Dr. Brian Druker | Knight Campus Distinguished Lecture, Oct 7, 2019


Well, thank you all for coming out this morning, and it’s just an absolute pleasure for me to be here amongst friends and to really help build this collaboration between the Knight campuses. So we start, President Schill, by dispelling rumors. Brad Pitt is not playing me in the movie, but yes, there is a movie being made and, hopefully, it will be on track. We’ll see. What I want to do in my talk today is to take you through a little bit
of my past. And one of the things that I’ve been so fortunate to be associated with is what many people call a breakthrough. We hear about changing a paradigm for cancer treatment. And what I get to do is I get to reflect on all the things that went into making this happen. And in part, this is not about science. It’s more about how science is done and how breakthroughs can happen in virtually any discipline. And we’ll start with the first precept. And that’s that breakthroughs take time. So you don’t you? Many of us think about breakthroughs as, oh, you wake up had this “ah
ha” moment. Well, it can happen that way, but often times it takes a lot of time to
incubate. I’ll take you back and take you 100 years ago. And imagine you went to a hospital and you had an infection. This is what your encounter would look like. What you have is a contagious disease, an infection just like any other. I’ve seen cases where it was transmitted by an inanimate object. You mustn’t be disheartened. There’re many as badly off as you. Many. Will I get well? You must come here twice weekly for sweat baths, observation and
medical supplies. The treatment consists largely in rubbing yourself with this ointment. Tell me doctor, will I get well? Rub a different part of the skin every night of the week so that no part of the skin is rubbed more than once weekly. Got this girl in Yuma, someone I love, who loves me. We had planned to be married as soon as I graduate. Tell me, doctor. Tell me the truth. Can we ever get married now? I’m afraid marriage is out of the question now. You may dress now. Does anybody ever get cured? Of Course. There’ve been many cures. Many. Yes, many cures. The reality is, if you’re diagnosed with an infection
in 1900. That was it. And the reality is there was really very little that couldbe done. If you look at the leading cause of death in 1900, the leading three were infectious diseases pneumonia, tuberculosis and diarrheal diseases. Life expectancy in the US was 47 years. If you had a child, they had a
1 in 10 chance of dying before the age of six because of infections. That was 1900. Now, if you come to 2000. Heart disease is number one. Cancer is number two and nipping at its heels to become number one. Pneumonia and other infectious disease has been relegated to number
five. And we think of often some said that infections are now an old person’s friend. The reality is we don’t fear infections. So what happened over the course of a century that allowed infections to become eradicated or treated and the first was just really simple things like putting refrigerators
in our homes. That was in the 1940s. Water treatment facilities was around the turn of the century. Those are the things that made our food and water supply safe. In the 1940s, antibiotics, penicillin, 1942. And that was in the lead
up to World War 2. If I had said to you antibiotics in 1900, you’d have thought I came from a different planet. What’s an antibiotic? It’s a specific therapy that can kill bacteria.How are you going to do that? And then in the 1950s, vaccinations, polio vaccine, 1955. So if you recast this into bigger items, it was prevention, specific therapies and modulating the immune system. Huge amounts of time went into to all these breakthroughs over the last
century to eradicate or treat infections. The disease I worked on is a disease called chronic myeloid leukemia, first described in 1845. We began to understand the molecular pathogenesis by the mid 1980s and a specific therapy in 2001. Again, a huge amount of time went into the breakthroughs to allow me to develop a specific therapy for this disease. Let’s go back to the clinical description 1845. Two pathologist, Bennett on the upper left and Virchow on the lower right, describe two cases of leukemia in 1845. Now think about 1845. How did they actually get these articles into
print? There were no typewriters. There were no computers. And the reality is they publish these two papers within six weeks of one another. And thereafter ensued a vigorous debate about who was really first. So nothing new in science or nature. Who’s first? I was first. No, you were first. Ultimately, Virchow capitulated that Bennett had described his case six weeks prior to his. And he gets the credit for being first. We’ll fast forward a good 150 years. Now we know that chronic myeloid leukemia makes up about 15 to 20 percent of all leukemias. There are about one to two cases per hundred thousand per year. That translates into about five thousand new patients per year in the United States. The disease can affect any age group, but the average age of onset is somewhere between 50 to 60 and, before the year 2000, the average life expectancy of a patient diagnosed with this leukemia was about three to five years. So second precept, if we’re going to break make a breakthrough, we need some knowledge. Let’s go back and visit our doctor and see what he’s up to. [Doctor calls for his associates] Everbody come here. Professor, you called. Gentlemen, it would seem that the germ of syphilis has been discovered. Really. By whom? By one Fritz Schaudinn. The German medical weekly sent me these proofs for approval. Listen. The spariket pallidum is a protozoa. It is a fine, steeply convoluted filament with six to 14 turns. It is decidedly mortal with forward turning and bending movements. Tell me, does that description put you in mind of anything else? Its decidedly mortal with forward, turning and bending movements. Why, that’s just like the tripenozoa Right! If the germ has been discovered, there’s cause to hope for a cure. Yes, there is hope. So there we are. If the cause has been discovered, there is hope for a cure. And whether that’s chronic myeloid leukemia, Alzheimers all the problems that the Knight Center’s going to work on here. It takes knowledge to be, to get to a breakthrough. So we need to understand what causes CML. The molecular pathogenesis. And here will start again with two pathologists, Nowell and Hungerford, working in Philadelphia, discovering an abnormal chromosome in the blood and bone marrow of patients with chronic myeloid leukemia. And they dubbed it the Philadelphia chromosome after the city in which they worked. And if you’re ever on the wards working with me, you’ve got a series of questions wrong. I’ll ask you, what city was the Philadelphia chromosome discovered? It’s not a trick question. But as you can often do in the last paragraph of an article, you can talk about the implications of the work. And before I get there, this was a science article from 1960 in its entirety. Three hundred words. That’s right. Quality and quantity don’t equate. But look at the last paragraph. They say the findings suggest a causal relationship between the chromosomal abnormality observed and chronic, granular, acidic or chronic myeloid leukemia. Supposedly in 1960, this was highly controversial. Most people didn’t believe them. They thought it was an associated abnormality that had nothing to do with the cause. Fortunately, Janet Rowley, working at the University of Chicago, from where your president hails, showed that this short chromosome here, this so-called Philadelphia
chromosome, a short chromosome 22, actually came about because of an exchange of chromosome material between two chromosomes, 9 and 22 making a short chromosome 22 and actually a long chromosome 9. Then by the 1980s, when people began to map oncogenes to chromosome locations, they realized that the ABL gene, which normally should be on chromosome 9, got translocated or removed to chromosome 22 and fused to make this BCR ABL fusion gene. Next, precept breakthroughs often occur when different fields converge. Often it’s different fields come together and then something opens
up. And when I think about the timeline of chronic myeloid leukemia, there are three critical threads that had to come together before anybody could move the field forward. The first is the entire field of tumor virology. Peyton Rouse, working at Rockefeller University, had a chicken – a farmer brought a chicken to him – and said, ‘my chicken has a tumor.’ And Professor Peyton Rouse said, well, there’s not much I can do for your chicken, but I’ll go study in the lab. And he took this chicken tumor and made this cell-free filtrate and passage it from animal to animal. And nobody believed him. Everybody thought there was cellular contamination. By the 1950s and 1960s, the entire field of tumor virology was born based on his seminal findings. His initial tumor harbored the oncogene V-SRC. Shortly after that, Herbert Abelson working at the NAH showed that there was another oncogenes in a different animal tumor called the V-ABL, and that was the initial set of oncogenes that were discovered by Bishop and Varmas in 1976. And the BCR-ABL was yet another oncogene. I mentioned the work of Nowell, Hungerford and Janet Rowley looking at chromosome banding, gene mapping that identified the BCR-ABL oncogene. And the last thread was protein phosphorylation, serine/threonine kinases is discovered in the 1930s. Tony Hunter discovered Tyrosine kinases in 1979. We’re celebrating the 40 year anniversary of that discovery. The first oncogene, the first oncogene V-SRC is a tyrosine kinase so is V-ABL and so is BRC-ACL. So when I started working in the lab in 1985, we began to think, could you now imagine specific therapies directed at these individual kinases? Last precept breakthroughs require seeing things differently and everyone thinks, oh, you gotta think outside the box. Well, I remember one of my first meetings with Phil Knight and I was complimenting on him him breaking open the entire field of footwear. And he said they’re just shoes. Well, what Phil Knight did as he saw the potential in shoes that nobody else saw, which was you could make running shoes and tennis shoes and basketball shoes. Everybody where I grew up, it was just tennis shoes and there were all the same. So he saw something right in front of him. So sometimes it’s not thinking outside the box. It’s seeing what’s right in front of you, staring you in the face. Now, I give you a couple of examples besides shoes. I’ll start with Velcro. The story of Velcro. This German sorry, this Swiss engineer, George de Mestral, went through a hike in the woods and he was wearing a velvet vest and he came back and his velvet vest was covered with burrs. Now, how many millions, if not billions of people have gone on hikes and had their clothes covered with burrs, pick the things off their clothes, got a prick from the thing and swore at it and went on with their day. He asked a critical question, which is ‘why!’ Why did these burrs stick so tightly to my velvet vest? Being an engineer, he happened to have a microscope down in his
basement. He went down and took a look. And lo and behold, his vest have loops, and the burrs had hooks. He made that into a fastener called Velcro right in front of anybody could have seen that. Penicillin, we’ll go to a medical example. Many of you heard the story, Alexander Fleming walked in the lab one
day, looked at this petri dish covered with a bacteria, and it had yeast contamination and the bacteria weren’t growing very well around the yeast. Now, having worked in a lab long enough, I can tell you I’ve gotten a lot of yeast contamination on my plates and usually just swear at them and throw them out and start over. He had a little bit more of a background. He had actually worked as a surgeon during World War 1 and grew tired of plucking bullets out of wounded soldiers, only to have them die,
weeks later, of infections. He dedicated his career to getting, going to the lab and finding ways of killing bacteria. And about 12 years later, that fateful day, after coming back from a vacation, he walked in the lab and instead of throwing out the plates, he looked at him and said, ‘why are the bacteria not growing as well around the yeast?’ and use that to isolate penicillin. So, again, right in front of them, anybody could have found it. So in 1985 when I started in the lab. Here’s what we knew about CML and BCR-ABL. In my view was the cause of the molecular abnormality of CML. It was present at all patients with this leukemia. In one of the first experiments I did was I mutated the kinase activity, made a kinase inactive protein and it had no function enabled, in terms of an oncagene. So, many people at that time, when I wrote my first paper, I said this is the causative abnormality. My mentor at that time said, no, you can’t say that. We don’t haven’t proven that yet. We know it’s the hallmark. So you can use hallmark, but not causation. To me it was like, well, it’s an oncagene. It drives the growth of cells. It’s the cause. Why don’t we just say it that way? And to me, it was pretty simple. You have a kinase that binds ATP, it transfers phosphate on to specific substrate proteins and that causes the excess growth of white cells. If you could imagine blocking binding to this specific kinase, you’d have an ideal targeted therapy for this leukemia. But another problem back in 1990, kinases were not a viable drug target. And here were the problems. First of all, I just showed you one kinase in the human genome. There about 500 of them. And the view was, if they all bind ATP, if you block one, you’re going to block them all. There’s never going to be specificity. So an impossible target to drug. Second, in the 1980s, 1990s, oncologists were a pretty pessimistic bunch. We were giving an awful lot of chemotherapy that didn’t work very well and the view was how are you ever going to give a single drug and have it work? Nobody’s ever done that. Third problem, they’re gonna be toxic. And here the view was the early knockout animals of specific kinases were embryonic lethal. The view is even if you add specificity, even if they work, you knock out one kinase and they’re gonna be incredibly toxic. But the death nail was this one, it was money. The reality is, is that drug companies looked at CML 5000 cases a year and the view was it’s going to cost us maybe a billion dollars, maybe two, to develop a drug. If you look at what the market will bear, maybe we’ll make 100 million
a year. Our patent life is 17 years. And by the time we get to market, it’s 12 years. So we’ll invest well over a billion. We may get 1.2 billion back and we have a 1 in 10 chance of success. Anyone want to take that that gamble? I don’t think there’d be many takers. Despite that, in 1993, Nick Lyden, who was working at a very large drug company, that I helped them set up a drug discovery program. When I moved to Oregon, he sent me a handful of compounds. One of them was here. Imatinib or Gleevec. And it was the best of the compounds that he sent me at specifically killing CML cells in my laboratory studies. And we published this paper in 1996. We could show that we knew we had an ABL inhibitor. It also inhibited a couple of other kinases. We added Kit to his profile. It specifically killed CML cells, both in vitro and in vivo in animal models. And the chemists at the drug company were able to make it into a highly bio available oral formulation, which meant when we went to clinical trials it was a pill. In 1998, about 21 years ago, we started clinical trials and this was the very first clinical trial in cancer that only enrolled patients with a single disease. Typically in a Phase 1 trial, It’s a dose finding study and you’ll take people with any type of cancer that have failed all other therapies to see if your medication is safe. I lobbied saying, “why would I give this drug to a lung cancer or a breast cancer or prostate cancer patient?” It has no chance of working. And how can I honestly tell him to enroll in this clinical trial? Why don’t we restrict it to patients with CML? I can honestly tell them it might work if we get two effective doses. And in addition, for the drug company, it was really reluctant to get into the study. We might see some early hints of activity. They bought into that and within six months, we reached 300 mg and above. And virtually every
single one of our patients benefited. So they had significant therapeutic benefits with minimal side effects. In patients with chronic stage disease, which is the majority of
patients, all but one of them had what’s called a complete haematology response, which means their blood counts returned to normal from dramatically elevated. Now, say when you develop a successful drug, you only need one patient to show that you have a successful drug, and this was my one patient. And this is the before and after picture. On the left, I was treating
withan old outdated drug called Hydrea and his white count would go up and then it would come down kind of on its own. His wife, who’s an accountant, would keep track of me. She loves numbers would do these create, though these were her graphs. And she’d come in to say, Dr. Druker, you’re not doing very well at controlling my husband’s blood counts, are you? And I sheepishly look back and say, ma’am, I’m doing the very best I can. I’d raise his dose of medication, his white count would bottom out and she’d go, ‘Dr. Druker, you got him in trouble his white count’s too low.’ And I’d lower the dose and they go right back up. In April ’99, we started him on 300 milligrams of Imatinib. By December, she said ‘Dr. Druker, you’re actually doing a pretty good job. We’re going to stop checking up on you.’ Now, the reality is the FDA doesn’t like anecdotes. We enrolled over a thousand patients on clinical trials and the results held. But to me, the real results were when you start to look at populations and this is a study out of Sweden and they’ve looked at every single decade over the last 50 years, 40 years, and they looked at how many years would people be expected to live as compared to the normal population. And what you can see is as of 2010, there is no difference in the life expectancy of somebody who’s age 55, diagnosed with CML compared to the regular population. Now, there are a couple important points about this slide. This is Sweden. The reality, if you live in Sweden, you have universal health care coverage, you have access to care, and these are your results. I’m ashamed to say that if you live in the United States, we don’t see these same results and we don’t see these same results because not everybody has insurance and not everybody has access to low cost medications. And if you live somewhere in even a lower resource country, the results aren’t going to be anywhere near as good. So the reality is we have a lot of work to do because I don’t think we’re all going to be moving to Sweden. So we have work to do. But again, if you have access to drug, if you can get costs down, you can see these kinds of results in this type of a country. Unfortunately, however, Imatinib is not perfect. And if you look at the number of people who relapse at about five years, it’s around 17 percent. So, again, not perfect. And we need to understand many of the precepts that I just used. Why do people relapse? So the reality is. We have an ABL kinase inhibitor. So the first question is, “Is the kinase still inhibited in people who relapse or not? Because if it is still inhibited in the patient’s leukemias growing and has to mean something else is now driving the growth of the cancer. If, however, the kinase is no longer inhibited, maybe the drug’s been pumped out of the cells. Maybe the target’s been amplified or perhaps has been mutated. Through lab studies, we’ve learned that about 60 percent of people who relapse are in this not inhibited category and it’s due to mutations scattered throughout the kinase domain. What I’m showing you here is this is the kind domain, the amino acid numbering and the vertical bars above are the frequency at which the various residues have been mutated. What that tells us now is we can develop second and third generation medications, much like has happened the HIV world where the targets are mutated and new drugs come along to target those mutations. So now, in 2019, we have four FDA approved medications. They targeted different spectrum of mutations. But every single one of those common mutations I show on the last slide can be shut down by one of these four drugs. So now, Imatinib is currently the standard of care. It is significantly prolonged life expectancy. Relapses are mostly due to kinase domain mutations. With these novel ABL inhibitors being used more and more in the upfront setting and CML is now considered a manageable condition. So when I see patients, I still see patients in clinic, I see somebody newly diagnosed. The first thing I tell him is I expect him to live a normal lifespan and I can start my conversation there. Then we can start talking about how we’re going to get them there. But I can at least see some anxiety being relieved as we get into those discussions. So where else has Imatinib of worked? As I started out earlier, I talked about its targets, PDGF receptor and Kit. Gastrointestinal stromal tumors, largely driven by kit mutations, and it’s worked remarkably well there. There’s about 2 or 3 percent of melanomas driven by kit mutations. Imatinib’s worked there. and then a couple of really rare disorders, hypereosinohillic syndrome, that’s rare skin’s sarcoma called DSFP, driven either by PD, driven by PDGF receptor mutations and Imatinib has worked significantly in those two diseases. Now, because oncologists are oncologists, we try, if something works, we figure oh let’s try it everywhere. And Imatinib got tried pretty much everywhere because all of its PDGF, is expressed in most cancers, but it hasn’t worked in hardly any of these other tumors, with the exception of the small percent of melanomas. So the reality is that despite expression of a target, unless it’s mutated and known to drive the growth, these drugs won’t work. Now, what lessons have we learned so we can begin to apply this to other diseases and other cancers? I’ve spent an awful lot of time trying to convince you, you’ve got to understand what you’re doing and it’s all about the target. And if you have a good target and a good drug, you can get good
results. And we made it look pretty simple. Now, for as long as I’ve had this slide in my deck, I’ve [inaudible] concentrated on target. Recently I’ve been a few examples where bad drugs almost killed good targets. One of them is a target in melanoma is a kinase called B RAF. And there were a series of drug trials of RAF inhibitors that didn’t target the mutant and they didn’t work. And then people began to question, well, maybe it’s not a good target. I looked at the data and said but it’s not targeting the mutated
enzyme. Why would you expect it to work? Fortunately, another drug company came in with a wonderful B RAF mutant specific inhibitor, got FDA approval and now they’re half a dozen other drugs coming along like it. So how are we going to translate the success of Imatinib to other cancers? First of all, in my view, we have to identify the appropriate therapeutic target. In general, I like to think about these as early driver, early
pathogenic events. Second of all, we have to treat early in the course of the disease and I’ll come back to this point. But the reality is, is that CML progresses to an acute leukemia in a three to five year timeframe. If you try Imatinib in the advanced acute leukemia, we see transient responses. If you treat in the chronic phase, we have people living a normal
lifespan. Well, we should be treating earlier before we get to advanced metastatic cancer. And I’ve said this for 15 years and it’s only now that people start to invest in early detection of cancer. And obviously we have to be precise about we’re doing matching the right patient to the right drug. Now, the reality is this slide has been the blueprint for the work that’s ongoing, both in my lab and at the Knight Cancer Institute. I’m going to talk to you about a huge effort we have at discovering and validating new targets. I won’t talk much about our precision oncology, but that’s a precision early detection. But that’s what that second bullet is all about. How do identify who to treat early? And the third is all of what I call precision oncology, matching the right patient with the right drug. So that’s what the Knight Cancer Institute focus areas are; Precision oncology, Precision early detection. Things I’ve been talking about for as long as I can remember and even before the first gift from Phil and Penny Knight. I’m going to focus mostly on what we’re doing in precision oncology because the early detection program is relatively nascent. But there’s just there’s a lot of exciting work there, but I do have to come in on time today. So I want to start about how do we accelerate identifying the right
targets? And right now, the world has been divided up into two camps. One camp has been sequence everything. And we started out 15 odd years
ago sequencing everything. And what we learn very quickly is we got a lot of information, a lot of data, but we got a lot of wheat with the chaff and separating the wheat from the chaff turned out to be a huge endeavor and we never could identify how to get there more quickly. What we then started doing was the other camp says, oh, you’ve got any functional screens. Take a cancer cell, take a whatever your favorite cell type is and see what will kill it. And you can do that by knocking down genes. You can do that by genome wide, crisper. Now, you can do that with drug screens. The problem with those screens is you never really know why you knock a gene down and it kills the cells. Well, why did that happen? We have the advantage of working with leukemia patients so I can get a blood sample. I can do these functional screens. I can do the genomic screens. And I can integrate all these results and try to accelerate progress. I can prioritize genomic data based on what the functional screens are trying to tell me. And I can take the functional screens and then use the genomic data to see what’s going on. So I’ll give you an example of what we did in a nine-month period going from a patient’s sample to a treatment. So we took a patient’s sample with a particular type of leukemia called chronic neutrophilic leukemia. We sequenced it and we found 20 or so genes that add mutations in it that looked interesting. This is a long time ago and using RNA I and there are a couple of of proteins if we knock them down, the cells were killed. We could kind of rank them in terms of what what we then did a drug screen, we could look at what drugs killed the cells. We could integrate all that and look at what the right targets were. And we ended up with this one here, the CSF3R or GCSF receptor. We then went to a bank of leukemia samples and Jeff Tyner and Julia Maxson, who’s a post-doc with Jeff and me, publish this paper in The New England Journal, again within nine months of getting the sample. What we learned was these CSF3R mutations define this disorder, much like BCR-ABL define CML. CSF3R defines chronic neutrophilic leukemia. And we knew from our
functional screens that the largest set of these mutations were sensitive to an already FDA approved drug, Ruxolitinib. So we treated a patient with Ruxolitinib and they responded. We now have done a very large national trial of Ruxolitinib it for this disorder. And our hope is it will be used to extend the label of this drug. So talk about fast going from a patient’s sample to defining pathogenic abnormality to a treatment. That’s how fast we want to be. Now. One of my one of my good friends, he’s the head of oncology at Stanford, gave a talk at a large society of oncologist, 40,000 strong, and he talked about the work I had done. And he said while Brian worked on a really stupid cancer called chronic myeloid leukemia. I didn’t take that personally, least not too much. But I did recognize that what he was saying is it was a pretty simple cancer, had one driving molecular abnormality, wasn’t really an advanced cancer. And I began to think, okay, let’s work on something a little tougher. So it began to work on acute myeloid leukemia and this is data over for 40 years showing you the survival of patients treated with standard therapy, which hasn’t changed. And you can see it’s about five or 10 percent if you’re diagnosed with this, leukemia and it hasn’t changed one bit. So talk about a tough challenge. We decided to take this on with our functional and genomic screening. And you put together an incredible collaborative group in this Beat AML program. We collected 950 patient samples from about 11 different academic medical centers. We put them through whole genome sequencing. We put them through our functional screens And we had one of our collaborators do immune screening. We got eleven to 15 different drug companies to give us their drugs for the screens. We collaborated with a large group of computationalists to actually help us analyze the data. And we published this all in a publicly accessible database. So a couple things. First of all, it was a huge collaborative effort.
Second of all, on that computational biology, this is where our collaboration with the University of Oregon is going to come in. There is massive amounts of data and we want that data be analyzed right here in Oregon. So that’s part of the goal of our data science collaboration. But just look at the not this. This project by the numbers, There was eleven academic medical centers, 950 samples. Thirty thousand..thirty seven thousand labs on our patients. This is the largest publicly accessible database with genomic data, functional data and clinical data with outcomes. And I’ve had people from around the country who can get into this database and look at their favorite gene, their favorite protein, their favorite pathway, and do an in silico experiment and get an answer within hours, not years. That’s what we’re trying to do to accelerate the ecosystem. Now, we’re also moving this into a large clinical trials sponsored by the Leukemia Lymphoma Society. And the idea here is to move the needle on patient treatment. We’re taking patients sequencing their leukemia and putting them on one of eleven different treatments. And the idea is to turn that data around in seven days and assign a treatment. And oh, I don’t want you to get lost in the weeds, I just want to focus
on the numbers. In two and a half years, we’ve enrolled almost 500 patients and assigned them to one of eleven different treatments. This has been an incredibly popular trial. Over 12 academic centers are participating in this. At least a half a dozen different drug companies put their best drugs into this to try to move them this along as quickly as possible. We’ve collaborated with the FDA on this project so that if one of these drugs looks good, they can move it through the registration quickly. Now, in the back of my mind, my friend who said I’ve worked on stupid cancers is probably still saying, well, come on, these are blood cancers. You can get those blood samples. Why don’t you work on something really tough, like breast cancer or lung cancer or something that kills lots more people? Well, we took that on too and with the recruitment of Joe Gray, Gordon
Mills. We’ve launched this trial called SMMART. And we can’t spell, so we’ve put two M’s in it. And it’s Serial Measurement of Molecular and Architectural Responses to Treatment. That’s a mouthful. But let me just walk you through what we’re doing. We start with a patient and get a biopsy and we run through it, a remarkable array of testing. And I’ll…I’ll show you what that is in a minute. We initiate treatment much like we’re doing in this Beat AML trial based on all of the data. But what’s unique about this trial is that we know that tumor is going to evolve. So one month later, we go back and re-biopsy, reanalyze and change the treatment around to see if we can stay ahead of the cancer. And, if we see the patients progressing on therapy, we go back and biopsy yet again and change our treatment. So the idea is we want the patient on our study to benefit as much as they can and we evolve the treatment to benefit each individual patient. On this slide, again, I don’t want you to get lost in the details, but I’m showing you all of the testing we do. And if you look up at the top, the first two or three is what most of
the rest of the world will do. They’ll sequence the cancer and hope that they find a match. But in my view, that’s about 15 percent of the data cancer has to give us. If we begin to think about what proteins are expressed, what pathways are activated? What’s the immune status of the cancer as well as immune status of all the surrounding cells? What’s the architecture of the cancer? How does that look in three dimensions? If you start to add in all of that complexity. Maybe we could get to 50 percent. That’s three times more data than anybody else in the world is getting to make a treatment decision. Of course you aren’t 100 percent, but we’re not going to get there right now. But if we can get three times as much information, how much better decisions are going to be? Now, the other point I want to make on this slide is another area for collaboration on data science. Do you know how much data we generate from each individual patient? We have to pull all these records. We have to integrate all this data and then we have to bring this to a tumor board to make a decision. So here’s another area where the data science initiative is absolutely critical to the success of our treatments. So how are we doing? We’re early days. We actually are up to 50 patients and the numbers are holding. But we’re seeing 70 percent of our patients benefit Now, these are typically 3rd line metastatic breast cancer patients. And if we saw a 15 percent response rate, we’d be ecstatic. We’re at 70 percent and remarkably, two complete responses. Three, whose whose disease doesn’t recur for four times longer than their past therapy. That’s unheard of. And that’s exactly what we’re seeing as we go to these 50 patients. So it hasn’t benefited 100 percent, but it’s significantly better than anybody would have expected. So the future that we see is to get a patient sample, put it through this sophisticated OMIC analysis. Have our computationalists tell us which are the important data elements we need to capture and design a treatment option within days. And we do think we can make this happen. So my view for what we have to do for the 21st century is pretty similar to what was done last century. We have to take a broad-based approach to cancer. Yes, we need many, many more specific treatments that target driving molecular abnormalities in cancer. We absolutely have to focus on earlier diagnosis and ultimately precision early diagnosis as well as precision prevention. And of course, we have to modulate the immune system and there are some remarkable immune therapies that have come along. But we also need to think about how we make them safer and how we use them earlier. My view is that if we do this in the 21st century, we’ll see similar things what we’ve seen for infectious diseases in the last century. In closing, I just have I’ve thousands and thousands of people to thank. This is my lab team and they’re just an incredible team. I have a whole backing of the Knight Cancer Institute family. Almost a thousand strong now. But the most important people I have that are backing me are my patients. Some have went on this journey and have been with me a long, long time. Now they’re doing the things they enjoy. This was the long…This is the longest patient continuously on Imatinib. January 1999 and still going strong. Over 20 and a half years, doing what she enjoys, which is gardening. The very first patient from Italy who came to visit me and got on our trial is doing what he does enjoys, which is dancing. A remarkable young woman who was diagnosed when she was 6 years old and is now a nurse at Doernbecher Children’s Hospital. One of our biggest supporters, who came to me almost 14 years ago and the first words out of his mouth were, ‘I want to walk my daughter down the aisle.’ That’s exactly what he did this past summer in threes, in Sisters. Some people doing remarkable things, this was one of the first patients from Australia, who traveled to see me in 1999. She was selected as one of the torch
bearers for the Sydney Olympics in 2000. But it all starts to add up. One patient at a time, one disease at a time, and pretty soon we have lots more people surviving and thriving despite the diagnosis of cancer. And this is my hope for the future. And with your work and our collaboration, I believe we can make this possible. Thank you very much.

Leave a Reply

Your email address will not be published. Required fields are marked *