由 sufang 在 五, 02/27/2015 – 10:08 發表 Oral Cancer HNSCC TCGA
S4.3. Low pass whole genome sequencing
Data file S5.3. Epigenetically silenced genes in head and neck squamous cell carcinoma
Data file S5.4. Results of all pairDwise comparisons of DNA methylation levels between tumor sites, HPV(+) smokers and non-smokers, HPV(+) and HPV(–) samples, and oropharynx only HPV(+) and HPV(–) samples
S6.1. Methods and statistical analysis
Figure S7.10. Decreased copy number and expression of miR-100-5p and let-7c-5p are correlated
Table S7.2. Increased mRNA expression associated with decreased miRD100 and letD7c expression in
Comprehensive genomic characterization of head and neck squamous cell carcinomas (pdf 4292; PubMed Link)
The Cancer Genome Atlas profiled 279 head and neck squamous cell carcinomas (HNSCCs) to provide a comprehensive landscape of somatic genomic alterations. Here we show that human-papillomavirus-associated tumours are dominated by helical domain mutations of the oncogene PIK3CA, novel alterations involving loss of TRAF3, and amplification of the cell cycle gene E2F1. Smoking-related HNSCCs demonstrate near universal loss-of-function TP53 mutations and CDKN2A inactivation with frequent copy number alterations including amplification of 3q26/28 and 11q13/22. A subgroup of oral cavity tumours with favourable clinical outcomes displayed infrequent copy number alterations in conjunction with activating mutations of HRAS or PIK3CA, coupled with inactivating mutations of CASP8, NOTCH1 and TP53. Other distinct subgroups contained loss-of-function alterations of the chromatin modifier NSD1, WNT pathway genes AJUBA and FAT1, and activation of oxidative stress factor NFE2L2, mainly in laryngeal tumours. Therapeutic candidate alterations were identified in most HNSCCs.
Five Figures in the Main Text:
Figure 1 | DNA copy number alterations. a, Copy number alterations by anatomic site and HPV status for squamous cancers. Lung squamous cell carcinoma (LUSC, n = 358) and cervical squamous cell carcinoma (CESC, n = 114). b, Unsupervised analysis of copy number alteration of HNSCC (n = 279) with associated characteristics. The rectangle indicates chromosome 7 amplifications in the purple cluster. NA, not available.
Figure 2 | Significantly mutated genes in HNSCC. Genes (rows) with significantly mutated genes (identified using the MutSigCV algorithm; q , 0.1) ordered by q value; additional genes with trends towards significance are also shown. Samples (columns, n = 279) are arranged to emphasize mutual exclusivity among mutations. Left, mutation percentage in TCGA. Right, mutation percentage in COSMIC (‘upper aerodigestive tract’ tissue). Top, overall number of mutations per megabase. Colour coding indicates mutation type.
Figure 3 | Candidate therapeutic targets and driver oncogenic events. Alteration events for key genes are displayed by sample (n = 279). TSG, tumour suppressor gene.
Figure 4 | Integrated analysis of genomic alterations. a,b,Samples (n=279) ared isplayed in columns and grouped by gene expression (a) or methylation (b) subtype (sub.). Unadjusted two-sided Fisher’s exact test P values assess the association of each genomic alteration. Methylation probe location of CpG islands, shores and shelves are shown on the left of b. Annotation shows HPV status and subtype (16, 33 and 35). CN, copy number.
Figure 5 | Deregulation of signalling pathways and transcription factors. Key affected pathways, components and inferred functions, are summarized in the main text and Supplementary Information section 7 for n = 279 samples. The frequency (%) of genetic alterations for HPV(–) and HPV(+) tumours are shown separately within sub-panels and highlighted. Also see Supplementary Fig. 7.15. Pathway alterations include homozygous deletions, focal amplifications and somatic mutations. Activated and inactivated pathways/genes, and activating or inhibitory symbols are based on predicted effects of genome alterations and/or pathway functions.
Ten Parts of Supplementary Information:
S1: Biospecimen collection and clinical data
S1.1. Biospecimen collection and clinical data
S1.2. HPV detection methods
S1.3. Survival analysis **
Figure S1.1. HPV status as a function of clinical and molecular characteristics
Figure S1.2. Receiver operating characteristic (ROC) curves in HPV-associated miRNAs in oropharyngeal HNSCC
Figure S1.3. DNA methylation signatures of HPV
Figure S1.4. Survival analysis for select clinical and genomic variables
Figure S1.5. Survival analysis for platform-specific subtypes
Table S1.1. Summary of clinical data
Data file S1.1. Data freeze clinical dataset (This file contains clinical and demographic information for all patients)
Data file S1.2. Summary of HPV detection results (This file contains the results of molecular analyses used to determine the HPV status of all patients. These include data from in situ hybridization, p16 staining, RNA and DNA sequencing, and the MassArray assay.)
Data file S1.3 Mutation signatures by HPV status (This file contains counts of base changes seen in APOBEC and smoking mutation signatures for HPV(+) and HPV(-) subjects. Fisher’s exact test p-values are also shown.)
S2: Copy number analysis
S2.1. SSNP array-based copy number analysis
S2.2. Structural alterations
Figure S2.1. GISTIC 2.0 analysis of significantly reoccurring focal alteration in 279 HNSCC tumors
Figure S2.2. GISTIC amplification and deletion peaks in lung squamous cell and cervical squamous cell carcinoma
Figure S2.3. Comparison of GISTIC 2.0 analyses of 243 HPV(–) and 36 HPV(+) head and neck tumors
Figure S2.4. Number of copy number segments in HPV(+) and HPV(–) samples
Data file S2.1. GISTIC amplification and deletion peak annotation in head and neck squamous cell (all samples, by HPV
status, and by site), lung squamous cell carcinoma, cervical squamous cell carcinoma (results from GISTIC analyses for head and neck (HNSC), lung, and cervical squamous cell carcinoma. Additional analyses for HNSC were conducted by HPV status and tumor site.)
Data file S2.2. Fisher’s exact test p-values for frequency comparisons of significantly reoccurring alterations by HPV
status and site (results of analyses of frequency comparisons of significantly reoccurring DNA copy number alterations by HPV status and tumor site. Fisher’s exact test p-values are also shown.)
S3: RNA sequencing
S3.1. RNA sequencing and expression quantification
S3.2. RNA-Seq for confirmation of somatic alterations reported in whole exome sequencing
S3.3. Gene fusion detection
S3.4. RNA-Seq for gene splicing and viral integration
Figure S3.1. RNA-Seq for confirmation of somatic alterations reported in whole exome sequencing
Figure S3.2. FGFR3-TACC3 fusion event
Figure S3.3. EGFR vIII mutant sample
Figure S3.4. Exon 14 skipping in MET+
Figure S3.5. Alterations of CDKN2A gene structure, copy number, and expression of its protein coding transcripts p16INK4A and p14ARF
Figure S3.6. Integration of DNA mutation type, copy number, and gene expression for CDKN2A+
Figure S3.7. Alterations of FAT1 gene structure, copy number, and expression
Figure S3.8. Integration of DNA mutation type, copy number, and gene expression for FAT1+
Figure S3.9. Integration of DNA mutation type, copy number, and gene expression for predicted driver genes relevant to HNSCC
Figure S3.10. Distribution of HPV integration breakpoints across the host genome
Figure S3.11. The KLK12 gene documents recurrent alternate transcription in HNSCC Figure S3.12. Heterogeneous TP63 isoform usage in HNSCC+
Data file S3.1. RNA-Seq predicted fusions ( contains the results of fusion detection analyses performed with MapSplice. )
Data file S3.2. Viral integration sites (information about HPV viral integration sites based on the analysis of RNA and DNA sequencing
data.)
Data file S3.3. SigFuge clustering results for alternatively spliced genes (the results of SigFuge analyses to detect differential expression of multiple gene isoforms. Uncorrected p-values are shown.)
S4: DNA sequencing: exome and genome
S4.1. Exome sequencing, high-pass whole genome sequencing, and data processing
S4.2. Mutation validation
S4.3. Low pass whole genome sequencing
Figure S4.1. Mutation validation counts by allelic fraction for HNSCC
Figure S4.2. Predicted coding impact by transcript base position and functional domain for selected genes
Data file S4.1. Summary of multiple MUTSIG analyses (the results of MutSig analyses to detect significantly mutated genes. Additional analyses were conducted by gene expression subtype, tumor site, and HPV status. Mutation counts by site and HPV status are also shown, as are Fisher’s exact test p-values.)
Data file S4.2. Structural aberration calls from BreakDancer and Meerkat
S5: Molecular Subtypes and Subset Analyses
S5.1. Detection of previously validated gene expression subtypes in HNSCC and correlation with lung squamous cell carcinoma
S5.2 Validation of selected genomic alterations of the gene expression subtypes S5.3. Subset analyses by genomic platform
Figure S5.1. Comparison of gene expression patterns in squamous cell carcinomas of the upper aerodigestive tract Figure S5.2. Comparison of select genes and expression subtype centroids for squamous cell carcinomas of the upper aerodigestive tract
Figure S5.3. DNA copy number in chromosome 7 by gene expression subtype
Figure S5.4. DNA copy number and gene expression of canonical oncogenes in chromosome 3q by gene expression subtype
Figure S5.5. Gene expression heatmap for 37 normal samples
Figure S5.6. miRs that are differentially abundant between tumor and adjacent normal samples
Figure S5.7. miRs that are differentially abundant between HPV(+) and HPV(D) samples
Figure S5.8. miRs that are differentially abundant between different anatomic sites
Data file S5.1. Summary of RNA differential abundance analyses
Data file S5.2. Summary of miRNA differential abundance analyses
Data file S5.3. Epigenetically silenced genes in head and neck squamous cell carcinoma
Data file S5.4. Results of all pairDwise comparisons of DNA methylation levels between tumor sites, HPV(+) smokers and non-smokers, HPV(+) and HPV(–) samples, and oropharynx only HPV(+) and HPV(–) samples
S5:
S5.1: This file contains the results of SAM analyses to identify differentially expressed genes. False discovery rate q- values are shown for tumor vs. normal, as well as comparisons based on tumor site, HPV status, and smoking status.
S5.2: This file contains the results of SAMseq analyses to identify differentially expressed miRNAs. False discovery
rate q-values and other summary statistics are shown for tumor vs. normal, as well as comparisons based on
tumor site, HPV status, smoking status, and miRNA subtype.
S5.3: This file contains the results of analyses that identified epigenetically silenced genes based on gene
expression levels in methylated and unmethylated samples. Test statistics, and corrected and uncorrected p-
values are also shown, as are correlations of methylation and expression levels.
S5.4: This file contains the results of analyses to identify differentially methylated genes. Test statistics, and
corrected and uncorrected p-values are shown for comparisons based on tumor site, HPV status, and smoking
status.
S6: Reverse phase protein array analysis
S6.1. Methods and statistical analysis
Figure S6.1. Protein expression of p16, pRb, and E2F1 by HPV status
Figure S6.2. RPPA analysis of EGFR as a function of EGFR amplification
Data file S6.1. RPPA antibodies
Data file S6.2. Data freeze samples with RPPA data available
S6.1: This file contains information about the 160 antibodies that were used in the reverse phase protein array
analyses.
S6.2: This file lists the barcodes for the n = 200 samples for which reverse phase protein array analyses were
performed.
S7: Pathways and integrated analysis
. S7.1. MEMo analysis of coDoccurring and mutually exclusive genomic events
. S7.2. Genomic aberrations in gene expression subtypes
. S7.3. Exploratory clustering / Unsupervised analysis of genomic platforms
. S7.4. Supervised integrated analysis of miRNA, gene expression, and copy number
. S7.5. Integrated pathway analysis using PARADIGM and PARADIGMDSHIFT
. S7.6. Somatic alteration in therapeutic targets
. Figure S7.1. CoDoccurrence and mutual exclusivity of select genomic events
. Figure S7.2. DNA copy number and gene expression in chromosome 11q
. Figure S7.3. DNA copy number and gene expression for HLA class 1 and lymphocyte signature genes
. Figure S7.4. Unsupervised clustering of reverse phase protein array data by nonDnegative matrix factorization (NMF) clustering
. Figure S7.5. Correlation of RPPA subtypes (by NMF clustering) and mutations
. Figure S7.6. Unsupervised clustering of miRNADSeq data
. Figure S7.7. Covariates, EMT scores and differentially abundant miRNAs by unsupervised cluster
. Figure S7.8. DNA methylation subtypes are associated with somatic mutations, EMT score, and target gene expression
. Figure S7.9. Cluster of clusters analysis
Figure S7.10. Decreased copy number and expression of miR-100-5p and let-7c-5p are correlated
. with increased CDK6 and E2F1 expression in head and neck cancer
Figure S7.11. Subtypes defined by PARADIGM integrated pathway levels
. Figure S7.12. Enriched subDnetwork for features significantly differentiated between HPV(+) and HPV(D) samples
. Figure S7.13. PARADIGMDSHIFT analysis of NFE2L2
Figure S7.14. PARADIGMDSHIFT analysis of NOTCH family genes
. Figure S7.15. Diversity and frequency of genetic changes leading to deregulation of signaling pathways and transcription factors in HPV (D), part 1 and HPV(+), part 2 HNSCC
. Table S7.1. miRNAs associated with NSD1Ddepleted/hypomethylated cluster
Table S7.2. Increased mRNA expression associated with decreased miRD100 and letD7c expression in
. deleted genomic regions
Table S7.3. Copy number loss of miRD100 and letD7c in tumor specimens
. Data File S7.1. Associations of integrated genomic events
. Data File S7.2. Summary of class labels from different platforms
. Data File S7.3. Summary of pathway activation
S7:
S7.1: This file contains p-values for the mutual exclusivity modules analyses presented in Figure S7.1. In addition,
uncorrected and corrected Fisher’s exact test p-values are shown for the associations presented in Figures 4A
and 4B.
S7.2: This file summarizes information about the subtypes identified by the RNA, miRNA, methylation, reverse phase
protein array, and PARADIGM analyses. Two-way tables show counts for all pairs of subtypes. Fisher’s exact
test p-values are also presented.
S7.3: This file identifies patients that exhibit alterations in the pathways described in Figures 5, S7.15 part 1, and
S7.15 part 2. For each patient, specific alterations are shown based on output from the cBioPortal.
S8: DNA methylation profiling
S9: miRNA sequencing
Table S9.1. Priorities for resolving annotation ambiguities for aligned miRNA-Seq reads
S10: Batch effects analysis
S10.1. Methods
S10.2. Results by platform
Figure S10.1. Hierarchical clustering for miRNA expression from miRNA-Seq data
Figure S10.2. PCA: First two principal components for miRNA expression from miRNA-Seq data, with samples connected
by centroids according to batch ID
Figure S10.3. PCA: First two principal components for miRNA expression from miRNA-Seq data, with samples connected
by centroids according to tissue source site
Figure S10.4. Hierarchical clustering plot for DNA methylation HM450 data
Figure S10.5. PCA for DNA methylation with samples connected by centroids according to batch ID
Figure S10.6. PCA for DNA methylation with samples connected by centroids according to tissue source site
Figure S10.7. Hierarchical clustering for mRNA expression from RNA-Seq data
Figure S10.8. PCA: First two principal components for RNA-Seq, with samples connected by centroids according to batch
ID
Figure S10.9. PCA: First two principal components for RNA-Seq, with samples connected by centroids according to tissue
source site
Figure S10.10. Hierarchical clustering for SNP6 data
Figure S10.11. PCA: First two principal components for SNP6, with samples connected by centroids according to
batch ID
Figure S10.12. PCA: First two principal components for SNP6, with samples connected by centroids according to
tissue source site