Analysis Overview
Thyroid Adenocarcinoma (Primary solid tumor)
23 September 2013  |  analyses__2013_09_23
Maintainer Information
Citation Information
doi:10.7908/C15H7DNX
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 Thyroid Adenocarcinoma (Primary solid tumor cohort) - 23 September 2013. Broad Institute of MIT and Harvard. doi:10.7908/C15H7DNX
Overview
Introduction

This is an overview of Thyroid Adenocarcinoma analysis pipelines from Firehose run "23 September 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

    • CHASM (Cancer-Specific High-throughput Annotation of Somatic Mutations)
      View Report | There are 4387 mutations identified by MuTect and 812 mutations with significant functional impact at BHFDR <= 0.25.

    • LowPass Copy number analysis (GISTIC2)
      View Report | There were 95 tumor samples used in this analysis: 10 significant arm-level results, 0 significant focal amplifications, and 0 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 | 

    • Mutation Assessor
      View Report | 

    • SNP6 Copy number analysis (GISTIC2)
      View Report | There were 494 tumor samples used in this analysis: 18 significant arm-level results, 1 significant focal amplifications, and 32 significant focal deletions were found.

  • 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 15 clinical features across 477 patients, 58 significant findings detected with P value < 0.05 and Q value < 0.25.

    • Correlation between copy number variation genes (focal events) and selected clinical features
      View Report | Testing the association between copy number variation 29 focal events and 15 clinical features across 475 patients, 14 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 55 arm-level events and 15 clinical features across 475 patients, 57 significant findings detected with Q value < 0.25.

    • Correlation between gene methylation status and clinical features
      View Report | Testing the association between 19702 genes and 15 clinical features across 474 samples, statistically thresholded by Q value < 0.05, 14 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 7 genes and 15 clinical features across 385 patients, 8 significant findings detected with Q value < 0.25.

    • Correlation between miRseq expression and clinical features
      View Report | Testing the association between 514 genes and 15 clinical features across 476 samples, statistically thresholded by Q value < 0.05, 13 clinical features related to at least one genes.

    • Correlation between mRNAseq expression and clinical features
      View Report | Testing the association between 17997 genes and 15 clinical features across 463 samples, statistically thresholded by Q value < 0.05, 13 clinical features related to at least one genes.

    • Correlation between RPPA expression and clinical features
      View Report | Testing the association between 175 genes and 15 clinical features across 211 samples, statistically thresholded by Q value < 0.05, 11 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 494 samples using the 35 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 5272 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 496 samples and 5272 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 255 most variable miRs. Consensus average linkage hierarchical clustering of 495 samples and 255 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 mature expression: consensus NMF
      View Report | We filtered the data to 255 most variable miRs. Consensus NMF clustering of 495 samples and 255 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 495 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 495 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 482 samples and 1500 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 mRNAseq gene expression: consensus NMF
      View Report | The most robust consensus NMF clustering of 482 samples using the 1500 most variable genes 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 | 175 proteins were selected. Consensus average linkage hierarchical clustering of 222 samples and 175 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 222 samples using 175 proteins 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.

  • Pathway Analyses

    • HotNet pathway analysis of mutation and copy number data
      View Report

    • PARADIGM pathway analysis of mRNASeq expression and copy number data
      View Report | There were 37 significant pathways identified in this analysis.

    • PARADIGM pathway analysis of mRNASeq expression data
      View Report | There were 48 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 29 peak regions and 10 molecular subtypes across 494 patients, 70 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 45 arm-level results and 10 molecular subtypes across 494 patients, 132 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 7 genes and 10 molecular subtypes across 401 patients, 30 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 = 482. Number of gene expression samples = 482. Number of methylation samples = 496.

    • Correlations between copy number and mRNAseq expression
      View Report | The correlation coefficients in 10, 20, 30, 40, 50, 60, 70, 80, 90 percentiles are 430, 1168, 1740, 2049, 2347, 2680, 3049, 3545, 4196, respectively.

Methods & Data
Input

  • Summary Report Date = Mon Oct 21 15:37:31 2013

  • Protection = FALSE

Copyright © 2013 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle