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Mayo Clinic s Dennis O Kane on Medical Genomics and Microarrays



PhD, SUNY at Stony Brook, 1979 in cellular and developmental biology

NIH postdoctoral fellow at the University of Georgia


Director of the Mayo Clinic Cancer Center’s molecular epidemiology/ microarray shared resource, co-director Mayo Clinic central clinical laboratory, co-director of the endocrine laboratory.


Research efforts focus on new technologies in clinical research and laboratory testing, especially microarray transcriptional profiling, genotyping, and laboratory automation. Has recently developed assays for high-throughput genotyping of diabetic neuropathies, Parkinson’s disease, and treatment-resistant depression.

Mayo Clinic researcher Dennis O’Kane, who spoke at the “Microarrays for Diagnostics” conference in San Diego last month, began his presentation on medical genomics with a warning: The human genome project will not deliver on its promise of reduced health care costs, but will actually raise costs overall. “New technology almost always increases medical costs,” he said. And when genetic tests and expression profiling allow diagnosis of risk factors or sub-clinical disease when the patient is still young, this could increase medical costs overall for the patient. However, the quality of patient care is expected to improve, he said.

Nevertheless, O’Kane said, these issues are not yet before us, as science and medicine has not reached the point yet where the exploding knowledge about genetic variation can be reliably applied to treatment of disease.

O’Kane went on to outline the current obstacles to making clinical use out of the information in microarray and other genomic studies. Following is a summary of his talk:

In the case of microarrays, scientists have been working with these tools for genomic research for over a decade, and clearly we’re in an exponentially expanding phase in terms of microarray work. But in terms of utilizing these results in clinical medicine, the field is currently still down at the base of the curve. Of 2,000 functional SNPs, how many are correlated to patient management and patient outcome? If we don’t have [SNPs] correlated to outcome, we aren’t going to have patients who need a test. Currently, there are few patients who would benefit from having genotyping performed.

Even if genetic variation is correlated to clinical outcome, the field must still clear intellectual property hurdles erected by companies that have patented particular genes and SNPs. Everyone is attempting to tie up little bits of IP with the result that no one can use any of it in a concrete, concerted manner.

Next, there is the issue of obtaining FDA clearance for a diagnostic test — something that few companies have even attempted, because there’s a fear of being the first one in. Any company that does hazard a try at clearance is going to face a medico-legal environment currently in flux over the issue of how and how much to regulate genetic testing. We’re in a transition period where no one knows what the next step is.

A final obstacle is in the clinic itself: clinicians do not comprehend how to utilize genomic information to treat patients. In the vocabulary of 20,000 words or so that medical students learn, you do not find words like ‘intron,’ ‘exon,’ and ‘microarray.’

It will take a minimum of five to eight years to reeducate existing clinicians in genomics, and four years of genomics courses should be integrated into the medical school curriculum.


Putting Microarray Data into Practice


Medical genomics should involve coupling genomic and clinical information with patient management. Molecular profiling, which the National Cancer Institute wants very badly to accomplish, can be done soon. With prognosis, we are at the stage where we can separate patients into different outcome groups [based] on some markers. But in the clinic, the things doctors want to have in their hands is a better way to dictate how patients would be treated.

In an expression profile of good vs. poor outcome in breast cancer, you have 20 percent of tumors that don’t recur within 10 years even though they had a bad prognostic profile based on the microarray. If you are a patient, it would be inappropriate for a doctor to say ‘We might as well not treat you based on your transcriptional profile, you are going to metastasize and probably die’. The patient would go somewhere else for a second opinion. More needs to be known not only about the basic biology of breast cancer, but how to treat based on the results of microarray tests. With Her2/neu, test ordering is coupled with treatment. That’s what most physicians want to see.

At the Mayo Clinic, researchers are looking at the centrosome, which is the motor in the cell cycle. When this becomes disregulated, there are multiple possibilities for spindle pulls during mitosis. You can end up with tripolar mitosis, and too many or too few chromosomes. Centrosome hypertrophy leads to aneuploid tumors. You can transcriptionally profile the centrosome. A number of trials doing this have found specific markers for genes involved in the structure of the centrosome. A researcher at the Coccid Corporation has found an inhibitor of centrosome function in vitro, and is beginning proof-of-principle studies to identify tumors that may be sensitive to a new type of chemotherapy. A link between transcriptional profiling and treatment may evolve in this scenario.

Also, Mayo investigators have been looking at antidepressants and the genotype of the CYP2D6 gene in the CYP450 pathway, using microarray-based genotyping and other methods. Preliminary results indicate that 42 percent of these patients had a deficiency in the CYP2D6 gene. In the future, genotyping can be used with other factors in determining which medications to use for individual patients and to determine medication dosing. The interrelationships of genetic variation with other factors must be studied.

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