Individual and joint performance of DNA methylation profiles, genetic risk score and environmental risk scores for predicting breast cancer risk

PurposeTo examine whether the associations of endogenous estrogens and testosterone with breast cancer risk differ between high- and low-risk women, as determined by the Gail model and the Rosner and Colditz model, and by family history of breast cancer.MethodsWe conducted a prospective nested case-control study within the Nurses' Health Study. From 1989 or 1990 until June 2000, blood samples were collected, 418 breast cancer patient cases were identified, and two controls (total n = 817) were matched to each case. We classified women as high or low risk based on their family history of breast cancer, their 5-year Gail risk score, and their 5-year Rosner and Colditz risk score. Multivariate relative risks (RR) and 95% CI were calculated by unconditional logistic regression, adjusting for matching and breast cancer risk factors.ResultsEstrone sulfate was statistically significantly associated with breast cancer risk among women with low (< 1.66%) and high (≥ 2.52%; 75th percentile) Gail predicted risk (fourth v first quartile RR = 3.6; 95% CI, 1.9 to 7.0; RR = 2.5; 95% CI, 1.2 to 5.1, respectively). Testosterone results were similar across strata of predicted risk, with two times the risk in the fourth (v first) quartile. Estradiol appeared more strongly associated with breast cancer in women with higher predicted risk (RR = 4.5; 95% CI, 2.1 to 9.5) compared with women with lower risk (RR = 2.1; 95% CI, 1.2 to 3.6), but differences were not statistically significant. Results were similar across predicted Rosner and Colditz risk scores.ConclusionThese data suggest that higher levels of endogenous estrogens and testosterone are associated with increased breast cancer risk, regardless of predicted risk or family history of breast cancer.

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Do Breast Cancer Risk Scores Work for You?

JNCI Journal of the National Cancer Institute ◽

10.1093/jnci/djab052 ◽

2021 ◽

Author(s):

Kathleen E Houlahan

Keyword(s):

Breast Cancer ◽

Breast Cancer Risk ◽

Cancer Risk ◽

Risk Scores

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Identifying individuals with high risk of Alzheimer’s disease using polygenic risk scores

Nature Communications ◽

10.1038/s41467-021-24082-z ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Ganna Leonenko ◽

Emily Baker ◽

Joshua Stevenson-Hoare ◽

Annerieke Sierksma ◽

Mark Fiers ◽

...

Keyword(s):

High Risk ◽

Risk Score ◽

Genetic Risk ◽

Prediction Accuracy ◽

Genetic Risk Score ◽

Low Risk ◽

Risk Scores ◽

Sample Mean ◽

Polygenic Risk ◽

Reliable Identification

AbstractPolygenic Risk Scores (PRS) for AD offer unique possibilities for reliable identification of individuals at high and low risk of AD. However, there is little agreement in the field as to what approach should be used for genetic risk score calculations, how to model the effect of APOE, what the optimal p-value threshold (pT) for SNP selection is and how to compare scores between studies and methods. We show that the best prediction accuracy is achieved with a model with two predictors (APOE and PRS excluding APOE region) with pT<0.1 for SNP selection. Prediction accuracy in a sample across different PRS approaches is similar, but individuals’ scores and their associated ranking differ. We show that standardising PRS against the population mean, as opposed to the sample mean, makes the individuals’ scores comparable between studies. Our work highlights the best strategies for polygenic profiling when assessing individuals for AD risk.

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DNA Methylation in Benign Breast Epithelium in Relation to Age and Breast Cancer Risk

Cancer Epidemiology Biomarkers & Prevention ◽

10.1158/1055-9965.epi-07-2582 ◽

2008 ◽

Vol 17 (5) ◽

pp. 1051-1059 ◽

Cited By ~ 56

Author(s):

David M. Euhus ◽

Dawei Bu ◽

Sara Milchgrub ◽

Xian-Jin Xie ◽

Aihua Bian ◽

...

Keyword(s):

Breast Cancer ◽

Dna Methylation ◽

Breast Cancer Risk ◽

Cancer Risk ◽

Breast Epithelium

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A streamlined model for use in clinical breast cancer risk assessment maintains predictive power and is further improved with inclusion of a polygenic risk score

PLoS ONE ◽

10.1371/journal.pone.0245375 ◽

2021 ◽

Vol 16 (1) ◽

pp. e0245375

Author(s):

Richard Allman ◽

Erika Spaeth ◽

John Lai ◽

Susan J. Gross ◽

John L. Hopper

Keyword(s):

Breast Cancer ◽

Risk Factors ◽

African American ◽

Breast Cancer Risk ◽

Cancer Risk ◽

Risk Score ◽

Clinical Risk Factors ◽

Polygenic Risk Score ◽

Gail Model ◽

Clinical Risk

Five-year absolute breast cancer risk prediction models are required to comply with national guidelines regarding risk reduction regimens. Models including the Gail model are under-utilized in the general population for various reasons, including difficulty in accurately completing some clinical fields. The purpose of this study was to determine if a streamlined risk model could be designed without substantial loss in performance. Only the clinical risk factors that were easily answered by women will be retained and combined with an objective validated polygenic risk score (PRS) to ultimately improve overall compliance with professional recommendations. We first undertook a review of a series of 2,339 Caucasian, African American and Hispanic women from the USA who underwent clinical testing. We first used deidentified test request forms to identify the clinical risk factors that were best answered by women in a clinical setting and then compared the 5-year risks for the full model and the streamlined model in this clinical series. We used OPERA analysis on previously published case-control data from 11,924 Gail model samples to determine clinical risk factors to include in a streamlined model: first degree family history and age that could then be combined with the PRS. Next, to ensure that the addition of PRS to the streamlined model was indeed beneficial, we compared risk stratification using the Streamlined model with and without PRS for the existing case-control datasets comprising 1,313 cases and 10,611 controls of African-American (n = 7421), Caucasian (n = 1155) and Hispanic (n = 3348) women, using the area under the curve to determine model performance. The improvement in risk discrimination from adding the PRS risk score to the Streamlined model was 52%, 46% and 62% for African-American, Caucasian and Hispanic women, respectively, based on changes in log OPERA. There was no statistically significant difference in mean risk scores between the Gail model plus risk PRS compared to the Streamlined model plus PRS. This study demonstrates that validated PRS can be used to streamline a clinical test for primary care practice without diminishing test performance. Importantly, by eliminating risk factors that women find hard to recall or that require obtaining medical records, this model may facilitate increased clinical adoption of 5-year risk breast cancer risk prediction test in keeping with national standards and guidelines for breast cancer risk reduction.

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Study the Effect of the Risk Factors in the Estimation of the Breast Cancer Risk Score Using Machine Learning

Asian Pacific Journal of Cancer Prevention ◽

10.31557/apjcp.2021.22.11.3543 ◽

2021 ◽

Vol 22 (11) ◽

pp. 3543-3551

Author(s):

Sam Khozama ◽

Ali Mayya

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Risk Factors ◽

Breast Cancer Risk ◽

Cancer Risk ◽

Risk Score

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Progression from islet autoimmunity to clinical type 1 diabetes is influenced by genetic factors: results from the prospective TEDDY study

Journal of Medical Genetics ◽

10.1136/jmedgenet-2018-105532 ◽

2018 ◽

Vol 56 (9) ◽

pp. 602-605 ◽

Cited By ~ 4

Author(s):

Andreas Beyerlein ◽

Ezio Bonifacio ◽

Kendra Vehik ◽

Markus Hippich ◽

Christiane Winkler ◽

...

Keyword(s):

Type 1 Diabetes ◽

Risk Score ◽

Genetic Risk ◽

Genetic Factors ◽

Genetic Risk Score ◽

Risk Scores ◽

Islet Autoimmunity ◽

Islet Autoantibody ◽

Clinical Type

BackgroundProgression time from islet autoimmunity to clinical type 1 diabetes is highly variable and the extent that genetic factors contribute is unknown.MethodsIn 341 islet autoantibody-positive children with the human leucocyte antigen (HLA) DR3/DR4-DQ8 or the HLA DR4-DQ8/DR4-DQ8 genotype from the prospective TEDDY (The Environmental Determinants of Diabetes in the Young) study, we investigated whether a genetic risk score that had previously been shown to predict islet autoimmunity is also associated with disease progression.ResultsIslet autoantibody-positive children with a genetic risk score in the lowest quartile had a slower progression from single to multiple autoantibodies (p=0.018), from single autoantibodies to diabetes (p=0.004), and by trend from multiple islet autoantibodies to diabetes (p=0.06). In a Cox proportional hazards analysis, faster progression was associated with an increased genetic risk score independently of HLA genotype (HR for progression from multiple autoantibodies to type 1 diabetes, 1.27, 95% CI 1.02 to 1.58 per unit increase), an earlier age of islet autoantibody development (HR, 0.68, 95% CI 0.58 to 0.81 per year increase in age) and female sex (HR, 1.94, 95% CI 1.28 to 2.93).ConclusionsGenetic risk scores may be used to identify islet autoantibody-positive children with high-risk HLA genotypes who have a slow rate of progression to subsequent stages of autoimmunity and type 1 diabetes.

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