Breast cancer mutation risk underestimated for Asian womenSeptember 12th, 2008 - 4:09 pm ICT by ANI
Washington, Sept 12 (ANI): Oncologists at the Stanford University School of Medicine have shown that the risk of breast cancer mutations among Asian women is highly underestimated.
Allison Kurian, MD, and her colleagues at the Stanford University School of Medicine were perplexed. Computer models designed to identify women who might have dangerous genetic mutations that increase their risk of breast and ovarian cancer worked well for white women. But they seemed to be less reliable for another ethnic group.
In the study, Allison Kurian, MD, used computer models designed to identify women who might have dangerous genetic mutations that increase their risk of breast and ovarian cancer
However, they were taken by surprise when they found that in a head-to-head comparison between whites and Asians, two of the most commonly used computer models failed in predicting the presence of mutations in almost half of the Asian women studied.
“We”ve been repeatedly surprised when Asian women who the models predicted would probably not have the mutations do in fact have them,” said Kurian.
She added: “Doctors and patients should have a higher level of suspicion when using these prediction models in Asian women, because they under-predicted the true number of clinically important mutations. We may have to consider more subtle patterns of family cancer history when considering genetic testing in this ethnic group.”
While it is believed that mutations in two genes - BRCA1 and BRCA2 are linked to the development of breast or ovarian cancer in carriers, it was found that, not every woman with a family history of cancer or who develops these cancers has these mutations.
Kurian and her colleagues used two of the most widely used computer models, named BRCAPRO and Myriad II, to predict the presence of the mutations in 200 white women and 200 Asian-American women at cancer genetics clinics in four locations: Stanford, the University of California-San Francisco, Queen’’s Medical Center in Honolulu and the British Columbia Cancer Center in Vancouver.
They sequenced the BRCA1 and BRCA2 genes of all of the study subjects and compared them to the models” predictions.
It was found that the models were highly accurate in predicting the presence of mutations in white women; one program identified 24 of the 25 women with BRCA1 or BRCA2 mutations and the other identified all 25.
However, both programs performed much worse in predicting the 49 Asian women in the study sample with mutations. One program predicted that only 25 of the 49 women would carry mutations, while the other recommended testing of 26 women.
“It’’s clear that these models are far from foolproof. These results emphasize the need for expert evaluation by a genetics professional to guide all clinical genetic testing,” said Kurian, who is also a member of the Stanford Cancer Center.
The study results point out the need for further investigation into the genetic variability of different ethnic groups.
In addition to previously identified, clinically important mutations of the genes, the researchers identified more “variants of unknown significance” in the BRCA1 and BRCA2 genes of Asian women than in white women.
Many of these variants probably don”t have any clinical effect. We know a lot more about the normal variability of these genes in white women. Many of these variants are probably just normal for members of a particular ethnic group, but we haven”t studied enough people in ethnic minority groups to know for sure, and further research needs to be done to distinguish variants of uncertain significance from truly harmful mutations,” said Kurian.
The study was published online in the Journal of Clinical Oncology. (ANI)
Tags: asian american women, asian women, brca1 and brca2, breast cancer, cancer genetics, cancer history, computer models, family cancer, genetic mutations, genetic testing, kurian, oncologists, ovarian cancer, prediction models, school of medicine, stanford university school, stanford university school of medicine, subtle patterns, true number, white women