Molecular Stratification of Clear Cell Renal Cell Carcinoma by Consensus Clustering Reveals Distinct Subtypes and Survival Patterns

Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA.
Genes & cancer 02/2010; 1(2):152-163. DOI: 10.1177/1947601909359929
Source: PubMed

ABSTRACT Clear cell renal cell carcinoma (ccRCC) is the predominant RCC subtype, but even within this classification, the natural history is heterogeneous and difficult to predict. A sophisticated understanding of the molecular features most discriminatory for the underlying tumor heterogeneity should be predicated on identifiable and biologically meaningful patterns of gene expression. Gene expression microarray data were analyzed using software that implements iterative unsupervised consensus clustering algorithms to identify the optimal molecular subclasses, without clinical or other classifying information. ConsensusCluster analysis identified two distinct subtypes of ccRCC within the training set, designated clear cell type A (ccA) and B (ccB). Based on the core tumors, or most well-defined arrays, in each subtype, logical analysis of data (LAD) defined a small, highly predictive gene set that could then be used to classify additional tumors individually. The subclasses were corroborated in a validation data set of 177 tumors and analyzed for clinical outcome. Based on individual tumor assignment, tumors designated ccA have markedly improved disease-specific survival compared to ccB (median survival of 8.6 vs 2.0 years, P = 0.002). Analyzed by both univariate and multivariate analysis, the classification schema was independently associated with survival. Using patterns of gene expression based on a defined gene set, ccRCC was classified into two robust subclasses based on inherent molecular features that ultimately correspond to marked differences in clinical outcome. This classification schema thus provides a molecular stratification applicable to individual tumors that has implications to influence treatment decisions, define biological mechanisms involved in ccRCC tumor progression, and direct future drug discovery.

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Available from: James D Brooks, Sep 27, 2015
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    • "Previous work revealed that genes overexpressed in samples with the ccA signature are enriched for genes implicated in angiogenesis and fatty acid, organic acid, and pyruvate metabolism. Genes overexpressed in samples displaying the ccB signature are enriched for cell differentiation , epithelial to mesenchymal transition, mitotic cell cycle, response to wounding, and TGF-b and Wnt signalling genes [10]. We further revealed that seven of nine specific genetic alterations that were validated in univariate analysis were enriched in ccB samples with 72% of samples harbouring at least one and up to six of these. "
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    ABSTRACT: Background Candidate biomarkers have been identified for clear cell renal cell carcinoma (ccRCC) patients, but most have not been validated. Objective To validate published ccRCC prognostic biomarkers in an independent patient cohort and to assess intratumour heterogeneity (ITH) of the most promising markers to guide biomarker optimisation. Design, setting, and participants Cancer-specific survival (CSS) for each of 28 identified genetic or transcriptomic biomarkers was assessed in 350 ccRCC patients. ITH was interrogated in a multiregion biopsy data set of 10 ccRCCs. Outcome measurements and statistical analysis Biomarker association with CSS was analysed by univariate and multivariate analyses. Results and limitations A total of 17 of 28 biomarkers (TP53 mutations; amplifications of chromosomes 8q, 12, 20q11.21q13.32, and 20 and deletions of 4p, 9p, 9p21.3p24.1, and 22q; low EDNRB and TSPAN7 expression and six gene expression signatures) were validated as predictors of poor CSS in univariate analysis. Tumour stage and the ccB expression signature were the only independent predictors in multivariate analysis. ITH of the ccB signature was identified in 8 of 10 tumours. Several genetic alterations that were significant in univariate analysis were enriched, and chromosomal instability indices were increased in samples expressing the ccB signature. The study may be underpowered to validate low-prevalence biomarkers. Conclusions The ccB signature was the only independent prognostic biomarker. Enrichment of multiple poor prognosis genetic alterations in ccB samples indicated that several events may be required to establish this aggressive phenotype, catalysed in some tumours by chromosomal instability. Multiregion assessment may improve the precision of this biomarker.
    European Urology 07/2014; 66(5). DOI:10.1016/j.eururo.2014.06.053 · 13.94 Impact Factor
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    • "If a higher interindividual variation in the expression of cancer-associated genes results from genetic heterogeneity, so that different tumors use different sets of cancer genes, one can expect that genetically homogeneous cancers would not show high interindividual variation in expression. Clear cell renal cell carcinoma (CCRCC) is believed to be one of the least heterogeneous cancers [35] with only two major subtypes (ccA and ccB) identified by expression profiling [36]. For CCRCC we used GSE781 GEO dataset [37]. "
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    ABSTRACT: Whole-genome profiling of gene expression is a powerful tool for identifying cancer-associated genes. Genes differentially expressed between normal and tumorous tissues are usually considered to be cancer associated. We recently demonstrated that the analysis of interindividual variation in gene expression can be useful for identifying cancer associated genes. The goal of this study was to identify the best microarray data-derived predictor of known cancer associated genes. We found that the traditional approach of identifying cancer genes--identifying differentially expressed genes--is not very efficient. The analysis of interindividual variation of gene expression in tumor samples identifies cancer-associated genes more effectively. The results were consistent across 4 major types of cancer: breast, colorectal, lung, and prostate. We used recently reported cancer-associated genes (2011-2012) for validation and found that novel cancer-associated genes can be best identified by elevated variance of the gene expression in tumor samples. The observation that the high interindividual variation of gene expression in tumor tissues is the best predictor of cancer-associated genes is likely a result of tumor heterogeneity on gene level. Computer simulation demonstrates that in the case of heterogeneity, an assessment of variance in tumors provides a better identification of cancer genes than does the comparison of the expression in normal and tumor tissues. Our results thus challenge the current paradigm that comparing the mean expression between normal and tumorous tissues is the best approach to identifying cancer-associated genes; we found that the high interindividual variation in expression is a better approach, and that using variation would improve our chances of identifying cancer-associated genes.
    BMC Genomics 03/2014; 15(1):223. DOI:10.1186/1471-2164-15-223 · 3.99 Impact Factor
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    • "ccA tumours relatively overexpressed genes associated with hypoxia, angiogenesis and fatty acid metabolism and carried a favourable prognosis in comparison with ccB tumours, which overexpressed a more aggressive panel of genes associated with epithelial-mesenchymal transition, cell cycle and wound healing. Interestingly, rates of VHL involvement were similar between the two groups [18]. A third, small (14%) cluster of tumours could also be identified, 82% of which were classed as VHL wild-type. "
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    ABSTRACT: Significant advances in our understanding of the biology of renal cell carcinoma (RCC) have been achieved in recent years. These insights have led to the introduction of novel targeted therapies, revolutionising the management of patients with advanced disease. Nevertheless, there are still no biomarkers in routine clinical use in RCC. Tools used routinely to determine prognosis have not changed over the past decade; classification remains largely morphology based; and patients continue to be exposed to potentially toxic therapy with no indication of the likelihood of response. Thus the need for biomarkers in RCC is urgent. Here, we focus on recent advances in our understanding of the genetics and epigenetics of RCC, and the potential for such knowledge to provide novel markers and therapeutic targets. We highlight on-going research that is likely to deliver further candidate markers as well as generating large, well-annotated sample banks that will facilitate future studies. It is imperative that promising candidates are validated using these resources, and in subsequent prospective clinical trials, so that future biomarkers may be used in the clinic to personalise patient care.
    BMC Medicine 09/2012; 10(1):112. DOI:10.1186/1741-7015-10-112 · 7.25 Impact Factor
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