 Okay, I'm not really going to talk a lot about the mechanics of whole genome sequencing. I'm going to describe how the Partner Center for Personalized Genetic Medicine is organized, describe the laboratory for molecular medicine, which is our CLIA-certified lab, talk a little bit about our plans to launch this service, which is very much along the lines of what Howard and David are doing at MCW, and focus in on gene insight lab and clinic as a critical piece of software and something that may be shareable with other members of the group. So how is PCPGM organized? We have a set of research cores, which are sort of the standard cores that are also used by our CLIA-certified lab, and these research cores both support research and support the clinical lab, and they are CLIA-certified. We're supporting about $140 million of research across Partners Health Care. Just for those of you that don't know, Partners Health Care is the umbrella organization for Massachusetts General Hospital and Brigham and Women's Hospital, and involves a number of ancillary facilities that are linked to those two academic medical centers. So this is all CLIA. We also have a very active RPDR and electronic medical record that's used for research. Sean Murphy actually runs this. It's not actually technically part of the center. Most of the research that's being done here is not genetic. There are about 3,000 projects that are going on, and over 4 million Partners patients that are being used for research using the electronic medical record. And this is the piece that's not yet built. We've just actually gotten approval to go forward with the Partners Biorepository for Medical Discovery, which is going to be a biorepository which will be linked to both the CLIA lab and to the electronic medical record. And our goal is to have the biorepository actually CLIA-certified as well, because going forward, we're doing this in a consented fashion, and I think the reasons for that are manyfold, but I think the most important is, is that we want to be able to do research on these patients, but also be able to push things forward in terms of clinical research and clinical care, and it's going to be a lot easier to do that if this is both CLIA-approved and if the patients are consented. So that's how the center is organized. So between the investigators that we're supporting here, currently there is a service crimson, which is now, I just became operative at Mass General, has been operative at the Brigham for the last three years, and that's supporting about $40 million in research. So between $140 or so million dollars worth of research that's using the EMR, another $150 that's using these cores, and the 40 here, we're supporting about $300 million of research for approximately 400, 500 partners investigators across the health care system. And I think, sorry, perhaps the most important part is the IT infrastructure that supports all of this, because without this IT infrastructure, we wouldn't be able to have samples flowing seamlessly from the CLIA lab to the research cores, to the biorepository, to other investigators, samples going to the Broad or wherever. So the IT infrastructure here is clearly the major investment in terms of the center and the way it works. So a little bit about the laboratory for molecular medicine. Heidi Rehm, who was at the previous NHGRI workshop with Robert Green last week, is the director of the lab. This lab's been in existence since 2003, and we do about 4,000 genetic tests a year. We do these tests for both the two partners, hospitals, but also for medical institutions around the country and in some cases around the world. The bulk of our testing is in cancer and cardiovascular disease. We do have some other oligogenic and monogenic traits that we test for, and currently we have tests for over 200 genes. The challenge here obviously, and this is an example of one of our first tests, both are two academic cancer centers, produced papers within a few months of each other looking at EGFR as a gene for a treatment response for small cell lung cancer, and within three months the lab had a test up and running to test patients, and we still do the bulk of the testing for Dana-Farber, although the MGH does their own testing now for EGFR. A little bit of context in terms of the evolution of clinical genetic testing, which I think gives maybe further justification, if you will, for the approach that Howard outlined earlier. You can see along here how the evolution of testing in the lab has occurred, and we've expanded tests and expanded the number of tests in the areas that we've been doing genotyping, but we're reaching the point now where it's going to cost as much to genotype the whole genome as it will, in fact, the cost of the genotyping will be less than the cost of our cardiarchip genotyping. The cardiarchip test, I think, costs on the order of $3,000. It's likely that the whole genome, at least the genomic sequencing portion of that test is going to be well below that in a year or so. So we're entering this logarithmic phase where whole genome sequencing is going to be relatively cheap, but the thing that's going to lag behind, as has been pointed out by a number of people already, is the analysis and the ability to build content to deliver to practitioners. That's going to be the major challenge going forward. It's not going to be the sequencing itself, and we anticipate that all of our tests now are targeted next generation sequencing tests, and we expect to be completely out of that business in two years, simply because it's just not going to be economically viable to do it. So our service is organized very similar to the way MCW has organized theirs. We're outsourcing the actual sequencing to Illumina and complete genomics. We're going to concentrate all of our efforts on data analysis using the existing infrastructure, the LMM, and all of the things that Howard talked about in terms of patient workup consent, oversight, clinical committees is all going to be in place, and we're probably going to launch this so we would like to launch it in July of 2012. The major factor in terms of whether we will be able to do that or not is going to depend on our IT infrastructure, I think. I think it's interesting to look at this from the context of the clinician's perspective. The amount of information that's going to be generated for clinicians is truly daunting, and new information can emerge on any of these variants at any time, and new forms of support are already needed to stay up to date on the limited number of variants that were identified using the clinical support tools that we have now, and the infrastructure, depending clinical process, need to be established to allow clinicians to retrieve and manage genetic results, to link clinician to experts capable of determining the implications of each of these genomic variants and keep them up to date. Obviously, this whole process, we've linked to the clinical genetics programs at the two hospitals, Mike Murray, who runs clinical genetics at the Brigham, and David Sweitzer, who's his comparable person at Mass General, are involved with Heidi and Robert Green, and me as we formulate the plans to launch this service. This also creates significant opportunities for partners' health care in particular because of tools that we've already developed and would like to share with people here. You've got this constant flow of cases that are going to the geneticists in the lab to sign up these cases. You've got this evolving knowledge base and the need to continuously update the electronic medical record and report information to clinicians. We've developed a tool that we call Gene Insight that we use both as a report-generating engine, but is used in the laboratory as a knowledge base. We keep information on all of the variants that we've genotyped in the lab, and we obviously use this to report the results to the electronic medical record. And both of these, I think, are going to turn out to be very important. Heidi Rehm has actually put in a U41 grant to address the issue that people have been talking about here, which is a huge issue, which is variants linked to clinical phenotype allele frequencies, allele frequencies in people that don't have the phenotype, standard ways of reporting and annotating variants. This is going to be a huge issue going forward. And if this group decides to make that an issue that people want to collaborate on, I think that we have several people in our center who would be very interested in participating in that. So this is what Gene Insight looks like if you're at a terminal in your office. This is a patient named Curious George who happens to have a cardiomyopathy, and he had a variant that was previously classified as of unknown significance that has been now reclassified as pathogenic. So we can update these in real time. We can push this information to the clinicians. We can provide decision support tools through this software and help to enable the clinicians to manage these patients and to help them seek support from genetics experts if they need that backup and support. So, and I think that this software, although it is clearly at the terminal end of the pipeline that Howard described, it's still software that is potentially valuable, and we'd be interested in sharing it with others and working with others who might want to use it. No, no, it's not sitting on a research database. And this is just the people who have helped get this center off the ground. Yes, Debbie. So, should I wait, or can everybody hear me? Great. Scott. Yes. Tell me what made you change from one category of pathogenic. That's a big change. For an individual variant? Yeah. So what was the level of evidence that made you make that switch? Yeah, I'm probably not the best, you know, I don't run the clinical lab. That's sort of like the question that Howard deferred to David. I think that the geneticists in the lab have a whole bunch of criteria that they use to decide whether something variant changes. They look at the literature, they're looking at a lot of different things. And I agree with you that that whole issue is one that is, well, I think it's not just a nightmare, it's one where there's not clear standards, and some people might do it one way or another, people might do it another too. I mean, people are beginning to look at this because it's such an important question, but I do think it is key because what has been reported in the literature, if you go today with modern databases, what in a family segregated and what was without functional studies or even some maybe not appropriate functional study caused or said was causal, you cannot hold up. Yeah, well I think that, you know, there's clearly variants that were found for monogenic disorders where the variants are in linkage to this equilibrium with the causal variant and it was reported that that was the causal variant. So I couldn't agree with you more that this is a huge area. I think it's an area that's going to demand a lot of attention. What I wanted to focus on here was not so much the process that we do that, but the fact that the software allows us to change the interpretation. I think that's going to be important because I think that going forward, this is not something where it's going to be set in stone. Yeah, but I think it's important also if you're taking data from a provider, what they're providing, let's say, an aqueous setting is not necessarily the end of, you know, no question. And a clear certification doesn't mean that an interpretation is set in stone. So you're looking at a lot of places to reduce costs and you're doing some of the whole genome sequencing then at the clear labs like Illumina. And when you look at these like 4,000 bucks and they'll do the alignment, call the variants with something that's good enough, do you see bringing that, I mean, I guess I'm wondering why bring that back if you have to re-invent all the analysis questions and all the initial labs set up? Is that a part that you really see needs to be in-house? I think that, you know, we have currently two high-seeks that are being used for research sequencing. We're not doing any of the whole, we haven't started doing whole genome sequencing ourselves in the research core. I think that whether we ultimately bring this in-house or how much we do at partners and how much we rely on Illumina and complete genomics is a fluid thing. It's not something that's been set in stone. I don't know that we'll, the one thing I'm pretty sure we're not going to do is we're not going to build the giant sequencing facility in Waltham to sequence all partners' patients. I don't think that's probably in the cards, but how much we actually do ourselves and how much we outsource I think is an open question. I think for us, the issue of the analysis and the interpretation is what's paramount. But when you say analysis and interpretation, I think that was different than what was referred to earlier, which is how do you call the variance? You're real concerned with analysis and as interpretation. So there are kind of two- No, I think we would include that as well. And I think that all of the issues that were described are issues that we are going to consider in terms of how we do this. Nothing here is set in stone. Virtually everything about this is a moving target. So how we decide to do this today may be very different from how we decide to do it, you know, six months or a year from now. Scott, you may have mentioned it, but can you clarify whether the gene insight is designed as a standalone tool or is it in any way linkable with an EMR to talk to EMR? It's absolutely linkable with an EMR. You know, several other health systems actually have been using it. We collaborate with Intermountain Health. They're using it. So it's definitely linkable with other software and other EMRs. Sir, I had the exact same question, but that means it's a standalone tool, because if different EMRs can talk to it, it means it's not tied to a specific EMR. Is that correct? That's correct. And then there's the laboratory, you know, that basically this resides between the clinical laboratories and the information systems, and so you really build one interface from a clinical site to a gene insight, and then you can interact with all the laboratories that use that. And so it just simplifies the interface construction with otherwise in a distributed single gene, gene by gene type of thing, you have an infinite number of interfaces that you have to build. Now, again, it may become a moot point if we all move into next-gen whole genomes, but at least at this point it was a very pragmatic solution. It's a follow-up on your question. As all of these modular components are proposed and developed by different people, is there any idea that in the end something like data.gov and semantic technologies will be somehow the milieu or the middle layer that all these things used to talk to each other? Anyone has a comment on that? So this is something that groups are talking about, and specifically since this involves a lot of moving and pushing of data around, the clinical genomics group of HL7 has specifically constituted a working group within their working group to look at whole genome and whole exome sequencing and how that data can be characterized, and they have other working groups that have looked at other types of data models within this arena for family history, and there are additional working groups that are outside of the genomic working group that do clinical decision support models, but essentially all building on a common standard language so that there's relatively seamless movement of the information that you want, as long as the information system is built using that type of a communication standard, in this case an HL7 version 2.x.