Several breast cancer risk assessment tools have been developed that combine known major risk factors. Risk models either predict risk of pathogenic mutation in BRCA1 or BRCA2, risk of developing invasive breast cancer, or both. Risk models can be useful in stratifying patients into risk categories to facilitate personalized screening and surveillance plans for clinical management of the patient.
1. To Identify Women Who May Benefit from Risk-Reducing Medications
The Gail model is used to determine risk for purposes of advising on use of medications to reduce risk. In the National Surgical Adjuvant Breast and Bowel Project (NSABP) P1[i] study, women at increased risk for breast cancer were defined as follows: 1) age 35 to 59 years with at least a 1.66% five-year risk for developing breast cancer by the Gail model; or 2) personal history of lobular carcinoma in situ (LCIS); or 3) over age 60 years of age. 13,388 such women were randomized to receive tamoxifen or placebo daily for five years. Tamoxifen reduced the risk of invasive breast cancer by 49% and reduced the risk of noninvasive cancer by 50%.
The reduced risk of breast cancer was only seen for estrogen-receptor expressing tumors. There was a 2.5-fold increase in risk of endometrial cancer in women taking tamoxifen and a decrease in hip and spine fracture risk. Blood clots causing stroke and deep vein thrombosis are increased in women taking tamoxifen [ii].
2. To Identify Women Who May Carry a Pathogenic Mutation in BRCA1 or BRCA2
The Tyrer-Cuzick (IBIS), Penn II, BOADICEA, and BRCAPRO are among the models that predict risk of pathogenic mutation. Women with risk of mutation estimated to be more than 10% are usually recommended for genetic testing, though there has been the recent suggestion to perform genetic testing much more broadly[iii] as many women who have pathogenic mutations do not have a suggestive family history.
3. To Identify Women Who Meet Criteria for High-Risk Screening MRI
Current American Cancer Society guidelines[iv] recommend annual screening MRI beginning by age 25 to 30 in women who have a lifetime risk (LTR) of breast cancer of 20 to 25% or more. Any of the models used to predict risk of a pathogenic mutation, or the Claus model, but NOT the Gail model, can be used to estimate lifetime risk for purposes of screening MRI guidelines. Annual screening MRI is also recommended in women who are known to carry pathogenic mutations in BRCA1 or BRCA2 (unless the woman has had bilateral mastectomy), and in women who are first-degree relatives of known mutation carriers but who are themselves untested. Women who are known to carry or are first-degree untested relatives of individuals with less common disease-causing mutations (such as those associated with Li-Fraumeni, Bannayan-Riley-Ruvalcaba, or Cowden syndrome) are also recommended for annual screening MRI. Finally, women with prior chest radiation therapy (such as for Hodgkin disease) between ages 10 and 30, and at least 8 years earlier, are at high risk for developing breast cancer[v], similar to BRCA1 or -2 carriers, and are also recommended for annual screening MRI.
[i] Fisher B, Costantino JP, Wickerham DL, et al. Tamoxifen for prevention of breast cancer: report of the National Surgical Adjuvant Breast and Bowel Project P-1 Study. J Natl Cancer Inst 1998; 90:1371-1388
[ii] http://www.ncbi.nlm.nih.gov/pubmed/19569248 and http://www.ncbi.nlm.nih.gov/pubmed/16288118
[iii] Gabai-Kapara E, Lahad A, Kaufman B, et al. Population-based screening for breast and ovarian cancer risk due to BRCA1 and BRCA2. Proceedings of the National Academy of Sciences of the United States of America 2014; 111:14205-14210
[iv] Saslow D, Boetes C, Burke W, et al. American Cancer Society guidelines for breast screening with MRI as an adjunct to mammography. CA Cancer J Clin 2007; 57:75-89
[v] Moskowitz CS, Chou JF, Wolden SL, et al. Breast cancer after chest radiation therapy for childhood cancer. J Clin Oncol 2014; 32:2217-2223[i]
Table 1 features details and live links to several commonly utilized breast cancer risk assessment models: Gail, Tyrer-Cuzick (IBIS), Penn II, and a link to a paper describing the Claus model[vi]. None of these models include breast density in risk calculations.
There are risk models that do include breast density in risk calculations.
- Breast Cancer Surveillance Consortium (BCSC) model[vii] was developed and validated in a large, ethnically diverse, prospective cohort of women undergoing screening mammography. It includes the risk factors with the greatest population attributable risks for breast cancer including age, breast density, family history, history of a breast biopsy, and a polygenic risk score (PRS) based on common genetic variations[viii]. The updated model is the only breast cancer risk assessment model that uses BI-RADS breast density and the only model to include the full range of breast biopsy results including hyperplasia, atypical hyperplasia and lobular carcinoma in situ.[ix],[x] A link to the BCSC model is also included in Table 1.
- Tyrer-Cuzick Model includes density - The latest version (v8) of the Tyrer-Cuzick (IBIS) model, based on input from Dr. Jennifer Harvey at the University of Virginia and Dr. Martin Yaffe at University of Toronto, includes breast density: http://www.ems-trials.org/riskevaluator (Windows/PC only). In this model, breast density is one of the top five factors determining breast cancer risk.
vi Claus EB, Risch N, Thompson WD. Autosomal dominant inheritance of early-onset breast cancer. Implications for risk prediction. Cancer 1994; 73:643-651
vii Tice JA, Cummings SR, Smith-Bindman R, et al: Using clinical factors and mammographic breast density to estimate breast cancer risk: development and validation of a new predictive model. Ann Intern Med 148:337-47, 2008
viii Vachon CM, Pankratz VS, Scott CG, et al: The contributions of breast density and common genetic variation to breast cancer risk. JNCI In press, 2014
ix Tice JA, O'Meara ES, Weaver DL, et al: Benign breast disease, mammographic breast density, and the risk of breast cancer. J Natl Cancer Inst 105:1043-9, 2013
x Tice J, Miglioretti DL, Li C, et al: Breast density and benign breast disease: Risk assessment to identify women at high risk of breast cancer. J Clin Oncol, In submission, 2015