Homologous recombination deficiency prediction from whole slide images using label refinement and foundation-model benchmarking in ovarian cancer
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Background
Homologous recombination deficiency (HRD) is clinically imperative in high-grade serous ovarian carcinoma (HGSOC), particularly because of its association with platinum sensitivity and benefit from poly(ADP-ribose) polymerase inhibitor (PARPi) therapy. However, public datasets rarely contain a complete combination of diagnostic haematoxylin and eosin (H&E) whole-slide images (WSIs), validated clinical HRD assay results, genomic scar scores, BRCA1 promoter methylation data, and treatment-response outcomes. This creates a major barrier for computational pathology studies seeking to develop clinically interpretable models of HRD or PARPi response from routine histology.
Objective
We performed an exploratory, leakage-controlled computational pathology benchmarking study to evaluate whether H&E WSIs from TCGA-OV contain a measurable morphology-linked signal associated with research-grade molecular HRD labels, and whether label refinement and pathology foundation-model embeddings alter predictive performance.
Methods
We assembled a frozen-primary TCGA-OV WSI cohort comprising 717 tissue-section/biospecimen slides from 316 patients. Diagnostic FFPE DX slides were excluded from model selection because of complete patient overlap with the frozen-primary cohort. Two HRD labels were evaluated: an initial mutation-only molecular label based on BRCA/HR-gene mutation evidence, and a refined methylation-enhanced molecular label that additionally incorporated BRCA1 promoter methylation. Feature extraction was performed using ResNet50, UNI, CONCH, Virchow2, Phikon-v2, and UNI2-h encoders. Patient-level attention-based multiple instance learning (ABMIL) was used with patient-as-bag modelling. Evaluation used patient-level grouped 5-fold x 5-repeat stratified cross-validation, with 25 folds total, bootstrap confidence intervals, and patient-level leakage control.
Results
The initial mutation-only label classified 78 patients as positive and 238 as negative. The refined methylation-enhanced label recovered 33 additional positives, resulting in 111 positive and 205 negative patients. Patient-level ABMIL using UNI2-h features achieved the strongest performance for the refined label, with AUROC 0.634 (95% CI 0.571-0.698), AUPRC 0.468 (95% CI 0.390-0.562), balanced accuracy 0.597, sensitivity 0.532, specificity 0.663, F1 score 0.494, and Brier score 0.233. The calibrated threshold was 0.512, yielding TN=136, FP=69, FN=52, and TP=59. Comparative models showed lower discrimination, including UNI2-h with the initial label (AUROC 0.628), Phikon-v2 refined (0.582), Virchow2 refined (0.582), CONCH initial (0.587), ResNet50 refined (0.570), and clinical baselines (AUROC 0.54-0.57).
Conclusions
TCGA-OV H&E WSIs contain a modest but reproducible morphology-linked signal associated with research-grade molecular HRD status. However, the AUROC around 0.63, absence of clinical HRD assay labels, lack of genomic scar endpoints in the implemented workflow, and absence of PARPi/platinum response targets prevent clinical interpretation. This study should be interpreted as a proof-of-concept benchmarking framework and methodological foundation for future H&E-based predictive modelling in clinically curated PARPi response cohorts.
