2O Prostate cancer BRCA mutation prediction using multiple-instance learning and histopathology
Carlos Macarro, Pablo Cresta Morgado, Sara Simonetti, D. Navarro, H. Alatoom, Christina Zatse, David Olmos, P.G. Nuciforo, Ana Vivancos, Rubén Olivera‐Salguero, T. Pascual, J. Carles Galceran, Elena Castro, J. Mateo, Raquel Pérez López · ESMO Open · 2024
BRCA1/2 alterations are associated with poor prognosis in prostate cancer (PCa). However, tumours with genomic loss of BRCA1/2, show increased response to PARP inhibitors and platinum-based chemotherapy. Access to genomic testing remains limited in routine clinical practice, and technical challenges arise with next-generation sequencing of small tumour biopsies. Phenotypic biomarkers of BRCA1/2 status can support the deployment of precision medicine strategies in the clinic. Deep-learning architectures such as attention-based Multiple Instance Learning (att-MIL) applied to digital pathology have shown promise in oncology.