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Nat Commun. 2016 Jul 15;7:12096. doi: 10.1038/ncomms12096.

Challenges in identifying cancer genes by analysis of exome sequencing data.

Author information

1
Cancer Cell Map Initiative (CCMI), 9500 Gilman Drive, La Jolla, California 92093, USA.
2
Department of Computer Science and Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA.
3
Department of Medicine, University of California San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA.
4
Moores Cancer Center, University of California San Diego, 3855 Health Sciences Drive, La Jolla, California 92093, USA.
5
Diller Family Comprehensive Cancer Center, University of California San Francisco, 1600 Divisadero Street, San Francisco, California 94115, USA.
6
Ludwig Institute for Cancer Research, University of California San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA.
7
Sage Bionetworks, Seattle, 110 Fairview Avenue North, Seattle, Washington 98109, USA.

Abstract

Massively parallel sequencing has permitted an unprecedented examination of the cancer exome, leading to predictions that all genes important to cancer will soon be identified by genetic analysis of tumours. To examine this potential, here we evaluate the ability of state-of-the-art sequence analysis methods to specifically recover known cancer genes. While some cancer genes are identified by analysis of recurrence, spatial clustering or predicted impact of somatic mutations, many remain undetected due to lack of power to discriminate driver mutations from the background mutational load (13-60% recall of cancer genes impacted by somatic single-nucleotide variants, depending on the method). Cancer genes not detected by mutation recurrence also tend to be missed by all types of exome analysis. Nonetheless, these genes are implicated by other experiments such as functional genetic screens and expression profiling. These challenges are only partially addressed by increasing sample size and will likely hold even as greater numbers of tumours are analysed.

PMID:
27417679
PMCID:
PMC4947162
DOI:
10.1038/ncomms12096
[Indexed for MEDLINE]
Free PMC Article

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