Analysis Overview
Bladder Urothelial Carcinoma (Primary solid tumor)
22 February 2013  |  analyses__2013_02_22
Maintainer Information
Citation Information
doi:10.7908/C1PN93SS
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Analysis Overview for Bladder Urothelial Carcinoma (Primary solid tumor cohort) - 22 February 2013. Broad Institute of MIT and Harvard. doi:10.7908/C1PN93SS
Overview
Introduction

This is an overview of Bladder Urothelial Carcinoma analysis pipelines from Firehose run "22 February 2013".

Summary

Note: These results are offered to the community as an additional reference point, enabling a wide range of cancer biologists, clinical investigators, and genome and computational scientists to easily incorporate TCGA into the backdrop of ongoing research. While every effort is made to ensure that Firehose input data and algorithms are of the highest possible quality, these analyses have not been reviewed by domain experts.

Results

  • Sequence and Copy Number Analyses

    • Copy number analysis (GISTIC2)
      View Report | There were 150 tumor samples used in this analysis: 27 significant arm-level results, 27 significant focal amplifications, and 36 significant focal deletions were found.

    • Mutation Analysis (MutSig v2.0)
      View Report | 

    • Mutation Analysis (MutSig vS2N)
      View Report | 

    • Mutation Analysis (MutSigCV v0.6)
      View Report | 

  • Correlations to Clinical Parameters

    • Correlation between copy number variation genes (focal) and selected clinical features
      View Report | Testing the association between subtypes identified by 63 different clustering approaches and 12 clinical features across 125 patients, one significant finding detected with Q value < 0.25.

    • Correlation between copy number variations of arm-level result and selected clinical features
      View Report | Testing the association between subtypes identified by 74 different clustering approaches and 12 clinical features across 125 patients, 8 significant findings detected with Q value < 0.25.

    • Correlation between gene methylation status and clinical features
      View Report | Testing the association between 17321 genes and 11 clinical features across 112 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.

    • Correlation between gene mutation status and selected clinical features
      View Report | Testing the association between mutation status of 3 genes and 11 clinical features across 28 patients, no significant finding detected with Q value < 0.25.

    • Correlation between miRseq expression and clinical features
      View Report | Testing the association between 587 genes and 11 clinical features across 122 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one genes.

    • Correlation between mRNAseq expression and clinical features
      View Report | Testing the association between 18283 genes and 11 clinical features across 115 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.

    • Correlation between RPPA expression and clinical features
      View Report | Testing the association between 175 genes and 11 clinical features across 53 samples, statistically thresholded by Q value < 0.05, 1 clinical feature related to at least one genes.

  • Clustering Analyses

    • Clustering of copy number data: consensus NMF
      View Report | The most robust consensus NMF clustering of 150 samples using the 63 copy number focal regions was identified for k = 6 clusters. We computed the clustering for k = 2 to k = 8 and used the cophenetic correlation coefficient to determine the best solution.

    • Clustering of Methylation: consensus NMF
      View Report | The 10268 most variable methylated genes were selected based on variation. The variation cutoff are set for each tumor type empirically by fitting a bimodal distriution. For genes with multiple methylation probes, we chose the most variable one to represent the gene. Consensus NMF clustering of 153 samples and 10268 genes identified 4 subtypes with the stability of the clustering increasing for k = 2 to k = 8 and the average silhouette width calculation for selecting the robust clusters.

    • Clustering of miRseq expression: consensus hierarchical
      View Report | We filtered the data to 150 most variable miRs. Consensus average linkage hierarchical clustering of 135 samples and 150 miRs identified 3 subtypes with the stability of the clustering increasing for k = 2 to k = 8 and the average silhouette width calculation for selecting the robust clusters.

    • Clustering of miRseq expression: consensus NMF
      View Report | We filtered the data to 150 most variable miRs. Consensus NMF clustering of 135 samples and 150 miRs identified 3 subtypes with the stability of the clustering increasing for k = 2 to k = 8 and the average silhouette width calculation for selecting the robust clusters.

    • Clustering of mRNAseq gene expression: consensus hierarchical
      View Report | The 1500 most variable genes were selected. Consensus average linkage hierarchical clustering of 122 samples and 1500 genes identified 3 subtypes with the stability of the clustering increasing for k = 2 to k = 8 and the average silhouette width calculation for selecting the robust clusters.

    • Clustering of mRNAseq gene expression: consensus NMF
      View Report | The most robust consensus NMF clustering of 122 samples using the 1500 most variable genes was identified for k = 3 clusters. We computed the clustering for k = 2 to k = 8 and used the cophenetic correlation coefficient to determine the best solution.

    • Clustering of RPPA data: consensus hierarchical
      View Report | The 150 most variable proteins were selected. Consensus average linkage hierarchical clustering of 54 samples and 150 proteins identified 3 subtypes with the stability of the clustering increasing for k = 2 to k = 8 and the average silhouette width calculation for selecting the robust clusters.

    • Clustering of RPPA data: consensus NMF
      View Report | The most robust consensus NMF clustering of 54 samples using the 150 most variable proteins was identified for k = 4 clusters. We computed the clustering for k = 2 to k = 8 and used the cophenetic correlation coefficient to determine the best solution.

  • Other Analyses

    • Correlate_Clinical_vs_Molecular_Signatures
      View Report | Testing the association between subtypes identified by 8 different clustering approaches and 12 clinical features across 128 patients, 3 significant findings detected with P value < 0.05 and Q value < 0.25.

  • Pathway Analyses

    • HotNet pathway analysis of mutation and copy number data
      View Report | There were 18 significant subnetworks identified in HotNet analysis.

    • PARADIGM pathway analysis of mRNASeq expression and copy number data
      View Report | There were 34 significant pathways identified in this analysis.

    • PARADIGM pathway analysis of mRNASeq expression data
      View Report | There were 47 significant pathways identified in this analysis.

  • Other Correlation Analyses

    • Correlation between copy number variation genes and molecular subtypes
      View Report | Testing the association between copy number variation of 63 peak regions and 8 molecular subtypes across 150 patients, 43 significant findings detected with Q value < 0.25.

    • Correlation between copy number variations of arm-level result and molecular subtypes
      View Report | Testing the association between copy number variation 76 arm-level results and 8 molecular subtypes across 150 patients, 15 significant findings detected with Q value < 0.25.

    • Correlation between gene mutation status and molecular subtypes
      View Report | Testing the association between mutation status of 3 genes and 8 molecular subtypes across 28 patients, no significant finding detected with P value < 0.05 and Q value < 0.25.

    • Correlation between mRNA expression and DNA methylation
      View Report | The top 25 correlated methylation probes per gene are displayed. Total number of matched samples = 122. Number of gene expression samples = 122. Number of methylation samples = 122.

    • Correlations between copy number and mRNAseq expression
      View Report | The correlation coefficients in 10, 20, 30, 40, 50, 60, 70, 80, 90 percentiles are 1069.7, 1853, 2487, 3091, 3741, 4445, 5153, 5899.6, 6696, respectively.

Methods & Data
Input

  • Summary Report Date = Mon Aug 5 18:53:16 2013

  • Protection = FALSE

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