Article

Cancer genomic profiling predicts pathogenicity of BRCA1 and BRCA2 variants

2026-03-06

Abstract excerpt

Accurate classification of BRCA1 and BRCA2 variants is essential for cancer risk assessment and therapy selection, yet over one-third remain variants of uncertain significance (VUS). Here, using 120,660 real-world cancer genomic profiles with BRCA1 or BRCA2 variants from a >800,000-sample cohort, we develop machine learning models that predict pathogenicity using clinical and tumor-derived features, including a pa...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 75748a48-943e-5d3b-a654-8bbe411c1992
DOI: 10.64898/2026.03.05.26347746

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Cancer genomic profiling predicts pathogenicity of BRCA1 and BRCA2 variantsDOI 10.64898/2026.03.05.26347746Read

Machine learning approach to assess the pathogenicity of BRCA1/2 genetic variants : brca-NOVUSSemantic proximity Read

Integrating breast tumour homologous recombination deficiency status to aid germline BRCA1 and BRCA2 variant classification.Semantic proximity Read

Validation of Genomic and Transcriptomic Models of Homologous Recombination Deficiency in a Real-World Pan-Cancer CohortSemantic proximity Read

Regulators of homologous recombination deficiency identified by machine learning using somatic multi-omics data.Semantic proximity Read

Integrating breast tumor homologous recombination deficiency status to aid germline<i>BRCA1</i>and<i>BRCA2</i>variant classificationSemantic proximity Read

Validation of genomic and transcriptomic models of homologous recombination deficiency in a real-world pan-cancer cohortSemantic proximity Read

Domain-Level Distribution of Pathogenic BRCA1/2 Somatic Mutations Shows No Evidence of Large Subtype-Specific Enrichment in Breast Cancer: A Three-Cohort Analysis Supporting Broad BRCA Testing.Semantic proximity Read

Azurify integrates cancer genomics with machine learning to classify the clinical significance of somatic variantsSemantic proximity Read

Pathogenic Germline Variants in BRCA1/2 and p53 Identified by Real-world Comprehensive Cancer Genome Profiling Tests in Asian PatientsSemantic proximity Read

Pathogenicity assessment of variants for breast cancer susceptibility genes based on BRCAness of tumor sample.Semantic proximity Read

Evidence-based recommendations for gene-specific ACMG/AMP variant classification from the ClinGen ENIGMA BRCA1 and BRCA2 Variant Curation Expert PanelSemantic proximity Read

DrABC: deep learning accurately predicts germline pathogenic mutation status in breast cancer patients based on phenotype data.Semantic proximity Read