March 18, 2010 — A number of common genetic variations associated with breast cancer have been identified in several populations of women, but a new study reports that they only modestly improve the performance of current models that are based on traditional risk factors.
In the March 18 issue of the New England Journal of Medicine, researchers report that adding data on 10 genetic variants to a standard clinical breast cancer risk model only slightly raised its predictive ability over the clinical model alone.
The area under the curve (AUC) was 58% for a standard risk model, which included information on age and 4 traditional risk factors. When 10 genetic variants were added, the AUC was 61.8%.
The Breast Cancer Risk Assessment Tool, also known as the Gail model, is currently used to estimate a woman's risk of developing invasive breast cancer. It takes into account factors such as reproductive history, breast cancer in close relatives, and previous breast biopsies.
"The Gail model has been in existence for 20 years and it is the most commonly used breast cancer risk tool," said lead author Sholom Wacholder, PhD, senior investigator in the Division of Cancer Epidemiology and Genetics at the National Cancer Institute in Bethesda, Maryland. "The model is widely used in clinical practice, and by women themselves."
"The 5- or 10-year estimated breast cancer risk provided by the Gail model is used for counseling, informing decisions about whether or not a woman should take tamoxifen, and for determining sample size in randomized prevention trials," he told Medscape Oncology.
Dr. Wacholder and colleagues concluded that although the Gail model and models that include single-nucleotide polymorphisms (SNPs) might help to identify groups of women who have an increased risk for breast cancer, they did not find that any of the models used in their study were able to accurately predict the development of breast cancer.
"In our study, we explored whether common genetic variants identified in recent genome-wide association studies can increase the clinical value of the Gail model," he said. "We found no clear benefit of including current SNP genotyping information in breast cancer risk assessment for most women. Based on these data, we do not recommend that women seek SNP genotyping as a means of better understanding their risk profile."
"As our understanding of the genetic and environmental determinants of cancer improves, we will be able to enhance our risk-prediction models," he added.
Disappointment Premature?
However, the disappointment expressed in this study might be a little premature, according to Peter Devilee, PhD, and Matti A. Rookus, PhD, from the Leiden University Medical Center and the Netherlands Cancer Institute in Amsterdam, writing in an accompanying editorial.
They note that positive family history is one of the major risk factors for breast cancer and, on average, nearly doubles the risk. Conversely, the 10 common breast cancer SNPs account for less than 5% of familial risk. Thus, the 10 SNPs that were evaluated in the study are "no more than the tip of the iceberg."
"A more pressing question is why, after the completion of several genome-wide association studies of breast cancer, only a dozen risk alleles have been identified," the editorialists write.
There are very few clues as to how the currently identified breast cancer SNPs function to increase the risk for disease, they point out. "It is plausible that once a cellular network, within which a SNP operates, has been unraveled, the resulting biologic defect will reveal a much stronger association with breast cancer than the SNP that led to the detection of the pathway," the editorialists write.
Bur for now, it is too early to incorporate SNP testing in women seeking advice on a personal level, they say. "At the population level, however, it is expected that future prediction models will provide sufficient discrimination to classify women according to relevant risk groups."
As previously reported by Medscape Oncology, new genetic variants have been added to a growing list of genes that increase susceptibility to breast cancer. But it remains unclear how these variants, collectively, will increase the ability to identify women who are at an elevated or reduced risk for breast cancer.
Genetic Variants Add Little to Prediction
In this study, the authors evaluated the contribution of 10 newly established common genetic variants, both as an alternative and as a supplement to the components of the Gail model. Their cohort consisted of 5590 case subjects and 5998 controls between the ages of 50 and 79 years.
Information and specimens were collected from participants in the Women's 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 Nurses' Health Study. Some data also came from the Polish Breast Cancer Study, a population-based case–control study conducted in Warsaw and Lodz, Poland. Follow-up was up to 15 years. All of the case subjects were women who had been diagnosed with invasive breast cancer.
Using information on traditional risk factors and 10 common genetic variants associated with breast cancer, the authors created several models to determine the absolute risk for breast cancer. For the nongenetic model, the authors used components of the Gail model to incorporate standard risk factors and reflect current clinical practice.
The inclusive model combined demographic factors, components of the Gail model, and the 10 SNPs.
They found that, overall, associations between each predictor and breast cancer risk were consistent with published data and were similar in the 2 models. Of note, the effect of family history on breast cancer risk "did not materially change after adjustment for SNPs," they point out.
The authors also observed that disease risk among women who carried 13 or more of a maximum of 20 risk-conferring variant alleles (4%) was nearly 3 times higher than for those who carried 6 or fewer variants (12%). For individual nongenetic factors, a history of breast biopsy yielded the greatest AUC (56.2%).
Case subjects were also classified according to quintiles of absolute risk, for both the inclusive and nongenetic models. The estimated annual risk was more than 0.575% for substantially more case subjects with the inclusive model than with the nongenetic model (27.7% vs 18.9%), and 47.2% were in the same category in both models.
Of the case subjects, 32.5% were in a higher-risk category with the inclusive model than with the nongenetic model and 20.4% were in a lower category; the corresponding percentages for control subjects was 26% and 28%. The authors write that they "found no evidence that a complex model involving interactions among Gail model components and SNPs performed materially better than the models described here (P > .10)."
The study was supported, in part, by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics of the National Cancer Institute, and by grants from the National Institutes of Health.
N Engl J Med. 2010;362:986-993, 1043-1045.
Δεν υπάρχουν σχόλια:
Δημοσίευση σχολίου