Analysis Overview for Skin Cutaneous Melanoma
(metastatic tumor cohort)
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Overview
Introduction

This is the analysis overview for Firehose run "26 March 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 244 tumor samples used in this analysis: 23 significant arm-level results, 24 significant focal amplifications, and 27 significant focal deletions were found.

    • Mutation Analysis (MutSig v1.5)
      View Report | 

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

    • Mutation Analysis (MutSigCV v0.9)
      View Report | 

  • Clustering Analyses

    • Clustering of copy number data by focal peak region with log2 ratio: consensus NMF
      View Report | The most robust consensus NMF clustering of 244 samples using the 51 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 11014 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 260 samples and 11014 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 RPPA data: consensus NMF
      View Report | The most robust consensus NMF clustering of 143 samples using the 175 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 175 most variable proteins were selected. Consensus average linkage hierarchical clustering of 143 samples and 175 proteins identified 5 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 232 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 mRNAseq gene expression: consensus hierarchical
      View Report | The 1500 most variable genes were selected. Consensus average linkage hierarchical clustering of 232 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 miRseq precursor expression: consensus NMF
      View Report | We filtered the data to 150 most variable miRs. Consensus NMF clustering of 235 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 precursor expression: consensus hierarchical
      View Report | We filtered the data to 150 most variable miRs. Consensus average linkage hierarchical clustering of 235 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.

  • Correlation Analyses

    • Correlation between copy number variations of arm-level result and selected clinical features
      View Report | Testing the association between copy number variation 73 arm-level results and 7 clinical features across 158 patients, 2 significant findings detected with Q value < 0.25.

    • Correlation between copy number variation genes (focal) and selected clinical features
      View Report | Testing the association between copy number variation 51 arm-level results and 7 clinical features across 158 patients, no significant finding detected with Q value < 0.25.

    • Correlation between gene methylation status and clinical features
      View Report | Testing the association between 17131 genes and 6 clinical features across 148 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.

    • Correlation between molecular cancer subtypes and selected clinical features
      View Report | Testing the association between subtypes identified by 8 different clustering approaches and 7 clinical features across 165 patients, one significant finding 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 125 genes and 7 clinical features across 146 patients, 3 significant findings detected with Q value < 0.25.

    • Correlation between RPPA expression and clinical features
      View Report | Testing the association between 175 genes and 6 clinical features across 100 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 18092 genes and 6 clinical features across 151 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.

    • Correlation between miRseq expression and clinical features
      View Report | Testing the association between 615 genes and 6 clinical features across 154 samples, statistically thresholded by Q value < 0.05, 1 clinical feature related to at least one genes.

    • Correlations between copy number and mRNAseq expression
      View Report | The correlation coefficients in 10, 20, 30, 40, 50, 60, 70, 80, 90 percentiles are 972, 1709, 2283, 2861, 3470.5, 4108, 4768.3, 5440, 6212, 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 = 232. Number of gene expression samples = 232. Number of methylation samples = 232.

    • Correlation between copy number variations of arm-level result and molecular subtypes
      View Report | Testing the association between copy number variation 78 arm-level results and 8 molecular subtypes across 244 patients, 32 significant findings detected with Q value < 0.25.

    • Correlation between copy number variation genes and molecular subtypes
      View Report | Testing the association between copy number variation of 51 peak regions and 8 molecular subtypes across 244 patients, 36 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 129 genes and 8 molecular subtypes across 225 patients, 2 significant findings detected with P value < 0.05 and Q value < 0.25.

  • Pathway Analyses

    • PARADIGM pathway analysis of mRNASeq expression data
      View Report | There were 64 significant pathways identified in this analysis.

    • PARADIGM pathway analysis of mRNASeq expression and copy number data
      View Report | There were 65 significant pathways identified in this analysis.

Methods & Data
Input

  • Run Prefix = analyses__2013_03_26

  • Summary Report Date = Sat Apr 20 08:45:42 2013

  • Protection = FALSE

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