Improving the prediction of cancer causing mutations
Cancer is generally caused by a combination of many specific mutations, called drivers. But cancer cells contain many other mutations that are not the cause of the cancer, but rather a consequence (passenger mutations). Also, high-throughput genome projects are identifying a huge number of somatic variants. Which ones are cancer-causing? How to distinguish the needle in the haystack?
A new computational method recently published in Genome Medicine by the research group led by Núria López-Bigas at the GRIB (UPF-IMIM), can help. Called transformed Functional Impact Score for Cancer (transFIC), it improves the assessment of the functional impact of tumor nonsynonymous single nucleotide variants (nsSNVs) by taking into account the baseline tolerance of genes to functional variants.
Other methods predicting the functional impact of cancer-causing somatic variants employ evolutionary information to assess the likely impact of an amino acid change on the structure or function of the altered protein. However, according to the authors, the ultimate effect of this amino acid change on the functioning of a cell depends on other factors as well, such as the particular role played by the altered protein in the cellular machinery. The more critical that role is, the less tolerant will the protein be to an amino acid change.
Their new method takes this feature into consideration, and has been shown to outperform previous ones. They distribute their new tool as a PERL script that users can download and use locally, and they have set up a web server which can be queried to obtain the transFIC of somatic cancer nsSNVs.
Gonzalez-Perez A, Deu-Pons J, Lopez-Bigas N. Improving the prediction of the functional impact of cancer mutations by baseline tolerance transformation. Genome Med. 2012 Nov 26;4(11):89