Format

Send to

Choose Destination
Nat Genet. 2020 Feb;52(2):208-218. doi: 10.1038/s41588-019-0572-y. Epub 2020 Feb 3.

Identification of cancer driver genes based on nucleotide context.

Author information

1
Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA. Felix_Dietlein@dfci.harvard.edu.
2
Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA. Felix_Dietlein@dfci.harvard.edu.
3
Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
4
Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
5
Centre for Genomic Regulation, Barcelona, Spain.
6
Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
7
Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA.
8
Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
9
Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA. EliezerM_VanAllen@dfci.harvard.edu.
10
Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA. EliezerM_VanAllen@dfci.harvard.edu.
11
Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. ssunyaev@rics.bwh.harvard.edu.
12
Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. ssunyaev@rics.bwh.harvard.edu.

Abstract

Cancer genomes contain large numbers of somatic mutations but few of these mutations drive tumor development. Current approaches either identify driver genes on the basis of mutational recurrence or approximate the functional consequences of nonsynonymous mutations by using bioinformatic scores. Passenger mutations are enriched in characteristic nucleotide contexts, whereas driver mutations occur in functional positions, which are not necessarily surrounded by a particular nucleotide context. We observed that mutations in contexts that deviate from the characteristic contexts around passenger mutations provide a signal in favor of driver genes. We therefore developed a method that combines this feature with the signals traditionally used for driver-gene identification. We applied our method to whole-exome sequencing data from 11,873 tumor-normal pairs and identified 460 driver genes that clustered into 21 cancer-related pathways. Our study provides a resource of driver genes across 28 tumor types with additional driver genes identified according to mutations in unusual nucleotide contexts.

PMID:
32015527
DOI:
10.1038/s41588-019-0572-y

Supplemental Content

Full text links

Icon for Nature Publishing Group
Loading ...
Support Center