Recommendations for the classification of germline variants in the exonuclease domain of POLE and POLD1

POLE 和 POLD1 外切酶结构域种系变异分类建议

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Abstract

BACKGROUND: Germline variants affecting the proofreading activity of polymerases epsilon and delta cause a hereditary cancer and adenomatous polyposis syndrome characterized by tumors with a high mutational burden and a specific mutational spectrum. In addition to the implementation of multiple pieces of evidence for the classification of gene variants, POLE and POLD1 variant classification is particularly challenging given that non-disruptive variants affecting the proofreading activity of the corresponding polymerase are the ones associated with cancer. In response to an evident need in the field, we have developed gene-specific variant classification recommendations, based on the ACMG/AMP (American College of Medical Genetics and Genomics/Association for Molecular Pathology) criteria, for the assessment of non-disruptive variants located in the sequence coding for the exonuclease domain of the polymerases. METHODS: A training set of 23 variants considered pathogenic or benign was used to define the usability and strength of the ACMG/AMP criteria. Population frequencies, computational predictions, co-segregation data, phenotypic and tumor data, and functional results, among other features, were considered. RESULTS: Gene-specific variant classification recommendations for non-disruptive variants located in the exonuclease domain of POLE and POLD1 were defined. The resulting recommendations were applied to 128 exonuclease domain variants reported in the literature and/or public databases. A total of 17 variants were classified as pathogenic or likely pathogenic, and 17 as benign or likely benign. CONCLUSIONS: Our recommendations, with room for improvement in the coming years as more information become available on carrier families, tumor molecular characteristics and functional assays, are intended to serve the clinical and scientific communities and help improve diagnostic performance, avoiding variant misclassifications.

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