Wild Type vs. Mutation
Many personalized medicine strategies are centered on identifying variations or mutations in normal genes that affect the efficacy, dosing, or toxicity of a drug. However, it is also sometimes more useful to determine whether a patient is wild type for a given gene. That appears to be the case with nod2, an immune system protein with a mutant form that is implicated in Crohn disease. nod2 interacts with rip2, and it’s the nod2/rip2 binding complex that sets up the signaling cascade.
Derek Abbott, M.D., Ph.D., assistant professor of pathology at Case Western Reserve University, has been working on nod2 and rip2 for a number of years, and his group found that two drugs used in non-small-cell lung cancer inhibit rip2, and, therefore, could be effective therapies in diseases characterized by too much activity of the nod2/rip2 complex such as Crohn’s or other inflammatory bowel disease.
However, this would only be true for patients who had a functioning, wild-type copy of the nod2 gene. Because the Crohn mutant is not the only possible mutation for nod2, a test for the Crohn mutation will not suffice, and in order to benefit from these drugs, a separate test is needed to confirm wild-type status. “In medicine, there’s a general emphasis on the mutation, rather than on the normal protein,” Dr. Abbott said. “Genetic studies tell you what goes wrong with the gene, sometimes it makes sense to know if you’re wild type for a gene.”
Simulated Clinical Decision Making
Peter Tonellato, Ph.D., visiting professor and senior research scientist at the Center for Biomedical Informatics and department of pathology at Harvard Medical School, discussed his work on the practical use of an individual’s whole genome data and information. He specifically described the tools, technologies, and thought processes behind the Laboratory for Personalized Medicine at Harvard, through a set of model experiments projecting clinical parameters and predictions based on genome data.
Dr. Tonellato used an algorithm that maps an individual genome onto a reference genome, and then identifies the individual’s clinically important sequence variants. These variants are then used to generate useful clinical information for sample patients. In one example, they predicted an optimal warfarin dose for James Watson—whose whole genome has been published. The predicted therapeutic dose for Watson (if he were to hypothetically need this medication) is 4.9 mg per day. This is very close to the recommended best-practice clinical dosing of 5 mg per day. Consequently, one might argue that the genetic variant does not contribute to the best-care practice.
However, when the same exercise was performed on an anonymous African American male genome, the resulting optimal dose was 6.9 mg per day. That difference then becomes clinically significant. “We didn’t contrive it, we just ran it. This is a case when genome data is materially important in actionable healthcare information,” said Dr. Tonellato.
In a second model experiment, Dr. Tonellato calculated the relative risk of breast cancer for an anonymous female based on height, weight, age at first birth, and simulated variants of BRCA1 and BRCA2 for that individual. For this representative female with no variants in BRCA1 and BRCA2, the relative lifetime risk of breast cancer was 24.8%, compared to 8% for the general population.
That risk rose to 91.9% with one variant of BRCA1 or BRCA2 and up again to 99.8% for other possible variants in BRCA1 and BRCA2. “Boiling the whole genome down to a few genotypes demonstrates that it is valuable and impacts healthcare decisions dramatically.
One anticipates that an individual’s genome will provide a lifetime of enriched data and information that will help guide preventive measures, diagnosis, and optimal treatment.”