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© 2003 American Society for Clinical Oncology Application of Breast Cancer Risk Prediction Models in Clinical PracticeFrom the University of Pennsylvania Cancer Center; Abramson Family Cancer Research Institute, Philadelphia, PA; and Hamilton Regional Cancer Centre, Hamilton, Ontario, Canada. Address reprint requests to Susan M. Domchek, MD, University of Pennsylvania Cancer Center, 14 Penn Tower, 3400 Spruce Street, Philadelphia, PA 19104; email: susan.domchek{at}uphs.upenn.edu.
Breast cancer risk assessment provides an estimation of disease risk that can be used to guide management for women at all levels of risk. In addition, the likelihood that breast cancer risk is due to specific genetic susceptibility (such as BRCA1 or BRCA2 mutations) can be determined. Recent developments have reinforced the clinical importance of breast cancer risk assessment. Tamoxifen chemoprevention as well as prevention studies such as the Study of Tamoxifen and Raloxifene are available to women at increased risk of developing breast cancer. In addition, specific management strategies are now defined for BRCA1 and BRCA2 mutation carriers. Risk may be assessed as the likelihood of developing breast cancer (using risk assessment models) or as the likelihood of detecting a BRCA1 or BRCA2 mutation (using prior probability models). Each of the models has advantages and disadvantages, and all need to be interpreted in context. We review available risk assessment tools and discuss their application. As illustrated by clinical examples, optimal counseling may require the use of several models, as well as clinical judgment, to provide the most accurate and useful information to women and their families.
THE GOAL of breast cancer risk assessment is to personalize management strategies for all women, with the aim of increasing survival in high-risk women while decreasing cost and complications in low-risk women. Management strategies now in place for families with BRCA1 and BRCA2 mutations are a clear demonstration of this principle. Female BRCA1 or BRCA2 mutation carriers have a lifetime risk of breast cancer of between 50% and 80% and a lifetime risk of ovarian cancer of between 10% and 40% (as reviewed in1). Thus, prophylactic oophorectomy, which reduces ovarian cancer incidence by more than 90% and breast cancer incidence by at least 50%,2–4 is indicated after childbearing for all mutation carriers. The administration of tamoxifen,5 use of oral contraceptives,6,7 prophylactic mastectomy,8–10 and an intensified screening program also are options in mutation carriers.11–13 However, these interventions should be avoided in relatives who do not carry a known familial mutation. Data support interventions in other high-risk women as well. Tamoxifen use was associated with a 49% reduction in invasive breast cancer in women with a 5-year risk of at least 1.7% (Gail model) or a prior diagnosis of lobular carcinoma-in-situ in the Breast Cancer Prevention Trial.14 Enrollment in the Study of Tamoxifen and Raloxifene trial is also a consideration for postmenopausal women. Women at lower risk may be best served by following standard mammography and health maintenance recommendations.15 Two types of models are used for breast cancer risk assessment. The first models developed estimate the risk of developing breast cancer over time; the most commonly used models being the Gail and Claus models.16,17 More recently, probability models that estimate the likelihood of detecting a BRCA1 or BRCA2 mutation in a given family or individual have been published. These models provide complementary information. There are four widely used prior probability models, referred to as Couch, Shattuck-Eidens, Frank, and BRCAPRO.18–21 Additional prior probability models exist, but they focus on specific ethnic populations22–24 or are aimed at identifying individuals to be referred to genetic counseling.25 Each model has unique attributes stemming from the methodology, sample size, and population characteristics used to create the model. Understanding the strengths and weaknesses of each model, as well as the clinical scenarios in which predictions from one model vary markedly from others, facilitates accurate breast cancer risk assessment.
Estimating the probability that an individual or family carries a mutation in BRCA1 or BRCA2 aids in selection of individuals most likely to receive informative results from genetic testing. Many centers use a prior probability of 5% to 10% as the lower bound for consideration of testing. The four widely used models for estimating prior probability are compared in Table 1  . An alternative approach when none of these models apply is to use data from studies that apply to a specific individual; for example, approximately 10% of non-Ashkenazi white women diagnosed with breast cancer before age 40 years have detectable BRCA1 mutations.26–29
Couch Model For the model derived from our own clinic population, we used heteroduplex analysis and logistic regression to examine 169 different families for germline mutations in BRCA1.18 The majority of families had breast cancer only (with a median number of breast cancers per family of 4.6); both breast and ovarian cancer were seen in 27% of families. The overall prevalence of BRCA1 mutations was 16% of 169 affected probands. Predictive factors included average age at breast cancer diagnosis in the family less than 55 years, ovarian cancer in the family (particularly in an individual with breast cancer), and Ashkenazi Jewish ancestry. The data are presented in tables with estimated rates of BRCA1 mutations as a function of family history. This model estimates the probability of finding a mutation within a family with at least two cases of breast cancer. It is not applicable to families with site-specific ovarian cancer, and because of the relatively small sample size, confidence intervals are wide, or could not be calculated, for some point estimates, such as for families with breast and ovarian cancer in a single individual. Probability estimates for unaffected individuals must be extrapolated from the closest affected relative without a built-in Bayesian adjustment for age. Finally, the model as initially published does not estimate the probability of finding a BRCA2 mutation. We have now refined this model to include both BRCA1 and BRCA2 mutation detection by full sequencing.30 Six hundred and fifteen families were included from the United Kingdom and the United States. There were at least two cases of breast or ovarian cancer in each family, and logistic regression analysis was used to examine associations between familial characteristics and the presence of a BRCA1 or BRCA2 mutation. Predictors included the number of women diagnosed with breast cancer before age 50 years, breast-ovarian multiple primary cases, ovarian or fallopian tube carcinomas, male breast cancer, and Ashkenazi Jewish ancestry. The data are presented in tables as in the original model, but a software package is under development that will be available free on request. To date, this model has only been published in abstract form, although a manuscript is in preparation and the model is being updated.30
Shattuck-Eidens Model Unlike the Couch model, the Shattuck-Eidens model can be applied to affected individuals without any family history of breast or ovarian cancer as well as to individuals in families with site-specific ovarian cancer. However, other than the proband, only one affected relative is used to determine the likelihood of detecting a mutation. As a result, a breast cancer patient with one affected sister is assigned the same probability of having a mutation as a woman with four affected sisters. As with the Couch model, the graphs cannot be used for unaffected individuals; the probabilities for such individuals must be derived from family relationships. Another important limitation of the Shattuck-Eidens model is that the prior probabilities are determined for individuals (and not families); therefore, probability can vary within a family. For example, a woman with breast cancer has a different prior probability than her sister with breast and ovarian cancer, even though their family history is identical. Finally, the model only predicts the probability of finding a BRCA1 mutation.
Frank Model Frank et al32 have published BRCA1 and BRCA2 prevalence tables on the basis of 10,000 individuals tested through Myriad Genetics. These data are empiric, rather than modeled, and they state the prevalence of mutations in a variety of clinical situations. Family history data were obtained from information written on the test requisition and, therefore, may be limited. In addition, there were no defined criteria for testing, these data being descriptive of all samples sent to Myriad Genetics for clinical testing, potentially incorporating a number of ascertainment biases. Myriad Genetics frequently updates mutation prevalence tables of individuals tested through its laboratories at http://www.myriad.com.
BRCAPRO
Prevalence Tables
Ductal Carcinoma-In-Situ There is a significantly lower incidence of ductal carcinoma-in-situ (DCIS) associated with invasive cancers in women with BRCA1 mutations compared with age-matched population cases, raising questions about how to incorporate this lesion into prior probability models.42 However, almost half (41%) of BRCA1-associated tumors in this study did have adjacent DCIS. Thus, although this difference may reflect an important biologic attribute of BRCA1, DCIS should be considered a BRCA1-associated lesion. Including DCIS when calculating prior probabilities is not straightforward, because all existing models are based only on invasive breast cancer. Using age of DCIS diagnosis likely introduces bias that inflates prior probability calculations because DCIS is thought to precede invasive cancer. The correction for this bias in Breast Cancer Linkage Consortium analyses has been to add 10 years to the diagnosis of DCIS; for example, DCIS diagnosed at age 40 years is incorporated into model calculations as an invasive cancer diagnosed at age 50 years. Presumably not all DCIS progresses to invasive breast cancer, and the time course of this progression is largely speculative; nonetheless, this correction may be the best solution currently available to incorporate DCIS into calculating the likelihood of finding a BRCA1 or BRCA2 mutation.
Lobular Carcinoma-In-Situ
Other Cancers
Male Breast Cancer
Race and Ethnicity
Although many data addressing breast cancer risk in first-degree relatives of patients are available,63–66 the Claus and Gail models were derived from large population-based data sets, provide estimates of absolute lifetime breast cancer risk, and are the most commonly used models17,16,67 (Table 3 ). The Gail model was used to determine eligibility for the Breast Cancer Prevention Trial14 and has since been modified (in part to adjust for race) and made available on the National Cancer Institute Web site (http://bcra.nci.nih.gov/brc/q1.htm). Estimates derived from the Claus model are based solely on family history, whereas the Gail model also incorporates reproductive variables, atypical hyperplasia, and a history of breast biopsies (regardless of histology—a point of criticism).67 One limitation of the Gail model is the inclusion of only first-degree relatives, which results in underestimating risk in the 50% of families with cancer in the paternal lineage. The Claus model incorporates maternal and paternal breast cancer history, first- and second-degree relatives, and age at breast cancer diagnosis.16,68 An expansion of the original Claus model estimates breast cancer risk in women with a family history of ovarian cancer.69 However, use of the Claus model requires a set of published tables limited to specific combinations of affected relatives (eg, two first-degree relatives, mother-maternal aunt, etc), and the tables do not include the commonly encountered mother-maternal grandmother pair. In this situation, a combination with the same degree of relatedness (eg, mother-maternal aunt, instead of mother-maternal grandmother) is the best approximation. Concordance of the two models is only fair, with the greatest discrepancies seen with nulliparity, multiple benign breast biopsies, and a strong paternal or first-degree family history.70,71
When the four prior probability models are applied to a specific family, discordant predictions can result. In Fig 1 , a family of Irish descent, three women were diagnosed with breast cancer before age 40 years, and the maternal grandmother was diagnosed with ovarian cancer at age 53 years. Having both breast and ovarian cancer within a family is suggestive of a BRCA mutation, reflected in the high carrier probability estimates: Couch (44% for BRCA1), Blackwood (60% for BRCA1 or BRCA2), Frank (59% for BRCA1 or BRCA2), Frank 2002 (47% to 56% for BRCA1 or BRCA2), and BRCAPRO (94% for BRCA1 or BRCA2). However, the Shattuck-Eidens model estimates the BRCA1 carrier probability for the proband at only 5% to 10%. These discrepant results occur because the Shattuck-Eidens model includes only the proband and one other affected relative. Thus, the Shattuck-Eidens model may not be suited to families with many affected members. Note that the estimate derived from the empiric data of Frank 200232 is consistent with modeled estimates from the Couch, Blackwood, and original Frank models, indicating that the populations being tested at Myriad Genetics and forming most of the research data sets are converging in terms of predictive characteristics.
Another example is shown in Fig 2 . LCIS in the proband is discounted in considering the likelihood of a BRCA1 or BRCA2 mutation, but not in evaluating risk of developing breast cancer. In this non-Ashkenazi family, Couch and Shattuck-Eidens model estimates are low (7.7% and 1% to 5%, respectively). The Frank model is not applicable because the second breast cancer was not diagnosed before age 50 years. The BRCAPRO model must be used carefully, because the choice of the proband will significantly affect the results. Using the affected premenopausal women (paternal aunt, indicated by double arrow in Fig 2) gives a prior probability of 55%, because both the premenopausal cancer and the male breast cancer are considered. However, using an affected postmenopausal woman (sister, indicated by triple arrow in Fig 2) as the proband, the prior probability decreases strikingly to 1%. Nonetheless, male breast cancer in a paternal first cousin, premenopausal breast cancer in a paternal aunt, and even the prostate cancer in the proband’s father raise suspicion for a BRCA2 mutation in the family. The Frank 2002 model gives probabilities of 4.4% for the unaffected proband and 11.2% for her affected sister. Only the Blackwood and BRCAPRO models incorporate male breast cancer, but with discrepant results—15% and 55%, respectively. No model incorporates cancers other than breast and ovarian cancer (such as the father’s prostate cancer). This example emphasizes disadvantages of considering only first- and second-degree relatives, the care that must be taken in choosing the proband while using the BRCAPRO computer software, and most importantly, the pitfalls of relying solely on models when considering genetic testing.
In addition to drawbacks specific to each model, there are general limitations to all pedigree-based assessments of hereditary breast and ovarian cancer. These include lack of family history information (in the case of adoption), small family size, deaths at young ages, and few women in a family, all of which may lead to underestimation of prior probabilities. Lack of verifiable cancer diagnosis also can lead to inaccurate prior probability assessment, particularly in regard to ovarian cancer. Unconfirmed stomach cancer could well be ovarian cancer and would significantly change prior probabilities.
Prior probability models also may be useful in considering discrepancies that arise when the Gail or Claus model is applied to an unaffected woman in a family with a high likelihood of having a BRCA1 or BRCA2 mutation. An example is shown in Fig 3
As a result, the Claus and Gail models may be best suited for individuals with low prior probability estimates. These models were derived from population-based series of women with breast cancer who were not selected for family history, and the Gail model is the only model that incorporates reproductive and histologic factors. Figure 4 illustrates a white woman with a single biopsy showing atypical hyperplasia at age 47 years and one first-degree relative with breast cancer. She has moderate reproductive risk factors (menarche at age 12 years, first birth at age 30 years) for a 5-year and lifetime Gail risk of breast cancer of 4.7% and 36%, respectively. However, her prior probabilities are quite low (< 5%), as is her Claus lifetime risk (9.6%). One point of confusion for patients is the apparent discrepancy in having a low prior probability and a high breast cancer risk; in such a situation, the individual’s breast cancer risk is simply unlikely to be due to a mutation in BRCA1 or BRCA2.
In conclusion, risk assessment clinics provide women with an estimation of their risk of developing breast cancer, as well as the likelihood that this risk can be explained by one of the known breast cancer susceptibility genes. This information is clinically relevant given current options regarding prevention and, particularly in the case of known BRCA1 and BRCA2 mutation carriers, prophylactic surgical options.2–4,8–10 Models exist for breast cancer risk assessment and for estimating the likelihood of having a BRCA1 and BRCA2 mutation. However, each model has optimal applications, largely a function of the methods by which they were developed. Calculating breast cancer risk and prior probabilities using several of the models may provide helpful ranges. Estimates from the Gail and Claus models can be readily calculated for all unaffected individuals in a risk assessment clinic. Prior probability models can be selected for specific clinical situations. We recommend consideration of BRCA1 and BRCA2 testing for women with more than 5% to 10% prior probability. Clinical judgment remains a key component in estimating prior probabilities, particularly in families with non–breast-ovarian cancers (eg, male breast cancer, pancreatic cancer, and early-onset prostate cancer) or individuals with multiple primary cancers. Ultimately, risk assessment is only an estimate. However, risk assessment tools can place breast cancer risk in context for women at all risk levels and allow for more focused management recommendations on the basis of this risk.
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ABSTRACT
INTRODUCTION
); men (
). Age is current or age at death (designated by a diagonal slash). Shading of upper left, breast cancer; shading of lower left, ovarian cancer. Arrow designates proband. BR Ca, breast cancer; OV Ca, ovarian cancer; d, died.
