For women, genetic testing offers hope to better understand the likelihood that they will develop breast cancer. But reality does not match the dream, at least not yet. Scientists at the National Institute of US cancer (NCI) report today that DNA does not risk much better predict a questionnaire about breast cancer. The small improvement does not justify the cost of the introduction of technology to the clinic, they say.
In recent years, several genetic mutations have been found that increase the risk of breast cancer of a woman. The best known are two mutations in tumor suppressor genes called BRCA1 (breast cancer susceptibility gene 1) and BRCA2 , which are thought to be present in 0.3% UK population. a harmful mutation or gene increases the lifetime risk of a woman from 12% to about 60%. Eighteen other genes were discovered more subtly influencing the risk of breast cancer of a woman.
In theory, testing of these genes may enable women to make more informed choices about how often undergo routine mammograms, for example, or, more radically, either taking cancer drugs such as tamoxifen prophylaxis. These decisions are being taken by patients in consultation with clinicians, on the basis of predicted risk of cancer provided by the so-called Gail model. This model calculates risk based on the answers to seven questions, including the age at which a woman began menstruating, the age at which she had her first child, and the number of relatives with breast cancer.
To find how much genetic screening measured to the Gail model-based questions, a cancer epidemiologist at the NCI team collected data from five studies originally used to isolate cancers within genetic risk factors. Four were cohort studies in which a healthy population was genetically screened early on and followed for 15 years to see who developed breast cancer and who does not.
In the new work, published today in The New England Journal of Medicine ( NEJM ), the researchers identified from these studies 550 women have developed breast cancer and 5998 who did not. Then they retrospectively calculated a prediction of cancer risk based on the data from each woman for 10 genetic risk factors at baseline. They then asked a simple question: What is the probability that a woman chosen at random from the group which will develop cancer have a higher risk of predicting a woman chosen at random who has not? For a completely useless model, the answer would be 50%; for a perfect model, the answer would be 100%.
The answer for genetic screening was 59.7%, while the response to the Gail model based questions was 58%. By combining the two, the researchers were able to produce a model with a predictive power of 61.8%. But this combination does not affect risk prediction, also called the score much for most individual patients. "There were very, very few cases where the new score was very different from the old score," said cancer epidemiologist Patricia Hartge of the National Cancer Institute in Bethesda, Maryland, a co-author of the study . She and her colleagues conclude that, given the cost involved, genetic screening is not useful in a clinical setting.
Nevertheless, Hartge remains optimistic about the future. it stresses that common genetic variants that they were tested were found there under 3 years. "is not it fascinating that we get the same ability to predict them we got 40 years of painstaking research on other risk factors? "Discoveries of several mutations, including eight found since this study began, should improve the reliability of genetic testing, she said.
cancer epidemiologist Paul Pharoah from the University of Cambridge in the UK , which published a similar analysis there 2 years in NEJM based on just seven genetic risk factors, agrees that genetic testing does not add much to the Gail model. But he questions the assessment of the new document that screening must be expensive: "The cost of one of these genetic tests really is trivial," says Pharoah. Therefore, genetic testing could be a cost effective way to decide which further screen, he said.
- This article has been corrected to take account of Patricia Hartge is currently affiliated with the National Institute of the National Institutes of Health cancer and not the University George Washington.
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