Welcome to Berry's In the Interim podcast, where we explore the cutting edge of innovative clinical trial design for the pharmaceutical and medical industries, and so much more. Let's dive in.
All right. Welcome everybody to, in the interim, and Don Berry has joined me again and we, we have a, a really cool topic today to talk about in the interim. And that is the I SPY two trial. Many people may have heard of the I Spy two trial, so we're gonna go back and remember a little bit of, uh, of this trial. We'll, we'll walk through how it became, what it was, what happened in the trial. So Don, you ready to tell us the story of I Spy two?
I sure am. Uh, thanks Scott. So I, SPY two has a, has a history, um, uh, and it revolves around Laura Essman. Of course. Laura Esserman was the Pi I. I was the co-PI of the trial. And this is back, we're talking, uh, like, uh, 2007 and eight. And she and I were part of a cooperative group called the C-A-L-G-B, the Cancer and Leukemia Group B, which itself has a long history. And we were in the breast committee, uh, of that, uh, cooperative group.
And we had designed trials and commiserated with each other, uh, about the lack of innovation and about what we wanted to do. And, um, we designed a trial. I Spy one. You may wonder how the two came about, but I spy one. Um. Where we were looking at neoadjuvant disease. Now this is part of the story. Um, in breast cancer, there were two kinds of categories of disease at the time.
One was adjuvant, which was, uh, patients who, uh, had typically had, uh, uh, uh, high risk disease, but were early patients, were newly diagnosed patients, uh, and they had, uh, bad tumors, you know, large tumors, um, uh, uh, tumors with, uh, positive lymph nodes and, uh, were receiving chemotherapy. Those with, um, estrogen receptor status positive were, we're receiving. Endocrine therapy, uh, Tamoxifen at that time.
Um, and that, and, and what you would do is you would treat the patients, uh, after they had surgery and after they had surgery, means that for almost all of them, the tumor's gone because we took it out. Uh, and so what was the endpoint? The endpoint was when it comes back. Now, you know, if you're into clinical trials, that when it comes back is a dicey thing.
Uh, it sometimes takes a long time and moreover, uh, which is of course good for the patient, uh, and moreover, it got better for the patient over time because these therapies worked.
and that
Um, and that was a problem. The problem was that pharmaceutical companies and cooperative groups and the like, um, uh, sponsors of, uh, these things and patients, patient advocacy groups, it was difficult to run these trials. The other, just to touch the base, the other part, uh, is metastatic disease. Once it recurs, it becomes, um, uh, let's say it recurs distantly and, and, uh, uh, the lungs or the brain or the bone or something. Uh, then it becomes metastatic disease.
So that's a different category. We're talking about the early, so-called early, uh, breast cancer.
So, so let me see if I, so the, the setting is typically when the diagnosis is made, the tumor's removed, and that's what adjuvant means. You go in, remove the tumor, and then you treat them with different therapies.
the training is called the Avant.
adjuvant. Is called
a adjuvant to
To surgery. To the surgery, and that trials ended up being extremely large because the endpoints took a long time. The treatments were reasonably effective, so clinical trials became incredibly uh, onerous to run in this patient population in breast cancer.
Yeah. E expensive, uh, onerous. Nobody wanted to do it. Uh, what could we do and what the CLGB said? Uh, and I was the faculty statistician and working with the committee and. Uh, par parcel of these decisions. Um, we should try the neoadjuvant approach. Uh, now this was novel. It was led by, uh, other ki uh, something called the, uh, N-S-A-B-P, another one of the cooperative groups that had led this.
Because the neoadjuvant approach is all you do is you exchange the surgery and the treatment, but that's a big deal because you're leaving the tumor in the body for like six months, but you're pummeling it with, um, uh, you know, lots of, uh, toxic, uh, therapies that kill lots of cells, including, of course, cancer cells. Um, so it was a big deal, but.
But
It had a benefit. The benefit was that you got to see whether the tumor responded to the therapy that you gave them before you took it out. So you left it in, you watched it carefully, um, that you saw whether the treatment that you gave, the treatment you gave could be different in a clinical trial. Of course it should be because you're learning. And, uh, when you go in and do surgery after, uh, six months of therapy, you see whether or not the tumor is still there.
And that in the neoadjuvant approach became the endpoint, uh, called pathologic complete response. You send the tissue to the pathologist and pathologists can't find any, uh, tumor. Okay? So that was sort of a risk. I described it to. Uh, the, my colleagues at the CLGB as betting the farm, uh, this had this, this was novel, um, and we did studies in different, um, uh, categories of the disease, different biomarker categories of the disease.
Uh, so I designed, uh, some of these trials and meanwhile Laura and I keep talking about what we're going to do, uh, and, um, we both wanted to do, uh, what I called the bandit approach, uh, where you have multi-arm bandit, where you do lots of therapies and you try 'em on different patients and you see which patients work. And this, uh, you know, I, I had tried to do some of this back at, uh, MD Anderson, where.
Uh, I was in am, uh, in the biostatistics department and we designed trials, uh, in various diseases and we, uh, Laos Push Eye, a faculty member there now at Yale. Uh, and I, uh, tried to do the, uh, this kind of thing where we would look at various therapies and go to pharmaceutical companies and try to sell it.
And it was a hard sell, uh, to have, uh, therapies from Eli Lilly, from Pfizer, from Merck that you're comparing to a control, but in the same trial as you're comparing to each other, you know, like. I, because I had the data, I could look to see how the Eli Lilly was doing versus the Merck. Uh, and that's, uh, a, a dicey thing. So we failed. Lache and I failed.
Um, uh, but we, in the context of the neoadjuvant approach, um, Laura and I mused about maybe we can do this in the neoadjuvant approach. And, uh, she is an amazing person. She, first of all, she never takes no for an answer. Uh, you say you can't do that, Laura. Oh, yes, we can. Uh, and so who's gonna fund this?
Uh, we were told by various people, uh, Anna Barker is a good friend of ours, and she was once the deputy director of the NCI and she was the head of, um, uh, committee in something called the FNIH, the foundation for the, uh, national Institutes of Health. Uh, that's a, uh, that it works with, with pharmaceutical companies, including the government. So it works across this. And, uh, she said, you'll never get this approved by the NCI. The CLGB studies were.
CLGB was funded by the NCI National Cancer Institute. Um, and we would have to get the, the, the funding from the NCI if we were to do this at CLGB. Um, and she told us, and others told us, and we knew actually this would never happen. Uh, I had personally spent years trying to get innovations into the NCI with a little bit of success, uh, but this would've been well beyond their, their, uh, ability to imagine this could happen.
Uh, so we, we worked with the FDA, I remember the FDA, I remember, uh, Rick Paster, who's the, was still is the head of, uh, the oncology, uh, telling me. But these are early patients. These are. Curable and you're experimenting with them, how can you do that?
that?
And so I said, uh, well, we're going to, uh, have a, a data monitoring committee made up of, uh, a couple of great statisticians and a couple of great clinicians, and they're gonna be meeting monthly and looking at the data, seeing how things are going, and making sure that nothing, and he was satisfied with that. So they allowed us to do this study, but then how to get the funding. And the funding was through the foundation for the NIH, uh, the initial funder. Um, but, uh, actually, Laura.
Was responsible for getting most of the funding. You know, uh, she, she was a surgeon who still is, um, and would treat patients and they would, uh, tell her that, uh, uh, she made them, she, that they were alive because of her. And therefore, since I'm the CEO of this big corporation will fund you. Uh, she got lots of funding that way. It was originally funded by, um, uh, the donations and philanthropy.
Uh, as time went along and as we got more and more, uh, companies involved with their drugs, uh, we passed the funding off onto them. So they had to, uh, pay to play.
So, so let's back up a little bit, but Sure. So you, I is, is I spy one funded or I Spy one? You, you described this as a bit of a pilot and from there you went out and were able to get this additional funding to bring in the first investigation arm.
Uh, yes. Um, uh, exactly. I Spy one was funded by the government. It was funded by the NCI. Uh, and it, it was not randomized or adaptively randomized. It was looking at patients, uh, in the neoadjuvant setting because, you know, the surgeons had to learn how to do, uh, the, this, how this thing, you know, not do surgery right away, but then come in and do surgery later. It was a new thing to surgeons. I. And, um, so it, I spy one had two endpoints.
Uh, I mean we, we were still looking at path pathologic, complete response, PCR, uh, but we were interested in could we predict PCR from an MRI that we give, uh, intermediate in time between the initial presentation and the initial therapies. Uh, you know, after, uh, three weeks, for example, on a therapy, maybe there's an effect. And maybe that effect could predict not only path cr, but also, uh, survival. So that's what I SPY one was about.
It was, uh, uh, uh, kind of a registry if you like, or, uh, it, it, it wasn't, uh, a randomized, we weren't trying to learn about therapies. There was a specific therapy that patients got who, who met the eligibility criteria.
So, so interesting people listening to this. You've described the neoadjuvant as a huge advantage to clinical trials and the ability to learn about the treatment. What do we know now about it? For the patient benefit? Um, there's no sense that this is better or worse for the patient, that that goes through neoadjuvant as opposed to adjuvant care.
Uh, boy, I'm, I'm gonna answer the question, but the only way I can answer the question is, uh, with, um, with, with hindsight. Uh, so at the time we thought that the benefit for the patient was this business about learning what benefits the tumor. When we would give therapies different experimental therapies, and we do the MRI, sometimes we looked after three weeks and the tumor was gone.
Yeah.
Um, and that, I mean, is an obvious advantage to the patient that, uh, you can do something. Uh. Uh, else if it, if it hasn't gone, I mean, you could stop. If it's not defected at all, you might want to do something else.
So it was getting the information that would help the individual patient, but it was also getting the information that would help in understanding which therapies are benning, benefiting which patients, and then we could, you know, emphasize those, those pairings, the right patient for the, or the right therapy for, for the individual patient. Um, and so it was this, uh, PCR that was the attraction for the clinical trialists that perhaps you could get approval.
Um, and in fact, this has happened since perhaps you could get approval for a therapy. Uh, that had a, uh, a, a great benefit on the path. CR rate, you know, improves a 30% rate to 50%, uh, because patients who got complete responses did extremely well. And it didn't matter what the therapy was, it didn't matter what the biomarker characteristics were.
If you got a, if you were disease free as far as they could tell at the time of surgery, uh, and then follow them, they did extremely well for overall survival and event-free survival. Now, I mentioned fast forward, uh, we're gonna have to go back to tell you about ipy two. You know, what it did. But, but, but fast forward with the neo adjunct approach. What happened? Was there are patients who come out of the surgery, and as I said, when they're Pat cr they do extremely well.
But if they don't have a pat cr, if they still have residual disease, they did poorly. The pharmaceutical companies latched onto this possibility and said, maybe we should, uh, treat those patients who have residual disease. And to your point earlier, Scott, about the size of the trials, when we have, uh, residual disease, um, the, uh, event rate is a lot greater because they, you know, they recur.
Um. Uh, much sooner than, and more likely than patients who get, uh, PCR patients, who gets get a PCR don't need additional therapy. Uh, and moreover, in a clinical trial, it would be difficult to see that there's a benefit for the therapy. 'cause everybody lives,
Hmm.
uh, not everybody, but, uh, most, most women. And, um, so they did this and they found, uh, depending on the, uh, biomarker characteristics that some therapies, immunotherapy for example, worked extremely well for patients who were so-called her two negative. Um, and the, um, the, the company would then go to the FDA and say, we've got this great therapy. Moreover, that's changing the course.
yeah.
When, and, and, and, and it means that patient, you could, if you've got a pharmaceutical, if you're a pharmaceutical company and you take using the neoadjuvant approach and you randomize patients to get your therapy versus the standard therapy, and you saw that there's a PCR benefit, you're still not going to get approved by the FDA because what happens is the patients who don't have a benefit, who have residual disease are getting other therapy that's effective.
Yeah.
So it's confounding. And so the neoadjuvant approach has completely changed, um, uh, breast cancer treatment and, uh, and approach. And it, it means that the original hope, it's sort of ironic, the original hope was to get a PCR. And now the, um, the, the benefit for the neoadjuvant approach is it really doesn't matter. You're just getting information about what therapy works and then you give some other therapy and they call it adjuvant after the surgery.
Mm.
So that's a whole different story that's not really part of IFI two, but it's part of, uh, the breast cancer, uh, uh, story RA.
Okay. So, so I, in the timeline of this, I Spy one is proof of concept that you can do neoadjuvant, there's no investigational therapies. You can get MRIs, you're getting data on PCR. So meanwhile, if, if a company wants to do a phase two trial before I SPY two in the adjuvant setting. It would be incredibly hard because disease-free survival after surgery is so very long. These trials would be huge and long.
It's hard to do drug development, so you turn it upside down and you're doing neoadjuvant. So you're ready for I SPY two, which is intervention in the neoadjuvant space and the funding. Are we ready to describe the I SPY two trial? Yes.
Yes.
Okay. Okay. Okay. So, um, do you want to describe what this trial looks like? You, you want to do, you described you want to do the bandit approach, you want multiple therapies simultaneously, but this is also about precision medicine in a sense.
Exactly. So the precision medicine aspect is, uh, breast cancer is a, probably the poster child of precision medicine. Um, it has biomarkers that are extremely important. Estrogen receptor status, progesterone receptor status, her two. Uh, status, um, of, uh, something called MammaPrint, or, which is very similar to Oncotype dx, which is a 21 gene. MammaPrint is a 70 gene, uh, uh, uh, biomarker multi, you know, poly marker. Um, uh,
So, so I-SPY 2 classifies. You described these, there's HER2 status, hormone receptor status and MammaPrint status. Each one is dichotomous, uh, for this. So women who come into I-SPY 2 fit into a subgroup, and they're, or a subtype, sorry, I SPY two created all kinds of new terminology, and there are eight subtypes by the classification of that when a woman comes in that their, their, their, uh, tumors classified in eight, eight different subtypes.
Uh, right. And then we want to see which subtypes benefit from which therapies. But now if you, uh, we will, what we want to do is, is, uh, tell the company. Your therapy is beneficial in this, the subtype, but not in that subtype. Now, how can you do that? There are, if there are eight subtypes, there are 255 different combinations of those subtypes and we can't do 255 different partitions of of cancer.
So we looked at what we call signatures, which are subsets of subtypes, and we looked at 10 that were the primary analysis. So every month we analyzed how well the various therapies in the trial were doing in these 10 different signatures. So one signature, uh, would be HER two positive, that as you described it, Scott has four subtypes. Uh, but we're looking not at those individual subtypes, but at the, at the, uh, all of them together. Uh, we also look at the HER two negative.
We look at the estrogen receptor positive, the negative. We look at the, uh, estrogen receptor negative, HER two negative, which is called triple negative disease. And that has two subtypes, namely MammaPrint positive, MammaPrint negative, um, that we don't, we're not advertising those subtypes as being an indication for your drug. It's the signatures which are indications for your drug.
So that was, uh, uh, a, a big deal to go to the FDA and say, we have these, uh, 10 different possible indications. Uh, and that in itself is a huge innovation, which by the way, uh, I SPY two is a phase two trial, and we're, we're trying to ready it for phase three. Uh, we've since then designed similar trials in, uh, GBM glioblastoma, uh, pancreatic cancer, uh, that have a smaller number of subtypes because they have, you know, different biomarker, uh, uh, science associated with them.
Um, and, but that the FDA has agreed with looking at these multiple signatures in a registration trial. Uh, so it's, it's, it's pie in the sky, but we're eating the pie.
mm. So, uh, a woman comes in and she belongs to one of these eight subtypes and is randomized among control and, uh, call it 20% chance she goes to control. And the other 80% she could go to the various investigational therapies that are there at a time. So this is your randomized bandits. In a way that if there are three therapies in the trial right now, she gets randomized among the three therapies.
And so, uh, you probably need to describe the response, adaptive randomization aspect and which is so critical to icey two.
Yeah. So, um, the bandit problem is that you have these, uh, multi arms and, and, uh, it's usually posed in the context of a single. Uh, subtype, um, where you want to treat patients effectively, uh, in the trial, which is, uh, you know, trying to blow, I've been trying to blow up the, uh, notion that you can't learn. You, you, you, you're not treating patients in a clinical trial. You're learning about patients. And I say, why can't we do both? Um, and we had done that.
Um, uh, I've been writing about it for many years. Uh, and, and when I went to MD Anderson, we actually started to do it. So we had trials where we, uh, adaptively, randomized. Uh, you get a, a randomization, but if a, if that therapy is doing well for your subtype, if you're the patient, you get that therapy with a higher probability. And what it means is that, uh, patients in the trial,
the trial,
um,
um,
on average have a higher, uh, PA PCR rate than uh, a, a standard trial because they're more likely to get a therapy that's doing well. And moreover, you're not exposing those patients to a therapy that's not doing well for your subtype. Um, and it it means that patients in, in the trial do better. Um, it also, uh, means that the, uh, overall patient. Uh, population does better.
Um, and you get more information about the therapies that are doing well in the subtype signature because they're getting it more often. So you get a bigger sample size. Um, because in, in and faster you learn faster, you learn better about where you really wanted to learn. You don't care how well it does in a therapy, in, in a subtype that doesn't do very well with it. Uh, you don't use that for, for those patients. You use it only for those patients who it is doing well.
okay. So, uh, when women come in, they're classified in their eight groups and the randomization, uh, probabilities for one subtype are different than another because the drugs are modeled as potentially having differential effect for those women.
Exactly.
And it might even be that they have no chance to get a particular therapy 'cause it's not doing well. And a very high chance to get another one that's doing well for women like them from, from, the drug side. The drug can focus on the women where it's having an effect and not randomize to those, that they're not. What's amazing about this is you can't really do this.
You can't do it very well if it's a single drug trial, because now you can't change the prevalence of various subsets without stopping and rolling them all together. It's almost undoable to do this precision unless you've got multiple things to give. Now, now you're doing these, I I, I know you wanna jump at this, but you're doing these adaptations and you're analyzing the data. Uh, the algorithms are being run and resetting.
The randomization weekly during the, i I think it varied during the time of Ipy two. I think initially it was actually daily, then weekly. Now your endpoint is six months. And you're trying to learn about what works and you don't wanna wait six months. And so you're trying to accelerate the learning to allow all of these things you just described to do better. So how do you do that?
Uh, thanks for the setup. Uh, uh, I mentioned earlier the MRI, we did MRIs in I spy one to learn about that and we, we learned that it is predictive what we di like to do. Is to use all of the patients. Uh, we don't wanna wait six months. Um, in the context of waiting for disease-free survival or overall survival, six months is a short time. But in the context of, uh, uh, I spy two, six months is a long time. Um, we get MRIs, we got MRIs, uh, MRIs at three weeks and at, uh, 12 weeks.
Uh, and we use that information to predict is it gonna be a PCR. Now here is where, uh, I reported to the, uh, data Safety Monitoring Board. Uh, every month. Um, and, uh, uh, I explained to them the trial and they kept learning about the trial. And after some period of time, uh, they said to me, said, Don, you told us about MRI and how you use MRI, uh, at least eight times. Could you tell us again?
So it's not an easy concept for those people that are used to, you know, looking at an endpoint and focusing on the endpoint. If you say we're gonna, we focus on the endpoint, being MRI, uh, first of all, nobody would, uh, accept that. It's not a surrogate for, uh, anything. But we looked at MRI, not with the notion that what we're gonna do is. Uh, uh, focus on MRI as the endpoint, but as an auxiliary endpoint, as a marker of how likely is it that it's gonna be a PCR?
So it, if if, there's no tumor, uh, that you can see on the MRI, that means there's a high probability is gonna be a PCR. So what we do is we do multiple imputation. We have this probability, and when we are doing the multiple imputation for all of the women in the trial who don't have. Six months, you know, who don't have the result of surgery. We're predicting the result of surgery, but it's probabilistic.
So, um, uh, there was a drug pembrolizumab, which you've seen advertisements for, for Keytruda, uh, from Merck, uh, which, uh, graduated. We call it graduate when it, we've learned what it's, what it's, uh, uh, signature is, uh, when no patient had results at six months. Um, and the, the way we could do that, we will talk about the time machine, uh, in a minute, I'm sure. Uh, the, the, the way we could do that is.
Uh, we had the algorithm that was making the patient assignment and making decisions about graduation. The drug would graduate, uh, no longer get, uh, patients, uh, but we would continue follow up, uh, when only one patient had the result of surgery in triple negative breast cancer. And, uh, how could we do that? It was because the algorithm was seeing that the therapy was melting the tumor away. And I went in and I looked at the data, I said, how can it do this?
And the answer was, you know, in the 12 patients who had, uh, results at 12 weeks. Um, 11 of them eventually had a PCR, uh, and 11 of them, uh, were, uh, and it was clear that they were gonna do extremely well. Um, so
so, so, and at the time I think one patient had been through six months and the model predicted something like a 63% PCR rate for the arm and, uh, with one patient. And by the time all the patients got through, it stopped for graduation, uh, at some point. It almost nailed exactly the PCR rate based on the MRIs.
it was exactly the sa the, the same as the, as the MRI, but now. Um, there's another aspect to this. How could it do this 60% and get it right with only one patient? Uh, the answer is the time machine and the controls. Now, you,
so hang on, hang on. Let's, let's, we're gonna be adaptive here, so for the first time, we're gonna call this part A of the podcast and we're gonna make people have to tune in for part B of the podcast and learn about the time machine. And thank you all for joining and join our next episode and learn about the time machine
And, and other things as well, predictive probabilities, for example, which are usually important in, uh, uh, being adaptive.
Alright, so thank you.
Thank you.