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Molecular Classification and Prediction of Metastatic Potential in Early Malignant Melanoma Yale University EliScholar – A Digital Platform for Scholarly Publishing at Yale Yale Medicine Thesis Digital Library School of Medicine 5-2006 Molecular classification and prediction of metastatic potential in early malignant melanoma: improvement of prognostic accuracy by quantitative in situ proteomic analysis Aaron J. Berger Yale University. Follow this and additional works at: http://elischolar.library.yale.edu/ymtdl Part of the Medicine and Health Sciences Commons Recommended Citation Berger, Aaron J., "Molecular classification and prediction of metastatic potential in early malignant melanoma: improvement of prognostic accuracy by quantitative in situ proteomic analysis" (2006). Yale Medicine Thesis Digital Library. 2226. http://elischolar.library.yale.edu/ymtdl/2226 This Open Access Dissertation is brought to you for free and open access by the School of Medicine at EliScholar – A Digital Platform for Scholarly Publishing at Yale. It has been accepted for inclusion in Yale Medicine Thesis Digital Library by an authorized administrator of EliScholar – A Digital Platform for Scholarly Publishing at Yale. For more information, please contact [email protected]. Molecular Classification and Prediction of Metastatic Potential Early Malignant Melanoma: Improvement of Prognostic Accuracy by Quantitative in situ Proteomic Analysis A Dissertation Presented to the Faculty of the Graduate School of Yale University in Candidacy for the Degree of Doctor of Philosophy By Aaron J. Berger Dissertation Advisor: David L. Rimm, M.D., Ph.D. May 2006 iii Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Abstract Molecular Classification and Prediction of Metastatic Potential in Early Malignant Melanoma: Improvement of Prognostic Accuracy by Quantitative in situ Proteomic Analysis Aaron J. Berger 2006 The incidence of cutaneous malignant melanoma continues to increase every year, and remains the leading cause of skin cancer death in industrialized countries. In spite of the aggressive nature of advanced melanoma, there are no standard biological assays in clinical usage that can predict metastasis. This may be due, in part, to the inadequacy of reproducible assessment of protein expression using traditional immunohistochemistry. This dissertation will discuss the use of tissue microarrays combined with quantitative in situ molecular analysis of protein expression to allow prediction of melanoma metastasis. Through the identification and validation of novel prognostic biomarkers, we seek to identify subsets of patients that are at high or low risk for melanoma recurrence or melanoma-related death. Some of these biomarkers may also serve as potential targets for future biologic therapy in melanoma, a disease for which no effective medical treatment is currently available. We demonstrate that quantitative assessment of a small number of markers is predictive of metastasis and outcome, augmenting the current system of prognosis. The dissertation begins with a brief introduction on the current state of melanoma diagnosis, staging, and treatment, as well as a review of current efforts to understand the biology of melanoma progression and metastasis. The fundamentals of tissue microarray technology are then described. Critical aspects of quantitative immunohistochemistry, i Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. including a description of the Automated Quantitative Analysis (AQUA) system developed in our laboratory, are also addressed. The second chapter demonstrates the use of tissue microarray technology to examine melanoma specimens by the current field standard, with a study of activating transcription factor 2 (ATF2); an example of semi- quantitative immunohistochemical analysis of protein expression. The third chapter provides validation of the AQUA technology on melanoma tissue by evaluation of the human homologue of murine double minute 2 protein (HDM2). Chapter four demonstrates an example of the critical—and beneficial—aspect of subcellular compartmentalization that the AQUA system provides, demonstrating that the ratio of cytoplasmic-to-nuclear expression of activator protein 2 (AP-2) predicts outcome in melanoma patients. The last chapter draws these concepts together and presents results from the analysis of 50 protein biomarkers in melanoma. It also introduces the use of a number of statistical methods (traditional and novel) employed to develop an optimal biomarker set for future analyses. n Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. This dissertation is dedicated to the memory of my friend Marco Palma. If he were alive, I think he’d be doing this type of work. I miss him with each day that passes. v Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Acknowledgements First and foremost, I would like to thank my mentor, David Rimm, M.D., Ph.D. I was drawn to him and his lab based on his deep interest in translational research, as well as his remarkable ability to describe extremely complicated concepts scientists, clinicians, and laypeople. I initially ‘discovered’ David as he was a guest lecturer in a seminar course, and then again as a section leader for the medical school Pathology course. After almost committing myself to another laboratory, I cam back from six months of medical school clerkships with a strong desire to work in David’s lab on clinically relevant problems in cancer research. The type of work we do in his lab will hopefully change the face of oncology care in our society. The tools developed and refined in our lab are unique and exciting. David is an outstanding mentor; he always puts his students first. His creativity, intellectual curiosity, and courage to pursue the answers to complex, almost ‘unanswerable’ questions are admirable traits that I hope I have acquired. In addition, his enthusiasm is infectious. The second person I would like to thank is Robert (Bob) Camp, M.D., Ph.D. Bob is probably one of the smartest people I know, and among the best dressed. Without Bob’s mind, ‘vision’ and programming skills, none of this work would be possible. He is an asset to this lab, the department, the world in general; a true genius. All members of the Rimm lab deserve recognition in terms of the assistance they have provided me on this journey. In particular, I would like to thank the graduate students (past and present) who have provided guidance and support (intellectual and emotional) throughout this process: Elayne Provost, Ph.D., Derek Pappas, Ph.D., Marisa Dolled-Filhart, Ph.D., Jena Giltnane, and Sharon Pozner. THANK YOU! Other vi Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. individuals who have provided tremendous assistance include Kyle DiVito and Melissa Cregger, who took many of my projects into their own hands. Additionally, Sophya Rodov and Harriet Kluger, M.D. were quite helpful. Jay Cowan, Maciej Zerkowski, Tom D ’Aquila, Mark Gustavson, Ph.D., Summar Siddiqui, Malini Harigopal, M.D., Gina Chung, M.D., Amanda Psyrri, M.D., and Carola Zalles, M.D. all became my friends and made graduate student life more palatable. The Tissue Microarray Facility, run by Lori Charette and Joanna Graham, deserves special mention. Without your hardwork and help, the puzzle ends up missing pieces. Critical collaborators on my projects include Dr. Stephan Ariyan, M.D. and his assistant Carolyn Truini, Michael Krauthammer, M.D., Ph.D. (Bioinformatics) and Antonio Subtil, M.D. (Dermatopathology). People who have made the Department of Pathology a truly hospitable and fun place to be include JoAnn Falato, Dagmar Moore, the business office crew (Janice, Rachel, and Beth), and JoAnn D ’Agostino. In terms of my personal life, I am grateful to all of my friends, but especially Lee Sylvestre for keeping everything in perspective. I would like to express thanks to my Dissertation Committee (Michael Kashgarian, M.D., Ruth Halaban, Ph.D., and Jeffrey Sklar, M.D., Ph.D.) for their feedback and suggestions, as well as their acceptance of this project as a meaningful endeavor. I also owe a debt of gratitude to the MD/PhD Program (Jim Jamieson, M.D., Ph.D. and Sue Sansone) for the support that they have provided (and continue to provide) in my attempt to become a physician-scientist. vii Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table of Contents A bstract ......................................................................................................................................i Dedication .................................................................................................................................v Acknowledgements ............................................................................................................... vi Table of Contents ............................................................................................................... viii Chapter 1: Introduction .........................................................................................................1 Current Understanding and Treatment of Melanoma ........................................................ 1 Prognostic Indicators in Melanoma .....................................................................................5 Molecular Classification of Melanoma..............................................................................13
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