Integration of genetic and epigenetic markers for risk stratification: opportunities and challenges

Common genetic susceptibility variants could be used for risk stratification in risk-tailored cancer screening and prevention programmes. Combining genetic variants with environmental risk factors would improve risk stratification. Epigenetic changes are surrogate markers of environmental exposures during individual's lifetime. Integrating epigenetic markers, in lieu of environmental exposure data, with genetic markers would potentially improve risk stratification. Epigenetic changes are reversible and acquired gradually, providing potentials for prevention and early detection strategies. The epigenetic changes are tissue-specific and stage-of-development-specific, raising challenges in choice of sample and timing for evaluation of cancer-associated changes. The Horizon 2020 funded research programme, FORECEE, using empirical data, will investigate the value of integration of epigenomics with genomics for risk prediction and prevention of women-specific cancers

Stochastic epigenetic outliers can define field defects in cancer

BACKGROUND: There is growing evidence that DNA methylation alterations may contribute to carcinogenesis. Recent data also suggest that DNA methylation field defects in normal pre-neoplastic tissue represent infrequent stochastic “outlier” events. This presents a statistical challenge for standard feature selection algorithms, which assume frequent alterations in a disease phenotype. Although differential variability has emerged as a novel feature selection paradigm for the discovery of outliers, a growing concern is that these could result from technical confounders, in principle thus favouring algorithms which are robust to outliers. RESULTS: Here we evaluate five differential variability algorithms in over 700 DNA methylomes, including two of the largest cohorts profiling precursor cancer lesions, and demonstrate that most of the novel proposed algorithms lack the sensitivity to detect epigenetic field defects at genome-wide significance. In contrast, algorithms which recognise heterogeneous outlier DNA methylation patterns are able to identify many sites in pre-neoplastic lesions, which display progression in invasive cancer. Thus, we show that many DNA methylation outliers are not technical artefacts, but define epigenetic field defects which are selected for during cancer progression. CONCLUSIONS: Given that cancer studies aiming to find epigenetic field defects are likely to be limited by sample size, adopting the novel feature selection paradigm advocated here will be critical to increase assay sensitivity

Inter-individual methylation variation and its relationship with evolution and cancer

Background: In recent years, epigenetics has gained interest among scientists involved in different research areas (e.g. cancer, molecular medicine, behavior, development). It is now clear that the environment influences the methylome by promoting methylation variation with possible effects on both healthy and disease-related phenotypes. I studied inter-individual DNA methylation variation in healthy individuals and in cancer tissues to identify possible factors influencing this variation. Results: Using the EPIC-Italy dataset (1 tissue, 83 males and 83 age-matched healthy females), I analyzed methylation variation values in relation to CpG cluster density and I found a strong association between them (p-value < 2.2*10-16). Furthermore, I found that genes related to CpGs with high methylation variation values were enriched for immunological pathways; instead, those associated with low variation were enriched for pathways related to basic cellular functions. Finally, I found an association between methylation variation values and signals of both ancient (p-value < 2.2*10-16) and recent selective pressure (p-value < 1*10-4). Furthermore, using TCGA datasets (4 healthy and cancer tissues), I analyzed methylation variation correlation in several tissues and in different conditions. I found that cancer tissues show higher methylation variation than healthy tissues (p-value < 2.2*10-16). Finally, I used a linear regression model to calculate Differentially Methylated CpGs (DM-CpGs) and I found that DM-CpGs always display higher inter-individual methylation variation especially in cancer (p-value < 2.2*10-16). Conclusion: These results indicate the presence in healthy subjects of an intricate interplay between genetics, epigenetics and evolutionary constraints that influence the inter-individual methylation variation. Furthermore, my results show an increase of inter-individual variation in cancer

Epigenetic outlier profiles in depression: A genome-wide DNA methylation analysis of monozygotic twins

Recent discoveries highlight the importance of stochastic epigenetic changes, as indexed by epigenetic outlier DNA methylation signatures, as a valuable tool to understand aberrant cell function and subsequent human pathology. There is evidence of such changes in different complex disorders as diverse as cancer, obesity and, to a lesser extent, depression. The current study was aimed at identifying outlying DNA methylation signatures of depressive psychopathology. Here, genome-wide DNA methylation levels were measured (by means of Illumina Infinium HumanMethylation450 Beadchip) in peripheral blood of thirty-four monozygotic twins informative for depressive psychopathology (lifetime DSM-IV diagnoses). This dataset was explored to identify outlying epigenetic signatures of depression, operationalized as extreme hyper- or hypo-methylation in affected co-twins from discordant pairs that is not observed across the rest of the study sample. After adjusting for blood cell count, there were thirteen CpG sites across which depressed co-twins from the discordant pairs exhibited outlying DNA methylation signatures. None of them exhibited a methylation outlier profile in the concordant and healthy pairs, and some of these loci spanned genes previously associated with neuropsychiatric phenotypes, such as GHSR and KCNQ1. This exploratory study provides preliminary proof-of-concept validation that epigenetic outlier profiles derived from genome-wide DNA methylation data may be related to depression risk

The impact of paternal metabolic health on sperm DNA methylation and fetal growth

Low birth weight is associated with cardiovascular disease and T2DM in later life. Paternal obesity and T2DM have been associated with an increased risk of fathering low birthweight offspring. Obesity is associated with epigenetic changes in blood, but few studies have replicated DNA methylation differences found in obese subjects. Animal studies have shown that obesity and insulin resistance are associated with DNA methylation changes in sperm, which in turn could mediate intergenerational effects. Such findings are lacking in humans. My PhD explored the association between paternal metabolic traits and the birth weight of his offspring. I then investigated whether DNA methylation signatures in spermatozoa of obese fathers could underlie any observed association with his offspring birthweight. First, I performed a prospective cohort study of 500 mother-father-offspring trios to identify paternal metabolic traits associated with an increased risk of fathering low birth weight offspring. Out of 390 trios, including 64 obese men and 48 growth restricted offspring, I did not discover any significant paternal metabolic traits associated with fathering low-birthweight offspring. However, I found that paternal (own) birth weight is associated with the birth weight of his offspring. This suggests that paternal genetic factors are more influential in determining his offspring’s growth in utero than are factors acquired during his lifetime. Second, I performed a systematic review of studies that had investigated DNA methylation in human sperm. From this review, I summarised current knowledge and generated recommendations for future research. I then performed the largest characterisation of matched human sperm and blood samples to date using the most comprehensive DNA methylation profiling array, the MethylationEPIC array. Results showed that the DNA methylomes of sperm and blood are highly discordant and in effect completely uncorrelated. Future studies of intergenerational effects will have to study germ cells, rather than blood. Lastly, I attempted to validate previously-identified DNA methylation signatures associated with male obesity. Despite comparing 96 well-characterised obese men with 96 lean men, I was unable to replicate any previously identified differentially methylated CpG sites associated with obesity, in their blood. In a linear regression model, I identified two CpG sites, cg07037944 and cg26651978, as being suggestive of an association with BMI. These results will contribute to a larger cohort study of 1000 obese and 1000 lean men that aims to identify a robust and reproducible DNA methylation profile associated with obesity. In conclusion, this thesis did not prove my pre-determined hypotheses. However, it does present findings which advance our understanding of the intriguing possibility that acquired parental metabolic phenotype may influence offspring birthweight through intergenerational inheritance of epigenetic marks

Trade-offs between causes of mortality in life history evolution: the case of cancers.

International audienceLittle is known about the relative importance of different causes of death in driving the evolution of senescence and longevity across species. Here we argue that cause-specific mortality may be shaped by physiological trade-offs between mortality components, challenging the theoretical view that physiologically independent processes should senesce at the same rate, or that interactions between causes of death will make selection blind to the effects of specific causes of death. We review the evidence that risk of cancers trades off with risks of mortality from other diseases, and investigate whether this might explain two of the most puzzling paradoxes in cancer evolution. First, among species, cancer prevalence is not a function of species’ size and longevity, despite the fact that cancer incidence is known to be a function of the number of cell divisions (and therefore of size) by unit of time (and therefore of longevity). Second, within species, despite the fact that genomic instability is thought to be the proximal cause of both cancer incidence and senescence, mortality rates rise with age while cancer incidence decelerates and declines at old ages. Building on a relatively novel theory from cellular biology, we construct a preliminary model to reveal the degree to which accumulation of senescent cells with age could explain this latter paradox. Diverting damaged stem cells towards a senescent-state reduces their risk of becoming tumorous; however, conversely, the accumulation of senescent cells in tissues compromises their rejuvenation capacity and functioning, leading to organismal senescence. Accumulation of senescent cells with age may then be optimal because it reduces cancer mortality at the cost of faster senescence from other causes. Evolution will drive species towards a balance between these two sources of mortality

Statistical and integrative system-level analysis of DNA methylation data

Epigenetics plays a key role in cellular development and function. Alterations to the epigenome are thought to capture and mediate the effects of genetic and environmental risk factors on complex disease. Currently, DNA methylation is the only epigenetic mark that can be measured reliably and genome-wide in large numbers of samples. This Review discusses some of the key statistical challenges and algorithms associated with drawing inferences from DNA methylation data, including cell-type heterogeneity, feature selection, reverse causation and system-level analyses that require integration with other data types such as gene expression, genotype, transcription factor binding and other epigenetic information