Family Structure, Size Could Affect Breast Cancer Risk Prediction Accuracy for BRCA Gene Testing
Behind the Cancer Headlines®
June 28, 2007
Researchers have found that the probability of the breast cancer gene mutation BRCA among women with a history of breast cancer is greater when the number of older, female relatives in the family is smaller, according to a study published in the Journal of the American Medical Association. This finding may challenge the accuracy of some breast cancer prediction models, which may not take family structure into account.
“Germline BRCA1 or BRCA2 gene mutations significantly increase a woman’s risk of breast cancer (50 percent - 85 percent) and ovarian cancer (16 percent - 50 percent),” the authors write. “Documented efficacy of screening and risk reduction interventions provides evidence for individualized risk management advice, making genetic cancer risk assessment (GCRA) a component of medically necessary care. Identifying appropriate candidates for GCRA is challenging.”
Jeffrey N. Weitzel, M.D., of the City of Hope, Duarte, Calif., and colleagues conducted a study to determine if genetic risk models underestimate the BRCA gene mutation prevalence for “single-case indicators,” that is, women with a limited family structure (less than two females who lived to age 45 or older in each lineage) who have early onset breast cancer and no family history of breast or ovarian cancers. “If true, a more accurate estimation of BRCA gene mutation probability may be needed for these women,” the researchers write.
The study included 1,543 women seen at U.S. high-risk clinics for genetic cancer risk assessment and BRCA gene testing, who were enrolled in the study between April 1997 and February 2007. Three hundred six of these women had breast cancer before age 50 years and no first- or second-degree relatives with breast or ovarian cancers.
Family structure was limited in 153 cases (50 percent). BRCA gene mutations were detected in 13.7 percent of participants with limited vs. 5.2 percent with adequate family structure. Participants with a limited family structure had an increased likelihood of being carriers of a BRCA gene mutation than those with adequate family structure.
“Although statistically intuitive, limited family structure is akin to the problem of ‘missing values’ and is a limitation of all common mutation probability models. Consequently, absent better models, cancer risk assessment practices and genetic testing guidelines need to be more inclusive of single cases of breast cancer when there is limited family structure,” the authors write. “The fact that commonly used models for estimating BRCA gene mutation probability were insensitive to family structure as a predictive factor is a cautionary note for community practitioners.”
“Given the significant effect of family structure, illustrated by limited accuracy of probability models for single cases of breast cancer, and the commonality of the missing-data problem, the databases of currently available probability models should be reanalyzed and limited family history recoded as a separate variable.”
In an accompanying editorial, Noah D. Kauff, M.D., and Kenneth Offit, M.D., M.P.H., of Memorial Sloan-Kettering Cancer Center, New York, write that the use of some genetic risk prediction models should be used cautiously.
“Weitzel and colleagues make a compelling argument that risk assessment models are likely not appropriate if only a limited number of informative relatives are available in either the maternal or paternal lineage. Perhaps more important than this specific conclusion are the implications this study has for use and interpretation of risk assessment models in general. The authors clearly demonstrate that in this cohort, several of the most commonly used risk assessment models overestimate mutation probability, and the resultant lifetime cancer risk, in the setting of adequate family structure. These same models underestimated the risk in the setting of less informative family structure.”
“Given these limitations, important questions are whether currently available models are appropriate to triage individuals for genetic testing or are adequate to provide cancer risk prediction and guidance of care in the absence of genetic testing.”
Journal of the American Medical Association, June 20, 2007