Abstract
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.
Highlights
An understanding of the genetic causes of cancer is a prerequisite for the administration of effective treatment regimes and the development of new drugs
We report on a novel method pathway and reach method (PAR) that assesses pathways of interest to identify those genes that are similar to a set of gold standard cancer driver genes using a guilty by resemblance doctrine
The curated list of 572 Cancer Genome Census (CGC) genes was much larger than the 125 genes of Vogelstein et al and the 254 genes of Lawrence et al, both of which were generated by computation
Summary
An understanding of the genetic causes of cancer is a prerequisite for the administration of effective treatment regimes and the development of new drugs. Cancer cells are different from normal cells in that they develop the capability to initiate their own cell division and ignore messages to stop dividing or to commit suicide via apoptosis. These defects are brought about by random genetic alterations that by chance rewire signaling pathways in a manner that favors uncontrolled cell division. Genes that are frequently mutated in tumors are readily identified as cancer driver genes. This approach fails to identify oncogenes that are activated by increased expression, tumor suppressor genes (TSGs) that are deactivated by suppressed expression, or driver genes that are rarely mutated. Expression levels of genes may be altered by methylation of their promoters or by the binding of micro RNA molecules to their
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