Analysis Overview
Uterine Corpus Endometrioid Carcinoma (Primary solid tumor)
23 September 2013  |  analyses__2013_09_23
Maintainer Information
Citation Information
doi:10.7908/C1VX0DWR
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 Uterine Corpus Endometrioid Carcinoma (Primary solid tumor cohort) - 23 September 2013. Broad Institute of MIT and Harvard. doi:10.7908/C1VX0DWR
Overview
Introduction

This is an overview of Uterine Corpus Endometrioid Carcinoma 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

    • LowPass Copy number analysis (GISTIC2)
      View Report | There were 106 tumor samples used in this analysis: 19 significant arm-level results, 9 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 504 tumor samples used in this analysis: 28 significant arm-level results, 49 significant focal amplifications, and 49 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 12 different clustering approaches and 5 clinical features across 478 patients, 22 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 98 focal events and 5 clinical features across 470 patients, 164 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 80 arm-level events and 5 clinical features across 470 patients, 79 significant findings detected with Q value < 0.25.

    • Correlation between gene methylation status and clinical features
      View Report | Testing the association between 20206 genes and 5 clinical features across 362 samples, statistically thresholded by Q value < 0.05, 4 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 38 genes and 5 clinical features across 248 patients, 10 significant findings detected with Q value < 0.25.

    • Correlation between miRseq expression and clinical features
      View Report | Testing the association between 556 genes and 5 clinical features across 469 samples, statistically thresholded by Q value < 0.05, 4 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, 3 clinical features related to at least one genes.

    • Correlation between mRNAseq expression and clinical features
      View Report | Testing the association between 18332 genes and 5 clinical features across 85 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.

    • 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, 3 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 504 samples using the 98 copy number focal regions 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 Methylation: consensus NMF
      View Report | The 10037 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 396 samples and 10037 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 259 most variable miRs. Consensus average linkage hierarchical clustering of 376 samples and 259 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 259 most variable miRs. Consensus NMF clustering of 376 samples and 259 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 504 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 504 samples and 150 miRs 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 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 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 mRNAseq gene expression: consensus hierarchical
      View Report | The 1500 most variable genes were selected. Consensus average linkage hierarchical clustering of 118 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 118 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 | 166 proteins were selected. Consensus average linkage hierarchical clustering of 200 samples and 166 proteins identified 2 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 166 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.

  • Pathway Analyses

    • Association of mutation, copy number alteration, and subtype markers with pathways
      View Report | There are 33 genes with significant mutation (Q value <= 0.1) and 342 genes with significant copy number alteration (Q value <= 0.25). The identified marker genes (Q value <= 0.01 or within top 2000) are 238 for subtype 1, 238 for subtype 2, 238 for subtype 3. Pathways significantly enriched with these genes (Q value <= 0.01) are identified :

    • HotNet pathway analysis of mutation and copy number data
      View Report | There were 66 significant subnetworks identified in HotNet analysis.

    • PARADIGM pathway analysis of mRNA expression and copy number data
      View Report | There were 28 significant pathways identified in this analysis.

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

    • PARADIGM pathway analysis of mRNASeq expression and copy number data
      View Report | There were 68 significant pathways identified in this analysis.

    • PARADIGM pathway analysis of mRNASeq expression data
      View Report | There were 66 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 98 peak regions and 12 molecular subtypes across 504 patients, 590 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 80 arm-level results and 12 molecular subtypes across 504 patients, 198 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 38 genes and 10 molecular subtypes across 248 patients, 26 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 = 118. Number of gene expression samples = 118. Number of methylation samples = 396.

    • Correlations between copy number and mRNA expression
      View Report | The correlation coefficients in 10, 20, 30, 40, 50, 60, 70, 80, 90 percentiles are -0.09319, -0.01, 0.05333, 0.1106, 0.16705, 0.2236, 0.2863, 0.35788, 0.4516, 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 1126.6, 1846, 2458.8, 3021.4, 3594, 4179, 4804.2, 5466, 6247, respectively.

Methods & Data
Input

  • Summary Report Date = Mon Oct 21 15:41:17 2013

  • Protection = FALSE

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