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1.
Figure 1.

Figure 1. From: Molecular Stratification of Clear Cell Renal Cell Carcinoma by Consensus Clustering Reveals Distinct Subtypes and Survival Patterns.

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.

A. Rose Brannon, et al. Genes Cancer. 2010 February;1(2):152-163.
2.
Figure 4.

Figure 4. From: Molecular Stratification of Clear Cell Renal Cell Carcinoma by Consensus Clustering Reveals Distinct Subtypes and Survival Patterns.

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.

A. Rose Brannon, et al. Genes Cancer. 2010 February;1(2):152-163.
3.
Figure 5.

Figure 5. From: Molecular Stratification of Clear Cell Renal Cell Carcinoma by Consensus Clustering Reveals Distinct Subtypes and Survival Patterns.

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.

A. Rose Brannon, et al. Genes Cancer. 2010 February;1(2):152-163.
4.
Figure 2.

Figure 2. From: Molecular Stratification of Clear Cell Renal Cell Carcinoma by Consensus Clustering Reveals Distinct Subtypes and Survival Patterns.

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.

A. Rose Brannon, et al. Genes Cancer. 2010 February;1(2):152-163.
5.
Figure 6.

Figure 6. From: Molecular Stratification of Clear Cell Renal Cell Carcinoma by Consensus Clustering Reveals Distinct Subtypes and Survival Patterns.

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.

A. Rose Brannon, et al. Genes Cancer. 2010 February;1(2):152-163.
6.
Figure 3.

Figure 3. From: Molecular Stratification of Clear Cell Renal Cell Carcinoma by Consensus Clustering Reveals Distinct Subtypes and Survival Patterns.

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).

A. Rose Brannon, et al. Genes Cancer. 2010 February;1(2):152-163.

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