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The Molecular Twin artificial-intelligence platform integrates multi-omic data to predict outcomes for pancreatic adenocarcinoma patients.

  • 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
Publication Date
Feb 01, 2024
eScholarship - University of California
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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.

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