Filtering and Interpretation Algorithm for Oncology based on VarClaM Conceptual Model

The accurate interpretation of genomic variants is crucial for hereditary cancer diagnostics and management. We present a standardized variant interpretation algorithm that adapts the American College of Medical Genetics and Genomics-Association for Molecular Pathology (ACMG-AMP) guidelines specifically for hereditary cancer assessment. This algorithm incorporates structured filtering criteria for identifying relevant variants and comprehensive interpretation metrics that leverage multiple data sources, including population databases, computational predictions, and clinical evidence. The framework employs four primary filtering tiers that evaluate structural variants, pathogenicity interpretations from established databases, rare null variants, and cancer-relevant genes. The interpretation system utilizes a quantitative scoring approach with 22 distinct criteria, each weighted according to strength of evidence, resulting in a refined nine-tier interpretation system ranging from benign to pathogenic. This workflow has been validated through expert review by the Chilean Cancer Hereditary Group and successfully tested on more than 30 clinical cases.