Research Perspectives:

Analysis of mutation, selection, and epistasis: an informed approach to cancer clinical trials

Jon F. Wilkins, Vincent L. Cannataro, Brian Shuch and Jeffrey P. Townsend _

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Oncotarget. 2018; 9:22243-22253. https://doi.org/10.18632/oncotarget.25155

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Jon F. Wilkins1, Vincent L. Cannataro2, Brian Shuch3,4 and Jeffrey P. Townsend2,5,6

1 Ronin Institute, Montclair, NJ, USA

2 Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, CT, USA

3 Department of Urology, Yale School of Medicine, New Haven, CT, USA

4 Department of Radiology, Yale School of Medicine, New Haven, CT, USA

5 Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA

6 Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA

Correspondence to:

Jeffrey P. Townsend, email:

Keywords: cancer; natural selection; evolution; mutation; epistasis

Received: February 19, 2018     Accepted: April 02, 2018     Published: April 27, 2018


Currently, drug development efforts and clinical trials to test them are often prioritized by targeting genes with high frequencies of somatic variants among tumors. However, differences in oncogenic mutation rate—not necessarily the effect the variant has on tumor growth—contribute enormously to somatic variant frequency. We argue that decoupling the contributions of mutation and cancer lineage selection to the frequency of somatic variants among tumors is critical to understanding—and predicting—the therapeutic potential of different interventions. To provide an indicator of that strength of selection and therapeutic potential, the frequency at which we observe a given variant across patients must be modulated by our expectation given the mutation rate and target size to provide an indicator of that strength of selection and therapeutic potential. Additionally, antagonistic and synergistic epistasis among mutations also impacts the potential therapeutic benefit of targeted drug development. Quantitative approaches should be fostered that use the known genetic architectures of cancer types, decouple mutation rate, and provide rigorous guidance regarding investment in targeted drug development. By integrating evolutionary principles and detailed mechanistic knowledge into those approaches, we can maximize our ability to identify those targeted therapies most likely to yield substantial clinical benefit.

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