 Good morning. It's certainly a pleasure to be here. I'm Steven Chanick, and one of the co-directors of the Center for Cancer Genomics. And Lou Stout, the other acting director, will speak in just a few minutes. It's really a tremendous pleasure to be here and to be able to be part of this particular meeting in a very important juncture in the time of the lifespan of TCGA and what will come after that. And I want to talk about two things briefly today. One is the tremendous value of TCGA, particularly in the realm where I come from, which is more of germline and asking questions of the relationship between germline and somatic alterations. And I think Lou is going to give you some very exciting things that he's seen. And then I'm going to interspersed a few comments about what some of the discussions are right now, as we're going forward and thinking about exactly what kinds of things the NCI is going to support and in what ways we'd like to do it, and those discussions are very active at this time. So let me just start with a very exciting set of analyses that came out of one of the first TCGA analyses in the ovarian cancer analysis, where there was a paper in JAMA not so long ago where they were looking at not only the germline, but the somatic alterations, and particularly asking the question of BRCA1 and BRCA2. And these generated, what I think, are very important key preliminary findings. And this is really, to me, the tremendous value of TCGA in establishing hypotheses and establishing findings that then larger data sets will clearly go ahead and hopefully replicate and be able to extend and potentially push into clinical implications. And I think this is an example where the findings between the TCGA study and a follow-up study that I was very intimately involved in certainly suggests that as we look at BRCA1 germline status and probably also the somatic alterations as well, this may have a tremendous implication with respect to survivorship in ovarian cancer and may be a factor to consider in the design of studies and particularly certain kinds of trials that are going to potentially go forward. So I think this is a very nice paradigm of thinking about where TCGA goes from discovery to characterization to eventually some, you know, version of clinical implications. And we know that those clinical implications, as much as we like to say that that's where we want to go, those are hard and arduous. And they're going to take time and they're going to take a multitude of studies and various activities to really put those in place. So the first paper is this paper that came out in JAMA at the beginning of 2012 where many of the authors are sitting here. So it's a little embarrassing to present the data to those authors. But what they looked at basically was looking at the clinical data that was available and asking the question of survivorship. Did the initial BRCA1 or BRCA2 mutation status have anything to do with separating those individuals on a survival curve compared to those individuals who don't have an underlying BRCA1 and BRCA2 mutation? You could see an overall survival shift between brackeness and, so to speak, the individuals not having mutations there. Certainly progression free survival saw this same trend. And then the platinum free survival. And this raised some very interesting questions about mechanisms of platinum therapy itself and where the bracken genes and mutations in those genes may be important in a funny way in sort of protecting those individuals. They then went a step further and looked at some of the very interesting somatic alterations, asking the question of the expression of BRCA, particularly BRCA1 and the mRNA. And then they've also looked at looking at specifically signatures of expression and methylation. So this was in our minds a very exciting finding that another group that I'm part of, the OCAC, a large ovarian cancer association consortium, pulled out a much larger number of individuals as opposed to just less than 500, which had been the initial study. Now taking this study and looking across nearly 3,900 individuals where we had very good clinical information where we knew quite a bit about the clinical histories and the risk factors, and then to look, can we see those same curves in place? And in fact, when we went forward, we certainly could. And we could actually see a separation between BRCA1 and BRCA2, which the initial study was not quite well enough powered to do that. But in a subsequent larger study, we could see that separation. And these are things that had clearly been suggested in the literature before in very small underpowered studies. But here, the power of really larger team science, and this is the other key issue I want to really emphasize in moving from initial hypotheses and preliminary observations to going to large amalgamated data sets, were tremendously valuable in being able to start to separate these things. Because it's really those kind of agnostic statistical criteria that I think that we eventually want to achieve in looking at our large data sets. And if we look at the five-year overall survivorship by BRCA status, that was very impressive. When we go look at the Kaplan-Meyer curves, we again see the same separation. And interestingly enough, we could actually look and see that individuals who'd received platinum had a different degree of response compared to those who didn't, again, seen very similar findings to what we had seen in that first paper. And so I think these kinds of supersizing are really a very important message for us to consider in thinking about what we're going to do with our TCGA data, where we're going to take it, what kinds of ways we can grab on to, whether it's germline or somatic or interactions between those, and take them to additional data sets that would be the basis of new grants, new approaches, and most importantly, potentially clinical approaches towards specifically treating these diseases. And when we were able to look specifically at the types of mutations in BRCA-1 and BRCA-2, we could see in BRCA-1 that perhaps, and again, I use the word perhaps here, because I'm not sure the statistics here are quite strong enough to say that we can unequivocally say that the type of mutation you have in BRCA-1 and where it is may be important in determining this. And again, this now tells us as we get to a more granular level, we're going to need larger and larger studies as we really move forward. So really, I think the story here I've already sort of projected is that BRCA-1 and BRCA-2 carriers potentially, I mean, do show a substantially improved survivorship compared to non-carriers in ovarian cancer, okay? And this is using primarily retrospectively or collected case series and cohort studies. We see a slight distinct change in the clinical course between BRCA-1 and BRCA-2, and that there's some survivorship that may be very importantly mapping to the actual types of mutations in BRCA-1. So these are sort of the next hypotheses that this community is now going after, and I think TCGA was very important in empowering and sort of moving this to a very important step. And then, of course, the implications for this for clinical trials are things that won't be realized for two, three, or four years from now, as those studies have to be suitably designed, integrating this information, accumulated, and then most importantly, analyzed once we have those particular studies far enough along. So I sort of use this as a paradigm to where I think we're thinking about where we'd like to see the TCGA data going. And this, again, comes from where I'm much more comfortable in my world of germline, but I think there's a very important interaction here with the somatic alterations that clearly will be followed up. So when we think about this, we now need to sort of take a step back. We know TCGA is moving along as a spectacular train. As Kenna has, and others have clearly been able to move this along, they've been spectacular advances in really beginning to reach those milestones and develop the data sets that are extremely useful for applying the hypotheses and testing and analyzing in the ways in which we want. And it's really the center for cancer genomics that's relatively recently been established that Barbara Wald had really been instrumental in cementing within the NCI structure. And then now Lou and myself have the fun of sort of taking this over for a period of time to establish what's the vision, what are the next sets of studies that are going to come. And to do that really, you know, we have to think about what kind of mission we have. This is a slide that Barbara had initially developed that I think sort of characterizes where we see the center for cancer genomics going to, you know, to develop and apply these cutting-edge genomic scientific tools to improve cancer prevention, a very important thing, cancer care and cancer detection. And so these discoveries will then go in many different ways as suggested here on this particular graph. And clearly there'll be iterations and revisitations of these data. And these data should form really the nucleus of establishing new hypotheses and new studies that clearly are going to need to go forward. So the discovery really is a key element. And we know that we have to think about what comes next. And so in our minds, over the next three to six months, I think there's going to be a very active dialogue and a very active discussion of putting in place what kinds of studies are going to be lined up behind TCGA. Because we know TCGA is on track for 2013, 2014, you know, realizing the milestones and those goals. And we have to exhort everyone to meet those deadlines, get to where they need to get the samples there, get the analysis going, get the pan-cancer analyses, all these things that we know are going to be very fruitful. Because behind that we're already considering what are we going to line up. And we know that it takes time, as Kenna just showed, the timelines from the point of identifying a study, getting the permissions, getting everyone to buy in, getting samples analyzed. There are very specific, you know, time frames that have to be met. So we are now very actively thinking what is it that's going to be put into the TCGA-like pipeline and how that's going to be configured as something that's a very active discussion at this moment. And as we hear the exciting presentations and the discussions in the hall today, I think we'll be in a very good position to try and really develop those. And it's really going to be a dialogue between those of us in the NCI and in the NHGRI and the community as to really what makes sense and where the opportunities are right at hand for the next year, the next two years, the next four years. So these strategic lessons are really crucial in our mind. And capitalizing on these structures are really crucial. And as I already alluded to, there will clearly be a continued fruitful partnership between NCI and NHGRI as we clearly go forward. So we want to build upon these strengths of these pipelines, particularly for the processing and characterization. But also the analytic tools, we know the hardest part about this is really the analyzing of the data and having to revisit it and look at it new creative ways. And the data sharing challenges are really quite, quite interesting. And I think the TCGA team has done a spectacular job in getting the data out and making it available to people. And how and in what way the policies at the NIH are going to evolve to take into account new technical opportunities, et cetera, are clearly a very active discussion right now. We also, I think, foresee that the kinds of projects that will be supported will be both top down and clearly the value of bottom up. And I made that illusion earlier. I think the importance of taking the findings from these studies and generating R01s and generating institution or PI-specific grants is very important in sort of fitting in and building the larger analyses. And that's something that's clearly going to be very important in the NCI portfolio, and I'm sure in the NHGRI as well. We have a major transition towards wanting to be clinically important here. And that transition is really crucial. But we know that we've got to be very careful that this is very arduous. It's filled with landmines. It's not as easy as it is to say to actually do it. And at the same time, we know that really the next clinical opportunities are going to come out of very basic discoveries. And so we can't lose sight of the value of basic discovery and of looking at the data sets in the most effective, intelligent ways. So really what kinds of large-scale questions? Two or three more slides. Unraveling cancer biology in our minds is very important. Getting at this question of drivers and mutations, drivers and passengers, window passengers, change places with drivers, and the like. I mean, this temporal nature is something that's going to be very important, and we're going to need numbers. And we're going to need very clever studies to be able to put those kinds of things together. So developing a kind of somatic molecular epidemiology, larger studies clearly, and we're going to have to pace together different kinds of things, asking questions of clonality and progression, a very hot topic, and what kinds of coverages are necessary, and what kinds of study designs clearly are going to be very important. The value of epidemiology in germline, and now asking questions of susceptibility when we want to look at adenos, CA of the lung, and really in terms of understanding what kinds of smoking and other types of exposures in much more exquisite details to be able to understand questions of risk that are both individual and, very importantly, their public health questions of risk. And these are two very different venues. The contribution to somatic events and those relationships between germline drivers, so to speak, of cancer, and the treatment stratification in pharmacogenomics are certainly going to be there, particularly as we look at response, toxicity, and, of course, outcome. So we sort of think about genome-related trials. Now is genome-informed trials, where we're going to learn things from things that have been actually collected, the value of prospective collections going forward is going to be very important, but they won't be reaped for a period of time. But the question is, how and what do we do with future clinical trials, and how do we start to integrate those into these kinds of analyses? Genome-driven trials, there'll be a lot of discussion about those, about where we actually have very remarkable phenotypes, and what can we learn in genomic characterization there. And then certainly the genomic analysis, not as a part of a trial. Using the archive samples, and gene environment analyses, the epidemiologic studies, these are all things that are going to be part of, I think, portfolio that's going to have to be built. It's not going to be done in any one particular venue, but probably a cross-section of the kinds of things that I've just outlined. So let me finish by saying that our current TCGA goals are really, I think, very important. Achieving those milestones per cancer site with the timely publications is really essential. These are the coin of the realm that we have to be sure we keep up with, conducting the pan-cancer analyses. The technical pilots were all very charged up and excited about seeing these to their completion, and particularly to the thorough completion to know what the next steps are going to be. And then most importantly, the forging of new solutions related to the integration, storage, and sharing of data. Things that only become more difficult as we generate more information. And lastly, the fortification of the collaborative spirit, because I think there's a very great American president, Woodrow Wilson, who made a very important statement, and this is my final comment to the TCGA community, and it should be in the first-person plural. We not only use all the brains we have, but all that we can borrow. And so I think the idea of really collaborating and being able to count on and work with others is really a central component of not only TCGA, but what comes out of TCGA and what directions it goes in in terms of large top-down, as well as bottom-up studies. So let me stop there and bring Lou up. Lou, do you have your computer over here?