So I might try to stall a little bit. Our second speaker has not quite gotten through traffic. Yeah. That said, I'm going to go ahead and start. I think everybody in this audience obviously knows why TCG exists and what our core objectives are. But I did want to review for some of the folks that might not know all of the history, that when TCG was launched in 2006, we did start as a pilot. And the pilot was to go after GBM, Lung Squamous, and Ovarian Cystadina Carcinoma. And in 2009, we expanded, I think now as many of you know, obviously, to 20 new tumor types. And I'm going to announce, again, kind of a somewhat even broader expansion to some new rare tumor types that we hope to finish by the end of next year. In order to stay on our current timeline, we would like to complete all acquisition to get to 11,000 qualified cases, which would account for the pilot phase of about 1,000 cases, which was our goal, and to the 10,000 case expansion. And as of today, we have about almost 8,000 qualifiable cases sitting at the BCR or having been shipped. So we're very close to that goal. And by the end of this year, we should hit that 8,000 out of 11,000 mark. The goals of TCGA remain the same today as they were in 2006. And that's to establish and demonstrate that team science can be an effective way to getting large data sets out to the community that the federal government and very, very talented scientists can develop a scalable pipeline that we've always had to make sure that the public stress begins with the highest quality samples. And about 20% of the audience today are the folks that are providing those clinical samples that make this project possible. And we have representatives also here, just a kind of hint, from SAIC Frederick, who are helping us continue the sample acquisition phase now, that if you have samples, we still want them. So that's always going to be my bottom line. We've determined, I think, that we can develop a large-scale parts list and make it publicly accessible. We're always on statistically robust sample sets, which is why TCGA attempts to go after 500 qualified cases of every diagnosis we look at, at least for our major tumor types. Again, the rare tumors are going to be an exception to that rule. And we always make the data publicly available. In fact, most of our investigators actually upload the data to the public domain and then download it before they actually do their own analyses. So TCGA has stayed true to a no-platform left-behind strategy, which I know frustrates some, but I think still provides the most comprehensive data set to the community. And therefore, I think, is really living up to what we tried to do initially. And so for every tumor type, every sample that comes in, we have an array of data that come out. And this is done right now through just over 150 tissue source sites all around the globe. We have tissue source sites in Pakistan, in Moscow, in Thailand, in Vietnam, in a new one in Colombia, South America, and even all the way to Hawaii. So they're all over the world. We do this with six cancer genomic characterization centers and the sequencing centers from NHGRI. And we've also added these seven genome data analysis centers that try to provide integrated analysis and portals for the community to utilize. And of course, we have the data coordinating centers with SRA International that provides all non-primary sequence data and CG Hub now at UCSC that provides the sequencing data to the community. While this is small, I wanted to make sure that at least people had it available online. This is the current TCGA research network. There are some changes. We've recently consolidated all samples to be sent to our Columbus, Ohio Biospecimen Core Resource. So Julie Gastair fosters here. If you have questions about how the BCR works, all samples are now flowing through a single unified pipeline at Nationwide Children's. We've added UCSC as a data portal for our sequence data away from NCBI. And I'll go over that briefly at the end. So here, if you have a favorite tumor, you're going to want to look along the bottom of our x-axis. And along the y, you'll see how many qualified cases have either been shipped or have qualified and are ready to be shipped from the BCR. If it has an asterisk on top, it means we've basically closed acquisition and are only open to specific acquisition from African-American patients or patients who would be racially defined as black on a census form, essentially. So anywhere in the world, we would take your samples as long as they qualify under that racial category. If your favorite tumor type is under a green bar, we are actively doing analysis and or preparing a manuscript right now. If you're in blue or even if you're in green and are on this end of the spectrum, this is where I'm bugging you probably know me well because I've bugged you on email or on the phone or in person at your house to try to get samples out of your freezers. I've yet to do that, but I'm getting very, very close. So don't leave your addresses with Josh outside. You'll see this is my favorite new little bar here is this gold bar for chromophobic kidney. This is our first rare tumor type that we ventured into with TCGA, and I'm gonna introduce this project to many in this audience today for the first time. So it became very clear if you looked at our sample acquisition graph with red being where samples were coming in and this is total unqualified cases. So this is total cases coming in our door. In red is the ideal curve, so to speak, to get to a number that would get to 11,000 cases by the end of this project, and the end of this project is late summer 2014. So in order for us to get to 11,000 qualified cases by the end of summer 2014, I have to have 11,000 qualifiable cases into Julie and her team at the Nationwide Children's BCR by December 15th, 2013. So we've just over a year to collect about 5,000 more samples in order to hit this metric. It's gonna be a very, very busy year, I hope for us. But the reality was that when I looked at our blue line, which is the sample, the reality of sample acquisition with it flattening out, assuming we never collect another case, it was clear that there was no way with the way sample acquisition was going that we were gonna hit, ever hit or meet this red line. And so in order to try to fill some of at least the case acquisition gap and realizing that many tumor types are still orphan in the international cancer genomics community and not being tackled, we started a rare cancer project. The first one we went after was chromophobe kidney that is now closed to accrual. But chromophobe kidney represents our first case where we have 50 qualified cases of a rare tumor. These other tumor types are currently being investigated with adrenal cortical carcinoma and uterine carcinose sarcoma acquisition hopefully ending by the end of this year, pheochromocytoma and paraganglioma hopefully being accrued in February and March of next year and the other tumor types hopefully being defined as we go on. That said, if it hasn't been done and you have potential of 100 cases that can come in, we will add your rare tumor as long as it's some malignant tumor type. So come find me if you have a good idea. And I will say Phymoma is one of these examples of the last TCGA meeting. We announced this and it was a smaller meeting that one of our ESC members came up and was very interested in Phymoma and that's now on the potential list. So if you have an interest in a potential, we might be able to slot you in. So between the pilot and the expansion and now I can speed up since our second speaker has arrived, I wanted to kind of go over what our core data sets are so that you know what to expect because things have absolutely changed between what we did in the pilot and what we're doing today. Every case now gets Affymetric Snip 6 Arrays, the Infinium Array which is now the 450K Array for methylation. Every case, we use RNA-Seq for both mRNA and mRNA. Every case gets a whole exome and 10 plus percent of cases get whole genomes. If there's enough residual tissue, we will do a reverse phase protein array with Gordon Mills and MD Anderson. Every case has minimum enrollment data, a pathology report and an H&E from both the frozen and the diagnostic slide. So we do have several groups now that are doing genotype phenotype correlations by trying to add some histopathology review on top of these images and pathology reports and those data are slowly becoming public also. So as of today, we have 7,136 cases that have been shipped and have initial biospecimen data at the DCC. Those other 900 cases-ish that we have in the pipeline are basically projected because we have them in hand but they have not yet qualified or been shipped yet. 5,800 plus of those cases have a minimum clinical data set. So this is basic clinical data diagnosis, data diagnosis, age of diagnosis, et cetera. 3,800, 3,900 have at least one year follow-up and about half of those have treatment information associated. We are going after everyone else every meeting I go to. We are going after at least minimum of one year follow-up, additional follow-up on all patients and we will go after additional years as funding allows. So far of those 7,100 cases, 105 samples have been shipped to our centers. So from one single case, we isolate RNA and DNA from tumor and normal and 105 samples have been shipped to our centers. 87% of those samples have a level one data set publicly available and we are currently hunting down with the help of Battelle and our quality management system. We are actively hunting down what has happened to those other 13,001 by one and the centers have just started getting emails from me asking for those data and some of them we will never find because there's bad data or a bad sample but we will at least try to account for where every single piece of data is but we do have an 87% return rate which is still fairly comprehensive. So I wanted to go over the timeline briefly in hopes that the community sitting in this room will help us shorten this timeline to getting the data out to the community. First and foremost, this starts with sample procurement without samples we can't have a project and from first touch to first touch meaning from first touch from the first time I've talked to a tissue source site through the first time I see a sample can be anywhere from three months to two years.  So you know how painful it can be to just get emails and emails and emails from me? It takes about 90 days at the BCR before we are able to qualify your case and ship it out to our centers. SNF data on average rolls around about 45 days after the cases have been shipped. Methylation a little later followed by MIR, RNA-seq, RNA-seq and exomes which average about 180 days depending on the center doing the sequencing. And then it takes us about 12 to 24 months from the time we have about half of our data set available to get a publication into the public domain. So from first touch to first public review you're looking at approximately five years. These are the people in the audience that hopefully you'll go talk to if you have questions about any part of our pipeline. I'm mostly responsible for tissue acquisition and the characterization centers. Roy Tarnuzzer, Martin Ferguson and John Demchick are responsible for help managing the BCR with Julie Gastair as well as clinical data, et cetera. Brad Ozenberger from the NHGRI for the sequencing centers. Li Ming runs our DCC and CG Hub from the program office with a team of folks and I did wanna recognize this very special human being right here, Julia who really did manage this meeting with the help of Josh Shapiro obviously outside. And Margie Scheff who most of you know from running all 20 plus of our analysis working groups. I'm not gonna go over this slide because we're running out of time but again it's also actually in your book our sample criteria. So I suggest if you're interested in what our sample criteria are, you flip to the program information pages and I have all of our really desperately seeking tumor types list there as well as our sample criteria. And ultimately of course our end goal is to make sure that all of these high quality samples end up in the public domain so that one single data set in this case the GBM data sets can lead to an immense amount of research. So this is every single paper that's actually used the primary GBM data set in a publication post release, post data release. Hopefully all of you know about the TCGA data portal and getting data, the DCC from SRA International. The team is here today and they will be giving a workshop this afternoon. If you wanna learn about their DCC 2.0, the revamp that should go live next year, we hope that you'll talk to them and give them some guidance on perhaps what you're looking for in terms of case studies for what our current DCC doesn't provide you but you might want to get out of a next version. We also have the team from Firehose here. This is the team from the Broad led by Gaddy Gatz and Linda Chin now at MD Anderson where we have ongoing pipeline analysis. So these are runs through their analytical engine that provides you. So if you clicked on our breast link, for example, you'd get some information on where we are with breast cancer and you could even get all the way down to just the plots, et cetera. So these are almost figure level with legends, et cetera. So you can get a snapshot of any tumor type you want for the active data. We also have, of course, representatives from David Halstow's team here and Anais Systems who manage the CG Hub, which is our sequencing repository. They think most people in the room have finally gotten onto but we have moved all of our data to the system and they will also be running a workshop at this meeting. So we hope that you guys will attend if you have any questions. I wanted to make sure also that everyone was aware that if you don't see your favorite tumor type on the TCGA list, there is a chance that the International Cancer Genomics Consortium is working on your favorite tumor type. So please go to their website. Brett Whitty actually is here from the ICGC Data Coordinating Center. So if you have questions, you can always hunt him down. I'm sure he'll be pleased that I just did that. I did not warn him, but he will be here to answer questions about their project as well. I wanted to, of course, acknowledge, very briefly before turning this over to Steven and Lou, the team in our office, and I apologize to Greg, but you didn't send me a picture, man. That's all I had. So here is the team from the office. Obviously thanks also to my two new supervisors and the direct co-directors for the Center for Cancer Genomics, Steven Chanick and Lou Stout, as well as the team who co-manages TCGA from NHGRI, Brad Ozenberger, Heidi Sophia, Lindsay Lund and Mark Geyer. And I think we'll just come up at the end if there are any questions for all of us at the end.


Very similar to this, the idea that literature should somehow be true to reality. The idea that textual elements, characters, dialogue, setting, images, should be believable, plausible, authentic, lifelike. Aspiring writers, therefore, are likely to hear the following advice. Characters must be convincing. Characters of dialogue must reflect the way people talk. Different characters should speak in different styles. Descriptions of places must ring true. Literary images must evoke familiar thoughts, sensations, emotions, memories, and so on. In short, writers have to get it right, so to speak. Here's an example. Cody and Katie are driving across Nebraska. Their names should sound real, appropriate, fitting. They have to ring true. The car must be described, make, model, color, condition. Is Cody the type of character who would drive a Japanese car or an American car? If Cody and Katie are in New York, readers should be able to feel the city. The skyscrapers, the yellow taxis, the smell of Chinese or Italian food, a man with a beard yelling in the middle of the street. When they stop in an old-fashioned diner, we need, once again, some very similitude. It's not enough to know that they had lunch. What did they order? What did the waitress look like?  Did Katie ask for extra butter with her pancakes? Did Cody flood his hamburger with extra ketchup? More importantly, did the ketchup remind him of the first dead animal that he had seen when he was six years old on his uncle's farm on a cold November morning? And don't forget to name that uncle and describe him. Here's an example from a famous American novel, Bright Lights, Big City, by Jay McInerney. The novel takes place in New York and every little detail is meticulously described to the point where readers feel that they are actually in New York. The main character remembers the apartment in which he used to live with his now estranged wife and we get to see everything, the imperfect ceiling, the windows that don't fit the frames, the smell of bread from the bakery downstairs, which reminds him of the croissants that he used to buy for her. We can also see random characters, a woman with a hive of rollers on her head walking a German shepherd on Seventh Avenue. All these details, all these characters are there for the sake of very similitude, the realist qualities of the text. As a counter-argument, other approaches emphasize the idea that literary elements do not represent things outside the text. Art by definition is artificial, including literary art. Poetry, for example, is beautiful, truthful or moving precisely because its formal features do not accommodate very similitude. Meter, rhyme scheme, stanzas, enjambment. Nobody speaks in broken lines of trocaic feet, right? And different characters in the plays of William Shakespeare, for example, do not display individual patterns of speech. They all speak the language of Shakespeare, which is exactly what makes it so beautiful. By the same token, the beauty of music stems from the fact that the sound of the piano or the violin or the saxophone or the electric guitar is completely unnatural. There is nothing in nature that sounds like these instruments. Similarly, when we go to the museum, we are not necessarily looking for paintings that reflect reality. When we go to the museum, we stand in front of the canvas and look at its textures, the shapes, the colors, the brushstrokes, the composition. If we apply this principle to literary fiction, the point is not to make it believable, so to speak, but to emphasize the formal features of the text, style, structure, tone, syntax. In other words, the textures of the written page. Paradoxically, this rejection of very similitude often allows us to see things clearly, experience intense emotions and come to some new, profound, meaningful understanding of reality. In other words, fiction becomes truthful precisely at the moment its artificiality is acknowledged.