Multi-modal digital twin model outperforms conventional biomarker stratification in pancreatic cancer

Abstract

Better patient selection for treatment is critical to improving both cancer care and therapeutic development in oncology. The ability to predict individual patient responses to cancer treatment ahead of time would transform cancer care with substantial impact on outcomes, quality of life and cost. We generated molecular digital twins of individual participants using a Bayesian foundation model of cancer (FarrSight®) in the COMPASS clinical trial (a non-randomized study of mFOLFIRINOX and gemcitabine + nab-paclitaxel in first-line advanced pancreatic cancer). We compared these individual digital twin predictions to the existing Moffitt classification of pancreatic ductal adenocarcinoma. Individual digital twin predictions of response to mFOLFIRINOX outperformed the Moffitt classification in the basal-like subtype with an AUC of 72.3% compared to the conventional biomarker AUC of 44.8%; overall accuracy of 65.8% vs. 47.4%; PPV of 60% vs. 40%; and NPV of 72.2% vs. 52.2%. Individual patient response predictions using models such as FarrSight® have the potential to better select patients for treatment with established therapeutics and in therapeutic development compared to biomarkers based on population averages.