Integrative genomic analysis of gemcitabine resistance in pancreatic cancer by patient-derived xenograft models.

Purpose Gemcitabine is most commonly used for pancreatic cancer (PC). However, the molecular features and mechanisms of the frequently occurred 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 present study, we established 66 patient-derived xenografts (PDXs) based on clinical PC specimens and treated them with gemcitabine. We generated multi-omics data (including whole exome-seq, RNA-seq, miRNA-seq 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 required resistance. Then, shRNA-based high-content screening was implemented to validate the function of the de-regulated genes. Results The comprehensive multi-omics analysis and functional experiment revealed that MRPS5 and GSPT1 had strong effects on cell proliferation, and CD55 and DHTKD1 contributed to gemcitabine resistance in PC cells. Moreover, we found miR-135a-5p was significantly associated with the prognosis of PC patients 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, HIF-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 PC and provide potential therapy targets for overcoming gemcitabine resistance.