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The Lancet Oncology

Monday, February 2, 2009

New Genetic Model Improves Breast Cancer Screening

Researchers using the OncoVue® (InterGenetics Inc,655 Research Parkway, Suite 300, Oklahoma City, OK 73104) clinical breast cancer model for assessing genetic risk had much higher accuracy when compared with the more commonly used Gail model, according to data presented at the 31st Annual CTRC-AACR San Antonio Breast Cancer Symposium (SABCS, December 10 - 14, 2008).

By using a combination of a questionnaire and a saliva test, the OncoVue® model takes into account genetic variation in single nucleotide polymorphisms, or SNPs, that the traditional Gail model, which bases risk calculation primarily on traditional risk factors like woman's own personal medical history (number of previous breast biopsies and the presence of atypical hyperplasia in any previous breast biopsy specimen), her own reproductive history (age at the start of menstruation and age at the first live birth of a child), and the history of breast cancer among her first-degree relatives (mother, sisters, daughters) to estimate her risk of developing invasive breast cancer over specific periods of time, does not.

The single nucleotide polymorphisms or SNPs used by the OncoVue model, are small genetic changes within DNA that underlie individuality, including disease risk.

The initial research considered 117 common SNPs as candidates, but during test development the list was ultimately narrowed to 22 SNPs in 19 genes that proved to be most informative in estimating a woman’s individualized risk of developing breast cancer.

‘Women are clamoring for genetic tests like this one. The traditional Gail model looks at personal and clinical risk factors, but not at the inherited genetic variation in SNPs,’ explained lead investigator Kathie Dalessandri, M.D., a physician scientist who led the research team along with colleagues at the University of California, San Francisco, and the Buck Institute for Age Research.

Buccal cell DNA had been collected from 177 women without breast cancer who comprised the control group and 169 women diagnosed with breast cancer between 1997 and 1999 in Marin County, California. This region has been recognized for many years as having higher than average breast cancer incidence and mortality rates. Despite their elevated risk, Marin women who develop breast cancer have similar risk factors as Marin women who do not develop breast cancer when assessed by the classical Gail model characteristics. The researchers theorized that the OncoVue model which integrates the influence of genetic variation along with personal and clinical information would be able to more accurately estimate risk for these Marin women.

DNA was genotyped for 22 SNP variants, and the researchers assessed the fraction of case and control patients who were assigned an OncoVue risk score greater than 1.5 times average likelihood of developing breast cancer between ages 30 and 69. In this blinded analysis, the OncoVue® score proved 2.4 times (p=0.036) more accurate than the Gail model characteristics in accurately identifying the Marin cases with their increased risk from Marin controls with their reduced risk.

In this blinded validation study OncoVue exhibited significantly improved performance, compared to the Gail model alone, in estimating individual risk among Marin County, California women. The improved performance of OncoVue was similar to that observed in two previous independent validation sets, thus, supporting the clinical utility of OncoVue for more accurate individualized breast cancer risk estimation.

Additionally, OncoVue exhibited a 51 percent improvement over the Gail model in assigning an elevated risk score to cases. The improvement in risk estimation is significant and Dalessandri believes the OncoVue model will soon become standard clinical practice for evaluating breast cancer risk. ‘Within the next few years there is going to be a definite paradigm shift toward prevention by analysis of genetic material rather than traditional risk factors,’ Dalessandri noted. ‘As these tests become more commonplace we will be able to more effectively target prevention and early intervention to those at highest risk.’

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