Analysis Overview
Head and Neck Squamous Cell Carcinoma (Primary solid tumor)
23 May 2013  |  analyses__2013_05_23
Maintainer Information
Citation Information
doi:10.7908/C15T3HHZ
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 Head and Neck Squamous Cell Carcinoma (Primary solid tumor cohort) - 23 May 2013. Broad Institute of MIT and Harvard. doi:10.7908/C15T3HHZ
Overview
Introduction

This is an overview of Head and Neck Squamous Cell Carcinoma analysis pipelines from Firehose run "23 May 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 368 tumor samples used in this analysis: 25 significant arm-level results, 24 significant focal amplifications, and 47 significant focal deletions were found.

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

    • Mutation Analysis (MutSig v2.0 and MutSigCV v0.9 merged result)
      View Report | 

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

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

  • Correlations to Clinical Parameters

    • Correlation between aggregated molecular cancer subtypes and selected clinical features
      View Report | Testing the association between subtypes identified by 10 different clustering approaches and 11 clinical features across 331 patients, 2 significant findings detected with P value < 0.05 and Q value < 0.25.

    • Correlation between copy number variation genes (focal) and selected clinical features
      View Report | Testing the association between copy number variation 71 arm-level results and 11 clinical features across 325 patients, 3 significant findings 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 copy number variation 79 arm-level results and 11 clinical features across 325 patients, one significant finding detected with Q value < 0.25.

    • Correlation between gene methylation status and clinical features
      View Report | Testing the association between 20139 genes and 9 clinical features across 306 samples, statistically thresholded by Q value < 0.05, 8 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 45 genes and 10 clinical features across 306 patients, 4 significant findings detected with Q value < 0.25.

    • Correlation between miRseq expression and clinical features
      View Report | Testing the association between 551 genes and 10 clinical features across 327 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one genes.

    • Correlation between mRNAseq expression and clinical features
      View Report | Testing the association between 18388 genes and 9 clinical features across 302 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one genes.

    • Correlation between RPPA expression and clinical features
      View Report | Testing the association between 174 genes and 9 clinical features across 212 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one genes.

  • 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 368 samples using the 71 copy number focal regions was identified for k = 8 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 4526 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 310 samples and 4526 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 mature expression: consensus hierarchical
      View Report | We filtered the data to 170 most variable miRs. Consensus average linkage hierarchical clustering of 319 samples and 170 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 miRseq mature expression: consensus NMF
      View Report | We filtered the data to 170 most variable miRs. Consensus NMF clustering of 319 samples and 170 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 356 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 NMF
      View Report | We filtered the data to 150 most variable miRs. Consensus NMF clustering of 356 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 303 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 303 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 | 174 proteins were selected. Consensus average linkage hierarchical clustering of 212 samples and 174 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 212 samples using 174 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.

  • Pathway Analyses

    • HotNet pathway analysis of mutation and copy number data
      View Report | There were 48 significant subnetworks identified in HotNet analysis.

    • PARADIGM pathway analysis of mRNASeq expression and copy number data
      View Report | There were 35 significant pathways identified in this analysis.

    • PARADIGM pathway analysis of mRNASeq expression data
      View Report | There were 39 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 71 peak regions and 10 molecular subtypes across 368 patients, 104 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 79 arm-level results and 10 molecular subtypes across 368 patients, 56 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 45 genes and 10 molecular subtypes across 306 patients, 19 significant findings 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 = 303. Number of gene expression samples = 303. Number of methylation samples = 310.

    • Correlations between copy number and mRNAseq expression
      View Report | The correlation coefficients in 10, 20, 30, 40, 50, 60, 70, 80, 90 percentiles are 956, 1703, 2292, 2903, 3563, 4252, 4949, 5670, 6472, respectively.

Methods & Data
Input

  • Summary Report Date = Wed Jun 26 16:40:37 2013

  • Protection = FALSE

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