Project Summary: This proposal outlines a five-year research and career development program aimed at building a data-driven, quantitative framework for the evolution of cancer resistance. The basis for this application draws heavily on the candidate’s prior MD/PhD training in cancer biology and evolutionary genomics through the combined Harvard-MIT Health Sciences Technology program, and leverages his current appointments as both a senior medical oncology fellow in the combined Dana Farber/Massachusetts General Hospital CancerCare Program as well as a post-doctoral research fellow at the Broad Institute under Drs. Gad Getz and Eric Lander. The joint experimental and computational aims proposed here represent a fundamentally new research agenda that draws on core expertise from each of the candidate’s supervising laboratories. Along with a series of relevant didactics and career building activities, these studies will form the basis of his transition to an independent tenure track position as a physician-scientist guided by the goal of enabling long-term cancer control. Abstract: The proliferation of targeted therapies and immunotherapies over the last decade has heralded an unprecedented era in cancer treatment. However, durable disease control is still the exception in advanced cancers, due in large part to the emergence of resistance. The objective of this work is to use a combination of experimental and computational approaches to shed light on the underlying evolutionary rules governing cancer resistance. This is guided by the central hypothesis that the evolution of resistance is in large part predictable from features of the pre-treatment genome and therapy. In particular, three Specific Aims will be evaluated: (1) To assess for trends in the diversity of resistance across 10 cancer cell line models, (2) To assess the role of mutational diversity in promoting resistance, and (3) To assess the role of therapy in defining the resistance bottleneck. Taken together, this work will advance the field by establishing both a mathematical and experimental foundation for understanding cancer resistance. At the same time, it will furnish an array of novel genetic and epigenetic targets for future basic and applied studies. Given that some modes of resistance may be shared across treatment modalities – and in particular between targeted therapy and immunotherapy – this work is anticipated to have broad relevance.