Cancer research has invested in cancer genome sequencing with the aim of identifying somatic mutations that drive tumorigenesis. Yet, the thousands of somatic mutations identified in cancer sequencing projects have little meaning without being able to distinguish the cancer-causing “drivers” from the neutral “passengers”. Identifying the mutations with a functional impact is one approach to distinguish driver from neutral mutations for further investigation.
Genes encoding critical proteins are likely to have a lower tolerance to non-synonymous single nucleotide variants (SNVs), compared with genes encoding less essential products. However, the tools available so far to assess the functional impact of mutations would not take this different baseline tolerance into account. Nuria López-Bigas and colleagues used germline variants from the 1000 Genomes Project data to assess the baseline tolerance of different proteins to functional variants, and then leveraged this information in a computational method to predict cancer driver mutations. This Method, transFIC (Transformed Functional Impact score for Cancer), performed better than any of the previous tools in predicting the functional impact of cancer somatic mutations.
As discussed by Vidhya Krishnan and Pauline Ng in an accompanying research highlight, this new bioinformatics method will be valuable for the clinic, as the identification of mutations with a functional impact may determine the choice and outcome of treatment, bringing us one step closer to personalized cancer medicine.