5 research outputs found
DataSheet_1_Combining the SMAC mimetic LCL161 with Gemcitabine plus Cisplatin therapy inhibits and prevents the emergence of multidrug resistance in cholangiocarcinoma.pdf
Cholangiocarcinoma (CCA) is a highly lethal gastrointestinal malignancy that has one of the worst prognoses among solid tumors. The combination of Gemcitabine + Cisplatin (GEM/CIS) remains the standard first-line treatment for advanced stage CCA. However, this drug combination yields only a modest objective response rate, and in cases that initially respond to this treatment, drug resistance commonly rapidly develops. To improve the efficiency of GEM/CIS therapy for CCA, a thorough understanding of the mechanism of GEM/CIS resistance in CCA is required. To that end – in this study, we developed several acquired GEM/CIS-resistant CCA cell lines and we screened those cell lines for acquired vulnerability. The screening process revealed that subset of CCA with GEM/CIS resistance acquired vulnerability to the small-molecule second mitochondrial-derived activator of caspases (SMAC) mimetics LCL161 and Birinapant. The observed acquired vulnerability was found to be associated with upregulation of an inhibitor of apoptosis protein 2 (cIAP2), a known target of SMAC mimetics. LCL161 or cIAP2-shRNA downregulated cIAP2 and restored the sensitivity to GEM/CIS in GEM/CIS-resistant CCA cell lines and in in vivo GEM/CIS-resistant xenograft models. A strong synergic effect was observed when LCL161 was added to GEM/CIS. Interestingly, this synergism was also observed in drug-naïve CCA cell lines, xenografts, and patient-derived organoids. This triplet therapy also prevented the emergence of multidrug-resistant CCA in in vitro and in vivo models. Our findings suggest that activation of cIAP2 allows CCA to escape GEM/CIS, and that suppression of cIAP2 reestablishes the apoptotic profile of CCA, thus restoring its vulnerability to GEM/CIS. The results of this study indicate that combining the SMAC mimetic LCL161 with GEM/CIS inhibits and prevents the emergence of multidrug resistance in CCA.</p
Dataset 1-genomics
Dataset containing files related to sequencing and annotation as a compressed .zip archive
Dataset 2-phenomics
Dataset containing files related to phenomic studies including GC-FID, GC/MS, HPLC, and phenotype microarrays
Additional file 3: of A computational framework for complex disease stratification from multiple large-scale datasets
Table S7. Estimated accuracy and standard deviation of the RFE procedure. Table S8. Accuracy and Kappa values of the Random Forest models in the training set. Table S9. Performances values for the Random Forest model in the testing set. Figure S11. Relative importance of the top 20 predictors building the final model of the RF. The importance axis is scaled, with the mRNA expression of CD3D scaled to 100% and the methylation state of POLA2 to 0% (not shown). (DOCX 18Â kb
Additional file 4: of A computational framework for complex disease stratification from multiple large-scale datasets
DIABLO sPLSDA model results. (DOCX 18966Â kb