A paper published in the New England Journal of Medicine (NEJM) this week, entitled “Performance of Common Genetic Variants in Breast-Cancer Risk Models,” has led several media outlets to declare that common genetic variants have nothing to add when it comes to predicting breast cancer risk.  Here we’ll explain how the results of this study have been misinterpreted.

Researchers from the National Cancer Institute and other institutions looked at data from 5,590 women with breast cancer and 5,998 women without the disease.  These women, all between 50 and 79 years old, had participated in one of five studies, four in the United States and one in Poland.  Since it’s known already which women have or have had breast cancer, and which have not, the researchers were able to use their data, both genetic and non-genetic, to test the predictive power of different types of risk calculations.

The study tested five different models.  The “demographic model” considered only age, year of entry into the study and which study the woman was originally part of.  The “nongenetic model” added in several variables that are part of the so-called “Gail model,” which is the standard model used in clinical practice today to counsel women about their risk.  These variables were the number of first-degree relatives with a diagnosis of breast cancer, age at menarche, age at first live birth, and number of previous breast biopsies.  Two models used the demographic information plus genetic information for 10 SNPs (they differed in details of how the genetic risk score was calculated).  Finally, the “inclusive model” combined demographics, the Gail model and the genetic information.

The genetic models and the nongenetic model performed about the same, with genetics doing just a little bit better.  Perhaps not surprisingly, the best model was the one that used the most information.  With the inclusive model, which is based on genetic and non-genetic information, there was a net 12% improvement in risk classification over the nongenetic model for women with breast cancer.

So, SNPs did add something.  The improvement in prediction seen when SNPs are added to the nongenetic model is about the same as the improvement seen when the Gail model information is added to the simplistic demographic model.  Discounting the benefit of adding SNP information to the Gail model implicitly discounts the utility of the Gail model itself.  Peter Devilee and Matti A. Rookus made this very point in a NEJM editorial that accompanied the study.

It’s also important to remember that the study of common variations and their effect on common diseases like breast cancer is a relatively young science.  Many more variants are bound to be discovered, and these will only help further refine risk predictions.

It’s true that there is a cost associated with collecting a person’s genetic information, while collecting the information needed for the Gail model is essentially free.  (In fact, a risk calculator based on the Gail model is available at the National Cancer Institute website.) But for those people who do have their genetic information in hand, the current study shows that it can improve the estimation of their risk for breast cancer.  It should be remembered, too, that not everyone is fortunate enough to have access to their family medical history. Adoptees, for example, cannot get an accurate picture of their breast cancer risk from the Gail model.  For such women, the ability to use their own genetics to help assess their risk for breast cancer is an option that should not be dismissed.

Ultimately, it’s not about genetics vs. non-genetics — it’s about getting accurate estimates of risk to help doctors catch cancers early on, when they’re easiest to beat. Researchers should welcome any tool that can help them move closer to keeping more women healthy.  Giving up on genetics this early on in the game would be a real disservice.

Note: A similar study can be found in Nature Precedings.  The results are consistent with the NEJM study regarding the improvement in risk prediction when SNP information is added to the Gail model.  This work also shows that the improvement is larger for women at intermediate risk who are more likely to be reclassified.  Senior author David Hinds was at Perlegen Sciences when this paper was written and is currently an employee of 23andMe, Inc.

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A new genetic screening process has helped researchers understand the genetic causes of cancer, such as how mutations accumulated in a person’s life can cause leukemia.

The study shows that by comparing a person’s own DNA to that of their cancerous cells, researchers can find DNA mutations that may have led to abnormal cell growth, or cancer.

After sequencing a single leukemia patient’s healthy and cancerous DNA, researchers at Washington University School of Medicine in St. Louis were able to pinpoint several mutations out of hundreds that appeared likely to have contributed to his cancer’s development. He is the second patient with acute myeloid leukemia (AML) to have his entire genome and that of his cancerous tissue fully sequenced, rather than just the portions that are known to be prone to cancer-causing mutations.

“If we only look at genes with known or suspected links to cancer, we’ll miss many mutations that are potentially relevant,” co-author Richard Wilson said in statement.

The study by Mardis et al., published in The New England Journal of Medicine, identified a total of 750 mutations in the patient’s AML genome. Most of them proved irrelevant, but 64 were likely to be cancer-related. Two previously known mutations were newly linked to leukemia.

Timothy Lay, senior author of the study, explained in a statement that most patients with this type of leukemia are treated similarly, at least in the beginning. This study’s patient, for example, received various chemotherapy drugs. Defining cancer mutations could help determine which patients need aggressive treatment, such as a stem cell transplant, and which could be effectively treated with less intense therapies.

Personalized sequencing of entire cancer genomes is possible now because the accuracy and cost of genome sequencing technology has dramatically improved. This study took only a few months at one-third the cost of the first AML patient, who was sequenced only one year ago.

To date 350 cancer mutations are known, but thousands of cancer genomes will need to be screened to truly explain the genetic basis for cancer. This information could be used not just to guide physicians to the most effective treatment, but also to inform patients about their prognosis.

But do patients even want to know?

A recent study published in the Journal of Genetic Counseling suggests they do. Researchers found that 98 of 99 patients with ocular melanoma, a rare, untreatable eye cancer, said they wanted to know if their cancer had a genetic marker that gave them a 50 percent chance of dying within five years. Patients were relieved when the risk was low, but even when the risk was high they were enabled to plan financially and make the most of their time alive.

This very personalized medicine will continue to be driven by improved diagnostic testing, finding more predictive disease markers, and new therapies directed at cancer-specific mutations, James Downing wrote in a recent editorial published this month in The New England Journal of Medicine. He believes this technology will likely be used clinically long before we have a complete knowledge of cancer genes.

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