 Thank you. Good morning. It's a pleasure to be here to discuss the findings from the Endometrial Disease Working Group. I always loved coming to Washington. Last time I was here I walked past the Government Accountability Office. Talk about an oxymoron. This time I was walking out of the subway and I walked out of the metro. I saw the VRE trains, so all the doctors in the audience will know that I took an immediate right turn and went the opposite direction. So endometrial cancer begins here on the lining of the uterus. It invades into the muscle of the uterus and then will spread to lymph nodes or to the omentum within the abdomen. There's two main types of endometrial cancer. There is low grade, glandular forming, endometrioid cancer, which has a low nuclear to cytoplasmic ratio, and preserved architecture. And there's a high grade serous type, which is more solid and as much more atypical nuclei. And these are essentially two different diseases. Now the complicating part is that it's very difficult for pathologists to differentiate these two types of tumors when they are high grade. And an article impressed here where three different pathologists from three different academic centers try to look at inter-observer variability. And in fact more than a third of the cases was a major disagreement when trying to classify high grade endometrial carcinoma. Type 1 cancers are endometrioid. They have a favorable outcome and often they're treated with radiation therapy when appropriate. The type 2 serous cancers, which are more aggressive and often metastatic, are treated with chemotherapy. So these two types have different treatment paradigms, which is somewhat schizophrenic among the professional community as far as who gets which type of treatment. But classification is the first important step to determining what treatment a patient receives. There's no mutations in endometrial cancer. P10 mutations, P353 mutations, and the ones you see on this slide are previously known and have varying frequencies between type 1 and type 2 cancers. And the outcomes are different, of course, like any other cancer early stage tumors do well. And for a disease that's spread beyond the uterus, the recurrence at about one year, 50% of patients will recur at about one year. But interesting to note is in the advanced recurrent cases, about 25% are serous or high grade. And in the early diagnostic cases, at the time of diagnosis, only about 15% of cases are serous or high grade. So when we started the TCGA project, we didn't want to collect all the tumors that were available because the ones that are commonly available are low grade, good prognostic tumors that do not result in death. And so that would not be the best cohort to study. And so we tried to skew the accrual towards the more aggressive cases that would lead to death and tried to accrue cases based on what we see in the recurrent disease setting, as shown here on this slide. And we did pretty good. We've collected 373 cases for the data freeze. The average age is about what you'd expect for endometrial cancer. We have 20% of patients that have recurred. And here you can see we have about 100 cases of each, including the grade 3 high grade endometrioid cases. And we have 50 of the serous cases. And we want to make sure we get to 50 cases. And so we reached our accrual goal here before doing the analysis. And as with most endometrial cancer, there's only a few deaths. Many patients will die of other causes. And so we're powering most of our outcomes on recurrence rather than death, which doesn't occur as frequently in endometrial cancer. Endometrial cancer being the fourth most common cancer among women leads to 8,000 deaths per year in the United States. To date, we have 400 and I'm sorry, 248 exome payers, just over 100 low-pass hole genomes. And you can see the other numbers here for the other platforms that have been used in this analysis, including almost 300 samples on the reverse phase protein arrays. So the first thing we'll look at is a copy number. And this is just organized based on histologic subtype. And you can see that as the histology becomes more aggressive, the copy number profiles become more complex. Now, if you do unsupervised clustering, as Andy Cherniak did, you can find four copy number groups here. And interestingly, the first copy number group is essentially diploid with no somatic copy number alterations. There's two predominantly endometrioid groups here in the middle that have few copy number alterations. And then this very complex group four here. And if you look at the second bar below the figure, the blue here are all the cirrus subtype cases. But mixed in here are a few other lighter colors. And in fact, one quarter of the high-grade endometrioid cases cluster with the cirrus tumors. And we refer to these as cirrus-like cases. If you look at progression-free survival, as you would expect, the cirrus group does much worse. But in fact, copy number three, which is essentially defined by this one-q broad amplification, and it is endometrioid in histology, also does significantly worse than the other two groups up here. If you look at the focal logistic peaks, again, you can see a copy number one right here. Copy number two has very few alterations. Copy number three, again, cluster three has this broad one-q amplicon. And then cluster four has extensive copy number alterations, like you would see in ovarian cirrus carcinoma, probably lung squamous, and in basal-like breast cancer. If you want to move to mutations, just the common mutations, just to sort of lay the landscape here, P10 mutations are most common in low-grade endometrioid tumors. P53 mutations are obviously common in the high-grade cirrus and in a portion of the high-grade endometrioids. And just for reference, the PIC3CA mutations are essentially equally distributed across these different histologic subtypes. So now we get more into the exome data, and this part of the figure, I'll take a few minutes to go through it. On the top here, you're seeing mutation rate, and there's a group of samples here that have a very high mutation rate, probably 100 times higher than most solid tumors on the order of 100 per megabase or more. This group is defined here by microsatellite instability and also has a high mutation rate, and these two groups over here have low mutation rates and are split up specifically based on copy number alterations, which you see in this row over here. This is again the cirrus cases are all over here, as I'll show you later, and the cirrus-like cases are here. But this is how we initially divided up the cohort, again based on mutation rate, microsatellite instability, and copy number alteration. Now moving on a little further, if we highlight this subgroup over here, these 17 cases which have very high mutation rates are all defined by universal mutations in polymerase E, which plays a role in transcription-coupled repair, and the very interesting thing we discovered in this study is that 75 percent or 13 of the 17 cases here actually have one of two hotspot mutations in pole E, and this results in a different mutation spectrum with a significantly greater frequency of transversions rather than transition mutations. And so this was quite exciting when this was discovered, and similar findings have been seen in colorectal. In particular, in the marker paper, they had a small group that had these ultramutator cases, and since that time they've also identified these hotspot mutations in colorectal. And this afternoon, David Wheeler will talk about this. If we take these four mutation spectrum groups and look at their outcome, we see two interesting things. As expected, again, the cirrus cases and the high-grade frequent copy number cases have a worse outcome. The two middle groups, which includes the microsatellite-stable and instable groups, have about the same progression-free survival, keeping in mind that the follow-up data in this particular study is somewhat limited compared to other studies in literature and clinical trials. It's controversial in endometrial cancer whether patients who have microsatellite instability do better or worse in colon cancer. It's almost uniformly agreed that those patients do better. And again, in endometrial cancer, we see here that they do the same. And in the subgroup of pole E mutations, although it's only 17 patients, it may be hard to draw conclusions, but so far, and some of these, despite the access right now being in days, some of these sensorines do get out to three to five years, there's not a single event in the pole E group. So it's possible this is a good prognostic subgroup and further data, further study will be required and this is under way by both David Wheeler and collaborators. So now if we add a few other rows to this figure, we can add in just P10 and P53 mutations. Again, you see all of the P53 mutations are here. Virtually all the P10 mutations lie in the other groups. And now we add on histology and grade. And again, these purple bars are cirrus cases. The dark gray here are the high grade cases. And so again, these are all the cirrus cases plus about 25% of the high grade and nemitrioid cases, which still are behaving like cirrus tumors and called cirrus like. Now, you always find interesting cases here. And this is really where the molecular data can certainly be value added on to what you otherwise would get without ever doing this. And here, before we get into that, we'll look at some of the significantly mutated genes is about 50 SMGs that are different, have differential frequencies between these groups. Some of the more attractive ones are highlighted here. Again, you can see PIC3CA mutations are fairly evenly distributed. Certainly, the ultramutator group has more mutations in every gene. ARID1A is not found in cirrus cases. KRES is not found in cirrus cases. Beta-catenin is interesting here because there's a very high frequency peak among the cases that have low mutation rate. And in fact, the hypermutator micro-satellite cases do not have a higher frequency, as you see in every other gene, suggesting that beta-catenin is really playing a role here in the nemitrioid low grade, low mutation rate cohort, and a few other genes are here. Now, getting to an interesting case here, this is zoomed in on one part of the figure. And you'll see at the bottom, this is a cirrus case, but in fact, it does not have a P53 mutation. There's no copy number alterations, and it has a very high mutation rate. This seemed odd, so we went into the patient portal, which you heard about yesterday, from the C-bio group at MSK. And if we pull up this patient's profile, you can see, in fact, there's a KRAS mutation, again, not what you'd expect in a cirrus case. And there's an ARID1A mutation, which is also less common in the cirrus cases. So then we went back to the histologic section, which now, gratefully, is fully available for all the TCGA cases. And we had one of our specialty pathologists look at this case, and he said definitively, this is an nemitrioid case, not a cirrus case, but in fact, it has some micropapillary architecture, which can be confusing to differentiate this from cirrus and nemitrioid. And he looked at the morphology, and based on some other studies he had done, suggested this case may actually have an MSH6 mutation. I did tell him that it had a high mutation rate, and CERIAC, we know CERIAC, Candle from WashU, just last night went back to look at the traces, and there may be an MSH6 insertion in a homopolymer tract, so it's not clear if that's a true mutation or not. But nonetheless, this is what would be a misclassified cirrus case that really only could be identified from doing these types of studies. We've done, the Vancouver group has done a microRNA sequencing and clustering, we found six microRNA subgroups. In addition, methylation was done both in this project on the 27 and 450k arrays. They developed some approaches to integrate that data. Again, like many other tumor types, there's four methylation subgroups. This group here is a hypermethylator group that has a simp-like phenotype that's seen in some of the other diseases, and again here are all the cirrus cases that have very low levels of DNA promoter methylation. The MD Anderson, Wei Zhang, and Yuzhan Lu have done expression clustering, identifying three gene expression clusters, giving them names of mitotic, hormonal, and immunoresponsive based on the components and members of these clusters. The hormonal group has increased expression both at the gene and protein level of the hormonal receptors, and looking at the progression for survival according to gene expression clusters. Again, the mitotic group which contains all the cirrus and cirrus-like cases, again does worse than the other two groups do about the same. Now, in this, and again you can see that confirmed by all the p53 mutations over here, and the p10 mutations over here, which really is the hallmark for differentiating these two subtypes. Now, I think for the first time in TCGA, we actually took the RPPA data and used it in a supervised manner to see if we could understand and validate the biology from the gene expression clusters here, and doing this on this heat map with 36 proteins. In fact, you can see that the mitotic cluster has increased expression of DNA and proliferative genes. Again, the hormonal cluster at the protein level we see the hormone receptors being active here, and in the immunoreactive cluster we see stat 3, as well as LKB1. Again, confirming what we think is a proper labeling of this cluster of immunoreactive. And now looking at the full RPPA data courtesy of Gordon Mills, that was a supervised RPPA analysis. This is the unsupervised RPPA clustering. Here there are five RPPA clusters. I'll give you one second to look over that slide. The first cluster in pink here has basically signaling on. The second cluster contains most of the serous cases. The third cluster is basically signaling pathways that are off. The fourth cluster has reactive proteins, as well as map kinase pathway, and the small fifth cluster, I believe, is a stromal signature here. The paradigm folks did the paradigm analysis, looking at the expression and copy number data, and I also identified five clusters, which you can see at the bottom, but two of them are quite small. The third cluster in the middle contains all the serous and serous-like cases, which has mick activation and interest. And here we see the p53 pathway is suppressed due to the p53 inactivating mutations. Cluster five has mick activity and hormonal activity, which is consistent with certainly this disease process. And here's interesting, in cluster one we see low mixed signaling, but very high wind signaling, which again one confirms the high frequency beta-catenin mutations, and I'll also show you some of the specific mutations on the next few slides. This is the, speaking of the next slides, this is the, from the C-bio group, I think names are on the next slide. This is the Ras-beta-catenin pathway, which is the most significant module that came up in their mutually exclusive mutation analysis. And what we have here, in these three different boxes throughout the whole figure, we have the hypermutator micro-satellite unstable cases. We have the micro- satellite stable and nemitrioid cases and the serous-like cases here. The serous-like cases have amplification of ERB-2, which may or may not be associated with sensitivity to herceptin, a trial previously done by the gynecological college group was negative, using mostly immunohistochemistry. The hypermutator samples have frequent KRAS mutations, and then again we see the frequent beta-catenin mutations here. Now this is a different mechanism of activation in which KRAS can stabilize beta- catenin as opposed to APC- associated degradation, which is seen in colon cancer. And the reason that this comes up is because the beta-catenin and the KRAS here are mutually exclusive that you see in multiple of the subgroups. Also identified were SOC-17 mutations with two hotspots shown on the bottom part of the figure. Looking back to one of our favorite pathways, PI3 kinase-AKT, this is certainly the disease where we see the most activity in the pathway. Again, the mutations are evenly distributed across the various subtypes. P10 mutations are more common, of course, in the end demitrioids. And here we see mutual exclusivity between PIC3R1 and PIC3CA. This is not a new finding. It was reported about two years ago by Gordon Mills and has also been reported by others. But what's interesting here is if you look at the PIC3CA mutations, we see the common exon 9 and exon 20 hotspots. But we also see frequent mutations here in exon 2, which is the P85 binding domain. And then if we look at P85 or PIC3R1, we see many mutations with one hotspot here in the SH2 domain, which is the domain that binds over here to the P85 domain of PIC3CA. And so, in fact, these mutations here are likely functionally related to interactions over here with these mutations. And for that reason, you see almost perfect exclusivity here between the two genes. And I would imagine these cases are just due to the hyper mutation status of these samples. And of course, a few of these samples in this group do have a high mutation rates, but do not have micro-satellite instability. Another interesting finding is that there's a very high frequency in P10 of the codon 130 hotspot mutations, more than a third of the endometrial cases. And in other diseases that have P10 mutations, this particular hotspot is mutated at a much lower rate. Looking at PIC3CA, again, we went over this just in the past couple of slides, but again, this is also different than you see in other diseases, particularly breast cancer, which has a lot of PIC3CA mutations and has targeted trials going on, but the spectrum is very different than we see here in endometrial cancer, which has clear implications. Superclusters were done, which you've seen before. Again, this is sort of summarizing all the various platforms. There were four different clusters identified. Again, the cluster that contained all the cirrus cases does worse, a common theme, of course, and the rest of the clusters show no difference in outcome based on this analysis. Finally, the question is whether uterine cirrus cases share similarities with ovarian cirrus and basal-like breast. In the breast cancer paper, they showed similarities with ovarian cirrus as far as cyclone amplifications, amic amplification, and BRCA mutations, as well as correlation of expression profiles between the ovarian cirrus and the basal-like breast subgroup. So we asked the question, well, does uterine cirrus also share the features because histologically and clinically they share many features. And so here is a multi-platform approach looking at copy number. Again, expression correlations between the uterine cirrus, ovarian cirrus, and basal group here. And again, now the methylation plots are here with the uterine cirrus, ovary cirrus, and basal-like breast looking very similar. And from the paradigm group, you can see the consensus clustering here with the uterine cirrus, basal-like breast, and all the ovarian cases based on the silhouette widths here. Unfortunately, not unfortunately, but when we look at the mutations, we do find the differences here. So they're very similar looking at multiple platforms. But when you do add in the various mutations, you see quite a few differences. Certainly ovarian cirrus and basal-like breast have a generally lower mutation rate, particularly PIC-3CA, Irby-2 amplifications, PIC-3R1-P10, and some of the other genes here are highly mutated in uterine cirrus cancer, which is again not a hypermutator subtype, but there are frequent mutations here, which you did not see an ovarian cancer, which generally has very few mutations overall, and you don't see them as commonly in basal-like breast. So there's many similarities. They share many genomic similarities, probably related to the shared high frequency of P53 mutations. And some of the GYN specialists and biologists ask the question, well, do all these cases come from the same place? Are these all tumors that begin in the fallopian tube and some of them fall down onto the endometrium and some of them fall out onto the ovary, giving you endometrial cancer and ovarian cancer? The mutation data would suggest that's not the case, but you easily could make the argument that they come from the same original site, but the microenvironment then induces them to become differentiated tumors. So to summarize, again, we've identified recurrent polymutations that are associated with the altered mutation spectrum and very high mutation rate, very active PI-3KT pathway, which certainly has ramifications for targeted inhibition. But again, one of the main points is that this genomic stratification can really complement or supplant histologic subtyping, particularly where there's poor inter-observer concordance. And this may have very important effects in whether a patient receives a completely different modality of treatment, radiotherapy versus chemotherapy after having a hysterectomy. And so in this era of precision medicine, these types of findings will help to design clinical trials and bring the targeted agents to the clinic in a rational manner. Just a few announcements before I finish. TCGA is doing a lot to combat cancer. I think we can do better with cigarette smoking. This is back to the government accountability office where they're helping you figure out where to smoke. And in the elevators of this building, you can have a $25 fine if you actually smoke in the elevators. I think the fine should be a little higher for smoking in the elevator. The endometrial group will meet tonight at five o'clock in salon two to discuss the manuscript and go over our punch list for the manuscript. What else do I have? There's many people to thank. I tried to include the key players on this slide. If I left you off, I'm very sorry. Elaine is the co-chair of the analysis working group with me and has been wonderful to work with. JJ Gao is our data wrangler. And Nikki Schultz has been coordinating all the figures for the manuscript. And so we're grateful to all those people. And I have 35 seconds. And so my last slide, oh, where did my last slide go? My last slide went away. Can I have my slides back? The last slide will show you that the AACR is having a special conference on ovarian cancer next May. And the reason I put this up is not to advertise the conference because people are studying ovarian cancer here. But they really came and said because of TCGA ovarian project, which was finished about a year or two ago, we now think it's time to have a special conference. And so now the work that's being done here is really, as we all know, being spread to the broader community. The subtitle should be from TCGA to clinic, but it's from concept to clinic. So if you're interested in this disease, come next September to sunny Miami. And thank you for your attention. Actually, I got a quick question. So I noticed the high degree of overlapping p10 loss and PI3 kinase mutation. Is that second? P10 loss or p10 mutation and PI3 kinase mutation co-occurred. Yes. Almost all of the p10 mutations have either picked three or picked three or one mutations in this cancer, but not others. And when you look at the protein, there is some correlations as to which dual mutation you have and what that functional effect is. I think basically in all these clinical trials, which you're involved with and I'm involved with, we have to basically look at the outcomes of the responses in light of these types of co-mutations and see what the significance is. So does the RPPA data show activation of markers downstream of PI3 kinase in all these samples? Gordon has previously published that there's more functional activity if you have a co-occurrence of loss of p10 protein with one of these, as opposed to just the mutation of the loss of protein. All right. Thank you, Doug.