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

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).

569 DNA samples from datasets A and B were sequenced using a custom library design covering a backbone of structural regions and the full-length sequence of 375 genes. Data were used to train, validate (dataset A), and test (dataset B) HR-SC, using BRCA1/BRCA2 status and a compendium of previously annotated copy number signatures. Lastly, HR-SC was compared with already established approaches to evaluate its predictive and prognostic role.

In dataset A, where the failure rate was 6.4%, HR-SC showed a sensitivity of 92%, a specificity of 94.73%, an accuracy of 93.18%, a positive predictive value (PPV) of 95.83%, and a negative predictive value (NPV) of 90%. In dataset B, where the failure rate was 4%, HR-SC showed a sensitivity of 90.16%, a specificity of 82.86%, an accuracy of 87.5%, a PPV of 90.16%, and an NPV of 82.86%. Univariate and multivariate survival analyses demonstrated its predictive role [progression-free survival (PFS): hazard ratio (HR) = 0.42, P < 0.0001; overall survival (OS): HR = 0.63, P = 0.036] and its prognostic role (PFS: HR = 0.56, P = 0.0095).

The study demonstrates that HR-SC is a novel, clinically feasible solution with a low failure rate for predicting HRR status in OC patients and underscores the importance of leveraging ML approaches for advancing precision oncology in the era of personalized medicine.