Analysis Overview
Lung Squamous Cell Carcinoma (Primary solid tumor)
15 July 2014  |  analyses__2014_07_15
Maintainer Information
Citation Information
doi:10.7908/C1833QT5
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 Lung Squamous Cell Carcinoma (Primary solid tumor cohort) - 15 July 2014. Broad Institute of MIT and Harvard. doi:10.7908/C1833QT5
Overview
Introduction

This is an overview of Lung Squamous Cell Carcinoma analysis pipelines from Firehose run "15 July 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

    • 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 490 tumor samples used in this analysis: 30 significant arm-level results, 30 significant focal amplifications, and 50 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 14 clinical features across 422 patients, 4 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 80 focal events and 14 clinical features across 420 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 80 arm-level events and 14 clinical features across 420 patients, one significant finding detected with Q value < 0.25.

    • Correlation between gene methylation status and clinical features
      View Report | Testing the association between 20265 genes and 14 clinical features across 288 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 17 genes and 14 clinical features across 178 patients, no significant finding detected with Q value < 0.25.

    • Correlation between miRseq expression and clinical features
      View Report | Testing the association between 543 miRs and 14 clinical features across 396 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 8 clinical features related to at least one miRs.

    • Correlation between mRNA expression and clinical features
      View Report | Testing the association between 17814 genes and 13 clinical features across 154 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 4 clinical features related to at least one genes.

    • Correlation between mRNAseq expression and clinical features
      View Report | Testing the association between 18516 genes and 14 clinical features across 419 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 8 clinical features related to at least one genes.

    • Correlation between mutation rate and clinical features
      View Report | Testing the association between 2 variables and 15 clinical features across 178 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 1 clinical feature related to at least one variables.

    • Correlation between RPPA expression and clinical features
      View Report | Testing the association between 174 genes and 14 clinical features across 195 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 490 samples using the 80 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 490 samples using the 80 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 4561 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 359 samples and 4561 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 | Median absolute deviation (MAD) was used to select 256 most variable miRs. Consensus ward linkage hierarchical clustering of 331 samples and 256 miRs identified 9 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 256 most variable miRs. Consensus NMF clustering of 331 samples and 256 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 135 most variable miRs. Consensus ward linkage hierarchical clustering of 467 samples and 135 miRs identified 3 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 467 samples and 150 miRs 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 hierarchical
      View Report | Median absolute deviation (MAD) was used to select 1500 most variable genes. Consensus ward linkage hierarchical clustering of 154 samples and 1500 genes identified 3 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 154 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 | Median absolute deviation (MAD) was used to select 1500 most variable genes. Consensus ward linkage hierarchical clustering of 489 samples and 1500 genes identified 3 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 489 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 174 most variable proteins. Consensus ward linkage hierarchical clustering of 195 samples and 174 proteins identified 4 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 195 samples using 174 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

    • Association of mutation, copy number alteration, and subtype markers with pathways
      View Report | There are 14 genes with significant mutation (Q value <= 0.1) and 192 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 and copy number data
      View Report | There were 46 significant pathways identified in this analysis.

    • PARADIGM pathway analysis of mRNA expression data
      View Report | There were 65 significant pathways identified in this analysis.

    • PARADIGM pathway analysis of mRNASeq expression and copy number data
      View Report | There were 31 significant pathways identified in this analysis.

    • PARADIGM pathway analysis of mRNASeq expression data
      View Report | There were 34 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 80 focal events and 12 molecular subtypes across 490 patients, 200 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 490 patients, 124 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 17 genes and 12 molecular subtypes across 178 patients, 8 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 = 358. Number of gene expression samples = 489. Number of methylation samples = 359.

    • Correlations between copy number and mRNA expression
      View Report | The correlation coefficients in 10, 20, 30, 40, 50, 60, 70, 80, 90 percentiles are -0.0195, 0.04656, 0.1071, 0.175, 0.24805, 0.3233, 0.4037, 0.4796, 0.5678, 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 1114, 1764, 2321, 2918, 3575, 4279, 5000, 5672, 6394, respectively.

Methods & Data
Input

  • Summary Report Date = Tue Sep 16 18:25:12 2014

  • Protection = FALSE

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