Format

Send to

Choose Destination
Nat Methods. 2017 Aug;14(8):782-788. doi: 10.1038/nmeth.4364. Epub 2017 Jul 17.

Comparison of algorithms for the detection of cancer drivers at subgene resolution.

Author information

1
Sanford Burnham Prebys Medical Discovery Institute, La Jolla, California, USA.
2
Department of Pathology and Cancer Center, Massachusetts General Hospital, Boston, Massachusetts, USA.
3
Harvard Medical School, Boston, Massachusetts, USA.
4
Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
5
Department of Experimental and Health Sciences, University Pompeu Fabra (UPF), Barcelona, Spain.
6
Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain.
7
Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain.
8
Barcelona Supercomputing Centre (BSC), Barcelona, Spain.
9
Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain.

Abstract

Understanding genetic events that lead to cancer initiation and progression remains one of the biggest challenges in cancer biology. Traditionally, most algorithms for cancer-driver identification look for genes that have more mutations than expected from the average background mutation rate. However, there is now a wide variety of methods that look for nonrandom distribution of mutations within proteins as a signal for the driving role of mutations in cancer. Here we classify and review such subgene-resolution algorithms, compare their findings on four distinct cancer data sets from The Cancer Genome Atlas and discuss how predictions from these algorithms can be interpreted in the emerging paradigms that challenge the simple dichotomy between driver and passenger genes.

PMID:
28714987
PMCID:
PMC5935266
DOI:
10.1038/nmeth.4364
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Nature Publishing Group Icon for PubMed Central
Loading ...
Support Center