Analysis Overview
Adrenocortical Carcinoma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C16Q1W60
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 Adrenocortical Carcinoma (Primary solid tumor cohort) - 02 April 2015. Broad Institute of MIT and Harvard. doi:10.7908/C16Q1W60
Overview
Introduction

This is an overview of Adrenocortical Carcinoma analysis pipelines from Firehose run "02 April 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 62 tumor samples in this analysis. The Benjamini-Hochberg-corrected p-value for enrichment of the APOBEC mutation signature in 5 samples is <=0.05. Out of these, 4 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 7418 mutations identified by MuTect and 234 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 90 tumor samples used in this analysis: 23 significant arm-level results, 19 significant focal amplifications, and 27 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 8 clinical features across 92 patients, 26 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 46 focal events and 8 clinical features across 90 patients, no significant finding 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 8 clinical features across 90 patients, 12 significant findings detected with Q value < 0.25.

    • Correlation between gene methylation status and clinical features
      View Report | Testing the association between 19519 genes and 7 clinical features across 80 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 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 119 genes and 7 clinical features across 62 patients, 2 significant findings detected with Q value < 0.25.

    • Correlation between miRseq expression and clinical features
      View Report | Testing the association between 518 miRs and 7 clinical features across 80 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 6 clinical features related to at least one miRs.

    • Correlation between mRNAseq expression and clinical features
      View Report | Testing the association between 17733 genes and 7 clinical features across 79 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 5 clinical features related to at least one genes.

    • Correlation between mutation rate and clinical features
      View Report | Testing the association between 2 variables and 9 clinical features across 90 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 4 clinical features related to at least one variables.

    • Correlation between RPPA expression and clinical features
      View Report | Testing the association between 192 genes and 7 clinical features across 46 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 5 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 90 samples using the 46 copy number focal regions 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 copy number data by peak region with threshold value: consensus NMF
      View Report | The most robust consensus NMF clustering of 90 samples using the 46 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 15062 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 80 samples and 15062 genes 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 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 78 samples and 647 miRs identified 3 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 78 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 129 most variable miRs. Consensus ward linkage hierarchical clustering of 80 samples and 129 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 80 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 79 samples and 1500 genes identified 4 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 79 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 192 most variable proteins. Consensus ward linkage hierarchical clustering of 46 samples and 192 proteins identified 8 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 46 samples using 192 proteins was identified for k = 2 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 | 91 samples and 857 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 756 miRs and 17733 mRNAs across 79 samples. After 2 filtering steps, the number of 48 miR:genes pairs were detected.

  • Pathway Analyses

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

    • PARADIGM pathway analysis of mRNASeq expression and copy number data
      View Report | There were 66 significant pathways identified in this analysis.

    • PARADIGM pathway analysis of mRNASeq expression data
      View Report | There were 50 significant pathways identified in this analysis.

    • Significant over-representaion 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, no significant overlapping gene sets are found.

  • Other Correlation Analyses

    • Correlation between copy number variation genes (focal events) and molecular subtypes
      View Report | Testing the association between copy number variation 46 focal events and 10 molecular subtypes across 90 patients, 154 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 90 patients, 211 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 119 genes and 10 molecular subtypes across 62 patients, no 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 = 79. Number of gene expression samples = 79. Number of methylation samples = 80.

    • Correlations between copy number and mRNAseq expression
      View Report | The correlation coefficients in 10, 20, 30, 40, 50, 60, 70, 80, 90 percentiles are 958.9, 1904, 2595, 3210.6, 3802, 4390, 4987.3, 5617.2, 6344, respectively.

Methods & Data
Input

  • Summary Report Date = Sat Aug 15 11:29:51 2015

  • Protection = FALSE

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