Detailed modeling of positive selection improves detection of cancer driver genes

Nature Communications
Siming ZhaoXin He

Abstract

Identifying driver genes from somatic mutations is a central problem in cancer biology. Existing methods, however, either lack explicit statistical models, or use models based on simplistic assumptions. Here, we present driverMAPS (Model-based Analysis of Positive Selection), a model-based approach to driver gene identification. This method explicitly models positive selection at the single-base level, as well as highly heterogeneous background mutational processes. In particular, the selection model captures elevated mutation rates in functionally important sites using multiple external annotations, and spatial clustering of mutations. Simulations under realistic evolutionary models demonstrate the increased power of driverMAPS over current approaches. Applying driverMAPS to TCGA data of 20 tumor types, we identified 159 new potential driver genes, including the mRNA methyltransferase METTL3-METTL14. We experimentally validated METTL3 as a tumor suppressor gene in bladder cancer, providing support to the important role mRNA modification plays in tumorigenesis.

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Citations

Jan 11, 2020·Frontiers in Oncology·Wenhui ZhengMing Gu
Aug 29, 2020·Journal of Hematology & Oncology·Chengwu ZengHengyou Weng
Jan 21, 2021·The EMBO Journal·P Cody He, Chuan He
Mar 17, 2020·Bioinformatics·Pramod ChandrashekarLi Liu
Jan 13, 2021·Cell & Bioscience·Jianzhong GuXinbing Sui
Jan 12, 2021·Journal of Genetics and Genomics = Yi Chuan Xue Bao·Sirvan KhalighiVinay Varadan
Feb 27, 2021·Cancer Control : Journal of the Moffitt Cancer Center·KuangZheng LiuMing Chen
Jun 18, 2021·Computational and Structural Biotechnology Journal·Song-Yao ZhangJia Meng

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Methods Mentioned

BETA
exome sequencing
nucleotide exchange
transfection
HiC
RNA Seq
acetylation
ion-exchange chromatography
Assay

Software Mentioned

MA
firehose
PolyPhen
GeneMania
OncodriveCLUST
MADGiC
PhyloP
MutSigCV
CBaSE
SiFT

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