recurrence in the LNs. 89 Figure 3-4. In vivo LN accumulation and distribution of MCP1-Gd in mice with metastatic LN recurrence (rMLN). A) rMLN is established in 7 days after surgical resection of primary B16F10-Luc2 tumors and confirmed through bioluminescence imaging. B) Representative MRI scan of C57BL/6 mouse with rMLN. Purple and red boxes denote rMLN and CLN, respectively. C) Normalized MRI signal (left) and SNR (right) indicate rMLN targeting of MCP1-Gd 3 h after injection. *p<0.05. N=3. D) Normalized radiance measurements from rMLNs and CLNs taken 3 h post-injection of MCP1-Gd confirm rMLN targeting. *p<0.05. N=3. E) Immunofluorescent stains of sectioned rMLNs and CLNs indicate greater CCR2 expression (green) and MCP1-Gd accumulation (red) in rMLNs. Scale bar = 250 µm. 3.3.5 Evaluation of MCP1-Gd Hitchhiking onto Monocytes in Vivo After demonstrating MCP1-Gd detection of MLNs and rMLNs, we then tested if monocyte hitchhiking improved MCP1-Gd targeting of MLNs in vivo. Flow cytometry confirmed that MLNs contained approximately 80% more CD11b+ /CCR2+ inflammatory monocytes than CLNs, corroborating studies that reported increased monocyte recruitment to LNs under inflammatory conditions (Figure 3-5A, p<0.01)390, 391. Then, we depleted monocytes in C57BL/6 mice through intraperitoneal injections of the CCR2 antagonist RS102895 to elucidate the role and contribution of monocyte hitchhiking on LN targeting (Figure 3-5B). Flow cytometry indicated >75% reduction 90 in CCR2+ cells following RS102895 treatment, confirming monocyte depletion. After injection of MCP1-Gd, ex vivo fluorescence imaging indicated 80% reduction in LN targeting in monocytedepleted mice (Figure 3-5C, p<0.005). However, no difference was observed in the scrMCP1-Gd group, which demonstrates that molecular targeting of CCR2 facilitates monocyte hitchhiking and enhances LN delivery. In addition, flow cytometry indicated that monocyte depletion reduced the percentage of LN cells bound with MCP1-Gd from 37.9% to <2% (p<0.005), while no change was observed in scrMCP1-Gd, further indicating that CCR2 targeting facilitates LN accumulation. Given that monocytes enter the LNs through HEVs in the inner cortex, we next investigated if monocyte-hitchhiking affected micelle distribution within the LN282. We sectioned LNs from mice injected with MCP1-Gd or scrMCP1-Gd and immunostained for CCR2, as well as LYVE1, which identifies lymphatic endothelial cells located in the outer capsule of LNs (Figure 3-5E). 48% of micelle signal was observed in the outer region of the LN in the MCP1-Gd group, increasing to 65% in the scrMCP1-Gd group, indicating that molecular targeting of CCR2 led to increased MCP1-Gd delivery into the LN cortex, where cells of the adaptive immunity are located. In totality, these mechanistic studies identified monocyte hitchhiking as the primary mechanism for MCP1- Gd accumulation in the LNs and corroborates recent studies reporting the potential of immune cell hitchhiking for enhancing nanoparticle delivery392, 393 . 91 Figure 3-5. Evaluation of monocyte role in LN accumulation of MCP1-Gd. A) Flow cytometry indicates increased number of CD11b+ /CCR2+ monocytes in MLNs. **p<0.01. N=4. B) Experimental timeline for in vivo monocyte depletion studies. C) Cy7 radiance measurements from ex vivo images of LNs collected from monocyte-depleted or PBS-treated mice injected with MCP1-Gd or scrMCP1-Gd indicate LN accumulation of MCP1-Gd is reduced by 80% following monocyte depletion. Data normalized to PBStreated mice. ***p<0.005. D) Flow cytometric analysis of LNs from monocyte-depleted or PBS-treated mice indicate monocyte depletion reduces the percentage of LN cells bound to MCP1-Gd by >90%. ***p<0.005. E) Immunofluorescent staining of LN sections from mice injected with MCP1-Gd or scrMCP1-Gd evaluating CCR2 (green), LYVE1 (blue), and micelle distribution (red). Scale bar = 250 µm. 3.3.6 In Vivo Safety and Biocompatibility of MCP1-Gd 92 LN screenings for metastatic recurrence are performed every 3 to 12 months following surgery, depending on the size and thickness of the primary tumor394. Hence, diagnostic tools used in these screenings must be safe for repeated use without inducing toxicity. Thus, the in vivo immunogenicity, plasma half-life, and biocompatibility of MCP1-Gd was characterized over the course of 4 weeks following weekly MCP1-Gd doses (Figure 3-6A). Recent literature has reported the development of anti-PEG immunogenic responses following repeated exposure of PEGylated biomaterials, resulting in accelerated blood clearance, reduced efficacy, and detrimental hypersensitivity reactions395-397. Hence, we tested the potential immunogenicity and characterized the plasma half-life of MCP1-Gd in mice before and after exposure to MCP1-Gd. As shown in Figure 3-6B, anti-PEG IgG and IgM titers remained unchanged over the duration of MCP1-Gd exposure, indicating that MCP1-Gd does not induce an immunogenic response against PEG. Then, we evaluated the plasma half-life of MCP1-Gd in naïve mice, as well as in mice that had been exposed to 3 prior doses of MCP1-Gd, as accelerated blood clearance of nanomaterials can indicate immunogenic response and reduced efficacy (Figure 3-6C) 398. No difference was observed in the plasma half-lives of MCP1-Gd in naïve and pre-exposed mice, further indicating the safety of repeated MCP1-Gd injection. MCP1-Gd clearance was also calculated, and while clearance in the pre-exposed mice increased ~22% compared to naïve mice, this difference did not reach statistical significance (0.204 mL/hr vs. 0.167 mL/hr, p=0.238). In addition, H&E-stained tissues indicated no morphological damage associated with repeated MCP1-Gd dosing, further corroborating its safety as an MRI contrast agent (Figure 3-6D). Because clinical gadolinium contrast agents like Magnevist have been reported to induce nephrotoxicity even after 1 dose, we then performed renal toxicity assays using plasma from mice treated with 4 weekly doses of MCP1-Gd (Figure 3-6E) 399, 400. In addition, hepatic toxicity was also evaluated, since PAMs are also cleared by the liver401. No differences were observed in the renal (BUN & creatinine) and hepatic (ALT & AST) health markers between mice treated with MCP1-Gd or PBS, confirming the long-term safety of MCP1-Gd. 93 Figure 3-6. In vivo safety and biocompatibility of MCP1-Gd. A) Timeline depicting experimental procedure for in vivo immunogenicity studies. B) ELISA evaluating anti-PEG IgG and IgM indicates nonimmunogenicity of MCP1-Gd. N=4. C) Plasma half-life of MCP1-Gd is unchanged between naïve mice and mice with prior exposure to MCP1-Gd following intravenous administration of micelles. N=4. D) H&E stains of tissues collected from mice treated weekly with MCP1-Gd or PBS indicate no morphological damage associated with MCP1-Gd treatment. I) Liver, II) Kidney, III) Spleen, IV) Lung, V) Heart, VI) Intestine, VII) Brain, VIII) Lymph node. Scale bar = 125 µm. E) Evaluation of blood chemistry markers indicate no effect of weekly MCP1-Gd treatment on renal (BUN, creatinine) or hepatic (ALT, AST) health in mice relative to PBS-treated controls. N=4. 3.4 Conclusion Despite the improved prognosis and survival benefit associated with chemo- and immunotherapy of lymph node (LN) metastasis and recurrence, early detection remains a 94 significant obstacle. Current clinical tools including biopsy and MRI are limited by health risks or lack sensitivity for detecting early metastasis402. Targeting nanoparticles to cancer-specific biomarkers as well as to tumor-tropic monocytes has been reported to increase chemotherapy drug delivery to tumor tissues but has yet to be explored in the context of metastatic LN imaging and diagnostics403. In this study, we developed MCP1-Gd micelles for the imaging and detection of LN metastasis and recurrence through molecular targeting of CCR2, which enables both metastatic cancer cell binding and monocyte hitchhiking for targeted delivery of Gd to metastatic LNs. MCP1-Gd was biocompatible in vitro and exhibited CCR2-specific binding and hitchhiking onto monocytes, resulting in enhanced transport across lymphatic endothelium. In vivo models of LN metastasis and LN recurrence were developed and utilized to verify MCP1-Gd targeting of metastatic LNs, which enabled earlier cancer detection than conventional MRI. Furthermore, we demonstrated that MCP1-Gd accumulation in LNs is facilitated by circulating monocytes, as depletion of these cells in vivo resulted in significant reduction in micelle targeting to the LNs. Given the mechanism of monocyte-targeting, it is plausible that MCP1-Gd could accumulate in tissues or pathologies characterized by high monocyte infiltration that are unrelated to lymph node metastasis, representing both a potential for use in broader diagnostic applications, as well as a potential for false-positives. Thus, additional studies examining MCP1-Gd biodistribution and accumulation within contexts of benign LN inflammation will be needed in the future to provide valuable insight into their sensitivity and capability as a diagnostic tool for recurrent LN metastasis. In summary, we report the potential of CCR2-targeting micelles for MRI detection of metastatic LNs. 95 Chapter 4. CD70-targeted Micelles Enhance HIF2α siRNA Delivery and Inhibit Oncogenic Functions in Patient-derived Clear Cell Renal Carcinoma Cells 4.1 Introduction, Objective, and Rationale Clear cell renal cell carcinoma (ccRCC) comprises 75–80% of all kidney cancers, with 50% of high-risk patients relapsing and a five-year survival rate of less than 20% for those with metastatic disease404-406. The current clinical standard for the systemic treatment of ccRCC consists of combination therapy using an immune checkpoint inhibitor (ICI), such as an anti-PD1 antibody with either a CTLA-4 antibody or a VEGFR tyrosine kinase inhibitor (TKI). However, adverse events (AEs) related to treatment toxicity remain common407. For example, in the CLEAR phase III clinical trial in which 352 patients were treated with a combination of the PD1 antibody pembrolizumab and the TKI lenvatinib, grade 3–4 AEs were observed in 82% of patients, resulting in 68% of patients receiving dose reductions and another 13% discontinuing treatment408. Thus, the development of efficacious alternative therapies that minimize patient AEs remains a major need409, 410 . To boost treatment efficacy and minimize toxicity, therapies targeted to specific molecular targets of ccRCC have been recently explored. For example, 70–90% of ccRCCs are characterized by loss of function of the Von Hippel–Lindau (VHL) tumor suppressor gene, which suppresses the activity of and destabilizes hypoxia-induced factors (HIFs)411-415. This has generated interest in the development of therapies targeting HIFs, especially HIF2α, which when overexpressed upregulates oncogenes including SLC2A1, CCND1, VEGFA, CXCR4, and CXCL12 that support cancer progression through increased glucose transport and glycolysis, cell cycle progression, angiogenesis, and cell migration416-419. To that end, in 2021, a selective inhibitor of HIF2α (belzutifan) was FDA approved for VHL disease and is in further clinical 96 development for metastatic ccRCC. Despite efficacy, belzutifan also suppresses HIF2α/erythropoetin in hepatocytes leading to anemia in 90% of treated patients420-423 . To limit off-target toxicity, nanocarriers with tunable size, charge, and surface properties can be utilized to increase the specificity of drug delivery to target tissues424-427. For example, studies by our and other groups have shown that nanoparticles less than 100 nm in size achieve the greatest kidney accumulation93, 428-430 . In this proof-of-concept study, we developed peptide amphiphile micelles (PAMs) that are 8–20 nm in size and incorporate siRNA targeted to HIF2α. In order to increase the specificity of HIF2α siRNA delivery to ccRCC cells, PAMs were functionalized with a targeting ligand capable of binding to CD70, a transmembrane protein reported to be highly expressed in ccRCCs431, 432. The chosen targeting ligand is a 13-mer peptide derived from CD27, the binding partner of CD70433. After successfully developing HIF2α-CD27 PAMs, we evaluated their siRNA release profile under intracellular glutathione concentrations. Then, to assess the efficacy of HIF2α-CD27 PAMs, we tested micelle binding to human patient tissue-derived ccRCC cells in vitro and evaluated their ability to inhibit cancer cell glucose transport, proliferation, release of angiogenic factors, and migration. Overall, the in vitro efficacy of HIF2α-CD27 PAMs in downregulating multiple oncogenic mechanisms and their clinical potential for ccRCC gene therapy are provided. 4.2 Materials and Methods 4.2.1 Materials and Cells Amino acids were purchased from Gyros Protein Technologies (Uppsala, Sweden) and Sigma Aldrich (St. Louis, MO, USA). PEGylated lipids were purchased from Avanti Lipids (Alabaster, AL, USA). Cy7 mono-N-hydroxysuccinimide (NHS) ester was purchased from Lumiprobe (Hunt Valley, MD, USA). DSPE-PEG2000-FITC was purchased from Creative PEGWorks (Durham, NC, USA). 97 HIF2α and control RNA duplexes were purchased from IDT Technologies (Coralville, IA, USA). Antibodies were purchased from Thermo Fisher (Waltham, MA, USA). Cell lines were purchased from the American Type Culture Collection (ATCC, Manassas, VA, USA). Cell culture reagents were purchased from Gibco (Waltham, MA, USA) and Sigma Aldrich. De-identified, human clear cell renal cell carcinoma (ccRCC) tissue samples (1 tumor per patient) were collected on an IRBapproved protocol, measured, evaluated for grade and stage, and then generously donated from UCLA Urology/PI-Dr. Brian Shuch. 4.2.2 Synthesis of HIF2α-CD27 Peptide Amphiphile Micelles CD27 (CRKAAQCDPCIPG) and scrambled CD27 (CQGPRACKADPIC) peptides were synthesized on a Rink amide resin using Fmoc-mediated solid phase peptide synthesis with an automated peptide synthesizer (PS3, Gyros Protein Technologies) and N-capped with an acetyl group. Peptide deprotection and cleavage from the resin was performed with two 2 h incubations with a 94:2.5:2.5:1 vol% mixture of trifluoroacetic acid (TFA):water:ethanedithiol:triisopropylsilane (TIS) 434. To control conjugation to DSPE-PEG2000-maleimide, peptides were synthesized with TFA-stable acetamidomethyl (Acm) groups protecting the side chains of the nonterminal cysteines, which were deprotected later with 10 eq. mercury acetate 435. Peptides were purified using reverse-phase high performance liquid chromatography (RP-HPLC, Prominence, Shimadzu, Columbia, MD, USA) on a Luna C18 column (Phenomenex, Torrance, CA, USA) at 55 °C using HPLC-grade water and acetonitrile (Fisher Scientific, Hampton, NH, USA) supplemented with 0.1% formic acid. Peptide purity was characterized using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS, Bruker, MA, USA). The expected m/z is 1403. 98 Pure peptides were then mixed with a 10% molar excess of DSPE-PEG2000-maleimide in water before adjustment of the pH to 7.2 436. The reaction was nitrogen purged and allowed to stir for 3 d before purification through RP-HPLC using a Luna C4 column (Phenomenex). Acm groups were removed using 10 eq. of mercury acetate, and the pure peptide amphiphiles were desalted through RP-HPLC and characterized through MALDI-TOF-MS. The expected m/z is 4343 m/z. Fluorescent DSPE-PEG2000-cy7 amphiphiles were synthesized by dissolving DSPE-PEG2000- amine in a 0.1 M sodium bicarbonate buffer, and then mixing it with 3-fold molar excess of cy7 NHS ester solubilized in dimethyl formamide (DMF, 10% of reaction volume). The reaction was allowed to shake overnight before purification using a Luna C4 column and characterization via MALDI-TOF-MS. HIF2α siRNA (sense: 5′-CUUGCAGUUUUACUAAAACACUGAA-3′, antisense: 5′-UUCAGUGUUUUAGUAAAACUGCAAGGG-3′) thiolated on the 5′ end of the sense strand was conjugated to DSPE-PEG2000-maleimide through a thioether bond by mixing the siRNA and lipid in nuclease-free water adjusted to pH 7.2 overnight 303. Conjugation of DSPE-PEG2000-HIF2α siRNA amphiphiles was confirmed through the gel electrophoresis assays described in 4.2.5. Micelles were self-assembled from peptide and siRNA amphiphiles through thin-film hydration. Peptide amphiphiles were dissolved in methanol, and a nitrogen stream was used to evaporate the methanol, leaving a thin lipid film that was then hydrated with nuclease-free water or PBS containing the siRNA amphiphiles, gently sonicated and vortexed, and heated to 40 °C for 30 min. Unless otherwise stated, siRNA and fluorescent amphiphiles were incorporated into micelles at 1 mol% and 10 mol%, respectively. 4.2.3 Dynamic Light Scattering (DLS) and Zeta Potential 99 Micelles were suspended at 50 μM in 1 mM NaCl in a folded capillary zeta cell for size, polydispersity, and zeta potential measurements using a Zetasizer Ultra instrument (Malvern Panalytical, Malvern, UK) at room temperature437. N = 3. 4.2.4 Transmission Electron Microscopy (TEM) Micelles were imaged using a FEI Talos F200C microscope (Thermo Fisher). An amount of 5 μL of a 25 μM micelle solution in water was placed directly onto a 400 mesh Carbon Type-B TEM grid (Ted Pella, Redding, CA, USA), washed with water, and then stained with 2% uranyl acetate before a final wash 438. Grids were left overnight in the dark prior to imaging. 4.2.5 Gel Electrophoresis Assay HIF2α-CD27 (500 ng siRNA, 13.3 μg CD27) PAMs or free HIF2α siRNA (500 ng siRNA) was loaded into the wells of a 2% (w/v) agarose gel containing 0.5 μg/mL ethidium bromide and RNA migration was observed using a ChemiDoc XRS+ imaging system (Bio-Rad, Hercules, CA, USA) after the application of 50 V for 90 min 439. For RNase treatment, samples were incubated for 1 h with 50 μg/mL RNase A (Thermo Fisher) in 5% fetal bovine serum (FBS) prior to gel electrophoresis. 4.2.6 Characterization of siRNA Release An amount of 300 μM HIF2α-CD27 PAMs was incubated with 10 mM or 1 μM glutathione (GSH) for up to 24 h in nuclease-free water and then diluted 5-fold in nuclease-free water immediately before gel electrophoresis was performed under the conditions detailed in 4.2.5. 100 Relative band intensity was quantified using ImageJ and used to calculate % siRNA release. N = 3. 4.2.7 Isolation and Culture of Patient-derived ccRCC Cells Freshly resected tumor samples procured at time of partial or radical nephrectomy were transported on ice before dicing finely using a pair of scalpels. Afterwards, samples were digested in low-serum culture media containing 2 mg/mL Type-II collagenase and 2% penicillinstreptomycin overnight at 37 °C, passed sequentially through 70 μm and 40 μm filters, and then washed twice in fresh RPMI before seeding onto collagen-coated tissue culture plates or flasks440 . Cells were cultured in RPMI supplemented with 10–20% FBS and 1% penicillin–streptomycin. 4.2.8 In Vitro Micelle Binding to Patient-derived ccRCC Cells Approximately 200,000 patient-derived ccRCC cells or HK-2 cells were seeded onto glass coverslips placed in the wells of a 6-well plate and allowed to adhere overnight. Then, 50 μM FITC-labeled CD27 or scrambled CD27 micelles were incubated with the cells for 30 min at 37 °C and fixed with fresh 4% paraformaldehyde (PFA) and blocked with 0.3 M glycine and 5% normal goat serum in PBS-Tween-20 (PBST). After blocking, cells were incubated with anti-CD70 primary antibodies (1:200) overnight at 4 °C and then AF594 secondary antibodies (1:500) for 1 h at room temperature. Cells were then counterstained with 2 μg/mL DAPI and mounted to Superfrost Plus microscope slides (Fisher) using Vectamount aqueous mounting media (Vector Laboratories, Burlingame, CA, USA) and sealing with clear nail polish. Microscope slides were allowed to set overnight in the dark at room temperature before imaging on an LSM 880 confocal microscope. In addition to microscopy, 10,000 cells were also seeded into black 96-well plates, incubated with 101 50 μM FITC-labelled micelles for 30 min, washed, and then fluorescence was measured using a Synergy H4 Hybrid microplate reader (Agilent, Santa Clara, CA, USA, n = 6). To block cell surface CD70, cells were incubated with anti-CD70 (1:20) for 30 min at 37 °C prior to micelle incubation. 4.2.9 IHC Staining and PAM Binding to ccRCC Patient Tissues ccRCC patient tumor samples were formalin-fixed and paraffin-embedded (FFPE) and sectioned at 10 μm. Sections were then stained with hematoxylin and eosin or prepared for immunofluorescent (IF) antibody staining. Briefly, for IF staining, tissue sections were heated to 100 °C in pH 6 citrate buffer for 15 min, blocked with 5% normal goat serum in TBS-Tween-20 (TBST) for 30 min, incubated with anti-CD70 (1:200) primary antibodies at 4 °C overnight, washed with TBST and then incubated for 1 h with Alexa Fluor 594-conjugated secondary antibodies (1:500) at room temperature. Following antibody staining, tissue sections were washed and then incubated with 10 μM CD27 micelles for 1 h before washing and counterstaining with 2 μg/mL DAPI. Coverslips were mounted using Prolong Gold mounting medium (Thermo Fisher) and allowed to cure overnight before imaging using a Zeiss LSM 880 confocal microscope (Zeiss, Oberkochen, DE). 4.2.10 In Vitro Transfection and mRNA Expression of Patient ccRCC Cells An amount of 100 μL micelles or free siRNA was added to the wells of a 12-well plate at a concentration of 5 μM RNA, and then 900 μL of a solution containing approximately 100,000 cells in 5% FBS/RPMI was added (n = 5). Cells were incubated for 48 h at 37 °C, after which the cells were lysed, and RNA was isolated using the mRNeasy Kit from Qiagen (Hilden, DE). cDNA was synthesized using the RT2 First Strand Kit according to manufacturer instructions (Qiagen). 102 Expression of HIF2A, CD70, SLC2A1, CCND1, VEGFA, CXCR4, CXCL12, and GAPDH was evaluated through real-time PCR with primer assays and the RT2 SYBR Green qPCR Mastermix (Qiagen) using a CFX384 Touch Real-Time PCR Detection System (Bio-Rad Laboratories), according to the manufacturer’s instructions. Fold-change in mRNA expression was calculated using the delta-delta Ct method. 4.2.11 MTS Assay In vitro micelle biocompatibility was evaluated by incubating micelles or siRNA with patientderived RCC or HK-2 cells for 48 h in 5% FBS/RPMI before adding MTS reagent (10% v/v, Abcam, Cambridge, UK) and incubating for 1 h. Afterwards, absorbance at 490 nm was quantified using a microtiter plate reader. Cell % viability was determined following blank subtraction by normalizing the treatment values to PBS-treated control wells. The effect of siRNA transfection on cell proliferation was determined by transfecting cells according to Section 4.2.10, replacing the transfection solution with fresh complete culture media, and then incubating for an additional 48–120 h before adding the MTS reagent for subsequent absorbance measurements (n = 5). 4.2.12 Glucose Uptake Assay 5,000 cells were plated in the wells of a 96-well plate (n = 4) and treated with HIF2α-CD27 PAMs, HIF2α-scrCD27 PAMs, scrHIF2α-CD27 PAMs, HIF2α siRNA, or PBS at an siRNA concentration of 500 nM for 48 h at 37 °C. Afterwards, the cell culture media was refreshed, and the cells were allowed to grow unperturbed for 24 h before evaluation of glucose uptake using the Glucose Uptake-Glo assay (Promega, Madison, WI, USA) according to the manufacturer’s 103 instructions. Glucose uptake was evaluated via luminescence measurements from a Synergy H4 Hybrid Microplate Reader. 4.2.13 Collection of ccRCC-conditioned Cell Culture Medium and in Vitro Culture and Growth of Endothelial Cells ccRCC cells were transfected as detailed in Section 4.2.10, then cell culture medium was refreshed. After 24 h, conditioned culture medium was collected and stored at 4 °C441 and 5000 HUVECs were seeded into the wells of a 96-well plate and allowed to adhere and grow over 48 h. Then, conditioned culture medium was diluted 1:1 in a 4:1 mixture of serum-free RPMI and endothelial cell growth medium. This mixture was added to the HUVECs, and the cells were allowed to grow for 72 h before evaluation of cell proliferation via MTS assay (n = 5)442 . 4.2.14 Wound Healing Assay 50,000 cells were seeded into the wells of a 48-well plate (n = 4) and cultured for 5 d to attain a confluent monolayer. Then, cells were treated with HIF2α-CD27 PAMs, HIF2α-scrCD27 PAMs, scrHIF2α-CD27 PAMs, HIF2α siRNA, or PBS according to Section 4.2.10. After treatment, the media in the wells was replaced with fresh culture media and a 200 uL pipette tip was used to create a single scratch on the surface of the wells, and brightfield images of the cells were taken with a Leica DMi8 microscope (Leica, Wetzlar, DE) 0, 3, 6, 9, 12, and 24 h after scratching443. A scalpel blade was used to mark the wells to ensure microscope images were taken in the same field-of-view for each timepoint. Wound closure was measured as the area of the scratch using ImageJ. 104 4.2.15 Statistical Analysis Data are expressed as mean ± SD. Statistical analysis between two groups was performed using a Student’s t-test. Comparisons between three or more groups were performed through analysis of variance (ANOVA) followed by post hoc Dunnett’s test for multiple comparisons. A pvalue ≤ 0.05 was considered to be statistically significant. 4.3 Results and Discussion 4.3.1 Synthesis of and Characterization of HIF2α-CD27 PAMs Based on our earlier studies developing nanoparticles that accumulate in the kidneys in vivo, peptide amphiphile micelles (PAMs) that are typically less than 20 nm in diameter and are able to penetrate the glomerular filtration barrier were developed for ccRCC401. To enhance ccRCC specificity, we incorporated a peptide targeted to the CD70 transmembrane protein expressed by ccRCCs (CD27 peptide). CD27 peptides or thiolated HIF2α siRNAs were conjugated to DSPEPEG2000-maleimide and self-assembled into HIF2α-CD27 PAMs under aqueous conditions at a 99:1 peptide:siRNA ratio (Figure 4-1A) 24. Non-targeting PAMs were synthesized using scrambled CD27 (scrCD27) peptides. Transmission electron microscopy (TEM) images confirmed micelles are uniform and spherical in morphology (Figure 4-1B), and dynamic light scattering (DLS) measurements showed the HIF2α-CD27 PAMs to be 13.8 ± 0.4 nm in diameter. Using zeta potential measurements, the surfaces of the CD27 PAMs became more negatively charged upon incorporation of the anionic siRNA as expected, from −28.8 ± 6.0 to −40.5 ± 13.6 (Figure 4-1C) 444, 445 . 105 Figure 4-1. Physicochemical characterization of HIF2α-CD27 PAMs. (A) Schematic of HIF2α-CD27 PAM self-assembly. (B) Transmission electron micrograph of HIF2α-CD27 PAMs. Scale bar = 200 nm. (C) Zeta potential of CD27 PAMs before and after HIF2α siRNA incorporation. N = 3. (D) Gel electrophoresis assay demonstrating incorporation of siRNA into PAMs at 1 mol% and protection of siRNA from RNAse-mediated degradation after 1 h. Lanes: (i) HIF2α-CD27 PAMs, (ii) HIF2α siRNA, (iii) HIF2α-CD27 PAMs incubated with RNAse-treated FBS for 1 h, (iv) HIF2α siRNA incubated with RNAse-treated FBS for 1 h. (E) Gel electrophoresis assay with PAMs up to 24 h after GSH treatment demonstrate rapid siRNA release following exposure to intracellular GSH levels (1 mM) and minimal siRNA release (<25%) after extracellular GSH exposure (1 μM). N = 3. To evaluate HIF2α siRNA incorporation into PAMs, a gel shift assay was performed to compare HIF2α-CD27 PAMs (500 ng siRNA, 13.3 μg PAM) and free HIF2α siRNA (500 ng siRNA) migration446. As shown in Figure 4-1D, following 90 min of electrophoresis at 50 V, >95% of HIF2α-CD27 PAMs (lane i) remained in the well, demonstrating that the HIF2α siRNA was successfully incorporated into the micelle. In contrast, the free HIF2α siRNA (lane ii) readily migrated down the gel. In addition, incorporation of the siRNA into micelles was found to protect the nucleic acid cargo from degradation, as approximately 90% of HIF2α-CD27 PAMs incubated with RNase-treated FBS for 1 h were retained in the well (lane iii), while unconjugated siRNA 106 incubated with RNase-treated FBS for 1h showed no signal in the gel (lane iv), indicating that the siRNA had been degraded into smaller oligonucleotides and migrated off the gel447, 448 . Upon internalization into the cell, HIF2α-CD27 PAMs experience intracellular levels of glutathione (GSH) up to 10 mM, which can be several orders of magnitude higher than extracellular GSH449-453. To characterize the release of therapeutic siRNA from the PAMs under intracellular conditions, additional gel electrophoresis assays were performed following PAM incubation with 10 mM GSH for up to 24 h454. siRNA release was calculated by comparing the band intensity in the well (non-released siRNA) to the band intensity further down the gel (released siRNA). As found in Figure 4-1E, >70% of the siRNA was released after 1 h incubation with intracellular levels of GSH, indicating that HIF2α-CD27 PAMs are capable of rapidly releasing their payload after internalization into target cells. In contrast, HIF2α-CD27 PAMs incubated at extracellular concentrations of GSH (1 μM) released <25% of siRNA cargo after 24 h. 4.3.2 HIF2α and Downstream Genes are Upregulated in Patient-derived ccRCC Cells To determine if HIF2α siRNA delivery represents a viable therapeutic strategy for ccRCC patients, baseline mRNA expression of HIF2A, CD70, and cancer-supporting downstream targets regulated by HIF2α such as SLC2A1, CCND1, VEGFA, CXCR4, and CXCL12 were evaluated in immortalized HK-2 renal proximal tubule cells and eight patient-derived primary ccRCC cell cultures (Figure 4-2A–G) 455-460. Patient characteristics for each of these cultures, including tumor grade, size, and stage are listed in Table 4-1 461-463 . 107 Figure 4-2. Baseline mRNA expression in patient-derived ccRCC cell cultures. Patient-derived ccRCC cells have increased (A) HIF2A, (B) CD70, (C) SLC2A1, (D) CCND1, (E) VEGFA, (F) CXCR4, (G) CXCL12 mRNA expression compared to HK-2 kidney epithelial cells. N = 8. * p < 0.05, ** p < 0.001, **** p < 0.0001. Table 4-1. ccRCC patient characteristics. *Metastatic ccRCC. As found in Figure 4-2, in general, the ccRCC samples had significantly higher baseline mRNA expression of HIF2A, SLC2A1, CCND1, VEGFA, and CXCR4 compared to the HK- 2 kidney epithelial cell line (p < 0.05). Although overall mRNA expression of CD70 and CXCL12 in the ccRCC samples was greater than that of the control HK-2 cells, the differences were not statistically significant which is attributed to the high variation in gene expression across the Sample Grade Tumor Size (cm) Stage 1 3 2.5 I 2 2 6.3 III 3 3 4.7 III 4 3 11 III 5 2 3.6 IV * 6 2 4.1 I 7 3 15.6 III 8 4 13.5 III 108 ccRCC samples. Analyzing the individual samples, 5 of the 8 (63%) patient-derived samples had significantly higher CD70 mRNA expression compared to the HK-2 control cells. Additionally, 7 of the 8 (88%) samples had increased CXCL12 mRNA expression (Table 4-2). Out of all samples, Sample 5 which was derived from a patient with metastatic ccRCC, had significantly higher mRNA expression for all the target genes compared to the HK-2 control cells (Table 4-3). Because of this, we hypothesized that Sample 5 would be the most susceptible to CD70-targeted HIF2α siRNA therapy and continued with this sample for subsequent in vitro studies. Table 4-2. Patient-derived ccRCC cells with higher HIF2α-related gene expression compared to HK-2 cells (p<0.05). Table 4-3. mRNA expression of individual ccRCC samples, normalized to HK-2 cells. N = 3. *p<0.05 relative to HK-2 cells. 4.3.3 CD70-targeting Micelles Bind to Patient-derived ccRCC Cells in Vitro To evaluate the specificity of HIF2α-CD27 PAMs for binding to CD70+ ccRCC cells, we incubated FITC-labelled CD27 PAMs with patient-derived ccRCC cells in vitro for 30 min (Figure 4-3A). Immunofluorescent antibody staining confirmed CD70 expression and that CD27 PAMs Gene N (%) HIF2A 5/8 (63%) CD70 5/8 (63%) SLC2A1 5/8 (63%) CCND1 8/8 (100%) VEGFA 7/8 (88%) CXCR4 7/8 (88%) CXCL12 7/8 (88%) Sample Gene 1 2 3 4 5 6 7 8 HIF2A 6.54 * ± 1.99 3.31 * ± 0.39 1.28 ± 0.39 1.47 ± 0.18 2.84 * ± 0.72 1.32 ± 0.19 2.61 * ± 0.50 2.52 * ± 0.67 CD70 0.81 ± 0.25 1.08 ± 0.14 2.23 * ± 0.36 2.22 * ± 0.45 3.38 * ± 0.24 1.48 * ± 0.22 0.02 * ± 0.00 1.48 * ± 0.18 SLC2A1 1.32 ± 0.08 4.63 * ± 1.10 1.86 * ± 0.77 1.75 * ± 0.17 3.22 * ± 0.68 2.75 * ± 0.47 0.79 ± 0.11 2.99 * ± 0.02 CCND1 8.83 * ± 1.23 8.82 * ± 1.02 11.75 * ± 1.55 11.58 * ± 0.28 8.27 * ± 1.22 4.04 * ± 0.80 21.09 * ± 3.35 12.00 * ± 2.54 VEGFA 1.91 * ± 0.18 5.20 * ± 0.95 0.95 ± 0.19 1.75 * ± 0.14 5.79 * ± 1.40 1.86 * ± 0.21 1.23 * ± 0.16 1.55 * ± 0.14 CXCR4 116.90 * ± 75.61 66.95 * ± 25.37 33.25 * ± 8.98 7.40 * ± 3.89 10.45 * ± 4.19 52.47 * ± 18.75 1.33 ± 0.55 77.82 * ± 14.67 CXCL12 393.77 * ± 219.76 0.21 * ± 0.13 1.83 * ± 0.44 11.59 * ± 3.00 4.06 * ± 1.85 20.73 * ± 7.77 109.59 * ± 18.41 4.38 * ± 2.76 109 colocalized with CD70 (Pearson’s colocalization coefficient R = 0.54). In contrast, minimal binding was observed in cells treated with scrCD27 PAMs (Figure 4-3B). To further confirm the specificity of HIF2α-CD27 PAMs for binding to CD70+ ccRCC cells, the binding of CD27 PAMs was also assessed following a 30 min incubation of the ccRCC cells with anti-CD70 polyclonal antibodies (Figure 4-3C). As expected, blocking CD70 prior to PAM treatment significantly decreased PAM binding to levels comparable to the scrCD70 control. To quantify these results, we repeated these experiments with cells grown in microtiter plates and measured the fluorescence signal and found that, in agreement with the qualitative microscopy images, CD27 PAMs had significantly more binding than scrCD27 PAMs, and that this increased binding was not observed if the cells were pre-treated with anti-CD70 (Figure 4-3D). The specificity of CD27 PAMs for CD70 is also apparent through our binding studies with the HK-2 cell line, which has been reported to have minimal CD70 expression464. When incubated with HK-2 cells for 30 min, no differences in micelle binding were observed between CD27 PAMs, scrCD27 PAMs, or CD27 PAMs after anti-CD70 incubation. 110 Figure 4-3. In vitro binding to patient-derived ccRCC cells and to tumor tissue sections. Confocal microscopy images of patient-derived ccRCC cells that were immunostained for CD70 (green), counterstained with DAPI (blue), and incubated for 30 min with 50 μM FITC-labeled micelles (red). (A) CD27 PAMs have increased binding to ccRCC cells in vitro compared to (B) scrCD27 PAMs. (C) CD27 PAM binding is reduced after pre-incubation with anti-CD70 antibodies, confirming specificity of CD27 PAMs for CD70. (D) CD27 PAMs have significantly increased binding compared to scrCD27 PAMs, but not if target cells are pre-treated with anti-CD70. N = 6. * p < 0.05. (E) ccRCC tumor tissue sections incubated for 1 h with 10 μM CD27 micelles have increased nanoparticle signal compared to (F) scrCD27 micelles. Scale bar = 50 μm. (G) Representative H&E-stained ccRCC tumor tissue section. Scale bar = 100 μm. In addition to patient-derived cells, binding studies were also performed ex vivo using formalinfixed, paraffin-embedded (FFPE) ccRCC tumor tissue sections by treating sections with fluorescently labeled 10 μM PAMs for 1 h (Figure 4-3E and 4-3F). Similar to Figure 4-3A–D, we confirmed that CD70 was expressed by IHC, and that CD27 PAMs bound to ccRCC tissue sections with greater specificity compared to scrCD27 PAMs. 4.3.4 HIF2α-CD27 PAM Treatment Reduces HIF2α mRNA Expression in Vitro To evaluate the efficacy of siRNA delivery with CD70-targeted PAMs, patient-derived ccRCC cells were treated with HIF2α-CD27 PAMs, HIF2α-scrCD27 PAMs, scrHIF2α-CD27 PAMs, free HIF2α siRNA, or PBS for 48 h at siRNA concentrations of 500 nM. Then, HIF2A and CD70 mRNA expression were assayed through qRT-PCR (Figure 4-4). Cells treated with HIF2α-CD27 PAMs were observed to have reduced HIF2A expression (31.2 ± 5.6%) relative to the PBS control (p < 0.05). Additionally, the HIF2A knockdown mediated by HIF2α-CD27 PAMs was larger than that of non-targeting HIF2α-scrCD27 PAMs (54.8 ± 9.6%, p < 0.05) and free HIF2α siRNA (65.9 ± 19.6%, p < 0.05), suggesting that incorporation of the HIF2α siRNA into a CD70-targeted micelle increased its uptake into the ccRCC cells and thus enhanced gene silencing, which corroborates the results observed in the binding studies in Figure 4-3. Similarly, HIF2α-CD27 PAM treatment also reduced CD70 mRNA expression compared to the PBS control (24.1 ± 12.0), as well as the 111 other treatment groups (p < 0.05), as also reported by other groups that found CD70 expression to be correlated to HIF2α expression464, 465 . Figure 4-4. mRNA expression of HIF2A (left) and CD70 (right) following 48 h treatment with HIF2α-CD27 PAMs. The 48 h HIF2α-CD27 PAM treatment (500 nM siRNA) significantly reduced HIF2A and CD70 mRNA expression compared to non-targeted PAM and free siRNA controls in patient-derived ccRCC cells. N = 5. * p < 0.05 relative to PBS. # p < 0.05 relative to HIF2α-CD27 PAMs. & p < 0.05 relative to HIF2αscrCD27 PAMs. 4.3.5 HIF2α-CD27 PAMs Inhibit in Vitro ccRCC Glucose Transport and Proliferation by Reducing SLC2A1 and CCND1 Expression To evaluate if the gene knockdown observed in HIF2A extended to its downstream targets as well, we conducted qRT-PCR on several downstream oncogenes, starting with SLC2A1, which controls glucose uptake466 . SLC2A1 modulates GLUT1, the transmembrane glucose transporter strongly expressed in ccRCC that contributes to increased glucose metabolism, ATP generation, and cell growth467, 468. To evaluate the effect of HIF2α-CD27 PAM treatment on GLUT1-mediated glucose transport, patient-derived ccRCC cells were treated with HIF2α-CD27 PAMs, HIF2αscrCD27 PAMs, scrHIF2α-CD27 PAMs, free HIF2α siRNA, or PBS for 48 h (500 nm siRNA), and then SLC2A1 mRNA expression was assayed through qRT-PCR (Figure 4-5A). HIF2α-CD27 PAMs significantly reduced SLC2A1 mRNA expression compared to HIF2α-scrCD27 PAMs, HIF2α siRNA, PBS-treated cells (p < 0.05), demonstrating the potential of CD70-targeted micelles 112 for siRNA delivery for ccRCC. To test if the significant knockdown at the mRNA level impacted cell phenotype, a glucose uptake assay was performed 24 h following siRNA treatment (Figure 4-5B). HIF2α-CD27 PAM treatment reduced glucose uptake by 47.8%, 32.8%, and 36.4% relative to PBS, HIF2α-scrCD27 PAMs, and free HIF2α siRNA, respectively (p < 0.05). Figure 4-5. Gene knockdown and functional effects of HIF2α-CD27 PAM treatment. 48 h HIF2α-CD27 PAM treatment (500 nM siRNA) reduces (A) SLC2A1 mRNA expression and (B) glucose transport relative to non-targeted PAMs, free siRNA, and PBS in patient-derived ccRCC cells. HIF2α-CD27 PAMs also reduce (C) CCND1 mRNA expression and (D) ccRCC proliferation 5 d following treatment. N = 4–6. * p < 0.05 relative to PBS. # p < 0.05 relative to HIF2α-CD27 PAMs. & p < 0.05 relative to HIF2α-scrCD27 PAMs. Given the ability of HIF2α-CD27 PAMs to restrict glucose metabolism in ccRCC cells, the effect of HIF2α-CD27 PAM treatment directly on ccRCC cell proliferation was examined. mRNA expression of CCND1, a downstream target of HIF2α that regulates cell cycle progression, was evaluated 48 h following HIF2α-CD27 PAM treatment (500 nM siRNA)469, 470. As shown in Figure 113 4-5C, HIF2α-CD27 PAM treatment reduced CCND1 expression by 76.8% (p < 0.05) and was observed to be more efficacious in knocking down CCND1 than all other groups (p < 0.05). Next, the effect of HIF2α-CD27 PAM treatment on ccRCC cell proliferation was evaluated 3 d and 5 d after treatment using the MTS proliferation assay. While there was only a 15% reduction in cell proliferation in the HIF2α-CD27 PAM-treated cells after 3 d, the anti-proliferative effect of micelle treatment was more pronounced 5 d after treatment, with a 54.4% reduction in cell proliferation relative to the PBS-treated control (Figure 4-5D, p < 0.05) and was more potent than HIF2αscrCD27 PAMs and free HIF2α siRNA (p < 0.05). These functional studies demonstrate the potential of HIF2α-CD27 PAMs to directly modulate metabolic pathways and their therapeutic ability to inhibit ccRCC cell growth and proliferation. 4.3.6 Anti-angiogenic Properties of HIF2α-CD27 PAMs As mentioned, anti-angiogenic TKIs are often used as the clinical standard for the ccRCC treatment. Thus, to understand how HIF2α-CD27 PAMs alter the induction of angiogenesis in ccRCC tumors, we first evaluated the mRNA expression of the angiogenic VEGFA gene following PAM treatment and found that HIF2α-CD27 PAMs reduced VEGFA expression by approximately 75% compared to the PBS group (p < 0.05 vs. PBS, HIF2α-scrCD27 PAMs, and free HIF2α siRNA, Figure 4-6A). Then, we collected and incubated conditioned culture medium from treated patientderived ccRCC cells with human umbilical vein endothelial cells (HUVECs) for 72 h. HUVECs cultured with conditioned medium from HIF2α-CD27 PAM-treated cells grew 40% slower than cells cultured with conditioned medium from PBS-treated cells, likely due to the reduced production and release of angiogenic growth factors in cells treated with HIF2α-CD27 PAMs (Figure 4-6B) 471, 472. These assays demonstrate the multiple therapeutic benefits of HIF2α-CD27 PAMs that may aid to slow vascularization and growth of tumors in future in vivo studies. 114 Figure 4-6. 48 h HIF2α-CD27 PAM treatment (500 nM siRNA) reduces (A) VEGFA mRNA expression in patient-derived ccRCC. (B) HUVECs cultured with HIF2α-CD27 PAM-conditioned culture medium have reduced proliferation relative to PBS-conditioned culture medium. N = 5. * p < 0.05 relative to PBS. # p < 0.05 relative to HIF2α-CD27 PAMs. & p < 0.05 relative to HIF2α-scrCD27 PAMs. 4.3.7 HIF2α-CD27 PAM Treatment Reduces Patient-derived ccRCC Cell Migration and Wound Closure Finally, to further verify the therapeutic effects of HIF2α-CD27 PAM treatment on ccRCC cell migration and mobility, testing was performed through wound healing assays on patient-derived ccRCC cells (Figure 4-7A). Cells were treated with HIF2α-CD27 PAMs for 48 h before an artificial wound was introduced to the monolayer and cell migration was imaged up for to 24 h. As shown in Figure 4-7A, HIF2α-CD27 PAM treatment slowed wound closure by approximately 80% compared to the PBS-treated control after 24 h (p < 0.05). HIF2α-scrCD27 PAM treatment slowed wound closure by approximately 50% (p < 0.05), while free HIF2α siRNA treatment did not have any significant effect on wound closure. These results are consistent with the qRT-PCR data, which show ~65% knockdown of chemotactic markers CXCR4 and CXCL12 following 48 h of HIF2α-CD27 PAM treatment, and lesser knockdown from the HIF2α-scrCD27 PAM and free HIF2α treatments (Figure 4-7B). As such, our studies collectively demonstrate that HIF2α-CD27 115 PAMs are capable of binding specifically to ccRCC cells and exert a therapeutic effect through the modulation of HIF2α-related oncogenic genes and function. Figure 4-7. Effect of HIF2α-CD27 PAMs on ccRCC migration. (A) 48 h HIF2α-CD27 PAM treatment (500 nM siRNA) slows wound closure of ccRCC cells over 24 h compared to PBS and free siRNA treatment. (B) 48 h HIF2α-CD27 PAM treatment (500 nM) reduces mRNA expression of CXCR4 (left) and CXCL12 (right). N = 4 or 5. * p < 0.05 relative to PBS. # p < 0.05 relative to HIF2α-CD27 PAMs. 4.4 Conclusion In summary, we report the successful synthesis and characterization of peptide amphiphile micelles incorporating siRNA targeted to HIF2α and CD70-targeting peptides and their ability to exert multiple anti-tumor effects in patient-derived ccRCC cells and tumor tissues. We confirmed that incorporating CD70-targeting peptides to PAMs increased micelle binding to both patientderived ccRCC cells in vitro and on ex vivo tissue sections. We report that HIF2α-CD27 PAMs were consistently more efficacious than both free siRNA and non-targeted PAMs in silencing gene expression and inhibiting their oncogenic functions in glucose metabolism, cell cycle progression, angiogenesis, and cell migration, highlighting the importance of both siRNA encapsulation and 116 molecular targeting, and demonstrating the potential of HIF2α-CD27 PAMs for ccRCC-specific drug delivery. As such, future studies will evaluate the in vivo efficacy of HIF2α-CD27 PAMs using a ccRCC mouse model towards evaluating its clinical potential. In addition, follow-up studies will also evaluate on-target toxicity effects, particularly in renal and hepatic cells that express HIF2α, as this has been a major limitation of clinical HIF2α inhibition therapies. 117 Chapter 5. Contributions to Nanomedicine for Cardiovascular Disease In addition to research conducted in the field of cancer nanomedicine, my work has also applied PAMs to contribute to other fields of nanomedicine, namely for the targeted treatment of the underlying causes of cardiovascular disease such as atherosclerosis and hypertension. 5.1 Immunization using ApoB-100 Peptide-linked Nanoparticles Reduce Atherosclerosis The work presented herein was performed in conjunction with the Prediman Shah group at Cedars Sinai Hospital in Los Angeles, California, and the following text is excerpted and adapted from our joint publication in JCI Insight, in which I am listed as a contributing author, and highlights my contributions to the study, specifically regarding nanoparticle synthesis and characterization, and in vivo biodistribution and colocalization473 . 5.1.1 Introduction, Objective, and Rationale Adaptive immune response against self-antigens such as apolipoproteins is a hallmark of human atherosclerosis474. P210 is an apolipoprotein B-100-related peptide that is the subject of investigation towards antigen-specific immune modulation, with previous work demonstrating that P210-specific CD8+ T cells in hypercholesterolemic mice can be detected by peptide-loaded synthetic soluble MHC-I pentamers475. These P210-specific CD8+ T cells increased in response to atherogenic diet, correlated with the extent of atherosclerosis, and localized to atherosclerotic plaques476. Additionally, P210 fragments and P210-specific antibodies have been detected in 118 plaques and circulation of patients with atherosclerotic cardiovascular disease (ASCVD), suggesting the involvement of P210 in human atherosclerotic disease477 . An outcome of various experimental strategies of P210 immune modulation is alteration of T cell responses to P210, suggesting that the peptide or its derivatives are self-antigens that provoke immune responses involved in atherosclerosis478. P210, when used in an active immunization strategy, has been demonstrated to CD8+ T cell response to reduce atherosclerosis, potentially by shifting the immune-dominant epitope. These experimental observations implicate immune response to P210 in atherogenesis and suggest that modification of the intrinsic immune response to P210 could reduce human atherosclerosis. In preclinical studies, immunogenic peptides are often conjugated as haptens to carrier molecules along with an adjuvant such as mineral salt to provoke an immune response to establish vaccine efficacy. Traditional aluminum salt–based vaccines are known to induce weak cell-mediated immune responses, limiting their clinical application and choice of antigens479. The evidence from work with P210 immunization in animal models shows the involvement of various cellular immune responses such as regulatory T cell or CD8+ T cell responses480. We therefore surmised that an approach targeting immune regulation of the response to P210 would be beneficial in atherosclerosis. One effective way to deliver antigens that provoke a regulatory response is to use the nanoparticle platform481 . Mechanistically, nanoparticles have favorable physicochemical properties that provide sizepreferential lymphatic transport, relatively long injection site retention and circulating time for contact with dendritic cells acting as adjuvants in subunit vaccines, and the induction of autoimmunity-specific regulatory immune responses482. A variety of nanoparticle platforms have been tested to target inflammation and to modulate immune function in atherosclerosis with wide potential in humans483. More importantly, nanoparticle-based vaccines are already in clinical use 119 to prevent COVID-19 infection and being tested in a clinical trial to treat autoimmune disease, such as celiac disease484 . In this study, we utilized the peptide amphiphile nanoparticle platform, in which the P210 is chemically conjugated to hydrophobic tails, facilitating subsequent self-assembly into well-defined peptide amphiphile micelles (PAMs)22. PAMs were used as nanocarriers because they are composed of biocompatible lipids and peptides and are chemically versatile, allowing the incorporation of multiple modalities, such as fluorescence and immunogenicity, into a single particle. Herein, we synthesized P210-PAMs and characterized their physical properties, in vitro uptake in dendritic cells, and in vivo biodistribution and retention in mice. 5.1.2 Materials and Methods Amphiphile Synthesis Peptide amphiphiles were synthesized by conjugating peptides to the 1’-3’-dihexadecyl Nsuccinyl-L-glutamate (diC16) hydrophobic tail22. DiC16 was synthesized by first mixing hexadecanol (22.4 g, 0.092 mol), L-glutamic acid (6.8 g, 0.047 mol), and para-toluenesulfonic acid (10.5 g, 0.051 mol) to yield 1’-3’-dihexadecyl L-glutamate, which was then purified through Buchner funnel filtration through acetone and identified through 1H-NMR, as shown in Figure 5-1A. This was then mixed with succinic anhydride in 1:1 tetrahydrofuran:chloroform to yield 1’-3’-dihexadecyl Nsuccinyl-L-glutamate (diC16). The crude diC16 was then crystallized overnight at 4 oC, purified through Buchner funnel filtration through diethyl ether, and identified via 1H-NMR, as shown in Figure 5-1B. 120 Figure 5-1. 1H-NMR analysis of 1’-3’-dihexadecyl L-glutamate (A) and diC16 (1’-3’-dihexadecyl N-succinylL-glutamate, B). One mmol of P210 or mouse serum albumin (MSA; QTALAELVKHKPKATAEQLK) peptides were synthesized on an automated peptide synthesizer (PS3, Protein Technologies, Tucson, AZ, USA) with Fmoc-mediated solid phase peptide synthesis. Then peptides were conjugated to 1 mmol diC16 overnight through a peptide bond using N,N-diisopropylethylamine (1.25 mmol) and 121 O-benzotriazole-N,N,N’,N’-tetramethyl-uronium-hexafluoro-phosphate (1.125 mmol). Peptide amphiphiles were then cleaved from the solid phase resin by shaking in a 95:2.5:2.5 % volume trifluoroacetic acid:triisopropylsilane:water solution for 2 hours, precipitated in ice-cold diethyl ether, and lyophilized. Peptide amphiphiles (PA) were purified using reverse-phase, highpressure liquid chromatography (RP-HPLC, Prominence, Shimadzu, Columbia, MD, USA) on a Luna C4 column (Phenomenex, Torrance, CA, USA) at 55 oC with 0.1% formic acid in water and acetonitrile mixtures as mobile phases. The purity of eluted peptide amphiphiles was characterized using matrix-assisted laser desorption/ionization time-of-flight mass spectroscopy (MALDI-TOF-MS, Bruker, MA, USA). As shown in Figure 5-2A, the expected mass peak for the P210 PA is 3058 g/mol, and as shown in Figure 5-2B, the expected mass peak for the MSA PA is 2882 g/mol. Fluorescently labeled diC16-cy7 amphiphiles were synthesized by reacting diC16 with cy7-amine, 1-ethyl-3-(3-dimethylaminopropyl)-carbodiimide (EDC), N-hydroxysuccinimide (NHS), and triethanolamine (TEA) in dimethyl sulfoxide (DMSO) at a 1:1.5:4:1:1 ratio diC16:cy7- amine:EDC:NHS:TEA. The EDC, NHS, and TEA were divided into five aliquots, with the first four aliquots added sequentially 2 h after the previous aliquot, while the fifth aliquot was added 12 h after the fourth aliquot. Afterwards, the reaction was stirred for an additional 24 h before purification through RP-HPLC. As shown in Figure 5-2C, the expected mass peak for the diC16- cy7 is 1326 g/mol. 122 Figure 5-2. MALDI characterization of P210 peptide (A, expected m/z: 3058), MSA peptide (B, expected m/z: 2882), and diC16-cy7 (C, expected m/z: 1326) amphiphiles. Micelle Assembly Micelles were prepared through thin-film hydration as previously reported22 . Briefly, peptide amphiphiles were dissolved and sonicated in methanol, before evaporation under a nitrogen stream into thin films. Films were hydrated in water or PBS, sonicated, and heated to 80 oC for 123 30 minutes before cooling to room temperature. Fluorescently labeled P210 or MSA PAMS were synthesized by mixing P210 or MSA PAs with diC16-cy7 at a 90:10 molar ratio. Micelle Characterization The shape and morphology of micelles were characterized through transmission electron microscopy (TEM). Seven µL of 100 µM P210 PAMs was placed onto 400 mesh carbon grids (Ted Pella, Redding, CA, USA) for 5 minutes, before excess liquid was wicked, and the grids were washed with water. The grids were then stained 2% uranyl acetate, washed again with water, and dried before imaging on a JEOL JEM 2100-F TEM (JEOL, Tokyo, Japan). Micelle size, polydispersity, and zeta potential were characterized using a Dynapro Nanostar system (Wyatt, Santa Barbara, CA, USA). One hundred µM of micelles were suspended in water and placed in a quartz cuvette with a platinum dip probe (n = 3) for size, polydispersity, and zeta potential analysis. Statistical Analysis Data are expressed as mean ± SD. Statistical analysis between two groups was performed using a Student's t-test. Comparisons of three or means were performed through analysis of variance (ANOVA) followed by post-hoc Tukey's tests for multiple comparisons. A p-value <0.05 was considered to be statistically significant. 5.1.3 Results and Discussion Synthesis, Characterization, and in Vitro Dendritic Cell Uptake of P210-PAMs 124 To enable efficient antigen delivery by protecting peptides from protease degradation and clearance and providing a scaffold for increased epitope density, P210 was incorporated into PAMs through covalent conjugation of the peptide to 1′-3′-dihexadecyl N-succinyl-L-glutamate (diC16) hydrophobic moieties. Hydrophobic interaction induced self-assembly of the diC16-P210 monomers into cylindrical micelles with an average diameter of 21.6 ± 1.1 nm, a polydispersity index of 0.152 ± 0.001, and a zeta potential of 2.7 ± 0.8 mV (Figures 5-1, 5-2, and 5-3A-C). We tested whether P210-PAM enters DCs and if P210 (or its fragment) can be co-stained with MHCI by conducting confocal experiments using FITC-labeled P210-PAMs. MHC-I was chosen as the pathway to visualize given prior data indicating the involvement of the MHC-I/CD8+ T cell pathway in P210 immunization, consistent with the reported characterization of cell-penetrating peptides like P210 to be cross-presented to MHC-I 485. Confocal microscopy demonstrated co-staining of FITC-labeled P210 with MHC-I molecule on the surface of mouse DCs (Figure 5-3 D-I). 125 Figure 5-3. Characterization of P210-PAM nanoparticles. (A) The majority of P210-PAMs are between 15 and 25 nm. N=3. Transmission electron microscopy of P210-PAM at low (B) and high (C) magnification. Scale bars: 200 nm (B), 50 nm (C). (D) Light microscopy of Giemsa-stained mouse BMDCs. Fixed BMDCs stained with (E) CD11c PE, (F) MHC-I APC, (G) FITC-P210-PAM, and (H) DAPI. (I) Color overlay and arrows indicating costaining. The last lysine of the P210 peptide was FITC labeled prior to PAM assembly. The experiment was replicated twice with similar results. Scale bars: 10 μm (D–F), 5 μm (G–I). In Vivo P210-PAM Biodistribution and Retention Effective immunization depends not only on the immunogenicity of antigens but also on their retention at the injection site486 . We hence characterized the biodistribution kinetics of fluorescently labeled P210-PAM injected subcutaneously into wild-type mice and imaged over a period of 7 days, showing 80%, 30%, and 15% retention in the injection site at 2, 5, and 7 days, respectively, with a calculated clearance half-life of 79.7 ± 29.2 hours (Figure 5-4A, 5-4B). 126 Immunofluorescence staining of the injection site showed colocalization of P210-PAM with F4/80+ macrophages and CD11c+ DCs (Figure 5-4C). MSA-PAM had percentage retention of 67%, 37%, and 11% at 2, 5, and 7 days, respectively, and a clearance half-life of 72.7 ± 29.2 hours (Figure 5-4D-F). 127 Figure 5-4. PAM imaging and retention in vivo. In vivo imaging of P210-PAM (A–C) or MSA-PAM (D–F) retention at the injection site of C57BL/6J mice over 168 hours. Percentage of signal intensity relative to 128 time 0 (immediately after injection) of P210-PAM (B) or MSA-PAM (E). N=4. Colocalization of fluorescently labeled P210-PAM (C) or MSA-PAM (F) with F4/80+ macrophages and CD11c+ DCs at the injection site at 48 hours. 5.1.4 Conclusion In this study, we successfully synthesized P210-PAMs and characterized their physical properties. In vitro studies confirmed the uptake, internalization, and MHC-I presentation of P210- PAMs in dendritic cells. In vivo biodistribution studies characterized the retention of P210-PAMs in the subcutaneous retention site in mice, with a half-life of approximately 80 hours at the injection site. Furthermore, immunofluorescent staining of injection sites indicated that P210-PAMs had greater colocalization with CD11c+ dendritic cells relative to F4/80+ macrophages. Further in vivo studies demonstrated that P210-PAM immunization reduced atherosclerosis in humanized atherosclerotic mouse models473 . 5.2 Peptide Amphiphiles Micelles for ROCK2 siRNA Delivery to Th17 T Cells towards the Treatment of Hypertension: Preliminary Results 5.2.1 Introduction, Objective, and Rationale Hypertension is widely recognized as a key driver of cardiovascular disease and renal endorgan damage. Despite the well-characterized role of T cell-mediated inflammation in the propagation and progression of hypertension, immunotherapeutic strategies within this context have been limited largely due to concerns regarding global suppression of these immune cells487 . Previous studies have identified that imbalances in the ratio of pro-inflammatory CD4+ T cells, specifically Th17 cells and anti-inflammatory CD4+ T cells, specifically regulatory T cells or Tregs, are predictive indicators of hypertension488. Th17 cells are characterized by high expression of IL17, and IL-21, both of which have been demonstrated to promote hypertension. For example, IL- 129 17-/- and IL-21-/- mouse models have both been observed to develop blunted hypertension relative to non-knockout mice489, 490. In addition, pharmacologic inhibition of these cytokines has been shown to reduce the development of hypertension in mice491. Rho-associated coiled-coil containing protein kinase 2 (ROCK2) is an enzyme that has been recently identified as a major driver of immunophenotypic polarization of CD4+ T cells to the pro-inflammatory Th17 phenotype, and thus represents a possible molecular target for gene therapy492. However, ROCK2 is expressed in other somatic cells and is vital in physiological processes such muscle contraction and maintenance of circadian rhythm493, 494. Thus, the development of a delivery strategy tailored specifically for ROCK2 knockdown in Th17 cells is needed to achieve therapeutic efficacy without disrupting crucial ROCK2-mediated physiological function in other cells. To that end, our group has developed peptide amphiphile micelles (PAMs) functionalized with anti-ROCK2 siRNA, as well as peptides for specific CD4 T cell binding and targeting. These peptides are derived from the V3 loop of the gp120 spike protein of the HIV-1 virus (GP120 peptides), which has been reported to facilitate CD4-binding and internalization into CD4 T cells495, 496. In this preliminary work, we synthesized PAMs incorporating GP120 peptides and ROCK2 siRNA (GP120-ROCK2 micelles) and characterized their physical properties, confirmed in vitro targeting of primary CD4 T cells, demonstrated in vitro ROCK2 knockdown of about 50%. Collectively, we demonstrate the efficacy and potential of CD4-targeted micelles for gene therapy. 5.2.2 Materials and Methods GP120 and scrGP120 Micelle Synthesis GP120 (CYNKRKRIHIGPGRAFYTTKNIIG) and scrambled GP120 (CIGPHKNRTGRYKGARIIFIYNTK) peptides were synthesized on a Wang resin using Fmocmediated solid phase peptide synthesis with an automated peptide synthesizer (PS3, Gyros 130 Protein Technologies)495-499. After several rinses with dichloromethane (DCM), peptide deprotection and cleavage from the resin was performed with two 2 h incubations with a 94:2.5:2.5:1 vol% mixture of trifluoroacetic acid (TFA):water:ethanedithiol:tri-isopropylsilane (TIS). After ether precipitation and lyophiliation, peptides were purified using reverse-phase high performance liquid chromatography (RP-HPLC, Prominence, Shimadzu, Columbia, MD, USA) on a Luna C18 column (Phenomenex, Torrance, CA, USA) at 55 °C using HPLC-grade water and acetonitrile (Fisher Scientific, Hampton, NH, USA) supplemented with 0.1% formic acid. Peptide purity was characterized using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS, Bruker, MA, USA). The expected m/z is 2807. Pure peptides were then mixed with a 10% molar excess of DSPE-PEG2000-maleimide in water before adjustment of the pH to 7.2. The reaction was nitrogen purged and allowed to stir for 72 h before purification through RP-HPLC using a Luna C4 column (Phenomenex) and characterized through MALDI-TOF-MS. The expected m/z is 5748 m/z. Fluorescent DSPEPEG2000-cy7 amphiphiles were synthesized by dissolving DSPE-PEG2000-amine in a 0.1 M sodium bicarbonate buffer, and then mixing it with 3-fold molar excess of cy7 NHS ester solubilized in dimethyl formamide (DMF, 10% of reaction volume). The reaction was allowed to shake overnight before purification using a Luna C4 column and characterization via MALDI-TOFMS. ROCK2 siRNA (sense: 5′-GAGAUUACCUUACGGAAAA-3′, antisense: 5′- UUUUCCGUAAGGUAAUCUC-3′) thiolated on the 5′ end of the sense strand was conjugated to DSPE-PEG2000-maleimide through a thioether bond by mixing the siRNA and lipid in nucleasefree water adjusted to pH 7.2 overnight. Conjugation of siRNA to DSPE-PEG2000-maleimide was confirmed via gel electrophoresis. Micelles were self-assembled from peptide and siRNA amphiphiles through thin-film hydration. Peptide amphiphiles were dissolved in methanol, and a nitrogen stream was used to evaporate the methanol, leaving a thin lipid film that was then hydrated with nuclease-free water or PBS 131 containing the siRNA amphiphiles, gently sonicated and vortexed, and heated to 40 °C for 30 min. siRNA and fluorescent amphiphiles were incorporated into micelles at 1 mol% and 10 mol%, respectively. Gel Electrophoresis GP120-ROCK2 micelles (500 ng siRNA) or free ROCK2 siRNA (500 ng siRNA) was loaded into the wells of a 2% (w/v) agarose gel containing 0.5 μg/mL ethidium bromide and RNA migration was observed using a ChemiDoc XRS+ imaging system (Bio-Rad, Hercules, CA, USA) after the application of 50 V for 90 min. For RNase treatment, samples were incubated for 30 min with 10 μg/mL RNase A (Thermo Fisher) prior to gel electrophoresis. Transmission Electron Microscopy (TEM) 400-mesh carbon TEM grids (Ted Pella, Redding, CA, USA) were spotted with 5 µL of 25 µM micelles suspended in MQ water for 5 min. Samples were then wicked to remove excess liquid and washed with MQ water. Grids were stained with 2% uranyl acetate for 2 min before washing with MQ water. Grids were dried overnight and then imaged on a FEI Talos F200C microscope (Thermo Fisher). Dynamic Light Scattering (DLS) Micelles were suspended at 25 µM in 1 mM NaCl, filtered, and then placed in a folded capillary zeta cell for size, zeta potential, and polydispersity measurements using a Zetasizer Ultra (Malvern Panalytical, Malvern, UK). N=3. 132 Primary T Cell Isolation Fresh spleens collected from C57/BL6 mice were rough-cut into 3-4 pieces using sterile scissors before mashing with the plunger of a luer-lok syringe through a 70 µm cell strainer and pelleted at 300g for 7 min. Cells were washed twice with cell culture media before treatment for 5 min at RT with red blood cell (RBC) lysis buffer. Cells were then washed and passed through a 40 µm cell strainer before counting and separation into CD4+ and CD8+ T cells using the appropriate magnetic-bead kits as instructed by the manufacturer (Miltenyi Biotec, Bergisch Gladbach, DE). Following isolation, T cells were seeded at a density of 1 million cells/mL into 96- well plates pre-coated with 100 µL of 2 µg/mL CD3 and CD28 antibodies. All isolated T cells were used within 72 h for experiments. In Vitro T Cell Binding 1 million T cells were incubated with 25 µM FITC-labeled micelles at 37 C for 1 h, washed twice with PBS and then seeded onto poly-lysine coated glass coverslips for immunostaining or into black 96-well plates for microtiter fluorescence measurements. T cells were immunostained with anti-CD4 primary antibodies and Alexa-Fluor 594-labeled secondary antibodies, counterstained with DAPI, and imaged using an LSM 880 inverted confocal microscope with a 63x oil objective. In Vitro T Cell Biocompatibility 5,000 T cells were incubated with 50 µM micelles for 48 h before evaluation of cell viability through the MTS assay according to manufacturer instructions (BioVision, Milpitas, CA, USA). % 133 Viability was calculated by normalizing blank-subtracted absorbance readings of treated samples with PBS-treated control cells. mRNA Expression of T cells Treated with GP120-ROCK2 Micelles CD4 T cells were incubated with micelles or free siRNA for 24 h at 37 °C in OptiMEM media, after which the cells were lysed, and RNA was isolated using the mRNeasy Kit from Qiagen (Hilden, DE). cDNA was synthesized using the RT2 First Strand Kit according to manufacturer instructions (Qiagen). Expression of ROCK2, ROCK1, and GAPDH was evaluated through realtime PCR with primer assays and the RT2 SYBR Green qPCR Mastermix (Qiagen) using a CFX384 Touch Real-Time PCR Detection System (Bio-Rad Laboratories), according to the manufacturer’s instructions. Fold-change in mRNA expression was calculated using the deltadelta Ct method. Statistical Analysis Data are expressed as mean ± SD. Statistical analysis between two groups was performed using a Student's t-test. Comparisons of three or means were performed through analysis of variance (ANOVA) followed by post-hoc Tukey's tests for multiple comparisons. A p-value <0.05 was considered to be statistically significant. 5.2.3 Results and Discussion Synthesis and Characterization of GP120-ROCK2 Micelles To synthesize GP120-ROCK2 micelles, cysteinated GP120 peptides and thiolated ROCK siRNA were conjugated to DSPE-PEG2000-maleimide as previously reported303, 436. Gel shift 134 assays were performed to evaluate siRNA incorporation into the micelle nanostructure (Figure 5- 5A). As shown in lanes i-iii, free siRNA and GP120-ROCK2 micelles synthesized below the critical micelle concentration (CMC) migrated down the length of the agarose gel, while GP120-ROCK2 micelles synthesized above the CMC remained within the well, indicating that the siRNA was successfully incorporated into the relatively large nanoparticle and thus did not migrate down the gel. TEM and DLS measurements confirmed the formation of spherical micelles approximately 12 nm in diameter (Figure 5-5B, 5-5C). Figure 5-5. Synthesis and characterization of GP120-ROCK2 micelles. A) Agarose gel electrophoresis shows successful incorporation of ROCK2 siRNA into GP120 micelles. i) ROCK2 siRNA ii) GP120- ROCK2 micelle (<CMC), iii) GP120-ROCK2 micelle, iv) ROCK2 siRNA + RNAse v) GP120-ROCK2 micelle (<CMC) + RNAse, vi) GP120-ROCK2 micelle + RNAse. B) Transmission electron micrograph of GP120-ROCK2 micelles. Scale bar = 50 nm. C) Size and zeta potential measurements of GP120-ROCK2 micelles. N=3. In Vitro Binding and Biocompatibility of GP120-ROCK2 Micelles to T Cells 135 The in vitro binding of GP120-ROCK2 micelles was evaluated using primary murine CD4 T cells as well as the human Jurkat T cell line. Primary CD8 T cells were also used as controls. As shown in Figure 5-6A, primary CD4 T cells and Jurkat cells both expressed CD4, while CD8 T cells did not, as expected. GP120 micelle binding in both confocal microscopy and microtiter experiments was found to be correlated with CD4 expression, with the most binding observed in the CD4 T cells, followed by the Jurkat T cells, and minimal binding observed in the CD8 T cells (Figure 5-6A, 5-6B). As expected, minimal binding was observed in all tested cells when incubated with a non-targeted micelle incorporating scrambled GP120 peptides. Then, in vitro biocompatibility of GP120-ROCK2 micelles was assessed by incubating CD4 T cells, Jurkat T cells, and CD8 T cells with 50 µM micelles for 48 h before evaluation of cell viability with the MTS assay (Figure 5-6C). As shown, micelle treatment did not induce cytotoxicity over 48 h. Figure 5-6. In vitro characterization of GP120 micelle binding and biocompatibility. A) Fluorescence microscopy images of GP120 and scrGP120 micelle binding to CD4+, Jurkat, and CD8+ T cells. B) Microtiter fluorescence measurements of GP120 and scrGP120 micelle binding to CD4+, Jurkat, and CD8+ T cells. *p<0.05. C) MTS viability assay of CD4+, Jurkat, and CD8+ T cells treated with GP120 or scrGP120 micelles for 48 h. 136 In Vitro siRNA Delivery to Primary CD4 T Cells In vitro micelle-mediated siRNA delivery and subsequent gene knockdown was evaluated through PCR. Primary CD4 T cells were isolated and treated with GP120-ROCK2 micelles for 24 h before lysis and PCR analysis for ROCK2 and ROCK1 mRNA expression (Figure 5-7). GP120- ROCK2 treatment was observed to reduce ROCK2 mRNA expression by 51% compared to PBStreated cells (Figure 5-7A, p<0.01). In addition, treatment with GP120 micelles incorporating control siRNA sequences, as well as free ROCK2 siRNA treatment had no effect on ROCK2 expression. Notably, ROCK2 siRNA complexed with the commercially available lipofection reagent Lipofectamine also exhibited no reduction in ROCK2 mRNA expression, highlighting the limitations in available technologies for T cell transfection. Importantly, GP120-ROCK2 micellemediated knockdown was specific to ROCK2, as mRNA expression of the closely related isoform ROCK1 was unaffected by any treatment (Figure 5-7B). Figure 5-7. In vitro siRNA delivery to primary CD4+ T cells. A) ROCK2 mRNA expression following 24 h treatment with GP120-ROCK2 micelles. **p<0.01. N=7. B) ROCK1 mRNA expression following 24 h treatment with GP120-ROCK2 micelles. N=7. 5.2.4 Conclusion 137 In summary, we have demonstrated the synthesis and characterization of GP120-ROCK2 micelles for siRNA delivery to CD4 T cells. In vitro studies confirmed the biocompatibility of micelle treatment, as well as CD4-dependent binding and ROCK2 knockdown of GP120-ROCK2 micelles. Future studies may evaluate in vivo delivery of siRNA in mouse models of hypertension, as well as investigate unintended delivery of GP120-ROCK2 micelles to somatic cells in which ROCK2 expression is high, such as smooth muscle cells. In addition, the results of these studies may be extrapolated for use in broader applications, given the versatility of the peptide amphiphile micelle (PAM )platform. Different payloads can be incorporated to manipulate different molecular targets in CD4 T cells. For example, regulatory T cells are another subset of CD4 T cells with potent antiinflammatory functions. Stimulating their activity may be beneficial within the context of hypertension. In addition, Tregs have been noted to be a major driver of tumor immunosuppression, so utilizing CD4-targeted micelles to target these cells could represent a viable cancer immunotherapy, although further engineering of the nanoparticle may be necessary to tailor delivery specifically to Tregs instead of other CD4 T cell subsets. In summary, the studies presented here demonstrate the potential of PAMs for CD4 T cell-targeted siRNA delivery. 138 Chapter 6. Conclusions and Future Work Despite the annual investment of over $5 billion in cancer research by the U.S. government alone, the current standards for the diagnosis and treatment of cancer are still inadequate, particularly in the context of metastatic cancers, as population-adjusted cancer mortality rates are largely unchanged from 100 years ago. However, clinical results with immunotherapies have generally shown an increase in patient survival and quality of life. A major drawback of these therapies, though, is the lack of predictability in patient response upon their use, indicating the need for other molecular targets for cancer therapies. Our work has aimed to demonstrate the feasibility of the cancer biomarkers CCR2 and HIF2α as molecular targets in cancer therapy and diagnostics. In our first aim, we synthesized and characterized the in vitro targeting, in vitro anti-cancer efficacy, and in vivo anti-tumor and immunomodulatory effects of KLAK-MCP-1 PAMs. Our studies highlighted the cancer-supportive role of CCR2-expressing monocytes, as KLAK-MCP-1 treatment exhibited anti-tumor effect without significant tumor accumulation, demonstrating the therapeutic efficacy in targeting cancer-promoting monocytes. In our second aim, we synthesized MCP1-Gd PAMs for the purpose of MRI imaging and early detection of lymph node metastasis. MCP1-Gd PAMs were found to accumulate specifically in metastatic lymph nodes in in vivo models of lymph node metastasis and metastatic lymph node recurrence, which enabled earlier detection relative to clinical control contrast agents. Further studies revealed that the driving mechanism for metastatic lymph node accumulation was PAM hitchhiking onto inflammatory monocytes trafficking the lymph nodes. This hitchhiking mechanism can likely be applied to target other cell populations for increased nanoparticle delivery to target tissues. 139 In our third aim, we performed a proof-of-concept study demonstrating the efficacy of PAMmediated HIF2α siRNA delivery to patient-derived ccRCC cells. Characterization studies confirmed that micelle incorporation protected the siRNA from RNAse-mediated degradation. In vitro studies confirmed siRNA delivery through analysis of target mRNA expression. Further studies demonstrated the functional inhibition of several disease-driving mechanisms including cancer cell proliferation, secretion of angiogenic factors, and migration following PAM-mediated siRNA delivery. The results of this work highlight several avenues of future study. For example, the molecular targeting of CCR2 was studied here purely in the context of cancer. As monocytes also have roles in other pathologies such as Alzheimer’s disease, this nanoplatform can potentially be applied to target these diseases as well. In addition, our studies demonstrated the ability of CCR2-targeted micelles to accumulate in metastatic lymph nodes. Given that the lymph nodes are the main hubs of the adaptive immune system, the micelles could potentially be applied within the context of vaccine delivery. Lastly, as cancer patients are always at risk of developing recurrence, the adaptation of diagnostic tools like MCP1-Gd PAMs to administration routes that are more amenable to patient compliance like oral or transdermal delivery is highly desirable, so future studies can aim to develop tools that are compatible with these administration routes. 140 References (1) Ahmad, F. B.; Anderson, R. N. The Leading Causes of Death in the US for 2020. JAMA 2021, 325 (18), 1829-1830. DOI: 10.1001/jama.2021.5469. (2) Siegel, R. L.; Miller, K. D.; Fuchs, H. E.; Jemal, A. Cancer Statistics, 2021. CA Cancer J Clin 2021, 71 (1), 7-33. DOI: 10.3322/caac.21654. (3) Dillekas, H.; Rogers, M. S.; Straume, O. Are 90% of deaths from cancer caused by metastases? Cancer Med 2019, 8 (12), 5574-5576. DOI: 10.1002/cam4.2474. (4) Schallier, D.; Decoster, L.; Braeckman, J.; Fontaine, C.; Degreve, J. 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Asset Metadata

Creator Trac, Noah Tom (author)

Core Title Molecularly targeted micelle nanoparticles for cancer drug delivery and lymph node metastasis detection

School Viterbi School of Engineering

Degree Doctor of Philosophy

Degree Program Biomedical Engineering

Degree Conferral Date 2023-12

Publication Date 11/29/2023

Defense Date 11/17/2023

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

Tag cancer,CCR2,micelle,molecular targeting,nanoparticle,OAI-PMH Harvest,peptide

Format theses (aat)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Chung, Eun Ji (committee chair), Gross, Mitchell Eric (committee member), Mackay, John Andrew (committee member), Shen, Keyue (committee member)

Creator Email noahttrac@gmail.com,ntrac@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC113778574

Unique identifier UC113778574

Identifier etd-TracNoahTo-12501.pdf (filename)

Legacy Identifier etd-TracNoahTo-12501

Document Type Dissertation

Format theses (aat)

Rights Trac, Noah Tom

Internet Media Type application/pdf

Type texts

Source 20231129-usctheses-batch-1109 (batch), University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

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