 So that was a real honor to be introduced by Harold. And I'll take a few minutes to tell you a story. Recently, I built a man cave in the basement. And I've been saving newspapers my entire life from important events in my life. And now I had the man cave. The newspapers are going to go up as posters. And of course, it's Red Sox win the World Series, the shuttle launch, things like this. But there was one from my four years in San Francisco, which the headline is, Giants Win Penant. So of course, that's why I saved that newspaper. But then in the lower right corner, I noticed Varmus and Bishop win Nobel Prize. Of course, quote, the visionary that I was. I didn't know Harold, didn't know Mike, and didn't know how important the discoveries they made at that time would be for us and for my own career. Really, that observation that cellular oncogenes were the thing that drove cancer, not tumor viruses per se, which was really sort of the thing that was being thought of at the time. That plus the discovery of tumor suppressors, I think, set the trajectory that we've been on for the last 20 years or so with the objective of understanding the combination of oncogenes and tumor suppressors that contribute to cancer and then making therapeutic advances based on that. So Harold, you'll be happy to know that, will be in the man caves. And so you were lucky enough that you won the Nobel Prize the day the Giants won the pennant. So the other interesting observation that happened during the last 20 years or so was the paradigm shift that was exemplified by the results of a matinib in the disease chronic byloginous leukemia. And I think this is well known, but still worth reflecting on. Of course, CML is interesting in that it was really the first time a somatic genetic lesion was discovered in 1960 by Nolan Hungerford. It only took us about 50 years to figure out what DNA was, what a kinase was, what phosphorylation was that you could make small molecules. But 50 years later, you have a drug, a matinib that's very effective at inhibiting the output of this oncogene. And this is the efficacy data in the United States not based on a clinical trial, but rather based on looking at the statistics of mortality and incidents per year in the United States just going to the Cancer Journal of Statistics and pulling out their estimates every year. So beginning in 1997, the incidence of CML, the number of new cases per year in the United States was around 4,500. And you can see that that's remained constant over the next 14 to 15 years. A matinib was introduced into the market in 2001 and thereafter mortality dropped from an annual mortality rate of 2,500 deaths per year down to around 440. Nylatinib and disatinib second generation Able inhibitors were introduced in 2010. Interestingly, the mortality rate in 2011 dropped a little bit. You can sort of speculate based on no data that that may be related to second generation inhibitors coming into the market. So there are a few things worth noting. First, when this really was going, people may forget that there were a bunch of naysayers that thought this was not going to really work. That is, yes, it was working transiently, but ultimately the dreaded CML stem cell would take off and patients would die because the stem cell population would progress, cancers were smart, they would find genetic ways around this. And in fact, if you look at not the incidence, but the prevalence of the disease, this is the number of patients now alive with CML. In 2007, it was around 25,000 patients. By 2010, it was around 37,000 patients. This just illustrates that the mortality per prevalent case is continuing to decline. So there has not been sort of the inexorable progression in this case. Now that being said, there was still a big question as to whether or not this would or would not translate from a relatively simple genetic disease and one that's hematologic in nature, and maybe even represent the benign precursor to advanced leukemia, would this really be relevant to the more complex solid tumors that we see at a later stage? And Matthew and I, along with Tom Lynch and Dan Haber and Harold and William Powell were fortunate to participate in this discovery of EGFR mutations in lung cancer, which illustrated that yes, you could have fairly dramatic therapeutic results with inhibitors of specifically activated mutant oncogenes even in solid tumors. So I'm not gonna go through the other many examples of where this paradigm has translated just to say that I think this is a translatable paradigm more broadly than just in CML. So now we can sort of look back and think of two different modes of drug development, an era which we're still are living in of empiric drug discovery where therapeutics were applied without knowledge to the underlying pathogenesis, generally to unselected patient populations looking for small benefit. I'm comparing in A, the intact trial of Orissa in unselected lung cancer patients to the power of a single patient observation, one patient with a mutation being treated having a dramatic response. So these two modes of drug development coexist today. I think we all hope to have more of B than of A and the question and a conversation for the rest of the talk is how do we make B better? And that of course involves sort of identifying the major limitations to making the drug discovery mechanism applicable to the genetics of cancer more robust, more reliable. So I'm gonna spend the rest of the talk talking about five key issues for this paradigm. The first is if you want to take on the genetic basis of cancer and this is your job, you have to know the genetic basis of cancer and for many years we have not known the genetic basis of cancer. So we need to know this to completion in my view and I resonate with Lou's comment that the genetic experiment has been done. It's been done in humans over and over and over again. We just haven't gotten the results yet. So what does it mean to do this to completion? And I know there are people who think we know all the single gene mutations. So what? We need to know this. We need to finish the atlas. We have a long way to go. We need to know this in all cancer types and all cancer subtypes. That's in and of itself a big challenge. We need to know it along the stage of cancer. In prostate cancer, what's the biggest question today? Yes, metastatic, but also the over treatment of pre-malignant or benign early stage prostate cancer. What do we know about the genetic differences between that disease and the type of disease that kills patients? Not very much. So we need to know it across the evolution of cancer and then we need to know it with a robust sample size and robust depth of analysis that the genetics we understand at the level of functional redundancy, we can understand cooperation and we can understand antagonistic genetic events. That is two things are mutually exclusive either because they're redundant or because they're antagonistic. We're nowhere near the sample sizes yet to make those claims. Matthew showed a few examples, but I'm guessing those are still on the verge of statistical significance. And that's when you're only trying to do any two genetic lesions. The second you wanna know three genetic lesions or four, we're still very underpowered with respect to the ability to do this. And I believe firmly when we have that power these genetics are gonna fall into well-defined pathways, common nodes of therapeutic intervention could be identified simply by looking at the genetic map. So I think that's the aspiration and the hope. This is your task I think and I'm not gonna spend any more time talking about it today. So that's the problem number one and I'm counting on you guys to solve that. Problem number two is where we have to start thinking more deeply which is even when we've known the genetic alterations in cancer some of the absolute best genetic alterations in cancer we have not made sufficient progress in turning that information into robust drug candidates. And that problem comes in two flavors. The oncogenes, so how many of the oncogenes are actually drugable today? Not very many. And it includes oncogenes like RAS which are mutant in 90% of pancreas cancer or ETV-1 which looks like it's translocated in between 80 to 90% of prostate cancer, BCL-2 which is a dominant oncogene in lymphoma. These types of oncogenes outside the kinases have really been refractory to drug discovery. Now I think they've been refractory for two reasons. One is they're in fact harder but two is people may not be trying hard enough and I think that's where industry also needs to play a more active role. I think a great example of an attack on this problem was the work of Steve Fessek and his colleagues at Abbott Pharmaceuticals who took 10 years and I think they're still working on this problem to try to make inhibitors of the interaction between BH3 peptides and the BCL-2 family members. And this is difficult because it's a very large surface, a very large protein-protein interaction, doesn't have the same tight, well-recognized binding structures that kinases do. Nonetheless, they were able over time to elaborate ABT263, which was a lead clinical candidate and now they have a second molecule going into the clinic attacking this class of oncogenes. So this is an example of taking on the question of quote, undruggable, challenging the notion that something's undruggable and making headway despite the skepticism that one could make a drug against this family. The second class of genetics that we talked about earlier is the tumor suppressor pathways and of course this is even more difficult because instead of having an activated gene which you could inhibit, you simply have the absence of a gene. Now I think this is where the concept of synthetic lethality is going to play a big role and I just wanted to show you a few examples that may not be so obvious to people that suggest this is really working. So the first is the example of the hedgehog pathway. So we know that mutations in the patched tumor suppressor gene occur in basal cell carcinoma and meduloblastoma and Phil Beachy's lab discovered the natural product cyclopamine as a natural product antagonist of the receptor smoothened. Now based on the way this pathway works it was predicted that patched deficiency would lead to constitutive activation of the smoothened receptor and the work from the Beachy lab suggested that antagonists of the smoothened receptor would reverse the phenotypic consequences of patched deficiency. Now this seems like an obvious one but synthetic lethality doesn't say it has to be in a parallel pathway or some magically unknown mechanism. This is or can be an example of synthetic lethality as well. So I just wanted to show you the example that we've been working on LDE 225 which is a smoothened inhibitor. It has an IC50 for human smoothened in in vitro assays of 11 nanomolar and in cellular assays of seven nanomolar. So the very potent non-natural product synthetic inhibitor of smoothened. We were very interested in the idea of targeting medulla blastoma and there was a lot of divergent opinions as to whether you do or don't need blood-brain barrier penetrating molecules to treat a CNS or cerebral lesion like medulla blastoma. Nonetheless we decided to make a CNS penetrating molecule. Here's an example of a preclinical study where a patch deficient orthograph from a mouse was transplanted into a skid mouse brain and treated with LDE 225 versus vehicle and you can see the untreated tumors grow very rapidly while the LDE treated tumors regress over time. So we had fairly strong evidence that in a patch deficient model in the right location we were able to affect a therapeutic response in a preclinical model. So medulla blastoma is interesting, you would think we would just sequence the patched gene and then take those patients and put them on the drug. It turns out, this is sort of three to four years ago, sequencing patched from paraffin embedded samples wasn't the easiest thing in the world because of the number of exons you have to sequence. Paraffin embedded sequencing has made a lot of progress since then, but in the end we basically made a gene expression signature to capture hedgehog activity as had been defined by some of the key investigators in this field by the transcriptional signature of the pathway rather than by the genetics. So we developed an expression signature using 40 medulla blastomas that were embedded and fresh in paraffin embedded tissue. A multi gene model was built using the elastic net model, the optimal model selection was validated using an independent data set and these five genes were selected for evaluation in the clinic using a QRT PCR assay. So four genes that are up in the hedgehog pathway and one gene that's down. So this drug has been through phase one in both basal cell and medulla blastoma and is now in phase two studies. This is an example of a pediatric patient who had a complete response to the drug. You can see at baseline had a tumor near the brain stem and the cerebellum and then by cycle five had no evidence of disease. The results so far using unselected medulla blastoma patients and retrospectively characterizing them for the signature is that all five patients who have a signature positive medulla blastoma have responded to the drug where none out of the patients who are signature negative have responded and this number on the right is now zero out of 21 signature negative patients. So in many ways this is an interesting control group we have a mechanism based signature, a mechanism based inhibitor and activity that seems to be strongly linked to the pathway signature. So this is an example as I said of this idea of synthetic lethality where a mutation in the cancer predisposes, it enhances viability of the cancer. Of course the drug itself has to be viable for the host but where the cell that bears the mutation plus the drug has a lethal phenotype in this case it would be the patch deficient medulla blastoma cell being lethal when exposed to LDE 225. So I'll just give you one more example of what I consider a synthetic lethal interaction and a successful attack on a tumor suppressor pathway and that involves the PI3 kinase pathway and in particular the tuber sclerosis gene. So tuber sclerosis is a fairly rare hereditary syndrome that's associated with a number of manifestations but on the cancer side it's associated with two interesting tumors, angiomyelipomas of the kidney and subopendymal giant cell astrocytoma, a tumor in the CNS. Now work in drosophila had shown that or suggested that in the absence of TSC M-tor kinase and S6 kinase would be constitutively dysregulated and in fact the first experiments of rapamycin analogs in a TSC deficient setting were done in drosophila and you could essentially reverse at least the larval phenotype in drosophila by treatment with rapamycin. So this suggested that rapamycin or rapamycin analogs like Everolimus would be particularly effective in TSC deficient settings. So this has been tested in children and adults with tuber sclerosis, first in the subopendymal giant cell astrocytoma setting but also in the angiomyelipomas. This is example of a patient with a fairly large subopendymal tumor which has not had a complete response but has had a fairly significant response. We've conducted a phase three trial led by David Frans and John Bisler at the University of Cincinnati and the results of the phase three trial are shown here in the first year of the trial, no patient with SEGA progressed on therapy. At the time this was a controlled trial against placebo because there's no approved therapy for SEGA. The overall partial response rate was 35% versus zero in the placebo and in the kidney tumors, the response rate is 53% versus 0%. And based on this data, the FDA approved a finitor or everlimus in this disease late last year. So again, to me this exemplifies the notion of synthetic lethality that a tumor suppressor gene a lesion like TSC predisposes preferentially to sensitivity to a torenhibitor compared to the normal cells bearing intact copies of TSC which are let's say relatively insensitive or at least give you a therapeutic index when a patient is treated with a rapamycin analog. So everlimus was also treated, was also studied in breast cancer and had fairly significant advances in progression free survival in the ER positive setting of breast cancer. Now this goes back to model A because honestly at the beginning of this trial I don't think anybody had any idea of why a rapamycin analog would work in ER positive breast cancer. I'm not sure we still understand why it would or wouldn't work but what's intriguing from the work that's going on in the TCGA project and I have to admit this was a very difficult figure for me to deconvolute but I've tried to simplify it to these two things which are in the ER positive subgroup of breast cancer. There's a fairly high rate of PI3 kinase mutation and one can hope or speculate that perhaps the effect of torenhibitors in ER positive breast cancer is greatest in the PI3 kinase mutant population. I have no data for that but fortunately we do in fact have the samples from this trial and those samples are being analyzed now in a collaboration with Foundation Medicine looking at about 500 genes for mutations to see whether or not there are or are not correlates with clinical benefit to torenhibitors in this trial. I'll say one thing about this because some people would think well now that you have an approved drug why would you go back and even bother to find out? I think that's a really good question that is a challenge for the industry. I happen to think there's some really good reasons. One is if you know you have a mutation you know the patient is more likely to benefit you're more likely to optimize a drug for that patient. The second is you're more likely to work through toxicity find a dose that the patient can tolerate find a treatment regimen the patient can tolerate rather than just simply give up because you didn't really understand why the patient would benefit in the first place. And then from an economic point of view let's face it if those are all the patients that are benefiting that's where all the money is being made anyway. So I'm quite hopeful that this is going to make sense not just scientifically but also from a clinical and maybe even economic point of view. So I want to give you one more tumor suppressor example because I think it's also motivated by the work of the TCGA which is the common mutation in the PI3 kinase pathways that are found in malignant glioma. The dominant mutation in the PI3 kinase pathway is not TSC1 and 2 in this case but is rather P10 but also PIC3CA and the regulatory subunit of PIC3CA shown further to the right on this slide are also commonly mutated. And I think this was a highlight of the TCGA glioma paper showing this sort of very dominant activation of the PI3 kinase pathway in glioma. So we've been working on PI3 kinase inhibitors and one of the central questions is which PI3 kinase subunit would be synthetic lethal with P10 deficiency? I wouldn't say this is locked down definitively but the preponderance of the evidence suggests that in the situation of loss of P10 as shown here where we've compared depletion of PI3 kinase beta with PI3 kinase alpha, P10 deficient cells as shown right here tend to be pretty dependent on PI3 kinase beta versus alpha and this is a PIC3CA knockdown in a P10 deficient cell shown here. Now because PI3 kinase alpha can probably take over we have not taken the strategy of making a beta selective inhibitor. There are other companies that are doing that. We've taken the strategy of trying to make a pan type one PI3 kinase inhibitors. People ask me, why don't you just make an alpha beta? I would if I could. It's not so easy to make an alpha beta dual specificity inhibitor because of the structural homology between alpha and delta makes that very difficult. So nonetheless, BCAM 120 is a PI3 kinase inhibitor that inhibits alpha, beta, gamma and delta. It has activity against the common alpha mutations. It also has very good blood-brain barrier penetrating properties. In fact, maybe a little too good. It accumulates in the brain over plasma concentrations. So this was also the intent of this program was to have a molecule that would work in glioma because of its BBB penetrating properties. This is exemplified in this study where we implanted a P10 deficient cell line in the brain. In the top panel, we're comparing BKM 120 to GDC0941, which is a type one PI3 kinase inhibitor that does not have brain penetration. And you can see in blue is the PK of the molecule. So you get very good brain exposure after dosing with BKM 120. GDC0941 does not have brain penetration. Phospho-AKT suppression is shown in yellow, so concordant with high exposure in the brain, we get rapid and profound diminution in phospho-AKT in the brain. And this is associated with the ability to prevent tumor genicity in orthotopically injected, in this case, breast cancer cells that are PI3 kinase dependent. So we're currently studying BKM 120 in glioma, and I don't have any results to share with you. I'm hoping it will be successful. But this is our attempt to again exploit a potential synthetic relationship between synthetic lethal relationship between P10 deficiency and PI3 kinase dependence. So more broadly, there have been a lot of attempts to now discover synthetic lethal interactions. And the dominant way people are trying to do this is by SHRNA screening. I would say like genetic sequencing, this has been noisy so far, but it hasn't been done to the extent that we need to do it. At a robust level with deep SHRNA libraries across large numbers of cell lines, in many cases, people are using isogenic pairs of cell lines, which we find has a lot of noise to it. Our approach has been to try to use cell line panels that are genetically defined, find SHRNAs that are selectively depleting or killing certain mutant cells. An example of this is shown here where beta-catenin SHRNAs are highly enriched for their depletion experiments in the APC-deficient subset of cell lines. So if you didn't know beta-catenin was a key player in the APC pathway, you would have discovered it as the very top hit in this particular screen. So I have a lot of faith still in these types of experiments. I just think that we're not at the point where they've been great yet. And I think they'll get better and better over time. So that's problem number two, and sort of the idea of taking on the oncogenes and the tumor suppressors by working on difficult-to-drug oncogenes and then trying to exploit systematically this notion of synthetic lethality for discovering drugable genes downstream of either tumor suppressors or, of course, undruggable oncogenes. Now, the third issue which we're going to face, and we face already, is resistance. So there's not going to be a single drug that wipes out cancer like EGFR mutant lung cancer. We know we're going to have to have combinations, and the reason for that is resistance develops to targeted agents. Now, this has caused a lot of hand-ringing, too, right after BCR-Able's success with Gleevec. Resistance developed. People said, oh, no. But shortly thereafter, I'm going to just skip to this, Charles Sawyer's and Neil Shaw discovered that the resistance was likely mediated by mutations in Able. Now, this also caused two lines of thought. One was, oh, no, the cancers are so smart. Others of us thought, wow, that is pretty exciting. I could have thought of three million other base pairs that might have been mutated that might have caused resistance. But in fact, these cancers chose or had to mutate Able in order to survive. To me, this suggested this concept of addiction was very powerful and also led to the notion that the next best thing you could do in CML was to make a better Able inhibitor. Now, it would contrast that to the situation with taxol. I'm still not sure we have any idea of what taxol resistance is. Why? Because we don't actually know how taxol works. We don't know why it works in ovarian cancer. We don't know why it works in lung cancer. The advantage in the targeted therapy paradigm is we generally know why the inhibitor is working. We generally can understand the mechanisms of resistance at a much faster rate and use that information to leverage further drug discovery. So in the case of Novartis, this led to the generation of a second molecule known as nylotinib. In the case of BMS, BMS developed disatinib. Both of them are more potent Able inhibitors. Nylotinib is a very interesting comparison because structurally, it's very similar to imatinib, as I've shown here. The central difference is that it is 10 times more potent at the cellular level. Gleevec is 220 nanomolar in cellular assays. Nylotinib is 20 nanomolar. For kit, we know that gleevec is a kit inhibitor. For kit, nylotinib and gleevec are fairly comparable. So the clinical trial that was done to compare nylotinib and imatinib was a test of whether more potent kinase inhibition matters or not. And I think that was answered dramatically in the yes, where the more potent kinase inhibitor, nylotinib, doubled the rate of major molecular response and complete molecular response. So that tells us that cells are really addicted to these genes, and we really need to inhibit them very well, at least in the case of CML. So in some cases, improved or enhanced target inhibition is going to be a method for overcoming resistance. We still wonder about this with EGFR, whether we really have the final best EGFR inhibitor yet or not. Such improved inhibitors will not only work in the resistance setting, but they will most likely become the better frontline therapy. So the question is, are there other opportunities? I mentioned EGFR. I wanted to share you one new opportunity that we've been working on where we now have clinical data, and that is targeting alt-translocations and lung cancer. So I think many of you know that Pfizer's drug chrysotinib was approved very rapidly after the discovery of the ELML-4 alt-translocations and lung cancer. Interestingly, chrysotinib is a potent elk inhibitor, but also a potent met inhibitor. In fact, it can be a little better on MET than elk. We have made a selective elk inhibitor that is 150 pycomolar in vitro assays and 3.2 micromolar on MET, so very selective for elk, and 27 nanomolar in cellular assays versus chrysotinib, which is 110 nanomolar, so about four to five-fold more potent in cellular assays than chrysotinib. In an EML-4 elk-driven xenograph, single three to six milligram doses are sufficient to regress the tumors completely. And if we look in the cell line encyclopedia, something I'll describe in a second across 600 cell lines, you can see for LDK, the three most sensitive cells are all elk-driven cell lines, and the gap between those and other, let's say, non-genetically elk-driven cells is quite large. So we're very encouraged by the profile of this drug. We didn't really know at that time what mechanisms of chrysotinib resistance would really be evident. It was, we were following pretty quickly on the heels of chrysotinib, but nonetheless, we went into chrysotinib refractory patients, and you can see essentially every patient is responding. The response rate at the, this is the data from the MGH in Alasha is 81% in chrysotinib refractory patients, really simply by making a more potent elk inhibitor. So we're very excited about this data, but again, I think it highlights the notion that really targeting the key oncogenes with potent inhibitors is one key mechanism for trying to prevent resistance. Now, that's not the only mechanism of resistance, and I think in the BRAF setting, we're seeing a quite different picture. So in the setting of BRAF, where we have well-defined downstream pathway, almost no BRAF mutations have been found as mediators of resistance to Vemurafinib or other BRAF inhibitors. Instead, the work of people like Neil Rosen and Levi Garroway and Richard Murray have identified a host of different ways that melanoma cells seem to be able to evolve resistance to the inhibitors. Now that could be also really bad news, but in my view, the good news is still pretty good. Why? Because almost every one of those mechanisms reactivates the MEK-ERC pathway. Again, I could have imagined mutations in the pietrichinase pathway or a lot of other pathways, but a dominant message that we're getting from studying resistance in the BRAF mutant setting is that pathway reactivation is critical for the development of resistance, and this has led a number of companies, GSK is sort of out front on this, to try to develop dual combinations of MEK-RAF inhibitors as a way to create a hurdle over which the cancer cell will not be able to get over. So I'm quite optimistic in fact that the study of resistance will ultimately lead us either to the best molecules and or to the best combinations. But this does lead to problem number four, which is we know that one drug is never enough. Now ideally the sequencing and the genetics of the cancer will tell us what we should be doing. We're of course not there yet because we're just starting to understand the single gene mutation frequency. Resistance may be another mechanism by which we get to the right combinations. We're also interested in trying to explore combination space by large scale systematic screening, and I'm just going to skip this and show you the project that's ongoing. This is a large scale combination screen that we're doing in collaboration with Zalicus. They're a company that used to be called Combinatorics, which probably makes why we're collaborating with them more sense. This screen is 70 compounds by 70 compounds over 100, it's actually 138 cell lines now. Using this type of combination grid, just to give you an idea of how hard this is, that's 27 million data points and it's taking us two years to just do that one experiment. And I still think it's underpowered myself. So some of the things that are emerging are expected. We can see the expected synergies between check inhibitors and gem cytobines or the antagonism between a microtubule stabilizer and topoisomerase inhibitors. But it remains to be seen when the data are complete, whether we're really going to get informative stratification of combinations by this sort of large scale screening. So just to close, I've mentioned a few of the translational infrastructure model systems we're using, but I think this has been another problem to the advancement of genetic and other forms of cancer therapy. That is lack of a preclinical translational infrastructure. And just to be very simplistic about it, how many papers have you read where the entire paper is about one cell line, right? Okay, that's one patient's cancer. We would never run a clinical trial with one patient's cancer. So we've had this problem that we've had very limited ability to profile preclinically the same number of samples from cancer of patients were about to treat clinically. So the idea of the cell line encyclopedia was to try to go from one cell line to this encyclopedia of 1,000 cell lines. This was a long-term collaboration with the Broad, which is ongoing, where we bought from commercial sources, 1,000 cancer cell lines. They were bought from commercial sources so that if you want the exact cell line we used, you can order it from the same source. So we bought them, took them out of the vial, grew them in a limited way and made DNA RNA as soon after purchase as we could so that the community can hopefully access as close to the same cell line as the data is here. Of course, then we've gone on to do the genetics and expression. When we started this project, there was no next-gen sequencing, so we had sort of aspiration of sequencing like 50 genes or something like that and now it's with the help of TCGA going well beyond that. So of course, the key now is to figure out a way to profile the encyclopedia, identify sensitive cells, and hope that amongst the sensitive cells, there's a biomarker that's enriched in the sensitive cells as compared to the largely insensitive cells. And the trick to this is having a system that allows you to do this. So this is a system that was built first at GNF and then put in place in Novartis. So it's a robotic system with automated cell culture as well as a compound handling. So this is the incubator. Those are plates in the incubator. Robot is retrieving the cells from the incubator. The cover of the cells comes off and then it goes on to the compound dispensing deck. So these are compounds being pipetted into the 1572 well plate. You can imagine trying to do that manually. And then after the dispensing of the plates plate goes back into the incubator. Three days later, it comes out of the incubator and then by cell tight or glow, the number of cells on the plate are measured. So with this system, we can profile about 3000 compounds with triplicate IC50 curves in about a two to three month period. In fact, the method of dispensing now has switched from pin dispensing to acoustic dispensing, which turns out to be much more rapid. So just to show you one example from the cell line encyclopedia and how this can motivate the clinical development, this is our PI3 kinase alpha inhibitor, BYL719. It has a single digit nanomolar activity against PI3 kinase alpha and reduced activity against beta, delta and gamma. So of course this compound has been run against the cell line encyclopedia and the types of profiles you're looking for are not the all green, which are all dead, or the all red, which are all live, but compounds that are of interest, are the ones that are gonna have heterogeneous sensitivities among the encyclopedia. With the help of the Broad, we've built a informatics platform for sifting through 50,000 features that are in combination genetics or expression, lineage, et cetera. Using compound response measures such as AMACS or IC50 or AUC, using those to categorize cells into sensitive refractory or intermediate, and then throwing out the intermediate and using the sensitive and refractory populations, building models that try to predict compound sensitivity and NICO is here and others that you can ask about exactly how this works, because I really don't know. But what's impressive to me is from 50,000 features, the number one predicted feature for PI3 kinase was PI3 kinase mutation. Now everybody's saying, okay, I already knew that. Still, for all of those of you who do a lot of large scale data analysis, having the right answer, not in the top 20, not in the top 10, at the number one position, I think is still pretty impressive. And you can see from the list that was true for a number of oncogenic proteins and their cognate therapeutics. The power of that data set for us is that when we went to do the clinical trial, the data were compelling enough that the clinical trial from the beginning was done in PI3 kinase mutant patients. So right away, the phase one was not an all comers dose escalation. Instead, only patients with PI3 kinase mutation were enrolled onto the phase one and dose escalation was done in those patients. And I can say we know it's well tolerated. We've seen significant signs of tumor shrinkage as shown here in a patient with PI3 kinase mutated ovarian cancer. So I think this has been really transformative for us, every one of our project teams now is waiting on an annual basis to see their compounds in this encyclopedia and to try to either validate existing therapeutic hypotheses or create new ones. Now we know that cell lines are deficient for many things. Number one is they grow on a plastic surface and that can't possibly replicate all of human cancer. We also know that cell lines don't even replicate human cancer because, for example, prostate cancer barely exists in its form as a cell line. So in parallel to that, we've been trying to establish primary tumor models. I know a lot of people are now doing this. We've been doing this since 2007. We've implanted around 2,200 tumors and now have 410 established primary tumors that can be propagated, frozen down as fragments, rethought and used as model systems. So we're in the middle of categorizing these since we started in 2007. First we were on arrays, now we're doing RNA-seq, then it's whole exome. So it's sort of a mishmash right now but we expect by the end of this year to finish profiling this and we're doing ongoing collections to try to fill in the gaps that now exist. So we hope that this will become not as facile as a cell line encyclopedia but still a second source of models that one can use to profile compounds and even mimic many clinical trials prior to the human clinical trial. So I'm just gonna close now by going through these five problems now as more statements of what we need to do. So first complete the cancer genome in depth. Work on validated but difficult to drug targets. Discover synthetic lethal drug targets in particular in the tumor suppressor arena. Study resistance pre-clinically. Don't wait till we get to the clinic but try to anticipate resistance and use that to drive either better therapeutic development or novel combinations. We need to discover those novel highly combinations and start testing them as early as possible in clinical development. And then finally we're continuing to build a robust pre-clinical translational infrastructure to allow more of us to sort of explore these questions at a level which will give us confidence and greater direction as we go into the clinic. So I just wanna thank the patients who have participated in our clinical trials and I have the privilege of working as many of you do with a great group of scientists, great collaborators at the Broad and elsewhere and I wanna thank them for all their help. So thanks. So Bill, thanks for really an inspirational presentation, really demonstrating some of the ways in which the cancer genomic research that all of us are doing here can start to lead to benefits directly for patients. So we have time for a few questions for Bill to follow up. So please go ahead. There's enthusiasm completely on the synthetic lethal approach. And I wanna share with you some success we've had with a one gene for a well SIRNA screening approach. Some of the successes we've had are a very high level of reproducibility of our hits, about 70 or 80%. Secondly, we get a much deeper menu of potential targets. We get the whole iceberg instead of the tip of the iceberg because we can query every gene in the one gene per well approach. And third, it's a new area we're very excited about is doing screens on primary patient derived tumor cultures with SIRNA. We can do the assays over a period of a week or two. And these are a couple of hits we've gone on to validate in preclinical models. We won synthetic lethal with P53 and mixed synthetic lethal in neuroblastoma model. I think 2A is, we agree that's a very good area to focus on. We actually have moved off of well by well screening to the pool screens, partly because you have the issue of transfection. And some cells are available to do transfection, others are not, but the heterogeneity of transfection was, at least at the scale, we wanted to do this somewhat difficult. We've done well by well lentiviral transduction, which turned was, as you can imagine, generating each lentivirus one at a time was quite the hurdle. So anyway, I think there's no right answer, but I'm glad to hear it's working. Greetings, it's a very, very fascinating talk. Just very naively, I have two comments and I'm interested in your suggestions. One is that I'm interested to know when is TCGA community is going to work on metastatic tumors, and so we can compare primary versus metastatic. That's one impediment. And the second thing is that when I worked on discovering subtypes of subtypes in breast cancer, what I did is data integration of different data types, copy number, mRNA expression, so on and so forth. So what I found is that there are many set lines that are represented of individual genomic alterations, but one of the impendments was that there were limitation of set lines that show the correlation of events as we see in the TCGA tumors. So what kind of comments or suggestions do you have to overcome those? Well, so maybe I can take the second question first, because that's the easy one. Is a thousand cancer cell lines enough to represent human cancer? Not even close, right? So yeah, the cancer cell line encyclopedia is a limited representation. It represents what it can represent. We're going to try this year starting to convert our primary human tumors, the PDX models into cell lines. I would like to see a effort where people who generate cell lines, either through a publication point of view or a grant renewal point of view, are asked to deposit them into ATCC, because I think there are a lot of cell lines out there that are not available or not readily available. So I'm with you that the cell line representation is not great accessing the ones that are available, making them readily available to do one thing. Clearly media growth conditions, different ways to grow cells, that's probably going to have to be important as well. With respect to your first question, like when is TCGA going to do metastasis since I'm not on the TCGA, I wouldn't know, but probably when they get the metastasis samples I would guess, right? Thank you. So Bill, could you expand on your, you said sort of off hand, you didn't like the isogenic cell idea, and we have very great abilities now to manipulate cells with exonucleases, and we know that we have very complicated heterogeneity genetically in cancer, and are we going to rely on the luck of whether you get a primary human xenograph to get a model for a particular type, or why can't we engineer it as a simple way? Certainly if you have no choice, then I wouldn't stay away from that. So a few comments, isogenic cell lines, in my experience, the ones that are created from cancer are not isogenic, or not necessarily isogenic. So if you take an oncogene and try to create the wild type version of the cell by knocking out the oncogene, that to me is sort of violating the very principle of the idea in the first place, and we've had specific examples where an isogenic pair was provided to us, and the wild type of the mutant cell line had basically deleted BCL2 during the isogenic process. The second is that the noise is difficult to overcome, and often what we've seen is you get hits that are differential because of the wild type cell line, not the mutant cell line, and this happened in the setting of VHL deficiency as well as in the piezrykinia setting, so that's been our experience. I was just thinking more, if you build up from an immortal, but not malignant clone and add things to it. Yeah, I'm not saying don't do it. I happen to like the genetic heterogeneity when you have one consistent lesion and 400 other things, because then if something's consistent, you've already controlled for other genetic events, but yeah, anyway, it's certainly harder to do these large scale panels, and as you said, if there are no cell line models for the disease you wanna study, then you can't be a stickler on principle from that point of view. Two comments and a question. So the first comment is about the metastatic cases. We are actively collecting triplets, so this is a source of germline, preferably blood, with the primary tumor, and then if the patient had a metastasis, even if the metastasis was exposed to treatment, we will run those as triplets in TCGA, so if you have them, we will take them. We are actively doing those. We have also a couple dozen recurrences for GBM and ovarian, et cetera, so those data are in the public domain. I did wanna also just make a comment, since most folks in this audience probably do not know about the TCGA collaboration with Novartis and Brode on the CCLE project. Those cell line exomes will be coming into the public domain through CG Hub at UCSC, and David Halser's group in the next couple of months followed in early spring, probably by the end of May, with those same lines and RNA-seq. So there was a decision that was made that those data will be made publicly accessible without having to go through the DAC process, so you should find those in CG Hub in 2013. My question is more, of course, Rot with fear about your statement that we should have to do all tumor types, all subtypes, et cetera, and whether an endeavor like that would need to, in your opinion, be continued to be led by the federal government as the person who is responsible for 10,000 sample procurement of a bigger project scarce. I was thinking much larger than that. Yeah, exactly, exactly. That's why I'm scared, so I'm curious whether you think it would be possible for a community-driven effort where the data are generated in individual sites and then data are deposited centrally. Sure, I mean, it's for you guys to decide on the model. I just don't, I just want to provide the message that we have a ways to go, and I'm with LOO, the genetic experiment has been done. It's like having taken yeast. You know, the reason we don't do forward genetics in mammalian cells is we couldn't sequence the genome. We did forward genetics in yeast because you could just sequence the genome and find the mutations. The forward genetic experiment has been done, and can we deconvolute it? Can we get enough little yeast tumors and find all the patterns that go together? I think it's very exciting. You know, it may take another log drop in sequencing costs, but every time I see Matthew talk, the chart looks like it's going down, but... So we sometimes envision a future where cancer is almost chronic, treated as a chronic disease. We do the genomics, treat cancer recurs, do the genomics again, treat again. What's your view on building the therapeutic armamentarium to make that successful? Yeah, I'm willing to accept it. I don't want to start at that proposition. The reason I don't want to start there is if I were a cancer patient, I wouldn't want to have to live with my cancer and the fear of it recurring all the time and the necessity to go back to the doctor and to take a treatment and to get another treatment. I don't think that's the greatest existence if we could actually get rid of the cancer. So why can't we get rid of the cancer? Well, I don't know the answer, but the past history says even diseases like testicular cancer, which people forget, looked worse than pancreas cancer. Patients died in weeks of testicular cancer is cured today. Now that may, yeah, again, that could be an exception or we may find other ways to engender that kind of curative response. Now why do I think it's sort of dangerous to go to the chronic therapy model? You are not going to cure patients with homeopathic doses of medications that cause no side effects. In fact, one of the downsides of Gleevec is in fact, it is so well tolerated, people now think we can treat melanoma and lung cancer with drugs that have zero side effects. Cancers, you need to inhibit the targets and cancers very potently. To do that, you're going to have side effects, but we can manage the side effects, we can find schedules, we can ameliorate side effects with other mechanisms. That's how it works for testicular cancer. That's how it's worked in lymphoma for many years. My fear is if we don't try to cure it, we will stop at doses that are subtherapeutic or subcurative. Now that being said, if we try all that and it doesn't work, I'm fine. We're treating to a model where we keep patients alive as long as we can. I think we should do everything we can to do that, but yeah, I'd rather aspire for the bigger cure than to retreat and never have the chance to get there. I think there's a question over here. As a clinician, I'll tell you one thing that today we genotype patients very routinely and our patients get biopsies on a regular basis, particularly at the time of disease progression. I do think there is an opportunity here for industry to collaborate with people like us to actually do sequencing studies, not up front, but at the time of disease progression. It used to be a difficult thing before to get repeat biopsies, but today we do this routinely. But I don't think the industry is there yet, at least in our experience, to support these studies, doing sequencing studies at the time of disease progression. Yes, so support can be a word for pay for, but I can tell you that we're very interested in this area. Of course, the first application is if we have our own trials, patients relapse, we're actually doing sequencing on biopsies when we get them. I'm in the process of setting up what we call a next gen diagnostics group at Novartis that in fact wants to collaborate off of Novartis trials and on Novartis trials to answer questions just like the one you proposed. There we imagine we're building the facility in the informatics that a clinician who had interesting samples might want them analyzed, we'd be willing to do it. So we are interested. I think Merck has had a pretty big investment in infrastructure in Florida around clinical samples. I don't know if it's focused on resistance or not, but I think people are pretty excited about that. So I just wanna say we'll take the last two questions from Dr. Medico and Dr. Getz, and then there probably are more questions yet to come, but we'll hold off on those for the people who are not yet up at the microphones. Okay, there's an intriguing problem about acquired resistance and the mutations that drive acquired resistance. Do they pre-exist in a small fraction of the cells or do they emerge de novo? And if so, how can they come so efficiently during treatment? What's your opinion now on this? I don't know. I think it's a great question and I was talking earlier about, or asking earlier about what is our actual sensitivity with NGS right now? And I think NGS is still not actually sensitive enough to answer the question. That is, if it's when in 10,000 alleles you can determine, that's probably not, that's probably, if you have a mutation at one in 10,000, that's a very common population of cells in the human body. So that's one issue is the technical limitation of NGS is still a problem. The second is at some point, it becomes a stochastic problem of biopsy, right? So a human has like 10 to the ninth cells somewhere in the body, you biopsy one place, as we've seen even in primary tumors, you don't know whether or not you've hit the biopsy point that would have the cell that might be mutated or not. In either case, I still think the answer is the same. I don't know how they generate them so efficiently, presumably mismatch repair and ongoing DNA repair issues, but the answer is the same, that is to create pressure on the cancer cell from more than one point where no one mutation and hopefully no two mutations even is sufficient to overcome the therapeutic pressure. So I think we can extrapolate from lymphoma where at least for one point it was four drugs, now I guess it's down to two, that combinations at least work at least in part by creating this pressure on the genetic evolution that the cancer cells in fact cannot overcome. Gatti. Hi Bill, great talk. I want to ask what's your take about heterogeneity in cancer and what happens if you find a driver, drugable mutation that occurs in 20% of the cancer cells? Do you act or not act on that event and what do you think will happen to the cancer? If it were in 20% of one patient cells, yeah, I wouldn't work on that. I think for me, the important message to me from the New England Journal heterogeneity paper was not the heterogeneity, it was the non-heterogeneous part. 30 mutations in that paper were completely conserved in every one of the tumor samples they sequenced. VHL was one of the founder mutations. We know that that's a driver in lung cancer. I want to know the earliest set of mutations that are persistently required for all the clones. That's my own bias. Now, what was interesting in that paper was this idea of sort of mutually, functionally redundant mutations. I think it was what, set B something and set B2 and KDM five something or other. Okay, so that sort of an interesting clue that maybe in fact during the evolution to the metastatic process, that pathway was in fact already turned on. So I would certainly want to look at that in the earlier stages and see if that pathway was already activated and whether this heterogeneity showing a pathway would be then relevant to the earlier stage of the tumor. Thanks.