The Development of a Statistical Model to Study How the Deletion of PD-1 Promotes Anti-Tumor Immunity The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Diallo, Alos Burgess. 2021. The Development of a Statistical Model to Study How the Deletion of PD-1 Promotes Anti-Tumor Immunity. Master's thesis, Harvard University Division of Continuing Education. Citable link https://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37367694 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA The Development of a Statistical Model to Study How the Deletion of PD-1 Promotes Anti-Tumor Immunity Alos Diallo A Thesis in the Field of Bioinformatics for the Degree of Master of Liberal Arts in Extension Studies Harvard University March 2021 1 2 Copyright 2021 [Alos Diallo] Abstract T-cells are an essential component to the immune system, but they do not act alone and are instead a component in the body’s immune system. PD-1 and its ligands PD-L1 and PD-L2 play an important role in the regulation of T-cells which are incredibly important to the treatment of cancer. Tumors have been able to hijack the PD-1 inhibitory pathway to evade our body’s immune response. PD-1 pathway blockade, therefore, can serve as an important approach for cancer immunotherapy. However, we do not fully understand the mechanism by which PD-1 regulates anti-tumor immunity. With the datasets derived from experiments by the Sharpe Lab, we hope to answer two important questions. First, what does PD-1 regulate in a cell intrinsic manner compared to bystander effects on other cells in the tumor micro-environment? Second, which gene expression changes predict response as opposed to resistance to tumor clearance when PD-1 is present or deleted? We hypothesize that a cell intrinsic loss of PD-1 is necessary for improved T-cell fitness and effector functions. This project aims to help answer these questions in three ways. First, the development of an RNA-sequencing pipeline allows the researchers in the lab to analyze the results of datasets that are generated. Second, conducting pathway analyses provides a broader picture of the gene expression landscape, including provide a more complete picture of the tumor micro-environment by indicating which pathways and cellular processes are enriched for the genes affected by the deletion of PD-1. Third, the development of a statistical model which makes predictions on which gene expression changes predict response as opposed to resistance. 3 The results of the statistical model indicate which genes are more closely related to PD-1 when it is deleted versus when it is present. This will help us better understand immune response as opposed to resistance to PD-1 cancer immunotherapy, and its effects on tumor growth. This model therefore provides a valuable tool to the community that would allow researchers to probe the gene expression landscape around PD-1. 4 Dedication This work is dedicated to my parents, Abou Diallo and Sue Burgess. My parents have been a driving force and a positive influence on me and my success. They met in Senegal where my mother worked as an American Peace Corps volunteer, and my father worked as an instructor. Both have had to continuously make sacrifices and work hard for the good of our family. This often times meant one of them had to work at night. As a child, I watched them sacrifice selflessly for my sister and me. My father, more specifically, was unable to finish his own education so that he could build a better life for us. I saw him give up his dream of finishing college in order to work and provide for our family. I have never once heard him complain or ask for anything in return. Selflessness is a quintessential quality in my father, it is a part of who he is as a man. He was present for me to provide encouragement and advice whenever I considered giving up. From him, I learned that duty, honor and respect are part of a code that one should live by. From my mother, I learned to be curious and inquisitive. She enjoys going through records at churches, libraries, and city offices so she can research our family genealogy. She is always trying to find difficult to obtain ingredients to recipes she would like to replicate because she thinks we would enjoy them. Her inquisitive nature even led to travel abroad during a time when women were not expected nor encouraged to do so. Traveling the US, to Europe, and finally to Africa. I have become a better scientist by taking after my mother’s curious nature and I have gleaned what it means to be a man by observing my v father. I am and will be forever grateful and appreciative for their love, advice, influence and support of me. ARMA virumque cano, Troiae qui primus ab oris Italiam, fato profugus, Laviniaque venit litora, multum ille et terris iactatus et alto vi superum saevae memorem Iunonis ob iram –Aneid - Vergil vi Acknowledgments I would like to thank my advisor, Professor Arlene Sharpe, whose support and guidance makes this project possible. The Sharpe Lab Post-Doctoral Fellow, Kristen Pauken, has provided not only scientific advice on immunology but also logistic assistance for which I am thankful for. Kelly Burke, who is also a Post-Doctoral Fellow in the Sharpe lab has provided thoughtful advice and edits during this process, helping me to get the paper to its finished state. I would like to acknowledge Vikram Juneja, as he generated the data that I used for the thesis, without which the project would not be possible. Sarah Hillman, who is the administrative assistant to the Chair Arlene Sharpe has helped me with setting up invaluable meetings and provided general logistics support, for which I am thankful. I would also like to thank Steven Denkin, my research advisor, who helped me through the entire thesis process. He provided encouragement and thoughtful feedback in both the development of my thesis as well as with my research proposal. I want to thank the Thesis Coordinator, Gail Dourian, for helping me with the administrative thesis process. I feel it is important to also thank the two people who taught me what I know about statistical modeling. Andrey Sivachenko and Victor Farutin. I will always be grateful for what you taught me and for your encouragement. I would especially like to thank my parents Sue Burgess and Abou Diallo for their encouragement and support. Through their love I have garnered the strength to succeed. Finally, I would like to thank my wife, Jiaoyuan Elisabeth Diallo for helping me with the vii edits to my thesis. She helped me with all of the proofreading for this work. In addition she encouraged me and supported me when I really needed it most. viii Table of Contents Dedication ............................................................................................................................v Acknowledgments............................................................................................................. vii List of Tables ..................................................................................................................... xi List of Figures ................................................................................................................... xii Chapter I. Introduction .........................................................................................................1 Cancer Immunotherapy ............................................................................................3 Machine learning in medicine ..................................................................................5 Goals ........................................................................................................................9 Research Problem ..................................................................................................11 Chapter II. Materials and Methods ....................................................................................13 RNA Sequencing analysis......................................................................................19 Pathway Analysis ...................................................................................................24 The Statistical Model .............................................................................................27 Chapter III. Results ............................................................................................................37 RNA Sequencing analysis......................................................................................41 Pathway Analysis ...................................................................................................55 The Statistical model..............................................................................................69 Chapter IV. Discussion ......................................................................................................73 Limitations .............................................................................................................80 Future work ............................................................................................................81 ix Chapter V. Conclusion .......................................................................................................83 Chapter VI. Appendix ........................................................................................................84