Hey, we're back live in San Francisco, Dave. We're not in Orlando, Florida. We're in Las Vegas. Again, in Las Vegas, just unbelievable. This is theCUBE, we are here at EMC World 2012 and this is the siliconangle.com, siliconangle.tv flagship telecast of theCUBE, where we go out to the events and talk to the smartest people we can find, extract the signal from the noise and share that with you. And I'm John Furrier, the founder of siliconangle.com and I'm with my co-host. I'm Dave Vellante of wikibond.org, John. This is year three. You know, we met 2010. We started doing theCUBE, EMC World was our first. It was our third EMC World. Very excited to be here with you. You know, Dave, this is really marks a milestone for us because in EMC 2010, we did theCUBE, our first ever Mark Hopkins prototype did at South by Southwest in 2009. And, you know, we kind of knew it felt good but we really weren't sure what it was going to turn into. But two years later, full two years we're on our third season. We've interviewed over 600 people and have had over six million views of our content where we go out and go in depth at these events that are not covered by anybody else to this scale. So I'm real proud to have worked with you and the silicon angle team to go out and actually cover really compelling content. And as it turns out, this is the hottest area in tech with Facebook's IPO just behind us last week and the future of the tech world, tech culture, tech industry. It's all revolving around cloud and big data and that is what EMC is banking on. They took that bet over a year ago. Cloud means big data and we're here to cover it wall to wall. And we're going to hear more cloud, more big data. Silicon Angle will be tomorrow in San Francisco for the HBase conference, which is a database on top of Hadoop. So we got it covered. We got it on siliconangle.com and wikibon.org for all the research. And Dave, what are you expecting this week here at EMC World 2012? What is your angle and what do you expect to hear from our amazing lineup of guests this week? Well, John, you alluded to EMC's marketing of a year, a year and a half ago talking about cloud meets big data. They definitely were ahead of the game and it's very clear that they got it right. That messaging is now being used by virtually everybody in the marketplace. Now, here's the thing. The theme this year is IT plus business transformation, transform your IT, your business and yourself. When you think about transforming IT, John, to me that's all about cloud. Now, wikibon just launched a survey last week of its practitioner community and we found that really, John, we've said it here in theCUBE, 2012 really is the year of the cloud and we've confirmed that. I want to give you some data points. Last year, 15% of the people we interviewed in the wikibon community said cloud is meaningless buzzword. Today, that number's down to 15%. Last year, only 9% said hybrid cloud was their primary strategy. That number shot up to 37%. So that's clearly happening. Now, on the big data side, so that's the IT transformation. The big data transformation or the business transformation is all about big data. We had a couple of questions in there on big data. John, the number one challenge that people have with big data is monetizing and getting value out of information. So they talk about the three Vs, variety, velocity and volume. There's a fourth and that's all about value and that's really what the big data transformation is all about and then, of course, there's the personal transformation and if you're an IT professional, that's all about getting cloud smart, becoming a cloud architect and if you're a business person, it's all about being good with data, good with numbers and actually finding ways to monetize big data. You know, EMC, Dave, has done an amazing job. We've talked about this on theCUBE and we're going to have Jeremy Burton who's the CMO of EMC on Thursday morning, I mean, Wednesday morning and their marketing has been fantastic for a couple of reasons. One is they saw the trend of cloud, they saw the trend of big data as well and they actually marketed to that. When in reality, the market was so early on, they've established a good position in there but at the end of the day, what we're seeing today from EMC in this show is the kickoff of a new set of positioning around EMC. They're going to expand the cloud meets big data positioning and take that messaging and the product and the value that they want to deliver to their customers to a whole nother level. They're talking about transformation for IT which is essentially data centers and corporate enterprises. Service providers, which is, you know, cloud providers and also individuals and that is ultimately where it's at and there's different themes around this. So EMC is packaged their product and portfolio of solutions to address the cloud meets big data. So for EMC, when they talk about cloud, they're referring to IT and to some extent service providers with all their infrastructure and solutions. When they talk about big data, they're really talking about business transformation and we covered this on theCUBE at SAP Sapphire last week and we've been doing it for over a year. Big data with Hadoop and unstructured data warehouses evolving is really about transforming and instrumenting the business and we're seeing massive disruption, Dave, in this area. So I think EMC's got it right. I think they have good messaging that they're building on and I think what they're going to do is extend that messaging and really deliver the solutions. And I got to tell you, from our research and our coverage on siliconangle.com that clearly this is resonating with the C level executives where they understand that the transformation both on the infrastructure side and the business is happening all the way down to the developer communities within these environments where the developers are adopting open source. Open source is a variable in all this. And so you got the perfect storm for innovation and ultimately value creation, as you said, and that is you got the top down approach with the C level and the bottoms up with the developer. So between those two, you're going to see the consumerization of IT hit the main stage this year. And I think EMC, again, is smart to be ahead of the curve on this. You know, John Paul Moretz, the CEO of VMware has talked about going beyond cost savings, going beyond consolidation and getting deeper into business integration. That is the land grab that's going on right now. Cloud is the enabler of that. Big data is the realization of that value. And so we have all the guests, all executives, we got customers, we got bloggers, we got analysts coming on. John, I really want to understand from the EMC executives what transformation means for them, how they're supporting customers getting from point A to point B without massive disruption, without having to rip and replace. That's the big challenge that IT executives tell us. Here's the problem is if you look at the data, about 60% of the spending that goes on around servers and storage, about 60% according to Wikibon is on labor. That's enormous. Now, IT is supposed to be about automation. And here we have a situation where labor is choking innovation. So that's why people always talk about 70% of investments are made on running the business, only 30% on growing the business and innovation. In order for that to flip, that has to change. So I think we're going to hear a lot about infrastructure that's converged. I'm interested in hearing about that and how you support these new big data applications, John. You know, Dave, you mentioned a good point about the labor and value, right? So here's my take on all this right now. In the industry right now, when I talk to CIOs and people in the trenches, the number one issue right now is what is my future going to be like? My future in terms of my job and my environment that I'm living in in terms of I'm CIO or if I'm an application developer. So this notion of labor is interesting. So under the covers is some interesting trends going on around infrastructure and big data. And that is that the skill set to operate these new infrastructures is transforming. So at the same time, the skill sets for in these job markets is changing because of social media, social infrastructure and the cloud, the different applications are evolving. So the consumerization of IT, although it's been kicked around for a long time, is really hitting mainstream and that affects the labor pool. So data science is a great example in the big data space, the notion of a data scientist is evolving as a job description. Very similar to the DBA was when the databases hit the scene. You know, we've talked about this with Hadoop. And so I find that very, very interesting. At the same time, the infrastructure is changing around business value, agile IT as I call it. Agility is a programming concept, but agile IT is essentially combining developer paradigms of agile development with agile operations. And that's the DevOps message. So, you know, we covered DevOps and SiliconANGLE DevOps.com and DevOpsANGLE.com. So that's a really, really important thing. Now underneath all this innovation around skill sets and disruption around infrastructure and applications is open source. Open source is becoming a very important variable in this equation. And that's an interesting balance between these big incumbent companies like EMC and VMware and others on how they balance the innovations in open source. Because in the open source communities, we're seeing massive innovation around performance and capabilities. The question is, will the incumbents embrace the open source community around this scale and innovation? And will they roll it into their solutions or will they try to kill the open source movements? Now my feeling is we're seeing with EMC and VMware they're embracing the open source. Green Plum has shifted their strategy much more open. Hadoop is becoming a standard around big data applications and infrastructure. So very interesting dynamic around the labor. It's a personal dynamic, but also the infrastructure is changing in that open source. Yeah, the other thing I want to point out, John, is this is now a third year doing theCUBE and we have seen the transformation of EMC. Now, of course, I've witnessed it first hand for, you know, 20, 30 years ago. EMC used to be a one product company. When Joe Tucci took over, he made a major effort to expand the portfolio, made a bunch of acquisitions. Not all of them worked out, but one of them worked out great. And that was VMware. And the acquisition of VMware has totally transformed this company. And that was sort of yesterday's news. Now we're seeing a total transformation around, as I said before, getting deeper into the business side, deeper business integration. And that's about transforming infrastructure to make it cloud ready, cloud like, hybrid cloud associations, and then bringing the big data piece to it. And that's something that EMC led the charge. I was frankly skeptical that an infrastructure company could actually lead that charge. EMC is clearly leading the messaging. We were at SAP Sapphire last week. John, you heard a little bit about big data. And clearly SAP is at the forefront of transforming organizations and helping them get to mobile. You know, but their big data messaging was not nearly as forceful as what you're going to hear this week from EMC. So I think the key is that EMC has to prove that it's not just marketing, that it's got substance and it's got real proof points and it can deliver real value. And that value is going to come from a combination of products and very importantly, services. And one of the areas of services that they're paying a great deal of attention to is the training and education of both cloud art. We talked about this at the top of the show. Cloud architects and data scientists. You know, Dave, I think my view on this show at EMC World here in Vegas is where the infrastructure is really changing. And if you look at what's happened over the past three years, initially EMC was positioning themselves as a private cloud, journey to the private cloud. That was our first cube gig where we came out and did the cube. But now it's evolved into a couple of different segmentation. So I'm looking at this marketplace segmenting in the following categories. IT organizations, service providers, developers, what's happening under the covers, and then the application market. So on the IT organization, the theme is literally IT as a service, consumerization of IT, but really agile IT as I call it. And so the IT organizations, that's about the data center, that's about servicing the business units and ultimately their customers. The service provider really is about the cloud. That's about the hybrid clouds. The hybrid cloud is emerging as the kind of de facto offering in the service provider markets. And those are the normal issues. Multitenancy, very Amazon-like, AWS, Rackspace, and the Citrix Solutions out there are doing that kind of hybrid cloud. And even HP recently announced them there. And then developer community, it's interesting because to make the private cloud and the agile IT work and the service providers, there's a lot of under the covers innovation that needs to happen. Automation, configuration management, provisioning, and ultimately data. We're going to hear from Pat Gelsinger. I'm sure he's going to talk about the role of data, value creation around data, and big data in particular. But these are all under the covers innovations that are actually to do items in the developer community. And finally, applications. The application market is very clear. Rapid application deployment on these infrastructure to deliver value to the customer. So there it is.  All those are core pillars in this new emerging infrastructure. I want to talk a little bit about, I want to go back to your comments about cloud. I'll tell you, again, I'll give you some of the data from the survey that we just launched. Commercial enterprises, John, are not relying on the public cloud as their sole or primary cloud strategy. It's just not happening. I mean, it's increasing, but not nearly to the extent that the hybrid cloud is. So last year in the survey, we saw 6% of organizations were standardizing on public cloud as their primary strategy today. That's up to 9%. Okay, not bad. But it's the hybrid cloud that is really taking off. It used to be private cloud. As you said, it was journey to the private cloud. And now it's the hybrid cloud that is really picking up momentum. We're here at EMC World 2012. This is SiliconAngle.tv's exclusive coverage of EMC World 2012. We are an independent media outlet where we go out to the events and extract the signal from the noise. If you're interested in EMC's perspective, go to EMC TV. Go to EMC.com. They have a lot of videos. They've got all their executives. Really, really, that's the corporate content. They're doing an amazing job. I got to say EMC TV. Dave has done an amazing job. Since day one when we came here three years ago at theCUBE, there was no EMC TV. Now, two years later, Jeremy Burden and Jonathan Martin and their team under Maggie Burke's leadership has evolved EMC TV into a really, really compelling organization where they are absolutely taking the message directly to their customers, directly to their partners, and they're producing high quality content from EMC's perspective. And I got to say I'm really impressed with that. We're excited to collaborate with them in a very social kind of coexistence way, bringing our independent analysis. And we're going to continue to break news. We're going to continue to cover this market like a blanket. Again, next course of the day, we got 60 interviews lined up. Plus, if not more, packed wall-to-wall coverage. We're going to be going live every day, today, tomorrow, and Wednesday, and we're wrapping up Wednesday night. So keep watching SiliconANGLE.com, SouthernANGLE.tv. And for more information, go to those sites, and also for research, go to wikibon.org. You know, John, you mentioned EMC TV. What I like about what EMC's doing there, and you mentioned this with Jeremy Burden, is they're putting a big investment there. They're investing in the way in which people want to receive information, which is video. At the same time, everybody knows it's EMC TV. I mean, Jeremy even said at one of these shows, hey, if you got something good to say, come on, on EMC TV. That's what it is, that's what they do. But at the same time, I think Maggie Burke and company recognize that collaborating with people like us, independent news organizations, independent analyst organizations, is good for them, because that validates what they're doing. And so, communities collaborate. We have a community, they have a community. We're collaborating. John Furrier and I are going to go up on the main stage today in Hall-A, right before the Joe Tucci keynote, and we are going to be on stage, providing independent perspective with Maggie Burke of EMC TV. John, I'm very excited about that. Let's talk about, what are we going to talk about? Because obviously, EMC is exciting about this new Mega Launch 2. So, the big news that we'll be talking about on the big stage and here in theCUBE this week is the fact that EMC is coming out with what they call Mega Launch 2, Dave. And that is building on the Mega Launch 1, which we covered in January of last year in London and New York, where they had the largest ever announcements in terms of EMC's history under Jeremy Burton's leadership, of course. And today, it's the same thing. They got 42 product announcements this week at EMC World, 14,000 attendees here in Las Vegas. EMC has totally evolved from a storage company to a absolute infrastructure player. And now with the big data messaging there, actually delivering great solutions. And I got to say, I'm very impressed with the messaging, but also the positioning. When I talked to customers out there in pharma, healthcare, and other verticals, big data is on everyone's mind around business re-engineering, process improvement, around making their business smarter, faster, and accelerating those cycle times to deliver value to their customers. But no one really knows what to do. I talk to people and they're like, we know what big data is, we don't know what it means for our business. That plays to their strengths here at EMC because their organization around the service and support absolutely have great relationships. They can bring that consulting organization, guys like Bill Schmarzo, Cube alumni, among others at EMC, under Howard Elias's group, Tony Kolish, Tom Roloff. They actually have a huge army of consultants out there and sales folks who can actually deliver these solutions. So EMC is absolutely smart. Now on that side, they can handhold the customers, go out and deliver value, but Pat Gelsinger's leadership has been amazing in the sense that they've taken a step back and said, hey, let's create our product portfolio. Let's change the game in terms of how we deliver our value to our customers from a product perspective. And you've seen the VNX, which is a channel friendly product. They've got Vplexes now getting some major traction. We're going to talk about that. And they have the normal stuff at most. Data backup and recovery, absolutely changing because of the virtualization impact. So, you know, market leading 66% in backup and recovery. EMC is perfectly poised and they got some headroom. They got time. So they got some time to develop some products, acquire some companies. So I think EMC, both from the product perspective, has room to grow. They're in a good position. Their organization in the field is going to be working with customers. And in this market, how early it is with big data and cloud evolving very rapidly, I see that rising up very fast. Yes, so John, in just a couple of minutes here, we have BJ Jenkins coming on, who's the president of EMC's BRS division. He was an executive on the East Coast in marketing. He went out there right after the data domain acquisition, worked side by side with Frank Slutman, took over the company. They haven't missed the beat. I'm very interested to hear how he sees backup transforming, what EMC is doing. So let's take a quick break. Well, let's take a break, but we might have some breaking news. So I've been on the phone all morning this morning, since six o'clock in the morning, checking some sources. I might have some really big breaking news here about EMC, about VMware. Let's take a break and we'll either do the news in the next segment or later in the day. I'm still waiting to get some, quote, confirmations off the record around some really big news that really impacts EMC's leadership and CEO and or VMware. So stay tuned here at SiliconANGLE. We'll be right back after this break.


Hey everyone, Dylan Schumacher, Citadel Defense, and it's time for more tactical book review. This is Small Unit Tactics by Matthew Luke. That's actually a pseudonym, it's not the author's real name. Full disclosure, this book was sent to me in exchange for my review. I'm going to be as honest as I can, but just want you to know that the author did reach out to me and send this book to me, and that's how we're getting this video. So this book content, pretty straightforward, right? It's Small Unit Tactics. So it's tactics at the squad and platoon level. And the way the content in this book is organized is you're going to do an ambush, and so they walk you through all the things you do, you know, from leaving the fob and maneuvering out and dealing with casualties and having choke points for accountability and break contact and assault through and all this stuff out to the ambush, then they talk about the ambush, right? So that is how the content of the book is organized. It's unique in that regard. I haven't read any of the books that kind of tell a story as they do that. One of the other things I like about this book is a lot of it is in color, right? So you have a lot of color diagrams and things like that, which you just don't see that often. So that's pretty cool. They have a lot of pictures that they pull from, you know, various operations or whatever in here. So that's cool. I enjoyed that aspect of it. It's probably the only small tactics, manual or book that I've read that is in color. So, hey, gotta give it gotta give it credit for what it is. So this book does cover some things that I haven't read in other books. And for that, I'm thankful, such as using a point man and kind of running a five-ish man fire team versus the traditional four-man fire team. I talked about, like I mentioned earlier, choke points and having accountability for people coming in and out. It is very detailed on its movement.  And I appreciate that. I appreciate kind of a step-by-step detailed nature. In my opinion, it's like a better version of the Ranger Ham book or, you know, any kind of standard military manual. I would tell you to read this over those because it's going to be a little bit more thorough and it's going to be more clear in its explanation. So for that, I am thankful. A couple things that I kind of didn't like or was whatever about is they have footnotes in here and then it's kind of putting these pithy quotes here and there. That's fine. I mean, if you're into that, that doesn't take away from the book, but I just thought that was a little silly at points. The pictures, honestly, I could do without. Like if they cut those, I don't really think they add any value. They look cool, but I don't think they really add any value to the actual learning process in my opinion. Other than that, would I recommend this book? Yes. Would it be the first tactics book I would recommend? No. I would probably point you to Light Infantry Tactics by Christopher Larson. That's probably the first one, or Small Unit Tactics Manual by Max Velocity. Actually, that would probably be the first one I pointed to. But one of those two, I think, gives a good job of giving the whole topic kind of broader stroke. This book does a better job of starting to drill down into specifics and getting in the more minute details. So if you've read a couple books already, I think you would do well to read this. I think this would add this in. If you're just getting into Small Unit Tactics and you're wondering where do I start, I don't think this is a bad book to start with, but I think there's some other books that would serve you better that will allow you to get more value out of this book after you've had a book or two under your belt. All in all, if that tickles your fancy, go ahead and get it. I think it's valuable. I think it's worth your time, and I think you will learn something. One of the other things that they talk about a lot in here is the use of machine guns and saws. So when I say machine guns, I usually mean like the M240, right? Something shooting the 308. And then the saw is the squad automatic weapon, which usually shoots a 556. I don't have a machine gun, unfortunately. And so this is a military tactics manual. You've got to understand that. I have never been in the military. I'm not a military guy. So for me, it's not as applicable when we start talking about anti-tank weapons and machine guns and claymores because I don't have access to those things. And as an American who wants to embrace my militaristic heritage, those just aren't tools available to me. So in that sense, it's helpful to know and have an understanding and an overview of that. But because those aren't things that are currently in my arsenal and they're not things that I'd likely be able to have my arsenal, there's not a lot of value for me in my position to be gained there. And so that may or may not apply to you. Again, if you're in the military and you do have access to those toys, you're going to get more value out of this book in that sense. But if you're like me, and you're just an everyday guy, and you have your rifle and your pistol and you're set up and whatever, and you have some other guys that also have that set up, when it comes time to talk about placing machine guns and everything and anti-tank weapons and claymores, that it's not really going to apply to you. So that's not the most applicable to your life situation at this time. That being said, one of the things that was helpful from this book and did drive it home to me was that machine guns are highly effective at causing enemy casualties and or suppressing the enemy, right? And that's been that way since about World War I. So for the last hundred years, you know, since machine guns have shown up, they've really changed the way we've done small unit tactics. I think that's all I have to say about this book, Small Unit Tactics by Matthew Luke, would suggest to read it if you've read some other books already. Hope you enjoy it. Do brave deeds and endure.


Hi everyone, I'm Chai Banlamoudi. I'm a graduate student at Kevin White Lab at the University of Chicago. First I want to thank the organizers for giving me an opportunity to present my work. I'm going to talk about the work we've been doing to identify recurrent fusions from the TCGA cohort. So the first part of my talk is going to be on the approach we take to identify these fusions and then I'm going to talk about some of the results and end up with some of the functional validations we've done on this. So cosmic maintains a database of known fusion genes. If you look at it similar to the some of the power law distributions we saw earlier today. So cosmic maintains a database of known fusion genes. About 1% of them, less than 10% of them should have a frequency of greater than 1%. So which is kind of interesting. So there's an interesting few interesting things from this is that if you look at the tissue specificity some of these known recurrent fusion genes for example tempers 2, ERG is very prostate cancer specific and some of the fusions like the FGFR3 tag are you see across multiple different tissue types. So there are a few interest a few future directions like how is this distribution going to change with larger sample sizes and more sensitive methods to detect fusions. So the basic there are two pieces of evidence that we need to identify make a fusion call. So the reads that are mapping to two different genes these are called discordant reads and the reads that are mapping to the fusion junction these we call as anchor reads and the specificity of this anchor read is determined by this anchor length which is the minimum of the two overhang regions the left and right. So this is the anchor length. So as you know one of the main challenges in the fusion discovery is the high false positive rate and this is primarily because of misalignment of this read to this fusion junction and this can happen in several different ways. First is if this read aligns to this read that's mapping to this junction get aligned to one of the two genes of the fusion incomplete and also it can align to another gene called gene C or it can align to another transcriptional locus and these things it's very incredibly hard to computationally identify these sorts of artifacts. So we attempted to control for some of these artifacts and we implemented this approach in a method called mojo. This identifies fusions between at canonical exon exon junctions. So the basic approach is similar to the most of the other methods that are out there but the novelty comes in the way we filter for these various artifacts and also the approach we take to align to identify these candidate fusion genes. So this is on I have a poster out so I'm not going to go into detail about this please feel free to come by. So obvious thing is like how are we going to evaluate this so the way I evaluated mojo in comparison with nine other published methods using a company of 18 cell line transcriptomes these have validated fusions and so I there's a list of 137 131 fusions with that are known with experimental validation and I'm comparing these with some of the other published methods and I want to make a point that to me all the parameters are consistent across all these methods so if you look at some of these methods that show similar sensitivity despite that you see like there's a significant discordance between the types of fusions they call so we're showing like very good sensitivity but what about false positive rate so instead of false positive I'm going to call these as low conference fusions because we don't have evidence to say that these are like false positives we don't have experimental evidence so you'll notice that some of them share some of these calls are shared by more more than one of these methods these could be polymorphic or these could be new fusions that haven't yet been validated but you see a lot of fusion calls that are singleton to that are specific to each of these methods and I should point out that some of these methods are designed to identify fusions involving intronic regions and breaks in the middle of exons therefore larger search space and like more number of calls so what I'm showing to the right is a false discovery rate plot so the anchor length is a proxy for like conference in a fusion call so the increasing number of anchor reads you expect to gain more specificity but it's I say it's a proxy it's a weak proxy because you can have a fusion call with only one anchor read because it's like low expressed so we've taken this this algorithm and ran this on 70 nearly 7200 tumor transcriptomes one thing I want to point out is the median runtime across all these methods is around four between four and five hours for an 80 million paired and read sample and these these are run on a machine with each node has has 24 cores and 32 gigs of memory so our so we're despite our efforts to control for like false positives since we are looking at large number of samples inevitably we're gonna see some recurrent fusions that are false positives so the way we accounted for that is as follows so we ran mojo in the highest sensitivity mode requiring only two discordant reads and one anchor read of 10 base pairs and then first the way we do singletons is singleton calls the one without the recurrence is requiring two anchor reads two proper anchor reads we define this as an anchor read one enough anchor read mapping to the junction with the largest overhang mapping to the other gene than the it's paired read maps to and for the recurrent fusions we pull the reads across all samples and we look for this sort of uniform distribution at the fusion junction and we felt out reads that are biased to the gene A or gene B these sorts of most likely alignment artifacts and then we require similar one fusion call with highest high stringency or high conference fusion call so we compile these fusion calls and then to account for some of the annotation artifacts in the human genome and transcriptome and also someone to take out some polymorphic fusions we ran our algorithm on 1800 nearly 1800 normal transcriptomes in the genotype and tissue expression database and will filter out all calls that overlap with this and similarly with the TCGA 674 paired normal transcriptomes so we identified close to sorry we identified 15,600 high conference somatic fusion calls so are we capturing all the known ones so so I compile a list of fusions from the different marker papers and we are capturing most of them the only one that we missed was the not it's a non-canonical fusion that has a break in the middle of the exon interestingly we're also capturing some of the fusions with more with much higher sensitivity for example fgfr3 tack 3 we are finding it at 20% frequency in the glioblastoma significantly higher than what's been reported before and some of the other fusions and in this tumor type you're also finding it some higher frequency it's interesting to know that 11 out of the 26 different tumor types we analyzed have at least one fusion one fgfr3 tack 3 fusion so looking at the distribution of these fusion calls so obviously breast and ovarian have large number of samples therefore you see higher number of calls and most likely because they have a lot of copy number alterations too so if you look at known cosmic and novel fusions approximately 9% of all the fusion calls have a gene involving a cosmic gene or is a novel or is a known cancer gene known fusion gene so and the distribution of number of fusions per sample across the different tumor types so some of the solid tumors have like higher number of fusion calls per tumor and you see some tumor types like the thyroid adrenal tumor type showing like very few fusions so one thing to look at is like are these fusions caused by local rearrangements or distal long-range rearrangements like translocations or rearrangements farther than 10 megabases so the interesting thing you see again with this thyroid cancer is that it has far fewer rearrangements but most of them are like distal and some of the leukemia as expected show like higher number of translocations and if you look at the distribution of samples that have at least one fusion or more than one fusion you see this sort of pattern where like tumors that are certainly very highly arranged like ovarian have like large number of fusions and some of them have greater than 20 these are candidates for chromatripsis and so on and another thing is approximately more than 75% of the thyroid tumors do not have a fusion yet there's like a quite a large fraction of them have like are unknown driver potentially driver fusion so again this is the same pattern with the ovarian I'm just trying to reiterate the large number of rearrangements in the ovarian tumor and you see fewer rearrangements but large number of translocations here and with the thyroid like fewer rearrangements and many known cancer genes so these are the some of the novel fusions we found there are some interesting ones in here for example this fusion in involving BMP R1B has is seen in breast ovarian and prostate and these are all hormone driven cancers and some fusions like ESR1 CCDC it's found in MCF7a cell line 2 there some of them are interesting and we're like following up on functional validation some on some of these so our approach for functional validations is we synthesize a fusion construct package them into wire particles and create stable cell lines expressing the fusion construct and perform three different types of assays proliferation invasion and evasion of apoptosis so I'm going to show some preliminary data on on the on 11 fusion constructs we built using this pipeline in MCF10a cell line and we did proliferation assays and we acid two different phenotypes the EDU and cell titer glow once for DNA synthesis and one for ATP metabolism so these are some of the so we have positive two positive controls here FGFR3 tag 3 and PMLR array you can see that some of these fusion show greater proliferation than FGFR3 tag 3 you can argue that FGFR3 tag 3 is not it's not as andogenous environment in MCF10a yet it's interesting we are replicating these assays in NIH3D3 cells you'll notice that the one odd oddity here is that we have two isoforms of this fusion one doesn't have a DNA binding domain and one has a DNA binding domain so the one with the DNA binding domain has significant show significant increase in proliferation so in summary we're repeating these assays in NIH3D cells and we're overexpressing full-length individual genes as controls and we're and then follow up with functional further functional validations analysis and so on so and other thing that I bring up is that recurrently fused genes not the fused gene pairs but the genes are also interesting and finally integrating this analysis with copy number alterations mutations and fusion events is of significant interest just looking at the bigger scale of like genomic instability and what drives these alterations so finally I want to thank Patien Lin a postdoc in our lab that has she spent an incredible amount of time and effort trying to set up these experimental validations and following up on them and so on and I want to thank my advisor Kevin and some of our helpful lab mates and and the bio-numbers team for providing the TCG data and the Beagle supercomputer for providing computing resources and so on thank you very much I'm interested in what the influence of depth of RNA sequencing would be on your discovery so in the mouth that we've been usually doing in TCGA the number of reads is that maxed out your discovery rate or can you model whether you do a lot better with much deeper RNA absolutely the more you the more you deeper you tend to pick up more clonal events so a large fraction of our fusions have only one or two supporting reads and we're able to pick them up because we have some samples that are deeply sequenced or are in those samples that that is actually a clonal event compared to the subclonal event so then using that can you model how if we were going to do a new project sure how deep would we want if you have an estimate of what's the clonality or what sort of clonal events or what's the purity of certain tumor type that you expect you you can model it we have enough data to look at that have you tried to validate the biarty PCR followed by Sanger sequencing some of the fusions you ever that's a very good point so we don't have material for some of or most of these so the way we are looking at is looking at the whole genome sequencing so I ask this question because I'm a little bit concerned for some calls in particular the FGFR3-TAC-3 fusion in GBM we have been studying the fusion for quite a long time and we definitely know that the frequency is not 20% in particular the problem they are might be I would suggest that in some batch of RNA-seq tumors there are real positives with a very high expression of the fusion gene that contaminate a lower level also all the other symbols in that batch so I would suggest that you give a look at the possibility to the validations yeah that is there's pretty yeah contamination is pretty hard to I wish you you are right in that case because that would make actually therapeutic trials so much easier if we had the frequency but unfortunately that's not the case I think we need to move on we'll take the remaining questions after the talk or at the poster session our next speaker is Audrey Foo from University of Chicago presenting on widespread genetic epistasis among cancer genes


ora ditemi la verità no cioè non avevo un compito semplice perché io parlo dopo Steve Noken e introduco adesso un premio Nobel no cioè è come cantare un grande concerto tra Bruce Springsteen e Bruce Springsteen cioè io sono il cantante locale no che canticchia tre canzoni ok fine della storia bene vi voglio introdurre al primo grande ospite di questa sera che il professor Takaki Kajita Takaki Kajita è stato insegnato del premio Nobel per la fisica nel 2015 insieme ad Arthur McDonald perché ha rivelato un fenomeno che si chiama oscillazione dei neutrini e rivelando l'oscillazione dei neutrini ha dimostrato che i neutrini hanno una massa ora i neutrini sono besti particolari è come se immaginatevi l'uomo di neanderthal l'uomo sapiens e l'uomo erectus ok sono tre specie diverse di un'unica famiglia la famiglia degli ominidi solo che omo sapiens e omo sapiens e l'uomo di neanderthal e l'uomo di neanderthal e l'uomo erectus e l'uomo erectus i neutrini no anche loro sono in tre famiglie diverse ma mutano fra loro sono in un certo senso uno e trino e questo mi ricorda qualcosa io volevo chiamare sul palco takaki kajita per l'entrazione e good evening well as the first talk on the messengers of the universe I'm going to talk about neutrinos now to begin this talk I want to show this slide and this is a diary written by Fujiwara teika and well actually this is about one thousand years ago and in one of the pages he wrote down some very spectacular event that occurred in the universe well to be honest I cannot read but anyway according to the people who can read the yellow part is the description of the very bright new star that emerged on May 1st 1006 and the green part is another description of a bright new star that happened in May 1,054 so clearly these descriptions tells us that people have interested in the events that occurred in the universe and well in the present day word these very bright new stars are supernova now because I'm going to talk about neutrinos and I want to talk about a little bit about the key features of neutrinos first neutrinos are fundamental particles like electrons and quarks and well of course you want to know a little bit more then neutrinos are something like electrons without electric charge in fact this feature has a very special consequence neutrinos can easily pass through even the art or even the even the sun so neutrinos can easily pass through stars and in fact this feature is very important because neutrinos can bring information of the fundamental processes at the center of the stars so because of this feature we think neutrinos are one of the key players in the multi messenger astronomy okay now I want to show you the neutrino detector and this is the cameo kanda experiment and this experiment was operated in the 1980s and 1990s and this is a large water detector located deep in underground and in fact this detector contained 3000 tons of very pure water and I want to show you another example this is another large water detector called IMB and this was actually bigger than cameo kanda it had 8000 tons of pure water inside this detector again this detector was located deep underground in united states by the way these detectors were constructed to observe proton decays not to observe neutrinos in fact in the 1970s new theories emerged and these theories predicted that protons should decay with the lifetime of about 10 to 30 years well 10 to 30 years is an extremely long lifetime well if protons do not decay or do decay our daily life doesn't change but scientifically this prediction was extremely important and therefore people constructed these kinds of detectors to observe proton decays unfortunately proton lifetime was longer than predicted so these experiments never observed proton decays however a lucky thing happened well let me tell you and this is the explanation of the life of the stars stars like this our sun the total life of the sun is about 10 bilion years and if the mass of the star is smaller than the sun then these are sorry these stars have a extremely long lifetime but if the mass of the star is happier than the sun then these stars have a dramatic life they have shorter lifetime but at the end of the year lifetime they have the very significant explosion this is called the supernova and after the supernova typically neutron star is generated or the mass of the star is much heavier than the sun then the life is even shorter and they have the supernova and at the end maybe black hole is formed so this was the thought I would say people expected that supernova was the event that occurred at the end of the life of a heavy star but we didn't have proof now in 1987 in February um there was a very bright new star observed in large magianic cloud that is the nearby galaxy next to our milky way galaxy so this star exploded and after the explosion the star was like that so this was the supernova explosion and as I said at that time two proton decay experiments cameo candy and imb were in operation by the way I have to say according to the theory of supernova explosion 10 to 58 neutrinos should be emitted in about 10 seconds this is really a huge number huge number of neutrinos should be omitted in 10 in 10 seconds therefore the neutrino detectors on the earth could observe these neutrinos and in fact the imb experiment and cameo candy experiment and furthermore the Russian bachstein experiment observed neutrinos related to the supernova explosion and with with these detectors a total of about 20 neutrinos were observed only 20 however these 20 neutrinos were enough to prove the basic understanding of the mechanism of the supernova explosion so we understood that supernova occurs at the end of the heavy star's life at the end of the heavy star's life all of the fusion processes finish and then the star collapse and then at the center of the star neutron star is formed and then all the materials are bounced and this way the supernova explosion occurs so we had only 20 events but this was really enough to understand the basic mechanism of the supernova explosion and because of this observation professor Koshiba the safety and overall presence in physics in 2002 by the way he was my thesis advisor so actually we are very happy to hear this news well I wanted to show you a video but unfortunately we have some trouble therefore I cannot show you the video but with this video I wanted to tell you the activities of the people who would like to reproduce the supernova explosion actually in the 1980s people thought that people they are able to reproduce the supernova explosion by the computer simulation by the way you know the sun and you know that the sun is a very good ball or a very good spherical shape therefore initially people thought that they can reproduce the supernova explosion by a computer simulation assuming the complete spherical symmetry complete ball however they were unable to reproduce or simulate the supernova explosion therefore in recent years people try to simulate the supernova explosion by a full three dimensional simulation and in fact this video wanted to show you the result of the full three dimensional simulation and in fact well this would show you how complicated the supernova explosion is but anyway I have to give up anyway because of the so dramatic observation of the supernova neutrino detection we had the next generation neutrino detector it is super cameo cande it is a 50,000 water detector so this is approximately one orders of magnitude bigger than the previous generation detectors and well in fact this detector is famous because of their discovery of the neutrino oscillations that shows that new neutrinos have small mass but unfortunately today we are talking about the messengers of the universe so I'm going to stick on the supernova neutrinos by the way this is another view of the super cameo cande detector here we take a photo from the top of the detector viewing the the downward and you can see the water and we have been waiting waiting waiting for the next supernova explosion well we have been waiting for more than 20 years but unfortunately super cameo cande so far was unable to observe supernova neutrino events so finally people decided to show you how the supernova neutrino detection could look like in super cameo cande so in this case I hope yeah so this is the detector now super neutrinos will be arriving in one second so this is the signal we would expect for a supernova explosion and in about 10 second all the neutrinos gone so this is the signal we would expect for the supernova explosion and in fact the real signal should be even more dramatic because super cameo cande will detect about 10,000 neutrino events in 10 seconds so this would be really fantastic information to know the detail of the supernova explosion so super cameo cande is still waiting for the next supernova explosion by the way we are now in the multi messenger astronomy era and now in addition to neutrinos we have gravitational wave detectors and we are going to hear more about gravitational wave detectors soon but anyway if a supernova explosion occurs in our galaxy then both gravitational wave detectors and neutrino detectors should observe the signal and by combining these two data we think we really understand the supernova mechanism of the supernova explosion and then soon after this supernova could also be observed by gamma rays that means by cta and therefore this way we expect that we understand the particular acceleration in supernova so in the multi messenger astronomy era neutrinos gravitational waves and gamma rays are going to be very important ok that's all from me