Deciphering oncogenic drivers: from single genes to integrated pathways.
Technological advances in next-generation sequencing have uncovered a wide spectrum of aberrations in cancer genomes. The extreme diversity in cancer mutations necessitates computational approaches to differentiate between the 'drivers' with vital function in cancer progression and those nonfunctional 'passengers'. Although individual driver mutations are routinely identified, mutational profiles of different tumors are highly heterogeneous. There is growing consensus that pathways rather than single genes are the primary target of mutations. Here we review extant bioinformatics approaches to identifying oncogenic drivers at different mutational levels, highlighting the strategies for discovering driver pathways and networks from cancer mutation data. These approaches will help reduce the mutation complexity, thus providing a simplified picture of cancer.
Jiajia
J
Chen
Maomin
M
Sun
Bairong
B
Shen
D000465:Algorithms; D000818:Animals; D001483:Base Sequence; D063646:Carcinogenesis / Q000235:genetics*; D004252:DNA Mutational Analysis / Q000379:methods*; D004273:DNA, Neoplasm / Q000235:genetics*; D006801:Humans; D000069550:Machine Learning; D008969:Molecular Sequence Data; D009154:Mutation / Q000235:genetics; D009857:Oncogenes / Q000235:genetics*; D010363:Pattern Recognition, Automated / Q000379:methods; D017422:Sequence Analysis, DNA / Q000379:methods*; D015398:Signal Transduction / Q000235:genetics*
D004273:DNA, Neoplasm
cancer driver; mutation; network; pathway