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We compared methods to project absolute risk, the probability of experiencing the outcome of interest in a given projection interval accommodating competing risks, for a person from the target population with missing predictors. Without missing data, a perfectly calibrated model gives unbiased absolute risk estimates in a new target population, even if the predictor distribution differs from the training data. However, if predictors are missing in target population members, a reference dataset with complete data is needed to impute them and to estimate absolute risk, conditional only on the observed predictors. If the predictor distributions of the reference data and the target population differ, this approach yields biased estimates. We compared the bias and mean squared error of absolute risk predictions for seven methods that assume predictors are missing at random (MAR). Some methods imputed individual missing predictors, others imputed linear predictor combinations (risk scores). Simulations were based on real breast cancer predictor distributions and outcome data. We also analyzed a real breast cancer dataset. The largest bias for all methods resulted from different predictor distributions of the reference and target populations. No method was unbiased in this situation. Surprisingly, violating the MAR assumption did not induce severe biases. Most multiple imputation methods performed similarly and were less biased (but more variable) than a method that used a single expected risk score. Our work shows the importance of selecting predictor reference datasets similar to the target population to reduce bias of absolute risk predictions with missing risk factors., Competing Interests: Declaration of conflicting interestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Laboratory and animal research support a protective role for vitamin D in breast carcinogenesis, but epidemiologic studies have been inconclusive. To examine comprehensively the relationship of circulating 25-hydroxyvitamin D [25(OH)D] to subsequent breast cancer incidence, we harmonized and pooled participant-level data from 10 U.S. and 7 European prospective cohorts. Included were 10,484 invasive breast cancer cases and 12,953 matched controls. Median age (interdecile range) was 57 (42-68) years at blood collection and 63 (49-75) years at breast cancer diagnosis. Prediagnostic circulating 25(OH)D was either newly measured using a widely accepted immunoassay and laboratory or, if previously measured by the cohort, calibrated to this assay to permit using a common metric. Study-specific relative risks (RRs) for season-standardized 25(OH)D concentrations were estimated by conditional logistic regression and combined by random-effects models. Circulating 25(OH)D increased from a median of 22.6 nmol/L in consortium-wide decile 1 to 93.2 nmol/L in decile 10. Breast cancer risk in each decile was not statistically significantly different from risk in decile 5 in models adjusted for breast cancer risk factors, and no trend was apparent (P-trend = 0.64). Compared to women with sufficient 25(OH)D based on Institute of Medicine guidelines (50- < 62.5 nmol/L), RRs were not statistically significantly different at either low concentrations (< 20 nmol/L, 3% of controls) or high concentrations (100- < 125 nmol/L, 3% of controls; ≥ 125 nmol/L, 0.7% of controls). RR per 25 nmol/L increase in 25(OH)D was 0.99 [95% confidence intervaI (CI) 0.95-1.03]. Associations remained null across subgroups, including those defined by body mass index, physical activity, latitude, and season of blood collection. Although none of the associations by tumor characteristics reached statistical significance, suggestive inverse associations were seen for distant and triple negative tumors. Circulating 25(OH)D, comparably measured in 17 international cohorts and season-standardized, was not related to subsequent incidence of invasive breast cancer over a broad range in vitamin D status., (© 2023. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Purpose: Women with unilateral breast cancer are increasingly opting for the removal of not only the involved breast, but also for the removal of the opposite uninvolved breast (contralateral prophylactic mastectomy [CPM]), although the risk of contralateral breast cancer (CBC) has decreased in recent years. Models to predict the absolute risk of CBC can help a woman decide whether to undergo CPM. Our objective is to illustrate that a better decision can be made if the patient and doctor also have estimates of the absolute risks of regional and distant recurrences and mortality from non-breast cancer causes., Materials and Methods: We based our analyses on two published models for CBC and published information on the hazards of regional and distant recurrences and non-breast cancer mortality. Assuming that CPM eliminates CBC but has no effect on other events, we calculated how much CPM reduces a woman's CBC risk and total risk from all these events for 10 hypothetical women with various subtypes of breast cancer and risk factors., Results: The risk of CBC and total risk vary greatly, depending on the breast cancer subtype. In some cases, a decision for or against CPM can be based on CBC risk alone, but in others, additional consideration of total risk may cause a woman to decline CPM., Conclusion: There is a potential to develop more informative tools for deciding on CPM. Realizing this potential will require more and better data to validate existing models of absolute CBC risk and to characterize the hazards of regional and distant recurrences and deaths from non-breast cancer causes for women with various subtypes of breast cancers and risk factors.

Background: External validation of risk models is critical for risk-stratified breast cancer prevention. We used the Individualized Coherent Absolute Risk Estimation (iCARE) as a flexible tool for risk model development and comparative model validation and to make projections for population risk stratification., Methods: Performance of two recently developed models, one based on the Breast and Prostate Cancer Cohort Consortium analysis (iCARE-BPC3) and another based on a literature review (iCARE-Lit), were compared with two established models (Breast Cancer Risk Assessment Tool and International Breast Cancer Intervention Study Model) based on classical risk factors in a UK-based cohort of 64 874 white non-Hispanic women (863 patients) age 35-74 years. Risk projections in a target population of US white non-Hispanic women age 50-70 years assessed potential improvements in risk stratification by adding mammographic breast density (MD) and polygenic risk score (PRS)., Results: The best calibrated models were iCARE-Lit (expected to observed number of cases [E/O] = 0.98, 95% confidence interval [CI] = 0.87 to 1.11) for women younger than 50 years, and iCARE-BPC3 (E/O = 1.00, 95% CI = 0.93 to 1.09) for women 50 years or older. Risk projections using iCARE-BPC3 indicated classical risk factors can identify approximately 500 000 women at moderate to high risk (>3% 5-year risk) in the target population. Addition of MD and a 313-variant PRS is expected to increase this number to approximately 3.5 million women, and among them, approximately 153 000 are expected to develop invasive breast cancer within 5 years., Conclusions: iCARE models based on classical risk factors perform similarly to or better than BCRAT or IBIS in white non-Hispanic women. Addition of MD and PRS can lead to substantial improvements in risk stratification. However, these integrated models require independent prospective validation before broad clinical applications., (Published by Oxford University Press 2019. This work is written by US Government employees and is in the public domain in the US.)

Background: U.S. breast cancer incidence has been changing, as have distributions of risk factors, including body mass index (BMI), age at menarche, age at first live birth, and number of live births. Methods: Using data for U.S. women from large nationally representative surveys, we estimated risk factor distributions from 1980 to 2008. To estimate ecologic associations with breast cancer incidence, we fitted Poisson models to age- and calendar year-specific incidence data from the NCI's Surveillance, Epidemiology and End Results registries from 1980 to 2011. We then assessed the proportion of incidence attributable to specific risk factors by comparing incidence from models that only included age and calendar period as predictors with models that additionally included age- and cohort-specific categorized mean risk factors. Analyses were stratified by age and race. Results: Ecologic associations usually agreed with previous findings from analytic epidemiology. From 1980 to 2011, compared with the risk factor reference level, increased BMI was associated with 7.6% decreased incidence in women ages 40 to 44 and 2.6% increased incidence for women ages 55 to 59. Fewer births were associated with 22.2% and 3.99% increased incidence in women ages 40 to 44 and 55 to 59 years, respectively. Changes in age at menarche and age at first live birth in parous women did not significantly impact population incidence from 1980 to 2011. Conclusions: Changes in BMI and number of births since 1980 significantly impacted U.S. breast cancer incidence. Impact: Quantifying long-term impact of risk factor trends on incidence is important to understand the future breast cancer burden and inform prevention efforts. Cancer Epidemiol Biomarkers Prev; 27(10); 1214-22. ©2018 AACR ., (©2018 American Association for Cancer Research.)

Background: Incorporation of polygenic risk scores and mammographic density into models to predict breast cancer incidence can increase discriminatory accuracy (area under the receiver operating characteristic curve [AUC]) from 0.6 for models based only on epidemiologic factors to 0.7. It is timely to assess what impact these improvements will have on individual counseling and on public health prevention and screening strategies, and to determine what further improvements are needed., Methods: We studied various clinical and public health applications using a log-normal distribution of risk., Results: Provided they are well calibrated, even risk models with AUCs of 0.6 to 0.7 provide useful perspective for individual counseling and for weighing the harms and benefits of preventive interventions in the clinic. At the population level, they are helpful for designing preventive intervention trials, for assessing reductions in absolute risk from reducing exposure to modifiable risk factors, and for resource allocation (although a higher AUC would be desirable for risk-based allocation). Other public health applications require higher AUCs that can only be achieved with risk predictors 1.6 to 8.8 times as strong as all those yet discovered combined. Such applications are preventing an appreciable proportion of population disease when employing a high-risk prevention strategy and deciding who should be screened for subclinical disease., Conclusions: Current and foreseeable risk models are useful for counseling and some prevention activities, but given the daunting challenge of achieving, for example, an AUC of 0.8, considerable effort should be put into finding effective preventive interventions and screening strategies with fewer adverse effects.

Endogenous estradiol and estrone are linked causally to increased risks of breast cancer. In this study, we evaluated multiple competing hypotheses for how metabolism of these parent estrogens may influence risk. Prediagnostic concentrations of estradiol, estrone, and 13 metabolites were measured in 1,298 postmenopausal cases of breast cancer and 1,524 matched controls in four separate patient cohorts. The median time between sample collection and diagnosis was 4.4 to 12.7 years across the cohorts. Estrogen analytes were measured in serum or urine by liquid chromatography-tandem mass spectrometry. Total estrogen levels (summing all 15 estrogens/estrogen metabolites) were associated strongly and positively with breast cancer risk. Normalizing total estrogen levels, we also found that a relative increase in levels of 2-hydroxylation pathway metabolites, or in the ratio of 2-hydroxylation:16-hydroxylation pathway metabolites, were associated inversely with breast cancer risk. These associations varied by total estrogen levels, with the largest risk reductions occurring in women in the highest tertile. With appropriate validation, these findings suggest opportunities for breast cancer prevention by modifying individual estrogen metabolism profiles through either lifestyle alterations or chemopreventive strategies. Cancer Res; 77(4); 918-25. ©2017 AACR ., (©2016 American Association for Cancer Research.)

Background: There is no model to estimate absolute invasive breast cancer risk for Hispanic women., Methods: The San Francisco Bay Area Breast Cancer Study (SFBCS) provided data on Hispanic breast cancer case patients (533 US-born, 553 foreign-born) and control participants (464 US-born, 947 foreign-born). These data yielded estimates of relative risk (RR) and attributable risk (AR) separately for US-born and foreign-born women. Nativity-specific absolute risks were estimated by combining RR and AR information with nativity-specific invasive breast cancer incidence and competing mortality rates from the California Cancer Registry and Surveillance, Epidemiology, and End Results program to develop the Hispanic risk model (HRM). In independent data, we assessed model calibration through observed/expected (O/E) ratios, and we estimated discriminatory accuracy with the area under the receiver operating characteristic curve (AUC) statistic., Results: The US-born HRM included age at first full-term pregnancy, biopsy for benign breast disease, and family history of breast cancer; the foreign-born HRM also included age at menarche. The HRM estimated lower risks than the National Cancer Institute's Breast Cancer Risk Assessment Tool (BCRAT) for US-born Hispanic women, but higher risks in foreign-born women. In independent data from the Women's Health Initiative, the HRM was well calibrated for US-born women (observed/expected [O/E] ratio = 1.07, 95% confidence interval [CI] = 0.81 to 1.40), but seemed to overestimate risk in foreign-born women (O/E ratio = 0.66, 95% CI = 0.41 to 1.07). The AUC was 0.564 (95% CI = 0.485 to 0.644) for US-born and 0.625 (95% CI = 0.487 to 0.764) for foreign-born women., Conclusions: The HRM is the first absolute risk model that is based entirely on data specific to Hispanic women by nativity. Further studies in Hispanic women are warranted to evaluate its validity., (Published by Oxford University Press 2016. This work is written by US Government employees and is in the public domain in the US.)

Importance: An improved model for risk stratification can be useful for guiding public health strategies of breast cancer prevention., Objective: To evaluate combined risk stratification utility of common low penetrant single nucleotide polymorphisms (SNPs) and epidemiologic risk factors., Design, Setting, and Participants: Using a total of 17 171 cases and 19 862 controls sampled from the Breast and Prostate Cancer Cohort Consortium (BPC3) and 5879 women participating in the 2010 National Health Interview Survey, a model for predicting absolute risk of breast cancer was developed combining information on individual level data on epidemiologic risk factors and 24 genotyped SNPs from prospective cohort studies, published estimate of odds ratios for 68 additional SNPs, population incidence rate from the National Cancer Institute-Surveillance, Epidemiology, and End Results Program cancer registry and data on risk factor distribution from nationally representative health survey. The model is used to project the distribution of absolute risk for the population of white women in the United States after adjustment for competing cause of mortality., Exposures: Single nucleotide polymorphisms, family history, anthropometric factors, menstrual and/or reproductive factors, and lifestyle factors., Main Outcomes and Measures: Degree of stratification of absolute risk owing to nonmodifiable (SNPs, family history, height, and some components of menstrual and/or reproductive history) and modifiable factors (body mass index [BMI; calculated as weight in kilograms divided by height in meters squared], menopausal hormone therapy [MHT], alcohol, and smoking)., Results: The average absolute risk for a 30-year-old white woman in the United States developing invasive breast cancer by age 80 years is 11.3%. A model that includes all risk factors provided a range of average absolute risk from 4.4% to 23.5% for women in the bottom and top deciles of the risk distribution, respectively. For women who were at the lowest and highest deciles of nonmodifiable risks, the 5th and 95th percentile range of the risk distribution associated with 4 modifiable factors was 2.9% to 5.0% and 15.5% to 25.0%, respectively. For women in the highest decile of risk owing to nonmodifiable factors, those who had low BMI, did not drink or smoke, and did not use MHT had risks comparable to an average woman in the general population., Conclusions and Relevance: This model for absolute risk of breast cancer including SNPs can provide stratification for the population of white women in the United States. The model can also identify subsets of the population at an elevated risk that would benefit most from risk-reduction strategies based on altering modifiable factors. The effectiveness of this model for individual risk communication needs further investigation.

Background: The role of estrogen metabolism in determining breast cancer risk and differences in breast cancer rates between high-incidence and low-incidence nations is poorly understood., Methods: We measured urinary concentrations of estradiol and estrone (parent estrogens) and 13 estrogen metabolites formed by irreversible hydroxylation at the C-2, C-4, or C-16 positions of the steroid ring in a nested case-control study of 399 postmenopausal invasive breast cancer case participants and 399 matched control participants from the population-based Shanghai Women's Health Study cohort. Odds ratios (ORs) and 95% confidence intervals (CIs) of breast cancer by quartiles of metabolic pathway groups, pathway ratios, and individual estrogens/estrogen metabolites were estimated by multivariable conditional logistic regression. Urinary estrogen/estrogen metabolite measures were compared with those of postmenopausal non-hormone-using Asian Americans, a population with three-fold higher breast cancer incidence rates. All statistical tests were two-sided., Results: Urinary concentrations of parent estrogens were strongly associated with breast cancer risk (ORQ4vsQ1 = 1.94, 95% CI = 1.21 to 3.12, Ptrend = .01). Of the pathway ratios, the 2-pathway:total estrogens/estrogen metabolites and 2-pathway:parent estrogens were inversely associated with risk (ORQ4vsQ1 = 0.57, 95% CI = 0.35 to 0.91, Ptrend = .03, and ORQ4vsQ1 = 0.61, 95% CI = 0.37 to 0.99, Ptrend = .04, respectively). After adjusting for parent estrogens, these associations remained clearly inverse but lost statistical significance (ORQ4vsQ1 = 0.65, 95% CI = 0.39 to 1.06, Ptrend = .12 and ORQ4vsQ1 = 0.76, 95% CI = 0.44 to 1.32, Ptrend = .28). The urinary concentration of all estrogens/estrogen metabolites combined in Asian American women was triple that in Shanghai women., Conclusions: Lower urinary parent estrogen concentrations and more extensive 2-hydroxylation were each associated with reduced postmenopausal breast cancer risk in a low-risk nation. Markedly higher total estrogen/estrogen metabolite concentrations in postmenopausal United States women (Asian Americans) than in Shanghai women may partly explain higher breast cancer rates in the United States., (Published by Oxford University Press 2016. This work is written by US Government employees and is in the public domain in the United States.)

Background: Higher body mass index (BMI) and circulating estrogen levels each increase postmenopausal breast cancer risk, particularly estrogen receptor-positive (ER(+)) tumors. Higher BMI also increases estrogen production., Methods: We estimated the proportion of the BMI-ER(+) breast cancer association mediated through estrogen in a case-control study nested within the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Participants included 143 women with invasive ER(+) breast cancer and 268 matched controls, all postmenopausal and never having used hormone therapy at baseline. We used liquid chromatography-tandem mass spectrometry to measure 15 estrogens and estrogen metabolites in baseline serum. We calculated BMI from self-reported height and weight at baseline. We estimated the mediating effect of unconjugated estradiol on the BMI-ER(+) breast cancer association using Aalen additive hazards and Cox regression models., Results: All estrogens and estrogen metabolites were statistically significantly correlated with BMI, with unconjugated estradiol most strongly correlated [Pearson correlation (r) = 0.45]. Approximately 7% to 10% of the effect of overweight, 12% to 15% of the effect of obesity, and 19% to 20% of the effect of a 5 kg/m(2) BMI increase on ER(+) breast cancer risk was mediated through unconjugated estradiol. The BMI-breast cancer association, once adjusted for unconjugated estradiol, was not modified by further adjustment for two metabolic ratios statistically significantly associated with both breast cancer and BMI., Conclusion: Circulating unconjugated estradiol levels partially mediate the BMI-breast cancer association, but other potentially important estrogen mediators (e.g., bioavailable estradiol) were not evaluated., Impact: Further research is required to identify mechanisms underlying the BMI-breast cancer association., (©2015 American Association for Cancer Research.)

Purpose: Breast cancer mortality began declining in many Western countries during the late 1980s. We estimated the proportion of improvements in stage- and age-specific breast cancer survival in the United States explained by tumor size or estrogen receptor (ER) status., Methods: We estimated hazard ratios for breast cancer-specific death from time of invasive breast cancer diagnosis in the National Cancer Institute's Surveillance, Epidemiology, and End Results 9 Registries Database from 1973 to 2010, with and without stratification by tumor size and ER status., Results: Hazards from breast cancer-specific death declined from 1973 to 2010, not only in the first 5 years after diagnosis, but also thereafter. Stratification by tumor size explained less than 17% of the improvements comparing 2005 to 2010 versus 1973 to 1979, except for women age ≥ 70 years with local (49%) or regional (38%) disease. Tumor size usually accounted for more of the improvement in the first 5 years after diagnosis than later. Additional adjustment for ER status (positive, negative, or unknown) from 1990 to 2010 did not explain much more of the improvement, except for women age ≥ 70 years within 5 years after diagnosis., Conclusion: Most stage-specific survival improvement in women younger than age 70 years old is unexplained by tumor size and ER status, suggesting a key role for treatment. In the first 5 years after diagnosis, tumor size contributed importantly for women ≥ 70 years old with local and regional stage, and stratification by tumor size and ER status explained even more of the survival improvement among women age ≥ 70 years., (© 2015 by American Society of Clinical Oncology.)

We investigated whether the gut microbiota differed in 48 postmenopausal breast cancer case patients, pretreatment, vs 48 control patients. Microbiota profiles in fecal DNA were determined by Illumina sequencing and taxonomy of 16S rRNA genes. Estrogens were quantified in urine. Case-control comparisons employed linear and unconditional logistic regression of microbiota α-diversity (PD&#95;whole tree) and UniFrac analysis of β-diversity, with two-sided statistical tests. Total estrogens correlated with α-diversity in control patients (Spearman Rho = 0.37, P = .009) but not case patients (Spearman Rho = 0.04, P = .77). Compared with control patients, case patients had statistically significantly altered microbiota composition (β-diversity, P = .006) and lower α-diversity (P = .004). Adjusted for estrogens and other covariates, odds ratio of cancer was 0.50 (95% confidence interval = 0.30 to 0.85) per α-diversity tertile. Differences in specific taxa were not statistically significant when adjusted for multiple comparisons. This pilot study shows that postmenopausal women with breast cancer have altered composition and estrogen-independent low diversity of their gut microbiota. Whether these affect breast cancer risk and prognosis is unknown., (Published by Oxford University Press 2015. This work is written by (a) US Government employee(s) and is in the public domain in the US.)

Accurately quantifying parent estrogens (PE) estrone (E1) and estradiol (E2) and their metabolites (EM) within breast tissue and serum may permit detailed investigations of their contributions to breast carcinogenesis among BRCA1/2 mutation carriers. We conducted a study of PE/EM in serum, nipple aspirate fluid (NAF), and ductal lavage supernatant (DLS) among postmenopausal BRCA1/2 mutation carriers. PE/EM (conjugated and unconjugated) were measured in paired serum/NAF (n = 22 women) and paired serum/DLS samples (n = 24 women) using quantitative liquid chromatography-tandem mass spectrometry (LC/MS/MS). The relationships between serum and tissue-specific PE/EM were measured using Pearson's correlation coefficients. Conjugated forms of PE/EM constituted the majority of estrogen in serum (88 %), NAF (59 %) and DLS (69 %). PE/EM in NAF and serum were highly correlated [E1 (r = 0.97, p < 0.0001), E2 (r = 0.90, p < 0.0001) and estriol (E3) (r = 0.74, p < 0.0001)] as they were in DLS and serum [E1 (r = 0.92, p < 0.0001; E2 (r = 0.70, p = 0.0001; E3 (r = 0.67, p = 0.0004)]. Analyses of paired total estrogen values for NAF and serum, and DLS and serum yielded ratios of 0.22 (95 % CI 0.19-0.25) and 0.28 (95 % CI 0.24-0.32), respectively. This report is the first to employ LC/MS/MS to quantify PE/EM in novel breast tissue-derived biospecimens (i.e., NAF and DLS). We demonstrate that circulating PE and EM are strongly and positively correlated with tissue-specific PE and EM measured in NAF and DLS among postmenopausal BRCA1/2 mutation carriers. If confirmed, future etiologic studies could utilize the more readily obtainable serum hormone levels as a reliable surrogate measure of exposure at the tissue level.

Introduction: Mammographic density is similar among women at risk of either sporadic or BRCA1/2-related breast cancer. It has been suggested that digitized mammographic images contain computer-extractable information within the parenchymal pattern, which may contribute to distinguishing between BRCA1/2 mutation carriers and non-carriers., Methods: We compared mammographic texture pattern features in digitized mammograms from women with deleterious BRCA1/2 mutations (n = 137) versus non-carriers (n = 100). Subjects were stratified into training (107 carriers, 70 non-carriers) and testing (30 carriers, 30 non-carriers) datasets. Masked to mutation status, texture features were extracted from a retro-areolar region-of-interest in each subject's digitized mammogram. Stepwise linear regression analysis of the training dataset identified variables to be included in a radiographic texture analysis (RTA) classifier model aimed at distinguishing BRCA1/2 carriers from non-carriers. The selected features were combined using a Bayesian Artificial Neural Network (BANN) algorithm, which produced a probability score rating the likelihood of each subject's belonging to the mutation-positive group. These probability scores were evaluated in the independent testing dataset to determine whether their distribution differed between BRCA1/2 mutation carriers and non-carriers. A receiver operating characteristic analysis was performed to estimate the model's discriminatory capacity., Results: In the testing dataset, a one standard deviation (SD) increase in the probability score from the BANN-trained classifier was associated with a two-fold increase in the odds of predicting BRCA1/2 mutation status: unadjusted odds ratio (OR) = 2.00, 95% confidence interval (CI): 1.59, 2.51, P = 0.02; age-adjusted OR = 1.93, 95% CI: 1.53, 2.42, P = 0.03. Additional adjustment for percent mammographic density did little to change the OR. The area under the curve for the BANN-trained classifier to distinguish between BRCA1/2 mutation carriers and non-carriers was 0.68 for features alone and 0.72 for the features plus percent mammographic density., Conclusions: Our findings suggest that, unlike percent mammographic density, computer-extracted mammographic texture pattern features are associated with carrying BRCA1/2 mutations. Although still at an early stage, our novel RTA classifier has potential for improving mammographic image interpretation by permitting real-time risk stratification among women undergoing screening mammography.

Background: Breast, endometrial, and ovarian cancers share some hormonal and epidemiologic risk factors. While several models predict absolute risk of breast cancer, there are few models for ovarian cancer in the general population, and none for endometrial cancer., Methods and Findings: Using data on white, non-Hispanic women aged 50+ y from two large population-based cohorts (the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial [PLCO] and the National Institutes of Health-AARP Diet and Health Study [NIH-AARP]), we estimated relative and attributable risks and combined them with age-specific US-population incidence and competing mortality rates. All models included parity. The breast cancer model additionally included estrogen and progestin menopausal hormone therapy (MHT) use, other MHT use, age at first live birth, menopausal status, age at menopause, family history of breast or ovarian cancer, benign breast disease/biopsies, alcohol consumption, and body mass index (BMI); the endometrial model included menopausal status, age at menopause, BMI, smoking, oral contraceptive use, MHT use, and an interaction term between BMI and MHT use; the ovarian model included oral contraceptive use, MHT use, and family history or breast or ovarian cancer. In independent validation data (Nurses' Health Study cohort) the breast and ovarian cancer models were well calibrated; expected to observed cancer ratios were 1.00 (95% confidence interval [CI]: 0.96-1.04) for breast cancer and 1.08 (95% CI: 0.97-1.19) for ovarian cancer. The number of endometrial cancers was significantly overestimated, expected/observed = 1.20 (95% CI: 1.11-1.29). The areas under the receiver operating characteristic curves (AUCs; discriminatory power) were 0.58 (95% CI: 0.57-0.59), 0.59 (95% CI: 0.56-0.63), and 0.68 (95% CI: 0.66-0.70) for the breast, ovarian, and endometrial models, respectively., Conclusions: These models predict absolute risks for breast, endometrial, and ovarian cancers from easily obtainable risk factors and may assist in clinical decision-making. Limitations are the modest discriminatory ability of the breast and ovarian models and that these models may not generalize to women of other races. Please see later in the article for the Editors' Summary.

An ideal preventive intervention would have negligible side effects and could be applied to the entire population, thus achieving maximal preventive impact. Unfortunately, many interventions have adverse effects and beneficial effects. For example, tamoxifen reduces the risk of breast cancer by about 50% and the risk of hip fracture by 45%, but increases the risk of stroke by about 60%; other serious adverse effects include endometrial cancer and pulmonary embolus. Hence, tamoxifen should only be given to the subset of the population with high enough risks of breast cancer and hip fracture such that the preventive benefits outweigh the risks. Recommendations for preventive use of tamoxifen have been based primarily on breast cancer risk. Age-specific and race-specific rates were considered for other health outcomes, but not risk models. In this paper, we investigate the extent to which modeling not only the risk of breast cancer, but also the risk of stroke, can improve the decision to take tamoxifen. These calculations also give insight into the relative benefits of improving the discriminatory accuracy of such risk models versus improving the preventive effectiveness or reducing the adverse risks of the intervention. Depending on the discriminatory accuracies of the risk models, there may be considerable advantage to modeling the risks of more than one health outcome., (Published 2012. This article is a US Government work and is in the public domain in the USA.)

Background: Estrogens are recognized causal factors in breast cancer. Interindividual variation in estrogen metabolism may also influence the risk of breast cancer and could provide clues to mechanisms of breast carcinogenesis. Long-standing hypotheses about how estrogen metabolism might influence breast cancer have not been adequately evaluated in epidemiological studies because of the lack of accurate, reproducible, and high-throughput assays for estrogen metabolites., Methods: We conducted a prospective case-control study nested within the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO). Participants included 277 women who developed invasive breast cancer (case subjects) and 423 matched control subjects; at PLCO baseline, all subjects were aged 55-74 years, postmenopausal and not using hormone therapy, and provided a blood sample. Liquid chromatography-tandem mass spectrometry was used to measure serum concentrations of 15 estrogens and estrogen metabolites, in unconjugated and conjugated forms, including the parent estrogens, estrone and estradiol, and estrogen metabolites in pathways defined by irreversible hydroxylation at the C-2, C-4, or C-16 positions of the steroid ring. We calculated hazard ratios (HRs) approximating risk in highest vs lowest deciles of individual estrogens and estrogen metabolites, estrogens and estrogen metabolites grouped by metabolic pathways, and metabolic pathway ratios using multivariable Cox proportional hazards models. All statistical tests were two-sided., Results: Nearly all estrogens, estrogen metabolites, and metabolic pathway groups were associated with an increased risk of breast cancer; the serum concentration of unconjugated estradiol was strongly associated with the risk of breast cancer (HR = 2.07, 95% confidence interval [CI] = 1.19 to 3.62). No estrogen, estrogen metabolite, or metabolic pathway group remained statistically significantly associated with the risk of breast cancer after adjusting for unconjugated estradiol. The ratio of the 2-hydroxylation pathway to parent estrogens (HR = 0.66, 95% CI = 0.51 to 0.87) and the ratio of 4-hydroxylation pathway catechols to 4-hydroxylation pathway methylated catechols (HR = 1.34, 95% CI = 1.04 to 1.72) were statistically significantly associated with the risk of breast cancer and remained so after adjustment for unconjugated estradiol., Conclusions: More extensive 2-hydroxylation of parent estrogens is associated with lower risk, and less extensive methylation of potentially genotoxic 4-hydroxylation pathway catechols is associated with higher risk of postmenopausal breast cancer.

For Hispanic women, the Breast Cancer Risk Assessment Tool (BCRAT; "Gail Model") combines 1990-1996 breast cancer incidence for Hispanic women with relative risks for breast cancer risk factors from non-Hispanic white (NHW) women. BCRAT risk projections have never been comprehensively evaluated for Hispanic women. We compared the relative risks and calibration of BCRAT risk projections for 6,353 Hispanic to 128,976 NHW postmenopausal participants aged 50 and older in the Women's Health Initiative (WHI). Calibration was assessed by the ratio of the number of breast cancers observed with that expected by the BCRAT (O/E). We re-evaluated calibration for an updated BCRAT that combined BCRAT relative risks with 1993-2007 breast cancer incidence that is contemporaneous with the WHI. Cox regression was used to estimate relative risks. Discriminatory accuracy was assessed using the concordance statistic (AUC). In the WHI Main Study, the BCRAT underestimated the number of breast cancers by 18% in both Hispanics (O/E = 1.18, P = 0.06) and NHWs (O/E = 1.18, P < 0.001). Updating the BCRAT improved calibration for Hispanic women (O/E = 1.08, P = 0.4) and NHW women (O/E = 0.98, P = 0.2). For Hispanic women, relative risks for number of breast biopsies (1.71 vs. 1.27, P = 0.03) and age at first birth (0.97 vs. 1.24, P = 0.02) differed between the WHI and BCRAT. The AUC was higher for Hispanic women than NHW women (0.63 vs. 0.58, P = 0.03). Updating the BCRAT with contemporaneous breast cancer incidence rates improved calibration in the WHI. The modest discriminatory accuracy of the BCRAT for Hispanic women might improve by using risk factor relative risks specific to Hispanic women.

Background: Although modifiable risk factors have been included in previous models that estimate or project breast cancer risk, there remains a need to estimate the effects of changes in modifiable risk factors on the absolute risk of breast cancer., Methods: Using data from a case-control study of women in Italy (2569 case patients and 2588 control subjects studied from June 1, 1991, to April 1, 1994) and incidence and mortality data from the Florence Registries, we developed a model to predict the absolute risk of breast cancer that included five non-modifiable risk factors (reproductive characteristics, education, occupational activity, family history, and biopsy history) and three modifiable risk factors (alcohol consumption, leisure physical activity, and body mass index). The model was validated using independent data, and the percent risk reduction was calculated in high-risk subgroups identified by use of the Lorenz curve., Results: The model was reasonably well calibrated (ratio of expected to observed cancers = 1.10, 95% confidence interval [CI] = 0.96 to 1.26), but the discriminatory accuracy was modest. The absolute risk reduction from exposure modifications was nearly proportional to the risk before modifying the risk factors and increased with age and risk projection time span. Mean 20-year reductions in absolute risk among women aged 65 years were 1.6% (95% CI = 0.9% to 2.3%) in the entire population, 3.2% (95% CI = 1.8% to 4.8%) among women with a positive family history of breast cancer, and 4.1% (95% CI = 2.5% to 6.8%) among women who accounted for the highest 10% of the total population risk, as determined from the Lorenz curve., Conclusions: These data give perspective on the potential reductions in absolute breast cancer risk from preventative strategies based on lifestyle changes. Our methods are also useful for calculating sample sizes required for trials to test lifestyle interventions.

Background: The Breast Cancer Risk Assessment Tool (BCRAT) of the National Cancer Institute is widely used for estimating absolute risk of invasive breast cancer. However, the absolute risk estimates for Asian and Pacific Islander American (APA) women are based on data from white women. We developed a model for projecting absolute invasive breast cancer risk in APA women and compared its projections to those from BCRAT., Methods: Data from 589 women with breast cancer (case patients) and 952 women without breast cancer (control subjects) in the Asian American Breast Cancer Study were used to compute relative and attributable risks based on the age at menarche, number of affected mothers, sisters, and daughters, and number of previous benign biopsies. Absolute risks were obtained by combining this information with ethnicity-specific data from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) program and with US ethnicity-specific mortality data to create the Asian American Breast Cancer Study model (AABCS model). Independent data from APA women in the Women's Health Initiative (WHI) were used to check the calibration and discriminatory accuracy of the AABCS model., Results: The AABCS model estimated absolute risk separately for Chinese, Japanese, Filipino, Hawaiian, Other Pacific Islander, and Other Asian women. Relative and attributable risks for APA women were comparable to those in BCRAT, but the AABCS model usually estimated lower-risk projections than BCRAT in Chinese and Filipino, but not in Hawaiian women, and not in every age and ethnic subgroup. The AABCS model underestimated absolute risk by 17% (95% confidence interval = 1% to 38%) in independent data from WHI, but APA women in the WHI had incidence rates approximately 18% higher than those estimated from the SEER program., Conclusions: The AABCS model was calibrated to ethnicity-specific incidence rates from the SEER program for projecting absolute invasive breast cancer risk and is preferable to BCRAT for counseling APA women.

Purpose: The Study of Tamoxifen and Raloxifene (STAR) demonstrated that raloxifene was as effective as tamoxifen in reducing the risk of invasive breast cancer (IBC) in postmenopausal women and had lower risks of thromboembolic events, endometrial cancer, and cataracts but had a nonstatistically significant higher risk of noninvasive breast cancer. There is a need to summarize the risks and benefits of these agents., Patients and Methods: Baseline incidence rates of IBC and other health outcomes, absent raloxifene and tamoxifen, were estimated from breast cancer chemoprevention trials; the Surveillance, Epidemiology and End Results Program; and the Women's Health Initiative. Effects of raloxifene and tamoxifen were estimated from STAR and the Breast Cancer Prevention Trial. We assigned weights to health outcomes to calculate the net benefit from raloxifene compared with placebo and tamoxifen compared with placebo., Results: Risks and benefits of treatment with raloxifene or tamoxifen depend on age, race, breast cancer risk, and history of hysterectomy. Over a 5-year period, postmenopausal women with an intact uterus had a better benefit/risk index for raloxifene than for tamoxifen. For postmenopausal women without a uterus, the benefit/risk ratio was similar. The benefits and risks of raloxifene and tamoxifen are described in tables that can help identify groups of women for whom the benefits outweigh the risks., Conclusion: We developed a benefit/risk index to quantify benefits from chemoprevention with tamoxifen or raloxifene. This index can complement clinical evaluation in deciding whether to initiate chemoprevention and in comparing the benefits and risks of raloxifene versus tamoxifen.

This paper defines absolute risk and some of its properties, and presents applications in breast cancer counseling and prevention. For counseling, estimates of absolute risk give useful perspective and can be used in management decisions that require weighing risks and benefits, such as whether or not to take tamoxifen to prevent breast cancer. Absolute risk models are also useful in designing intervention trials to prevent breast cancer and in assessing the potential reductions in absolute risk of disease that might result from reducing exposures that are associated with breast cancer. In these applications, it is important that the risk model be well calibrated, namely that it accurately predicts the numbers of women who will develop breast cancer in various subsets of the population. Absolute risk models are also needed to implement a 'high risk' prevention strategy that identifies a high-risk subset of the population and focuses intervention efforts on that subset. The limitations of the high-risk strategy are discussed, including the need for risk models with high discriminatory accuracy, and the need for less toxic interventions that can reduce the threshold of risk above which the intervention provides a net benefit. I also discuss the potential use of risk models in allocating prevention resources under cost constraints. High discriminatory accuracy of the risk model, in addition to good calibration, is desirable in this application, and the risk assessment should not be expensive in comparison with the intervention., (This article is a U.S. Government work and is in the public domain in the U.S.A. Published in 2011 by John Wiley & Sons, Ltd.)

Objective: To investigate, among women with breast cancer, how postdiagnosis diet quality and the combination of diet quality and recreational physical activity are associated with prognosis., Methods: This multiethnic, prospective observational cohort included 670 women diagnosed with local or regional breast cancer. Thirty months after diagnosis, women completed self-report assessments on diet and physical activity and were followed for 6 years. Cox proportional hazards models were used to estimate hazard ratios (HR) and 95% confidence intervals for death from any cause and breast cancer death., Results: Women consuming better-quality diets, as defined by higher Healthy Eating Index-2005 scores, had a 60% reduced risk of death from any cause (HR(Q4:Q1): 0.40, 95% CI: 0.17, 0.94) and an 88% reduced risk of death from breast cancer (HR(Q4:Q1): 0.12, 95% CI: 0.02, 0.99). Compared with inactive survivors consuming poor-quality diets, survivors engaging in any recreational physical activity and consuming better-quality diets had an 89% reduced risk of death from any cause (HR: 0.11, 95% CI: 0.04, 0.36) and a 91% reduced risk of death from breast cancer (HR: 0.09, 95% CI: 0.01, 0.89). Associations observed were independent of obesity status., Conclusion: Women diagnosed with localized or regional breast cancer may improve prognosis by adopting better-quality dietary patterns and regular recreational physical activity. Lifestyle interventions emphasizing postdiagnosis behavior changes are advisable in breast cancer survivors.

Purpose: The increased risk of breast cancer (BC) among women receiving chest radiotherapy for Hodgkin's lymphoma (HL) is well-established. However, there are no large population-based studies that describe overall survival (OS) and cause-specific survival (CSS) compared with women with first primary BC., Methods: For 298 HL survivors who developed BC (HL-BC group) and 405,223 women with a first or only BC (BC-1 group), actuarial OS and CSS were compared, accounting for age, BC stage, hormone receptor status, sociodemographic status, radiation for HL, and other variables. All patients were derived from the population-based Surveillance, Epidemiology, and End Results program., Results: OS among patients with HL-BC was significantly inferior that of to patients with BC-1: 15-year OS was 48% versus 69% (P < .0001) for localized BC, and 33% versus 43% (P < .0001) for regional/distant BC. Patients with HL-BC had a significantly increased seven-fold risk (P < .0001) of death from other cancers (ie, not HL or BC) compared with patients with BC-1. Mortality from heart disease among patients with HL-BC with either localized or regional/distant disease was also significantly increased (hazard ratio = 2.22, P = .04; and hazard ratio = 4.28, P = .02, respectively) compared with patients with BC-1. Although 10-year BC-CSS was similar for patients with HL-BC and BC-1 with regional/distant disease, it was inferior for patients with localized BC (82% v 88%, respectively; P = .002)., Conclusion: Women with HL may survive a subsequent diagnosis of BC, only to experience significant excesses of death from other primary cancers and cardiac disease. Greater awareness of screening for cardiac disease and subsequent primary cancers in patients with HL-BC is warranted.

Objectives: We prospectively examined nonrecreational physical activity and sedentary behavior in relation to breast cancer risk among 97 039 postmenopausal women in the National Institutes of Health-AARP Diet and Health Study., Methods: We identified 2866 invasive and 570 in situ breast cancer cases recorded between 1996 and 2003 and used Cox proportional hazards regression to estimate multivariate relative risks (RRs) and 95% confidence intervals (CIs)., Results: Routine activity during the day at work or at home that included heavy lifting or carrying versus mostly sitting was associated with reduced risk of invasive breast cancer (RR = 0.62; 95% CI = 0.42, 0.91; P(trend) = .024)., Conclusions: Routine activity during the day at work or home may be related to reduced invasive breast cancer risk. Domains outside of recreation time may be attractive targets for increasing physical activity and reducing sedentary behavior among postmenopausal women.

Background: Guidelines from the American Cancer Society recommend annual breast magnetic resonance imaging (MRI) screening for women with a projected lifetime risk of ≥20% based on risk models that use family history. Because MRI screening is costly and has limited specificity, estimates of the numbers of U.S. women with ≥20% breast cancer risk would be useful., Methods: We used data from the 2000 and 2005 National Health Interview Survey and the National Cancer Institute (NCI) Breast Cancer Risk Assessment Tool (i.e., Gail model 2 with a revision for African Americans) to calculate estimates of U.S. women by age and race/ethnicity categories with a lifetime absolute breast cancer risk of ≥20%. Distributions of 5-year and lifetime absolute risk of breast cancer were compared across demographic groups., Results: We estimated that 1.09% (95% confidence interval, 0.95-1.24%) of women age 30 to 84 years have a lifetime absolute breast cancer risk of ≥20%, which translates to 880,063 U.S. women eligible for MRI screening. The 5-year risks are highest for white non-Hispanics and lowest for Hispanics. The lifetime risks decrease with age and are generally highest for white non-Hispanics, lower for African American non-Hispanic, and lowest for Hispanics., Conclusion: We provide national estimates of the number of U.S. women who would be eligible for MRI breast screening and distributions of 5-year and lifetime risks of breast cancer using the NCI Breast Cancer Risk Assessment Tool., Impact: These estimates inform the potential resources and public health demand for MRI screening and chemopreventive interventions that might be required for U.S. women., (©2010 AACR.)

Background: Inflammation and immune response have potential prognostic implications for breast cancer survivors. We examined how postdiagnosis diet quality is cross-sectionally related to biomarkers of inflammation and adipose-derived hormones among breast cancer survivors and determined whether physical activity or body size modified any observed associations., Methods: Participants included 746 women diagnosed with stage 0 to IIIA breast cancer. Thirty months after diagnosis, the women completed food frequency questionnaires. We scored diet quality with the Healthy Eating Index (HEI)-2005. Serum concentrations of C-reactive protein (CRP), serum amyloid A, leptin, and adiponectin were measured in fasting 30 mL blood samples. Log biomarker values were regressed on quartiles of HEI-2005 scores in multivariate models, and beta scores were exponentiated and expressed as geometric means within quartiles of HEI-2005 scores., Results: Women with better versus poor quality postdiagnosis diets, as defined by higher HEI-2005 scores (Q4 versus Q1), had lower concentrations of CRP (1.6 mg/L versus 2.5 mg/L), but no significant difference in concentrations of serum amyloid A, leptin, or adiponectin. Among women not engaging in recreational physical activity after diagnosis, better diet quality was associated with lower CRP concentrations (2.5 mg/L versus 5.0 mg/L), but no association was observed among women engaging in any recreational physical activity (1.4 mg/L versus 1.6 mg/L; P heterogeneity = 0.03)., Conclusions: Among breast cancer survivors, a better-quality diet seems to be associated with lower levels of chronic inflammation., Impact: Lower levels of chronic inflammation have been associated with improved survival after breast cancer., ((c) 2010 AACR.)

Elevated mammographic density (MD) is one of the strongest risk factors for sporadic breast cancer. Epidemiologic evidence suggests that MD is, in part, genetically determined; however, the relationship between MD and BRCA1/2 mutation status is equivocal. We compared MD in unaffected BRCA1/2 mutation carriers enrolled in the U.S. National Cancer Institute's Clinical Genetics Branch's Breast Imaging Study (n = 143) with women at low-to-average breast cancer risk enrolled in the same study (n = 29) or the NCI/National Naval Medical Center's Susceptibility to Breast Cancer Study (n = 90). The latter were BRCA mutation-negative members of mutation-positive families or women with no prior breast cancer, a Pedigree Assessment Tool score <8 (i.e., low risk of a hereditary breast cancer syndrome) and a Gail score <1.67. A single experienced mammographer measured MD using a computer-assisted thresholding method. We collected standard breast cancer risk factor information in both studies. Unadjusted mean percent MD was higher in women with BRCA1/2 mutations compared with women at low-to-average breast cancer risk (37.3% vs. 33.4%; P = 0.04), but these differences disappeared after adjusting for age and body mass index (34.9% vs. 36.3%; P = 0.43). We explored age at menarche, nulliparity, age at first birth, menopausal status, number of breast biopsies, and exposure to exogenous hormonal agents as potential confounders of the MD and BRCA1/2 association. Taking these factors into account did not significantly alter the results of the age/body mass index-adjusted analysis. Our results do not provide support for an independent effect of BRCA1/2 mutation status on mammographic density.

Background: Genomewide association studies have identified multiple genetic variants associated with breast cancer. The extent to which these variants add to existing risk-assessment models is unknown., Methods: We used information on traditional risk factors and 10 common genetic variants associated with breast cancer in 5590 case subjects and 5998 control subjects, 50 to 79 years of age, from four U.S. cohort studies and one case-control study from Poland to fit models of the absolute risk of breast cancer. With the use of receiver-operating-characteristic curve analysis, we calculated the area under the curve (AUC) as a measure of discrimination. By definition, random classification of case and control subjects provides an AUC of 50%; perfect classification provides an AUC of 100%. We calculated the fraction of case subjects in quintiles of estimated absolute risk after the addition of genetic variants to the traditional risk model., Results: The AUC for a risk model with age, study and entry year, and four traditional risk factors was 58.0%; with the addition of 10 genetic variants, the AUC was 61.8%. About half the case subjects (47.2%) were in the same quintile of risk as in a model without genetic variants; 32.5% were in a higher quintile, and 20.4% were in a lower quintile., Conclusions: The inclusion of newly discovered genetic factors modestly improved the performance of risk models for breast cancer. The level of predicted breast-cancer risk among most women changed little after the addition of currently available genetic information., (2010 Massachusetts Medical Society)

Introduction: In Asian and Asian-American women, soy consumption is associated with reduced breast cancer risk, perhaps due to its effects on estrogen production or metabolism. In a sample of Asian-American women, we investigated the associations of usual adult soy intake with the urinary concentrations of 15 estrogens and estrogen metabolites (EM) measured using liquid chromatography-tandem mass spectrometry., Methods: Participants included 430 Chinese-American, Japanese-American, and Filipino-American women, ages 20 to 55 years, and living in San Francisco-Oakland (California), Los Angeles (California), or Oahu (Hawaii). They were postmenopausal (n = 167) or premenopausal in luteal phase (n = 263) when 12-hour urine samples were collected. Robust linear regression was used to assess soy tertiles as predictors of log-transformed EM measures. Individual and grouped EM were considered as concentrations (pmol/mg creatinine) and as percentages of total EM (%EM)., Results: Factor analysis confirmed that EM groups defined by metabolic pathways appropriately captured covariation in EM profiles. Total EM concentrations were not significantly associated with soy in premenopausal or postmenopausal women. Among all women, %2-hydroxylated EM and %4-hydroxylation pathway EM were 16% higher (P(trend) = 0.02) and 19% higher (P(trend) = 0.03) in the highest versus lowest soy tertiles, respectively. In contrast, 16% hydroxylated EM were 11% lower (P(trend) < 0.01). Results were consistent across ethnic and menopausal groups and after adjustment for westernization measured by birthplace (Asia or United States)., Discussion: Findings suggest that regular soy intake is associated with increased ratios of 2:16-pathway EM and with higher relative levels of 4-hydroxylated EM. The observed variations in estrogen metabolism might modify breast cancer risk.

Background: Adding genotypes from seven single-nucleotide polymorphisms (SNPs), which had previously been associated with breast cancer, to the National Cancer Institute's Breast Cancer Risk Assessment Tool (BCRAT) increases the area under the receiver operating characteristic curve from 0.607 to 0.632., Methods: Criteria that are based on four clinical or public health applications were used to compare BCRAT with BCRATplus7, which includes the seven genotypes. Criteria included number of expected life-threatening events for the decision to take tamoxifen, expected decision losses (in units of the loss from giving a mammogram to a woman without detectable breast cancer) for the decision to have a mammogram, rates of risk reclassification, and number of lives saved by risk-based allocation of screening mammography. For all calculations, the following assumptions were made: Hardy-Weinberg equilibrium, linkage equilibrium across SNPs, additive effects of alleles at each locus, no interactions on the logistic scale among SNPs or with factors in BCRAT, and independence of SNPs from factors in BCRAT., Results: Improvements in expected numbers of life-threatening events were only 0.07% and 0.81% for deciding whether to take tamoxifen to prevent breast cancer for women aged 50-59 and 40-49 years, respectively. For deciding whether to recommend screening mammograms to women aged 50-54 years, the reduction in expected losses was 0.86% if the ideal breast cancer prevalence threshold for recommending mammography was that of women aged 50-54 years. Cross-classification of risks indicated that some women classified by BCRAT would have different classifications with BCRATplus7, which might be useful if BCRATplus7 was well calibrated. Improvements from BCRATplus7 were small for risk-based allocation of mammograms under costs constraints., Conclusions: The gains from BCRATplus7 are small in the applications examined. Models with SNPs, such as BCRATplus7, have not been validated for calibration in independent cohort data. Additional studies are needed to validate a model with SNPs and justify its use.

One purpose for seeking common alleles that are associated with disease is to use them to improve models for projecting individualized disease risk. Two genome-wide association studies and a study of candidate genes recently identified seven common single-nucleotide polymorphisms (SNPs) that were associated with breast cancer risk in independent samples. These seven SNPs were located in FGFR2, TNRC9 (now known as TOX3), MAP3K1, LSP1, CASP8, chromosomal region 8q, and chromosomal region 2q35. I used estimates of relative risks and allele frequencies from these studies to estimate how much these SNPs could improve discriminatory accuracy measured as the area under the receiver operating characteristic curve (AUC). A model with these seven SNPs (AUC = 0.574) and a hypothetical model with 14 such SNPs (AUC = 0.604) have less discriminatory accuracy than a model, the National Cancer Institute's Breast Cancer Risk Assessment Tool (BCRAT), that is based on ages at menarche and at first live birth, family history of breast cancer, and history of breast biopsy examinations (AUC = 0.607). Adding the seven SNPs to BCRAT improved discriminatory accuracy to an AUC of 0.632, which was, however, less than the improvement from adding mammographic density. Thus, these seven common alleles provide less discriminatory accuracy than BCRAT but have the potential to improve the discriminatory accuracy of BCRAT modestly. Experience to date and quantitative arguments indicate that a huge increase in the numbers of case patients with breast cancer and control subjects would be required in genome-wide association studies to find enough SNPs to achieve high discriminatory accuracy.

Background: The Breast Cancer Risk Assessment Tool of the National Cancer Institute (NCI) is widely used for counseling and determining eligibility for breast cancer prevention trials, although its validity for projecting risk in African American women is uncertain. We developed a model for projecting absolute risk of invasive breast cancer in African American women and compared its projections with those from the Breast Cancer Risk Assessment Tool., Methods: Data from 1607 African American women with invasive breast cancer and 1647 African American control subjects in the Women's Contraceptive and Reproductive Experiences (CARE) Study were used to compute relative and attributable risks that were based on age at menarche, number of affected mother or sisters, and number of previous benign biopsy examinations. Absolute risks were obtained by combining this information with data on invasive breast cancer incidence in African American women from the NCI's Surveillance, Epidemiology and End Results Program and with national mortality data. Eligibility screening data from the Study of Tamoxifen and Raloxifene (STAR) trial were used to determine how the new model would affect eligibility, and independent data from the Women's Health Initiative (WHI) were used to assess how well numbers of invasive breast cancers predicted by the new model agreed with observed cancers., Results: Tables and graphs for estimating relative risks and projecting absolute invasive breast cancer risk with confidence intervals were developed for African American women. Relative risks for family history and number of biopsies and attributable risks estimated from the CARE population were lower than those from the Breast Cancer Risk Assessment Tool, as was the discriminatory accuracy (i.e., concordance). Using eligibility screening data from the STAR trial, we estimated that 30.3% of African American women would have had 5-year invasive breast cancer risks of at least 1.66% by use of the CARE model, compared with only 14.5% by use of the Breast Cancer Risk Assessment Tool. The numbers of cancers predicted by the CARE model agreed well with observed numbers of cancers (i.e., it was well calibrated) in data from the WHI, except that it underestimated risk in African American women with breast biopsy examinations., Conclusions: The CARE model usually gave higher risk estimates for African American women than the Breast Cancer Risk Assessment Tool and is recommended for counseling African American women regarding their risk of breast cancer.

Objective: Clarifying age-specific female breast cancer risks and interactions may provide important etiologic clues., Method: Using a population-based case-control study in Poland (2000-2003) of 2,386 incident breast cancer cases and 2,502 control subjects aged 25-74 years, we estimated age-specific breast cancer incidence rates according to risk factors., Results: Breast cancer risks were elevated among women with positive family history (FH), younger age at menarche, older age at first full-term birth, nulliparity, exogenous hormonal usage, and reduced physical activity (PA). Notwithstanding overall risks, we observed statistically significant quantitative (non-crossover) and qualitative (crossover) age interactions for all risk factors except for FH and PA. For example, nulliparity compared to parity reduced breast cancer risk among women ages 25-39 years then rates crossed or reversed, after which nulliparity increased relative risks among women ages 40-74 years., Conclusion: Though quantitative age interactions could be expected, qualitative interactions were somewhat counterintuitive. If confirmed in other populations, qualitative interactions for a continuous covariate such as age will be difficult to reconcile in a sequential (multistep or monolithic) 'stochastic' breast cancer model. Alternatively, the reversal of relative risks among younger and older women suggests subgroup heterogeneity with different etiologic mechanisms for early-onset and late-onset breast cancer types.

Background: The Gail model 2 (GM) for predicting the absolute risk of invasive breast cancer has been used for counseling and to design intervention studies. Although the GM has been validated in US populations, its performance in other populations is unclear because of the wide variation in international breast cancer rates., Methods: We used data from a multicenter case-control study in Italy and from Italian cancer registries to develop a model (IT-GM) that uses the same risk factors as the GM. We evaluated the accuracy of the IT-GM and the GM using independent data from the Florence-European Prospective Investigation Into Cancer and Nutrition (EPIC) cohort. To assess model calibration (i.e., how well the model predicts the observed numbers of events in subsets of the population), we compared the number of expected incident breast cancers (E) predicted by these models with the number of observed incident breast cancers (O), and we computed the concordance statistic to measure discriminatory accuracy., Results: The overall E/O ratios were 0.96 (95% confidence interval [CI] = 0.84 to 1.11) and 0.93 (95% CI = 0.81 to 1.08) for the IT-GM and the GM, respectively. The IT-GM was somewhat better calibrated than GM in women younger than 50 years, but the GM was better calibrated when age at first live birth categories were considered (e.g., 20- to 24-year age-at-first-birth category E/O = 0.68, 95% CI = 0.53 to 0.94 for the IT-GM and E/O = 0.75, 95% CI = 0.58 to 1.03 for the GM). The concordance statistic was approximately 59% for both models, with 95% confidence intervals indicating that the models perform statistically significantly better than pure chance (concordance statistic of 50%)., Conclusions: There was no statistically significant evidence of miscalibration overall for either the IT-GM or the GM, and the models had equivalent discriminatory accuracy. The good performance of the IT-GM when applied on the independent data from the Florence-EPIC cohort indicates that GM can be improved for use in populations other than US populations. Our findings suggest that the Italian data may be useful for revising the GM to include additional risk factors for breast cancer.

Background: To improve the discriminatory power of the Gail model for predicting absolute risk of invasive breast cancer, we previously developed a relative risk model that incorporated mammographic density (DENSITY) from data on white women in the Breast Cancer Detection Demonstration Project (BCDDP). That model also included the variables age at birth of first live child (AGEFLB), number of affected mother or sisters (NUMREL), number of previous benign breast biopsy examinations (NBIOPS), and weight (WEIGHT). In this study, we developed the corresponding model for absolute risk., Methods: We combined the relative risk model with data on the distribution of the variables AGEFLB, NUMREL, NBIOPS, and WEIGHT from the 2000 National Health Interview Survey, with data on the conditional distribution of DENSITY given other risk factors in BCDDP, with breast cancer incidence rates from the Surveillance, Epidemiology, and End Results program of the National Cancer Institute, and with national mortality rates. Confidence intervals (CIs) accounted for variability of estimates of relative risks and of risk factor distributions. We compared the absolute 5-year risk projections from the new model with those from the Gail model on 1744 white women., Results: Attributable risks of breast cancer associated with DENSITY, AGEFLB, NUMREL, NBIOPS, and WEIGHT were 0.779 (95% CI = 0.733 to 0.819) and 0.747 (95% CI = 0.702 to 0.788) for women younger than 50 years and 50 years or older, respectively. The model predicted higher risks than the Gail model for women with a high percentage of dense breast area. However, the average risk projections from the new model in various age groups were similar to those from the Gail model, suggesting that the new model is well calibrated., Conclusions: This new model for absolute invasive breast cancer risk in white women promises modest improvements in discriminatory power compared with the Gail model but needs to be validated with independent data.

Background: Many women develop breast cancer after treatment for Hodgkin lymphoma (HL) at a young age. We estimated this future risk, taking into account age and calendar year of HL diagnosis, HL treatment information, population breast cancer incidence rates, and competing causes of death., Methods: Relative risks of breast cancer for categories defined by radiation dose to the chest (0, 20- < 40 Gy, or > or = 40 Gy) and use of alkylating agents (yes or no) were estimated from a case-control study conducted within an international population-based cohort of 3817 female 1-year survivors of HL diagnosed at age 30 years or younger from January 1, 1965, through December 31, 1994. To compute cumulative absolute risks of breast cancer, we used modified standardized incidence ratios to relate cohort breast cancer risks to those in the general population, enabling application of population-based breast cancer rates, and we allowed for competing risks by using population-based mortality rates in female HL survivors., Results: Cumulative absolute risks of breast cancer increased with age at end of follow-up, time since HL diagnosis, and radiation dose. For an HL survivor who was treated at age 25 years with a chest radiation dose of at least 40 Gy without alkylating agents, estimated cumulative absolute risks of breast cancer by age 35, 45, and 55 years were 1.4% (95% confidence interval [CI] = 0.9% to 2.1%), 11.1% (95% CI = 7.4% to 16.3%), and 29.0% (95% CI = 20.2% to 40.1%), respectively. Cumulative absolute risks were lower in women treated with alkylating agents., Conclusions: Breast cancer projections varied considerably by type of HL therapy, time since HL diagnosis, and age at end of follow-up. These estimates are applicable to HL survivors treated with regimens of the past and can be used to counsel such patients and plan management and preventive strategies. Projections should be used with caution, however, in patients treated with more recent approaches, including limited-field radiotherapy and/or ovary-sparing chemotherapy.