Analysis Overview for Glioblastoma Multiforme
(primary solid tumor cohort)
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)


Unique Tumor Sample Counts
TumorBCRClinicalCNLowPMethylationmRNAmRNAseqmiRmiRseqRPPAMAF
GBM59856556304055421614910214291

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Overview
Introduction

This is the analysis overview for Firehose run "17 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 563 tumor samples used in this analysis: 23 significant arm-level results, 24 significant focal amplifications, and 46 significant focal deletions were found.

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

    • Mutation Analysis (MutSig vS2N)
      View Report | 

  • Clustering Analyses

    • Clustering of copy number data: consensus NMF
      View Report | The most robust consensus NMF clustering of 563 samples using the 70 copy number focal regions 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 Methylation: consensus NMF
      View Report | The 8016 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 112 samples and 8016 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 RPPA data: consensus NMF
      View Report | The most robust consensus NMF clustering of 214 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.

    • Clustering of RPPA data: consensus hierarchical
      View Report | The 150 most variable proteins were selected. Consensus average linkage hierarchical clustering of 214 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 mRNA expression: consensus NMF
      View Report | The most robust consensus NMF clustering of 529 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 mRNA expression: consensus hierarchical
      View Report | The 1500 most variable genes were selected. Consensus average linkage hierarchical clustering of 529 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 154 samples using the 1500 most variable genes 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 mRNAseq gene expression: consensus hierarchical
      View Report | The 1500 most variable genes were selected. Consensus average linkage hierarchical clustering of 154 samples and 1500 genes identified 6 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 miR expression: consensus NMF
      View Report | We filtered the data to 150 most variable miRs. Consensus NMF clustering of 491 samples and 150 miRs 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 miR expression: consensus hierarchical
      View Report | We filtered the data to 150 most variable miRs. Consensus average linkage hierarchical clustering of 491 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.

  • Other Analyses

    • The boxplot mRNA array based expression data
      View Report

    • Identification of putative miR direct targets
      View Report | This pipeline use a relevance network approach to infer putative miR:mRNA regulatory connections. All miR:mRNA pairs that have correlations < -0.3 and have predicted interactions in three sequence prediction databases (Miranda, Pictar, Targetscan) define the final network.

  • Correlation Analyses

    • Preprocessing of clinical data
      View Report | Clinical data for tier 1 clinical variables are generated.

    • Correlation between copy number variations of arm-level result and selected clinical features
      View Report | Testing the association between copy number variation 79 arm-level results and 5 clinical features across 544 patients, 10 significant findings detected with Q value < 0.25.

    • Correlation between copy number variation genes and selected clinical features
      View Report | Testing the association between copy number variation of 70 peak regions and 5 clinical features across 544 patients, 15 significant findings detected with Q value < 0.25.

    • Correlation between gene methylation status and clinical features
      View Report | Testing the association between 17243 genes and 5 clinical features across 45 samples, statistically thresholded by Q value < 0.05, no clinical feature related to at least one genes.

    • Correlation between molecular cancer subtypes and selected clinical features
      View Report | Testing the association between subtypes identified by 10 different clustering approaches and 5 clinical features across 564 patients, 2 significant findings detected with P value < 0.05 and Q value < 0.25.

    • Correlation between gene mutation status and selected clinical features
      View Report | Testing the association between mutation status of 181 genes and 5 clinical features across 276 patients, 2 significant findings detected with Q value < 0.25.

    • Correlation between RPPA expression and clinical features
      View Report | Testing the association between 171 genes and 5 clinical features across 210 samples, statistically thresholded by Q value < 0.05, 1 clinical feature related to at least one genes.

    • Correlation between mRNA expression and clinical features
      View Report | Testing the association between 12042 genes and 5 clinical features across 519 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.

    • Correlation between mRNAseq expression and clinical features
      View Report | Testing the association between 18190 genes and 5 clinical features across 152 samples, statistically thresholded by Q value < 0.05, 2 clinical features related to at least one genes.

    • Correlation between miR expression and clinical features
      View Report | Testing the association between 534 miRs and 5 clinical features across 482 samples, statistically thresholded by Q value < 0.05, 2 clinical features related to at least one miRs.

    • Correlations between copy number and mRNA expression
      View Report | The correlation coefficients in 10, 20, 30, 40, 50, 60, 70, 80, 90 percentiles are -0.0239, 0.0158, 0.05463, 0.1034, 0.1552, 0.20402, 0.2514, 0.30306, 0.3724, respectively.

    • Correlations between copy number and mRNAseq expression
      View Report | The correlation coefficients in 10, 20, 30, 40, 50, 60, 70, 80, 90 percentiles are 953.4, 1848.8, 2387.2, 2921, 3435, 3984, 4584.8, 5254, 6118.6, respectively.

    • Correlations between copy number and miR expression
      View Report | The correlation coefficients in 10, 20, 30, 40, 50, 60, 70, 80, 90 percentiles are -0.05517, -0.01362, 0.00507, 0.02226, 0.04025, 0.07948, 0.1342, 0.20986, 0.27194, respectively.

    • Correlation between mRNA expression and DNA methylation
      View Report | The top 25 correlated methylation probes per gene are displayed. Total number of matched samples = 47. Number of gene expression samples = 154. Number of methylation samples = 47.

  • Pathway Analyses

    • HotNet pathway analysis of mutation and copy number data
      View Report | There were 21 significant subnetworks identified in HotNet analysis.

    • PARADIGM pathway analysis of mRNA expression data
      View Report | There were 71 significant pathways identified in this analysis.

    • PARADIGM pathway analysis of mRNA expression and copy number data
      View Report | There were 101 significant pathways identified in this analysis.

    • PARADIGM pathway analysis of mRNASeq expression data
      View Report | There were 52 significant pathways identified in this analysis.

    • PARADIGM pathway analysis of mRNASeq expression and copy number data
      View Report | There were 40 significant pathways identified in this analysis.

    • Association of mutation, copy number alteration, and subtype markers with pathways
      View Report | There are 98 genes with significant mutation (Q value <= 0.1) and 181 genes with significant copy number alteration (Q value <= 0.25). The identified marker genes (Q value <= 0.01 or within top 2000) are 2000 for subtype 1, 2000 for subtype 2, 2000 for subtype 3. Pathways significantly enriched with these genes (Q value <= 0.01) are identified :

Methods & Data
Input

  • Run Prefix = awg_gbm__2013_02_17

  • Summary Report Date = Mon Feb 25 13:41:35 2013

  • Protection = FALSE

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