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On the Leading Edge of Cancer Care and IT Development at Memorial Sloan-Kettering

June 17, 2013
by Mark Hagland
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David Artz, M.D., CMIO at Memorial Sloan-Kettering Cancer Center, is helping to facilitate new bridges between research and patient care

David Artz, M.D., CMIO at Memorial Sloan-Kettering Cancer Center in Manhattan, has been very busy lately working with his colleagues to enable new waves of clinical—and IT—innovation.  Indeed, he is helping to support exciting new realms of cancer research—the data from which is becoming bigger and bulkier by the day. Dr. Artz spoke recently with HCI Editor-in-Chief Mark Hagland about his current work. Below are excerpts from that interview.

What have you and your colleagues been up to lately?

If you look at new things that have come along in the industry in the last ten years—for instance, beginning in the early to mid-1990s through the end of the 1990s—PACS [picture archiving and communications systems] kind of came through as an innovation in the workflow; and more recently, digital pathology has emerged. But now we’re noticing a new field in diagnostics, where molecular pathology is branching off from pathology, involving genomics and personalized care.

David Artz, M.D.

And it’s really affecting cancer care. You can think of cancer as a genetic disease, not necessarily an inherited genetic disease, but one that moves forward at the cellular level, within the DNA and RNA of the cell. And there’s been a research modality called sequencing, which is now making its way into the clinical arena. We have a few specific mutations that have a known significance in terms of the care of the patient. And you can characterize an illness not only by the way it looks under a microscope, or by the different chemicals it may absorb, but also by the profile of the mutations within a particular tumor.

For actual treatment purposes, with a known actual mutation, it’s been only a handful so far. But the way that the instruments work now, and they’ve evolved significantly over the past three years—when you run a panel, you run a panel that captures, for the same price, the small number of known, potentially actionable mutations, and a much larger panel of what we’ll call mutations of interest, which differentiate the tumor from normal tissue, but we don’t yet know their significance. And the equipment and the testing modalities that we use, are evolving rapidly. The newest modalities, which we started running this year, are called next-generation sequencers.

In the last few months, we’ve switched to next-generation sequencers in the clinical lab. And now we have a new “green space” in health IT, which we’ve been working to address, because now we have a new challenge. At a research institution, first, we get a massive amount of new data, and have to figure out how to manage it. We have to create ways to incorporate this data into the medical record; we have to inform clinicians when their patients have a positive mutation of interest; and at a research institution, we have to inform the clinicians on a routine basis, kind of as a push, so that they know, if they’re doing a trial or study of a particular tumor or mutation, that they’re aware of which patients are positive for the mutation of interest that they want to study.

And what are the mechanics of how you do that informing?

We now do a push e-mail on a routine basis that says to Dr. X, these are your patients in the last week who have this tumor site with this histology and this mutation, since the last time we’ve tested. And that lets them know that they could enroll them in a particular study.

And things are complex on multiple levels, correct?

Yes;  we’ve had to create the computer systems that allow people to be able to search for how many patients we have who have a particular mutation or combination of mutations, and combine that search query with all of the other data in the data warehouse. And at a cancer center, that includes some very rich, very specific data. There are two other things that we have at cancer centers that make the data very high-quality: one is that we have the pathology on all patients. Because in cancer care, particular with solid tumors, we don’t treat until we know the type of cancer, with few exceptions. So at this institution, by policy, if someone is treated elsewhere, we re-run the pathology. And in addition to that, we have something that many hospitals have, which is that we’re part of a program on tumor registries that captures about 70 percent of all tumors in the United States. That program is run by the Division on Cancer at the Chicago-based American College of Surgeons.

And because of a number of factors, including the use of a special code set called ICD-O, for oncology, we have this rich description of clinical data that is accurate; and it’s very useful for us to merge that with the molecular data, because now I have a very accurate description, for example, of everyone who has colon adenocarcinoma. We have a strict definition of that, and we can now look and see among those patients, who has particular mutations.

Are you storing actual images?