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Passenger Mutations in More Than 2,500 Cancer Genomes: Overall Molecular Functional Impact and Consequences

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  • Sushant Kumar, Yale University
  • ,
  • Jonathan Warrell, Yale University
  • ,
  • Shantao Li, Yale University
  • ,
  • Patrick D McGillivray, Yale University
  • ,
  • William Meyerson, Yale University
  • ,
  • Leonidas Salichos, Yale University
  • ,
  • Arif Harmanci, Yale University, University of Texas Health Sciences Center
  • ,
  • Alexander Martinez-Fundichely, Weill Cornell Medical College, Weill Cornell Medicine
  • ,
  • Calvin W Y Chan, German Cancer Research Center (DKFZ), Heidelberg University
  • ,
  • Morten Muhlig Nielsen
  • Lucas Lochovsky, Yale University
  • ,
  • Yan Zhang
  • Xiaotong Li, Yale University
  • ,
  • Shaoke Lou, Yale University
  • ,
  • Jakob Skou Pedersen
  • Carl Herrmann, German Cancer Research Center (DKFZ), Health Data Science Unit, Medical Faculty Heidelberg and BioQuant
  • ,
  • Gad Getz, The Broad Institute of MIT and Harvard, Cambridge, MA, USA, Massachusetts General Hospital Center for Cancer Research, Harvard Medical School
  • ,
  • Ekta Khurana, Weill Cornell Medical College, Weill Cornell Medicine
  • ,
  • Mark B Gerstein, Yale University

The dichotomous model of "drivers" and "passengers" in cancer posits that only a few mutations in a tumor strongly affect its progression, with the remaining ones being inconsequential. Here, we leveraged the comprehensive variant dataset from the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) project to demonstrate that-in addition to the dichotomy of high- and low-impact variants-there is a third group of medium-impact putative passengers. Moreover, we also found that molecular impact correlates with subclonal architecture (i.e., early versus late mutations), and different signatures encode for mutations with divergent impact. Furthermore, we adapted an additive-effects model from complex-trait studies to show that the aggregated effect of putative passengers, including undetected weak drivers, provides significant additional power (∼12% additive variance) for predicting cancerous phenotypes, beyond PCAWG-identified driver mutations. Finally, this framework allowed us to estimate the frequency of potential weak-driver mutations in PCAWG samples lacking any well-characterized driver alterations.

Original languageEnglish
Pages (from-to)915-927.e16
Publication statusPublished - Mar 2020

    Research areas

  • PCAWG, additive-efects, cancer genomics, deleterious passengers, driver mutations, molecular impact, passenger mutations, weak drivers

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ID: 180111446