HR-SC-an academic-developed machine learning framework to classify HRD-positive ovarian cancer patients and predict sensitivity to olaparib.

ABSTRACT:

Background

High-grade serous ovarian cancer (OC) patients with defects in the homologous recombination repair (HRR) pathway benefit from poly (ADP-ribose) polymerase inhibitor (PARPi) maintenance therapy. Clinically approved methods for identifying HRR status suffer from limitations, such as high failure rates and costs, leading to the clinical need for innovative approaches. To this aim, we developed Homologous Recombination Signature Classifier (HR-SC), a machine learning (ML) algorithm that integrates BRCA1/BRCA2 status and copy number signatures, leveraging the availability of OC samples recruited from two international clinical trials, namely PAOLA-1 (dataset A) and MITO16A/MaNGO-OV2 (dataset B).

Patients and methods

569 DNA samples from datasets A and B were sequenced

SUBMITTER: Beltrame L 

PROVIDER: S-EPMC12148384 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature