Analysis Overview
Stomach and Esophageal carcinoma (Primary solid tumor)
21 August 2015  |  analyses__2015_08_21
Maintainer Information
Citation Information
doi:10.7908/C10P0Z9K
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Analysis Overview for Stomach and Esophageal carcinoma (Primary solid tumor cohort) - 21 August 2015. Broad Institute of MIT and Harvard. doi:10.7908/C10P0Z9K
Overview
Introduction

This is an overview of Stomach and Esophageal carcinoma analysis pipelines from Firehose run "21 August 2015".

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

    • Analysis of mutagenesis by APOBEC cytidine deaminases (P-MACD).
      View Report | There are 289 tumor samples in this analysis. The Benjamini-Hochberg-corrected p-value for enrichment of the APOBEC mutation signature in 4 samples is <=0.05. Out of these, 2 have enrichment values >2, which implies that in such samples at least 50% of APOBEC signature mutations have been in fact made by APOBEC enzyme(s).

    • CHASM 1.0.5 (Cancer-Specific High-throughput Annotation of Somatic Mutations)
      View Report | There are 83461 mutations identified by MuTect and 2837 mutations with significant functional impact at BHFDR <= 0.25.

    • LowPass Copy number analysis (GISTIC2)
      View Report | There were 158 tumor samples used in this analysis: 21 significant arm-level results, 19 significant focal amplifications, and 14 significant focal deletions were found.

    • Mutation Analysis (MutSig 2CV v3.1)
      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 625 tumor samples used in this analysis: 28 significant arm-level results, 39 significant focal amplifications, and 60 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 12 clinical features across 628 patients, 67 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 99 focal events and 12 clinical features across 625 patients, 65 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 82 arm-level events and 12 clinical features across 625 patients, 7 significant findings detected with Q value < 0.25.

    • Correlation between gene methylation status and clinical features
      View Report | Testing the association between 20581 genes and 12 clinical features across 580 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 11 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 788 genes and 9 clinical features across 289 patients, 3 significant findings detected with Q value < 0.25.

    • Correlation between miRseq expression and clinical features
      View Report | Testing the association between 514 miRs and 12 clinical features across 620 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 10 clinical features related to at least one miRs.

    • Correlation between mRNAseq expression and clinical features
      View Report | Testing the association between 18768 genes and 12 clinical features across 184 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 9 clinical features related to at least one genes.

    • Correlation between mutation rate and clinical features
      View Report | Testing the association between 2 variables and 10 clinical features across 289 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 196 genes and 12 clinical features across 483 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 10 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 625 samples using the 99 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 625 samples using the 99 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 7059 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 580 samples and 7059 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 | Median absolute deviation (MAD) was used to select 647 most variable miRs. Consensus ward linkage hierarchical clustering of 522 samples and 647 miRs identified 4 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 522 samples and 647 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 | Median absolute deviation (MAD) was used to select 128 most variable miRs. Consensus ward linkage hierarchical clustering of 620 samples and 128 miRs identified 5 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 620 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 | Median absolute deviation (MAD) was used to select 1500 most variable genes. Consensus ward linkage hierarchical clustering of 184 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 184 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 | Median absolute deviation (MAD) was used to select 196 most variable proteins. Consensus ward linkage hierarchical clustering of 483 samples and 196 proteins identified 3 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 483 samples using 196 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.

  • Other Analyses

    • Aggregate Analysis Features
      View Report | 627 samples and 1765 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 by sequencing data
      View Report | The CLR algorithm was applied on 776 miRs and 18768 mRNAs across 183 samples. After 2 filtering steps, the number of 85 miR:genes pairs were detected.

  • Pathway Analyses

    • GSEA Class2: Canonical Pathways enriched in each subtypes of mRNAseq_cNMF in STES-TP
      View Report | basic data info

    • PARADIGM pathway analysis of mRNASeq expression and copy number data
      View Report | There were 32 significant pathways identified in this analysis.

    • PARADIGM pathway analysis of mRNASeq expression data
      View Report | There were 39 significant pathways identified in this analysis.

    • Significant over-representation of pathway genesets for a given gene list
      View Report | For a given gene list, a hypergeometric test was tried to find significant overlapping canonical pathway gene sets. In terms of FDR adjusted p.values, top 5 significant overlapping gene sets are listed as below.

  • Other Correlation Analyses

    • Correlation between copy number variation genes (focal events) and molecular subtypes
      View Report | Testing the association between copy number variation 99 focal events and 10 molecular subtypes across 625 patients, 617 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 82 arm-level events and 10 molecular subtypes across 625 patients, 425 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 788 genes and 8 molecular subtypes across 289 patients, 1345 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 = 184. Number of gene expression samples = 184. Number of methylation samples = 580.

    • Correlations between copy number and mRNAseq expression
      View Report | The correlation coefficients in 10, 20, 30, 40, 50, 60, 70, 80, 90 percentiles are 1046.5, 1741, 2336, 2946, 3621, 4307, 5003, 5689, 6399, respectively.

Methods & Data
Input

  • Summary Report Date = Mon Nov 9 00:19:33 2015

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