1. The Molecular Twin artificial-intelligence platform integrates multi-omic data to predict outcomes for pancreatic adenocarcinoma patients.
- Author
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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, and Hendifar, Andrew
- Subjects
Humans ,Adenocarcinoma ,Pancreatic Neoplasms ,Multiomics ,Artificial Intelligence ,Carcinoma ,Pancreatic Ductal ,Intelligence - Abstract
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.
- Published
- 2024