Additional file 1: Figure S1. of DNA methylation subgroups in melanoma are associated with proliferative and immunological processes
Creators: name: Lauss, Martin
name: Ringnér, Markus
name: Karlsson, Anna
name: Harbst, Katja
name: Busch, Christian
name: Geisler, Jürgen
name: Lønning, Per
name: Staaf, Johan
name: Jönsson, Göran
Publisher: name: Figshare
Year: 2015
Subjects: subject: Cell Biology
subject: Genetics
subject: FOS: Biological sciences
subject: FOS: Biological sciences
subject: Molecular Biology
subject: Pharmacology
subject: 19999 Mathematical Sciences not elsewhere classified
subject: FOS: Mathematics
subject: FOS: Mathematics
subject: Developmental Biology
subject: Cancer
subject: 110309 Infectious Diseases
subject: FOS: Health sciences
subject: FOS: Health sciences
subject: Plant Biology

description: Robustness of methylation subtypes. Principal component analysis to monitor data bias due to technical variables in the Bergen (A) and TCGA (B) cohorts, respectively. The entire set of 473,864 CpGs was used for principal component analysis. The heatmaps indicate the association of a sample annotation to each of the principal components. The strength of association is specified by the log10 p-value of the linear model with the respective principal component as dependent variable and sample annotation as regressor. DNAconc = DNA concentration. Technical variables of TCGA data are termed as defined by the TCGA consortium. TCGA = The Cancer Genome Atlas Consortium. Abbreviations as in Figure 1 of the main manuscript. (C) Sample overlap when using different CpG sets for group discovery in Bergen and TCGA data, respectively. For group discovery we used CpGs with variant methylation between melanoma and melanocytes. CpGs were required to be either methylated or de-methylated in at least 10 tumors for our final subtypes. The CpG set for 10 tumors contained 9,886 melanoma-methylated and 5,236 melanoma-demethylated CpGs. The heatmap displays the overlap of the resulting three consensus clusters at various tumor cutoffs. (D) Heatmap of TCGA sample co-occurrence between unsupervised consensus clusters and methylation subtypes obtained from (supervised) nearest centroid classification. Figure S2. Promoter island consensus clusters. The group discovery CpG set was reduced to CpGs located within 1500 bp upstream of transcription start sites and within a CpG island. In total this analysis includes 947 CpGs, of which 930 are hypermethylated, and 17 are hypomethylated in tumors as compared to melanocytes. Two-group consensus solutions were favorable to three-group solutions in Bergen and TCGA data, respectively. Figure S3. Signature expression across methylation subtypes in TCGA data. (A) ESTIMATE scores and tumor purity. P-value from Kruskal-Wallis test. (B) Mean expression values of gene modules and GO-term ‘cell cycle’. P-value from anova. TCGA tumor data. Figure S4. Aberrant methylation in melanoma compared to colon, lung and breast cancer. (A) Venn diagrams of aberrant methylation on the CpG and gene level. (B) Aberrant methylation in ES-cell chromatin context. Chromatin categories as in ENCODE project. (C) Aberrant methylation in melanocyte chromatin context. Chromatin categories as in Epigenome Roadmap project. Figure S5. Driver gene mutations and methylation subtypes in the TCGA cohort. (A) Number of non-silent mutations per sample across methylation subtypes. (B) Non-silent and hotspot mutations of reported melanoma driver genes across subtypes. Figure S6. Expression of MAPK and PI(3)K pathway genes across methylation subtypes. (A) Expression of MAPK and PI(3)K pathway. fdr = false discovery rate for p-values from anova using Benjamini-Hochberg adjustment. (B) Cross-table of gene expression and methylation subtypes. (PDF 865 kb)