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Poster 854_Champions_Oncology_AACR2023 (3).pdf

Poster

AACR 2023 Poster

A Pharmaco-Pheno-Multiomic Integration Analysis of Pancreatic Cancer: A Highly Predictive Biomarker Model of Biomarkers of Gemcitabine + Nab Paclitaxel Sensitivity and Resistance

Summary

Pancreatic cancer patients have low overall survival rates, with current therapies showing less than 50% initial effectiveness and high mortality shortly after diagnosis. A deeper understanding of tumor biology and resistance mechanisms could reveal new therapeutic targets and improve patient stratification. In this study, we analyzed multi-omic datasets to identify predictive biomarkers for Gemcitabine + Nab Paclitaxel sensitivity using a pharmaco-phenotypic-multiomic (PPMO) model.

  • The data were integrated into a pharmaco-phenotypic-multiomic (PPMO) model to predict therapeutic sensitivity or resistance using sparse partial least squares (sPLS).

  • Key cellular discriminants associated with Gemcitabine + Nab Paclitaxel resistance were identified, including increased TPRV6 RNA, MUC13 protein, and USP42 mutation.

  • The PPMO model successfully predicted drug response profiles for 4/5 additional pancreatic cancer samples, suggesting its potential as a predictive diagnostic tool.

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