Literature DB >> 29887214

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

Brian H Shirts1, Eric Q Konnick2, Sarah Upham2, Tom Walsh3, John Michael O Ranola2, Angela L Jacobson2, Mary-Claire King3, Rachel Pearlman4, Heather Hampel4, Colin C Pritchard2.   

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.
Copyright © 2018 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

Entities:  

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

Mesh:

Year:  2018        PMID: 29887214      PMCID: PMC6035155          DOI: 10.1016/j.ajhg.2018.05.001

Source DB:  PubMed          Journal:  Am J Hum Genet        ISSN: 0002-9297            Impact factor:   11.025


  40 in total

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10.  UMD-MLH1/MSH2/MSH6 databases: description and analysis of genetic variations in French Lynch syndrome families.

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Journal:  Database (Oxford)       Date:  2013-05-31       Impact factor: 3.451

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