 Thank you Richard and thank the local organisers for inviting me to talk at this prestigious event so I'm actually going to tell you about a Essentially, it's a cancer that's already published As far as the the market paper is concerned But I will be talking about two new data sets that we've added quite recently Those of the transcript are in sequencing and that's both the messenger RNA sequencing and also micro RNA sequencing Previously these things had been studied and at the micro array level So just a little background about the high-grade serious ovarian cancer cohort that the TCGA consortium has has collected Little background that most most deaths From it are the result of this advanced stage high-grade serious ovarian cancer about 70 percent of all ovarian cancer patients And the TCGA group had published last year this marker paper in which a cohort of 489 tumors were studied primarily at the expression level for Messenger RNA and micro RNA DNA copy number Evaluations as well as DNA methylation And and additionally and some you've heard already from the Broad Institute this 316 cases of of tumor and normal exome sequencing to complement the data set and Fundamental messages coming from that marker paper included that the disease is defined by and characterized by very simple Mutational spectrum in which TP 53 mutations predominant in almost 96 So almost all patients have these TP 53 mutations and and also Characteristically high frequency of somatic copy number alterations both focal gains and focal losses And then that was in stark contrast to to previous Gear Blastoma multi-formie study in which there was very low copy number The aims of this study that I'll tell you about in the next 10 minutes Are essentially the transcriptome sequencing and to use this transcriptome sequencing primarily the the RNA sequence To define subtypes Importantly structural variants that could not be established well with microarray based technologies And alternative spliced transcripts to name but a few So the data set is described in this slide We received 490 tumor total RNA samples from the biospecimen core resource repository These samples had been collected from 15 different tissue source sites across the world And we were able to generate RNA sequence libraries For sequencing of 420 of those all of which have been submitted to the cancer genome hub and the data coordinating center of these 420 300 What we do in high quality? Expression data sets that pass very stringent quality control metrics Those expression profiles have likewise been submitted to the DCC a Further 485 Samples have micro RNA sequences again submitted publicly available And then the preliminary analysis that we performed on these data sets are listed at the bottom of the slide here Include unsupervised consensus clustering to identify subtypes I'll talk to you a little about that that the micro RNA anti-correlations with the gene isoform expression Very briefly touch on that and then a little more on on our fusion identification Using two platforms our in-house transibis and then the University of Chicago's fusion finder Algorithm So to touch on Subtypes in this slide. I'm showing Figure two from the the ovarian marker paper published last year and in this study. There were four different subtypes Defined Corresponding here differentiated immunoreactive miz and chymal and prohibitive subtypes When we perform similar unsupervised cluster analysis using our sequence based expression profiles from 300 tumors we are we identify potentially two additional groupings and this NMF cluster is illustrated here with both co-franetic scores showing High high values here and the adverse average silhouette widths also supporting and that there may be additional Clusters to the to the four previously published of course if we then look for the Correspondence of the samples within this new six cluster solution to the existing four We see four discrete Clusters that the map Almost identically to to those prior published these are our clusters four one two and five But then additionally we see these two slightly smaller clusters cluster six and cluster three for which the samples don't map to To a single Predefined locus and so this adds some support to the fact that there may be additional subtypes Within this data that we're seeing through the through the sequencing work Those are the two additional ones that We can do perform the same analysis for micro RNA sequencing Again in the in the consortium publication three robust micro RNA clusters were identified We also see Reasonably robust evidence for for six clusters and here we're putting Some of the top driver micro RNA signatures On to each of these clusters many of them are familiar to those of you working on patent cancer and multiple different tumor types But in this case unlike the RNA sequencing data, we see very little correspondence between the the novel Cluster solution and those existing Previously and clearly we need to dig deeper into these analysis to identify Perhaps add p-values to these Bezier curves to identify whether there are enrichments between certain clusters With these expression signatures in hand we can turn to to ask questions such as the interplay between micro RNA and messenger RNA and here just to give an example is is a Relationship that was actually published by Chad Creighton and others last year Between this micro RNA 29a and the the Locust DNM T3a DNA methyl transferase a gene and what we're showing here are the the Expression based for each of the six sub clusters that we've identified for RNA sequencing The expression of DNM T3a in each of those clusters and we can see for example in the gray cluster increasing RNA expression Conversely if we look at this bottom plot This is the expression profile for the micro RNA 29a and we see decreasing expression Corresponding so anti correlated with the RNA But we only see this this trend in in cases for which the micro RNA binding site is present in our isoform and this is where the sequencing gives us additional resolution that that May not be captured in micro array experiments so in the example shown The three top isoforms of this gene all contain a micro RNA binding site This shorter isoform is absent and has no expression correlation with that of the micro RNA Turning now to the gene fusion detection within this this cohort We've applied our in-house Assembly and analysis pipeline trans abyss to all four hundred and twenty cases and identified about four four thousand three hundred Candidate fusions in the absence of total RNA remaining total RNA for verification, we've turned to orthogonal approaches and we Have been working with Kevin Whites group of the University of Chicago Their group have been running UC fusion finder on this same cohort And looking only at the intersection. We identify approximately 1500 such gene fusions called by both platforms of these 1564 are recurrent that is present in in two or more cases and The distribution of these is is very interesting so and really In stark contrast to other studies such as the acute myeloid leukemia study in ovarian We see a high degree of duplication and and this is consistent with the findings in the marker paper of Copy number focal copy number gains and losses and so Many around this circus plot those arcs that are linked in the same chromosome block are essentially the result of Duplication there are very few cases of translocation for ovarian, but you can see the density of the Of the recurrent gene fusions That's all that's being plotted here in contrast with AML the very much many more in frame fusions Indicated by the the green color and the thickness of these bars is corresponding to the level of recurrence So many more highly prevalent Fusion events and the result largely of translocations within AML and so very stark differences If we tease apart the ovarian fusion events into both in frame and outer frame We identify the most recurrent In frame events in this chart and the colors here indicate Events that are seen in the middleman database of chromosome aberrations in cancer. So those in purple Are known fusion events where both gene partners Have been previously reported those highlighted in green Where a single gene member of that gene fusion partner have been previously reported and then the remainder in gray are Entirely novel gene fusion constructs identified through our analysis to draw your attention to That there's a single case of TFG GPR 128, which is a known polymorphism within the database of genomic variants and So the most highly prevalent gene fusion event we have Is in frame is this mecom or MDS one and EVI one complex locus And this was observed to be focally amplified in in over 20 percent of the ovarian tumors in in the TCG Earlier report and of interest mecom is a target of a couple of FDA approved therapeutic compounds listed here And like I said, we've identified these In-frame fusions in approximately three percent of this cohort Primarily the diffusion events fuse the exon one of mecom to an entire transcript Of a novel partner and as cartooned in this slide Mecom and the partner genes as a result of duplication events are present on chromosome 3 band q2 6.2 And we have a fusion between this exon one of mecom and in this particular case in which six Patient samples contain the fusion we have the entire transcript locus for this leucine rich repeat containing protein of interest the the five prime end of Mecom contains a 12 amino acid signature sequence Which has previously been shown to recruit map kinases smat 3 and and so 39 h1 and so transcriptional core oppressors and the like so we've now taken the gene fusion partners for for all 1500 events and identified Pathways which may be Linked to these to these genes so of the 2500 unique genes We see an enrichment within the cosmic database 105 of these genes Are seen in in the cancer sensors as causally implicated with cancer Some of the pathways Listed on this slide are familiar to many of you and if we then remove these 105 genes from the total set The the one remaining pathway is the ubiquitin mediated proteolysis and so certainly this Wants further investigation So to summarize we've generated mRNA seek and microRNA seek for 420 and 485 of these of TCGA ovarian samples unsupervised clustering of the expression profiles identifies Potentially additional sample groupings and an exploration of putative microRNA and mRNA interactions Identify significant expression anti-correlations Including the example I provided that was previously published In contrast to other cancers AML being an example at duplication is the primary rearrangement leading to gene fusions and is consistent with the TCGA publication And me confusions are the most recurrent in frame events that we've identified within this tumor type So ongoing work includes the identification of recurrent partial tandem duplications and internal tandem duplications and My colleague Lucas Swanson is is here with poster number 106. I encourage you to visit Further pursuit of this me come especially in light of the therapeutic target is warranted and of course differential expression discriminator gene analysis and further integration with existing and novel TCGA data sets is Is in the pipeline so I thank you for your attention I thank my colleagues at the BC cancer agency genome science center, and I'll happily take any questions I'm for a quick question or two I guess so the correlative Observation of the large number of fusions with the overall level of genomic rearrangement in ovarian What point do you say there is there's a strong causative association? You know the the genome is rearranged because that disease wants to see more fusions So certainly it's it's key. I believe that the TP 53 is is mutated in almost all if not all of these cases And so genome rearrangement is clearly An integral part of this disease and quite different to many of the other tumor types we see Whether the whether the transcription fusions I mean I think it must be looked at a pathway analysis because the highest Recurrency we've seen is still relatively low at around three percent And so whether it's a combinatorial driving of the disease Needs further exploration Thank you. We better move on we got Thank you very much