Dataset Information

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

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 singleand 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 singleomic 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): Orbitrap Fusion Lumos, Orbitrap Fusion

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Blood Plasma

SUBMITTER: Niveda Sundararaman

LAB HEAD: Jennifer Van

PROVIDER: PXD037038 | Pride | 2024-01-23

REPOSITORIES: Pride

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Project description:Aberrant protease activity has been implicated in the etiology of various prevalent diseases including neurodegeneration and cancer, in particular metastasis. Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) has recently been established as a key technology for bioanalysis of multiple biomolecular classes such as proteins, lipids, and glycans. However, it has not yet been systematically explored for investigation of a tissue’s endogenous protease activity. In this study, we demonstrate that different tissues, spray-coated with substance P as a tracer, digest this peptide with different time-course profiles. Furthermore, we reveal that distinct cleavage products originating from substance P are generated transiently and that proteolysis can be attenuated by protease inhibitors in a concentration-dependent manner. To show the translational potential of the method, we analyzed protease activity of gastric carcinoma in mice. Our MSI and quantitative proteomics results reveal differential distribution of protease activity – with strongest activity being observed in mouse tumor tissue, suggesting the general applicability of the workflow in animal pharmacology and clinical studies.Aberrant protease activity has been implicated in the etiology of various prevalent diseases including neurodegeneration and cancer, in particular metastasis. Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) has recently been established as a key technology for bioanalysis of multiple biomolecular classes such as proteins, lipids, and glycans. However, it has not yet been systematically explored for investigation of a tissue’s endogenous protease activity. In this study, we demonstrate that different tissues, spray-coated with substance P as a tracer, digest this peptide with different time-course profiles. Furthermore, we reveal that distinct cleavage products originating from substance P are generated transiently and that proteolysis can be attenuated by protease inhibitors in a concentration-dependent manner. To show the translational potential of the method, we analyzed protease activity of gastric carcinoma in mice. Our MSI and quantitative proteomics results reveal differential distribution of protease activity – with strongest activity being observed in mouse tumor tissue, suggesting the general applicability of the workflow in animal pharmacology and clinical studies.

Dataset Information

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

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