Integrating DNA methylation measures to improve clinical risk assessment: are we there yet? The case of BRCA1 methylation marks to improve clinical risk assessment of breast cancer

Wong, Ee Ming; Southey, Melissa C.; Terry, Mary Beth

doi:10.1038/s41416-019-0720-2

Cited by 22 publications

(14 citation statements)

References 78 publications

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“…Involvement of epigenetic alterations in the development of various diseases have been previously demonstrated and is being confirmed in increasingly larger-scale epigenetic studies ( Wong et al , 2020 ). It is also predicted that epigenetic alterations in a higher than currently anticipated number of cancer-predisposing genes might affect cancer risk ( Widschwendter et al , 2018 ).…”

Section: Resultsmentioning

confidence: 67%

ramr: an R/Bioconductor package for detection of rare aberrantly methylated regions

2021

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Motivation With recent advances in the field of epigenetics, the focus is widening from large and frequent disease- or phenotype-related methylation signatures to rare alterations transmitted mitotically or transgenerationally (constitutional epimutations). Merging evidence indicate that such constitutional alterations, albeit occurring at a low mosaic level, may confer risk of disease later in life. Given their inherently low incidence rate and mosaic nature, there is a need for bioinformatic tools specifically designed to analyse such events. Results We have developed a method (ramr) to identify aberrantly methylated DNA regions (AMRs). ramr can be applied to methylation data obtained by array or next-generation sequencing techniques to discover AMRs being associated with elevated risk of cancer as well as other diseases. We assessed accuracy and performance metrics of ramr and confirmed its applicability for analysis of large public data sets. Using ramr we identified aberrantly methylated regions that are known or may potentially be associated with development of colorectal cancer and provided functional annotation of AMRs that arise at early developmental stages. Availability The R package is freely available at https://github.com/BBCG/ramr and https://bioconductor.org/packages/ramr. Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Resultsmentioning

confidence: 67%

ramr: an R/Bioconductor package for detection of rare aberrantly methylated regions

2021

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“…Involvement of epigenetic alterations in the development of various diseases have been previously demonstrated and is being confirmed in increasingly larger-scale epigenetic studies (Wong et al, 2020). It is also predicted that epigenetic alterations in a higher than currently anticipated number of cancerpredisposing genes might affect cancer risk (Widschwendter et al, 2018).…”

Section: Resultsmentioning

confidence: 78%

ramr: an R package for detection of rare aberrantly methylated regions

Nikolaienko

Lønning

Knappskog

2020

Preprint

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Motivation: With recent advances in the field of epigenetics, the focus is widening from large and frequent disease-or phenotype-related methylation signatures to rare alterations transmitted mitotically or transgenerationally (constitutional epimutations). Merging evidence indicate that such constitutional alterations, albeit occurring at a low mosaic level, may confer risk of disease later in life. Given their inherently low incidence rate and mosaic nature, there is a need for bioinformatic tools specifically designed to analyse such events. Results: We have developed a method (ramr) to identify aberrantly methylated DNA regions (AMRs). ramr can be applied to methylation data obtained by array or next-generation sequencing techniques to discover AMRs being associated with elevated risk of cancer as well as other diseases. We assessed accuracy and performance metrics of ramr and confirmed its applicability for analysis of large public data sets. Using ramr we identified aberrantly methylated regions that are known or may potentially be associated with development of colorectal cancer and provided functional annotation of AMRs that arise at early developmental stages. Availability and implementation: The R package is freely available at https://github.com/BBCG/ramr

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“…A recent comparison of several of the most commonly used clinical models (i.e., Gail, BRCAPRO, BCSC, Claus, IBIS) in a large, predominantly White US screening population indicated that the models were generally well-calibrated (O/E range: 0.78-0.97) but with only moderate discrimination (AUC range: 0.61-0.64) [304]. Expansion of these models to include multiple biomarkers (e.g., mammographic density and/or other imaging features, polygenic risk scores, endogenous hormones, epigenetics, metabolomics), and the development and validation of models across race/ethnicity and by tumor subtype is ongoing, and likely to lead to model improvement [305][306][307][308][309][310][311][312][313][314][315][316].…”

Section: Risk Prediction Modelsmentioning

confidence: 99%

Cancer Progress and Priorities: Breast Cancer

Houghton

Hankinson

2021

Cancer Epidemiology, Biomarkers &Amp; Prevention

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DCIS) is more common than lobular carcinoma in situ (LCIS), and while both are considered risk factors for invasive breast cancer, LCIS is not considered to be a lesion capable of becoming malignant [19,20]. However, the etiology and natural history of in situ tumors is not well known.Invasive breast cancer is classified by histology, to guide clinical treatment, into invasive ductal (70-80% of breast cancer), invasive lobular (5-15% of breast cancer), and other less common types such as papillary tumors [17,19]. Immunohistochemical (IHC) staining for the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) guide the use of targeted therapies. The use of other tests such as gene array profiling or other IHC markers are not as commonly used clinically [17,[21][22][23]. Several main intrinsic molecular subtypes have been identified using gene micro-arrays: Luminal A, Luminal B, Basal Like, and Normal Like [17,[24][25][26]. Further classifications of primarily triple negative tumors have identified the Claudin-low subtype and six triple-negative molecular subtypes [27,28]. Several gene panels (e.g., PAM50) have been developed as less expensive options with a similar ability to classify tumors into molecular subtypes [17,29,30]. In 2013, the St. Gallen consensus agreed on surrogate definitions of the intrinsic subtypes that could be approximated by IHC staining of ER, PR, and HER2 as well as grade and proliferation [31] (Table 1), though several studies have noted that the agreement may be low [18,32]. Disparities in the United StatesPrior to age 40, US Black women have the highest breast cancer incidence rates, after which rates are highest among White women (Figure 2). American Indian/ Alaska Native women have the lowest rates until age 74. Asian/ Pacific Islander women have similar incidence rates as White and Black women until age 45, after which they have the lowest incidence rates. Black women have the highest mortality rates at all ages followed by White women (e.g., 68.2 per 100,000 in Blacks vs 46.5 per 100,000 in Whites at age 60-64). Mortality rates for American Indian/ Alaska Native and Asian/ Pacific Islander women are similar until about age 60, at which point Asian/ Pacific Islander women have the lowest mortality rates (e.g., 30.0 per 100,000 in Asian/ Pacific Islander vs 33.2 per 100,000 in American Indian/ Alaska Native at age 60-64).While breast cancer incidence rates have either declined or remained stable since the early 2000s among White women, incidence rates among Black women have continued to increase (Figure 3) [33]. In 2016, the age-standardized incidence rate was 128.2 per 100,000 among Black women versus 132.7 per 100,000 among White women [4]. While incidence rates are lower, Black women experience higher breast cancer mortality than White women; further, that gap has continued to widen even as survival has increased overall (Figure 3) [33]. The 2016 age-standardized mortality rate was 27.3 per 100,000 Black women versus 19.6 per 100,00...

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Integrating DNA methylation measures to improve clinical risk assessment: are we there yet? The case of BRCA1 methylation marks to improve clinical risk assessment of breast cancer

Cited by 22 publications

References 78 publications

ramr: an R/Bioconductor package for detection of rare aberrantly methylated regions

ramr: an R/Bioconductor package for detection of rare aberrantly methylated regions

ramr: an R package for detection of rare aberrantly methylated regions

Cancer Progress and Priorities: Breast Cancer

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