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The Contributions of Breast Density and Common Genetic Variation to Breast Cancer Risk
Abstract
We evaluated whether a 76-locus polygenic risk score (PRS) and Breast Imaging Reporting and Data System (BI-RADS) breast density were independent risk factors within three studies (1643 case patients, 2397 control patients) using logistic regression models. We incorporated the PRS odds ratio (OR) into the Breast Cancer Surveillance Consortium (BCSC) risk-prediction model while accounting for its attributable risk and compared five-year absolute risk predictions between models using area under the curve (AUC) statistics. All statistical tests were two-sided. BI-RADS density and PRS were independent risk factors across all three studies (P interaction = .23). Relative to those with scattered fibroglandular densities and average PRS (2(nd) quartile), women with extreme density and highest quartile PRS had 2.7-fold (95% confidence interval [CI] = 1.74 to 4.12) increased risk, while those with low density and PRS had reduced risk (OR = 0.30, 95% CI = 0.18 to 0.51). PRS added independent information (P < .001) to the BCSC model and improved discriminatory accuracy from AUC = 0.66 to AUC = 0.69. Although the BCSC-PRS model was well calibrated in case-control data, independent cohort data are needed to test calibration in the general population.
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... Studies included in this review presented the risk for different types of BC. Most predicted the risk of developing overall BC (n = 25) [12,24,25,[43][44][45][46]49,51,54,56,58,[61][62][63][64][65][66][67][69][70][71][72]74,75] or invasive BC (n = 11) [47,48,50,52,53,55,57,59,63,68,73], and BC subtypes such as ER-positive (n = 11) [7,12,45,[47][48][49][50]52,57,67,70] and ER-negative (n = 10) [12,45,[47][48][49][50]52,57,67,70]. Most studies were conducted in the United States (n = 7) [7,43,44,57,59,61,74], the United Kingdom (n = 5) [25,45,62,69,75], Sweden (n = 3) [23,46,66] and Australia (n = 3) [47,48,73]. ...
... Studies included in this review presented the risk for different types of BC. Most predicted the risk of developing overall BC (n = 25) [12,24,25,[43][44][45][46]49,51,54,56,58,[61][62][63][64][65][66][67][69][70][71][72]74,75] or invasive BC (n = 11) [47,48,50,52,53,55,57,59,63,68,73], and BC subtypes such as ER-positive (n = 11) [7,12,45,[47][48][49][50]52,57,67,70] and ER-negative (n = 10) [12,45,[47][48][49][50]52,57,67,70]. Most studies were conducted in the United States (n = 7) [7,43,44,57,59,61,74], the United Kingdom (n = 5) [25,45,62,69,75], Sweden (n = 3) [23,46,66] and Australia (n = 3) [47,48,73]. ...
... However, newer methods such as Bayesian approaches [7,59] or risk prediction algorithms [72] were also used. Nineteen studies presented only the development of a risk model [7,43,[46][47][48][49]52,[54][55][56][57]59,[62][63][64][65][66][67]71], and fourteen presented only its validation [23][24][25]44,45,50,51,53,58,61,69,[73][74][75]. Nine included a method of internal validation [49,52,56,57,59,60,65,67,71], and four studies externally validated their model [12,68,70,72]. ...
Simple Summary
Several risk prediction tools have been developed to better stratify women according to their risk of developing breast cancer (BC) and inform prevention and early detection strategies. Many recent versions of these tools now incorporate a polygenic risk score (PRS) that uses the aggregated effect of common genetic variants, also known as single nucleotide polymorphisms (SNP), as a reliable predictor to estimate BC risk. However, the characteristics of each tool in terms of PRS development, population, and risk factors included vary considerably, which may affect their predictive performance and limit their use in public health practices. Thus, this systematic review characterizes BC risk prediction tools incorporating a PRS and explores the factors that can influence their ability to predict a woman’s risk of developing BC during her lifetime.
Abstract
Single nucleotide polymorphisms (SNPs) in the form of a polygenic risk score (PRS) have emerged as a promising factor that could improve the predictive performance of breast cancer (BC) risk prediction tools. This study aims to appraise and critically assess the current evidence on these tools. Studies were identified using Medline, EMBASE and the Cochrane Library up to November 2022 and were included if they described the development and/ or validation of a BC risk prediction model using a PRS for women of the general population and if they reported a measure of predictive performance. We identified 37 articles, of which 29 combined genetic and non-genetic risk factors using seven different risk prediction tools. Most models (55.0%) were developed on populations from European ancestry and performed better than those developed on populations from other ancestry groups. Regardless of the number of SNPs in each PRS, models combining a PRS with genetic and non-genetic risk factors generally had better discriminatory accuracy (AUC from 0.52 to 0.77) than those using a PRS alone (AUC from 0.48 to 0.68). The overall risk of bias was considered low in most studies. BC risk prediction tools combining a PRS with genetic and non-genetic risk factors provided better discriminative accuracy than either used alone. Further studies are needed to cross-compare their clinical utility and readiness for implementation in public health practices.
... Two new risk prediction approaches have recently emerged, namely polygenic risk score (PRS) and artificial intelligence (AI). PRSs estimate a woman's risk of breast cancer based on susceptibility loci identified through genome wide association studies [6]. AI algorithms, in contrast, identify discriminative image patterns from full-field mammograms to categorize a woman's risk of developing breast cancer in the future [7]. ...
... In strategies 5 ('PRS + no screening for low-risk' , hereafter) and 6 ('PRS + biennial screening for low-risk' , hereafter), screening pathways were the same as in strategies 3 and 4; however, risk stratification was performed using PRS instead of AI. All women underwent genetic testing at age 40 in which 76 single nucleotide polymorphisms (SNPs) known to be associated with breast cancer were genotyped [6]. ...
... It also meant that fewer low-risk women were incorrectly predicted to be at high risk, leading to reduction in screening and fewer false-positive diagnoses. In our model, accuracy of breast cancer risk prediction using AI and PRS was measured using area under the receiver operating characteristic curve (AUC) obtained from published studies [6,7]. As real-world clinical decisions will also likely utilize information on other demographic and personal risk factors (such as weight, family history and breast density) in addition to AI or PRS, we used AUC values for models based on both AI or PRS and other risk factors. ...
Background
Current guidelines for mammography screening for breast cancer vary across agencies, especially for women aged 40–49. Using artificial Intelligence (AI) to read mammography images has been shown to predict breast cancer risk with higher accuracy than alternative approaches including polygenic risk scores (PRS), raising the question whether AI-based screening is more cost-effective than screening based on PRS or existing guidelines. This study provides the first evidence to shed light on this important question.
Methods
This study is a model-based economic evaluation. We used a hybrid decision tree/microsimulation model to compare the cost-effectiveness of eight strategies of mammography screening for women aged 40–49 (screening beyond age 50 follows existing guidelines). Six of these strategies were defined by combinations of risk prediction approaches (AI, PRS or family history) and screening frequency for low-risk women (no screening or biennial screening). The other two strategies involved annual screening for all women and no screening, respectively. Data used to populate the model were sourced from the published literature.
Results
Risk prediction using AI followed by no screening for low-risk women is the most cost-effective strategy. It dominates (i.e., costs more and generates fewer quality adjusted life years (QALYs)) strategies for risk prediction using PRS followed by no screening or biennial screening for low-risk women, risk prediction using AI or family history followed by biennial screening for low-risk women, and annual screening for all women. It also extendedly dominates (i.e., achieves higher QALYs at a lower incremental cost per QALY) the strategy for risk prediction using family history followed by no screening for low-risk women. Meanwhile, it is cost-effective versus no screening, with an incremental cost-effectiveness ratio of $23,755 per QALY gained.
Conclusions
Risk prediction using AI followed by no breast cancer screening for low-risk women is the most cost-effective strategy. This finding can be explained by AI’s ability to identify high-risk women more accurately than PRS and family history (which reduces the possibility of delayed breast cancer diagnosis) and fewer false-positive diagnoses from not screening low-risk women.
... To our knowledge, no other study has examined absolute risk estimates with a similar design. Instead, other PRS evaluations have relied on input from external sources to convert relative to absolute risks [20,21,[36][37][38][39][40]. ...
... Several studies have shown that women with a high PRS are at increased risk of breast cancer [19-21, 37-39, 42]. One of the most recent publications was made [19,21,27,[37][38][39]42], the PRS was found to be moderately to strongly associated with breast cancer, primarily when comparing the top 3 percentile PRS-groups to the lowest (0-25%) PRS-group. ...
... Upon adding PRS to age, the increase in AUC was limited to 2-3% more pairs assigning higher risk to the breast cancer case. Although the AUC in our analysis are slightly higher than in other PRS evaluations [21,[36][37][38][39]42], two studies have reported similarly small improvements in AUC of 3-4% [37,38], while two other studies reported an increase of 7-9% when adding SNPs/PRS to existing risk factors [21,39]. Yet, the AUC does not inform on whether the small improvement could translate into sufficient discrimination of clinically defined subgroups, supporting different screening recommendations. ...
Background
Polygenic risk scores (PRS) could potentially improve breast cancer screening recommendations. Before a PRS can be considered for implementation, it needs rigorous evaluation, using performance measures that can inform about its future clinical value.
Objectives
To evaluate the prognostic performance of a regression model with a previously developed, prevalence-based PRS and age as predictors for breast cancer incidence in women from the Estonian biobank (EstBB) cohort; to compare it to the performance of a model including age only.
Methods
We analyzed data on 30,312 women from the EstBB cohort. They entered the cohort between 2002 and 2011, were between 20 and 89 years, without a history of breast cancer, and with full 5-year follow-up by 2015. We examined PRS and other potential risk factors as possible predictors in Cox regression models for breast cancer incidence. With 10-fold cross-validation we estimated 3- and 5-year breast cancer incidence predicted by age alone and by PRS plus age, fitting models on 90% of the data. Calibration, discrimination, and reclassification were calculated on the left-out folds to express prognostic performance.
Results
A total of 101 (3.33‰) and 185 (6.1‰) incident breast cancers were observed within 3 and 5 years, respectively. For women in a defined screening age of 50–62 years, the ratio of observed vs PRS-age modelled 3-year incidence was 0.86 for women in the 75–85% PRS-group, 1.34 for the 85–95% PRS-group, and 1.41 for the top 5% PRS-group. For 5-year incidence, this was respectively 0.94, 1.15, and 1.08. Yet the number of breast cancer events was relatively low in each PRS-subgroup. For all women, the model’s AUC was 0.720 (95% CI: 0.675–0.765) for 3-year and 0.704 (95% CI: 0.670–0.737) for 5-year follow-up, respectively, just 0.022 and 0.023 higher than for the model with age alone. Using a 1% risk prediction threshold, the 3-year NRI for the PRS-age model was 0.09, and 0.05 for 5 years.
Conclusion
The model including PRS had modest incremental performance over one based on age only. A larger, independent study is needed to assess whether and how the PRS can meaningfully contribute to age, for developing more efficient screening strategies.
... In the current clinical environment, there are several breast cancer risk models used for specific purposes 4,7 for example: the Gail model 8 is used to advise on chemoprevention for reducing risk; the Tyrer-Cuzick 9 , BRCAPRO 10,11 and Claus 12 models are useful for determining if supplemental imaging with magnetic resonance might be beneficial 4 . The Breast Cancer Screening Consortium (BCSC) model 13,14 may be useful for determining if women with dense breasts require supplemental screening 13 . These models do not use the same set of risk factors and are useful for different subpopulations 15 . ...
... The standard measure for breast density in the US used for clinical reporting is the BI-RADS ordinal composition classification provided by the attending radiologist, originally developed for masking, or indicating when mammography may be ineffective. This measure is also used for risk prediction in both the BCSC 13,14 and Tyrer-Cuzick models. The Tyrer-Cuzick model (and other models including the BCSC and Claus models) is also available in a widely used commercial mammography reporting software product (https:// magvi ew. ...
Mammography shifted to digital breast tomosynthesis (DBT) in the US. An automated percentage of breast density (PD) technique designed for two-dimensional (2D) applications was evaluated with DBT using several breast cancer risk prediction measures: normalized-volumetric; dense volume; applied to the volume slices and averaged (slice-mean); and applied to synthetic 2D images. Volumetric measures were derived theoretically. PD was modeled as a function of compressed breast thickness (CBT). The mean and standard deviation of the pixel values were investigated. A matched case–control (CC) study (n = 426 pairs) was evaluated. Odd ratios (ORs) were estimated with 95% confidence intervals. ORs were significant for PD: identical for volumetric and slice-mean measures [OR = 1.43 (1.18, 1.72)] and [OR = 1.44 (1.18, 1.75)] for synthetic images. A 2nd degree polynomial (concave-down) was used to model PD as a function of CBT: location of the maximum PD value was similar across CCs, occurring at 0.41 × CBT, and PD was significant [OR = 1.47 (1.21, 1.78)]. The means from the volume and synthetic images were also significant [ORs ~ 1.31 (1.09, 1.57)]. An alternative standardized 2D synthetic image was constructed, where each pixel value represents the percentage of breast density above its location. Several measures were significant and an alternative method for constructing a standardized 2D synthetic image was produced.
... Previous studies widely observed a strong association between breast density, which is the ratio of the amount of fibroglandular tissue in the breast and the amount of fatty tissue, and increased breast cancer risk. While the most important factors for breast cancer risk would be the patient's age and family history, mammographic breast density is widely considered a strong risk factor for breast cancer that is not specific to the breast side [2][3][4][5][6][7]. Higher density means the glands are located close to each other. ...
... This estimated breast composition features as a function of the patient's age provide important data in clinical or scientific assessments of breast imaging, in which breast density influences the diagnostic accuracy or radiation dose exposure. Our data acquired in this study may be further used in models estimating the individual breast cancer risk as breast density is an important independent risk factor for the development of breast cancer [2][3][4][5][6][7]. ...
Objectives: Breast density is considered an independent risk factor for the development of breast cancer. This study aimed to quantitatively assess the percent breast density (PBD) and the mammary glands volume (MGV) according to the patient’s age and breast quadrant. We propose a regression model to estimate PBD and MGV as a function of the patient’s age. Methods: The breast composition in 1027 spiral breast CT (BCT) datasets without soft tissue masses, calcifications, or implants from 517 women (57 ± 8 years) were segmented. The breast tissue volume (BTV), MGV, and PBD of the breasts were measured in the entire breast and each of the four quadrants. The three breast composition features were analyzed in the seven age groups, from 40 to 74 years in 5-year intervals. A logarithmic model was fitted to the BTV, and a multiplicative inverse model to the MGV and PBD as a function of age was established using a least-squares method. Results: The BTV increased from 545 ± 345 to 676 ± 412 cm3, and the MGV and PBD decreased from 111 ± 164 to 57 ± 43 cm3 and from 21 ± 21 to 11 ± 9%, respectively, from the youngest to the oldest group (p < 0.05). The average PBD over all ages were 14 ± 13%. The regression models could predict the BTV, MGV, and PBD based on the patient’s age with residual standard errors of 386 cm3, 67 cm3, and 13%, respectively. The reduction in MGV and PBD in each quadrant followed the ones in the entire breast. Conclusions: The PBD and MGV computed from BCT examinations provide important information for breast cancer risk assessment in women. The study quantified the breast mammary gland reduction and density decrease over the entire breast. It established mathematical models to estimate the breast composition features—BTV, MGV, and PBD, as a function of the patient’s age.
... Over the past two decades, many risk factors have been independently associated with the risk of developing breast cancer. Genetic risk factors can explain up to 40 % of the inherited breast cancer risk (defined as a doubled familial breast cancer risk) [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26]. This is contrasted with risk factors that are not associated with genetic risk, which include, for example, reproductive health parameters, weight, or lifestyle factors [27]. ...
... This is contrasted with risk factors that are not associated with genetic risk, which include, for example, reproductive health parameters, weight, or lifestyle factors [27]. Some risk factors, such as breast density, are partly determined by genetic factors and partly by other risk factors [8,14,15,23,28,29,30]. With this in mind, breast density plays a central role in determining the risk of breast cancer. ...
With abemaciclib (monarchE study) and olaparib (OlympiA study) gaining approval in the adjuvant treatment setting, a significant change in the standard of care for patients with early stage breast cancer has been established for some time now. Accordingly, some diverse developments are slowly being transferred from the metastatic to the adjuvant treatment setting. Recently, there have also been positive reports of the NATALEE study.
Other clinical studies are currently investigating substances that are already established in the metastatic setting. These include, for example, the DESTINY Breast05 study with trastuzumab deruxtecan and the SASCIA study with sacituzumab govitecan.
In this review paper, we summarize and place in context the latest developments over the past months.
... Simple cysts exhibit a similar occurrence in dense (6.6%) and non-dense breasts (4.8%), indicating that cyst formation may be less influenced by breast density. (22). ...
Introduction: Breast cancer is the second most common malignant tumor worldwide and the leading cause of cancer death among women in developing regions, including the Middle East and North Africa. This study aims to evaluate Mammogram Density Estimation for Assessing Breast Cancer Risk in Intermediate-Risk Women at Warith International Cancer Institution. Methods: The study included 261 women aged 40-78 who visited the Warith International Cancer Institution in Karbala, Iraq, between May 2023 and April 2024. Participants were divided into two groups based on mammographic breast density (MBD) according to the BI-RADS classification: non-dense (fatty and scattered fibroglandular) and dense (heterogeneously dense and dense) breasts. Demographic and clinical data were collected, and the association between breast density and various breast conditions was analyzed using logistic regression. Results: For malignant cases, the odds ratio (OR) for invasive ductal carcinoma (IDC) was 1.04, suggesting no significant difference in the likelihood of IDC between dense and non-dense breasts. The OR for ductal carcinoma in situ (DCIS) was 1.60, indicating a slightly higher, but not statistically significant, likelihood of DCIS in dense breasts. The overall OR for malignant cases was 1.26, showing a trend towards a higher likelihood of malignancy in dense breasts, but it was not statistically significant. For benign conditions, the OR for fibrocystic changes was 1.76, suggesting a higher likelihood in dense breasts, but the results were not statistically significant. Similarly, the ORs for fibroadenoma (1.27) and simple cysts (1.40) showed a slight increase in dense breasts but without statistical significance. Abscess and duct ectasia were less likely in dense breasts, but the results were not statistically significant. Conclusion: The study findings suggest that some breast conditions may be more common in dense breasts, but the differences are not statistically significant in many cases. Further targeted research is needed to understand better the relationships between breast density and various breast pathologies.
... [19][20][21] The PRSMAN panel was designed to target 883 PRS-associated variants in breast, ovarian, and other cancer types that were published by July 2020 (Table S1). 8,10,[15][16][17][22][23][24][25][26][27][28][29][30][31][32][33][34][35] The Roche probe design algorithm excluded 39 SNPs from the PRSMAN panel before its production. The final 844 targeted SNPs are listed in Table S2. ...
Background
The polygenic risk score (PRS) allows the quantification of the polygenic effect of many low‐penetrance alleles on the risk of breast cancer (BC). This study aimed to evaluate the performance of two sets comprising 77 or 313 low‐penetrance loci (PRS77 and PRS313) in patients with BC in the Czech population.
Methods
In a retrospective case‐control study, variants were genotyped from both the PRS77 and PRS313 sets in 1329 patients with BC and 1324 noncancer controls, all women without germline pathogenic variants in BC predisposition genes. Odds ratios (ORs) were calculated according to the categorical PRS in individual deciles. Weighted Cox regression analysis was used to estimate the hazard ratio (HR) per standard deviation (SD) increase in PRS.
Results
The distributions of standardized PRSs in patients and controls were significantly different (p < 2.2 × 10⁻¹⁶) with both sets. PRS313 outperformed PRS77 in categorical and continuous PRS analyses. For patients in the highest 2.5% of PRS313, the risk reached an OR of 3.05 (95% CI, 1.66–5.89; p = 1.76 × 10⁻⁴). The continuous risk was estimated as an HRper SD of 1.64 (95% CI, 1.49–1.81; p < 2.0 × 10⁻¹⁶), which resulted in an absolute risk of 21.03% at age 80 years for individuals in the 95th percentile of PRS313. Discordant categorization into PRS deciles was observed in 248 individuals (9.3%).
Conclusions
Both PRS77 and PRS313 are able to stratify individuals according to their BC risk in the Czech population. PRS313 shows better discriminatory ability. The results support the potential clinical utility of using PRS313 in individualized BC risk prediction.
... Tree validation studies added genetic information as a polygenic risk score to the model. Tey achieved AUC values of 0.69 [32], 0.65 [59], and 0.72 [58], whereby the latter applied to the prediction of oestrogen receptorpositive breast cancer. ...
Purpose. Breast cancer is the most common cancer among women globally, with an incidence of approximately two million cases in 2018. Organised age-based breast cancer screening programs were established worldwide to detect breast cancer earlier and to reduce mortality. Currently, there is substantial anticipation regarding risk-adjusted screening programs, considering various risk factors in addition to age. The present study investigated the discriminatory accuracy of breast cancer risk prediction models and whether they suit risk-based screening programs. Methods. Following the PICO scheme, we conducted an overview of reviews and systematically searched four databases. All methodological steps, including the literature selection, data extraction and synthesis, and the quality appraisal were conducted following the 4-eyes principle. For the quality assessment, the AMSTAR 2 tool was used. Results. We included eight systematic reviews out of 833 hits based on the prespecified inclusion criteria. The eight systematic reviews comprised ninety-nine primary studies that were also considered for the data analysis. Three systematic reviews were assessed as having a high risk of bias, while the others were rated with a moderate or low risk of bias. Most identified breast cancer risk prediction models showed a low prognostic quality. Adding breast density and genetic information as risk factors only moderately improved the models’ discriminatory accuracy. Conclusion. All breast cancer risk prediction models published to date show a limited ability to predict the individual breast cancer risk in women. Hence, it is too early to implement them in national breast cancer screening programs. Relevant randomised controlled trials about the benefit-harm ratio of risk-adjusted breast cancer screening programs compared to conventional age-based programs need to be awaited.
... 12,17 The BCSC model was found to have the highest discriminatory accuracy among women with known increased mammographic breast density. A study by Vachon et al 18 found that breast density and genetic variation in the BCSC are important risk factors in estimating breast cancer risk with the BCSC model. In relation to the intermediate risk group, the BCSC model considers factors including breast density and personal history of atypia and LCIS. ...
Breast cancer screening guidelines vary for women at intermediate risk (15%-20% lifetime risk) for developing breast cancer across jurisdictions. Currently available risk assessment models have differing strengths and weaknesses, creating difficulty and ambiguity in selecting the most appropriate model to utilize. Clarifying which model to utilize in individual circumstances may help determine the best screening guidelines to use for each individual.
... The European Food Safety Authority (EFSA) reported that no association was found between isoflavone intake and uterine cancer or thyroid function. In the same report, the EFSA also concluded that no significant association was observed between isoflavone consumption and mammographic density, which is associated with breast cancer (Boyd, 2013; EFSA Panel on Food Additives Nutrient Sources added to Food, 2015; Vachon et al., 2015). Of particular note, Symbiota® contains approximately 18 mg/100 g of total isoflavones (genistein, daidzein, and glycitein), which is comparable to other soy-based foods (Table 2). ...
A safety evaluation was performed of Symbiota®, which is made by a proprietary anaerobic fermentation process of soybean with multistrains of probiotics and a yeast. The battery of genotoxicity studies showed that Symbiota® has no genotoxic effects. Safety and tolerability were further assessed by acute or repeated dose 28‐ and 90‐day rodent studies, and no alterations in clinical observations, ophthalmological examination, blood chemistry, urinalysis, or hematology were observed between the control group and the different dosing groups (1.5, 5, and 15 mL/kg/day). There were no adverse effects on specific tissues or organs in terms of weight and histopathology. Importantly, the Symbiota® treatment did not perturb hormones and other endocrine‐related endpoints. Of note, the No‐Observed‐Adverse‐Effect‐Level was determined to be 15 mL/kg/day in rats. Moreover, a randomized, double‐blind, placebo‐controlled clinical trial was recently conducted with healthy volunteers who consumed 8 mL/day of placebo or Symbiota® for 8 weeks. Only mild adverse events were reported in both groups, and the blood chemistry and blood cell profiles were also similar between the two groups. In summary, this study concluded that the oral consumption of Symbiota® at 8 mL/day by the general population does not pose any human health concerns.
... Risk-based screening could optimise benefits while minimising harm (Harkness et al., 2020). There are several calculators that can measure the risk of breast cancer, such as the Tyrer-Cuzick Risk Calculator, the BCSC Risk Calculator, and the BCRAT Calculator (Himes et al., 2016;Matsuno et al., 2011;Vachon et al., 2015). The criteria for risk factors that they use vary, but in general they produce a calculation which combines more than one risk. ...
Although mammography is the gold standard for breast cancer screening, the World Health Organization recommends clinical breast examination (CBE) as the preferred early detection method in countries with limited resources. However, its effectiveness as a ‘stand-alone’ screening modality compared with other techniques remains unclear. Therefore, we evaluated a risk-based opportunistic breast cancer screening programme using three modalities. Between June and December 2018, we conducted a cross-sectional study in Yogyakarta, Indonesia, of women aged >40 years with at least one risk factor for breast cancer. Subjects underwent CBE, mammography, and ultrasonography. We calculated the proportion of breast lesions detected through each modality and compared their mass size. A total of 503 eligible subjects were screened. Five cases of potential malignant lesions were detected; pathological tests conducted for 4 of them confirmed breast cancer diagnoses. A combined assessment of mammography and ultrasonography examinations revealed 343 breast lesions (68.2%), whereas CBE screening detected only 76 breast lesions (15.1%). The mean lesion sizes detected by mammography or ultrasonography, but not through CBE, were significantly smaller (p-values of 0.037 and 0.007 for mammography and ultrasonography, respectively). In conclusion, mammography and ultrasonography produced higher detection rates for benign and malignant breast lesions compared with CBE.
... The BCSC risk models have moderate discrimination, they do not consider potentially useful predictors such as family history of breast cancer in second-degree relatives, genetic polymorphisms, or quantitative image-based mammographic features, and it is unclear how commonly they are used in clinical practice. However, the BCSC invasive breast cancer risk model is well calibrated in comparison to other models and externally validated in three cohorts.[41][42][43] To our knowledge, the BCSC advanced cancer risk model is the only available risk model for advanced breast cancer. ...
Background
There are no consensus guidelines for supplemental breast cancer screening with whole‐breast ultrasound. However, criteria for women at high risk of mammography screening failures (interval invasive cancer or advanced cancer) have been identified. Mammography screening failure risk was evaluated among women undergoing supplemental ultrasound screening in clinical practice compared with women undergoing mammography alone.
Methods
A total of 38,166 screening ultrasounds and 825,360 screening mammograms without supplemental screening were identified during 2014–2020 within three Breast Cancer Surveillance Consortium (BCSC) registries. Risk of interval invasive cancer and advanced cancer were determined using BCSC prediction models. High interval invasive breast cancer risk was defined as heterogeneously dense breasts and BCSC 5‐year breast cancer risk ≥2.5% or extremely dense breasts and BCSC 5‐year breast cancer risk ≥1.67%. Intermediate/high advanced cancer risk was defined as BCSC 6‐year advanced breast cancer risk ≥0.38%.
Results
A total of 95.3% of 38,166 ultrasounds were among women with heterogeneously or extremely dense breasts, compared with 41.8% of 825,360 screening mammograms without supplemental screening (p < .0001). Among women with dense breasts, high interval invasive breast cancer risk was prevalent in 23.7% of screening ultrasounds compared with 18.5% of screening mammograms without supplemental imaging (adjusted odds ratio, 1.35; 95% CI, 1.30–1.39); intermediate/high advanced cancer risk was prevalent in 32.0% of screening ultrasounds versus 30.5% of screening mammograms without supplemental screening (adjusted odds ratio, 0.91; 95% CI, 0.89–0.94).
Conclusions
Ultrasound screening was highly targeted to women with dense breasts, but only a modest proportion were at high mammography screening failure risk. A clinically significant proportion of women undergoing mammography screening alone were at high mammography screening failure risk.
... In our survey (Supplementary Table S1, Fig. 2), we considered the BCRAT, BOADICEA, BRCAPRO, IBIS and BCSC scores (Gail et al. 1989;Parmigiani et al. 1998;Tice et al. 2008), in addition to the combination of various clinical parameters. The original studies took different approaches to combine clinical scores with PRSs, from simple multiplication (Dite et al. 2016;Vachon et al. 2015;van Veen et al. 2018), via the simultaneous inclusion of both scores in a logistic regression or Cox model (Husing et al. 2012;Lall et al. 2019;Zhang et al. 2018), to the direct inclusion of the early PRS of Mavaddat et al. (2015) into the BOADICEA score (Choudhury et al. 2021;Lakeman et al. 2020). ...
Since their first appearance in the context of schizophrenia and bipolar disorder in 2009, polygenic risk scores (PRSs) have been described for a large number of common complex diseases. However, the clinical utility of PRSs in disease risk assessment or therapeutic decision making is likely limited because PRSs usually only account for the heritable component of a trait and ignore the etiological role of environment and lifestyle. We surveyed the current state of PRSs for various diseases, including breast cancer, diabetes, prostate cancer, coronary artery disease, and Parkinson disease, with an extra focus upon the potential improvement of clinical scores by their combination with PRSs. We observed that the diagnostic and prognostic performance of PRSs alone is consistently low, as expected. Moreover, combining a PRS with a clinical score at best led to moderate improvement of the power of either risk marker. Despite the large number of PRSs reported in the scientific literature, prospective studies of their clinical utility, particularly of the PRS-associated improvement of standard screening or therapeutic procedures, are still rare. In conclusion, the benefit to individual patients or the health care system in general of PRS-based extensions of existing diagnostic or treatment regimens is still difficult to judge.
... We used a nested case-control dataset from the Nurses' Health Study to compare the model's performance to existing models. Our results are consistent with studies that have investigated the association of mammographic density and PRS with breast cancer risk [46][47][48], including those that have incorporated a risk prediction model [49,50]. We used the classic version of IBIS (without SNPs or mammographic density) because this was the only version available in the Nurses' Health Study. ...
Purpose
We compared a simple breast cancer risk prediction model, BRISK (which includes mammographic density, polygenic risk and clinical factors), against a similar model with more risk factors (simplified Rosner) and against two commonly used clinical models (Gail and IBIS).
Methods
Using nested case–control data from the Nurses’ Health Study, we compared the models’ association, discrimination and calibration. Classification performance was compared between Gail and BRISK for 5-year risks and between IBIS and BRISK for remaining lifetime risk.
Results
The odds ratio per standard deviation was 1.43 (95% CI 1.32, 1.55) for BRISK 5-year risk, 1.07 (95% CI 0.99, 1.14) for Gail 5-year risk, 1.72 (95% CI 1.59, 1.87) for simplified Rosner 10-year risk, 1.51 (95% CI 1.41, 1.62) for BRISK remaining lifetime risk and 1.26 (95% CI 1.16, 1.36) for IBIS remaining lifetime risk. The area under the receiver operating characteristic curve (AUC) was improved for BRISK over Gail for 5-year risk (AUC = 0.636 versus 0.511, P < 0.0001) and for BRISK over IBIS for remaining lifetime risk (AUC = 0.647 versus 0.571, P < 0.0001). BRISK was well calibrated for the estimation of both 5-year risk (expected/observed [E/O] = 1.03; 95% CI 0.73, 1.46) and remaining lifetime risk (E/O = 1.01; 95% CI 0.86, 1.17). The Gail 5-year risk (E/O = 0.85; 95% CI 0.58, 1.24) and IBIS remaining lifetime risk (E/O = 0.73; 95% CI 0.60, 0.87) were not well calibrated, with both under-estimating risk. BRISK improves classification of risk compared to Gail 5-year risk (NRI = 0.31; standard error [SE] = 0.031) and IBIS remaining lifetime risk (NRI = 0.287; SE = 0.035).
Conclusion
BRISK performs better than two commonly used clinical risk models and no worse compared to a similar model with more risk factors.
... While LCIS has been shown to increase risk of both DCIS and invasive cancer, the BCSC model specifically calculates risk prediction for invasive cancer development. 11 The purpose of this study is to evaluate the accuracy and discrimination of the BCSC model in predicting the development of invasive breast cancer among women with LCIS. ...
PurposeThe Breast Cancer Surveillance Consortium (BCSC) model predicts risk of invasive breast cancer risk based on age, race, family history, breast density, and history of benign breast disease, including lobular carcinoma in situ (LCIS). However, validation studies for this model included few women with LCIS. We sought to evaluate the accuracy of the BCSC model among this cohort. Methods
Women with LCIS diagnosed between 1983 and 2017 were identified from a prospectively maintained database. The BCSC score was calculated; those with prior breast cancer, unknown breast density, aged <35 or >74 or history of chemoprevention use were excluded. The Kaplan-Meier method was used to estimate incidence rates. Time-dependent receiver operating characteristic (ROC) analysis was used to analyze the discriminative capacity of the model. Results1302 women with LCIS were included. At a median follow-up of 7 years, 152 women (12%) developed invasive cancer (6 with bilateral disease). Cumulative incidences of invasive breast cancer were 7.1% (95% CI 5.6, 8.7) and 13.3% (95% CI 10.9,15.6), respectively, and the median BCSC risk scores were 4.9 and 10.4, respectively, at 5 and 10 years. The median 10year BCSC score was significantly lower than the 10-year Tyrer-Cuzick score (10.4 vs 20.8, p<0.001). The ROC curve scores (AUC) for BCSC at 5 and 10 years were 0.59 (95% CI 0.52, 0.66) and 0.58 (95% CI 0.52, 0.64), respectively. Conclusion
The BCSC model has moderate accuracy in predicting invasive breast cancer risk among women with LCIS with fair discrimination for risk prediction between individuals.
... Long-term studies have shown the BOADICEA, IBIS/ Tyrer-Cuzick, and BCSC risk models to be well calibrated overall, though they have less discriminatory ability at the level of individual patients (35)(36)(37). The addition of polygenic risk scores (38)(39)(40)(41) and advances in artificial intelligence (AI) using digital mammogram features in addition to traditional risk factors (42-44) have improved risk prediction and (45). The use of traditional risk models and polygenic risk scores, however, may not be equally valid for all women. ...
Objective
To assess effectiveness of a web-based educational intervention on women’s health care provider knowledge of breast cancer risk models and high-risk screening recommendations.
Methods
A web-based pre- and post-test study including 177 U.S.-based women’s health care providers was conducted in 2019. Knowledge gaps were defined as fewer than 75% of respondents answering correctly. Pre- and post-test knowledge differences (McNemar test) and associations of baseline characteristics with pre-test knowledge gaps (logistic regression) were evaluated.
Results
Respondents included 131/177 (74.0%) physicians; 127/177 (71.8%) practiced obstetrics/gynecology. Pre-test, 118/177 (66.7%) knew the Gail model predicts lifetime invasive breast cancer risk; this knowledge gap persisted post-test [(121/177, 68.4%); P = 0.77]. Just 39.0% (69/177) knew the Gail model identifies women eligible for risk-reducing medications; this knowledge gap resolved. Only 48.6% (86/177) knew the Gail model should not be used to identify women meeting high-risk MRI screening guidelines; this deficiency decreased to 66.1% (117/177) post-test (P = 0.001). Pre-test, 47.5% (84/177) knew the Tyrer-Cuzick model is used to identify women meeting high-risk screening MRI criteria, 42.9% (76/177) to predict BRCA1/2 pathogenic mutation risk, and 26.0% (46/177) to predict lifetime invasive breast cancer risk. These knowledge gaps persisted but improved. For a high-risk 30-year-old, 67.8% (120/177) and 54.2% (96/177) pre-test knew screening MRI and mammography/tomosynthesis are recommended, respectively; 19.2% (34/177) knew both are recommended; and 53% (94/177) knew US is not recommended. These knowledge gaps resolved or reduced.
Conclusion
Web-based education can reduce important provider knowledge gaps about breast cancer risk models and high-risk screening recommendations.
... Existing studies of European ancestries have consistently shown that there is at least a twofold difference in breast cancer risk between the lowest and highest quartiles of PRS distributions [1,[3][4][5]. The addition of PRS to clinical risk models has also been shown to improve discrimination and reclassification abilities [6,7]. Ongoing clinical trials are examining the effect of personalized medicine on breast cancer screening and prevention using PRS [8][9][10]. ...
PurposeThis study aimed to develop an ancestry-specific polygenic risk scores (PRSs) for the prediction of breast cancer events in Japanese females and validate it in a longitudinal cohort study.Methods
Using publicly available summary statistics of female breast cancer genome-wide association study (GWAS) of Japanese and European ancestries, we, respectively, developed 31 candidate genome-wide PRSs using pruning and thresholding (P + T) and LDpred methods with varying parameters. Among the candidate PRS models, the best model was selected using a case-cohort dataset (63 breast cancer cases and 2213 sub-cohorts of Japanese females during a median follow-up of 11.9 years) according to the maximal predictive ability by Harrell’s C-statistics. The best-performing PRS for each derivation GWAS was evaluated in another independent case-cohort dataset (260 breast cancer cases and 7845 sub-cohorts of Japanese females during a median follow-up of 16.9 years).ResultsFor the best PRS model involving 46,861 single nucleotide polymorphisms (SNPs; P + T method with PT = 0.05 and R2 = 0.2) derived from Japanese-ancestry GWAS, the Harrell’s C-statistic was 0.598 ± 0.018 in the evaluation dataset. The age-adjusted hazard ratio for breast cancer in females with the highest PRS quintile compared with those in the lowest PRS quintile was 2.47 (95% confidence intervals, 1.64–3.70). The PRS constructed using Japanese-ancestry GWAS demonstrated better predictive performance for breast cancer in Japanese females than that using European-ancestry GWAS (Harrell’s C-statistics 0.598 versus 0.586).Conclusion
This study developed a breast cancer PRS for Japanese females and demonstrated the usefulness of the PRS for breast cancer risk stratification.
... Bei den nicht genetischen Risikofaktoren stehen die mammografische Dichte [16][17][18] und reproduktive Faktoren im Fokus [19][20][21][22][23][24][25][26][27]. Zusätzlich sind insbesondere Alter bei Menarche und bei Menopause und die Anzahl der Kinder ebenso wie die Dauer des Stillens gut untersuchte Risikofaktoren [19,21]. ...
Zusammenfassung
In dieser Übersichtsarbeit werden neueste Entwicklungen in der Prävention von Brustkrebs und Behandlung von Patientinnen mit frühen Krankheitsstadien mit Mammakarzinom zusammengefasst. Die Ermittlung von individuellen Erkrankungsrisiken nach molekularen Subtypen wurde in einer großen epidemiologischen Studie untersucht. Im Bereich der Behandlung gibt es neue Daten zur Langzeitnachbeobachtung der Aphinity-Studie ebenso wie neue Daten zur neoadjuvanten Therapie von HER2-positiven Patientinnen mit Atezolizumab. Biomarker wie Residual Cancer Burden wurden im Zusammenhang mit einer Pembrolizumab-Therapie untersucht. Eine Untersuchung des Genomic-Grade-Indexes bei älteren Patientinnen reiht sich ein in die Gruppe von Studien, die versucht, durch moderne Multigentests Patientinnen zu identifizieren, bei denen eine Chemotherapie vermieden werden kann, weil diese eine exzellente Prognose haben. Diese und weitere Aspekte der neuesten Entwicklungen bei der Diagnostik und Therapie des Mammakarzinoms werden in dieser Übersichtsarbeit beschrieben.
... In recent years, some risk models have been updated to include additional genomic information, typically the effects of rare PV in other genes (ATM, CHEK2 and PALB2) [2,3] and the joined effect of single nucleotide polymorphisms (SNPs) summarized in polygenic risk scores (PRS) [4][5][6]. Indeed, in the general population, some studies suggested that stratification of women according to their risk of breast cancer based on their PRS could personalize screening and prevention strategies [7][8][9][10]. Several PRS for breast cancer have been defined and validated in women of European ancestry from the large multi-centric and multicountry studies conducted by the Breast Cancer Association Consortium (BCAC). ...
Background
Three partially overlapping breast cancer polygenic risk scores (PRS) comprising 77, 179 and 313 SNPs have been proposed for European-ancestry women by the Breast Cancer Association Consortium (BCAC) for improving risk prediction in the general population. However, the effect of these SNPs may vary from one country to another and within a country because of other factors.
Objective
To assess their associated risk and predictive performance in French women from (1) the CECILE population-based case-control study, (2) BRCA1 or BRCA2 (BRCA1/2) pathogenic variant (PV) carriers from the GEMO study, and (3) familial breast cancer cases with no BRCA1/2 PV and unrelated controls from the GENESIS study.
Results
All three PRS were associated with breast cancer in all studies, with odds ratios per standard deviation varying from 1.7 to 2.0 in CECILE and GENESIS, and hazard ratios varying from 1.1 to 1.4 in GEMO. The predictive performance of PRS313 in CECILE was similar to that reported in BCAC but lower than that in GENESIS (area under the receiver operating characteristic curve (AUC)=0.67 and 0.75, respectively). PRS were less performant in BRCA2 and BRCA1 PV carriers (AUC = 0.58 and 0.54 respectively).
Conclusion
Our results are in line with previous validation studies in the general population and in BRCA1/2 PV carriers. Additionally, we showed that PRS may be of clinical utility for women with a strong family history of breast cancer and no BRCA1/2 PV, and for those carrying a predicted PV in a moderate-risk gene like ATM, CHEK2 or PALB2.
... Several studies have shown that the addition of PRS to the classical breast cancer risk factors improves risk stratification. Vachon et al. [50], based on the analysis of three casecontrol studies (1643 cases and 2397 controls) conducted in Germany and the USA, found that a 76-locus PRS and breast density were independent risk factors and that incorporating the PRS into the BCSC risk model improved the model fit and net reclassification for case patients. For instance, the PRS further stratified the risk associated with extremely dense breasts such that those with the lowest PRS had an odds ratio of 0.91, while those in the highest PRS had a 2.7-fold increased risk compared with women with scattered fibroglandular densities and average PRS. ...
The aim of this study was to assess the acceptability and feasibility of offering risk-based breast cancer screening and its integration into regular clinical practice. A single-arm proof-of-concept trial was conducted with a sample of 387 women aged 40–50 years residing in the city of Lleida (Spain). The study intervention consisted of breast cancer risk estimation, risk communication and screening recommendations, and a follow-up. A polygenic risk score with 83 single nucleotide polymorphisms was used to update the Breast Cancer Surveillance Consortium risk model and estimate the 5-year absolute risk of breast cancer. The women expressed a positive attitude towards varying the frequency of breast screening according to individual risk and, especially, more frequently inviting women at higher-than-average risk. A lower intensity screening for women at lower risk was not as welcome, although half of the participants would accept it. Knowledge of the benefits and harms of breast screening was low, especially with regard to false positives and overdiagnosis. The women expressed a high understanding of individual risk and screening recommendations. The participants’ intention to participate in risk-based screening and satisfaction at 1-year were very high.
... Although these models are commonly used in clinical practice, few studies have examined their accuracy among women with high-risk lesions [8][9][10]. While LCIS has been shown to increase risk of both DCIS and invasive cancer, the BCSC model specifically calculates risk prediction for invasive cancer development [11]. The purpose of this study is to evaluate the accuracy and discrimination of the BCSC model in predicting the development of invasive breast cancer among women with LCIS, and to compare the accuracy of the BCSC model to the Tyrer-Cuzick model. ...
Purpose
The Breast Cancer Surveillance Consortium (BCSC) model predicts risk of invasive breast cancer risk based on age, race, family history, breast density, and history of benign breast disease, including lobular carcinoma in situ (LCIS). However, validation studies for this model included few women with LCIS. We sought to evaluate the accuracy of the BCSC model among this cohort.
Methods
Women with LCIS diagnosed between 1983 and 2017 were identified from a prospectively maintained database. The BCSC score was calculated; those with prior breast cancer, unknown breast density, age < 35 years or > 74 years, or with history of chemoprevention use were excluded. The Kaplan–Meier method was used to estimate incidence rates. Time-dependent receiver operating characteristic (ROC) analysis was used to analyze the discriminative capacity of the model.
Results
1302 women with LCIS were included. At a median follow-up of 7 years, 152 women (12%) developed invasive cancer (6 with bilateral disease). Cumulative incidences of invasive breast cancer were 7.1% (95% CI 5.6–8.7) and 13.3% (95% CI 10.9–15.6), respectively, and the median BCSC risk scores were 4.9 and 10.4, respectively, at 5 and 10 years. The median 10-year BCSC score was significantly lower than the 10–year Tyrer-Cuzick score (10.4 vs 20.8, p < 0.001). The ROC curve scores (AUC) for BCSC at 5 and 10 years were 0.59 (95% CI 0.52–0.66) and 0.58 (95% CI 0.52–0.64), respectively.
Conclusion
The BCSC model has moderate accuracy in predicting invasive breast cancer risk among women with LCIS with fair discrimination for risk prediction between individuals.
... 10,11 Recent studies have considered the value of including an SNP-based PRS into risk prediction algorithms showing promising results. [12][13][14][15] We collected data for classical breast cancer risk factors and mammographic density of 57,902 women attending screening, who were aged 46 to 73 years at entry to the Predicting Risk of Cancer at Screening (PROCAS) study. 16 More than 10,000 women also provided saliva DNA samples for initial validation of 18-breast cancer susceptibility SNP PRS (SNP18). ...
Purpose
There is great promise in breast cancer risk stratification to target screening and prevention. It is unclear whether adding gene panels to other risk tools improves breast cancer risk stratification and adds discriminatory benefit on a population basis.
Methods
In total, 10,025 of 57,902 women aged 46 to 73 years in the Predicting Risk of Cancer at Screening study provided DNA samples. A case–control study was used to evaluate breast cancer risk assessment using polygenic risk scores (PRSs), cancer gene panel (n = 33), mammographic density (density residual [DR]), and risk factors collected using a self-completed 2-page questionnaire (Tyrer-Cuzick [TC] model version 8). In total, 525 cases and 1410 controls underwent gene panel testing and PRS calculation (18, 143, and/or 313 single-nucleotide polymorphisms [SNPs]).
Results
Actionable pathogenic variants (PGVs) in BRCA1/2 were found in 1.7% of cases and 0.55% of controls, and overall PGVs were found in 6.1% of cases and 1.3% of controls. A combined assessment of TC8-DR-SNP313 and gene panel provided the best risk stratification with 26.1% of controls and 9.7% of cases identified at <1.4% 10-year risk and 9.01% of controls and 23.3% of cases at ≥8% 10-year risk. Because actionable PGVs were uncommon, discrimination was identical with/without gene panel (with/without: area under the curve = 0.67, 95% CI = 0.64-0.70). Only 7 of 17 PGVs in cases resulted in actionable risk category change. Extended case (n = 644)–control (n = 1779) series with TC8-DR-SNP143 identified 18.9% of controls and only 6.4% of stage 2+ cases at <1.4% 10-year risk and 20.7% of controls and 47.9% of stage 2+ cases at ≥5% 10-year risk.
Conclusion
Further studies and economic analysis will determine whether adding panels to PRS is a cost-effective strategy for risk stratification.
... This is due to both the absolute higher amount of fibroglandular tissue within the breast and the breast composition [8]. Breast density is independent of other personal risk factors typically used for breast cancer risk prediction, and complementary when used in conjunction [9,10]. Breast density is estimated to account for 26% of breast cancers in postmenopausal women [11]. ...
Breast density is an independent risk factor for the development of breast cancer and also decreases the sensitivity of mammography for screening. Consequently, women with extremely dense breasts face an increased risk of late diagnosis of breast cancer. These women are, therefore, underserved with current mammographic screening programs. The results of recent studies reporting on contrast-enhanced breast MRI as a screening method in women with extremely dense breasts provide compelling evidence that this approach can enable an important reduction in breast cancer mortality for these women and is cost-effective. Because there is now a valid option to improve breast cancer screening, the European Society of Breast Imaging (EUSOBI) recommends that women should be informed about their breast density. EUSOBI thus calls on all providers of mammography screening to share density information with the women being screened. In light of the available evidence, in women aged 50 to 70 years with extremely dense breasts, the EUSOBI now recommends offering screening breast MRI every 2 to 4 years. The EUSOBI acknowledges that it may currently not be possible to offer breast MRI immediately and everywhere and underscores that quality assurance procedures need to be established, but urges radiological societies and policymakers to act on this now. Since the wishes and values of individual women differ, in screening the principles of shared decision-making should be embraced. In particular, women should be counselled on the benefits and risks of mammography and MRI-based screening, so that they are capable of making an informed choice about their preferred screening method.
Key Points
• The recommendations in Figure 1 summarize the key points of the manuscript
... In the field of breast cancer, large-scale studies and reproducible methods have facilitated the development and validation of polygenic risk prediction models in European populations. Studies that examined the effect of PRS have consistently reported positive associations between PRS and breast cancer risk [16,[108][109][110][111][112][113][114]. Although several PRSs have been developed, one of the current best performing PRS incorporates information from 313 SNPs (PRS 313 ) [16]: compared with women in the middle quintile (40th-60th percentile at population average risk), those in the highest 1% of the PRS 313 distribution had approximately four-fold greater risk for breast cancer [16]. ...
Genome-wide association studies (GWASs) have shown that the genetic architecture of cancers are highly polygenic and enabled researchers to identify genetic risk loci for cancers. The genetic variants associated with a cancer can be combined into a polygenic risk score (PRS), which captures part of an individual’s genetic susceptibility to cancer. Recently, PRSs have been widely used in cancer risk prediction and are shown to be capable of identifying groups of individuals who could benefit from the knowledge of their probabilistic susceptibility to cancer, which leads to an increased interest in understanding the potential utility of PRSs that might further refine the assessment and management of cancer risk. In this context, we provide an overview of the major discoveries from cancer GWASs. We then review the methodologies used for PRS construction, and describe steps for the development and evaluation of risk prediction models that include PRS and/or conventional risk factors. Potential utility of PRSs in cancer risk prediction, screening, and precision prevention are illustrated. Challenges and practical considerations relevant to the implementation of PRSs in health care settings are discussed.
Polygenic risk scores (PRSs) can significantly enhance breast cancer risk prediction when combined with clinical risk factor data. While many studies have explored the value-add of PRSs, little is known about the potential impact of gene-by-gene or gene-by-environment interactions towards enhancing the risk discrimination capabilities of multi-modal models combining PRSs with clinical data. In this study, we integrated data on 318 individual genotype variants along with clinical data in a neural network to explore whether gene-by-gene (i.e., between individual variants) and/or gene-by-environment (between clinical risk factors and variants) interactions could be leveraged jointly during training to improve breast cancer risk prediction performance. We benchmarked our approach against a baseline model combining traditional univariate PRSs with clinical data in a logistic regression model and ran an interpretability analysis to identify feature interactions. While our model did not demonstrate improved performance over the baseline, we discovered 248 (<1%) statistically significant gene-by-gene and gene-by-environment interactions out of the ~53.6k possible feature pairs, the most contributory of which included rs6001930 (MKL1) and rs889312 (MAP3K1), with age and menopause being the most heavily interacting non-genetic risk factors. We also modeled the significant interactions as a network of highly connected features, suggesting that potential higher-order interactions are captured by the model. Although gene-by-environment (or gene-by-gene) interactions did not enhance breast cancer risk prediction performance in neural networks, our study provides evidence that these interactions can be leveraged by these models to inform their predictions. This study represents the first application of neural networks to screen for interactions impacting breast cancer risk using real-world data.
Introduction. Personalized web-based clinical decision tools for breast cancer prevention and screening could address knowledge gaps, enhance patient autonomy in shared decision-making, and promote equitable care. The purpose of this review was to present evidence on the availability, usability, feasibility, acceptability, quality, and uptake of breast cancer prevention and screening tools to support their integration into clinical care. Methods. We used the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews Checklist to conduct this review. We searched 6 databases to identify literature on the development, validation, usability, feasibility, acceptability testing, and uptake of the tools into practice settings. Quality assessment for each tool was conducted using the International Patient Decision Aid Standard instrument, with quality scores ranging from 0 to 63 (lowest-highest). Results. We identified 10 tools for breast cancer prevention and 9 tools for screening. The tools included individual (e.g., age), clinical (e.g., genomic risk factors), and health behavior (e.g., alcohol use) characteristics. Fourteen tools included race/ethnicity, but no tool incorporated contextual factors (e.g., insurance, access) associated with breast cancer. All tools were internally or externally validated. Six tools had undergone usability testing in samples including White (median, 71%; range, 9%–96%), insured (99%; 97%–100%) women, with college education or higher (60%; 27%–100%). All of the tools were developed and tested in academic settings. Seven (37%) tools showed potential evidence of uptake in clinical practice. The tools had an average quality assessment score of 21 (range, 9–39). Conclusions. There is limited evidence on testing and uptake of breast cancer prevention and screening tools in diverse clinical settings. The development, testing, and integration of tools in academic and nonacademic settings could potentially improve uptake and equitable access to these tools.
Highlights
There were 19 personalized, interactive, Web-based decision tools for breast cancer prevention and screening. Breast cancer outcomes were personalized based on individual clinical characteristics (e.g., age, medical history), genomic risk factors (e.g., BRCA1/2), race and ethnicity, and health behaviors (e.g., smoking). The tools did not include contextual factors (e.g., insurance status, access to screening facilities) that could potentially contribute to breast cancer outcomes. Validation, usability, acceptability, and feasibility testing were conducted mostly among White and/or insured patients with some college education (or higher) in academic settings. There was limited evidence on testing and uptake of the tools in nonacademic clinical settings.
Refinement of breast cancer risk estimates with a polygenic-risk score (PRS) may improve uptake of risk-reducing endocrine therapy (ET). A previous clinical trial assessed the influence of adding a PRS to traditional risk estimates on ET use. We stratified participants according to PRS-refined breast cancer risk and evaluated ET use and ET-related quality of life (QOL) at 1-year (previously reported) and 2-year follow-ups. Of 151 participants, 58 (38.4%) initiated ET, and 22 (14.6%) discontinued ET by 2 years; 42 (27.8%) and 36 (23.8%) participants were using ET at 1- and 2-year follow-ups, respectively. At the 2-year follow-up, 39% of participants with a lifetime breast cancer risk of 40.1% to 100.0%, 18% with a 20.1% to 40.0% risk, and 16% with a 0.0% to 20.0% risk were taking ET (overall P = 0.01). Moreover, 40% of participants whose breast cancer risk increased by 10% or greater with addition of the PRS to a traditional breast cancer-risk model were taking ET versus 0% whose risk decreased by 10% or greater (P = 0.004). QOL was similar for participants taking or not taking ET at 1- and 2-year follow-ups, although most who discontinued ET did so because of adverse effects. However, these QOL results may have been skewed by the long interval between QOL surveys and lack of baseline QOL data. PRS-informed breast cancer prevention counseling has a lasting, but waning, effect over time. Additional follow-up studies are needed to address the effect of PRS on ET adherence, ET-related QOL, supplemental breast cancer screening, and other risk-reducing behaviors.
Prevention Relevance
Risk-reducing medications for breast cancer are considerably underused. Informing women at risk with precise and individualized risk assessment tools may substantially affect the incidence of breast cancer. In our study, a risk assessment tool (IBIS-polygenic-risk score) yielded promising results, with 39% of women at highest risk starting preventive medication.
Breast cancer is the most common neoplasm and main cause of cancer mortality in women; its incidence is still rising due to the high prevalence of its risk factors. Breast imaging plays a key role in the workflows of breast cancer, from secondary prevention through screening, to clinical diagnosis and follow-up. The aim of this chapter is to provide an overview of breast imaging modalities involved in breast cancer diagnostic pathways in women, along with their uses and roles in different settings as well as to outline future perspectives.
There are many active or recently completed breast cancer screening and treatment trials in 2023 that have the potential to fundamentally change the way breast radiologists practice medicine. Breast cancer screening trials may provide evidence to support supplemental screening beyond mammography to include US, contrast-enhanced mammography, and breast MRI. Furthermore, there are multiple efforts to support risk-adaptive screening strategies that would personalize screening modalities, frequencies, and ages of initiation. For breast cancer treatment, aims to reduce overtreatment may provide nonsurgical treatment options for women with low-risk breast cancer. Breast radiologists must be familiar with the study designs, major inclusion and exclusion criteria, and principal endpoints in order to determine when and how the study results should influence clinical care. As multidisciplinary team members, breast radiologists will have major roles in the success or failure of these trials as they transition from research to actual clinical practice.
PURPOSE
We extended the Breast Cancer Surveillance Consortium (BCSC) version 2 (v2) model of invasive breast cancer risk to include BMI, extended family history of breast cancer, and age at first live birth (version 3 [v3]) to better inform appropriate breast cancer prevention therapies and risk-based screening.
METHODS
We used Cox proportional hazards regression to estimate the age- and race- and ethnicity-specific relative hazards for family history of breast cancer, breast density, history of benign breast biopsy, BMI, and age at first live birth for invasive breast cancer in the BCSC cohort. We evaluated calibration using the ratio of expected-to-observed (E/O) invasive breast cancers in the cohort and discrimination using the area under the receiver operating characteristic curve (AUROC).
RESULTS
We analyzed data from 1,455,493 women age 35-79 years without a history of breast cancer. During a mean follow-up of 7.3 years, 30,266 women were diagnosed with invasive breast cancer. The BCSC v3 model had an E/O of 1.03 (95% CI, 1.01 to 1.04) and an AUROC of 0.646 for 5-year risk. Compared with the v2 model, discrimination of the v3 model improved most in Asian, White, and Black women. Among women with a BMI of 30.0-34.9 kg/m ² , the true-positive rate in women with an estimated 5-year risk of 3% or higher increased from 10.0% (v2) to 19.8% (v3) and the improvement was greater among women with a BMI of ≥35 kg/m ² (7.6%-19.8%).
CONCLUSION
The BCSC v3 model updates an already well-calibrated and validated breast cancer risk assessment tool to include additional important risk factors. The inclusion of BMI was associated with the largest improvement in estimated risk for individual women.
Overview
Breast cancer in women remains a major medical problem with significant public health and societal ramifications, including issues related to screening, risk factors, prevention, diagnosis, treatment, and survival following diagnosis. Major advances have markedly improved the understanding of clinical phenotypes, as well as the biologic pathways that drive tumor growth and resistance. This research has led to dramatic changes in treatment that have contributed to a significant reduction in breast cancer mortality over the last two decades, and is the basis of ongoing clinical research. Molecular profiling has provided insights into the heterogeneity of breast cancer subtypes; combining biology and tumor burden has allowed stratification of both risk and treatment to begin the process of individualizing screening, prevention, and treatment. As new information accumulates, new paradigms of management become the standard of care reflected in international guidelines. Our challenge is to apply new formation and treatment appropriately and effectively, and to understand both response and resistance. Information obtained from molecular, biologic, and pathologic investigations and clinical trials provides the major focus of this chapter.
Simple Summary
Breast cancer (BC) is the major cause of cancer-related deaths in women worldwide. In addition to genetic diagnostics for variants in high-risk genes, there is a need for better risk stratification to target high-risk individuals. The polygenic risk score (PRS) has emerged as a valuable addition to help sorting women into different risk categories for BC development. This study aimed to evaluate the impact of adding a PRS, based on 313 genetic variants, to standard genetic testing for 382 German women with BC or a family history of the disease. By incorporating the PRS into risk prediction models, meaningful changes in 10-year risks were observed in 13.6% of individuals. Additionally, the inclusion of the PRS led to clinically significant changes in prevention recommendations for 12.0% of cases, supporting the use of the PRS for BC risk assessment in genetic counselling.
Abstract
Single nucleotide polymorphisms are currently not considered in breast cancer (BC) risk predictions used in daily practice of genetic counselling and clinical management of familial BC in Germany. This study aimed to assess the clinical value of incorporating a 313-variant-based polygenic risk score (PRS) into BC risk calculations in a cohort of German women with suspected hereditary breast and ovarian cancer syndrome (HBOC). Data from 382 individuals seeking counselling for HBOC were analysed. Risk calculations were performed using the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm with and without the inclusion of the PRS. Changes in risk predictions and their impact on clinical management were evaluated. The PRS led to changes in risk stratification based on 10-year risk calculations in 13.6% of individuals. Furthermore, the inclusion of the PRS in BC risk predictions resulted in clinically significant changes in 12.0% of cases, impacting the prevention recommendations established by the German Consortium for Hereditary Breast and Ovarian Cancer. These findings support the implementation of the PRS in genetic counselling for personalized BC risk assessment.
Background:
Breast cancer is a complex, multifactorial disease influenced by many genetic factors. Besides the relatively rare pathogenic variants in high or moderate penetrant cancer predisposition genes, breast cancer risk is modified by numerous low risk alleles considered to be polygenic genetic factors. While the risks associated with individual polygenic loci are negligible, its cumulative effect can reach clinically significant values and it can be expressed as a polygenic risk score (PRS). PRS is recently considered to be a possible tool improving assessment of absolute and cumulative risks at the individual level.
Purpose:
Several single nucleotide polymorphism sets for PRS assessment have recently been developed and prepared for their implementation into clinical practice. The following text aims to explain the fundamental principles of the PRS assessment and its interpretation as a candidate prediction tool. The use of the PRS should always depend on genetic analysis of pathogenic variants in cancer predisposition genes including its current limitations.
Breast cancer risk is highly variable within the population and current research is leading the shift toward personalized medicine. By accurately assessing an individual woman's risk, we can reduce the risk of over/undertreatment by preventing unnecessary procedures or by elevating screening procedures. Breast density measured from conventional mammography has been established as one of the most dominant risk factors for breast cancer; however, it is currently limited by its ability to characterize more complex breast parenchymal patterns that have been shown to provide additional information to strengthen cancer risk models. Molecular factors ranging from high penetrance, or high likelihood that a mutation will show signs and symptoms of the disease, to combinations of gene mutations with low penetrance have shown promise for augmenting risk assessment. Although imaging biomarkers and molecular biomarkers have both individually demonstrated improved performance in risk assessment, few studies have evaluated them together. This review aims to highlight the current state of the art in breast cancer risk assessment using imaging and genetic biomarkers.
Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 6 is August 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
Purpose:
Artificial intelligence (AI) algorithms improve breast cancer detection on mammography, but their contribution to long-term risk prediction for advanced and interval cancers is unknown.
Methods:
We identified 2,412 women with invasive breast cancer and 4,995 controls matched on age, race, and date of mammogram, from two US mammography cohorts, who had two-dimensional full-field digital mammograms performed 2-5.5 years before cancer diagnosis. We assessed Breast Imaging Reporting and Data System density, an AI malignancy score (1-10), and volumetric density measures. We used conditional logistic regression to estimate odds ratios (ORs), 95% CIs, adjusted for age and BMI, and C-statistics (AUC) to describe the association of AI score with invasive cancer and its contribution to models with breast density measures. Likelihood ratio tests (LRTs) and bootstrapping methods were used to compare model performance.
Results:
On mammograms between 2-5.5 years prior to cancer, a one unit increase in AI score was associated with 20% greater odds of invasive breast cancer (OR, 1.20; 95% CI, 1.17 to 1.22; AUC, 0.63; 95% CI, 0.62 to 0.64) and was similarly predictive of interval (OR, 1.20; 95% CI, 1.13 to 1.27; AUC, 0.63) and advanced cancers (OR, 1.23; 95% CI, 1.16 to 1.31; AUC, 0.64) and in dense (OR, 1.18; 95% CI, 1.15 to 1.22; AUC, 0.66) breasts. AI score improved prediction of all cancer types in models with density measures (PLRT values < .001); discrimination improved for advanced cancer (ie, AUC for dense volume increased from 0.624 to 0.679, Δ AUC 0.065, P = .01) but did not reach statistical significance for interval cancer.
Conclusion:
AI imaging algorithms coupled with breast density independently contribute to long-term risk prediction of invasive breast cancers, in particular, advanced cancer.
Thirty-eight states and the District of Columbia (DC) have dense breast notification (DBN) laws that mandate varying levels of patient notification about breast density after a mammogram, and these cover over 90% of American women. On March 10, 2023, the Food and Drug Administration issued a final rule amending regulations under the Mammography Quality Standards Act for a national dense breast reporting standard for both patient results letters and mammogram reports. Effective September 10, 2024, letters will be required to tell a woman her breasts are “dense” or “not dense,” that dense tissue makes it harder to find cancers on a mammogram and also increases the risk of developing cancer. Women with dense breasts will also be told that other imaging tests in addition to a mammogram may help find cancers. The specific density category can be maintained (e.g. if mandated by state “inform” law) or added. Reports to providers must include the BI-RADS density category. Implementing appropriate supplemental screening should be based on patient risk for missed breast cancer on mammography; such an assessment should include consideration of breast density and other risk factors. This article discusses strategies for implementation. Currently 15 states plus DC have varying individual state insurance laws for supplemental breast imaging; in addition, Oklahoma requires coverage for diagnostic breast imaging. A federal insurance bill, the Find It Early Act, has been introduced which would ensure no-cost screening and diagnostic imaging for women with dense breasts or at increased risk and close loopholes in state laws.
Breast cancer is the most common cancer in women globally with enormous associated morbidity, mortality and economic impact. Prevention of breast cancer is a global public health imperative. To date, most of our global efforts have been directed at expanding population breast cancer screening programs for early cancer detection and not at breast cancer prevention efforts. It is imperative that we change the paradigm. As with other diseases, prevention of breast cancer starts with identification of individuals at high risk, and for breast cancer this requires improved identification of individuals who carry a hereditary cancer mutation associated with an elevated risk of breast cancer, and identification of others who are at high risk due to non-genetic, established modifiable and non-modifiable factors. This article will review basic breast cancer genetics and the most common hereditary breast cancer mutations associated with increased risk. We will also discuss the other non-genetic modifiable and non-modifiable breast cancer risk factors, available risk assessment models and an approach to incorporating screening for genetic mutation carriers and identifying high-risk women in clinical practice. A discussion of guidelines for enhanced screening, chemoprevention and surgical management of high-risk women is beyond the scope of this review.
Accurate breast cancer risk prediction could improve risk-reduction paradigms if thoughtfully used in clinical practice. Identification of at-risk women is the first step in tailoring risk screening and risk-reduction protocols to women's needs. Using the UK Biobank, we validated a simple risk model to predict breast cancer risk in the general population. Our simple breast cancer risk (BRISK) model integrates a combination of impactful breast cancer-associated risk factors including extended family history and polygenic risk allowing for the removal of moderate factors currently found in comprehensive traditional models. Using two versions of BRISK, differing by 77-single-nucleotide polymorphisms (SNP) versus 313-SNP polygenic risk score integration, we found improved discrimination and risk categorization of both BRISK models compared with one of the most well-known models, the Breast Cancer Risk Assessment Tool (BRCAT). Over a 5-year period, at-risk women classified ≥3% 5-year risk by BRISK had a 1.829 (95% CI = 1.710–1.956) times increased incidence of breast cancer compared with the population, which was higher than the 1.413 (95% CI = 1.217–1.640) times increased incidence for women classified ≥3% by BCRAT.
Prevention Relevance
In this prospective population-based cohort study, we show the improved performance of a new risk assessment model compared with a gold-standard model (BCRAT). The classification of at-risk women using this new model highlights the opportunity to improve risk stratification and implement existing clinical risk-reduction interventions.
Optimal healthcare policies require assessment of cancer risk at the individual level, especially for more common diseases like breast cancer. As outlined in a chapter titled “hereditary breast cancer and pathogenic germline variants” in this book, there are a complex array of environment and genetic risk factors with uncertain interactions between these. Therefore assessment of cancer risk must be approached and evaluated in a systematic and structured manner that is based on various risk assessment tools that have been developed, validated, and applied in the clinical setting [1–8].
Mammographic breast density is widely accepted as an independent risk factor for the development of breast cancer. In addition, because dense breast tissue may mask breast malignancies, breast density is inversely related to the sensitivity of screening mammography. Given the risks associated with breast density, as well as ongoing efforts to stratify individual risk and personalize breast cancer screening and prevention, numerous studies have sought to better understand the factors that impact breast density, and to develop and implement reproducible, quantitative methods to assess mammographic density. Breast density assessments have been incorporated into risk assessment models to improve risk stratification. Recently, novel techniques for analyzing mammographic parenchymal complexity, or texture, have been explored as potential means of refining mammographic tissue-based risk assessment beyond breast density.
Overview
Breast cancer is the most common cancer among women in the United States and growing in incidence worldwide, while mortality due to breast cancer is slowly decreasing. The last two decades have witnessed many changes in the diagnostic landscape both in terms of imaging studies and laboratory tests, most notably next‐generation sequencing both on tumor and blood. Our understanding of breast oncogenesis, tumor heterogeneity, and metastasis has deepened. Segmental mastectomy and sentinel lymph node (SLN) biopsy have been used in the majority of cases coupled with radiation therapy. In cases managed with mastectomy, radiation of one to three positive nodes has been utilized more often. Gene expression profiling has been used to identify women with hormone receptor‐positive (HR+) breast cancer who would benefit from adjuvant chemotherapy. Targeted therapy has been developed for HR+, HER2 amplified, and triple‐negative breast cancer. Immune checkpoint inhibitors have improved the management of triple‐negative disease. poly(adenosine diphosphate [ADP]–ribose) (PARP) inhibitors have improved outcomes for breast cancer patients with BRCA1/2 mutations. Combinations of targeted therapies and hormonal therapies have proved effective for adjuvant therapy and in recurrent disease.
This review summarizes recent developments in the prevention and treatment of patients with early-stage breast cancer. The individual disease risk for different molecular subtypes was investigated in a large epidemiological study. With regard to treatment, new data are available from long-term follow-up of the Aphinity study, as well as new data on neoadjuvant therapy with atezolizumab in HER2-positive patients. Biomarkers, such as residual cancer burden, were investigated in the context of pembrolizumab therapy. A Genomic Grade Index study in elderly patients is one of a group of studies investigating the use of modern multigene tests to identify patients with an excellent prognosis in whom chemotherapy may be avoided. These and other aspects of the latest developments in the diagnosis and treatment of breast cancer are described in this review.
There has been increased interest in the use of clinical risk prediction models for decision-making in medicine for patient care. This has been accelerated through the focus on precision medicine, the revolution in omics data, and increasing use of randomized controlled trial and electronic health record databases. These models are expected to assist diagnostic assessment, prognostication, and therapeutic decision-making. Randomized controlled trial data are highly relevant for modeling treatment benefit and treatment effect heterogeneity. The development and validation of prediction models requires careful methodology and reporting, and an evidence-based approach is needed to bring risk prediction models to clinical practice. This chapter provides an overview of the key steps and considerations to develop and validate risk prediction models. We comment on the role of clinical trials throughout the process. A risk prediction model for the occurrence of breast cancer is used as an example.
Objective:
There is growing interest in more risk-based approaches to breast cancer screening in Australia. This would require more detailed reporting of BreastScreen data for factors of interest in the assessment and monitoring of risk-based screening. This review assesses the current and potential availability and reporting of BreastScreen data for this purpose.
Methods:
We systematically searched governmental BreastScreen reports and peer-reviewed literature to assess current and potential availability of outcomes for predetermined factors including breast cancer risk factors and factors important for implementing, monitoring or evaluating risk-based screening. Outcomes evaluated were BreastScreen Performance Indicators routinely included in BreastScreen Australia monitoring reports, and key tumour characteristics.
Results:
All outcomes were reported annually by age group, except for tumour hormone receptor status, nodal involvement and grade. Screening participation was reported nationally for many factors important for risk-based screening; other reporting was ad hoc or unavailable.
Conclusions:
There is potential to build on BreastScreen's existing high-quality national data collection and reporting systems to inform and support risk-based breast screening. Implications for public health: Enhanced BreastScreen data collection and reporting would improve the evidence base and support evaluation of risk-based screening and improve the detail available for benchmarking any future changes to the program.
Description: Update of the 2004 U. S. Preventive Services Task Force (USPSTF) recommendation on screening for glaucoma. Methods: The USPSTF reviewed evidence on the benefits and harms of screening for glaucoma and of medical and surgical treatment of early glaucoma. Beneficial outcomes of interest included improved vision-related quality of life and reduced progression of early asymptomatic glaucoma to vision-related impairment. The USPSTF also considered evidence on the accuracy of glaucoma screening tests. Population: This recommendation applies to adults who do not have vision symptoms and are seen in a primary care setting. Recommendation: The USPSTF concludes that the current evidence is insufficient to assess the balance of benefits and harms of screening for primary open-angle glaucoma in adults. (I statement)
Purpose: Mammographic characteristics are known to be correlated to breast cancer risk. Percent mammographic density (PMD), as assessed by computer-assisted methods, is an established risk factor for breast cancer. Along with this assessment the absolute dense area (DA) of the breast is reported as well. Aim of this study was to assess the predictive value of DA concerning breast cancer risk in addition to other risk factors and in addition to PMD.
Methods: We conducted a case control study with hospital-based patients with a diagnosis of invasive breast cancer and healthy women as controls. A total of 561 patients and 376 controls with available mammographic density were included into this study. We describe the differences concerning the common risk factors BMI, parital status, use of hormone replacement therapy (HRT) and menopause between cases and controls and estimate the odds ratios for PMD and DA, adjusted for the mentioned risk factors. Furthermore we compare the prediction models with each other to find out whether the addition of DA improves the model.
Results: Mammographic density and DA were highly correlated with each other. Both variables were as well correlated to the commonly known risk factors with an expected direction and strength, however PMD (rho = -0.56) was stronger correlated to BMI than DA (rho = -0.11). The group of women within the highest quartil of PMD had an OR of 2.12 (95% CI: 1.25-3.62). This could not be seen for the fourth quartile concerning DA. However the assessment of breast cancer risk could be improved by including DA in a prediction model in addition to common risk factors and PMD.
Conclusions: The inclusion of the parameter DA into a prediction model for breast cancer in addition to established risk factors and PMD could improve the breast cancer risk assessment. As DA is measured together with PMD in the process of computer-assisted assessment of PMD it might be considered to include it as one additional breast cancer risk factor that is obtained from breast imaging.
PURPOSETo update the 2009 American Society of Clinical Oncology guideline on pharmacologic interventions for breast cancer (BC) risk reduction. METHODSA systematic review of randomized controlled trials and meta-analyses published from June 2007 through June 2012 was completed using MEDLINE and Cochrane Collaboration Library. Primary outcome of interest was BC incidence (invasive and noninvasive). Secondary outcomes included BC mortality, adverse events, and net health benefits. Guideline recommendations were revised based on an Update Committee's review of the literature.
tamoxifen, raloxifene, arzoxifene, lasofoxifene, exemestane, and anastrozole.RecommendationsIn women at increased risk of BC age ≥ 35 years, tamoxifen (20 mg per day for 5 years) should be discussed as an option to reduce the risk of estrogen receptor (ER) -positive BC. In postmenopausal women, raloxifene (60 mg per day for 5 years) and exemestane (25 mg per day for 5 years) should also be discussed as options for BC risk reduction. Those at increased BC risk are defined as individuals with a 5-year projected absolute risk of BC ≥ 1.66% (based on the National Cancer Institute BC Risk Assessment Tool or an equivalent measure) or women diagnosed with lobular carcinoma in situ. Use of other selective ER modulators or other aromatase inhibitors to lower BC risk is not recommended outside of a clinical trial. Health care providers are encouraged to discuss the option of chemoprevention among women at increased BC risk. The discussion should include the specific risks and benefits associated with each chemopreventive agent.
TERT-locus SNPs and leukocyte telomere measures are reportedly associated with risks of multiple cancers. Using the Illumina custom genotyping array iCOGs, we analyzed ∼480 SNPs at the TERT locus in breast (n = 103,991), ovarian (n = 39,774) and BRCA1 mutation carrier (n = 11,705) cancer cases and controls. Leukocyte telomere measurements were also available for 53,724 participants. Most associations cluster into three independent peaks. The minor allele at the peak 1 SNP rs2736108 associates with longer telomeres (P = 5.8 × 10(-7)), lower risks for estrogen receptor (ER)-negative (P = 1.0 × 10(-8)) and BRCA1 mutation carrier (P = 1.1 × 10(-5)) breast cancers and altered promoter assay signal. The minor allele at the peak 2 SNP rs7705526 associates with longer telomeres (P = 2.3 × 10(-14)), higher risk of low-malignant-potential ovarian cancer (P = 1.3 × 10(-15)) and greater promoter activity. The minor alleles at the peak 3 SNPs rs10069690 and rs2242652 increase ER-negative (P = 1.2 × 10(-12)) and BRCA1 mutation carrier (P = 1.6 × 10(-14)) breast and invasive ovarian (P = 1.3 × 10(-11)) cancer risks but not via altered telomere length. The cancer risk alleles of rs2242652 and rs10069690, respectively, increase silencing and generate a truncated TERT splice variant.
Breast cancer is the most common cancer among women. Common variants at 27 loci have been identified as associated with susceptibility to breast cancer, and these account for ∼9% of the familial risk of the disease. We report here a meta-analysis of 9 genome-wide association studies, including 10,052 breast cancer cases and 12,575 controls of European ancestry, from which we selected 29,807 SNPs for further genotyping. These SNPs were genotyped in 45,290 cases and 41,880 controls of European ancestry from 41 studies in the Breast Cancer Association Consortium (BCAC). The SNPs were genotyped as part of a collaborative genotyping experiment involving four consortia (Collaborative Oncological Gene-environment Study, COGS) and used a custom Illumina iSelect genotyping array, iCOGS, comprising more than 200,000 SNPs. We identified SNPs at 41 new breast cancer susceptibility loci at genome-wide significance (P < 5 × 10(-8)). Further analyses suggest that more than 1,000 additional loci are involved in breast cancer susceptibility.
Introduction
Mammographic density is a strong risk factor for breast cancer. Image acquisition technique varies across mammograms to limit radiation and produce a clinically useful image. We examined whether acquisition technique parameters at the time of mammography were associated with mammographic density and whether the acquisition parameters confounded the density and breast cancer association.
Methods
We examined this question within the Mayo Mammography Health Study (MMHS) cohort, comprised of 19,924 women (51.2% of eligible) seen in the Mayo Clinic mammography screening practice from 2003 to 2006. A case-cohort design, comprising 318 incident breast cancers diagnosed through December 2009 and a random subcohort of 2,259, was used to examine potential confounding of mammogram acquisition technique parameters (x-ray tube voltage peak (kVp), milliampere-seconds (mAs), thickness and compression force) on the density and breast cancer association. The Breast Imaging Reporting and Data System four-category tissue composition measure (BI-RADS) and percent density (PD) (Cumulus program) were estimated from screen-film mammograms at time of enrollment. Spearman correlation coefficients (r) and means (standard deviations) were used to examine the relationship of density measures with acquisition parameters. Hazard ratios (HR) and C-statistics were estimated using Cox proportional hazards regression, adjusting for age, menopausal status, body mass index and postmenopausal hormones. A change in the HR of at least 15% indicated confounding.
Results
Adjusted PD and BI-RADS density were associated with breast cancer (p-trends < 0.001), with a 3 to 4-fold increased risk in the extremely dense vs. fatty BI-RADS categories (HR: 3.0, 95% CI, 1.7 - 5.1) and the ≥ 25% vs. ≤ 5% PD categories (HR: 3.8, 95% CI, 2.5 - 5.9). Of the acquisition parameters, kVp was not correlated with PD (r = 0.04, p = 0.07). Although thickness (r = -0.27, p < 0.001), compression force (r = -0.16, p < 0.001), and mAs (r = -0.06, p = 0.008) were inversely correlated with PD, they did not confound the PD or BI-RADS associations with breast cancer and their inclusion did not improve discriminatory accuracy. Results were similar for associations of dense and non-dense area with breast cancer.
Conclusions
We confirmed a strong association between mammographic density and breast cancer risk that was not confounded by mammogram acquisition technique.
Over the last decade several breast cancer risk alleles have been identified which has led to an increased interest in individualised risk prediction for clinical purposes.
We investigate the performance of an up-to-date 18 breast cancer risk single-nucleotide polymorphisms (SNPs), together with mammographic percentage density (PD), body mass index (BMI) and clinical risk factors in predicting absolute risk of breast cancer, empirically, in a well characterised Swedish case-control study of postmenopausal women. We examined the efficiency of various prediction models at a population level for individualised screening by extending a recently proposed analytical approach for estimating number of cases captured.
The performance of a risk prediction model based on an initial set of seven breast cancer risk SNPs is improved by additionally including eleven more recently established breast cancer risk SNPs (P = 4.69 × 10-4). Adding mammographic PD, BMI and all 18 SNPs to a Swedish Gail model improved the discriminatory accuracy (the AUC statistic) from 55% to 62%. The net reclassification improvement was used to assess improvement in classification of women into low, intermediate, and high categories of 5-year risk (P = 8.93 × 10-9). For scenarios we considered, we estimated that an individualised screening strategy based on risk models incorporating clinical risk factors, mammographic density and SNPs, captures 10% more cases than a screening strategy using the same resources, based on age alone. Estimates of numbers of cases captured by screening stratified by age provide insight into how individualised screening programs might appear in practice.
Taken together, genetic risk factors and mammographic density offer moderate improvements to clinical risk factor models for predicting breast cancer.
Breast cancer is the most common cancer among women. To date, 22 common breast cancer susceptibility loci have been identified accounting for ∼8% of the heritability of the disease. We attempted to replicate 72 promising associations from two independent genome-wide association studies (GWAS) in ∼70,000 cases and ∼68,000 controls from 41 case-control studies and 9 breast cancer GWAS. We identified three new breast cancer risk loci at 12p11 (rs10771399; P = 2.7 × 10(-35)), 12q24 (rs1292011; P = 4.3 × 10(-19)) and 21q21 (rs2823093; P = 1.1 × 10(-12)). rs10771399 was associated with similar relative risks for both estrogen receptor (ER)-negative and ER-positive breast cancer, whereas the other two loci were associated only with ER-positive disease. Two of the loci lie in regions that contain strong plausible candidate genes: PTHLH (12p11) has a crucial role in mammary gland development and the establishment of bone metastasis in breast cancer, and NRIP1 (21q21) encodes an ER cofactor and has a role in the regulation of breast cancer cell growth.
Genome-wide association studies have identified several common genetic variants associated with breast cancer risk. It is likely, however, that a substantial proportion of such loci have not yet been discovered.
We compared 296,114 tagging single-nucleotide polymorphisms in 1694 breast cancer case subjects (92% with two primary cancers or at least two affected first-degree relatives) and 2365 control subjects, with validation in three independent series totaling 11,880 case subjects and 12,487 control subjects. Odds ratios (ORs) and associated 95% confidence intervals (CIs) in each stage and all stages combined were calculated using unconditional logistic regression. Heterogeneity was evaluated with Cochran Q and I(2) statistics. All statistical tests were two-sided.
We identified a novel risk locus for breast cancer at 9q31.2 (rs865686: OR = 0.89, 95% CI = 0.85 to 0.92, P = 1.75 × 10(-10)). This single-nucleotide polymorphism maps to a gene desert, the nearest genes being Kruppel-like factor 4 (KLF4, 636 kb centromeric), RAD23 homolog B (RAD23B, 794 kb centromeric), and actin-like 7A (ACTL7A, 736 kb telomeric). We also identified two variants (rs3734805 and rs9383938) mapping to 6q25.1 estrogen receptor 1 (ESR1), which were associated with breast cancer in subjects of northern European ancestry (rs3734805: OR = 1.19, 95% CI = 1.11 to 1.27, P = 1.35 × 10(-7); rs9383938: OR = 1.18, 95% CI = 1.11 to 1.26, P = 1.41 × 10(-7)). A variant mapping to 10q26.13, approximately 300 kb telomeric to the established risk locus within the second intron of FGFR2, was also associated with breast cancer risk, although not at genome-wide statistical significance (rs10510102: OR = 1.12, 95% CI = 1.07 to 1.17, P = 1.58 × 10(-6)).
These findings provide further evidence on the role of genetic variation in the etiology of breast cancer. Fine mapping will be needed to identify causal variants and to determine their functional effects.
The Gail model is widely used for the assessment of risk of invasive breast cancer based on recognized clinical risk factors. In recent years, a substantial number of single-nucleotide polymorphisms (SNPs) associated with breast cancer risk have been identified. However, it remains unclear how to effectively integrate clinical and genetic risk factors for risk assessment.
Seven SNPs associated with breast cancer risk were selected from the literature and genotyped in white non-Hispanic women in a nested case-control cohort of 1664 case patients and 1636 control subjects within the Women's Health Initiative Clinical Trial. SNP risk scores were computed based on previously published odds ratios assuming a multiplicative model. Combined risk scores were calculated by multiplying Gail risk estimates by the SNP risk scores. The independence of Gail risk and SNP risk was evaluated by logistic regression. Calibration of relative risks was evaluated using the Hosmer-Lemeshow test. The performance of the combined risk scores was evaluated using receiver operating characteristic curves. The net reclassification improvement (NRI) was used to assess improvement in classification of women into low (<1.5%), intermediate (1.5%-2%), and high (>2%) categories of 5-year risk. All tests of statistical significance were two-sided.
The SNP risk score was nearly independent of Gail risk. There was good agreement between predicted and observed SNP relative risks. In the analysis for receiver operating characteristic curves, the combined risk score was more discriminating, with area under the curve of 0.594 compared with area under the curve of 0.557 for Gail risk alone (P < .001). Classification also improved for 5.6% of case patients and 2.9% of control subjects, showing an NRI value of 0.085 (P = 1.0 × 10⁻⁵). Focusing on women with intermediate Gail risk resulted in an improved NRI of 0.195 (P = 8.6 × 10⁻⁵).
Combining validated common genetic risk factors with clinical risk factors resulted in modest improvement in classification of breast cancer risks in white non-Hispanic postmenopausal women. Classification performance was further improved by focusing on women at intermediate risk.
Germline BRCA1 mutations predispose to breast cancer. To identify genetic modifiers of this risk, we performed a genome-wide association study in 1,193 individuals with BRCA1 mutations who were diagnosed with invasive breast cancer under age 40 and 1,190 BRCA1 carriers without breast cancer diagnosis over age 35. We took forward 96 SNPs for replication in another 5,986 BRCA1 carriers (2,974 individuals with breast cancer and 3,012 unaffected individuals). Five SNPs on 19p13 were associated with breast cancer risk (P(trend) = 2.3 × 10⁻⁹ to P(trend) = 3.9 × 10⁻⁷), two of which showed independent associations (rs8170, hazard ratio (HR) = 1.26, 95% CI 1.17-1.35; rs2363956 HR = 0.84, 95% CI 0.80-0.89). Genotyping these SNPs in 6,800 population-based breast cancer cases and 6,613 controls identified a similar association with estrogen receptor-negative breast cancer (rs2363956 per-allele odds ratio (OR) = 0.83, 95% CI 0.75-0.92, P(trend) = 0.0003) and an association with estrogen receptor-positive disease in the opposite direction (OR = 1.07, 95% CI 1.01-1.14, P(trend) = 0.016). The five SNPs were also associated with triple-negative breast cancer in a separate study of 2,301 triple-negative cases and 3,949 controls (P(trend) = 1 × 10⁻⁷) to P(trend) = 8 × 10⁻⁵; rs2363956 per-allele OR = 0.80, 95% CI 0.74-0.87, P(trend) = 1.1 × 10⁻⁷
We determined whether the association between breast density and breast cancer risk and cancer severity differs according to menopausal status and postmenopausal hormone therapy (HT) use.
We collected data on 587,369 women who underwent 1,349,027 screening mammography examinations; 14,090 women were diagnosed with breast cancer. We calculated 5-year breast cancer risk from a survival model for subgroups of women classified by their Breast Imaging Reporting and Data System (BIRADS) breast density, age, menopausal status, and current HT use, assuming a body mass index of 25 kg/m(2). Odds of advanced (ie, IIb, III, IV) versus early (ie, I, IIa) stage invasive cancer was calculated according to BIRADS density.
Breast cancer risk was low among women with low density (BIRADS-1): women age 55 to 59 years, 5-year risk was 0.8% (95% CI, 0.6 to 0.9%) for non-HT users and 0.9% (95% CI, 0.7% to 1.1%) for estrogen and estrogen plus progestin users. Breast cancer risk was high among women with very high density (BIRADS-4), particularly estrogen plus progestin users: women age 55 to 59 years, 5-year risk was 2.4% (95% CI, 2.0% to 2.8%) for non-HT users, 3.0% (95% CI, 2.6% to 3.5%) for estrogen users, and 4.2% (95% CI, 3.7% to 4.6%) for estrogen plus progestin users. Advanced-stage breast cancer risk was increased 1.7-fold for postmenopausal HT users who had very high density (BIRADS-4) compared to those with average density (BIRADS-2).
Postmenopausal women with high breast density are at increased risk of breast cancer and should be aware of the added risk of taking HT, especially estrogen plus progestin.
Breast cancer is the most common cancer in women in developed countries. To identify common breast cancer susceptibility alleles, we conducted a genome-wide association study in which 582,886 SNPs were genotyped in 3,659 cases with a family history of the disease and 4,897 controls. Promising associations were evaluated in a second stage, comprising 12,576 cases and 12,223 controls. We identified five new susceptibility loci, on chromosomes 9, 10 and 11 (P = 4.6 x 10(-7) to P = 3.2 x 10(-15)). We also identified SNPs in the 6q25.1 (rs3757318, P = 2.9 x 10(-6)), 8q24 (rs1562430, P = 5.8 x 10(-7)) and LSP1 (rs909116, P = 7.3 x 10(-7)) regions that showed more significant association with risk than those reported previously. Previously identified breast cancer susceptibility loci were also found to show larger effect sizes in this study of familial breast cancer cases than in previous population-based studies, consistent with polygenic susceptibility to the disease.
It has been shown in several studies that antihormonal compounds can offer effective prophylactic treatment to prevent breast cancer. In view of the low participation rates in chemoprevention trials, the purpose of this study was to identify the characteristics of women taking part in a population-based mammography screening program who wished to obtain information about the risk of breast cancer and then participate in the the International Breast Cancer Intervention Study II (IBIS-II) trial, a randomized double-blind controlled chemoprevention trial comparing anastrozole with placebo. A paper-based survey was conducted in a population-based mammography screening program in Germany between 2007 and 2009. All women who met the criteria for the mammography screening program were invited to complete a questionnaire. A total of 2,524 women completed the questionnaire, and 17.7% (n = 446) met the eligibility criteria for the IBIS-II trial after risk assessment. The women who wished to receive further information about chemoprevention were significantly younger (P < 0.01) and had significantly more children (P = 0.03) and significantly more relatives with breast cancer (P < 0.001). There were no significant differences between the participants with regard to body mass index or hormone replacement therapy. Normal mammographic findings at screening were the main reason (42%) for declining to participate in the IBIS-II trial or attend risk counseling. The ultimate rate of recruitment to the IBIS-II trial was very low (three women). Offering chemoprevention to women within a mammography screening unit as part of a paper-based survey resulted in low participation rates for both, the survey and the final participation in the IBIS-II trial. More individualized approaches and communication of breast cancer risk at the time of the risk assessment might be helpful to increase the participation and the understanding of chemopreventive approaches.
Introduction
The stroma is the supportive framework of biologic tissue in the breast, consisting of various proteins such as the proteoglycans, decorin and lumican. Altered expression of decorin and lumican is associated with breast tumors. We hypothesized that genetic variation in the decorin (DCN) and lumican (LUM) genes may contribute to breast cancer.
Methods
We investigated associations of 14 common polymorphisms in the DCN and LUM genes with 798 breast cancer cases and 843 controls from Mayo Clinic, MN, USA. One polymorphism per gene with the strongest risk association in the Mayo Clinic sample was genotyped in 4,470 breast cancer cases and 4,560 controls from East Anglia, England (Studies of Epidemiology and Risk Factors in Cancer Heredity (SEARCH)).
Results
In the Mayo Clinic sample, six polymorphisms were associated with breast cancer risk (Ptrend ≤ 0.05). The association with LUM rs2268578, evaluated further in SEARCH, was positive, although the odds ratios (OR) were weaker and not statistically significant. ORs were 1.4 (95% confidence interval [CI], 1.1 to 1.8) for heterozygotes and 2.2 (95% CI, 1.1 to 4.3; P2 df = 0.002) for homozygotes in the Mayo Clinic sample, and were 1.1 (95% CI, 0.9 to 1.2) for heterozygotes and 1.4 (95% CI, 1.0 to 2.1; P2 df = 0.13) for homozygotes in the SEARCH sample. In combined analyses, the ORs were 1.1 (95% CI, 1.0 to 1.2) for heterozygotes and 1.6 (95% CI, 1.2 to 2.3; P2 df = 0.005) for homozygotes. Positive associations for this polymorphism were observed for estrogen receptor-positive tumors in both the Mayo Clinic sample (OR for heterozygotes = 1.5, 1.1 to 1.9 and OR for homozygotes = 2.5, 1.2 to 5.3;P2 df = 0.001) and the SEARCH sample (OR for heterozygotes = 1.0, 0.9 to 1.1 and OR for homozygotes = 1.6, 1.0 to 2.5; P2 df = 0.10). In combined analyses, the ORs were 1.1 (95% CI, 0.9 to 1.2) for heterozygotes and 1.9 (95% CI, 1.3 to 2.8; P2 df = 0.001) for homozygotes.
Conclusions
Although LUM rs2268578 was associated with breast cancer in the Mayo Clinic study, particularly estrogen receptor-positive breast cancer, weaker and modest associations were observed in the SEARCH sample. These modest associations will require larger samples to adequately assess the importance of this polymorphism in breast cancer.
We conducted a three-stage genome-wide association study (GWAS) of breast cancer in 9,770 cases and 10,799 controls in the Cancer Genetic Markers of Susceptibility (CGEMS) initiative. In stage 1, we genotyped 528,173 SNPs in 1,145 cases of invasive breast cancer and 1,142 controls. In stage 2, we analyzed 24,909 top SNPs in 4,547 cases and 4,434 controls. In stage 3, we investigated 21 loci in 4,078 cases and 5,223 controls. Two new loci achieved genome-wide significance. A pericentromeric SNP on chromosome 1p11.2 (rs11249433; P = 6.74 x 10(-10) adjusted genotype test, 2 degrees of freedom) resides in a large linkage disequilibrium block neighboring NOTCH2 and FCGR1B; this signal was stronger for estrogen-receptor-positive tumors. A second SNP on chromosome 14q24.1 (rs999737; P = 1.74 x 10(-7)) localizes to RAD51L1, a gene in the homologous recombination DNA repair pathway. We also confirmed associations with loci on chromosomes 2q35, 5p12, 5q11.2, 8q24, 10q26 and 16q12.1.
Genome-wide association studies (GWAS) have identified seven breast cancer susceptibility loci, but these explain only a small fraction of the familial risk of the disease. Five of these loci were identified through a two-stage GWAS involving 390 familial cases and 364 controls in the first stage, and 3,990 cases and 3,916 controls in the second stage. To identify additional loci, we tested over 800 promising associations from this GWAS in a further two stages involving 37,012 cases and 40,069 controls from 33 studies in the CGEMS collaboration and Breast Cancer Association Consortium. We found strong evidence for additional susceptibility loci on 3p (rs4973768: per-allele OR = 1.11, 95% CI = 1.08-1.13, P = 4.1 x 10(-23)) and 17q (rs6504950: per-allele OR = 0.95, 95% CI = 0.92-0.97, P = 1.4 x 10(-8)). Potential causative genes include SLC4A7 and NEK10 on 3p and COX11 on 17q.
We carried out a genome-wide association study among Chinese women to identify risk variants for breast cancer. After analyzing 607,728 SNPs in 1,505 cases and 1,522 controls, we selected 29 SNPs for a fast-track replication in an independent set of 1,554 cases and 1,576 controls. We further investigated four replicated loci in a third set of samples comprising 3,472 cases and 900 controls. SNP rs2046210 at 6q25.1, located upstream of the gene encoding estrogen receptor alpha (ESR1), showed strong and consistent association with breast cancer across all three stages. Adjusted odds ratio (95% CI) were 1.36 (1.24-1.49) and 1.59 (1.40-1.82), respectively, for genotypes A/G and A/A versus G/G (P for trend 2.0 x 10(-15)) in the pooled analysis of samples from all three stages. We also found a similar, albeit weaker, association in an independent study comprising 1,591 cases and 1,466 controls of European ancestry (P(trend) = 0.01). These results strongly implicate 6q25.1 as a susceptibility locus for breast cancer.
In recent years, multiple imputation has emerged as a convenient and flexible paradigm for analysing data with missing values. Essential features of multiple imputation are reviewed, with answers to frequently asked questions about using the method in practice.
Multivariable regression models are powerful tools that are used frequently in studies of clinical outcomes. These models can use a mixture of categorical and continuous variables and can handle partially observed (censored) responses. However, uncritical application of modelling techniques can result in models that poorly fit the dataset at hand, or, even more likely, inaccurately predict outcomes on new subjects. One must know how to measure qualities of a model's fit in order to avoid poorly fitted or overfitted models. Measurement of predictive accuracy can be difficult for survival time data in the presence of censoring. We discuss an easily interpretable index of predictive discrimination as well as methods for assessing calibration of predicted survival probabilities. Both types of predictive accuracy should be unbiasedly validated using bootstrapping or cross-validation, before using predictions in a new data series. We discuss some of the hazards of poorly fitted and overfitted regression models and present one modelling strategy that avoids many of the problems discussed. The methods described are applicable to all regression models, but are particularly needed for binary, ordinal, and time-to-event outcomes. Methods are illustrated with a survival analysis in prostate cancer using Cox regression.
Estrogen receptor (ER)-negative breast cancer shows a higher incidence in women of African ancestry compared to women of European ancestry. In search of common risk alleles for ER-negative breast cancer, we combined genome-wide association study (GWAS) data from women of African ancestry (1,004 ER-negative cases and 2,745 controls) and European ancestry (1,718 ER-negative cases and 3,670 controls), with replication testing conducted in an additional 2,292 ER-negative cases and 16,901 controls of European ancestry. We identified a common risk variant for ER-negative breast cancer at the TERT-CLPTM1L locus on chromosome 5p15 (rs10069690: per-allele odds ratio (OR) = 1.18 per allele, P = 1.0 × 10?10). The variant was also significantly associated with triple-negative (ER-negative, progesterone receptor (PR)-negative and human epidermal growth factor-2 (HER2)-negative) breast cancer (OR = 1.25, P = 1.1 × 10?9), particularly in younger women (<50 years of age) (OR = 1.48, P = 1.9 × 10?9). Our results identify a genetic locus associated with estrogen receptor negative breast cancer subtypes in multiple populations.
Estrogen receptor (ER)-negative tumors represent 20–30% of all breast cancers, with a higher proportion occurring in younger women and women of African ancestry1 . The etiology2 and clinical behavior3 of ER-negative tumors are different from those of tumors expressing ER (ER positive), including differences in genetic predisposition4 . To identify susceptibility loci specific to ER-negative disease, we combined in a meta-analysis 3 genome-wide association studies of 4,193 ER-negative breast cancer cases and 35,194 controls with a series of 40 follow-up studies (6,514 cases and 41,455 controls), genotyped using a custom Illumina array, iCOGS, developed by the Collaborative Oncological Gene-environment Study (COGS). SNPs at four loci, 1q32.1 (MDM4, P = 2.1 × 10−12 and LGR6, P = 1.4 × 10−8), 2p24.1 (P = 4.6 × 10−8) and 16q12.2 (FTO, P = 4.0 × 10−8), were associated with ER-negative but not ER-positive breast cancer (P > 0.05). These findings provide further evidence for distinct etiological pathways associated with invasive ER-positive and ER-negative breast cancers.
Breast cancer is the most common cancer among women. To date, 22 common breast cancer susceptibility loci have been identified accounting for ∼8% of the heritability of the disease. We attempted to replicate 72 promising associations from two independent genome-wide association studies (GWAS) in ∼70,000 cases and ∼68,000 controls from 41 case-control studies and 9 breast cancer GWAS. We identified three new breast cancer risk loci at 12p11 (rs10771399; P = 2.7 × 10(-35)), 12q24 (rs1292011; P = 4.3 × 10(-19)) and 21q21 (rs2823093; P = 1.1 × 10(-12)). rs10771399 was associated with similar relative risks for both estrogen receptor (ER)-negative and ER-positive breast cancer, whereas the other two loci were associated only with ER-positive disease. Two of the loci lie in regions that contain strong plausible candidate genes: PTHLH (12p11) has a crucial role in mammary gland development and the establishment of bone metastasis in breast cancer, and NRIP1 (21q21) encodes an ER cofactor and has a role in the regulation of breast cancer cell growth.
The Breast Cancer Association Consortium (BCAC) has been established to conduct combined case-control analyses with augmented statistical power to try to confirm putative genetic associations with breast cancer. We genotyped nine SNPs for which there was some prior evidence of an association with breast cancer: CASP8 D302H (rs1045485), IGFBP3 -202 C --> A (rs2854744), SOD2 V16A (rs1799725), TGFB1 L10P (rs1982073), ATM S49C (rs1800054), ADH1B 3' UTR A --> G (rs1042026), CDKN1A S31R (rs1801270), ICAM5 V301I (rs1056538) and NUMA1 A794G (rs3750913). We included data from 9-15 studies, comprising 11,391-18,290 cases and 14,753-22,670 controls. We found evidence of an association with breast cancer for CASP8 D302H (with odds ratios (OR) of 0.89 (95% confidence interval (c.i.): 0.85-0.94) and 0.74 (95% c.i.: 0.62-0.87) for heterozygotes and rare homozygotes, respectively, compared with common homozygotes; P(trend) = 1.1 x 10(-7)) and weaker evidence for TGFB1 L10P (OR = 1.07 (95% c.i.: 1.02-1.13) and 1.16 (95% c.i.: 1.08-1.25), respectively; P(trend) = 2.8 x 10(-5)). These results demonstrate that common breast cancer susceptibility alleles with small effects on risk can be identified, given sufficiently powerful studies.
Breast cancer exhibits familial aggregation, consistent with variation in genetic susceptibility to the disease. Known susceptibility genes account for less than 25% of the familial risk of breast cancer, and the residual genetic variance is likely to be due to variants conferring more moderate risks. To identify further susceptibility alleles, we conducted a two-stage genome-wide association study in 4,398 breast cancer cases and 4,316 controls, followed by a third stage in which 30 single nucleotide polymorphisms (SNPs) were tested for confirmation in 21,860 cases and 22,578 controls from 22 studies. We used 227,876 SNPs that were estimated to correlate with 77% of known common SNPs in Europeans at r(2) > 0.5. SNPs in five novel independent loci exhibited strong and consistent evidence of association with breast cancer (P < 10(-7)). Four of these contain plausible causative genes (FGFR2, TNRC9, MAP3K1 and LSP1). At the second stage, 1,792 SNPs were significant at the P < 0.05 level compared with an estimated 1,343 that would be expected by chance, indicating that many additional common susceptibility alleles may be identifiable by this approach.
Mammographic features are associated with breast cancer risk, but estimates of the strength of the association vary markedly between studies, and it is uncertain whether the association is modified by other risk factors. We conducted a systematic review and meta-analysis of publications on mammographic patterns in relation to breast cancer risk. Random effects models were used to combine study-specific relative risks. Aggregate data for > 14,000 cases and 226,000 noncases from 42 studies were included. Associations were consistent in studies conducted in the general population but were highly heterogeneous in symptomatic populations. They were much stronger for percentage density than for Wolfe grade or Breast Imaging Reporting and Data System classification and were 20% to 30% stronger in studies of incident than of prevalent cancer. No differences were observed by age/menopausal status at mammography or by ethnicity. For percentage density measured using prediagnostic mammograms, combined relative risks of incident breast cancer in the general population were 1.79 (95% confidence interval, 1.48-2.16), 2.11 (1.70-2.63), 2.92 (2.49-3.42), and 4.64 (3.64-5.91) for categories 5% to 24%, 25% to 49%, 50% to 74%, and >= 75% relative to < 5%. This association remained strong after excluding cancers diagnosed in the first-year postmammography. This review explains some of the heterogeneity in associations of breast density with breast cancer risk and shows that, in well-conducted studies, this is one of the strongest risk factors for breast cancer. It also refutes the suggestion that the association is an artifact of masking bias or that it is only present in a restricted age range.