Ionizing radiations induce shared epigenomic signatures unraveling adaptive mechanisms of cancerous cell lines with or without methionine dependency.

BACKGROUND: Although radiation therapy represents a core cancer treatment modality, its efficacy is hampered by radioresistance. The effect of ionizing radiations (IRs) is well known regarding their ability to induce genetic alterations; however, their impact on the epigenome landscape in cancer, notably at the CpG dinucleotide resolution, remains to be further deciphered. In addition, no evidence is available regarding the effect of IRs on the DNA methylome profile according to the methionine dependency phenotype, which represents a hallmark of metabolic adaptation in cancer. METHODS: We used a case-control study design with a fractionated irradiation regimen on four cancerous cell lines representative of HCC (HepG2), melanoma (MeWo and MeWo-LC1, which exhibit opposed methionine dependency phenotypes), and glioblastoma (U251). We performed high-resolution genome-wide DNA methylome profiling using the MethylationEPIC BeadChip on baseline conditions, irradiated cell lines (cumulative dose of 10 Gy), and non-irradiated counterparts. We performed epigenome-wide association studies to assess the effect of IRs and methionine-dependency-oriented analysis by carrying out epigenome-wide conditional logistic regression. We looked for epigenome signatures at the locus and single-probe (CpG dinucleotide) levels and through enrichment analyses of gene ontologies (GO). The EpiMet project was registered under the ID#AAP-BMS_003_211. RESULTS: EWASs revealed shared GO annotation pathways associated with increased methylation signatures for several biological processes in response to IRs, including blood circulation, plasma membrane-bounded cell projection organization, cell projection organization, multicellular organismal process, developmental process, and animal organ morphogenesis. Epigenome-wide conditional logistic regression analysis on the methionine dependency phenotype highlighted several epigenome signatures related to cell cycle and division and responses to IR and ultraviolet light. CONCLUSIONS: IRs generated a variation in the methylation level of a high number of CpG probes with shared biological pathways, including those associated with cell cycle and division, responses to IRs, sustained angiogenesis, tissue invasion, and metastasis. These results provide insight on shared adaptive mechanisms of the epigenome in cancerous cell lines in response to IR. Future experiments should focus on the tryptic association between IRs, the initiation of a radioresistance phenotype, and their interaction with methionine dependency as a hallmark of metabolic adaptation in cancer.

Epigenetic quantification of circulating immune cells in peripheral blood of triple-negative breast cancer patients.

BACKGROUND: A shift in the proportions of blood immune cells is a hallmark of cancer development. Here, we investigated whether methylation-derived immune cell type ratios and methylation-derived neutrophil-to-lymphocyte ratios (mdNLRs) are associated with triple-negative breast cancer (TNBC). METHODS: Leukocyte subtype-specific unmethylated/methylated CpG sites were selected, and methylation levels at these sites were used as proxies for immune cell type proportions and mdNLR estimation in 231 TNBC cases and 231 age-matched controls. Data were validated using the Houseman deconvolution method. Additionally, the natural killer (NK) cell ratio was measured in a prospective sample set of 146 TNBC cases and 146 age-matched controls. RESULTS: The mdNLRs were higher in TNBC cases compared with controls and associated with TNBC (odds ratio (OR) range (2.66-4.29), all Padj. < 1e-04). A higher neutrophil ratio and lower ratios of NK cells, CD4 + T cells, CD8 + T cells, monocytes, and B cells were associated with TNBC. The strongest association was observed with decreased NK cell ratio (OR range (1.28-1.42), all Padj. < 1e-04). The NK cell ratio was also significantly lower in pre-diagnostic samples of TNBC cases compared with controls (P = 0.019). CONCLUSION: This immunomethylomic study shows that a shift in the ratios/proportions of leukocyte subtypes is associated with TNBC, with decreased NK cell showing the strongest association. These findings improve our knowledge of the role of the immune system in TNBC and point to the possibility of using NK cell level as a non-invasive molecular marker for TNBC risk assessment, early detection, and prevention.

Epigenetic modelling of former, current and never smokers.

BACKGROUND: DNA methylation (DNAm) performs excellently in the discrimination of current and former smokers from never smokers, where AUCs > 0.9 are regularly reported using a single CpG site (cg05575921; AHRR). However, there is a paucity of DNAm models which attempt to distinguish current, former and never smokers as individual classes. Derivation of a robust DNAm model that accurately distinguishes between current, former and never smokers would be particularly valuable to epidemiological research (as a more accurate smoking definition vs. self-report) and could potentially translate to clinical settings. Therefore, we appraise 4 DNAm models of ternary smoking status (that is, current, former and never smokers): methylation at cg05575921 (AHRR model), weighted scores from 13 CpGs created by Maas et al. (Maas model), weighted scores from a LASSO model of candidate smoking CpGs from the literature (candidate CpG LASSO model), and weighted scores from a LASSO model supplied with genome-wide 450K data (agnostic LASSO model). Discrimination is assessed by AUC, whilst classification accuracy is assessed by accuracy and kappa, derived from confusion matrices. RESULTS: We find that DNAm can classify ternary smoking status with reasonable accuracy, including when applied to external data. Ternary classification using only DNAm far exceeds the classification accuracy of simply assigning all classes as the most prevalent class (63.7% vs. 36.4%). Further, we develop a DNAm classifier which performs well in discriminating current from former smokers (agnostic LASSO model AUC in external validation data: 0.744). Finally, across our DNAm models, we show evidence of enrichment for biological pathways and human phenotype ontologies relevant to smoking, such as haemostasis, molybdenum cofactor synthesis, body fatness and social behaviours, providing evidence of the generalisability of our classifiers. CONCLUSIONS: Our findings suggest that DNAm can classify ternary smoking status with close to 65% accuracy. Both the ternary smoking status classifiers and current versus former smoking status classifiers address the present lack of former smoker classification in epigenetic literature; essential if DNAm classifiers are to adequately relate to real-world populations. To improve performance further, additional focus on improving discrimination of current from former smokers is necessary.

An Efficient Approach to Screening Epigenome-Wide Data.

Screening cytosine-phosphate-guanine dinucleotide (CpG) DNA methylation sites in association with some covariate(s) is desired due to high dimensionality. We incorporate surrogate variable analyses (SVAs) into (ordinary or robust) linear regressions and utilize training and testing samples for nested validation to screen CpG sites. SVA is to account for variations in the methylation not explained by the specified covariate(s) and adjust for confounding effects. To make it easier to users, this screening method is built into a user-friendly R package, ttScreening, with efficient algorithms implemented. Various simulations were implemented to examine the robustness and sensitivity of the method compared to the classical approaches controlling for multiple testing: the false discovery rates-based (FDR-based) and the Bonferroni-based methods. The proposed approach in general performs better and has the potential to control both types I and II errors. We applied ttScreening to 383,998 CpG sites in association with maternal smoking, one of the leading factors for cancer risk.

Integrative modeling of multi-platform genomic data under the framework of mediation analysis.

Given the availability of genomic data, there have been emerging interests in integrating multi-platform data. Here, we propose to model genetics (single nucleotide polymorphism (SNP)), epigenetics (DNA methylation), and gene expression data as a biological process to delineate phenotypic traits under the framework of causal mediation modeling. We propose a regression model for the joint effect of SNPs, methylation, gene expression, and their nonlinear interactions on the outcome and develop a variance component score test for any arbitrary set of regression coefficients. The test statistic under the null follows a mixture of chi-square distributions, which can be approximated using a characteristic function inversion method or a perturbation procedure. We construct tests for candidate models determined by different combinations of SNPs, DNA methylation, gene expression, and interactions and further propose an omnibus test to accommodate different models. We then study three path-specific effects: the direct effect of SNPs on the outcome, the effect mediated through expression, and the effect through methylation. We characterize correspondences between the three path-specific effects and coefficients in the regression model, which are influenced by causal relations among SNPs, DNA methylation, and gene expression. We illustrate the utility of our method in two genomic studies and numerical simulation studies.

Integrative analyses for omics data: a Bayesian mixture model to assess the concordance of ChIP-chip and ChIP-seq measurements.

The analysis of different variations in genomics, transcriptomics, epigenomics, and proteomics has increased considerably in recent years. This is especially due to the success of microarray and, more recently, sequencing technology. Apart from understanding mechanisms of disease pathogenesis on a molecular basis, for example in cancer research, the challenge of analyzing such different data types in an integrated way has become increasingly important also for the validation of new sequencing technologies with maximum resolution. For this purpose, a methodological framework for their comparison with microarray techniques in the context of smallest sample sizes, which result from the high costs of experiments, is proposed in this contribution. Based on an adaptation of the externally centered correlation coefficient ( Schäfer et al. 2009 ), it is demonstrated how a Bayesian mixture model can be applied to compare and classify measurements of histone acetylation that stem from chromatin immunoprecipitation combined with either microarray (ChIP-chip) or sequencing techniques (ChIP-seq) for the identification of DNA fragments. Here, the murine hematopoietic cell line 32D, which was transduced with the oncogene BCR-ABL, the hallmark of chronic myeloid leukemia, was characterized. Cells were compared to mock-transduced cells as control. Activation or inhibition of other genes by histone modifications induced by the oncogene is considered critical in such a context for the understanding of the disease.

Biologic and epigenetic impact of commuting to work by car or using public transportation: a case-control study.

BACKGROUND AND AIMS: Commuting by public transportation (PT) entails more physical activity and energy expenditure than by cars, but its biologic consequences are unknown. METHODS: In 2009-2010, we randomly sampled New York adults, usually commuting either by car (n=79) or PT (n=101). Measures comprised diet and physical activity questionnaires, weight and height, white blood cell (WBC) count, C reactive protein, (CRP) gene-specific methylation (IL-6), and global genomic DNA methylation (LINE-1 methylation). RESULTS: Compared to the 101 PT commuters, the 79 car drivers were about 9 years older, 2 kg/m(2) heavier, more often non-Hispanic whites, and ate more fruits and more meats. The 2005 guidelines for physical activity were met by more car drivers than PT users (78.5% vs. 65.0%). There were no differences in median levels of CRP (car vs. PT: 0.6 vs. 0.5mg/dl), mean levels of WBC (car vs. PT: 6.7 vs. 6.5 cells/mm(3)), LINE-1 methylation (car vs. PT: 78.0% vs. 78.3%), and promoter methylation of IL-6 (car vs. PT: 56.1% vs. 58.0%). CONCLUSIONS: PT users were younger and lighter than car drivers, but their commute mode did not translate into a lower inflammatory response or a higher DNA methylation, maybe because, overall, car drivers were more physically active.