Transcript: Cancer Genomics and Genetic Testing in the Era of Precision Medicine: What is a Precision Model of Care?

Transcript:

Gordon Kuntz: Welcome to Oncology Innovations, a Journal of Clinical Pathways podcast. Focus on candid discussions with innovators aiming to advance quality and value throughout the cancer care ecosystem. I'm your host, Gordon Kuntz. I'm a consultant with almost 20 years experience on oncology clinical pathways in the business of oncology.

I've worked with oncology practices, pharma, payers, GPOs and pathway developers, basically every aspect of the oncology ecosystem. I'm really looking forward to today's podcast. We're joined today by Dan Rhodes, CEO of Strata Oncology. Strata is a genomic testing company focused on oncology. I recently met Dan, who has a very interesting background, which I know you'll be interested in hearing more about. Welcome, Dan.

Dan Rhodes: Thank you, Gordon. It's great to be with you.

Gordon Kuntz: Today, we're going to be exploring genomic and NGS testing. Dan, this is a fascinating topic to me. It seems like there's just an absolute proliferation of testing types and options, companies that are providing different tests. I'll be really interested in hearing your perspective on these technologies and the different use cases that you see.

If we could we start off really with your background, we talked about this earlier, and I think your story is a wonderful one. You've been working in the oncology field for over 20 years. Tell us about your professional journey and really what fuels your passion around this field.

Dan Rhodes: Yeah, so you're right. I have been working on this for 20 plus years. Cancer genomics and its application is really the only thing I've worked on in my professional career. This began back in the year 2000. I was studying gene expression patterns, kidney cancer, differentiating those from patients with poor outcomes from those with good outcomes, and really became enamored by the idea that we could use the human genome, and then our ability to characterize the cancer genome, to really drive our understanding, and then ultimately better treatments and better outcomes for patients battling cancer.

This really has been a lifelong pursuit and a belief that through genomics and data analysis, we could unlock better and smarter treatments, and potentially even drive towards cure. That's really been the driver for me. This led me to graduate school at University of Michigan, where I joined the MD PhD program, and pursued a PhD in Cancer Genomics and Bioinformatics. Continued my study of cancer gene expression. We actually built a really incredible database, accumulating all of the published data from groups around the world, and put a data analysis platform on top of that.

Interesting story, it was a great project for a PhD, but it also had commercial legs. The pharma industry was very interested in accessing these data and analyses. I actually finished my PhD, but then left medical school to build a company around this cancer genome database. We built a very successful company here in Ann Arbor called Compendia. Wonderful experience for me, to really focus on the broad scale implementation and application of the work, to really try to drive and power the pharma industry.

For many years, I was able to really get to know pharma, and how they develop drugs and bring those drugs to market. Really, a fascinating experience for me, that then transitioned in more into the diagnostics world, After Life Technologies, which became Thermo Fisher Scientific acquired that company, Compendia, had the opportunity there to lead our development of next generation sequencing assays for cancer. Really moving from the data and the analysis and the insights into the physical world, like developing tests that could characterize patients' tumors, and really help inform on optimal therapy. Wonderful experience there.

Continuing to work with pharma and advancing diagnostics that ultimately led me to where I am now, where I'm the co-founder and CEO of Strata Oncology. That sort of lifelong pursuit continues. Our mission at Strata is really to ensure that every patient battling cancer receives their best possible treatment. We think the field has made great strides, really advancing targeted therapies, using NGS to help select targeted therapies.

At Strata, we're not satisfied with informing on targeted therapies. We're really trying to take that next leap and say, "How can we really optimize all therapeutic modalities, and the use of really all types of therapies in cancer to realize that mission that every patient receives their best possible treatment?"

Gordon Kuntz: What you're describing is a version of a precision medicine. How would you define a precision model of care, and how is that different than the traditional model we see today for cancer care?

Dan Rhodes: Yeah, really important question. Our vision for a precision model of care is one in which every treatment decision is optimized to a patient's tumor molecular profile. Let's talk about where we are today. Today, there are a class of therapies, targeted therapies that are indeed optimized to a patient's profile. Predominantly, these are single gene mutations that associate with response to a specific targeted therapy. A wonderful set of advances over the last couple of decades. Many of these therapies have incredible outcomes for patients with the particular molecular profile.

That sort of class is really where precision oncology really ends today. The majority of other therapeutic modalities, immunotherapy, angiogenesis inhibitors, antibody drug conjugates, these are really applied in, I would say, in a non-precision model, whereby pharma has advanced these therapies in large tumor type indications, all comers indications, as opposed to really optimizing the use of these therapies in specific molecular subsets.

I think that problem, what the result of that is, we have a lot of therapies that are approved and have between 10 and 30% response rates. We're treating a whole population, but only a subpopulation is really benefiting. Our belief is we can identify that subpopulation for most, if not all, of these therapeutic modalities, if we do the right molecular profiling and develop the right algorithms around those.

I'd say another challenge or problem we see today is we may have one partially effective therapy, let's say it benefits 20 or 25% of patients, and then a second partially effective therapy comes along, another 20 or 25% of patients. Well, the approach today is to combine those two therapies in the first line or in the second line, for example. The better approach would be to identify the 25% of patients that benefit from therapy one, and the 25% of patients that benefit from therapy, and apply those therapies specifically.

That sounds obvious, but it's not necessarily easy to do, and it's not the conventional paradigm. I think this is really where we're seeking to move the needle, to change the paradigm, and to really usher in a world where every one of these therapies that we have available to us is optimized, and we've got the molecular profile defined to optimize the use of that therapy.

We think that'll drive better outcomes and response rates in those populations, but also better utilization, to spare patients from unnecessary side effects if the therapy was never going to work in the first place, and to save the healthcare system dollars on unnecessary drug spend. We think it's a win/win all the way around.

Gordon Kuntz: Is part of that, the way that we're currently, we're kind of in this transition period it sounds like, from systemic therapy to precision therapy, and is part of the reason why we have these doublet, triplet therapies going on. Is that a legacy of pharma's marketing? Traditionally, they want to serve the drug to as many patients as is reasonable, which if you can deliver the drug to 100 patients, and it works for 20 or 30 of them, that's great, but it's not so great for the system. It's not so great for the other patients that it didn't work for.

Dan Rhodes: Yeah, I think that's right. I think it's really an incentives issue. You see this often, where pharma often brings a medicine to market in the late line, and then seeks to move it up in line of therapy. And as opposed to trying to define the precise population, it's really a simpler path to bring the drug to market, to just combine it with that first line standard of care. I think this dates back to really how we optimize chemotherapies and chemotherapy combinations.

I think that same mentality, it works to bring these therapies to market. I just don't think it's the most efficient way to utilize these therapies. It's not the best way necessarily for patients and for the system as a whole. The pharma industry really isn't incented to solve that problem. They're incented to go build the next therapy that's going to have a higher response rate.

I don't think we have to put this all on pharma. I really think it's companies like Strata, I think it's payers and providers that really need to work together to better optimize really all therapies once they come to market, because I think we can identify additional patient populations who could benefit, so we could expand the utility of those therapies. But we could also sort of reign in their unnecessary use to improve side effect profiles, cost profiles, et cetera.

Gordon Kuntz: Excellent. So I know the strata isn't sort of a “one trick pony” in terms of the testing that you do. What kind of precision medicine testing do you think benefits patients the most and why?

Dan Rhodes: Right. So I think of precision medicine testing for patients really in two stages. I think stage one is comprehensive mutation profiling to inform untargeted therapy. And that's really becoming a standard of care today. It's being led in lung cancer where we have a whole number of targeted therapies available. And so I think that comprehensive mutation profiling is sort of the first piece. But then I think that the problem is though, mutations really don't matter for many other therapeutic classes like immunotherapy antibody drug conjugates, and really any sort of target directed antibody-based therapy.

So then the question is, well, how then do we inform on those treatments? We think the answer is with quantitative RNA, with profiling the gene expression levels of a number of biological pathways, a number of targets, and then developing predictive models for each of those therapeutic classes. And that's really where we focus. We think this is the next wave, the second wave of precision oncology advancement. We have to move beyond mutations, move into these quantitative gene expression algorithms, and then we can get to a truly predictive set of algorithms to optimize each treatment decision.

Gordon Kuntz: So we know that there's a number... As you said, some of the comprehensive genetic profiling is becoming a standard of care, but it isn't quite there yet. What would you say are the top three challenges facing precision medicine broadly as we think about how to move to a fuller adoption?

Dan Rhodes: Yeah, so I'll come back to the earlier point we discussed, which is incentives is a challenge. I think that with the pharma industry and just the weight it carries and the investments that it makes and the studies that it runs. With the incentives aligned with maximizing utilization, maximizing indications, I think that's a counter incentive to the precision medicine pursuit in some cases. In other cases it's not. If the drug is dependent on the biomarker for response then it works, the system works. But if it's not that perfect one to one, then the incentives is a challenge. I would say another challenge has been really data and clinical utility demonstration. So a fair question is, in some tumor types, why wouldn't I just do a small number of single gene tests? Why do I need that comprehensive genomic profile?

So I think it's incumbent upon us as test makers and pharma as drug makers to expand the precision treatments in many tumor types to really push us over that tipping point where it just is impossible to get away with a couple of single gene markers. So I think that's one, that data and clinical utility demonstration. We're moving that needle with our RNA-based predictive algorithms, which there really is no single gene test that could substitute for a gene expression algorithm. So I think having truly distinct and substantial clinical utility above and beyond what you can do with single gene testing, I think that's a key barrier, but opportunity.

And then I think the third I would highlight would be reimbursement and coverage. I think Medicare has done a great job of really leading the way and thinking about the clinical utility and application of this type of testing. I commend both CMS broadly with national to national coverage decisions that they've made, but also Palmetto MolDX and the local coverage decisions. The private payer community has been slow to adopt, and I think that's a barrier for us in precision medicine. If patients are getting stuck with a bill for a three or five thousand dollar test, that causes real problems for those patients and for their providers in running those types of tests. So I think we're moving in the right direction. I think we've got great tailwinds, but it's still a barrier today.

Gordon Kuntz: So how do you feel clinical pathways will play in guiding appropriate tests, therapy utilization in a precision care model setting?

Dan Rhodes: So I think clinical pathways are absolutely critical. The knowledge required today to care for patients with cancer. All of the testing options, the biomarkers required, even for just standard of care treatments, let alone for the emerging investigational treatments in clinical trials, the knowledge required to care for patients is growing rapidly. We see this as especially challenging in the community setting where oncologists are caring for patients across the whole spectrum of tumor types. It's nearly impossible to keep up with all of the new approvals and therapies moving in lines of therapy, moving in combinations, the biomarker profiles, it's almost impossible to keep up. So we really see the only way forward is health system endorsed clinical pathways that really guide providers on the testing and treatment journey. And so we're a big advocate of driving and advancing clinical pathways. And as we advance our new biomarker profiles, we'd really like to do it in a clinical pathway framework, such that it's not another sort of one-off test that needs to be considered along with dozens of others.

Gordon Kuntz: So what do you think some of the common misperceptions are about precision medicine?

Dan Rhodes: Yeah, that's a good question. We certainly face some of these misconceptions every day. I think one of them is that precision medicine has the potential to benefit all patients. And the reality is today, it's only a fraction of patients and so we're often compelling providers to go through exhaustive testing for a patient, even though we know in many cases it's not going to turn anything up. So I think that's a misconception that it is going to work for everyone. I think another is that precision therapies are going to lead to a cure. And they do sometimes, but often they're our best treatment, but they're not yet the cure. We've got first generation ALK inhibitors and then second and then third, and now average timeline on those types of therapies is three or four years, which is remarkable for advanced lung cancer. But it's still not a cure, it's still not a panacea. There's still more work to do.

Dan Rhodes: So I think realizing that this is a journey, we're going in the right direction, and we all have to work together to realize the full opportunity here is the message we deliver. But I think we still face these misconceptions that it's going to work for everyone and that these targeted therapies or precision selected immunotherapies are going to cure. And I think they do sometimes, but in the majority of the time, they still don't.

Gordon Kuntz: Right. Are some of those misconceptions driven by the fact that the innovation curve is so steep with this? You've mentioned your own career, you've been thinking about these issues for a number of years, but as I talk to people in different testing companies, every time I talk to them, there's six new things that they've got available. And is part of it just a function of the rapid pace of innovation, which unfortunately keeps some of this out of the realm of standard of care because it's innovating so quickly that things like pathways have a hard time keeping up with that. Payers have a hard time keeping up with that as well. Is that part of the function? And we certainly don't want to slow down the pace of change, but how do you address that?

Dan Rhodes: Hey, I think this is a really good question, Gordon, and I don't know that we have the answer, but one way that we're approaching this is ideally every one of these new advances would have large scale perspective randomized phase three trials, and that data would move through the publication and guidelines and pathways sort of paradigm. But it's just too big to do that for all of the innovation that we're seeing, and to do that at a pace that then brings these innovations into standard of care in academia, but also in the community setting.

So one way that we're trying to address this is, instead of turning everything into a prospective randomized phase three trial, is to leverage real-world data. And so with all of the testing that we're doing at Strata, we're also partnering with health systems to align the clinical outcome data, the treatment outcome data from patients who have been tested so that we can look backward and run virtual real-world trials to generate evidence. So, I think that's one way to accelerate the evidence generation, to use all of this real world data that's available to us but that still needs to turn into guidelines and pathways and reimbursed tests. And so, I think maybe we have to work together on that one, Gordon, because there's certainly work to do.

Gordon Kuntz: Absolutely. So clearly, that's a barrier. What are the other barriers to broader access? Is it ease of simply being able to order the testing for community or academic? You mentioned reimbursement and one of the things that you'd also mentioned was the tests aren't for every patient. How do we determine which patients? You don't get a tattoo when you're born with, "Test me later." How do we know which patients are going to benefit the most and how do we solve some of those barriers?

Dan Rhodes: There definitely are just workflow and operational barriers that we see and I think, if you step through the process, there's challenges at every step in the process. So, placing the order, doing a one-off test order and transcribing information from electronic health record into an order form and maybe a dozen different order forms. That's a real barrier. So, we and others are tackling that through EHR integration and really trying to get to push-button. But I think even better than push-button ordering is really a reflex ordering paradigm, whereby we define specific clinical scenarios and the testing required in those scenarios and then that testing is automatically ordered. So, whether it's at diagnosis or whether it's at biopsy of metastatic disease, it doesn't even require a set of one-off, one doc, one patient thinking around, "What test do I order?"

It's just built into the system. The reflex order just happens automatically. We're starting to do that with health system partners and I think that's key, but that's just step one is ordering the test. Now, we've got to identify the right sample and get that sample to the laboratory. That's another real pain point. One that we've worked hard on. One of the keys here is working with pathology to make a standard practice of defining the best possible sample for molecular testing at the point of initial case review. So then, you don't have to go back into the archives and try to figure out the appropriate example.

So, I think that's an important step there. Then, on the results side, I think that EHR integration, so those data flow back into the native environment that clinicians are using to care for their patients, I think that's another critical one. So, those would be some of the areas that I think, just operationally, we have to solve. Our vision for this is that precision testing is really just a background service that a health system is providing on behalf of their providers and patients so that when the provider comes into the room to see, let's say, a new patient with an advanced lung cancer diagnosis, all of that profiling's already done. All of that data and those insights. And then the treatments are right there in front of the clinician in their EHR environment. So, that's... Well, we're not there yet. It's a far-out vision, but that's what we need to shoot for.

Gordon Kuntz: Okay. Do you see the opportunity... You've mentioned in health systems. Do you see the opportunity for health systems to run their own tests? Or is that, in your view, really best maintained at a more centralized, expert kind of level?

Dan Rhodes: Yes. You know, this is a really good question. I've been on both sides of this fence in the roles that I've had. Here's what I see. The pace of innovation is too fast for the manufacturers of reagents and software to keep up and to deliver the appropriate tools into hospital labs. And also, the scale required to do this in a cost-effective manner is high, so the only reason that we can make a business out of this is because we run hundreds of samples at a time and it's automated with robotics and software and it required tens of millions of dollars of investment to get to this scaled sort of point.

So, I think my answer would be, today, I think, no. I think this is best with central labs that can make the investments to keep on the bleeding edge of that innovation curve. I think, though, when we look into the future, I think this does kit up and become distributable into hospital labs. But what I've seen is, hospital labs make the investment, thinking they're going to get in the game, bring up an assay, but by the time they bring up the assay, they're already out of date. And so, I think we're progressing too quickly now for individual labs to bear that burden.

Gordon Kuntz: Yes. So, one of the challenges that I've heard about in precision medicine is... You described a lot of the other challenges and, of course, there's the payer access and prior authorization and all those sorts of things you kind of already talked about. But when all that happens and the oncologist gets the report back, it's not always clear what to do and what does it mean. And so, how do you address that? Again, in the face of this very rapid innovation, when new tests, new information are always being introduced and you have a community oncologist who might not be accessing that test every day or even once a month, but you've got three new tests in the meantime. How do you provide that education on an ad hoc basis around the particular finding that's come out of that analysis?

Dan Rhodes: This is a critical question, Gordon, and when I look back at how our field has progressed... In early days, it really was, "Right. We're adding new content, new biomarkers and tests and we've got treatment associations and we're just exploding that data back to the provider and they're getting pages and pages of associations with trials that they don't understand and that is just untenable." We had a provider in the community setting, the leader of a provider group, say to us, "If you don't turn this into one page that my community oncologist can interpret in one minute, we're turning all of this testing off because you are causing more problems than problems you're solving with these reports." So, this was probably four or five years ago. Major eye opening experience for us because we were sort of following what everyone else was doing, in terms of reporting.

So, we really pivoted hard in terms of how we report and what we do now. And this is unique, I believe, in the industry, is our Chief Medical Officer and his team of molecular pathologists provide a clinical impression statement on each case, to really boil down what matters and given the patients context and the molecular findings, what specific treatments or trials should be considered and why. And so, that has really changed it for our providers. They focus on that clinical impression statement. They've come to really trust our medical team as thought leaders. They trust us to stay up on all of the latest testing and treatment associations and then really put it into context for them, one patient at a time. And I think that's just required today. Ideally, we imagine some future state where our software can do that automatically but today, there's no replacing that expert molecular pathologist who's been collaborating with oncologists to treat patients for their career. There's no replacing that in that interface and we've found that to be the key.

Gordon Kuntz: So, that's really taking precision medicine and making it even more personalized, I suppose is one way to look at it.

Dan Rhodes: Yes, that's right. That's right. And, you know, as these... You might say, "Oh, gosh. That's too big of a burden for a testing company," but what we've found is, as our team grows in the experience, they become better and better at it and they've seen the patterns. They see that first, let's say, K-Ras(G12C) mutant lung cancer, post-approval, and they know how to put that into context and then that can translate to the next dozens of patients with that profile. So, I think having this expert team is important and then our providers, too, have relationships with that expert team. So, they can call them up and say, "Hey. This is the first time I'm seeing this. You've explained this to me, but I don't understand. Walk me through this." And you have to be able to provide that level of service to give providers the comfort to act upon these new, emerging biomarker findings.

Gordon Kuntz: And I would think that the real-world data that you're collecting and analyzing would be a marvelous feedback loop into that as well. Right? So, your medical team is seeing what they think is the right thing to do. You look at the real-world data and you see, "What was the outcome related to this?"

Dan Rhodes: You've got it, Gordon. And so, we're often referencing our real-world database and, you know, "In 88 patients with a similar profile, treated this way, their time on therapy was substantially longer than patients treated another way.” So, that's exactly what we're doing.

Gordon Kuntz: That's fantastic. So, again, you all are always looking for something new. You're always developing something new. What are some of the emerging testing strategies that have the potential to really change the current standard of care treatment approaches? And what's the anticipated impact for all key stakeholders? Thinking about patients, providers, the health systems and payers, and I'm going to repeat patients in that as well.

Dan Rhodes: Yeah. Well, let's talk about one of our new test algorithms that I'm most excited about, and that's our immunotherapy algorithm. So, the state of the art today in immunotherapy is, either immunotherapy is prescribed, all comers, for a particular tumor type, or there may be a PD-L1 immunohistochemistry test in front of an immunotherapy prescription.

But it's widely known that PD-L1 is an okay enrichment tool, but not a great biomarker. It sometimes can leave responsive patients behind. But the bigger problem is, still only small proportions of patients are responding to immunotherapy, even in the PD-L1 positive groups.

So, we set out to build a fully optimized immunotherapy biomarker, and we were successful in doing that, and our real-world evidence shows this. The biomarker approach that we took was to really combine the current state-of-the-art biomarkers, but then also layer in a number of other potential biomarkers, in an optimized algorithm.

So, we have a five-marker algorithm. So, it combines tumor mutation burden, PD-L1, PD-1, the proliferative state of the tumor, and some other sort of tumor micro-environment factors. And this optimized algorithm is very powerful in predicting time on therapy to immunotherapy, pan tumor.

And so, we validated in lung cancer, in melanomas, bladder cancers, really in all tumor types, this algorithm can stratify responders from non-responders to immunotherapy. And so, we're just now in the midst of publishing our findings on this and rolling this biomarker out.

So, what is the impact of this biomarker? It's really twofold. One is, today, immunotherapy is only indicated for about half of patients with advanced cancer. So, our biomarker identifies 10% of patients outside of those current indications who we believe are likely to benefit from immunotherapy. So, that's one. Expanding the benefit of immunotherapy.

But then within the 50% or so of patients who are getting immunotherapy today, our biomarker identifies the responsive population and the non-responsive population. So that, then, leads to the opportunity to really refine the utilization of immunotherapy in these indications that we have today. In some cases, there's treatment choice, and if we say biomarker low, the clinician might opt for the alternative treatment. But if we say biomarker high, we would prioritize immunotherapy.

So, we think this biomarker will drive increased use and benefit from immunotherapy in a small segment, but also, then, better utilization across a whole range of tumor types that immunotherapy is being applied today.

And I think we need to point out that immunotherapy is not without side effects. While the majority of patients do very, very well from a side-effects perspective, a minority of patients, maybe sub-10%, have substantial side effects. So, if we're treating a patient who we know from the biomarker profile is unlikely to derive any benefit, but then still bringing all the side effects along, that's just bad care for the patient.

Not to mention the cost of having that patient on immunotherapy for three to six months plus, when there was really no opportunity for benefit. So, from an impacting stakeholders perspective, maybe we drive better outcomes by identifying more patients, but then we think we can drive better utilization on label, which will lead to fewer side effects and cost savings for the health care system.

So, that's an example of one of our new test algorithms that we're in the midst of bringing to market that we're very, very excited about and think can really transform how we think about the use of immunotherapy.

Gordon Kuntz: Thank you for that. That was actually very informative. Is there anything else you want us to know about Strata and sort of where you're headed next?

Dan Rhodes: I'd give you one more kind of area that we're very excited about. So, immunotherapy would be first, and then second would be antibody drug conjugates.

So, as a field, we've been working on antibody drug conjugates for probably 15 years plus, but only recently, in the last several years, has the pharma industry really dialed in how to bring effective antibody drug conjugates to market. And last year, we had three landmark approvals of new antibody drug conjugates, and we're anticipating half a dozen more in the next, call it, 12 to 18 months.

But these ADCs are being brought to market kind of one tumor type at a time. So, the next big area of innovation that we see is taking a biomarker algorithm approach, pan tumor, and applying these very impactful therapies to broader populations, but biomarker-selected populations.

So, we think this will maybe be the next big thing in oncology care. Immunotherapy was really transformative before that, targeted therapies. We think precision-selected antibody drug conjugates will be the next big thing, and so that's where we're investing, in terms of advancing the biomarkers and running prospective trials.

Gordon Kuntz: Well, it sounds like we may have to have this conversation about once a month, given the pace of change, but I think that's going to do it for today. So, that wraps up our episode of Oncology Innovations, and thank you so much, Dan for joining us today.

And as always, thank you to the Journal of Clinical Pathways for producing this episode. Please download, rate, review and subscribe to the podcast. For more episodes, you can visit www.journalofclinicalpathways.com, or you can find us on Apple Podcasts, Google Podcasts, and Spotify. Also, be sure to share Oncology Innovations with a friend or colleague.

Let me know your reactions to episodes, questions, or recommended topics for future episodes. You can find me on LinkedIn, or you can send an email to Gordon@gordonkuntz.com to request specific topics and guests. Thanks, Dan. See you next time.

Dan Rhodes: Thanks, Gordon. Take care.