Analysis Overview
Breast Invasive Carcinoma (Primary solid tumor)
17 October 2014  |  analyses__2014_10_17
Maintainer Information
Citation Information
doi:10.7908/C1X34W9S
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 Breast Invasive Carcinoma (Primary solid tumor cohort) - 17 October 2014. Broad Institute of MIT and Harvard. doi:10.7908/C1X34W9S
Overview
Introduction

This is an overview of Breast Invasive Carcinoma 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

    • Analysis of mutagenesis by APOBEC cytidine deaminases (P-MACD).
      View Report | There are 977 tumor samples in this analysis. The Benjamini-Hochberg-corrected p-value for enrichment of the APOBEC mutation signature in 224 samples is <=0.05. Out of these, 214 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 45188 mutations identified by MuTect and 3592 mutations with significant functional impact at BHFDR <= 0.25.

    • LowPass Copy number analysis (GISTIC2)
      View Report | There were 19 tumor samples used in this analysis: 15 significant arm-level results, 2 significant focal amplifications, and 2 significant focal deletions were found.

    • 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 1079 tumor samples used in this analysis: 28 significant arm-level results, 29 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 12 different clustering approaches and 12 clinical features across 1043 patients, 73 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 67 focal events and 12 clinical features across 1025 patients, 51 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 12 clinical features across 1025 patients, 28 significant findings detected with Q value < 0.25.

    • Correlation between gene methylation status and clinical features
      View Report | Testing the association between 19976 genes and 12 clinical features across 701 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 10 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 150 genes and 12 clinical features across 959 patients, 7 significant findings detected with Q value < 0.25.

    • Correlation between miRseq expression and clinical features
      View Report | Testing the association between 500 miRs and 12 clinical features across 1023 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 10 clinical features related to at least one miRs.

    • Correlation between mRNA expression and clinical features
      View Report | Testing the association between 17814 genes and 12 clinical features across 526 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 8 clinical features related to at least one genes.

    • Correlation between mRNAseq expression and clinical features
      View Report | Testing the association between 18296 genes and 12 clinical features across 1039 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 11 clinical features related to at least one genes.

    • Correlation between mutation rate and clinical features
      View Report | Testing the association between 2 variables and 13 clinical features across 959 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 8 clinical features related to at least one variables.

    • Correlation between RPPA expression and clinical features
      View Report | Testing the association between 142 genes and 12 clinical features across 409 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 9 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 1079 samples using the 67 copy number focal regions was identified for k = 5 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 1079 samples using the 67 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 4945 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 732 samples and 4945 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 668 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 668 samples and 647 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 miRseq precursor expression: consensus hierarchical
      View Report | Median absolute deviation (MAD) was used to select 125 most variable miRs. Consensus ward linkage hierarchical clustering of 1078 samples and 125 miRs identified 6 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 1078 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 526 samples and 1500 genes identified 5 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 526 samples using the 1500 most variable genes was identified for k = 8 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 1091 samples and 1500 genes identified 6 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 1091 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 142 most variable proteins. Consensus ward linkage hierarchical clustering of 409 samples and 142 proteins identified 5 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 409 samples using 142 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 | 1097 samples and 665 features are included in this feature table. The figures below show which genomic pair events are co-occurring and which are mutually-exclusive.

  • Pathway Analyses

    • Association of mutation, copy number alteration, and subtype markers with pathways
      View Report | There are 94 genes with significant mutation (Q value <= 0.1) and 173 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, 2000 for subtype 4, 2000 for subtype 5, 2000 for subtype 6. 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 55 significant pathways identified in this analysis.

    • PARADIGM pathway analysis of mRNA expression data
      View Report | There were 51 significant pathways identified in this analysis.

    • PARADIGM pathway analysis of mRNASeq expression and copy number data
      View Report | There were 55 significant pathways identified in this analysis.

    • PARADIGM pathway analysis of mRNASeq expression data
      View Report | There were 55 significant pathways identified in this analysis.

  • Other Correlation Analyses

    • Correlation between copy number variation genes (focal events) and molecular subtypes
      View Report | Testing the association between copy number variation 67 focal events and 12 molecular subtypes across 1079 patients, 644 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 12 molecular subtypes across 1079 patients, 567 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 152 genes and 12 molecular subtypes across 976 patients, 46 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 = 730. Number of gene expression samples = 1091. Number of methylation samples = 732.

    • Correlations between copy number and mRNA expression
      View Report | The correlation coefficients in 10, 20, 30, 40, 50, 60, 70, 80, 90 percentiles are -0.00666, 0.04188, 0.09312, 0.15662, 0.2375, 0.3264, 0.40948, 0.4862, 0.5712, 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 939.1, 1495, 2014, 2605, 3248, 3920.6, 4590.7, 5234, 5987.9, respectively.

Methods & Data
Input

  • Summary Report Date = Wed Jan 21 16:09:42 2015

  • Protection = FALSE

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