Comprehensive molecular characterization of clear cell renal cell carcinoma Cancer Genome Atlas Research N Nature 2013 499 7456 43 9 10.1038/nature12222

1] Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas 77030, USA. [2] Dan L Duncan Cancer Center, Baylor College of Medicine, Houston, Texas 77030, USA.
Nature (Impact Factor: 41.46). 06/2013; 499(7456). DOI: 10.1038/nature12222
Source: PubMed


Genetic changes underlying clear cell renal cell carcinoma (ccRCC) include alterations in genes controlling cellular oxygen sensing (for example, VHL) and the maintenance of chromatin states (for example, PBRM1). We surveyed more than 400 tumours using different genomic platforms and identified 19 significantly mutated genes. The PI(3)K/AKT pathway was recurrently mutated, suggesting this pathway as a potential therapeutic target. Widespread DNA hypomethylation was associated with mutation of the H3K36 methyltransferase SETD2, and integrative analysis suggested that mutations involving the SWI/SNF chromatin remodelling complex (PBRM1, ARID1A, SMARCA4) could have far-reaching effects on other pathways. Aggressive cancers demonstrated evidence of a metabolic shift, involving downregulation of genes involved in the TCA cycle, decreased AMPK and PTEN protein levels, upregulation of the pentose phosphate pathway and the glutamine transporter genes, increased acetyl-CoA carboxylase protein, and altered promoter methylation of miR-21 (also known as MIR21) and GRB10. Remodelling cellular metabolism thus constitutes a recurrent pattern in ccRCC that correlates with tumour stage and severity and offers new views on the opportunities for disease treatment.

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Available from: Christopher Ricketts, Jul 03, 2014
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    • "From these results, we concluded that among the eight selected miRs miR-21, miR-126, miR-221 and let7b might be critically involved in the development of ccRCC with TT. Several studies already gave evidence that all four dysregulated miRs are involved in biological processes controlling malignant transformation and progression of tumor cells by the identification or prediction of various target mRNAs and pathways controlled by these miRs [13], [20], [26], [27], [28], [29]. Even if our study is limited by the lack of functional and molecular analysis concerning the interaction between miRs and potential target genes, the presented data might provide the basis for further investigations. "
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    ABSTRACT: Clear cell renal cell carcinoma (ccRCC) characterized by a tumor thrombus (TT) extending into the inferior vena cava (IVC) generally indicates poor prognosis. Nevertheless, the risk for tumor recurrence after nephrectomy and thrombectomy varies. An applicable and accurate prediction system to select ccRCC patients with TT of the IVC (ccRCC/TT) at high risk after nephrectomy is urgently needed, but has not been established up to now. To our knowledge, a possible role of microRNAs (miRs) for the development of ccRCC/TT or their impact as prognostic markers in ccRCC/TT has not been explored yet. Therefore, we analyzed the expression of the previously described onco-miRs miR-200c, miR-210, miR-126, miR-221, let-7b, miR-21, miR-143 and miR-141 in a study collective of 74 ccRCC patients. Using the expression profiles of these eight miRs we developed classification systems that accurately differentiate ccRCC from non-cancerous renal tissue and ccRCC/TT from tumors without TT. In the subgroup of 37 ccRCC/TT cases we found that miR-21, miR-126, and miR-221 predicted cancer related death (CRD) accurately and independently from other clinico-pathological features. Furthermore, a combined risk score based on the expression of miR-21, miR-126 and miR-221 was developed and showed high sensitivity and specificity to predict cancer specific survival (CSS) in ccRCC/TT. Using the combined risk score we were able to classify ccRCC/TT patients correctly into high and low risk cases. The risk stratification by the combined risk score (CRS) will benefit from further cohort validation and might have potential for clinical application as a molecular prediction system to identify high- risk ccRCC/TT patients.
    PLoS ONE 10/2014; 9(10):e109877. DOI:10.1371/journal.pone.0109877 · 3.23 Impact Factor
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    • "To assess the potential relevance of MEK and/or mTOR inhibition in ccRCC we examined reverse phase protein array data (RPPA) from the TCGA clear cell kidney cancer project (KIRC) to determine the relative activation state of these pathways in human RCC [39]. Reverse phase protein arrays are a highly validated technique allowing the assessment of protein expression across hundreds of proteins simultaneously and because of the multiplatform nature of the TCGA allows for correlations to other genomic aspects of a tumor. "
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    ABSTRACT: Rapamycin derivatives allosterically targeting mTOR are currently FDA approved to treat advanced renal cell carcinoma (RCC), and catalytic inhibitors of mTOR/PI3K are now in clinical trials for treating various solid tumors. We sought to investigate the relative efficacy of allosteric versus catalytic mTOR inhibition, evaluate the crosstalk between the mTOR and MEK/ERK pathways, as well as the therapeutic potential of dual mTOR and MEK inhibition in RCC. Pharmacologic (rapamycin and BEZ235) and genetic manipulation of the mTOR pathway were evaluated by in vitro assays as monotherapy as well as in combination with MEK inhibition (GSK1120212). Catalytic mTOR inhibition with BEZ235 decreased proliferation and increased apoptosis better than allosteric mTOR inhibition with rapamycin. While mTOR inhibition upregulated MEK/ERK signaling, concurrent inhibition of both pathways had enhanced therapeutic efficacy. Finally, primary RCC tumors could be classified into subgroups [(I) MEK activated, (II) Dual MEK and mTOR activated, (III) Not activated, and (IV) mTOR activated] based on their relative activation of the PI3K/mTOR and MEK pathways. Patients with mTOR only activated tumors had the worst prognosis. In summary, dual targeting of the mTOR and MEK pathways in RCC can enhance therapeutic efficacy and primary RCC can be subclassified based on their relative levels of mTOR and MEK activation with potential therapeutic implications.
    PLoS ONE 09/2014; 9(9):e104413. DOI:10.1371/journal.pone.0104413 · 3.23 Impact Factor
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    • "For a wide variety of cancer types, recent studies have identified genes that are significantly associated with cancer risk, onset and progression (e.g. Cancer Genome Atlas Research Network, 2008, 2012a, b, 2013; Kandoth et al., 2013). "
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    ABSTRACT: Motivation: Ancestral character state reconstruction describes a set of techniques for estimating phenotypic or genetic features of species or related individuals that are the predecessors of those present today. Such reconstructions can reach into the distant past and can provide insights into the history of a population or a set of species when fossil data are not available, or they can be used to test evolutionary hypotheses, e.g. on the co-evolution of traits. Typical methods for ancestral character state reconstruction of continuous characters consider the phylogeny of the underlying data and estimate the ancestral process along the branches of the tree. They usually assume a Brownian motion model of character evolution or extensions thereof, requiring specific assumptions on the rate of phenotypic evolution.Results: We suggest using ridge regression to infer rates for each branch of the tree and the ancestral values at each inner node. We performed extensive simulations to evaluate the performance of this method and have shown that the accuracy of its reconstructed ancestral values is competitive to reconstructions using other state-of-the-art software. Using a hierarchical clustering of gene mutation profiles from an ovarian cancer dataset, we demonstrate the use of the method as a feature selection tool.Availability and implementation: The algorithm described here is implemented in C++ as a stand-alone program, and the source code is freely available at mchardy@hhu.deSupplementary information: Supplementary data are available at Bioinformatics online.
    Bioinformatics 09/2014; 30(17):i527-i533. DOI:10.1093/bioinformatics/btu477 · 4.98 Impact Factor
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