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

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.

Figure 1.

Flowchart diagram depicts the order of analyses. (A) Delineation of steps taken to identify clear cell renal cell carcinoma (ccRCC) subtypes. (B) Diagram of analyses to characterize and validate identified subtypes.

Figure 2.

Consensus matrixes demonstrate the presence of only two core clusters of clear cell renal cell carcinoma (ccRCC). Consensus matrix heat maps demonstrate the presence of two clusters within all clear cell tumors (A) and invariance of the two ccRCC core clusters using (B) k = 2, (C) k = 3, and (D) k = 4 cluster assignments for each cluster method. Red areas identify the similarity between samples and display samples clustered together across the bootstrap analysis. ccA is color coded in green, ccB in blue.

Figure 3.

Pathway analysis of subtypes shows that ccA and ccB are highly dissimilar. (A) Heat map of the 6,213 probes differentially expressed between ccA and ccB as determined by SAM analysis; false discovery rate (FDR) < 0.000001. (B-G) Magnified heat maps of the genes from (A) that populate the ccA (B-D) or ccB (E-G) overexpressed Molecular Signatures Database curated gene sets of Brentani angiogenesis (B), beta-oxidation (C), HSA00071 fatty acid metabolism (D), epithelial to mesenchymal transition (EMT) up (E), transforming growth factor beta (TGFβ) C4 up (F), and Wnt targets (G).

Figure 4.

Logical analysis of data (LAD) probes separate ccA and ccB tumor clusters. (A) Gene expression data for core arrays and 120 LAD probes. These probes were selected using LAD and leave-one-out analysis from 1,075 distinguishing probes with P value <0.000001. (B) Semi-quantitative reverse transcription PCR validates the ability of a subset of the LAD probes to clearly distinguish between ccA and ccB tumors.

Figure 5.

Validation of logical analysis of data (LAD) probes in the validation data set show the existence of two clear cell renal cell carcinoma (ccRCC) clusters. Consensus matrix of 177 ccRCC tumors determined by 111 probes corresponding to the 120 LAD probes. Red areas identify samples clustered together across the bootstrap analysis. Two distinct clusters are visible, validating the ability of the LAD probe set to classify ccRCC tumors into ccA or ccB subtypes from other array platforms.

Figure 6.

Classification of tumors from the validation data set by logical analysis of data (LAD) prediction shows that subtypes have differing survival outcomes. In total, 177 ccRCC tumors were individually assigned to ccA, ccB, or unclassified (uncl) by LAD prediction analysis, and cancer-specific survival (A) and overall survival (B) were calculated via Kaplan-Meier curves. The ccB subtype had a significantly decreased survival outcome compared to ccA, while unclassified tumors had an intermediate survival time (log rank P < 0.01). (C) Cancer-specific survival for intermediate (Fuhrman grades 2-3) tumors shows significant difference between subtypes. (D) Cancer-specific survival for high grade (Fuhrman grade 4) shows a trend of better survival for ccA tumors.