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

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.