Genome‐wide association study of peripheral blood DNA methylation and conventional mammographic density measures

Li, Shuai; Dugué, Pierre Antoine; Baglietto, Laura; Severi, Gianluca; Wong, Ee Ming; Nguyen, Tuong L.; Stone, Jennifer; English, Dallas R.; Southey, Melissa C.; Giles, Graham G.; Hopper, John L.; Milne, Roger L.

doi:10.1002/ijc.32171

Cited by 14 publications

(12 citation statements)

References 26 publications

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“…We have applied a new method for making inference about causation by examining familial confounding, called ICE FALCON [34], to data on twin pairs. We found no evidence that conventional DNA methylation in blood is having a causal effect on conventional mammographic density (Cumulus), or vice versa [35]. We will apply this approach to study our new mammogram-based risk factors.…”

Section: Discussionmentioning

confidence: 96%

Going Beyond Conventional Mammographic Density to Discover Novel Mammogram-Based Predictors of Breast Cancer Risk

Hopper

Nguyen

Schmidt

et al. 2020

JCM

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This commentary is about predicting a woman’s breast cancer risk from her mammogram, building on the work of Wolfe, Boyd and Yaffe on mammographic density. We summarise our efforts at finding new mammogram-based risk predictors, and how they combine with the conventional mammographic density, in predicting risk for interval cancers and screen-detected breast cancers across different ages at diagnosis and for both Caucasian and Asian women. Using the OPERA (odds ratio per adjusted standard deviation) concept, in which the risk gradient is measured on an appropriate scale that takes into account other factors adjusted for by design or analysis, we show that our new mammogram-based measures are the strongest of all currently known breast cancer risk factors in terms of risk discrimination on a population-basis. We summarise our findings graphically using a path diagram in which conventional mammographic density predicts interval cancer due to its role in masking, while the new mammogram-based risk measures could have a causal effect on both interval and screen-detected breast cancer. We discuss attempts by others to pursue this line of investigation, the measurement challenge that allows different measures to be compared in an open and transparent manner on the same datasets, as well as the biological and public health consequences.

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Section: Discussionmentioning

confidence: 96%

Going Beyond Conventional Mammographic Density to Discover Novel Mammogram-Based Predictors of Breast Cancer Risk

Hopper

Nguyen

Schmidt

et al. 2020

JCM

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“…Few studies mentioned exclusion of cross-hybridizing probes, probes containing SNPs, and probes located on X-chromosomes (Table 1 and Table S2 ). Most studies reporting global methylation analysis across all included probes or a predefined set of probes used methylation beta-values in conditional or unconditional logistic regression models, with only three out of nine studies considering all important confounders for adjustment [ 23 , 24 , 27 ]. Most studies reporting probe-wise differential methylation analysis used methylation beta-values, conditional or unconditional logistic regression models, Bonferroni’s correction for multiple comparisons, with only three out of 16 studies considering all important confounders for adjustment [ 23 , 24 , 27 ] (Table 1 and Table S2 ).…”

Section: Resultsmentioning

confidence: 99%

Epigenome-wide DNA methylation and risk of breast cancer: a systematic review

et al. 2020

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Background DNA methylation is a potential biomarker for early detection of breast cancer. However, robust evidence of a prospective relationship between DNA methylation patterns and breast cancer risk is still lacking. The objective of this study is to provide a systematic analysis of the findings of epigenome-wide DNA methylation studies on breast cancer risk, in light of their methodological strengths and weaknesses. Methods We searched major databases (MEDLINE, EMBASE, Web of Science, CENTRAL) from inception up to 30th June 2019, for observational or intervention studies investigating the association between epigenome-wide DNA methylation (using the HM450k or EPIC BeadChip), measured in any type of human sample, and breast cancer risk. A pre-established protocol was drawn up following the Cochrane Reviews rigorous methodology. Study selection, data abstraction, and risk of bias assessment were performed by at least two investigators. A qualitative synthesis and systematic comparison of the strengths and weaknesses of studies was performed. Results Overall, 20 studies using the HM450k BeadChip were included, 17 of which had measured blood-derived DNA methylation. There was a consistent trend toward an association of global blood-derived DNA hypomethylation and higher epigenetic age with higher risk of breast cancer. The strength of associations was modest for global hypomethylation and relatively weak for most of epigenetic age algorithms. Differences in length of follow-up periods may have influenced the ability to detect associations, as studies reporting follow-up periods shorter than 10 years were more likely to observe an association with global DNA methylation. Probe-wise differential methylation analyses identified between one and 806 differentially methylated CpGs positions in 10 studies. None of the identified differentially methylated sites overlapped between studies. Three studies used breast tissue DNA and suffered major methodological issues that precludes any conclusion. Overall risk of bias was critical mainly because of incomplete control of confounding. Important issues relative to data preprocessing could have limited the consistency of results. Conclusions Global DNA methylation may be a short-term predictor of breast cancer risk. Further studies with rigorous methodology are needed to determine spatial distribution of DNA hypomethylation and identify differentially methylated sites associated with risk of breast cancer. Prospero registration number CRD42020147244

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“…We calculated various methylation scores for smoking, alcohol consumption and BMI, the main lifestyle factors that cause changes to DNA methylation and increase the risk of cancer. 12-14 , 39-41 Several of these were associated with certain types of cancer and associations were only moderately attenuated after adjustment for sociodemographic, anthropometric and lifestyle factors, including several questionnaire-collected variables relating to smoking history (pack-years, age at starting, time since quitting). These associations were nevertheless generally relatively small, except for smoking scores and lung and urothelial cancer risk, for which we reported some of the findings previously.…”

Section: Discussionmentioning

confidence: 99%

Methylation scores for smoking, alcohol consumption, and body mass index and risk of seven types of cancer

Dugué

et al. 2021

Preprint

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Methylation marks of exposure to health risk factors may be useful markers of cancer risk as they might better capture current and past exposures than questionnaires, and reflect different individual responses to exposure. We used data from seven case-control studies nested within the Melbourne Collaborative Cohort Study of blood DNA methylation and risk of colorectal, gastric, kidney, lung, prostate and urothelial cancer, and B-cell lymphoma (N cases=3,123). Methylation scores (MS) for smoking, body mass index (BMI), and alcohol consumption were calculated based on published data as weighted averages of methylation values. Rate ratios (RR) and 95% confidence intervals for association with cancer risk were estimated using conditional logistic regression and expressed per standard deviation increase of the MS, with and without adjustment for health-related confounders. The contribution of MS to discriminate cases from controls was evaluated using the area under the curve (AUC). After confounder adjustment, we observed: large associations (RR~1.5-1.7) with lung cancer risk for smoking MS; moderate associations (RR~1.2-1.3) with urothelial cancer risk for smoking MS and with mature B-cell neoplasm risk for BMI and alcohol MS; moderate to small associations (RR~1.1-1.2) for BMI and alcohol MS with several cancer types and cancer overall. Generally small AUC increases were observed after inclusion of several MS in the same model (colorectal, gastric, kidney, urothelial cancers: +3%; lung cancer: +7%; B-cell neoplasms: +8%). Methylation scores for smoking, BMI, and alcohol consumption show independent associations with cancer risk, and may provide some improvements in risk prediction.

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Genome‐wide association study of peripheral blood DNA methylation and conventional mammographic density measures

Cited by 14 publications

References 26 publications

Going Beyond Conventional Mammographic Density to Discover Novel Mammogram-Based Predictors of Breast Cancer Risk

Going Beyond Conventional Mammographic Density to Discover Novel Mammogram-Based Predictors of Breast Cancer Risk

Epigenome-wide DNA methylation and risk of breast cancer: a systematic review

Methylation scores for smoking, alcohol consumption, and body mass index and risk of seven types of cancer

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