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Additional file 1: Additional figures and tables.

Additional file 1: Fig. S1. Datasets and experimental design. Fig. S2. Detailed comparison of the WID general clock and other epigenetic clocks, and assessments of cell composition in response to menopause, hormone replacement and cancer. Fig. S3. Detailed epidemiological assessment of the WID general clock age acceleration. Fig. S4. Association of the WID general, epithelial and immune clocks with menopause, replicative age, and solo-WCGW methylation. Fig. S5. Association of WID-relative-epithelial- and -immune-age with chronological age and immune cell proportion and breast cancer subtype. Fig. S6. Epidemiological associations of the WID-relative-epithelial-age. Fig. S7. Epidemiological associations of the WID-relative-immune-age. Table S1. Epidemiological and clinical characteristics of the training set. Table S2. Epidemiological and clinical characteristics of the validation set 1. Table S3. WID-REA quantiles and pre-menopausal breast cancer risk. Table S4. Ages of individuals in the breast tissue sets. Table S5. Epidemiological and clinical characteristics of Validation set 2. Table S6. List of samples used to assess the performance of age prediction.

Additional file 2. Is the protocol for Clinical Trial 1, ‘Mifepristone treatment prior to insertion of a levonorgestrel releasing intrauterine system for improved bleeding control – a randomized controlled trial’ (EudraCT registration number 2009-009014-40).

Additional file 1: Supplementary Methods. Fig. S1. Elevated levels of estradiol and progesterone in BRCA1 and BRCA2 mutation carriers compared to controls. Fig. S2. Epithelial cell composition in normal tissues adjacent to TNBC compared to cancer-free breasts. Fig. S3. Enrolment and randomisation in Clinical Trial 1. Fig. S4. Enrolment and randomisation in Clinical Trial 2. Fig. S5. Enrolment in Clinical Trial 3. Fig. S6. Effect of mifepristone treatment on breast epithelium cell subtype composition. Fig. S7. Effect of mifepristone treatment on breast epithelium cell subtype composition – RNA-seq data. Fig. S8. The WID-Breast5 index. Fig. S9. The Ki67 index. Fig. S10. Comparing the Ki67 index with the WID-Breast29 index. Fig. S11. Comparing the change (before and after ulipristal acetate) in percentage of immunohistochemistry Ki67-positive cell with change in Ki67-index in DNAme Dataset 4 (Clinical trial 3, ‘A pilot prevention study of the effects of the anti- progestin Ulipristal Acetate (UA) on surrogate markers of breast cancer risk’). Table S1a. Summary statistics about the 20 healthy women (12 BRCA1 mutation carriers and 8 BRCA1 wild type) enrolled in the trial to measure estradiol and progesterone levels throughout the menstrual cycle (see also Figs. 1 and 2). Table S1b. Summary statistics about the 21 healthy BRCA mutation carriers enrolled in Clincal Trial 2 who provided samples for DNAme analysis as part of DNAme Set 3. Table S2. The 37 WID-Breast29 CpGs. Table S3. Summary statistics about the donors of the breast cancer biopsies and matched surrounding normal tissue samples. Table S4. The reference CpGs identified for luminal progenitor, mature luminal, and basal breast epithelial cells. Table S5. Summary statistics about the 15 healthy women who provided samples for RT-PCR and RNA-seq before and after mifepristone treatment.

Additional file 4. Is the protocol for Clinical Trial 3, ‘A pilot prevention study of the effects of the anti- progestin Ulipristal Acetate (UA) on surrogate markers of breast cancer risk’ (EudraCT registration number: 2015-001587-19).

Additional file 3. Is the protocol for Clinical Trial 2, ‘The effect of a progesterone receptor modulator on breast tissue in women with BRCA1 and 2 mutations’ (EudraCT registration number: 2012-003703-35).

Additional file 5. Review history.

Additional file 1. Supplementary figures and tables: Figure S1. Cell-type composition in the combined Discovery Set and Diagnostic Validation Set as determined by the EpiDISH algorithm (A). Distribution of p-values after comparing hrHPV-positive controls to CIN3+ cases in the Discovery Set (based on a linear regression model with adjustment for age and immune cell proportion) (B). Distribution of the estimated epithelial and immune delta-betas (C). Performance of ridge and lasso classifiers based on out-of-bag estimates from 10-fold cross validation on the Discovery Set (D). Odds ratios when comparing the genomic annotation of the 5,000 CpGs comprising the WID-CIN-index to the 777,005 CpGs that were used in the analysis (E). Figure S2. Dependence of the WID-CIN-index on immune cell proportion in the hrHPV-positive controls and CIN3+ cases from the Diagnostic Validation Set (A). Dependence of the WID-CIN-index on age in the hrHPV-positive controls and CIN3+ cases from the Diagnostic Validation Set (B). The WID-CIN-index in different subgroups and HPV genotypes from the Diagnostic Validation Set (C). The other high risk genotype category consists of genotypes 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 66 and 68. Only samples that tested positive for one of the three genotype categories were included in the plot. Figure S3. The cell-type composition of samples from the predictive validation set based on the EpiDISH algorithm (A). Dependence of the WID-CIN-index on age in the hrHPV-positive controls and CIN3+ cases from the Predictive Validation Set (B). Kaplan-Meier curves from the Predictive Validation Set in which samples have been split into those below and above the 75% specificity cutoff (C). Table S1. Summary of hrHPV genotypes in the Discovery Set (A), Diagnostic Validation Set (B) and Predictive Validation Set (C). The other high risk genotype category consists of genotypes 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 66 and 68. Note that rows may not sum to the total sample number as some samples tested postive for multipe genotypes.

Background Epigenetic heterogeneity within a tumour can play an important role in tumour evolution and the emergence of resistance to treatment. It is increasingly recognised that the study of DNA methylation (DNAm) patterns along the genome – so-called ‘epialleles’ – offers greater insight into epigenetic dynamics than conventional analyses which examine DNAm marks individually. Results We have developed a Bayesian model to infer which epialleles are present in multiple regions of the same tumour. We apply our method to reduced representation bisulfite sequencing (RRBS) data from multiple regions of one lung cancer tumour and a matched normal sample. The model borrows information from all tumour regions to leverage greater statistical power. The total number of epialleles, the epiallele DNAm patterns, and a noise hyperparameter are all automatically inferred from the data. Uncertainty as to which epiallele an observed sequencing read originated from is explicitly incorporated by marginalising over the appropriate posterior densities. The degree to which tumour samples are contaminated with normal tissue can be estimated and corrected for. By tracing the distribution of epialleles throughout the tumour we can infer the phylogenetic history of the tumour, identify epialleles that differ between normal and cancer tissue, and define a measure of global epigenetic disorder. Conclusions Detection and comparison of epialleles within multiple tumour regions enables phylogenetic analyses, identification of differentially expressed epialleles, and provides a measure of epigenetic heterogeneity. R code is available at github.com/james-e-barrett. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1753-2) contains supplementary material, which is available to authorized users.

Metabonomics, also known as metabolomics, is concerned with the study of metabolite profiles in humans, animals, plants and other systems in order to assess their health or other status and their responses to experimental interventions. Metabonomics is thus widely used in disease diagnosis and in understanding responses to therapies such as drug administration. Pharmacometabonomics, also known as pharmacometabolomics, is a related methodology but with a prognostic as opposed to diagnostic thrust. Pharmacometabonomics aims to predict drug effects including efficacy, safety, metabolism and pharmacokinetics, prior to drug administration, via an analysis of pre-dose metabolite profiles. This article will review the development of pharmacometabonomics as a new field of science that has much promise in helping to deliver more effective personalised medicine, a major goal of twenty-first century healthcare.