Breast Cancer Susceptibility—Towards Individualised Risk Prediction

  • Inge M. M. Lakeman
  • Marjanka K. Schmidt
  • Christi J. van Asperen
  • Peter DevileeEmail author
Open Access
Cancer Genomics (K Snape and H Hanson, Section Editors)
Part of the following topical collections:
  1. Topical Collection on Cancer Genomics


Purpose of Review

Breast cancer is the most common cancer among females in developed countries. Strategies such as early detection by breast cancer screening can reduce the burden of disease but have disadvantages including overdiagnosis and increased cost. Stratification of women according to the risk of developing breast cancer, based on genetic and lifestyle risk factors, could improve risk-reduction and screening strategies by targeting those most likely to benefit.

Recent Findings

Breast cancer risk is partly determined by genetic factors including rare pathogenic variants in susceptibility genes and common low-risk variants. Other risk factors include alcohol use, smoking, reproductive factors, hormonal factors, family history, mammographic density, BMI, and body height. Ideally, all risk factors are combined into an individual breast cancer lifetime risk score, but this requires knowledge about their interactions as well as accurate effect sizes. A few risk models seem to be sufficiently developed to inform clinical risk management to minimise cancer risk of those at increased risk and avoid overtreatment of those at decreased risk.


In this review, we briefly summarise the breast cancer susceptibility factors and discuss avenues towards combining all these factors to create individual risk scores.


Breast cancer Gene panel Polygenic risk score Individualised breast cancer risk prediction 


Breast cancer is the most common cancer among women in Europe, with approximately 523,000 cases diagnosed annually [1] and remains a leading cause of death among adult women. Primary prevention of breast cancer by endocrine therapy has side effects and is not absolute, whereas prophylactic surgery is very effective [2] but socially and emotionally burdensome. Secondary prevention by early detection through mammographic screening can reduce mortality but at the cost of overdiagnosis and the burden of false-positive results [3, 4]. Stratification of women according to the risk of developing breast cancer could provide a persuasive rationale for surgical intervention as well as improve the efficacy of risk-reduction and screening strategies by tailoring starting age and frequency [5, 6•].

Box 1 Definition of breast cancer risk

Clinically, definitions such as low, moderate, and high breast cancer risk are often used. However, this can reflect relative or absolute risks. For a given relative risk (RR), absolute risk can vary between countries depending on cancer incidences. Another term often used is lifetime risk, which is the absolute risk of breast cancer over the period of a woman’s life. Here, we define moderate risk as RR = 2 to 4, high risk as RR > 4, and low or population risk as RR < 2.

To accurately assess a woman’s risk, it is important to take all risk factors into account. Having a positive family history is one of the main risk factors for breast cancer. For women with a first-degree relative with breast cancer, the risk for developing breast cancer is twofold compared with women without such a family history [7]. Approximately 25% of this familial relative risk (FRR) is explained by (likely) pathogenic variants in a small number of genes, and a further 18% by the currently known common low-risk variants, mostly single nucleotide polymorphisms (SNPs) [8, 9, 10•, 11]. Besides the familial relative risk, other risk factors such as mammographic density and lifestyle factors are also important. In this review, we briefly summarise the breast cancer susceptibility factors, and then discuss avenues towards combining all these factors to create individual risk scores, and towards the identification of factors capable of explaining the remainder of familial relative risk.

Rare Genetic Variation Associated with Breast Cancer

The definition of ‘rare’ variation is somewhat arbitrary but is generally taken as to occur in < 0.5% of the general population. Indeed, we currently know that some variants associated with risk to breast cancer are extremely rare (< 0.001%), others moderately rare (~ 0.1%), or even almost ‘common’ (~ 1%). In addition, the risks conferred by these variants may vary from less than twofold to over tenfold. Classic linkage analysis in multiple-case families discovered some of the genes, but many were discovered by DNA sequencing of candidate genes. The best-known examples of linkage-detected genes are BRCA1 and BRCA2 [12, 13]. Pathogenic variants in either gene, each with a joint allele frequency of ~ 0.1%, will lead to a high risk of breast and ovarian cancer [14, 15]. Other genes, particularly TP53, PTEN, STK11, CDH1, and NF1, were discovered because of their association with typical familial cancer syndromes of which breast cancer is one feature [16, 17, 18, 19, 20]. Accordingly, their prevalence in the population is extremely rare. These findings also underscore the pleiotropic effects that some DNA variations display by predisposing to cancers of diverse tissue origin. Yet for most breast cancer genes discovered so far, the most conspicuous ‘other’ cancer with which an association has been firmly established is ovarian cancer. Another ‘syndromic’ gene is ATM; pathogenic variants in ATM act in a recessive way to cause Ataxia telangiectasia, a neurodegenerative disorder, but heterozygous carriers are at moderately increased risk for breast cancer [21]. The discovery that BRCA1, BRCA2, and ATM are involved in DNA damage repair, and that BRCA2 is a Fanconi anaemia gene [22], suggested that other DNA repair genes might also confer breast cancer susceptibility. Sequence analysis of these candidates then led to the discovery of CHEK2, BARD1, PALB2, NBN, and RAD51D [23, 24, 25, 26, 27] as breast cancer genes, although evidence is sometimes limited to specific variants in populations of specific ethnic background [26]. Breast cancer risks in these five genes are generally moderate, with the exception of loss-of-function variants in PALB2, which can lead to breast cancer risks comparable to BRCA2 [26, 28].

There is a long list of genes, including BRIP1, FANCC, FANCM, MEN1, MRE11A, PPM1D, RAD50, RAD51B, RAD51C, RECQL, and XRCC2, for which an association with breast cancer has been reported in a few studies, but for which replication in sufficiently large samples of cases and controls and establishment of effect sizes are still lacking. In fact, BARD1 and RAD51D were only recently confirmed in such analyses as moderate-risk genes [29•]. Finally, a long-standing issue is whether the Lynch syndrome genes (MLH1, MSH2, MSH6, and PMS2) and MUTYH are associated with breast cancer risk. Interpretation of breast cancer incidence in studies of Lynch syndrome families is complicated due to various biases (e.g., ascertainment). The issue remains controversial to date, even though a recent study again found an association between pathogenic variants in MSH6 and breast cancer risk [30]. More detailed discussions on the association of gene variants and breast cancer and the corresponding risks can be found in reviews by Wendt et al., Easton et al., and Graffeo et al. [26, 27, 31].

Box 2 Classification of gene variants

The ACMG has recommended a five-tier classification system, which has been adopted by many countries [32]. These classes are (1) Benign, (2) Likely Benign, (3) VUS, (4) Likely Pathogenic, (5) Pathogenic. For VUS, the pathogenicity and hence the association with disease risk are unknown, usually because they result in a similarly shaped amino acid or reside in a part of the gene not essential for its function.

Challenges in Risk Assessment and Clinical Translation

Once a gene has been repeatedly associated with breast cancer, other challenges arise that may hamper introduction into the clinic. One is allelic diversity and the notion that different types of variants (e.g., nonsense versus missense changes) might confer different breast cancer risks [26]. For BRCA1 and BRCA2, the effect of mutation-position on the relative risks for breast and ovarian cancer has been firmly established [33]. Furthermore, several missense changes have been identified in BRCA1 and BRCA2 that cause much more moderate risks than the typical loss-of-function variants [34•, 35]. Conversely, while most pathogenic variants in ATM will give an intermediate breast cancer risk, one specific missense mutation (c.7271C>G) seems to reach a level of risk approaching that of BRCA1/2 pathogenic variants [36, 37]. The presence of allelic diversity in breast cancer genes also highlights the difficulties we are still having with establishing pathogenicity for each variant. This seems straightforward for protein-truncating variants (although exceptions exist [38]), but for many missense and ‘spliceogenic’ variants the impact on protein function (and, by inference, on cancer risk) is hard to predict. The many in silico tools available for this purpose still perform poorly with respect to clinical standards, and for virtually all genes listed above, well-calibrated high-throughput functional analyses in model systems are lacking [39]. As a result, many variants detected by sequencing in these genes are still classified as Variants of Uncertain Significance (VUS).

Another challenge is to establish the penetrance of pathogenic variants and the corresponding breast cancer risks with sufficient accuracy. With some exceptions, there is still much uncertainty surrounding the magnitude and precision of the risks conferred by pathogenic variants in the genes. One problem underlying this issue is ascertainment bias in the sample used in the analyses. Patient series consisting mostly of women with a positive family history are almost certainly overestimating risk due to the enrichment of other risk factors. This is especially true for tumour syndrome genes, investigation of which is usually triggered by the syndrome criteria. For example, the penetrance of TP53 variants was initially estimated to be very high [40]. But with the introduction of gene panel sequencing, pathogenic variants in TP53 were also reported in families who do not fulfil the classical criteria of Li-Fraumeni Syndrome [41]. These families show older ages of onset of breast cancer [42], suggesting lower penetrance of at least some TP53 pathogenic variants. This is consistent with recent estimates of the prevalence of pathogenic germline TP53 variants in the general population [43], which are also much higher than expected on the basis of the prevalence of Li-Fraumeni Syndrome alone. The other problem is the rarity of variants, which necessitates the analysis of very large case-control series in order to sufficiently narrow down confidence intervals of risk estimates. For this reason, we have reasonably good breast cancer risk estimates for the 1100delC variant in CHEK2, which occurs in ~ 0.5% of the general population in Europe [44, 45] and the USA [29•, 45], but not for most other, much rarer variation in this gene. To establish an odds ratio of 2 with a 95% confidence interval of 1.4–2.8, conferred by a variant with an allele frequency of 0.01%, it would require genotyping 100,000 cases and 100,000 controls. Larger numbers are needed for lower risks and lower allele frequencies.

Gene Panel Studies—Non-BRCA1/2 Genes

Gene panel sequencing (GPS) has become a diagnostic reality in cancer genetics. Due to the lower costs and improving data quality, it became possible to test multiple genes in addition to BRCA1 and BRCA2 in a single assay, driven by a desire to explain familial clustering of breast cancer in more families and thus impact clinical management. As explained above, the frequency of pathogenic variants found in clinic-based series of familial cases is dependent on the selection criteria of the families included. The highest frequencies, up to 10%, of pathogenic variants are still found in the BRCA1 and BRCA2 genes in familial breast cancer cases [46, 47, 48]. Pathogenic variants in non-BRCA1/2 genes are found in 3.7–6.2% of the cases [29•, 46, 47, 48, 49, 50]. The highest frequencies of pathogenic variants in non-BRCA1/2 genes are found in CHEK2, ATM and PALB2 [29•]. However, this increased diagnostic yield comes at the expense of a large proportion of detected VUS, which poses a significant clinical problem. Gene panel studies have found a VUS in 13.6–41.6% of the cases [46, 48, 49, 51]. This means that for every pathogenic variant found in a case, two to three cases with VUS are detected. Furthermore, gene panels contain many genes for which the relevance to breast cancer is unknown or uncertain, as outlined above. Due to these uncertainties, most of the test results of commercial gene panels do not translate well into cancer risk assessment. Even the relatively well-defined cancer risks conferred by BRCA1 and BRCA2 are influenced by mutation position and mutation class, as well as by non-genetic exposures and lifestyle factors [35, 52, 53]. Therefore, the gain in clinical utility of testing genes for which evidence of their association with breast cancer is still ill-defined remains limited [26, 54].

SNPs and Polygenic Risk Scores

Since 2005, genome-wide association studies, using SNP arrays and very large case-control samples, enabled the identification of common low-risk variants for breast cancer [11]. Collaborative groups, such as the Breast Cancer Association Consortium (BCAC), have currently identified ~ 180 SNPs as significantly associated with breast cancer [10•]. The first substantial batch of SNPs was found by the Collaborative Oncologic Gene-environment Study (COGS) in 2013, coordinated by BCAC, which was subsequently confirmed and extended by combining with other GWAS data [55]. Another 65 loci were detected after the introduction of the OncoArray, a SNP array with a much denser SNP coverage than COGS [10•]. Some of the associated SNPs are more strongly associated with Estrogen Receptor (ER)-negative or ER-positive subtypes of breast cancer [10•, 56•]. The currently known SNPs explain 18% of the familial relative risk for breast cancer, but a much greater proportion (~ 40%) can be explained when variants that can be reliably imputed from the OncoArray data are included [10•, 57•]. To validate these latter SNPs, very large case-control studies are needed to reach genome-wide significance levels of association because many of these are expected to be relatively rare (< 5%) and/or of very small effect sizes.

The breast cancer–associated SNP alleles are distributed normally throughout the general population. This means that, in contrast to pathogenic variants in breast cancer susceptibility genes, all individuals in the population carry a certain number of risk alleles, with most individuals carrying the average number. Individually, these risk alleles confer a very small increase in breast cancer risk but their joint effect may be a substantially higher [8]. In the absence of evidence of clear interactions between SNPs [8, 58], a simple log-additive (or multiplicative) model combines all SNPs into a single Polygenic Risk Score (PRS).

Many different PRSs for breast cancer have been published in recent years (Table 1). Most studies have generated PRSs for overall unilateral breast cancer, a few have addressed ER status-specific PRS-models with the use of subtype-specific odds ratios of certain SNPs. Subtype-specific PRSs can potentially be useful to guide clinical management for chemoprevention and other prevention strategies. Two studies [74, 75] have used a PRS to predict contralateral breast cancer, and two have studied the PRS as risk modifier in rare gene mutation carriers (BRCA1, BRCA2, and CHEK2) [72•, 73•]. The number of SNPs, their allele frequencies, and effect sizes determine the discriminatory and predictive power of a PRS. Predictive power of a PRS is usually expressed as odds ratio (OR) per standard deviation unit of the distribution; discriminatory power is assessed by the area under the curve (AUC). The number of SNPs included in a PRS is not strongly correlated with the overall effect size or the AUC. This is because the SNPs detected in the earliest studies, although smaller in number, generally have higher effect sizes than those detected more recently in studies with more statistical power. Including large numbers of SNPs at lower than genome-wide significance thresholds may increase predictive power of the PRS but at the expense of being less specific [57•].
Table 1

Effect size and AUC of Polygenic Risk Scores






Effect size




Overall BC

ER-positive BC

ER-negative BC



McCarthy et al. 2015 [59]

Prospective cohort; women referred for breast biopsy




Per SD

OR = 2.30[1.06–4.99] P = 0.035


0.685 [0.620–0.750] (including age)

Dite et al. 2015 [60]

Population based case-control; women < 50 yr




Per SD, adjusted for age group

OR = 1.46 [1.29–1.64], p = 2E–16


0.61 [0.58–0.65]

Mavaddat et al. 2015 [8]

Case-control study




Per SD

OR = 1.55 [1.52–1.58]

OR = 1.63[1.60–1.67]

OR = 1.45 [1.40–1.49]

0.622 [0.619–0.627]

Naslund-koch et al. 2016 [61]

Danish population study




5th in comparison to 1st quintile

HR = 1.82[1.53–2.18]



Li et al. 2016 [62]

Prospective cohort; breast cancer families




Per SD

HR = 1.38[1.22–1.56] P = 2.9E-7


0.59 [0.55–0.63]

Shieh et al. 2016 [63]

Nested case-control study; mammography screening cohort




4th quartile in comparison to 1st quartile

OR = 2.54[1.69–3.82], P < 0.001


0.60 [0.57–0.64]

Muranen et al. 2016 [64]

Case-control study




Per SD

OR = 1.56 [1.45–1.68] P = 9.2E-31



Muranen et al. 2016 [64]

Breast cancer families




Per SD

OR = 1.82 [1.55–2.13] P = 1.8E-13



Maas et al. 2016 [65]

Prospective cohort; nested case-control study



24; 92

10th decile in comparison to 1th decile

OR = 2.79 (24 SNPs)


0.623 [92 SNPs]

Cuzick et al. 2017 [66]

Prospective nested case-control study




Interquartile range

OR = 1.37 [1.16–1.79]; p valuethinsp;< 0.001

OR = 1.44 [1.16–1.79] P < 0.001

OR = 0.99 [0.61–1.61] P = 0.10

0.55 [0.51–0.60]

Shieh et al. 2017 [67]

Nested case-control study; mammography screening cohort

110 (ER-positive)



Per SD


OR = 1.58 [1.06–2.36] P = 0.02


0.68 [0.61–0.75] P = 0.07

Evans et al. 2017 [68]

Case-control study; women attending a familial risk clinic

364 (112 BRCA1/2+)

1605 (691 BRCA1/2+)


Interquartile range

Non-carriers: OR = 1.55 [1.29–1.87]


Non-carriers: 0.59 [0.55–0.63]

van Veen et al. 2018 [69]

Prospective cohort study




Interquartile range

OR = 1.56 [1.38–1.77]



Zhang et al. 2018 [70]

Nested case-control study




4th quartile in comparison to 1st quartile

RR = 2.5 [2.2–2.8]



Khera et al. 2018 [71]

Case-control study; UK biobank participants




5th quintile in comparison with remainder

OR = 2.07 [1.97–2.19], P = 3.4E–159


0.69 [0.68–0.69], including age

Mavaddat et al. 2019 [57•]

Prospective case-control studies




Per SD

OR = 1.61[1.57–1.65]

OR = 1.45 [1.37–1.53]

OR = 1.35 [1.27–1.43]

0.630 [0.628–0.651]

Mavaddat et al. 2019 [57•]

Case-control study; UK biobank




Per SD

HR = 1.59 [1.54–1.64]




Muranen et al. 2017 [72•]

CHEK2*1100delC carriers

39,139 (624 carriers)

40,063 (224 carriers)


Per SD

Carriers: OR = 1.59 [1.21–2.09], P = 0.0008; Non-carriers: OR = 1.58 [1.55–1.62], P < 1.0E-10



Kuchenbaecker et al. 2017 [73•]

BRCA1 mutation carriers




Per SD

HR = 1.14 [1.11–1.17], P = 1.8E–18

HR = 1.11 [1.08–1.15], P = 3.5E–13

HR = 1.27 [1.23–1.31], P = 8.2E–53

Overall BC: 0.541 [0.530–0.551]

Kuchenbaecker et al. 2017 [73•]

BRCA2 mutation carriers




Per SD

HR = 1.22 [1.17–1.28], P = 7.2E–20

HR = 1.22 [1.16–1.27], P = 4.0E–19

HR = 1.15 [1.10–1.20], P = 6.8E–10

Overall BC: 0.566 [0.551–0.581]


Sawyer et al. 2012 [74]

Case-control study; Familial breast cancer cohort

126 contralateral BC

711 unilateral BC


4th quartile in comparison with 1st quartile

OR = 1.96 [1.17–3.70]



Robson et al. 2017 [75]

Population-based case-control study, < 55 yr

1459 contralateral BC

2126 unilateral BC


4th quartile in comparison with 1st quartile

OR = 1.75 [1.41–2.18]



BC, breast cancer; SD, standard deviation; AUC, area under the curve

A limitation of many PRSs is that most SNPs contained in it are discovered in European-descent populations and their effects cannot be translated directly to other ethnicities. Studies are ongoing to define breast cancer–associated SNPs and evaluate the European-descent-derived PRSs in Asian and African-American populations.

For all PRS-models, the AUC is modest, but should this alone preclude their application as an individual test to predict if a woman will develop breast cancer or not? A comparison with gene panel testing, which is widely used in the clinic for this purpose, is illustrative. A PRS has been shown to be capable of stratifying women into different risk categories in a clinically meaningful way [8, 62, 73•, 74], but the most relevant clinical information of the PRS is in the extreme tails of the distribution. And because these tails concern the general population (as opposed to gene carriers only), the associated attributable risks of the PRS are in fact far greater than that achieved by gene panel testing. For example, the best performing PRS at this moment includes 313 SNPs with an association at a p value threshold three orders below genome-wide significance (P < 10−5). For this PRS, in the general population, 35% of all breast cancers occur in women in the highest quintile and only 9% of all breast cancers in the lowest quintile [57•]. Women in the top 1% of the PRS313 are at fourfold elevated risk relative to population average (95% CI 3.34–4.89), a risk-level defined in many countries as ‘high’. In comparison, BRCA1 mutation carriers explain < 2% of all breast cancer in Western Caucasian populations [76] and comprise ~ 0.1% of the general population. Implementation research is ongoing to introduce the PRS into clinical genetic testing, e.g. in the Netherlands, Germany, the UK and the USA. An example of how individual PRS testing could aid risk counselling in the setting of familial breast cancer is shown in Fig. 1, which highlights how two individuals that would otherwise have received the same risk assessment (sisters in generation IV) on the basis of their identical family history, are clearly classified into distinct risk classes on the basis of their PRS313.
Fig. 1

Standardised Polygenic Risk Scores for breast cancer cases and their female relatives. In this non-BRCA1/2 breast cancer family, multiple family members were genotyped by SNP array. For all genotyped individuals, the SNP313 Polygenic Risk Score (PRS) was calculated. The individual PRSs are standardised to population controls in the BCAC dataset (mean = 0 and SD = 1 in controls). The numbers in the figure are therefore Z-scores of the individual PRSs. A higher Z-score indicates a higher breast cancer risk

Another potential application of the PRS is in deciding when and how frequent women should undergo breast cancer screening [6•, 77]. In most countries running such screening programs, women are offered screening above a certain age, usually between 45 and 50, when their breast cancer risk exceeds a certain cost-effective level. Women in the lowest quintile of the PRS313 in fact never reach that threshold, whereas those in the highest quintile will attain this level of risk before age 40 years [57•].

Hormonal, Environmental and Lifestyle Risk Factors

A number of non-genetic risk factors are presently firmly established as being associated with breast cancer. Besides age, these include physical factors such as body height and weight [78, 79]. For weight, breast cancer risk is dependent on menopausal status. Weight gain and obesity (BMI > 30) after menopause are associated with an increase in postmenopausal breast cancer [78]. It is likely that higher oestrogen levels underlie this effect in postmenopausal women [80]. A higher mammographic density due to a high proportion of connective and glandular relative to adipose tissue leads to a higher risk for breast cancer [81, 82]. Hormonal factors influencing breast cancer risk include the use of oral contraception and hormone replacement therapy (HRT) [83, 84], as well as age at menarche and menopause [85]. Reproductive history (age of first childbirth or nulliparity) may have similar impact on mammary gland biology [82, 86]. The lifestyle factors like alcohol use and smoking increase breast cancer risk as well, while physical activity and breastfeeding seem to act protectively [87, 88, 89]. Finally, a personal history of benign breast disease also signifies an increased breast cancer risk [82].

Combining Risk Factors

Since any woman will have only a single certain risk level at a given moment in time to develop breast cancer over the course of her life, genetic and non-genetic risk factors must somehow combine to define that risk. A major challenge for individual breast cancer risk prediction, therefore, is to design risk calculation models that accommodate all known risk factors, which require knowledge about the underlying model and how they interact. Through the large international consortia such as BCAC, data to design and validate such models are now forthcoming. There are now much more accurate estimates on how the PRS can modify the breast and ovarian cancer risks conferred by pathogenic variants in BRCA1, BRCA2, and CHEK2 [72•, 73•] (Table 1). This can help inform choices and timing of preventive surgery or chemoprevention. The interaction between the 1100delC variant in CHEK2 and the PRS appears to follow a simple multiplicative interaction, but the per SD hazard ratio estimates in BRCA1 and BRCA2 carriers were smaller than those in general population (Table 1). In BRCA1 carriers, the PRS based on SNPs associated with ER-negative disease showed a much stronger association with breast cancer risk in comparison with the ER-positive PRS, consistent with the predominant ER-negative tumour subtype in BRCA1 carriers [73•]. These issues highlight the complexity of some of these interactions and underscore the necessity of large prospective cohort studies to validate these models. A similar deviation from simple multiplicative interactions has been found for individuals with rare pathogenic variants in more than one breast cancer–associated gene [90]. There is limited evidence for interaction between SNPs and lifestyle/hormonal factors [91]. For environmental factors (e.g. reproductive factors, BMI and alcohol intake), the PRS can, in general, be combined in a multiplicative way [92].

Breast Cancer Risk Prediction Models

Currently, predicting whether a woman will develop primary breast cancer or not is mainly done within Cancer Family Clinics. Healthy women who are worried because of their family history for breast cancer can be referred by their general practitioner to such a clinic; alternatively, breast cancer patients with a clear family history are referred by oncologists, also because of the potential impact a gene diagnosis may have for their therapeutic options. The major incentive behind these referrals is the possibility to detect a high-risk variant in BRCA1, BRCA2 and, more recently, PALB2. As set forth above, however, such variants are found in < 10% of all referred families. For women from non-BRCA1/2 breast cancer families, breast cancer risk is often based on family history alone, although more than 20 risk prediction algorithms known today [93] include other risk factors as well. Some well-known risk prediction algorithms are the Gail model, BRCAPRO, Tyrer-Cuzick and the breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA). Depending on what the model predicts and for which population, the most appropriate model can be used.

The Gail model predicts breast cancer lifetime risks for women older than 35 years and is widely studied and validated. It includes hormonal risk factors, breast biopsies and affected first-degree relatives [93, 94]. The Chen model extends this by incorporating mammographic breast density as well [95]. The BRCAPRO model calculates breast cancer lifetime risks and the risk of contralateral breast cancer. The calculation is based on family history, the prevalence of BRCA1 and BRCA2 pathogenic variants, population incidence rates and pathological markers for breast cancers [96]. The Tyrer-Cuzick model incorporates hereditary (first- and second-degree relatives with breast or ovarian cancer), hormonal and environmental risk factors (age, BMI, menarche, reproductive factors, menopause and HRT) and pathological variables (breast biopsies and benign breast pathology). Mammographic density will be incorporated in the model in an upcoming version [93]. BOADICEA calculates breast cancer lifetime risks and contralateral cancer risks for women with a family history of breast cancer [97]. The model includes tumour pathology characteristics, current cancer incidences and pathogenic variants in ATM, BRCA1, BRCA2, CHEK2 and PALB2. For BOADICEA, family history is not restricted to a number of relatives or a particular degree.

Several studies have shown an improved discriminative power between breast cancer cases and controls by combining the PRS with a breast cancer risk prediction tool [60, 63, 66, 69]. In one study [62], new breast cancer lifetime risks for women from breast cancer families were calculated by adding the PRS to family-based risk prediction. For up to 23% of the women, screening recommendations, as stipulated by local management guidelines, could alter.

The BOADICEA model has recently been extended to accommodate a broad range of genetic and non-genetic risk factors for breast cancer, adding mammographic density, reproductive factors, age at menarche and menopause, use of hormones, BMI, body height, alcohol use and the SNP313 PRS to the previous version [98•]. This is the first time that so many factors are combined into a single model. Unsurprisingly, the potential for risk stratification was the greatest when all risk factors were used for risk prediction. Of all factors, the PRS had the largest contribution in risk stratification. Without knowledge of the genetic status of a woman for the rare genes, or family history, the lifetime breast cancer risk varied from 2.8% for the lowest to 30.6% for the highest percentile of the PRS. The model assumes that the risk factors and the PRS act multiplicatively, consistent with evidence from previous studies but not yet formally demonstrated for PRS313. Similarly, the assumption that the PRS313 combines multiplicatively with the effects of rare truncating variants in the five breast cancer genes will need validation. Finally, the current BOADICEA model uses population breast cancer risks of several countries but the UK risk factor distributions and therefore may require tailoring for application in other populations.


Approximately half of familial relative risk of breast cancer can be explained by the genes and variants identified over the past three decades. In order to be able to maximally exploit a woman’s genomic data for breast cancer risk prediction, we will have to detect the genetic factors underlying the remaining half. To do so, researchers must face the conundrum of genome-wide significance and costs. Restricting to protein-coding regions by whole-exome sequencing, a so-called burden-type association analysis (counting presumable loss-of-function variants in cases and controls), and using a Bonferroni-corrected significance level of p < 2.5 × 10−6 will require data on at least 10,000 cases and 10,000 controls to be sufficiently powered. For whole-genome sequencing, not only the costs per sample are several-fold higher than for exome sequencing, genome-wide significance is at least 50-fold more stringent, requiring many more samples to be analysed. In addition, functional annotation of intronic and intergenic variants, to guide which variants to include in the association analysis, is still in its infancy.

Epidemiology has firmly established and quantified the role of many non-genetic factors in causing breast cancer. Currently, computational models are being built that integrate available knowledge so as to allow highly personal risk estimates. Ultimately, such models will empower women to exploit these risk estimates and take appropriate actions to lower this risk (many risk factors are modifiable). While there are many challenges still to overcome (particularly the lack of evidence to demonstrate improved clinical or economic outcomes), the use of genomic and personal lifestyle data in breast cancer prediction seems imminent.


Compliance with Ethical Standards

Conflict of Interest

Inge M.M. Lakeman, Marjanka K. Schmidt, Christi J. van Asperen and Peter Devilee each declare no potential conflicts of interest.

Human and Animal Rights

All reported studies/experiments with human or animal subjects performed by the authors have been previously published and complied with all applicable ethical standards (including the Helsinki declaration and its amendments, institutional/national research committee standards, and international/national/institutional guidelines).


Papers of particular interest, published recently, have been highlighted as: • Of importance

  1. 1.
    Ferlay J, Colombet M, Soerjomataram I, Dyba T, Randi G, Bettio M, et al. Cancer incidence and mortality patterns in Europe: estimates for 40 countries and 25 major cancers in 2018. Eur J Cancer (Oxford, England : 1990). 2018;103:356–87. Scholar
  2. 2.
    Li X, You R, Wang X, Liu C, Xu Z, Zhou J, et al. Effectiveness of prophylactic surgeries in BRCA1 or BRCA2 mutation carriers: a meta-analysis and systematic review. Clin Cancer Res. 2016;22(15):3971–81. Scholar
  3. 3.
    Bleyer A. Screening mammography: update and review of publications since our report in the New England Journal of Medicine on the magnitude of the problem in the United States. Acad Radiol. 2015;22(8):949–60. Scholar
  4. 4.
    Myers ER, Moorman P, Gierisch JM, Havrilesky LJ, Grimm LJ, Ghate S, et al. Benefits and harms of breast cancer screening: a systematic review. JAMA. 2015;314(15):1615–34. Scholar
  5. 5.
    Burton H, Chowdhury S, Dent T, Hall A, Pashayan N, Pharoah P. Public health implications from COGS and potential for risk stratification and screening. Nat Genet. 2013;45(4):349–51. Scholar
  6. 6.
    • Pashayan N, Morris S, Gilbert FJ, Pharoah PDP. Cost-effectiveness and benefit-to-harm ratio of risk-stratified screening for breast cancer: a life-table model. JAMA Oncol. 2018;4(11):1504–10. This study describes how risk-based, as opposed to age-based breast cancer screening programs could improve cost-effectiveness, reduce overdiagnosis, and maintain the benefits of screening.Google Scholar
  7. 7.
    Familial breast cancer: collaborative reanalysis of individual data from 52 epidemiological studies including 58,209 women with breast cancer and 101,986 women without the disease. Lancet (London, England). 2001;358(9291):1389–99.
  8. 8.
    Mavaddat N, Pharoah PD, Michailidou K, Tyrer J, Brook MN, Bolla MK, et al. Prediction of breast cancer risk based on profiling with common genetic variants. J Natl Cancer Inst. 2015;107(5).
  9. 9.
    Michailidou K, Hall P, Gonzalez-Neira A, Ghoussaini M, Dennis J, Milne RL, et al. Large-scale genotyping identifies 41 new loci associated with breast cancer risk. Nat Genet. 2013;45(4):353–61.Google Scholar
  10. 10.
    • Michailidou K, Lindstrom S, Dennis J, Beesley J, Hui S, Kar S, et al. Association analysis identifies 65 new breast cancer risk loci. Nature. 2017;551(7678):92–+. The last in a series of GWA studies, which ultimately formed the basis of the most discriminatory breast cancer PRS currently available.Google Scholar
  11. 11.
    Lilyquist J, Ruddy KJ, Vachon CM, Couch FJ. Common genetic variation and breast cancer risk - past, present, and future. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2018.
  12. 12.
    Miki Y, Swensen J, Shattuck-Eidens D, Futreal PA, Harshman K, Tavtigian S, et al. A strong candidate for the breast and ovarian cancer susceptibility gene BRCA1. Science (New York, NY). 1994;266(5182):66–71.Google Scholar
  13. 13.
    Wooster R, Bignell G, Lancaster J, Swift S, Seal S, Mangion J, et al. Identification of the breast cancer susceptibility gene BRCA2. Nature. 1995;378(6559):789–92. Scholar
  14. 14.
    Antoniou A, Pharoah PD, Narod S, Risch HA, Eyfjord JE, Hopper JL, et al. Average risks of breast and ovarian cancer associated with BRCA1 or BRCA2 mutations detected in case series unselected for family history: a combined analysis of 22 studies. Am J Hum Genet. 2003;72(5):1117–30. Scholar
  15. 15.
    Chen S, Parmigiani G. Meta-analysis of BRCA1 and BRCA2 penetrance. J Clin Oncol. 2007;25(11):1329–33. Scholar
  16. 16.
    Nelen MR, Padberg GW, Peeters EA, Lin AY, van den Helm B, Frants RR, et al. Localization of the gene for Cowden disease to chromosome 10q22-23. Nat Genet. 1996;13(1):114–6. Scholar
  17. 17.
    Hearle N, Schumacher V, Menko FH, Olschwang S, Boardman LA, Gille JJ, et al. Frequency and spectrum of cancers in the Peutz-Jeghers syndrome. Clin Cancer Res. 2006;12(10):3209–15. Scholar
  18. 18.
    Pharoah PD, Guilford P, Caldas C. Incidence of gastric cancer and breast cancer in CDH1 (E-cadherin) mutation carriers from hereditary diffuse gastric cancer families. Gastroenterology. 2001;121(6):1348–53.Google Scholar
  19. 19.
    Madanikia SA, Bergner A, Ye X, Blakeley JO. Increased risk of breast cancer in women with NF1. Am J Med Genet A. 2012;158a(12):3056–60. Scholar
  20. 20.
    Malkin D, Li FP, Strong LC, Fraumeni JF Jr, Nelson CE, Kim DH, et al. Germ line p53 mutations in a familial syndrome of breast cancer, sarcomas, and other neoplasms. Science (New York, NY). 1990;250(4985):1233–8.Google Scholar
  21. 21.
    Renwick A, Thompson D, Seal S, Kelly P, Chagtai T, Ahmed M, et al. ATM mutations that cause ataxia-telangiectasia are breast cancer susceptibility alleles. Nat Genet. 2006;38(8):873–5. Scholar
  22. 22.
    D’ Andrea AD, Grompe M. The Fanconi anaemia/BRCA pathway. Nat Rev Cancer. 2003;3(1):23–34.Google Scholar
  23. 23.
    Meijers-Heijboer H, van den Ouweland A, Klijn J, Wasielewski M, de Snoo A, Oldenburg R, et al. Low-penetrance susceptibility to breast cancer due to CHEK2(*)1100delC in noncarriers of BRCA1 or BRCA2 mutations. Nat Genet. 2002;31(1):55–9. Scholar
  24. 24.
    Rahman N, Seal S, Thompson D, Kelly P, Renwick A, Elliott A, et al. PALB2, which encodes a BRCA2-interacting protein, is a breast cancer susceptibility gene. Nat Genet. 2007;39(2):165–7. Scholar
  25. 25.
    Bogdanova N, Feshchenko S, Schurmann P, Waltes R, Wieland B, Hillemanns P, et al. Nijmegen breakage syndrome mutations and risk of breast cancer. Int J Cancer. 2008;122(4):802–6. Scholar
  26. 26.
    Easton DF, Pharoah PD, Antoniou AC, Tischkowitz M, Tavtigian SV, Nathanson KL, et al. Gene-panel sequencing and the prediction of breast-cancer risk. N Engl J Med. 2015;372(23):2243–57. Scholar
  27. 27.
    Wendt C, Margolin S. Identifying breast cancer susceptibility genes - a review of the genetic background in familial breast cancer. Acta Oncol. 2019;58:1–12. Scholar
  28. 28.
    Antoniou AC, Casadei S, Heikkinen T, Barrowdale D, Pylkas K, Roberts J, et al. Breast-cancer risk in families with mutations in PALB2. N Engl J Med. 2014;371(6):497–506.Google Scholar
  29. 29.
    • Couch FJ, Shimelis H, Hu CL, Hart SN, Polley EC, Na J, et al. Associations between cancer predisposition testing panel genes and breast cancer. JAMA Oncol. 2017;3(9):1190–6. One of the first studies to attempt to replicate the many putative breast cancer genes by association in a large-scale case-control design.Google Scholar
  30. 30.
    Lu HM, Li S, Black MH, Lee S, Hoiness R, Wu S, et al. Association of breast and ovarian cancers with predisposition genes identified by large-scale sequencing. JAMA Oncol. 2018;5(1):51–7. Scholar
  31. 31.
    Graffeo R, Livraghi L, Pagani O, Goldhirsch A, Partridge AH, Garber JE. Time to incorporate germline multigene panel testing into breast and ovarian cancer patient care. Breast Cancer Res Treat. 2016;160(3):393–410. Scholar
  32. 32.
    Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med. 2015;17(5):405–24. Scholar
  33. 33.
    Rebbeck TR, Mitra N, Domchek SM, Wan F, Friebel TM, Tran TV, et al. Modification of BRCA1-associated breast and ovarian cancer risk by BRCA1-interacting genes. Cancer Res. 2011;71(17):5792–805.Google Scholar
  34. 34.
    • Shimelis H, Mesman RLS, Von Nicolai C, Ehlen A, Guidugli L, Martin C, et al. BRCA2 hypomorphic missense variants confer moderate risks of breast cancer. Cancer Res. 2017;77(11):2789–99. In vitro testing of BRCA2 functionality can help to identify variants conferring moderate breast cancer risks.Google Scholar
  35. 35.
    Moghadasi S, Meeks HD, Vreeswijk MP, Janssen LA, Borg A, Ehrencrona H, et al. The BRCA1 c. 5096G>A p.Arg1699Gln (R1699Q) intermediate risk variant: breast and ovarian cancer risk estimation and recommendations for clinical management from the ENIGMA consortium. J Med Genet. 2018;55(1):15–20. Scholar
  36. 36.
    Southey MC, Goldgar DE, Winqvist R, Pylkas K, Couch F, Tischkowitz M, et al. PALB2, CHEK2 and ATM rare variants and cancer risk: data from COGS. J Med Genet. 2016;53(12):800–11. Scholar
  37. 37.
    Bernstein JL, Teraoka S, Southey MC, Jenkins MA, Andrulis IL, Knight JA, et al. Population-based estimates of breast cancer risks associated with ATM gene variants c.7271T>G and c.1066-6T>G (IVS10-6T>G) from the breast cancer family registry. Hum Mutat. 2006;27(11):1122–8.Google Scholar
  38. 38.
    Thompson ER, Gorringe KL, Rowley SM, Li N, McInerny S, Wong-Brown MW, et al. Reevaluation of the BRCA2 truncating allele c.9976A > T (p.Lys3326Ter) in a familial breast cancer context. Sci Rep. 2015;5:14800.Google Scholar
  39. 39.
    Toland AE, Andreassen PR. DNA repair-related functional assays for the classification of BRCA1 and BRCA2 variants: a critical review and needs assessment. J Med Genet. 2017;54(11):721–31. Scholar
  40. 40.
    Chompret A, Brugieres L, Ronsin M, Gardes M, Dessarps-Freichey F, Abel A, et al. P53 germline mutations in childhood cancers and cancer risk for carrier individuals. Br J Cancer. 2000;82(12):1932–7. Scholar
  41. 41.
    O'Shea R, Clarke R, Berkley E, Giffney C, Farrell M, O'Donovan E, et al. Next generation sequencing is informing phenotype: a TP53 example. Familial Cancer. 2018;17(1):123–8. Scholar
  42. 42.
    Rana HQ, Gelman R, LaDuca H, McFarland R, Dalton E, Thompson J, et al. Differences in TP53 mutation carrier phenotypes emerge from panel-based testing. J Natl Cancer Inst. 2018;110(8):863–70. Scholar
  43. 43.
    de Andrade KC, Frone MN, Wegman-Ostrosky T, Khincha PP, Kim J, Amadou A, et al. Variable population prevalence estimates of germline TP53 variants: a gnomAD-based analysis. Hum Mutat. 2019;40(1):97–105. Scholar
  44. 44.
    Consortium TCBCC-C. CHEK2*1100delC and susceptibility to breast cancer: a collaborative analysis involving 10,860 breast cancer cases and 9,065 controls from ten studies. Am J Hum Genet. 2004;74(6):1175–82.Google Scholar
  45. 45.
    Schmidt MK, Hogervorst F, van Hien R, Cornelissen S, Broeks A, Adank MA, et al. Age- and tumor subtype-specific breast cancer risk estimates for CHEK2*1100delC carriers. J Clin Oncol. 2016;34(23):2750–60. Scholar
  46. 46.
    Tung N, Battelli C, Allen B, Kaldate R, Bhatnagar S, Bowles K, et al. Frequency of mutations in individuals with breast cancer referred for BRCA1 and BRCA2 testing using next-generation sequencing with a 25-gene panel. Cancer. 2015;121(1):25–33. Scholar
  47. 47.
    Desmond A, Kurian AW, Gabree M, Mills MA, Anderson MJ, Kobayashi Y, et al. Clinical actionability of multigene panel testing for hereditary breast and ovarian cancer risk assessment. JAMA Oncol. 2015;1(7):943–51. Scholar
  48. 48.
    Lerner-Ellis J, Khalouei S, Sopik V, Narod SA. Genetic risk assessment and prevention: the role of genetic testing panels in breast cancer. Expert Rev Anticancer Ther. 2015;15(11):1315–26. Scholar
  49. 49.
    Kapoor NS, Curcio LD, Blakemore CA, Bremner AK, McFarland RE, West JG, et al. Multigene panel testing detects equal rates of pathogenic BRCA1/2 mutations and has a higher diagnostic yield compared to limited BRCA1/2 analysis alone in patients at risk for hereditary breast cancer. Ann Surg Oncol. 2015;22(10):3282–8. Scholar
  50. 50.
    Thompson ER, Rowley SM, Li N, McInerny S, Devereux L, Wong-Brown MW, et al. Panel testing for familial breast cancer: calibrating the tension between research and clinical care. J Clin Oncol. 2016.
  51. 51.
    Susswein LR, Marshall ML, Nusbaum R, Vogel Postula KJ, Weissman SM, Yackowski L, et al. Pathogenic and likely pathogenic variant prevalence among the first 10,000 patients referred for next-generation cancer panel testing. Genet Med. 2015.
  52. 52.
    Kuchenbaecker KB, Hopper JL, Barnes DR, Phillips KA, Mooij TM, Roos-Blom MJ, et al. Risks of breast, ovarian, and contralateral breast cancer for BRCA1 and BRCA2 mutation carriers. JAMA. 2017;317(23):2402–16. Scholar
  53. 53.
    Rebbeck TR, Friebel TM, Friedman E, Hamann U, Huo D, Kwong A, et al. Mutational spectrum in a worldwide study of 29,700 families with BRCA1 or BRCA2 mutations. Hum Mutat. 2018;39(5):593–620. Scholar
  54. 54.
    Turnbull C, Sud A, Houlston RS. Cancer genetics, precision prevention and a call to action. Nat Genet. 2018;50(9):1212–8. Scholar
  55. 55.
    Michailidou K, Beesley J, Lindstrom S, Canisius S, Dennis J, Lush MJ, et al. Genome-wide association analysis of more than 120,000 individuals identifies 15 new susceptibility loci for breast cancer. Nat Genet. 2015;47(4):373–80. Scholar
  56. 56.
    • Milne RL, Kuchenbaecker KB, Michailidou K, Beesley J, Kar S, Lindstrom S, et al. Identification of ten variants associated with risk of estrogen-receptor-negative breast cancer. Nat Genet. 2017;49(12):1767–78. Definitive evidence that some SNPs predispose specifically to ER-negative breast cancer subtypes.Google Scholar
  57. 57.
    • Mavaddat N, Michailidou K, Dennis J, Lush M, Fachal L, Lee A, et al. Polygenic risk scores for prediction of breast cancer and breast cancer subtypes. Am J Hum Genet. 2019;104(1):21–34. Presentation of the most predictive breast cancer PRS available to date, consisting of 313 SNPs.Google Scholar
  58. 58.
    Milne RL, Herranz J, Michailidou K, Dennis J, Tyrer JP, Zamora MP, et al. A large-scale assessment of two-way SNP interactions in breast cancer susceptibility using 46 450 cases and 42 461 controls from the breast cancer association consortium. Hum Mol Genet. 2014;23(7):1934–46.Google Scholar
  59. 59.
    McCarthy AM, Keller B, Kontos D, Boghossian L, McGuire E, Bristol M, et al. The use of the Gail model, body mass index and SNPs to predict breast cancer among women with abnormal (BI-RADS 4) mammograms. Breast Cancer Res. 2015;17:1. Scholar
  60. 60.
    Dite GS, MacInnis RJ, Bickerstaffe A, Dowty JG, Allman R, Apicella C et al. Breast cancer risk prediction using clinical models and 77 independent risk-associated SNPs for women aged under 50 years: Australian Breast Cancer Family Registry. Cancer Epidemiol Biomarkers Prev. 2015;1055–9965.
  61. 61.
    Naslund-Koch C, Nordestgaard BG, Bojesen SE. Common breast cancer risk alleles and risk assessment: a study on 35,441 individuals from the Danish general population. Ann Oncol. 2016.
  62. 62.
    Li H, Feng B, Miron A, Chen X, Beesley J, Bimeh E, et al. Breast cancer risk prediction using a polygenic risk score in the familial setting: a prospective study from the breast cancer family registry and kConFab. Genet Med. 2016;19:30–5. Scholar
  63. 63.
    Shieh Y, Hu D, Ma L, Huntsman S, Gard CC, Leung JW, et al. Breast cancer risk prediction using a clinical risk model and polygenic risk score. Breast Cancer Res Treat. 2016;159(3):513–25. Scholar
  64. 64.
    Muranen TA, Mavaddat N, Khan S, Fagerholm R, Pelttari L, Lee A, et al. Polygenic risk score is associated with increased disease risk in 52 Finnish breast cancer families. Breast Cancer Res Treat. 2016;158(3):463–9. Scholar
  65. 65.
    Maas P, Barrdahl M, Joshi AD, Auer PL, Gaudet MM, Milne RL, et al. Breast cancer risk from modifiable and nonmodifiable risk factors among White women in the United States. JAMA Oncol. 2016;2(10):1295–302. Scholar
  66. 66.
    Cuzick J, Brentnall AR, Segal C, Byers H, Reuter C, Detre S, et al. Impact of a panel of 88 single nucleotide polymorphisms on the risk of breast cancer in high-risk women: results from two randomized tamoxifen prevention trials. J Clin Oncol. 2017;35(7):743–50. Scholar
  67. 67.
    Shieh Y, Hu D, Ma L, Huntsman S, Gard CC, Leung JWT, et al. Joint relative risks for estrogen receptor-positive breast cancer from a clinical model, polygenic risk score, and sex hormones. Breast Cancer Res Treat. 2017;166(2):603–12. Scholar
  68. 68.
    Evans DG, Brentnall A, Byers H, Harkness E, Stavrinos P, Howell A, et al. The impact of a panel of 18 SNPs on breast cancer risk in women attending a UK familial screening clinic: a case-control study. J Med Genet. 2017;54(2):111–3. Scholar
  69. 69.
    van Veen EM, Brentnall AR, Byers H, Harkness EF, Astley SM, Sampson S, et al. Use of single-nucleotide polymorphisms and mammographic density plus classic risk factors for breast cancer risk prediction. JAMA Oncol. 2018;4(4):476–82. Scholar
  70. 70.
    Zhang X, Rice M, Tworoger SS, Rosner BA, Eliassen AH, Tamimi RM, et al. Addition of a polygenic risk score, mammographic density, and endogenous hormones to existing breast cancer risk prediction models: a nested case-control study. PLoS Med. 2018;15(9):e1002644. Scholar
  71. 71.
    Khera AV, Chaffin M, Aragam KG, Haas ME, Roselli C, Choi SH, et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet. 2018;50(9):1219–24. Scholar
  72. 72.
    • Muranen TA, Greco D, Blomqvist C, Aittomaki K, Khan S, Hogervorst F, et al. Genetic modifiers of CHEK2*1100delC-associated breast cancer risk. Genet Med. 2017;19(5):599–603. A Major loss-of-function variant in CHEK2 acts multiplicatively with the PRS to increase breast cancer risk.Google Scholar
  73. 73.
    • Kuchenbaecker KB, McGuffog L, Barrowdale D, Lee A, Soucy P, Dennis J, et al. Evaluation of polygenic risk scores for breast and ovarian cancer risk prediction in BRCA1 and BRCA2 mutation carriers. J Natl Cancer Inst. 2017;109(7). . Detailed description of how the PRS modifies breast and ovarian cancer risks conferred by BRCA1 and BRCA2.
  74. 74.
    Sawyer S, Mitchell G, McKinley J, Chenevix-Trench G, Beesley J, Chen XQ, et al. A role for common genomic variants in the assessment of familial breast cancer. JClinOncol. 2012;30(35):4330–6. Scholar
  75. 75.
    Robson ME, Reiner AS, Brooks JD, Concannon PJ, John EM, Mellemkjaer L, et al. Association of common genetic variants with contralateral breast cancer risk in the WECARE study. J Natl Cancer Inst. 2017;109(10).
  76. 76.
    Ford D, Easton D, Peto J. Estimates of the gene frequency of BRCA1 and its contribution to breast and ovarian cancer incidence. Am J Hum Genet. 1995;57:1457–62.Google Scholar
  77. 77.
    Pharoah PD, Antoniou AC, Easton DF, Ponder BA. Polygenes, risk prediction, and targeted prevention of breast cancer. N Engl J Med. 2008;358(26):2796–803.Google Scholar
  78. 78.
    Lahmann PH, Hoffmann K, Allen N, van Gils CH, Khaw KT, Tehard B, et al. Body size and breast cancer risk: findings from the European prospective investigation into cancer and nutrition (EPIC). Int J Cancer. 2004;111(5):762–71. Scholar
  79. 79.
    Green J, Cairns BJ, Casabonne D, Wright FL, Reeves G, Beral V. Height and cancer incidence in the million women study: prospective cohort, and meta-analysis of prospective studies of height and total cancer risk. Lancet Oncol. 2011;12(8):785–94. Scholar
  80. 80.
    Key TJ, Appleby PN, Reeves GK, Roddam A, Dorgan JF, Longcope C, et al. Body mass index, serum sex hormones, and breast cancer risk in postmenopausal women. J Natl Cancer Inst. 2003;95(16):1218–26.Google Scholar
  81. 81.
    McCormack VA, dos Santos Silva I. Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis. Cancer Epidemiol Biomarkers Prev. 2006;15(6):1159–69. Scholar
  82. 82.
    Nelson HD, Zakher B, Cantor A, Fu R, Griffin J, O'Meara ES, et al. Risk factors for breast cancer for women aged 40 to 49 years: a systematic review and meta-analysis. Ann Intern Med. 2012;156(9):635–48. Scholar
  83. 83.
    Beral V, Reeves G, Bull D, Green J. Breast cancer risk in relation to the interval between menopause and starting hormone therapy. J Natl Cancer Inst. 2011;103(4):296–305. Scholar
  84. 84.
    Hunter DJ, Colditz GA, Hankinson SE, Malspeis S, Spiegelman D, Chen W, et al. Oral contraceptive use and breast cancer: a prospective study of young women. Cancer Epidemiol Biomarkers Prev. 2010;19(10):2496–502. Scholar
  85. 85.
    Cancer CGoHFiB. Menarche, menopause, and breast cancer risk: individual participant meta-analysis, including 118 964 women with breast cancer from 117 epidemiological studies. Lancet Oncol. 2012;13(11):1141–51. Scholar
  86. 86.
    Breast cancer and breastfeeding: collaborative reanalysis of individual data from 47 epidemiological studies in 30 countries, including 50302 women with breast cancer and 96973 women without the disease. Lancet (London, England). 2002;360(9328):187–95.
  87. 87.
    Hamajima N, Hirose K, Tajima K, Rohan T, Calle EE, Heath CW Jr, et al. Alcohol, tobacco and breast cancer--collaborative reanalysis of individual data from 53 epidemiological studies, including 58,515 women with breast cancer and 95,067 women without the disease. Br J Cancer. 2002;87(11):1234–45. Scholar
  88. 88.
    Gram IT, Park SY, Kolonel LN, Maskarinec G, Wilkens LR, Henderson BE, et al. Smoking and risk of breast cancer in a racially/ethnically diverse population of mainly women who do not drink alcohol: the MEC study. Am J Epidemiol. 2015;182(11):917–25. Scholar
  89. 89.
    Pizot C, Boniol M, Mullie P, Koechlin A, Boniol M, Boyle P, et al. Physical activity, hormone replacement therapy and breast cancer risk: a meta-analysis of prospective studies. Eur J Cancer. 2016;52:138–54. Scholar
  90. 90.
    Turnbull C, Seal S, Renwick A, Warren-Perry M, Hughes D, Elliott A, et al. Gene-gene interactions in breast cancer susceptibility. Hum Mol Genet. 2012;21(4):958–62. Scholar
  91. 91.
    Rudolph A, Chang-Claude J, Schmidt MK. Gene-environment interaction and risk of breast cancer. Br J Cancer. 2016;114(2):125–33. Scholar
  92. 92.
    Rudolph A, Song M, Brook MN, Milne RL, Mavaddat N, Michailidou K, et al. Joint associations of a polygenic risk score and environmental risk factors for breast cancer in the breast cancer association consortium. Int J Epidemiol. 2018;47:526–36. Scholar
  93. 93.
    Cintolo-Gonzalez JA, Braun D, Blackford AL, Mazzola E, Acar A, Plichta JK, et al. Breast cancer risk models: a comprehensive overview of existing models, validation, and clinical applications. Breast Cancer Res Treat. 2017;164(2):263–84. Scholar
  94. 94.
    Gail MH, Brinton LA, Byar DP, Corle DK, Green SB, Schairer C, et al. Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. J Natl Cancer Inst. 1989;81(24):1879–86.Google Scholar
  95. 95.
    Chen J, Pee D, Ayyagari R, Graubard B, Schairer C, Byrne C, et al. Projecting absolute invasive breast cancer risk in white women with a model that includes mammographic density. J Natl Cancer Inst. 2006;98(17):1215–26. Scholar
  96. 96.
    Mazzola E, Blackford A, Parmigiani G, Biswas S. Recent enhancements to the genetic risk prediction model BRCAPRO. Cancer Informat. 2015;14(Suppl 2):147–57. Scholar
  97. 97.
    Lee AJ, Cunningham AP, Kuchenbaecker KB, Mavaddat N, Easton DF, Antoniou AC. BOADICEA breast cancer risk prediction model: updates to cancer incidences, tumour pathology and web interface. Br J Cancer. 2014;110(2):535–45. Scholar
  98. 98.
    • Lee A, Mavaddat N, Wilcox AN, Cunningham AP, Carver T, Hartley S, et al. BOADICEA: a comprehensive breast cancer risk prediction model incorporating genetic and nongenetic risk factors. Genet Med. 2019. This paper describes the most elaborate breast cancer risk prediction model available to date, including potential clinical applications.

Copyright information

© The Author(s) 2019

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • Inge M. M. Lakeman
    • 1
  • Marjanka K. Schmidt
    • 2
  • Christi J. van Asperen
    • 3
  • Peter Devilee
    • 1
    • 4
    Email author
  1. 1.Department of Human GeneticsLeiden University Medical CentreLeidenThe Netherlands
  2. 2.Department of Molecular Pathology, and Department of Psychosocial Research and EpidemiologyNetherlands Cancer Institute—Antoni van Leeuwenhoek HospitalAmsterdamthe Netherlands
  3. 3.Department of Clinical GeneticsLeiden University Medical CentreLeidenthe Netherlands
  4. 4.Department of PathologyLeiden University Medical CentreLeidenthe Netherlands

Personalised recommendations