Additional file 1: of Systems-epigenomics inference of transcription factor activity implicates aryl-hydrocarbon-receptor inactivation as a key event in lung cancer development

Additional file containing all Additional file 1: Figures S1–S10 and Table S2. (PDF 1142 kb

Additional file 2: of Stochastic epigenetic outliers can define field defects in cancer

GSEA result tables of hypervariable DVCs, as identified using iEVORA, in the normal breast study comparing normal breast from healthy women to normal breast adjacent to breast cancer. There are 4 tables, corresponding to hypervariable DVCs mapping to TSS1500, TSS200 or 1st Exon regions, and which are hypermethylated (dvUPdmUP) or hypomethylated (dvUPdmDN) in normal-adjacent tissue, as well as hypervariable DVCs mapping to gene-body or 5′UTR regions, which are hypermethylated (dvUPdmUP-GB) or hypomethylated (dvUPdmDN-GB) in normal-adjacent tissue. In each case, the columns label the number of genes in the MSigDB database list (nList), the number present prior to iEVORA analysis (nRep), the corresponding fraction (fRep), the number of genes overlapping with the iEVORA selected list (nOVLAP), the corresponding odds ratio (OR) and one-tailed Fisher test P-value (P-value), the adjusted P-value using Benjamini-Hochberg correction, and the gene symbols of the genes present in the overlap. (XLS 54 kb

Additional file 1: of The multi-omic landscape of transcription factor inactivation in cancer

This document contains all Supplementary Tables and all Supplementary Figures, plus their associated legends/captions. (PDF 3658 kb

Corruption of the Intra-Gene DNA Methylation Architecture Is a Hallmark of Cancer

<div><p>Epigenetic processes - including DNA methylation - are increasingly seen as having a fundamental role in chronic diseases like cancer. It is well known that methylation levels at particular genes or loci differ between normal and diseased tissue. Here we investigate whether the intra-gene methylation architecture is corrupted in cancer and whether the variability of levels of methylation of individual CpGs within a defined gene is able to discriminate cancerous from normal tissue, and is associated with heterogeneous tumour phenotype, as defined by gene expression. We analysed 270985 CpGs annotated to 18272 genes, in 3284 cancerous and 681 normal samples, corresponding to 14 different cancer types. In doing so, we found novel differences in intra-gene methylation pattern across phenotypes, particularly in those genes which are crucial for stem cell biology; our measures of intra-gene methylation architecture are a better determinant of phenotype than measures based on mean methylation level alone (K-S test in all 14 diseases tested). These per-gene methylation measures also represent a considerable reduction in complexity, compared to conventional per-CpG beta-values. Our findings strongly support the view that intra-gene methylation architecture has great clinical potential for the development of DNA-based cancer biomarkers.</p></div

Number of probes per genomic region and gene, of 18272 annotated genes.

<p>Number of probes per genomic region and gene, of 18272 annotated genes.</p

Correlation of expression to intra-gene methylation architecture, for matched BRCA samples.

<p>Expression was taken as the response variable, with one of mean -score, mean derivative and methylation variance as one covariate predictor, together with mean methylation as a second covariate predictor. (a) The proportion of genes with at least one covariate significant (FDR ), and the proportion of genes with neither covariate significant. (b) The proportion of significant genes (i.e., the proportion of the genes represented by the left of each pair of bars in a) which are significant due to one, or the other, or both covariates. For the genes which are significant due to only one covariate predictor, the proportions of these genes for which the significance is due to positive or negative correlation are indicated on the bars with/and \ respectively. There are many genes with expression not significantly predicted by mean methylation but significantly predicted by mean -score, mean derivative, or methylation variance.</p

Heatmap of the mean -score for the top 50 genes found by the meta-analysis.

<p>Mean -scores for tumour (T) and healthy (H) samples are displayed in a heatmap according to the colour code for the top 50 meta-analysis genes (top 50 most consistently unstable genes). The heatmap shows the extent to which the instability is consistent (high mean -score, red) across cancer patients as compared to healthy patients (low mean -score, blue). For each tissue type healthy samples appear to the right of tumour samples; where no space is available the (H) label is omitted. Abbreviations: R (READ), B (BLCA), K(KIRP), P (PAAD).</p

Number of samples in each data set.

<p>Number of samples in each data set.</p

Dynamical Network Biomarker (DNB) in cervical carcinogenesis.

<p>The changes in the relevance score of the LBC1-B CpG module, termed a DNB, as a function of disease stage: the stages shown are N(HPV−), N(HPV+), preCIN2(HPV−), preCIN2(HPV+), CIN2+, CC stages 1,2 and 3. Note that the samples from stages N(HPV+), preCIN2(HPV−) and preCIN2(HPV+) were drawn from completely independent test sets and that these samples exhibit relevance scores which are intermediate between N(HPV−) and CIN2+, in line with their disease stage. Darkred dashed line indicates hypothetical switching point in the transition from cytologically normal cells at risk of CIN2+ to CIN2+. (Abbrev: N = Normal, preCIN2+: precursor CIN2+ cells, CIN2+ = cervical intraepithelial neoplasia of grade 2 or higher, CC = cervical cancer).</p

Distributions of per-gene AUCs calculated from intra-gene methylation measures.

<p>Each box displays the values of the AUCs for the 1000 most significant genes for a particular tumour type and intra-gene methylation measure. The mean -score predicts phenotype better than the other three measures in all 14 tumour types. Tumour type abbreviations are as follows: Bladder Urothelial Carcinoma (BLCA), Breast Invasive Carcinoma (BRCA), Colon Adenocarcinoma (COAD), Head and Neck Squamous Cell Carcinoma (HNSC), Kidney Renal Clear Cell Carcinoma (KIRC), Kidney Renal Papillary Cell Carcinoma (KIRP), Liver (LIHC), Lung Adenocarcinoma (LUAD), Lung Squamous Cell Carcinoma (LUSC), Pancreatic Adenocarcinoma (PAAD), Prostate Adenocarcinoma (PRAD), Rectum Adenocarcinoma (READ), Thyroid Carcinoma (THCA), and Uterine Corpus Endometrioid Carcinoma (UCEC).</p