Analysis Overview
Ovarian Serous Cystadenocarcinoma (Primary solid tumor)
17 October 2014  |  analyses__2014_10_17
Maintainer Information
Citation Information
doi:10.7908/C1JD4VP0
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Analysis Overview for Ovarian Serous Cystadenocarcinoma (Primary solid tumor cohort) - 17 October 2014. Broad Institute of MIT and Harvard. doi:10.7908/C1JD4VP0
Overview
Introduction

This is an overview of Ovarian Serous Cystadenocarcinoma analysis pipelines from Firehose run "17 October 2014".

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 1.0.5 (Cancer-Specific High-throughput Annotation of Somatic Mutations)
      View Report | There are 89 mutations identified by MuTect and 2 mutations with significant functional impact at BHFDR <= 0.25.

    • Mutation Analysis (MutSig 2CV v3.1)
      View Report | 

    • Mutation Analysis (MutSig v1.5)
      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 579 tumor samples used in this analysis: 31 significant arm-level results, 33 significant focal amplifications, and 38 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 14 different clustering approaches and 8 clinical features across 587 patients, 6 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 71 focal events and 8 clinical features across 571 patients, 11 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 8 clinical features across 571 patients, 12 significant findings detected with Q value < 0.25.

    • Correlation between gene methylation status and clinical features
      View Report | Testing the association between 14047 genes and 8 clinical features across 574 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 1 clinical feature related to at least one genes.

    • Correlation between gene mutation status and selected clinical features
      View Report | Testing the association between mutation status of 19 genes and 5 clinical features across 316 patients, no significant finding detected with Q value < 0.25.

    • Correlation between miR expression and clinical features
      View Report | Testing the association between 817 miRs and 8 clinical features across 560 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 3 clinical features related to at least one miRs.

    • Correlation between miRseq expression and clinical features
      View Report | Testing the association between 415 miRs and 7 clinical features across 453 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 2 clinical features related to at least one miRs.

    • Correlation between mRNA expression and clinical features
      View Report | Testing the association between 18632 genes and 8 clinical features across 562 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 3 clinical features related to at least one genes.

    • Correlation between mRNAseq expression and clinical features
      View Report | Testing the association between 18555 genes and 5 clinical features across 261 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 2 clinical features related to at least one genes.

    • Correlation between mutation rate and clinical features
      View Report | Testing the association between 2 variables and 6 clinical features across 316 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 2 clinical features related to at least one variables.

    • Correlation between RPPA expression and clinical features
      View Report | Testing the association between 165 genes and 7 clinical features across 407 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 2 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 579 samples using the 71 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 copy number data by peak region with threshold value: consensus NMF
      View Report | The most robust consensus NMF clustering of 579 samples using the 71 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 2419 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 582 samples and 2419 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 miR expression: consensus hierarchical
      View Report | Median absolute deviation (MAD) was used to select 205 most variable miRs. Consensus ward linkage hierarchical clustering of 568 samples and 205 miRs identified 10 subtypes with the stability of the clustering increasing for k = 2 to k = 10.

    • Clustering of miR expression: consensus NMF
      View Report | We filtered the data to 150 most variable miRs. Consensus NMF clustering of 568 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 mature expression: consensus hierarchical
      View Report | Median absolute deviation (MAD) was used to select 647 most variable miRs. Consensus ward linkage hierarchical clustering of 22 samples and 647 miRs identified 6 subtypes with the stability of the clustering increasing for k = 2 to k = 10.

    • Clustering of miRseq mature expression: consensus NMF
      View Report | We filtered the data to 647 most variable miRs. Consensus NMF clustering of 22 samples and 647 miRs 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 miRseq precursor expression: consensus hierarchical
      View Report | Median absolute deviation (MAD) was used to select 103 most variable miRs. Consensus ward linkage hierarchical clustering of 453 samples and 103 miRs identified 4 subtypes with the stability of the clustering increasing for k = 2 to k = 10.

    • Clustering of miRseq precursor expression: consensus NMF
      View Report | We filtered the data to 150 most variable miRs. Consensus NMF clustering of 453 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 mRNA expression: consensus hierarchical
      View Report | Median absolute deviation (MAD) was used to select 1500 most variable genes. Consensus ward linkage hierarchical clustering of 569 samples and 1500 genes identified 3 subtypes with the stability of the clustering increasing for k = 2 to k = 10.

    • Clustering of mRNA expression: consensus NMF
      View Report | The most robust consensus NMF clustering of 569 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 | Median absolute deviation (MAD) was used to select 1500 most variable genes. Consensus ward linkage hierarchical clustering of 261 samples and 1500 genes identified 3 subtypes with the stability of the clustering increasing for k = 2 to k = 10.

    • Clustering of mRNAseq gene expression: consensus NMF
      View Report | The most robust consensus NMF clustering of 261 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 | Median absolute deviation (MAD) was used to select 165 most variable proteins. Consensus ward linkage hierarchical clustering of 412 samples and 165 proteins identified 4 subtypes with the stability of the clustering increasing for k = 2 to k = 10.

    • Clustering of RPPA data: consensus NMF
      View Report | The most robust consensus NMF clustering of 412 samples using 165 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.

  • Other Analyses

    • Aggregate Analysis Features
      View Report | 597 samples and 420 features are included in this feature table. The figures below show which genomic pair events are co-occurring and which are mutually-exclusive.

    • Identification of putative miR direct targets
      View Report | This pipeline use a relevance network approach to infer putative miR:mRNA regulatory connections. All miR:mRNA pairs that have correlations < -0.3 and have predicted interactions in three sequence prediction databases (Miranda, Pictar, Targetscan) define the final network.

  • Pathway Analyses

    • Association of mutation, copy number alteration, and subtype markers with pathways
      View Report | There are 13 genes with significant mutation (Q value <= 0.1) and 221 genes with significant copy number alteration (Q value <= 0.25). The identified marker genes (Q value <= 0.01 or within top 2000) are 2000 for subtype 1, 2000 for subtype 2, 2000 for subtype 3. Pathways significantly enriched with these genes (Q value <= 0.01) are identified :

    • PARADIGM pathway analysis of mRNA expression and copy number data
      View Report | There were 79 significant pathways identified in this analysis.

    • PARADIGM pathway analysis of mRNA 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 30 significant pathways identified in this analysis.

    • PARADIGM pathway analysis of mRNASeq expression data
      View Report | There were 43 significant pathways identified in this analysis.

  • Other Correlation Analyses

    • Correlation between copy number and miR expression
      View Report

    • Correlation between copy number variation genes (focal events) and molecular subtypes
      View Report | Testing the association between copy number variation 71 focal events and 14 molecular subtypes across 579 patients, 76 significant findings detected with P value < 0.05 and 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 events and 14 molecular subtypes across 579 patients, 42 significant findings detected with P value < 0.05 and Q value < 0.25.

    • Correlation between gene mutation status and molecular subtypes
      View Report | Testing the association between mutation status of 19 genes and 14 molecular subtypes across 316 patients, one significant finding 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 = 261. Number of gene expression samples = 261. Number of methylation samples = 582.

    • Correlations between copy number and mRNA expression
      View Report | The correlation coefficients in 10, 20, 30, 40, 50, 60, 70, 80, 90 percentiles are 0.0169, 0.066, 0.1239, 0.20298, 0.2942, 0.38462, 0.45889, 0.531, 0.60113, 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 1013.3, 1706.6, 2340, 3048, 3788.5, 4557.8, 5248, 5863, 6487, respectively.

Methods & Data
Input

  • Summary Report Date = Wed Jan 21 17:18:55 2015

  • Protection = FALSE

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