SummaryBackgroundVarious factors—including age, family history, inflammation, reproductive factors, and tubal ligation—modulate the risk of ovarian cancer. In this study, our aim was to establish ...whether women with, or at risk of developing, ovarian cancer have an imbalanced cervicovaginal microbiome. MethodsWe did a case-control study in two sets of women aged 18–87 years in the Czech Republic, Germany, Italy, Norway, and the UK. The ovarian cancer set comprised women with epithelial ovarian cancer and controls (both healthy controls and those diagnosed with benign gynaecological conditions). The BRCA set comprised women with a BRCA1 mutation but without ovarian cancer and controls who were wild type for BRCA1 and BRCA2 (both healthy controls and those with benign gynaecological conditions). Cervicovaginal samples were gathered from all participants with the ThinPrep system and then underwent 16S rRNA gene sequencing. For each sample, we calculated the proportion of lactobacilli species (ie, Lactobacillus crispatus, Lactobacillus iners, Lactobacillus gasseri, and Lactobacillus jensenii), which are essential for the generation of a protective low vaginal pH, in the cervicovaginal microbiota. We grouped samples into those in which lactobacilli accounted for at least 50% of the species present (community type L) and those in which lactobacilli accounted for less than 50% of the species present (community type O). We assessed the adjusted association between BRCA1 status and ovarian cancer status and cervicovaginal microbiota community type, using a logistic regression model with a bias reduction method. FindingsParticipants were recruited between Jan 2, 2016, and July 21, 2018. The ovarian cancer set (n=360) comprised 176 women with epithelial ovarian cancer, 115 healthy controls and 69 controls with benign gynaecological conditions. The BRCA set (n=220) included 109 women with BRCA1 mutations, 97 healthy controls wild type for BRCA1 and BRCA2 and 14 controls with a benign gynaecological condition wild type for BRCA1 and BRCA2. On the basis of two-dimensional density plots, receiver–operating characteristic curve analysis, and age thresholds used previously, we divided the cohort into those younger than 50 years and those aged 50 years or older. In the ovarian cancer set, women aged 50 years or older had a higher prevalence of community type O microbiota (81 61% of 133 ovarian cancer cases and 84 59% of 142 healthy controls) than those younger than 50 years (23 53% of 43 cases and 12 29% of 42 controls). In the ovarian cancer set, women younger than 50 years with ovarian cancer had a significantly higher prevalence of community type O microbiota than did age-matched controls under a logistic regression model with bias correction (odds ratio OR 2·80 95% CI 1·17–6·94; p=0·020). In the BRCA set, women with BRCA1 mutations younger than 50 years were also more likely to have community type O microbiota than age-matched controls (OR 2·79 95% CI 1·25–6·68; p=0·012), after adjustment for pregnancy (ever). This risk was increased further if more than one first-degree family member was affected by any cancer (OR 5·26 95% CI 1·83–15·30; p=0·0022). In both sets, we noted that the younger the participants, the stronger the association between community type O microbiota and ovarian cancer or BRCA1 mutation status (eg, OR for community type O for cases aged <40 years in the ovarian cancer set 7·00 95% CI 1·27–51·44, p=0·025; OR for community type O for BRCA1 mutation carriers aged <35 years in the BRCA set 4·40 1·14–24·36, p=0·031). InterpretationThe presence of ovarian cancer, or factors known to affect risk for the disease (ie, age and BRCA1 germline mutations), were significantly associated with having a community type O cervicovaginal microbiota. Whether re-instatement of a community type L microbiome by using, for example, vaginal suppositories containing live lactobacilli, would alter the microbiomial composition higher up in the female genital tract and in the fallopian tubes (the site of origin of high-grade serous ovarian cancer), and whether such changes could translate into a reduced incidence of ovarian cancer, needs to be investigated. FundingEU Horizon 2020 Research and Innovation Programme, EU Horizon 2020 European Research Council Programme, and The Eve Appeal.
Diverse molecular alterations associated with smoking in normal and precursor lung cancer cells have been reported, yet their role in lung cancer etiology remains unclear. A prominent example is ...hypomethylation of the aryl hydrocarbon-receptor repressor (AHRR) locus, which is observed in blood and squamous epithelial cells of smokers, but not in lung cancer.
Using a novel systems-epigenomics algorithm, called SEPIRA, which leverages the power of a large RNA-sequencing expression compendium to infer regulatory activity from messenger RNA expression or DNA methylation (DNAm) profiles, we infer the landscape of binding activity of lung-specific transcription factors (TFs) in lung carcinogenesis. We show that lung-specific TFs become preferentially inactivated in lung cancer and precursor lung cancer lesions and further demonstrate that these results can be derived using only DNAm data. We identify subsets of TFs which become inactivated in precursor cells. Among these regulatory factors, we identify AHR, the aryl hydrocarbon-receptor which controls a healthy immune response in the lung epithelium and whose repressor, AHRR, has recently been implicated in smoking-mediated lung cancer. In addition, we identify FOXJ1, a TF which promotes growth of airway cilia and effective clearance of the lung airway epithelium from carcinogens.
We identify TFs, such as AHR, which become inactivated in the earliest stages of lung cancer and which, unlike AHRR hypomethylation, are also inactivated in lung cancer itself. The novel systems-epigenomics algorithm SEPIRA will be useful to the wider epigenome-wide association study community as a means of inferring regulatory activity.
A common difficulty in large-scale microarray studies is the presence of confounding factors, which may significantly skew estimates of statistical significance, cause unreliable feature selection ...and high false negative rates. To deal with these difficulties, an algorithmic framework known as Surrogate Variable Analysis (SVA) was recently proposed.
Based on the notion that data can be viewed as an interference pattern, reflecting the superposition of independent effects and random noise, we present a modified SVA, called Independent Surrogate Variable Analysis (ISVA), to identify features correlating with a phenotype of interest in the presence of potential confounding factors. Using simulated data, we show that ISVA performs well in identifying confounders as well as outperforming methods which do not adjust for confounding. Using four large-scale Illumina Infinium DNA methylation datasets subject to low signal to noise ratios and substantial confounding by beadchip effects and variable bisulfite conversion efficiency, we show that ISVA improves the identifiability of confounders and that this enables a framework for feature selection that is more robust to model misspecification and heterogeneous phenotypes. Finally, we demonstrate similar improvements of ISVA across four mRNA expression datasets. Thus, ISVA should be useful as a feature selection tool in studies that are subject to confounding.
An R-package isva is available from www.cran.r-project.org.
MOTIVATION: The standard paradigm in omic disciplines has been to identify biologically relevant biomarkers using statistics that reflect differences in mean levels of a molecular quantity such as ...mRNA expression or DNA methylation. Recently, however, it has been proposed that differential epigenetic variability may mark genes that contribute to the risk of complex genetic diseases like cancer and that identification of risk and early detection markers may therefore benefit from statistics based on differential variability. RESULTS: Using four genome-wide DNA methylation datasets totalling 311 epithelial samples and encompassing all stages of cervical carcinogenesis, we here formally demonstrate that differential variability, as a criterion for selecting DNA methylation features, can identify cancer risk markers more reliably than statistics based on differences in mean methylation. We show that differential variability selects features with heterogeneous outlier methylation profiles and that these play a key role in the early stages of carcinogenesis. Moreover, differentially variable features identified in precursor non-invasive lesions exhibit significantly increased enrichment for developmental genes compared with differentially methylated sites. Conversely, differential variability does not add predictive value in cancer studies profiling invasive tumours or whole-blood tissue. Finally, we incorporate the differential variability feature selection step into a novel adaptive index prediction algorithm called EVORA (epigenetic variable outliers for risk prediction analysis), and demonstrate that EVORA compares favourably to powerful prediction algorithms based on differential methylation statistics. CONCLUSIONS: Statistics based on differential variability improve the detection of cancer risk markers in the context of DNA methylation studies profiling epithelial preinvasive neoplasias. We present a novel algorithm (EVORA) which could be used for prediction and diagnosis of precursor epithelial cancer lesions. AVAILABILITY: R-scripts implementing EVORA are available from CRAN (www.r-project.org). CONTACT: a.teschendorff@ucl.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.
Identifying molecular alterations in normal tissue adjacent to cancer is important for understanding cancer aetiology and designing preventive measures. Here we analyse the DNA methylome of 569 ...breast tissue samples, including 50 from cancer-free women and 84 from matched normal cancer pairs. We use statistical algorithms for dissecting intra- and inter-sample cellular heterogeneity and demonstrate that normal tissue adjacent to breast cancer is characterized by tens to thousands of epigenetic alterations. We show that their genomic distribution is non-random, being strongly enriched for binding sites of transcription factors specifying chromatin architecture. We validate the field defects in an independent cohort and demonstrate that over 30% of the alterations exhibit increased enrichment within matched cancer samples. Breast cancers highly enriched for epigenetic field defects, exhibit adverse clinical outcome. Our data support a model where clonal epigenetic reprogramming towards reduced differentiation in normal tissue is an important step in breast carcinogenesis.
With the rapid expansion of aging biology research, the identification and evaluation of longevity interventions in humans have become key goals of this field. Biomarkers of aging are critically ...important tools in achieving these objectives over realistic time frames. However, the current lack of standards and consensus on the properties of a reliable aging biomarker hinders their further development and validation for clinical applications. Here, we advance a framework for the terminology and characterization of biomarkers of aging, including classification and potential clinical use cases. We discuss validation steps and highlight ongoing challenges as potential areas in need of future research. This framework sets the stage for the development of valid biomarkers of aging and their ultimate utilization in clinical trials and practice.
Menopause accelerates biological aging Levine, Morgan E.; Lu, Ake T.; Chen, Brian H. ...
Proceedings of the National Academy of Sciences - PNAS,
08/2016, Letnik:
113, Številka:
33
Journal Article
Recenzirano
Odprti dostop
Although epigenetic processes have been linked to aging and disease in other systems, it is not yet known whether they relate to reproductive aging. Recently, we developed a highly accurate ...epigenetic biomarker of age (known as the “epigenetic clock”), which is based on DNA methylation levels. Here we carry out an epigenetic clock analysis of blood, saliva, and buccal epithelium using data from four large studies: the Women’s Health Initiative (n = 1,864); Invecchiare nel Chianti (n = 200); Parkinson’s disease, Environment, and Genes (n = 256); and the United Kingdom Medical Research Council National Survey of Health and Development (n = 790). We find that increased epigenetic age acceleration in blood is significantly associated with earlier menopause (P = 0.00091), bilateral oophorectomy (P = 0.0018), and a longer time since menopause (P = 0.017). Conversely, epigenetic age acceleration in buccal epithelium and saliva do not relate to age at menopause; however, a higher epigenetic age in saliva is exhibited in women who undergo bilateral oophorectomy (P = 0.0079), while a lower epigenetic age in buccal epithelium was found for women who underwent menopausal hormone therapy (P = 0.00078). Using genetic data, we find evidence of coheritability between age at menopause and epigenetic age acceleration in blood. Using Mendelian randomization analysis, we find that two SNPs that are highly associated with age at menopause exhibit a significant association with epigenetic age acceleration. Overall, our Mendelian randomization approach and other lines of evidence suggest that menopause accelerates epigenetic aging of blood, but mechanistic studies will be needed to dissect cause-and-effect relationships further.
Breast cancer is one of the most common cancers in humans and will on average affect up to one in eight women in their lifetime in the United States and Europe. The Women's Health Initiative and the ...Million Women Study have shown that hormone replacement therapy is associated with an increased risk of incident and fatal breast cancer. In particular, synthetic progesterone derivatives (progestins) such as medroxyprogesterone acetate (MPA), used in millions of women for hormone replacement therapy and contraceptives, markedly increase the risk of developing breast cancer. Here we show that the in vivo administration of MPA triggers massive induction of the key osteoclast differentiation factor RANKL (receptor activator of NF- B ligand) in mammary-gland epithelial cells. Genetic inactivation of the RANKL receptor RANK in mammary-gland epithelial cells prevents MPA-induced epithelial proliferation, impairs expansion of the CD49fhi stem-cell-enriched population, and sensitizes these cells to DNA-damage-induced cell death. Deletion of RANK from the mammary epithelium results in a markedly decreased incidence and delayed onset of MPA-driven mammary cancer. These data show that the RANKL/RANK system controls the incidence and onset of progestin-driven breast cancer.
The 27k Illumina Infinium Methylation Beadchip is a popular high-throughput technology that allows the methylation state of over 27,000 CpGs to be assayed. While feature selection and classification ...methods have been comprehensively explored in the context of gene expression data, relatively little is known as to how best to perform feature selection or classification in the context of Illumina Infinium methylation data. Given the rising importance of epigenomics in cancer and other complex genetic diseases, and in view of the upcoming epigenome wide association studies, it is critical to identify the statistical methods that offer improved inference in this novel context.
Using a total of 7 large Illumina Infinium 27k Methylation data sets, encompassing over 1,000 samples from a wide range of tissues, we here provide an evaluation of popular feature selection, dimensional reduction and classification methods on DNA methylation data. Specifically, we evaluate the effects of variance filtering, supervised principal components (SPCA) and the choice of DNA methylation quantification measure on downstream statistical inference. We show that for relatively large sample sizes feature selection using test statistics is similar for M and β-values, but that in the limit of small sample sizes, M-values allow more reliable identification of true positives. We also show that the effect of variance filtering on feature selection is study-specific and dependent on the phenotype of interest and tissue type profiled. Specifically, we find that variance filtering improves the detection of true positives in studies with large effect sizes, but that it may lead to worse performance in studies with smaller yet significant effect sizes. In contrast, supervised principal components improves the statistical power, especially in studies with small effect sizes. We also demonstrate that classification using the Elastic Net and Support Vector Machine (SVM) clearly outperforms competing methods like LASSO and SPCA. Finally, in unsupervised modelling of cancer diagnosis, we find that non-negative matrix factorisation (NMF) clearly outperforms principal components analysis.
Our results highlight the importance of tailoring the feature selection and classification methodology to the sample size and biological context of the DNA methylation study. The Elastic Net emerges as a powerful classification algorithm for large-scale DNA methylation studies, while NMF does well in the unsupervised context. The insights presented here will be useful to any study embarking on large-scale DNA methylation profiling using Illumina Infinium beadarrays.
Despite a myriad of attempts in the last three decades to diagnose ovarian cancer (OC) earlier, this clinical aim still remains a significant challenge. Aberrant methylation patterns of linked CpGs ...analyzed in DNA fragments shed by cancers into the bloodstream (i.e. cell-free DNA) can provide highly specific signals indicating cancer presence.
We analyzed 699 cancerous and non-cancerous tissues using a methylation array or reduced representation bisulfite sequencing to discover the most specific OC methylation patterns. A three-DNA-methylation-serum-marker panel was developed using targeted ultra-high coverage bisulfite sequencing in 151 women and validated in 250 women with various conditions, particularly in those associated with high CA125 levels (endometriosis and other benign pelvic masses), serial samples from 25 patients undergoing neoadjuvant chemotherapy, and a nested case control study of 172 UKCTOCS control arm participants which included serum samples up to two years before OC diagnosis.
The cell-free DNA amount and average fragment size in the serum samples was up to ten times higher than average published values (based on samples that were immediately processed) due to leakage of DNA from white blood cells owing to delayed time to serum separation. Despite this, the marker panel discriminated high grade serous OC patients from healthy women or patients with a benign pelvic mass with specificity/sensitivity of 90.7% (95% confidence interval CI = 84.3-94.8%) and 41.4% (95% CI = 24.1-60.9%), respectively. Levels of all three markers plummeted after exposure to chemotherapy and correctly identified 78% and 86% responders and non-responders (Fisher's exact test, p = 0.04), respectively, which was superior to a CA125 cut-off of 35 IU/mL (20% and 75%). 57.9% (95% CI 34.0-78.9%) of women who developed OC within two years of sample collection were identified with a specificity of 88.1% (95% CI = 77.3-94.3%). Sensitivity and specificity improved further when specifically analyzing CA125 negative samples only (63.6% and 87.5%, respectively).
Our data suggest that DNA methylation patterns in cell-free DNA have the potential to detect a proportion of OCs up to two years in advance of diagnosis and may potentially guide personalized treatment. The prospective use of novel collection vials, which stabilize blood cells and reduce background DNA contamination in serum/plasma samples, will facilitate clinical implementation of liquid biopsy analyses.
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