Integrative Genomic Analysis of Gemcitabine Resistance in Pancreatic Cancer by Patient-derived Xenograft Models.

Purpose: Gemcitabine is most commonly used for pancreatic cancer. However, the molecular features and mechanisms of the frequently occurring resistance remain unclear. This work aims at exploring the molecular features of gemcitabine resistance and identifying candidate biomarkers and combinatorial targets for the treatment.

Experimental Design: In this study, we established 66 patient-derived xenografts (PDXs) on the basis of clinical pancreatic cancer specimens and treated them with gemcitabine. We generated multiomics data (including whole-exome sequencing, RNA sequencing, miRNA sequencing, and DNA methylation array) of 15 drug-sensitive and 13 -resistant PDXs before and after the gemcitabine treatment. We performed integrative computational analysis to identify the molecular networks related to gemcitabine intrinsic and acquired resistance. Then, short hairpin RNA-based high-content screening was implemented to validate the function of the deregulated genes.

Results: The comprehensive multiomics analysis and functional experiment revealed that and had strong effects on cell proliferation, and and contributed to gemcitabine resistance in pancreatic cancer cells. Moreover, we found miR-135a-5p was significantly associated with the prognosis of patients with pancreatic cancer and could be a candidate biomarker to predict gemcitabine response. Comparing the molecular features before and after the treatment, we found that PI3K-Akt, p53, and hypoxia-inducible factor-1 pathways were significantly altered in multiple patients, providing candidate target pathways for reducing the acquired resistance.

Conclusions: This integrative genomic study systematically investigated the predictive markers and molecular mechanisms of chemoresistance in pancreatic cancer and provides potential therapy targets for overcoming gemcitabine resistance.