Source: National Cancer Institute
Friday, March 19, 2010
Scientists report that breast cancer risk assessment models, which predict
a woman's chance of developing breast cancer, do not perform better when
they include common inherited genetic variants recently linked to the
disease. Therefore, recommendations for breast cancer screening or treatments
will remain unchanged for most women. The study, led by investigators
from the National Cancer Institute (NCI), part of the National Institutes
of Health, appears in the March 18, 2010, New England Journal of Medicine.
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"In the past three years, genome-wide association studies have
identified multiple common genetic variants associated with breast cancer.
The extent to which adding these variants to existing models could improve
clinical recommendations had not been tested in a large population of
women prior to this study," said Sholom Wacholder, Ph.D., senior
investigator in NCI's Division of Cancer Epidemiology and Genetics (DCEG). "When
we included these newly discovered genetic factors, we found some improvement
in the performance of risk models for breast cancer, but it was not enough
improvement to matter for the great majority of women."
Findings from genome-wide association studies (GWAS) to date have pinpointed
several locations in the human genome, called single-nucleotide polymorphisms
(SNPs), where genetic variation is associated with cancer risk. SNPs
are the most common type of variation, affecting just a single building
block of DNA. SNPs are used in GWAS to identify chromosome regions that
are associated with disease. Studies to characterize the biologic effects
of the variants associated with breast cancer are now being conducted
to help clarify their role in breast cancer risk.
To test whether genetic information from recent genome-wide association
studies would increase the value of breast cancer risk models, Wacholder
and colleagues combined data from five studies: the Nurses' Health Study;
the Womens’ Health Initiative Observational Study; the American Cancer
Society Cancer Prevention Study II Nutrition Cohort; the Prostate, Lung,
Colorectal and Ovarian Cancer Screening Trial; and the Polish Breast
Cancer Study, in order to provide more reliable and accurate estimates
than those available from any single study. These studies, altogether,
included 5,590 breast cancer patients and 5,998 women without cancer.
The women were predominately white and between the ages of 50 and 79.
The team assembled information for each participant on established risk
factors and on the 10 SNPs recently found to be associated with breast
cancer risk in analyses of GWAS.
Next, the investigators examined the predictive accuracy of the Gail
model"the most commonly used breast cancer risk model"for this
group of women. The Gail model uses information on a woman's own personal
medical and reproductive history, as well as the history of breast cancer
among her first-degree relatives (mother, sisters, and children) to estimate
her risk of developing invasive breast cancer within the next five years,
or over her lifetime. The investigators then tested the accuracy of a
SNP model and found that it was as good as the Gail model alone. An inclusive
model, using both SNPs and Gail factors, performed only slightly better
than either model alone.
For most women in the study, the inclusive model did not substantially
change their personal estimated risk of developing breast cancer beyond
the Gail model calculations. Overall, using the inclusive model reclassified
26 percent of women to a higher risk category; 28 percent to a lower
risk category; and left 46 percent in the same category of risk score.
The shifts from one category to another were generally too small to influence
clinical decision-making.
The authors emphasized that the genome-wide association studies represent
an early stage in our understanding of the inherited components of breast
cancer risk. "We can expect to identify more genetic determinants
of breast cancer, and to learn more about those we have already found," said
Wacholder. "This information, along with our increasing knowledge
of non-genetic factors, should allow us to steadily improve our risk
prediction models for breast cancer."
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and their families, through research into prevention and cancer biology,
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