RESUMO
The role of cytosine methylation in the structure and function of enhancers is not well understood. In this study, we investigate the role of DNA methylation at enhancers by comparing the epigenomes of the HCT116 cell line and its highly demethylated derivative, DKO1. Unlike promoters, a portion of regular and super- or stretch enhancers show active H3K27ac marks co-existing with extensive DNA methylation, demonstrating the unexpected presence of bivalent chromatin in both cultured and uncultured cells. Furthermore, our findings also show that bivalent regions have fewer nucleosome-depleted regions and transcription factor-binding sites than monovalent regions. Reduction of DNA methylation genetically or pharmacologically leads to a decrease of the H3K27ac mark. Thus, DNA methylation plays an unexpected dual role at enhancer regions, being anti-correlated focally at transcription factor-binding sites but positively correlated globally with the active H3K27ac mark to ensure structural enhancer integrity.
Assuntos
Metilação de DNA , Elementos Facilitadores Genéticos , Epigênese Genética , Histonas/metabolismo , Processamento de Proteína Pós-Traducional , Acetilação , Sítios de Ligação , Montagem e Desmontagem da Cromatina , Citosina , Células HCT116 , Histonas/genética , Humanos , Fatores de TempoRESUMO
BACKGROUND: Germline variants explain more than a third of prostate cancer (PrCa) risk, but very few associations have been identified between heritable factors and clinical progression. OBJECTIVE: To find rare germline variants that predict time to biochemical recurrence (BCR) after radical treatment in men with PrCa and understand the genetic factors associated with such progression. DESIGN, SETTING, AND PARTICIPANTS: Whole-genome sequencing data from blood DNA were analysed for 850 PrCa patients with radical treatment from the Pan Prostate Cancer Group (PPCG) consortium from the UK, Canada, Germany, Australia, and France. Findings were validated using 383 patients from The Cancer Genome Atlas (TCGA) dataset. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: A total of 15,822 rare (MAF <1%) predicted-deleterious coding germline mutations were identified. Optimal multifactor and univariate Cox regression models were built to predict time to BCR after radical treatment, using germline variants grouped by functionally annotated gene sets. Models were tested for robustness using bootstrap resampling. RESULTS AND LIMITATIONS: Optimal Cox regression multifactor models showed that rare predicted-deleterious germline variants in "Hallmark" gene sets were consistently associated with altered time to BCR. Three gene sets had a statistically significant association with risk-elevated outcome when modelling all samples: PI3K/AKT/mTOR, Inflammatory response, and KRAS signalling (up). PI3K/AKT/mTOR and KRAS signalling (up) were also associated among patients with higher-grade cancer, as were Pancreas-beta cells, TNFA signalling via NKFB, and Hypoxia, the latter of which was validated in the independent TCGA dataset. CONCLUSIONS: We demonstrate for the first time that rare deleterious coding germline variants robustly associate with time to BCR after radical treatment, including cohort-independent validation. Our findings suggest that germline testing at diagnosis could aid clinical decisions by stratifying patients for differential clinical management. PATIENT SUMMARY: Prostate cancer patients with particular genetic mutations have a higher chance of relapsing after initial radical treatment, potentially providing opportunities to identify patients who might need additional treatments earlier.
Assuntos
Fosfatidilinositol 3-Quinases , Neoplasias da Próstata , Células Germinativas , Mutação em Linhagem Germinativa , Humanos , Masculino , Recidiva Local de Neoplasia/genética , Fosfatidilinositol 3-Quinases/genética , Prostatectomia , Neoplasias da Próstata/cirurgia , Neoplasias da Próstata/terapia , Proteínas Proto-Oncogênicas c-akt/genética , Proteínas Proto-Oncogênicas p21(ras)/genética , Serina-Treonina Quinases TORRESUMO
The study of epigenetic heterogeneity at the level of individual cells and in whole populations is the key to understanding cellular differentiation, organismal development, and the evolution of cancer. We develop a statistical method, epiG, to infer and differentiate between different epi-allelic haplotypes, annotated with CpG methylation status and DNA polymorphisms, from whole-genome bisulfite sequencing data, and nucleosome occupancy from NOMe-seq data. We demonstrate the capabilities of the method by inferring allele-specific methylation and nucleosome occupancy in cell lines, and colon and tumor samples, and by benchmarking the method against independent experimental data.