Cancer driver genes: a guilty by resemblance doctrine

PeerJ

article

Emilie Ramsahai¹  Vrijesh Tripathi¹  Melford John² 

[1] Department of Mathematics and Statistics, The University of the West Indies;Department of Preclinical Sciences, The University of the West Indies

关键词: Signal transduction;    Network topology;    m-reach;    Cancer;    Novel driver genes;    Pathways;   

DOI  :  10.7717/peerj.6979

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

A major benefit of expansive cancer genome projects is the discovery of new targets for drug treatment and development. To date, cancer driver genes have been primarily identified by methods based on gene mutation frequency. This approach fails to identify culpable genes that are not mutated, rarely mutated, or contribute to the development of rare forms of cancer. Due to the complexity of the disease and the sheer volume of data, computational methods may encounter a NP-complete problem. We have developed a novel pathway and reach (PAR) method that employs a guilty by resemblance approach to identify cancer driver genes that avoids the above problems. Essentially PAR sifts through a list of genes of biological pathways to find those that are common to the same pathways and possess a similar 2-reach topology metric as a reference set of recognized driver genes. This approach leads to faster processing times and eliminates any dependency on gene mutation frequency. Out of the three pathways, signal transduction, immune system, and gene expression, a set of 50 candidate driver genes were identified, 30 of which were new. The top five were HGF, E2F1, C6, MIF, and CDK2.

【 授权许可】

CC BY