Breast Cancer Risk Assessment Models Research Articles

Genetic Susceptibility as a Possible Risk Factor for Breast Cancer.

Abstract Background: Established risk factors for breast cancer (BC) only explain 25% to 47% of BC incidence. The Gail model is commonly used to assess BC risk and determine eligibility for prevention trials. Unfortunately, it has a discriminatory accuracy of 58% at the individual level. A greater understanding of BC risk factors is needed to offer improved risk stratification and prevention. It is plausible that BC risk may be related to deficiencies or abnormalities in DNA repair and cell cycle checkpoints because of their importance in maintaining genomic integrity. Recent epidemiologic studies support this hypothesis. In this pilot project, the bleomycin mutagen sensitivity assay (MSS), a measure of genetic susceptibility was performed on women of low, intermediate, and high risk based on commonly used risk models to determine if there was any correlation between MSS and established BC risk factors.Methods: Women in the Prevention Clinic at RPCI consented to donate blood for this study as part of the Data Bank and BioRepository (DBBR). Gail and Claus Models were calculated using CancerGene. Women were stratified into three groups low (<20%), intermediate (20%-35%), and high (> 35%) lifetime BC risk. For logistic regression analysis, women were grouped into low risk (<20%) and high risk (≥20%). MSS in blood lymphocytes were performed at LCCC using fresh blood samples. The mean values of MSS were compared between low, intermediate, and high risk groups using Wilcoxon-Mann-Whitney tests. Multivariate logistic regression was used to analyze the relationship between BC risk groups and MSS.Results: A total of 78 women were enrolled in the study. MSS were performed on 70 blood samples and 67 had evaluable slides. There were 30, 26 and 11 women in the low, intermediate, and high risk groups respectively. The mean age was 49.1 years. There was no statistical difference between the three groups with respect to age, race, menopausal status, proportion of patients with a family history of BC or another cancer, and smoking status (never, former, or current). The mean MSS score was not significantly different among the three risk groups: mean ±SD = 0.86±0.37, 0.84±0.37 (p=0.565), and 0.76+0.44 (0.361) for low, intermediate and high risk groups respectively. Spearman correlation revealed that MSS score did not correlate with Gail lifetime BC risk score (r=-0.054, p=0.668) or Claus model-10 year risk score (r=-0.057, p=0.722). Using the median in low risk women as a cut point, when women who had higher MSS score were compared with women had lower MSS score, the adjusted OR was 1.16 (95% CI, 0.29 to 4.57), adjusted for age, race, smoking status, BMI and menopausal status. When the MSS score were categorized into quartiles, no significant dose-response relationship was observed.Conclusions: In this pilot study, there was no correlation between commonly used breast cancer risk assessment models based on hormonal, family and biopsy history and the MSS in our study population. Previous case-control studies have consistently shown that MSS is significantly associated with BC risk. The lack of correlation between mutagen sensitivity and Gail or Claus risk score suggests that mutagen sensitivity may measure the host susceptibility factors that are not considered by Gail or Claus risk models. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 6067.

Interaction between Family History of Breast Cancer and the Remaining Gail Risk Factors.

Abstract Background: The Gail breast cancer risk assessment model takes into account the relative contribution of various breast cancer risk factors (RF) and provides 5-year and lifetime breast cancer risk estimates. The purpose of this analysis was to determine the relative contribution of the Gail model RF in patients with and without family history of breast cancer (FHBC).Materials and Methods: Patients from the RUTH trial, consisting of postmenopausal women with or at high risk for coronary artery disease (CAD), were included in this analysis, with the exception of women ≥86 years of age or with medical history of any breast cancer, ductal carcinoma in situ, or lobular carcinoma in situ. Patients with missing values for RF were also excluded. For the resulting study population (N=10015), the risk score was calculated using the breast cancer risk assessment (BCRA) tool from the National Cancer Institute. A descriptive analysis of the BCRA risk scores for each RF stratified based on the presence or absence of a FHBC was performed. Risk factors were categorized as follows: advancing age (<65 or ≥65; 65 was the mean age); age at menarche (>12 or ≤12), number of biopsies (None or ≥1), age at first live child birth (<30 or ≥30; nulliparty included in <30 group per model). Interaction plots based on the two-way analysis of variance (ANOVA) model were generated for each category of the RF.Results: Of the 10015 women, 9123 (91.1%) did not have FHBC, while 892 (8.9%) did. Among women without FHBC, the mean BCRA score (%) was 1.54±0.40; 62.2% were 65 years or older, 28.8% had early menarche, 8.4% had first child at ≥30 years of age, and 8.1% had a previous breast biopsy. Among women with FHBC, mean BCRA score was 3.46±1.24; 70.3% were 65 years or older, 30.4% had early menarche, 9.3% had first child at ≥30 years of age, and 15.4 % had a previous breast biopsy. A statistically significant (p<0.001) amplification in the risk conferred by advancing age, previous biopsy, and early menarche was observed in patients with FHBC compared with patients with no family history for the disease (Figure 1). In contrast, the relative contribution to risk by late childbearing is more pronounced (p<0.001) in patients without FHBC.Discussion: Family history is an independent and important determinant of breast cancer risk in postmenopausal women. Other RF have a different influence on the overall risk estimate in women with FHBC when compared to women without FHBC. Increased risk was observed for advancing age, early menarche, and previous breast biopsy. A different pattern was seen for late childbearing. While the data were obtained from a large cohort of women, further analysis may be required to determine if these results are generalizable for all women regardless of menopausal status or CAD.Figure 1. Mean BCRA risk within each risk factor group, stratified by FHBC. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 6062.

Breast Cancer Risk Estimation Using the OncoVue® Model Compared to Combined GWAS Single Nucleotide Polymorphisms.

Abstract Background: We have developed a new breast cancer risk assessment model (OncoVue) that integrates information from 22 single nucleotide polymorphisms (SNPs) and 5 personal risk factors. It has been shown to effectively stratify risk in three independent validation populations. Genome wide association studies (GWAS) have identified seven candidate SNPs independently associated with breast cancer risk. Using estimates of relative risks and allele frequencies derived from these studies, a theoretical examination of these SNPs has concluded that they have the potential to modestly improve clinical risk estimation. We have examined both the OncoVue model and these GWAS SNPs in the same case-control population to determine their utility in risk estimation.Materials and Methods: A randomly selected subset of participants (376 cases and 982 controls) that had enrolled in a larger case-control study conducted in six distinct geographic regions of the United States were examined. DNAs were genotyped for the 22 SNP variants in OncoVue and combined with personal factors to calculate the risk scores for the individual participants. DNAs were also genotyped for the following GWAS SNPs: rs2981582 (FGFR2), rs3817198 (LSP1), rs889312 (MAP3K), rs4415084 (MRPS30), rs13281615 (POU5F1P1), rs13387042 (TNP1), and rs3803662 (TOX3, formerly TNRCR9). Association of individual GWAS SNP genotypes with breast cancer risk was evaluated by calculating odds ratios (ORs). Assuming independent contribution of each SNP to risk, a combined GWAS risk score was calculated using a multiplicative model. Positive likelihood ratios (PLRs) were calculated using a risk threshold of 1.5-fold the control population mean risk to evaluate the proportion of individuals placed at elevated risk by OncoVue compared to the combined GWAS risk scores.Results: OncoVue exhibited significant ability to stratify risk at the 1.5-fold mean with a PLR of 2.2 and at a risk level of 2.5-fold mean the PLR increased to 5.0. In analyses of individual GWAS SNPs, statistically significant associations with breast cancer were identified for homozygous carriers of the rare allele for the FGFR2, MAP3K and TOX3 genes. However, the PLR for the combined GWAS risk scores at 1.5-fold mean was 1.0 indicating no ability to discriminate risk in this study population.Conclusions: In this case-control population OncoVue effectively stratified risk by accurately assigning elevated risk to breast cancer cases. In the same population, a combined risk score produced from seven GWAS SNPs did not effectively stratify risk. These results indicate that the OncoVue model has clinical utility for identifying elevated risk women who might benefit from additional screening and prevention. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 3177.

Breast Cancer Risk Asssessment in the High Risk Marin County Population Using OncoVue® Compared to SNPs from Genome Wide Association Studies.

Abstract Background: Models for accurate assessment of individualized risk of developing breast cancer are important for effectively guiding clinical decisions regarding early detection and prevention. Marin County (MC), California (located immediately north of San Francisco) has long been recognized as a population with high incidence rates of breast cancer. In a population-based case-control study conducted from 1997 to 1999 in MC, no classical epidemiological risk factors such as age at menarche, age at first live birth, number of first-degree relatives with breast cancer, and history/outcome of previous breast biopsies were associated with breast cancer risk. Thus, the widely used National Cancer Institute-Breast Cancer Risk Assessment Tool (BCRAT) or Gail Model is not an effective risk assessment tool for this population. Recently, we found that a new breast cancer risk assessment model (OncoVue) that integrates information from 22 single nucleotide polymorphisms (SNPs) and 5 personal risk factors effectively stratified risk in the MC population. Several genome wide association studies (GWAS) have also recently derived a consensus of seven independent candidate SNPs strongly associated with breast cancer risk that are not included in the OncoVue model. Here we have evaluated these SNPs from GWAS to determine their utility in risk estimation and stratification of the high risk MC population.Materials and Methods: Study DNAs were genotyped for the following SNPs in seven genes: rs2981582 (FGFR2), rs3817198 (LSP1), rs889312 (MAP3K), rs4415084 (MRPS30), rs13281615 (POU5F1P1), rs13387042 (TNP1), and rs3803662 (TOX3, formerly TNRCR9). The analyses were performed on white women ages 30-69 and included 164 cases and 173 controls that were genotyped for all seven SNPs. Association of individual genotypes with breast cancer risk was evaluated by calculating odds ratios (ORs). Composite risk for each individual in the study was calculated by assigning risks from published studies for each genotype and multiplying them. The clinical utility of composite risk scores was evaluated by calculating positive likelihood ratios (PLR) as the proportion of patients with breast cancer with an elevated risk estimate divided by the proportion of disease-free individuals with an elevated risk estimate.Results: Carriers of the rare allele for FGFR2, MAP3K, and TNP1 exhibited statistically significant associations with breast cancer risk. Over a range of risk levels, composite risk scores exhibited PLRs of approximately 1.0 but at the highest end of the risk spectrum a PLR of 2.3 indicates useful risk stratification. The women at this risk level represent <10% of the control population.Conclusions: In the high risk MC population the seven GWAS SNPs stratify risk at the highest end of the risk. In contrast, overall OncoVue exhibits higher PLRs at high risk levels and may prove to be more clinically useful for directing prevention and screening decisions. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 3057.

Assessment of breast cancer risk prediction models in a high-risk population.

Abstract Abstract #4074 Background: A variety of risk assessment models have been developed to quantify a woman's risk for developing breast cancer. Although the Gail model (GM) is the most widely utilized model, there are limitations. The Tyrer-Cuzick model (TCM), which has been validated in the United Kingdom (UK), addresses these limitations but its performance in other populations is unclear. The purpose of this study was to evaluate the predictive ability of the TCM in a cohort of high-risk women from the New York metropolitan area and compare the results to the GM.
 Methods: The Women At Risk (WAR) Registry provided the study population. Due to the age limitation of the Gail model, we excluded women who were under 35 and over 80 years of age. Calculation of lifetime Gail scores included the following: age, race, age at first menses, age at first live birth, number of first degree relatives with breast cancer, number of previous breast biopsies, and atypical hyperplasia. These variables were also included in the lifetime Tyrer-Cuzick scores as well as information on menopausal status, use of hormone replacement therapy, LCIS, and age at diagnosis for relatives with ovarian and breast cancer. The Receiver Operating Characteristic (ROC) and estimated area under the ROC curve (AUC) with 95% confidence intervals (CI) were used to assess prediction accuracy of both models.
 Results: Out of a total study population of 1523 women, 82 (5%) developed breast cancer during a median follow-up of 56 months. The median age of women without breast cancer was 49 years, and the median age of women who developed breast cancer was 54 years. The AUC and 95% CI for the Gail model was 0.547 (0.479 to 0.615) and 0.501 (0.433 to 0.569) for the Tyrer-Cuzick model. Our results indicate poor discriminatory accuracy for both models, which performed no better than pure chance (0.5).
 
 Discussion: Quantitative breast cancer risk assessment models are critically important in developing effective risk management strategies. Although the TCM addresses some limitations of the GM, it is unclear how well it performs in populations outside the UK due to the wide variation in international breast cancer rates. In addition, the TCM excludes more recently identified risk factors such as breast density. Bayesian techniques may ultimately lead to a better risk assessment tool as they allow continual modification of the model as new risk factors are identified. Accurate breast cancer risk prediction is a complex undertaking and further studies are warranted in order to achieve truly comprehensive and widely applicable models. Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 4074.

The American Cancer Society guidelines for breast screening with magnetic resonance imaging

The American Cancer Society (ACS) guidelines for screening with breast magnetic resonance imaging (MRI) recommend MRI for women who have a lifetime risk > or = 20% of developing breast cancer. Genetic testing for breast cancer gene (BRCA) mutations is offered to women who have a risk > or = 10% of carrying a mutation. The objectives of the current study were 1) to identify the number of women in a breast cancer screening population who had > or = 20% lifetime breast cancer risk and, thus, were candidates for screening MRI; and 2) to determine the number of women who had > or = 10% risk of BRCA mutation yet had <20% lifetime risk of breast cancer and, thus, may not have been identified as candidates for MRI screening. From 2003 to 2005, women who underwent screening mammography completed a self-administered questionnaire regarding breast cancer risk factors. For each patient, the lifetime breast cancer risk and the risk of BRCA mutation was determined by using the computerized BRCAPRO breast cancer risk-assessment model. Of 18,190 women, 78 (0.43%) had > or = 20% lifetime risk of breast cancer, all of whom had > or = 10% risk of carrying a BRCA mutation. An additional 374 women (2.06%) had <20% lifetime breast cancer risk but > or = 10% risk of mutation. Overall, there were 183 (1%) predicted mutation carriers, 27 women (0.15%) who had > or = 20% lifetime risk of breast cancer, and 62 women (0.34%) who had > or = 10% risk of mutation but <20% lifetime breast cancer risk. The ACS guidelines for breast MRI screening may systematically exclude MRI screening for many women who have a substantial risk for BRCA mutation. The current results demonstrated a need for greater awareness of breast cancer risk factors in the screening mammography population, so that high-risk women can be identified and given access to genetic testing and counseling regarding all risk-reducing interventions.

Differences and similarities in breast cancer risk assessment models in clinical practice: which model to choose?

To show differences and similarities between risk estimation models for breast cancer in healthy women from BRCA1/2-negative or untested families. After a systematic literature search seven models were selected: Gail-2, Claus Model, Claus Tables, BOADICEA, Jonker Model, Claus-Extended Formula, and Tyrer-Cuzick. Life-time risks (LTRs) for developing breast cancer were estimated for two healthy counsellees, aged 40, with a variety in family histories and personal risk factors. Comparisons were made with guideline thresholds for individual screening. Without a clinically significant family history LTRs varied from 6.7% (Gail-2 Model) to 12.8% (Tyrer-Cuzick Model). Adding more information on personal risk factors increased the LTRs and yearly mammography will be advised in most situations. Older models (i.e. Gail-2 and Claus) are likely to underestimate the LTR for developing breast cancer as their baseline risk for women is too low. When models include personal risk factors, surveillance thresholds have to be reformulated. For current clinical practice, the Tyrer-Cuzick Model and the BOADICEA Model seem good choices.

Breast cancer risk-assessment models

There are two main questions when assessing a woman for interventions to reduce her risks of developing or dying from breast cancer, the answers of which will determine her access: What are her chances of carrying a mutation in a high-risk gene such as BRCA1 or BRCA2? What are her risks of developing breast cancer with or without such a mutation? These risks taken together with the risks and benefits of the intervention will then determine whether an intervention is appropriate. A number of models have been developed for assessing these risks with varying degrees of validation. With further improvements in our knowledge of how to integrate risk factors and to eventually integrate further genetic variants into these models, we are confident we will be able to discriminate with far greater accuracy which women are most likely to develop breast cancer.

Cancer Risk Prediction Models: A Workshop on Development, Evaluation, and Application

Cancer researchers, clinicians, and the public are increasingly interested in statistical models designed to predict the occurrence of cancer. As the number and sophistication of cancer risk prediction models have grown, so too has interest in ensuring that they are appropriately applied, correctly developed, and rigorously evaluated. On May 20-21, 2004, the National Cancer Institute sponsored a workshop in which experts identified strengths and limitations of cancer and genetic susceptibility prediction models that were currently in use and under development and explored methodologic issues related to their development, evaluation, and validation. Participants also identified research priorities and resources in the areas of 1) revising existing breast cancer risk assessment models and developing new models, 2) encouraging the development of new risk models, 3) obtaining data to develop more accurate risk models, 4) supporting validation mechanisms and resources, 5) strengthening model development efforts and encouraging coordination, and 6) promoting effective cancer risk communication and decision-making.

Strengths and Limitations of Breast Cancer Risk Assessment

To evaluate current definitions of breast cancer risk and breast cancer risk assessment models, including the Gail, Claus, and BRCAPRO models, and discuss potential markers to enhance and standardize individual risk assessment. Published articles, conference proceedings, and textbooks. Defining high risk for breast cancer development is explored, and options for high-risk women are discussed. The risk factors frequently used for risk evaluation, including age, age at menarche, age at first live birth, past history of breast biopsy, family history of breast cancer, and the presence of atypical hyperplasia, are reviewed. Current models of breast cancer risk assessment are limited. Exploring the progression from healthy tissue to malignancy through techniques such as fine needle aspiration, ductal lavage, and nipple aspiration may lead to more precise individualized risk prediction. More accurate information regarding personal breast cancer risk is necessary. Oncology nurses may facilitate the use of appropriate tools that provide the most individualized risk assessment.

Breast cancer risk assessment models: applicability to African-American women.

Mortality rates are higher among African-American women with breast cancer than they are among white women. This population subset can benefit from available risk reduction strategies. Optimal public health gains from chemoprevention strategies depend on the ability to assess accurately the risk for the individual. However, it is not known if existing breast cancer prediction models are accurate predictors of the disease among African-American women. Literature was reviewed for breast cancer risk prediction models and their validation studies. Reported data were also reviewed regarding the strength of established breast cancer risk factors for African-American women. The two currently accepted breast cancer risk assessment models, the Gail Model and the Claus Model, were designed primarily to provide risk assessments for white women. Neither model has been validated in African-American women. Reported data are inconsistent regarding the prevalence and strength of risk factors included in these models. Efforts should be made to validate existing risk assessment models in African-American women and future research should be directed at the identification of more reliable risk assessment features.

Validation of a breast cancer risk assessment model in women with a positive family history.

Gail et al. developed a statistical model for estimating the risk of developing breast cancer in white women screened annually with mammography. This model is used for counseling and for admission to clinical trials. We evaluated the model prospectively in a cohort of women with a family history of breast cancer. We followed women who participated in the American Cancer Society 1987 Texas Breast Screening Project. The model was evaluated by comparing the observed (O) and expected (E) numbers of breast cancers using composite background rates from both the Breast Cancer Detection and Demonstration Project and the Surveillance, Epidemiology, and End Results program of the National Cancer Institute. Data were partitioned by adherence to American Cancer Society screening guidelines. The Gail et al. model predicted the risk well among women who adhered to the American Cancer Society guidelines (O/E = 1.12; 95% confidence interval = 0.75-1.61) but overpredicted risk for women who did not adhere to the guidelines. There was an indication that the model overpredicted risk for women younger than 60 years old and underpredicted risk in women aged 60 years and older. Overall, the Gail et al. model accurately predicts risk in women with a family history of breast cancer and who adhere to American Cancer Society screening guidelines. Thus, the model should be used as it was intended, for women who receive annual mammograms.