Medicine, Faculty of
DSpace
UBCV
Bhasin, Satyam
Doctor of Philosophy - PhD
University of British Columbia
Breast cancer (BCa) is a significant cause of cancer-related deaths among females globally. Approximately 45% of BCa patients develop treatment resistance or suffer from a lack of targeted therapies. Previously, we have shown that Semaphorin 3C (SEMA3C) is an autocrine growth factor that drives the growth and treatment resistance of various cancers. This study aims to investigate the functional role of SEMA3C in breast cancer progression and treatment resistance. My analysis of clinical datasets revealed elevated levels of SEMA3C mRNA in breast tumors compared to normal adjacent tissue. SEMA3C expression was positively correlated with the expression of various oncogenes implicated in breast cancer. Both estrogen receptor (ER) positive (ER⁺/HER2ˉ) BCa and triple negative breast cancer (TNBC) cells exhibited higher levels of SEMA3C protein compared to a non-cancer mammary epithelial cell line (MCF10A). Stimulation with recombinant SEMA3C activated EGFR, MAPK and AKT signaling in both ER⁺ and TNBC cells. Conversely, SEMA3C silencing inhibited ER expression, EGFR, MAPK, and AKT signaling, while inducing apoptosis. Silencing of SEMA3C significantly suppressed the growth of ER⁺ BCa and TNBC cells, indicating a growth dependency on SEMA3C. Tamoxifen-resistant cells (TamC3, TamR3) remained reliant on SEMA3C for growth and survival, suggesting its persistence in treatment resistance. Additionally, SEMA3C suppression enhanced the efficacy of certain chemotherapies and targeted therapies in TNBC cells. Additionally, treatment with SEMA3C pathway inhibitors, B1SP (Fc fusion protein) and ALS, attenuated SEMA3C-induced signaling and growth in both ER⁺ and TNBC cells. This study highlights the functional role of SEMA3C in breast cancer signaling and growth, suggesting its potential as a therapeutic target. Targeting SEMA3C may offer benefits in improving treatment outcomes for breast cancer patients, particularly in cases of endocrine treatment resistance and TNBC.
SEMAPHORIN 3C (SEMA3C) AS A POTENTAL THERAPEUTIC TARGET IN BREAST CANCER by Satyam Bhasin B. Tech, NIIT University, 2017 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Interdisciplinary Oncology) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) March 2024 © Satyam Bhasin, 2024 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Semaphorin 3C (SEMA3C) as a potential therapeutic target in breast cancer submitted by Satyam Bhasin in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Interdisciplinary Oncology Examining Committee: Christopher J Ong, Associate Professor, Department of Urologic Sciences, UBC Supervisor Yuzhuo Wang, Professor, Department of Urologic Sciences, UBC Supervisory Committee Member Wan Lam, Professor, Department of Pathology and Laboratory Medicine, UBC University Examiner Chinten James Lim, Associate Professor, Department of Pediatrics, UBC University Examiner Additional Supervisory Committee Members: Artem Cherkasov, Professor, Department of Urologic Sciences, UBC Co-Supervisor Christopher Maxwell, Associate Professor, Department of Pediatrics, UBC Supervisory Committee Member iii Abstract Breast cancer (BCa) is a significant cause of cancer-related deaths among females globally. Approximately 45% of BCa patients develop treatment resistance or suffer from a lack of targeted therapies. Previously, we have shown that Semaphorin 3C (SEMA3C) is an autocrine growth factor that drives the growth and treatment resistance of various cancers. This study aims to investigate the functional role of SEMA3C in breast cancer progression and treatment resistance. My analysis of clinical datasets revealed elevated levels of SEMA3C mRNA in breast tumors compared to normal adjacent tissue. SEMA3C expression was positively correlated with the expression of various oncogenes implicated in breast cancer. Both estrogen receptor (ER) positive (ER+/HER2-) BCa and triple negative breast cancer (TNBC) cells exhibited higher levels of SEMA3C protein compared to a non-cancer mammary epithelial cell line (MCF10A). Stimulation with recombinant SEMA3C activated EGFR, MAPK and AKT signaling in both ER+ and TNBC cells. Conversely, SEMA3C silencing inhibited ER expression, EGFR, MAPK, and AKT signaling, while inducing apoptosis. Silencing of SEMA3C significantly suppressed the growth of ER+ BCa and TNBC cells, indicating a growth dependency on SEMA3C. Tamoxifen-resistant cells (TamC3, TamR3) remained reliant on SEMA3C for growth and survival, suggesting its persistence in treatment resistance. Additionally, SEMA3C suppression enhanced the efficacy of certain chemotherapies and targeted therapies in TNBC cells. Additionally, treatment with SEMA3C pathway inhibitors, B1SP (Fc fusion protein) and ALS, attenuated SEMA3C-induced signaling and growth in both ER+ and TNBC cells. This study highlights the functional role of SEMA3C in breast cancer signaling and growth, suggesting its potential as a therapeutic target. Targeting SEMA3C may offer benefits in improving treatment iv outcomes for breast cancer patients, particularly in cases of endocrine treatment resistance and TNBC. v Lay Summary Despite advances in breast cancer (BCa) therapeutics, BCa remains a leading cause of cancer-related death in females worldwide, necessitating the continuing development of effective treatments and new targeted therapies for patients developing treatment resistance. This study investigates the role of a putative growth factor, Semaphorin 3C (SEMA3C) in breast cancer progression and treatment resistance. Our findings revealed high SEMA3C expression in breast tumors compared to healthy breast tissue and strong correlations with the expression of BCa oncogenes. In two different breast cancer subtypes, SEMA3C was found to be able to activate cancer growth signals. Remarkably, depleting breast cancer cells of SEMA3C decreases cancer cell growth and induces cell death. In-house developed anti-SEMA3C drugs showed promising efficacy independently and in combination with FDA approved drugs when tested on breast cancer cells. These findings underscore the potential of SEMA3C as a promising therapeutic target for personalized treatment approaches in breast cancer patients, paving the way for improved clinical outcomes. vi Preface The work surrounding this thesis was carried out at the Vancouver Prostate Centre under the supervision of Dr. Christopher Ong. On work presented in this thesis, Dr. Christopher Ong and Satyam Bhasin conceptualized the research aims; Dr. Christopher Ong, James Peacock, and Satyam Bhasin designed the experiments; Satyam Bhasin, Christopher Dusek, James Peacock conducted the experiments; Dr. Christopher Ong and Satyam Bhasin analyzed and interpreted the results. Drs. Artem Cherkasov, Christopher Maxwell, Yuzhuo Wang, provided project supervision and feedback on experimental results. The data presented in this thesis are part of three different manuscripts in distinct stages of preparation for which I am listed as first or a co-author. Chapter 2: Dependency of ER+ breast cancer cells on Semaphorin 3C (SEMA3C) for growth A version of Chapter 2 is published as a research article in MDPI Cells • Bhasin S, Dusek C, Peacock JW, Cherkasov A, Wang Y, Gleave M, Ong CJ. Dependency of Tamoxifen Sensitive and Resistant ER+ Breast Cancer Cells on Semaphorin 3C (SEMA3C) for Growth. Cells. 2023; 12(13):1715. https://doi.org/10.3390/cells12131715 • I conceptualized, designed, performed all the experimental validation (expect figures Appendix D and F within this document), formal analysis, writing—original draft preparation, writing—review and editing. Dusek C and Peacock JW supported in generating figures Appendix D and F. Peacock JW and Ong CJ provided guidance on methodology and reviewing of the manuscript. Ong CJ, vii Wang Y, Gleave M and Cherkasov A did project supervision, administration and funding acquisition. Chapter 3: Understanding significance of SEMA3C signaling pathway in Triple Negative Breast Cancer A version of Chapter 3 makes up a manuscript titled “Role of Semaphorin 3C (SEMA3C) in maintaining growth and survival in triple negative breast cancer cells” which is currently in submission under following title: • Bhasin S, Dusek C, Peacock JW, Tam KJ, Cherkasov A, Wang Y, Gleave M, Ong CJ. Role of Semaphorin 3C (SEMA3C) in maintaining growth and survival of Triple negative breast cancer cells. • I conceptualized, designed, performed all the experimental, formal analysis, writing—original draft preparation, writing—review and editing. Dusek C and Peacock JW, Tam KJ, Ong CJ were involved in reviewing and editing of the manuscript. Ong CJ provided guidance on methodology. Ong CJ, Wang Y, Gleave M and Cherkasov A did project supervision, administration and funding acquisition. Chapter 4: In-silico review of Semaphorin 3C (SEMA3C) receptor complex to develop next generation inhibitors Introduction and foundation for Chapter 4 have been published as a research article in Journal of Endocrine Society. • Lee CCW, Munuganti RSN, Peacock JW, Dalal K, Jiao IZF, Shepherd A, Liu L, Tam KJ, Sedgwick CG, Bhasin S, Lee KCK, Gooding L, Vanderkruk B, Tombe T, Gong Y, Gleave ME, Cherkasov A, Ong CJ. Targeting Semaphorin 3C in viii Prostate Cancer with Small Molecules. J Endocr Soc. 2018 Oct 11;2(12):1381-1394. https://doi.org/10.1210/js.2018-00170 • I was involved in performing and analyzing experiments as shown in Figure 4.1 of this document and editing of the manuscript. Rest of the manuscript experiments and its results (not included in thesis) were performed by Lee CCW, Munuganti RSN, Peacock JW, Dalal K, Jiao IZF, Shepherd A, Liu L, Tam KJ, Segwick CG, Lee KCK, Gooding L, Vanderkruk B, Tombe T and Gong Y. Ong CJ and Cherkasov A conceptualized and designed the experiments. Lee CCW, Munuganti RSN and Tam KJ were involved in writing the original draft of manuscript. I (Bhasin S), Dalal K, Tam KJ, and Ong CJ were involved in reviewing and editing. Ong CJ, Gleave M and Cherkasov A did project supervision, administration and funding acquisition. ix Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ......................................................................................................................... ix List of Tables .............................................................................................................................. xiii List of Figures ............................................................................................................................. xiv List of Abbreviations ...................................................................................................................xv Acknowledgements ................................................................................................................... xvii Dedication ................................................................................................................................. xviii Chapter 1: Introduction ................................................................................................................1 1.1 Introduction ........................................................................................................................... 1 1.2 Breast Cancer ........................................................................................................................ 1 1.2.1 Pathological and Molecular Classification .................................................................... 2 1.2.2 Genomic landscape of Breast Cancer ............................................................................ 4 1.2.3 Signaling Pathways ........................................................................................................ 5 1.2.4 Current Treatment Landscape ........................................................................................ 7 1.2.5 Treatment Challenges .................................................................................................. 10 1.3 Autocrine Growth Factor Hypothesis ................................................................................. 11 1.4 Semaphorins ........................................................................................................................ 11 1.4.1 Emergence of semaphorins as both a promoter and suppressor of tumors .................. 16 1.4.2 Semaphorin 3C (SEMA3C) ......................................................................................... 17 1.4.2.1 Pan-Cancer ............................................................................................................ 17 1.4.2.2 Breast cancer ......................................................................................................... 19 1.4.2.3 Rationale for investigating SEMA3C in breast cancer ......................................... 21 1.5 Hypothesis, objective and specific aims ............................................................................. 22 1.5.1 Objective and Hypothesis ............................................................................................ 22 1.5.2 Experimental Plan ........................................................................................................ 23 Chapter 2: Dependency of tamoxifen sensitive and resistant ER+ breast cancer cells on Semaphorin 3C for growth .........................................................................................................25 2.1 Introduction ......................................................................................................................... 25 2.2 Materials and Methods ........................................................................................................ 26 x 2.2.1 Cell lines ...................................................................................................................... 26 2.2.2 Antibodies, chemicals and reagents ............................................................................. 26 2.2.3 External datasets utilized in this study ......................................................................... 27 2.2.4 RNA Extraction and qPCR .......................................................................................... 28 2.2.5 Immunoblotting ............................................................................................................ 28 2.2.6 Cell stimulation ............................................................................................................ 28 2.2.7 Plexin B1 and SEMA3C silencing ............................................................................... 29 2.2.8 Cell growth assay ......................................................................................................... 30 2.2.9 Sub-G0/G1 DNA content Assay .................................................................................. 30 2.2.10 Production of SEMA3C: FL and B1SP ..................................................................... 31 2.2.11 Statistical Analysis and Software Used ..................................................................... 32 2.3 Results ................................................................................................................................. 32 2.3.1 SEMA3C is significantly upregulated in ER+ breast cancer and its expression is positively correlated with the expression of Estrogen Receptor (ER) .................................. 32 2.3.2 SEMA3C is an ER-induced gene that is highly expressed in ER-positive breast cancer cell lines ............................................................................................................................... 35 2.3.3 SEMA3C activates RTK signaling pathways in breast cancer cells via Plexin-B1 ......... ............................................................................................................................... 39 2.3.4 ER+ breast cancer cell lines show dependency on SEMA3C for cell growth, and its depletion leads to the induction of pro apoptotic proteins .................................................... 41 2.3.5 SEMA3C pathway inhibitor downregulates oncogenic signaling, inhibits growth and exhibits synergistic behavior with endocrine therapy drugs ................................................. 44 2.3.6 Tamoxifen-Resistant ER+ breast cancer cells remain dependent on SEMA3C for signaling and growth ............................................................................................................. 47 2.4 Discussion ........................................................................................................................... 49 Chapter 3: Understanding significance of SEMA3C signaling pathway in Triple Negative Breast Cancer ...............................................................................................................................53 3.1 Introduction ......................................................................................................................... 53 3.1.1 Current clinical challenges ........................................................................................... 53 3.2 Methods ............................................................................................................................... 55 3.2.1 Kaplan-Myer Plot Generation ...................................................................................... 55 3.2.2 Cell lines ...................................................................................................................... 55 3.2.3 Antibodies and chemical reagents ................................................................................ 56 3.2.4 Immunoblotting ............................................................................................................ 56 3.2.5 Cell stimulation ............................................................................................................ 57 3.2.6 Plexin B1 and SEMA3C silencing ............................................................................... 57 3.2.7 Growth Assay ............................................................................................................... 58 3.2.8 G0/G1 Assay ................................................................................................................ 59 3.2.9 Production of SEMA3C: FL, B1SP and ALS .............................................................. 59 3.2.10 Statistical Analysis ..................................................................................................... 60 3.3 Results ................................................................................................................................. 60 3.3.1 SEMA3C expression is prognostic for metastatic progression of TNBC and moderately expressed in three distinct TNBC cell lines ....................................................... 60 xi 3.3.2 Depletion of SEMA3C in TNBC cells lead to downregulation of receptor tyrosine kinase pathways, growth, and activation of apoptosis .......................................................... 64 3.3.3 SEMA3C siRNA treatment augments the cytotoxicity of anticancer agents and in-house developed SEMA3C pathway inhibitor(s) showcase therapeutic potential in-vitro .. 67 3.4 Discussion ........................................................................................................................... 75 Chapter 4: In-silico review of Semaphorin 3C (SEMA3C) receptor complex to support development of next generation small molecule inhibitors ......................................................79 4.1 Introduction ......................................................................................................................... 79 4.1.1 Protein-Protein Interaction Inhibitors (iPPI) and its major challenges ........................ 81 4.1.2 Semaphorin 3 family signaling .................................................................................... 81 4.2 Methods ............................................................................................................................... 82 4.2.1 Homology modeling to develop SEMA3C protein structure and identification of receptor binding sites ............................................................................................................ 82 4.2.2 In-silico screening ........................................................................................................ 82 4.2.3 In-vitro screening (Displacement Assay) ..................................................................... 84 4.3 Results ................................................................................................................................. 84 4.3.1 In-silico model of SEMA3C and potential receptor complex binding sites on SEMA3C ............................................................................................................................... 84 4.3.2 Drug ability assessment of 2nd generation small molecule inhibitors SEMA3C via large scale in-silico screening and preliminary in-vitro displacement assay ........................ 87 4.4 Discussion ........................................................................................................................... 90 Chapter 5: Discussion ..................................................................................................................92 5.1 Conclusion and General Discussion ................................................................................... 92 5.2 Limitation of this work ....................................................................................................... 98 5.3 Future Studies ..................................................................................................................... 99 5.4 Concluding Remarks and Significance ............................................................................. 103 References ...................................................................................................................................104 Appendices ..................................................................................................................................118 Appendix A. Time dependent estradiol treatment and estrogen receptor knockdown impact on SEMA3C mRNA expression. ................................................................................................. 118 Appendix B. Densitometry analysis of SEMA3C protein expression after overnight estradiol treatment (nM) ........................................................................................................................ 119 Appendix C. ESR1 binding region on SEMA3C locus identified by ChIP-seq studies analyzed on Integrative Genome Browser (hg38) ................................................................................. 120 Appendix D. SEMA3C induces receptor tyrosine kinase signaling in MCF10A ................... 121 Appendix E. Absence of caspase 3 in MCF7 cells leads to no cleaved caspase 3 expression in MCF7 cells post SEMA3C siRNA knockdown...................................................................... 122 Appendix F. B1SP dose response growth curve on ER+ breast cancer cells .......................... 123 Appendix G. Densitometry analysis of B1SP inhibition of SEMA3C induced tyrosine kinase signaling .................................................................................................................................. 124 xii Appendix H. TNBC patients shows lower overall survival within high SEMA3C group ..... 125 Appendix I. EGFR signal rescue in MDA-MB-468 suggests SEMA3C’s role in regulating EGFR protein expression potentially via downstream effects of SEMA3C signaling in positive feedback loop .......................................................................................................................... 126 Appendix J. ALS dose response growth curve on TNBC cells .............................................. 127 Appendix K. Plexin-B1 acts as a potential co-receptor for SEMA3C induced MAPK/AKT signaling in TNBC cells .......................................................................................................... 128 xiii List of Tables Table 1.1 Cancer Genetic Dependencies for SEMA3C. ............................................................... 18 Table 2.1 Publicly available datasets used for SEMA3C expression studies. .............................. 27 xiv List of Figures Figure 1.1. Schematic representation of semaphorins and its receptors. ...................................... 13 Figure 1.2. Semaphorin 3 family receptor complex (SEMA3A-NRP1-PLXNA2). ..................... 15 Figure 1.3. Analysis of SEMA3C expression in breast tumors. ................................................... 20 Figure 1.4. Positive correlation of SEMA3C mRNA expression with validated breast cancer oncogenes. ..................................................................................................................................... 20 Figure 1.5. ER+ breast cancer patients with mutation in PIK3CA expresses higher SEMA3C mRNA expression. ........................................................................................................................ 21 Figure 2.1. Semaphorin 3C (SEMA3C) mRNA expression associated with different breast cancer subtypes and correlation with estrogen receptor expression. ........................................................ 34 Figure 2.2. Semaphorin 3C (SEMA3C) expression confirmation in ER+ breast cancer cells and its regulation by ER in MCF7 and T47D. ..................................................................................... 38 Figure 2.3. SEMA3C activates receptor tyrosine kinase pathways via the Plexin-B1 receptor in breast cancer cells. ........................................................................................................................ 40 Figure 2.4. SEMA3C silencing in ER+ breast cancer cells lead to decreases in oncogenic signaling, cell survival and activation of pro-apoptotic pathways. ............................................... 43 Figure 2.5. B1SP impacts SEMA3C: PlexinB1 mediated cell growth, signaling and showcasing positive combi-nation effect with endocrine therapy drugs. ......................................................... 46 Figure 2.6. Tamoxifen-resistant cells continue to exhibit dependency on Semaphorin 3C (SEMA3C) for their growth and survival. .................................................................................... 49 Figure 3.1. Clinical relevance of SEMA3C expression in TNBC patients and its ability to activate receptor tyrosine kinase pathway in TNBC cells. ........................................................... 64 Figure 3.2. SEMA3C silencing in TNBC cells leads to decrease in oncogenic signaling, EMT marker, cell survival and activation of pro-apoptotic pathways. .................................................. 66 Figure 3.3. Growth inhibition heatmap of SEMA3C siRNA in combination with different FDA approved drugs. ............................................................................................................................. 70 Figure 3.4. SEMA3C siRNA showcase positive drug combination effect with chemotherapy and RTKi drugs in TNBC cells. .......................................................................................................... 72 Figure 3.5. Anti-SEMA3C therapeutics confirms its druggability potential in TNBC cells. ....... 75 Figure 4.1. Summary of proof-of-concept study establishing evidence of druggability potential of SEMA3C in prostate cancer cells. ................................................................................................ 80 Figure 4.2. Structural information of SEMA3C receptor complex from in-silico analysis. ......... 86 Figure 4.3. Druggability assessment of the new binding site on SEMA3C. ................................ 90 xv List of Abbreviations ADC: Antibody drug conjugates AI: Aromatase inhibitor AR: Androgen Receptor BCa: Breast Cancer BL-1: Basal like-1 BL-2: Basal like-2 CDX: Cell-line derived xenografts CISH: Chromogenic in situ hybridization CK: Cytokeratin CM: Conditioned media CNA: Copy-number alterations CSS: Charcoal Strip Serum CancerGD: Cancer Genetic Dependencies DFS: Disease-Free survival DMFS: Distant metastasis free survival EMT: Epithelial to mesenchymal transition ER+: Estrogen receptor positive ER-: ER negative ER: Estrogen receptor EREs: Estrogen response elements FACS: Fluorescence activated cell sorting FBS: Fetal Bovine Serum FISH: Fluorescence in situ hybridization GAP: GTPase activating protein GE: Gene expression GPCR: G-protein coupled receptors GPI: Glycosylphosphatidylinositol HER2+: HER2 positive HER2-E: HER2 enriched HER2/ERBB2: Human epithelial growth factor receptor 2 HRD: Homologues recombination deficiency IDC: Invasive Ductal Carcinoma ILC: Invasive lobular carcinoma IM: Immunomodulatory Ig: Immunoglobulin KD: Knockdown LAR: Luminal androgen receptor M: Mesenchymal MOE: Molecule Operating Environment MSL: Mesenchymal stem-like NRP: Neuropilin OS: Overall survival PDX: Patient derived xenografts xvi PLXN: Plexin, PPI: Protein-Protein Interaction PR-A: Progesterone receptor A PR-B: Progesterone receptor B PR: Progesterone receptor PROTAC: Proteolysis-targeting chimeric PSI: Plexin-semaphorin-integrin RFS: Recurrence free survival RMSD: Root mean square deviation RTKi: RTK inhibitor RTKs: Receptor tyrosine kinase S3C: Semaphorin 3C SEMA3C-DM: Semaphorin 3C double mutant SEMA3C-FL: Recombinant SEMA3C SEMA3C: Semaphorin 3C SERD: Selective ER degrader TFs: Transcription factor TILs: Tumor-infiltrating lymphocytes TK: Tyrosine kinase TNBC: Triple negative breast cancer TamR: Tamoxifen resistant TamS: Tamoxifen-sensitive UNS: Unstable tumor cluster WGS: Whole-genome sequencing cCREs: cis-Regulatory Elements iPPI: Protein-Protein Interaction inhibitors pCR: pathological complete response qPCR: Quantitative PCR xvii Acknowledgements I would like to acknowledge my supervisor, Dr. Christopher Ong, for taking me on as an international graduate student who just finished his bachelor’s and saw my passion for conducting translational research. My experience in his lab taught me innumerable immeasurable things such as how to navigate novel research ideas, freedom to learn at own pace, importance of collaboration and his mentorship was core to that. I am also eternally grateful to my supervisory committee, Dr. Artem Cherkasov, Dr. Christopher Maxwell, and Dr. Yuzhuo Wang. I would not have succeeded or survived without their constant guidance and steadfast support. I couldn’t have wished for a kinder committee or a richer graduate experience. Words cannot convey the gratitude I feel toward past and present colleagues at the Vancouver Prostate Centre and at BC Cancer Agency who carried me through hard times and supported my ideas, no matter how crazy they were. In particular, I would like to thank Dr. Kevin J Tam, Jim Peacock, Christopher Dusek, Dr. Wan Lam, Nikita Telkar, Dr. Shabnam Massah and Dr. Liangliang Liu. I would also like to acknowledge my family and friends who encouraged me in ways you will never know. Thank you also to my past mentors who made research fun and taught me everything I know. Finally, I would like to acknowledge the various funding agencies for their interest in and support of my research. It means a lot when others see the hope in your work; I could not have done it without them. xviii Dedication To my parents (Kavita and Chander) and my brother Shivam, this is for you. Thank you for believing in me and supporting me throughout this journey!1 Chapter 1: Introduction 1.1 Introduction Cancer cells possess a set of functional capabilities that allow them to transition from normalcy to neoplastic growth states. These capabilities are crucial for their ability to form malignant tumors. In 2011, Hanahan and Weinberg identified eight acquired hallmarks and two enabling hallmarks. The eight acquired hallmarks include the ability to sustain proliferative signaling, evade growth suppressors, resist cell death, enable replicative immortality, induce/access vasculature, activate invasion and metastasis, reprogram cellular metabolism, and avoid immune destruction (1). The two enabling hallmarks are genome instability and tumor-promoting inflammation (1). These components have gradually become incorporated as core components of the hallmarks of cancer conceptualization. In a more recent elaboration of this concept in 2022 additional new hallmarks and enabling characteristics of cancers have been suggested, such as "unlocking phenotypic plasticity," "non-mutational epigenetic reprogramming," "polymorphic microbiomes," and "senescent cells"(2). Despite significant progress in understanding these hallmarks and their role in cancer development and progression, there currently is no comprehensive understanding of the pathways involved and their interconnectivity. Therefore, gaining a deeper understanding of the networks involved will be crucial for the development of effective new therapies. 1.2 Breast Cancer Breast cancer is a prevalent issue worldwide, affecting women the most. According to Xu et al., (3) there were approximately 2.3 million new cases and over 685,000 deaths reported starting this decade. Despite the significant improvement in survival rates in the last 20 years, the incidence of this disease is still on the rise globally. 2 1.2.1 Pathological and Molecular Classification Terminal ductal lobular unit is the functional unit of the breast where a majority of breast tumors arise. Breast ducts consist of a bi-layered epithelium of luminal and myoepithelial cells, which are surrounded by an immune, fibroblast and adipose rich stroma. At diagnosis, based on biopsy histology of the breast lesions, tumors are classified into in situ carcinoma or invasive carcinoma depending on whether there is a spread of malignant cells from the breast lobules or ducts into the surrounding stroma. The most common pre-invasive breast cancer is ductal carcinoma in situ of which only 20-30% of cases progress to invasive breast cancer, but a gap in predicting whether this pre-invasive lesion will lead to an invasive or metastatic disease still exists. On the other hand, invasive carcinoma is a heterogenous group of diseases that is further subdivided based on cell morphology of which invasive ductal carcinoma (IDC) accounts for 60-75% of the cases. Followed by invasive lobular carcinoma (ILC) (10-15%) and rare invasive carcinoma (0.1-7%), a 3-tiered grading system (Bloom-Richardson) based on differentiation and proliferative activity further divides invasive carcinomas into for prognostic evaluation (4). Breast tumors are categorized beyond their morphological classification based on the expression of the estrogen receptor (ER), progesterone receptor (PR), and human epithelial growth factor receptor 2 (HER2/ERBB2). This categorization results in three main clinical groups: ER+, HER2+, and triple-negative breast cancer (TNBC). Around 70% of all breast cancers are classified as Estrogen Receptor Positive (ER+) tumors. HER2-positive (HER2+) tumors, on the other hand, can be further divided into two subgroups: HER2+ER+ (which accounts for approximately 70% of HER2+ tumors) and HER2+ER- (which accounts for the remaining 30%). HER2 status is determined through immunohistochemistry and further assessed through chromogenic or fluorescence in situ hybridization (CISH and FISH, respectively) to 3 determine gene amplification. HER2+ tumors, make up approximately 15% of all breast cancers and exhibit significant biological heterogeneity. Attention is increasingly being paid to HER2-low breast cancer, which is characterized by low to moderate HER2 expression detectable through immunohistochemistry but without ERBB2 amplification. TNBC accounts for around 15-20% of all breast cancers, is characterized by the absence of expression of ER, PR, and HER2 and encompasses diverse subtypes that are generally characterized by expression of EGFR and cytokeratin CK5 and CK14. These tumors often follow an aggressive clinical course associated with younger age, higher grade at diagnosis, and poor prognosis; TNBC are prone to early recurrence, metastasis, particularly to the lung and/or brain, and account for a disproportionately high fraction of breast cancer mortalities (5). With the help of RNA-seq studies, we have a better understanding of breast cancer heterogeneity and have an improved patient stratification leading to clinically better therapy outcomes. There are five intrinsic molecular subtypes that have emerged as a result of pioneering microarray expression technologies (6,7): Luminal A, luminal B, HER-2 enriched (HER2-E), basal-like and claudin-low. Luminal A tumors are a type of breast cancer that are typically low-grade and express a strong luminal gene signature, including ESR1, GATA3, XBP1, and FOXA1. These tumors are ER+PR+ and have a more favorable relapse-free survival and overall survival post-treatment compared to other breast cancer subtypes. On the other hand, Luminal B tumors are also ER+ but exhibit lower luminal gene expression and higher expression of proliferative genes. HER2-enriched (HER2-E) tumors, which represent 15-20% of breast cancer cases, are characterized by HER2/ERBB2 amplification on chromosome 17q12 and intermediate expression of the luminal gene signature. While HER2-E tumors largely overlap with HER2 positivity as determined by immunohistochemistry or FISH, not all clinically based HER2+ tumors are of the 4 HER2-E molecular subtype. It is worth noting that the HER2-E molecular profile can align with HER2 tumors (8). A majority (70%) of HER2+ER- tumors are HER2-E. On the other hand, basal tumors represent 15-20% of patients and are highly proliferative while displaying augmented expression of basal cytokeratins, EGFR, and low expression of the luminal A signature (4). Furthermore, they are characterized by high chromosomal instability and have a strong association with germline BRCA1 mutations. 80% of basal tumors are TNBC. The last molecular level subtype called claudin-low are typically triple-negative tumors that display low expression of proliferation markers, adhesion proteins, and luminal differentiation genes. The claudin-low profile also shows enrichment in mesenchymal features and immune cell infiltrate, high representation among metaplastic and medullary carcinomas, and poor sensitivity to chemotherapy (9). 1.2.2 Genomic landscape of Breast Cancer The comprehensive analysis of breast tumors using whole-genome sequencing (WGS), whole-exome sequencing, copy-number profiling, and transcriptomic analysis has resulted in a complete catalogue of genetic diversity. As a result of this, new molecular drivers have been discovered (8,10–14). The identification of driver and passenger mutations posed a significant obstacle due to the extensive number of alterations and considerable level of heterogeneity in tumors. These studies revealed in breast cancer that, PIK3CA and TP53 mutations were the main mutations, while only a small number of other genes exhibited coding mutations in more than 5% of tumors (12). Furthermore, specific genetic alterations have been associated with distinct molecular subtypes of breast cancer, illuminating their role in the development of varying tumor phenotypes (8). 5 Interestingly, luminal A tumors possess the highest number of genes with recurring mutations yet display the lowest overall count of mutations and copy-number alterations (CNAs). Among the frequently mutated genes in these tumors are PIK3CA (occurring in approximately 45% of cases), along with GATA3, MAP3K1, and CDH1 (15). On the other hand, luminal B tumor genomes have a higher frequency of TP53 mutations (8). In HER2-E tumors, there is a significant increase in the number of mutations, commonly found in TP53 (75%) and PIK3CA (40%), and a high level of amplification of ERBB2/HER2 (16). The genomic landscapes of basal-like tumors are intricate, showcasing CNAs, rearrangements, and a significant mutational burden. These tumors often contain inactivating mutations in TP53, with a prevalence of over 80%. These mutation and copy-number profiles leads to intratumoral heterogeneity, which is linked to poorer outcomes across various subtypes of breast cancer (17). In the following sections, we discuss signaling pathways that are upregulated in breast tumors across different subtypes and current treatment landscape for these patients. 1.2.3 Signaling Pathways In estrogen receptor-positive (ER+) breast cancer, the development of cancer is influenced by steroid hormones like estrogen and progesterone. The main driving force behind tumorigenesis in ER+ luminal breast cancer is ER-alpha, which is encoded by the ESR1 gene (18). The ER transcriptional network involves the cooperative functions of GATA3 and the pioneering factor FOXA1. In luminal breast cancer, there is a high occurrence of loss-of-function mutations in GATA3 (10%-15%), whereas FOXA1 mutations are rare. Loss of GATA3 is associated with the epithelial to mesenchymal transition (EMT), tumor progression, metastasis, and poor disease-free survival (19). Progesterone and its progesterone receptors (PR-A and PR-B), which are ER target genes themselves, play a significant role in breast cancer development 6 (20). However, the interaction between PR and ER is complex, as they can associate on chromatin in the presence of ligand, leading to the attenuation of breast cancer cell proliferation (21). Additionally, the androgen receptor (AR), which is expressed in up to 90% of ER+ breast cancer, has been shown to repress ER-regulated cell-cycle genes and may act as a tumor suppressor in ER+ disease (22) and act as a driver for ER negative breast cancer. ERBB receptor tyrosine kinase such as HER2 is of critical importance in breast cancer as an orphan receptor which gets primarily activated by heterodimerization with other ERBB family members but in HER2+ overexpressing breast cancer its homodimerizes in a ligand independent manner (23). Another ERBB family receptor tyrosine kinase EGFR is overexpressed in 60% of TNBC which activates similar downstream signaling pathways that overlap with ER and HER2 induced signaling. Two well validated downstream signaling pathways across breast cancer subtypes are PI3K-AKT and MAPK pathway. The PI3K-AKT pathway is a complex group of lipid kinases responsible for mediating signaling by receptor tyrosine kinases and other kinases, ultimately leading to AKT and mTOR signaling (24). Negative regulators, particularly PTEN, control the amplitude of transduction following pathway activation. Mutations in various components of this pathway, such as the p110a catalytic subunit of PIK3CA, are prevalent in luminal A and HER2-amplified tumors, while low-frequency mutations in AKT1, PTEN, and PIK3R1 also contribute to pathway stimulation (8). Additionally, amplification of PIK3CA is detectable in all tumor types. Basal-like tumors often exhibit deletion/mutation of PTEN (over 35%), as well as amplification of AKT3 (around 11%), and less common mutations in PIK3CA (8%). The activation of the RAS family of proto-oncogenes is triggered by various cell surface receptors, including receptor tyrosine kinases. This stimulation leads to the sequential activation 7 of the MAPK and ERK signaling cascades. While RAS genes are commonly mutated oncogenes in human cancer, breast cancer patients have rare occurrences of RAS mutations (<5%). Nevertheless, around 50% of breast tumors exhibit abnormal RAS pathway signaling due to constitutive upstream signaling, such as HER2 overexpression. The oncogenic activation of RAS/MAPK is linked to a significant decrease in tumor-infiltrating lymphocytes (TILs) and lower survival rates in TNBC patients. In addition to the ERK pathway, MAP3K1 can modulate MAPK-JNK signaling by phosphorylating substrates like MAP2K4. Luminal cancers often exhibit relatively frequent somatic mutations in MAP2K4 and MAP3K1 (approximately 12% in total), which are sometimes concurrent with PIK3CA mutations (25). 1.2.4 Current Treatment Landscape While clinicopathologic features continue to underpin the diagnostic workup of a newly diagnosed breast cancer, approaches to therapy are increasingly informed by the intrinsic subtype. In addition, there is an adaptation of genomic assays (e.g., BRCA1/2 germline status, RNA-based prognostic assays) to drive treatment decision due to their prognostic and predictive utility. For patients classified as ER+, targeting the ER pathway is fundamental in treating these patients. Endocrine therapy with the selective ER modulator tamoxifen or an aromatase inhibitor (AI), which blocks estradiol synthesis, has long been the mainstay for early and advanced ER+ disease (26). Whether woman underwent menopause or not plays a critical role in which type of endocrine therapy is prescribed e.g., AIs (exemestane, letrozole, and anastrozole) can only be used in the post-menopausal setting, ovarian ablation through surgery or suppression is increasingly being incorporated into endocrine regimens for younger women, who exhibit increased risk of early and late relapse. In adjuvant setting or high-risk individuals both tamoxifen and aromatase inhibitors can also be used for prevention of ER+ tumors (27). Another 8 ER inhibitor is Fulvestrant, which is more often used as a second line of treatment. It has dual properties as an ER inhibitor and selective ER degrader (SERD) and generally is used for advanced/metastatic disease. Intense efforts are underway to identify more potent oral SERDS or proteolysis-targeting chimeric (PROTAC) mediated degraders of ER (28). HER2 amplified breast cancer have an exemplar targeted therapy called trastuzumab which is a humanized monoclonal antibody (29). This antibody targets an extracellular domain in HER2, attenuates intracellular RAS/ MEK/ERK signaling, PI3K/AKT/mTOR signaling, and induces Fc-receptor- mediated antibody-dependent cell-mediated cytotoxicity. Its clinical use has radically improved the survival rates to 90% in the early disease setting and durable responses are commonly achieved in the advanced setting. Other HER2 inhibitors include small molecule tyrosine kinase inhibitors such as neratinib (which irreversibly inhibits EGFR, HER2, and HER4) and tucatinib (HER2-specific), which can cross the blood-brain barrier (30). Within the case of HER2 targeted drugs, antibody drug conjugates (ADC) have taken the center stage where potent killer (cytotoxic) payloads are covalently bound to the monoclonal antibody via a cleavable synthetic linker. In many cases, the ADC is internalized, but the cleavable linkers can also release the cytotoxic payload to neighboring tumor cells, resulting in a potent bystander effect. The first ADC of its kind, T-DM1, contained the powerful payload emtansine and showed positive responses in brain metastases among certain patients (31). A novel ADC called ADCT-DXd (DS-8201), which incorporates the topoisomerase I inhibitor deruxtecan as its payload, has also demonstrated high activity, even in cases where T-DM1 was ineffective (32,33). Interestingly, a small group of patients with HER2-low tumors responded to trastuzumab in early studies, suggesting that its benefits may extend beyond HER2-positive disease (34). This observation has been supported by the finding that T-DXd significantly improves outcomes 9 compared to standard chemotherapy in heavily pre-treated patients (35). Notably, most patients in these studies had ER-positive disease, highlighting the role of HER2 signaling in driving resistance in ER-positive breast cancer. These discoveries have significant clinical implications and are likely to impact clinical practice guidelines. Understanding the positioning of these tumors within the luminal subtype could be valuable for developing future treatment algorithms. TNBC, which exhibits high tumor heterogeneity have very limited options of treatment. Surgery and chemotherapy were the only clinically approved treatment for these patients until few years ago. Treatment naïve TNBC patients recently began the use of immunotherapy combined with neoadjuvant chemotherapy which improved progression free survival (36,37). Patients with germline or somatic mutations in BRCA1/2 are eligible for PARP inhibitors (Olaparib, talazoparib) that block single-strand DNA repair in the homologues recombination deficiency (HRD) setting. Both olaparib and talazoparib were highly effective in pre-treated patients with a germline mutation (38,39). Wild type BRCA1/2 TNBC patients, however do not respond to this therapy. The progress made in ADC has also had a positive impact on patients with TNBC, and this progress has now been replicated in TNBC through the recent development of sacituzumab govitecan (SG). SG is a humanized IgG1k monoclonal antibody that specifically targets trophoblast cell surface antigen 2 (TROP2), a glycoprotein that is excessively expressed in 80% of breast cancers with a poor prognosis. This ADC is connected to the topoisomerase I inhibitor SN-38 by means of a hydrolysable linker, and it has shown remarkable effectiveness in patients with TNBC who have undergone extensive prior treatments (40). But TNBC still lacks early-stage targeted approved treatments against pathways that include PI3K/AKT/mTOR, EGFR, RAS/MAPK, and JAK/STAT which are known to be upregulated in TNBC (41). 10 1.2.5 Treatment Challenges Even though there have been improvements in the treatment landscape of breast cancer patients, the challenge of treatment resistance and disease progression still exists across each subtype. In the case of ER+ breast cancer, endocrine therapy resistance is underpinned by multiple mechanisms including the acquisition of ESR1 mutations (42), ligand-independent ER activation or upregulation of the PI3K-AKT and MAPK pathways. For example, genomic profiling of ER+ tumors and/or circulating tumor- DNA (ctDNA) in both early- and late-stage ER+ disease has revealed multiple sub-clonal resistance mutations in these pathways (43–45). Diverse mechanisms of resistance to anti-HER2 therapy have also been elucidated, including loss or concealment of the extracellular domain, activating mutations in HER2 or HER3/ERBB3, and activating mutations in downstream pathways including the MEK/ERK, PI3K/AKT/mTOR, and cyclin D1/CDK4/6 pathways (30). In a striking example of parallel genetic evolution in response to PI3K inhibitor treatment in the metastatic setting, convergent loss of PTEN function was observed at multiple sites of metastasis, each with different PTEN alterations, underscoring hyperactivation of the PI3K-AKT axis as a major cause of acquired resistance (46). As PI3K is a significantly deregulated pathway across all subtypes of breast cancer, the identification of upstream regulators of PI3K pathway activation and targetable components is paramount. In the case of TNBC, various therapeutic strategies were tested such as EGFR antibodies and small molecule inhibitors with either carboplatin or cisplatin but only 20% of patients have been shown to benefit from this strategy (47). To improve the therapeutic outcome, taxane-based chemotherapy (paclitaxel and cisplatin) is administered for the treatment of breast cancer in various settings but certain subtypes of TNBC are not responsive (48). Analyzing different research literature in context of breast cancer treatment resistance irrespective of subtype, 11 consistently mentions similar downstream signaling pathways that get activated after acquiring mutations in oncogenic receptors upstream or transcription factors within localized or metastases disease setting (4). This suggests tumor cell dependency on specific growth factors that have the ability to stimulate multiple pathways to compensate for treatment induced downregulation of alternate pathways. I would like to elaborate more on this consistent uncontrolled growth signaling within cancer cells in our next section. 1.3 Autocrine Growth Factor Hypothesis Cancer hallmarks are cellular traits that can distinguish cancer cells from normal cells. The first and arguably the most fundamental of all cancer hallmarks to be identified is the cancer cells’ acquired capability for autonomous growth signaling (2). Whereas normal cells require exogenously derived mitogenic growth signals to proliferate, cancer cells invariably show a greatly reduced dependence on exogenous growth stimulation for sustaining proliferative signaling. A key mechanism by which cancer cells acquire this capability is through the autocrine production of growth factors which in turn activate cognate growth stimulatory receptors on the same cells. Two prototypical examples of cancer-associated autocrine growth factors are PDGF and TGFα produced by glioblastomas and sarcomas, respectively (49). These autocrine growth factors might play a major role in driving tumor growth across different stages of cancer progression and currently presents as a major bottleneck in developing successful cancer therapies. 1.4 Semaphorins The semaphorins play crucial roles in development and the maintenance of various physiological processes. Seminal research demonstrated that a protein derived from chicken brain extract could induce the collapse of explanted dorsal root ganglia (50,51). Subsequent 12 studies revealed that semaphorins establish molecular gradients that guide cell movement (52), and they also have essential functions in cardiac development and the immune system (53,54), as well as other cellular processes (55). These proteins have been identified in worms, chicks, flies, mammals, and viruses. They are expressed in different tissues across these species. Semaphorins can be either secreted or bound to the cell membrane, and the family consists of 20 members. The distinguishing feature of the Sema protein family is the sema domain, which is an extracellular domain approximately 500 amino acids long. This domain adopts a seven-blade beta-propeller fold structure and allows for classification into classes 3 to 7 based on various structural characteristics such as Immunoglobulin (Ig) domains, basic domains, thrombospondin repeats, transmembrane domains, or glycosylphosphatidylinositol (GPI)-linkages (Figure 1.1). Additionally, a viral-specific class, designated V, has been identified. Classes are divided into individual members, with each member having a specific nomenclature within the semaphorin system. The naming convention for semaphorins includes the prefix 'SEMA' followed by the numerical class and a letter representing the member. For instance, SEMA3C refers to semaphorin class 3, member C. Semaphorins undergo post-translational modifications like glycosylation and proteolytic cleavage, which subtly influence their function. While class 3 semaphorins are secreted by the cell, classes 4 through 7 are associated with the cell membrane. 13 All semaphorins contain a sema and PSI domain but are further separated into eight different classes (vertebrates and invertebrates) based on combinations of structural features including an Ig domain, a basic domain, thrombospondin repeats, or GPI-linkages. Vertebrates have plexins A1–4, plexins B1–3, PlexinC1, and PlexinD1. Plexins contain a sema domain, a PSI domain, and an Ig-like, plexins, transcription factors (IPT) domain. Structurally, the cytoplasmic domain of the plexin contains two GTPase activating protein (GAP) domains, including one GTPase-binding domain and one PDZ domain. In invertebrates, semaphorins 1 and 2 signal through Plexin A and Plexin B. In vertebrates, semaphorins 3, 5, and 6 signals via Plexin A, B, D while semaphorin 3 requires a combination of neuropilins (Nrp1 or Nrp2) for signal transduction. Neuropilin is a transmembrane receptor composed of two complement-like (CUB) domains, two FV/FVIII clotting factor-like domains, one meprin-like MAM domain, and a short cytoplasmic tail (56). Image used in accordance with Creative Commons CC-BY license-based on Frontiers Copyright statement. Figure 1.1. Schematic representation of semaphorins and its receptors. 14 There are various protein families that serve as receptors for semaphorins, and in certain instances, there are co-receptors that aid in enhancing the signaling response. Most semaphorins bind to plexins (PLXN), which are transmembrane receptors with an extracellular sema domain that interacts with semaphorins, as well as an intracellular domain that contains a GTPase activating protein (GAP) homology domain, which is highly conserved (Figure 1.1). Plexins are categorized into four classes (A-D). Apart from their intrinsic catalytic activity, PLXNs also function through communication with receptor tyrosine kinases at the plasma membrane. For instance, PLXNB1 and c-Met collaborate to drive invasive growth. Additionally, PLXND1 has been found to activate ErbB2 and EGFR signaling, promoting invasiveness and metastatic spread to the lungs in vivo (57,58). As mentioned earlier, in some cases, additional co-receptors, particularly Neuropilin (NRP) 1/2, are necessary to mediate signaling for Class 3 semaphorins. Neuropilins lack intrinsic enzymatic activity allowing no transduction of semaphorin signaling but they help to stabilize the Sema/Plexin interactions. The SEMA3 family's sema domain participates in protein-protein interactions with plexin and neuropilin receptors (59–62). Furthermore, all semaphorins possess a Plexin-semaphorin-integrin (PSI) domain located at the C-terminus of the sema domain. Studies involving SEMA3A, NRP1, and PLXNA2 have demonstrated that ternary complexes consisting of one semaphorin, one neuropilin, and one plexin dimerize at the cell surface in class 3 semaphorins. This initial dimerization is potentially followed by further oligomerization (Figure 1.2), leading to the initiation of cellular signaling through crosstalk with other cell surface proteins (63). While NRP-PLXN crystal structures have been resolved, it is presumed that other class 3 semaphorins follow a similar paradigm to SEMA3A. There is a certain level of redundancy within this mechanism, as some semaphorins bind to a shared plexin receptor, while in other 15 cases, one semaphorin can bind to multiple plexin receptors (52). Semaphorins exert their effects through autocrine, paracrine, and juxtacrine signaling and have been implicated in a wide range of biological functions, including tissue morphogenesis and immunity (53,54). Additionally, altered semaphorin expression has been observed in various cancers (64). Janssen et al (63) have provided a description of the (A) ternary complex dimers of SEMA3A-NRP1-PLXNA2, revealing their crystal structure. (B) The dimeric SEMA3A molecules are found to associate with two PLXNA2 molecules, which are embedded in the plasma membrane. This structural arrangement is further strengthened by the presence of two NRP1 molecules. It is suggested that the oligomerization of this complex at the cell surface might be necessary for effective signal transduction. Images used with permission from Springer Nature Group (Copyright Clearance Center’s RightsLink® service). Figure 1.2. Semaphorin 3 family receptor complex (SEMA3A-NRP1-PLXNA2). 16 1.4.1 Emergence of semaphorins as both a promoter and suppressor of tumors Given the pleiotropic nature of semaphorin function, in particular its roles in cell motility and cytoskeletal rearrangement (65), it is not surprising that semaphorins are associated with virtually every hallmark of cancer (64). As described by Hanahan and Weinberg (1) at some point, all cancer cells gain the ability to produce the very growth factors to which they also respond. Semaphorin signals have a multifaceted role in tumor development, which is still under investigation. They can regulate cancer cell behavior, but also control tumor angiogenesis and the function of immune cells recruited from the circulation, among other things. Some of the semaphorins exhibit a tumor suppressor role than a tumor promoter role. SEMA5A and SEMA3C are highly expressed in various types of cancer and have been found to promote cell growth and invasiveness in pancreatic tumors and endothelial cells, respectively (66,67). Cancer cells are known to disable mechanisms that lead to programmed cell death and stimulate angiogenesis to meet the increasing oxygen demands of a growing tumor. SEMA4D and SEMA3C have disrupted regulation in numerous cancers and have been shown to trigger endothelial cell activation and angiogenesis (67,68). SEMA3E promotes metastasis of colorectal, breast, and lung cancer cells to the lung (57). In breast cancer, SEMA3A is downregulated and induces integrin activity and cell adhesion. SEMA3B is downregulated in multiple cancers and has been found to induce apoptosis in lung cancer cells by inhibiting the PI3K signaling pathway and activating caspase-3 (69–71). Generally, SEMA3D, and SEMA3G are believed to have tumor suppressor activity, while SEMA3C, SEMA3E, and SEMA4D have both pro-tumor activity(72–74). Although the roles of semaphorins in cancer are becoming increasingly evident, the mechanistic details surrounding their involvement in cancer are not yet fully understood. 17 1.4.2 Semaphorin 3C (SEMA3C) 1.4.2.1 Pan-Cancer SEMA3C was initially discovered for its roles in neurogenesis and cardiac development (75,76). Its receptors include NRP1, NRP2, PLXNB1, and PLXND1 (77–79). SEMA3C is upregulated in response to chemotherapy and radiation treatment (80). The Cancer Genetic Dependencies (CancerGD) initiative integrates mutational profiling with loss-of-function genetic screens in a panel of cancer cell lines to identify genetic dependencies (such as oncogene addiction, non-oncogene addiction and synthetic lethality) associated with cancer driver gene mutations (81). Interestingly, from analysis of public CancerGD data (https://depmap.org/portal/), an increased dependency on SEMA3C was identified in cancer cells harboring mutations in corresponding cancer driver genes (PTEN, PIK3CA, NF1, MYC, TP53) (Table 1) commonly found in different breast cancer subtypes at the time of diagnosis or acquired during treatment resistance (82). Both clinical data analysis and lab-based experiments have started to uncover the mechanistic role of SEMA3C signaling. We recently discovered that semaphorin 3C (SEMA3C) is an autocrine growth factor that drives prostate cancer growth and treatment resistance through transactivation of multiple Receptor tyrosine kinases (RTKs) such as HER2, EGFR and MET in a cognate ligand independent manner. Furthermore, over-expression of SEMA3C conferred castration resistant growth of LNCaP cells in-vitro and xenografts in vivo (78). High expression of SEMA3C significantly contributes to cancer progression in prostate, pancreatic, gastric, 18 cervix, hepatocellular, and glioblastoma cancers by promoting tumor growth, invasion, and metastasis (78,83–90)and have been shown to regulate PD-L1 expression positively (86). From mining the scientific literature and public databases, we collated a number of cancer driver genes that are known to regulate SEMA3C expression in a direct or indirect manner (oncogenic transcription factors (AR (91), FOXA1 (91), Myc (92), SOX4 (93), YAP1 (94)) and tumor suppressor deletion (TP53 (94,95)) for which SEMA3C has been validated as a target gene. By analyzing pan-cancer ChIP-seq data, we found that many of these oncogenic transcription factor (TFs) directly binds to the SEMA3C gene locus, and the TF binding sites are clustered around three distinct enhancer regions located in the proximal promoter, intron 1 and intron 2 of SEMA3C gene. Importantly, alterations in these SEMA3C-regulating cancer driver genes are also frequently observed in different breast cancer subtypes and are associated with a poor prognosis. Table 1.1 Cancer Genetic Dependencies for SEMA3C. 19 1.4.2.2 Breast cancer Tissue microarray analyses of 343 invasive ductal breast carcinomas revealed that SEMA3C was expressed in 90% of the cases and was positively correlated with tumor grade (p<0.001) (96). To expand on this observation, we wanted to assess relevance of SEMA3C in breast cancer using publicly available TCGA datasets which revealed RNA expression of breast tumors exhibited the highest levels of SEMA3C expression (proteinatlas.org) (97) compared to other cancer types, and by comparing its expression in breast normal vs. breast tumors using GEPIA2 (98), we confirmed higher mRNA expression in breast tumors (n=1085) than matched normal TCGA and GTex samples (n=291) (p < 0.05) (Figure 1.3). 20 (A) RNA expression of SEMA3C is higher in breast cancer across different tumors in TCGA dataset available on protein atlas (image used from protein atlas) and (B) comparison of SEMA3C mRNA expression between breast tumor (n=1085) and normal (n=291) samples from TCGA and GTEx dataset reveals higher mean expression of SEMA3C in tumor samples (Log2FC threshold >0.5) using GEPIA2. *p<0.05 mean ± standard error of the mean (SEM). Using GEPIA2, we also observed significant positive correlation (Spearman) of SEMA3C mRNA expression with known oncogenic genes in breast cancer (MAP2K1, MAPK1, MAPK3, PIK3CA and AKT) (Figure 1.4). Spearman correlation identified significant positive association between mRNA expression of SEMA3C and mRNA of oncogenes commonly observed in breast cancer (MAP2K1, MAPK1, MAPK3, PIK3CA and AKT1); each dot represents data from one sample. mean ± standard error of the mean (SEM). Figure 1.3. Analysis of SEMA3C expression in breast tumors. Figure 1.4. Positive correlation of SEMA3C mRNA expression with validated breast cancer oncogenes. 21 Additionally, PIK3CA mutations or PTEN loss can induce estrogen-independent growth of breast cancer cells. Our analysis of clinical data (Figure 1.5) analyzing SEMA3C mRNA expression in ER+ breast cancer patients (99) with PIK3CA mutation and previously published studies (100) identified a strong correlation of these events with SEMA3C expression. In an external study, SEMA3C silencing has been shown to inhibit cell invasion of MCF7 and MDA-MB-231 cells (101). But the mechanistic role and the therapeutic potential of SEMA3C in breast cancer (second leading cause of cancer death in women) has not been studied yet. Analysis of clinical data identified a strong correlation of SEMA3C mRNA expression with PIK3CA mutants in ER+ breast cancer patients. ****p<0.0001; mean ± standard error of the mean (SEM). 1.4.2.3 Rationale for investigating SEMA3C in breast cancer Tyrosine kinase (TK) pathways are the Achilles heel of many cancer types (102). TKs transduce growth stimulatory signals and are commonly deregulated in cancer. In fact, the use of TK pathway inhibitors has led to major breakthroughs in treatment of melanoma (KIT, PDGFR), breast cancer (HER2), colorectal cancer (EGFR, VEGF) and non-small cell lung cancer (EGFR) Figure 1.5. ER+ breast cancer patients with mutation in PIK3CA expresses higher SEMA3C mRNA expression. 22 (103). In breast cancer, multiple RTKs including EGFR, HER2, and MET have been shown to play crucial roles in breast cancer biology (104). However, despite the important roles of these TKs in breast cancer, relatively little is known about the cognate and non-cognate growth factors that activate these TK pathways. The ability of SEMA3C to simultaneously activate EGFR, HER2, and MET and its downstream tyrosine signaling pathways such as: MAPK, AKT and Src (78) is noteworthy as each of these TKs is a well-established driver oncogene implicated in growth and metastasis of a variety of cancers especially in breast cancer and this paradigm of “one ligand being capable of activating numerous TKs” may explain why targeting of these TKs individually have not demonstrated meaningful results in breast cancer clinical trials (104). Concomitant activation of multiple TKs by SEMA3C would render inhibiting one TK alone insufficient due to compensation from secondary and tertiary TK pathways. After observing high SEMA3C expression in breast tumors along with its association with multiple oncogenes commonly expressed in breast cancer patients, these findings suggest that understanding the signaling mechanism and targeting SEMA3C may be necessary to achieve meaningful clinical efficacy in treatment of breast cancer. 1.5 Hypothesis, objective and specific aims 1.5.1 Objective and Hypothesis Given the numerous similarities observed between breast and prostate cancer (105), we have the opportunity to utilize the existing knowledge and technologies used to inhibit SEMA3C in treatment-resistant prostate cancer. By doing so, we can gain insights into the functional role of SEMA3C in breast cancer and establish preclinical evidence supporting its potential as a novel therapeutic approach for breast cancer. The primary goal of this research is to investigate the role of SEMA3C in the context of breast cancer. Currently, there are various molecular paradigms 23 that contribute to our understanding of the development of breast cancer, and my research aims to establish a connection between these paradigms and SEMA3C for the first time. Given that the estrogen receptor is a known driver of ER+ breast cancer, a significant part of my work focuses on establishing the link between the estrogen receptor and SEMA3C in both pre- and post-endocrine therapy settings. Additionally, we have conducted novel studies to determine the prognostic and functional role of SEMA3C in TNBC and have gained insights from in-silico analysis of the SEMA3C receptor complex. If successful, these objectives will provide evidence supporting the notion that SEMA3C plays a role in breast cancer progression through established pro-cancer pathways. My hypothesis is that SEMA3C is a transcriptional target of the estrogen receptor and can activate TK pathways (MAPK & AKT), making it a potential target for breast cancer therapy in combination with current FDA approved drugs. In a broader sense, we believe that there is a complex relationship between SEMA3C and different subtypes of breast cancer. To test this hypothesis, my specific aims are: Specific Aim 1: To determine whether SEMA3C is regulated by ER, and can it drive ER+ breast cancer signaling. Specific Aim 2: To determine whether SEMA3C can promote progression and induce therapeutic sensitivity in ER negative breast cancer (TNBC). Specific Aim 3: To analyze in-silico SEMA3C receptor complex and establish framework to develop next generation small molecule inhibitors against SEMA3C. 1.5.2 Experimental Plan To begin, SEMA3C protein expression in breast cancer cell lines were benchmarked to levels in a non-cancer cell line (MCF10A). To determine if SEMA3C is an ER-regulated gene in 24 specific aim 1, we use ER-positive cell lines (MCF7, T47D) to test whether SEMA3C expression is estrogen-inducible. We also showcase a continued dependency of SEMA3C in tamoxifen resistant cells (TamC3, TamR3). Additionally, we use ChIP Atlas IGV software to analyze for the presence of estrogen response elements (EREs) in the SEMA3C locus. For specific aim 2, TNBC cell lines: MDA-MB-468, MDA-MB231 and BT-549 are used as they represent different subtypes of TNBC based on gene clustering. These cell lines are utilized to study the signaling effect of SEMA3C in ER negative breast cancer. For specific aim 1 and 2, a) we perform experiments involving functional studies (qPCR, Western blot, FACS, in-vitro growth assays) to determine if expression of SEMA3C can promote oncogenic signaling and growth in different breast cancer cells followed by utilizing in-house developed anti-SEMA3C inhibitors as proof-of-principle to confirm druggability potential of SEMA3C as therapeutic target and study impact of SEMA3C’s inhibition on overall therapy efficacy. For specific aim 3, we utilize MOE software (chemical computing group, 2019) to analyze the 3D protein structure and peer reviewed structural studies of SEMA3 family to predict SEMA3C structure and binding sites of its receptor complex. Using that knowledge, we conduct large scale in-silico screening algorithm to select potential hits (small molecule compounds) and validate the inhibitory potential of proposed small molecule binding site. 25 Chapter 2: Dependency of tamoxifen sensitive and resistant ER+ breast cancer cells on Semaphorin 3C for growth 2.1 Introduction Hormone receptor positive (ER+/PR+/HER2-) breast cancer (BCa) commonly known as estrogen receptor positive (ER+) breast cancer represents approximately 65-70% of new breast cancer cases diagnosed each year and accounts for the highest number of breast cancer related deaths. Of all breast cancers, 75-80% have positive ER status (106,107). ER+ BCa cells are activated by the ER which is a ligand-dependent transcription factor that is believed to regulate cancer growth by inducing expression of downstream target genes involved in cell growth, survival, and proliferation (108,109). However, the identity of ER regulated genes that drive cancer growth and survival are poorly understood. The ER pathway is the primary driver of ER+ subtypes, and as such, endocrine treatments are the first-line therapy for ER+ BCa patients. These treatments include selective ER modulators (e.g. Tamoxifen), selective ER degraders (e.g. Fulvestrant), and aromatase inhibitors (e.g. Letrozole) (110). While most patients initially respond to endocrine therapy, the clinical response is transient, as approximately 40% of the patients experience treatment resistance to ER-targeting therapies (111). Although many mechanisms for achieving endocrine resistance have been described, most endocrine resistant BCa cells remain dependent on the ER pathway (112,113). This suggests that critical ER regulated genes are necessary for driving cancer growth and survival; therefore, the identification of new ER-regulated targets that control growth and survival is urgently needed. 26 However, the mechanism by which SEMA3C drives breast cancer progression and endocrine resistance is poorly understood, the aim of this study is to further characterize the role of SEMA3C in driving growth and survival in tamoxifen sensitive and resistant ER+ breast cancer cells. 2.2 Materials and Methods 2.2.1 Cell lines MCF10A and T47D cell lines were purchased from the American Type Culture Collection (ATCC, Manassas, VA, USA). MCF7 (authenticated by IDEXX Bioanalytics Case #16382-2019), TamR3 and TamC3 (authenticated by AmpFLSTR Identifiler PCR Amplification kit (Applied Biosystems, Foster City, CA) according to the manufacturer’s protocol) cell lines were kindly provided by Dr A. Cherkasov (Vancouver Prostate Centre). MCF10A were maintained in growth medium as recommended by American Type Culture Collection (ATCC, Manassas, VA, USA). MCF7 and T47D cell-lines were maintained as recommended in (114). TamC3 were maintained in media lacking tamoxifen, but containing the diluent (0.1% ethanol) in phenol-red-free RPMI containing 10% charcoal-stripped fetal bovine serum (CSS) and 1X Insulin-Transferrin-Selenium and TamR3 were maintained in tamoxifen (1µM in ethanol) in phenol-red-free RPMI containing 10% charcoal-stripped fetal bovine serum and 1X Insulin-Transferrin-Selenium. All cell lines were mycoplasma free. Experiments were carried out on cells grown in their respective growth medium unless otherwise specified. 2.2.2 Antibodies, chemicals and reagents Antibodies to the following were used at 1:1000 dilution unless specified: p-EGFR (Y1068), p42/44 pMAPK(T202/Y204), p42/44 MAPK(L34F12, 1:2000), AKT (9272), p-AKT (Ser473, D9E), cleaved PARP (Asp214), PARP (9542), Estrogen Receptor alpha (D8H8) cleaved 27 caspase-3 (Asp 175, 5A1E, 9664), caspase-3 (9662) , cleaved caspase-8 (Asp 374, E6H8S,), caspase-8 (1C12, 9746) (Cell Signaling Inc., Beverly, MA) SEMA3C (N-20, 1:2000); Plexin B1 (H300, 1:500), t-EGFR (C-2, 1:1000) (Santa Cruz Biotechnology, Santa Cruz, CA), Actin (A2066, 1:5000), Vinculin (1:5000) (Roche Diagnostics, Laval, PQ), LICOR secondary antibodies- anti Rabbit IR680 and IR800, anti mouse IR680 and IR800 (Rockland, Gilbertsville, PA), anti Goat680 (Pierce Biotechnology, Rockford, IL). Antibodies were used according to manufacturer’s protocol. The following chemicals were used: Tamoxifen (68392-35-8; Sigma-Aldrich), Fulvestrant (I4409-25MG; MiliporeSigma), Estradiol (E2758; Sigma-Aldrich), Charcoal Strip Serum (12676029; Gibco), RNase A (12091021; Invitrogen), Propidium Iodide (P3566; Invitrogen), Insulin-transferrin-selenium (41400045; Gibco). 2.2.3 External datasets utilized in this study We utilized three publicly available databases to analyze SEMA3C mRNA expression from studies consisting of tumor expression data from breast cancer patients and genome wide differential expression cell-line based assays: 1] Cbioportal (115,116) 2] GEO-DATSET (117). STUDY NAME Number of samples utilized for analysis Database Sample Grouping Breast Cancer (METABRIC) (99,118) 1764 Cbioportal ER+/HER2- (n=1257); HER2+ (n=198); ER-/HER2- (n=309) Breast Invasive Carcinoma (TCGA, Firehose Legacy) (119) 1101 Cbioportal ER+/HER2- (n=437); All samples (n=1101) GSE67916 (120) 18 GEO TamR1 (3 replicates); TamR4 (3 replicates); TamR7 (2 replicates); TamR8 (2 replicates); TamS (8 replicates) GSE11324 (121) 12 GEO 0hr (3 replicates); 3hr (3 replicates); 6hr (3 replicates); 12hr (3 replicates) GSE27473 (122) 6 GEO MCF7-Scr (3 replicates); MCF7-silenced ER (3 replicates) Table 2.1 Publicly available datasets used for SEMA3C expression studies. 28 2.2.4 RNA Extraction and qPCR Total RNA was extracted from cultured cells using the PureLink RNA kit (Invitrogen). Total RNA (1.5-2µg) was reverse transcribed using High-Capacity cDNA Reverse Transcription Kit (Applied Biosystem). Real-time monitoring of PCR amplification of cDNA was performed using SEMA3C (Hs00170762_ml) and GAPDH (Hs03(9)29097_gl (Applied Biosystems) on ABI PRISM 7900 HT Sequence Detection System (Applied Bio-systems) with SYBR gene expression Master Mix (Applied Biosystems, Burlington, ON). Target gene expression was normalized to GAPDH levels in respective samples as an internal standard. 2.2.5 Immunoblotting Cells were lysed with 100 µL lysis buffer (50mM Tris pH 7.5, 150 mM NaCl, 1% NP-40, 10 mM NaF, 10% Glycerol) containing Complete and PhosStop phosphatase inhibitors (Roche, Mississauga, ON). Whole cell lysates were centrifuged at 14,000 rpm for 15 min at 4◦C. The protein concentration in cleared whole cell lysates was identified using BCA kit (Thermo Scientific, Rockville, IL). Protein lysates (30-50µg) were analyzed by SDS-PAGE (6-10%) and transferred by Western Blot to nitrocellulose membranes (Bio-Rad, Hercules, CA). Membranes were blocked in PBS containing 5% BSA or TBS Odyssey buffer (Licor, Lincoln, NE) if probing with phospho-antibodies. The primary antibody dilution was conducted as per manufacturer's protocol. Membranes were washed 3X with TBST followed by appropriate IRdye-conjugated secondary antibodies for 1h. Blots were washed again 3X for 5 min with TBST. Detection was by image analysis using a LICOR infrared imager and Image Studio Lite version 3.1 software. 2.2.6 Cell stimulation MCF7 and T47D were seeded at 1x105 cells/well in a six well plate in RPMI medium containing 10% FBS for two days. After two days, the medium was replaced with RPMI phenol-red free 29 and serum starved for 24h. MCF10A cells were treated similarly under its respective recommended media and for serum starvation we utilized MEBM without phenol red for 5 hours. The medium was changed again just prior to stimulation to remove endogenous secreted SEMA3C. For SEMA3C stimulations across different expriments: MCF7, T47D and MCF10A were treated with SEMA3C for 10 min at the indicated concentrations followed by a wash with PBS. For B1SP, MCF7 and T47D cells were treated with B1SP for 90 min and following B1SP treatment, cells were treated with SEMA3C for 10 min without re-moving media followed by a wash with PBS. To study the effect of tamoxifen on SEMA3C expression in tamoxifen resistant cells, TamC3 and TamR3 were seeded at 5 x 105 cells/ml in a 10 cm dish in their growth media conditions except 0.1 % ethanol or 1µM tamoxifen respectively for three days in order to see the effect. After three days, these cells were seed-ed into a 6-well plate at 1x105 cells/well and were treated with either Ethanol (95%) or tamoxifen (0.1µM) for 48h. All of these treatments were carried at 37◦C in 5% CO2. 2.2.7 Plexin B1 and SEMA3C silencing siRNA targeting Plexin-B1 and SEMA3C were validated: siPlexin-B1-1 (Hs_PLXNB1_6, Qiagen, Montreal, PQ)- CCGGGUGGAAUUUAUCCUUGAUU, siSEMA3C-1 (Ambion, Life Technologies Corporation, Carlsbad, CA)- CAC-CAUCCUUUAGACUACATT. MCF7 and T47D cells were reverse transfected with either scramble siRNA (siScr) (AUCAAACUGUUGUCAGCGCUGUU), siPlexin-B1-1 (25nM) or siSEMA3C-1 (25nM) using RNAiMax (Invitrogen, Mississauga, ON) as described by the manufacturer. For immunoblotting, transfected cells were incubated for 72 hours then washed with PBS and then lysed for SEMA3C siRNA experiment. To study Plexin-B1 siRNA knockdown impact on SEMA3C mediated signaling after 72 hours we serum starved cells overnight and stimulated cells with exogenous 30 next day. Plexin-B1 (45µg) and SEMA3C (40µg) levels were analyzed by SDS-PAGE. For cell growth assay, cells were reverse transfected in 96-well flat bottom plate and proliferation was monitored over 4 days (siPlexin-B1) and 5 days (siSEMA3C) with readings taken at every alternate day starting from Day 0. 2.2.8 Cell growth assay The cell growth of MCF10A (2000 cells/well), MCF7 (1500 cells/well) and T47D (1500 cells/well), TamC3 (1500 cells/well) and TamR3 (2200 cells/well) were assessed in 96-well plate using Presto Blue Viability Reagent (Life Technologies Corporation, Carlsbad, CA) according to manufacturer’s protocol. Fluorescence was measured at excitation 535 nm and emission 612 nm using a Tecan F500 plate reader (Tecan, Austria, GmbH). To detect cell growth for: 1) siRNA silencing we conducted reverse-transfection within a 96-well plate and analyzed data at different time points. 2) For showcasing dependence of cells on SEMA3C: Plexin-B1 complex for growth, exogenous SEMA3C (1µM) was added to wells in presence of charcoal strip serum. 3) For dose response, cell growth inhibition by B1SP and during synergy assay we allowed cells to attach to the wells of the 96-well plate overnight and then administered B1SP, Fulvestrant or tamoxifen the next day at the indicated concentrations in respective experiments (see results section). All growth assays were done in six biological replicates and at least two technical replicates. 2.2.9 Sub-G0/G1 DNA content Assay Flow Cytometry analysis of propidium iodide-stained nuclei was performed as shown previously (100). MCF7 and T47D cells were reverse-transfected with SEMA3C siRNA (20nM) in 6-well plates, allowed to attach overnight in RPMI containing 2% FBS and were forward-transfected again the next day. 48 hours later, cells were trypsinized, washed in 1 ml PBS and then fixed in 1 ml 80% ethanol overnight at 4C. Fixed cells were incubated in DNase-free RNase A reagent (50 31 µg/ml) and propidium iodide (50 µg/ml) at 4℃ for 1 hour. The stained cells were analyzed for relative DNA content on a Becton Dickesnon FACSCanto II flow cytometer (BD Biosciences, Mississauga, ON). 2.2.10 Production of SEMA3C: FL and B1SP CHO-S cells were transduced with SEMA3C-FL (recombinant SEMA3C) and B1SP-expressing lentivirus at 100 MOI. For SEMA3C: FL, cells (40 million) were seeded in Cellstack (2-stack) culture chambers (Corning Incorporated, Tewksbury, MA) in a volume of 200 ml DMEM, supplemented with 5% FBS (Life technologies, Gaithersburg, MD), heparin (25μg/ml) and blasticidin (5μg/ml). Conditioned media (CM) was collected after 48h and then replenished with the same medium containing 2% FBS. The CM was collected every 48h and replenished. Harvested CM was then centrifuged at 2000 RPM for 5 min and then filtered (0.22μm). Filtrates were purified over HisTrap excel (GE Healthcare, Uppsala, Sweden). Recombinant SEMA3C-FL was then buffer exchanged and concentrated on an Amicon Ultra-15 centrifugal filter device (MWCO, 10kDa), to 2.0 mg/ml (Merck Millipore, Carrigtwohill, Co, Cork, IRL). The protein sample was then desalted on a PD midiTrap G-25 column as directed by the manufacturer, (GE Healthcare, Buckinghamshire, UK), and then filter sterilized using a 0.22μm filter. B1SP was produced from CHO-S B1SP Cells (2x108) which were seeded and cultured in 5L Wave Bag (GE Healthcare, Uppsala Sweden) using 75%, Freestyle CHO expression medium/25% Optimem supplemented with 8mM Glutamine and Penicillin/Streptomycin (5.0ml/L), (Life technologies, Gaithersburg, MD) at 37◦C and 8% CO2. CM was harvested three times over a period of 4 weeks and B1SP was purified using specific purification process described previously (78). 32 2.2.11 Statistical Analysis and Software Used The clinical data and in-vitro data were assessed using Student’s t-test and ANOVA. All data are expressed as the mean ± standard error of the mean (SEM) (only for densitometry analysis we used mean ± standard deviation (SD)). Comparisons between two means were performed using a student’s t-test. Comparisons among multiple means were performed with a one-way ANOVA followed by Fisher’ protected least significant difference test (StatView 512, Brain Power, Inc., Calabasas, CA, USA). GraphPad Prism software (Version No. 8.4.3) was used to calculate the statistical significance and generate graphs. ChIP-Atlas was used as a source for CHIP-Seq and DNA-seq studies and IGV (Integrative Genomics Viewer) browser (Version 2.14.0) was used to analyze the files. Only in case of densitometry analysis of SEMA3C expression and phosphorylation of MAPK and AKT under B1SP treatment was performed using ImageJ 1.53e (Wayne Rasband and contributors National Institutes of Health, USA) based on two independent experiments. The Synergy Finder Plus tool was used to calculate synergy scores. The threshold of statistical significance was set at *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001. 2.3 Results 2.3.1 SEMA3C is significantly upregulated in ER+ breast cancer and its expression is positively correlated with the expression of Estrogen Receptor (ER) We investigated SEMA3C mRNA expression in 1764 breast cancer patients using clinical data from a METABRIC study on cbioportal (see Methods for details on the sample grouping in each dataset) (99,118). We found SEMA3C’s mRNA expression to be the highest in ER+/HER2-breast cancer patients when compared to all other subtypes, including HER2+ and Triple-Negative breast cancer (p < 0.0001) (Figure 2.1A). Furthermore, by segregating the patients based on their ER status (positive or negative) we observed that SEMA3C mRNA 33 expression was significantly higher in ER-positive tumor samples (TCGA, Firehose Legacy), providing further confirmation of this positive association (119)(Figure 2.1B). Gene co-expression studies can shed light on the mechanisms underlying breast cancer development and progression. To define the co-expression relationship between SEMA3C and the estrogen receptor, we used mRNA expression data of SEMA3C and ESR1 from a TCGA study (Firehose Legacy-Breast Invasive Carcinoma) with 1100 samples (119). We performed gene correlation analysis (Pearson correlation) and found a positive correlation between SEMA3C and ER-alpha mRNA expression levels (p < 0.0001) (Figure 2.1C). Interestingly, selecting ER+/HER2- patients within this study revealed similar significant positive correlations (p < 0.0001) between SEMA3C and ESR1 mRNA expression (Figure 2.1C). Taken together, these findings suggest a potential functional relationship between SEMA3C and ER, warranting further investigations to elucidate the functional relationship between SEMA3C and ER in promoting tumorigenesis. 34 (A) Analyzing a dataset of 1764 patients showed higher SEMA3C mRNA expression in the ER+/HER2ˉ- breast cancer subtype, and (B) in the TCGA (Firehose Legacy) study we confirmed that SEMA3C expression was significantly higher in patients with ER+ diagnosis. (C) Furthermore, analyzing breast cancer patients (all subtypes or ER+/HER2ˉ_ subset) in the TCGA study (Firehose Legacy) revealed ESR1 and SEMA3C mRNA expression had significant positive Pearson’s correlation (each dot represents data from one sample; **** p < 0.0001). mean ± standard error of the mean (SEM). Figure 2.1. Semaphorin 3C (SEMA3C) mRNA expression associated with different breast cancer subtypes and correlation with estrogen receptor expression. 35 2.3.2 SEMA3C is an ER-induced gene that is highly expressed in ER-positive breast cancer cell lines To investigate the potential functional or regulatory relationship between SEMA3C and ER, we performed a data mining analysis of GEO datasets (GSE11324, GSE27473) generated by Carroll et al. (121) and Al Saleh et al. (122), studying genome-wide effects of the activation or depletion of the ER pathway. Our analysis from these studies revealed that SEMA3C mRNA was robustly induced by estradiol in a time-dependent manner in MCF7 cells (Appendix A). This was consistent with another external gene expression profiling study where SEMA3C was found among list of genes which were upregulated by ER pathway (123). Moreover, SEMA3C mRNA expression was significantly suppressed upon ER silencing, indicating that SEMA3C is an ER-regulated gene (Appendix A). Importantly, in the Cheng et al. study, where they studied upregulated genes post estrogen activation by utilizing the GSE11324 dataset, we identified SEMA3C among the top 100 genes induced by estradiol in that study (p < 0.0001) (124) (Figure 2.2A), further supporting the notion that SEMA3C expression is tightly linked to ER signaling in breast cancer. To further explore the molecular mechanisms underlying SEMA3C’s role in breast cancer, we conducted in-vitro cell-based experiments. First, we investigated the levels of SEMA3C protein in a panel of breast cancer cell lines. Consistent with our clinical observations, we found that SEMA3C protein expression is significantly elevated in ER+ breast cancer cell lines, MCF7 and T47D, as compared to MCF10A, which are immortalized non-malignant breast epithelial cells that possess many characteristics of normal breast cells (Figure 2.2B). Our observation of higher SEMA3C expression in breast cancer cell lines corroborates the clinical observation made earlier, in which the transcript levels of SEMA3C were higher in breast cancer 36 tissues as compared to adjacent normal mammary tissues (101). Next, to confirm whether SEMA3C is an ER-regulated gene, we treated MCF7 and T47D cells (previously grown in charcoal stripped serum for 4 days) with estradiol in a dose-dependent manner and monitored SEMA3C mRNA and protein levels. A significant dose-dependent (0.1–1 nM) increase in SEMA3C mRNA and protein was observed after an overnight treatment with estradiol (Figures 2.2C and Appendix B). In addition, after identifying 1 nM as a sufficient dose of estradiol to induce SEMA3C expression, we conducted a similar study to Carroll et al.,(121) but at the protein level, to observe the gradual increase in SEMA3C expression in a time-dependent manner (Figure 2.2C). To investigate the role of ER in regulating SEMA3C expression, we performed siRNA knockdown experiments targeting ER in MCF7 and T47D cells. Our results showed that SEMA3C protein expression levels were decreased upon ER siRNA knockdown (Figure 2.2C). These findings suggest that ER plays a critical role in regulating SEMA3C expression in ER+ breast cancer cells, further supporting the notion that SEMA3C is an ER-regulated gene. To gain a better understanding of how ER might be regulating SEMA3C expression, we analyzed ER-ChIP Seq studies conducted on MCF7 (SRX4451213, SRX4367193, SRX4451208) and T47D (SRX5702620, SRX1961156, SRX1161199) from the ChIP-Atlas database (125,126). Based on three independent studies for each cell line, we identified significant ER binding peaks upstream or on the SEMA3C locus (Upstream/Promoter, Intron 1, Intron 2) suggesting ER interaction on the SEMA3C locus (Figure 2.2D). With the help of DNA-seq studies from the ChIP-Atlas, we were able to show chromatin accessibility in these regulatory regions to allow the potential binding of ER-alpha (SRX193608, SRX193608) (Figure 2.2D). These ER-binding regions on the SEMA3C locus were consistent with studies conducted by Tam et al., (91). 37 Furthermore, these regions have also been identified as cis-Regulatory Elements (cCREs) (EH38E2566990, EH38E2566989, EH38E3782829, EH38E2566941) (127). With help of JASPAR (128), we were able to identify multiple ER binding motifs (MA0112.2 and MA0112.3) in SEMA3C regulatory regions (Upstream/Promoter, Intron 1, Intron 2) which overlapped with the ER ChIP seq peaks observed earlier (Figure 2.2D and Appendix C). 38 (A) Volcano plot analysis of differential gene expression dataset pre- and post-estrogen treatment (GSE11324). (B) Western Blot of SEMA3C protein expression in MCF10A, MCF7 and T47D. (C) Increase in protein and mRNA expression of SEMA3C by estradiol (E2) in a dose- and time-dependent manner along with siRNA ER-alpha knockdown, leading to a decrease in SEMA3C expression. (D) Confirmation of ER-alpha binding onto regulatory Figure 2.2. Semaphorin 3C (SEMA3C) expression confirmation in ER+ breast cancer cells and its regulation by ER in MCF7 and T47D. 39 sites in the SEMA3C locus using ChIP-Atlas (IGV) and ERE binding motif sequences (MA0112.2, MA0112.3). * p < 0.05. mean ± standard error of the mean (SEM). 2.3.3 SEMA3C activates RTK signaling pathways in breast cancer cells via Plexin-B1 To investigate SEMA3C’s potential role as a driver of breast cancer, we needed to in-duce SEMA3C signaling via the stimulation of ER+ breast cancer cells and examine whether SEMA3C can activate RTK signaling pathways that are commonly observed in breast cancer. Our previous findings indicate that SEMA3C functions as a secreted soluble autocrine growth factor that drives cancer growth by transactivating multiple RTKs such as EGFR, HER2 and MET via Plexin B1 (78). To determine whether SEMA3C can activate these RTKs, we treated MCF10A cells with recombinant SEMA3C (0–1µM). Our results showed that MCF10A cells expressed EGFR and MET but not HER2 (Appendix D). Additionally, SEMA3C treatment induced the phosphorylation of MAPK and AKT, as depicted in (Figure 2.3A). In ER+ breast cancer patients, the AKT and MAPK signaling pathways are known to be constitutively active due to downstream effects of ER (129,130). To investigate whether SE-MA3C activates these pathways in ER+ breast cancer cells, we treated MCF7 and T47D cells with recombinant SEMA3C in a dose-dependent manner. Our results indicated that SEMA3C treatment activated MAPK (Thr202/Tyr204) and AKT (Ser473) proteins in a dose-dependent manner (Figure 2.3B). Based on our previous report, which suggested that SEMA3C transactivates multiple RTKs through its interaction with Plexin B1, we conducted plexin B1 silencing experiments to investigate whether SEMA3C drives signaling through Plexin B1 in ER+ breast cancer. We confirmed that both MCF7 and T47D cells express high levels of Plexin B1 (Figure 2.3C) and established that Plexin-B1 serves as the receptor for SEMA3C in ER+ breast cancer cells. The siRNA knockdown (20 nM) of Plexin-B1 attenuated cell signaling and 40 growth, even in the presence of recombinant SEMA3C (Figure 2.3C, D). These findings provide further evidence that SEMA3C is a potential therapeutic target in the treatment of breast cancer. Figure 2.3. SEMA3C activates receptor tyrosine kinase pathways via the Plexin-B1 receptor in breast cancer cells. 41 (A) Exogenous treatment with recombinant SEMA3C on MCF10A leads to an increase in phosphoactivation of AKT and MAPK. (B) Exogenous treatment with recombinant SEMA3C leads to the activation of AKT and ERK pathways in a dose-dependent manner in MCF7 and T47D. (C) Plexin-B1 siRNA knockdown (25 nM), showcasing the significant effect on SEMA3C signaling and (D) impacting growth in MCF7 and T47D, confirming SEMA3C signal via Plex-in-B1. * p < 0.05; *** p<0.001. mean ± standard error of the mean (SEM). 2.3.4 ER+ breast cancer cell lines show dependency on SEMA3C for cell growth, and its depletion leads to the induction of pro apoptotic proteins After exploring cell signaling pathways activated by SEMA3C, we wanted to understand whether ER+ breast cancer cells were dependent on SEMA3C-induced signaling. Therefore, we aimed to investigate whether SEMA3C silencing could impact tumor cell growth. To this end, we conducted a siRNA knockdown of SEMA3C (25 nM) on MCF7 and T47D cells, and 72 h post-treatment we analyzed cell signaling pathways known to be upregulated in ER+ breast cancer via immunoblotting. We observed a decrease in phospho-AKT and phospho-MAPK signals (Figure 2.4A) and saw a significant impact in cell growth on both of the cell models, even in the presence of serum, within five days (Figure 2.4B). As already known, the ER pathway is the main driver of tumor progression in ER+ breast cancer and cells. Utilizing this knowledge, we wanted to study whether the activation of the ER pathway would overcome the effect of SEMA3C silencing on growth, as observed previously. We performed reverse siRNA knockdown on cells using siSEMA3C or siScramble (siScr) in respective wells in a 96-well plate in presence of CSS. The following day, we treated half of the siRNA-treated wells with 0.1 nM estradiol to activate the ER pathway, and the other half with EtOH (95% v/v) as a control for estradiol treated wells. Within a few days, we started to observe a similar growth inhibition pattern as seen in (Figure 2.4B), irrespective of whether they were treated with estradiol or EtOH. Notably, the treatment with estradiol was unable to reverse the growth inhibition caused by SEMA3C knockdown (Figure 2.4C). This observation suggested 42 that SEMA3C signaling might have a positive feedback effect on the ER pathway. To confirm this, we examined ER protein expression in MCF7 post-SEMA3C siRNA knockdown. As seen in Figure 2.4C, we confirmed that SEMA3C can affect ER expression, suggesting crosstalk between SEMA3C and the ER signaling pathway. Next, we wanted to elucidate the mechanism underlying the impact of SEMA3C knockdown on cell growth. We examined whether this effect was, in part, due to induction of cell apoptosis. By conducting SEMA3C silencing (25 nM) in a six-well plate and collecting conditioned media, along with whole cell protein lysate, we stained the cells with propidium iodide and observed a higher percentage of cells in the sub G0/G1 fraction, which was indicative of apoptotic cell death (Figure 2.4D). Through immunoblotting, we were able to confirm at the protein level an increase in cleaved caspase-8 and PARP (Figure 2.4E) in SEMA3C knockdown cells, in addition to cleaved caspase-3 detection only in T47D, but not in MCF7, which are known to be caspase-3 deficient (Appendix E). These findings suggest that SEMA3C knockdown induces apoptosis in MCF7 and T47D cells, which may contribute to the growth inhibition observed earlier. 43 (A) siRNA knockdown (25 nM) of SEMA3C in MCF7 and T47D cells resulted in downregulation of p-p44/42 (Thr202/Tyr204)/AKT (Ser473) within 72h, and (B) led to cell growth inhibition in both of the cell models in-vitro. (C) MCF7 show-cased their dependency on SEMA3C, shown by the growth curve under different conditions (Scr or siSEMA3C/EtOH or Estradiol (1nM)), with an inability of estradiol to rescue growth, probably due to the downregulation of ER. (D) siRNA knockdown in MCF7 and T47D induced apoptosis as detected by flow cytometry with confirmation of (E) increase in expression of pro-apoptotic proteins c-PARP/c-Caspase-8 within 72h. * p < 0.05; ** p < 0.01; **** p <0.0001. mean ± standard error of the mean (SEM). Figure 2.4. SEMA3C silencing in ER+ breast cancer cells lead to decreases in oncogenic signaling, cell survival and activation of pro-apoptotic pathways. 44 2.3.5 SEMA3C pathway inhibitor downregulates oncogenic signaling, inhibits growth and exhibits synergistic behavior with endocrine therapy drugs Our study indicates that SEMA3C is a potential therapeutic target in ER+ breast cancer, as it drives the activation of RTK signaling pathways, and SEMA3C knockdown significantly impacts cell growth. To assess its druggability potential within ER+ breast cancer, we conducted proof-of-concept testing using B1SP, a novel SEMA3C pathway inhibitor, to lay the groundwork for the development of SEMA3C-targeted therapies in ER+ breast cancer. As shown above, we observed that SEMA3C is involved in the activation of MAPK and AKT signaling pathways and that SEMA3C siRNA treatment has an impact on cell growth, hinting that this growth factor might have a therapeutic potential. Currently, there are no known SEMA3C-targeted therapies in the clinic. We wanted to conduct a proof-of-concept analysis confirming SEMA3C as a druggable target in ER+ breast cancer. Therefore, we utilized our in-house-developed therapeutic protein inhibitor of SEMA3C signaling, which was engineered as a Plexin B1: Fc fusion decoy protein to functionally disrupt SEMA3C-induced RTK activation and is called B1SP. Its role has been validated in treatment resistant prostate cancer both in-vitro and in vivo (78). To determine which concentration of B1SP can be a used to study its therapeutic potential in ER+ breast cancer cells, we conducted a dose response growth assay on MCF7 and T47D cells (Appendix F). B1SP inhibited the SEMA3C-induced activation of oncogenic signaling pathways (AKT and MAPK) in both MCF7 and T47D cells in a dose-dependent manner (Figure 2.5A) supported with densitometry analysis (Appendix G), and then induced 45 significant (p < 0.01) growth inhibitory effects on MCF7 and T47D cells within 72h (Figure 2.5B). We further wanted to identify whether B1SP in combination with currently approved first- and second-line endocrine therapy (Tamoxifen and Fulvestrant) had any synergistic or antagonistic effects, by studying the effect on cellular growth in endocrine therapy naïve MCF7 and T47D cells. Synergy results in better inhibition at a lower concentration for each of the two drugs in combination than observed individually. It is critical to assess whether B1SP can improve efficacy of these approved drugs. For this experiment, we created a matrix with a range of different treatment concentrations of drugs (B1SP = 0–1.6µM; Tamoxifen = 0–1.6µM; Fulvestrant = 0–0.5µM) based on internal and external studies for the two combinations (B1SP–tamoxifen and B1SP–Fulvestrant), and the cells were analyzed 96h post-treatment. We represent these findings in form of 2D heatmap (Figure 2.5C-D) calculating synergy score for each combination (for e.g. B1SP =1.6µM and Tamoxifen = 0.4µM) and developing an overall mean synergy score. For the experiment, the expected drug combination synergy responses were calculated based on the HSA reference model, using Synergy Finder Plus (131)to identify relationships between B1SP and the endocrine therapy drugs. For the both combination, potential synergy was observed based on HSA synergy score (score closer to 10 and above likely synergistic or additive (= −10≤ score≤10) in both MCF7 and T47D. However, the B1SP–Fulvestrant combination in MCF7 exhibited an additive effect rather than synergistic. This would likely be due to differences in the mechanism of action of these two endocrine therapies and different genetic background of these MCF7 cells. 46 (A) B1SP treatment in a dose-dependent manner affects SEMA3C-mediated signaling along with densitometry analysis. (B) B1SP-impacted cell growth within 72 h in the presence of full-serum conditions in MCF7 and T47D cells. (C) B1SP and Tamoxifen combination on tamoxifen-sensitive cells, showcasing synergistic behavior (D) Figure 2.5. B1SP impacts SEMA3C: PlexinB1 mediated cell growth, signaling and showcasing positive combi-nation effect with endocrine therapy drugs. 47 B1SP and Fulvestrant combination on Fulvestrant sensitive cells showcased synergistic and additive behavior based on Synergy Finder Plus HSA Scoring method. ** p < 0.01; mean ± standard error of the mean (SEM). 2.3.6 Tamoxifen-Resistant ER+ breast cancer cells remain dependent on SEMA3C for signaling and growth After exhibiting its functional and potential therapeutic role in tamoxifen-sensitive cells (MCF7, T47D), the next step was to understand the relevance of SEMA3C-dependent signaling in endocrine therapy resistant cells. To begin with, we analyzed the expression levels of SEMA3C protein expression in tamoxifen resistant (TamR) cells generated via two different conditions, representing distinct endocrine therapy strategies vs. tamoxifen sensitive (MCF7) ER+ breast cancer cell lines cells along with MCF10A (132). We observed high expression levels of SEMA3C in tamoxifen resistant cells (TamC3 and TamR3) when compared with MCF7 (Figure 2.6A). This finding was consistent with the analysis of the global gene expression dataset from GSE67916, which compared the expression levels of genes between tamoxifen-sensitive (TamS) and -resistant cells (TamR) (120) (Figure 2.6B). Treating TamR cells with tamoxifen in their normal growth conditions over 48h had no effect or led to enhanced SEMA3C protein levels, suggesting the dependence of resistant cells on SEMA3C post-endocrine therapy resistance (Figure 2.6C). MCF7 cells had low expressions of SEMA3C under estradiol-deficient conditions, which mimicked tamoxifen’s mechanism of action, in which estradiol was unable able to activate the ER pathway (Figure 2.2C). Therefore, the next step was to explore whether these cells showcased any dependency on SEMA3C, as shown previously in tamoxifen-sensitive cells. It was clearly seen in our SEMA3C knockdown studies (25 nM) that it led to a decrease in the phosphorylation of known upregulated RTK pathways (EGFR) and ER expression, as observed in these cells and in the majority of tamoxifen resistant tumors (132) (Figure 2.6D), 48 followed by a significant inhibitory effect on the growth of TamR and TamC3 cells (Figure 2.6E). As seen in (Figure 2.6F-G), these cells continued to show sensitivity to B1SP treatment for growth (1µM) and signaling (0.5µM). This might suggest, in some ER+ breast cancer cases, that the consistent activation of SEMA3C is exploited by the tumor cell population to evade treatment efficacy. A.SEMA3CBeta-ActinSEMA3CB.D.SEMA3CsiS3CsiSCRp-EGFR (Y1068)t-EGFRp-Aktt-Aktp-MAPKt-MAPKBeta-ActinTamR3siS3CsiSCRBeta-ActinTamC3 TamR3Scr siS3C Scr siS3CER-alphaSEMA3CTamC349 (A) Basal-level SEMA3C protein expression in tamoxifen-sensitive (MCF7) vs. tamoxifen-resistant cells (TamC3, TamR3). (B) Global gene expression analysis of SEMA3C expression between MCF7-tamoxifen sensitive cells vs. MCF7-derived tamoxi-fen-resistant cells. (C) High levels of SEMA3C expression detected 48h post-tamoxifen treatment. SEMA3C siRNA silencing (D) downregulates receptor tyrosine kinase signaling (EGFR, AKT and MAPK) and estrogen receptor (ER) expression, (E) with growth inhibitory effect in TamR cells. (F-G) TamR cells respond to B1SP therapeutic effects, leading to significant decreases in SE-MA3C-mediated cell signaling and growth. *p<0.05; *** p < 0.001; ****p<0.0001. mean ± standard error of the mean (SEM). 2.4 Discussion Here, we show that SEMA3C is significantly upregulated in ER+ breast cancer patients and cell lines, and its expression is modulated by the estrogen receptor. Importantly, we demonstrate that silencing SEMA3C inhibits the MAPK and AKT signaling pathways, promotes apoptosis, and suppresses the growth of both tamoxifen-sensitive and resistant ER+ breast cancer cells. We further demonstrate the druggable potential of SEMA3C by using the B1SP Fc fusion protein, a SEMA3C pathway inhibitor that displayed comparable inhibitory effects on growth and signaling of ER+ breast cancer cells as those observed during SEMA3C silencing. These findings underscore the emerging role of SEMA3C in promoting ER+ breast cancer growth and survival and suggest that targeting SEMA3C may represent a potential therapeutic strategy for ER+ breast cancer patients. Figure 2.6. Tamoxifen-resistant cells continue to exhibit dependency on Semaphorin 3C (SEMA3C) for their growth and survival. 50 We examined the role of SEMA3C in the context of ER+ breast cancer, with the aim of defining SEMA3C as a novel potential therapeutic target for this breast cancer subtype. Notably, among the various subtypes of breast cancer, ER+ breast cancer exhibited the highest expression of SEMA3C mRNA in clinical samples. Importantly, we observed a positive association between the expression of SEMA3C and ER in patients with ER+ breast cancer. Our data further indicated that SEMA3C functions as an ER-induced growth factor that activates and maintains sustained proliferative and survival signaling pathways in both tamoxifen-sensitive and -resistant ER+ breast cancer cells. Significantly, SEMA3C pathway inhibition led to the attenuation of cell signaling, growth and ER expression of these cells. This study highlights the therapeutic potential of targeting SEMA3C in the treatment of ER+ breast cancer, with particular relevance for hormone-resistant cases. Among all breast cancer subtypes, ER+ breast cancer expresses the highest levels of SEMA3C transcripts. This observation was consistent with the study presented by Zhang et al. (72). Furthermore, our study revealed that SEMA3C protein levels were significantly higher in ER+ breast cancer cell lines MCF7 and T47D compared to the immortalized normal epithelial cell line MCF10A. These results are in line with the findings of Cole-Healy et al. (96), who reported that SEMA3C protein levels progressively increased during the transition from normal to invasive breast cancer. This study demonstrates that SEMA3C is an ER-induced autocrine growth factor that drives the signaling and growth of ER+ breast cancer, mainly by activating MAPK and AKT pathways. Our findings validate previous research showing that SEMA3C knockdown inhibits the proliferation of ER+ breast cancer cells and extends these insights by demonstrating that SEMA3C is an autocrine growth factor that stimulates the growth of ER+ breast cancer cell lines. 51 Importantly, our investigation revealed that SEMA3C activates MAPK and AKT pathways, which are essential for sustaining cancer cell proliferative signaling, a fundamental hallmark of cancer, making it a potential therapeutic target. SEMA3C also plays a critical role in the activation of phospho-EGFR levels, a key player in endocrine resistance of ER+ breast cancer cells (133), suggesting that targeting SEMA3C could be effective in overcoming endocrine resistance. We further identified ER as a key transcriptional regulator of SEMA3C expression in ER+ breast cancer cells, establishing SEMA3C as a tumor vulnerability in ER+ breast cancer and providing a mechanistic link between the SEMA3C-ER pathway and growth signaling in ER+ breast cancer. Targeting SEMA3C could be a potential strategy for combating ER+ breast cancer, highlighting its potential as a therapeutic target in precision oncology. Importantly, we showed that SEMA3C is a novel, tractable therapeutic target for the treatment of ER+ BCa patients. Our findings revealed that B1SP treatment inhibited clinically relevant signaling pathways and the growth of ER+ breast cancer cells, similar to its effect on inhibiting prostate cancer growth. These results suggest that targeting SEMA3C could be a possible therapeutic strategy for ER+ breast cancer patients and could provide a viable treatment option for patients who progress on endocrine therapy. Our findings on the role of SEMA3C in ER+ breast cancer have potential implications for breast cancer research and treatment. This study provided novel insights into the biology of ER+ breast cancer, highlighting the potential role of SEMA3C in driving tumor progression and maintaining tumor cell proliferation, especially in the context of endocrine resistance. These findings fit into the current understanding of breast cancer biology, which emphasizes the importance of targeting specific pathways and vulnerabilities in cancer cells for effective 52 treatment. These results suggest that targeting SEMA3C could be a possible strategy for personalized medicine and precision oncology, especially for patients with ER+ breast cancer who are resistant to current therapies. Moreover, the study’s findings could pave the way for the development of new and more effective SEMA3C inhibitors with fewer off-target effects and less toxicity, leading to improved outcomes for breast cancer patients. Therefore, the study’s broader implications extend beyond the field of breast cancer to the broader context of cancer research and treatment. But there is another subtype of breast cancer known as TNBC which is known to have upregulation of similar pathways as seen in endocrine resistant tumors. Until few years ago the only treatment option for this breast cancer subtype was chemotherapy which we know is non-specific and patients suffer from adverse side effects. Still there are very limited targeted options that are available and only beneficial to handful of patients. Therefore, there is an urgent need for novel targets which are involved in driving TNBC disease progression. In next chapter we wanted to explore SEMA3C’s potential as therapeutic target in TNBC based on our current findings. 53 Chapter 3: Understanding significance of SEMA3C signaling pathway in Triple Negative Breast Cancer 3.1 Introduction 3.1.1 Current clinical challenges Approximately 20-25% of breast cancer cases diagnosed are triple-negative breast cancer (TNBC) which is associated with poor prognosis (134). Although there has been a sustained decline in mortality rates over the last decades in breast cancer, patients still are suffering from recurrence and progression specifically for this subtype (135). TNBC tumors are identified with basal histopathological subtype with high pathological grade and stage (Stage III/IV) (136). At the moment of diagnosis, these tumors exhibit lymph node involvement, and the identification of lymph node metastasis has been recognized as an independent prognostic factor for TNBC. Regardless of the high initial clinical response rates to neoadjuvant therapy, patients with TNBC still exhibit a poorer prognosis compared to other breast cancer patients. They have a higher risk of distant metastasis and experience a shorter Disease-Free Survival (DFS) when diagnosed at a younger age. Dent et al. study (137) predicted that distant recurrence peaked after three years of treatment and have a mortality rate of 40% within five years of diagnosis. Due to lack of expression of ER, PR and HER2, the clinically validated therapeutics administered in other breast cancer subtypes cannot be prescribed to TNBC patients. Due to lack of biomarkers for targeted therapies, conventional therapies (surgery + chemotherapy) are in many cases not effective, leading to tumor recurrence due to long-term residual TNBC cancer cells in primary tumors and/or metastatic lesions. Due to high tumor heterogeneity within TNBC, efforts have been made to conduct molecular subtyping based on gene expression clustering. TNBC tumors 54 have been categorized into four to six distinct molecular subtypes based on the expression of characteristic gene expression (GE) profiles. Most promising method developed by Lehman et al. (138) classified TNBC tumors into six molecular subtypes using 14 publicly available datasets and confirmed their results with other seven datasets (n=386). The examination of gene ontologies and differential gene expression led to the identification of seven tumor clusters, each with a unique gene signature. Among these clusters were six stable groups, including the mesenchymal (M), mesenchymal stem-like (MSL), basal like-1 (BL-1) and -2 (BL-2), luminal androgen receptor (LAR), and immunomodulatory (IM) subtypes. Additionally, there was one unstable tumor cluster (UNS) that expressed genes present in the other six clusters. Lehmann et al. additionally employed the TNBC patient tumor-derived gene expression signature to identify TNBC cell lines corresponding to each of these subtypes. These cell lines serve as valuable models for conducting functional tests on new targeted agents with clinical significance. We took the benefit of this analysis to select specific cell lines representing diverse molecular subtype seen within TNBC patients. Due to the lack of biomarkers for targeted therapies and an aggressive, often invasive disease progression, there are few FDA-approved drug treatments for TNBC till date. Therefore, it is an urgent need to develop effective treatments for this aggressive BCa subtype. Previously, it has been shown that SEMA3C’s mean expression is higher in TNBC tumor samples (96). But its impact on patient’s prognosis and the mechanistic role of SEMA3C in driving TNBC progression is limited. In this chapter, we take benefit of our learnings from role of SEMA3C in driving treatment sensitive and resistant ER+ breast cancer in-vitro and studies conducted in other solid tumors (prostate, gastric, pancreatic, cervix) which are known to have similar upregulated oncogenic pathways as observed in TNBC. We hypothesize that SEMA3C 55 plays a role in autocrine signaling in TNBC leading to activation of cell-line specific gene signature which maintains their growth and signaling. To validate SEMA3C’s role in TNBC, in our study we focus on understanding the clinical relevance of SEMA3C within TNBC patients, conduct in-vitro based assays (exogenous SEMA3C cell stimulation, siRNA knockdown, therapeutic intervention) to establish pre-clinical evidence for SEMA3C as a potential growth factor involved in driving TNBC progression followed by its potential as therapeutic target. 3.2 Methods 3.2.1 Kaplan-Myer Plot Generation SEMA3C expression correlation with overall survival (OS), relapse-free survival (RFS) and distant metastasis-free survival (DMFS) was analyzed using online Kaplan–Meier Plotter software (https://kmplot. com/analysis/index.php?p=service&cancer=breast) with the following settings selected: Affymetrix ID / Gene symbol; 203788_at, Split patients by; upper tercile vs lower tercile; Survival (n=404); RFS (n=534) or DMFS (n=424), St Gallen/PAM50 subtyping = basal; ER status = IHC-ER negative (n=2168), PR status= IHC-PR negative (n=1989), HER2 status= array- HER2 negative (n=6262) (139). For Kaplan–Meier analysis, patients were ranked by SEMA3C expression from lowest-to-highest then divided into tercile groups (T1-T3), with T3 being the highest expressing patients, and median months overall survival computed per quartile group of patients. 3.2.2 Cell lines MCF10A were purchased from the American Type Culture Collection (ATCC, Manassas, VA, USA). MDA-MB-468, MDA-MB-231 and BT-549 (authenticated by Huntsman lab) were kindly provided by Dr A. Cherkasov (Vancouver Prostate Centre). MCF10A were maintained in growth medium as recommended by American Type Culture Collection (ATCC, Manassas, VA, USA). 56 MDA-MB-468, MDA-MB-231 and BT-549 cell-lines were maintained RPMI + 10% FBS (114). All cell lines were mycoplasma free. Experiments were carried out on cells grown in their respective growth medium unless otherwise specified. 3.2.3 Antibodies and chemical reagents Antibodies to the following were used at 1:1000 dilution unless specified: p-EGFR (Y1068), p42/44 pMAPK(T202/Y204), p42/44 MAPK(L34F12, ), AKT (9272), p-AKT (Ser473, D9E), cleaved PARP (Asp214), PARP (9542), cleaved caspase-3 (Asp 175, 5A1E, 9664), caspase-3 (9662) (Cell Signaling Inc., Beverly, MA) SEMA3C (N-20, 1:2000), t-EGFR (C-2) (Santa Cruz Biotechnology, Santa Cruz, CA), Plexin-B1 (C-terminal region) (PP1841, RCM Biosciences), Actin (A2066, 1:5000), Vinculin (1:5000) (Roche Diagnostics, Laval, PQ), LICOR secondary antibodies- anti Rabbit IR680 and IR800, anti-mouse IR680 and IR800 (Rockland, Gilbertsville, PA), anti Goat680 (Pierce Biotechnology, Rockford, IL). Antibodies were used according to manufacturer’s protocol. The following chemicals were used: Cisplatin (S1166), Paclitaxel (S1150), Erlotinib (S1023), Cabozantinib (S1119) (Selleck Chemicals, Houston, TX), RNase A (12091021; Invitrogen), Propidium Iodide (P3566; Invitrogen). 3.2.4 Immunoblotting Cells were lysed with 100 µL lysis buffer (50mM Tris pH 7.5, 150 mM NaCl, 1% NP-40, 10 mM NaF, 10% Glycerol) containing Complete and PhosStop phosphatase inhibitors (Roche, Mississauga, ON, Canada). Whole cell lysates were centrifuged at 14,000 rpm for 15 min at 4 °C. The protein concentration in cleared whole cell lysates was identified using BCA kit (Thermo Scientific, Rockville, IL, USA). Protein lysates (30–40 µg) were analyzed by SDS-PAGE (6–10%) and transferred by Western Blot to nitrocellulose membranes (Bio-Rad, Hercules, CA, USA). Membranes were blocked in PBS containing 5% BSA or TBS Odyssey 57 buffer (Licor, Lincoln, NE, USA) if probing with phospho-antibodies. The primary antibody dilution was conducted as per the manufacturer’s protocol. Membranes were washed 3x with TBST followed by appropriate IRdye-conjugated secondary antibodies for 1h. Blots were washed again 3X for 5 min with TBST. Detection was via image analysis using a LICOR infrared imager and Image Studio Lite version 3.1 software. 3.2.5 Cell stimulation MDA-MB-468 were seeded at 1 × 105 cells/well in a six-well plate in RPMI medium containing 10% FBS for two days. After two days, the medium was replaced with RPMI phenol-red-free and serum starved for 24h. The medium was changed again just prior to stimulation to remove endogenous-secreted SEMA3C. For SEMA3C stimulations: MDA-MB-468 were treated with SEMA3C for 10 min at the indicated concentrations followed by a wash with PBS. For EGFR signaling rescue experiment MDA-MB-468 cells were treated with SEMA3C (0.5µM) overnight. In each cell stimulation experiment SEMA3C treatment was followed by a wash with PBS. All these treatments were carried out at 37°C in 5% CO2. 3.2.6 Plexin B1 and SEMA3C silencing siRNA targeting Plexin-B1 and SEMA3C were validated: siPlexin-B1-1 (Hs_PLXNB1_6, Qiagen, Montreal, PQ)- CCGGGUGGAAUUUAUCCUUGAUU, siSEMA3C-1 (Ambion, Life Technologies Corporation, Carlsbad, CA)- CAC-CAUCCUUUAGACUACATT. MDA-MB-468, MDA-MB-231 and BT-549 cells were reverse transfected with either scramble siRNA (siScr) (AUCAAACUGUUGUCAGCGCUGUU), siSEMA3C-1 (20nM) or siPlexin-B1-1 (20nM) using RNAiMax (Invitrogen, Mississauga, ON) as described by the manufacturer. For immunoblotting, transfected cells were incubated for 72 hours then washed with PBS followed whole-cell lysis for SEMA3C siRNA experiment. In case of pulse chase experiment transfected 58 cells were incubated for 48h and then replaced by RPMI phenol-red-free containing SEMA3C (0.5µM) overnight. To study Plexin-B1 siRNA knockdown impact on SEMA3C mediated signaling after 72 hours we serum starved cells overnight (RPMI phenol-red-free) and stimulated cells with exogenous SEMA3C next day. 30 µg -35µg of protein was loaded and levels were analyzed with SDS-PAGE. For cell growth assays, cells were reverse transfected in a 96-well flat bottom plate and proliferation was monitored over 6 days (siSEMA3C) with readings taken at Day 0, 3 and 6. 3.2.7 Growth Assay The cell growths of MDA-MB-468 (2500 cells/well), MDA-MB-231 (1800 cells/well) and BT-549 (1500 cells/well), were assessed in 96-well plates using Presto Blue Viability Reagent (Life Technologies Corporation, Carlsbad, CA, USA) according to the manufacturer’s protocol. Fluorescence was measured at excitation 535 nm and emission 612 nm using a Tecan F500 plate reader (Tecan, Grodig, Austria, GmbH). To detect cell growth for (1) siRNA silencing, we conducted reverse-transfection within a 96-well plate and analyzed data at different time points. (2) For synergy assay, siRNA reverse transfection was conducted within 96-well plate and 48h later, media was aspirated containing siRNA and replaced with fresh media containing cisplatin, erlotinib and cabozantinib at the indicated concentrations in respective experiments (see results section). (3) For cell growth inhibition by B1SP and ALS we allowed cells to attach to the wells of the 96-well plate overnight and then administered the wells with respective SEMA3C inhibitor concentration and took the reading at 72h after treatment. All growth assays were done in six biological replicates and at least two technical replicates. 59 3.2.8 G0/G1 Assay The flow cytometry analysis of propidium iodide-stained nuclei was performed as shown previously (100). MDA-MB-468, MDA-MB-231 and BT-549 cells were reverse-transfected once with SEMA3C siRNA (20 nM) in 6-well plates, allowed to attach overnight in RPMI + 10% FBS and next day replace with RPMI containing 2% FBS. Then, 48h later for cells were trypsinized, washed in 1 mL PBS and then fixed in 1 mL 80% ethanol overnight at 4°C. Fixed cells were incubated in DNase-free RNase A reagent (50 µg/mL) and propidium iodide (50 µg/mL) at 4 °C for 1 h. The stained cells were analyzed for relative DNA content on a Becton Dickesnon FACSCanto II flow cytometer (BD Biosciences, Mississauga, ON, Canada). 3.2.9 Production of SEMA3C: FL, B1SP and ALS CHO-S cells were transduced with SEMA3C-FL- or ALS, B1SP-expressing lentivirus at 100 MOI. For SEMA3C: FL and ALS, cells (40 million) were seeded in Cellstack (2-stack) culture chambers (Corning Incorporated, Tewksbury, MA, USA) in a volume of 200 mL DMEM, supplemented with 5% FBS (Life technologies, Gaithersburg, MD, USA), heparin (25 μg/mL) and blasticidin (5 μg/mL). Conditioned media (CM) were collected after 48h and then replenished with the same medium containing 2% FBS. The CM were collected every 48h and replenished. Harvested CM were then centrifuged at 2000 RPM for 5 min and then filtered (0.22 μm). Filtrates were purified over HisTrap excel (GE Healthcare, Uppsala, Sweden). Recombinant SEMA3C-FL and ALS was then buffer exchanged and concentrated on an Amicon Ultra-15 centrifugal filter device (MWCO, 10 kDa), to 2.0 mg/mL (MiliporeSigma, Oakville, ON, Canada). The protein sample was then desalted on a PD midiTrap G-25 column, as directed by the manufacturer (GE Healthcare, Buckingham-shire, UK), and then filter sterilized using a 0.22 μm filter. B1SP was produced from CHO-S B1SP Cells (2 × 108), which were seeded and 60 cultured in a 5 L Wave Bag (GE Healthcare, Uppsala, Sweden) using 75% Freestyle CHO expression medium/25% Op-timem supplemented with 8 mM glutamine and penicillin/streptomycin (5.0 ml/L) (Life technologies, Gaithersburg, MD, USA) at 37°C and 8% CO2. CM were harvested three times over a period of 4 weeks and B1SP was purified using the specific purification process described previously (78). 3.2.10 Statistical Analysis The clinical data and in-vitro data were assessed using Student’s t-test. All data were expressed as the mean ± standard error of the mean (SEM). Comparisons between two means were performed using a student t-test. GraphPad Prism software (Version No. 8.4.3) was used to calculate the statistical significance and generate graphs. Densitometry analysis of SEMA3C expression, phosphorylation of EGFR, AKT for SEMA3C rescue experiment and ALS treatment were performed using ImageJ 1.53 e (Wayne Rasband and contributors, National Institutes of Health, USA). The Synergy Finder Plus tool was used to calculate drug combination scores. The thresholds of statistical significance were set at * p < 0.05, ** p < 0.01, ***p<0.001, ****p<0.0001 3.3 Results 3.3.1 SEMA3C expression is prognostic for metastatic progression of TNBC and moderately expressed in three distinct TNBC cell lines To assess overall survival, we looked at patient’s tumors which were classified as “basal” (ER-/HER2-) based on StGallen criteria (n=404), high SEMA3C expression was associated with lower overall survival with survival difference between high vs low group being 29 months (Figure 3.1A; p=0.03; HR 1.56). This observation holds true even when we used PAM50 selection criteria to select “basal” classified tumor patients (n = 431; Appendix H). As clinically 61 known, TNBC patients are predisposed to recurrence and metastasis more than other breast cancer subtypes, therefore it is critical to assess whether SEMA3C expression impacts recurrence and metastasis within this subtype. For this part of analysis, we selected patients based on their negative ER, PR, and HER2 status via IHC or array and compared recurrence free survival (RFS) and distant-metastasis free survival (DMFS) in TNBC patients distributed across lower tercile (SEMA3C low) vs upper tercile (SEMA3C high) expression. Shorter time to RFS (n=534) was more pronounced in SEMA3C high patients (Figure 3.1B; p= 0.021; HR 1.54). For DMFS (n=434), high SEMA3C levels were more strongly associated with relatively short time to distant metastasis (upper tercile survival 27.04 versus lower tercile survival 39.47 months) in TNBC patients (Figure 3.1C; p = 0.0079; HR 1.8). Specifically analyzing its role in predicating treatment response, post-chemotherapy TNBC patients with high SEMA3C levels exhibited lower RFS (p=0.08; HR 1.52) and DMFS (p=0.095; HR 1.63) as compared to SEMA3C low patients (Figure 3.1 D-E). Hence, elevated levels of SEMA3C expression might be indicative of poor TNBC patient outcomes. Analyzing the impact of high SEMA3C levels which can play a role in disease progression by faster tumor recurrence or distant metastasis after adjuvant/neo-adjuvant therapy led us to a select diverse TNBC cell line models to understand SEMA3C’s role in tumor signaling and driving progression. As described earlier we utilized the Lehmann et al. study which used the TNBC patient tumors derived gene expression (GE) signature to reveal TNBC cell lines for each of these subtypes which represent clinically relevant cell line models for the functional testing of novel targeted agents. Therefore, we selected MDA-MB-468, BT-549, and MDA-MB-231 which represent three different genomic signature classes. Consistent with previously made observations by Cole-Healy et al.,(96) we found that SEMA3C protein 62 expression is elevated in TNBC cell lines, as compared to MCF10A, which are immortalized non-malignant breast epithelial cells that possess many characteristics of normal breast cells (Figure 3.1F). Our observation of higher SEMA3C in these breast cancer cell lines corroborates the clinical observation made earlier, in which the transcript levels of SEMA3C were higher in breast cancer tissues as compared to adjacent normal mammary tissues (Figure 1.3). In TNBC patients, multiple RTK pathways are upregulated due to high tumor heterogeneity across each TNBC patient. In our case, we specifically focused on looking into EGFR signaling due to previously established association between SEMA3C inducing EGFR signaling (78) which is frequently overexpressed and involved in treatment resistance for TNBC patients. We selected MDA-MB-468 cells which are known to have upregulated EGFR levels and investigated whether SEMA3C can induce EGFR phosphorylation and downstream pathways in a dose dependent manner. Through these experiments, it was indicated that exogenous SEMA3C treatment activated EGFR (Y1068), Src (Y416) and MAPK (Thr202/Tyr204) proteins in a dose dependent manner (Figure 3.1G). 63 64 (A-C) Kaplan–Meier analysis (OS, RFS, DMFS) of SEMA3C high vs low expressing TNBC patients irrespective of treatment type. (D-E) Kaplan–Meier analysis (RFS, DMFS) of SEMA3C high vs low expressing TNBC patients post-chemotherapy (F) Moderate protein expression of SEMA3C in triple negative breast cells which is consistent between external and internal studies (G) followed its ability to modulate signaling pathways in MDA-MB-468 in dose dependent manner (0, 0.5µM and 1µM). 3.3.2 Depletion of SEMA3C in TNBC cells lead to downregulation of receptor tyrosine kinase pathways, growth, and activation of apoptosis After exploring cell signaling pathways activated by SEMA3C, we wanted to understand whether TNBC cells were dependent on SEMA3C-induced signaling. Therefore, we aimed to investigate whether SEMA3C silencing could impact TNBC cell signaling. To this end, we conducted a siRNA knockdown of SEMA3C (20 nM) on MDA-MB-468, MDA-MB-231 and BT-549, and 72h post-treatment we analyzed cell signaling pathways known to be upregulated in TNBC via immunoblotting. We observed a decrease in phospho-EGFR, phospho-AKT and phospho-MAPK signals across three different TNBC cells (Figure 3.2A). Observing the impact of SEMA3C KD in MDA-MB-468, showcase significant drop in both phospho- and total levels of EGFR which suggested a complex relationship between SEMA3C and EGFR. As mentioned earlier these cells are known to overexpress EGFR, we wanted to confirm whether SEMA3C can rescue these levels after a transient knockdown. After 48h of reverse transfection with SEMA3C siRNA, PBS and SEMA3C (0.5µM) were added to respective wells for 24h in low serum conditions to study whether SEMA3C can rescue EGFR levels and used MAPK as a negative control. Conducting immunoblotting on whole cell lysate and densitometry analysis (Appendix I) revealed SEMA3C’s potential involvement in regulation of EGFR levels in MDA-MB-468 possibly with help of activation of transcription factors downstream of SEMA3C signaling in a positive feedback loop. This observation needs further additional experiments to thoroughly Figure 3.1. Clinical relevance of SEMA3C expression in TNBC patients and its ability to activate receptor tyrosine kinase pathway in TNBC cells. 65 validate SEMA3C-EGFR regulation axis which is currently out of scope of this chapter and thesis. The decrease in signaling is followed by significant impact on the cell growth across all three TNBC cell models, even in the presence of serum, within three days (Figure 3.2B). Next, we wanted to elucidate whether the mechanism underlying the impact of SEMA3C knockdown on cell growth is similar to what we observed in ER+ breast cancer cells. We examined whether this effect was, in part, due to induction of apoptosis. By conducting SEMA3C silencing (20 nM) in a six-well plate and collecting conditioned media, along with whole cell protein lysate, we stained the cells with propidium iodide and observed a higher percentage of cells in the sub G0/G1 fraction, which was indicative of apoptosis (Figure 3.2C). Through immunoblotting, we were able to confirm at the protein level an increase in cleaved caspase-3 and PARP (Figure 3.2D) in SEMA3C knockdown cells suggesting induction of apoptosis. It is critical to note that as each of these TNBC cell lines represent a different sub-class within TNBC and the impact of SEMA3C KD (signaling, growth, apoptosis) tends to be asymmetric based on each cell line genetic makeup. Overall, these findings suggest that SEMA3C knockdown induces apoptosis in both ER+ and TNBC cells, which may contribute to the growth inhibition observed earlier. 66 siRNA knockdown (20 nM) of SEMA3C in MDA-MB-468, MDA-MB-231 and BT-549 cells resulted in (A) downregulation of p–EGFR (Y1068), p-p44/42(Thr202/Tyr204) and p-AKT (Ser473) within 72h, followed by (B) Figure 3.2. SEMA3C silencing in TNBC cells leads to decrease in oncogenic signaling, cell survival and activation of pro-apoptotic pathways. 67 impact of signaling downregulation leading to cell growth inhibition in all three TNBC cell models in-vitro (C) cell apoptosis detected by flow cytometry with confirmation of (D) increase in expression of pro-apoptotic proteins c-PARP/c-Caspase-3 within 72h (MDA-MB-468, MDA-MB-231) and 96h (BT-549). * p < 0.05; **** p<0.0001. mean ± standard error of the mean (SEM). 3.3.3 SEMA3C siRNA treatment augments the cytotoxicity of anticancer agents and in-house developed SEMA3C pathway inhibitor(s) showcase therapeutic potential in-vitro Chemotherapy is currently the only principal therapeutic option in TNBC. Preferred regimes include taxanes, anthracyclines, and platinum-based regimes in neoadjuvant and adjuvant setting (140,141). Despite better pathological complete response (pCR) rates after neoadjuvant chemotherapy, prognosis of TNBC patients is worse as compared to non-TNBC tumors; this phenomenon is known as “triple negative paradox” (142). Therefore, improving chemo treatment sensitivity in TNBC is a highly interesting avenue which can prolong TNBC patient survival and delay chemo-resistance. Our study within TNBC patient samples indicates that SEMA3C has a prognostic value and through in-vitro assays we confirmed its role in activation of RTK signaling pathways. Also, SEMA3C knockdown significantly impacts cell growth which makes it a potential therapeutic target in TNBC. To understand whether there is benefit of using SEMA3C targeted therapy within current therapy regime of TNBC we utilized different SEMA3C pathway inhibition mechanisms either as a monotherapy or in combination with chemotherapy or RTK inhibitors (RTKi). To begin, we selected two distinct chemotherapy classes and two RTKi (EGFR-specific/multi-kinase) to establish dose sensitivity for each cell line currently used in our study. After conducting a dose response for each drug in three TNBC cells (based on external papers) (138,143), the IC50’s identified for each drug across each cell line had similar values as reported earlier (Figure 3.3 A). This helped us in identifying which drugs are to be utilized for 68 combination assay on specific cell lines. For example, in chemotherapy combination, we observed cisplatin sensitivity was observed in MDA-MB-468 but not MDA-MB-231 and BT-549. This observation is supported by Lehman et al. (138) confirming earlier that basal-like TNBC subtype is more sensitive to cisplatin than metastatic TNBC subtype (MDA-MB-231 and BT-549). We wanted to identify whether SEMA3C siRNA in combination with cisplatin had any positive drug combination effects as observed with anti-SEMA3C treatment with Tamoxifen or Fulvestrant in ER+ breast cancer cells (MCF7, T47D) by studying the effect on cellular growth in cisplatin resistant MDA-MB-231 and BT-549 cells (144). As it is critical to assess whether SEMA3C siRNA can improve efficacy of these approved drugs and potentially induce chemotherapy sensitization. For this experiment, we created a matrix with a range of different treatment concentrations of drugs (chemotherapy or RTKi) for e.g., (SEMA3C siRNA = 0–20nM; Cisplatin = 0–10µM) and 96h later for each combination growth inhibition (%) and synergy score is calculated. We depicted growth inhibition (%) for each concentration combination in a heat map (Figure 3.3B). 69 70 (A) IC50 values of currently prescribed and in-trials drugs on TNBC cells. (B-D) Growth inhibition (%) heatmap of three different drug combination across three different TNBC cells For these experiments, the expected drug combination responses were calculated based on the HSA reference model, using Synergy Finder Plus (131) to identify relationships between SEMA3C siRNA and the specific therapy. For the BT-549, SEMA3C siRNA-Cisplatin combination revealed a potential synergy effect based on mean HSA synergy score (≥10 likely synergistic) (Figure 3.4A). However, the same combination in MDA-MB-231 (Figure 3.4A) exhibited a mean additive effect rather than synergistic (additive = −10≤ HSA score ≤10). This difference in drug combination effect would likely be due to different molecular pathways that might get upregulated to evade growth inhibitory effects from this combination in MDA-MB-231. Currently there are a lack of targeted therapies in TNBC at large, except TROP2, PARPi, PD-L1 inhibitors from which only a small subset of TNBC patients benefit(4). It has already been established that certain RTK’s (EGFR, MET, VEGFR, RET) are involved in treatment resistance in TNBC such as EGFR overexpression incidence ranges from 13-76% and observed more in TNBC than other breast cancer subtypes (145). Targeting these RTKs alone has not been successful in the clinic (146) due to tumor heterogeneity of TNBC which led to different RTKs being involved in driving the tumor population. To confirm whether SEMA3C inhibition in combination with different RTKi (selective or multi) will improve RTKi efficacy via positive drug combination effects, we conducted similar combination assays on MDA-MB-468 and BT549, this time with Erlotinib (MDA-MB-468, 0-2µM; BT-549, 0-5µM) and Cabozantinib (MDA-MB-468, 0-5µM; BT-549, 0-5µM) based on their lower sensitivity to these drugs (Figure Figure 3.3. Growth inhibition heatmap of SEMA3C siRNA in combination with different FDA approved drugs. 71 3.3A) probably due to different RTKs expression (104). Through our analysis we clearly saw that SEMA3C siRNA alone at low concentration of 10 nM and 20 nM (Figure 3.3 C-D) can substantially impact the growth in these cells leading to synergistic effect (BT-549 = siRNA + erlotinib) and additive effects (BT-549 = siRNA + Cabozantinib; MDA-MB-468 = siRNA + Erlotinib; MDA-MB-468 = siRNA + Cabozantinib) when SEMA3C signaling pathway is inhibited in addition to inhibitor specific RTK signaling inhibition (Figure 3.4B-C). This varied effect can be due to unique mechanism of action for RTKi and distinct TNBC subtype represented by the different TNBC cells. Interestingly, >90% of FDA approved combination therapies in market are additive rather than synergistic (147). We are already aware of SEMA3C role as an autocrine growth factor which is known to activate different RTKs in a cognate ligand independent manner (78) which can lead to similar inhibitory potential of Erlotinib or Cabozantinib alone. Additionally, toxicity studies are required to find the optimal drug combination partner. But overall, combining SEMA3C pathway inhibition with different types of therapies resulted in positive drug combination effects. 72 (A) siRNA and cisplatin combination treatment on cisplatin-resistant TNBC cells (BT-549, MDA-MB-231) cells Figure 3.4. SEMA3C siRNA showcase positive drug combination effect with chemotherapy and RTKi drugs in TNBC cells. 73 showcase synergy in BT-549 and additive effect in MDA-MB-231 based on HSA scoring. (B-C) siRNA and receptor tyrosine kinase inhibitors (erlotinib or cabozantinib) combination showed mean synergy in BT-549 for erlotinib and for the rest additive effect based on HSA scoring. Another way of SEMA3C pathway inhibition is via different versions of decoy proteins (called biologics) which can act as inhibitor by blocking protein-protein interactions. In the case of TNBC, we used two different in-house developed biologics B1SP and ALS (Figure 3.5A). These two inhibitors differ in their structure, B1SP acts as decoy Plexin-B1 receptor which binds to endogenous SEMA3C secreted from the cells and ALS which acts as an inactivated SEMA3C competing with endogenous SEMA3C in binding to cell-surface receptors. Due to production challenges within 2022-23, we were limited on the number of assays we were able to perform using B1SP and ALS. We could only conduct a growth assay on TNBC cells with B1SP which showcased growth inhibition at 1µM (Figure 3.5B). For ALS, we observed >50% inhibition across three distinct TNBC cells at 5µM (Figure 3.5C) based on a dose response assay (Appendix J). The difference in the inhibitory effect of these two biologics can be attributed due to different SEMA3C expression, mechanism of action and complex pathways which are unique to each of these cell lines. For proof-of-concept, we utilized MDA-MB-468 which had a median response to ALS treatment across three different TNBC cells. These cells overexpress EGFR, therefore we wanted to confirm if ALS treatment impact on cell growth in-vitro is via RTK pathway downregulation. Treating MDA-MB-468 with ALS in low serum condition for 24h indeed revealed ALS can inhibit signaling in these cells at a concentration as low as 1.5µM (Figure 3.5D). This is in parallel to observations made in SEMA3C siRNA KD in (Figure 3.2A) which showcase targeting SEMA3C alone can lead to significant growth inhibitory effects in-vitro. 74 75 (A) 2D schematic representation of B1SP and ALS (B) 1µM B1SP (C) 5µM ALS impacted cell growth within 72h in TNBC cells and (D) ALS downregulates oncogenic signaling (in MDA-MB-468) in the presence of full-serum condition. *p<0.05, ** p<0.01; ***p<0.001; ****p<0.0001; mean ± standard error of the mean (SEM). 3.4 Discussion Several evidence suggest that SEMA3C is highly expressed across different subtypes of breast cancer. Within the scope of our study, we showcase SEMA3C’s mechanistic role in driving signaling in both ER+ breast cancer and TNBC cells. Various treatment options are available for therapy naïve non-TNBC subtypes but for TNBC there is no specific targeted therapy that is widely used except chemotherapy. In this chapter, we showcased high SEMA3C expression in TNBC patients irrespective of treatment they underwent resulted in lower OS, RFS and DMFS by at least 11 months. Followed by confirmation of its protein expression across different TNBC cell lines which are uniquely clustered within three different categories which collectively impart the tumor promoting inflammation, invasion and metastasis hallmarks of cancer (138). There are specific oncogenic pathways which are upregulated in TNBC such as the higher expression of EGFR which is associated with poor survival (145). PI3K-AKT-mTOR is known for its involvement in cell proliferation, survival and migration. Both BT549 and MDA-MB-468 have PTEN mutations (148) which are consistent with their higher observed levels of p-AKT (149,150). To address this, we conducted different in-vitro based assays which showcased dependency of TNBC cells on SEMA3C-induced signaling. First, via SEMA3C:DM exogenous treatment and second by siRNA KD assays which resulted in activation or downregulation of EGFR, Src, AKT and MAPK signaling respectively. Tam et al., showcased upregulation of CD44 (marker for EMT) in breast cancer cells (MCF10A, MDA-MB-468) with low metastatic phenotype, in SEMA3C overexpressing cells (151). This suggests disruption of the SEMA3C pathway would inhibit Figure 3.5. Anti-SEMA3C therapeutics confirms its druggability potential in TNBC cells. 76 TNBC cell growth and survival which we confirmed by siRNA KD growth assay and detection of upregulation of apoptosis markers in absence of SEMA3C through immunoblotting and flow cytometry. TNBC resistance pathways and proofs through our study showed that SEMA3C plays an active role in maintenance of these pathways. Previous studies have also reported that avoidance of apoptosis and RTK upregulation confer resistance to chemotherapy in several cancer models including TNBC (152). After seeing impact (upregulation or downregulation) of these pathways by SEMA3C siRNA KD, this motivated us to see if suppressing SEMA3C expression (siRNA silencing) in combination with chemotherapy can lead to chemo-sensitization on mesenchymal TNBC subtype cells (MDA-MB-231 and BT-549) which are known to be least responsive to cisplatin treatment (144). The combination assay revealed two interesting things- a) siRNA SEMA3C does improve the chemosensitivity of these two cell lines b) transient siRNA KD alone has the ability to perform better than chemotherapy treatment alone in-vitro. The former observation when analyzed by HSA synergy algorithm in Syngerfinder plus (131) resulted in SEMA3C KD and chemotherapy having positive drug combination effects (additive or synergistic) on cisplatin resistant MDA-MB-231 and BT-549 cells in-vitro. The positive drug combination effect was also observed when high EGFR expressers (MDA-MB-468) and metastatic TNBC cells (BT-549) were treated with RTKi (Erlotinib/Cabozantinib). This synergy may be achieved through the impacting downstream pathways (AKT and MAPK) indirectly which are involved in anti-apoptotic effects. These observations can implicate, SEMA3C downregulation can inhibit the growth and invasion of triple negative breast cancer. Additionally, can also impart direct cytotoxicity in these tumors although needs to be confirmed by in-vivo studies. In our study, we also showcased SEMA3C druggability potential in TNBC via 77 in-house developed biologics (ALS and B1SP) which led to >50% inhibition in different TNBC cells at low concentration (1µM-5µM). The varied effects observed across different TNBC cell lines as mentioned earlier can be attributed due to distinct genomic background of each cell line used in this study. In addition to clinical relevance of high expression of SEMA3C on patient survival and disease progression, its co-receptor NRPs [neuropilin1/2] are also found to be widely co-expressed in breast cancer. A study demonstrated that NRP-1 was expressed more abundantly in high grade tumors and in patients with a poorer prognosis (153). Recently, Brett-Holey et al., (154) confirmed higher level of NRP-1 was significantly associated with worse survival in TNBC patients. Our preliminary signaling experiments confirmed Plexin-B1 as a potential co-receptor involved in SEMA3C mediated signaling in TNBC cells (Appendix K). This suggests a clinical relevance of SEMA3C receptor complex in TNBC progression. Although further investigation is needed to reveal the mechanistic effect of SEMA3C mediated signaling via Plexin-B1 (other plexins) in association with Neuropilin. In summary, for the first time, we showcase the impact of SEMA3C expression on survival in TNBC patients. In-vitro based experiments, revealed TNBC cells depend on SEMA3C for growth and signaling, and its inhibition was associated with decreased levels of AKT and MAPK, increased apoptosis, and chemo sensitization to anticancer drugs. It has the potential to be deployed as a monotherapy or packaged within a multimodal regimen in neo-adjuvant/adjuvant setting. These results provide preclinical proof of principle for the targeting of SEMA3C as one of the therapeutic targets in triple negative breast cancer. Targeting SEMA3C via biologics has been validated in-vivo in treatment resistant prostate cancer and currently in final stages of pre-clinical studies. But in order to successfully develop a successful anti-78 SEMA3C therapy additional studies focusing on other therapeutic strategies are also recommended such as development of small molecule inhibitors. They have less invasive therapy administration, lower immunogenicity and current next-generation AI based discovery tools can develop high affinity and specific small molecules. Therefore, in our next chapter we expand on for the first time an in-silico analysis of SEMA3C receptor complex. Utilizing the structural information of SEMA3 family, we conduct large scale in-silico drug screening which validates this targeting strategy and gives an opportunity to develop a successful anti-SEMA3C therapy in future. 79 Chapter 4: In-silico review of Semaphorin 3C (SEMA3C) receptor complex to support development of next generation small molecule inhibitors 4.1 Introduction Developing a wide variety of therapeutic strategies to target SEMA3C signaling pathway is essential as the process of drug development (pre-clinical to clinical settings) typically has high risk of failure. Therefore, a diversified approach pursuing parallel strategies to target SEMA3C would accelerate drug development and counter-balance the high failure risk experienced during drug development. As seen in chapters 2 and 3 (and previous work in our laboratory on prostate (78), pancreatic and bladder – not published yet) we have successfully developed in-house “biologics” (B1SP, ALS) showcasing their efficacy both in-vitro and in-vivo. We believe an alternate strategy to target SEMA3C is via small molecules which are orally available than IV injection used for biologics administration. Furthermore, from mouse genetic studies loss of SEMA3C is predicted to have limited function in adults therefore making it a clinically safe and relevant target (155). We previously have conducted a proof-of-concept study (Figure 4.1A-C) where we concluded that SEMA3C can be targeted through small molecules which make it a druggable target (156). But the major caveats of this study were 1) mediocre significance of binding pocket, which majorly overlaps with SEMA3C-Plexin binding site, 2) utilization of NCI dataset for in-silico compound screening that has a limited size compound library, 3) Transient binding of lead compounds. To overcome these limitations, we need to first understand potential binding sites of its receptor on SEMA3C and large library of compounds to screen for potential small molecule inhibitors. 80 (A) Homology-modeled structure of human SEMA3C (left); binding mode of compound 20526 (referred to as D13 throughout this report) at the binding region of NRP1 (in circle) with chemical structure of top three lead compounds. (B) Inhibition of prostate cancer cell growth by first-generation lead compounds (D13, D36, and D90) dose dependently inhibited growth in LNCaP and DU145 cells. Cells were incubated with various concentrations of compound (starting concentration at 200µM, 1:2 serial dilution) for 3 d at which point cell viability was assessed using PrestoBlue. (C) Comparison of small molecule binding site on SEMA3C with other SEMA3C family members Data represent mean ± SEM. Figure 4.1. Summary of proof-of-concept study establishing evidence of druggability potential of SEMA3C in prostate cancer cells. 81 4.1.1 Protein-Protein Interaction Inhibitors (iPPI) and its major challenges Small molecule drugs are one of the known therapeutic strategies used for the treatment of various diseases. In the case of SEMA3C inhibition, there is an important aspect we need to discuss while identifying drugs for a particular target which is we cannot treat every target class the same. They have their own unique characteristics. Most of the current small molecule drugs for treatment across various types of cancers belong to target class of G-protein coupled receptors (GPCR), kinase/non-kinase, ion channels or nuclear receptors. But these only cover 324 druggable targets in clinic as compared to 300000 potential protein-protein interaction which plays a significant role in disease progression and resistance, and SEMA3C belongs to iPPI class. This target class has specific properties such as a large surface area of contact, a lack of deep cavities where a small molecule can bind, flatter in shape and more hydrophobic interactions (157,158). SEMA3C is an attractive therapeutic target from multiple standpoints: biologically, it fills tumorigenic roles and correlates with intensified disease state while its extracellular localization renders it readily accessible by pharmacological agents (83). 4.1.2 Semaphorin 3 family signaling Semaphorin 3 family interaction with one of its co-receptors neuropilin-1/2 is a critical interaction as it provides structural integrity to the Semaphorin: Plexin complex because in absence of neuropilin, semaphorins can bind to plexins but with lower affinity and cannot induce plexin-dependent signaling (63,159,160). Structural studies conducted on SEMA3A helped in identifying a 70aa stretch in sema domain which is unique to Semaphorin 3 protein family (161). Currently there is no known crystallized SEMA3C: Plexin: Neuropilin receptor complex due to difficulty of working with membranes in their native environment which can give details 82 about exact binding locations of these co-receptors on SEMA3C. Therefore, our goal is to propose an in-silico model of SEMA3C: Plexin: Neuropilin receptor complex by using homology modeling and parallel structural studies conducted on other Sema 3 family members identifying potential binding sites on SEMA3C. Utilizing this knowledge will help us predict a new-binding pocket with high potency and conduct in-silico screening to identify lead compounds. Validation of this proposed strategy can help us lay foundation for future next-generation small molecule inhibitors of SEMA3C. 4.2 Methods 4.2.1 Homology modeling to develop SEMA3C protein structure and identification of receptor binding sites The full-length protein sequence of the SEMA3C (UniProt ID: Q99985) was searched against the Protein Data Bank (PDB) (162) and a crystal structure of SEMA3A (PDB ID: 4GZ8) (63)was identified as the best matching template, with 46% sequence identity across the 345 amino acids of the SEMA domain and PSI domain compared to the human SEMA3C. Using SEMA3A crystal structure as its template (160) we modeled SEMA3C structure at root-mean-square deviation (RMSD) of 0.6Å. This computational model was developed and operated in MOE (Chemical Computing Group, Montreal, Québec, CA) (163). Using this model, we identified potential binding sites on SEMA3C for its co-receptors based on observations from two external studies (63,161) to identify potential active site region to be utilized for docking. 4.2.2 In-silico screening The predicted protein structure of SEMA3C was then prepared using the Protein Preparation Wizard of Schrodinger 2016-3 software (with OPLS3 force field) (164). MOE (Chemical Computing Group, 2019) “site finder” tool was used to specifically identify an active site with 83 hot-spot residues (Asp231, Asp237, Lys239, Gln346, Phe349, Asn350, Arg374, Pro375, Gly376, Thr377, Cys378) used for large scale in-silico screening within the proposed region on SEMA3C (249 – 400 aa) which interacts with Neuropilin. To identify suitable sites for small molecule binding on the SEMA3C, a total of 10 million drug-like molecules (in-stock, 3D representation, with molecular weights from 250 to 500 Dalton, and logP ≤5, Charge = -2 to 2 and pH= 7.4) were extracted from the ZINC15 database (165) based on protein-protein interaction inhibitors (iPPI). Each of the 10 million small molecules was docked against the docking grid using the program Glide (166) (standard precision mode with default parameters) from the Schrodinger 2016-3 package (Schrödinger, New York, NY, USA). The top docking pose of each molecule was imported into the molecular operating environment (MOE, Chemical Computing Group, Montreal, Québec, CA) (163). Molecules with docking scores lower than −5 (lower is better) were further docked with the FRED program, OEDocking (OpenEye Scientific Software, Santa Fe, NM, USA) (167) (up to 500 conformers were generated for each molecule and were docked using FRED with default parameters), and the corresponding RMSD values were calculated for the top poses. All the molecules with a RMSD ≤ 3Å were retained and were docked again using ICM software (Molsoft LLC, San Diego, CA, USA) (168). For the poses predicted by ICM (default parameters), RMSD values were calculated against Glide and only the molecules with RMSD ≤ 3Å were retained. This provided us with 229,000 compounds. Within this set, more than 10 ligand/protein ligand properties were assessed which are essential for protein-protein interaction inhibitors (iPPI). Predicted docking pKi was calculated for each molecule using a custom MOE SVL script (163). Other properties like ADMET (absorption, distribution, metabolism, excretion, toxicity) and pharmacokinetics predictions were also calculated using computational programs such as ADMET Predictor (Simulations Plus, Lancaster, CA, USA) 84 (169), FAF-Drugs (170), and quantitative estimate of drug-likeness (QED) (171). Consensus scoring matrix for molecules with MW between 250 and 500 Dalton (oral application). The total score for each molecule is calculated based on the criteria mentioned in the table. Molecules with total consensus scores ≥ 7 were retained and subjected to clustering to generate a pharmacophore. 4.2.3 In-vitro screening (Displacement Assay) NRP1-Fc (sigma, fusion protein with the Fc domain from IgG) was immobilized onto an A3-plex plate via incubation at 4°C overnight. After overnight incubation, Blocker A (PBS + 3% BSA) was added to the wells incubated for 1h at room temperature to prevent non-specific binding of other reagents to the bottom of the wells. After blocking, 50μl mix of compounds (10 μM) with SEMA3C:DM (25nM) were incubated at room temperature for 2h. This step allows potential lead compounds to prevent binding of SEMA3C:DM to NRP1-Fc. After this step, wells were incubated with 25μl of SEMA3C-targeting anti-mouse 1H5 antibody (developed-in house) for 1 hour at room temperature. Next, we incubated wells with the SULFO-TAG goat anti-mouse detection antibody (0.5μg/ml) for 1h followed by addition of 1X Read Buffer T Surfactant free and data is read on Meso-Scale Discovery- QuickPlex SQ120. The plate was washed with wash buffer (PBS; 0.05% Tween 20, Sigma-Aldrich, P9416) three times after each step of incubation. 4.3 Results 4.3.1 In-silico model of SEMA3C and potential receptor complex binding sites on SEMA3C Using SEMA3A structure identified via X-ray diffraction, we were able to predict SEMA3C protein structure (Figure 4.2A) using homology modeling in MOE (Chemical 85 Computing Group, 2019) (163). To develop a lead small molecule inhibitor which can inhibit SEMA3C signaling, we first need to identify interaction sites on SEMA3C. It has been validated through various experiments (63,159) that semaphorins bind to their co-receptors (NRPs and Plexins) via its sema-domain which makes it a critical hot spot to target via a small molecule. To identify these hotspots on our in-silico model of SEMA3C we conducted a thorough literature review (159–161) to help us identify binding patterns on semaphorins and especially SEMA3 family members which are the only semaphorins known to bind neuropilin. Our findings suggest that SEMA3 family, first bind to neuropilin which provides them structural stability to bind to plexins and in the absence of neuropilin binding SEMA3 family can bind to plexin with high KD but cannot induce downstream signaling. a1 domain of neuropilin is involved in the semaphorin binding but there is also interaction between C-terminal domain of SEMA3C and b1 domain of Neuropilin. Semaphorin dimer formation is critical for semaphorin-receptor complex as monomeric semaphorin’s can bind to plexins but no signaling occurs. Region 345-420 aa is unique to SEMA3 family as few researchers consider it to be a NRP1 binding site, but as mentioned above there are few other residues which are important in NRP1 binding shown via mutagenesis study. SEMA3A dimerization sites are 70% similar and 50% identical to SEMA3C dimer site. There are some residues which overlap in SEMA3C dimer and NRP1 binding site. As mentioned, 166-235 aa residues in SEMA3 family decide the specificity of which plexins it can bind. These observations are presented in the form of the surface map on the SEMA3C protein model (Figure 4.2B) with proposed residues on SEMA3C which are involved in dimerization, NRP and Plexin binding (Figure 4.2 C). 86 (A) Homology-modeled structure of human SEMA3C designed from SEMA3A (4GZ8) as template with RMSD of 0.6Å. (B) Surface map of binding site location on SEMA3C for homo-dimerization, Plexin (left) and Neuropilin-1 (right) (C) Binding site location and residues on SEMA3C involved during SEMA3C receptor complex formation. Figure 4.2. Structural information of SEMA3C receptor complex from in-silico analysis. 87 4.3.2 Drug ability assessment of 2nd generation small molecule inhibitors SEMA3C via large scale in-silico screening and preliminary in-vitro displacement assay From previous analysis we learnt that neuropilin interaction with SEMA3 family members (for e.g., SEMA3C) is an essential step in receptor complex formation to initiate downstream signaling. We hypothesize that identifying a binding pocket within proposed neuropilin binding site on SEMA3C will help us develop novel small molecules with high affinity (currently no compounds are being developed against SEMA3C). Utilizing this knowledge, we identified a druggable pocket on SEMA3C (Figure 4.3A) which was used for in-silico screening. Understanding how unique this active site was as compared to other SEMA3 family member as this region interaction is only restricted to this semaphorin family, we identified 29-76% “similarity” among other SEMA3 family members (Figure 4.3B) with SEMA3F having the most similar residues in the active site as SEMA3C. Across all family the only conserved residues were in 372- 376 aa region and uniqueness of this binding pocket on SEMA3C might prevent potential off-target effects. We performed a large-scale virtual screening of 10 million drug-like molecules from the ZINC15 database (165) (please see the Materials and Methods section for details on the binding site). Using our established virtual screening protocols (172), the binding poses and scores of these 10 million molecules were calculated using three docking programs—Glide (166), ICM (168), and OEDocking (167). High-scoring small molecules (229,000 compounds) with consistent docking poses (evaluated by the corresponding root-mean-square deviation, RMSD) were subjected to ADMET (absorption, distribution, metabolism, excretion, toxicity) and pharmacokinetics filtering using computational programs including ADMET Predictor (169), FAF-Drugs (170), and Quantitative Estimate of Drug-likeness (QED) (171). Based on the 88 consensus scoring of the above computational predictions (e.g., docking, RMSD, ADMET), we selected 115 top candidate compounds for experimental validation. As mentioned earlier, one of the major limitations of previous proof-of-concept studies validating SEMA3C as a druggable target via small molecules was transient binding of lead compounds. To confirm whether our large-scale virtual screening protocols using an extensive database resulted in “hits” that fits in target site we randomly selected one of the top 115 candidates (SM_898/E0240) selected for in-vitro screening and conducted molecular dynamic (MD) analysis using MOE (163)by benchmarking it against an old lead compound (D13) (156). We ran MD for 30ns at 3000 frames, in MD simulation of Protein-Drug interaction, a desired drug would sit in the pocket for longer period time. Its RMSD should not be more variable than 1-2Å of the protein’s RMSD. As seen in (Figure 4.3C), E0240/SM_898 bound in the active site in more stable manner for a longer time period than old lead compound D13 which almost move away from the binding site hinting towards low-affinity binding. Next, we assessed 49 of the 115 candidates that emerged from the in-silico screen by assessing their ability to prevent SEMA3C binding to its biological receptor, NRP1. We used an electrochemiluminescence method (Meso-Scale Discovery- QuickPlex SQ120) approach to measure the displacement of SEMA3C from NRP1 using purified recombinant proteins. NRP1 was immobilized on 96-well plates followed by the addition of SEMA3C:DM with or without small molecules; Followed by a wash, SEMA3C binding antibody (1H5) was then added for an hour and sulfo-tagged detection antibody was then added to measure luminesce generated after electric signal is passed through each well. Only when sulfo-tagged detection is bound to its target will it generate a signal. Therefore, a compound was identified as a “hit” when it disrupted SEMA3C-NRP1 interaction and provided >50% drop in signal compared with DMSO control 89 (Figure. 4.3D). From this set of 49 compounds originally selected from the in-silico screen, three hits (SM_898, SM_568 and SM_880) were identified at 10µM concentration. 90 (A) Identification of novel binding site with NRP1 binding region on SEMA3C along with specific residues that interacts with small molecules. (B) Assessing uniqueness of the active binding site on SEMA3C by overlapping with other human Sema 3 family members residues. (C) Molecular Dynamics analysis of the first-generation lead compound (D13) to one of the top hits (SM_898) from large scale in-silico screening depicting higher stability in the new binding site. (D) Meso-Scale Discovery (QuickPlex SQ 120MM) assay to showcase Protein-Protein interaction (NRP1:SEMA3C) displacement by 49 out of 115 top hits (Orange = Positive Control; Green = Compounds with >50% displacement of NRP1:SEMA3C interaction; Data represent mean ± SEM) 4.4 Discussion In recent years, SEMA3C has been characterized as a promising drug target in solid tumors such as prostate, pancreatic, cervical and through recent studies now in breast cancer too. To address the lack of anti-SEMA3C therapies, we engaged a computer-aided drug design pipeline to virtually screen 10 million compounds against the SEMA3C-NRP1 interface in the sema domain of SEMA3C. As a result of the computational screen, a total of 115 compounds were selected for experimental validation which first confirmed via MD to have better binding affinity in the active site than first-generation lead compounds (D13 vs SM_898). Out of 49 of 115 compounds, three of them already demonstrated preliminary inhibitory activity via PPI displacement assay. However, cell-based assays (IC50 and signaling) are required along with potential hits to be docked/experimentally screened on SEMA3F to test specificity of the compounds. Success of the current preliminary in-silico screening highlights the need of expanding the compound database and utilize enhanced docking algorithm for the remaining (approximately 1 billion) drug-like compounds in the ZINC databases for the identification of additional high potent molecules with anti-SEMA3C activities that might have been missed to due current in-silico approach. The strategy applied in this study lays the foundation to further improve the in-silico and in-vitro screening process to develop another set of lead compounds Figure 4.3. Druggability assessment of the new binding site on SEMA3C. 91 this time via deep docking model and deep QSAR methods (173,174) developed by Dr. Artem Cherkasov which will help us screen a wider database of compounds (1 billion or more) with higher success rate for a potent lead compound. Applying the docking algorithm discussed in this chapter (with the same number of CPUs) on 1 billion chemical structures would take 2000 days or 5 years but with deep docking this can happen in a relatively short period. In addition, we can utilize the power of Alpha-fold (175) to use predicted complete SEMA3C protein structure which was not available before this study was conducted to further enhance the small molecule development process. 92 Chapter 5: Discussion 5.1 Conclusion and General Discussion In summary, our findings identify SEMA3C as a novel therapeutic target for ER+ breast cancer and TNBC patients who are either treatment naïve or have experienced disease progression after standard of care procedure. Even though there are many advancements in the breast cancer treatment landscape, there are still patients in both breast cancer subtypes that suffer from treatment resistance (post-endocrine therapy) or lack of treatment options in the case of TNBC patients. Therefore, identification of a non-oncogenic autocrine growth factor, which can play a role in activating multiple RTK downstream signaling pathways across breast cancer types, is necessary to overcome therapeutic challenges faced in the clinic. In our study, we showcased the ability of SEMA3C signaling pathway to maintain the growth and survival for ER+ breast cancer and TNBC cells. This research brings a fresh perspective by uncovering previously limited knowledge about the role of SEMA3C in breast cancer progression, SEMA3C receptor complex binding hotspots and in-vitro proof-of-concept studies validating its role as a druggable target via small molecule or biologics. Public Cancer Genetic Dependencies (CancerGD) (81) data showcased an increased dependency on SEMA3C for cancer cells harboring mutations in corresponding cancer driver genes. This observation made us postulate that various oncogenic mutations may convergently activate autocrine SEMA3C production to promote cancer growth and treatment resistance. Oncogene addiction refers to the observation that cancer cells remain dependent on (or addicted to) one or a few cancer driver genes for their sustained proliferation and/or survival; thus, providing a rationale for molecular targeted therapy against the addiction (176). However, not all cancer driver genes are readily druggable. Based on observations made in Cancer GD database 93 and by previously conducted similar experiments in our lab (data not shown here) across multiple cancer types, we think that the addiction of cancer cells to some oncogenes and tumor suppressor genes may, in part, be due to their ability to upregulate autocrine SEMA3C expression leading to self-sustained proliferative RTK signaling. This suggests that cancer cells harboring certain oncogenic mutations (PTEN, PIK3CA, NF1, MYC, TP53) may exhibit a dependency on SEMA3C referred to as a non-oncogene addiction (177) and may, in turn, be susceptible to SEMA3C pathway inhibitors such as B1SP. If SEMA3C is the primary growth factor that is required by breast cancer cells to achieve autonomous cell growth in different subtypes of breast cancer cells, then potent endocrine therapy or chemotherapy will initially inhibit growth by significantly reducing ER-dependent or independent SEMA3C expression. The strong selective pressure imposed by this therapy will then force cancer cells to undergo cellular rewiring and adaptation to achieve continued growth (178). If breast cancer cells are truly addicted to autocrine SEMA3C for its growth and survival, then the potent selective pressure imposed by different therapies will prompt cells to re-engage SEMA3C expression by any means such as re-wiring of its gene regulatory circuits as seen in tamoxifen resistant cells (having similar or higher SEMA3C protein levels) and high SEMA3C expressors having lower RFS and DMFS in TNBC patients post chemotherapy. In our study among all breast cancer subtypes, ER+ breast cancer expresses the highest levels of SEMA3C transcripts. This observation was consistent with the study presented by Zhang et al. (179). But findings of Cole-Healy et al., (96) reported that SEMA3C protein levels were higher in tumor samples of TNBC than ER+ breast cancer patients. This discrepancy can be attributed to poor correlation between mRNA and protein levels plus mRNA analysis is based on TCGA datasets which uses bulk tumor for RNA-seq experiments whereas Cole-Healy used 94 slides of tumor sections for tissue microarray. Additionally, our in-vitro experiments showed consistently higher SEMA3C protein expression in breast cancer cells than non-cancer cell line (MCF10A) which is consistent with observation made in Figure 1.3B. The work presented here has significant implications for understanding the potential mechanisms that drive the progression of ER+ and TNBC. Irrespective of different levels of SEMA3C protein expression, dependency of different breast cancer cells belonging to luminal and basal class suggests its critical role in driving different hallmarks of cancer (resisting cell death, sustained proliferative signaling and invasiveness). The validity of this statement is yet to be determined by additional experiments, however, the evidence provided in this context compels us to put forth this particular scenario. In lieu of this model, SEMA3C levels might compensate for depletion of other tumor promoting factors by having the ability to drive multiple RTK pathways. Additionally, we speculate that the downregulation of different DNA and non-DNA binding factors that bind to SEMA3C locus will eventually be overcome by mutation(s) in regulatory pathways favoring re-activation of SEMA3C expression. Hence, it will be crucial to ascertain alterations in the expression of SEMA3C in relation to these occurrences to validate our approach which we will discuss later in this chapter. Nevertheless, the findings detailed in this report satisfy objectives stated in the Hypothesis, objectives, and specific aims section and are consistent with the hypothesis of SEMA3C’s role in maintaining RTK pathway activation, evasion of apoptosis and its ability to improve efficacy of current treatments in two distinct subtypes of breast cancer. To establish biological relevance of a new biomarker in breast cancer progression, first we need to analyze patient tumor samples to establish some type of clinical significance with molecular or prognostic tumor characteristics. In our study, we observed positive correlation of 95 SEMA3C mRNA expression with mRNA expression of well-known oncogenes along with impact of high SEMA3C expression associated with poor patient prognosis. Through our analysis of ER+ breast cancer samples and in-vitro experiment we identified SEMA3C to be one of the top genes positively regulated by ER. Additionally, we reported a novel finding where SEMA3C siRNA KD impacted ER expression in both tamoxifen sensitive and resistant cells which suggests a critical relationship between SEMA3C-ER axis to maintain ER+ breast cancer progression. Although numerous downstream targets of ER have been identified in large-scale studies, accompanying work to validate these downstream targets impact on cancer progression does not always occur. Given that SEMA3C was shown to be regulated by ER and further showcased by different assays that it drives cancer progression, one might speculate that abnormal ER signaling, which is characteristic of ER+ breast cancer progression maintains elevated SEMA3C levels before and after endocrine therapy. In our study we consistently observed in two different breast cancer subtypes, SEMA3C’s ability to maintain AKT and MAPK signaling pathway across different cell lines. Accumulating data suggests that PI3K/AKT activation plays a crucial role in endocrine resistance in ER+ breast cancer (180). Preclinical data supports that PI3K and AKT can activate ERα independently in the absence of estrogen by phosphorylating it at Ser167 (181). This interaction between ER and PI3K/AKT/mTOR pathway hyperactivation causes breast cancer cells to lose sensitivity to endocrine therapy. PIK3CA mutations are detected in close to 50% of ER+ breast cancer patients, contributing to endocrine resistance (182,183). In-vitro experiments on MCF7 and T47D which have different onco-driver mutations in PI3KCA (E545K, H1047R) (148), showcased consistently high SEMA3C protein expression. Furthermore, another mechanism of resistance involves upregulation of receptor tyrosine kinases (RTKs) such as EGFR, which leads to 96 enhanced activation of the downstream PI3K/AKT signaling pathway (184). We also provided supportive evidence of SEMA3C's ability to maintain these resistance pathways in tamoxifen-resistant cells and TNBC cells and inhibiting the SEMA3C signaling pathway downregulated phospho-EGFR, -MAPK and -AKT. Nevertheless, the development of resistance to chemotherapy remains a common occurrence, primarily attributed to the acquisition of anticancer resistance and acquired drug resistance (185). Unfortunately, the precise mechanisms underlying chemoresistance in breast cancer have yet to be fully elucidated. Currently, research has indicated that the PI3K/AKT pathway plays a pivotal role in promoting chemotherapy resistance by influencing multidrug resistance-related proteins and exerting anti-apoptotic effects (186). Furthermore, studies have revealed a positive correlation between AKT phosphorylation and cell viability, migration, and apoptosis in breast cancer, potentially leading to the induction of chemoresistance (187). Therefore, blocking an upstream regulator of PI3K/AKT signaling can restore the sensitivity of cancer cells to chemotherapy drugs and thus overcome drug resistance as observed in SEMA3C siRNA combination studies. Similarly, MAPK pathway is also known to be involved in driving breast cancer treatment resistance (188). There has been evidence of crosstalk between these two clinically validated pathways (AKT/MAPK) in breast cancer which compensate for each other when either one is downregulated via targeted therapies leading to constituent growth signaling after a transient inhibitory stage (178,189). Wnt pathway is another oncogenic pathway which is involved in driving treatment resistance including in breast cancer (190) and Hao et al., (191) recently published a study revealing SEMA3C drives Wnt signaling via β-catenin nuclear accumulation in a Rac1-dependent process, leading to transactivation of Wnt target genes in 97 absence of Wnt ligand. Overall, through clinical data analysis and in-vitro experiments we found that SEMA3C ability to induce these pathways and drives tumor progression in ER+ BCa and TNBC. These findings suggest that targeting these pathways, either alone or in combination with subtype specific therapies could be a potential strategy for overcoming treatment resistance and improving outcomes for breast cancer patients. With current advancements of drug development technology, there are multiple modalities we can utilize to develop a successful therapeutic strategy. In our study we showcased two different modalities (small molecules, biologics) that have the potential to be further developed as anti-SEMA3C therapy in breast cancer. Success of developing a SEMA3C targeted therapy will depend on understanding structural biology of SEMA3C induced signaling- Worzfeld et al., (192) has showcased Plexin-B1 role in HER2+ breast cancer. We expanded this observation of SEMA3C co-receptors supporting SEMA3C induced signaling in ER+ BCa cells and TNBC cells using inhibitors which blocks SEMA3C interaction with its known receptors (Plexin-B1 and NRP1). The proposed mechanism of action would be, SEMA3C binds to Plexin-B1 and Neuropilin which leads to activation of tumor promoting signaling via Plexin intracellular domain or binding of SEMA3C receptor complex to RTKs in a cognate ligand independent manner which activates MAPK, AKT and other currently undiscovered pathways induced by SEMA3C in breast cancer. Until now, there is no crystal structure of SEMA3C receptor complex, therefore in our study we utilized SEMA3 family unique structural characteristics and other studies to propose binding regions on SEMA3C in form of surface map. With current AI based technologies we can predict these interactions and binding hotspots with a higher precision which can help us develop protein-protein interaction inhibitors (biologics and small molecules). 98 Combination therapies have emerged as a powerful approach for combating breast cancer (193), and our study suggests that targeting SEMA3C in combination with endocrine therapy/RTKi/ chemotherapy could be a highly effective strategy. We have demonstrated the synergistic and additive effects of B1SP and SEMA3C siRNA with different drugs currently administered in clinic. By selecting biologically relevant and non-lethal drug targets with the least side effects, we can ensure that patients receive the best possible treatment with minimal harm. SEMA3C-deficient mice are indistinguishable from their wild-type littermates, and Plexin-B1 deficient mice remain fertile and viable (155,194). This provides further evidence that SEMA3C inhibition could be a well-tolerated and relatively safe option for breast cancer patients and should be further explored in clinical trials as a potential therapeutic approach. Currently only known human disease associated with SEMA3C gene defect is Hirschsprung disease (195) which affects the large intestine (colon) and causes problems with passing stool but if intervened earlier can lead to full lives with milder impact on quality of life (196). In addition, there are few limitations of our studies which needs to be addressed and we propose future studies which can further build on current findings to holistically understand why and how SEMA3C plays a role in breast cancer which can aid us in moving from pre-clinical to clinical settings. 5.2 Limitation of this work A limitation of our study is the use of cell line models for ER+ and TNBC breast cancer. Cell line-specific phenomena are inherent to in-vitro-based experimentation. While we used multiple cell lines for each breast cancer subtype (in total five) which are widely used in breast cancer research and can provide valuable insights into cancer biology, they do not always fully recapitulate the complexity of the disease in vivo. Therefore, more research is needed in different 99 model systems to further verify the importance of these findings. Additionally, further studies with larger sample sizes providing us with updated clinical data are needed to confirm our findings and to explore the potential of SEMA3C as a biomarker and therapeutic target for ER+ breast cancer and TNBC. In general, results that are consistent across multiple cell lines and tissue types can help discriminate principles that are context-specific from those that can be more universally applied. Lupo et al., (197) suggested that SEMA3A and SEMA3C are preferentially expressed by cancer associated fibroblast in tumor microenvironment in PDAC which contributes to aggressive molecular subtype of PDAC as compared to mouse tumor organoids overexpressing semaphorins showed reduced tumor growth and lack of metastatic competence. Discordant function across different cell or tissue types can be explained by the fact that cells of different tissues are decorated with different proteins which in turn influence micro-environmental context and function. The biological activities of semaphorins are refined by proteolytic cleavage and post-translational modifications and are likely further fine-tuned by the repertoire of neuropilin and plexin receptors present on the recipient tumor cell. Studies in more diverse cell types (ER+/HER2+) will be useful in distinguishing between context-specific versus more widely applicable principles of Semaphorin 3C biology. Further, these studies will bring to light whether Semaphorin 3C can elicit different functions through different plexin members. 5.3 Future Studies For next steps we would like to expand on current findings by focusing on four key areas: a) mapping molecular mechanism of SEMA3C regulation b) identify binding partners of SEMA3C during signaling cascade c) Deep learning-based discovery of anti-SEMA3C small molecule inhibitor d) Conducting in-vivo studies to validate the in-vitro efficacy of B1SP and ALS. 100 In this study we only showcased upregulation and downregulation effect on SEMA3C mediated by ER pathway along with external ChIP seq data revealing ER binding sites on SEMA3C locus in MCF7 and T47D. However, there is need for studies showcasing exact binding location on SEMA3C locus by luciferase studies and potential co-factors that are involved in SEMA3C regulation in ER+ breast cancer. For luminal androgen receptor TNBC subtype, our suggestion is SEMA3C will be regulated by androgen receptor (AR) as we have previously provided the proof of AR regulation of SEMA3C (91). In case of other ER negative TNBC subtypes, it will be essential to first computationally analyze gene regulatory networks within these cells to narrow down to a top list of transcription factors that might be involved in SEMA3C regulation and perform in-vitro experiments to confirm the new findings. Epigenomic analysis via DNA-seq or ATAC-seq can further help us understand how pioneering transcription factors impact SEMA3C locus across different breast cancer subtypes and treatment conditions. In terms of SEMA3C signaling pathway, one of the limitations was the lack of evidence of upstream tyrosine kinase receptors (extracellular or intracellular) that might be playing a role in upregulation in activation of MAPK and AKT pathway after SEMA3C exogenous treatment or downregulation. In our previous studies in prostate cancer, we have provided evidence of interaction between different RTKs and SEMA3C-receptor complex. Another novel approach within ER+ breast cancer which can be tested is to identify whether secreted SEMA3C autocrine signaling mechanism has an impact on GPR30 (extracellular membrane bound ER) which is known to activate EGFR pathway (198) by conducting SEMA3C siRNA knockdown and protein-protein interaction studies. We believe conducting KinexTM antibody phosphorylation microarray and co-immunoprecipitation assays post SEMA3C exogenous treatment on breast cancer cells can provide us with holistic view of different RTKs that respond to SEMA3C 101 mediated signaling in breast cancer in absence of their ligand. Utilizing the findings from these genomic and proteomic studies will support us to stratify breast cancer patients which can benefit from anti-SEMA3C therapy and provide us insight into patient prognosis. We have showcased two established therapeutic modalities; small molecules and biologics to be utilized as SEMA3C inhibitors. Targeting SEMA3C and semaphorins would be more feasible due to their extracellular localization, in contrast to the challenge of targeting intracellular or nuclear proteins. The latter requires the drug to traverse one or two membranes, respectively, before reaching their desired target. Small molecule development against SEMA3C is still nascent stages as compared to B1SP and requires further experimentation before testing in in-vivo models. Within our study, we identified a potential binding site with significant impact on key structural binding process of SEMA3C complex formation and preliminary experiments revealed this approach feasible. Since when these experiments performed, there has been drastic changes that occurred within drug discovery research after introduction of new in house developed AI-based algorithm (e.g. Deep Docking proposed by Cherkasov lab) (173) to perform large in-silico screening which was deemed closer to impossible in the past. Therefore, a scientifically better approach will be to redock SEMA3C hotspot binding site, this time using deep docking algorithm to screen for compounds that might have been filtered out previously and utilize new full length SEMA3C structure proposed by alpha-fold. Simultaneously, we can perform site-directed mutagenesis studies on SEMA3C to identify additional hot-spot residues which can increase specificity of small molecules developed against SEMA3C against other SEMA3 family members. Here we tried to use as many distinct cell lines as possible with unique genotypes to analyze SEMA3C’s role in breast cancer in-vitro. As for the next steps we would like to validate 102 our findings in-vivo. There are two model system we can focus on, cell-line and patient derived xenografts (CDX and PDX) (199,200), CDX is relevant for studying aggressive malignant or pleural effusions as studied in MDA-MB-231 cell line but not all well characterized breast cancer cell lines (luminal A-MCF7, T47D; HER2+- SKBR3; TNBC- MDA-MB-468) can be studied in-vivo using this model (201). In CDX’s model for preclinical testing of novel agents and/or treatment schedules, site of transplantation should also be considered (202). Subcutaneous injection (ectopic), or by implanting cells in the mouse mammary gland (orthotopic) which is more complicated can provide distinct effect on microenvironment of these sites, particularly the tumor vasculature, significantly impacts tumor growth rate (203), drug delivery and therapeutic efficacy. They can be used for studying biological processes, and to some degree, metastatic potential but they have a poor record of predicting clinically effective therapies (204) and references therein). PDX utilize xenotransplantation of human cancer cells or tumor pieces into mice as it preserves many relevant features of the primary human tumor, including growth kinetics, histological features, behavioral characteristics (such as invasiveness and metastatic capacity) and most importantly, response to therapy (205–208). The success in establishing a sustainable resource of stably transplantable PDX models, which accurately preserve the characteristics of the original tumor, can be attributed to the utilization of hormone supplementation and the specific site of implantation, such as intraductal delivery (207). In terms of mice that can be used for these studies are NOD-SCID mice crossed with IL2γ receptor null mice allowing for a highest level of tumor engraftment, long lifespan, permissive for development of human immune system and stromal growth (209,210). 103 5.4 Concluding Remarks and Significance In summary, SEMA3C intersects with various nodes of breast cancer cell biology, either subtype agnostic or specific. These include the AKT and MAPK signaling pathways, ER-SEMA3C regulation axis, and the evasion of apoptosis, all of which contribute to cancer growth. Additionally, SEMA3C plays a role in poor patient survival in TNBC, and its targeting can lead to improving the efficacy of drugs currently in use for ER+ and TNBC. This thesis commenced with a discussion surrounding the importance of understanding SEMA3C’s role in driving another hormone dependent cancer apart from prostate cancer. The results demonstrate the functional consequences of cellular deregulation when SEMA3C levels increase due to ER mediated pathway or other mechanisms. This deregulation can initiate cellular programs that are not normally observed under physiological conditions, thereby destabilizing the cells. While it is tempting to consider SEMA3C as a mechanism for aggressive disease caused by various oncogenic factors, further studies using in-vivo models and clinical data are necessary for confirmation. In conclusion, this research highlights several novel aspects of SEMA3C signaling in different breast cancer subtypes that currently lack effective treatment options. We hope that this work stimulates discussions on SEMA3C's potential roles in breast cancer progression, as well as in other female-based cancers such as cervix and ovarian cancer and inspires the development of novel strategies to target it. 104 References 1. Hanahan D, Weinberg RA. Hallmarks of cancer: The next generation. Vol. 144, Cell. 2011. p. 646–74. 2. Hanahan D. Hallmarks of Cancer: New Dimensions. Vol. 12, Cancer Discovery. American Association for Cancer Research Inc.; 2022. p. 31–46. 3. Xu Y, Gong M, Wang Y, Yang Y, Liu S, Zeng Q. Global trends and forecasts of breast cancer incidence and deaths. Sci Data. 2023 May 27;10(1):334. 4. Nolan E, Lindeman GJ, Visvader JE. Deciphering breast cancer: from biology to the clinic. Vol. 186, Cell. Elsevier B.V.; 2023. p. 1708–28. 5. Orrantia-Borunda E, Anchondo-Nuñez P, Acuña-Aguilar LE, Octavio Gómez-Valles F, Adriana Ramírez-Valdespino C. Subtypes of Breast Cancer. In: Mayrovitz HN, editor. Breast Cancer. Brisbane: Exon Publications; 2022. 6. Herschkowitz JI, Simin K, Weigman VJ, Mikaelian I, Usary J, Hu Z, et al. Identification of conserved gene expression features between murine mammary carcinoma models and human breast tumors. Genome Biol. 2007 May 10;8(5). 7. Sørlie. Therese, Perou C, Tibshirani R, Aas T, Geisler S, Johnsen H, et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A. 2001 Sep 11;98(19):10869–74. 8. Koboldt DC, Fulton RS, McLellan MD, Schmidt H, Kalicki-Veizer J, McMichael JF, et al. Comprehensive molecular portraits of human breast tumours. Nature. 2012 Oct 4;490(7418):61–70. 9. Prat A, Parker J, Karginova O, Fan C, Livasy C, Herschkowitz J, et al. Phenotypic and molecular characterization of the claudin-low intrinsic subtype of breast cancer. Breast Cancer Research. 2010 Sep 2;12. 10. Ellis MJ, Ding L, Shen D, Luo J, Suman VJ, Wallis JW, et al. Whole-genome analysis informs breast cancer response to aromatase inhibition. Nature. 2012 Jun 21;486(7403):353–60. 11. Curtis C, Shah SP, Chin SF, Turashvili G, Rueda OM, Dunning MJ, et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature. 2012 Jun 21;486(7403):346–52. 12. Stephens PJ, Tarpey PS, Davies H, Van Loo P, Greenman C, Wedge DC, et al. The landscape of cancer genes and mutational processes in breast cancer. Nature. 2012 Jun 21;486(7403):400–4. 13. Banerji S, Cibulskis K, Rangel-Escareno C, Brown KK, Carter SL, Frederick AM, et al. Sequence analysis of mutations and translocations across breast cancer subtypes. Nature. 2012 Jun 21;486(7403):405–9. 14. Shah SP, Roth A, Goya R, Oloumi A, Ha G, Zhao Y, et al. The clonal and mutational evolution spectrum of primary triple-negative breast cancers. Nature. 2012 Jun 21;486(7403):395–9. 15. Ciriello G, Gatza ML, Beck AH, Wilkerson MD, Rhie SK, Pastore A, et al. Comprehensive Molecular Portraits of Invasive Lobular Breast Cancer. Cell. 2015 Oct 8;163(2):506–19. 105 16. Ng CKY, Martelotto LG, Gauthier A, Wen HC, Piscuoglio S, Lim RS, et al. Intra-tumor genetic heterogeneity and alternative driver genetic alterations in breast cancers with heterogeneous HER2 gene amplification. Genome Biol. 2015 May 22;16(1). 17. Pereira B, Chin SF, Rueda OM, Vollan HKM, Provenzano E, Bardwell HA, et al. The somatic mutation profiles of 2,433 breast cancers refines their genomic and transcriptomic landscapes. Nat Commun. 2016 May 10;7. 18. Miziak P, Baran M, Błaszczak E, Przybyszewska-Podstawka A, Kałafut J, Smok-Kalwat J, et al. Estrogen Receptor Signaling in Breast Cancer. Vol. 15, Cancers. Multidisciplinary Digital Publishing Institute (MDPI); 2023. 19. Mehra R, Varambally S, Ding L, Shen R, Sabel MS, Ghosh D, et al. Identification of GATA3 as a breast cancer prognostic marker by global gene expression meta-analysis. Cancer Res. 2005 Dec 15;65(24):11259–64. 20. Brisken C. Progesterone signalling in breast cancer: A neglected hormone coming into the limelight. Vol. 13, Nature Reviews Cancer. 2013. p. 385–96. 21. Mohammed H, Russell IA, Stark R, Rueda OM, Hickey TE, Tarulli GA, et al. Progesterone receptor modulates ERα action in breast cancer. Nature. 2015 Jul 16;523(7560):313–7. 22. Hickey TE, Selth LA, Chia KM, Laven-Law G, Milioli HH, Roden D, et al. The androgen receptor is a tumor suppressor in estrogen receptor–positive breast cancer. Nat Med. 2021 Feb 1;27(2):310–20. 23. Yarden Y, Sliwkowski M. Untangling the ErbB signalling network. Nature Reviews Molecular Cell Biology . 2001 Feb 1;2:127–37. 24. Hoxhaj G, Manning BD. The PI3K–AKT network at the interface of oncogenic signalling and cancer metabolism. Vol. 20, Nature Reviews Cancer. Nature Research; 2020. p. 74–88. 25. Semba T, Wang X, Xie X, Cohen EN, Reuben JM, Dalby KN, et al. Identification of the JNK-Active Triple-Negative Breast Cancer Cluster Associated with an Immunosuppressive Tumor Microenvironment. J Natl Cancer Inst. 2022 Jan 1;114(1):97–108. 26. Burstein HJ. Systemic Therapy for Estrogen Receptor–Positive, HER2-Negative Breast Cancer. New England Journal of Medicine. 2020 Dec 24;383(26):2557–70. 27. Cuzick J. Preventive therapy for cancer. Lancet Oncol. 2017 Aug;18(8):472–82. 28. Hernando C, Ortega-Morillo B, Tapia M, Moragón S, Martínez MT, Eroles P, et al. Oral selective estrogen receptor degraders (Serds) as a novel breast cancer therapy: Present and future from a clinical perspective. Vol. 22, International Journal of Molecular Sciences. MDPI; 2021. 29. Slamon D, Leyland-Jones B, Shak S, Fuchs H, Paton V, Bajamonde A, et al. Use of Chemotherapy plus a Monoclonal Antibody against HER2 for Metastatic Breast Cancer That Overexpresses HER2. N Engl J Med. 2011 Mar 15;344:783–92. 30. Swain SM, Shastry M, Hamilton E. Targeting HER2-positive breast cancer: advances and future directions. Vol. 22, Nature Reviews Drug Discovery. Nature Research; 2023. p. 101–26. 31. Verma S, Miles D, Gianni L, Krop IE, Welslau M, Baselga J, et al. Trastuzumab Emtansine for HER2-Positive Advanced Breast Cancer. New England Journal of Medicine. 2012 Nov 8;367(19):1783–91. 106 32. Cortés J, Kim SB, Chung WP, Im SA, Park YH, Hegg R, et al. Trastuzumab Deruxtecan versus Trastuzumab Emtansine for Breast Cancer. New England Journal of Medicine. 2022 Mar 24;386(12):1143–54. 33. Modi S, Saura C, Yamashita T, Park YH, Kim SB, Tamura K, et al. Trastuzumab Deruxtecan in Previously Treated HER2-Positive Breast Cancer. New England Journal of Medicine. 2020 Feb 13;382(7):610–21. 34. Paik S, Kim C, Wolmark N. HER2 Status and Benefit from Adjuvant Trastuzumab in Breast Cancer. N Engl J Med. 2008 Mar 27;358:1409–11. 35. Modi S, Jacot W, Yamashita T, Sohn J, Vidal M, Tokunaga E, et al. Trastuzumab Deruxtecan in Previously Treated HER2-Low Advanced Breast Cancer. New England Journal of Medicine. 2022 Jul 7;387(1):9–20. 36. Mittendorf EA, Zhang H, Barrios CH, Saji S, Jung KH, Hegg R, et al. Neoadjuvant atezolizumab in combination with sequential nab-paclitaxel and anthracycline-based chemotherapy versus placebo and chemotherapy in patients with early-stage triple-negative breast cancer (IMpassion031): a randomised, double-blind, phase 3 trial. The Lancet. 2020 Oct 10;396(10257):1090–100. 37. Schmid P, Cortes J, Dent R, Pusztai L, McArthur H, Kümmel S, et al. Event-free Survival with Pembrolizumab in Early Triple-Negative Breast Cancer. New England Journal of Medicine. 2022 Feb 10;386(6):556–67. 38. Litton JK, Rugo HS, Ettl J, Hurvitz SA, Gonçalves A, Lee KH, et al. Talazoparib in Patients with Advanced Breast Cancer and a Germline BRCA Mutation . New England Journal of Medicine. 2018 Aug 23;379(8):753–63. 39. Robson M, Im SA, Senkus E, Xu B, Domchek SM, Masuda N, et al. Olaparib for Metastatic Breast Cancer in Patients with a Germline BRCA Mutation . New England Journal of Medicine. 2017 Aug 10;377(6):523–33. 40. Bardia A, Hurvitz SA, Tolaney SM, Loirat D, Punie K, Oliveira M, et al. Sacituzumab Govitecan in Metastatic Triple-Negative Breast Cancer. New England Journal of Medicine. 2021 Apr 22;384(16):1529–41. 41. Garrido-Castro AC, Lin NU, Polyak K. Insights into molecular classifications of triple-negative breast cancer: Improving patient selection for treatment. Vol. 9, Cancer Discovery. American Association for Cancer Research Inc.; 2019. p. 176–98. 42. Jeselsohn R, Buchwalter G, De Angelis C, Brown M, Schiff R. ESR1 mutations-a mechanism for acquired endocrine resistance in breast cancer. Vol. 12, Nature Reviews Clinical Oncology. Nature Publishing Group; 2015. p. 573–83. 43. Griffiths JI, Chen J, Cosgrove PA, O’Dea A, Sharma P, Ma C, et al. Serial single-cell genomics reveals convergent subclonal evolution of resistance as patients with early-stage breast cancer progress on endocrine plus CDK4/6 therapy. Nat Cancer. 2021 Jun 1;2(6):658–71. 44. Kingston B, Cutts RJ, Bye H, Beaney M, Walsh-Crestani G, Hrebien S, et al. Genomic profile of advanced breast cancer in circulating tumour DNA. Nat Commun. 2021 Dec 1;12(1). 45. Razavi P, Chang MT, Xu G, Bandlamudi C, Ross DS, Vasan N, et al. The Genomic Landscape of Endocrine-Resistant Advanced Breast Cancers. Cancer Cell. 2018 Sep 10;34(3):427-438.e6. 107 46. Juric D, Castel P, Griffith M, Griffith OL, Won HH, Ellis H, et al. Convergent loss of PTEN leads to clinical resistance to a PI(3)Kα inhibitor. Nature. 2015 Feb 12;518(7538):240–4. 47. Baselga J, Gómez P, Greil R, Braga S, Climent MA, Wardley AM, et al. Randomized phase II study of the anti-epidermal growth factor receptor monoclonal antibody cetuximab with cisplatin versus cisplatin alone in patients with metastatic triple-negative breast cancer. Journal of Clinical Oncology. 2013 Jul 10;31(20):2586–92. 48. Ghersi D, Wilcken N, Simes RJ. A systematic review of taxane-containing regimens for metastatic breast cancer. Vol. 93, British Journal of Cancer. 2005. p. 293–301. 49. Hanahan D, Weinberg RA. The Hallmarks of Cancer Review evolve progressively from normalcy via a series of pre. Vol. 100, Cell. 2000. 50. Kolodkin AL, Matthes DJ, Goodman CS. The semaphorin Genes Encode a Family of Transmembrane and Secreted Growth Cone Guidance Molecules. Vol. 75, Cell. 1993. 51. Luo Y, Raible D, Raper JA. Collapsin: A Protein in Brain That Induces the Collapse and Paralysis of Neuronal Growth Cones. Vol. 75, Cell. 1993. 52. Kruger RP, Aurandt J, Guan KL. Semaphorins command cells to move. Vol. 6, Nature Reviews Molecular Cell Biology. 2005. p. 789–800. 53. Behar O, Golden J, Mashimo H, Schoen F, Fishman M. Semaphorin III is needed for normal patterning and growth of nerves, bones and heart. Nature. 1996 Oct 10;383:525–8. 54. Hall K, Boumsell L, Schultze J, Boussiotis V, Dorfman D, Cardoso A, et al. Human CD100, a novel leukocyte semaphorin that promotes B-cell aggregation and differentiation. Proc Natl Acad Sci U S A. 1996 Oct;93(21):11780–5. 55. Yazdani U, Terman JR. The semaphorins. Genome Biol. 2006 Mar 30;211. 56. Hu S, Zhu L. Semaphorins and Their Receptors: From Axonal Guidance to Atherosclerosis. Front Physiol. 2018 Oct 12;9. 57. Casazza A, Finisguerra V, Capparuccia L, Camperi A, Swiercz JM, Rizzolio S, et al. Sema3E-Plexin D1 signaling drives human cancer cell invasiveness and metastatic spreading in mice. Journal of Clinical Investigation. 2010 Aug 2;120(8):2684–98. 58. Giordano S, Corso S, Conrotto P, Artigiani S, Gilestro G, Barberis D, et al. The semaphorin 4D receptor controls invasive growth by coupling with Met. Nat Cell Biol. 2002 Sep;4(9):720–4. 59. Chen H, Ché A, He Z. Neuropilin-2, a Novel Member of the Neuropilin Family, Is a High Affinity Receptor for the Semaphorins Sema E and Sema IV but Not Sema III. Vol. 19, Neuron. 1997. 60. He Z, Tessier-Lavigne M. Neuropilin Is a Receptor for the Axonal Chemorepellent Semaphorin III. Vol. 90, Cell. 1997. 61. Kolodkin AL, Levengood D V, Giger RJ, Ginty DD. Neuropilin Is a Semaphorin III Receptor. Vol. 90, Cell. 1997. 62. Tamagone L, Artigiani S, Chen H, He Z, Ming GL, Song H jun, et al. Plexins Are a Large Family of Receptors for Transmembrane, Secreted, and GPI-Anchored Semaphorins in Vertebrates. Cell. 1999 Oct 1;99(1):71–80. 63. Janssen BJC, Malinauskas T, Weir GA, Cader MZ, Siebold C, Jones EY. Neuropilins lock secreted semaphorins onto plexins in a ternary signaling complex. Nat Struct Mol Biol. 2012 Dec;19(12):1293–9. 108 64. Rehman M, Tamagnone L. Semaphorins in cancer: Biological mechanisms and therapeutic approaches. Vol. 24, Seminars in Cell and Developmental Biology. Elsevier Ltd; 2013. p. 179–89. 65. Serini G, Valdembri D, Zanivan S, Morterra G, Burkhardt C, Caccavari F, et al. Class 3 semaphorins control vascular morphogenesis by inhibiting integrin function. Nature. 2003 Jul 24;424(6947):391–7. 66. Sadanandam A, Varney ML, Singh S, Ashour AE, Moniaux N, Deb S, et al. High gene expression of semaphorin 5A in pancreatic cancer is associated with tumor growth, invasion and metastasis. Int J Cancer. 2010 Sep 1;127(6):1373–83. 67. Banu N, Teichman J, Dunlap‐Brown M, Villegas G, Tufro A, Banu N, et al. Semaphorin 3C regulates endothelial cell function by increasing integrin activity. The FASEB Journal. 2006 Oct;20(12):2150–2. 68. Conrotto P, Valdembri D, Corso S, Serini G, Tamagnone L, Comoglio PM, et al. Sema4D induces angiogenesis through Met recruitment by Plexin B1. Blood. 2005 Jun 1;105(11):4321–9. 69. Castro-Rivera E, Ran S, Brekken RA, Minna JD. Semaphorin 3B inhibits the phosphatidylinositol 3-kinase/Akt pathway through neuropilin-1 in lung and breast cancer cells. Cancer Res. 2008 Oct 15;68(20):8295–303. 70. Castro-Rivera E, Ran S, Thorpe P, Minna JD. Semaphorin 3B (SEMA3B) induces apoptosis in lung and breast cancer, whereas VEGF165 antagonizes this effect. Proceedings of the National Academy of Sciences. 2004 Aug 3;101(31):11432–7. 71. Tomizawa Y, Sekido Y, Kondo M, Gao B, Yokota J, Roche J, et al. Inhibition of lung cancer cell growth and induction of apoptosis after reexpression of 3p21.3 candidate tumor suppressor gene SEMA3B. Proceedings of the National Academy of Sciences. 2001 Nov 20;98(24):13954–9. 72. Zhang X, Klamer B, Li J, Fernandez S, Li L. A pan-cancer study of class-3 semaphorins as therapeutic targets in cancer. BMC Med Genomics. 2020 Apr 3;13. 73. Yang W, Hu J, Uemura A, Tetzlaff F, Augustin HG, Fischer A. Semaphorin‐3C signals through Neuropilin‐1 and PlexinD1 receptors to inhibit pathological angiogenesis. EMBO Mol Med. 2015 Oct;7(10):1267–84. 74. Nasarre P, Gemmill RM, Drabkin HA. The emerging role of class-3 semaphorins and their neuropilin receptors in oncology. Vol. 7, OncoTargets and Therapy. Dove Medical Press Ltd.; 2014. p. 1663–87. 75. Poschel AW, Adams RH, Betz H. Murine Semaphorin D/Collapsin Is a Member of a Diverse Gene Family and Creates Domains Inhibitory for Axonal Extension. Vol. 14, Neuron. 1995. 76. Gitler AD, Lu MM, Epstein JA. PlexinD1 and semaphorin signaling are required in endothelial cells for cardiovascular development. Dev Cell. 2004 Jul;7(1):107–16. 77. Christie SM, Hao J, Tracy E, Buck M, Yu JS, Smith AW. Interactions between semaphorins and plexin-neuropilin receptor complexes in the membranes of live cells. Journal of Biological Chemistry. 2021 Aug 1;297(2). 78. Peacock JW, Takeuchi A, Hayashi N, Liu L, Tam KJ, Al Nakouzi N, et al. SEMA 3C drives cancer growth by transactivating multiple receptor tyrosine kinases via Plexin B1 . EMBO Mol Med. 2018 Feb;10(2):219–38. 109 79. Tamagnone L, Comoglio PM. Signalling by semaphorin receptors: cell guidance and beyond. Trends Cell Biol. 2000 Sep;10(9):377–83. 80. Yamada T, Endo R, Gotoh M, Hirohashi S. Identification of semaphorin E as a non-MDR drug resistance gene of human cancers. Proceedings of the National Academy of Sciences. 1997 Dec 23;94(26):14713–8. 81. Tsherniak A, Vazquez F, Montgomery PG, Weir BA, Kryukov G, Cowley GS, et al. Defining a Cancer Dependency Map. Cell. 2017 Jul 27;170(3):564-576.e16. 82. Ellis MJ, Perou CM. The genomic landscape of breast cancer as a therapeutic roadmap. Cancer Discov. 2013 Jan;3(1):27–34. 83. Hui DHF, Tam KJ, Jiao IZF, Ong CJ. Semaphorin 3C as a therapeutic target in prostate and other cancers. Vol. 20, International Journal of Molecular Sciences. MDPI AG; 2019. 84. Xu X, Zhao Z, Guo S, Li J, Liu S, You Y, et al. Increased semaphorin 3c expression promotes tumor growth and metastasis in pancreatic ductal adenocarcinoma by activating the ERK1/2 signaling pathway. Cancer Lett. 2017 Jul 1;397:12–22. 85. Tomizawa S, Takano S, Eto R, Takayashiki T, Kuboki S, Ohtsuka M. Semaphorin 3 C enhances putative cancer stemness and accelerates peritoneal dissemination in pancreatic cancer. Cancer Cell Int. 2023 Dec 1;23(1). 86. Zhang D, Lindstrom A, Kim EJ, Hwang C Il, Hall ML, Lin TY, et al. SEMA3C Supports Pancreatic Cancer Progression by Regulating the Autophagy Process and Tumor Immune Microenvironment. Front Oncol. 2022 Jun 16;12. 87. Miyato H, Tsuno NH, Kitayama J. Semaphorin 3C is involved in the progression of gastric cancer. Cancer Sci. 2012 Nov;103(11):1961–6. 88. Liu R, Shuai Y, Luo J, Zhang Z. SEMA3C Promotes Cervical Cancer Growth and Is Associated With Poor Prognosis. Front Oncol. 2019 Oct 9;9. 89. Feng X, Zhu K, Liu J, Chen J, Tang J, Liang Y, et al. The evaluative value of Sema3C and MFN2 co-expression detected by immunohistochemistry for prognosis in hepatocellular carcinoma patients after hepatectomy. Onco Targets Ther. 2016 May 30;9:3213–21. 90. Zhang H, Ma H, Zhang W, Duan D, Zhu G, Cao W, et al. Increased Expression of Sema3C Indicates a Poor Prognosis and Is Regulated by miR-142-5p in Glioma. Vol. 43, Biol. Pharm. Bull. 2020. 91. Tam KJ, Dalal K, Hsing M, Cheng CW, Khosravi S, Yenki P, et al. Androgen receptor transcriptionally regulates semaphorin 3C in a GATA2-dependent manner. Oncotarget. 2017 Feb 7;8(6):9617–33. 92. Reymann S, Borlak J. Transcription profiling of lung adenocarcinomas of c-myc-transgenic mice: Identification of the c-myc regulatory gene network. BMC Syst Biol. 2008 May 22;2. 93. Liao YL, Sun YM, Chau GY, Chau YP, Lai TC, Wang JL, et al. Identification of SOX4 target genes using phylogenetic footprinting-based prediction from expression microarrays suggests that overexpression of SOX4 potentiates metastasis in hepatocellular carcinoma. Oncogene. 2008 Sep 18;27(42):5578–89. 94. Mello SS, Valente LJ, Raj N, Seoane JA, Flowers BM, McClendon J, et al. A p53 Super-tumor Suppressor Reveals a Tumor Suppressive p53-Ptpn14-Yap Axis in Pancreatic Cancer. Cancer Cell. 2017 Oct 9;32(4):460-473.e6. 95. Yoon H, Liyanarachchi S, Wright FA, Davuluri R, Lockman JC, de la Chapelle A, et al. Gene expression profiling of isogenic cells with different TP53 gene dosage reveals 110 numerous genes that are affected by TP53 dosage and identifies CSPG2 as a direct target of p53. Proceedings of the National Academy of Sciences. 2002 Nov 26;99(24):15632–7. 96. Cole-Healy Z, Vergani P, Hunter K, Brown NJ, Reed MWR, Staton CA. The relationship between semaphorin 3C and microvessel density in the progression of breast and oral neoplasia. Exp Mol Pathol. 2015;99(1):19–24. 97. Uhlen M, Zhang C, Lee S, Sjöstedt E, Fagerberg L, Bidkhori G, et al. A pathology atlas of the human cancer transcriptome. Science (1979). 2017 Aug 18;357(6352). 98. Tang Z, Kang B, Li C, Chen T, Zhang Z. GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res. 2019 Jul 1;47(W1):W556–60. 99. Pereira B, Chin SF, Rueda OM, Vollan HKM, Provenzano E, Bardwell HA, et al. The somatic mutation profiles of 2,433 breast cancers refines their genomic and transcriptomic landscapes. Nat Commun. 2016 May 10;7. 100. Peacock JW, Palmer J, Fink D, Ip S, Pietras EM, Mui ALF, et al. PTEN Loss Promotes Mitochondrially Dependent Type II Fas-Induced Apoptosis via PEA-15. Mol Cell Biol. 2009 Mar 1;29(5):1222–34. 101. Malik MFA, Satherley LK, Davies EL, Ye L, Jiang WG. Expression of Semaphorin 3C in Breast Cancer and its Impact on Adhesion and Invasion of Breast Cancer Cells. Anticancer Res. 2016 Mar;36(3):1281–6. 102. Weinstein IB. Addiction to Oncogenes--the Achilles Heal of Cancer. Science (1979). 2002 Jul 5;297(5578):63–4. 103. Regad T. Targeting RTK signaling pathways in cancer. Vol. 7, Cancers. MDPI AG; 2015. p. 1758–84. 104. Butti R, Das S, Gunasekaran VP, Yadav AS, Kumar D, Kundu GC. Receptor tyrosine kinases (RTKs) in breast cancer: Signaling, therapeutic implications and challenges. Vol. 17, Molecular Cancer. BioMed Central Ltd.; 2018. 105. Risbridger GP, Davis ID, Birrell SN, Tilley WD. Breast and prostate cancer: more similar than different. Nat Rev Cancer. 2010 Mar 11;10(3):205–12. 106. Giaquinto AN, Sung H, Miller KD, Kramer JL, Newman LA, Minihan A, et al. Breast Cancer Statistics, 2022. CA Cancer J Clin. 2022 Nov;72(6):524–41. 107. Acheampong T, Kehm RD, Terry MB, Argov EL, Tehranifar P. Incidence Trends of Breast Cancer Molecular Subtypes by Age and Race/Ethnicity in the US from 2010 to 2016. JAMA Netw Open. 2020 Aug 17;3(8):E2013226. 108. Chi D, Singhal H, Li L, Xiao T, Liu W, Pun M, et al. Estrogen receptor signaling is reprogrammed during breast tumorigenesis. Proc Natl Acad Sci U S A. 2019;166(23):11437–43. 109. Clusan L, Ferrière F, Flouriot G, Pakdel F. A Basic Review on Estrogen Receptor Signaling Pathways in Breast Cancer. Vol. 24, International Journal of Molecular Sciences. Multidisciplinary Digital Publishing Institute (MDPI); 2023. 110. Hanker AB, Sudhan DR, Arteaga CL. Overcoming Endocrine Resistance in Breast Cancer. Vol. 37, Cancer Cell. Cell Press; 2020. p. 496–513. 111. Nardone A, De Angelis C, Trivedi M V., Osborne CK, Schiff R. The changing role of ER in endocrine resistance. Breast. 2015 Nov 1;24:S60–6. 111 112. Shiino S, Kinoshita T, Yoshida M, Jimbo K, Asaga S, Takayama S, et al. Prognostic Impact of Discordance in Hormone Receptor Status Between Primary and Recurrent Sites in Patients With Recurrent Breast Cancer. Clin Breast Cancer. 2016 Aug 1;16(4):e133–40. 113. Hartkopf AD, Grischke EM, Brucker SY. Endocrine-Resistant Breast Cancer: Mechanisms and Treatment. Vol. 15, Breast Care. S. Karger AG; 2020. p. 347–54. 114. Dai X, Cheng H, Bai Z, Li J. Breast cancer cell line classification and Its relevance with breast tumor subtyping. Vol. 8, Journal of Cancer. Ivyspring International Publisher; 2017. p. 3131–41. 115. Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, et al. The cBio Cancer Genomics Portal: An open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012 May;2(5):401–4. 116. Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, et al. Integrative Analysis of Complex Cancer Genomics and Clinical Profiles Using the cBioPortal. Sci Signal. 2013 Apr 2;6(269). 117. Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, et al. NCBI GEO: Archive for functional genomics data sets - Update. Nucleic Acids Res. 2013 Jan 1;41(D1). 118. Curtis C, Shah SP, Chin SF, Turashvili G, Rueda OM, Dunning MJ, et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature. 2012 Jun 21;486(7403):346–52. 119. Grossman RL, Heath AP, Ferretti V, Varmus HE, Lowy DR, Kibbe WA, et al. Toward a Shared Vision for Cancer Genomic Data. New England Journal of Medicine. 2016;375(12):1109–12. 120. Elias D, Vever H, Lænkholm A V., Gjerstorff MF, Yde CW, Lykkesfeldt AE, et al. Gene expression profiling identifies FYN as an important molecule in tamoxifen resistance and a predictor of early recurrence in patients treated with endocrine therapy. Oncogene. 2014 May 26;34(15):1919–27. 121. Carroll JS, Meyer CA, Song J, Li W, Geistlinger TR, Eeckhoute J, et al. Genome-wide analysis of estrogen receptor binding sites. Nat Genet. 2006 Nov 23;38(11):1289–97. 122. Al Saleh S, Al Mulla F, Luqmani YA. Estrogen receptor silencing induces epithelial to mesenchymal transition in human breast cancer cells. PLoS One. 2011;6(6). 123. Chang EC, Frasor J, Komm B, Katzenellenbogen BS. Impact of estrogen receptor β on gene networks regulated by estrogen receptor α in breast cancer cells. Endocrinology. 2006;147(10):4831–42. 124. Cheng R, Qi L, Kong X, Wang Z, Fang Y, Wang J. Identification of the Significant Genes Regulated by Estrogen Receptor in Estrogen Receptor-Positive Breast Cancer and Their Expression Pattern Changes When Tamoxifen or Fulvestrant Resistance Occurs. Front Genet. 2020 Sep 29;11. 125. Oki S, Ohta T, Shioi G, Hatanaka H, Ogasawara O, Okuda Y, et al. Ch IP ‐Atlas: a data‐mining suite powered by full integration of public Ch IP ‐seq data . EMBO Rep. 2018 Dec;19(12). 126. Abugessaisa I, Kasukawa T. Practical Guide to Life Science Databases. Practical Guide to Life Science Databases. Springer Nature; 2022. 1–224 p. 112 127. Moore JE, Purcaro MJ, Pratt HE, Epstein CB, Shoresh N, Adrian J, et al. Expanded encyclopaedias of DNA elements in the human and mouse genomes. . Nature. 2020;583(7818):699–710. 128. Castro-Mondragon JA, Riudavets-Puig R, Rauluseviciute I, Berhanu Lemma R, Turchi L, Blanc-Mathieu R, et al. JASPAR 2022: The 9th release of the open-access database of transcription factor binding profiles. Nucleic Acids Res. 2022 Jan 7;50(D1):D165–73. 129. Pagano MT, Ortona E, Dupuis ML. A Role for Estrogen Receptor alpha36 in Cancer Progression. Vol. 11, Frontiers in Endocrinology. Frontiers Media S.A.; 2020. 130. Miricescu D, Totan A, Stanescu-Spinu II, Badoiu SC, Stefani C, Greabu M. PI3K/AKT/mTOR Signaling Pathway in Breast Cancer: From Molecular Landscape to Clinical Aspects. Int J Mol Sci. 2020 Dec 26;22(1):173. 131. Zheng S, Wang W, Aldahdooh J, Malyutina A, Shadbahr T, Tanoli Z, et al. SynergyFinder Plus: Toward Better Interpretation and Annotation of Drug Combination Screening Datasets. Genomics Proteomics Bioinformatics. 2022 Jun 1;20(3):587–96. 132. Leung E, Kannan N, Krissansen GW, Findlay MP, Baguley BC. MCF-7 breast cancer cells selected for tamoxifen resistance acquire new phenotypes differing in DNA content, phospho-HER2 and PAX2 expression, and rapamycin sensitivity. Cancer Biol Ther. 2010 May 1;9(9):717–24. 133. Cheng GJ, Leung EY, Singleton DC. In vitro breast cancer models for studying mechanisms of resistance to endocrine therapy. Vol. 3, Exploration of Targeted Anti-tumor Therapy. Open Exploration Publishing Inc; 2022. p. 297–320. 134. Borri F, Granaglia A. Pathology of triple negative breast cancer. Vol. 72, Seminars in Cancer Biology. Academic Press; 2021. p. 136–45. 135. Yang R, Li Y, Wang H, Qin T, Yin X, Ma X. Therapeutic progress and challenges for triple negative breast cancer: targeted therapy and immunotherapy. Vol. 3, Molecular Biomedicine. Springer; 2022. 136. Agarwal G, Nanda G, Lal P, Mishra A, Agarwal A, Agrawal V, et al. Outcomes of Triple-Negative Breast Cancers (TNBC) Compared with Non-TNBC: Does the Survival Vary for All Stages? World J Surg. 2016 Jun 1;40(6):1362–72. 137. Dent R, Trudeau M, Pritchard KI, Hanna WM, Kahn HK, Sawka CA, et al. Triple-negative breast cancer: Clinical features and patterns of recurrence. Clinical Cancer Research. 2007 Aug 1;13(15):4429–34. 138. Lehmann BD, Shyr Y, Pietenpol JA, Lehmann BD, Bauer JA, Chen X, et al. Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. Journal of Clinical Investigation. 2011;121(7):2750–67. 139. Győrffy B. Survival analysis across the entire transcriptome identifies biomarkers with the highest prognostic power in breast cancer. Comput Struct Biotechnol J. 2021 Jan 1;19:4101–9. 140. Polley MYC, Leon-Ferre RA, Leung S, Cheng A, Gao D, Sinnwell J, et al. A clinical calculator to predict disease outcomes in women with triple-negative breast cancer. Vol. 185, Breast Cancer Research and Treatment. Springer; 2021. p. 557–66. 141. Han HS, Vikas P, Costa RLB, Jahan N, Taye A, Stringer-Reasor EM. Early-Stage Triple-Negative Breast Cancer Journey: Beginning, End, and Everything in Between. American Society of Clinical Oncology Educational Book. 2023 Jun;(43). 113 142. O’Reilly D, Sendi M Al, Kelly CM. Overview of recent advances in metastatic triple negative breast cancer. World J Clin Oncol. 2021 Mar 24;12(3):164–82. 143. Lefebvre C, Allan AL. Anti-proliferative and anti-migratory effects of EGFR and c-Met tyrosine kinase inhibitors in triple negative breast cancer cells. Precis Cancer Med. 2021 Mar 1;4(March). 144. Hill DP, Harper A, Malcolm J, McAndrews MS, Mockus SM, Patterson SE, et al. Cisplatin-resistant triple-negative breast cancer subtypes: Multiple mechanisms of resistance. BMC Cancer. 2019 Nov 4;19(1). 145. Nakai K, Hung MC, Yamaguchi H. A perspective on anti-EGFR therapies targeting triple-negative breast cancer. Am J Cancer Res. 2016;6(8):1609–23. 146. Tolaney SM, Ziehr DR, Guo H, Ng MR, Barry WT, Higgins MJ, et al. Phase II and Biomarker Study of Cabozantinib in Metastatic Triple-Negative Breast Cancer Patients. Oncologist. 2017 Jan 1;22(1):25–32. 147. Hwangbo H, Patterson SC, Dai A, Plana D, Palmer AC. Additivity predicts the efficacy of most approved combination therapies for advanced cancer. Nat Cancer. 2023; 148. Hollestelle A, Elstrodt F, Nagel JHA, Kallemeijn WW, Schutte M. Phosphatidylinositol-3-OH kinase or RAS pathway mutations in human breast cancer cell lines. Molecular Cancer Research. 2007 Feb;5(2):195–201. 149. Marty B, Maire V, Gravier E, Rigaill G, Vincent-Salomon A, Kappler M, et al. Frequent PTEN genomic alterations and activated phosphatidylinositol 3-kinase pathway in basal-like breast cancer cells. Breast Cancer Research. 2008 Dec 3;10(6). 150. Umemura S, Yoshida S, Ohta Y, Naito K, Osamura RY, Tokuda Y. Increased phosphorylation of Akt in triple-negative breast cancers. Cancer Sci. 2007 Dec;98(12):1889–92. 151. Tam KJ, Hui DHF, Lee WW, Dong M, Tombe T, Jiao IZF, et al. Semaphorin 3 C drives epithelial-to-mesenchymal transition, invasiveness, and stem-like characteristics in prostate cells. Sci Rep. 2017 Sep 13;7(1):11501. 152. Newton EE, Mueller LE, Treadwell SM, Morris CA, Machado HL. Molecular Targets of Triple-Negative Breast Cancer: Where Do We Stand? Vol. 14, Cancers. MDPI; 2022. 153. Escudero-Esparza. PGF isoforms, PLGF-1 and PGF-2 and the PGF receptor, neuropilin, in human breast cancer: Prognostic significance. Oncol Rep. 2009 Dec 28;23(2). 154. Tang YH, Rockstroh A, Sokolowski KA, Lynam LR, Lehman M, Thompson EW, et al. Neuropilin-1 is over-expressed in claudin-low breast cancer and promotes tumor progression through acquisition of stem cell characteristics and RAS/MAPK pathway activation. Breast Cancer Research. 2022 Dec 1;24(1). 155. Feiner L, Webber A, Brown C, Lu MM, Jia L, Feinstein P, et al. Targeted disruption of semaphorin 3C leads to persistent truncus arteriosus and aortic arch interruption. Development. 2001 Aug;128(16):3061–70. 156. Lee CCW, Munuganti RSN, Peacock JW, Dalal K, Jiao IZF, Shepherd A, et al. Targeting Semaphorin 3C in Prostate Cancer with Small Molecules. J Endocr Soc. 2018 Dec 1;2(12):1381–94. 157. Ivanov AA, Khuri FR, Fu H. Targeting protein-protein interactions as an anticancer strategy. Vol. 34, Trends in Pharmacological Sciences. 2013. p. 393–400. 114 158. Jin L, Wang W, Fang G. Targeting protein-protein interaction by small molecules. Vol. 54, Annual Review of Pharmacology and Toxicology. Annual Reviews Inc.; 2014. p. 435–56. 159. Janssen BJC, Robinson RA, Pérez-Brangulý́ F, Bell CH, Mitchell KJ, Siebold C, et al. Structural basis of semaphoring-plexin signalling. Nature. 2010 Oct 28;467(7319):1118–22. 160. Nogi T, Yasui N, Mihara E, Matsunaga Y, Noda M, Yamashita N, et al. Structural basis for semaphorin signalling through the plexin receptor. Nature. 2010 Oct 28;467(7319):1123–7. 161. Antipenko A, Himanen JP, van Leyen K, Nardi-Dei V, Lesniak J, Barton WA, et al. Structure of the Semaphorin-3A Receptor Binding Module. Neuron. 2003 Aug;39(4):589–98. 162. Rose PW, Bi C, Bluhm WF, Christie CH, Dimitropoulos D, Dutta S, et al. The RCSB Protein Data Bank: New resources for research and education. Nucleic Acids Res. 2013 Jan 1;41(D1). 163. Chemical Computing Group. Molecular Operating Environment. Montréal; 2018. 164. Schrodinger L. Maestro. New York, NY, USA; 2016. 165. Sterling T, Irwin JJ. ZINC 15 - Ligand Discovery for Everyone. J Chem Inf Model. 2015 Nov 23;55(11):2324–37. 166. Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, et al. Glide: A New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy. J Med Chem. 2004 Mar 25;47(7):1739–49. 167. McGann M. FRED and HYBRID docking performance on standardized datasets. J Comput Aided Mol Des. 2012;26(8):897–906. 168. Neves MAC, Totrov M, Abagyan R. Docking and scoring with ICM: The benchmarking results and strategies for improvement. J Comput Aided Mol Des. 2012;26(6):675–86. 169. Simulations Plus. ADMET Predictor. Lancaster, CA, USA; 2018. 170. Lagorce D, Bouslama L, Becot J, Miteva MA, Villoutreix BO. FAF-Drugs4: Free ADME-tox filtering computations for chemical biology and early stages drug discovery. Bioinformatics. 2017 Nov 15;33(22):3658–60. 171. Bickerton GR, Paolini G V., Besnard J, Muresan S, Hopkins AL. Quantifying the chemical beauty of drugs. Nat Chem. 2012 Feb;4(2):90–8. 172. Ban F, Dalal K, Li H, LeBlanc E, Rennie PS, Cherkasov A. Best Practices of Computer-Aided Drug Discovery: Lessons Learned from the Development of a Preclinical Candidate for Prostate Cancer with a New Mechanism of Action. Vol. 57, Journal of Chemical Information and Modeling. American Chemical Society; 2017. p. 1018–28. 173. Gentile F, Agrawal V, Hsing M, Ton AT, Ban F, Norinder U, et al. Deep Docking: A Deep Learning Platform for Augmentation of Structure Based Drug Discovery. ACS Cent Sci. 2020 Jun 24;6(6):939–49. 174. Tropsha A, Isayev O, Varnek A, Schneider G, Cherkasov A. Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR. Nat Rev Drug Discov. 2023 Dec 8; 175. Varadi M, Anyango S, Deshpande M, Nair S, Natassia C, Yordanova G, et al. AlphaFold Protein Structure Database: Massively expanding the structural coverage of protein-115 sequence space with high-accuracy models. Nucleic Acids Res. 2022 Jan 7;50(D1):D439–44. 176. Weinstein IB, Joe AK. Mechanisms of Disease: Oncogene addiction - A rationale for molecular targeting in cancer therapy. Vol. 3, Nature Clinical Practice Oncology. 2006. p. 448–57. 177. Nagel R, Semenova EA, Berns A. Drugging the addict: non‐oncogene addiction as a target for cancer therapy. EMBO Rep. 2016 Nov;17(11):1516–31. 178. Cremers CG, Nguyen LK. Network rewiring, adaptive resistance and combating strategies in breast cancer. Vol. 2, Cancer Drug Resistance. OAE Publishing Inc.; 2019. p. 1106–26. 179. Zhang X, Klamer B, Li J, Fernandez S, Li L. A pan-cancer study of class-3 semaphorins as therapeutic targets in cancer. BMC Med Genomics. 2020 Apr 3;13(S5):45. 180. Yap TA, Bjerke L, Clarke PA, Workman P. Drugging PI3K in cancer: Refining targets and therapeutic strategies. Vol. 23, Current Opinion in Pharmacology. Elsevier Ltd; 2015. p. 98–107. 181. Campbell RA, Bhat-Nakshatri P, Patel NM, Constantinidou D, Ali S, Nakshatri H. Phosphatidylinositol 3-kinase/AKT-mediated activation of estrogen receptor α: A new model for anti-estrogen resistance. Journal of Biological Chemistry. 2001 Mar 30;276(13):9817–24. 182. Miller TW, Balko JM, Fox EM, Ghazoui Z, Dunbier A, Anderson H, et al. ERα-dependent E2F transcription can mediate resistance to estrogen deprivation in human breast cancer. Cancer Discov. 2011 Sep;1(4):338–51. 183. Huang D, Tang L, Yang F, Jin J, Guan X. PIK3CA mutations contribute to fulvestrant resistance in ER-positive breast cancer . Am J Transl Res. 2019;11(9):6055–65. 184. Mansouri S, Feizi N, Mahdi A, Majidzadeh-A K, Farahmand L. A Review on The Role of VEGF in Tamoxifen Resistance. Anticancer Agents Med Chem. 2018;18(14):2006–9. 185. Olgen S. Overview on Anticancer Drug Design and Development. Curr Med Chem. 2018;25(15):1704–19. 186. Zhang L, Li Y, Wang Q, Chen Z, Li X, Wu Z, et al. The PI3K subunits, P110α and P110β are potential targets for overcoming P-gp and BCRP-mediated MDR in cancer. Mol Cancer. 2020 Jan 17;19(1). 187. Gao C, Yuan X, Jiang Z, Gan D, Ding L, Sun Y, et al. Regulation of AKT phosphorylation by GSK3β and PTEN to control chemoresistance in breast cancer. Breast Cancer Res Treat. 2019 Jul 30;176(2):291–301. 188. Haagenson KK, Wu GS. The role of MAP kinases and MAP kinase phosphatase-1 in resistance to breast cancer treatment. Vol. 29, Cancer and Metastasis Reviews. 2010. p. 143–9. 189. Kinnel B, Singh SK, Oprea-Ilies G, Singh R. Targeted Therapy and Mechanisms of Drug Resistance in Breast Cancer. Vol. 15, Cancers. MDPI; 2023. 190. Xu X, Zhang M, Xu F, Jiang S. Wnt signaling in breast cancer: biological mechanisms, challenges and opportunities. Vol. 19, Molecular Cancer. BioMed Central Ltd; 2020. 191. Hao J, Han X, Huang H, Yu X, Fang J, Zhao J, et al. Sema3C signaling is an alternative activator of the canonical WNT pathway in glioblastoma. Nat Commun. 2023 Dec 1;14(1). 116 192. Worzfeld T, Swiercz JM, Looso M, Straub BK, Sivaraj KK, Offermanns S. ErbB-2 signals through Plexin-B1 to promote breast cancer metastasis. J Clin Invest. 2012 Apr;122(4):1296–305. 193. Cortés J, Im SA, Holgado E, Perez-Garcia JM, Schmid P, Chavez-MacGregor M. The next era of treatment for hormone receptor-positive, HER2-negative advanced breast cancer: Triplet combination-based endocrine therapies. Vol. 61, Cancer Treatment Reviews. W.B. Saunders Ltd; 2017. p. 53–60. 194. Fazzari P, Penachioni J, Gianola S, Rossi F, Eickholt BJ, Maina F, et al. Plexin-B1 plays a redundant role during mouse development and in tumour angiogenesis. BMC Dev Biol. 2007;7. 195. Tilghman JM, Ling AY, Turner TN, Sosa MX, Krumm N, Chatterjee S, et al. Molecular Genetic Anatomy and Risk Profile of Hirschsprung’s Disease. New England Journal of Medicine. 2019 Apr 11;380(15):1421–32. 196. Meinds RJ, van der Steeg AFW, Sloots CEJ, Witvliet MJ, de Blaauw I, van Gemert WG, et al. Long-term functional outcomes and quality of life in patients with Hirschsprung’s disease. British Journal of Surgery. 2019 Mar 1;106(4):499–507. 197. Lupo F, Delfino P, Filippini D, Vicentini C, Corbo V. Role of SEMA3A and SEMA3C in tumor progression and metastatic processes in PDAC (abstract). Cancer Res. 2020;80(16 Suppl). 198. Mo Z, Liu M, Yang F, Luo H, Li Z, Tu G, et al. GPR30 as an initiator of tamoxifen resistance in hormone-dependent breast cancer. Breast Cancer Research. 2013 Dec 29;15(6):R114. 199. Neve RM, Chin K, Fridlyand J, Yeh J, Baehner FL, Fevr T, et al. A collection of breast cancer cell lines for the study of functionally distinct cancer subtypes. Cancer Cell. 2006;10(6):515–27. 200. Hoffman RM. Patient-derived orthotopic xenografts: Better mimic of metastasis than subcutaneous xenografts. Vol. 15, Nature Reviews Cancer. Nature Publishing Group; 2015. p. 451–2. 201. Holliday DL, Speirs V. Choosing the right cell line for breast cancer research. Breast Cancer Research. 2011 Aug 12;13(4):215. 202. Fung AS, Lee C, Yu M, Tannock IF. The effect of chemotherapeutic agents on tumor vasculature in subcutaneous and orthotopic human tumor xenografts. BMC Cancer. 2015 Dec 12;15(1). 203. Fleming JM, Miller TC, Meyer MJ, Ginsburg E, Vonderhaar BK. Local regulation of human breast xenograft models. J Cell Physiol. 2010 Sep;224(3):795–806. 204. Whittle JR, Lewis MT, Lindeman GJ, Visvader JE. Patient-derived xenograft models of breast cancer and their predictive power. Vol. 17, Breast Cancer Research. BioMed Central Ltd.; 2015. 205. Hidalgo M, Amant F, Biankin A V., Budinská E, Byrne AT, Caldas C, et al. Patient-derived Xenograft models: An emerging platform for translational cancer research. Cancer Discov. 2014;4(9):998–1013. 206. Tentler JJ, Tan AC, Weekes CD, Jimeno A, Leong S, Pitts TM, et al. Patient-derived tumour xenografts as models for oncology drug development. Vol. 9, Nature Reviews Clinical Oncology. 2012. p. 338–50. 117 207. Derose YS, Wang G, Lin YC, Bernard PS, Buys SS, Ebbert MTW, et al. Tumor grafts derived from women with breast cancer authentically reflect tumor pathology, growth, metastasis and disease outcomes. Nat Med. 2011 Nov;17(11):1514–20. 208. Marangoni E, Vincent-Salomon A, Auger N, Degeorges A, Assayag F, De Cremoux P, et al. A new model of patient tumor-derived breast cancer xenografts for preclinical assays. Clinical Cancer Research. 2007 Jul 1;13(13):3989–98. 209. Puchalapalli M, Zeng X, Mu L, Anderson A, Glickman LH, Zhang M, et al. NSG mice provide a better spontaneous model of breast cancer metastasis than athymic (nude) mice. PLoS One. 2016 Sep 1;11(9). 210. Shultz LD, Goodwin N, Ishikawa F, Hosur V, Lyons BL, Greiner DL. Human cancer growth and therapy in immunodeficient mouse models. Cold Spring Harb Protoc. 2014 Jul 1;2014(7):694–708. 118 Appendices Appendix A. Time dependent estradiol treatment and estrogen receptor knockdown impact on SEMA3C mRNA expression. Appendix A. Time dependent estradiol treatment and estrogen receptor knockdown impact on SEMA3C mRNA expression. (A) mRNA expression of SEMA3C increases in a time dependent manner under estradiol treatment (100nM) as analyzed in GSE11324 (top) and (B) significant decrease in SEMA3C expression during ESR1 knockdown as analyzed in GSE27473. 119 Appendix B. Densitometry analysis of SEMA3C protein expression after overnight estradiol treatment (nM) Appendix B. Densitometry analysis of SEMA3C protein expression after overnight estradiol treatment. Quantification of SEMA3C protein levels in MCF7 and T47D cells after an overnight treatment with estradiol in a dose dependent manner. ***p<0.001; ****p<0.0001. mean ± standard deviation (SD). 120 Appendix C. ESR1 binding region on SEMA3C locus identified by ChIP-seq studies analyzed on Integrative Genome Browser (hg38) Appendix C. ESR1 binding region on SEMA3C locus identified by ChIP-seq studies analyzed on Integrative Genome Browser (hg38). ESR1 binding peaks region derived from ChIP-Seq studies on SEMA3C locus showcasing ERE motifs in all three regulatory regions on both positive and negative strand. 121 Appendix D. SEMA3C induces receptor tyrosine kinase signaling in MCF10A Appendix D. SEMA3C induces receptor tyrosine kinase signaling in MCF10A. Exogenous treatment with recombinant SEMA3C (1uM) for 10 minutes on MCF10A leads to increase in phosphorylation of EGFR and MET. 122 Appendix E. Absence of caspase 3 in MCF7 cells leads to no cleaved caspase 3 expression in MCF7 cells post SEMA3C siRNA knockdown Appendix E. Absence of caspase 3 in MCF7 cells leads to no cleaved caspase 3 expression in MCF7 cells post SEMA3C siRNA knockdown. Confirmation of absence (MCF7) and presence (T47D) of caspase-3 dependent pro-apoptotic signal post SEMA3C siRNA treatment (25nM) benchmarked with positive control used for caspase-3 and cleaved caspase-3 detection (NIH3T3). Beta-Actin Caspase-3 C-Caspase-3 SEMA3C MCF7 Scr siS3C T47D Scr siS3C NIH3T3^ Ctrl SS* *Staurosporine ^ Positive Control for Caspase-3 detection 123 Appendix F. B1SP dose response growth curve on ER+ breast cancer cells Appendix F. B1SP dose response growth curve on ER+ breast cancer cells. MCF7 (1500 cells/well) and T47D cells (1500 cells/well) were plated in a 96-well plate and were treated with different B1SP concentrations in 1:2 serial dilution after letting them attach overnight. Cell viability was tested using presto-blue 72h after B1SP addition. mean ± standard error mean (SEM) 00.06250.1250.25 0.5 10.00.51.0T47DConcentration (uM)Relative Growth124 Appendix G. Densitometry analysis of B1SP inhibition of SEMA3C induced tyrosine kinase signaling Appendix G. Densitometry analysis of SEMA3C induced tyrosine kinase signaling inhibition by B1SP (uM). Quantification of phosphor-MAPK and AKT protein levels in MCF7 and T47D cells after an 2h treatment with B1SP in a dose dependent manner (0-1uM). **p<0.01; ***p<0.001; ****p<0.0001. mean ± standard deviation (SD). 125 Appendix H. TNBC patients shows lower overall survival within high SEMA3C group Appendix H. TNBC patients with high SEMA3C expression shows lower overall survival. Using PAM50 subtyping method TNBC patients with high SEMA3C expression have significantly lower survival rate with median difference in survival of 35 months. 126 Appendix I. EGFR signal rescue in MDA-MB-468 suggests SEMA3C’s role in regulating EGFR protein expression potentially via downstream effects of SEMA3C signaling in positive feedback loop Appendix I. EGFR signal rescue in MDA-MB-468 suggests SEMA3C’s role in regulating EGFR protein expression potentially via downstream effects of SEMA3C signaling in positive feedback loop. 20nM SEMA3C siRNA reverse transfection for 48h on MDA-MB-468 resulted in decrease in EGFR levels and overnight treatment with exogenous SEMA3C (0.5uM) was able to induce a mild rescue of EGFR levels in low serum conditions. 127 Appendix J. ALS dose response growth curve on TNBC cells Appendix J. ALS dose response growth curve on TNBC cells. MDA-MB-468 (2500 cells/well), MDA-MB-231 (1800 cells/well) and BT-549 (1500 cells/ell) were plated in a 96-well plate and were treated with different ALS concentrations in 1:2 serial dilution after letting them attach overnight. Cell viability was tested using presto-blue 72h after B1SP addition. mean ± standard error mean (SEM) 00.310.621.25 2.5 50.00.51.0MDA-MB-468Concentration (uM)Relative Growth00.310.621.25 2.5 50.00.51.0MDA-MB-231Concentration (uM)Relative Growt h00.310.621.25 2.5 50.00.51.0BT-549Concentration (uM)Relative Growth128 Appendix K. Plexin-B1 acts as a potential co-receptor for SEMA3C induced MAPK/AKT signaling in TNBC cells Appendix K. Plexin-B1 acts as a potential co-receptor for SEMA3C induced MAPK/AKT signaling in TNBC cells. (A) Basal expression of Plexin-B1 (100kDa fragment) in three different TNBC cell lines. (B) 20nM Plexin-B1 siRNA reverse transfection for 72h on MDA-MB-231 and BT-549 followed by 10 min treatment with exogenous SEMA3C (0.5uM) after serum starvation overnight resulted in decrease in phospho- MAPK and AKT levels as compared positive control (Scr siRNA + SEMA3C (0.5uM)) wells.
Thesis/Dissertation
eng
Interdisciplinary Oncology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Ong, Christopher J.
Cherkasov, Artem
Semaphorin 3c (SEMA3C) as a potential therapeutic target in breast cancer
Text