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AACR 2023

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



Gilad Silberberg, Clare Killick-Cole, Yaron Mosesson, Haia Khoury, Xuan Ren, Mara Gilardi, Daniel Ciznadija, Paolo Schiavini, Marianna Zipeto, Michael Ritchie



The overall survival of patients diagnosed with Pancreatic Cancer remains low. Initial responses to current therapeutic interventions are below 50%, leading to a high mortality rate shortly after diagnosis. To date, only a companion diagnostic, non-specific for pancreatic cancer, has been approved for this indication. A better understanding of the tumor cell biology and resistance mechanisms may shed light onto novel therapeutic targets that improve long-term outcomes and improved patient stratification. In this study, we performed an exhaustive analysis to identify predictive biomarkers for Gemcitabine + Nab Paclitaxel sensitivity using multiomic datasets. These datasets were integrated in a pharmaco-phenotypic-multiomic (PPMO) model predictive of therapeutic sensitivity or resistance, using sparse partial least squares (sPLS). Our results reveal major cellular discriminants in genomic variants, transcriptomics, and most pronouncedly in proteomics data. Tumors exhibiting Gemcitabine + Nab Paclitaxel resistance associate with increased TPRV6 RNA expression, MUC13 protein expression, and USP42 mutation, among others.  Prospective application of the PPMO integration model was able to accurately predict Gemcitabine + Nab Paclitaxel response profiles for 4/5 additional Pancreatic samples, therefore suggesting a potential application as a predictive diagnostic tool.