 Hello everyone. I'm sure many of you are hungry now, but please bear with me for the next 10-15 minutes. My name is Kadir Akdemir. I am a postdoctoral fellow in Linda Chin's lab. And first of all, I would like to thank the organizers to give me the opportunity to present our work about understanding the evolution of the epigenome in melanoma progression. So melanoma cells can exhibit plastic behavior and in this study, authors have showed that eponominal immunotherapy, melanoma cells can go to reversible de-differentiation process, which results in the resistance to the therapy and the tumor growth. The fact that this cellular state changes reversible suggests an underlying epigenetic component of this therapy resistance. And recently, several groups have re-emphasized the role of epigenomic changes in the human medical disease, such as the case of loss of five hydroxy metal cytosine in melanoma, or H3K4 monometallation signatures in human colon cancer. And AACR Cancer Epigenome Task Force recommends establishment of international cancer epigenome project to map a defined number of cancer epigenomes that might help us to develop a better therapeutic strategies. So the overarching question in this project is how does epigenome contribute to melanoma progression? And we would like to understand what changes on chromatin when the normal melanocytes became primary melanoma and from primary melanoma to metastatic melanoma. So first we start with the cell line system, and the cell line system first initially developed by David Fisher's lab, where they immortalized the primary human melanocytes with tetorexpression as well as P3 double negative CDK4 mutation and BRF V600 mutation. However, these alterations were not enough to produce tumors in mouse. So we call them non-tumorgenic, and when we add P10 knockdown in addition to these alterations, as you can see that there's a drastic formation of the tumor in mouse, but we need to over-express C-Met to get the metastatic tumors in the mouse. So we call this SHP10 level as pro-tumorgenic and SHP10-SHC-Met over-expression as metastatic. So initially we focus on the changes happening from non-tumorgenic to pro-tumorgenic cell lines, and we are utilizing a method called high-throughput chip sequencing, which is initially developed by Edo Amit by then at the LV Vragemstab at the Broad Institute, which helps you to parallelize the chip protocol, and currently we can chip all encode-validated antibodies for a given cell type. So this is our initial results. When we compare the pro-tumorgenic epigenome to the non-tumorgenic epigenome, what we observe a global loss of histone acetylations, as you can see for different acetylation marks, and these changes are generally happening around the transcription start site of the genes that can be associated with apoptosis, DNA replication, or cell cycle. And more interestingly, when we check the deacetylated enhancer regions, the motifs that we found was were putative tumor suppressors such as FOXO3 or RUNX1, and what we think is going on is that pre-existing chromatin landscape is affecting the binding of these tumor suppressors, and thereby affecting the regulatory functions. So if the chromatin is highly acetylated, these transcription factors can bind and affect the gene regulation. However, the chromatin is lower acetylated than these transcription factors cannot bind effectively. And there is a supporting evidence coming from a recent publication where the authors show that initially enhancers marks are influential for the FOXO3 binding and downstream regulation. So next we switch our focus from the cell line data, and we would like to know what is happening from normal melanocyte to melanoma tumors. For this purpose, we acquired 10 melanoma tumor samples that is profiled by the TCGA project. And we had to optimize our HD-chip-sync method to use a small number of cells as the tissue source was finite, so currently we can chip an antibody for 1,000 to 10,000 cells, and so far we have profiled 36 histone marks, two forms of RNA polymerase, three histone variants, and genomic insulator CT-CF for eight tumor samples, and so generated six billion reads. And we think this is the biggest cancer histone modification data set, but we are still in the data production stage. As you can see for certain certain marks, we are missing enough coverage for the certain tumors. So the important question that we would like to know answer is that which developmental pathways are hijacked during melanomogenesis, and the idea came from a recent cell paper from John Stem Group at University of Washington, where they studied the DNA's hypersensitive sites at the human cells. DNA hypersensitivity, I'd say, is a way to measure the regulatory regions in a given cell type, and these sites are generally in a good concordance with the active histone marks. So the overall message from this paper is that compared to the normal counterparts, the cancer cells acquire a certain amount of regulatory regions, and many of these regions are either overlapping with the sites that were active at embryonic stem cells, or they are active in the different lineages. So the take-home message is new oncogenic sites that are actually older developmental pathways, and cancer cells do not reinvent the wheel every time. They use the already existing arsenal of the regulatory sites. So then we would like to compare the changes happening from normal men's to melanoma tumors, the one that in the normal human development from embryonic stem cells to precursor cells and the fetal or adult issue, and for this purpose we are using data coming from another NIH funded project, Roadmap Epigenomics, where they profiled 127 different human body cell types. And here I am showing a initial result for the H3K4 mono-metallation site evolution, and these results are overlapping with what is observed for the DNAs data. Once, when we looked at the sites that are obtained in melanoma compared to the melanocytes, 24% of these sites were active at the embryonic stem cells. And 70% were active in the different lineages. Only 6% are novel or unknown. Here I am showing an example site that is gaining some K4 mono-metallation for certain tumors, melanoma tumors. And when you check the DNA signal, you can see these sites were open in different cell types. And just to be sure, this signal is not coming from the bulk tissue with the different cell types. I profiled the DNAs hypersensitivity coming from melanoma or melanoma, melanocyte cell lines. And the sites gained H3K4 mono-metallation in melanoma tumors exhibit a very high DNA signal in melanoma, but not in melanocytes. Another evidence is that when we look at another region that gained K4 mono-metallation in melanoma, what we observe is that in addition to the modest increase for DNAs, what we also observe a binding site gaining of MITF, lineage-specific master regulator, in melanoma, nothing in melanocytes. So that suggests these gained sites are somewhat functional for the melanoma genesis. And lastly, what we would like to also do is to check, to annotate the non-coding variants, in cancer genome, by integrating our epigenomic data sets. Given that the third promoter mutations have been recently reported, I think we are just scratching the surface for the non-coding mutation functionality. And first, we focus on the GVAS locations. FTO, GINF, GVAS has been identified for over 12,000 melanoma patients compared to the 55,000 controls. And first, I am showing you our cell line data. As you can see, in our protomergenic cell lines, we are losing the active histone marks. And we also check these sites for our human tumor data. And you can see the similar trend. In normal melanocytes, we have a scrolled signal for H3K2-7a saturation. In melanoma cells, this signal is gone. As well as you can see, this DNA's hypersensitive signal is also going down in melanoma cells. And the cells are losing their MITF binding site. So as a summary, what we think is that understanding the epigenome in the human cancers, in this case, melanoma, could help us to understand where to target or what the mutations to prioritize. And the can improve the therapeutic strategies. With that, I would like to thank my mentor, Dr. Linda Chin, and take a moment to thank a close collaborator and a good friend, Kunal Rai, who is doing all the chips in this project, as well as Emily Q. With all the lab members, the computational group in our development and the rest of the collaborators. With that, I would like to thank all of you for your time and patience. I will answer any questions. So is this a TCGA funded project, or is this outside of the TCGA? So this is outside. Okay, and where are you going to make the sequencing results available? So we are still in the data production stage, and we are doing the QCs just to be sure that whatever we are producing is the real tumor samples. But once we have the good quality data, we are planning to do the data. Okay, thank you very much. So this concludes the morning session. Just housekeeping. It looks like we have lunch until two o'clock, and then the poster session starts, and then session five starts at three o'clock in this room.