 while we all munch, and she is going to discuss how we should be considering the human genome from a patient's point of view. Great. Thanks very much, Eric and Larry and others for inviting me here, and I know that my good friend Teri is worried about her flight, so I will make this fairly quick. And I'm not going to use slides. I usually pretend and be a poser to be a scientist with groups and typically use slides and talk about data, et cetera. But instead I'm going to come to you purely from a patient-consumer-public kind of point of view in a very brief perspective around what that means. I am the parent of two kids with a disease called pseudosanthoma-elasticum, and that's a rare genetic condition that causes calcification mineralization of the middermis of the skin, the arteries, and the worst part about it is the Brooks membrane, which causes vision loss around age 30 or 40 for most individuals. Our kids were diagnosed two days before Christmas in 1994. We were shocked just like any parents who think that they have two normal children, and everybody once your kids are past a certain age realized none of them are normal. But we pretty much thought we had two healthy kids, they were six and four, and essentially learned this horrible thing and didn't understand at all what it meant. So 1994, there was some pretty good genetics going on, at least in terms of gene discovery, et cetera. We pretty much walked into that as two parents would, expecting that, of course, once the diagnosis came, which came from our dermatologist, that in fact somebody would tell us everything else we needed to know about the disease, and they'd also give us the treatment. We quickly learned, in fact, by the time the Christmas season was over that there was very little known about the disease, which is typical of the 7,000 rare genetic disorders, and that if anything was to be done, we were going to have to do it. So my husband and I, by some time around New Year's, had read every journal article we could find, and my background is I have a master's in religious studies. I was a college chaplain. My husband was a trade school graduate who had done fire protection engineering for the Boston hospitals. So we found ourselves wallowing through all this technical language that we had never heard of. We were buying ourselves encyclopedias and dictionaries, and we eventually understood enough to know that not enough was known. We also learned really quickly that the culture around science and medicine was not what we expected, and instead of answers, in fact, scientists were coming to us for the blood and tissue of our children. The first ones that came, day after Christmas, we said, sure, absolutely take these vials of blood from our kids. The second ones that came about two days after that, these were Sibs, and they were trying to do studies to find the gene, said, in fact, we'd like blood as well. And we said, wait a minute, these are little kids. Go get the blood from the other guys, and they said, are you kidding? This was Harvard, the other was Mount Sinai Hospital. No way were people going to collaborate. So that shocked us as parents that people in the science realm competed rather than cooperated around especially such important things as somebody's health. And what we did is we set up a blood and tissue bank, believing that while all these cats supposedly were not to be herded, in fact, you can herd cats if you move the food, and if we made the blood and tissue the food, then they would come to us. And in fact, they have, we have now a 30 lab consortium of scientists around the world working on the disease. So fast forward, we found the gene ourselves, my husband and I working in a lab at Harvard at night, and went on to clone the gene, and then patent the gene with other universities that were involved. There were two of them. All of that gave us a real interesting perspective because we essentially plunged ourselves into being somewhat citizen scientists. Again, no background in this, but getting kind of the, on the, on the spot, on the street education we needed to do the work we needed to do, we thought. And at the same time, we kept thinking this system is not really very good if every single disease has to have this happen to it. Of course, in the common disease realm, it's completely different, although not so different in some regard. So a lot of the competition certainly exists. A lot of the not learning one from another, one lab from another, one research community from another exists. And so we've begun to think about those things as well. So when we think about all the stuff that you all have been listening to this morning and will continue to listen to this afternoon, we're certainly very excited. And I have been intimately involved with NHGRI and with other institutes at NIH working with them. In fact, to support the science every way that I can from lobbying for appropriations on Capitol Hill to being part of planning committees and work groups and that sort of thing. And at the same time, you have to ask, what does this mean, though, to those people in the trenches? So since the time that my kids were diagnosed, I've met thousands of people with thousands of diseases and worked with thousands of other support groups because now I'm president and CEO of Genetic Alliance, which is an umbrella for about 1200 of those groups. And what we see, of course, is real hope but wariness about hype. And that when we hear about these discoveries and we learn the latest, most wonderful thing, we're delighted and then soon what the community does is worry that that advance will not translate right away. And we all well know and certainly the scientists know and you writers know that these discoveries don't translate really fast, that this is really work in terms of lifetimes but we're really trying to get it done in terms of years or decades. And so there's a real tension between that hope and hype and we try to balance that tension by certainly continuing to support discovery and really support the kinds of translational efforts that are being made but at the same time try to be very pragmatic. My husband coined a term for it during the time that we were starting up and he called it tempered urgency. That our urgency is huge. Our kids now are 20 and 22. They will be losing their vision somewhere around 30 or 40 years old and so we have many less years to get done what we need done. It's still a long way to go and we still look for the day that we can make a difference. I think we also like to try to think in terms of systems and so some of those include for example working with Congress and as you all know Congress has a great interest in the biomedical infrastructure of the country and both Republicans and Democrats at different times and some who become both a Republican and a Democrat are interested in this issue and what we find is that they often think throwing money at the cause will make a difference. We've seen that that's not always successful and one wonders again is that are there ways that we could as a nation, as a people, as a community make a difference and so we started looking at this issue carefully over the last couple of years. Again this mounting frustration from parents of children who in some ways I'm quite lucky. My kids will have a normal lifespan. They may be blind but they'll have a normal lifespan. I work with other families whose kids potentially will die at age 13 or 11 or five and that's much more severe. So how do we keep their hope alive, get something done so that they can be able to see results and at the same time follow these practical courses that science need and when we work with Congress and talk to them about is just money gonna make a difference, we think not and so some of the things that we're looking at are programs that we've undertaken for example one we call GrandRx which is Gateway to Rare and Neglected Disease Therapeutics whereby we've said the system doesn't work, there are many roadblocks in it. Some have been named like the Valley of Death, the medicinal chemistry kind of roadblock. There are others around cohort development which we could go on for hours about. There are others around actual clinical trial development et cetera and certainly even some really ones that should be very minor like high throughput and assay development that just isn't what academic labs are able to do so easily. So essentially what we've done is brought together a group of umbrella organizations like the National Organization of Rare Disorders, Faster Cures, Foundation Fighting Blindness, et cetera and said how can we share our information, our infrastructure and our resources to get something done today and also to encourage the right kind of collaboration throughout industry, throughout government, et cetera. I think I'm most and certainly all these activities are huge and they're going to take a long time and they're gonna take a rethinking on all of our parts about what we share and what we don't share, what's pre-competitive and what's not. I think I'm most struck though in all of this journey to go back to where I come from as a parent by my own children and what they declared when they were about 16 and 14 years old. When we were very frustrated saying at that point we were far five, six years into having the gene, we still didn't have a treatment, we still don't and we were very frustrated that we hadn't done much and our daughter and our son kind of together playing off one another that night said, you spend all your time fighting disease while we're living with disease and when you shift your focus and understand what it means to live with disease you'll understand how important what you're doing is and how much you succeeded already. So for me, certainly to have children who can keep my eyes on the prize is helpful and I think if all of us keep in mind what truly matters, the individuals that are suffering it will really help all of us. Thank you. For a few questions for Sharon. So please step up to the microphone if you're willing to step away from your sandwich. That might be hard. What? Not too many questions. It looks like not even so. Please. And you could use the short microphone again. That's that wonderful short microphone. I really admire your work Sharon. Congratulations on everything you've achieved. I work in publications that are trying to communicate risk to the layman and as you, we've all heard we don't really know where we are. You know, we correlate things and then we try to make a statement about your risk. And I'd like to hear your take on that statement as it's, you know, since you're in the business of interpreting it personally and dealing with it politically, what do you think we should say about risk to the public? So I think that's a terrific question. I don't know if you saw today, 23 and me had a 96 well plate get mixed up and they sent out the wrong results to their people, including one mother who apparently said, oh my God, my son was switched at the hospital. He is not related to me or my husband. So which could be quite traumatic. I think we're in a really difficult age. I'm excited by it. I kind of like the disruptiveness of trying to figure out what do we communicate? How do we communicate it? I mean, I think there's some good work going on with Colleen McBride with Bob Green looking at what do people understand when they hear something. I think all of us know terribly well that we hear what we hear and that as much as we try to communicate, it's really gonna be tough to get people to understand the level of complexity around the kind of stuff we heard this morning. So my kind of pitch always is to get as much information as possible out to everyone so that there could be interpreters because there will be, science writers are gonna interpret, disease groups will interpret hospitals, clinics, et cetera. And then also to make sure that people have support to understand that they are hearing what they're hearing and that can take many forms, certainly genetic counseling, but I think also the various forms, et cetera, that have begun to be set up around what does this information mean? I think we're a long way from knowing what it means and I think the quicker we can get more people's clinical information available and correlated to the actual data will make a huge difference, whether it's a bunch of hurdles there that we're trying to overcome. Hi, my name is Deborah Levinson. I'm here for the American Journal of Medical Genetics and I'm wondering if you could comment on services like private access, which lets patients or the families of patients who are potential research subjects get together with researchers who are looking for such subjects and set, and it allows those patients or their families to set the amount of privacy they desire. I'm wondering if you can talk about the advantages and disadvantages of such a service. So I'll be really brief and also give the disclosure that in fact, we're genetic alliances in a partnership with Private Access. You can go to privateaccess.com and look up the system, essentially allowing people to set their privacy preferences the way you do on Facebook and we are actually quite excited by this and that's why we're in a partnership with them because we believe that the more people who enter projects like this that allow them to set a privacy preference and then researchers to go in and search for those people, contact them as they wish. Some people full privacy protection, some none, will allow people to be able to be more interactive in their own research, also allow researchers to re-contact them and get increasing levels of participation in research. Catherine Talman, American Dietetic Association. So with your experience and all the people that you deal with who have been genetically tested and know these diseases that they have, how has that affected their ability to get health insurance, disability insurance, long-term care, jobs, what kind of practical advice do you have for that? So certainly in the case of employment and regular health insurance, GINA, the Genetic Information Non-Discrimination Act protects them now as long as the disease is not manifest. So we're just talking about just genetic information or familial information. In the cases of long-term care and other issues, it was still a really tough and in fact we know from some of the studies that others have done if people are tested and they don't reveal the test of long-term care insurer and go and get long-term care insurance before they manifest the disease. So I think there's still a lot of work to be done there and there are people like Anna Eshoo in Congress who are interested in protecting those people as well. Thank you. So we are gonna move right along and we're gonna have our fourth panel, although this might not actually be a panel, we're gonna do one and then the other, right, for time reasons, zoom together. Okay, so this is on population genomics and the first speaker is Terry Minolio who runs NHGRI's Office of Population Genomics and she'll be talking about population studies beyond GWAS considering genes and the environment. Thanks, Eric and sorry for the time pressures but you guys are reporters so you're used to that. So one of the things that's new since 10 years ago is that NHGRI has moved into the population genomics area. We've defined this sort of ourselves as a way of kind of putting together epidemiology and genomic technologies to apply these technologies to people with lots and lots of different characteristics that are available for research and that and to learn really how genotype relates to phenotype and then also to develop new population resources for this kind of work. Population based research is an advantage because the findings can be generalized to everyone, not just those coming for clinical care or not just those with rare diseases. The diseases, traits or risk factors that characterize those people that are often referred to as phenotypes and environmental exposures tend in studies like this to be defined in sort of standardized, reproducible, valid, transportable ways so that you can compare across studies. Generally these need to be large to reduce spurious findings sort of coincidental kinds of things and allow meaningful subgroup analyses, particularly for groups that are not the majority population in the US. You heard earlier from Francis Collins. He's for many years has been arguing that the US needs a large prospective study of genes and environment. In 2004, he wrote this nature commentary in which he concluded that the time is right for the US to consider such a project. Hundreds of thousands of people coming together in a large research program to identify genetic and environmental influences on disease. Just recently we held a workshop to sort of examine what has happened in the past six years since he wrote that editorial in terms of what have we learned about doing these kinds of studies. This was just in January. And one that we learned from particularly was the UK Biobank, a study of 500,000 adults that began about three years ago. As you can see here, they now have 484,000 people. That's actually as of 3 p.m. today. So it's ahead of time but of course that's a Greenwich Daylight time or whatever they call it. So we're a little bit ahead of ourselves. And the BBC News actually about a month ago noted that they were close to signing up their 500,000 participants. They project to be finished end of this month, beginning of next month. That's at least six months ahead of time and considerably under budget, not unlike the genome project. So challenges in conducting this kind of research, one has to have good measures of environmental exposures. You have to have good measures of phenotypes and it would be great if you could get them from electronic medical records so that they're sort of already available and already collected in the course of clinical care. That does sort of take away the population basis of this because you have to have people who are coming to the doctor in the UK. That's not a problem because everybody has a doctor in the US, it's more of an issue. Representativeness versus inclusiveness is a whole debate among epidemiologists that I won't go into in great detail. Can you have consent that sort of lives and breathes with the science? So as the science evolves, you don't have to go back for consent each time or you can go back for consent each time if participants want you to do that. Can you have widespread data sharing and can you use existing studies and sort of in some ways kind of push them together instead of collecting people anew? One of the things that the Biobank has shown is that there may be some major advantages to sort of changing the way we routinely do business at the NIH. We tend to have sort of distributed models as we call them where we establish a clinical center at an academic site like this one and actually many of them and have them all send data into a central coordinating center which may also itself be an academic site. All of those begin at the beginning of a study, stay open throughout the course of the study. And the Women's Health Initiative is a good example of that kind of design. Another possibility which Biobank has used is to start with your coordinating center which may also be an academic site but then set up temporary clinics in places like warehouses, open office space, wherever there might be available low cost space in areas where there's lots of transportation, et cetera and shut them down once they've kind of exhausted the area of available participants so that you're constantly sort of moving from one place to another. This has been termed a centralized administration and actually the National Health Nutrition Examination Survey uses this model, although they use vans to do their exams. So key points that we learned about these kinds of studies is that recruitment is very, it's useful to have a high recruitment yield so that of all the people that you invite, 80, 90% of them come in, you avoid biases that way. That doesn't happen anymore. It may be used to happen in the 1920s but certainly not now. And probably it's okay to go ahead and just invite lots and lots of people in as long as you get a diversity of them from lots of different backgrounds including racial, ethnic diversity, socioeconomic, geographic, cultural, you probably are okay. You'd like to set it up in a population that allows follow up. So setting it up in a clinic that has electronic records that you can link to. You want to make sure that the cost per, or recognize the cost per participant recruited is the driving force and cost efficiency and cost efficiency is really important in these kinds of studies. Centralized model may be a better approach. And in funding it's important to recognize that you need sort of clear straightforward lines of leadership and organizational authority. I think I'm gonna skip over gene environment interactions because time is short just to mention one that you probably may have seen in high school long ago. This is the peppered moth and here is the wild type moth and the sort of mutant variant moth on a soot colored tree and then a soot colored moth and the regular moth on a lichen covered tree. This is a gene environment interaction where these moths recognize that in order to survive they needed to change their spots as it were. But really the environmental factor here is this fellow, the Paris major who was going after them without him or her, there wouldn't have been the importance of this variation. We have the same kinds of interactions with our environment ourselves and our genetic factors. This is a huge area and really in order to be able to study it you need very large numbers of people because not all of them carry the soot colored gene and not all of them live in environments with a Paris major here. So as I said I'll skip over this just to make the point that genes in an environment, when you kind of contrast the ease of measuring the environment is very difficult, variability is high, you can have a variety of biases in the information you collect and understanding the temporal relationship to disease development can be very hard. So Larry asked me to sort of look forward in terms of what might be the six easy ways that we would implement a large U.S. prospective study. Well we'd first like to find inexpensive, simple and reliable measures of key environmental exposures, key things like diet, not an easy thing to measure, not one that we're likely to be able to crack anytime soon. Oops, well I'm not gonna be able to go up. So we'd like to find inexpensive, simple, reliable measures of a broad range of phenotypes, a broad range of diseases that you can measure really quite easily, again not a simple thing to do. Another simple thing, cut recruitment or exam costs by 90% or more, the Biobank is doing their study well under a tenth of what we had estimated would cost a similar study in the U.S. to be done. Very challenging in this environment. It would be great to find standardized, readily accessible and reliable electronic medical records. There are no such things in the U.S. They are coming, maybe 10 years from now, we may have them, but they certainly wouldn't be penetrating all of the medical systems in the U.S. We certainly need to mirror the full richness and diversity of the U.S. population in the recruited sample and there are some groups that are much harder to recruit than others and a lot more constraints in them and that and that's gonna be another challenge as well. And then finally, investing in responsibility in the study leadership and having them backed by simple and clear lines of authority. So in many ways we're trying to change some paradigms here in order to get this done. I'm a big Gary Larson fan and much like these guys, hey, they're lighting their arrows, can they do that? So what we're trying to do is not burn the house down but at least change a few paradigms. I think I'll stop there and then you'll introduce Charles, yeah. I can, okay, great. And our next speaker in this panel is an intramural researcher at NHRI, Charles Rotimi, who will talk about race and ethnicity and genetics defining the population to be studied and interpretation of group genomic data. Here somewhere, right there, got it. Good afternoon, I'll try to speed up also. What I wanted to share with you some of the difficulty that we experience in defining populations and that we definitely include myself in this struggle. So typically when we are designing study or when we are interpreting study, we tend to do it along the lines of continent, national, ethnic and tribal orientation and then we have issues dealing with admixture where we really know that people are coming from different human populations that have been isolated from each other for a long time and that long time could be 10 generations. It could be much longer than that. And then we have issues dealing with ancestry. As reporters, I think you have to look at data when they have been presented and ask very serious questions. One of the things that you typically hear is Africa. And the question where I usually ask is are you talking about Africa or are you talking about sub-Saharan Africa? So even at the level of continent, we have confusion in terms of defining population. Typically when people say Africa, they are indeed, in terms of genetics, they are indeed referring to sub-Saharan Africa, not Africa, because for the most part, as you will see in this slide, that people in North Africa are typically clustered with other European ancestry populations to define what we call white, okay? So in the context of the US census, for example, the US government, white is defined as Europe, Middle East, and North Africa. So you can see where the confusions can come in, even at the level of continents. So those are some of the difficulty. In terms of the, again, the US census, one of the things that is most constant by the US census is that it's going to change. The definition of race and ethnicity and how we identify them is going to change every 10 years. You almost can bet on it. And because of that, it becomes very, very difficult to actually understand what it means and to be able to use it in any kind of systematic manner. And the question I typically ask my genetics friend is, if you have a variable that you know will change that much, will you have any confidence on any terms of putting in your model? The answer, technically, is no. But in the context of race and ethnicity, we are very comfortable with that lack of robustness because we think we know what it is and so we constantly use it. So in terms of, again, the US census, there is a good disclaimer when you go to the US census that what they are trying to do is not to ascribe any kind of biological and biological meaning or genetic meaning to the group that they are defining. They are trying to collect political, economic, and social data in the way that they can monitor certain things in the environment and how society is structured and how that structure is impacting on people's health, people's economic well-being, the neighborhoods that people live in. So, but in science, we tend to ignore this disclaimer and use it as if it's some kind of very robust set of variables and that creates also some problems. For example, the group called Hispanic can claim any racial group they like, really. So this is the most recent category that you do find in the 2010 US census. What you do see is we do have issues dealing with ethnicity, whether you are Hispanic or not Hispanic and if you are Hispanic, what part of Hispanic origin are you? And then the issue of race, you have white, black, traditional kind of category in Native American and then you have all these other categories here. These are national and ethnic and all the combined. So there's really no clarity in terms of what you actually define when you are developing this model. For example, when we did the HAAP map, we didn't say Chinese, we say HON Chinese. But when you look at this category here, you just say it's Chinese. So you can see where things get very, very positive and difficult. So part of the difficulty again is really the fact that we all started somewhere in Africa. So really beneath our skin, we are all Africans. So it depends on the level of resolution that you are talking about or how many generations that you are talking about that you have to now say somebody was still in Africa or somebody went out of Africa, again looking at this kind of migration. But the important thing is that this migration is ongoing. It did not stop because people left Africa. So there's still migration going on between in Africa and outside of Africa also. So as a result of this, a lot of the variation that we see is indeed shared. But there are some that are not shared because people needed to survive new environment. So there are again some rare variants or variants that are not commonly shared. So in the context of Africa, again this is really problematic because just because of the evolutionary history of the continent, it is indeed the oldest part of... Human beings have lived the longest here and because of that you do see all sorts of mixing. By using language and genetic variation, Siratishkoff and her group were able to come up with what she called about six clusters. But those are indeed very, very difficult things to understand because these are constant migration processes. So you do have one of the real, I think important information that came out of this study is that you do have a lot of ad-mixing going on, but they are ancient ad-mixing that is going on, not in terms of what you talk about when you refer it to African-Americans, for example, or Mexican-American. So it was the implication of that diversity. Recently, I'm sure you saw this publication in Nature where the sequencing of Africans or Southern African was published. One of the things that came out very clearly in this study is that just like what we've been doing in the past by taking one African population and comparing to European Asians and we're able to draw these clean clusters, you can also do the same thing within Africa. Again, it just depends on the level of resolution you're talking about and number of markers that you have. So the question that I'll ask for you, what does it mean, therefore, to be able to distinguish or form these clusters? I think it's a question that we need to think about. So this is huge. And I think genetics is needed for a long time that whenever you sequence an African genome, you always find new things. On average, these two Bushmen in our study, this is a quote from these investigators, say they are more different than when you compare them to a European or an Asian. And they are even more different compared to West Africans. So what do we mean when we say African? Yeah, when we are using that phrase. Okay. The danger of group label, and I'll quickly go through this. So the question I have is, how do we interpret differences in drug response by group? When group definition is imprecise, fluid and time dependent. And can we tell you how an individual will respond based on group data? I think there's a confusion. Group identity is confused with group ancestry. For example, when you say African-American, African-American can have all sorts of combination of ancestry. So when you treat African-Americans as one group, what are you really doing at a genetic level? Or even at a cultural level. So who is black? I'm sure you know these two guys here who are excellent golfers. Completely, they look in terms of skin complexion. You can say they look very, very similar, but very different ancestry background, very, very different. These are Aborigines. This is from Ethiopia, Maasai. And you have all sorts of combinations of yes. So who is black? One of the interesting translation, I think that have happened in genetics, at least for common condition, is this HLA variant that is highly, highly predicted or predicted of somebody getting hyper-reactivity to this particular avocado drug. And before the FDA made its decision about who to screen, there was some discussion about maybe we should screen only Europeans. Because it made sense because the prevalence was about 8%, and you needed to screen about 14% to get the advantage. But that was because of the wrong interpretation of that we were putting in terms of the imaginative variation for this particular variant. So what we did recently was we wanted to look at how frequent is this variant when we look at different half-mar populations. So we look at about 11 half-mar populations. What you do see is that the highest frequency was reported again among the Indians that were in the half-mar project, okay? And in Africa, there was huge variability. Again, here I'm referring to the Maasai and the Yoruba specifically. And among the Maasai, it was about 14%. We couldn't find it among the Yorubas. So in Europe, you find that it's about anywhere from about 3% to about 8%. So the point I want to make with this slide is that the label African or black render radically different allele frequency invisible, as you can see here. So what wants me to be again very, very careful. The wrong public health decisions could have been reached, but FDA got you right because FDA said everybody should be screened. Yeah, so the wrong public health could have been reached if we have gone with our poor understanding. This is another slide I wanted to use to demonstrate for African Americans why we need to be very careful when we use the word African American as if it's a monolithic group, okay? So this is a study that was published in 2008 in Nature Medicine, looking at beta blocker and the genetic basis of response to beta blocker. So one of the things that came out from this very well done study was the fact that it looks like about 40% of African Americans have what you may classify as natural beta blocker. So they really don't need a beta blocker, they won't benefit from it if you give it to them. And that's precisely what you will see here, okay? So for those that don't have that particular variant, if you give them a beta blocker, they do quite well, okay? They do better. But for those that have that particular variant, there's no difference whether you give them beta blocker or not. So what happens when you combine 40% and 60% you come to the conclusion that maybe beta blocker doesn't work very well in African American, but that's precisely wrong conclusion again. So and the reason for that is very clear. This was a study that was done again recently published in a PNAS that shows very clearly that among African American, the distribution of ancestry is very, very different. Some can have almost zero West African ancestry, while some might have almost 100% European ancestry and they are self-identified as African American. Hence you see those kind of variability when you look at it from a genetic level, okay? So the take home message here is that individuals cannot be treated as representative for all those who physically resemble them or have some of the same ancestry. That's really the take home message. And I will end with this slide to say that when we are defining race and we are using race and we think we know what it is, I think you need to really question yourself and ask the question that of every human being on this planet, if I touch them on their shoulder and they turn around and look at me, can I really put them in one ratio group? If the answer to that question is no, then the concept of race is indeed very difficult to deal with. So the point I want to make here is that using geneticists to define race, it's like slicing soup. It's this mix, no matter how you put it. And I thank you for it. Okay, we are opening this dual panel to questions. Reporters are getting tired. Post lunch. Post lunch. Here's a question. I'm Jocelyn Kaiser of Science Magazine. Terry, I just wanted to ask you, so where are you with this possible U.S. Sure, yeah, now those six easy steps that I mentioned are the things that we're trying to explore now. So if you take a look at the Common Fund, which is the NIH sort of central research plan for kind of new and innovative ideas, this is on it as an FY 11, so something we're starting in October of this year, an FY 11 exploratory project, basically. And so we'll be looking at particularly these issues of, can we recruit through systems with electronic medical records, such as HMOs or large scale integrated medical systems and that. Can we accept low recruitment yields? Will we be able to get a wide representation of ethnic and socioeconomic and rural urban areas and that? So all of those are sort of exploratory things right now. So then what would it take to actually launch the study? Would it take a separate appropriation or? It's unclear, you know, of course, it depends on how much it would cost. If we could do it for the cost of the Biobag, then probably not. I suspect that even if we were to adopt all of their approaches and all of them worked here, which is a big question, it still would probably cost us three to four times what it costs them to do it because of our funding structures are just differently organized. But even then, you know, that might be something that could be absorbed, it might not and so I don't think we really know until we have a good cost estimate for it. Terry, I mean, in hearing Charles's talk and thinking about what a large population-based study would look like in the lessons from Biobank, what are sort of the implications of what can be generalized from the experience in England in terms of their population diversity, what would have to be faced in the United States? Yeah, I like to think of population diversity in Britain as being, you know, like whales. I mean, that's really different than the rest of Britain, but at least in their perspective it is. Now they have a small minority population, but it's really small. It's like four or 5% of their population is sort of the colonies come home as it were. So South Asian in particular and other African in that areas. So they did not go into any really extraordinary effort to try to get those groups because the numbers were so small. I think we would definitely need to do that. Now how diverse do you get? How small a subgroup do you get to? I think that's something else that we'll need to be exploring in the next year or so and figure out exactly what subgroups we're talking about. And costs will drive a lot of this, of course. I probably will have to. I mean, you know, sometimes the perfect standard may be good as they say. Right, right. Please. Steve Sternberg, USA Today. Are there any lessons to be learned from decode in the Iceland experience? There are. I think, you know, one of the clear lessons that was learned from that is that there can be small subgroups that are violently opposed to an idea when the vast majority of a population is not opposed to it and can really kind of bring down a proposal. So in Iceland, as you may recall, there was a proposal for what's called an opt-out model where basically they would take everybody in the population link them all to their medical records unless people said they didn't want to be part of that. The vast majority of the Icelandic population, like most Scandinavian populations, are very positive about research and had no problem with that at all. A small, very vocal subgroup had tremendous problems with it. And basically, probably because, or perhaps because there wasn't enough groundwork late, that particular part of decode was never done. So they never did link to the medical records in the way that they had proposed. What they did was to do sort of a traditional, you know, bring people in, get their consent, and then link to their records. So that's an important lesson. And I think the Vanderbilt experience with their bio-view repository where they did extensive consultations within the community to address these kinds of concerns, they've lived with an opt-out model and it's done quite well. So I think the community consultation part, which the UK Biobank has done extremely well, is one lesson from them. Another is that an automated robotic biorepository is critical to success of these very large-scale studies. And so that's probably something we need to build as well. Yeah, hi, I'm C. Darcy from The Gray Sheet. I had a question for Mr. Routine. So I gather from your discussion about how mixed everybody is in the race, there's almost, it sounds like there's almost no point in picking out a certain population based on their phenotype and then saying, we're just gonna test this population for such and such a disease. Because really almost anybody could have it because everybody's so mixed. Is that your conclusion or what would you do? No, that's not my conclusion. My conclusion is that the way we use these terms are so fluid that they really don't have precise boundaries. But it's still important, especially in the context of gene environment interactions, to define the population that you're studying. For example, when I'm doing a study in Africa, I don't just say Africa. I say Europe, but in Ibado. Because what I'm trying to understand is how being a Euroband, with all your budgetary background, your cultural background, how that will inform my own design of my study and how I will study, I'll go about doing the various studies. So the idea is not to ignore grouping, but is to define group more precisely and not to use these global labels that we use in the way we do our study. That's really the problem. But then what do you do about someone who in some place in their history they immigrated to America and they don't even know themselves what a tribe in Africa or what place in Africa they might have come from originally? And you could ask them that prior to an exam or something like that. So precisely, again, if you look at African-American, for example, I do a study among African-American and I'll tell you the only definition I have that I can consistently use to define an African-American is the descendant of the middle passage. Any other definition is imprecise and it will not do. And that definition is not genetic. It's the- What does that mean exactly? That is the people who have ancestors who came through the slave trade to the America are the only way you can consistently define what an African-American is. So in that context, I am not an African-American because I came from Nigeria 20 years ago to the United States. I am not an African-American. Okay. Well, do we have one more? Take one more and then we'll move on. I'm Rita Rubin from USA Today. What, from your presentation, what does that mean for drugs? I keep thinking of Baidal and kind of the reaction when that came on the market that this is a drug for black patients and like, well, so who's black? Are people still pursuing that line of research for drugs or, as you said, looking more for genetic variations that could be targeted and not just if a person appears to be black or white? I think the answer is yes and no. People are still pursuing that kind of design strategy. People are still using these global labels to do the initial recruitment for studies. But later on, people do know that not all members of that particular group will carry that variant that you are interested in studying. For the example that I gave you with Baylor Blockers, for example. So the fact that you are looking at, quote, African-American doesn't mean that that particular gene or variant is a characteristic of that group. It is not because not all members of that group will have it. It may be more frequent in one group versus another. It doesn't mean that it defines that group. I think that's part of the problem that we encounter. So the answer to your question is yes, unfortunately some of this is still going on because we right now don't have a better way to do some of these things. So we physically use this proxies to move forward. But it's also important to use this group in the context of gene environment interaction. That's the point I really want to make. Because being an African-American within the US experience means something very specific. In terms of social experiences. And if you're trying to understand how that social experience puts somebody at risk for getting a certain disease, then it becomes a reasonable design strategy to use. Okay, well I want to thank Charles and Terry for their presentation. We will move on now to panel number five, medical application of genomic research. And our first speaker is Les Beesiker, another investigator from NHGRI's Intramural Program who will be discussing Genome Sequencing in Medical Care and Research. And specifically talking about the ClinSeq project. Good afternoon. Hopefully you guys aren't totally into post-lunch hypoglycemia yet. Close, I can tell. All right, I'll try and keep it brief and hopefully at least somewhat lively. See if we can get you to stay with us here. So I am what my English colleagues disparagingly refer to as a medic. This is what scientists say when they want to sort of keep the clinicians in their place. And my job is to figure out how to use these technologies to answer questions. And I have a very simple job. I have to answer just three questions that my patients ask. They ask, what's wrong with me? What caused it? And what can we do about it? Really simple questions. And like Sharon Terry said, you would think that for most of the things that people walk into a clinician's office and ask those questions for that there would actually be answers to those questions. And as Sharon discovered in her particular case, and I can tell you for thousands of other cases, it is in fact not the case. And so we have to address that. And so what we want to try and do is use these genomic technologies to try and answer those embarrassingly simple, yet difficult questions. And if you take a little bit of a step back from that, the bigger picture is, is that we want to begin to develop a paradigm for individualized or personalized medicine. And I think I was told Francis mentioned that concept this morning. Is that correct, Eric? Personalized medicine. The notion being that we want to be able to pinpoint risks and give individual risks and individual treatments to patients that are appropriate and tailored to them. And this is based on a couple of fairly simple ideas. All of us know that our family histories tell us that we have more or less susceptibilities to various diseases. Very well known and that's evidence that there is a genetic or inherited susceptibility to treats. We also know that for most diseases, if you diagnose them and treat them earlier as opposed to later, the outcome is better. In fact, the costs are less. And in fact, it may be the case that if you diagnose very early, that is before any symptoms arise, pre-symptomatic diagnosis, that may be even more powerful. We also know that there is huge inter-individual variation in responses to treatments. Charles wrote to me, talked about that a second ago. The response of an individual patient to a treatment, both its efficacy, how well it works to treat the disease, as well as the side effects that that drug generates varies a lot from person to person. And much of that variation is probably attributable to inherited differences in genes. So, we know that there's a strong theoretical basis for personalized and individualized medicine. How do we go about doing it? What we need to do is develop a research basis that allows us to make these predictions, because that's what this business really is. We're in the prediction business. This is a tough business to be in. All of you have been involved, I'm sure, with the medical system at some point or another, and what docs do is they evaluate you and they're making predictions and trying to figure out what's the right thing to do for you based on what they think is going to happen to you based on whatever parameters they have. So these considerations led us to develop a research program called ClinSeq. And the ClinSeq project is a project that is designed to pilot large-scale medical sequencing or medical genomics, if you will, and develop abilities to correlate genetic inter-individual variation with traits, diseases, susceptibilities, liabilities to disease. So our initial focus, we had to pick a target to focus the study on. Our initial focus was atherosclerosis or coronary artery disease. And we picked that trait because we already know that it has a complex genetic architecture. That is, it has significant environmental components, and it has significant genetic components, and there are common genetic variants that raise or lower your susceptibility to coronary vascular disease, as well as there are rare variants that massively increase or potentially decrease your susceptibility to this trait. And we thought that would be an excellent target to pilot large-scale sequencing to answer these kinds of questions. So how are we doing it? So this study started back in 2007, and we're recruiting 1,500 patients to the NIH Clinical Center up in Bethesda, Maryland for patients to come, and they undergo about a half-day clinical evaluation to assess their phenotype, collect blood for sequencing, and that DNA from that blood is sent up to the NIH Intramural Sequencing Center. You heard from Jim Mulligan, where the genes are sequenced. Now, initially, we started out because this study began in 2007. We were using that old-fashioned technology that Jim Mulligan mentioned to you. PCR amplification and 3730 capillary sequencing to sequence about 200, 250 genes that were known or suspected to be associated with coronary heart disease. Now, the technologies have increased as fast as we predicted, and so this study is changing as fast as we can, and we are rapidly expanding into sequencing entire exomes, or all exons of the genes of these patients, as well as in a few selected cases, which you'll hear about in a bit, the entire genome sequence of selected individuals from this cohort. And what our goal is, is to take this massive fire hose of data that we're generating and is flowing out of the sequencing center and take all of the variants that we can find and associate them with the traits, the phenotypes that the patients have, again, in order to try and derive from that associations of predictions of what variants go with what diseases. So there's two attributes of the study I wanna emphasize to you. The first is that when you do whole genome interrogation at this level, you are acquiring data on both rare and common variation. There's been a lot of information, a lot of things in the media as well about chip testing or gene chips. And remember that those technologies are high throughput, but they only assess genetic variants that you already know you should be looking for, most of them being relatively common variants. And most of those variants are associated with relatively modest changes in risk, higher or lower. It's very common to find a common gene variant that changes your risk from one being the baseline to maybe 1.07, that is 7% more likely to get a disease. And as you well know, most people in the population don't respond very well to these minor statistical kind of arguments like this. Patients wanna know, am I gonna get it? I don't care if it's 7% more in the population, I wanna know if it's me or not. And again, that's what this personalized medicine is about. The other interesting thing about the study that we're developing and trying to push forward is that we are returning the results from the sequencing study to the patient. And our assumption here is this, is that this is a scale of data you heard about terabytes and terabits of information and gigabases of sequence. These are scales of information and data that people just cannot integrate on a personal level. When I'm in a room with a patient, trying to explain the results of a single gene test, if I talk for 20 or 30 minutes to that patient, that patient is saturated with information before I'm done talking. That's one patient, one gene, one variant. So imagine me sitting in a room with maybe a computer or a laptop and going through three gigabases of data. It's not gonna work. So what do we have to do? We have to figure out what are the subsets of these massive data sets that the patients actually want back? Because the assumption should be that different people will want different things. What are they going to do with the data? How do they interpret it? And how will they use it to change and modify their healthcare to their advantage taking into account their histories, their predispositions, their preferences? And we think this is a key attribute of this study is we want to hear this from the patients. There's no data on this, essentially none. And it's a bad situation because whenever I talk about this issue, my colleagues are always raising their hands and saying I think we should do this and I think we should do that. Opinions are rampant when there's no data. What I want is data. I want to know what the patients want, what the patients think and how the patients will use it. And that will guide how we can go forward. The third thing I want to emphasize is that we're trying to change the medical paradigm a bit here. Most medical diagnosis is based upon a patient going to a doc with a symptom, a complaint, a concern. The doctor evaluating that patient, developing a set of possible explanations for those symptoms and ordering some tests that address those possibilities and trying to make a diagnosis. That's a way to approach medicine from a symptomatic sense. But with this sort of technology, you don't have to think about which genetic test to order because we're doing them all. When you sequence the entire genome of a patient, you're doing all genetic tests for all genes. And so you can actually flip the entire question around. You don't have to wait for the patient to come to you with a symptom. You can go into the patient's genome. You can sift that for variants that you know are associated with disease and ask the patient, do you have this trait in your family? Do you have manifestations of this disorder? Do you have symptoms? Or you can examine the patient and look for a specific finding because of a variant that you know is there. This is potentially a very, very powerful approach to medicine. Now the truth is, this is not a novel. We're already practicing this in medicine in a different way. You might call it newborn screening, all right? You screen hundreds of thousands of babies when they're born for traits that are going to affect a couple hundred of them. They're usually some terrible metabolic disease that usually within one to three weeks after birth will cause that patient to severely metabolically decompensate and often die. But we're not gonna wait for that to happen. We do newborn screening and we pick these babies out and we say, nope, this one needs this very, very specialized management. And we're going to do that before they get sick because we know we can keep them alive, keep them healthier if we do it before they crash and after. So what we're doing basically is using the genome to try and generalize that approach to medicine. An example of what we've done in the study is one of the genes in our 250-canid genes is a gene called LDLR, the Low Density Lipoprotein Receptor, a gene known to cause early onset heart disease. Causes a trait called familial hypercholesterolemia, an autosomal dominant rare form of very high cholesterol. We sequenced that gene and several, about eight of our patients popped up with rare variants in that gene, including one patient who was a lady who was just diagnosed having garden variety high cholesterol, did not have the diagnosis of familial hypercholesterolemia. We brought her in, she wanted her results from that study. We counseled her based on that and turns out when we probed and pushed harder, she had a huge family history of people with severe early onset high cholesterol and early onset heart attacks and now we're working through that family to identify the people who are at risk, test them and get them treated. And this is not a patient who came to us and said, would you please figure out why I have early onset hypercholesterolemia? She came into the study, sequester, looked at the sequence, had a hit and we worked from the sequence data back toward the patient. So this is what this whole project is about from my perspective, is this individualized, personalized genomic approach to medicine where you take patients, you apply this technology to answer these simple questions that they have. What's wrong with me? What caused it and what can I do about it? I'll stop there and we'll take some questions later. Can we just sit up here? Sit, sit. Larry, sit, sit. And we have with us to give a short presentation, a participant of the ClinSeq project. Rick DelSantro is here, who is a volunteer and a participant in this ClinSeq project and we thought it would be interesting for you to hear some of his thoughts about his participation in this. No slides. Good afternoon. I'm Rick DelSantro, I am a participant. I have no slides, I have no formal presentation just here to let you know my experience. I think that might be helpful for you. I got started in the study in an unusual way. I bought into the myth, I bought into the story that if you ate well and if you worked out all the time, kept yourself active and healthy, kept your cholesterol low, your triglycerides low, all would be well and it's unfortunately not true. I lost my mom at age 69 to her third open heart surgery. I had a 37 year old brother, totally asymptomatic, low cholesterol, low triglycerides in good physical shape, have emergency double bypass surgery. I had a sister, very similar, 47 years old, had two stents put in for blockages and so I forced myself upon a cardiologist because my general practitioner, God love him, said, God, you're a specimen, you're wonderful, you're totally fine. So I went and saw a cardiologist and he looked at me literally, he picked my file up and looked at everything and said, what are you doing here? And I said, no, you gotta hear my family history and then throw me out of your office. And so he did and he said, okay, well based upon that, why don't you go get a heart scan? And I did and what they found out was that I had off the charts calcification and so he said, well, you better come back and we'll do a couple of more studies, we'll put you through some stress tests. Now, I've run an Ironman before so the stress tests were very easy for me. That was not a big deal and they really proved nothing other than I think that I was physically fit because they couldn't find anything. So I said, well, am I off to hook? And he said, well, the only way you'll ever really know is to have angioplasts done or I mean a catheterization done. And I said, well, okay, then let's do that because I really wanna know what are my blockages and how bad are they? And so that day when I had it done, I was lying in Sibley Hospital and I was relaxing for four or six hours on my back, whichever it was, because I couldn't move. And somebody came over to me and said, hey, there's this really interesting study going on at NIH. It's on heredity and coronary artery disease and would you be interested in potentially participating in it? And I said, well, sure, maybe let me see what it's all about and if I am, I'll call. And so I made a couple of calls over there and left messages because they're all very busy folks and somebody got back to me and said, hey, we'd love to have you come in. And apparently that was a good thing. So in I came and I remember probably about a month after I'd been there, I'd gotten a call from, I think it was Dr. Clausen, Turner Clausen, who I don't think is doing the study anymore, but at the time, he seemed almost giddy about my results and what they had found that about my family history and asked if my entire family would participate in the study to which I said, look, it's wonderful tests. It's more than you'll ever know about yourself. You'll go a lot deeper than probably your doctors will. So why don't you all participate? And they, of course, I have seven brothers and sisters and scattered across the country. And I think one by one, they've all come in and they've been a part of this study. And one day going forward after that had all begun, I found out that they were doing a full body gene sequencing on me, which I was like, look, I was looking at them like they had antlers on their head. I'm no idea what they were talking about. But they said, no, no, no, we're gonna go through all of your genes and we're gonna study everything and we're gonna be able to tell you things about yourself that you don't know and potentially you don't even wanna know. I said, ah, you know, I think I wanna know. I mean, tell me, I'm not the kind of person that worries about anything before it happens. I wanna know what it is and then I'll worry. Otherwise, I'd be worried all the time. So they did and they ran some tests and they called me just before the holidays, it was ironic and they said, well, we found something in your gene sequencing, but we can't really tell you what it is. I'm like, huh, right before the holidays, huh? Said yes, you have to come in and meet with the doctor. It's like, okay, great. So I said, well, look, that's fine. I'll come in and meet with them. And they said that they had found something called HNPP, I think is what it's called. They're hereditary neuropathy, pressure palsy. Again, meant nothing to me, may mean something all of you, but meant absolutely nothing to me. And they said, do you ever experience numbness? And I said, yeah, do you ever experience numbness in your extremities? I was like, yeah, all the time. Did you ever know what caused it? And I said, no, I had no idea. I always thought it was maybe associated with my back, putting pressure on my nerve endings, but this is something that candidly had happened for me for since my 20s. And it's gotten increasingly worse. There's nothing you can do about it, by the way. It's just a hereditary thing. And so, but knowing was great because I now had an explanation for what I was doing or what was happening to me. And it at least allowed me to understand things I should and should not do to help alleviate or prevent it from going on for longer periods of times. Now some of these numbness spells go on for days, some for weeks and some for months. And they said, well, there is a pretty good chance that some of your siblings may have this as well. So if you want, you can call and share with them and see if any of them have had similar things, and which I did, and we've now come to find out that probably half of my family has that as well as a hereditary trait, which is interesting to know. And one of my brothers actually has it probably worse than me. And yes, as I stand here today, I can tell you I do have numbness in certain parts of my body. It's just something I live with and deal with. It's not debilitating in any way. It's just annoying is all it really is. So here's what I can tell you. The study for me has been fabulous. It's very eye-opening. It's really great to understand what's going on inside the hollowed halls of NIH, right? Because to me, NIH was the stop on the Metro. It was something you pass by as you're going through Bethesda, right? But once I got into that campus, I couldn't believe how much was actually going on there, how big the campus was, and how committed the entire staff is to what they're doing. For me again, and that's the only perspective I can give you is mine. It's really been rewarding to participate in the study and to hear things and find out things about myself, about my family. And now they're actually going generations and generations and I think that we've just found out who some of my ancestors from Italy are and so I think they're even drilling back that far in this history as they try to find traits specific of my concern really, I have to tell you, is coronary artery disease. But anything else I find is sort of icing on the cake. Two last thoughts and I'll shut up and sit down and let you guys get on with important stuff. But one is, is that last time I was in to see Dr. Besiker, he explained to me that they had found or were able to determine things for which I am a carrier. And we went through a list of, I think it was four items I was a carrier for and really happy to say that none of them were horrible things. They were things but they were, in my opinion, small and not that significant, which was great because I have two small children and would love to know what potentially they could be in store for. So I think from that standpoint it was very, very important. The other thing I will tell you is that sort of the one thing that has always resonated from what they said to me when I began participating in the study is, we're not sure if anything that we find out in the study will necessarily help you. And it may not help your children, but we're pretty darn sure it's gonna help your children's children. And for me that was all the motivation I needed to participate in the study. So from a layman's perspective, from a participant's perspective, is what they're doing good? Absolutely. Is it rewarding to participate in it? Yeah. Is it interesting to know what you have and what you're exposed to and explanations for things? Yeah, from my perspective it is. I'm not sure everybody else would feel the same way, but again, that's all I can tell you is me. So appreciate having a few minutes. I guess I'm gonna stick around and answer questions if anybody has any of me, but enjoy your afternoon. Thank you. Okay, one more speaker before we're gonna open up for questions. We're gonna have the third speaker. We're gonna shift gears from general exploration of genomes to focusing in on cancer in particular and Brad Ozenberger, a member of our NHGRI's Extramural Staff is gonna talk about a large project that is going on at NIH that he's heavily involved in called the Cancer Genome Atlas. Thank you, Eric. Thank you for being here. I'm gonna talk a little bit about the Cancer Genome Atlas, a little shift from the previous talks, a little bit back towards some basic research, but some that are gonna be impacting patients soon. The Cancer Genome Atlas or what we call TCGA is jointly run by the National Cancer Institute and the National Human Genome Research Institute. It's been going on now for a bit and I'll give you a brief update that start with some of these grim statistics for cancer. In the United States, 10 million people have cancer. 1.4 million will be diagnosed this year and 700,000 will die from cancer this year. And this hasn't changed a lot in 20 years. Like Francis Collins said this morning, 20 years ago, one of the calls for the Human Genome Project was so we could begin to understand cancer. Cancer is a disease of the genome. Each one of these 700,000 deaths is due to a somatic change, so a change in the genome in that tumor. We was only in the death of the patient. And by somatic mutation, these are changes different from what you were born with. So changes in the genome that are occurring in the tumor that you were born with. So this is in a way obvious genomic problem, DNA sequencing problem in that we have the normal DNA. We have the person's germline DNA and can take the tumor DNA and compare those and identify every change that has occurred. And that's the goal of the Cancer Genome Atlas Project is to do such a thing. And this is just really just now possible. This is another version of some of the grass you saw this morning. This is the output in billions of bases of just the NHGRI supported large scale sequencing centers. Through the end of the Human Genome Project, there were just incremental changes, nice steady climb, it was fantastic. But then in going from 07 to 08, notice the scale changes by 1,000. This was when we began to implement the new sequencing instruments. And you can see, so we project, this slide's actually a couple of months old, this is probably gonna be quite a bit higher, but we project, we're going to produce greater than a thousand fold more sequence than we did just two to three years ago. So this has made it possible now to do the large numbers of cancer specimens that the comprehensive genomic analysis is now feasible and this is what we're undertaking. So with NCI, we've set up TCGA to take cancer specimens, very tightly quality controlled, each specimen and a tissue sample to provide the germline DNA is processed centrally in a core facility that then sends the DNA and RNA to a variety of genome characterization centers and sequencing centers, where we do everything we can do on them. So they get gene expression analysis, SNP arrays and comprehensive genomic sequencing. All on the same samples. Okay, so we have all the types of genomic data, all on the single samples. Of course, all this is combined with as rich of clinical data that we can obtain. All this is now going out and this is a community resource project. All the data are being released as we get them. They are available through NCI's data resources and NCBI NIH databases. And all these data are integrated and we begin to develop this comprehensive knowledge of these particular cancers. This is kind of a glimpse of the future. This is a single cancer patient, an ovarian cancer patient, what we call a genome card. And this is what we're building towards. We're still, we have to have these databases, these rich data to begin to understand the cancer, what's going on in the cancer. So part of it's that, but what we see in the future is that this is going to lead to individualized treatment for each tumor, each cancer patient. This is a single ovarian patient, the genome. The cancer was sequenced 31 times over. This is deep coverage of the cancer genome, every base. The normal DNA was sequenced 30 times. We can see there were only 17, 1,786 changes, single base changes in that genome. There are a lot of other changes. These are copy number variations where pieces have been duplicated or deleted. This is the circus diagram of this cancer genome, showing all the chromosomes in a circle and recombination that's going on. Ovarian cancer is typically quite scrambled by the time it's extracted, this is a late stage tumor. And we get highlights, the genes that are mutated, the ones that we suspect may be involved in this cancer, various tidbits of information about this cancer. But anyway, just to get a glimpse of where we're going to be going with, being able to take a cancer and begin to figure out what this particular tumor may respond to. One of our large scale sequencing center directors told me the other day that almost daily he gets requests from somebody who's just been diagnosed with cancer. Can you sequence my genome and help me figure out what therapies might be best? Just quickly where TCGA is now, we started with a pilot a few years ago to do glioblastoma and ovarian cancers. As we were coming towards the end of the pilot phase, the Recovery Act economic stimulus package hit and a fairly substantial bolus of money was allocated to TCGA to try to accelerate it and expand it. And we indicated that we would be able to add 20 tumors to the project over this next two years or so. This is that list. These tumors, we already have data sets becoming available at NIH for each of these, some major killers such as colon. The most prominent killer in the United States, lung cancer, is here. And then NCI is accruing all these other tumor types and these are being queued up to follow. And this is on a very fast track. And we will begin to generate data on all of these in the next year. We're not in this alone with the dissemination of DNA sequencing in this sort of technology. Of course, many countries have their own interests and there's a lot of benefit in sharing our knowledge and working together across the globe. So TCGA participates in the International Cancer Genome Consortium involving some 11 countries and I think over 20 projects now. There was a big marker paper describing the International Cancer Genome Consortium in the April 15th issue of nature. You can see things, people getting behind this and this is a big commitment for a country to say they're going to do this cancer. For example, China doing gastric cancer means they're making a big commitment to do hundreds of samples across the whole extent of the genome. So just to wrap up, TCGA is well underway. We are really excited about the impact this is going to make on cancer research and we're already seeing results reaching all the way to the clinic in glioblastoma. We intend to do enough samples to go way down in power, build our database to detect genes that are mutated even as less than 5% of patients that get that. So really discover and catalog the entire breadth of tumors that may occur, ever mutations that may occur in a given tumor. Of course, we'll reveal all the biomarkers, the new therapeutic targets that we can. And as I mentioned, leading as we build this database, begin to understand the underlying biology, we'll begin to see the individualized approaches to therapy that will be enabled. Importantly, you've heard a lot of technology talks, descriptions from the NHGRI folks today and TCGA is breaking a lot of ground. Just this scale of genomic analysis is requiring huge new pushes in technology and computational approaches in Vivian Bonazzi's talk about managing the data. TCGA is used to kind of break that ground in many cases across NIH. And finally, the cancer patients will soon and forever benefit from these data as we generate them, thank you. Three sim is now open for questions. We're here. Hi, it's Renata Miles with the NCI Cancer Bulletin and I actually had a question for Rick. From the data they found with sequencing in genome, whether consequences to the treatment, perhaps for the calcification? Well, to my best knowledge right now, there hasn't been anything. I think Dr. Besiker can probably address that better. In my particular case, I mean, I'm obviously started on some statin to help with the cholesterol buildup, but from what I can tell, I still have to eat well and I still have to work out, but I've had some choices I've had to make unfortunately, but nothing yet that has been in my mind a breakthrough, but I'm very optimistic. That's correct. So we haven't yet identified the gene variant that causes the high susceptibility to coronary artery disease and the absence of the usual things as Rick beautifully outlined, there's all those risk factors that he doesn't have and they have the high incidence of heart disease in spite of that. And that's the goal of this, trying to find the variant that causes that because we would sure like to be able to do that because Rick and his family are being missed. Everything the medical system is designed to look for that they don't have. We wanna know who the families are that have that trait so we can evaluate and treat them aggressively, which we can't do now. Over here. Hi, I'm Rita Rubin from USA Today. I was kind of part of my question, but you know, Dr. Bysigar, the woman you were talking about who has a family, who has family hypocholesterolemia. I mean, it almost seems to me that you're also implying that doctors are not doing a great job of family histories and because this is something, as you said, you started to probe and I've written a lot back 25 years about familial hypocholesterolemia and I know that you can see that in the families. Just one other good question is, and I wondered, I'll ask you, Mr. Dosantor, it sounds like all your siblings were agreeable about participating, but what have you got? I mean, he has a big family. What happens when not all the siblings are agreeable and because, like you learned about the HNPP? I mean, I don't know, I'm kind of on the fence about what I want to know and what I don't want to know, so. Well, I'll let you, me. Okay, yeah, it's a great question. I didn't think necessarily all my family would be agreeable and it turned out that they were, but it's a real concern. I mean, there are people, I'm sure, amongst us today that don't want to know, that may not want to know and just say, look, let me go through life as life is and not be alerted to things and because the other side of finding out is what if you have something really to worry about, then do you get all consumed by that? And so I just looked at it and said, I understand that, but I also understand that there's a greater need to go down this road because if, in my case, with coronary artery disease, if there is something that my children can do from birth that would counteract it short of eating well and staying active, then that's worth knowing and is that something that maybe would consume them or make them worried or is that something where they would allow themselves to focus on prevention? Just one other note on the question of family backgrounds. Now I have had a wonderful, he just retired, wonderful general practitioner who I thought was great, but I will tell you, I pleaded with him about my family history and he never proactively did anything for me. So I think just as, again, as a layman, just not a scientist, just as an individual like you folks are, although you probably understand a lot more of that than I do, there probably is not enough family history being done today in backgrounds on families, but I'll let you answer. That's a great question, when we got into this project, we put in the LDLR gene and the APOB gene because they were basically positive controls for the study because my view of that was the same as yours, this is an old story, we know what these diseases are, we know how to diagnose them, we know how to treat them, but let's sort of pepper the study with some positive control so we see if we can find things and hit on them, et cetera, but it turned out to be much more than that, which is we found the patients who had these variants and for most of the patients, the patients who were in our study were themselves correctly diagnosed as having hypercholesterolemia and were mostly well treated, but just as Rick says, the docs never asked them, does anyone else in your family have this? And we know that it's inherited as a dominance and 50% of the people in the family are at very high risk of this devastating trait that is very readily treatable and can extend life by a couple of decades and it was a shock to me and it made me reflect on our entire medical system is not geared toward thinking this way about disease, but using the power of whether it's family history or a whole genome sequence, using genetics to find the patients who you need to be working really, really hard on and leveraging that information to save lives and the system's really not set up to do that and it may turn out, we're a technology loving people and we'd like our machines and our giga this and mega that and it may turn out that this kind of information is the way we can actually do that. When I actually have a patient, I sit down with a room and I sit down with him and I say you have a variant in this gene, it causes this and this is how it's inherited then all these doors open and we can start to work on that problem with this medical genetics paradigm that we know how to do and that may be what it takes to open these doors and start to do this kind of work. Hi, I'm Annie Semino, I'm a health communication specialist with the National Institute of Nursing Research and I have a question about tissue samples. I'm not really clear on how they're selected or acquired, if they're from one individual or many and as we learn more about how cancer grows, will that impact how they're selected or used? Yeah, the initial list for TCJ were actually built by incidents because by more, they were higher impact research because they're more patients with that type of cancer but also meant that there were greater stores. So TCJ began with existing stores and clinical centers of the specimens and the advantage of that also is that we had the full clinical record. So we generally have how the patient responded to treatment and other things. Now NCI is also building a prospective accrual network for the future where samples will be collected, specifically patients will be consented for the project and then they'll be followed over time. The sample accrual is for a project of the scale is always difficult and requires both the stores, legacy collections as well as the prospective. Catherine Talmadge, when you map out a person and the cancer cells genetically, how far in advance of that person getting cancer can you predict they're going to get it? I'm thinking particularly something like pancreatic cancer where people are diagnosed usually when it's too late. Yeah, ovarian cancer is such a tragic example where it's generally not diagnosed until very late stages. And so actually what we're studying for the most part in TCGA are in fact those late stage tumors. But from that we believe we'll be able to kind of go back in the evolution a little bit and be able to discern some of the earlier events that are occurring. And there's the potential then to discover new biomarkers that maybe will allow a much earlier diagnosis. And that's true of all the tumor types. We hope to actually be able to kind of dissect out that evolution. Okay, well I'd like to thank this panel for their participation. And we are miraculously back on schedule. Essentially we are now scheduled for a 10 minute break. So let's take the break. You can talk to these panelists. There's also some drinks in the back. And we will reconvene at about 25 after.