Table 1 Outline of strategies that can be used to enrich for, identify and refine candidate driver genes and mechanisms in cancer: the underlying rationale, experimental approaches and computational tools
Strategy | Rationale | Tools/data sources |
|---|---|---|
Increasing sample size and clinical focus | Decrease variability due to different disease states | Statistical approaches; power calculations |
Detect driver versus passenger events | Determine significance of recurrent mutations after controlling for background mutation rate, gene size and regional complexity | MutSig; MuSIC; GISTIC2.0 |
Individual gene characteristics | Computationally predict functional consequences | ActiveDriver; CHASM; Polyphen2; SIFT; Mutationtaster |
Integrative analysis and known characteristics of cancer drivers | Genomic characteristics of defined drivers inform functionally relevant events | Aligning genomic datasets (SNV, CNV, SV, methylation) |
Pathway and network analysis | Heterogeneity of individual genetic aberrations contributing to common mechanisms | MsigDB; GeneGO; Reactome; PINA; PARADIGM |
Integrative multidimensional data analysis | Orthogonal readouts of disease using different experimental approaches and model systems | GEMM; chemically induced models; mutagenesis screens; shRNA screens |
Clinical correlation | Association with clinical characteristics may inform functional roles | Statistical methods |