Somatic selection distinguishes oncogenes and tumor suppressor genes

Abstract Motivation Functions of cancer driver genes vary substantially across tissues and organs. Distinguishing passenger genes, oncogenes (OGs) and tumor-suppressor genes (TSGs) for each cancer type is critical for understanding tumor biology and identifying clinically actionable targets. Although many computational tools are available to predict putative cancer driver genes, resources for context-aware classifications of OGs and TSGs are limited. Results We show that the direction and magnitude of somatic selection of protein-coding mutations are significantly different for passenger genes, OGs and TSGs. Based on these patterns, we develop a new method (genes under selection in tumors) to discover OGs and TSGs in a cancer-type specific manner. Genes under selection in tumors shows a high accuracy (92%) when evaluated via strict cross-validations. Its application to 10 172 tumor exomes found known and novel cancer drivers with high tissue-specificities. In 11 out of 13 OGs shared among multiple cancer types, we found functional domains selectively engaged in different cancers, suggesting differences in disease mechanisms. Availability and implementation An R implementation of the GUST algorithm is available at https://github.com/liliulab/gust. A database with pre-computed results is available at https://liliulab.shinyapps.io/gust. Supplementary information Supplementary data are available at Bioinformatics online.

[1]  Rong Chen,et al.  Human genomic disease variants : A neutral evolutionary explanation , 2012 .

[2]  N. Ridgway,et al.  Epidermal growth factor receptor (EGFR) in lung cancer: an overview and update. , 2011, Journal of thoracic disease.

[3]  E. Lander,et al.  Comprehensive assessment of cancer missense mutation clustering in protein structures , 2015, Proceedings of the National Academy of Sciences.

[4]  Steven J. M. Jones,et al.  Comprehensive Characterization of Cancer Driver Genes and Mutations , 2018, Cell.

[5]  Kazuhiko Nakagawa,et al.  First- and Second-Generation EGFR-TKIs Are All Replaced to Osimertinib in Chemo-Naive EGFR Mutation-Positive Non-Small Cell Lung Cancer? , 2019, International journal of molecular sciences.

[6]  Joshua M. Stuart,et al.  The Cancer Genome Atlas Pan-Cancer analysis project , 2013, Nature Genetics.

[7]  Roland Rad,et al.  Tissue-specific tumorigenesis: context matters , 2017, Nature Reviews Cancer.

[8]  Obi L. Griffith,et al.  SciClone: Inferring Clonal Architecture and Tracking the Spatial and Temporal Patterns of Tumor Evolution , 2014, PLoS Comput. Biol..

[9]  C. Cole,et al.  The COSMIC Cancer Gene Census: describing genetic dysfunction across all human cancers , 2018, Nature Reviews Cancer.

[10]  Jane E. Visvader,et al.  Cells of origin in cancer , 2011, Nature.

[11]  Steven J. M. Jones,et al.  Comprehensive molecular characterization of gastric adenocarcinoma , 2014, Nature.

[12]  Li Ding,et al.  Protein-structure-guided discovery of functional mutations across 19 cancer types , 2016, Nature Genetics.

[13]  Manfred Westphal,et al.  EGFR as a Target for Glioblastoma Treatment: An Unfulfilled Promise , 2017, CNS Drugs.

[14]  K. Kinzler,et al.  Evaluating the evaluation of cancer driver genes , 2016, Proceedings of the National Academy of Sciences.

[15]  Wei Zhao,et al.  Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas. , 2018, Cell systems.

[16]  M. Stratton,et al.  Statistical Analysis of Pathogenicity of Somatic Mutations in Cancer , 2006, Genetics.

[17]  B. Schuster-Böckler,et al.  The effects of mutational processes and selection on driver mutations across cancer types , 2017, Nature Communications.

[18]  D. Hanahan,et al.  The Hallmarks of Cancer , 2000, Cell.

[19]  Gilles Louppe,et al.  Understanding variable importances in forests of randomized trees , 2013, NIPS.

[20]  Marc J. Williams,et al.  Identification of neutral tumor evolution across cancer types , 2016, Nature Genetics.

[21]  Vanessa E. Gray,et al.  Evolutionary diagnosis method for variants in personal exomes , 2012, Nature Methods.

[22]  J. Plotkin,et al.  The Population Genetics of dN/dS , 2008, PLoS genetics.

[23]  Matthew Stephens,et al.  Detailed modeling of positive selection improves detection of cancer driver genes , 2019, Nature Communications.

[24]  Andrea Sottoriva,et al.  Between-Region Genetic Divergence Reflects the Mode and Tempo of Tumor Evolution , 2017, Nature Genetics.

[25]  C. Harris,et al.  Oncogenes and tumor-suppressor genes. , 1991, Environmental health perspectives.

[26]  Anne-Pascale Meert,et al.  Management of EGFR mutated nonsmall cell lung carcinoma patients , 2015, European Respiratory Journal.

[27]  Paul S Mischel,et al.  Differential sensitivity of glioma- versus lung cancer-specific EGFR mutations to EGFR kinase inhibitors. , 2012, Cancer discovery.

[28]  Andrea Sottoriva,et al.  Cancer Evolution and the Limits of Predictability in Precision Cancer Medicine , 2016, Trends in cancer.

[29]  Michael S. Lawrence,et al.  Passenger hotspot mutations in cancer driven by APOBEC3A and mesoscale genomic features , 2019, Science.

[30]  A. Godzik,et al.  Comparison of algorithms for the detection of cancer drivers at subgene resolution , 2017, Nature Methods.

[31]  M. McMahon,et al.  PIK3CA‐mutated melanoma cells rely on cooperative signaling through mTORC1/2 for sustained proliferation , 2017, Pigment cell & melanoma research.

[32]  Ananda L Roy,et al.  Pathophysiology of TFII-I: Old Guard Wearing New Hats. , 2017, Trends in molecular medicine.

[33]  Li Liu,et al.  Evolutionary balancing is critical for correctly forecasting disease-associated amino acid variants. , 2013, Molecular biology and evolution.

[34]  Philippe Blache,et al.  SOX9 is an atypical intestinal tumor suppressor controlling the oncogenic Wnt/ß-catenin signaling , 2016, Oncotarget.

[35]  Julian M. Hess,et al.  Passenger Hotspot Mutations in Cancer , 2019, bioRxiv.

[36]  T. Chan,et al.  Therapeutic targeting of tumor suppressor genes , 2015, Cancer.

[37]  Chris Sander,et al.  Pan-Cancer Analysis of Mutation Hotspots in Protein Domains. , 2015, Cell systems.

[38]  Tom H. Pringle,et al.  The human genome browser at UCSC. , 2002, Genome research.

[39]  K. Kinzler,et al.  Cancer Genome Landscapes , 2013, Science.

[40]  Martin H. Schaefer,et al.  Cell type-specific properties and environment shape tissue specificity of cancer genes , 2016, Scientific Reports.

[41]  Matthew Mort,et al.  A meta‐analysis of nonsense mutations causing human genetic disease , 2008, Human mutation.

[42]  L. Sleire,et al.  Drug repurposing in cancer. , 2017, Pharmacological research.