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Exome-wide analysis of bi-allelic alterations identifies a Lynch phenotype in The Cancer Genome Atlas.

Published Web Location

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6138910/
No data is associated with this publication.
Abstract

Background

Cancer susceptibility germline variants generally require somatic alteration of the remaining allele to drive oncogenesis and, in some cases, tumor mutational profiles. Whether combined germline and somatic bi-allelic alterations are universally required for germline variation to influence tumor mutational profile is unclear. Here, we performed an exome-wide analysis of the frequency and functional effect of bi-allelic alterations in The Cancer Genome Atlas (TCGA).

Methods

We integrated germline variant, somatic mutation, somatic methylation, and somatic copy number loss data from 7790 individuals from TCGA to identify germline and somatic bi-allelic alterations in all coding genes. We used linear models to test for association between mono- and bi-allelic alterations and somatic microsatellite instability (MSI) and somatic mutational signatures.

Results

We discovered significant enrichment of bi-allelic alterations in mismatch repair (MMR) genes and identified six bi-allelic carriers with elevated MSI, consistent with Lynch syndrome. In contrast, we find little evidence of an effect of mono-allelic germline variation on MSI. Using MSI burden and bi-allelic alteration status, we reclassify two variants of unknown significance in MSH6 as potentially pathogenic for Lynch syndrome. Extending our analysis of MSI to a set of 127 DNA damage repair (DDR) genes, we identified a novel association between methylation of SHPRH and MSI burden.

Conclusions

We find that bi-allelic alterations are infrequent in TCGA but most frequently occur in BRCA1/2 and MMR genes. Our results support the idea that bi-allelic alteration is required for germline variation to influence tumor mutational profile. Overall, we demonstrate that integrating germline, somatic, and epigenetic alterations provides new understanding of somatic mutational profiles.

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