Predicting benefit from PARP inhibitors using deep learning on H&E-stained ovarian cancer slides
Frederik Marmé, Eva Krieghoff‐Henning, Lennard Kiehl, Christoph Wies, Jan Hauke, Eric Hahnen, Philipp Harter, Philip C. Schouten, Tobias Brodkorb, Mohamad Kayali, Florian Heitz, C. Zamagni, Antonio González‐Martín, Isabelle Treilleux, Stefan Kommoss, Katharina Prieske, Timo Gaiser, Stefan Fröhling, Isabelle Ray‐Coquard, Éric Pujade-Lauraine, Titus J. Brinker
Abstract
PURPOSE: Ovarian cancer patients with a Homologous Recombination Deficiency (HRD) often benefit from polyadenosine diphosphate-ribose polymerase (PARP) inhibitor maintenance therapy after response to platinum-based chemotherapy. HR status is currently analyzed via complex molecular tests. Predicting benefit from PARP inhibitors directly on histological whole slide images (WSIs) could be a fast and cheap alternative. PATIENTS AND METHODS: We trained a Deep Learning (DL) model on H&E stained WSIs with "shrunken centroid" (SC) based HRD ground truth using the AGO-TR1 cohort (n = 208: 108 training, 100 test) and tested its ability to predict HRD as evaluated by the Myriad classifier and the benefit from olaparib in the PAOLA-1 cohort (n = 447) in a blinded manner. RESULTS: In contrast to the HRD prediction AUROC of 72 % on hold-out, our model only yielded an AUROC of 57 % external. Kaplan-Meier analysis showed that progression free survival (PFS) in the PARP inhibitor treated PAOLA-1 patients was significantly improved in the HRD positive group as defined by our model, but not in the HRD negative group. PFS improvement in PARP inhibitor-treated patients was substantially longer in our HRD positive group, hinting at a biologically meaningful prediction of benefit from PARP inhibitors. CONCLUSION: Together, our results indicate that it might be possible to generate a predictor of benefit from PARP inhibitors based on the DL-mediated analysis of WSIs. However, further studies with larger cohorts and further methodological improvements will be necessary to generate a predictor with clinically useful accuracy across independent patient cohorts.