Analysis Overview
Prostate Adenocarcinoma (Primary solid tumor)
15 January 2014 | analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C1NV9GPR
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 Prostate Adenocarcinoma (Primary solid tumor cohort) - 15 January 2014. Broad Institute of MIT and Harvard. doi:10.7908/C1NV9GPR
Overview
Introduction

This is an overview of Prostate Adenocarcinoma analysis pipelines from Firehose run "15 January 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 9334 mutations identified by MuTect and 544 mutations with significant functional impact at BHFDR <= 0.25.

- LowPass Copy number analysis (GISTIC2)
  View Report | There were 115 tumor samples used in this analysis: 16 significant arm-level results, 4 significant focal amplifications, and 16 significant focal deletions were found.

- Mutation Analysis (MutSig v1.5)
  View Report |

- Mutation Analysis (MutSig v2.0 and MutSigCV v0.9 merged result)
  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 278 tumor samples used in this analysis: 20 significant arm-level results, 24 significant focal amplifications, and 30 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 14 clinical features across 185 patients, 18 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 54 focal events and 14 clinical features across 184 patients, 12 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 57 arm-level events and 14 clinical features across 184 patients, 23 significant findings detected with Q value < 0.25.

- Correlation between gene methylation status and clinical features
  View Report | Testing the association between 20015 genes and 13 clinical features across 185 samples, statistically thresholded by Q value < 0.05, 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 6 genes and 14 clinical features across 171 patients, 3 significant findings detected with Q value < 0.25.

- Correlation between miRseq expression and clinical features
  View Report | Testing the association between 474 miRs and 13 clinical features across 179 samples, statistically thresholded by Q value < 0.05, 9 clinical features related to at least one miRs.

- Correlation between mRNAseq expression and clinical features
  View Report | Testing the association between 18226 genes and 13 clinical features across 185 samples, statistically thresholded by Q value < 0.05, 10 clinical features related to at least one genes.

- Correlation between RPPA expression and clinical features
  View Report | Testing the association between 189 genes and 13 clinical features across 143 samples, statistically thresholded by Q value < 0.05, 6 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 278 samples using the 54 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 278 samples using the 54 copy number focal regions 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 Methylation: consensus NMF
  View Report | The 3616 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 300 samples and 3616 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 | We filtered the data to 242 most variable miRs. Consensus average linkage hierarchical clustering of 259 samples and 242 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 NMF
  View Report | We filtered the data to 242 most variable miRs. Consensus NMF clustering of 259 samples and 242 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 | We filtered the data to 150 most variable miRs. Consensus average linkage hierarchical clustering of 259 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 precursor expression: consensus NMF
  View Report | We filtered the data to 150 most variable miRs. Consensus NMF clustering of 259 samples and 150 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 mRNAseq gene expression: consensus hierarchical
  View Report | The 1500 most variable genes were selected. Consensus average linkage hierarchical clustering of 278 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 mRNAseq gene expression: consensus NMF
  View Report | The most robust consensus NMF clustering of 278 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 | 189 proteins were selected. Consensus average linkage hierarchical clustering of 164 samples and 189 proteins 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 164 samples using 189 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.

- Pathway Analyses

- HotNet pathway analysis of mutation and copy number data
  View Report | There were 27 significant subnetworks identified in HotNet analysis.

- PARADIGM pathway analysis of mRNASeq expression and copy number data
  View Report | There were 56 significant pathways identified in this analysis.

- PARADIGM pathway analysis of mRNASeq expression data
  View Report | There were 54 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 40 focal events and 10 molecular subtypes across 278 patients, 38 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 67 arm-level events and 10 molecular subtypes across 278 patients, 35 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 7 genes and 10 molecular subtypes across 249 patients, 9 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 = 278. Number of gene expression samples = 278. Number of methylation samples = 300.

- Correlations between copy number and mRNAseq expression
  View Report | The correlation coefficients in 10, 20, 30, 40, 50, 60, 70, 80, 90 percentiles are 646.5, 1450, 1815, 2176, 2541, 2943, 3401.5, 3991, 4794, respectively.

Methods & Data
Input

- Summary Report Date = Fri Feb 28 12:49:00 2014

- Protection = FALSE

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