GRTS KOL Webinar Transcript August 18, 2021
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welcome to the gritstone bio KOL webinar on Neo antigen oncology programs and colorectal cancer. At this time all attendees are in listen only mode. A question and answer session will follow the formal presentations. If you’d like to submit a question, you may do so by using the q&a textbox at the bottom of the webcast player or by emailing your questions to questions at lifestyle advisors.com. As a reminder, this call is being recorded and a replay will be made available on the gritstone bio website following the conclusion of the event. I’d now like to turn the call over to your host Dr. Andrew Allen, co founder president and chief executive officer of gritstone bio, please go ahead, Andrew.
Andrew Allen (CEO): Very good. Thank you, Tara. Good morning, everybody. This is Andrew Allen. I’m the CEO and as you heard one of the founders of gritstone bio, and I’ll be making some forward looking statements during this presentation.
We’ll be spending the next hour covering a couple of important topics. I’ll begin with a brief overview of the gritstone bio neoantigen immunotherapy program with a focus on the personalized program called granite, which we’ll be presenting data at ESMO. In September, just a month or so away. I’ll give you a quick reminder of the data that will be showing at that presentation. Dr. Dan catenacci from the University of Chicago will be giving a mini oral presentation. And we will be focusing on a couple of relatively new elements. And we thought it would be useful today to cover some background to prepare our audiences for the data that we’ll be seeing at ESMO. In particular, we have an interest in colorectal cancer, which is something you probably haven’t been thinking about a lot recently, if you’re operating in the immuno oncology space. And as our guest Professor Michael Overman from MD Anderson will show. Unfortunately, current immunotherapy has little utility. In the treatment of colorectal cancer, and progress over we’ll review the current state of play for colorectal cancer therapy. An important biomarker for assessing early efficacy with novel immunotherapies appears to be circulating tumor DNA. And this is a new area. And again, we thought it would be helpful if Professor Overman who usefully possesses expertise in this domain as well would provide some background on what circulating tumor DNA is, and how it can be used in clinical practice and in the development of novel therapeutics with some very striking new data suggesting its strong value in assessing early efficacy from novel immunotherapies. We’ll take about 40 minutes, 45 minutes for this and then we’ll open up for a q&a session at the end.
As I mentioned, our guest speaker is Professor Michael Overman. Michael is a professor of gastrointestinal medical oncology at the MD Anderson Cancer Center in Houston. And you can read more of his background here. He does possess obviously clear expertise in colorectal cancer and has also worked extensively and published in the field of circulating tumor DNA. Hopefully, you’ll enjoy learning from him today.
So let me begin with an overview of our neoantigen oncology program. We have two programs, one which is patient-specific, referred to as granite, and one off-the-shelf platform referred to as slate, the mini oral presentation at ESMO will be with granite. And so our focus today is primarily on the granite platform.
This is the therapeutic hypothesis that we’ve been pursuing for several years now since we formed gritstone. And I’m happy to say that I think our therapeutic hypothesis has withstood the test of time and really is unchanged from when it was first articulated in 2015. And for those of you with good memories who’ve been working with us for a while, you’ll remember that the field really was born in human the human environment at least in late 2014 when Professor Tim Chan and Naya Rizvi both at Memorial Sloan Kettering at the time, published some seminal work in the New England Journal of Medicine, suggesting that when a patient with melanoma responded to a checkpoint inhibitor, initially that was with you avoid, the therapeutic response was likely attributable to the development of T cells, CD eight killer T cells that were recognizing tumor cells expressing Neoantigens in the context of class one HLA molecules. And we realized early on those patients who had high tumor mutational burden tumors, such as melanoma, or patients with often high PDL, one tumors would typically have abundant new antigen reactive CD8T cells in their tumor of baseline. And that this indeed, was one of the strongest predictors of response to checkpoint inhibitor therapy, be that with anti ctla four or more recently, of course, with anti PD one or anti PDL one antibodies. And so today, as of course, we all know the standard of care for many of these tumors with high mutational burden or high PDL1 is to treat with monotherapy with checkpoint inhibitors, or combination chemotherapy plus a checkpoint inhibitor.
Unfortunately, Most solid tumors, which of course are the burden the majority of the causes of cancer death in the United States are relatively low tumor mutational burden and or low PDL one. And typically these tumors do not respond to checkpoint inhibitor therapy. And the thesis is that the reason they don’t respond is that there is no neoantigen reactive T cells at baseline. So by treating with a checkpoint, there is no substrate. And that worst, all you’ll get is autoimmunity and activation of autoreactive T cells, but there are no neoantigen reactive T cells to provide therapeutic benefit. Our therapeutic hypothesis, therefore, is that we will provide the patient with new antigen reactive CDA T cells using an immunotherapy vaccine-based approach. And that when you then administer checkpoints, you now have perhaps the elements required to drive clinical benefit for that patient.
To address the hypothesis, we needed to solve two problems. First of all, we know that there are many mutations in the tumor, but only a minor fraction comprises true Neoantigens. In other words, mutated peptides that are expressed in the tumor cell, processed through the proteasomal machinery and then presented on the surface of the tumor cell by HLA class one. So correct identification of which mutations will form true Neoantigens was a key challenge. Furthermore, once you’ve identified the class one Neoantigens, you then need to be able to generate CD8T cells against those in a human being, and priming a CD8 response is a big ask of immunotherapy. And indeed, most vaccines can not prime a de novo CDA T cell response. So these were the two problems that we had to solve when we formed gritstone bio.
In terms of that first problem, we don’t have time today to go into this in detail. But we used a sophisticated human tumor inflammation-derived machine learning model approach to solving this challenge. And what we did was we took human tumors, sequence them, so we derived the DNA and RNA sequence information. And then we performed an elegant form of mass spectrometry, originally developed by john hunt and Vic engelhart, in the 90s at UVA, whereby you isolate the HLA molecules from the surface of your sample, in this case, tumor cells, and you then elute the peptides and sequence those peptides by mass spectrometry. So you end up having a catalog of all of the peptides that were indeed found on the surface of your sample, the tumor cells, and you can then correlate that with the sequence data associated with that same sample. And this is where machine learning mathematics comes to the fore, then can start to test which are the genomic features that predict the probability of presentation. So we did this iteratively on 1000s of human tumor samples. And we’ve now developed this high-quality prediction model, we don’t have to do any of that complicated mass spec work, all we need to do now is sequence a tumor. And just for the sequence stage alone, we can predict with a high positive predictive value, which mutations will be Neoantigens. And we’ve now applied the same technology to pathogens in particularly to viruses. Because again, the same problem pertains to particularly novel viruses, which regions the virus will function as good antigens. So clearly, this platform technology has broad utility. As I say, this is published in Nature biotech a couple of years ago, and we have now issued us patents covering this approach. We’ve also been working more recently on class two prediction, which is a harder problem, and perhaps important, and we can speak about that at another time.
So we’ve clearly solved this first problem, we now predict new answers with high accuracy. The next problem was to be able to deliver them to the human patient, to drive a strong CDH response. And our team led by Karen use, originally our Chief Scientific Officer, now head of r&d, identified that the ad no virus was the most potent CDA priming vector known. So we use a chimpanzee add no virus for the prime boosting with the same ad, the virus is problematic, because you end up generating neutralizing antibodies to the surface protein of the virus. And therefore you switch vectors, you give the same antigens, but in a different vector. And we’ve been pioneering the use of a self-amplifying RNA vector, which of course now is becoming widely discussed through the realms of COVID. So this is our basic platform a prime and boost the same new actions delivered in these two different vaccine vectors. They’re given a simple intramuscular injections. So this is very straightforward for the patient. And of course many of us now know how easy it is to receive an intramuscular lipid nanoparticle because of course, that’s what sets in the Madonna and BioNTech COVID-19 vaccines.
So this is the schema for granite, we take a routine core needle biopsy, nothing complicated. This is routine material available essentially for every cancer patient. And we ship that to our site where we perform DNA and RNA sequencing. All of the mutations are identified plugged into the edge prediction model, and every mutation is given a probability of being a Neoantigen, there may be several 100 mutations in a particular tumor. We rank the mutations based on probability and take the top 20, which we then plug into the in silico generator, and the sequences derived for the personalized immunotherapy. We make that in our California facility, put it into vials, and it’s then shipped to the sites for simple intramuscular injection in patients.
We’ve been running for the last couple of years, a phase one two study exploring this platform in patients with advanced solid tumors. And of course, this is a fate that started as a phase one study, so we’re treating patients who’ve exhausted regular available therapy. The phase one completed last year, we’ve been using a flat dose of chimpanzee adenovirus 10 to the 12 viral particles. We’ve been doing a dose escalation on the self amplifying RNA because this was first inhuman. Every patient receives standard intravenous nivola map to block PD one. And we’ve been introducing subcutaneous ipilimumab at a low dose to further augment the immune response. We completed phase one, as I said last year and took the higher dose level through interphase two where we’ve been focusing primarily on patients with microsatellite stable colorectal cancer and gastro esophageal adenocarcinoma. We have a handful of patients with non small cell lung cancer. And it is these data both long term follow up from the phase one as well as data from the phase two that we’ll be presenting at ESMO In a month’s time. we will be focusing on microsatellite stable colorectal cancer in the second part of that talk, because this is where we’ve elected to pursue the program and advance it into phase two clinical trials. And we have two randomized phase two trials in different contexts in microsatellite stable colorectal cancer. And we’ll be providing more details on those trials at the ESMO presentation that we’ll be giving after the mini oral and those trials are due to begin in early 2022. We’ll be providing you obviously with all of the relevant data from the phase one to program, the safety data as well as the routine efficacy data assessed with standard radiology. But we’ll also be providing information on circulating tumor DNA, which as I say, is demonstrating its utility fairly broadly now to assess efficacy, particularly with novel immunotherapies. And so for these reasons, we thought it would be helpful today to invite Professor Overman to give a background in the current state of treatment for microsatellite, stable colorectal cancer, and the utility of circulating tumor DNA. So he’ll give a talk with two chapters. And with that, let me hand over to Professor Overman, Michael.
Professor Michael Overman: Thank you, Andrew. Yes, I’m glad to be here today candidate really give a background on where we stand with colorectal cancer, I think an area that we need to kind of have success with immune therapy, we’ve had a lot of efforts and so kind of where that stands. And I think we’re the potential potentially is and then and then really kind of I think a bulk of the talks will be more talking and kind of on circling tumor DNA, which really has an exciting future. I think we’re all aware of that, and really has some great kind of aspects in regards to our ability to, you know, develop drugs, I think a novel fashions look at the kind of novel areas we haven’t really explored before and kind of utilization and kind of the immune therapy space where I think there is some real interesting data that we see really being a benefit for us kind of going forward.
Here kind of my disclosures, and then to start off again, do so. So some simple kind of slides just to kind of give the scope of what we’re talking about colorectal cancer, if you look across the genders really common related cancer that we see in patients. So, clearly there is a population need for us to improve on, on our current kind of standards. You know, this is a process that starts early in polyp So, anyone 45 and older should be getting colonoscopies, that that really is fundamentally something we have to stat just because that is a crux of potential success in a population basis. And then there are you know, kind of different locations across the corner which do have prognostic and also have some predictive information in regards to kind of anti EGFR based therapy utilization.
You know, we often kind of talk and I think, you know, stage four metastatic disease is where we really kind of discussed, you know, novel therapeutics and engagement and so that is somewhat what we’ll see about kind of later on as I talk but, but I think the the point here is that you know, there is often a larger population patients and really this kind of locally advanced stage two, three, where we actually do give therapy to patients, right. So standard surgery, a good portion of patients are cured with surgery, but we give, you know, Agilent therapy, extra therapy to patients to help improve that cure fraction. And here is a space where I think certainly humidity clearly we’ll see kind of roles being played out in the future where we’re giving therapy to everyone, most of those patients actually aren’t getting any benefit from therapy. So we’re kind of benefiting a small fraction. And so clearly, can we identify that group better. And that really gives us I think, a window to actually start thinking about treating with novel therapies, even in that space, which I’m barely excited about.
In regards to kind of, you know, where we stand with approaches within colorectal cancer, you know, metastatic disease. And so this is kind of the slide that kind of gives you that sense. You know, frontline is systemic chemotherapy, either a doublet or triplet regiments. They’re deficient, Mr. pair is a subset will kind of briefly touch base on again, in the future, but, but that’s the one subset where we do have immune therapy kind of as now a de facto standard in the frontline. But again, you know, 5% of metastatic disease, so really a small subset in the second line, again, we’re really kind of, you know, chemotherapy depends on what’s kind of given the frontline is kind of the standard. So we’re not taking oxalic Platinum based. And then there are some molecular subsets that are that are starting to have some success. So So B, RAF is going to, you know, rare subsets again, so those are probably, you know, four or 5%, kind of each of those of you you tally them up. And the third line, you know, we have some agents right graph of the past one or two we’ll talk about but, but agents that clearly have limited activity would be the general sense, I think, at the bottom there, you kind of see that that benefit playing out, right. So pretty clear, we have, you know, a nice benefit in frontline and declines as you go along. And, and again, that benefit, though, then again, is, you know, disease control for a period of time.
And so here are just some slides quickly to kind of just reiterate a little bit what you saw on that table form. And so here are two, large recent kind of phase three studies, looking at various different chemo combinations upfront. And there could be a lot of different studies out there that would look at this, but what you see is, I think, the few points so one is, you know, the median kind of is, you know, kind of 9-10 months approaching a year, if you look at some triple A data, and then what you see is the all the curves, though, kind of are approaching back to baseline, right, so we actually have control for a period of time. So even in the frontline, which we consider to be very kind of active and beneficial therapy, in the big picture sense, are not really benefiting our patients. And I think clearly an angle where we would like to do better with immune therapy and in improving that tail, the curve. second line you see here, so similar, but just curves, you know, for progression-free read, falling much quicker down to baseline, right, so you’re kind of half your benefit and progression-free survival. It’s kind of seen with two examples here, kind of with chemotherapy. And then kind of in again, that would say, the refractory setting or third line, we have these oral agents task one, a two, a regular Rafa nib, in which, you know, clearly half the patients are getting zero benefit, and then there is a benefit to the range of the patients in regards to kind of slowing down to disease, stable disease. But if you look at the medians, they’re pretty limited benefit. Right. And I think that the issue here is the toxicity spectrum is real. And so the benefit toxicity ratio here it is really not great. And so I think, a, you know, a, an area where there is a low kind of level of bar to exceed, to kind of get entry into colorectal cancer does exist.
And then just to kind of, I think, you know, close out a little bit in, in the just big background in colorectal cancer, you know, this is kind of what we mentioned before the different categorizations. And I think, metastatic disease, clearly you can see that curve, it stands out, it’s, it’s, you know, approaching back to present survival of zero. So clearly an area where we need to improve, and that’s where we’ve done a lot of drug development, I would say, you know, that regional curve, though, there is a number of patients there that are relapsing and dying from disease, right. And so if in theory, if you can identify those patients, another large population of patients where we could really try to optimize outcomes as well exists.
So what about the immune therapy space within colon cancer and again, you know, 95% color to cancer is kind of proficient in mismatch repair microsatellite stable, and so that’s kind of the group we’re looking at here. And I think, you know, the data is pretty clear from early efforts to look at this space that we really just do not have activity right those curves one would say are clearly overlapping. Alright, so so you have you know, I tetherless Mab, Bevis ism AB combination that was looked at in the maintenance setting you have a devalue Mab tremelimumab So PDL, one ctla, four PDL, one anti veg F. Also in the table up top, you see another kind of large study that the MLA study that looked at cobimetinib and it has Alyssa abera Tesla lism, AB and RAF nib and, and consistently across all these different studies, there was actually really no activity really seen with regards to kind of these approaches really setting the stage for the fact that you know, in proficient mismatch repair, we really need to have, you know, novel approaches that actually have probably more logic and support behind it, besides kind of the classic success of you know, PD one based kind of efforts that we see another tumor types.
The one exception, though, is here, right. So this is that deficient mismatch repair subset, where, you know, robust positive study Kino 177, here, where you see your Pember lism ab arm with that tail, the curve, you know, markedly superior to kind of chemotherapy in the frontline setting. So clearly, colorectal cancer can be immune responsive, right, that now that the beauty of deficient mismatch repair is that you have a number of mutations, they already have pre-existing recognition that’s happened, right. And so, so the challenge other side of the coin is that the proficient you know, the mutation load is much lower, and so that, that pre existing immune recognition is likely not present. And that’s kind of one of the likely keys that’s kind of driving our lack of success.
So, to kind of round out the landscape and CRC, what kind of you heard a little bit is that, you know, we have a clear common problem, we have five-year survival rates in the metastatic setting that are poor, we have a number of lines of setting lines of therapy across different disease settings, you know, for a second and third, but in each case, we have, you know, need within those spaces. And then I think, you know, immune therapy for proficient mismatch repair, in the large randomized phase three studies that we’ve seen so far, have really shown no activity, you know, we could talk about some, some novel approaches in the q&a a little bit, but really have not found meaningful success in that space. And I think this kind of begs us to ask the question of, you know, how can we be doing this better? And I think, you know, that kind of leads a little bit into the, what we’re going to be talking about in the next session was CT DNA. So can we actually be, you know, approaching and studying and designing trials with more novel approaches to really kind of get cleaner kind of populations and cleaner kind of readouts? And then also, can we look at, you know, novel based approaches that, you know, we have to think are different than kind of the classic, you know, throwing your PD, one, PDL, one across the board and other tumor types, that’s just not what’s going to work and proficient mismatch repair microsatellite, stable colorectal cancer.
And so kind of with this immune therapy, colorectal tie in, you know, one question that we will see, and this kind of leads us into that circling team, to certainly tumor DNA kind of space. Is that, you know, can we actually use a tool too early or tell us success? Right, that that’d be really great. If you’re looking from a trial design kind of standpoint, can you find that earlier, right, this keynote 177 trial, went to full enrollment full kind of follow up of two years, for every single patient before they kind of report it out due to this kind of need to really kind of get this long term kind of readout, because your response rate wasn’t capturing that, right, you see the response rate difference here, but clearly not capturing that differential with a progression-free survival. Right, you know, um, and that is kind of an immune therapy phenomenon we see. I mean, here’s another recent novel immune therapy agent, again, looking at uveal melanoma, which is, was different than cutaneous. So you really has limited mutations.
And there’s this novel kind of by specific agent, that’s kind of looking at CD three, and gb 100. And, and has demonstrated activity here. So you can see kind of overall survival, but again, that that response rate, readout doesn’t really give you that benefit, that you see with kind of longer kind of follow up. And so kind of making the point that, you know, immune therapy based assessments, potentially, could we be doing better with kind of a different approach a little bit. And here is kind of that, you know, sub analysis from the study I just showed you with that agent looking at, you know, CT DNA as potentially that ability to kind of give you that readout in regards to kind of reduction, right, so and, and the beauty here is you can look early to see this reduction, because you can see it happened pretty, pretty quickly. And so a, you know, there’s various different kind of thresholds for reduction or benefit. And so we can talk a little bit about that. And different studies have used different kind of criterias. But clearly, that gives you a pretty nice quick readout on what you’re seeing kind of long term and patients.
Okay, so so I’m going to kind of move now, I think this point of kind of the circling tumor today, and what can we do with it? What are we seen with it? You know, what can it kind of lead to us in the future. And I do think this is really a powerful emerging biomarker, I’m sure probably everyone on the call probably has already believes in that, and probably agrees with that. But I think you know, from an academic perspective, there is no doubt that this is something we see as going to be fairly transformative for the practice of colorectal oncology in the future. I think there’s no doubt and so, so this is one of those background slides I like. So this is, you know, Ryan Corcoran from Mass General who’s done a lot of certainly tumor DNA work and read it really nice review, kind of just hitting the high points of the various different, you know, alterations, genomic alterations, one kind of pull out from circulating tumor DNA, the different mechanisms of doing that, and clearly the benefit of next-gen sequencing to be able to capture a lot of different alterations.
And then you know, the ability to go how deep and sensitive the test can be in regards to limit of detection. And I think a few of the points, you know, it is quantitative. And I think that’s really just fantastic that you have this nice quantitative readout that’s both sensitive and specific, right. And there are different approaches and how you do this. But it really is beneficial in that sense. And you have this quick, half-life turnover time. So it’s really kind of a rapid kind of readout that you have of your current state of tumor. And so here is an example of a tumor informed approach, there’s really kind of, you know, panel based testing, which we see and, you know, tumor based testing, you can apply that to circulating tumor DNA, your plasma, or you can do tumor in form. So you sequence your tumor, you know, what alterations there are, and you actually then go for those alterations, generally, at a much kind of lower limit of detection threshold, a deeper level of detection, looking for those specific alterations. And so here is an example in a Tara and kind of one company out there kind of in this space, doing a tumor in form that has a number of different studies and data that will kind of touch based on as we go through.
So, so here are really I think, you know, we’re where if you break down, where can we see this biomarker really playing a key role, and here are a number of those different categorizations. Right, so So, early diagnostic prognostication, monitoring for minimal residual disease, that’s kind of that that resected kind of stage biomarker of therapeutic efficacy. And I think, this is interesting an idea of, you know, earlier efficacy readout, right, and then also on the issue of immune therapy, where we’re radiology does have some real challenges and kind of immune therapy response. And we’ll see some of the hints of that kind of later on. And then detection of resistant mutations, and this would be an area that you would say is already kind of really come to the forefront, right? If you’re looking for targeted, you know, escape from targeted therapies, one of the best ways you can do this is actually the totality of tumor and that really is a circulating tumor DNA assessment, right. So so that really is already kind of in, I would say, clinical practice in a number of different settings And even in Colorectal, and kind of the idea of anti EGFR resistance and retreatment space, right?
So now kind of dive into all of these, because they’re kind of, you know, in themselves really kind of deep talk, it’s but kind of just hit on some of the high points. And I think, you know, some of the fairly simple ones is that, you know, prognostic could this be a good surrogate for the amount of disease there, and I think if anyone’s ever, you know, looked at a resist measurement and tried to pull out kind of burden of disease from resist, people will realize clinically, it’s okay, but it really underestimates kind of the extent of what what is present there, right, you’re, you’re measuring a limited number of sites, and you’re measuring those lesions that are kind of easy to measure. Right. So this is, you know, potentially a nice quantitative assessment of prognostication just from baseline amount, right? So here’s kind of a study looking at this, again, this is one of these kind of solid tumor base tumor type studies. So a number of different solid tumors in which got Pember lism ab, and then really asking the simple kind of question of what is the baseline do in regards to prognostication and prognosticate fairly well, as you can see here, both your delineation with overall survival and progression free survival. And so this was kind of a tumor and forum based kind of approach. Here is another study looking again, this is a duralumin. But this is kind of a panel based kind of approach. This was, you know, garden 360. But the same kind of finding, again, multiple different solid tumors that received immune therapy, looking at kind of your baseline and stratifying it here fairly simply, you know, median, showing that you do have this prognostic benefit. So, kind of a surrogate marker for presumed kind of amount of disease present and quantitative and the ability to kind of assess that.
Okay, so the next kind of areas is this monitoring of minerals, disease, and I know everyone’s aware of some of the data that’s come out that you know,
We’ve seen actually, over a number of years really looking encouraging for this. And I think the thing that really kind of strikes me as you see this space kind of evolve is, is really just the consistency of the data.
And it just is really just dramatic. And that and I think that’s led really to, to this idea of, you know, potential for engagement in this population. And that’s happening in the clinical trial space, and I really think is one that is just a fantastic and interesting space, because you’re able to catch patients, from an immunotherapy perspective, where the disease burden is actually at fairly low, right. So you don’t have your refractory advanced patients with a ton of disease, rapid growth kinetics. And so potentially enables you to kind of engage and give time for actually, you know, immune,
you know, recognition and activation and eradication to potentially happen of the tumor. And so, so here is this example. So, again, colorectal cancer localized. And then you look at your CT DNA, you know, positive negative after that surgical resection, and you can clearly see positive as dramatically worse outcome. Right. I mean, that that has a ratio of 14 is one of those hazard ratios you never see, right, that that is kind of just dramatically striking. I think, you know, the one caveat you’ll see here is that the negative group still has recurrences, right. And so that’s the area of kind of iteration and development, that’s still ongoing, but But clearly, the positive group rate outcome is so poor, that it is very easy. And it is being made the statement that this group should have novel therapeutics applied to them, right? I mean, I, you look at those curves, and and it’s almost certain those patients are going to relapse, and need to have, you know, approaches done that, that address that and so able to pick out that group, and target them. And so here’s just another same kind of example, again, tumor informed, can I say this is from work from Australia, by Dr. Chi. And you can see here, again, that has a ratio of 80, right, just this phenomenon, as a comparison, you can look at our clinical risk factors, these are composite of various different clinical factors and your hazard ratio, there are three, right? I mean, just really No, no differential in regards to the ability to discriminate a group that’s going to kind of relapse from a group that’s not and so this really is kind of, I think, you know, there’s there’s a number of randomized large trials, even kind of engaging this space right now. Now, that time with immune therapy, I think is really kind of nicely shown here. And something that we would kind of be making the statement to people about as well is that, you know, this was a study says that Tesla lism, Ab urothelial, cancer Agilent, therapy, negative study, right? So so negative study in primary alpha disease free survival of our survival. But yet, then of you, you look into the data, and you actually say, well, let’s sub stratified by circling tumor DNA status, you actually see a differential A positive kind of effect within that certainly tumor positive population. Now, that’s the minority of the patients, right. So you have a minority, that patients that actually have this really high risk of having an event. And so you’re looking at a population with a really high event rate, you can actually then much easier to see your distinction in regards to potential efficacy or potential benefit. And so as you can see, here, in the cDNA, positive group, you have a nice hedge ratio demonstrated a benefit from a Tesla lism ab, in that subgroup, and so, kind of the ability to kind of use this biomarker to kind of enrich for the population that you want to study that you think you might have success with, and, and can actually work in that regard. Right. So So I think, a great demonstration of the potential, and that new population that we could study and study in a smarter way, and I think, also allows you to actually accept a lot more toxicity because, you know, kind of their outcome is so worse long term. So, okay, so So finally, so biomarker therapeutic efficacy, and so I think this is one that that has interest I mentioned before and I think, you know, one aspect of time here is immune therapy, where we will see some interesting data with immune therapy night, there’s challenges we’ve, you know, pseudo progression is clearly one that that does happen, I think, you know, a rare phenomenon, but, you know, potentially 10% of patients and I think, can create a lot of confusion in in data read out a little bit. And so I think there is real potential to kind of make that cleaner.
So, here are kind of just
two studies looking at that kind of ability of circulating tumor DNA
change over time, really demonstrating kind of that, that that benefit, right, so here is a work again, from Ryan Corcoran group Mass General, on the one side of the slide, this is colorectal cancer patients. And here you’re looking at kind of a decrease in certainly tumor DNA, over time, demonstrates really dramatic differential regards to who’s benefiting so really kind of you know, pretty clean, read out objective of
benefit from therapy and and one of the beauties here is you can actually capture this ctdna change early on in therapy even well before you’re kind of initial kind of restaging studies. So kind of the ability of for rapid assessment and ability of really a quantitative kind of exact assessment in the other studies shown there, so this is non small cell lung cancer urothelial combination study, the same issue again here with immune therapy devalue Mab and and what you see here, so the red is clearly the group that that is doing worse. And that’s the group where you have your chain and certainly tumor days increasing. So your variant allele frequency is going up. So suggesting that tumor is getting worse progression. And that pretty clearly delineates that out. Now, the green and blue are groups that are doing better, right, so the distinction there, those are both groups that have a decline in encircling tumor DNA, I think that the green arm actually looks better. And that’s really kind of this this novel kind of unique subset in which you’re actually looking for a eradication of certainly tumor today. So you’re taking your super lean tumor today, to zero. So this kind of novel idea of can you have an endpoint where you kind of eradicate you’re certainly human day, and that even identifies even a better group of outcome. And that kind of clearly is what you see here. And that’s one of these endpoints that I think we have a lot of interest in is the ability to kind of eradicate circling tumor DNA and really demonstrating kind of a better endpoint from that population. And if you even go back to this study I showed you before your affiliate cancer, Tesla lism, AB and what you see here is that the group that got the Tesla lism ab therapy, you can really even distinguish that group better of who’s really benefiting by the group that actually has eradication of the circling tumor DNA. So So you go search entrepreneur positive to negative, versus kind of certainly tumor today that that remained detectable. And so this ability to do develop undetectable certainty for today is actually I think, a real clean, nice endpoint of efficacy, that we can see stratifying kind of patients that benefit.
And so, here, we’re looking, again, kind of a same kind of conceptual kind of idea, and I think we’re going to kind of look, we saw this study before, in regards to kind of baseline prognostic, and here real similar curves with this idea of, you know, predictive change over time as as a marker of efficacy, right. So both overall survival and present viable, very similar, you know, solid tumor data sets. And you can see here that, you know, the the decrease or increase really delineates groups that benefit, right, so, um, and then I think the the final point that, that I want to kind of touch base on a little bit is kind of listed here. And so this is really the idea of how can this you know, play out and help us in in immune therapy space and and I’ve showed you some really fantastic hers was circling tumor DNA. And I think the obvious question, one would return to what Well, how do those compare in regards to radiographic kind of outcomes, which we more commonly see? I think the the points I’d make there is if you go back to some of those prior studies, you’ll see that certainly tumor DNA often is performing better than than radiographic outcomes. I think it’s also a one point of time earlier, that’s kind of performing better than a serial kind of radiographic data capture. And then you can see here, I think, really kind of the, the challenge of new therapy and we see this in patients. So if you look, you know, for deficient mismatched pair colorectal cancer patients, you know, we treat for two years that we stopped therapy and the vast majority of patients where we stopped therapy on we still have disease left on the scan, you know, so, so radiographic disease assessment does have some limitations in our immune therapy assessment. And so, so here, this a little bit complicated in colors, but really kind of the, the the blue green, right, so it would be the two kind of colors to look at together. And that’s really identifying a cohorts where you have an increasing circling tumor DNA, and that increases are going to identify as the two curves that are doing worse. All right. pretty clear cut in that regard. Now, what’s the difference there is that you know, one of those was progression on on first kind of restaging. The other one was kind of clinical benefit on first restaging. But But clearly, both have very similar outcomes. And you would say certainly even a better capturing that and I think there is no doubt from you know, historical expectations. You know, stable disease in oncology can be a host of benefit and no benefit patients, right? You have your indolent biology that’s hidden within the stable disease category, you have your therapeutic benefit that’s hidden within the stable disease category. And really the idea of stratifying that is fantastic and ability to really tease out the group that’s getting that clear benefit. And then if you look at your orange and red groups, so that’s kind of
The area where you have a decrease in circulating tumor DNA, you can see those two curves are the curves that are doing best on the Kaplan Meier.
And so the distinction between those is that on those two different curves, one group had radiographic progression on first scan, that’s kind of the the yellow color there. And then the other group had clinical benefit on first scan. And so this is where that, you know, radiographic progression and pseudo progression challenge comes in where you actually can see the group that has, you know, progression radiographically, potentially as being Miss called, because they actually have, you know, an initial immune response with swelling, inflammation that’s kind of clouding the assessment of disease.
And so, um, to conclude, kind of, by, you know, stating kind of what I’ve kind of reiterated a little bit on on the circling tumor DNA side of things is that we do have this really powerful new tool that I think we need to start using across the board to better understand clinical benefit and patients, I really think the the objective kind of quantitative nature of this should be incorporated across the board. So we really have clean kind of readouts and and early readouts of kind of our benefit in patients. Um, you know, that the panel based versus tumor informed kind of discussion does exist, there’s spaces for panel based spaces for tumor informed, I think we’ve seen a lot of robust data. And what I showed you mostly was tumor informed based. And so I think if you do have the tumor present, a tumor informed based essay is really kind of a better based essay, and boasts kind of your your sensitivity specificity in regards to what you’re detecting, and how deep you can detect.
You know, early detection, I think gives you the ability to help really pick out the patients that you want to treat, it opens up a new population to treat and opens up a rationale to be treating those patients with a very aggressive therapy. Um, and then I think, finally, cDNA demonstrates, you know, a biomarker that, I think as clear benefit in that immune therapy space, right, I kind of focused a little bit on immune therapy here, you know, I think, in the more classic kind of, you know, chemotherapy, you know, it’s a potential earlier readout of benefit, which I think is exciting across the board, but I think within the immune therapy space, it really does help you kind of delineate the some of the challenges you run into with kind of immune therapy, pseudo progression, and then immune therapy, residual disease, seen on scans and really kind of assessing better what what kind of those two kind of radiographic challenges really are on the underlying basis. And so, one couldn’t, you know, even make the the statement that, you know, is is certainly tumor DNA here to replace, you know, radiographic imaging. And I think we could argue that point, but I think we’re gonna see, clearly integration and clearly adjustment of kind of screening, scanning intervals based on this as we kind of move further down the line with a circulating tumor DNA. And so I will stop there, and then turn it over to Andrew, and I think, open for questions. Fantastic. Thank you, Mike. Perfect and great timing. So let’s begin the q&a session. Now. We’ll start with some oral questions. So let me hand back to our moderator, Tara to host that piece of this.
Great, thank you, Andrew. At this time, we’ll be conducting a question and answer session. If you’d like to submit a question you may do so by using the q&a textbox at the bottom of the webcast player, or by emailing your questions to questions at lifestyle advisors comm to our analysts, We now invite you to join via zoom, please hold while we pull for questions.
So the first question comes from Mark from from callin, Mark, please go ahead and unmute your line.
It thanks for taking the questions. And thanks for the great overview, Dr. Freeman.
Maybe it is to Part One for Andrew and then also for Michael Bay for Dr. Freeman to chime in, just interact with the ctdna essays
you’re using in this in your current studies and then plan to in your planned Advent study studies. I guess how closely tied? Are those essays in the genes you’re going to be looking at?
To the genes that are at the new antigens are actually included in the vaccine? And I guess, is it important to capture antigen potential mutations that are outside of the vaccine to capture things like antigens, right.
Yeah, thanks, Mark. Great question. The the data you’ll see a desmo arise from work that we do internally. And obviously we take what you would classify as a tumor informed approach, because obviously, we understand these tumors exhaustively at baseline. And so we can actually track all of the mutations. We do ultra deep sequencing, and we can track all of the mutations that we detect in plasma over time, and then we can highlight that
20 that are in the vaccine. And we can start to address questions as to whether we see evidence of subclones forming. Because obviously, what you might see is that you might see response across all mutations, for example, and then you might see breakout of the subclone, where some mutations stay suppressed, and others then start to come up. And also we can find novel mutations in plasma as well that were not there at baseline. And then, because obviously, we’re constantly looking for non germline sequence, we can actually detect novel mutations in plasma. So this does speak to the richness of this approach, which of course, massively exceeds what you could accomplish with corneal biopsies. So those are the data that you’ll be seeing that desmo. As we move forward, we will migrate over to a more
clear enabled FDA enabled platform, as opposed to our research grade sequencing, for obvious reasons. So there will be an evolution. But as you can see, as you saw from from Mike’s presentation, there are now robust, tumor informed sequencing strategies, and the terror Archer a couple of companies and the terror in particular has an FDA breakthrough designation for their diagnostic and commercializing it actively, as we speak with I think, pretty, pretty good success. So I think this is a very rich field, we can do a bit more in the research environment than we can in the career slash FDA environment. And you’ll see that migration over time, Mike anything, gentlemen?
No, I mean, I grew up and I think, you know, the, there’s a lot of interesting kind of sub fonality questions and and, you know, kind of recognition questions by looking at the the depth of kind of a tumor informed kind of assay. So I think there is a lot of interesting kind of understanding of tumor dynamics that that you can gain by by looking kind of broad light. And so I think, you know, I be interested to see kind of how that plays out over time and some of these tumor types related to kind of, you know, risk response status and benefit and kind of what you’re seeing on the various different mutations.
sorry, Mark, did you have another question or Was that it?
I’ll jump back in with you, the other people can have. Okay, great. Thanks for the questions mark. Our next question comes from Sean Lee. From hc Wainwright, Sean, please go ahead and unmute your line.
Good morning, guys. This is Shawn and thanks for taking my questions. My first question is, over the last year or so we’ve seen civil readouts from the combination of immunotherapies and targeted therapies in MS CRC, but the results have been have been mixed. So I was wondering whether the government has any thoughts on these results? And do you see any of them as changing the treatment paradigm over the coming years and how would you say they would play together with the upcoming immunotherapies such as Greiner?
Yeah, yeah. So I mean, I think you know, you’re speaking to kind of the number of different PD one PDL, one and kind of vet Jeff tyrosine kinase inhibitor combinations that are out there, you know, there, there’s a ton of them, you know, all kind of the, the small level phase two kind of setting.
You know, the volumetric rafted clearly the first one that kind of popped out, that that seemed really encouraging, and I don’t know, it’s a you, we’ve seen a lot of different studies, you know, with
even that same combination, not not achieving the same level of activity. And it’s been very kind of across the board. You know, I think there clearly is some, some patients responding to, I think, a PD one veggie FTK. I mean, I’ve clinically seen that myself. And so I think it is encouraging, you know, kind of what the fundamental statement that that there is the ability to engender, you know, kind of an immune response and microsatellite stable provision, those would be our Colorado cancer. Right. I think that’s a big take home mom. I think the the challenge, you know, clearly, as you expect, you know, going from phase two to phase three, you know, your your, your outcomes and efficacy don’t get better, they get worse. Right. So So, so you’re starting at a pretty heterogeneous low bar and expecting to go lower. And so I think there are some, you know, challenges with with that going forward, though, I think we will see that probably be be put into, you know, play and people looking at it. I I do believe and I do think you know that the issue of of tumor site is one of those kind of key explanations that’s out there in regards to, you know, the kind of immune suppressive nature of the liver microenvironment. And I think there’s some great, you know, preclinical data that supports that. And I think that is kind of a theme that we’re seeing a little bit so, you know, is there a just a real simple kind of, you know, clinical context and metastasis strategy
That might be at play, and kind of taking that combination for it. And I think that probably is a little bit true. I would say I think the benefit rate is going to be low, you know, no matter how you cut it, I think going to a large randomized study, you’re going to see a low subset population potentially benefit if it makes it I, I do think, you know, again, in the refractory CRC space, your your hurdle is low to cross. So, so I think you can come in with a fairly low response rate and, you know, benefit rate and potentially, you know, make a benefit, though. I think the one challenge with with immune therapy is that, you know, the, if you benefit 10%, but then 90% have no benefit, would you actually even fail against, you know, regorafenib, where you’re benefiting? 50% and 50%? don’t benefit at all right, you know, so I think there is still a unknown
outcome there, though, I am kind of would say, I think, encouraged that there is hits of activity, but I don’t see it as being a clear cut, guaranteed kind of break into kind of therapy, I’d have to tell you, yeah.
Thank you for your thoughts on that. Um, my second question is for focusing on the circulating tumor DNA, from a regulatory regulatory perspective, we see that the FDA has recently been very positive on CT DNA as a as a diagnostic tool. On the on prognostic or on a monitoring therapeutic therapeutic response front, how do you see the agents you see respond to that? status? his stance on that? And how do you see that will evolve in the coming years?
Yeah, I mean, I can give a little bit of my clinical sense, I, you know, I regulatory,
better sense on on regulatory space a little bit, but but, um, you know, I think, yeah, no, I mean, I think the data has been just consistent, right. And FDA, you know, pays attention to that, and sees that, you know, so so I, I think you’re right, they have kind of
been very open and very encouraged. And, you know, there’s a number of large studies going forward, that that kind of support that, you know, prognostic utility is, is nice, but, you know, again, for us in, you know, the therapeutic space, you know, you really want to predict and to have something that helps to change therapy, writer, adjust therapy or alter therapy. And so, so I think globally, you know, the the on therapy, early evaluation, we have a lot of interest in, you know, we have studies ongoing, looking at early time points to DNA kinetics to actually change therapy. Right. You know, and I think that’s kind of clearly in a clinical trial space, but but one you can foresee going forward and being one that we use more consistently in a number of different settings.
Just a quick follow up on that, for your own patients in both in clinical studies and for treatment is certainly into my da something that you break through regularly monitoring yourself.
Yeah, good question. So, um, I think, you know, not every patient right, I think we do it in a in a number of studies, we have it, I think we capture plasmin, almost everyone going on a study these days for for kind of that utility, within clinical trial, kind of understanding, I think, you know, clinical practice, right, where I would say we utilize it is, is really in the kind of the proven situations I’ve kind of demonstrated so that that kind of post resection setting, you know, that is clearly a setting where we’re engaging as a standard clinical practice approach. And then I would say the area of you know, targeted therapy kind of evolution, so that in Colorado cancer is really anti EGFR therapy, and an idea of re challenging with anti EGFR therapy. Right. So so so that would be the two spaces where I’d say in clinical practice, we are kind of using it.
Great. That’s all I have. Thank you for taking my questions. Great. Thank you, Shawn. If I may, I’ll turn now to a couple of the submitted oral written questions. And let’s start. Do you have any other oral questions? Nope. Go ahead, Andrew. Okay.
So, we have a couple of questions on ctdna. Let’s tackle those first, just keeping the conversation flow. First question comes from Funko. Wang, are there any studies of ctdna that contradict with the value of CT DNA to predict therapeutic benefit? And then a follow up question, is there any prospective study to look at the predictive utility of CT DNA? So Mike, we showed a lot of supportive data, are you aware of negative data saying that cDNA is inaccurate or unhelpful assessing response? Yeah, you know, I think
I mean, there’s there’s
no there’s no you know, clean kind of thing I can push to put my finger on for published data, but But no, clearly, I mean, the issue is, you know, is um, depends a little bit on
The the methodology you’re using, right in the limited detection, but when you’re close to limited detection, or you have kind of false positives related to clonal, hematopoiesis, or something, you can get confusion of of your readout. Right. You know, so you definitely see, you know, when we do often more at Anderson panel based testing, just because we have an internal panel that we use, and, and that does become a more of an issue than tumor informed, I think tumor informed, it’s a little bit cleaner in that regard. But but in a panel based testing space, you know, you do have potentially some some confusing kind of germline related challenges that that pop in and that then, you know, completely kind of throws out the utility a little bit, I think, on the other side, for both essays, if you’re close to the limit of detection, you can see some variation there that that can give you some confusing results, you know, positive, negative, positive, negative, but but you’re just fluxing around a limited detection and and are you really seeing meaningful changes? Or is it just kind of your, your, your technology is kind of pushing, it’s kind of, you know, ability to consistently provide you the readout you want? And so, so those would be where I would say you you have issues, I think, you know, outside of that, I think it’s consistent. Yeah. Oh, um, is kind of what I would say, you know, for practice in regards to kind of early prediction, I think. Yeah, I mean, you know, that’s a clinical trial space question.
You know, for the most part, we are using it in this immune pseudoprogression space. You know, I think that is a clinical challenge we have. And in certain settings, one that you really, you know, you don’t have time, just keep treating and let it play out. You know, so that is one I think clinically, we are doing it kind of as Early Assessment. But I think that most of that is really on a clinical trial. So we have clinical trials, we’re looking at, you know, a two week readout. ctdna did that help guide you so at a month, you’re changing therapy, right, instead of our standard waiting, you know, two months, you know, two to three months to change? Yep. Yeah. My sort of response in part to your question previously shown is that you asked about the regulatory perspective, I think what we can see is that the regulators are looking for other ways to assess the clinical benefit of immunotherapy. And they held their odacc. Committee recently, to really address the question that they’ve approved several different checkpoint inhibitors on the basis of response rate, with durable responses observed often in relatively small numbers of patients. And then of course, after an accelerated approval using that surrogate endpoint, the confirmatory study is required. And those confirmatory studies have been reading out negative using a PFS endpoint in an overall population. And obviously, that is puts everybody in a difficult position where of course, we don’t want to deny patients therapies that can help them and we don’t have perfect predictive tools. But at the same time, confirmatory studies, obviously are part of the accelerated approval framework. So I think the FDA is looking for new approaches. And I think ctdna is probably top of their list as being a potential endpoint that might help bridge that gap. Would you call does that make sense to you, Mike? I agree with that. Yep. Yep. Okay. Next question comes from Yuan Popoff. is going from positive to negative ctdna, during treatment, a better predictor of survival than radiographic tumor reduction?
Yeah, I mean, good question. I mean, I,
I think, you know, radiographic reduction is, is a spectrum. Right. Right. So So I think it kinda, you know, how you kind of cut that and define that a little bit.
But, but, you know, I mean, I think, you know, driving to undetectable CT DNA is a very robust marker for, you know, success. I mean, I think, again, it’s, it’s kind of, you know, limited detection kind of space a little bit, and we’re, what are we talking about? But, um, I think in a general sense, it potentially can be a much cleaner readout, I think, you know, again, you know, for resist percent reduction, you know, we have, you know, decreased, you know, to kind of 30%, that’s just stable disease, right. And then beyond that, you know, you have your partial response categorization. Right. And, and why do you have that buffer around stable disease, right, is because, you know, measuring lesions on a scan is, is fundamentally a lot of, you know, there, there’s a inner physician in our measurement that that clearly occurs, and, you know, scans look different from, you know, time point to time point, you know, um, and so there’s just inherent challenge with that. Right. So I do think you have the real ability to be a lot more quantitative and clean in your readout. And so I think potentially, yes, I think the key is just how you’re kind of cutting your your kind of response status a little bit in your comparison and setting but but in a general term, I would kind of say, yeah, I think I think it is probably going to be cleaner. Thank you. The last questions currently on my list, so please do to send in written questions if you have any further. The last question is what is the competitive edge