Deep learning predicts HRD and platinum response from histology slides in breast and ovarian cancer

ABSTRACT Breast and ovarian cancers harboring homologous recombination deficiencies (HRD) can benefit from platinum-based chemotherapies and PARP inhibitors. Standard diagnostic tests for detecting HRD utilize molecular profiling, which is not universally available especially for medically underserved populations. Here, we trained a deep learning approach for predicting genomically derived HRD scores from routinely sampled hematoxylin and eosin (H&E)-stained histopathological slides. For breast cancer, the approach was externally validated on three independent cohorts and allowed predicting patients’ response to platinum treatment. Using transfer learning, we demonstrated the method’s clinical applicability to H&E-images from high-grade ovarian tumors. Importantly, our deep learning approach outperformed existing genomic HRD biomarkers in predicting response to platinum-based therapies across multiple cohorts, providing a complementary approach for detecting HRD in patients across diverse socioeconomic groups. One-Sentence Summary A deep learning approach outperforms molecular tests in predicting platinum response of HRD cancers from histological slides.