Analysis Overview
Lung Squamous Cell Carcinoma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C1MG7NKM
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Analysis Overview for Lung Squamous Cell Carcinoma (Primary solid tumor cohort) - 02 April 2015. Broad Institute of MIT and Harvard. doi:10.7908/C1MG7NKM
Overview
Introduction

This is an overview of Lung Squamous Cell Carcinoma analysis pipelines from Firehose run "02 April 2015".

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 177 tumor samples in this analysis. The Benjamini-Hochberg-corrected p-value for enrichment of the APOBEC mutation signature in 110 samples is <=0.05. Out of these, 58 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 81240 mutations identified by MuTect and 3670 mutations with significant functional impact at BHFDR <= 0.25.

    • 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 501 tumor samples used in this analysis: 29 significant arm-level results, 30 significant focal amplifications, and 53 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 15 clinical features across 495 patients, 34 significant findings detected with P value < 0.05 and Q value < 0.25.

    • Correlation between APOBEC groups and selected clinical features
      View Report | Testing the association between APOBEC groups identified by 2 different apobec score and 15 clinical features across 177 patients, 4 significant findings detected with Q value < 0.25.

    • Correlation between APOBEC signature variables and clinical features
      View Report | Testing the association between 3 variables and 15 clinical features across 177 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 3 clinical features related to at least one variables.

    • Correlation between copy number variation genes (focal events) and selected clinical features
      View Report | Testing the association between copy number variation 83 focal events and 15 clinical features across 493 patients, 4 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 82 arm-level events and 15 clinical features across 493 patients, no significant finding detected with Q value < 0.25.

    • Correlation between gene methylation status and clinical features
      View Report | Testing the association between 20222 genes and 15 clinical features across 361 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 6 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 15 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 545 miRs and 15 clinical features across 469 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 14 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 18514 genes and 15 clinical features across 492 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 9 clinical features related to at least one genes.

    • Correlation between mutation rate and clinical features
      View Report | Testing the association between 2 variables and 16 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, 8 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 501 samples using the 83 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 501 samples using the 83 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 4538 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 370 samples and 4538 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 647 most variable miRs. Consensus ward linkage hierarchical clustering of 337 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 337 samples and 647 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 136 most variable miRs. Consensus ward linkage hierarchical clustering of 478 samples and 136 miRs identified 4 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 478 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 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 501 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 501 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.

  • Other Analyses

    • Aggregate Analysis Features
      View Report | 504 samples and 546 features are included in this feature table. The figures below show which genomic pair events are co-occurring and which are mutually-exclusive.

    • Identification of putative miR direct targets by sequencing data
      View Report | The CLR algorithm was applied on 815 miRs and 18514 mRNAs across 342 samples. After 2 filtering steps, the number of 47 miR:genes pairs were detected.

  • 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 212 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 :

    • GSEA Class2: Canonical Pathways enriched in each subtypes of mRNAseq_cNMF in LUSC-TP
      View Report | basic data info

    • 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 63 significant pathways identified in this analysis.

    • PARADIGM pathway analysis of mRNASeq expression and copy number data
      View Report | There were 30 significant pathways identified in this analysis.

    • PARADIGM pathway analysis of mRNASeq expression data
      View Report | There were 34 significant pathways identified in this analysis.

    • Significant over-representaion of pathway genesets for a given gene list
      View Report | For a given gene list, a hypergeometric test was tried to find significant overlapping canonical pathway gene sets. In terms of FDR adjusted p.values, top 5 significant overlapping gene sets are listed as below.

  • Other Correlation Analyses

    • Correlation between copy number variation genes (focal events) and molecular subtypes
      View Report | Testing the association between copy number variation 83 focal events and 12 molecular subtypes across 501 patients, 511 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 82 arm-level events and 12 molecular subtypes across 501 patients, 389 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, 17 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 = 370. Number of gene expression samples = 501. Number of methylation samples = 370.

    • Correlations between APOBEC_MutLoad_MinEstimate and mRNAseq expression
      View Report | The correlation coefficients in 10, 20, 30, 40, 50, 60, 70, 80, 90 percentiles are -0.091, -0.0558, -0.03001, -0.0077, 0.0131, 0.03478, 0.0571, 0.08304, 0.1193, respectively.

    • Correlations between copy number and mRNA expression
      View Report | The correlation coefficients in 10, 20, 30, 40, 50, 60, 70, 80, 90 percentiles are -0.0198, 0.0476, 0.108, 0.17566, 0.2485, 0.32334, 0.4037, 0.47952, 0.56726, 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 1032, 1662.8, 2221, 2807.6, 3454.5, 4145, 4805.3, 5443, 6107, respectively.

Methods & Data
Input

  • Summary Report Date = Sat Aug 15 14:21:48 2015

  • Protection = FALSE

Copyright © 2015 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle