Title

Comprehensive Characterization of Cancer Driver Genes and Mutations

Authors

Matthew H. Bailey, Washington University in St. Louis
Collin Tokheim, Johns Hopkins University
Eduard Porta-Pardo, Centro Nacional de Supercomputación
Sohini Sengupta, Washington University in St. Louis
Denis Bertrand, A-Star, Genome Institute of Singapore
Amila Weerasinghe, Washington University in St. Louis
Antonio Colaprico, Interuniversity Institute of Bioinformatics in Brussels
Michael C. Wendl, Washington University School of Medicine in St. Louis
Jaegil Kim, Broad Institute
Brendan Reardon, Broad Institute
Patrick Kwok Shing Ng, University of Texas MD Anderson Cancer Center
Kang Jin Jeong, University of Texas MD Anderson Cancer Center
Song Cao, Washington University in St. Louis
Zixing Wang, University of Texas MD Anderson Cancer Center
Jianjiong Gao, Memorial Sloan-Kettering Cancer Center
Qingsong Gao, Washington University in St. Louis
Fang Wang, University of Texas MD Anderson Cancer Center
Eric Minwei Liu, Weill Cornell Medicine
Loris Mularoni, IRB Barcelona - Institute for Research in Biomedicine
Carlota Rubio-Perez, IRB Barcelona - Institute for Research in Biomedicine
Niranjan Nagarajan, A-Star, Genome Institute of Singapore
Isidro Cortés-Ciriano, Harvard Medical School
Daniel Cui Zhou, Washington University in St. Louis
Wen Wei Liang, Washington University in St. Louis
Julian M. Hess, Broad Institute
Venkata D. Yellapantula, Washington University in St. Louis
David Tamborero, IRB Barcelona - Institute for Research in Biomedicine
Abel Gonzalez-Perez, IRB Barcelona - Institute for Research in Biomedicine
Chayaporn Suphavilai, A-Star, Genome Institute of Singapore
Jia Yu Ko, A-Star, Genome Institute of Singapore
Ekta Khurana, Weill Cornell Medicine
Peter J. Park, Harvard Medical School
Eliezer M. Van Allen, Broad Institute

Document Type

Journal Article

Publication Date

4-5-2018

Journal

Cell

Volume

173

Issue

2

DOI

10.1016/j.cell.2018.02.060

Keywords

driver gene discovery; mutations of clinical relevance; oncology; structure analysis

Abstract

© 2018 Identifying molecular cancer drivers is critical for precision oncology. Multiple advanced algorithms to identify drivers now exist, but systematic attempts to combine and optimize them on large datasets are few. We report a PanCancer and PanSoftware analysis spanning 9,423 tumor exomes (comprising all 33 of The Cancer Genome Atlas projects) and using 26 computational tools to catalog driver genes and mutations. We identify 299 driver genes with implications regarding their anatomical sites and cancer/cell types. Sequence- and structure-based analyses identified >3,400 putative missense driver mutations supported by multiple lines of evidence. Experimental validation confirmed 60%–85% of predicted mutations as likely drivers. We found that >300 MSI tumors are associated with high PD-1/PD-L1, and 57% of tumors analyzed harbor putative clinically actionable events. Our study represents the most comprehensive discovery of cancer genes and mutations to date and will serve as a blueprint for future biological and clinical endeavors. A comprehensive analysis of oncogenic driver genes and mutations in >9,000 tumors across 33 cancer types highlights the prevalence of clinically actionable cancer driver events in TCGA tumor samples.

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