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1.  Discriminatory Accuracy from Single-Nucleotide Polymorphisms in Models to Predict Breast Cancer Risk 
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, 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), which 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.
doi:10.1093/jnci/djn180
PMCID: PMC2528005  PMID: 18612136
2.  Discriminatory Accuracy From Single-Nucleotide Polymorphisms in Models to Predict Breast Cancer Risk 
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.
doi:10.1093/jnci/djn180
PMCID: PMC2528005  PMID: 18612136
3.  Risk Prediction for Breast, Endometrial, and Ovarian Cancer in White Women Aged 50 y or Older: Derivation and Validation from Population-Based Cohort Studies 
PLoS Medicine  2013;10(7):e1001492.
Ruth Pfeiffer and colleagues describe models to calculate absolute risks for breast, endometrial, and ovarian cancers for white, non-Hispanic women over 50 years old using easily obtainable risk factors.
Please see later in the article for the Editors' Summary
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
Editors' Summary
Background
In 2008, just three types of cancer accounted for 10% of global cancer-related deaths. That year, about 460,000 women died from breast cancer (the most frequently diagnosed cancer among women and the fifth most common cause of cancer-related death). Another 140,000 women died from ovarian cancer, and 74,000 died from endometrial (womb) cancer (the 14th and 20th most common causes of cancer-related death, respectively). Although these three cancers originate in different tissues, they nevertheless share many risk factors. For example, current age, age at menarche (first period), and parity (the number of children a woman has had) are all strongly associated with breast, ovarian, and endometrial cancer risk. Because these cancers share many hormonal and epidemiological risk factors, a woman with a high breast cancer risk is also likely to have an above-average risk of developing ovarian or endometrial cancer.
Why Was This Study Done?
Several statistical models (for example, the Breast Cancer Risk Assessment Tool) have been developed that estimate a woman's absolute risk (probability) of developing breast cancer over the next few years or over her lifetime. Absolute risk prediction models are useful in the design of cancer prevention trials and can also help women make informed decisions about cancer prevention and treatment options. For example, a woman at high risk of breast cancer might decide to take tamoxifen for breast cancer prevention, but ideally she needs to know her absolute endometrial cancer risk before doing so because tamoxifen increases the risk of this cancer. Similarly, knowledge of her ovarian cancer risk might influence a woman's decision regarding prophylactic removal of her ovaries to reduce her breast cancer risk. There are few absolute risk prediction models for ovarian cancer, and none for endometrial cancer, so here the researchers develop models to predict the risk of these cancers and of breast cancer.
What Did the Researchers Do and Find?
Absolute risk prediction models are constructed by combining estimates for risk factors from cohorts with population-based incidence rates from cancer registries. Models are validated in an independent cohort by testing their ability to identify people with the disease in an independent cohort and their ability to predict the observed numbers of incident cases. The researchers used data on white, non-Hispanic women aged 50 years or older that were collected during two large prospective US cohort studies of cancer screening and of diet and health, and US cancer incidence and mortality rates provided by the Surveillance, Epidemiology, and End Results Program to build their models. The models all included parity as a risk factor, as well as other factors. The model for endometrial cancer, for example, also included menopausal status, age at menopause, body mass index (an indicator of the amount of body fat), oral contraceptive use, menopausal hormone therapy use, and an interaction term between menopausal hormone therapy use and body mass index. Individual women's risk for endometrial cancer calculated using this model ranged from 1.22% to 17.8% over the next 20 years depending on their exposure to various risk factors. Validation of the models using data from the US Nurses' Health Study indicated that the endometrial cancer model overestimated the risk of endometrial cancer but that the breast and ovarian cancer models were well calibrated—the predicted and observed risks for these cancers in the validation cohort agreed closely. Finally, the discriminatory power of the models (a measure of how well a model separates people who have a disease from people who do not have the disease) was modest for the breast and ovarian cancer models but somewhat better for the endometrial cancer model.
What Do These Findings Mean?
These findings show that breast, ovarian, and endometrial cancer can all be predicted using information on known risk factors for these cancers that is easily obtainable. Because these models were constructed and validated using data from white, non-Hispanic women aged 50 years or older, they may not accurately predict absolute risk for these cancers for women of other races or ethnicities. Moreover, the modest discriminatory power of the breast and ovarian cancer models means they cannot be used to decide which women should be routinely screened for these cancers. Importantly, however, these well-calibrated models should provide realistic information about an individual's risk of developing breast, ovarian, or endometrial cancer that can be used in clinical decision-making and that may assist in the identification of potential participants for research studies.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001492.
This study is further discussed in a PLOS Medicine Perspective by Lars Holmberg and Andrew Vickers
The US National Cancer Institute provides comprehensive information about cancer (in English and Spanish), including detailed information about breast cancer, ovarian cancer, and endometrial cancer;
Information on the Breast Cancer Risk Assessment Tool, the Surveillance, Epidemiology, and End Results Program, and on the prospective cohort study of screening and the diet and health study that provided the data used to build the models is also available on the NCI site
Cancer Research UK, a not-for-profit organization, provides information about cancer, including detailed information on breast cancer, ovarian cancer, and endometrial cancer
The UK National Health Service Choices website has information and personal stories about breast cancer, ovarian cancer, and endometrial cancer; the not-for-profit organization Healthtalkonline also provides personal stories about dealing with breast cancer and ovarian cancer
doi:10.1371/journal.pmed.1001492
PMCID: PMC3728034  PMID: 23935463
4.  Breast cancer risk prediction and individualised screening based on common genetic variation and breast density measurement 
Introduction
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.
Methods
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.
Results
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.
Conclusions
Taken together, genetic risk factors and mammographic density offer moderate improvements to clinical risk factor models for predicting breast cancer.
doi:10.1186/bcr3110
PMCID: PMC3496143  PMID: 22314178
5.  Discriminatory accuracy and potential clinical utility of genomic profiling for breast cancer risk in BRCA-negative women 
Several single nucleotide polymorphisms (SNPs) are associated with an increased risk of breast cancer. The clinical utility of genotyping individuals at these loci is not known. Subjects were 519 unaffected women without BRCA mutations. Gail, Claus, and IBIS models were used to estimate absolute breast cancer risks. Subjects were then genotyped at 15 independent risk loci. Published per-allele and genotype-specific odds ratios were used to calculate the composite cumulative genomic risk (CGR) for each subject. Affected age- and ethnicity-matched BRCA mutation-negative women were also genotyped as a comparison group for the calculation of discriminatory accuracy. The CGR was used to adjust absolute breast cancer risks calculated by Gail, Claus and IBIS models to determine the proportion of subjects whose recommendations for chemoprevention or MRI screening might be altered (reclassified) by such adjustment. Mean lifetime breast cancer risks calculated using the Gail, Claus, and IBIS models were 19.4, 13.0, and 17.7%, respectively. CGR did not correlate with breast cancer risk as calculated using any model. CGR was significantly higher in affected women (mean 3.35 vs. 3.12, P = 0.009). The discriminatory accuracy of the CGR alone was 0.55 (SE 0.019; P = 0.006). CGR adjustment of model-derived absolute risk estimates would have altered clinical recommendations for chemoprevention in 11–19% of subjects and for MRI screening in 8–32%. CGR has limited discriminatory accuracy. However, the use of a genomic risk term to adjust model-derived estimates has the potential to alter individual recommendations. These observations warrant investigation to evaluate the calibration of adjusted risk estimates.
doi:10.1007/s10549-010-1215-2
PMCID: PMC3310430  PMID: 20957429
Breast cancer risk; Single nucleotide polymorphism; Risk prediction model
6.  Using SNP genotypes to improve the discrimination of a simple breast cancer risk prediction model 
It has been shown that, for women aged 50 years or older, the discriminatory accuracy of the Breast Cancer Risk Prediction Tool (BCRAT) can be modestly improved by the inclusion of information on common single nucleotide polymorphisms (SNPs) that are associated with increased breast cancer risk. We aimed to determine whether a similar improvement is seen for earlier onset disease. We used the Australian Breast Cancer Family Registry to study a population-based sample of 962 cases aged 35 to 59 years and 463 controls frequency matched for age and for whom genotyping data was available.
Overall, the inclusion of data on seven SNPs improved the area under the receiver operating characteristic curve (AUC) from 0.58 (95% confidence interval [CI]=0.55–0.61) for BCRAT alone to 0.61 (95% CI=0.58–0.64) for BCRAT and SNP data combined (p<0.001). For women aged 35 to 39 years at interview, the corresponding improvement in AUC was from 0.61 (95% CI=0.56–0.66) to 0.65 (95% CI=0.60–0.70; p=0.03), while for women aged 40 to 49 years at diagnosis, the AUC improved from 0.61 (95% CI=0.55–0.66) to 0.63 (95% CI=0.57–0.69; p=0.04). Using previously used classifications of low, intermediate and high risk, 2.1% of cases and none of the controls aged 35 to 39 years, and 10.9% of cases and 4.0% of controls aged 40 to 49 years were classified into a higher risk group.
Including information on seven SNPs associated with breast cancer risk improves the discriminatory accuracy of BCRAT for women aged 35 to 39 years and 40 to 49 years. Given the low absolute risk for women in these age groups, only a small proportion are reclassified into a higher category for predicted 5-year risk of breast cancer.
doi:10.1007/s10549-013-2610-2
PMCID: PMC4059776  PMID: 23774992
Breast cancer; risk prediction; single nucleotide polymorphism; Breast Cancer Risk Assessment Tool
7.  Potential Usefulness of Single Nucleotide Polymorphisms to Identify Persons at High Cancer Risk: An Evaluation of Seven Common Cancers 
Journal of Clinical Oncology  2012;30(17):2157-2162.
Purpose
To estimate the likely number and predictive strength of cancer-associated single nucleotide polymorphisms (SNPs) that are yet to be discovered for seven common cancers.
Methods
From the statistical power of published genome-wide association studies, we estimated the number of undetected susceptibility loci and the distribution of effect sizes for all cancers. Assuming a log-normal model for risks and multiplicative relative risks for SNPs, family history (FH), and known risk factors, we estimated the area under the receiver operating characteristic curve (AUC) and the proportion of patients with risks above risk thresholds for screening. From additional prevalence data, we estimated the positive predictive value and the ratio of non–patient cases to patient cases (false-positive ratio) for various risk thresholds.
Results
Age-specific discriminatory accuracy (AUC) for models including FH and foreseeable SNPs ranged from 0.575 for ovarian cancer to 0.694 for prostate cancer. The proportions of patients in the highest decile of population risk ranged from 16.2% for ovarian cancer to 29.4% for prostate cancer. The corresponding false-positive ratios were 241 for colorectal cancer, 610 for ovarian cancer, and 138 or 280 for breast cancer in women age 50 to 54 or 40 to 44 years, respectively.
Conclusion
Foreseeable common SNP discoveries may not permit identification of small subsets of patients that contain most cancers. Usefulness of screening could be diminished by many false positives. Additional strong risk factors are needed to improve risk discrimination.
doi:10.1200/JCO.2011.40.1943
PMCID: PMC3397697  PMID: 22585702
8.  Evaluation of breast cancer risk assessment packages in the family history evaluation and screening programme 
Journal of Medical Genetics  2003;40(11):807-814.
Introduction: Accurate individualised breast cancer risk assessment is essential to provide risk–benefit analysis prior to initiating interventions designed to lower breast cancer risk. Several mathematical models for the estimation of individual breast cancer risk have been proposed. However, no single model integrates family history, hormonal factors, and benign breast disease in a comprehensive fashion. A new model by Tyrer and Cuzick has addressed these deficiencies. Therefore, this study has assessed the goodness of fit and discriminatory value of the Tyrer–Cuzick model against established models namely Gail, Claus, and Ford.
Methods: The goodness of fit and discriminatory accuracy of the models was assessed using data from 1933 women attending the Family History Evaluation and Screening Programme, of whom 52 developed cancer. All models were applied to these women over a mean follow up of 5.27 years to estimate risk of breast cancer.
Results: The ratios (95% confidence intervals) of expected to observed numbers of breast cancers were 0.48 (0.37 to 0.64) for Gail, 0.56 (0.43 to 0.75) for Claus, 0.49 (0.37 to 0.65) for Ford, and 0.81 (0.62 to 1.08) for Tyrer–Cuzick. The accuracy of the models for individual cases was evaluated using ROC curves. These showed that the area under the curve was 0.735 for Gail, 0.716 for Claus, 0.737 for Ford, and 0.762 for Tyrer–Cuzick.
Conclusion: The Tyrer–Cuzick model is the most consistently accurate model for prediction of breast cancer. The Gail, Claus, and Ford models all significantly underestimate risk, although the accuracy of the Claus model may be improved by adjustments for other risk factors.
doi:10.1136/jmg.40.11.807
PMCID: PMC1735317  PMID: 14627668
9.  Prevention of Breast Cancer in Postmenopausal Women: Approaches to Estimating and Reducing Risk 
Background
It is uncertain whether evidence supports routinely estimating a postmenopausal woman's risk of breast cancer and intervening to reduce risk.
Methods
We systematically reviewed prospective studies about models and sex hormone levels to assess breast cancer risk and used meta-analysis with random effects models to summarize the predictive accuracy of breast density. We also reviewed prospective studies of the effects of exercise, weight management, healthy diet, moderate alcohol consumption, and fruit and vegetable intake on breast cancer risk, and used random effects models for a meta-analyses of tamoxifen and raloxifene for primary prevention of breast cancer. All studies reviewed were published before June 2008, and all statistical tests were two-sided.
Results
Risk models that are based on demographic characteristics and medical history had modest discriminatory accuracy for estimating breast cancer risk (c-statistics range = 0.58–0.63). Breast density was strongly associated with breast cancer (relative risk [RR] = 4.03, 95% confidence interval [CI] = 3.10 to 5.26, for Breast Imaging Reporting and Data System category IV vs category I; RR = 4.20, 95% CI = 3.61 to 4.89, for >75% vs <5% of dense area), and adding breast density to models improved discriminatory accuracy (c-statistics range = 0.63–0.66). Estradiol was also associated with breast cancer (RR range = 2.0–2.9, comparing the highest vs lowest quintile of estradiol, P < .01). Most studies found that exercise, weight reduction, low-fat diet, and reduced alcohol intake were associated with a decreased risk of breast cancer. Tamoxifen and raloxifene reduced the risk of estrogen receptor–positive invasive breast cancer and invasive breast cancer overall.
Conclusions
Evidence from this study supports screening for breast cancer risk in all postmenopausal women by use of risk factors and breast density and considering chemoprevention for those found to be at high risk. Several lifestyle changes with the potential to prevent breast cancer should be recommended regardless of risk.
doi:10.1093/jnci/djp018
PMCID: PMC2720698  PMID: 19276457
10.  Prediction of breast cancer risk by genetic risk factors, overall and by hormone receptor status 
Journal of medical genetics  2012;49(9):601-608.
Objective
There is increasing interest in adding common genetic variants identified through genome wide association studies (GWAS) to breast cancer risk prediction models. First results from such models showed modest benefits in terms of risk discrimination. Heterogeneity of breast cancer as defined by hormone-receptor status has not been considered in this context. In this study we investigated the predictive capacity of 32 GWAS-detected common variants for breast cancer risk, alone and in combination with classical risk factors, and for tumors with different hormone receptor status.
Material and Methods
Within the Breast and Prostate Cancer Cohort Consortium (BPC3), we analyzed 6009 invasive breast cancer cases and 7827 matched controls of European ancestry, with data on classical breast cancer risk factors and 32 common gene variants identified through GWAS. Discriminatory ability with respect to breast cancer of specific hormone receptor-status was assessed with the age- and cohort-adjusted concordance statistic (AUROCa). Absolute risk scores were calculated with external reference data. Integrated discrimination improvement (IDI) was used to measure improvements in risk prediction.
Results
We found a small but steady increase in discriminatory ability with increasing numbers of genetic variants included in the model (difference in AUROCa going from 2.7 to 4%). Discriminatory ability for all models varied strongly by hormone receptor status
Discussion and Conclusion
Adding information on common polymorphisms provides small but statistically significant improvements in the quality of breast cancer risk prediction models. We consistently observed better performance for receptor positive cases, but the gain in discriminatory quality is not sufficient for clinical application.
doi:10.1136/jmedgenet-2011-100716
PMCID: PMC3793888  PMID: 22972951
breast cancer; risk prediction; genetic factors; hormone receptor status
11.  Validation of a Breast Cancer Risk Prediction Model Developed for Black Women 
Background
A breast cancer risk prediction model for black women, developed from data in the Women’s Contraceptive and Reproductive Experiences (CARE) study, has been validated in women aged 50 years or older but not among younger women or for specific breast cancer subtypes.
Methods
We assessed calibration and discrimination of the CARE model in the Black Women’s Health Study (BWHS) with data from 45 942 women aged 30 to 69 years at baseline.
Results
During a mean follow-up of 9.5 years, we identified 852 invasive breast cancers. The CARE model predicted 749.6 breast cancers, yielding an expected-to-observed (E/O) ratio of 0.88 (95% confidence interval [CI] = 0.82 to 0.94). The E/O ratio did not appreciably differ between women aged less than 50 years and those aged 50 years or older. The model underpredicted risk to the greatest degree among women aged 25 years or older at birth of first child (E/O = 0.71, 95% CI = 0.63 to 0.81); the model was well calibrated among women aged less than 25 years at birth of first child. The prevalence of later age at birth of first child was higher in the BWHS than in the CARE study, and breast cancer incidence was higher in the BWHS compared with national rates used in the CARE model. With respect to discriminatory accuracy, the concordance statistic was 0.57 (95% CI = 0.55 to 0.59) for breast cancer overall, 0.59 (95% CI = 0.57 to 0.61) for estrogen receptor (ER)-positive breast cancer, and 0.54 (95% CI = 0.50 to 0.57) for ER-negative breast cancer.
Conclusions
The CARE model underpredicted breast cancer risk in the BWHS, at least in part because of older age at first birth in this cohort, which led to higher breast cancer incidence rates. Our results suggest that inclusion of age at first birth may improve model performance. Discriminatory accuracy was modest and worse for ER-negative breast cancer.
doi:10.1093/jnci/djt008
PMCID: PMC3691941  PMID: 23411594
12.  Using Clinical Factors and Mammographic Breast Density to Estimate Breast Cancer Risk: Development and Validation of a New Predictive Model 
Annals of internal medicine  2008;148(5):337-347.
Background
Current models for assessing breast cancer risk are complex and do not include breast density, a strong risk factor for breast cancer that is routinely reported with mammography.
Objective
To develop and validate an easy-to-use breast cancer risk prediction model that includes breast density.
Design
Empirical model based on Surveillance, Epidemiology, and End Results incidence, and relative hazards from a prospective cohort.
Setting
Screening mammography sites participating in the Breast Cancer Surveillance Consortium.
Patients
1 095 484 women undergoing mammography who had no previous diagnosis of breast cancer.
Measurements
Self-reported age, race or ethnicity, family history of breast cancer, and history of breast biopsy. Community radiologists rated breast density by using 4 Breast Imaging Reporting and Data System categories.
Results
During 5.3 years of follow-up, invasive breast cancer was diagnosed in 14 766 women. The breast density model was well calibrated overall (expected–observed ratio, 1.03 [95% CI, 0.99 to 1.06]) and in racial and ethnic subgroups. It had modest discriminatory accuracy (concordance index, 0.66 [CI, 0.65 to 0.67]). Women with low-density mammograms had 5-year risks less than 1.67% unless they had a family history of breast cancer and were older than age 65 years.
Limitation
The model has only modest ability to discriminate between women who will develop breast cancer and those who will not.
Conclusion
A breast cancer prediction model that incorporates routinely reported measures of breast density can estimate 5-year risk for invasive breast cancer. Its accuracy needs to be further evaluated in independent populations before it can be recommended for clinical use.
PMCID: PMC2674327  PMID: 18316752
13.  Personalized estimates of breast cancer risk in clinical practice and public health 
Statistics in medicine  2011;30(10):1090-1104.
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 predict 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.
doi:10.1002/sim.4187
PMCID: PMC3079423  PMID: 21337591
absolute risk; allocation of prevention resources; breast cancer; calibration; crude risk; cumulative incidence; discriminatory accuracy; disease prevention; designing disease prevention trials; high risk prevention strategy; risk versus benefit
14.  Identification of a BRCA2-Specific Modifier Locus at 6p24 Related to Breast Cancer Risk 
Gaudet, Mia M. | Kuchenbaecker, Karoline B. | Vijai, Joseph | Klein, Robert J. | Kirchhoff, Tomas | McGuffog, Lesley | Barrowdale, Daniel | Dunning, Alison M. | Lee, Andrew | Dennis, Joe | Healey, Sue | Dicks, Ed | Soucy, Penny | Sinilnikova, Olga M. | Pankratz, Vernon S. | Wang, Xianshu | Eldridge, Ronald C. | Tessier, Daniel C. | Vincent, Daniel | Bacot, Francois | Hogervorst, Frans B. L. | Peock, Susan | Stoppa-Lyonnet, Dominique | Peterlongo, Paolo | Schmutzler, Rita K. | Nathanson, Katherine L. | Piedmonte, Marion | Singer, Christian F. | Thomassen, Mads | Hansen, Thomas v. O. | Neuhausen, Susan L. | Blanco, Ignacio | Greene, Mark H. | Garber, Judith | Weitzel, Jeffrey N. | Andrulis, Irene L. | Goldgar, David E. | D'Andrea, Emma | Caldes, Trinidad | Nevanlinna, Heli | Osorio, Ana | van Rensburg, Elizabeth J. | Arason, Adalgeir | Rennert, Gad | van den Ouweland, Ans M. W. | van der Hout, Annemarie H. | Kets, Carolien M. | Aalfs, Cora M. | Wijnen, Juul T. | Ausems, Margreet G. E. M. | Frost, Debra | Ellis, Steve | Fineberg, Elena | Platte, Radka | Evans, D. Gareth | Jacobs, Chris | Adlard, Julian | Tischkowitz, Marc | Porteous, Mary E. | Damiola, Francesca | Golmard, Lisa | Barjhoux, Laure | Longy, Michel | Belotti, Muriel | Ferrer, Sandra Fert | Mazoyer, Sylvie | Spurdle, Amanda B. | Manoukian, Siranoush | Barile, Monica | Genuardi, Maurizio | Arnold, Norbert | Meindl, Alfons | Sutter, Christian | Wappenschmidt, Barbara | Domchek, Susan M. | Pfeiler, Georg | Friedman, Eitan | Jensen, Uffe Birk | Robson, Mark | Shah, Sohela | Lazaro, Conxi | Mai, Phuong L. | Benitez, Javier | Southey, Melissa C. | Schmidt, Marjanka K. | Fasching, Peter A. | Peto, Julian | Humphreys, Manjeet K. | Wang, Qin | Michailidou, Kyriaki | Sawyer, Elinor J. | Burwinkel, Barbara | Guénel, Pascal | Bojesen, Stig E. | Milne, Roger L. | Brenner, Hermann | Lochmann, Magdalena | Aittomäki, Kristiina | Dörk, Thilo | Margolin, Sara | Mannermaa, Arto | Lambrechts, Diether | Chang-Claude, Jenny | Radice, Paolo | Giles, Graham G. | Haiman, Christopher A. | Winqvist, Robert | Devillee, Peter | García-Closas, Montserrat | Schoof, Nils | Hooning, Maartje J. | Cox, Angela | Pharoah, Paul D. P. | Jakubowska, Anna | Orr, Nick | González-Neira, Anna | Pita, Guillermo | Alonso, M. Rosario | Hall, Per | Couch, Fergus J. | Simard, Jacques | Altshuler, David | Easton, Douglas F. | Chenevix-Trench, Georgia | Antoniou, Antonis C. | Offit, Kenneth
PLoS Genetics  2013;9(3):e1003173.
Common genetic variants contribute to the observed variation in breast cancer risk for BRCA2 mutation carriers; those known to date have all been found through population-based genome-wide association studies (GWAS). To comprehensively identify breast cancer risk modifying loci for BRCA2 mutation carriers, we conducted a deep replication of an ongoing GWAS discovery study. Using the ranked P-values of the breast cancer associations with the imputed genotype of 1.4 M SNPs, 19,029 SNPs were selected and designed for inclusion on a custom Illumina array that included a total of 211,155 SNPs as part of a multi-consortial project. DNA samples from 3,881 breast cancer affected and 4,330 unaffected BRCA2 mutation carriers from 47 studies belonging to the Consortium of Investigators of Modifiers of BRCA1/2 were genotyped and available for analysis. We replicated previously reported breast cancer susceptibility alleles in these BRCA2 mutation carriers and for several regions (including FGFR2, MAP3K1, CDKN2A/B, and PTHLH) identified SNPs that have stronger evidence of association than those previously published. We also identified a novel susceptibility allele at 6p24 that was inversely associated with risk in BRCA2 mutation carriers (rs9348512; per allele HR = 0.85, 95% CI 0.80–0.90, P = 3.9×10−8). This SNP was not associated with breast cancer risk either in the general population or in BRCA1 mutation carriers. The locus lies within a region containing TFAP2A, which encodes a transcriptional activation protein that interacts with several tumor suppressor genes. This report identifies the first breast cancer risk locus specific to a BRCA2 mutation background. This comprehensive update of novel and previously reported breast cancer susceptibility loci contributes to the establishment of a panel of SNPs that modify breast cancer risk in BRCA2 mutation carriers. This panel may have clinical utility for women with BRCA2 mutations weighing options for medical prevention of breast cancer.
Author Summary
Women who carry BRCA2 mutations have an increased risk of breast cancer that varies widely. To identify common genetic variants that modify the breast cancer risk associated with BRCA2 mutations, we have built upon our previous work in which we examined genetic variants across the genome in relation to breast cancer risk among BRCA2 mutation carriers. Using a custom genotyping platform with 211,155 genetic variants known as single nucleotide polymorphisms (SNPs), we genotyped 3,881 women who had breast cancer and 4,330 women without breast cancer, which represents the largest possible, international collection of BRCA2 mutation carriers. We identified that a SNP located at 6p24 in the genome was associated with lower risk of breast cancer. Importantly, this SNP was not associated with breast cancer in BRCA1 mutation carriers or in a general population of women, indicating that the breast cancer association with this SNP might be specific to BRCA2 mutation carriers. Combining this BRCA2-specific SNP with 13 other breast cancer risk SNPs also known to modify risk in BRCA2 mutation carriers, we were able to derive a risk prediction model that could be useful in helping women with BRCA2 mutations weigh their risk-reduction strategy options.
doi:10.1371/journal.pgen.1003173
PMCID: PMC3609647  PMID: 23544012
15.  Breast density and parenchymal texture measures as potential risk factors for Estrogen-Receptor positive breast cancer 
Proceedings of SPIE  2014;9035:90351D-.
Accurate assessment of a woman’s risk to develop specific subtypes of breast cancer is critical for appropriate utilization of chemopreventative measures, such as with tamoxifen in preventing estrogen-receptor positive breast cancer. In this context, we investigate quantitative measures of breast density and parenchymal texture, measures of glandular tissue content and tissue structure, as risk factors for estrogen-receptor positive (ER+) breast cancer. Mediolateral oblique (MLO) view digital mammograms of the contralateral breast from 106 women with unilateral invasive breast cancer were retrospectively analyzed. Breast density and parenchymal texture were analyzed via fully-automated software. Logistic regression with feature selection and was performed to predict ER+ versus ER− cancer status. A combined model considering all imaging measures extracted was compared to baseline models consisting of density-alone and texture-alone features. Area under the curve (AUC) of the receiver operating characteristic (ROC) and Delong’s test were used to compare the models’ discriminatory capacity for receptor status. The density-alone model had a discriminatory capacity of 0.62 AUC (p=0.05). The texture-alone model had a higher discriminatory capacity of 0.70 AUC (p=0.001), which was not significantly different compared to the density-alone model (p=0.37). In contrast the combined density-texture logistic regression model had a discriminatory capacity of 0.82 AUC (p<0.001), which was statistically significantly higher than both the density-alone (p<0.001) and texture-alone regression models (p=0.04). The combination of breast density and texture measures may have the potential to identify women specifically at risk for estrogen-receptor positive breast cancer and could be useful in triaging women into appropriate risk-reduction strategies.
doi:10.1117/12.2043710
PMCID: PMC4112103  PMID: 25075270
Digital Mammography; Breast Percent Density (PD%); Parenchymal Texture; Breast Cancer Risk; Receptor
16.  Identification of PTHrP(12-48) as a plasma biomarker associated with breast cancer bone metastasis 
Background
Breast cancer bone metastasis (BM) is a complication that significantly compromises patient survival due, in part, to the lack of disease-specific biomarkers that allow early and accurate diagnosis.
Methods
Using mass spectrometry protein profiling, plasma samples were screened from 3 independent breast cancer patient cohorts with and without clinical evidence of bone metastasis.
Results
The results identified 13 biomarkers that classified all 110 patients with a sensitivity of 91% and specificity of 93% [receiver operating characteristics area under the curve (AUC=1.00)]. The most discriminatory protein was subsequently identified as a unique 12-48aa peptide fragment of parathyroid hormone-related protein (PTHrP). PTHrP(12-48) was significantly increased in BM patients plasma compared with patients without BM (p<0.0001). Logistic regression models were used to evaluate the diagnostic potential of PTHrP(12-48) as a single biomarker or in combination with the measurement of the clinical marker N-telopeptide of type I collagen (NTx). The PTHrP(12-48) and NTx logistic regression models were not significantly different and classified the patient groups with high accuracy (AUC=0.85 and 0.95) respectively. Interestingly, in combination with serum NTx, the plasma concentration of PTHrP(12-48) increased diagnostic specificity and accuracy (AUC=0.99).
Conclusions
These data demonstrate that PTHrP(12-48) circulates in breast cancer patient plasma and is a novel and predictive biomarker of breast cancer BM. Importantly, the clinical measurement of PTHrP(12-48) in combination with NTx improves the detection of breast cancer BM.
Impact
In summary, we present the first validated, plasma biomarker signature for diagnosis of breast cancer BM that may improve the early diagnosis of high-risk individuals.
doi:10.1158/1055-9965.EPI-12-1318-T
PMCID: PMC3651837  PMID: 23462923
plasma profiling; biomarkers; breast cancer; bone metastasis; bone resorption
17.  Genetic and Clinical Predictors for Breast Cancer Risk Assessment and Stratification Among Chinese Women 
Background
Most of the genetic variants identified from genome-wide association studies of breast cancer have not been validated in Asian women. No risk assessment model that incorporates both genetic and clinical predictors is currently available to predict breast cancer risk in this population.
Methods
We analyzed 12 single-nucleotide polymorphisms (SNPs) identified in recent genome-wide association studies mostly of women of European ancestry as being associated with the risk of breast cancer in 3039 case patients and 3082 control subjects who participated in the Shanghai Breast Cancer Study. All participants were interviewed in person to obtain information regarding known and suspected risk factors for breast cancer. The c statistic, a measure of discrimination ability with a value ranging from 0.5 (random classification) to 1.0 (perfect classification), was estimated to evaluate the contribution of genetic and established clinical predictors of breast cancer to a newly established risk assessment model for Chinese women. Clinical predictors included in the model were age at menarche, age at first live birth, waist-to-hip ratio, family history of breast cancer, and a previous diagnosis of benign breast disease. The utility of the models in risk stratification was evaluated by estimating the proportion of breast cancer patients in the general population that could be accounted for above a given risk threshold as predicted by the models. All statistical tests were two-sided.
Results
Eight SNPs (rs2046210, rs1219648, rs3817198, rs8051542, rs3803662, rs889312, rs10941679, and rs13281615), each of which reflected a genetically independent locus, were found to be associated with the risk of breast cancer. A dose–response association was observed between the risk of breast cancer and the genetic risk score, which is an aggregate measure of the effect of these eight SNPs (odds ratio for women in the highest quintile of genetic risk score vs those in the lowest = 1.85, 95% confidence interval = 1.58 to 2.18, Ptrend = 2.5 × 10−15). The genetic risk score, the waist-to-hip ratio, and a previous diagnosis of benign breast disease were the top three predictors of the risk of breast cancer, each contributing statistically significantly (P < .001) to the full risk assessment model. The model, with a c statistic of 0.6295 after adjustment for overfitting, showed promise for stratifying women into different risk groups; women in the top 30% risk group accounted for nearly 50% of the breast cancers diagnosed in the general population.
Conclusion
A risk assessment model that includes both genetic markers and clinical predictors may be useful to classify Asian women into relevant risk groups for cost-efficient screening and other prevention programs.
doi:10.1093/jnci/djq170
PMCID: PMC2897876  PMID: 20484103
18.  Association between Melanocytic Nevi and Risk of Breast Diseases: The French E3N Prospective Cohort 
PLoS Medicine  2014;11(6):e1001660.
Using data from the French E3N prospective cohort, Marina Kvaskoff and colleagues examine the association between number of cutaneous nevi and the risk for breast cancer.
Please see later in the article for the Editors' Summary
Background
While melanocytic nevi have been associated with genetic factors and childhood sun exposure, several observations also suggest a potential hormonal influence on nevi. To test the hypothesis that nevi are associated with breast tumor risk, we explored the relationships between number of nevi and benign and malignant breast disease risk.
Methods and Findings
We prospectively analyzed data from E3N, a cohort of French women aged 40–65 y at inclusion in 1990. Number of nevi was collected at inclusion. Hazard ratios (HRs) for breast cancer and 95% confidence intervals (CIs) were calculated using Cox proportional hazards regression models. Associations of number of nevi with personal history of benign breast disease (BBD) and family history of breast cancer were estimated using logistic regression. Over the period 15 June 1990–15 June 2008, 5,956 incident breast cancer cases (including 5,245 invasive tumors) were ascertained among 89,902 women. In models adjusted for age, education, and known breast cancer risk factors, women with “very many” nevi had a significantly higher breast cancer risk (HR = 1.13, 95% CI = 1.01–1.27 versus “none”; ptrend = 0.04), although significance was lost after adjustment for personal history of BBD or family history of breast cancer. The 10-y absolute risk of invasive breast cancer increased from 3,749 per 100,000 women without nevi to 4,124 (95% CI = 3,674–4,649) per 100,000 women with “very many” nevi. The association was restricted to premenopausal women (HR = 1.40, ptrend = 0.01), even after full adjustment (HR = 1.34, ptrend = 0.03; phomogeneity = 0.04), but did not differ according to breast cancer type or hormone receptor status. In addition, we observed significantly positive dose–response relationships between number of nevi and history of biopsy-confirmed BBD (n = 5,169; ptrend<0.0001) and family history of breast cancer in first-degree relatives (n = 7,472; ptrend = 0.0003). The main limitations of our study include self-report of number of nevi using a qualitative scale, and self-reported history of biopsied BBD.
Conclusions
Our findings suggest associations between number of nevi and the risk of premenopausal breast cancer, BBD, and family history of breast cancer. More research is warranted to elucidate these relationships and to understand their underlying mechanisms.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
In 2012, nearly 1.7 million women worldwide discovered they had breast cancer, and about half a million women died from the disease. Breast cancer begins when cells in the breast acquire genetic changes that allow them to divide uncontrollably and to move around the body (metastasize). Uncontrolled cell division leads to the formation of a lump that can be detected by mammography (a breast X-ray) or by manual breast examination. Breast cancer is treated by surgical removal of the lump, or, if the cancer has started to spread, by removal of the whole breast (mastectomy). Surgery is usually followed by radiotherapy or chemotherapy to kill any remaining cancer cells. Because the female sex hormones estrogen and progesterone stimulate the growth of some tumors, drugs that block hormone receptors are also used to treat receptor-positive breast cancer. Nowadays, the prognosis (outlook) for women with breast cancer is good, and in developed countries, nearly 90% of affected women are still alive five years after diagnosis.
Why Was This Study Done?
Several hormone-related factors affect a woman's chances of developing breast cancer. For example, women who have no children or who have them late in life have a higher breast cancer risk than women who have several children when they are young because pregnancy alters sex hormone levels. Interestingly, the development of moles (nevi)—dark skin blemishes that are risk factors for the development of melanoma, a type of skin cancer—may also be affected by estrogen and progesterone. Thus, the number of nevi might be a marker of blood hormone levels and might predict breast cancer risk. In this prospective cohort study, the researchers test this hypothesis by investigating the association between how many moles a woman has and her breast cancer risk. A prospective cohort study enrolls a group (cohort) of people, determines their baseline characteristics, and follows them over time to see which characteristics are associated with the development of specific diseases.
What Did the Researchers Do and Find?
In 1990, the E3N prospective cohort study enrolled nearly 100,000 French women (mainly school teachers) aged 40–65 years to investigate cancer risk factors. The women completed a baseline questionnaire about their lifestyle and medical history, and regular follow-up questionnaires that asked about cancer occurrence. In the initial questionnaire, the women indicated whether they had no, a few, many, or very many moles. Between 1990 and 2008, nearly 6,000 women in the cohort developed breast cancer. Using statistical methods to calculate hazard ratios (an “HR” compares how often a particular event happens in two groups with different characteristics; an HR greater than one indicates that a specific characteristic is associated with an increased risk of the event), the researchers report that women with “very many” nevi had a significantly higher breast cancer risk (a higher risk that was unlikely to have occurred by chance) than women with no nevi. Specifically, the age-adjusted HR for breast cancer among women with “very many” nevi compared to women with no nevi was 1.17. After adjustment for a personal history of benign (noncancerous) breast disease and a family history of breast cancer (two established risk factors for breast cancer), the association between nevi and breast cancer risk among the whole cohort became nonsignificant. Notably, however, the association among only premenopausal women remained significant after full adjustment (HR = 1.34), which corresponded to an increase in ten-year absolute risk of invasive breast cancer from 2,515 per 100,000 women with no nevi to 3,370 per 100,000 women with “very many” nevi.
What Do These Findings Mean?
These findings suggest that among premenopausal women there is a modest association between nevi number and breast cancer risk. This noncausal relationship may indicate that nevi and breast diseases are affected in similar ways by hormones or share common genetic factors, but the accuracy of these findings may be limited by aspects of the study design. For example, self-report of nevi numbers using a qualitative scale may have introduced some inaccuracies into the estimates of the association between nevi number and breast cancer risk. Most importantly, these findings are insufficient to support the use of nevi counts in breast cancer screening or diagnosis. Rather, together with the findings reported by Zhang et al. in an independent PLOS Medicine Research Article, they suggest that further studies into the biological mechanisms underlying the relationship between nevi and breast cancer and the association itself should be undertaken.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001660.
This study is further discussed in a PLOS Medicine Perspective by Fuhrman and Cardenas
An independent PLOS Medicine Research Article by Zhang et al. also investigates the relationship between nevi number and breast cancer risk
The US National Cancer Institute provides comprehensive information about cancer (in English and Spanish), including detailed information for patients and professionals about breast cancer; it also has a fact sheet on moles
Cancer Research UK, a not-for profit organization, provides information about cancer, including detailed information on breast cancer
The UK National Health Service Choices website has information and personal stories about breast cancer; the not-for profit organization Healthtalkonline also provides personal stories about dealing with breast cancer
More information about the E3N prospective cohort study is available; detailed information is available in French
doi:10.1371/journal.pmed.1001660
PMCID: PMC4051602  PMID: 24915306
19.  Prediction of lung cancer risk in a Chinese population using a multifactorial genetic model 
BMC Medical Genetics  2012;13:118.
Background
Lung cancer is a complex polygenic disease. Although recent genome-wide association (GWA) studies have identified multiple susceptibility loci for lung cancer, most of these variants have not been validated in a Chinese population. In this study, we investigated whether a genetic risk score combining multiple.
Methods
Five single-nucleotide polymorphisms (SNPs) identified in previous GWA or large cohort studies were genotyped in 5068 Chinese case–control subjects. The genetic risk score (GRS) based on these SNPs was estimated by two approaches: a simple risk alleles count (cGRS) and a weighted (wGRS) method. The area under the receiver operating characteristic (ROC) curve (AUC) in combination with the bootstrap resampling method was used to assess the predictive performance of the genetic risk score for lung cancer.
Results
Four independent SNPs (rs2736100, rs402710, rs4488809 and rs4083914), were found to be associated with a risk of lung cancer. The wGRS based on these four SNPs was a better predictor than cGRS. Using a liability threshold model, we estimated that these four SNPs accounted for only 4.02% of genetic variance in lung cancer. Smoking history contributed significantly to lung cancer (P < 0.001) risk [AUC = 0.619 (0.603-0.634)], and incorporated with wGRS gave an AUC value of 0.639 (0.621-0.652) after adjustment for over-fitting. This model shows promise for assessing lung cancer risk in a Chinese population.
Conclusion
Our results indicate that although genetic variants related to lung cancer only added moderate discriminatory accuracy, it still improved the predictive ability of the assessment model in Chinese population.
doi:10.1186/1471-2350-13-118
PMCID: PMC3573944  PMID: 23228068
Chinese; Cumulative risk; Genetic risk score; Lung cancer; Risk assessment
20.  Association between Cutaneous Nevi and Breast Cancer in the Nurses' Health Study: A Prospective Cohort Study 
PLoS Medicine  2014;11(6):e1001659.
Using data from the Nurses' Health Study, Jiali Han and colleagues examine the association between number of cutaneous nevi and the risk for breast cancer.
Please see later in the article for the Editors' Summary
Background
Cutaneous nevi are suggested to be hormone-related. We hypothesized that the number of cutaneous nevi might be a phenotypic marker of plasma hormone levels and predict subsequent breast cancer risk.
Methods and Findings
We followed 74,523 female nurses for 24 y (1986–2010) in the Nurses' Health Study and estimate the relative risk of breast cancer according to the number of cutaneous nevi. We adjusted for the known breast cancer risk factors in the models. During follow-up, a total of 5,483 invasive breast cancer cases were diagnosed. Compared to women with no nevi, women with more cutaneous nevi had higher risks of breast cancer (multivariable-adjusted hazard ratio, 1.04, 95% confidence interval [CI], 0.98–1.10 for 1–5 nevi; 1.15, 95% CI, 1.00–1.31 for 6–14 nevi, and 1.35, 95% CI, 1.04–1.74 for 15 or more nevi; p for continuous trend = 0.003). Over 24 y of follow-up, the absolute risk of developing breast cancer increased from 8.48% for women without cutaneous nevi to 8.82% (95% CI, 8.31%–9.33%) for women with 1–5 nevi, 9.75% (95% CI, 8.48%–11.11%) for women with 6–14 nevi, and 11.4% (95% CI, 8.82%–14.76%) for women with 15 or more nevi. The number of cutaneous nevi was associated with increased risk of breast cancer only among estrogen receptor (ER)–positive tumors (multivariable-adjusted hazard ratio per five nevi, 1.09, 95% CI, 1.02–1.16 for ER+/progesterone receptor [PR]–positive tumors; 1.08, 95% CI, 0.94–1.24 for ER+/PR− tumors; and 0.99, 95% CI, 0.86–1.15 for ER−/PR− tumors). Additionally, we tested plasma hormone levels according to the number of cutaneous nevi among a subgroup of postmenopausal women without postmenopausal hormone use (n = 611). Postmenopausal women with six or more nevi had a 45.5% higher level of free estradiol and a 47.4% higher level of free testosterone compared to those with no nevi (p for trend = 0.001 for both). Among a subgroup of 362 breast cancer cases and 611 matched controls with plasma hormone measurements, the multivariable-adjusted odds ratio for every five nevi attenuated from 1.25 (95% CI, 0.89–1.74) to 1.16 (95% CI, 0.83–1.64) after adjusting for plasma hormone levels. Key limitations in this study are that cutaneous nevi were self-counted in our cohort and that the study was conducted in white individuals, and thus the findings do not necessarily apply to other populations.
Conclusions
Our results suggest that the number of cutaneous nevi may reflect plasma hormone levels and predict breast cancer risk independently of previously known factors.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
One woman in eight will develop breast cancer during her lifetime. Breast cancer begins when cells in the breast acquire genetic changes that allow them to divide uncontrollably (which leads to the formation of a lump in the breast) and to move around the body (metastasize). The treatment of breast cancer, which is diagnosed using mammography (a breast X-ray) or manual breast examination and biopsy, usually involves surgery to remove the lump, or the whole breast (mastectomy) if the cancer has started to metastasize. After surgery, women often receive chemotherapy or radiotherapy to kill any remaining cancer cells and may also be given drugs that block the action of estrogen and progesterone, female sex hormones that stimulate the growth of some breast cancer cells. Globally, half a million women die from breast cancer each year. However, in developed countries, nearly 90% of women affected by breast cancer are still alive five years after diagnosis.
Why Was This Study Done?
Several sex hormone–related factors affect breast cancer risk, including at what age a woman has her first child (pregnancy alters sex hormone levels) and her age at menopause, when estrogen levels normally drop. Moreover, postmenopausal women with high circulating levels of estrogen and testosterone (a male sex hormone) have an increased breast cancer risk. Interestingly, moles (nevi)—dark skin blemishes that are a risk factor for the development of melanoma, a type of skin cancer—often darken or enlarge during pregnancy. Might the number of nevi be a marker of hormone levels, and could nevi counts therefore be used to predict an individual's risk of breast cancer? In this prospective cohort study, the researchers look for an association between number of nevi and breast cancer risk among participants in the US Nurses' Health Study (NHS). A prospective cohort study enrolls a group of people, determines their baseline characteristics, and follows them over time to see which characteristics are associated with the development of certain diseases. The NHS, which enrolled 121,700 female nurses aged 30–55 years in 1976, is studying risk factors for cancer and other chronic diseases in women.
What Did the Researchers Do and Find?
In 1986, nearly 75,000 NHS participants (all of whom were white) reported how many nevi they had on their left arm. Over the next 24 years, 5,483 invasive breast cancers were diagnosed in these women. Compared to women with no nevi, women with increasing numbers of nevi had a higher risk of breast cancer after adjustment for known breast cancer risk factors. Specifically, among women with 1–5 nevi, the hazard ratio (HR) for breast cancer was 1.04, whereas among women with 15 or more nevi the HR was 1.35. An HR compares how often a particular event occurs in two groups with different characteristics; an HR greater than one indicates that a specific characteristic is associated with an increased risk of the event. Over 24 years of follow-up, the absolute risk of developing breast cancer was 8.48% in women with no nevi but 11.4% for women with 15 or more nevi. Notably, postmenopausal women with six or more nevi had higher blood levels of estrogen and testosterone than women with no nevi. Finally, in a subgroup analysis, the association between number of nevi and breast cancer risk disappeared after adjustment for hormone levels.
What Do These Findings Mean?
These findings support the hypothesis that the number of nevi reflects sex hormone levels in women and may predict breast cancer risk. Notably, they show that the association between breast cancer risk and nevus number was independent of known risk factors for breast cancer, and that the risk of breast cancer increased with the number of nevi in a dose-dependent manner. These findings also suggest that a hormonal mechanism underlies the association between nevus number and breast cancer risk. Because this study involved only white participants, these findings may not apply to non-white women. Moreover, the use of self-reported data on nevus numbers may affect the accuracy of these findings. Finally, because this study is observational, these findings are insufficient to support any changes in clinical recommendations for breast cancer screening or diagnosis. Nevertheless, these data and those in an independent PLOS Medicine Research Article by Kvaskoff et al. support the need for further investigation of the association between nevi and breast cancer risk and of the mechanisms underlying this relationship.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001659.
An independent PLOS Medicine Research Article by Kvaskoff et al. also investigates the relationship between nevi and breast cancer risk
The US National Cancer Institute provides comprehensive information about cancer (in English and Spanish), including detailed information for patients and professionals about breast cancer; it also has a fact sheet on moles
Cancer Research UK, a not-for profit organization, provides information about cancer, including detailed information on breast cancer
The UK National Health Service Choices website has information and personal stories about breast cancer; the not-for profit organization Healthtalkonline also provides personal stories about dealing with breast cancer
More information about the Nurses' Health Study is available
doi:10.1371/journal.pmed.1001659
PMCID: PMC4051600  PMID: 24915186
21.  Risk prediction for complex diseases: application to Parkinson disease 
Purpose
The aim of this study was to evaluate the risk of Parkinson disease using clinical and demographic data alone and when combined with information from genes associated with Parkinson disease.
Methods
A total of 1,967 participants in the dbGAP NeuroGenetics Research Consortium data set were included. Single-nucleotide polymorphisms associated with Parkinson disease at a genome-wide significance level in previous genome-wide association studies were included in risk prediction. Risk allele scores were calculated as the weighted count of the minor alleles. Five models were constructed. Discriminatory capability was evaluated using the area under the curve.
Results
Both family history and genetic risk scores increased risk for Parkinson disease. Although the fullest model, which included both family history and genetic risk information, resulted in the highest area under the curve, there were no significant differences between models using family history alone and those using genetic information alone.
Conclusion
Adding genome-wide association study–derived genotypes, family history information, or both to standard demographic risk factors for Parkinson disease resulted in an improvement in discriminatory capacity. In the full model, the contributions of genotype data and family history information to discriminatory capacity were similar, and both were statistically significant. This suggests that there is limited overlap between genetic risk factors identified through genome-wide association study and unmeasured susceptibility variants captured by family history. Our results are similar to those of studies of other complex diseases and indicate that genetic risk prediction for Parkinson disease requires identification of additional genetic risk factors and/or better methods for risk prediction in order to achieve a degree of risk prediction that is clinically useful.
doi:10.1038/gim.2012.109
PMCID: PMC3687522  PMID: 23222663
genetics; Parkinson disease; risk prediction
22.  The role of the fat mass and obesity associated gene (FTO) in breast cancer risk 
BMC Medical Genetics  2011;12:52.
Background
Obesity has been shown to increase breast cancer risk. FTO is a novel gene which has been identified through genome wide association studies (GWAS) to be related to obesity. Our objective was to evaluate tissue expression of FTO in breast and the role of FTO SNPs in predicting breast cancer risk.
Methods
We performed a case-control study of 354 breast cancer cases and 364 controls. This study was conducted at Northwestern University. We examined the role of single nucleotide polymorphisms (SNPs) of intron 1 of FTO in breast cancer risk. We genotyped cases and controls for four SNPs: rs7206790, rs8047395, rs9939609 and rs1477196. We also evaluated tissue expression of FTO in normal and malignant breast tissue.
Results
We found that all SNPs were significantly associated with breast cancer risk with rs1477196 showing the strongest association. We showed that FTO is expressed both in normal and malignant breast tissue. We found that FTO genotypes provided powerful classifiers to predict breast cancer risk and a model with epistatic interactions further improved the prediction accuracy with a receiver operating characteristic (ROC) curves of 0.68.
Conclusion
In conclusion we have shown a significant expression of FTO in malignant and normal breast tissue and that FTO SNPs in intron 1 are significantly associated with breast cancer risk. Furthermore, these FTO SNPs are powerful classifiers in predicting breast cancer risk.
doi:10.1186/1471-2350-12-52
PMCID: PMC3089782  PMID: 21489227
23.  Evaluating breast cancer risk projections for Hispanic women 
Breast cancer research and treatment  2011;132(1):10.1007/s10549-011-1900-9.
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.
doi:10.1007/s10549-011-1900-9
PMCID: PMC3827770  PMID: 22147080
Hispanic; Breast cancer; Risk prediction; Risk assessment; BCRAT
24.  Risk Factor Modification and Projections of Absolute Breast Cancer Risk 
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.
doi:10.1093/jnci/djr172
PMCID: PMC3131219  PMID: 21705679
25.  Mammographic density, breast cancer risk and risk prediction 
In this review, we examine the evidence for mammographic density as an independent risk factor for breast cancer, describe the risk prediction models that have incorporated density, and discuss the current and future implications of using mammographic density in clinical practice. Mammographic density is a consistent and strong risk factor for breast cancer in several populations and across age at mammogram. Recently, this risk factor has been added to existing breast cancer risk prediction models, increasing the discriminatory accuracy with its inclusion, albeit slightly. With validation, these models may replace the existing Gail model for clinical risk assessment. However, absolute risk estimates resulting from these improved models are still limited in their ability to characterize an individual's probability of developing cancer. Promising new measures of mammographic density, including volumetric density, which can be standardized using full-field digital mammography, will likely result in a stronger risk factor and improve accuracy of risk prediction models.
doi:10.1186/bcr1829
PMCID: PMC2246184  PMID: 18190724

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