domingo, 24 de junio de 2018

Using Somatic Mutations from Tumors to Classify Variants in Mismatch Repair Genes. - PubMed - NCBI

2018 May 31. pii: S0002-9297(18)30162-9. doi: 10.1016/j.ajhg.2018.05.001. [Epub ahead of print]

Using Somatic Mutations from Tumors to Classify Variants in Mismatch Repair Genes.

Shirts BH1, Konnick EQ2, Upham S2, Walsh T3, Ranola JMO2, Jacobson AL2, King MC3, Pearlman R4, Hampel H4, Pritchard CC2.

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Abstract

Present guidelines for classification of constitutional variants do not incorporate inferences from mutations seen in tumors, even when these are associated with a specific molecular phenotype. When somatic mutations and constitutional mutations lead to the same molecular phenotype, as for the mismatch repair genes, information from somatic mutations may enable interpretation of previously unclassified variants. To test this idea, we first estimated likelihoods that somatic variants in MLH1, MSH2, MSH6, and PMS2 drive microsatellite instability and characteristic IHC staining patterns by calculating likelihoods of high versus low normalized variant read fractions of 153 mutations known to be pathogenic versus those of 760 intronic passenger mutations from 174 paired tumor-normal samples. Mutations that explained the tumor mismatch repair phenotype had likelihood ratio for high variant read fraction of 1.56 (95% CI 1.42-1.71) at sites with no loss of heterozygosity and of 26.5 (95% CI 13.2-53.0) at sites with loss of heterozygosity. Next, we applied these ratios to 165 missense, synonymous, and splice variants observed in tumors, combining in a Bayesian analysis the likelihood ratio corresponding with the adjusted variant read fraction with pretest probabilities derived from published analyses and public databases. We suggest classifications for 86 of 165 variants: 7 benign, 31 likely benign, 22 likely pathogenic, and 26 pathogenic. These results illustrate that for mismatch repair genes, characterization of tumor mutations permits tumor mutation data to inform constitutional variant classification. We suggest modifications to incorporate molecular phenotype in future variant classification guidelines.

KEYWORDS:

Bayesian analysis; Lynch syndrome; Lynch-like; colorectal cancer; likelihood-ratio; loss of heterozygosity; molecular phenotype; tumor mutations; variant classification guidelines; variant of uncertain significance