Leveraging cancer mutation data to predict the pathogenicity of germline missense variants

ABSTRACT Innovative and easy-to-implement strategies are needed to improve the pathogenicity assessment of rare germline missense variants. Somatic cancer driver mutations identified through large-scale tumor sequencing studies often impact genes that are also associated with rare Mendelian disorders. The use of cancer mutation data to aid in the interpretation of germline missense variants, regardless of whether the gene is associated with a hereditary cancer predisposition syndrome or a non-cancer-related developmental disorder, has not been systematically assessed. We extracted putative cancer driver missense mutations from the Cancer Hotspots database and annotated them as germline variants, including presence/absence and classification in ClinVar. We trained two supervised learning models (logistic regression and random forest) to predict variant classifications of germline missense variants in ClinVar using Cancer Hotspot data (training dataset). The performance of each model was evaluated with an independent test dataset generated in part from searching public and private genome-wide sequencing datasets from ∼1.5 million individuals. Of the 2,447 cancer mutations, 691 corresponding germline variants had been previously classified in ClinVar: 426 (61.6%) as likely pathogenic/pathogenic, 261 (37.8%) as uncertain significance, and 4 (0.6%) as likely benign/benign. The odds ratio for a likely pathogenic/pathogenic classification in ClinVar was 28.3 (95% confidence interval: 24.2-33.1, p < 0.001), compared with all other germline missense variants in the same 216 genes. Both supervised learning models showed high correlation with pathogenicity assessments in the training dataset. There was high area under precision-recall curve values of 0.847 and 0.829 for logistic regression and random forest models, respectively, when applied to the test dataset. With the use of cancer and germline datasets and supervised learning techniques, our study shows that cancer mutation data can be leveraged to improve the interpretation of germline missense variation potentially causing rare Mendelian disorders. AUTHOR SUMMARY Our study introduces an approach to improve the interpretation of rare genetic variation, specifically missense variants that can alter proteins and cause disease. We found that published evidence from somatic cancer sequencing studies may be relevant to understanding the impact of the same variant in the context of rare inherited (Mendelian) disorders. By using widely available datasets, we noted that many cancer driver mutations have also been observed as rare germline variants associated with inherited disorders. This intersection led us to employ machine learning techniques to assess how cancer mutation data can predict the pathogenicity of germline variants. We trained machine learning models and tested them on a separate dataset curated by searching public and private genome-wide sequencing data from over a million participants. Our models were able to successfully identify pathogenic genetic changes, demonstrating strong performance in predicting disease-causing variants. This study highlights that cancer mutation data can enhance the interpretation of rare missense variants, aiding in the diagnosis and understanding of rare diseases. Integrating this approach into current genetic classification frameworks could be beneficial, and opens new avenues for leveraging existing cancer research to benefit broader genetic research and diagnostics for rare genetic conditions.