ORIC Pharmaceuticals, Inc. (ORIC) Earnings Call Transcript & Summary

Great. Welcome, everyone. I'm Yigal Nochomovitz. I'm one of the biotech analysts here at Citi. Welcome to the panel entitled Aiming for the Bull's Eye - Latest Advances in Targeted Oncology, and it's my great pleasure to have with me 4 distinguished expert speakers from IDEAYA Biosciences, Yujiro Hata, who is the CEO; from ORIC Pharmaceuticals, Jacob Chacko, also CEO; from Rain Therapeutics, Avanish Vellanki, Chairman and CEO; and last but not least, from Syndax Pharmaceuticals, Briggs Morrison, also CEO of the company. So welcome, gentlemen. Thank you very, very much for participating. As a starting point, I think it would be great for those listeners less familiar with each of your pipelines for each of you to go through and just give a brief 2 to 3-minute overview of your company and the pipeline to level set the discussion. So, Avanish, you want to kick it off?

Sure. Happy to. And, Yigal, thanks for having us. So as an introduction Rain Therapeutics, again, I guess we're all precision oncology-focused businesses here. But our lead program is an MDM2 small molecule oral inhibitor that we in-licensed from Daiichi Sankyo late last year. And we licensed this program in -- after a large Phase I study, we've now taken it into a pivotal Phase III study for a certain subset of patients with liposarcoma. And we're going to begin an extensive clinical development path for this program because of our feeling that this was a truly differentiated compound in MDM2 landscape. In contrast to me, the other big pharmas that have looked at the MDM2 landscape in the past, we're actually applying a very specific precision oncology strategy. There's ways to develop the MDM inhibitors in a non-precision approach and certainly with a precision approach. And with all 3 studies that we're expecting to start in the next few months, we already have a pivotal study enrolling. We are selecting for patients with MDM2 gene amplification. So it's absolutely a precision strategy with our lead MDM2 inhibitor. We expect to have data for the pivotal study of liposarcoma sometime in 2023. Our second indicator -- our second trial is going to be a basket study, selecting for patients across tumor type with a certain threshold of gene amplification of MDM2. That will commence enrolling in the next few months and that will have data -- interim data in the second half of next year. And the third trial, which should start by early 2022 is in a small population called intimal sarcoma, which is also an MDM2 gene amplified population, and that will have data -- interim data by late next year. So beginning of the second half of next year, we expect this nice stream of clinical news flow. And much earlier in the pipeline, we have a program targeting RAD52, a novel target in the DNA damage response pathway. We were compelled by this target really because the industry has not progressed, looking at -- in terms of looking at this target as a therapeutic strategy. RAD52 exists in pathways that are independent of the BRCA pathway, independent of the pathways where PARP plays a role. So in patients with homologous recombination deficiencies in BRCA, BRCA-mutant patients in ovarian, breast and prostate cancer that have maybe put on a PARP therapy and relapse, we think RAD52 is going to be one of those increasingly relied upon targets. And we have no clinical competition in the space. So in totality, those are the two programs we have at Rain Therapeutics, each one targeting a very specific genetic profile amongst patients. To wrap up, Rain Therapeutics, we're based in the San Francisco Bay Area, have about 45 people today. And we recently IPO-ed in April of this year with a cash runway well into 2024.

Perfect. Jacob, do you want to go over ORIC?

Sure, Yigal. Thanks. Thanks for having us. So ORIC stands for Overcoming Resistance In Cancer and that in a nutshell is the mission of the company. The company was founded in 2015 with that broad-based mission by a group of drug developers who have a long history of developing commercially-marketed drugs within the oncology space. Of course, resistance is ubiquitous within oncology. So we're not trying to be all things resistance related in oncology. And based on the team that we've recruited to ORIC, we've planted our flags in certain specific areas to sort of limit where we tend to focus. And that would be at a high-level precision oncology, of course. We've got the Ignyta clinical team that developed entrectinib, and for that reason, spend a lot of our time in the precision oncology space and find the topics you're going to discuss today quite relevant. Second area of expertise is key tumor dependencies, specific pathways related to innate, acquired and bypass resistance mechanisms. And then the third, just given the founders of the company, Charles Sawyers and Rich Heyman, is hormone dependent cancers. And so everything you see in our pipeline today, checks at least one, if not multiple of those boxes. Ideally, we're checking precision oncology with each one of the programs. I think another unique thing about the way that we built the company was we made a commitment to essentially build internal drug discovery capabilities at the company, but also to simultaneously try to supplement that with opportunistic in-licensing of assets through business development. It's very, very hard to do that successfully. I think very few companies in the oncology space that are able to do both of them. So far, we have been able to do that successfully. So the lead program is ORIC-101. It's a glucocorticoid receptor antagonist. This came out of a work that was originally done in Charles Sawyers Lab looking at causes of resistance in prostate cancer, essentially, why were patients becoming resistant to drugs like enzalutamide and apalutamide. So beyond that, we've now expanded that mechanistic hypothesis. So ORIC-101 is in 2 different Phase Ib studies right now. One of them is a combination with enzalutamide in prostate cancer. The other is a combination with Abraxane in various solid tumors. That was an internally-discovered program. The second program is a small molecule inhibitor of CD73. And that's obviously a target that's quite relevant within the adenosine axis. Again, that's a program that we developed internally at ORIC through our internal drug discovery. We believe it's one of the most differentiated programs out there. It's about to start dosing its first patient within the next quarter or so. As a single agent, that's a unique aspect of that program because most others in that space are doing combination approaches. And then beyond that, we in license the next 2 programs. So there's a PRC2 inhibitor, which is relevant to prostate cancer and some other tumor types that we in-licensed from Mirati last year. And then finally, an EGFR/HER2 Exon 20 program that we in-licensed from a private South Korean company last year as well. So those last 2 programs that I mentioned will file their INDs within the next quarter or so. So really, within the next 3 to 6 months, we'll have 4 clinical-stage programs, all adhering to those areas of expertise that I mentioned to you, but precision oncology is a huge aspect for everything that we do. Company went public in April of last year. So we've been public now about 1.5 years. And looking forward to your questions today on the panel.

Perfect. Yujiro?

Yes. So IDEAYA Biosciences was founded about 6 years ago. Our core focus is to be the leading synthetic lethality-focused precision medicine oncology company. Today, we've now advanced 2 programs into the clinic. We have 2 more development candidate goals for what we believe are potential first-in-class targets before the end of the year. Folks may know that we also have a very broad partnership with GlaxoSmithKline, total deal valued at $3 billion to $5 billion range, of which we hold 50-50 U.S. profit rights for 2 of the 3 programs. Within synthetic lethality, I would say, really -- we're really, I think, positioning our pipeline now with our clinical molecule IDE397, where we did report recent data on that just a month or so ago, is to be the industry leader in MTAP deletion. So here, of course, IDE397 Phase I, and we also have additional programs there that we're advancing to, again, enhance our capabilities in MTAP deletion.

Perfect. And Briggs?

Yes, great. Thanks so much for inviting us to join the panel. Our mission at Syndax is to realize a future in which people with cancer live longer and better than ever before. And we hope to achieve this by identifying breakthrough medicines that can improve the lives of people with cancer. We have 2 programs in clinical development today that we believe have the potential to address quite important unmet medical needs. Our first one is our menin inhibitor, which targets patients who have either MLLr or NPM1c driven AML or ALL. We've presented Phase I data showing that our molecule in a heavily pretreated population of patients was safe and well tolerated with no patients discontinuing for drug-related AE, and we were, of course, thrilled to see very clear evidence of monotherapy activity in patients with relapsed or refractory acute leukemia who have either of these 2 genetic lesions, the MLLr rearrangement or NPM1c mutations. We had an overall response rate of 48%. Two-thirds of those responders achieved MRD-negative status. The CR/CRh rate stands right now at about 23% as of our last update. So really very promising, both efficacy and safety in the Phase I study. And I think we'll talk about that. What do you -- how do you design these targeted therapy protocols. The goal of Phase I was to define the recommended Phase II dose, and we now have a recommended dose selection. And we're looking forward to updating data from this ongoing Phase I trial at a medical meeting at the end of the year and anticipate starting our registration cohorts for that molecule quite soon. Our second molecule is a CSF-1R inhibitor for chronic graft versus host disease. Although some might not consider this targeted oncology in the way we are discussing today. It's obviously targeted to block the interaction of CSF-1 with its receptor and thus inhibiting the proliferation and differentiation of monocytes and macrophages. We're studying that in chronic graft versus host disease. Last year at ASH, we presented our proof-of-concept data, showing that the drug looked like it was safe and effective for that disease. And we have an ongoing registration program in chronic graft versus host disease with the data anticipated in 2023.

Great. So to kick off the discussion, which I hope will prompt some good debate kind of ask a broad-based question, which is very relevant for all 4 of you. What makes a good target in precision oncology? What are the characteristics of a good target? What are the characteristics of a bad target? And how do you know that you have a good target if you don't have any clinical data? So Jacob could be smiling and maybe you want to take that first.

Yes. I think that's the -- you're asking the key question, Yigal, which is -- I always think of the -- in precision oncology that, I guess, Mike Tyson quote, everyone's got a plan until you get punched in the face. And it feels like that sometimes in precision oncology, where I think you obviously -- you're looking for targets that have, at least what you believe to be a mechanistic link to the disease based on preclinical work. And, obviously, there's a whole host of things you can do preclinically to try to validate that. At least in our case, and I'm sure with your other panelists today, you try to do a lot of work to figure out, is the tumor truly addicted to that target because addiction is a different thing than just it happens to be influenced by a particular target. And I think that -- if you can establish that preclinically through knockout models, shRNA, just a whole host of different modalities, I think the next big question you want to ask yourself is, is there something you can measure as you -- related to that target from a precision oncology angle or multiple things you can measure preclinically, but then also importantly, clinically. And is there something that is practical in patients where you're going to be able to get biopsies, but the thing that you want to measure -- you're going to be able to measure, is it a binary thing like a gene fusion? Or is it more on the spectrum of overexpression and amplification where you've got really a continuous gradient. And so just really understanding the characteristics of that target. So once you go beyond the mechanistic link of the disease, then kind of establishing what is it you're going to measure, what type of measurements will it be and then are those measurements going to be practical in patients? That's kind of the thought process we walk through initially as we consider new targets internally. But the reason I gave the Mike Tyson quote is because once you get in the clinic, you kind of discover new things about the target, of course, and just some of the practical realities. And although you mandate biopsies, you don't always get biopsies. Or sometimes you get biopsies and they don't have cells that you need to measure what you're trying to measure. So there's always a little bit of uncertainty and ambiguity in what you're trying to do. And then, of course, with all the diseases that we're trying to -- that this group is trying to study, depending on what line of therapy you're coming in, that target may be a great target, but the disease itself may be quite heterogeneous at that point. And so your target may only be one of the explanatory factors in which case, you've now got to drill down a layer even deeper on the translational exercise to really find the subset of patients that you're most focused on and sort of sort out the ones that aren't quite relevant. So it's a big -- I guess the summary there is it's a pretty comprehensive and systematic process that we undertake at ORIC in terms of identifying which targets are good targets and ones that we want to pursue and laying out the plan as to how we would go about doing it. But you've got to kind of stay dynamic as you get in the clinic in terms of figuring that out.

Makes a lot of sense. Anyone else want to dive in and contribute?

Yes. I'll just add to. I think Jacob summarized it very nicely. I think we've all learned over many, many years that, as they say, cancer is a genetic disease. And so it's -- understanding the genetic abnormalities, and I think everybody on this panel in their companies today and in their previous companies have worked on targets where the target -- there's a genetic abnormality in the cancer cell that is reproducibly present and seems to be a driver of the disease. And then you develop a -- I think that's what we mean by precision oncology. And I think that's what most of us have been focused on. And then the question is to develop a therapy. That gives you a patient selection tool because you're looking for patients that have that abnormality. In our case, it's either the MLLr fusion or the NPM1 mutation. So we know who to treat and you develop a therapeutic that targets that pathway, either directly hitting that as a fusion protein or I think as others on the panel have -- and we take advantage of the genetic background of the tumor to identify patients where your therapeutic is going to work.

Yigal, maybe I'll just add. I mean, we're in the synthetic lethality space, it's a little bit different. But I think for us, there are sort of 2 areas we think about -- there's obviously more than that, but I think 2 key areas. One is this concept around selective essential targets versus pan-essential. And I think there is a lot of research and literature out there that pen-essential targets, which, again, will affect all cells at some point, from a precision medicine oncology target, typically, the therapeutic window has been more difficult and challenging. And I would say, clinical POS there has also been typically more challenging. Where selective essential, they're not these pan-essential genes. But when you're in a specific -- very specific genetic biomarker setting, ideally, you see exquisite activity. And there's examples like that of ALK and others where we've just seen a lot faster time to approval as an example. The second, I think, is really around how robust is the biomarker because you look at a lot of literature and data out there. They're looking at very specific settings. But how robust is it? When you go to different tumor types, different situations, how intact is that biomarker? And then I think lastly is does the tumor care. If you inhibited in that biomarker setting what happens is that, do you really see single agent aggression? Do you see stasis? Do you see slight growth? I think for us, those really are the three pieces.

I'll jump in with a quick point. So [indiscernible] Mike Tyson quote, I'd say I got to use that internally. I think the heterogeneity is a big issue. You're absolutely right. I completely agree with that. For our programs, specifically in the basket strategy, I think finding at what point does a specific abnormality become oncogenic is really critical. And the bioinformatics approach that we've used at Rain in the MDM2/p53 axis was an example of that, what level of amplification of MDM2 truly gestures that now the amplification is the oncogenic driver, not something else. And so we've gone through those analyses to actually look at broad patient populations across public databases that gestures that specific copy numbers matter, below which it may be harder to evidence a positive outcome. So the bioinformatics approaches, I think, certainly can help in addressing that heterogeneity.

Well, you all touched on some important themes and you touched on the theme of biomarker, which I wanted to address. So with respect to biomarkers, I mean once you have a target identified, does the biomarker sort of follow by default just based on the understanding of the target? Or are there situations where the biomarker may not be obvious, and you need to do additional preclinical work or discovery work to find a good biomarker? That that makes sense.

Go ahead, Briggs, [ we're going to see ].

Yes. So again, I think what we're talking about, Yigal, are patient selection biomarkers, right? We're trying to find who are the patients who are most likely to respond to our therapy so that we can enrich for those patients and see a very strong efficacy signal early on in the development of our program. And so as I said, I think if you have -- I think all the panels have talked about a patient selection approach where they know whether it's, again, the specific driver itself or the context, such as BRCA mutation and what have you, one of those or amplification of the gene, that is the patient selection biomarker. In our case, we're fortunate because the patient selection biomarker is a standard test that's already used today in the clinic, so we don't have to develop anything. But there are other situations where I think the company does have to develop a CLIA certified tool to enroll and enrich for those patients that have the patient selection biomarker of interest.

Okay.

So, Yigal, I think also part of your question was around sort of where does the biomarker piece fall in the drug discovery process, if I said it correctly. But I think for us, there's kind of 2 buckets for that. One is there are just certain patient population biomarkers we're interested in. Just -- not saying this is specific, but just as an example [indiscernible]. And then we'll sort of interrogate what are the various synthetic lethal targets against that biomarker. And so there it's -- we know we have a strategic intent and a biomarker, and then it's really the target discovery process starts. And then the flip side is we know there is a pathway that we think is really biologically important and it falls in sort of a broader biomarker category. Someone just mentioned BRCA. So HRD is a great example of that. But we know HRD is a big subset and multiple mutations and genes fall in HRD, then it's spending the time really trying to parse out what in HRD is the most sensitive, right? And so that's kind of the flip approach is you get excited in a set of targets and a pathway. And then you're really trying to parse out the biomarker within a family of genes.

Sometimes, I think the tumor types can help when all the patients have it. And you don't need actually any sort of selection process. When we look at our strategy in liposarcoma, 100% of patients have this MDM2 gene amplification. So no companion diagnostic needed. That's perfect. Intimal sarcoma, the vast majority of patients have MDM2 gene amp, no companion diagnostic needed. So it's fortunate when certain tumor types nearly ubiquitously actually portray the genetic profile you're looking for.

Thanks, Avanish. [ You led into my ] -- go ahead. Sorry.

[indiscernible] add on to what Avanish said because it triggered a thought in my head, which is that we do -- from the very get-go, when we're working on programs internally, we think at that point what the biomarker strategy is. So at the same time as we're developing the therapeutic often sort of the direct biomarkers and then some of the indirect things that you measure as well, just that you're ensuring that you're getting proper exposure and good target engagement. But when we actually get into the clinic, and this kind of ties into what Avanish said, we actually generally take the approach of being permissive in terms of inclusion criteria. So trying to enroll a broad range of patients and obviously, measuring the biomarkers across all of them, at least in the Phase I, but especially for something like the glucocorticoid receptor, GR, where we're sort of blazing the trail in terms of just the role of GR in the oncology space, how much GR is enough to make a difference in terms of a targeted therapeutic. And so that's why it's actually equally beneficial to us in the early days of the trial to be enrolling patients with no or low GR status as it is with patients with moderate or high GR status. That all kind of informs decisions that we'll be making for later phases of the trial.

It's an interesting point that you should take the exact opposite approach. So we actually were with a high threshold, a high cut-off to try to increase the probability of getting a signal. And then if there's a strong signal then unravel it and kind of loosen up the criteria. So we should take the other approach.

Yes.

And, Avanish, a moment ago, you mentioned companion diagnostics. So I'd like to get a sense from all 4 of you when in the drug development process do you really get serious about the companion diagnostic? I mean, is that something that's done even before you enroll your first patient? Or do you wait till you have that POC from the Phase I before you really get serious about the companion diagnostic? Just help us through that thought process.

Yes. I can kick that off. So for our situation, we're waiting for POC with the basket study, the Phase II study, we want to make sure that we're seeing something before we incur the expense. That is not a trivial expense as everybody knows in terms of development and companion diagnostics. So we want to make sure that we can leverage some of the preexisting NGS assays out there to -- as part of our Phase II study, leverage some of the internal assays at Memorial Sloan Kettering, MD Anderson for the basket study. And then if there is a true rationale in selecting for these patients with a certain degree of MDM2 gene amplification, then we will develop a companion diagnostic. But we are not approaching it -- starting with a companion diagnostic first.

Yes. Maybe, Yigal, just -- I think for us, there's a lot of different biomarkers we've evaluated or we're pursuing through our programs. I would say that we put a higher priority towards biomarkers that are on key NGS platforms already that are being tested in centers and hospitals, Foundation Medicine, [ Kara ] are seeing them because we think that piece is -- yes, it creates an extra challenge in even just sort of capturing patients and enrolling quickly. So if we can, we like to go with biomarkers that are already on these key NGS platforms. But we have developed our own specific CLIA certified, let's say, [ IAT ] assays. But in general, I would say, we prefer ones that are already on the NGS platforms.

Any other thoughts?

We start -- like the others, we start thinking about the companion diagnostics early, early in development. But I think in terms of actually pursuing a companion diagnostic, as you would think about that in the more formal sense, we kind of wait for proof-of-concept in terms of just the more formal discussions that go around that. I think from my observation, 2 things have changed over the last 5, 10 years. One is FDA does feel like it's become a bit more permissive in terms of you don't necessarily have to have a companion diagnostic day 1 that your drug is approved. And then I think secondly, just with the diagnostic field in general advancing quite a bit, it really allows companies like us to do exactly what Yujiro said, which is you try to sort of piggyback off of diagnostics that are already widely available. I remember back at the last company at Ignyta, when we were looking for TRK and ROS fusions, in the early days, TRK fusion weren't even on in NGS samples. And so there's a lot of work done to just kind of work with the commercial diagnostic companies to get through [ some of those deals ]. And so we do think through those kind of considerations now. But by and large you find that the diagnostic companies are covering the kind of things you want to measure.

And just 10 seconds, yes, I think liquid biopsy testing, to that point, those panels are also just growing by the day. And I think it's going to just continue in that fashion.

I think you all touched on the theme of my next question when you were referencing patient selection for some of your early-stage trials. But the question is, what are the key design considerations for a trial of a targeted oncology product? What do you need to get right? What are the pitfalls? I mean clearly, patient selection is really important. But are there other critical factors that you need to get right in a Phase I proof-of-concept oncology trial? What do you -- and of course, what are the mistakes that people make that should be avoided?

Yes. Maybe I'll jump in. So I think the patient selection, at least in our experience, has been really quite important. So when we started our trial, similar to the conversation, Jacob and Avanish were having earlier, we actually got a request from FDA to not select based upon the genetic lesions that we were interested in with the idea being -- although we had very, very good preclinical data validating those lesions, their pushback was, well, you haven't actually studied all the other lesions and how do you know your drug doesn't work in something else. So let's not be -- let's not focus too much too fast. As we went -- we started down the path, we saw exactly what we predicted we would see, which is efficacy in the genetic lesions that we were interested in and we did not see efficacy in small numbers of patients, but we weren't seeing efficacy in the ones that didn't have the lesion. And so we went back to the FDA and said, we'd like to really just focus on these two for now. It doesn't mean we wouldn't go back and maybe do some additional, but I think to the point Avanish was making, if you can't see a signal in the population of patients that you really think you should see a signal, well, then you're done. If you do see a signal in the population of patients where you really think you should, then you can broaden it out and look at other things as well. So that's the way we've approached it. We've tried to really quickly focus in on the 2 genetic lesions where we thought the drug would work. And we're seeing a well-tolerated safety profile and efficacy, and that gives us a clear path to development in those genetic lesions. I think the only other thing -- comment I would make, having done this in a number of different settings, the -- understanding your dose is obviously pretty important. And looking for opportunities to increase the number of patients you study at any given dose just gives you a better ability to pick the dose base, not just on safety but on efficacy. So if you're studying the target population that has your genetic lesion, you're getting an efficacy signal and you're getting a safety signal, and you can put those 2 together when you go to pick your recommended Phase II dose.

Yes. I think that was a pretty comprehensive answer. I think maybe just the only other piece I'd add is, I think for a lot of us, if you do a biomarker-driven trial in your Phase I, let's say, dose escalation, which is what we're doing for our MTAP trial, we're seeing a lot of different tumor types. We're seeing half a dozen or more. And as we've seen in a lot in the precision oncology space, you don't always see the same signal in each tumor type. So I think that's an important one. Is there -- lung cancer, just an example, as we saw in other spaces or for whatever reason, there is more sensitivity there. That obviously forms your expansion decisions. But ideally, you're continuing still some basket, right, to continue [indiscernible]. So I think if you could get out of that escalation and have a good understanding which tumor type within that biomarker if you're seeing more sensitivity is quite valuable.

I'll add to that Yigal to just address your question about pitfalls and what are the -- what's ongoing. One of the things we spent a lot of time thinking about is the presence of co-drivers, right. So having a co-driver there that can completely obfuscate the primary selection metric that you're using is an important thing to consider. So if we start seeing ALK fusions, for example, in patients with MDM2 gene amplification, how frequent is it? And does the frequency matter? If it's a really small frequency, then maybe it doesn't matter. You can kind of go after the full patient population. 30%, 40% occurrence, again, looking at that kind of population metric and finding how frequent is something that could detract from the primary selection metric is important. So understanding additional co-drivers of those tumors matters a whole heck of a lot.

Okay. Maybe we can move into a bit of a company-specific lightning around, so to speak. So perhaps for Jacob, I'm just curious you have an asset called or ORIC-533, which is CD73 inhibitor, just had the IND cleared as I believe. Now this is obviously a fairly competitive space, the CD73 axis. So I'd love to hear your thoughts as to how you see a 533 as potentially differentiated and how your clinical development strategy might differentiate from your competitors.

Yes. Yigal, I think you're being generous by saying it's a fairly competitive space. I think it's a hypercompetitive space and we really acknowledge that. We think of the competition in probably 3 big buckets. Initially, a lot of the leading players in the CD73 space, AZ and others, especially the large pharmas, were antibody approaches to try to drive CD73. Part of that being a -- it was a chemistry challenge to come up with a small molecule oral inhibitor of CD73. So we and Arcus and others have done lots of profiling of the small molecule CD73 inhibitors versus the antibodies and shown pretty conclusively that the small molecules are able to achieve much, much better adenosine shutdown than the antibodies have, at least preclinically. I think the second big bucket of competition is really the small molecule oral inhibitors of CD73 -- I should say more broadly, the small molecule inhibitors of CD73 because some companies do have IV approaches there. And then the third big bucket is really just the broader adenosine pathway because I think you'll find people who would argue which node of the adenosine pathway, whether that's CD73, CD39 or the A2A receptor antagonist matters most. Clearly, we've placed our bet in the CD73 part of that space. Now what we've done is a lot of preclinical profiling, looking at ability to shut down adenosine, ability to keep up the cytokine levels and the CD8+ T cell levels in a high AMP environment. In other words, to kind of replicate the very high adenosine levels that you get in the tumor microenvironment. And whether it's versus the antibodies versus the CD73 -- in the small molecule CD73 inhibitors or the broader A2A receptor antagonist, ORIC-533, our asset is able to retain that potency, literally orders of magnitude more than the competition. And so that's something where we feel very convinced that, at least preclinically, we've got a great profile of this molecule and are quite excited to see how that stacks up against the competition that I mentioned. Now the additional angle that we sort of pursued in order to try to get even more daylight from the competition was for those that follow the space, you know that every single company in the CD73 space is doing big combination or triplet or quadruplet type studies. And so one of the areas that we decided to try to [ zag ] when everyone else is thinking is that we worked with an outside KOL last year who had established a mechanistic hypothesis in an undisclosed tumor where the belief was that CD73 could actually lead to single-agent activity in that tumor. So we pursued that work with that outside KOL, validated that preclinically. And actually, we're quite compelled by what we saw preclinically with our CD73 inhibitors in that type of a MoU. So we committed that after we filed the IND earlier this year, and that IND was cleared about a month or 2 ago that we'd be pursuing a single-agent strategy in that undisclosed tumor type. We'll keep it undisclosed as long as we can for competitive reasons because, obviously, there's a lot of players in the space, especially the large pharmas that could pivot there quite quickly once they realize that there may be a mechanistic rationale for targeting that disease. But at some point here, we'll have to come clean on what the disease is. But that's the way that we think that we stack up with a better preclinical profile, but also hopefully a differentiated clinical strategy.

This undisclosed tumor type is just -- is it not obvious or it's just obvious, but overlooked?

I would say it's not obvious in terms of the mechanistic link, although this KOL has done some work that does establish that mechanistic link to calm some of the investors out there. We've at least given them the reassurance. This is not some small, tiny niche tumor. It's one that as soon as we say, the tumor or the KOL, for that matter, people will know exactly what we're talking about.

Got it. Okay. All right. We look forward to hearing more about that.

Your analyst is putting you on the spot.

That's my job. All right. Yujiro, if I could ask you a fairly specific question, I believe you're expecting to present PK or PD data for your lead assets for the MAT2A inhibitor, I think, later this year for 397. Could you just help us level set and discuss what investors should expect on that data readout and what we expect to learn from that data readout?

Sure. Maybe just 30 seconds broad context. So MAT2A synthetic lethal with MTAP deletion, there are several key nodes in this pathway that folks are pursuing, I think, several very high-quality companies. And I think there is a view from several players that MTAP synthetic lethal perhaps is really the next big space, hopefully, the industry could open up. And the implication is that it's 15% of all solid tumors. So it's really, I think, an enormous opportunity for patients we hope in the clinical community. And I would say that a lot of what we're trying to do is somewhat similar. We're trying to reduce a key biomarker in the tumor called tumor SDMA. And we all want to try to reduce it at this fairly robust level of 80%, 90%-plus reduction because we know that when we do that, at least preclinically, we actually see very robust activity in the MTAP setting, including regressions. And I think what's been elusive for the field, Yigal, here is the historical assets in the space have not been able to do that consistently at least with a real therapeutic window. So for us, that's really the objective of this update is we want to keep the investor base and our analysts focused, can we robustly reduce the tumor PD of SDMA, but do it with the therapeutic window that the industry has just not seen before. And if we can achieve that, we think that's a big milestone. And then we'll just -- we'll take it to the next step after that. So yes, that's the goal. We don't know if we'll have the complete tumor PD data set because we're continuing the dose escalation, but our plan is to present some subset of the data we have for tumor PD, and that dose escalation is going well.

Got it. And Briggs, as far as I understand it, later this year, you will be providing an update for the Phase I monotherapies trial for 5613. So given that, could you just help us set expectations for the data in terms of number of patients that you're going to report on, what we should expect in terms of response rate by subpopulation as well as how much durability data we should expect from this readout?

Yes. Thanks so much, Yigal. Yes. So we will be presenting a medical conference at the end of the year, update on the Phase I portion. I think probably a common theme amongst all of us is the joke I'd like to make is Phase I is the new Phase II. So even though it's a Phase I trial in heavily treated patients, the questions we get asked are the questions one typically used to get asked at the end of your Phase II program, but that's just the way life goes these days. So we will -- yes -- we'll have at least a 50 patients' worth of safety data and at least 40 patients in the 2 genetic lesions that we're looking at. We'll be able to give a breakdown on efficacy, including overall response rate, CR/CRh rate, the MRD negative rate. And as you asked, importantly, a first look at durability of response, which we haven't presented yet. We're excited to be able to do that. And then given the number of patients we've treated, we can now, I think, start breaking out the efficacy parameters for the two genetic lesions, MLLr versus NPM1, and there have been questions about how the efficacy of the drug in those two distinct populations. So I think we'll be at a position to start to answer that with the data we have at the end of the year.

Okay, great. And Avanish for you, obviously, you mentioned the MDM2 inhibitor. I'd be curious to get a sense, just historically, what some of the challenges have been with the MDM2 inhibitors that preceded you and how RAIN-32 is differentiated from those prior attempts?

Sure. Happy to. And I'll be -- I'll try to be brief here. So you're right, MDM2 inhibition has been around for a long time. It's not new and big pharmas spend a lot of dollars trying to figure out this mechanism and various programs have failed before us. One of the things that we're seeing with some of the prior generation MDM2 inhibitors has really been in cracking this nut around the therapeutic index and an intermittent dosing schedule that preserves efficacy, but alleviating this really problematic on target toxicity. MDM2 inhibition, we all have MDM2 in normal hematopoietic cells. So the on-target tox is cytopenia. Thrombocytopenia, namely has been the kiss of death for a lot of these MDM2 inhibitors that came before us. And what we're seeing with some of the other MDM2 programs is they did suffer from tissue accumulation. When you have tissue accumulation, it's hard to devise an intermittent dosing schedule that allows patients to resolve their toxicities. So that's been a problem. It's a hard schedule to figure out. Milademetan, or RAIN-32, our program benefits from no tissue accumulation and because of that fact that there has been a very rationally-designed dose schedule, it's kind of unique, three days on, 11 days off, that's two weeks. Three days on, you can achieve a very high Cmax induced [ apoptosis ]. But if it was 11 days off, patients are able to resolve any of those MDM2 associated toxicities and amenable to redosing the next window. And so ultimately, we think that safety profile has led to improved efficacy. Patients were able to stay on drug. And that's the reason that when we keep on talking about the prior clinical data that has demonstrated a tripling to quadrupling of PFS versus the standard of care and even more than doubling versus other MDM2 inhibitors, it's because of this ability to keep patients on therapy and because of this way to solve the on-target cytopenias that MDM2 inhibitors have been historically associated with.

Got it. Got it. Jacob, could we just talk a bit about ORIC-101 and the combo work with Xtandi? I believe you're going to be presenting some data in prostate later this year. So could you level set there and just help us understand what to expect, how much data, what will we learn? Will we get a read on PSA50 response? How will this help investors evaluate the opportunity?

I'll be really quick here. You've all anecdotally phrased that Phase I is the new Phase II, so based on the questions that we get from investors, and we try to remind them, this is a Phase I. And so this will be roughly 25 patients' worth of data that we'll present at a major medical conference. About half of those patients coming from the dose escalation portion of the study, half coming from the expansion portion of the study. And amongst those patients, we're not selecting for GR status, but just percentage-wise, a handful of them ought to be GR positive. And so amongst that group, we're essentially looking at standard traditional Phase I metrics. So are we able to combine safely with enzalutamide, how do the peak target engagement look? And then can we see some anecdotal evidence of anti-tumor activity in that smaller group that happens to be GR positive. When I say anti-tumor activity, I define it quite broadly. So that could be PSA responders, it could be PFS measures, PSA stabilization, any of the above because really just -- we are in Phase I here, and we are signal seeking and to the points that we made earlier in the panel, it's a heterogeneous population. So there's a lot of work going on to figure out what exactly is the right subset that we'd be focused on for later studies.

Okay. Well, in the remaining few minutes, let's just do a quick level set. A few housekeeping questions, if I may. Just help us with the catalyst calendar for the next 6 to 12 months, what are the key data points that each of you are expecting over the next year? And just also quickly, if you wouldn't mind just mentioning where you stand from a financial perspective in terms of your cash runway? So want to start, Jacob?

Yigal, so we'll have the prostate readout later this year that I mentioned. We anticipate next year, providing another update on both the Abraxane combination and the prostate combination expansion cohorts for ORIC-101. CD73 that we mentioned, ORIC-533, which we'll be dosing its first patient here in the next quarter or so. We'd expect to have the earliest data from that undisclosed tumor type by the end of next year. And then finally, the last two programs, the PRC-2 inhibitor in prostate cancer and then the EGFR Exon 20 inhibitor, both filing the INDs in the next quarter or so, we'd expect to have probably the earliest data, most likely first half 2023 for both of those. We are in a strong cash position, ended last quarter with over $305 million of cash on the balance sheet with cash runway into 2024.

Got it. Avanish?

Yes. So our clinical [indiscernible] won't start until the second half of next year. That will come from the basket study first, followed by intimal sarcoma and then the pivotal data in 2023. We have nonclinical data that's going to be presented in the second half of this year. So that will inform us as just some new tumor opportunities where we can truly gauge where there's MDM2 dependence. So we've previously announced on our 2Q earnings call that at the Triple Cancer Conference later this year, we're presenting new nonclinical data on Merkel cell carcinoma as well as a novel patient population with ER-positive breast cancer. That's non-clinical data. But no new clinical news for until the second half of next year. In terms of cash flow, just to answer the other part of your question, $165 million at the end of 2Q. A lot of our studies are small, single arm studies. And even our pivotal strategy is a 160-patient randomized study. So they're not expensive trials. So $165 million does also take us into 2024.

Got it. Okay. Yujiro?

I'll just address financials first. So we have about $400 million in cash pro forma. We've guided towards the 2025 cash runway. That does not include any potential milestones from GSK. As I think, Yigal, you know there is sort of a key milestone as it relates to the tumor PD data that we just talked about for MAT2A that would trigger a potential $50 million non-dilutive payment, which obviously would further extend that runway. We have two clinical readouts we're targeting before the end of the year. One is for the crizotinib combo with darovasertib that's with Pfizer. So we're continuing to guide towards that. And then the second clinical readout we just talked about with the tumor PD data for the MAT2A program. And then lastly, I would say, 2 more development candidates we're targeting before the end of the year. And obviously, if we can achieve that, that would start giving us line of sight for 4 clinical programs in the future, 3 of which were homegrown. So, yes, we've got a lot of things we're trying to get done from now till end of December.

Got it. Briggs?

Yes. So we talked earlier about the clinical data we'll be presenting at a medical conference at the end of the year on 5613, our menin inhibitor. We'll also, at the end of the year, be presenting updated data on our CSF1R antibody. There we'll have roughly 40 patients' worth of data. You may recall, we had some Phase I data we presented last year at ASH. And based upon that, we expanded a 23-patient cohort at our -- what we anticipate being our label dose, the 1 mg per kg. And so we'll have somewhere between around 25 patients' worth of data at that dose, which we think will significantly derisk the registration program that we have ongoing because that's the -- we anticipate being our registered dose. The other thing is that we'll be starting the registration trials for our menin inhibitor. And depending on how quickly enrollment goes and the sample sizes that are required by FDA, it's possible that sometime next year, we would have some of the pivotal data from those programs. The pivotal data from Axatilimab will be available in 2023. And to your question about cash, we ended the second quarter with $253 million in cash, which we think is plenty of runway to take us into 2023 and importantly, covers the development costs, both for Axatilimab and for the menin registrational programs.

Great. Well, thank you all so much, very much, very, very much for the interesting discussion. I hope you enjoyed it. And Jacob plan to use the Mike Tyson quote in my published research. So look forward to that.

Sounds great, Yigal.