Analysis Overview for Colon/Rectal Adenocarcinoma
Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/Harvard Medical School)
Overview
Introduction

This is the analysis overview for Firehose run "25 August 2012".

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

- Copy number analysis (GISTIC2)
  View Report | There were 575 tumor samples used in this analysis: 24 significant arm-level results, 28 significant focal amplifications, and 47 significant focal deletions were found.

- Mutation Analysis (MutSig)
  View Report | MAF used for this analysis: COADREAD.final_analysis_set.maf

- Clustering Analyses

- Clustering of copy number data: consensus NMF
  View Report | The most robust consensus NMF clustering of 575 samples using the 75 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 Methylation: consensus NMF
  View Report | The 6858 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 349 samples and 6858 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 RPPA data: consensus NMF
  View Report | The most robust consensus NMF clustering of 399 samples using the 150 most variable 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.

- Clustering of RPPA data: consensus hierarchical
  View Report | The 150 most variable proteins were selected. Consensus average linkage hierarchical clustering of 399 samples and 150 proteins 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 mRNA expression: consensus NMF
  View Report | The most robust consensus NMF clustering of 224 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 mRNA expression: consensus hierarchical
  View Report | The 1500 most variable genes were selected. Consensus average linkage hierarchical clustering of 224 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 83 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 83 samples and 1500 genes 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 expression: consensus NMF
  View Report | We filtered the data to 150 most variable miRs. Consensus NMF clustering of 255 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 expression: consensus hierarchical
  View Report | We filtered the data to 150 most variable miRs. Consensus average linkage hierarchical clustering of 255 samples and 150 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.

- Correlation Analyses

- Correlation between copy number variations of arm-level result and selected clinical features
  View Report | Testing the association between copy number variation 78 arm-level results and 11 clinical features across 575 patients, 22 significant findings detected with Q value < 0.25.

- Correlation between copy number variation genes and selected clinical features
  View Report | Testing the association between copy number variation of 75 peak regions and 11 clinical features across 575 patients, 53 significant findings detected with Q value < 0.25.

- Correlation between molecular cancer subtypes and selected clinical features
  View Report | Testing the association between subtypes identified by 7 different clustering approaches and 11 clinical features across 585 patients, 14 significant findings detected with P value < 0.05.

- Correlation between gene mutation status and selected clinical features
  View Report | Testing the association between mutation status of 538 genes and 10 clinical features across 224 patients, 2 significant findings detected with Q value < 0.25.

- Correlation between mRNA expression and clinical features
  View Report | Testing the association between 17814 genes and 10 clinical features across 224 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one genes.

- Correlations between copy number and mRNA expression
  View Report | The correlation coefficients in 10, 20, 30, 40, 50, 60, 70, 80, 90 percentiles are -0.0307, 0.0222, 0.0714, 0.1338, 0.204, 0.28, 0.34934, 0.4228, 0.51106, 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 888, 1953, 2596, 3174, 3732, 4279, 4859, 5512, 6310, respectively.

- Correlation between mRNA expression and DNA methylation
  View Report | The top 25 correlated methylation probe(s) per gene are displayed. Total number of matched samples = 223 Number of gene expression samples = 238 Number of methylation samples = 224

- Other Analyses

- Preprocessing of clinical data
  View Report | Clinical data for tier 1 clinical variables are generated.

- Association of mutation, copy number alteration, and subtype markers with pathways
  View Report | There are 308 genes with significant mutation (Q value <= 0.1) and 490 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 data
  View Report | There were 54 significant pathways identified in this analysis.

- PARADIGM pathway analysis of mRNA expression and copy number data
  View Report | There were 48 significant pathways identified in this analysis.

Methods & Data
Input

- Run Prefix = awg_pancan8__2012_08_25

- Summary Report Date = Fri Sep 14 18:04:17 2012

- Protection = FALSE

Copyright © 2012 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
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