Modeling Cancer Predisposition: Profiling Li-Fraumeni
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MODELING CANCER PREDISPOSITION: PROFILING LI-FRAUMENI SYNDROME PATIENT-DERIVED CELL LINES USING BIOINFORMATICS AND THREE-DIMENSIONAL CULTURE MODELS Amruta Rajendra Phatak Submitted to the faculty of the University Graduate School in partial fulfillment of the requirements for the degree Doctor of Philosophy in the Department of Medical and Molecular Genetics, Indiana University December 2015 Accepted by the Graduate Faculty, Indiana University, in partial fullfillment of the requirements for the degree of Doctor of Philosohphy. ____________________________________ Brittney-Shea Herbert, Ph.D., Chair Doctoral Committee ____________________________________ Yunlong Liu, Ph.D. ____________________________________ Marc Mendonca, Ph.D. October 07, 2015 ____________________________________ Clark Wells, Ph.D. ii DEDICATION I dedicate this dissertation to my beloved mentors Drs. Brittney-Shea Herbert and Madhuri Sharon and Aai (my mother), without whom this work would not have been possible. iii ACKNOWLEDGEMENTS I am indebted to Dr. Brittney-Shea Herbert for her guidance and constant support throughout my graduate career. My training as a doctoral student would have been impossible without your encouragement and vision - I thank you for being the loving and caring mentor. I sincerely thank each of my committee members for their time and constructive criticism that has been crucial in my training. I thank Dr. Yunlong Liu for his immense support, suggestions and generosity for the use of various bioinformatics tools and advice. I have thoroughly learned a lot through meetings and your classes. I thank Dr. Clark Wells for his advice and questions regarding 3D cultures and microscopy I have received throughout the project. I thank Dr. Marc Mendonca for the advice, constant support and feedback. I also would like to thank my former committee members Drs. Brenda Grimes and John Turchi for their expert insight and suggestions. I also want to thank Drs. Milan Radovich, Yang Sun and Gosia Malgorzata for their generosity for bioinformatics tools and confocal microscope - I am truly grateful for letting me use state-of-the-art equipment for my work. I also thank Dr. Sophie Leliévre and her lab for helpful discussions of the work. I thank my former labmates Drs. Elizabeth Phipps, Jillian Koziel, Alyssa Sprouse and Catherine Steding for their camaraderie throughout my graduate years - I truly cherish the time I have spent with you! I thank Lauren Bringman, Brandon Lane, Laura iv Mairs, Cathy Wallmuth and Dr. Luo Na for help with microscopy equipment and their friendship. I also thank Ayowumi, Sujata, Punittee, Yash, Gaurav and all my friends for providing the much-needed reassurance and laughs when I needed it the most! I cannot thank Dr. Kenneth Cornetta enough for his immense support during my difficult time and helping me find the perfect laboratory! I thank the present and former MMGE department staff Mrs. Joan Charlesworth, Mrs. Donna Johnson, Mrs. Peggy Knope, Mrs. Margie Day and Mrs. Jean Good for all the help throughout the years! I am truly blessed to have loving parents and Abhi – I thank you for your loving care and understanding. I am grateful to my doting grandparents who believed in me and I thank you all for giving me the strength inspiration and making me laugh during all our conversations. v Amruta Rajendra Phatak MODELING CANCER PREDISPOSITION: PROFILING LI-FRAUMENI SYNDROME PATIENT-DERIVED CELL LINES USING BIOINFORMATICS AND THREE-DIMENSIONAL CULTURE MODELS Although rare, classification of over 200 hereditary cancer susceptibility syndromes accounting for ~5-10% of cancer incidence has enabled the discovery and understanding of cancer predisposition genes that are also frequently mutated in sporadic cancers. The need to prevent or delay invasive cancer can partly be addressed by characterization of cells derived from healthy individuals predisposed to cancer due to inherited “single-hits” in genes in order to develop patient-derived samples as preclinical models for mechanistic in vitro studies. Here, we present microarray-based transcriptome profiling of Li-Fraumeni syndrome (LFS) patient-derived unaffected breast epithelial cells and their phenotypic characterization as in vitro three-dimensional (3D) models to test pharmacological agents. In this study, the epithelial cells derived from the unaffected breast tissue of a LFS patient were cultured and progressed from non-neoplastic to a malignant stage by successive immortalization and transformation steps followed by growth in athymic mice. These cell lines exhibited distinct transcriptomic profiles and were readily distinguishable based upon their gene expression patterns, growth characteristics in monolayer and in vitro 3D cultures. Transcriptional changes in the epithelial-to-mesenchymal transition gene signature contributed to the unique phenotypes observed in 3D culture for each cell line of the progression series; the fully transformed vi LFS cells exhibited invasive processes in 3D culture with disorganized morphologies due to cell-cell miscommunication, as seen in breast cancer. Bioinformatics analysis of the deregulated genes and pathways showed inherent differences between these cell lines and targets for pharmacological agents. After treatment with small molecule APR-246 that restores normal function to mutant p53, we observed that the neoplastic LFS cells had reduced malignant invasive structure formation from 73% to 9%, as well as an observance of an increase in formation of well-organized structures in 3D culture (from 27% to 91%) by stereomicroscopy and confocal microscopy. Therefore, the use of well- characterized and physiologically relevant preclinical models in conjunction with transcriptomic profiling of high-risk patient derived samples as a renewable laboratory resource can potentially guide the development of safer and more effective chemopreventive approaches. Brittney-Shea Herbert, Ph.D., Chair vii TABLE OF CONTENTS LIST OF TABLES ............................................................................................................ xii LIST OF FIGURES ......................................................................................................... xiv ABBREVIATIONS ....................................................................................................... xviii CHAPTER ONE: Introduction ............................................................................................1 1.1. Cancer predisposition syndromes: insights to mechanisms of cancer and genotypic/phenotypic aspects ..............................................................................................1 1.2. Cancer Transcriptomics: burgeoning applications of the computational toolbox ........5 1.3. Relevance of cell lines as preclinical models for cancer research ................................7 1.4. Relevance of preclinical three-dimensional in vitro models .........................................9 1.5. Epithelial-to-mesenchymal transition in cancer development ....................................14 1.6. Rationale and overall objective ...................................................................................17 CHAPTER TWO: Materials and Methods ........................................................................21 2.1. Reagents ......................................................................................................................21 2.2. The Li-Fraumeni syndrome HME50 cell progression series ......................................25 2.3. Cell culture ..................................................................................................................27 2.4. Gene expression profiling of HME50 series ...............................................................27 2.4.1. Microarray data import and normalization ..................................................27 2.4.2. Detection of differentially regulated genes ..................................................30 2.4.3. IPA® pathway analyses ...............................................................................30 2.4.4. Gene Set Enrichment Analysis ....................................................................34 2.5. Modeling stages of breast cancer and phenotypic reversion in 3D culture ................38 viii 2.5.1. Three-dimensional (3D) Matrigel® overlay culture and stereomicroscope imaging of HME50 cell lines ....................................................38 2.5.2. Modified 3D Matrigel® embed culture and confocal imaging of HME50 cell lines ...................................................................................................42 2.5.3. Treatment of HME50 cell lines with pharmacological agents .....................46 2.5.4. Immunofluorescence, image acquisition and statistical analyses of HME50 acini in 3D cultures ..................................................................................47 CHAPTER THREE: Results .............................................................................................49 3.1. HME50 cell lines exhibit distinct morphologies and growth characteristics in monolayer and 3D Matrigel® culture ................................................................................49 3.1.1. Characteristics of HME50 progression series in monolayer culture ...........49 3.1.2. HMET cells adopt stellate-like acinar morphology in 3D culture ...............52 3.2. HME50 cell lines can be delineated by their distinct gene expression patterns .........65 3.2.1. Principal Components Analysis conclusively