Analysis Overview for Uterine Corpus Endometrioid Carcinoma
Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/Harvard Medical School)
Overview
Introduction

This is the analysis overview for Firehose run "25 August 2012".

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 443 tumor samples used in this analysis: 29 significant arm-level results, 47 significant focal amplifications, and 50 significant focal deletions were found.

  • Clustering Analyses

    • Clustering of copy number data: consensus NMF
      View Report | The most robust consensus NMF clustering of 443 samples using the 97 copy number focal regions was identified for k = 7 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 10362 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 334 samples and 10362 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 200 samples using the 150 most variable proteins 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 RPPA data: consensus hierarchical
      View Report | The 150 most variable proteins were selected. Consensus average linkage hierarchical clustering of 200 samples and 150 proteins 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 mRNA expression: consensus NMF
      View Report | The most robust consensus NMF clustering of 54 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 mRNA expression: consensus hierarchical
      View Report | The 1500 most variable genes were selected. Consensus average linkage hierarchical clustering of 54 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 266 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 266 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 expression: consensus NMF
      View Report | We filtered the data to 150 most variable miRs. Consensus NMF clustering of 377 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 hierarchical
      View Report | We filtered the data to 150 most variable miRs. Consensus average linkage hierarchical clustering of 377 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 79 arm-level results and 5 clinical features across 422 patients, 48 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 97 peak regions and 5 clinical features across 422 patients, 148 significant findings detected with Q value < 0.25.

    • Correlation between gene methylation status and clinical features
      View Report | Testing the association between 20238 genes and 5 clinical features across 313 samples, statistically thresholded by Q value < 0.05, 5 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 7 different clustering approaches and 5 clinical features across 430 patients, 28 significant findings detected with P value < 0.05.

    • Correlation between RPPA expression and clinical features
      View Report | Testing the association between 166 genes and 5 clinical features across 200 samples, statistically thresholded by Q value < 0.05, 2 clinical features related to at least one genes.

    • Correlation between mRNA expression and clinical features
      View Report | Testing the association between 17814 genes and 5 clinical features across 54 samples, statistically thresholded by Q value < 0.05, 1 clinical feature related to at least one genes.

    • Correlation between mRNAseq expression and clinical features
      View Report | Testing the association between 18744 genes and 5 clinical features across 266 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 551 genes and 5 clinical features across 372 samples, statistically thresholded by Q value < 0.05, 2 clinical features related to at least one genes.

    • Correlations between copy number and mRNA expression
      View Report | The correlation coefficients in 10, 20, 30, 40, 50, 60, 70, 80, 90 percentiles are -0.10414, -0.0138, 0.0519, 0.11058, 0.1705, 0.23282, 0.2964, 0.3696, 0.46436, 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 895.5, 1620, 2105, 2542, 2998, 3455, 3946, 4501, 5189, 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 = 153. Number of gene expression samples = 266. Number of methylation samples = 334.

  • Other Analyses

    • PARADIGM pathway analysis of mRNA expression data
      View Report | There were 59 significant pathways identified in this analysis.

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

Methods & Data
Input

  • Run Prefix = analyses__2012_08_25

  • Summary Report Date = Thu Sep 27 12:12:18 2012

  • Protection = FALSE

Copyright © 2012 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle