10-year performance of four models of breast cancer risk: a validation study

Terry, Mary Beth; Liao, Yuyan; Whittemore, Alice S.; Leoce, Nicole; Buchsbaum, Richard; Zeinomar, Nur; Dite, Gillian S.; Chung, Wendy K.; Knight, Julia A.; Southey, Melissa C.; Milne, Roger L.; Goldgar, David E.; Giles, Graham G.; McLachlan, Sue Anne; Friedlander, Michael; Weideman, Prue; Glendon, Gord; Nesci, Stephanie; Andrulis, Irene L.; John, Esther M.; Phillips, Kelly Anne; Daly, Mary B.; Buys, Saundra S.; Hopper, John L.; MacInnis, Robert J.

doi:10.1016/s1470-2045(18)30902-1

Cited by 136 publications

(127 citation statements)

References 35 publications

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“…This article focused on the ability of PRSs to add to the Tyrer–Cuzick model and mammographic density. The ability of the latter has been assessed in several studies, including PROCAS . An informative way to compare predictive ability of the domains is to consider the proportion of women who are (accurately) determined to be in high‐risk groups, where more intensive surveillance or prevention measures might be cost‐effective.…”

Section: Discussionmentioning

confidence: 99%

“…The ability of the latter has been assessed in several studies, including PROCAS. 10,11,13,19,[29][30][31] An informative way to compare predictive ability of the domains is to consider the proportion of women who are (accurately) determined to be in high-risk groups, where more intensive surveillance or prevention measures might be cost-effective. Table 3 shows that in this sample the number of high-risk women (controls) is approximately doubled by including mammographic density, and approximately tripled by including SNPs, as earlier predicted.…”

Section: Discussionmentioning

confidence: 99%

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A case–control evaluation of 143 single nucleotide polymorphisms for breast cancer risk stratification with classical factors and mammographic density

Brentnall

Veen

Rafiq

et al. 2019

Intl Journal of Cancer

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Panels of single nucleotide polymorphisms (SNPs) stratify risk for breast cancer in women from the general population, but studies are needed assess their use in a fully comprehensive model including classical risk factors, mammographic density and more than 100 SNPs associated with breast cancer. A case–control study was designed (1,668 controls, 405 cases) in women aged 47–73 years attending routine screening in Manchester UK, and enrolled in a wider study to assess methods for risk assessment. Risk from classical questionnaire risk factors was assessed using the Tyrer–Cuzick model; mean percentage visual mammographic density was scored by two independent readers. DNA extracted from saliva was genotyped at selected SNPs using the OncoArray. A predefined polygenic risk score based on 143 SNPs was calculated (SNP143). The odds ratio (OR, and 95% confidence interval, CI) per interquartile range (IQ‐OR) of SNP143 was estimated unadjusted and adjusted for Tyrer–Cuzick and breast density. Secondary analysis assessed risk by oestrogen receptor (ER) status. The primary polygenic risk score was well calibrated (O/E OR 1.10, 95% CI 0.86–1.34) and accuracy was retained after adjustment for Tyrer–Cuzick risk and mammographic density (IQ‐OR unadjusted 2.12, 95% CI% 1.75–2.42; adjusted 2.06, 95% CI 1.75–2.42). SNP143 was a risk factor for ER+ and ER− breast cancer (adjusted IQ‐OR, ER+ 2.11, 95% CI 1.78–2.51; ER− 1.81, 95% CI 1.16–2.84). In conclusion, polygenic risk scores based on a large number of SNPs improve risk stratification in combination with classical risk factors and mammographic density, and SNP143 was similarly predictive for ER‐positive and ER‐negative disease.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A case–control evaluation of 143 single nucleotide polymorphisms for breast cancer risk stratification with classical factors and mammographic density

Brentnall

Veen

Rafiq

et al. 2019

Intl Journal of Cancer

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“…Prospective, independent validation of multiple breast cancer risk models also provides perspective on how sensitive model performance estimates are to different assumptions guiding each risk model. For example, comparison of models that have very different model inclusion and modeling assumptions regarding the use of cancer family history, non-genetic data, and genetic data can provide valuable evidence on the quantitative impact of different model assumptions on the predicted absolute risk [3]. For example, comparing models that consider only first-degree family history data with other pedigree models can help evaluate the added value of second-degree family history.…”

Section: Comparison Of Multiple Modelsmentioning

confidence: 99%

Translate but validate: necessary steps in improving the use and utility of cancer risk models

Terry

2020

Cancer Causes Control

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“…BACKGROUND Current cancer risk prediction models are largely grouped based on the extent of family history and genetic data in addition to information relating to non-genetic risk factors such as lifestyle and the environment. Pedigree-based models, which are often used for genetic counselling and risk assessment and for making decisions about chemoprevention and risk-reducing surgeries, 1 have been improved through the integration of genetic information about high and intermediate penetrant cancer genes. [2][3][4] Three such breast cancer models-BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm model), IBIS (the International Breast cancer Intervention Study model) and BRCAPRO-have the ability to predict the probability of carrying a pathogenic breast cancer susceptibility variant in BRCA1/2 as well as the absolute risk of developing breast cancer, with higher discrimination for the former than the latter.…”

mentioning

confidence: 99%

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

2020

Self Cite

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Current risk prediction models estimate the probability of developing breast cancer over a defined period based on information such as family history, non-genetic breast cancer risk factors, genetic information from high and moderate risk breast cancer susceptibility genes and, over the past several years, polygenic risk scores (PRS) from more than 300 common variants. The inclusion of additional data such as PRS improves risk stratification, but it is anticipated that the inclusion of epigenetic marks could further improve model performance accuracy. Here, we present the case for including information on DNA methylation marks to improve the accuracy of these risk prediction models, and consider how this approach contrasts genetic information, as identifying DNA methylation marks associated with breast cancer risk differs inherently according to the source of DNA, approaches to the measurement of DNA methylation, and the timing of measurement. We highlight several DNA-methylation-specific challenges that should be considered when incorporating information on DNA methylation marks into risk prediction models, using BRCA1, a highly penetrant breast cancer susceptibility gene, as an example. Only after careful consideration of study design and DNA methylation measurement will prospective performance of the incorporation of information regarding DNA methylation marks into risk prediction models be valid.

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10-year performance of four models of breast cancer risk: a validation study

Cited by 136 publications

References 35 publications

A case–control evaluation of 143 single nucleotide polymorphisms for breast cancer risk stratification with classical factors and mammographic density

A case–control evaluation of 143 single nucleotide polymorphisms for breast cancer risk stratification with classical factors and mammographic density

Translate but validate: necessary steps in improving the use and utility of cancer risk models

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

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