ArticleHuman mutation2026
Bayesian Integration of Tumor Mutational Signatures and Somatic Features Refines Pathogenicity Assessment of Germline Mismatch Repair Variants.
Article in Human mutation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Variants of uncertain significance (VUS) in mismatch repair (MMR) genes represent a persistent bottleneck in germline interpretation for Lynch syndrome, creating a critical opportunity to leverage tumor biology to refine pathogenicity assessment. Although tumor features such as microsatellite instability (MSI) and immunohistochemistry (IHC) are routinely evaluated, they are typically interpreted separately from germline classification, and their quantitative contribution within ACMG/AMP frameworks remains poorly defined. We therefore analyzed paired germline and tumor sequencing data from 1110 tumors across 1073 patients with colorectal or endometrial cancer to determine whether mismatch repair-deficient (MMR-d) mutational signatures can be quantitatively integrated into Bayesian germline variant interpretation. Using COSMIC single-base substitution signatures, tumors were classified as MMR-d or MMR proficient, and an empirically derived likelihood ratio (LR) quantified the association between MMR-d signatures and pathogenic germline MMR variants. The presence of an MMR-d signature increased the likelihood of an underlying pathogenic germline MMR variant approximately eightfold (LR ≈ 8; log10 LR ≈ 0.90), whereas its absence provided moderate-to-strong benign evidence (LR ≈ 0.156; log10 LR ≈ -0.81). Applying this integrative framework to 45 germline MMR VUS, joint modeling of tumor mutational signatures with additional somatic and variant-level evidence resulted in clinically significant reclassification of 38 (84.4%) variants, including three reclassified as pathogenic or likely pathogenic and 35 as likely benign. A total of 16 downgraded variants were independently downgraded by Invitae. These findings demonstrate that tumor mutational signatures can be formally incorporated into Bayesian germline interpretation, transforming tumor data into quantitative pathogenicity evidence and offering a principled strategy to reduce VUS burden in hereditary cancer genetics.
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