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Nat Cancer ; 5(2): 299-314, 2024 Feb.
Article in English | MEDLINE | ID: mdl-38253803

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.


Subject(s)
Adenocarcinoma , Carcinoma, Pancreatic Ductal , Pancreatic Neoplasms , Humans , Adenocarcinoma/genetics , Adenocarcinoma/surgery , Pancreatic Neoplasms/genetics , Pancreatic Neoplasms/surgery , Multiomics , Artificial Intelligence , Carcinoma, Pancreatic Ductal/genetics , Carcinoma, Pancreatic Ductal/surgery , Intelligence
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