Wednesday, March 17, 2010
"Performance of Common Genetic Variants in Breast-Cancer Risk Models"
Remember when we did this for heart disease risk? FAIL WHALE.....
Do you think it will happen again?
10 common genetic variants
I had to create a couple of pages on SNPedia for this list FYI.....
Cases and controls-WHI, ACS CPSII Nutrition Cohort, Nurses Health Study, Prostate/Lung/Colorectal/Ovarian Cancer Screening Trial, and Polish Breast Cancer Study.
Cases-Woman who had received diagnosis of invasive breast cancer.
Risk Models Used-A hybrid of the Gail model.....I.E. Not exactly the Gail Model.
1. First degree relatives with breast cancer
2. Age at Menarche
3. Age at first live birth
4. Number of Breast Biopsies
They acknowledge that they were unable to get atypical hyperplasia and Mammographic density. Both of which have improved Gail.
So, This Gail is a little hobbled and not the best predictive model.......
The studied models- 5 logistic regression models
I don't have the supplementary tables and methods yet.
The nongenetic model-Gail Model
The Demographic/Genetic Variant Count Model-included number of alleles.
The Demographic/Genetic Individual Variant-Accounted for individual effects of each SNP
The Inclusive Model-Gail, Genetics Demographics
The Demographic Model
When we do these sorts of statistical analyses we look for a couple of things.
A. Number of people reclassified and how?
B. The Area Under the ROC Curve
The AUC is a good way of seeing whether a model or test is worthless or useful. And how much MORE useful than another test.
1. The Inclusive Model Yielded and AUC of 61.8%
2. The Nongenetic Model yielded an AUC of 58%
3. The Genetic Individual Variant Yielded an AUC of 59.7%
4. The Genetic Variant Count Yielded an AUC of 58.8%
5. Breast Biopsy BY ITSELF Yielded an AUC of 56.2%
That is a 3.8% difference in Yield from Genes and without Genes integrated into the weaker Gail Model.
Lastly, they asked. Well, does this Inclusive Model do a good job of discrimination of High risk vs. low risk.
The Answer- It determines lower risk better than Gail. It does not determine higher risk better.
The authors of this study have stated that
"As in Diabetes and cardiovascular disease, the addition of the common SNPs added little to the predictive value of the clinical models. On the basis of theoretical models, Gail has shown that increases in the AUC similar to those observed here and not sufficiently large to improve meaningfully the identification of women who might benefit from tamoxifen prophylaxis or screening mammography"
The addition of these factors only creates a minimal statistical increase that is of no useful clinical benefit.
The Sherpa Says: If the press says "gene tests fail to improve risk assessment" You can be assured that the DTCG industry is no longer the darlings. If instead they say "Improvement in risk model" well, then you have chance to woo them back! It Depends.......