Analysis Overview
Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C1902336
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Analysis Overview for Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (Primary solid tumor cohort) - 28 January 2016. Broad Institute of MIT and Harvard. doi:10.7908/C1902336
Overview
Introduction

This is an overview of Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma analysis pipelines from Firehose run "28 January 2016".

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 194 tumor samples in this analysis. The Benjamini-Hochberg-corrected p-value for enrichment of the APOBEC mutation signature in 148 samples is <=0.05. Out of these, 141 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 23267 mutations identified by MuTect and 339 mutations with significant functional impact at BHFDR <= 0.25.

    • LowPass Copy number analysis (GISTIC2)
      View Report | There were 50 tumor samples used in this analysis: 18 significant arm-level results, 4 significant focal amplifications, and 9 significant focal deletions were found.

    • Mutation Analysis (MutSig 2CV v3.1)
      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 295 tumor samples used in this analysis: 25 significant arm-level results, 26 significant focal amplifications, and 37 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 39 clinical features across 307 patients, 76 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 39 clinical features across 194 patients, 2 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 39 clinical features across 194 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 6 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 63 focal events and 39 clinical features across 295 patients, 25 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 39 clinical features across 295 patients, 15 significant findings detected with Q value < 0.25.

    • Correlation between gene methylation status and clinical features
      View Report | Testing the association between 16894 genes and 39 clinical features across 307 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 19 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 20 genes and 39 clinical features across 194 patients, 2 significant findings detected with Q value < 0.25.

    • Correlation between miRseq expression and clinical features
      View Report | Testing the association between 543 miRs and 39 clinical features across 307 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 20 clinical features related to at least one miRs.

    • Correlation between mRNAseq expression and clinical features
      View Report | Testing the association between 18187 genes and 39 clinical features across 304 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 23 clinical features related to at least one genes.

    • Correlation between mutation rate and clinical features
      View Report | Testing the association between 2 variables and 40 clinical features across 194 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 8 clinical features related to at least one variables.

    • Correlation between RPPA expression and clinical features
      View Report | Testing the association between 192 genes and 38 clinical features across 173 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 13 clinical features related to at least one genes.

  • Clustering Analyses

    • Clustering of copy number data by focal peak region with absolute value: consensus NMF
      View Report | The most robust consensus NMF clustering of 295 samples using the 63 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 copy number data by peak region with threshold value: consensus NMF
      View Report | The most robust consensus NMF clustering of 295 samples using the 63 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 most robust consensus NMF clustering of 307 samples using the 9125 most variable genes was identified for k = 3 clusters. We computed the clustering for k = 2 to k = 10 and uused the cophenetic correlation coefficient and the average silhouette width calculation to determine 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 294 samples and 647 miRs identified 5 subtypes with the stability of the clustering increasing for k = 2 to k = 10.

    • Clustering of miRseq mature expression: consensus NMF
      View Report | The most robust consensus NMF clustering of 294 samples using the 647 most variable miRs was identified for k = 3 clusters. We computed the clustering for k = 2 to k = 10 and uused the cophenetic correlation coefficient and the average silhouette width calculation to determine 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 307 samples and 135 miRs identified 5 subtypes with the stability of the clustering increasing for k = 2 to k = 10.

    • Clustering of miRseq precursor expression: consensus NMF
      View Report | The most robust consensus NMF clustering of 307 samples using the 150 most variable miRs was identified for k = 3 clusters. We computed the clustering for k = 2 to k = 10 and uused the cophenetic correlation coefficient and the average silhouette width calculation to determine the robust clusters.

    • 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 304 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 304 samples using the 1500 most variable genes was identified for k = 4 clusters. We computed the clustering for k = 2 to k = 10 and uused the cophenetic correlation coefficient and the average silhouette width calculation to determine the robust clusters.

    • Clustering of RPPA data: consensus hierarchical
      View Report | Median absolute deviation (MAD) was used to select 192 most variable proteins. Consensus ward linkage hierarchical clustering of 173 samples and 192 proteins identified 5 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 173 samples using the 192 most variable proteins was identified for k = 3 clusters. We computed the clustering for k = 2 to k = 10 and uused the cophenetic correlation coefficient and the average silhouette width calculation to determine the robust clusters.

  • Other Analyses

    • Aggregate Analysis Features
      View Report | 308 samples and 450 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 795 miRs and 18187 mRNAs across 304 samples. After 2 filtering steps, the number of 118 miR:genes pairs were detected.

  • Pathway Analyses

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

    • PARADIGM pathway analysis of mRNASeq expression and copy number data
      View Report | There were 53 significant pathways identified in this analysis.

    • PARADIGM pathway analysis of mRNASeq expression data
      View Report | There were 46 significant pathways identified in this analysis.

    • Significant over-representation of pathway gene sets for a given gene list
      View Report | For a given gene list, a hypergeometric test was tried to find significant overlapping canonical pathways using 1320 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 63 focal events and 10 molecular subtypes across 295 patients, 260 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 10 molecular subtypes across 295 patients, 217 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 20 genes and 10 molecular subtypes across 194 patients, 39 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 = 304. Number of gene expression samples = 304. Number of methylation samples = 307.

    • 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 -0.14214, -0.0981, -0.0672, -0.042, -0.0175, 0.006, 0.031, 0.0613, 0.10194, 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 933.7, 1481.4, 2008, 2548, 3175, 3841, 4516, 5207.6, 5939.3, respectively.

Methods & Data
Input

  • Summary Report Date = Thu Apr 7 15:30:32 2016

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