Table 2.
Summary of methods for identifying cancer driver modules
| Method | Description and reference | Additional information |
|---|---|---|
| Cancer driver module identification | ||
| Using mutual exclusivity of mutations | ||
| CoMEt | Identifies cancer genes by using the exact statistical test to test mutual exclusivity of genomic events and applies techniques to do simultaneous analysis for mutually exclusive alterations 4 | It has a low computational complexity |
| WeSME | Discovers cancer drivers by evaluating the mutual exclusivity of mutations of gene pairs 20 | It can only detect driver gene pairs (i.e. only two driver genes in each module) |
| MEMo | Analyses mutual exclusivity of mutated genes in subnetworks to identify mutual exclusivity modules in cancer 17 | It depends on the prior biological knowledge of gene interactions |
| Others: using mutations, gene expression, gene network | ||
| iMCMC | Uses the cancer genomic data including mutations, CNAs, and gene expression from cancer patients to identify mutated core modules in cancer 42 | It provides flexibility by using two input parameters to balance different sources of data |
| NetBox | Uses biological networks to assess network modules statistically and identify core pathways in GBM 21 | It is only used for Glioblastoma |
| TieDIE | Applies network diffusion to discover the relationship of genomic events and changes in cancer subtypes 43 | It has a high computational cost |
| CICERO | Uses RNA sequencing data and extensive annotation to detect driver fusions with a local assembly-based algorithm 44 | It may miss low-expressed gene fusions |
| Hamilton et al. | Uses the pan-cancer dataset of TCGA and the miRNA target data of AGO-CLIP to detect a pan-cancer oncogenic miRNA superfamily with a central core seed motif 45 | It discovers a miRNA driver superfamily consisting of miR-17, miR-19, miR-130, miR- 93, miR-18, miR-455 and miR-210 |