Literature DB >> 25407795

Widespread genetic epistasis among cancer genes.

Xiaoyue Wang1, Audrey Q Fu1, Megan E McNerney2, Kevin P White1.   

Abstract

Quantitative genetic epistasis has been hypothesized to be an important factor in the development and progression of complex diseases. Cancers in particular are driven by the accumulation of mutations that may act epistatically during the course of the disease. However, as cancer mutations are uncovered at an unprecedented rate, determining which combinations of genetic alterations interact to produce cancer phenotypes remains a challenge. Here we show that by using combinatorial RNAi screening in cell culture, dense and often previously undetermined interactions among cancer genes were revealed by assessing gene pairs that are frequently co-altered in primary breast cancers. These interacting gene pairs are significantly associated with survival time when co-altered in patients, indicating that genetic interaction mapping may be leveraged to improve risk assessment. As many of these interacting gene pairs involve known drug targets, personalized treatment regimens may be improved by overlaying genetic interactions with mutational profiling.

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Year:  2014        PMID: 25407795     DOI: 10.1038/ncomms5828

Source DB:  PubMed          Journal:  Nat Commun        ISSN: 2041-1723            Impact factor:   14.919


  22 in total

1.  Cancer genetics: Leveraging functional data for driver genes.

Authors:  Darren J Burgess
Journal:  Nat Rev Genet       Date:  2014-12-09       Impact factor: 53.242

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Journal:  Oncoimmunology       Date:  2018-07-23       Impact factor: 8.110

3.  WeSME: uncovering mutual exclusivity of cancer drivers and beyond.

Authors:  Yoo-Ah Kim; Sanna Madan; Teresa M Przytycka
Journal:  Bioinformatics       Date:  2017-03-15       Impact factor: 6.937

Review 4.  Concepts in solid tumor evolution.

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Journal:  Trends Genet       Date:  2015-02-27       Impact factor: 11.639

5.  EPIMUTESTR: a nearest neighbor machine learning approach to predict cancer driver genes from the evolutionary action of coding variants.

Authors:  Saeid Parvandeh; Lawrence A Donehower; Katsonis Panagiotis; Teng-Kuei Hsu; Jennifer K Asmussen; Kwanghyuk Lee; Olivier Lichtarge
Journal:  Nucleic Acids Res       Date:  2022-07-08       Impact factor: 19.160

Review 6.  Precision Oncology: The Road Ahead.

Authors:  Daniela Senft; Mark D M Leiserson; Eytan Ruppin; Ze'ev A Ronai
Journal:  Trends Mol Med       Date:  2017-09-05       Impact factor: 11.951

Review 7.  Tissue-specific tumorigenesis: context matters.

Authors:  Günter Schneider; Marc Schmidt-Supprian; Roland Rad; Dieter Saur
Journal:  Nat Rev Cancer       Date:  2017-03-03       Impact factor: 60.716

8.  GWAS of three molecular traits highlights core genes and pathways alongside a highly polygenic background.

Authors:  Nasa Sinnott-Armstrong; Sahin Naqvi; Manuel Rivas; Jonathan K Pritchard
Journal:  Elife       Date:  2021-02-15       Impact factor: 8.140

9.  Positive epistasis between disease-causing missense mutations and silent polymorphism with effect on mRNA translation velocity.

Authors:  Robert Rauscher; Giovana B Bampi; Marta Guevara-Ferrer; Leonardo A Santos; Disha Joshi; David Mark; Lisa J Strug; Johanna M Rommens; Manfred Ballmann; Eric J Sorscher; Kathryn E Oliver; Zoya Ignatova
Journal:  Proc Natl Acad Sci U S A       Date:  2021-01-26       Impact factor: 12.779

10.  Gene network inference by fusing data from diverse distributions.

Authors:  Marinka Žitnik; Blaž Zupan
Journal:  Bioinformatics       Date:  2015-06-15       Impact factor: 6.937

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