 Please join me in welcoming today's speaker, Dr. Howard McLeod. Thank you. Appreciate it. And I have the conflict of interest shown here for completion. So it's great to be back here and to have a chance to catch up with what's happening here on campus and also to update you a little bit on what's going on in the field of pharmacogenomics. The issue around that we're trying to solve with pharmacogenomics hasn't changed and never well changed. And that is that we now have many different active therapies for the treatment of most diseases. And the changes that will occur will be that there will be even more therapeutic options for these diseases. And I'm sure you have your favorite disease that does not currently have a therapy, so it's most diseases. But for many of the common diseases, we have a lot of different choices to make. If you take something common like high blood pressure, there are over 100 FDA-approved drugs or drug combinations for the treatment of high blood pressure. And so how do you sit down and pick one of them for the initial treatment of a given patient? And too often what we do is pick something that we're familiar with or that we know how to spell or that we have some sort of affinity to that would cause us to start with that medicine. And if it doesn't work, we'll try something else in a different class or a different family of some sort. And so that really speaks to the need for something more precise in how we choose from amongst the various medicines that are out there. Often we're taught medicine as if it's a Michelin three-star restaurant. And if you had the good fortune to be at one of those restaurants, you go there and there's no menu. There's a list of what you're going to receive because the chef has chosen it and there's a bunch of things that are very small and have a French name, taste amazing and you wonder what that was. But you don't have a choice. Here is the menu. Really we're much more like golden corral where you go in there and there's 20 different entrees. And if you don't like one of the entrees, you pick one of the other 19 entrees and go with that or too often you pick more than one of the other 19 entrees. And the idea that we have all these choices to make is a very different way of thinking than we're normally taught. Really we need to be thinking about how do we best choose from amongst these options and how do we choose what the next therapy would be should that not work. It's a different philosophy that we need to start building on. And the reason why we need to do that is that there is so much variation in the response to most every type of therapy. If you work in bone disease, bisphosphonates are quite effective and there's not as much variability there. With some bacterial infections, not a lot of variability in response. But in most other areas, important areas like psychiatric disease, cancer, viral diseases, HIV, hepatitis, many of the other diseases that you work on, there's variation. Someone will respond to the most common therapy, about half the people won't, they need a different therapy, half of those people will respond, eventually you might find a therapy for everyone. But it won't be the same therapy in the way that we normally think about it. And so understanding that variability is part of the goal. Just to be clear, the goal is not to be perfect right away. If we could choose from amongst those 100 FDA approved antihypertensive drugs and narrow it down to 25 drugs, that would be in advance. We want to go down to the point where we know exactly the medicine that's the right one for you. But even in advance of filtering down to a smaller group to choose from would be an important advance, ruling out some of the other options that are there. And so this good is the enemy of, great is the enemy of perfect, or good is the enemy of great, or whatever you would like to say it, the idea that we could be smarter than we are now is really the goal. Toxices also remain unpredictable. And when you look at the reasons why medicines don't work, sometimes it's because of the biology. It's because the patients don't take the medicine. I know from one of the big advances that has been made over the last couple of decades that the statin therapy is for cholesterol. These medicines, if taken, will have a significant public health impact. But at the end of the first year of prescription, only one-third of patients are still taking the medicine as prescribed. Many of them have stopped altogether. And I know from my own parents that when taking a statin, they had a family reunion coming up, or they were going on a cruise, or there was some of the thing happening, they didn't really like the muscle pain. They just stopped it for just for a week or two, just to feel better, and then forgot to start it back up. And so many of the advances that we have with medicines will only happen if the patients take the medicines, and too often the adverse events, the unpredictable toxicities are part of that. And then, of course, there's the issue that none of us want to acknowledge, and that is that these medicines actually cost money. And who knew? I mean, the Europeans knew, but we really have a situation where we can no longer ignore that factor. If you're well-insured, you will likely have a 10% copay on your insurance. And if you're taking one of the new amazing anti-cancer drugs, they can be as much as $20,000 per month. So if you're spending $2,000 of your own money, your copay for that month, on something, typically when you spend $2,000 per month, you've chosen what color it was, whether it had leather seats, satellite radio, other things like that, usually a triton at the front, Maserati in the back, these sorts of things. And instead, you're spending them on a medicine that may or may not work. And so we have some changes that have occurred in terms of the way we think about things, even for the well-insured. And this idea of value is something that has to be part of our equation. And I know, at least I can always speak for myself, I hate the idea of having to even think about that. But if we're going to take our science and make it real, we have to have some of these realities at least be part of the late translational phase for this. Now, when we approach these sorts of problems, typically we think about it as a yes-no type of, you're going to respond, you're not going to respond, or you're going to get the side effect, you're not going to get the side effect. In reality, probability or probabilistic data is enough. I mentioned the statin medicines. You don't take statins because someone just had a heart attack. I mean, actually, you do for secondary prevention. But you try to take it early to decrease the probability of having a coronary event. Same with anti-diabetic drugs. You don't wait until someone has lost a kidney or gone blind to start treating their diabetes. When you see uncontrolled glucose, you try to control it now to decrease the probability that those kind of inorgan effects will occur. Same with anti-hypertensives. You don't wait for a stroke, you treat it now to decrease the probability of a stroke. And most of our preventive medicine is that same way. Here's an increasing age on the x-axis, increasing number of cases on the y-axis for colon cancer. And you see it right about this age here. There's an inflection point, the number of cases go up. That's when you start offering colonoscopy to try to detect early cases, polyps or early cases of cancer, to try to deal with that in that very treatable stage. And so the concepts are there. But yet when it comes to medicines, we don't really approach it in that same way. And it's time to start thinking about what is the science behind this, what is the epidemiology behind this, and can we act on it? Now the reason to focus on medicines is that they're an important part of our treatment for most diseases. But they're also an important cause of morbidity and mortality in this country. They're the, at least the most recent reports, have the adverse drug reactions of the fifth leading cause of death in the United States. There was a report from last night from the British Medical Journal showing that, there's a study from Johns Hopkins, showing that adverse events in general, not just the drug events, were the third leading cause of mortality in the United States. So it's medical mistakes, including those around medicines, are an important issue that we need to deal with. And if you look at other industries, the way they've dealt with errors is much more systematic and much more objective than we currently approach things in the biomedical sciences. And so there's some opportunities to go there. Adverse events are heavily litigated. Many of these things are predictable. And so there's an opportunity. When we look at, not only are the large number of cases of death, but a lot of emergency room visits that can occur. And if you look at a place like in psychiatric areas, see almost 90,000 patients visit the emergency department each year due to a drug-induced adverse event just in the psychiatric area alone. So the problem is quite a substantial one. And if you happen to go to an emergency department and you wonder why there's so many people there, some of them will be because of the medicines. Now, sometimes it's the patient not doing what they're supposed to. So if you take a full dose of insulin and then you forget to eat, you've contributed to the reason why you need to go to the emergency department. If you take too many of your pain meds, you may have needed them, but there is a patient component of that. But often the difficulties are things that are not predictable and not related to dose. And in that case, there are, in many cases, genomic reasons why we could act on that. I mentioned before that these therapies are expensive. And so there is an opportunity for value. It could be that $2,000 per month for that anti-cancer drug I mentioned before is a fantastic value. Because you have the quality divided by cost equation for value. Or it could be that it's a complete waste of money and you need to know that and move on to something else. And so the idea that we can be looking at what is the end game and moving towards the more basic elements is really important as we look at these end points. And some of these things will require human studies. It's kind of difficult to do some of these studies in mice. They're not insured. They don't have a health budget for each year. But the idea of doing some of these mechanistic studies to drive towards these end points is very important. Now, the things I've been talking about in terms of framing the clinical problem for medications. The solutions have had a number of different names over the decades. They've been called personalized medicine, stratified medicine, individualized therapy, patient driven medicine, tailored therapy if you're in the UK. As I'll show in one of the slides is pharmacogenetics, pharmacogenomics. But last year when the president called it precision medicine, we now all call what we do precision medicine. And the reason why we call it precision medicine is really simple. The chances of getting funded are much higher if we call it precision medicine. The reality is we're not very precise. I don't like using the word precision medicine outside of a grant application because I don't think we're very precise. Precision medicine is more of an aspirational term. Someday we may be precise in the way that we prescribe medicines. And as opposed to what is reality now. And so it is a goal. It is something we need to work towards. But it is definitely just an opportunity. Now, the circles here, back in the late 50s, the term pharmacogenetics was coined. It first came at least in the English print at that time. And that was referring to some of the interactions between the genome in twins and response to medications and showing that there was a heritable component. And at least inferring that there were some genetics involved in that. The genes weren't known, but the term was applied. With the human genome project, we had to convert to genomics because we're looking at more than one gene. We're looking at multiple genes. It's a genome of opportunity to try to understand the drugs. And therefore, it was made forward. As we started taking this into practice, it became personalized medicine in terms of trying to take patient factors and understand how to combine them with things like genomic data to individualize the choice of therapy or the choice of dose or the choice of monitoring that you might do for a given patient. And then as mentioned, precision medicine is now taking this on in a much broader sense, trying to really look at multiple factors of patient data. Much more than one can think about using your own brain to try to really pull this together to guide patient care. And I won't spend a lot of time on that except at the very end, come back to it. But I wanted to put that framework because there's so much buzz around precision medicine right now. But in reality, it's just part of a natural progression of trying to take things like the genome and understand how do we become more realistic? How do we add some of the layers of complexity that are there, that are real, and put them forward? The idea that one gene will drive most any disease is just not true. Take a great example, lexistic fibrosis. There's a lot of variability amongst patients who have the same genetic variance. And some of that is due to other genetic modifiers. Some of that is due to how aggressive they've been with a pulmonary hygiene and other aspects, and it all comes together to focus in on a patient and their lung function. And the same is true around many of these other areas, including medicine. We need to know other aspects of the patient to really pull it together. The other thing is that personalized precision medicine, pharmacogenomics, whatever you want to call it, has really always been part of the way medicine is practiced. There's always been a component where a doctor has looked at the patient, had some attribute about their lifestyle, or about their size, or about some other feature, and used it. I, during part of my training, I went to spend one year in Scotland and ended up staying over there on faculty for eight years. And part of that time was up at Aberdeen, where they were having their 500 year anniversary as a medical school. 1495 is when they were founded. And so they had a great library, and so you could go in and had to put the funny gloves on, cotton gloves, and you could look at some of the old texts. And once you got to know that an F was actually an S and all these other copper print type of things, you could read some of these old texts, and I read an old translation of an even older Greek book that talked about how they had the patient P on the ground. And if certain types of worms or flies appeared at that spot, they would give them this kind of bark. And if a different type of bug or worm or fly came up at that spot, they would give them another type of bark. So the idea of personalizing medicine is not new. The idea of using biomarkers to drive therapy is not new. Now that was not a clear approved assay. I'm sure some regulatory issues around where you peed on the ground for that. But the concept of taking individual patient data and selecting a therapy has been around for a long time. It's just that the level of precision, I guess, that we're applying has changed and improved as we go forward. But medicine will never be truly personalized. I know your mom told you you were special. But you're amongst a group of special people. You're not the only one that's special. I have a little mug that says, world's greatest dad. And it was such a disappointment when one of my colleagues had his own mug that said world's greatest dad. I don't know what my kids were thinking, giving him that mug. But the idea that we think we're special, we need our medicine, well, will never occur for practical reasons, for costs, for other elements, regulatory elements. But we can make it so that from amongst the medicines that are out there, the ones that are most likely to have the highest probability of working will be there. Now some of this is all good to the next slide is towards issues like greater customer accountability. And those of us on the more clinical side just hate terms like that. But for some of the reasons I already mentioned in terms of the amount of contribution patients are making now to the cost of care that's becoming a feature. And then that's changing really the practical expectations, the process. I mean, if you're a health system and don't have an app for someone to be able to pull it out and look at their chart and order a refill or medicine or whatever, they're going to try to move on to a health system that it does. And so there are practical things that are out there that are really driving this forward. Now the technology has been a big driver for pharmacogenomics, personalized medicine, precision medicine, especially even the last five years. The amount that one can do is just astounding. And if you think back 10 years, which I know it's hard to do, it's amazing what we have summer students that come in and can generate more data and interpret that data better than a whole lab full of PhDs could 10 years before that. Because things are not as handcrafted as they used to be, you can now do things in massively parallel approaches. And so the technology is there. We can look at many different genes in a very rapid fashion. There's even some technologies where you can take something that basically plugs into your iPhone, spit on it, wait 12 minutes and get it up to five SNP genotypes back. And so in some places like the intensive care unit, you can get a genotype result back faster than you can get a blood gas back. And have some results in which to act on. Infectious disease is changing dramatically because of the technology, where the idea that one would wait for a culture to come back is becoming a foreign part of the history of medicine. Because one can get more rapid turnaround, especially in CNS infection. Rapid turnaround results on whether this is virus, fungus, whatever it might be, and act on it accordingly. I mentioned about the patient burden. If you're paying more, you want to have more say. And that's natural, and we definitely see that out on the clinical side. There's less personalized care. If you are lucky, I have a doctor that I go to. And it's kind of an old fashioned notion these days. Many people go to a clinic and might have a doctor of record. But if they're not available, we'll see one of the other doctors that are there for that practice. And that's become more common, and certainly there's elements there. And then the issues of even countries like the United States can't really afford to treat 100% of people with a expensive medicine that will only benefit about one-fifth of the patients. And certainly in the cancer area, it's relatively common that drugs will work in a 20% subset of patients. Yet we don't often know who those are until after treatment. And so that has certainly been a big driver of how things go forward. Now, one of these last background slides is I put up there just because it's often really easy to get lost in your favorite technology. This is a genomic series, so there's a lot of focus on DNA and could be a copy number of DNA or methylation of DNA, but DNA, you could also expand this out to protein or RNA, functional imaging, blood levels circulating tumor cells in the case of cancer, other measures like that. Lots of different ways of trying to measure a patient and understand what is it about them that would cause you to do one type of treatment versus another type of treatment. And I mention all these, and not that it is an exhaustive list, but there's more than one way of trying to do this. And too often, it's the old, if you have a hammer, everything looks like a nail type situation, where we do DNA genome science, therefore the answer, doesn't matter what the question is, the answer is DNA genome science. And really the answers are going to be as we combine these things. Now, I'm going to use a lot of DNA examples in the remaining time, but part of that is because of the practicality that DNA technology is ready. It's being used. It's great for discoveries. It has application on the clinical side, whereas some of the other technologies are not quite that far along. DNA is also quite easy to obtain. DNA is very stable. They got DNA from King Tut. You can get DNA from a lot of different things. DNA is also of, you can really get it almost anywhere in the world, and it'll be stable enough to get to a laboratory. You can get DNA from blood, from buckle scrapings, from hair follicles. I like to joke that you can get DNA from anything except OJ's glove. So it's basically, there's lots of different ways of getting DNA that can be valuable to the patient. And so I'll talk about DNA, but really as we're thinking about how do we take this forward, it's taking whatever is robust in helping us look at the patient and try to drive that into practice. Last little background piece is just to remind you that as we get towards the patient side of things, we have to deal with the complexities that patients have more than one set of genomes. In this example, there's a tumor genome and the normal genomes. And this might be toxicity and efficacy if you want to keep it in a more simplistic type of model. But the same is true with viral disease and for there's some data that even things like heart failure have a somatic component in terms of changes that have occurred over time. And so the idea that we have this simplistic model where we can measure a genome and an A genome will be important for a patient is really not true. And we need to be ready and thinking about, all right, how do we deal with multiple genomes and the way they're applied? Now, pharmacogenomics is not new, as I mentioned, nor is it something that will start happening in the future. It's something where there are changes already. Now, there are over 160 drugs where the FDA has put genomic information somewhere in the package insert. So you know when you pick up your prescription and they take out the bottle and there's that wad of paper at the bottom? I'm not recommending you read it, but I would like you to recycle it. So as you pull it out, if you happen to read it, there's a bunch of different sections that talk about dosing and administration or toxicities or this or that. And somewhere within that, around 160 drugs have genomic information. A smaller list, and most of them are shown here on this page, have genomics in the dosing and administration section. And that's the section that is supposed to be read by prescribers, and it is read by the app makers that prescribers use at least. It's read by insurance companies and it's also read by litigators. So there's kind of the three main audiences of that section. And these are examples where in some cases here, it's from here up changes in tumor. Some of these where the FDA has approved the drug, this drug for only patients who have this translocation or this translocation or this mutation is in this gene for this drug's case and the list goes on. And so you have the examples where it's very specific and you have others where it's really more of a general prognostic terms. You have also germline examples, normal DNA examples, where it could be metabolism, it could be the immune system in terms of hypersensitivity reaction, could be a response to certain medicines in the case of cystic fibrosis. But the idea that genomic information that is already out there for use is something that's been around for a while and is growing each year. The FDA has a nice site that has these examples that one could easily pull up. Now, very few of these examples are such that it would be malpractice to manage a patient without testing. Certainly in the germline side, I would say really only the abacavir example would be a malpractice type of situation. But rather, there's information saying, well, if you have this genetic information, here's what you can do with it in terms of starting with a lower dose or starting with a higher dose or whatever it might be for that. And so it's the type of information that is valuable if you have it already, but unless you're an early adopter, you're not gonna necessarily order that test. And so you see some areas like HIV with abacavir that pretty much every HIV patient is evaluated for this particular HLA molecule, if I get the arrow back, to identify risk of hypersensitivity reaction. Whereas you take some of the other examples and it's much less common for the genomics to be applied. Even for the same gene, this gene here is important for nausea and vomiting, for antidepressants, pain control. You'll see the psychiatrist doing a lot more testing than the oncologist for even though the oncologist might use a lot more of certain medicines. And so some of it is cultural, if you will, in terms of early adopting personalities versus otherwise. The other thing is even within an area like cancer where there's a lot of activity around tumor sequencing, there's still a lot of elements where the germline, the normal genome is important for medicines that are there. So pain control, nausea and vomiting control, antidepressants, stimulants, blood thinners, all have a genomic component in the dosage administration section. And at least some centers are now using more of this information as it's applied. There are guidelines that are now being produced. So there's something called CPIC, the Clinical Pharmacogenetics Implementation Consortium. There's around 140 institutions in 23, I think it is, countries that are involved in putting together a large number of different guidelines that are available on the PharmGKB, the Pharmacogenomics Knowledgebase.org website. And these guidelines are now working their way into the national registry and other places where guidelines are produced. And so the field is maturing from that standpoint. The way things are being applied in Pharmacogenomics are really around the testing for avoidance in many cases. And that is identifying a patient who would have a high risk of a hypersensitivity reaction or a bad reaction, try to avoid using that medicine. Testing for inclusion, so with some of the cancer examples, if you have a particular variant, you are eligible to have that medicine. If you don't have it, you are not eligible type of thing. Stratification in terms of whether someone is high risk or low, needs to have a different type of therapy. And then testing for explanation. It's my term for that. It's basically someone takes a medicine, something really bad happens to them, and you wanna figure out why. And so often you'll see testing done to say, well, did that patient crash and burn because of this genetic problem? And if so, I will now avoid a whole class of medicines, or was it something else of which now the whole therapies are available to be used? And so that idea is being applied. And so you see it for areas like pain control. Now, there is no prospective randomized trial that I'm aware of that has done genotype guided pain control versus not genotype guided pain control. But there are studies showing, for example, with codeine, that if you have the extra copies of the gene, you'll convert it to morphine more quickly. And there have been a number of fatalities with neonates and in the pediatric situation to the point where many pediatric centers no longer use codeine at all. They don't genotype the patient, they just don't use the drug at all because of these situations. It also really highlights the issue. If you have an awesome therapy and a not so awesome therapy, it would take an amazing bit of data for you to not use the awesome therapy. That's a generic name, not a brand name. And so awesome is gonna rule every time. But if you have two equal options and you have to pick one for the patient, even a feather of data would cause you to shift one way or the other in terms of choosing that medicine. And so really much of medicine is tiebreaker type decisions where you've got a number of equal options. You've got a number of different ways of treating pain. And if you knew that oxycodone will work much of the time but not all the time in someone who is deficient for this particular gene, you would shift to something that doesn't require that particular medicine. They're not all shown here, but for example, Hydromorphone or some of the other drugs that don't require metabolic activation. The idea that 10% of patients cannot activate a pain med and that somewhere between three and 5% hyper-activate a pain med means that somewhere between 10 and 15% of all your patients are gonna have trouble with those medicines. And that's a big percentage just based on one gene and one class of drugs. And so the idea that we can start using this information not to choose whether someone gets to a menace or not blindly, but rather shifting the scale to one versus another is the way we're seeing this starting to play out. Same with anti-imetics. Again, there's a number of different medicines. If you're an anesthesiologist you're used to using a densitron and you don't wanna use another medicine. A densitron is the one that's worked. Anesthesiologists are a very fine-tuned group of individuals. They have very clear protocols in terms of how they do things. They're the most objective, or at least I think, the most objective practitioners of medicine among all the different specialties. And they need a good reason to not do what they normally do. And so we're seeing even our own anesthesiologists now using SIP2D6 to tip the scale from using a densitron to one of these other medicines. I suggested they just stop using a densitron, but no, that's what they're used to using. And then I said, well, it's only 3% of patients that have extra copies of SIP2D6. So do you really care? And they looked at me funny, he said, we worry about a one in 1,000 event. 3% is huge in our world. And so again, it comes back to the context. If you ask an oncologist, if she is worried about 3% of the patients having nausea and vomiting, they would say, we would be delighted if it was only 3%. If you ask anesthesiologists, they're terrified of 3%. So again, context really matters as we see this start to be applied. And then the last little background piece is around, are there enough patients with these abnormalities to really make it worthwhile? And so we've seen places like Vanderbilt and Moffitt and others start to do preemptive type genomics. So this is a paper from almost two years ago now, came out of Vanderbilt. And they looked at this example for Clopidogril, in the cardiac situation, one of the statins, this genotype affects muscle pain for the statins. These genes have influenced the dose of Warfarin, the blood thinner. This gene influences whether you're gonna get severe neutropenia from this medicine for arthritis and dermatologic disorders, GI disorders, and leukemia. Here's in the solid organ transplant setting, this particular immunosuppressant. And if you look in some of them, there's very common. The yellow is at least one actionable variant. Some of these, it's quite common to have an actionable variant. Others, it's much less common. And then it's very rare for there to be a extreme risk type of variant. But when you add it all together, 86.5% of patients had at least one actionable variant. And an additional almost 5% had a high risk variant. And so it ends up being over 90% of patients, just with these five examples, over 90% of patients had something that was actionable in terms of preemptively preparing for the choice of medicine. Now, if you are choosing a statin and you don't have genomic information, you might wanna just start with one now. But if you had that information already, it would cause you to shift from one to versus another. Just like you may not really wanna know that someone's renal dysfunction. But if you know that they have a creatinine clearance of 40, you're gonna adjust a renal-extreated drug, even if there's not a randomized clinical trial showing you exactly what you should do. And so what we're seeing now is this with these preemptive strategies, more genomics being done when the patient arrives at the health system so that it's preloaded into the electronic medical record and can be acted on as we go forward. But these and the follow-on studies have shown that it's not the minority of patients that have something to think about. It's the majority that have something to prepare for as you change practice. Now, there's still shifting gears to the more basic discovery elements. There's still a lot of discovery that's needed. There are very few precision medicine, pharmacogenomics, whatever you wanna call it, genome-wide association studies, and even fewer where next-generation sequencing has been applied. They're starting to come out, but if you look in the NHGRI G-Wash catalog, it's somewhere around 4% of the phenotypes are drug-related in some way. And that's any type of medicine, psychiatric meds, cancer meds, et cetera. Very small number of investigations have been done. And if you look at the number of patients and people across the country that get a prescription medicine, and you look at what those prescription medicines are, very little has been done to really understand how do genes influence the choice of medicine, the choice of dose, the choice of monitoring for a given patient. And so there's a lot of work still to be done for that. Part of the issue as well is that replication data sets are difficult to obtain. So one example that wasn't planned, but that came online this morning was a study that we did out of the NCI's Clinical Trials Cooperative Group. In this case, one of the groups called the Alliance did this particular trial. Used to be Cancer Leukemia Group B. It was the historic name for that particular study. And so this was in prostate cancer, people who had advanced prostate cancer, and they were given this particular chemotherapy drug plus a placebo, or this chemotherapy drug plus an anti-vascular agent. And the bottom line was that the addition of the anti-vascular agent did not change overall survival for the patients. It did change progression-free survival, but overall survival was the primary endpoint. But we could go in then and look at the different toxicities that occurred for these patients. And so there's just over 1,000 patients of which 864 registered for the pharmacogenomic study. And then when you filter out some of the filters for population stratification, ends up with just over 600 patients on which we could study for the initial phase. And there's a number of end points one can look at for these kinds of studies. So the chemotherapy drug causes neutropenia and causes neuropathy. The anti-vascular agent causes high blood pressure, protein in the urine, clotting, and bleeding. And so we can look at these different features and look at what are the genome predictors of these particular end points. Let's skip that in the interest of time. So one can go in and do a model. For example, for neuropathy, which I'll show you some data from right now, that was the paper that came out this morning. And for neuropathy, one can go in and include as well this variant co-factor. So if someone already has long-standing diabetes, they're gonna either have or be at high risk for neuropathy. One needs to know that as we're looking at drug-induced effects, what is the baseline that's there? So the models we would use in the past were relatively simple. Here's a group of patients treated with this drug. This drug causes neuropathy as one of its side effects. We look at who experienced neuropathy. Maybe we normalize it per dose. So we can take into account dose adjustments that occurred and who completed the two years without getting neuropathy. And so that's very much a yes-no type of model. But unfortunately, we have all these competing risks that occur. There are patients that stop the drug because their disease progressed. There are others that died while on therapy. Some that had some other severe toxicity that caused them to stop taking the medicine. Others still that just got fed up with being on the trial and decided to go do something else. And they're allowed to do that. We have to take into account. And so by taking a more complex model, like a competing risk analysis model, one can then go in and functionally do censoring around these different events. Because if someone withdrew from a study after the second cycle of therapy, they didn't have enough time to get neuropathy. They might have gotten it. But to include them in the no neuropathy group would really be a false thing. And part of that is because the, what's shown here are a number of different endpoints. In black is death or disease progression. Green it's other treatment associated adverse events. And you see early on it's these other events that are occurring. And so there's a lot of other events, non neuropathy events, neuropathy shown in red, that are competing here. And so these patients that had these events never had a chance to really get neuropathy. And so we need to model them accordingly. And the reason for belaboring this point is that there are so few pharmacogenomic studies. I would hate for us to go and do some and use inappropriate statistical models. Every one of the studies that are done are precious. We don't have enough. If you're doing studies in diabetes, there are so many diabetes studies that you can have a few crappy ones because they'll be overwhelmed by some great ones. But for pharmacogenomics, we don't have enough studies. We need to do them well. And so going in with some kind of, hey, I have Excel, I can do statistics type of mindset really needs to be overcome. And as we're thinking about these approaches, and especially in places where it's hard to find good statisticians, we need to be doing more collaboration with the people who know how to do these more complex models. I don't know how to do them. I could go and buy a stats package and stick it on my PC or Apple and do something with it. But by having folks that really know how to do this stuff, one can start to really get at the truth here. Now, as we look at neuropathy as the endpoint, we get these genome-wide associations to these so-called Manhattan plots. Now, this particular Manhattan plot has a couple of features to it. First of all, you can tell a real statistician did this analysis because if it was me, each of the chromosomes would be a different color and each dot would be a little bit bigger and we'd actually be able to see everything. Secondly, I had to draw circles around the dot so you could actually see it because of that. Second thing, it resembles Manhattan, Kansas, much more than Manhattan, New York. And so we have a few hits, if you will, nothing up into the 10 to the minus eighth range, but something to go forward. And then as we put in more of the adjustment factors, the clinical features, we do start to get some genome-wide significance with some of these features. So what's shown here is a list of genes that had some level of genome significance and, as we looked at the functional data, had some sort of biologic plausibility. And I show this for two reasons. One, because we did it and the follow-on slides are featuring this particular example, the VAC-14 gene, but also because this is not a very smart way to do it. We did this analysis this way, but really what it does is it opens us up to be fooled by naming. So if anything has the word sell-death in it, automatically it's the best candidate gene you've ever found for whatever the phenotype is that you're looking at because you can make a great story for why it is the gene. Whereas if it's K-I-A-A-7-4-3-9, it doesn't really have a ringing to it. You're trying to, actually that probably does have a ring to it, but the idea that you're trying to use that and come up with some kind of plausibility, no one knows what those genes are. We don't have a clue. And yet they might be the right gene in terms of how they're going forward. And so as we're doing genome-wise studies, we need to be thinking about, are we being fooled by the naming, or are there more things that are gonna be important for us? So taking that caveat in mind, we then looked at this variant in VAC-14 that was one of the candidate genes with functional stability. This stabilizes one of the proteins that's involved in chocomeric tooth, a inherited peripheral neuropathy syndrome. And so looking at VAC-14, the variant, there was an increase in the risk of neuropathy. There was a gene dose effect, if you will, that occurred. And so that was encouraging. We then went in and did some studies with IPS cells, using some cells that have been differentiated into sensory neurons. And I'll point down to one particular circle so they don't have to get lost in what is figure three of the paper. What's shown in this particular panel right here is increasing dose of the drug and looking at the relative branching unit, so a measure of neural outgrowth in this particular study. And you see there's a difference between those that had had the VAC-14 gene knocked down compared to those that had a scrambled control in the particular experiment. There was a series of additional studies that are shown in the paper to show this. And so that's interesting from a biologic plausibility standpoint that we've now shown in an in vitro system that we can see a difference between the cells expressing and not expressing, or at least not over expressing or highly expressing the VAC-14 gene. And then one can go into VAC-14 knockout mice, heterozygotes, the homozygotes aren't viable. And what one can see is that there's a difference between, sorry, this is a ugly looking slide, the journal hasn't redrawn it for us yet, so this is a little bit more raw than you're used to seeing. But the blue X are the mice that are heterozygous for this gene and were treated with vehicle and the red X are those same mice treated with the drug. And you can see a real drop in the hind paw withdrawal threshold, one of the many measures of neuropathy that was performed in these particular mouse studies. But you can also see that even in the wild type mouse, there was a drop in neuropathy and differences did occur in here. So we have this in vitro data, or X vivo data that shows that we have a difference in these genes, but it's all very unsatisfactory. And part of the reason why is that replication data sets for pharmacogenomics are very difficult to obtain. The clinical trial that I just showed you that we used for this discovery, we're not gonna go and do the trial again. You do a trial once and then the winner, if you will, of the trial goes on and faces the next opponent in the next clinical trial. You don't do a trial twice just so you have a data set on which to do replication. It's not considered ethical in many cases. So you have a situation where you're really stuck with a data set for discovery and some level of biologic plausibility, hopefully with some mouse and cell line data to help back it up. And then have to really go forward into something more prospective in terms of intervention. And so there's a lot of opportunities to think of creative ways of generating the kind of data we need to really be able to do more robust replication at the same level that someone working on diabetes or many other diseases have in terms of these large cohorts. The other thing is that, just wanna go back to the title slide here. There's a bunch of different people that have to be involved in these studies. And this is just a small list. This isn't even the complete author list. But the idea that a number of folks that are running the clinical trial that are experts in the different types of phenotyping in the clinical trial, that are experts in the IPS cells and in the knockout mouse models that are involved in high level statistical modeling, all these folks have to be involved and preferably from the start and not involved in the usual way. So often what'll happen in statistics is that there's something that is referred to fondly by statisticians as the statistical autopsy. They're brought a data set, usually in pieces, and says, can you fix this? As opposed to, hey, we're gonna do a clinical trial. Can you help us design a really robust trial that will help us answer a question? And the idea that these folks get involved from the start, it makes a world of difference. It's a pain in the butt. Because first of all, they don't speak any language that I've ever seen. It's no language known to man. It's all a version of Greek that has a lot of numbers in it. They think in 12 dimensions, not the ones that we were involved in. But they can make us so much smarter as we go forward. And so team science, not team as in tennis where you have a team of physical therapists and psychologists and hitting coaches like whatever they help you. But team as in hockey, I guess, that's what's on right now, or basketball, where you take one of them out and you're in trouble. And so we need really more of that to be happening. Now, one of the things we also can do is once we have enough literature is not do the discovery component, but go straight to a replication type component. So this is a study that came out a couple of months ago from our group just as I had the slides ready, so it was easy to use. This is a study of ovarian cancer. It's really quite a boring study. It was dosataxyl or pacotaxyl, two chemical cousins of each other, and then combined with this one dose of platinum drug, normalized to blood level. And that was the randomization. And the end result was there was no difference in survival or overall survival or progression free survival. And this has become the standard of care. So over a decade later, after this trial, the standard of care for the treatment of ovarian cancer is still this particular regimen. Now, if you're involved with ovarian cancer or know someone who's had it and you might have gone to the ovarian cancer clinic, you'll have noticed a number of women using walkers. And you think, how unfortunate this disabled woman got ovarian cancer. You know, that's how unlucky. Typically what has happened is that she walked in, started her therapy, but the therapy is so toxic to the nerves, peripheral nerves in particular, that she no longer can feel her hands and it's trouble buttoning her blouse and whatever else. Can't put a piano very well anymore, things like that. And it's trouble feeling her feet and to the point where she's stumbling and gonna fall and hit her head. And so she's now using a walker to be able to keep forward. And the way I was taught, and the people who were taught around my era and really up until recently were taught, that in cancer, you need to almost kill the patient in order to kill the cancer. And that was the mindset that was done. And certainly it was appropriate when you take drugs like the alkylating drugs from back in the 50s and 60s where the more you use, the more killing you get of the tumor. And so you need to surf that careful line. And so, is a woman really willing to not have as much therapy to control her cancer in order to avoid the nerve toxicity? And that sort of has kept things back. And so we were looking at this, we had this large dataset. It had been maturing now for a number of years. We had robust toxicities that had been audited. So we decided that we were gonna go and do a discovery study. But then we realized that there had been many discovery studies already done. There were also studies looking at the nerve function biology, there were inherited neuropathies. There was some of the end points that had come out of these studies were pharmacokinetic or pharmacodynamic genes. And there was a lot of underpowered data sitting out there already that really was cluttering the literature. And so why not go in and just say, all right, any variant that has been shown in some study, no matter how small, to be associated with neuropathy, let's evaluate it in this context. And so it was kind of a bummer because we were looking forward to doing a million SNPs and instead we ended up doing 1,261, which just seems so old school to do such a small number. But those were the ones that had some robust association to go forward. And we took the 1,000 women from the study, our statisticians pulled out using a randomization algorithm, half of the patients, and evaluated the 1,261 SNPs and looked at the association with a grade of peripheral neuropathy in those patients. 69 SNPs were met our statistical threshold. And so these 69 variants went into the next 500 women on this study. And there was some directional correction. There were four variants that came out to be important. And one thing from these four variants and these four separate genes, firstly, there was about a doubling of risk. So each one of the variants contributed something, but a doubling of risk is not enough to change practice. It's enough to be interested, but you really need more than that in terms to change practice. Usually somewhere around four times, otherwise your four will change practice. And so that was interesting. But if you look at the accumulation of these variants, when you look at all of the variants, the population attributable risk was about 85%. So a lot of the variability could be explained by these particular variants. And one could go in and look at a kind of a, I guess, variant dose effect. The more variants you had, the higher your risk of neuropathy that was seen in this particular study. Well, that's interesting. It was a replication, not a discovery data set, so that's interesting. But what about the original problem of needing to really induce toxicity in order to cause control of the disease? We looked at these variants, especially those in the highest toxicity risk. We looked at a bunch of different cut points, but this is the one we put in the paper. And basically, we could not find a way of separating out in terms of progression of free survival or overall survival based on these predictors of neuropathy. So what this does is opens up the idea that neuropathy is not required in order to control the cancer. That the genes that, at least from our study, appear to be regulating or being associated with, I should say, neuropathy are not predictors of outcome in these women. And so the idea that we can use this data to now do some additional prospective studies, and we are, but also to start doing some drug development. Now, some of these examples, for example, BCL2, there's already an anti-BCL2 therapy that was approved earlier this year for a type of lymphoma that is on the market. And then there's at least the possibility of doing some intervention with some of these other types of genes. And so it opens up some drug development opportunities in addition to the possibility that it could predict prior to the start of therapy what the level of toxicity a woman might have. We've now gone further and using next generation sequencing technologies identified that there are some additional levels of risk that are out there. And so in this particular study, we're taking patients that had very severe response to these medicines, so these anti-cancer drugs. So you have them one or two doses and their nerves just melt away. And in that case, the patient seemed to have a underlying Charcot-Marie tooth, a peripheral neuropathy syndrome. Now, if you go to a neurologist, they would probably pick it up every time. But if you go to an oncologist, they don't really notice if someone shuffles in as opposed to walks in or if someone has a bit of a limp or it certainly don't look at someone's reflexes in most cases, they don't take people's shoes off and look and see whether they have high arches which is often associated with these syndromes. It's just not something that is part of normal practice. And so it's basically that iceberg under the water. Just because you didn't see it doesn't mean it won't sink the ship. And so we're now finding that and others are now too, that these inherited neuropathy syndromes, the moderate penetrant versions are out there. They're really not noticeable clinically very much and they're really waiting to cause problems. And so these types of discovery pieces are now allowing us to look at our preemptive strategies to the point where we are now looking for Charcot-Marie II syndrome genes, cardiomyopathy genes, a number of apathy genes prospectively in all of our patients to identify those few that are at super high risk of these very severe toxicities. Now part of it also is trying to come back to those dollar signs that I mentioned earlier. And that is we can really use the cost element in our favor as we try to take pharmacogenomics from being discovery science, including some of the stuff I just showed you, into implementation type of science. And so one example is with this anti-fungal drug called Voroconazole. It's used to treat fungal infection, but we use it in a context of a cancer center as prophylaxis for our myeloid leukemia patients. So without prophylaxis there is a high incidence, at least high in r-ray, in our mind could be as high as 30% incidence of death by fungal infection. But prophylaxis it's reduced quite dramatically. Well, as some of you will know, yeah I took that out, some of you will know this particular drug can be inactivated by a liver enzyme called the CYP-2C-19 and a substantial amount of the population has an overactive ability to get rid of the drug. So it's right around 28% of Moffat cancer center patients similar across centers in the United States have a high ability to get rid of this drug. And so a normal dose for everyone else is very inadequate and you can't really measure blood levels. You never get to the point where you're getting prophylaxis. You might as well not be using the medicine at all or you need to know to switch to a different medicine for this particular case. So there's a number of clinical trials and clinical studies rather that have been performed on this. We have the clinical case done nicely. But if you go to your administrators and say, hey, we have this strong clinical case, we would really like to implement this in our practice setting. What you'll get is that is a very, that's very interesting. Patient safety is paramount. So we would like you to go and do another clinical trial and we would like you to do some cost-effective mistakes. But if you go and do some of those economic analyses using actual data from your center, you get a very different response. So what's shown here is some analysis that was done by Neil Mason at our place around the cost of fungal infections and the treatment of and the genotyping for fungal infections in the context of myeloid leukemia. And basically what it came down to is that the myeloid leukemia patients that have a fungal infection cost us just under $30,000 more to manage in the first year than the patients who do not get a fungal infection. And what happens is if you can prevent one case, you can pay for testing in spades. And this sort of situation, when we went to our administration and said, hey, we would like to do this, here's the clinical case for doing this. Here's the economic using our actual data, not data from Kaiser or some other place, but our actual data. There was nowhere to go but yes. And so one of the things that we're doing now a lot is we try to take our pharmacogenomic examples and bring them all the way across the line to patient care is working with these economic folks, the folks in finance, the people who never thought we'd even have coffee with, much less collaborate with, to make the case. Because if we can go in and say, here is the clinical case, because you wouldn't go forward without a good solid clinical case, and here is the economics, you can get to yes much quicker. And I drop that in there, not so much for the NIH folks because it's a different model here, but for the folks that are gonna watch this later and see as they think about their health system, if you can make the dollars work, almost anything will happen. And so the idea that we only work on the fine tuning our next-gen sequencing machines is only gonna get us so far. We need to have the rest of that going, and we don't need to do that. We don't need to be those people, but we need to be collaborating with those people to really make this forward. All right, so we're gonna come back, swing back to the way genomics has started to change practice, especially in the area of cancer. So as I mentioned before, we have the germline genome, for example, things like peripheral neuropathy. The cancer genomics is really becoming a normal part of care. And often it's for the selection amongst equals. So if you have a new diagnosis of lung cancer, there are a bunch of FDA-approved options that might be the right ones for you if you have a particular genotype. And so having a small focused panel that allows you to make that decision is certainly normal practice in many, if not all of the United States-based cancer centers. But after you run out of the first and second line options, which is where most of the randomized clinical trial data is, you have a patient that's fit, wants to do more. You often don't know what to do next. I mean, you can pick a medicine, but what is really something that's gonna actually help that person? And the idea that genomic information will influence that is becoming a reality, coming back to this tiebreaker type medicine. So you have these two clinical trials, or this clinical trial and this off-label use of a medicine, what do you pick? And the genomic data is often driving that. And it's really changing the way practices happen. So it wasn't that long ago that a tumor in the colon would be called a colon cancer. Or maybe it has glandular formation under a hematoxylineus and stain. So it's an adenocarcinoma of the colon. Or maybe there was a K-RAS mutation, mutation in the K-RAS gene. And so it's a K-RAS mutant, adenocarcinoma of the colon. You can kinda see the theme that's building up on that. Except that's just been blown on its head. Now this tumor is a P53 mutant, EP300-deleted DDX-3X lost cancer with all these variants of unknown significance. With the handwriting being one of our clinicians who before getting this report was a world expert on this disease. And then after getting this report is a babbling idiot. Trying to figure out what in the world they're gonna do with their life because they certainly don't understand cancer anymore. What we've found is that, there's the old information, data, knowledge, wisdom. We're generating lots of data and information. We have very little knowledge and no wisdom. We are just so data-rich that it's paralyzing. And so the idea that we can take this and try to move it forward is critical. And what we're seeing, we have initiatives now at the NIH that are helping drive this, but there is such a lack of informatically informed people involved on the clinical end. You have people that are involved in health IT, electronic medical records, but people involved in trying to take information about a patient and help turn it into a decision are very few and far between. And so there's a big opportunity and a big problem sitting there as we sequence all these patients. And sequencing cancer patients is not something you might do every once in a while. And at a large center like ours, we're the third largest cancer center in the nation, we're sequencing about 120 patients a week. So it's a lot. It's not something that we might do. It's something that we do. And so it becomes a real problem very quickly as we go forward. But it also opens up opportunities. So this is a patient with Lyoma or sarcoma, very few FDA approved drugs for this disease. They, she ended up with a metastasis to the lung. She received this doublet of therapy for three months and then it didn't work. This doublet for four months, then it didn't work. This kinase inhibitor, which was FDA approved finally, is for three months and it no longer worked. She was fit, she wanted to do more, but she didn't know what to do. And so we could just put her on whatever trial we happen to have available, or we could try to look further. And so we sequenced her tumor. And it's not so important all the details or I would have made it larger. But the findings that there came back and there wasn't anything that her oncologist, who is an international expert in treatment of sarcomas, thought was actionable. But because of all this problem, we've generated a couple changes in the way we practice. First of all, genomics, the pharmacogenomics in particular now has turned into the equivalent of radiology at our institution. You might like to read your own CT scans, but a radiologist reads it anyway. And the same thing has now happened with tumor sequencing and germline, for that matter, sequencing. That every single case is read by the personalized medicine clinical service. And so they all get seen, a report is put in the electronic medical record. About a third of the time the report is we have nothing to add to the pathology report. Call us if you need us. Word is a little bit nicer than that. About a third of the time, there's a few changes. You know, this trial is no longer open or no longer accruing. You know, think about this option as well. And then another third of the time, there's some real complex work that has to be done. We've also generated something called the Clinical Genomics Action Committee. Now it's a molecular tumor board, but the reason we didn't call it a molecular tumor board is that too often, molecular tumor boards are academic freak shows. You go there and you say, whoa, this particular patient we're talking about, she had a Jack-2 amplification. So you'd go in this group and you say, she has a Jack-2 amplification. And everyone around the room would go, whoa, that's crazy, that hasn't been seen for a while. Next, as opposed to what do we do with this and is it actionable and how do we act on it? And so we have a large group of different disciplines that are all in the one room, trying to work through what happens. And I'll come back to a point about that in terms of the way our basic science colleagues are contributing. But in this particular case, Mahila Druda, a sarcoma medical oncologist whose patient this was, was presented the case. And a couple of the folks here, in particular a couple of the leukemia and lymphoma myeloma folks, they said, oh, oh, Jack-2 amplification is well known in our area. And at least with in vitro data and some of the initial clinical studies makes a patient more responsive to PD1 and PDL1 inhibitors, immunotherapy. Well, immunotherapy doesn't work in sarcoma, except in some of these patients that have these particular features that make them more relevant. And so based on the report we worked up, she ended up going on a PDL1 inhibitor trial in August of 2014 and is still on it. The previous therapies, the longest one worked for four months. This one's worked for a lot longer than that. It will not cure her. She still has viable disease that is stable sitting there, not decreasing, not really resectable. So it will at some point in time come and get her. But it has bought her way more time than before. And this is an anecdote that should not drive your practice in any way, shape, or form. But as we build up more and more of this, we start learning some of the rules whereby we can now objectively demonstrate, and that's future tense, that this genomic sequencing really makes a difference in terms of outcomes. Now, whether it influences survival, I would certainly like it to, but even time on therapy is important. Those of you in the oncology area, the patients that haunt you, and certainly haunt me, are the ones that died three weeks before an FDA approved therapy or a clinical trial event of a therapy became available that switched to a more curative type environment. Those gastrointestinal stromal cell tumors where now once a matinee became available, they became a, if not curable, at least lifelong controllable disease. And if I had had the drug three weeks earlier, that patient would still be alive. And so the idea that we're trying to cure people is definitely true. But the idea that we're trying to bridge them, have them living quality life, a quality life long enough, is also an important part of this. And we need to be thinking about those kinds of end points as well. Last little piece on this is we have several of our cell biology and molecular oncology PhD colleagues that now come to every one of these meetings. And the reason they came initially was because we had examples of patients with variants where there was no human data, no clinical trial data, nothing really, but there was some cell line data. And so we're like, well, in the land of the blind, the one eyed man is king. If we had some data, we could try to devise a more objective way of treating this patient than just picking a trial we happened to have open. And so we looked at, had a couple of the papers. Well, it turns out the papers were from our own institution. There was a guy down the hall that was doing this work. And so he said, well, could you come and actually tell us the story behind the story? You know, well, when he came, he said, oh yeah, this variant, you know, it does, you know, at least in cell lines, it doesn't respond as raffinative, but it does to some other drug. And they're very, they're terrified that someone's gonna mistake them for a clinical doctor. Listen, I'm not a clinical doctor. But I do know this about cells. I do know this about mice. And often, you know, we're at a situation where we're trying to choose up from amongst a couple of equal therapies. And even some cell line or mouse data might be enough to say, well, let's go with this versus this. Because we're not talking about therapy and no therapy. We're talking about what we use first. And if it doesn't work, what we use second. So the idea, you know, even when you look at a clinical trial in your favorite journal, you'll see this, you know, survival curves and they're usually wide enough that you can see a difference between the two. And you think, oh, there's winner and loser. No, there's first line therapy and second line therapy. You know, and, you know, we often say, oh, the winner of that trial was X. But then if we go to a patient after the winner stops working, we don't say, well, we're gonna try to lose your therapy on you now. Rather, it's the next best option. And so our whole mindset in terms of how we do trials, how we interpret them, how we go forward needs to be changed as we try to apply things like pharmacogenomics into practice. So I'm gonna skip that in the interest of time and hit the last little bit. You know, one of the things is just a reminder that there are a lot of genomes that patients have. You know, it's not just the germ line and the somatic. But in fact, within the tumors, there are a bunch of different genomes. It's not like there's lung cancer. It's a bunch of different types of flavors of lung cancer within that particular mass. And so we don't really have good models for thinking about in vivo or ex vivo setting, how do we deal with all these different populations? You know, the population, the folks dealing with population evolution or whatever you call it, really haven't jumped into the practicalities of what we're trying to think about and how do we evolve new ways of therapy. And I think as pharmacogenomics matures, in many of the diseases we're working on, we're gonna be seeing more and more of those elements. The last little piece is as we go into people, we really need to have more diversity. So when we talk about American being a melting pot, but really in some cases, it's really more like a carton of eggs. It's a bunch of people living in the same place, but not necessarily with the same diversity. And the reason this matters is that this is a study that is not yet published, but hopefully will be soon, where we looked at 127,000 patients over these three disease areas, over these different time periods. This Todd Nipper, myself, and two of our FDA colleagues. And basically what we saw is that over time, there was a doubling of the number of countries that were involved in pivotal clinical trials, the approval trials for the FDA. So globalization has occurred, no surprise, but we just quantitated it in a little bit bigger fashion. And you can see over time, here's the different countries that got involved and that's great. But what we found is that in 1997, just over 90% of the pivotal clinical trial patients were self-described as Caucasian. In 2012, we now have 2014 data, it had gone all the way down to only 82% of the clinical trial participants being Caucasian. Basically a lot of the growth that had occurred meant that there were more white people on trials except they were from the Ukraine and Argentina as opposed to actual diversity happening with globalization. And that matters. And look around the room, look around wherever you're gonna be tonight, look around every place. There's a bunch of people from a bunch of places that are needing help just even within this country. And the idea that we need to be doing these trials is important and at the pharmacogenomic level, it matters. What's shown here is a number of different genetic predictors for pharmacogenomics. And what's on the x-axis is different countries separated by continent. So here's Africa, here's Asia, here's Europe, here's the Middle East, here's Central America, sorry, here's Central America and here's South America. And using the data from the New England Journal Papers, here is the predicted average weekly dose of warfarin, very low dose in many of the Asian countries except India, which is much more similar to Europe in terms of its metabolism. Much higher dose needed in Africa, et cetera. Here's a risk of GI toxicity from one of the anti-malaria drugs. Again, a lot of diversity. Risk of muscle pain from Simvastatin, again, a lot of diversity. And then if you look within countries, taking the New England Journal study, here's the average dose for a US population, very similar in Mexico. The Nigerian and Ghana populations need a much higher dose. The Chinese and Japanese populations are much lower dose, but there's a lot of variability even within that geographic label. And the reason for bringing this point up is that as we extend pharmacogenomics into the clinic, we need to be making sure that we capture some of this diversity. We're doing a project now with China. They have 56 ethnic groups in China. So there's the Han Chinese that everybody knows about, and then there's a whole bunch of other groups. And 1% of the Chinese population is a lot of people. So these groups are not very common, except millions of people that are in each of these groups. And so the idea of trying to understand the diversity, we heard about from a couple lectures ago, population genetics, we need to know this sort of data not just so we can understand where we came from, but understand where we're going in terms of trying to make good health policy and individualized therapy for our patients. So I'm gonna stop with this particular slide on the Precision Medicine Initiative, and the way I view the Precision Medicine Initiative is it's a 3D printer. It's gonna be a massive million man or whatever person cohort, the majority of the money spent for that, but it is a 3D printer. The 3D printer is pretty cool, but what's even cooler is what you do with it. And so this cohort that is being built is the start of some amazing science. And so I would encourage all of you, no matter what area you're working in, to not be thinking like we often do, what a blank waste of money, but rather how can this be a strategic advantage for helping answer some of the questions that we've been working on. And I'm not part of the PMI, I don't even have a t-shirt with the PMI label on it. I'm an outsider looking at it, but after I got over the, what a waste of money this is gonna be part, I looked and said, wait a minute, this could be really powerful if we get involved and help shape it now, because 3D printers can make some really stupid little plastic toys or it can make a new valve for our heart. And it's up to us what we use our 3D printer for, but I think it was a great opportunity to take pharmacogenomics and really push it to the point where it's known to be useful and areas where it's known to not be useful as opposed to where we are now where we think we have some hits, we think we have some misses, but we really don't know what's all about. So I'll stop at that point, a little bit of time for questions and thank you very much for your attention.