Genomics Of Drug Sensitivity In Cancer Project Research Articles

GSK2126458 has the potential to inhibit the proliferation of pancreatic cancer uncovered by bioinformatics analysis and pharmacological experiments

BackgroundPancreatic cancer is one of the most serious digestive malignancies. At present, there is an extreme lack of effective strategies in clinical treatment. The purpose of this study is to identify key genes and pathways in the development of pancreatic cancer and provide targets for the treatment of pancreatic cancer.MethodsGSE15471 and GSE62165 were used to screen differentially expressed genes by GEO2R tool. Hub genes prognostic potential assessed using the GEPIA and Kaplan–Meier plotter databases. The drug susceptibility data of pan-cancer cell lines is provided by The Genomics of Drug Sensitivity in Cancer Project (GDSC). Finally, the effects of PI3K–Akt signaling pathway inhibitors on cell viability of pancreatic cancer cells were detected by cell proliferation and invasion assays.ResultsA total of 609 differentially expressed genes were screened and enriched in the focal adhesion, phagosome and PI3K–Akt signaling pathway. Of the 15 hub genes we found, four were primarily associated with the PI3K–Akt signaling pathway, including COL3A1, EGF, FN1 and ITGA2. GDSC analysis showed that mTOR inhibitors are very sensitive to pancreatic cancer cells with mutations in EWSR1.FLI1 and RNF43. Cell proliferation and invasion results showed that mTOR inhibitors (GSK2126458) can inhibit the proliferation of pancreatic cancer cells.ConclusionsThis study suggested that the PI3K–Akt signaling pathway may be a key pathway for pancreatic cancer, our study uncovered the potential therapeutic potential of GSK2126458, a specific mTOR inhibitor, for pancreatic cancer.

Abstract LB-121: The novel ATR inhibitor M4344 and CHK1 inhibitor SRA737 overcome chemoresistance in SLFN11-negative cells in combination treatment with DNA-damaging agents

Abstract Precision medicine is an unmet need for DNA-damaging agents with potentially severe side effects. Loss of Schlafen 11 (SLFN11) expression is frequently detected in ~50% cancer cell lines of the NCI, the Broad Institute cancer cell line panel (CCLE), and the Genomics of Drug Sensitivity in Cancer project (GDSC). Deficiency of SLFN11 expression causes chemoresistance to a broad range of DNA-damaging agents, including topoisomerase I (TOP1) inhibitors, topoisomerase II (TOP2) inhibitors, alkylating agents, DNA synthesis inhibitors and PARP inhibitors. Moreover, gene silencing of SLFN11 is observed in small cell lung cancer patient-derived xenograft models with acquired drug resistance. To identify synthetic lethal therapeutic targets to overcome chemoresistance in SLFN11 deficient cells, we performed a genome-wide RNAi screen with the human druggable genome siRNA library by using camptothecin (CPT), a TOP1 inhibitor, in SLFN11 wild-type (WT) and knock-out (KO) prostate cancer DU145 cells. Gene Ontology analysis identified the inhibition of ATR-mediated DNA repair pathway genes (ATR, CHK1, BRCA2 and RPA1) is synergistic with CPT in SLFN11 KO cells, whereas inhibition of the RNA metabolism-related genes (POLR2, PSMD3, POLR2F and PSMD11) had higher combination effects in SLFN11 WT cells. The synergistic effects by depletion of ATR-mediated DNA repair pathway in SLFN11 KO cells were validated by additional siRNAs-mediated cell viability assays. To determine whether inhibition of ATR-mediated pathway can be applied clinically, we tested the new clinical ATR (M4344) and CHK1 (SRA737) inhibitors. Treatment with non-toxic-doses of M4344 and SRA737 reversed drug resistance of the SLFN11 KO cells to TOP1 inhibitors [CPT, and clinically used topotecan and LMP400 (indotecan)]. Additionally, the same synergistic effects by combination of the ATR/CHK1inhibitors and CPT were detected in isogenic CCRF-CEM SLFN11 WT and KO leukemic lymphoblasts cells and non-isogenic DMS114 and H446 small cell lung cancer cells. We also confirmed synergy with ATR/CHK1 inhibitors in combination of other clinical DNA-damaging agents (TOP2 inhibitor: etoposide, alkylating agent: cisplatin, and PARP inhibitor: talazoparib). Molecular changes induced by the combination treatment were examined in the cell cycle by assessing DAPI and EdU incorporation, cell death with Annexin V, and DNA damage response by confocal microscopy with M4344 and CPT. Co-treatment with ATR inhibitor and CPT resulted in G2/M arrest and apoptotic cell death, and formation of micronuclei and fragmented nuclei in SLFN11 KO cells, compared with SLFN11 WT cells. Collectively, our results provide a new therapeutic rationale for the clinical development of combination treatments of chemotherapeutic DNA-targeted agents with ATR/CHK1 inhibitors based on SLFN11 status. Citation Format: Ukhyun Jo, Yasuhisa Murai, Junko Murai, Shar-yin N Huang, Sirisha Chakka, Lu Chen, Ken Cheng, Yves Pommier. The novel ATR inhibitor M4344 and CHK1 inhibitor SRA737 overcome chemoresistance in SLFN11-negative cells in combination treatment with DNA-damaging agents [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr LB-121.

A novel mutation panel for predicting etoposide resistance in small-cell lung cancer.

PurposePlatinum-based chemotherapy, consisting of etoposide and cisplatin (EP), has been the cornerstone of therapy for extensive-stage small-cell lung cancer (ES-SCLC) for decades. Despite the marked initial sensitivity of SCLC to chemotherapy, EP regimens cannot avoid the emergence of drug resistance in clinical practice. With the rise of new chemotherapy regimens in recent years and the primary resistance or insensitivity of ES-SCLC to EP regimens, it is desirable to be able to identify patients with resistant or insensitive ES-SCLC.MethodsThe sequencing and drug sensitivity data of SCLC cell lines were provided by The Genomics of Drug Sensitivity in Cancer Project (GDSC). The data regarding sensitivity to etoposide of 54 SCLC cell lines were analyzed, and etoposide-sensitive cell lines and etoposide-resistant cell lines were differentiated according to the IC50 values defined by the GDSC. ROC curve analysis was performed on all mutations and combinations of mutations to select the optimal panel to predict resistance to etoposide.ResultsROC analysis of etoposide resistance revealed that the most significant single gene mutation indicating resistance to etoposide was CSMD3, and the accuracy of predicting resistance to etoposide proved to be the highest when there was any mutation in CSMD3/PCLO/RYR1/EPB41L3, area under the curve =0.804 (95% confidence interval: 0.679–0.930,P<0.001).ConclusionThis study found that a panel with four genes (CSMD3, EPB41L3, PCLO, and RYR1) can accurately predict sensitivity to etoposide. These findings provide new insights into the overall treatment for patients with ES-SCLC that is resistant or insensitive to etoposide.

A novel mutation panel for predicting etoposide in small cell lung cancer.

e20090 Background: Platinum-based chemotherapy, consisting of etoposide and cisplatin (EP), has been the cornerstone of therapy for extensive-stage small cell lung cancer (ES-SCLC) for decades. Despite the marked initial sensitivity of SCLC to chemotherapy, EP regimens cannot avoid the emergence of drug resistance in clinical practice. With the rise of new chemotherapy regimens in recent years and the primary resistance or insensitivity of ES-SCLC to EP regimens, it is desirable to be able to identify patients with resistant or insensitive ES-SCLC. Methods: The sequencing and drug sensitivity data of SCLC cell lines were provided by The Genomics of Drug Sensitivity in Cancer Project (GDSC); the data regarding of sensitivity to etoposide of 54 SCLC cell lines were analyzed, and etoposide-sensitive cell lines and etoposide-resistant cell lines were differentiated according to the IC50 values defined by the GDSC. ROC curve analysis was performed on all mutations and combinations of mutations to select the optimal panel to predict resistance to etoposide. Results: Receiver Operating Characteristic(ROC) analysis of etoposide resistance revealed that the most significant single gene mutation indicating resistance to etoposide was CSMD3, and the accuracy of predicting resistance to etoposide proved to be the highest when there was any mutation in CSMD3/PCLO/RYR1/EPB41L3, area under the curve (AUC) = 0.804 (95% confidence interval (CI): 0.679-0.930, p &lt; 0.001). Conclusions: This study found that a panel with four genes (CSMD3, EPB41L3, PCLO, and RYR1) can accurately predict sensitivity to etoposide. These findings provide new insights into the overall treatment for patients with ES-SCLC that is resistant or insensitive to etoposide.

Dual-Layer Strengthened Collaborative Topic Regression Modeling for Predicting Drug Sensitivity.

An effective way to facilitate the development of modern oncology precision medicine is the systematical analysis of the known drug sensitivities that have emerged in recent years. Meanwhile, the screening of drug response in cancer cell lines provides an estimable genomic and pharmacological data towards high accuracy prediction. Existing works primarily utilize genomic or functional genomic features to classify or regress the drug response. Here in this work, by the migration and extension of the conventional merchandise recommendation methods, we introduce an innovation model on accurate drug sensitivity prediction by using dual-layer strengthened collaborative topic regression (DS-CTR), which incorporates not only the graphic model to jointly learn drugs and cell lines feature from pharmacogenomics data but also drug and cell line similarity network model to strengthen the correlation of the prediction results. Using Genomics of Drug Sensitivity in Cancer project (GDSC) as benchmark datasets, the 5-fold cross-validation experiment demonstrates that DS-CTR model significantly improves drug response prediction performance compared with four categories of state-of-the-art algorithms as for both Receiver Operator Curve (ROC) and the Area Under Receiver Operator Curve (AUC). By uncovering the unknown cell-drug associations with advanced literature evidences, our novel model DS-CTR is validated and supported. The model also provides the possibility to make the discovery of new anti-cancer therapeutics in the preclinical trials cheaper and faster.