Dataset Information

Interplay between pVHL and mTORC1 pathways in clear-cell renal cell carcinoma.

ABSTRACT: mTOR complex 1 (mTORC1) is implicated in cell growth control and is extensively regulated. We previously reported that in response to hypoxia, mTORC1 is inhibited by the protein regulated in development and DNA damage response 1 (REDD1). REDD1 is upregulated by hypoxia-inducible factor (HIF)-1, and forced REDD1 expression is sufficient to inhibit mTORC1. REDD1-induced mTORC1 inhibition is dependent on a protein complex formed by the tuberous sclerosis complex (TSC)1 and 2 (TSC2) proteins. In clear-cell renal cell carcinoma (ccRCC), the von Hippel-Lindau (VHL) gene is frequently inactivated leading to constitutive activation of HIF-2 and/or HIF-1, which may be expected to upregulate REDD1 and inhibit mTORC1. However, mTORC1 is frequently activated in ccRCC, and mTORC1 inhibitors are effective against this tumor type; a paradox herein examined. REDD1 was upregulated in VHL-deficient ccRCC by in silico microarray analyses, as well as by quantitative real-time PCR, Western blot, and immunohistochemistry. Vhl disruption in a mouse model was sufficient to induce Redd1. Using ccRCC-derived cell lines, we show that REDD1 upregulation in tumors is VHL dependent and that both HIF-1 and HIF-2 are, in a cell-type-dependent manner, recruited to, and essential for, REDD1 induction. Interestingly, whereas mTORC1 is responsive to REDD1 in some tumors, strategies have evolved in others, such as mutations disrupting TSC1, to subvert mTORC1 inhibition by REDD1. Sequencing analyses of 77 ccRCCs for mutations in TSC1, TSC2, and REDD1, using PTEN as a reference, implicate the TSC1 gene, and possibly REDD1, as tumor suppressors in sporadic ccRCC. Understanding how ccRCCs become refractory to REDD1-induced mTORC1 inhibition should shed light into the development of ccRCC and may aid in patient selection for molecular-targeted therapies.

SUBMITTER: Kucejova B

PROVIDER: S-EPMC3234675 | biostudies-literature | 2011 Sep

REPOSITORIES: biostudies-literature

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Interplay between pVHL and mTORC1 pathways in clear-cell renal cell carcinoma.

Kucejova Blanka B Peña-Llopis Samuel S Yamasaki Toshinari T Sivanand Sharanya S Tran Tram Anh T TA Alexander Shane S Wolff Nicholas C NC Lotan Yair Y Xie Xian-Jin XJ Kabbani Wareef W Kapur Payal P Brugarolas James J

Molecular cancer research : MCR 20110728 9

mTOR complex 1 (mTORC1) is implicated in cell growth control and is extensively regulated. We previously reported that in response to hypoxia, mTORC1 is inhibited by the protein regulated in development and DNA damage response 1 (REDD1). REDD1 is upregulated by hypoxia-inducible factor (HIF)-1, and forced REDD1 expression is sufficient to inhibit mTORC1. REDD1-induced mTORC1 inhibition is dependent on a protein complex formed by the tuberous sclerosis complex (TSC)1 and 2 (TSC2) proteins. In cle ...[more]

PMID: 21798997

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Project description:Kidney Renal Clear Cell Carcinoma (KIRC) is one of fatal genitourinary diseases and accounts for most malignant kidney tumours. KIRC has been shown resistance to radiotherapy and chemotherapy. Like many types of cancers, there is no curative treatment for metastatic KIRC. Using advanced sequencing technologies, The Cancer Genome Atlas (TCGA) project of NIH/NCI-NHGRI has produced large-scale sequencing data, which provide unprecedented opportunities to reveal new molecular mechanisms of cancer. We combined differentially expressed genes, pathways and network analyses to gain new insights into the underlying molecular mechanisms of the disease development.Followed by the experimental design for obtaining significant genes and pathways, comprehensive analysis of 537 KIRC patients' sequencing data provided by TCGA was performed. Differentially expressed genes were obtained from the RNA-Seq data. Pathway and network analyses were performed. We identified 186 differentially expressed genes with significant p-value and large fold changes (P < 0.01, |log(FC)| > 5). The study not only confirmed a number of identified differentially expressed genes in literature reports, but also provided new findings. We performed hierarchical clustering analysis utilizing the whole genome-wide gene expressions and differentially expressed genes that were identified in this study. We revealed distinct groups of differentially expressed genes that can aid to the identification of subtypes of the cancer. The hierarchical clustering analysis based on gene expression profile and differentially expressed genes suggested four subtypes of the cancer. We found enriched distinct Gene Ontology (GO) terms associated with these groups of genes. Based on these findings, we built a support vector machine based supervised-learning classifier to predict unknown samples, and the classifier achieved high accuracy and robust classification results. In addition, we identified a number of pathways (P < 0.04) that were significantly influenced by the disease. We found that some of the identified pathways have been implicated in cancers from literatures, while others have not been reported in the cancer before. The network analysis leads to the identification of significantly disrupted pathways and associated genes involved in the disease development. Furthermore, this study can provide a viable alternative in identifying effective drug targets.Our study identified a set of differentially expressed genes and pathways in kidney renal clear cell carcinoma, and represents a comprehensive computational approach to analysis large-scale next-generation sequencing data. The pathway and network analyses suggested that information from distinctly expressed genes can be utilized in the identification of aberrant upstream regulators. Identification of distinctly expressed genes and altered pathways are important in effective biomarker identification for early cancer diagnosis and treatment planning. Combining differentially expressed genes with pathway and network analyses using intelligent computational approaches provide an unprecedented opportunity to identify upstream disease causal genes and effective drug targets.

Project description:BackgroundThe VHL protein (pVHL) is a multiadaptor protein that interacts with more than 30 different binding partners involved in many oncogenic processes. About 70 % of clear cell renal cell carcinoma (ccRCC) have VHL mutations with varying impact on pVHL function. Loss of pVHL function leads to the accumulation of Hypoxia Inducible Factor (HIF), which is targeted by current targeted treatments. In contrast to nonsense and frameshift mutations that highly likely nullify pVHL multipurpose functions, missense mutations may rather specifically influence the binding capability of pVHL to its partners. The affected pathways may offer predictive clues to therapy and response to treatment. In this study we focused on the VHL missense mutation pattern in ccRCC, and studied their potential effects on pVHL protein stability and binding partners and discussed treatment options.MethodsWe sequenced VHL in 360 sporadic ccRCC FFPE samples and compared observed and expected frequency of missense mutations in 32 different binding domains. The prediction of the impact of those mutations on protein stability and function was assessed in silico. The response to HIF-related, anti-angiogenic treatment of 30 patients with known VHL mutation status was also investigated.ResultsWe identified 254 VHL mutations (68.3 % of the cases) including 89 missense mutations (35 %). Codons Ser65, Asn78, Ser80, Trp117 and Leu184 represented hotspots and missense mutations in Trp117 and Leu 184 were predicted to highly destabilize pVHL. About 40 % of VHL missense mutations were predicted to cause severe protein malfunction. The pVHL binding domains for HIF1AN, BCL2L11, HIF1/2α, RPB1, PRKCZ, aPKC-λ/ι, EEF1A1, CCT-ζ-2, and Cullin2 were preferentially affected. These binding partners are mainly acting in transcriptional regulation, apoptosis and ubiquitin ligation. There was no correlation between VHL mutation status and response to treatment.ConclusionsVHL missense mutations may exert mild, moderate or strong impact on pVHL stability. Besides the HIF binding domain, other pVHL binding sites seem to be non-randomly altered by missense mutations. In contrast to LOF mutations that affect all the different pathways normally controlled by pVHL, missense mutations may be rather appropriate for designing tailor-made treatment strategies for ccRCC.

Dataset Information

Interplay between pVHL and mTORC1 pathways in clear-cell renal cell carcinoma.

Publications

Interplay between pVHL and mTORC1 pathways in clear-cell renal cell carcinoma.

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