Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Expression data from 786-O renal cell cancer cells treated with pentamidine

ABSTRACT: While early stages of clear cell renal cell carcinoma (ccRCC) are curable, survival outcome for metastatic ccRCC remains poor. The purpose of the current study was to apply a new individualized bioinformatics analysis (IBA) strategy to these transcriptome data in conjunction with Gene Set Enrichment Analysis of the Connectivity Map (C-MAP) database to identify and reposition FDA-approved drugs for anti-cancer therapy. We demonstrated that one of the drugs predicted to revert the RCC gene signature towards normal kidney, pentamidine, is effective against RCC cells in culture and in a RCC xenograft model. Most importantly, pentamidine slows tumor growth in the 786-O human ccRCC xenograft mouse model. To determine which genes are regulated by pentamidine in a human RCC cell line, 786-O, we treated these cells with pentamidine and performed transcriptional profiling analysis. We used microarrays to determine the set of genes regulated by pentamidine in 786-O renal cell cancer cells. Total RNA was isolated from 786-O cells treated with 25 Î¼M pentamidine or vehicle control (DMSO) for 6 hours and hybridized to Affymetrix microarrays.

ORGANISM(S): Homo sapiens

SUBMITTER: Manoj Bhasin

PROVIDER: E-GEOD-54709 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Computational repositioning and preclinical validation of pentamidine for renal cell cancer.

Zerbini Luiz Fernando LF Bhasin Manoj K MK de Vasconcellos Jaira F JF Paccez Juliano D JD Gu Xuesong X Kung Andrew L AL Libermann Towia A TA

Molecular cancer therapeutics 20140501 7

Although early stages of clear cell renal cell carcinoma (ccRCC) are curable, survival outcome for metastatic ccRCC remains poor. We previously established a highly accurate signature of differentially expressed genes that distinguish ccRCC from normal kidney. The purpose of this study was to apply a new individualized bioinformatics analysis (IBA) strategy to these transcriptome data in conjunction with Gene Set Enrichment Analysis of the Connectivity Map (C-MAP) database to identify and reposi ...[more]

PMID: 24785412

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Project description:Altered metabolism is an important part of malignant transformation of tumor cells. Oncogenic transformation may reprogram tumor metabolism and render tumor cells addicted to extracellular nutrients. Such nutrient addictions associated with oncogenic mutations may offer therapeutic opportunities; however, it remains difficult to predict these nutrient addictions. Here, we performed a nutrigenetic screen to determine the phenotypes of isogenic pairs of clear-cell renal cancer cells (ccRCC) with or without VHL upon the deprivation of individual amino acids. We identified that cystine deprivation triggered rapid programmed necrosis in VHL-deficient RCC, but not in their VHL-restored counterparts. Similar cystine addiction was also observed in VHL-deficient primary RCC tumors cells. Blockage of cystine uptake significantly delayed xenograft growth of ccRCC. Importantly, cystine deprivation triggered similar metabolic changes regardless of VHL status. Therefore, metabolic differences due to cystine deprivation are not different enough to readily explain the distinct fate of life vs. death in VHL-deficient and restored cell.. Instead, we found that increased levels of TNFÎ± associated with VHL loss in the VHL-deficient RCC force them to rely on intact RIPK1 to inhibit apoptosis. However, this pre-existing elevated TNFÎ± in the VHL-deficient ccRCC renders these cells susceptible to the necrosis signaling triggered by cystine deprivation. In addition, we identified that cystine-deprived necrosis in VHL-deficient RCC depends on reciprocal amplification of the Src-p38-Noxa signaling and TNFÎ±-RIP1/3-MLKL necrosis pathways that culminate in MLKL oligomerization and programmed necrosis. Together, our data reveal that the contextual cystine-addictions in VHL-deficient ccRCC is dependent on activating pre-existing oncogenic pathways to trigger programmed necrosis. RNA was extracted by RNAeasy kits (Qiagen) from the RCC4 Vec and VHL-reconstituted cells which were exposed to the control full DMEM or cystine deprived DMEM media for 6 hours (three replicates in each condition).

Project description:Renal cell carcinoma (RCC) is one of the ten most common malignancies. Even though recent paradigm changes led to inclusion of immune checkpoint inhibitor combination therapy in treatment of naive metastatic RCC, insight in the biology and management of RCC would still favour the use of a combination of kinase inhibitors to target multiple pathways, presenting the possibility of enhanced efficacy and reduced development of resistance. We have used the streamlined-feedback system control (s-FSC) technique, a phenotypic approach, to identify optimized drug combinations (ODC) in 5 different human RCC lines. This approach requires no a priori mechanistic information on drug mechanism of action, and uses statistical modelling circumventing the need to experimentally screen large numbers of combinations. Different ODC with up to 90% effectivity were obtained for the 5 cell lines, importantly, by combining drug at doses that individually only inhibit 5-25% cell viability. Cell viability was reduced and apoptosis induced, accompanied by a general inhibition of downstream survival and growth promoting effector kinase RPS6. Global phosphoproteomics analysis of the cell lines revealed that in all cases where the ODC showed >50% efficacy, the most active kinases were targets of the selected drugs in the ODC. Interestingly, we observed considerable overlap in the top 20 most active kinases in all 5 cell lines, suggesting a universal ODC might be effective in all cells. Indeed, a combination of erlotinib and dasatinib, targeting EGFR and EPHA2/SRC signaling respectively, combined with AZD4547 or axitinib (targeting VEGFR and FGFR signaling), displayed excellent activity. The effect of the 786-O cell-line specific ODC was analysed in more detail using phosphoproteomics coupled to kinase activity analysis using the INKA pipeline.approaches. As expected, the activity of the main targets of erlotinib and dasatinib was reduced, but the activity of several untargeted kinases, including AXL, PTK2 and MET, remained high or increased, having potential implications for the development of resistance. Addition of crizotinib, targeting MET and PTK2, to the 3-drug ODC but not exchange with axitinib, further enhanced drug combination efficacy. In conclusion, we show that the phenotypic s-FSC method is highly effective in selecting optimized combinations of drugs, and that phosphoproteomics analysis can help further refine the selection of drugs included in the screen or final drug mixture.

Dataset Information

Expression data from 786-O renal cell cancer cells treated with pentamidine

Publications

Computational repositioning and preclinical validation of pentamidine for renal cell cancer.

Similar Datasets

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