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. 2012 Nov 26;4(11):89.
doi: 10.1186/gm390. eCollection 2012.

Improving the Prediction of the Functional Impact of Cancer Mutations by Baseline Tolerance Transformation

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Free PMC article

Improving the Prediction of the Functional Impact of Cancer Mutations by Baseline Tolerance Transformation

Abel Gonzalez-Perez et al. Genome Med. .
Free PMC article

Abstract

High-throughput prioritization of cancer-causing mutations (drivers) is a key challenge of cancer genome projects, due to the number of somatic variants detected in tumors. One important step in this task is to assess the functional impact of tumor somatic mutations. A number of computational methods have been employed for that purpose, although most were originally developed to distinguish disease-related nonsynonymous single nucleotide variants (nsSNVs) from polymorphisms. Our new method, transformed Functional Impact score for Cancer (transFIC), improves the assessment of the functional impact of tumor nsSNVs by taking into account the baseline tolerance of genes to functional variants.

Figures

Figure 1
Figure 1
The distribution of MutationAssessor functional impact scores of nonsynonymous single nucleotide variants differs significantly in proteins belonging to different functional groups. (a) Candlestick representation of the distributions of MutationAssessor (MA) scores of germline single nucleotide variants (SNVs) in genes in all Gene Ontology Molecular Function (GOMF) categories, ordered from higher to lower mean. (b,c) Thirty least-tolerant and 30 most-tolerant GOMF groups of nsSNVs ordered by their mean MA scores. Groups in the lower end of the tolerance scale (less tolerant) correspond to essential GOMF categories, involved in signal transduction, transcription, and translation. On the other hand, the most tolerant molecular functions correspond mainly to metabolic-related activities.
Figure 2
Figure 2
Outline of the method to transform the scores. (a) Functional impact scores (FISs) of all germline single nucleotide variants (SNVs) from the 1000 Genomes Project are computed. (b) SNVs are partitioned into subsets according to the category of the genes that harbor them (for example, Gene Ontology Molecular Function). (c) FISs of a given cancer somatic mutation are computed and transformed using the distribution of the scores of SNVs in the same category as the protein where the mutation under analysis occurs. We give these transformed scores the generic name transFIC (transformed Functional Impact scores in Cancer).
Figure 3
Figure 3
Transformed Functional Impact for Cancer (transFIC) systematically outperforms original scores in the task of differentiating cancer driver mutations from neutral variants. (a) Performance of GOMF transFIC is compared to the three original functional impact scores (FISs) classifying the nine proxy datasets, using as cutoff the value of FIS (or transFIC) that maximizes the Mathews correlation coefficient (MCC) in each case. (b) Performance of GOMF transFIC is compared to the original score of CHASM (q-value cutoff <0.05) in two proxy datasets after removal of mutations within the training set of CHASM.
Figure 4
Figure 4
Complementary cumulative distribution of the three transFIC of subsets of nonsynonymous single nucleotide variants from COSMIC. (a-c) Complementary cumulative distribution of transFIC SIFT (a), transFIC PPH2 (b) and transFIC MA (c) of nonrecurrent (blue), recurrent (orange) and highly recurrent (red) COSMIC mutations.

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