Project description: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 pancreatic cancer and provide potential therapy targets for overcoming gemcitabine resistance.
Project description: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 pancreatic cancer and provide potential therapy targets for overcoming gemcitabine resistance.
Project description:We investigated the gene expression changes in a library of small cell lung carcinoma (SCLC) patient-derived xenograft (PDX) models.
Project description:PDXs were established from cancer cells contained in ascitic effusions from patients with high-grade serous ovarian cancer, never treated with Bevacizumab. Tumors were propagated in female NOD/SCID mice by intraperitoneal injection of tumor cells. Two PDX models, one B20-4.1.1-Resistant (PDOVCA 69, n=5 samples/group) and one B20-4.1.1-Sensitive (PDOVCA 62, n=4 samples/group), were used to evaluate the transcriptional effects of B20-4.1.1 treatment with respect to untreated controls by microarrays. The biological material coming from the ascites of the two PDX models was analyzed at sacrifice following anti-VEGF treatment or control (PBS) treatment.