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

ABSTRACT:

Background

Identifying breast cancer patients with DNA repair pathway-related germline pathogenic variants (GPVs) is important for effectively employing systemic treatment strategies and risk-reducing interventions. However, current criteria and risk prediction models for prioritizing genetic testing among breast cancer patients do not meet the demands of clinical practice due to insufficient accuracy.

Methods

The study population comprised 3041 breast cancer patients enrolled from seven hospitals between October 2017 and 11 August 2019, who underwent germline genetic testing of 50 cancer predisposition genes (CPGs). Associations among GPVs in different CPGs and endophenotypes were evaluated using a case-control analysis. A phenotype-based GPV risk prediction model named DNA

SUBMITTER: Liu J 

PROVIDER: S-EPMC8876403 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature