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The DJ/dS Ratio Test Reveals Hundreds of Novel Putative Cancer Drivers

Overview
Journal Mol Biol Evol
Specialty Biology
Date 2015 Apr 16
PMID 25873590
Citations 5
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Abstract

Computational tools with a balanced sensitivity and specificity in identification of candidate cancer drivers are highly desired. In this study, we propose a new statistical test, namely the dJ/dS ratio test, to compute the relative mutation rate of exon/intron junction sites (dJ) to synonymous sites (dS); observation of dJ/dS ratio larger than 1 in cancer indicates positive selection for splicing deregulation, a signature of cancer driver genes. Using this method, we analyzed the data from The Cancer Genome Atlas and identified hundreds of novel putative cancer drivers. Interestingly, these genes are highly enriched in biological processes related to the development and maintenance of multicellularity, paralleling a previous finding that cancer evolves back to be unicellular by knocking down the multicellularity-associated genetic network.

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References
1.
. Comprehensive molecular profiling of lung adenocarcinoma. Nature. 2014; 511(7511):543-50. PMC: 4231481. DOI: 10.1038/nature13385. View

2.
Vogelstein B, Papadopoulos N, Velculescu V, Zhou S, Diaz Jr L, Kinzler K . Cancer genome landscapes. Science. 2013; 339(6127):1546-58. PMC: 3749880. DOI: 10.1126/science.1235122. View

3.
Li W, Wu C, Luo C . A new method for estimating synonymous and nonsynonymous rates of nucleotide substitution considering the relative likelihood of nucleotide and codon changes. Mol Biol Evol. 1985; 2(2):150-74. DOI: 10.1093/oxfordjournals.molbev.a040343. View

4.
Sjoblom T, Jones S, Wood L, Parsons D, Lin J, Barber T . The consensus coding sequences of human breast and colorectal cancers. Science. 2006; 314(5797):268-74. DOI: 10.1126/science.1133427. View

5.
Ding L, Getz G, Wheeler D, Mardis E, McLellan M, Cibulskis K . Somatic mutations affect key pathways in lung adenocarcinoma. Nature. 2008; 455(7216):1069-75. PMC: 2694412. DOI: 10.1038/nature07423. View