 Thank you. Today I'll present the genomic characteristic of invasive lobular breast cancer on behalf of the TCGA breast cancer analysis working group. So we know that breast cancer is the most commonly diagnosed malignancy in women with about 230,000 cases a year. There are two prominent forms of the disease. Invasive ductal carcinoma, which is derived from the ducts, and accounts for between 50 and 80 percent of all cases. And then invasive lobular carcinoma, which arises from the lobule, and accounts for 10 to 15 percent of the cases, or somewhere between 25 and 30,000 cases a year. There's also a smaller subset of patients, maybe four to five percent, that have a mixed histology having both ductal and lobular characteristics. So for the goals of this project, in particular goals of the project, as I'll talk about today, is to try to identify, or begin to identify, genomic differences between invasive ductal and invasive lobular carcinoma. And then we look within the invasive lobular carcinoma patients to determine whether this is a single disease, or whether this is a group of diseases characterized by different genomic profiles. So we first started by developing our data freeze. We have 817 samples. Importantly, the pathology of each of these samples was essentially re-reviewed by a group of pathologists led by Andy Beck here at the TCGA. Of these 817 samples, 490 are ductal, 127 are lobular, 88 have a mixed histology, and there are 112 patients with other histologies. Within the, within these groups, if we look at the molecular subtypes, these are basal like or triple negative, hormone receptor negative. Her two enriched lumenol A or lumenol B, you can see clear differences in the profiles between ductal and lobular. And this, to some degree, reflects the biology of the disease. Lobular tumors tend to be hormone receptor negative, or estrogen receptor, progesterone receptor negative, and have a low mitotic index that tend to be slower-growing tumors. In contrast, ductal tumors, as was pointed out yesterday in the PAMCAN-12 analysis presentation by John Stewart, tend to be much more diverse, with basal like tumors almost exhibiting a complete novel phenotype. So you can imagine if you do a direct comparison between ductal and lobular, that underlying molecular subtype would clearly confound the analysis. And this is true if we do look at identifying genes that are differentially expressed between ductal and lobular during a two-class SAM analysis. Here it goes to the lobular group of genes that are dysregulated very much mimic what's dysregulated in the lumenol A ductal samples. And it's only when you do a direct comparison between lumenol A ductal and lobular that you're able to identify genes that are differentially expressed in each subgroup. But when compared to normal adjacent tissue, we see no upregulation of those genes. These include a number of genes, including e-CAD-heron, which is lost in lobular tumors. And this is a characteristic of this disease. It's either lost on the RNA level or mutated in almost all samples. If we look at the, by David analysis, look at the genes that are dysregulated within these groups, we see that the lobular tumors show upregulation of ATM network signaling, a large number of immune signaling pathways as well as MAP kinase signaling, whereas the ductal tumors see upregulation of mick target pathways and e-CAD-heron stabilization. So we next ask whether or not we could identify genomic specific point mutations within ductal and lobular tumors. One of the concerns we had with looking at DNA whole exome sequencing was that ductal tumors tend to have a much lower cellularity. And that led us to be concerned that we may be underpowering our ability to identify variants in these tumors. So in addition to whole exome sequencing, we used data from a program called UNCER where it was developed by Matt Wilkerson at UNC that combines RNA sequencing analysis with DNA sequencing analysis to identify additional variants specifically in low-purity samples. On top of that, we used a program called ABRA, developed by Lyle Mose, who incidentally has a poster this afternoon on this, which allows us to detect small and medium-sized variants, I'm sorry, detect variants in small and medium-sized indels. So combining these three approaches, we were able to develop an integrated MAF. Comparing the integrated MAF variant calls in a sample by sample comparison against the DNA MAF, you can see that there's an increase in variant calls pretty uniformly in the integrated MAF. During this on a gene-by-gene basis, we see a similar profile where genes that are mutated at a higher frequency, genes like PIC3CA and P53, see an increase in variant calls in the integrated MAF, and even genes that are mutated at a low frequency here in this inset, you can see also shown increase. In particular, we identified CDH1, or eCAT hearing, which is important in lobular tumors, where it's mutated in about 7 or 8 percent of the time identified as being mutated using the DNA MAF, but close to 15 percent of the time in the integrated MAF. So using in this integrated MAF in combination with just the calls, Giovanni was able to begin to identify in looking at all ductile versus all lobular tumors, we're able to begin to identify the frequency of mutations in lobular or ductile samples. So here we see in green, CDH1 mutations occur in about 65 percent of patients, whereas lobular patients, whereas mic amplification occurs in about 25 percent of ductile tumors. And one of the things that we identified when we were doing this direct comparison of all ductile against all lobular was genes like P53 and also Erby 2 were identified, and we know that P53 is mutated in almost 90 percent or maybe 100 percent of basal like tumors, whereas Erby 2 is specifically amplified, which is shown here in red, in HER2 amplified or HER2 positive tumors. So we reduced our analysis to look specifically at the liminal A ductile and liminal A lobular tumors. Here again, frequency of mutations in liminal A lobular tumors versus liminal A ductile tumors. You see CDH1 mutations occur more than 65 percent of the time. We still see P53 mutations in mic amplification, albeit at a lower percentage than what we saw when we looked at all samples. None of the interesting things that we determined was that GATA3 is mutated in about 20 percent of ductile tumors, whereas this immediate downstream target FoxA1 is mutated in about 10 percent of the lobular tumors. Adding a layer of complexity to that, when we look at the protein expression analysis, we see that lobular tumors have a loss of GATA3 protein. So what this suggests to us is that dysregulation of this pathway is very important in these tumors, but whether the tumors derived from the lobular or from the duct, there may be two different mechanisms deactivating the same pathway. Taking a step back and looking at this on a more global scale, we used paradigm analysis that was introduced yesterday by Chris Benz to look at patterns of associated signaling pathways. And my point here is not for you to read all of this, but more to point out that there are nearly 2,000 features that were dysregulated between the two subtypes of cancer. In particular, in ductile tumors, we see a loss of CDH1 signaling, which is consistent with mutations that occur in about 65 percent of patients, as well as loss of mRNA that occurs in these patients and protein of this gene, or protein of loss. In addition, we see loss of mixed signaling relative to ductile, which is consistent with amplification of MIC in the ductile samples. We see low XBP1 signaling, which is consistent with FoxA1 mutations, estrogen receptor, in addition to FoxA1 results in XBP1 activation. We see increased P53 damage response in the labular tumors, consistent with increased P53 mutations in ductile tumors. And we see increased immune related signaling, which is consistent not only the RNA expression that I showed a few slides back, but also some methylation data that I don't have the time to get into. So in addition, so as we were going through these different analyses, looking at ductile versus lobby, one of the things that we observed was that lobular tumors seem to have increased variability. It doesn't seem to be a single disease. So we asked whether or not we could begin to identify molecular subtypes initially based on RNA sequencing. So we used Consensus Cluster Plus to identify three initial groups. You can see these here in a sample-by-sample comparison. To turn this from a qualitative to a quantitative subgroup classification strategy, we took those samples that had a positive silhouette with, or those samples that were the best representatives of each group, and used a program called Clank, developed a 90 gene centroid-based classifier. So now we can quantitatively assign not only these group samples, but other samples to these groups. When we began to look at the characteristics of these groups, we did it first at a two-class SAM analysis to identify differentially expressed genes in each group. We found that with an FDR of zero, 988 genes were dysregulated. The majority of these, about 700, were dysupregulated in class one. These include EGFR and a number of other oncogenes, a number of keratins, chelocrins and cladins, as well as a number of ATM, or DNA damage response proteins, or genes, and also some reactive stromal type proteins or genes. In class two, we see 268 genes that are upregulated, including a large number of immune-related genes, including LCK and interferon gamma, which are master regulators of this process. Then interestingly, in group three, we see almost a decrease or a void of upregulated genes, suggesting that these genes, this group is characterized not by what's being over expressed by what's being lost, in that case, perhaps DNA damage response, or tumor suppressors. When we can then compare these 988 genes that are upregulated in the labular groups to either ductal or normal adjacent tissue, two things became very apparent. First is that class one has a very similar gene expression profile to the normal adjacent tissue. And then secondly, the ductal tumors seem to have a profile very similar to both class two and three. If we quantitatively assess this using our 90 genes centroid-based predictor, we actually see that's true. All 94 adjacent normal tissues or samples are classified as class one, whereas 60 percent of ductal samples are classified as class two and remaining 30 percent are classified as class two. Interestingly, the same profile was examined when we looked at micro RNA, where class one looked very similar to adjacent normal, and class two and three look more like the ductal tumors. Coincidentally, also we see that a reduction in micro RNA expression in class three samples. So when we looked at the genes that were dysregulated in class one, not only do we see that there are a number of oncogenes, but we also saw that there are a number of genes that are associated with the reactive stromal or reactive stromal subtypes that have been previously identified by Gordon Mills Group. So we did a direct comparison, and what we see is that in class one, almost 90 percent, over 90 percent of the samples in class one also can be classified as the RPPA reactive subtype, significantly showing significant enrichment of this relationship, whereas classes two and three, there's no obvious relationship. Consistent with this, when we looked at RPPA data, this is goofed up a little bit here, what we saw was that a NEXIN 1, Cavialin 1, Collagen 4, and MyH11 were all over expressed in class one, whereas RBM 15 was downregulated, consistent with the profiles of these, expected profiles of these genes in the reactive subclass. Beyond the relationship between class one and the reactive subtype, we also showed by paradigm analysis that a number of pathways are altered, particularly with the over expression of PDGF receptor signaling and loss of FoxA1 signaling, and in particular FoxM1 subnig work was the only network that was identified that had more than five downstream nodes being dysregulated. Consistent with that, when we look at RPPA data, and this is messed up, we see increased phosphorylated SARC and phosphorylated STAT3 and Group 1, consistent with PDGF receptor signaling, and downregulation of FoxM1 expression, consistent with downregulation of the FoxM1 subnetwork. So we then looked at class two, and by two class seminalysis, we identified a number of genes that were dysregulated that were related to immune related signaling. So we then ran a number of gene expression analysis signatures that could measure B cell, T cell, macrophage, etc. signaling, and we found that there's a significant up-regulation of these signatures in class two compared to all other two classes. Beyond that, we also see an increased proliferation is measured by a PAM50 gene expression signature measure proliferation in class two. Importantly, what we find is that both class, both of these features can be reproduced in the independent metabolic data set. So what this data suggests is that, whereas our direct comparison between ductal and lobular tumors show that lobular tumors were enriched for this immune related signaling, we've now been able to restrict this immune related signaling to specifically to class two tumors. Consistent with these analysis, a paradigm analysis by Christina Yao showed up regulation of interferon gamma signaling, consistent with immune related signaling, as well as increased FoxM1 expression or signaling related to high proliferation. So to collectively, what I've shown here is that we've developed a unique integrated math that utilizes both DNA exome sequencing and mRNA sequencing to identify unique variants in both ductal and lobular tumors. Comparing ductal versus lobular tumors, we see that FoxA1, CDH1 mutations, as well as GATA3 under expression is associated with lobular tumors and GATA3 mutations are expressed specifically in ductal patients. We see altered signaling of a number of pathways and in data that I have time to discuss, we've identified differentiated expressed microRNA and methylation patterns in these two sets of tumors. Likewise, looking specifically at the lobular class classes, beyond genomic alterations, we're going to show that there's a strong microenvironment condition that seems to be affecting tumor genesis, where the class 1 is associated with the reactive stromal subtype and class 2 has a strong immune component. So this is work that was done by the breast cancer analysis working group. I've tried to highlight what each person has done along the way and I apologize to those people whose work I wasn't able to include today and I'll be happy to take any questions if there's time. Tom Giordano, just to, you know, so I sign up breast cancers, lobular carcinoma is what I consider sort of a sneaky cancer, infiltrates, and so I would think that there's a lot of opportunity for, you know, having to correct for varying proportions of stroma, normal breast tissue, and so how did you guys deal with that in this project? We haven't really addressed that too much depth yet. When we look at the lobular class, as we see that there's basically similar amounts of tumor components. Class 3 is a little bit increased in tumor component. Okay, so you looked at, like, tumor purity and... By both absolute, using absolute, looked at tumor purity. If we look at the pathology calls, group 1 seems to have a slightly increased amount of normal adjacent tissue or normal tissue, maybe 5% higher. Okay. But in terms of, actually, dealing with it on a practical matter, we've tried to incorporate micro mRNA sequencing to improve, boost our calls and variants. In some of the direct comparisons, we've done some normal backgrounds, let's say the RNA sequencing analysis, we've done background subtraction to get rid of some of the normal adjacent genes that would be upregulated. And then the other point is, just a question is, there's a subtype called pleomorphic lobular carcinoma. Right. And do you guys have any of those and any hope to get some? Or... You know, I actually just got an email about that from Chuck this morning. I'm not sure, there may be a few in there, but not very many. Yeah, it'd be interesting to see. Okay. Great. Thanks, Mike. Next presentation is Brady Bernard on line up, identifying deleterious mutations using protein domain alignment. Ready?