I'd like now, in the third part of my talk, to discuss how we can extend the imatinib paradigm to other diseases.
We've heard about how imatinib results in remarkable and durable responses in patients with chronic myeloid leukemia,
as well as gastrointestinal stromal tumor and a of couple of other diseases,
but I'd really like to delve into in this part of my talk is what lessons have we learned from the development of imatinib,
and how can that be applied to other diseases, and particularly other cancers.
So, let's start with where has imatinib worked?
It's worked in tumors in which ABL, KIT or PDGF receptor have a critical role in the growth and survival of the cancer.
So, where is that? In chronic myeloid leukemia, we're targeting the BCR-ABL oncogene,
in gastrointestinal stromal tumor, we're targeting a mutated KIT,
and in hypereosinophilic syndrome, we're targeting again a fusion to the PDGF receptor alpha, that activates it.
So, in each of these tumors, we have an example of one of the targets of imatinib being critical for the growth and survival of the cancer
and if you target it, you'll see rapid, dramatic and durable responses.
But imatinib actually has been tried in lots of other tumors.
Brain tumors, breast cancer, prostate, melanoma, and numerous others.
And in these clinical trials, which have been a little bit more empiric, imatinib has not worked very well, if at all.
And the reason presumably that it hasn't worked is that despite the fact that many of these tumors express targets of imatinib,
they're not critically dependent on one of those targets.
So, what lessons can we learn from the clinical trials of imatinib?
And we need to start first with target expression and how does that relate to response.
Now, I want to start with a hypothetical clinical trial.
And in our hypothetical clinical trial, we're going to enroll one hundred patients.
We're going to put one caveat on our clinical trial. We're going to vary the target frequency.
So, in our first group of patients, all of our patients will express the target, and in our last group of patients,
only ten percent. So, we'll vary the frequency at which our target of our drug is expressed.
We'll set the target response rate at sixty percent.
So what you can see is that in the patients, this group of patients, our response rate's sixty percent, and
and in this group of patients, our response rate is only 6 percent.
Now, in this clinical trial, we'd congratulate ourselves for developing a very successful agent,
and in this group of patients, we'd say that this agent wasn't very effective.
But the reality is is that in this clinical trial, we were very smart. We selected the right patients.
In this clinical trial, we weren't very smart, and we didn't select the right patients.
But the reality is that our response rate was always sixty percent. It's just whether you selected the right patients,
or the wrong patients. Now, does that have any bearing on reality?
And if we look at imatinib responses in advanced malignancies,
we have a situation with BCR-ABL in CML and gastrointestinal stromal tumor with KIT,
where all the patients enrolled in our clinical trial express a target of imatinib.
And our response rate is between fifty to sixty percent,
in advanced cancers, so blast crisis or metastatic gastrointestinal stromal tumor, a fifty to sixty percent response rate.
So, in this example, it looks like expression of a molecular target correlates with a response to an agent directed against that target.
But we really need to delve a little bit more deeply and ask a more pertinent question:
is expression really sufficient to predict responses?
So, we're going to go back to our hypothetical clinical trial.
But here, we're going to say, take our hundred patients and we're going to have all of them expressing our target.
So, a hundred percent of our patients express the target.
We're actually going to even bump our response rate a little bit.
So, we'll set our response rate at 80 percent.
But we're going to put one condition on whether patients respond or not.
They have to have some evidence that the target is activated,
in other words, that it's critical to the growth and survival of the tumor.
And we'll vary that from 90 percent down to 10 percent.
Now, let's look at what effect that has on our response rate.
In this group of patients, the response rate is 72 percent, and in this group of patients the response rate is 8 percent.
Now, in this clinical trial, we'd say that, first of all, we'd say this is a very successful agent, of course,
but we'd say that target expression correlates with response.
And in fact, we're wrong. We're wrong because it's actually target activation.
It looks like target expression because almost all the patients that express the target express an active target.
Now, in this example, there are a couple of things that we'd conclude.
First of all, we'd conclude that target expression doesn't correlate with response.
And in fact, we're right. Target expression, all have it, only eight percent respond.
So, target expression doesn't correlate with response.
But we'd also conclude that this agent has no activity, or virtually no activity, but we're wrong.
It has 80 percent response rate, we just selected our patients wrong.
So, I'm actually going to show you examples of both this first example and this last example,
of high response rate with all the patients expressing, target having, target activation.
And examples here, where all the patients expressed the target, response rates are low,
but we just need to understand what this ten percent of patients are.
So, let's start with gastrointestinal stromal tumors.
The most common KIT activating mutation is this exon 11 mutation.
If you have it, your response rate is 80 percent. In contrast, if you express wild-type KIT, no mutations, and
you express the target, but it doesn't have any mutations, your response rate is under 20 percent.
So, this was work done by Mike Heinrick in Oregon and Jonathan Fletcher in Boston.
So, here it's absolutely clear that mutational status predicts response.
You have mutated KIT, 80 percent response. No mutations in KIT, very low response.
So, here what this example tells us is that expression of a target doesn't guarantee response to an agent that modulates that target.
You need something else, you need some evidence of activation,
some evidence that the target is actually critical to the growth and survival of the tumor.
But for those of you that are listening carefully, when you looked at this example,
it wasn't in fact a zero percent response in these patients that expressed the wild-type KIT.
And in fact, some of these patients in this sixteen to eighteen percent that are responding,
no mutations in KIT, but rapid, dramatic responses compared to all these other patients that didn't respond.
So, what Mike and Jonathan did is they went on to look at these patients a little bit more carefully.
And what they found was that in fact, even though these patients had wild-type KIT,
about one-third of them had PDGF receptor activating mutations.
Now, we know that imatinib is capable of inhibiting the PDGF receptor
and in fact, one of these sets of mutations was imatinib sensitive.
And two-thirds of these patients had good responses.
So, now what we've learned is that in some patients who have wild-type KIT,
they have PDGF receptor activating mutations that are sensitive to imatinib that are driving the growth of these gastrointestinal stromal tumors
and that's why they respond.
So, what about another example?
Gefitinib and Erlotinib, also known as Iressa and Tarceva.
These two inhibitors inhibit the EGF receptor kinase.
Now, as it turns out, the EGF receptor kinase is broadly expressed in cancer.
These agents were tried in advanced non-small cell lung cancer,
all of which expressed the epidermal growth factor receptor.
But the response rate was disappointingly low, only ten to twenty percent.
But, as it turns out, there's no correlation, I said all, virtually all, the patients with non-small cell lung cancer express EGF receptor,
but only ten to twenty percent respond.
But hidden in this clinical trial was a clinical finding.
These ten to twenty percent of patients who were responding were having very rapid, very dramatic and relatively durable responses.
And the patients who had these responses were females that never smoke,
and had this slightly unusual histology, which was known as a bronchoavelolar histology.
Well, as it turns out, a number of laboratories took that clinical finding, some patients were having rapid, dramatic response,
and asked, why does that occur?
And what they found was that responding patients had EGF receptor mutations.
As it turns out, these EGF receptor mutations are more sensitive to inhibitors than the wild-type receptor.
So, both of these examples, the gastrointestinal stromal tumor example and EGF receptor inhibitor example
tell you that careful study of subsets of patients reveal important insights.
So, again, going back to one of the early points of my talk,
you see an effect in patients, you need to go back to the lab to study why that occurs
so you can learn how to design your trials even better, identify responding patients, enroll those in your clinical trials,
and now you can identify the right 100 patients to enroll in your clinical trial and get a 68 percent response rate.
So now we're in the position to ask a relatively broader question:
what if the response to a molecularly targeted agent is low?
The first question I want to ask: is the target even expressed?
The next question I want to ask: is the target modulated by your agent?
If you have an EGF receptor or an ABL inhibitor and you're not shutting down your target, you'd never expect to see a response.
But if your target's expressed and your target's modulated by your agent,
then you have to ask a more critical question: is this target really as good as you think it is?
Is the target critical to the growth and survival of the tumor?
Or, is there a subset of patients who are responding well? And as we saw in the gastrointestinal stromal tumor trials,
and in the Iressa and Tarceva trials, the EGF receptor inhibitors,
there in fact was a subset of patients who were responding extremely well and those patients deserve more careful study.
So, what lessons have we learned from the clinical trials?
And if nothing else, what we've learned is, it's the target!
The target turns out to be the most critical factor that drives whether we have a good agent or not.
And if we have a good target, and a good drug, we really can get good results, and it really can be that simple.
But let me remind you what we had, why we had such a good target, what makes BCR-ABL such a good target.
First of all, we have a causative molecular abnormality of the leukemia.
And in the early stages of the disease, it's likely it's the sole oncogenic event,
so we have a causative, early molecular pathogenic event that's driving the growth and survival of the tumor.
But in addition, we actually ran a pretty smart clinical trial.
And it wasn't because we were so smart, it was actually because we had it pretty easy.
We actually could select patients that had a cancer driven by activated ABL,
and enroll them in a clinical trial of an ABL inhibitor.
And we could select those patients because they had the Philadelphic chromosome, or BCR-ABL.
So, we only enrolled patients in our ABL inhibitor trial that had a disease driven by activated ABL.
And so, because of that, we actually, as I said, ran a pretty smart clinical trial.
What about KIT, why is that such a good target?
Well, as it turns out, KIT mutations are again an early, pathogenic event.
If patients undergo surgery for other abdominal disorders and less than one millimeter gastrointestinal tumors are found,
they already have KIT mutation. And these KIT mutations were acquired before many of the other cytogenetic abnormalities
that are observed in metastatic gastrointestinal stromal tumor.
And lastly, there's a familial syndrome of gastrointestinal stromal tumors and these families have germ line KIT mutations,
again, an early molecular pathogenic event.
We got some other news, or lesson, from the clinical trials of imatinib and this is some pretty old news.
Anybody who treats any disease knows the earlier you treat in the course of a disease, the better your responses are going to be.
So, if we look at our imatinib clinical trials in chronic myeloid leukemia, you can see newly diagnosed chronic phase patients,
very high response rate, you get to blast crisis, only 8 percent of patients have their blood counts return to completely normal.
So, the earlier you treat in the course of the disease, the better your responses are going to be.
So, if we're going to translate the success of imatinib to other malignancies,
it's my opinion that the first task is to identify the right targets.
And in my view, the right targets are going to be the early molecular pathogenic events.
And these events are carried on into the advanced malignancies, so even in blast crisis of CML,
we see a fifty to sixty percent response rate, even in metastatic gastrointestinal stromal tumor, fifty to sixty percent response rate,
and even in metastatic lung cancer, between a fifty to seventy percent response rate to targeted agents against the early, molecular pathogenic events.
But if we're really going to make major impacts, we have to treat early in the course of the disease.
And to treat early in the course of the disease, we have to identify the patients early.
And to do that, we need very reliable techniques. I wouldn't want a technique that's overly sensitive.
By sensitive I mean it would detect a thousand patients that might have disease,
and only a hundred of them turn out to have the disease. We need diagnostic techniques for early detection
where if you have a positive result, you have the disease or will develop the disease.
Those are the types we need if we're going to actually make an impact and start a treatment.
Because we wouldn't want to be treating patients that don't actually have the disease.
But the third thing we have to do is we have to run smarter clinical trials.
And those smarter clinical trials will match the right patients with the right drug.
If you have activated ABL driving your disease, you need an ABL inhibitor.
If you have activated PDGF driving your disease, you need a PDGF receptor inhibitor, and so on and so forth.
So out of that understanding comes the ability to match the right patient with the right drug.
So, in my view, in the twenty-first century, we have several goals.
First of all, we have to identify all the molecular pathogenic events in all cancers.
We have to develop improved diagnostic and imaging techniques so we can identify patients as early as possible
in the course of their disease. We also have to get faster drug discovery;
we want this drug discovery, instead of taking fifteen years, to take three to five years.
And lastly, a major goal of the 21st century will be to actually understand our own individual risk for cancer development,
based on genetic analyses. Because ultimately what that will mean is that we can actually move to preventative therapy,
based on molecularly targeted therapies based on our own individual genetic analyses and individual risks.
Now, that might seems like a daunting task, to do all of this in a hundred years or less,
and hopefully it could be significantly less, with the kind of technologies we currently have in hand today.
But, what I'd like to do is have you think about the last hundred years.
So, if we go back to 1900, the leading three causes of death in most industrialized countries were infectious diseases.
So, in the United States, in 1900, the three leading causes of death: pneumonia, tuberculosis and diarrheal diseases,
infectious diseases. Cancer isn't even on this list, it was actually down about number 8.
The reality is in 1900, life expectancy in the United States was about 47 years, people didn't live long enough to get cancer.
But where are we in the year 2000?
Cancer is now number two and in the next ten to fifteen years,
it's projected that it will have the indubitable, or enviable, unenviable view of being number one as the leading cause of death in this country.
So, what happened between 1900 and 2000 to take infectious diseases from numbers one, two and three
down to number six or less?
And there were many factors. The main factors, however, were things like improved sanitation
and refrigeration, and if you think about chlorination of water, that was introduced in 1903,
pasteurization of milk, 1908, having refrigerators in most of our houses was around 1940, 1945.
These are the things that made our food and water supply safe.
In the 1940's, antibiotics, not even conceived of in the 1900's, began to come into common use.
Penicillin, 1942. And antibiotics truly opened people's eyes in that era to what specific, target therapies could do for infectious diseases.
And, in the 1950's, vaccinations, like the polio vaccine, 1955. Before 1955, there were massive epidemics of polio,
and now polio is on the verge of being eradicated.
So, if we re-cast this, this is prevention, this is specific treatments and this immune modulation.
So, if we think about moving forward into the twenty-first century,
and impacting on cancer, we need to take a very similar, broad-based approach.
We need specific therapies directed at critical targets.
We'd like to have a Gleevec for every single cancer,
but it's only going to come about through a precise understanding of molecular pathogenic events.
We need to do better prevention and early diagnosis.
And also, we need to think about modulating the immune system to harness the power of the immune system to target and eradicate cancers,
particularly in patients with minimal residual diseases.
So, my hope is that for the twenty-first century, we can take a very similar approach to the approach we took in the last century
for infectious diseases and sometime in the next ten to twenty to even fifty years,
we'll see significant impacts on cancer and that by the time my children, your children, grandchildren,
get to be our ages, cancer will no longer be the feared disease that it is today.
So, in closing, I'd like to thank all the people that work at the Oregon Health and Science University Cancer Institute,
in my lab, as well as in our clinics that have helped me on these clinical studies.
Novartis, who organized many of the clinical studies and who are essential to the pre-clinical studies.
All the people who have helped put people on our imatinib studies,
as well as the funding agencies who have funded me over the long haul,
particularly the Leukemia and Lymphoma Society, the National Institutes of Health and most recently,
the Howard Hughes Medical Institute.
But in closing, the people I'd really like to thank are the patients.
And the patients are the people who really spurred me to become an advocate for a drug like imatinib.
These are also the pioneers who went on a phase I trial of a completely unknown agent
and these patients are now, who have gone on this journey with me,
are now here today doing the things they truly enjoy doing.
Like this patient, gardening, like this patient, dancing, this patient spending time with her children and grandchildren,
that she thought she'd never live to see. This patient, like many others, traveling to Oregon, for our clinical trials.
Or, some patients doing truly remarkable things, like this patient,
who was actually the first patient who was from Australia treated with imatinib,
she came to Oregon, received imatinib, returned to Australia and was selected as one of the torch-bearers
for the Sydney Olympics in the year 2000.
But, much like I've take each one of these patients one at a time, my hope is we'll take each cancer one at a time,
and as we take each cancer one at a time, it all begins to add up.
And these are my phase I patients now who are a large group of patients,
many patients throughout the world have benefitted from understanding leading to better treatments.
And my hope is that this is exactly what we'll see in cancer therapy over the next twenty to fifty years.
More and more patients alive, surviving and thriving, despite a diagnosis of cancer.
Thank you very much.
