Although a few cancer genes are mutated in a high proportion of tumours of a given type (>20%), most are mutated at intermediate frequencies (2–20%). To explore the feasibility of creating a comprehensive catalogue of cancer genes, we analysed somatic point mutations in exome sequences from 4,742 human cancers and their matched normal-tissue samples across 21 cancer types. We found that large-scale genomic analysis can identify nearly all known cancer genes in these tumour types. Our analysis also identified 33 genes that were not previously known to be significantly mutated in cancer, including genes related to proliferation, apoptosis, genome stability, chromatin regulation, immune evasion, RNA processing and protein homeostasis. Down-sampling analysis indicates that larger sample sizes will reveal many more genes mutated at clinically important frequencies. We estimate that near-saturation may be achieved with 600–5,000 samples per tumour type, depending on background mutation frequency. The results may help to guide the next stage of cancer genomics.
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- Supplementary Information (1.4 MB)
This file contains Supplementary Figures 1-9 and legends for Supplementary Tables 1-6 (see separate files for tables).
- Supplementary Table 1 (28 KB)
This file contains a list of source datasets analyzed in this work, and references to the corresponding publications.
- Supplementary Table 2 (709 KB)
This file contains the 260 significantly mutated cancer genes found by analysis with the MutSig suite (see Supplementary Information file for full legend).
- Supplementary Table 3 (16 KB)
This file contains a list of the 21 tumor types studied, and the significantly mutated genes found by the MutSig suite in each tumor type (see Supplementary Information file for full legend).
- Supplementary Table 4 (58 KB)
The file contains a list of references reporting the identification of candidate cancer genes (see Supplementary Information file for full legend).
- Supplementary Table 5 (21 KB)
This file contains a list of references to biological literature supporting the 33 novel candidate cancer genes with clear and compelling connections to cancer biology.
- Supplementary Table 6 (24 KB)
This file contains a summary of the analysis comparing the performance of each of the three MutSig metrics separately, in pairwise combinations, and all three combined as in the main analysis (see Supplementary Information file for full legend).