Steve Greenberg

Big breakthroughs can have humble beginnings. Steven A. Greenberg, MD, a faculty member in the Department of Neurology, followed an unconventional path into academic medicine and has forged an unconventional career by bringing the power of bioinformatic technology to bear on medical questions. Trained as a physician rather than a researcher, Greenberg’s patient-driven questions led him to discovering a new therapeutic target for treating dermatomyositis (DM) — a severely debilitating disease that affects the skin and muscle. There are about 40,000 to 60,000 patients with DM in the U.S., but to Greenberg, the distinction between a “rare” disease and a “common” disease is an artificial one. Insights about the underlying biology of a disease like DM, Greenberg has postulated, could have profound implications for broader areas of biology and far-reaching applications.

The target that Greenberg pinpointed led to a collaboration with industry to develop a therapy. Earlier this year, results of two phase 2 studies finding that the drug candidate was well tolerated, exceeded a primary endpoint of decreasing skin disease and demonstrated a significant decreasing trend over time for measures of muscle function.

Clinical & Research News and Mass General Brigham Innovation spoke with Greenberg about what led to his discovery of a new target and how basic research discoveries can yield clinical advancements.

How does your background in mathematics influence your perspective on biology and medicine?

SG: By nature, I’m a mathematician in terms of how I think about things, so as an undergraduate, I specialized in number theory. I really enjoyed it and debated about going into medicine. But I was attracted to a specific medical school program: the Harvard-MIT Health Sciences and Technology program. It offered a middle ground between quantitative mathematical thinking and sciences and medicine.

After that, I became a more traditional physician instead of a quantitative medical researcher and completed my medical internship, a residency in neurology, and a fellowship in neuromuscular disease.

I went into clinical private practice and began seeing patients, but in the 1990s, there was a revolution in medicine that was known as the Human Genome Project.

This attempt to sequence the first human genome produced a lot of very exciting technologies, including quantitative technologies requiring the strengths of mathematically inclined people. I saw an opening and I left clinical private practice to do a second fellowship and shifted into academic medicine.

What led you to study dermatomyositis?

SG: I am interested in neuromuscular disease — it’s the area of clinical neurology that intrigued me most. I became particularly interested in myositis after seeing patients in my practice with serious disorders affecting skin and muscle. When I came to the Brigham, the head of the Division of Neuromuscular Medicine was Anthony Amato, MD. His main area of focus was muscle disease and so my interest in it grew.

And my interest also grew because of the technology. In the early 2000s, bioinformatic technology involved something called gene chips and they provided a new window for looking into disease tissue samples.

For 50 years, the field of medicine mostly relied on the microscope to examine tissue samples and help discern the causes of these diseases. But gene chip technology gave us a brand-new view into human tissue samples to try to understand disease.

How did gene chip technology open the door for your work?

SG: Gene chip technology was ideally suited to understanding muscle disease because we perform muscle biopsies on patients all the time. I thought, ‘I can bring this technology to bear fruit on these diseases.’

My interest in research has always been about understanding human disease. I was never interested in research for research’s sake; I wanted to understand human diseases and this technology presented the opportunity to do so.

Gene chip technology allowed us to study the activity of approximately 25,000 genes in a single tissue sample. Suddenly, we could do experiments, process a tissue sample overnight and create huge datasets.

And that’s just from one muscle biopsy. When you start doing this on hundreds of muscle biopsy experiments, you end up with tens of millions of data points. That’s when it becomes a bioinformatics and big data question.

It’s possible to take a very academic approach to thinking about this technology. But I didn’t build my career around the science of bioinformatics. I built my career around using the tools practically to understand human diseases.

How do you connect this back to what you’ve seen as a physician?

SG: When I began working in this area, opinions in the literature varied about the cause of various forms of myositis. For the first time, this technology allowed us to look whole-scale at the activity of genes in the muscle tissue samples from our patients, and we saw there were different patterns of gene activity in the different forms — a pattern unique to dermatomyositis and one unique for inclusion body myositis. There was no precedent for it.

How did these insights lead to new treatment?

SG: These insights pointed to a specific biology. I immediately noticed in dermatomyositis muscle a group of genes that were extremely elevated compared to normal and they all sat in one area of biology called type 1 interferon biology.

This was so new that we didn’t know how to interpret it. It took time, but I was so impressed with the data that I stuck with it until I could understand the pattern of activity occurring in dermatomyositis muscle and its implications.

The leap from association to cause required other science, not just gene chip technology. So, after years of running my science programs from a desktop computer without any laboratory space, I eventually moved into laboratory space, hiring people and conducting conventional microscopic science and cell culture experiments to further work some of this out.

The key data have always been in the experiments pointing to a pathway. Once I came to understand there was a Type 1 interferon that was driving this disease and being produced in muscle, there were several interferon options to target. I let the data guide me.

For a couple of years, I was the only person focused on the promise of this target. Eventually, I connected with a dermatologist at Stanford, David Fiorentino, who was doing very similar work with skin biopsies that I was doing with muscle biopsies. We started to talk and found out I was seeing the same thing in muscle that he was seeing in skin. We published a paper together over it.

Now that this antibody has been brought through to clinical trials for dermatomyositis, we’re at an exciting moment, both in terms of our understanding of what may be causing and connecting diseases, and also in developing new, evidence-based treatments for patients.


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