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GNPS - Profiling of pancreatic adenocarcinoma using artificial intelligence-based integration of multi-omic and computational pathology features: lipid

ABSTRACT: Contemporary analyses focused on a limited number of clinical and molecular features have been unable to accurately predict clinical outcomes in pancreatic ductal adenocarcinoma (PDAC). Here we describe a novel, conceptual approach and use it to analyze clinical, computational pathology, and molecular (DNA, RNA, protein, and lipid) analyte data from 74 patients with resectable PDAC. Multiple, independent, machine learning models were developed and tested on curated single and multi-omic feature/analyte panels to determine their ability to predict clinical outcomes in patients. The multi-omic models predicted recurrence with an accuracy and positive predictive value (PPV) of 0.90, 0.91, and survival of 0.85, 0.87, respectively, outperforming every single-omic model. In predicting survival, we defined a parsimonious model with only 589 multi-omic analytes that had an accuracy and PPV of 0.85. Our approach enables discovery of parsimonious biomarker panels with similar predictive performance to that of larger and resource consuming panels and thereby has a significant potential to democratize precision cancer medicine worldwide.

INSTRUMENT(S): Shimadzu Nexera LC-30AD UHPLC system

ORGANISM(S): Homo Sapiens (ncbitaxon:9606)

SUBMITTER: Jennifer Van Eyk

PROVIDER: MSV000093118 | GNPS | Sun Oct 15 23:05:00 BST 2023

REPOSITORIES: GNPS

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GNPS - Profiling of pancreatic adenocarcinoma using artificial intelligence-based integration of multi-omic and computational pathology features: lipid

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