Dismantling the coarse paradigm of cancer 'Drivers' and 'Passengers’
Distinguished Speaker Series: AI & Health Science
September 19, 2025 • 11 am
Clinical and Translational Research Center (CTRC) in the Murphy Room 5019AB, Downtown Buffalo
Bio
Jeffrey P. Townsend is an internationally recognized expert in statistical genomics, evolutionary biology, and cancer research. He holds the Elihu Professorship of Biostatistics at the Yale School of Public Health and an appointment in the Department of Ecology and Evolutionary Biology at Yale University. Dr. Townsend is Co-Director of the Genomics, Genetics, and Epigenetics Research Program at Yale Cancer Center and recently completed his term as Co-Chair-Elect and Co-Chair of the American Association for Cancer Research (AACR) Cancer Evolution Working Group.
His interdisciplinary research applies cutting-edge methods from evolutionary biology—including theory on mutation, selection, population genetics, molecular evolution, and phylogenetics—to challenges in basic science, public health, and medicine. He has made major contributions to evolutionary biology, molecular developmental mycology, and the ecology of human disease. In recent years, his work has focused on quantifying the fitness effects of cancer-associated mutations, revealing how mutational processes and evolutionary forces shape tumorigenesis, therapeutic response, and resistance. His integrative approach incorporates statistical modeling and large-scale data analysis to advance precision oncology and deepen understanding of immune evasion, treatment resistance, and cancer disparities.
His research has been published in leading journals and has had substantial influence in both public health and clinical practice.
Jeffrey P. Townsend, PhD
Elihu Professor of Biostatistics
Professor of Ecology and Evolutionary Biology, Yale University
Abstract
The prevailing binary classification of somatic mutations in cancer as either "drivers" or "passengers" has offered a useful heuristic but obscures the complex, quantitative, and context-dependent nature of tumor evolution. In this talk, I will present a refined framework that quantifies the evolutionary impact of individual mutations by integrating gene-specific mutation rates, mutational signatures, and scaled selection coefficients derived from large-scale cancer genomics data. Drawing on recent work across multiple cancer types—including lung, prostate, esophageal, and cholangiocarcinoma—I will show that selection is not an all-or-none phenomenon, but rather a continuum shaped by mutational processes, tumor microenvironment, and genetic interactions. Moreover, I will demonstrate how selective pressures vary dynamically across disease stages and exposure contexts, and how epistasis between mutations can amplify or suppress their evolutionary significance. This quantitative evolutionary approach enables a principled redefinition of cancer "drivers" and reveals how ostensibly neutral or weakly selected mutations can contribute to tumorigenesis and therapeutic resistance in specific evolutionary landscapes. By dismantling the coarse paradigm of drivers and passengers, we open the door to more precise, predictive, and clinically actionable models of cancer evolution.