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How Does a Lawrence Livermore Scientist View Pharmacogenetics?


At A Glance

Name: Harvey Mohrenweiser

Age: 62

Title: Senior Biomedical Scientist; Chair, Program of Education and Workforce Training, Lawrence Livermore National Laboratory

Education: PhD in biochemistry from Michigan State University


Harvey Mohrenweiser may have been an odd choice to co-author a new pharmacogenomics book. Though he is a geneticist and biochemist by training, Mohren-weiser happens to be a senior scientist at the United States’ premiere nuclear-weapons facility, Lawrence Livermore National Laboratory.

But his outsider’s perspective, combined with broad experience and a wealth of contacts, allowed him to offer a unique take on an industry that has been analyzed into homogeneity. In fact, with 8,000 researchers and a $1.5 billion annual budget, Lawrence Livermore devotes just two scientists — Mohrenweiser and a colleague — to study genetic variation and toxicology.

SNPtech Reporter caught up with Mohrenweiser recently:

Where does pharmacogenomics stand today in toxicology and pharmacology?

Pharmacogenomics is really at the interface of pharmacology and genetics. And in reality the underpinning of both pharmacology and toxicology are one and the same. In fact we’re often talking about the same genes and the same principles, though pharmacologists focus on treatment of disease and toxicologists talk about the interface of exposure and disease.

They’re different disciplines when we think about them in the academic world, but they’re really not.

Which of the two disciplines seems to you to have taken greater advantage of pharmacogenomics technologies?

Oh I think pharmacology has historically, but I think increasingly we are beginning to understand the important role of genetic variation in risk of disease. And it runs the gamut from those families where there is a genetic variation, which predisposes one to a very, very high risk … to large numbers of different variants and different types of variation, all of which elevate someone’s risk twofold rather than a hundred- or thousand-fold, as you see in the cancer families.

Appreciate that this paradigm holds for almost all common diseases. We go back to the two who won a Nobel prize for their research into lipid metabolism [Joseph L. Goldstein and Michael S. Brown]. They found that a few families had had a major defect in lipid metabolism. So this area of using [families’ data] as a sort of starting material to hunt genes that give you very high risk, or the cytochrome P450 gene, [is] where there’s large amounts of variation in the face of an exposure to individuals who are at two- to three-fold in elevated risk.

But you must appreciate that the genes for cancers or lipid metabolism are generally rare. They get lots of activity, press, and lots of public relations, but probably all of the cancer genes account for 3 to 5 percent of all cancer cases.

Turn it around, and one of the most significant risk factors for cancer is a first-degree relative with cancer. So even in what we call sporadic cancer … one of the most significant risk factors is having a family history. It’s still a genetic trait, it just doesn’t segregate nicely so human-genetics books can study them.

Do you think there are certain diseases that may validate pharmacogenomics, and will the current availability and access to tissue samples strengthen that notion?

It depends. If you’re looking for families or sets of individuals at very high risk. We have harvested lots of those families. We’re increasingly talking about finding new genes or finding genes associated with disease in relatively small numbers of samples — like four, five, or six families. So this is where the more general aspects of pharmacogenetics are increasingly moving toward — not family studies but association studies.

We’re increasingly in a place where it’s not unlike the human-genome program — 10 to 12 years ago now — where we can generate data. The rate-limiting step is, How do we analyze it? Genotyping is becoming increasingly cheap, tissue arrays are available, gene-expression arrays are available, scaleable proteomics technologies are becoming available — and the real challenge is, you can generate the data, but how do you make sense of it? I’m increasingly arriving at a position where I think our ability to analyze the data and our need for new statistical approaches are increasingly going to become rate-limiting steps as we figure out how chips become cheaper, genotyping becomes cheaper, protein chips increasingly are available — and in the same vein there are large numbers of cohorts being pulled together by both the public and private sectors. The NCI, the NIH have increasingly focused on getting people with cohorts to work together so there are some fairly large cohorts of samples available.

When I came to Livermore I spent 10 years working on the human-genome project. The thing that had one of the major impacts of the human-genome project was the change in attitude in the ownership of data, and the increasing pressure that data and resources were out into the public domain. And that was a real change in the human-genome project. You know, historically, scientists sat on their data, they controlled parts of data. Molecular biology and the genome program have been a great equalizer because they have changed the attitude of people. And I think that is showing in epidemiology and pharmacogenomics where cohorts are increasingly available to at least an expanding circle, if not everybody. Having access to samples is becoming easier.

You once said that the task of using pharmacogenomics technology to glean valuable clinical data is like “finding the bits of precious metal in a fast-flowing stream.” What do you see down the road? Will the pan become bigger or will the stream begin to slow down?

It depends on which stream you’re in. And appreciate at least from my perspective, the disease families are really prime territory for the pharmaceutical industry. Because the genes to the diseases that are being identified in these families are really druggable targets. So as we find more and more of these families and understand the biology as to why these folks are at risk, it tells us a lot about disease processes. And, as we understand that biology, this is where the pharmacology and the pharmaceutical industry can focus on finding new drugs. The flip side of that — and I think in this case the stream is going to run even faster in the future — is in the area of individualized medicine.

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