- Osipov, Arsen;
- Nikolic, Ognjen;
- Gertych, Arkadiusz;
- Parker, Sarah;
- Singh, Pranav;
- Filippova, Darya;
- Dagliyan, Grant;
- Ferrone, Cristina;
- Zheng, Lei;
- Moore, Jason;
- Tourtellotte, Warren;
- Van Eyk, Jennifer;
- Theodorescu, Dan;
- Hendifar, Andrew
Contemporary analyses focused on a limited number of clinical and molecular biomarkers have been unable to accurately predict clinical outcomes in pancreatic ductal adenocarcinoma. Here we describe a precision medicine platform known as the Molecular Twin consisting of advanced machine-learning models and use it to analyze a dataset of 6,363 clinical and multi-omic molecular features from patients with resected pancreatic ductal adenocarcinoma to accurately predict disease survival (DS). We show that a full multi-omic model predicts DS with the highest accuracy and that plasma protein is the top single-omic predictor of DS. A parsimonious model learning only 589 multi-omic features demonstrated similar predictive performance as the full multi-omic model. Our platform enables discovery of parsimonious biomarker panels and performance assessment of outcome prediction models learning from resource-intensive panels. This approach has considerable potential to impact clinical care and democratize precision cancer medicine worldwide.