Analysis Overview
Acute Myeloid Leukemia (Primary blood derived cancer - Peripheral blood)
22 February 2013  |  analyses__2013_02_22
Maintainer Information
Citation Information
doi:10.7908/C147482F
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Analysis Overview for Acute Myeloid Leukemia (Primary blood derived cancer - Peripheral blood cohort) - 22 February 2013. Broad Institute of MIT and Harvard. doi:10.7908/C147482F
Overview
Introduction

This is an overview of Acute Myeloid Leukemia analysis pipelines from Firehose run "22 February 2013".

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 191 tumor samples used in this analysis: 14 significant arm-level results, 6 significant focal amplifications, and 15 significant focal deletions were found.

    • Mutation Analysis (MutSig v2.0)
      View Report | 

    • Mutation Analysis (MutSig vS2N)
      View Report | 

  • Correlations to Clinical Parameters

    • Correlation between copy number variation genes (focal) and selected clinical features
      View Report | Testing the association between subtypes identified by 20 different clustering approaches and 3 clinical features across 191 patients, 6 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 subtypes identified by 22 different clustering approaches and 3 clinical features across 191 patients, 4 significant findings detected with Q value < 0.25.

    • Correlation between gene methylation status and clinical features
      View Report | Testing the association between 16403 genes and 3 clinical features across 169 samples, statistically thresholded by Q value < 0.05, 3 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 24 genes and 3 clinical features across 197 patients, 5 significant findings detected with Q value < 0.25.

    • Correlation between miRseq expression and clinical features
      View Report | Testing the association between 367 genes and 3 clinical features across 187 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one genes.

    • Correlation between mRNAseq expression and clinical features
      View Report | Testing the association between 17276 genes and 3 clinical features across 173 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one genes.

  • Clustering Analyses

    • Clustering of copy number data: consensus NMF
      View Report | The most robust consensus NMF clustering of 191 samples using the 22 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 Methylation: consensus NMF
      View Report | The 9843 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 194 samples and 9843 genes identified 6 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 187 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 NMF
      View Report | We filtered the data to 150 most variable miRs. Consensus NMF clustering of 187 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 | The 1500 most variable genes were selected. Consensus average linkage hierarchical clustering of 173 samples and 1500 genes 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.

    • Clustering of mRNAseq gene expression: consensus NMF
      View Report | The most robust consensus NMF clustering of 173 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.

  • Other Analyses

    • Correlate_Clinical_vs_Molecular_Signatures
      View Report | Testing the association between subtypes identified by 6 different clustering approaches and 3 clinical features across 200 patients, 5 significant findings detected with P value < 0.05 and Q value < 0.25.

  • Pathway Analyses

    • HotNet pathway analysis of mutation and copy number data
      View Report

    • PARADIGM pathway analysis of mRNASeq expression and copy number data
      View Report | There were 60 significant pathways identified in this analysis.

    • PARADIGM pathway analysis of mRNASeq expression data
      View Report | There were 77 significant pathways identified in this analysis.

  • Other Correlation Analyses

    • Correlation between copy number variation genes and molecular subtypes
      View Report | Testing the association between copy number variation of 20 peak regions and 6 molecular subtypes across 191 patients, 38 significant findings detected with 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 22 arm-level results and 6 molecular subtypes across 191 patients, 20 significant findings detected with Q value < 0.25.

    • Correlation between gene mutation status and molecular subtypes
      View Report | Testing the association between mutation status of 24 genes and 6 molecular subtypes across 197 patients, 23 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 = 170. Number of gene expression samples = 173. Number of methylation samples = 170.

    • Correlations between copy number and mRNAseq expression
      View Report | The correlation coefficients in 10, 20, 30, 40, 50, 60, 70, 80, 90 percentiles are 448.9, 1236.8, 1858, 2193, 2500, 2829, 3196, 3646.2, 4381.1, respectively.

Methods & Data
Input

  • Summary Report Date = Mon Aug 5 19:55:04 2013

  • Protection = FALSE

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