In this JCO Precision Oncology Article Insights episode, Jiasen He summarizes "Predictive Impact of Tumor Mutational Burden on Real-World Outcomes of First-Line Immune Checkpoint Inhibition in Metastatic Melanoma” by Dr. Miles C. Andrews, et al. published on June 07, 2024.

The guest on this podcast episode has no disclosures to declare.

Jiasen He:

Hello and welcome to the JCO Precision Oncology Article Insights. I'm your host, Jiasen, and today we'll be discussing the JCO Precision Oncology article, "Predictive Impact of Tumor Mutational Burden on Real-World Outcomes of First-Line Immune Checkpoint Inhibition in Metastatic Melanoma," by Dr. Miles C. Andrews and colleagues. This study was supported by Foundation Medicine, a for-profit company that conducts FDA-regulated molecular diagnostics, including assays used to measure tumor mutational burdens, or TMB, as described in this article.

Immune checkpoint inhibitor (ICI) therapy has become a cornerstone in the treatment of metastatic melanoma. They work by activating the patient's own immune system, representing a fundamentally different approach from traditional chemotherapy. Several biomarkers have emerged as promising tools to predict ICI therapy response, and TMB is one of the most extensively studied. TMB is defined as the number of somatic mutations per megabase of an interrogated genome sequence. In the KEYNOTE-158 study, patients with high TMB showed better response rates and longer progression-free survival compared to those with low TMB, which led to the FDA tumor-agnostic approval of TMB as a biomarker to guide ICI therapy.

In this manuscript, Dr. Andrews and colleagues set out to answer an important question: does TMB predict outcomes of ICI therapy in real-world patients with advanced melanoma? To explore this, they analyzed de-identified data from the nationwide Flatiron Health-Foundation Medicine Clinico-Genomic Database (CGDB). To be included, patients needed to have had at least two visits to a Flatiron Health clinic and a Foundation Medicine Comprehensive Genomic Profiling report. Eligible patients had received first-line treatment with either monotherapy (nivolumab or pembrolizumab) or dual therapy with the combination of ipilimumab and nivolumab for metastatic melanoma. They also needed a tissue-based TMB score from either the FoundationOne or FoundationOne CDx genomic test. For this study, TMB less than 10 mutations per megabase was considered low TMB; TMB equal to or more than 10 mutations per megabase was considered high TMB; and TMB equal to or more than 20 mutations per megabase was considered very high TMB. Of the 497 patients in the final cohort, 29% had low TMB, while 71% had high TMB, and 50% had very high TMB.

The authors observed that patients with very high TMB were more often male, had BRAF wild-type tumors, and were more likely to receive anti-PD-1 monotherapy. This group also had tumors more commonly sampled from brain and lung metastases. Patients with high TMB but not very high TMB were more likely to carry the BRAF V600K mutation and were least likely to have lung metastases. Meanwhile, those with low TMB tended to be younger and had disease limited to non-visceral sites. As expected, the presence of ultraviolet mutation signatures, a known driver of melanoma, was strongly associated with TMB. UV signatures were found in just 18% of the low TMB group, but in 89% of the high TMB and 93% of the very high TMB group. High TMB was found to be prognostic of improved real-world progression-free survival (PFS) and overall survival (OS) in patients receiving both monotherapy and dual immune checkpoint inhibitors, even after adjusting for other established prognostic factors. Interestingly, in the low TMB group, overall survival was likely confounded by the availability of effective second-line targeted therapy, particularly for BRAF-mutant patients. These patients had better outcomes compared to their BRAF wild-type counterparts, likely reflecting a greater reliance on salvage therapy in low TMB patients who derived less benefit from first-line immunotherapy.

The authors then further examined the ICI outcomes using stepwise TMB thresholds, with TMB less than 10 as low, 10 to 19 as high, and equal to or more than 20 as very high. For those receiving ICI monotherapy, both PFS and OS were highest in the very high TMB group, followed by the high TMB group, and lowest in the low TMB group. However, in patients treated with dual ICI therapy, the results diverged. While low TMB patients still had the poorest outcomes, those with high TMB (mutations 10 to 19 per megabase) had better PFS and overall survival than those with very high TMB (mutations equal to or more than 20 per megabase).

The authors then conducted exploratory multivariable modeling, showing that among very high TMB patients with BRAF mutations, dual ICI therapy was associated with a significantly higher hazard ratio compared to monotherapy. They concluded that dual ICI may not benefit, and could even harm, patients with very high TMB, whereas those with TMB between 10 and 20 mutations per megabase may get more from the intensified regimen. Importantly, as the authors stated in the manuscript, we need to note that in this cohort, very high TMB patients were more likely to have brain metastases at treatment initiation, be male, and lack BRAF V600E/K mutations—all factors associated with poorer prognosis. This might partially explain inferior outcomes to dual ICI in very high TMB patients, as patients were not randomly assigned to therapy in this retrospective, real-world study. As such, these findings should be interpreted with caution and validated in future studies.

In summary, this study showed that in a real-world setting, high tumor mutational burden predicts better outcomes with immune checkpoint inhibitor therapy in patients with advanced melanoma. Interestingly, the authors found that dual ICI therapy may offer no added benefit for patients with very high TMB compared to ICI monotherapy. However, this was a retrospective, non-randomized study, and the cohorts were imbalanced for some known risk factors, which could confound outcomes. As a result, these findings should be interpreted with caution and will need to be validated in future prospective studies.

Thank you for tuning into JCO Precision Oncology Article Insights. Don't forget to subscribe and join us next time as we explore more groundbreaking research shaping the future of oncology. Until then, stay informed and stay inspired.

The purpose of this podcast is to educate and to inform. This is not a substitute for professional medical care and is not intended for use in the diagnosis or treatment of individual conditions.

Guests on this podcast express their own opinions, experience, and conclusions. Guest statements on the podcast do not express the opinions of ASCO. The mention of any product, service, organization, activity, or therapy should not be construed as an ASCO endorsement.

JCO PO author Dr. Dean A. Regier at the Academy of Translational Medicine, University of British Columbia (UBC), and the School of Population and Public Health, BC Cancer Research Institute shares insights into his JCO PO article, “Clinical Effectiveness and Cost-Effectiveness of Multigene Panel Sequencing in Advanced Melanoma: A Population-Level Real-World Target Trial Emulation.”

Host Dr. Rafeh Naqash and Dr. Regier discuss the real-world clinical effectiveness and cost-effectiveness of multigene panels compared with single-gene BRAF testing to guide therapeutic decisions in advanced melanoma.

Dr. Rafeh Naqash:Hello and welcome to JCO Precision Oncology Conversations, where we bring you engaging conversations with authors of clinically relevant and highly significant JCO PO articles. I'm your host, Dr. Rafeh Naqash, Podcast Editor for JCO Precision Oncology and Assistant Professor at the OU Health Stephenson Cancer Center in the University of Oklahoma.

Today, we are excited to be joined by Dr. Dean A. Regier, Director at the Academy of Translational Medicine, Associate Professor at the School of Population and Public Health, UBC Senior Scientist at the British Columbia Cancer Research Institute, and also the senior author of the JCO Precision Oncology article entitled "Clinical Effectiveness and Cost-Effectiveness of Multigene Panel Sequencing in Advanced Melanoma: A Population-Level Real-World Target Trial Emulation."

At the time of this recording, our guest's disclosures will be linked in the transcript. Dean, welcome to our podcast and thank you for joining us today.

Dr. Dean Regier:Thank you. I'm delighted to be here.

Dr. Rafeh Naqash:So, obviously, you are from Canada, and medicine, or approvals of drugs to some extent, and in fact approvals of gene testing to some extent is slightly different, which we'll come to learn about more today, compared to what we do in the US—and in fact, similarly, Europe versus North America to a large extent as well.

Most of the time, we end up talking about gene testing in lung cancer. There is a lot of data, a lot of papers around single-gene panel testing in non-small cell lung cancer versus multigene testing. In fact, a couple of those papers have been published in JCO PO, and it has shown significant cost-effectiveness and benefit and outcomes benefit in terms of multigene testing. So this is slightly, you know, on a similar approach, but in a different tumor type. So, could you tell us first why you wanted to investigate this question? What was the background to investigating this question? And given your expertise in health economics and policy, what are some of the aspects that one tends or should tend to understand in terms of cost-effectiveness before we go into the results for this very interesting manuscript?

Dr. Dean Regier:Yeah, of course, delighted to. So, one of the reasons why we're deeply interested in looking at comparative outcomes with respect to single- versus multigene testing— whether that's in a public payer system like Canada or an insurer system, a private system in the United States— is that the question around does multigene versus single-gene testing work, has not typically tested in randomized controlled trials. You don't have people randomized to multigene versus single-gene testing.

And what that does, it makes the resulting evidence base, whether it's efficacy, safety, or comparative cost-effectiveness, highly uncertain. So, the consequence of that has been uneven uptake around the world of next-generation sequencing panels. And so if we believe that next-gen sequencing panels are indeed effective for our patients, we really need to generate that comparative evidence around effectiveness and cost-effectiveness. So we can go to payers, whether it be single payer or a private insurer, to say, "Here are the comparative outcomes." And when I say that uptake has been uneven, uptake there's been actually plenty, as you know, publications around that uneven uptake, whether it be in Europe, in the United States, in Canada. And so we're really interested in trying to produce that evidence to create the type of deliberations that are needed to have these types of technologies accessible to patients. And part of those deliberations, of course, is the clinical, but also in some contexts, cost-effectiveness.

And so, we really start from the perspective of, can we use our healthcare system data, our learning healthcare system, to generate that evidence in a way that emulates a randomized controlled trial? We won't be able to do these randomized controlled trials for various, like really important and and reasons that make sense, quite frankly. So how can we mimic or emulate randomized controlled trials in a way that allows us to make inference around those outcomes? And for my research lab, we usually think through how do we do causal inference to address some of those biases that are inherent in observational data. So in terms of advanced melanoma, we were really interested in this question because first of all, there have been no randomized controlled trials around next-gen sequencing versus single-gene testing. And secondly, these products, these ICIs, immune checkpoint inhibitors, and BRAF and MEK inhibitors, they are quite expensive. And so the question really becomes: are they effective? And if so, to what extent are they cost-effective? Do they provide a good reason to have information around value for money?

Dr. Rafeh Naqash:So now going to the biology of melanoma, so we know that BRAF is one of the tumor-agnostic therapies, it has approvals for melanoma as well as several other tumor types. And in fact, I do trials with different RAF-RAS kinase inhibitors. Now, one of the things that I do know is, and I'm sure some of the listeners know, is the DREAMseq trial, which was a melanoma study that was an NCI Cooperative Group trial that was led by Dr. Mike Atkins from Georgetown a couple of years back, that did show survival benefit of first-line immunotherapy sequencing. It was a sequencing study of whether to do first-line BRAF in BRAF-mutant melanoma followed by checkpoint inhibitors, or vice versa. And the immune checkpoint inhibitors followed by BRAF was actually the one that showed benefit, and the trial had to stop early, was stopped early because of the significant benefit seen.

So in that context, before we approach the question of single-gene versus multigene testing in melanoma, one would imagine that it's already established that upfront nivolumab plus ipilimumab, for that matter, doublet checkpoint inhibitor therapy is better for BRAF-mutant melanoma. And then there's no significant other approvals for melanoma for NRAS or KIT, you know, mucosal melanomas tend to have KIT mutations, for example, or uveal melanomas, for that matter, have GNAQ, and there's no targeted therapies. So, what is the actual need of doing a broader testing versus just testing for BRAF? So just trying to understand when you started looking into this question, I'm sure you kind of thought about some of these concepts before you delved into that.

Dr. Dean Regier:I think that is an excellent question, and it is a question that we asked ourselves: did we really expect any differences in outcomes between the testing strategies? And what did the real-world implementation, physician-guided, physician-led implementation look like? And so, that was kind of one of the other reasons that we really were interested is, why would we go to expanded multigene panel sequencing at all? We didn't really expect or I didn't expect an overall survival a priori. But what we saw in our healthcare system, what happened in our healthcare system was the implementation in 2016 of this multigene panel. And this panel covered advanced melanoma, and this panel cost quite a bit more than what they were doing in terms of the single-gene BRAF testing. And so when you're a healthcare system, you have to ask yourself those questions of what is the additional value associated with that?

And indeed, I think in a healthcare system, we have to be really aware that we do not actually follow to the ideal extent randomized controlled trials or trial settings. And so that's the other thing that we have to keep in mind is when these, whether it's an ICI or a BRAF MEK inhibitor, when these are implemented, they do not look like randomized controlled trials. And so, we really wanted to emulate not just a randomized controlled trial, but a pragmatic randomized controlled trial to really answer those real-world questions around implementation that are so important to decision making.

Dr. Rafeh Naqash:Sure. And just to understand this a little better: for us in the United States, when we talk about multigene testing, we generally refer to, these days, whole-exome sequencing with whole-transcriptome sequencing, which is like the nuclear option of of the testings, which is not necessarily cheap. So, when you talk about multigene testing in your healthcare system, what does that look like? Is it a 16-gene panel? Is it a 52-gene panel? What is the actual makeup of that platform?

Dr. Dean Regier:Excellent question. Yeah, so at the time that this study is looking at, it was 2016, when we, as BC Cancer—so British Columbia is a population right now of 5.7 million people, and we have data on all those individuals. We are one healthcare system providing health care to 5.7 million people. In 2016, we had what I call our "home-brew" multigene panel, which was a 53-gene panel that was reimbursed as standard of care across advanced cancers, one of them being advanced melanoma. We have evolved since then. I believe in 2022, we are using one of the Illumina panels, the Focus panel. And so things have changed; it's an evolving landscape. But we're specifically focused on the 53-gene panel. It was called OncoPanel. And that was produced in British Columbia through the Genome Sciences Centre, and it was validated in a single-arm trial mostly around validity, etc.

Dr. Rafeh Naqash:Thank you for explaining that. So now, onto the actual meat and the science of this project. So, what are some of the metrics from a health economy standpoint that you did look at? And then, methodology-wise, I understand, in the United States, we have a fragmented healthcare system. I have data only from my institution, for that matter. So we have to reach out to outside collaborators and email them to get the data. And that is different for you where you have access to all the data under one umbrella. So could you speak to that a little bit and how that's an advantage for this kind of research especially?

Dr. Dean Regier:Yeah. In health economics, we look at the comparative incremental costs against the incremental effectiveness. And when we think about incremental costs, we think not just about systemic therapy or whether you see a physician, but also about hospitalizations, about all the healthcare interactions related to oncology or not that a patient might experience during their time or interactions with the healthcare system. You can imagine with oncology, there are multiple interactions over a prolonged time period depending on survival. And so what we try to do is we try to—and the benefit of the single-payer healthcare system is what we do is we link all those resource utilization patterns that each patient encounters, and we know the price of that encounter. And we compare those incremental costs of, in this case, it's the multigene panel versus the single-gene panel. So it's not just the cost of the panel, not just the cost of systemic therapy, but hospitalizations, physician encounters, etc.

And then similarly, we look at, in this case, we looked at overall survival - we can also look at progression-free survival - and ask the simple question, you know, what is the incremental cost per life-year gained? And in that way, we get a metric or an understanding of value for money. And how we evaluate that within a deliberative priority setting context is we look at safety and efficacy first. So a regulatory package that you might get from, in our case, Health Canada or the FDA, so we look at that package, and we deliberate on, okay, is it safe and is it effective? How many patients are affected, etc. And then separately, what is the cost-effectiveness? And at what price, if it's not cost-effective, at what price would it be cost-effective? Okay, so for example, we have this metric called the incremental cost-effectiveness ratio, which is incremental cost in the numerator, and in this case, life-years gained in the denominator. And if it is around $50,000 or $100,000 per life-year gained—so if it's in that range, this ratio—then we might say it's cost-effective. If it's above this range, which is common in oncology, especially when we talk about ICIs, etc., then you might want to negotiate a price. And indeed, when we negotiate that price, we use the economic evaluation, that incremental cost-effectiveness ratio, as a way to understand at what price should we negotiate to in order to get value for money for the healthcare system.

Dr. Rafeh Naqash:Thank you for explaining those very interesting terminologies. Now, one question I have in the context of what you just mentioned is, you know, like the drug development space, you talked about efficacy and safety, but then on the safety side, we talk about all-grade adverse events or treatment-related adverse events—two different terminologies. From a healthcare utilization perspective, how do you untangle if a patient on a BRAF therapy got admitted for a hypoxic respiratory failure due to COPD, resulting in a hospitalization from the cost, overall cost utilization, or does it not matter?

Dr. Dean Regier:We try to do as much digging into those questions as possible. And so, this is real-world data, right? Real-world data is not exactly as clean as you'd get from a well-conducted clinical trial. And so what we do is we look at potential adverse event, whether it's hospitalization, and the types of therapies around that hospitalization to try- and then engage with clinicians to try to understand or tease out the different grades of the adverse event. Whether it's successful or not, I think that is a real question that we grapple with in terms of are we accurate in delineating different levels of adverse events? But we try to take the data around the event to try to understand the context in which it happens.

Dr. Rafeh Naqash:Thank you for explaining that, Dean.

So, again to the results of this manuscript, could you go into the methodology briefly? Believe you had 147 patients, 147 patients in one arm, 147 in the other. How did you split that cohort, and what were some of the characteristics of this cohort?

Dr. Dean Regier:So, the idea, of course, is that we have selection criteria, study inclusion criteria, which included in our case 364 patients. And these were patients who had advanced melanoma within our study time period. So that was 2016 to 2018. And we had one additional year follow. So we had three total years. And what we did is that we linked our data, our healthcare system data. During this time, because the policy change was in 2016, we had patients both go on the multigene panel and on the single-gene BRAF testing. So, the idea was to emulate a pragmatic randomized controlled trial where we looked at contemporaneous patients who had multigene panel testing versus single-gene BRAF testing.

And then we did a matching procedure—we call it genetic matching. And that is a type of matching that allows us to balance covariates across the patient groups, across the multigene versus BRAF testing cohorts. The idea again is, as you get in a randomized controlled trial, you have these baseline characteristics that look the same. And then the hope is that you address any source selection or confounding biases that prohibit you to have a clean answer to the question: Is it effective or cost-effective? So you address all those biases that may prohibit you to find a signal if indeed a signal is there.

And so, what we did is we created—we did this genetic matching to balance covariates across the two cohorts, and we matched them one-to-one. And so what we were able to do is we were able to find, of those 364 patients in our pool, 147 in the multigene versus 147 in the single-gene BRAF testing that were very, very similar. In fact, we created what's called a directed acyclic graph or a DAG, together with clinicians to say, “Hey, what biases would you expect to have in these two cohorts that might limit our ability to find a signal of effectiveness?” And so we worked with clinicians, with health economists, with epidemiologists to really understand those different biases at play. And the genetic matching was able to match the cohorts on the covariates of interest.

Dr. Rafeh Naqash:And then could you speak on some of the highlights from the results? I know you did survival analysis, cost-effectiveness, could you explain that in terms of what you found?

Dr. Dean Regier:We did two analyses. The intention-to-treat analysis is meant to emulate the pragmatic randomized controlled trial. And what that does is it answers the question, for all those eligible for multigene or single-gene testing: What is the cost-effectiveness in terms of incremental life-years gained and incremental cost per life-years gained? And the second one was around a protocol analysis, which really answered the question of: For those patients who were actually treated, what was the incremental effectiveness and cost-effectiveness? Now, they're different in two very important ways. For the intention-to-treat, it's around population questions. If we gave single-gene or multigene to the entire population of advanced melanoma patients, what is the cost-effectiveness? The per-protocol is really around that clinical question of those who actually received treatment, what was the incremental cost and effectiveness? So very different questions in terms of population versus clinical cost and effectiveness.

So, for the intention-to-treat, what we found is that in terms of life-years gained is around 0.22, which is around 2.5 months of additional life that is afforded to patients who went through the multigene panel testing versus the single-gene testing. That was non-statistically significant from zero at the 5% level. But on average, you would expect this additional 2.5 months of life. The incremental costs were again non-statistically significant, but they're around $20,000. And so when we look at incremental cost-effectiveness, we can also look at the uncertainty around that question, meaning what percentage of incremental cost-effectiveness estimates are likely to be cost-effective at different willingness-to-pay thresholds? Okay? So if you are willing to pay $100,000 to get one gain of life-years, around 52.8% of our estimates, in terms of when we looked at the entire uncertainty, would be cost-effective. So actually that meets the threshold of implementation in our healthcare system. So it's quite uncertain, just over 50%. But what we see is that decision-makers actually have a high tolerance for uncertainty around cost-effectiveness. And so, while it is uncertain, we would say that, well, the cost-effectiveness is finely balanced.

Now, when we looked at the population, the per-protocol population, those folks who just got treatment, we actually have a different story. We have all of a sudden around 4.5 or just under 5 months of life gained that is statistically significantly different from zero, meaning that this is a strong signal of benefit in terms of life-years gained. In terms of the changes in costs or the incremental costs, they are larger again, but statistically insignificant. So the question now is, to what extent is it cost-effective? What is the probability of it being cost-effective? And at the $100,000 per life-year gained willingness-to-pay, there was a 73% chance that multigene panel testing versus single-gene testing is cost-effective.

Dr. Rafeh Naqash:So one of the questions I have here, this is a clarification both for myself and maybe the listeners also. So protocol treatment is basically if you had gene testing and you have a BRAF in the multigene panel, then the patient went on a BRAF treatment. Is that correct?

Dr. Dean Regier:It's still physician choice. And I think that's important to say that. So typically what we saw in both in our pre- and post-matching data is that we saw around 50% of patients, irrespective of BRAF status, get an ICI, which is appropriate, right? And so the idea here is that you get physician-guided care, but if the patient no longer performs on the ICI, then it gives them a little bit more information on what to do next. Even during that time when we thought it wasn't going to be common to do an ICI, but it was actually quite common.

Dr. Rafeh Naqash:Now, did you have any patients in this study who had the multigene testing done and had an NRAS or a KIT mutation and then went on to those therapies, which were not captured obviously in the single-gene testing, which would have just tried to look at BRAF?

Dr. Dean Regier:So I did look at the data this morning because I thought that might come up in terms of my own questions that I had. I couldn't find it, but what we did see is that some patients went on to clinical trials. So, meaning that this multigene panel testing allowed, as you would hope in a learning healthcare system, patients to move on to clinical trials to have a better chance at more appropriate care if a target therapy was available.

Dr. Rafeh Naqash:And the other question in that context, which is not necessarily related to the gene platform, but more on the variant allele frequency, so if you had a multigene panel that captured something that was present at a high VAF, with suspicion that this could be germline, did you have any of those patients? I'm guessing if you did, probably very low number, but I'm just thinking from a cost-effective standpoint, if you identify somebody with germline, their, you know, first-degree relative gets tested, that ends up, you know, prevention, etc. rather than somebody actually developing cancer subsequently. That's a lot of financial gains to the system if you capture something early. So did you look at that or maybe you're planning to look at that?

Dr. Dean Regier:We did not look at that, but that is a really important question that typically goes unanswered in economic evaluations. And so, the short answer is yes, that result, if there was a germline finding, would be returned to the patient, and then the family would be able to be eligible for screening in the appropriate context. What we have found in economic evaluations, and we've recently published this research, is that that scope of analysis is rarely incorporated into the economic evaluation. So those downstream costs and those downstream benefits are ignored. And when you- especially also when you think about things like secondary or incidental findings, right? So it could be a germline finding for cancer, but what about all those other findings that we might have if you go with an exome or if you go with a genome, which by the way, we do have in British Columbia—we do whole-genome and transcriptome sequencing through something called the Personalized OncoGenomics program. That scope of evaluation, because it's very hard to get the right types of data, because it requires a decision model over the lifetime of both the patients and potentially their family, it becomes very complicated or complex to model over patients' and families' lifetime. That doesn't mean that we should not do it, however.

Dr. Rafeh Naqash:So, in summary Dean, could you summarize some of the known and unknowns of what you learned and what you're planning in subsequent steps to this project?

Dr. Dean Regier:Our North Star, if you will, is to really understand the entire system effect of next-generation sequencing panels, exome sequencing, whole genomes, or whole genomes and transcriptome analysis, which we think should be the future of precision oncology. The next steps in our research is to provide a nice base around multigene panels in terms of multigene versus single-gene testing, whether that be colorectal cancer, lung cancer, melanoma, etc., and to map out the entire system implications of implementing next-generation sequencing panels.

And then we want to answer the questions around, “Well, what if we do exomes for all patients? What if we do whole genomes and transcriptomes for all patients? What are the comparative outcomes for a true tumor-agnostic precision oncology approach, accounting for, as you say, things like return of results with respect to hereditary cancers?” I think the challenge that's going to be encountered is really around the persistent high costs of something like a whole-genome and transcriptome sequencing approach. Although we do see the technology prices going down—the "$1,000 genome" or “$6,000 genome" on whatever Illumina machine you might have—that bioinformatics is continuing to be expensive.

And so, there are pipelines that are automated, of course, and you can create a targeted gene report really rapidly within a reasonable turnaround time. But of course, for secondary or what I call level two analysis, that bioinformatics is going to continue to be expensive. And so, we're just continually asking that question is: In our healthcare system and in other healthcare systems, if you want to take a precision oncology approach, how do you create the pipelines? And what types of technologies really lend themselves to benefits over and above next-generation sequencing or multigene panels, allowing for access to off-label therapies? What does that look like? Does that actually improve patients? I think some of the challenges, of course, is because of heterogeneity, small benefiting populations, finding a signal if a signal is indeed there is really challenging. And so, what we are thinking through is, with respect to real-world evidence methods and emulating randomized controlled trials, what types of evidence methods actually allow us to find those signals if indeed those signals are there in the context of small benefiting populations?

Dr. Rafeh Naqash:Thank you so much, Dean. Sounds like a very exciting field, especially in the current day and age where cost-effectiveness, financial toxicity is an important aspect of how we improve upon what is existing in oncology. And then lots more to be explored, as you mentioned.

The last minute and a half I want to ask about you as an individual, as a researcher. There's very few people who have expertise in oncology, biomarkers, and health economics. So could you tell us for the sake of our trainees and early career physicians who might be listening, what was your trajectory briefly? How did you end up doing what you're doing? And maybe some advice for people who are interested in the cost of care, the cost of oncology drugs - what would your advice be for them very briefly?

Dr. Dean Regier:Sure. So I'm an economist by training, and indeed I knew very little about the healthcare system and how it works. But I was recruited at one point to BC Cancer, to British Columbia, to really try to understand some of those questions around costs, and then I learned also around cost-effectiveness. And so, I did training in Scotland to understand patient preferences and patient values around quality of care, not just quantity of life, but also their quality of life and how that care was provided to them. And then after that, I was at Oxford University at the Nuffield Department of Population Health to understand how that can be incorporated into randomized control trials in children. And so, I did a little bit of learning about RCTs. Of course, during the way I picked up some epidemiology with deep understanding of what I call econometrics, what others might call biostatistics or just statistics.

And from there, it was about working with clinicians, working with epidemiologists, working with clinical trialists, working with economists to understand the different approaches or ways of thinking of how to estimate efficacy, effectiveness, safety, and cost-effectiveness. I think this is really important to think through is that we have clinical trialists, we have people with deep understanding of biostatistics, we have genome scientists, we have clinicians, and then you add economists into the mix. What I've really benefited from is that interdisciplinary experience, meaning that when I talk to some of the world's leading genome scientists, I understand where they're coming from, what their hope and vision is. And they start to understand where I'm coming from and some of the tools that I use to understand comparative effectiveness and cost-effectiveness. And then we work together to actually change our methods in order to answer those questions that we're passionate about and curious about better for the benefit of patients.

So, the short answer is it's been actually quite a trajectory between Canada, the UK. I spent some time at the University of Washington looking at the Fred Hutch Cancer Research Center, looking at precision oncology. And along the way, it's been an experience about interdisciplinary research approaches to evaluating comparative outcomes. And also really thinking through not just at one point in time on-off decisions—is this effective? Is it safe? Is it cost-effective?—not those on-off decisions, but those decisions across the lifecycle of a health product. What do those look like at each point in time? Because we gain new evidence, new information at each point in time as patients have more and more experience around it. And so what really is kind of driving our research is really thinking about interdisciplinary approaches to lifecycle evaluation of promising new drugs with the goal of having these promising technologies to patients sooner in a way that is sustainable for the healthcare system.

Dr. Rafeh Naqash:Awesome. Thank you so much for those insights and also giving us a sneak peek of your very successful career.

Thank you for listening to JCO Precision Oncology Conversations. Don't forget to give us a rating or review, and be sure to subscribe so you never miss an episode. You can find all ASCO shows at asco.org/podcast. Thank you.

The purpose of this podcast is to educate and to inform. This is not a substitute for professional medical care and is not intended for use in the diagnosis or treatment of individual conditions.

Guests on this podcast express their own opinions, experience, and conclusions. Guest statements on the podcast do not express the opinions of ASCO. The mention of any product, service, organization, activity, or therapy should not be construed as an ASCO endorsement.

In this JCO PO Article Insights episode, host Harold Tan summarizes Low Kynurenine Levels Among Exceptional Responders on Phase Ib Trial of the HDAC Inhibitor Abexinostat with Pazopanib by Tsang et al, published November 07, 2024.

Harold Nathan Tan:

Welcome to JCO Precision Oncology Article Insights, where we explore cutting-edge discoveries in the world of cancer treatment and research. I'm Harold Nathan Tan, your host, and today we're taking a focused look at a compelling phase Ib trial led by Dr. Tsang, which investigates a combination of abexinostat, a histone deacetylase inhibitor, with pazopanib, a VEGF-targeting tyrosine kinase inhibitor, in patients with advanced solid tumors.

VEGF inhibition has long been an established therapeutic strategy across a wide range of tumor types, including colorectal, ovarian, sarcoma, and renal cell carcinoma. These agents function by disrupting tumor angiogenesis, effectively limiting oxygen and nutrient delivery to malignant cells and contributing to improved survival outcomes. However, over time, acquired resistance remains a significant challenge.

A key mechanism implicated in this resistance involves the upregulation of hypoxia-inducible factor 1-alpha, or HIF-1-alpha for short, a master regulator of angiogenesis that restores VEGF signaling under hypoxic conditions. Interestingly, HIF-1-alpha overexpression is mediated by histone deacetylases, especially HDAC2. Preclinical studies suggest that HDAC2 inhibition can suppress tumor cell migration and downregulate HIF-1-alpha activity, effectively disabling a critical escape pathway used by tumors under VEGF pressure. Moreover, combining HDAC inhibition with VEGF blockade has demonstrated synergy in pazopanib-resistant tumor models, forming a compelling rationale for this dual approach.

The phase Ib trial by Tsang et al. was designed to evaluate the safety, tolerability, and preliminary efficacy of this dual-targeted approach in patients with heavily pretreated advanced solid tumors. A dose-expansion cohort focused on individuals with renal cell carcinoma, allowing for more detailed evaluation in this population.

A central component of this study was the incorporation of biomarker analysis, particularly regarding HDAC2 expression levels. The results were noteworthy. Patients with high HDAC2 expression achieved a progression-free survival of 7.7 months compared to only 3.5 months in those with low expression. Even more compelling, overall survival reached 32.3 months for those with a high HDAC2 expression versus just 9.2 months for those with low expression. This suggests the potential role for HDAC2 as a predictive biomarker for response to combination HDAC and VEGF-targeted therapy.

The authors also explored the metabolic landscape of these patients, conducting metabolomic analysis focused on kynurenine, a key tryptophan catabolite known to contribute to the immune suppression in the tumor microenvironment. Its reduction is driven by HIF-1-alpha and inflammatory cytokines, including interleukin-6 and tumor necrosis factor-alpha. What they found was striking. Exceptional responders, defined as patients with treatment responses lasting more than 3 years, had consistently lower levels of kynurenine both before and after treatment. This finding introduces kynurenine as a potential metabolic biomarker. It suggests that patients with lower kynurenine levels may have a less immunosuppressive microenvironment, making them more responsive to the combined effects of HDAC inhibition and VEGF blockade. Of note, VEGF levels themselves did not significantly differ between responders and nonresponders, highlighting that the treatment benefit is not purely VEGF-mediated but likely driven by epigenetic and metabolic modulation.

On the safety front, the combination of abexinostat and pazopanib was generally well tolerated. However, this study did report a correlation between higher plasma concentrations of abexinostat and an increased incidence of thrombocytopenia, a class effect associated with HDAC inhibitors.

This trial introduces several key considerations for future research. First, it calls for validation of HDAC2 as a predictive biomarker. If confirmed in larger cohorts, HDAC2 expression could be used to select patients most likely to benefit from HDAC inhibitor-based regimens, transforming how we approach trial enrollment and treatment planning. Second, the link between low kynurenine and exceptional response supports further investigation into how metabolic pathways can influence treatment response to combined HDAC and VEGF inhibition.

Overall, HDAC inhibitors hold significant promise in precision oncology. Realizing their full therapeutic potential requires a deeper understanding of HDAC biology, refined combination strategies, and thorough preclinical and clinical evaluations tailored to individual patient profiles. This study exemplifies the potential of epigenetic-metabolic crosstalk as a therapeutic vulnerability and underscores the importance of precision stratification in clinical trial design. As research in this space progresses, the integration of molecular, epigenetic, and metabolic profiling will be essential in optimizing the use of HDAC inhibitors and expanding their role within precision oncology.

Thank you for tuning into JCO Precision Oncology Article Insights. Don't forget to subscribe and join us next time as we explore more groundbreaking research shaping the future of oncology. Until then, stay informed and stay inspired.

The purpose of this podcast is to educate and to inform. This is not a substitute for professional medical care and is not intended for use in the diagnosis or treatment of individual conditions.

Guests on this podcast express their own opinions, experience, and conclusions. Guest statements on the podcast do not express the opinions of ASCO. The mention of any product, service, organization, activity, or therapy should not be construed as an ASCO endorsement.

JCO PO author Dr. Timothy Showalter at Artera and University of Virginia shares insights into his JCO PO article, “Digital Pathology–Based Multimodal Artificial Intelligence Scores and Outcomes in a Randomized Phase III Trial in Men With Nonmetastatic Castration-Resistant Prostate Cancer” . Host Dr. Rafeh Naqash and Dr. Showalter discuss how multimodal AI as a prognostic marker in nonmetastatic castration-resistant prostate cancer may serve as a predictive biomarker with high-risk patients deriving the greatest benefit from treatment with apalutamide.

TRANSCRIPT 

Dr. Rafeh Naqash: Hello and welcome to JCO Precision Oncology Conversations where we'll bring you engaging conversations with authors of clinically relevant and highly significant JCO PO articles. I'm your host, Dr. Rafeh Naqash, podcast Editor for JCO Precision Oncology and assistant professor at the OU Health Stephenson Cancer Center at the University of Oklahoma.

Today, we are excited to be joined by Dr. Timothy Showalter, Chief Medical Officer at Artera and professor of Radiation Oncology at the University of Virginia and author of the JCO Precision Oncology article entitled, “Digital Pathology Based Multimodal Artificial Intelligence Scores and Outcomes in a Randomized Phase 3 Trial in Men with Non-Metastatic Castration Resistant Prostate Cancer.”

At the time of this recording, our guest’s disclosures will be linked in the transcript.

Dr. Showalter, it's a pleasure to have you here today.

Dr. Timothy Showalter: It's a pleasure to be here. Thanks for having me.

Dr. Rafeh Naqash: I think this is going to be a very interesting discussion, not just from a biomarker perspective, but also in terms of how technologies have evolved and how we are trying to stratify patients, trying to escalate or deescalate treatments based on biomarkers. And this article is a good example of that.

One of the things I do want to highlight as part of this article is that Dr. Felix Feng is the first author for this article. Unfortunately, Dr. Felix Feng passed away in December of 2024. He was a luminary in this field of prostate cancer research. He was also the Chair of the NRG GU Committee as well as Board of Directors for RTOG Foundation and has mentored a lot of individuals from what I have heard. I didn't know Dr. Feng but heard a lot about him from my GU colleagues. It's a huge loss for the community, but it was an interesting surprise for me when I saw his name on this article as I was reviewing it. Could you briefly talk about Dr. Feng for a minute and how you knew him and how he's been an asset to the field?

Dr. Timothy Showalter: Yeah. I'm always happy to talk about Felix whenever there's an opportunity. You know, I was fortunate to know Felix Feng for about 20 years as we met during our residency programs through a career development workshop that we both attended and stayed close ever since. And you know, he's someone who made an impact on hundreds of lives of cancer researchers and other radiation oncologists and physicians in addition to the cancer patients he helped, either through direct clinical care or through his innovation. For this project in particular, I first became involved soon after Felix had co-founded Artera, which is, you know the company that developed this. And because Felix was such a prolific researcher, he was actually involved in this and this research project from all different angles, both from the multimodal digital pathology tool to the trial itself and being part of moving the field forward in that way. It's really great to be able to sort of celebrate a great example of Felix's legacy, which is team science, and really moving the field forward in terms of translational projects based on clinical trials. So, it's a great opportunity to highlight some of his work and I'm really happy to talk about it with you.

Dr. Rafeh Naqash: Thanks, Tim. Definitely a huge loss for the scientific community. And I did see a while back that there was an international symposium organized, showcasing his work for him to talk about his journey last year where more than 200, 250 people from around the globe actually attended that. That speaks volumes to the kind of impact he's had as an individual and impact he's had on the scientific side of things as well.

Dr. Timothy Showalter: Yes. And we just had the second annual Feng Symposium the day before ASCO GU this year with, again, a great turnout and some great science highlighted, as well as a real focus on mentorship and team science and collaboration.

Dr. Rafeh Naqash: Thank you so much for telling us all about that. Now going to what you guys published in JCO Precision Oncology, which is this article on using a biomarker approach to stratify non-metastatic prostate cancer using this artificial intelligence based H&E score. Could you tell us the background for what started off this project? And I see there is a clinical trial data set that you guys have used, but there's probably some background to how this score or how this technology came into being. So, could you superficially give us an idea of how that started?

Dr. Timothy Showalter: Sure. So, the multimodal AI score was first published in a peer reviewed journal back in 2022 and the test was originally developed through a collaboration with the Radiation Therapy Oncology Group or Energy Oncology Prostate Cancer Research Team. The original publication describes development and validation of a risk stratification tool designed to predict distant metastasis and prostate cancer specific mortality for men with localized prostate cancer. And the first validation was in men who were treated with definitive radiation therapy. There have been subsequent publications in that context and there's a set of algorithms that have been validated in localized prostate cancer and there's a test that's listed on NCCN guidelines based on that technology.

The genesis for this paper was really looking at extending that risk stratification tool that was developed in localized prostate cancer to see if it could one, validate in a non-metastatic castrate refractory prostate cancer population for patients enrolled on the SPARTAN trial. And two, whether there was a potential role for the test output in terms of predicting benefit from apalutamide for patients with non-metastatic prostate cancer. For patients who are enrolled on the SPARTAN study, almost 40% of them had H&E stain biopsy slide material available and were eligible to be included in this study.

Dr. Rafeh Naqash: Going a step back to how prostate cancer, perhaps on the diagnostic side using the pathology images is different as you guys have Gleason scoring, which to the best of my knowledge is not necessarily something that most other tumor types use. Maybe Ki-67 is somewhat of a comparison in some of the neuroendocrine cancers where high Ki-67 correlates with aggressive biology for prognosis. And similarly high Gleason scores, as we know for some of the trainees, correlates with poor prognosis. So, was the idea behind this based on trying to stratify or sub-stratify Gleason scoring further, where you may not necessarily know what to do with the intermediate high Gleason score individual tumor tissues?

Dr. Timothy Showalter: Well, yeah. I mean, Gleason score is a really powerful risk stratification tool. As you know, our clinical risk groupings are really anchored to Gleason scores as an important driver for that. And while that's a powerful tool, I think, you know, some of the original recognition for applying computer vision AI into this context is that there are likely many other features located in the morphology that can be used to build a prognostic model.

Going back to the genesis of the discovery project for the multimodal AI model, I think Felix Feng would have described it as doing with digital pathology and computer vision AI what can otherwise be done with gene expression testing. You know, he would have approached it from a genomic perspective. That's what the idea was. So, it's along the line of what you're saying, which is to think about assigning a stronger Gleason score. But I think really more broadly, the motivation was to come up with an advanced complementary risk stratification tool that can be used in conjunction with clinical risk factors to help make better therapy recommendations potentially. So that was the motivation behind it.

Dr. Rafeh Naqash: Sure. And one of the, I think, other important teaching points we try to think about, trainees of course, who are listening to this podcast, is trying to differentiate between prognostic and predictive scores. So, highlighting the results that you guys show in relation to the MMAI score, the digital pathology score, and outcomes as far as survival as well as outcomes in general, could you try to help the listeners understand the difference between the prognostic aspect of this test and the predictive aspect of this test?

Dr. Timothy Showalter: So let me recap for the listeners what we found in the study and how it kind of fits into the prognostic and the predictive insights. So, one, you know, as I mentioned before, this is ultimately a model that was developed and validated for localized prostate cancer for risk stratification. So, first, the team looked at whether that same tool developed in localized prostate cancer serves as a prognostic tool in non-metastatic castrate-refractory prostate cancer. So, we applied the tool as it was previously developed and identified that about 2/3 of patients on the SPARTAN trial that had specimens available for analysis qualified as high risk and 1/3 of patients as either intermediate or low risk, which we called in the paper ‘non-high risk’. And we're able to show that the multimodal AI score, which ranges from 0 to 1, and risk group, was associated with metastasis free survival time to second progression or PFS 2 and overall survival. And so that shows that it performs as a prognostic tool in this setting. And this paper was the first validation of this tool in non-metastatic castrate-refractory prostate cancer. So, what that means to trainees is basically it helps you understand how aggressive that cancer is or better stratify the risk of progression over time. So that's the prognostic performance.

Dr. Rafeh Naqash: Thank you for trying to explain that. It's always useful to get an example and understand the difference between prognostic and predictive. Now again, going back to the technology, which obviously is way more complicated than the four letter word MMAI, I per se haven't necessarily done research in this space, but I've collaborated with some individuals who've done digital pathology assessments, and one of the projects we worked on was TIL estimation and immune checkpoint related adverse events using some correlation and something that one of my collaborators had sent to me when we were working on this project as part of this H&E slide digitalization, you need color deconvolution, you need segmentation cell profiling. Superficially, is that something that was done as part of development of this MMAI score as well?

Dr. Timothy Showalter

You need a ground truth, right? So, you need to train your model to predict whatever the outcome is. You know, if you're designing an AI algorithm for Ki-67 or something I think you mentioned before, you would need to have a set of Ki-67 scores and train your models to create those scores. In this case, the clinical annotation for how we develop the multimodal AI algorithm is the clinical endpoints. So going back to how this tool was developed, the computer vision AI model is interpreting a set of features on the scan and what it's trying to do is identify high risk features and make a map that would ultimately predict clinical outcomes. So, it's a little bit different than the many digital pathology algorithms where the AI is being trained to predict a particular morphological finding. In this case, the ground truth that the model is trained to predict is the clinical outcome.

Dr. Rafeh Naqash: Sure. And from what you explained earlier, obviously, tumors that had a high MMAI score were the ones that were benefiting the most from the ADT plus the applausive. Is this specific for this androgen receptor inhibitor or is it interchangeable with other inhibitors that are currently approved?

Dr. Timothy Showalter: That's a great question and we don't know yet. So, as you're alluding to, we did find that the MMAI risk score was predictive for benefit from apalutamide and so it met the statistical definition of having a significant interaction p value so we can call it a predictive performance. And so far, we've only looked in this population for apalutamide. I think you're raising a really interesting point, which is the next question is, is this generalizable to other androgen receptor inhibitors? There will be future research looking at that, but I think it's too early to say.

Just for summary, I think I mentioned before, there are about 40% of patients enrolled on the SPARTAN study had specimens available for inclusion in this analysis. So, the SPARTAN study did show in the entire clinical trial set that patients with non-metastatic castrate-refractory prostate cancer benefited from apalutamide. The current study did show that there seems to be a larger magnitude of benefit for those patients who are multimodal AI high risk scores. And I think that's very interesting research and suggests that there's some interaction there. But I certainly would want to emphasize that we have not shown that patients with intermediate or low risk don't benefit from apalutamide. I think we can say that the original study showed that that trial showed a benefit and that we've got this interesting story with multimodal AI as well.

Dr. Rafeh Naqash: Sure. And I think from a similar comparison, ctDNA where ctDNA shows prognostic aspects, I treat people with lung cancer especially, and if you're ctDNA positive at a 3 to 4-month period, likely chances of you having a shorter disease-free interval is higher. Same thing I think for colorectal cancers. And now there are studies that are using ctDNA as an integral biomarker to stratify patients positive/negative and then decide on escalation/de-escalation of treatment. So, using a similar approach, is there something that is being done in the context of the H&E based stratification to de-intensify or intensify treatments based on this approach?

Dr. Timothy Showalter: You're hitting right on the point in the most promising direction. You know, as we pointed out in the manuscript, one of the most exciting areas as a next step for this is to use a tool like this for stratification for prospective trials. The multimodal AI test is not being used currently in clinical trials of non-metastatic castrate-refractory prostate cancer, which is a disease setting for this paper. There are other trials that are in development or currently accruing where multimodal AI stratification approach is being taken, where you see among the high-risk scores, at least in the postoperative setting for a clinical trial that's open right now, high risk score patients are being randomized to basically a treatment intensification question. And then the multimodal AI low risk patients are being randomized to a de-intensification experimental arm where less androgen deprivation therapy is being given. So, I think it's a really promising area to see, and I think what has been shown is that this tool has been validated really across the disease continuum. And so, I think there are opportunities to do that in multiple clinical scenarios.

Dr. Rafeh Naqash: Then moving on to the technological advancements, very fascinating how we've kind of evolved over the last 10 years perhaps, from DNA based biomarkers to RNA expression and now H&E. And when you look at cost savings, if you were to think of H&E as a simpler, easier methodology, perhaps, with the limitations that centers need to digitalize their slides, probably will have more cost savings. But in your experience, as you've tried to navigate this H&E aspect of trying to either develop the model or validate the model, what are some of the logistics that you've experienced can be a challenge? As we evolve in this biomarker space, how can centers try to tackle those challenges early on in terms of digitalizing data, whether it's simple data or slides for that matter?

Dr. Timothy Showalter: I think there's two main areas to cover. One, I think that the push towards digitalization is going to be, I think, really driven by increasing availability and access to augmentative technologies like this multimodal AI technology where it's really adding some sort of a clinical insight beyond what is going to be generated through routine human diagnostic pathology. I think that when you can get these sorts of algorithms for patient care and have them so readily accessible with a fast turnaround time, I think that's really going to drive the field forward. Right now, in the United States, the latest data I've seen is that less than 10% of pathology labs have gone digital. So, we're still at an early stage in that. I hope that this test and similar ones are part of that push to go more digital.

The other, I think, more interesting challenge that's a technical challenge but isn't about necessarily how you collect the data, but it certainly creates data volume challenges, is how do you deal with image robustness and sort of translating these tools into routine real-world settings. And as you can imagine, there's a lot of variation for staining protocols, intensity scanner variations, all these things that can affect the reliability of your test. And at least for this research group that I'm a part of that has developed this multimodal AI tool can tell you that the development is sophisticated, but very data and energy intensive in terms of how to deal with making a tool that can be consistent across a whole range of image parameters. And so that presents its own challenges for dealing with a large amount of compute time and AI cycles to make robust algorithms like that. And practically speaking, I think moving into other diseases and making this widely available, the size of data required and the amount of cloud compute time will be a real challenge.

Dr. Rafeh Naqash: Thank you for summarizing. I can say that definitely, you know, this is maybe a small step in prostate cancer biomarker research, but perhaps a big step in the overall landscape of biomarker research in general. So definitely very interesting.

Now, moving on to the next part of the discussion is more about you as a researcher, as an individual, your career path, if you can summarize that for us. And more interestingly, this intersection between being part of industry as well as academia for perhaps some of the listeners, trainees who might be thinking about what path they want to choose.

Dr. Timothy Showalter: Sure. So, as you may know, I'm a professor at the University of Virginia and I climbed the academic ladder and had a full research grant program and thought I'd be in academia forever. And my story is that along the way, I kind of by accident ended up founding a medical device company that was called Advaray and that was related to NCI SBIR funding. And I found myself as a company founder and ultimately in that process, I started to learn about the opportunity to make an impact by being an innovator within the industry space. And that was really the starting point for me. About four years ago, soon after Felix Feng co-founded Artera, he called me and told me that he needed me to join the company. For those who were lucky to know Felix well, at that very moment, it was inevitable that I was going to join Artera and be a part of this. He was just so persuasive. So, I will say, you know, from my experience of being sort of in between the academic and industry area, it's been a really great opportunity for me to enter a space where there's another way of making an impact within cancer care. I've gotten to work with top notch collaborators, work on great science, and be part of a team that's growing a company that can make technology like this available.

Dr. Rafeh Naqash: Thank you so much, Tim, for sharing some of those thoughts and insights. We really appreciate you discussing this very interesting work with us and also appreciate you submitting this to JCO Precision Oncology and hopefully we'll see more of this as this space evolves and maybe perhaps bigger more better validation studies in the context of this test.

Thank you for listening to JCO Precision Oncology Conversations. Don't forget to give us a rating or review and be sure to subscribe so you never miss an episode. You can find all ASCO shows at asco.org/podcast.

The purpose of this podcast is to educate and to inform. This is not a substitute for professional medical care and is not intended for use in the diagnosis or treatment of individual conditions.

Guests on this podcast express their own opinions, experience and conclusions. Guest statements on the podcast do not express the opinions of ASCO. The mention of any product, service, organization, activity, or therapy should not be construed as an ASCO endorsement.

In this JCO Precision Oncology Article Insights episode, Natalie DelRocco summarizes "Digital Pathology–Based Multimodal Artificial Intelligence Scores and Outcomes in a Randomized Phase III Trial in Men With Nonmetastatic Castration-Resistant Prostate Cancer" by Felix Y. Feng, et al published January 31, 2025. Come back for the next episode where JCO Precision Oncology Conversations host, Dr. Rafeh Naqash interviews the author of the JCO PO article discussed, Dr. Tim Showalter.

TRANSCRIPT

Natalie DelRocco: Hello and welcome to JCO Precision Oncology Article Insights. I'm your host Natalie Del Rocco. Today, we'll be discussing the article, “Digital Pathology-Based Multimodal Artificial Intelligence Scores and Outcomes in a Randomized Phase III Trial in Men With Nonmetastatic Castration-Resistant Prostate Cancer.” We will also be discussing the accompanying editorial, “Leveraging Artificial Intelligence to Improve Risk Stratification in Nonmetastatic Castration-Resistant Prostate Cancer.” So, we're going to start by summarizing the original report and then we'll jump into a few of the high-level interpretations that were supplied by the editorial.  

The original report by Feng et. al. describes the application of multimodal artificial intelligence to data collected on a nonmetastatic castration-resistant prostate cancer. We will call this disease moving forward NMCRPC, a Clinical Trial. So, we're looking at data from an NMCRPC clinical trial. The SPARTAN trial was a randomized phase three trial and this study compared metastasis-free survival as the primary endpoint for those treated with traditional androgen deprivation therapy or ADT to those treated with androgen deprivation therapy plus apalutamide. Other secondary endpoints included progression-free survival and overall survival, but the primary endpoint there was metastasis-free survival or MFS. This study found that the addition of apalutamide resulted in a significantly longer median metastasis-free survival compared to androgen deprivation therapy alone. And we should note that this is a double-blind placebo-controlled trial. In the overall study, 1,207 patients participated and over the course of this study histopathology slides were collected and they were digitized for future use. And that future use is what we are going to be discussing today. 

The authors do note that there are currently no good biomarkers for use in NMCRPC. The authors seem to be inspired by the ArteraAI prostate test, which was a recent application of multimodal artificial intelligence models. But in localized prostate cancer as opposed to NMCRPC, the authors constructed a multimodal artificial intelligence model or an MMAI model. They applied this to the SPARTAN trial with the intention of developing a risk score that could be used for risk stratification in NMCRPC. And we should note here that multimodal artificial intelligence or MMAI is a broad class of artificial intelligence models, and they can analyze different types of data at one time, hence the term multimodal. So in this example, the author's primary data source of interest were those digitized histopathology images because histopathology tells you a lot about NMCRPC. The authors though also wanted their model to consider traditional clinical factors that are known to be prognostic such as Gleason score, tumor stage, PSA level, and age. So those two different types of data, those histopathology images and that traditional clinical data are the two different types of data that make this model multimodal. So we should note here importantly, after dropping missing data, 420 patients contribute to this model, the MMAI model. 

The authors generate a risk score from this MMAI model and they categorize that risk score into low, intermediate, and high risk groups using clinical knowledge. The authors found in their results that an increase in this MMAI risk score was associated with an increased hazard of metastasis-free survival event with a hazard ratio from a Cox proportional hazards model of 1.72. To summarize how the authors arrived here, they derived a risk score from this MMAI model which incorporates both imaging and regular data. They plugged this risk score into a Cox proportional hazards mode,l modeling metastasis-free survival and they found that an increase in that MMAI based risk score is associated with increased hazard of metastasis-free event with a hazard ratio of 1.72, which is quite large. Additionally, the risk score seemed to be associated with PFS2 and OS, which were two of the secondary endpoints from the SPARTAN clinical trial, though the effect sizes were more modest. Those are the highlights from the original report, the methods and the results. 

The accompanying editorial notes that both histopathology and Gleason score specifically are very critical to understanding prostate cancer, and Gleason score alone is not sufficient to summarize the complexity of the disease, although it is a well validated prognostic factor for prostate cancer. So this makes MMAI an excellent tool in the setting described by the authors. We have an existing prognostic factor that doesn't describe the entire picture of the disease by itself and so we can use those digitized histopathology slides to help bolster our understanding and provide the model more information. MMAI allows you to do this because it can take in different types of data. So that was the main conclusion of the editorial. 

They also summarize a number of recent validations of MMAI models in prostate cancer research, noting that it will be an important tool for risk stratification and has already been shown to outperform classical techniques. The editorial though does highlight a number of weaknesses of this paper, limitations and I think the most important one to highlight, and we touched on this earlier, is that 420 patients from the SPARTAN clinical trial contributed to the development of this MMAI score. That is a small proportion of the roughly 1200 patients that did participate in the SPARTAN clinical trial. So we have a small subgroup analysis that can be limiting and this model will need to be validated in a broader population in the future.

Thank you for listening to JCO Precision Oncology Article Insights. Don't forget to give us a rating or a review and be sure to subscribe so that you never miss an episode. You can find all ASCO shows at asco.org/podcasts.

The purpose of this podcast is to educate and to inform. This is not a substitute for professional medical care and is not intended for use in the diagnosis or treatment of individual conditions.

Guests on this podcast express their own opinions, experience and conclusions. Guest statements on the podcast do not express the opinions of ASCO. The mention of any product, service, organization, activity or therapy should not be construed as an ASCO endorsement.

In this JCO Precision Oncology Article Insights episode, Jiasen He summarizes “Midline Low-Grade Gliomas of Early Childhood: Focus on Targeted Therapies.” by Dr. Ludmila Papusha et al. published on July 08, 2024.

TRANSCRIPT 

Jiasen He: Hello and welcome to JCO Precision Oncology Article Insights. I'm your host Jiasen He, a JCO Journal's Editorial Fellow. Today, I will provide a summary on “Midline Low-Grade Gliomas of Early Childhood: Focus on Targeted Therapies.” This is an observational study by Dr. Ludmila Papusha and colleagues that investigated the use of target therapies in early childhood with midline low grade glioma.

Low grade glioma located in the hypothalamic chiasmatic region, thalamus and the brain stem are classified as midline low grade gliomas. Due to their unique locations, complete surgical resection is usually not able to be achieved. In young children with low grade glioma, radiation therapy is generally not favored. Traditionally, chemotherapy regimens such as carboplatin and vincristine have been used. However, as Dr. Papusha noted, this population often exhibits poor response to chemotherapy. With a growing understanding of the RAS-RAF-MEK pathways in low grade glioma, targeted therapy has emerged as a promising treatment option for this group. However, limited data is available regarding the mutation status of midline low grade glioma in early childhood and real world evidence on their response to targeted therapy remains scarce.

Dr. Papusha's research aimed to address this critical gap by evaluating the effectiveness of targeted therapy in midline gliomas of early childhood. In this observational study, 40 patients under the age of three with midline low grade glioma were enrolled. Somatic genetic aberrations associated with activation of RAS-RAF signaling pathway were identified in 95% of the cohort with BRAF fusion being the most common aberration followed by the BRAF V600E mutation. These findings confirm the presence of targetable mutations in this specific population and provide a foundation for the use of targeted therapy.

Diencephalic syndrome is a rare neurologic disorder typically affecting infants and young children with tumors located in the diencephalon. In this cohort, 43% of the optic pathway and hypothalamic gliomas manifested diencephalic syndrome. Among 30 patients who received first line chemotherapy, primary carboplatin and vincristine, the two-year and five-year progression-free survival rate were only 24% and 6.4% respectively, indicating that most patients experience disease progression with chemotherapy. Targeted therapy was administered to 27 patients of whom 22 experienced disease progression during or after chemotherapy. A total of 26 patients were available for evaluation. Dr. Papusha reported that all patients benefited from targeted therapy with 12 achieving a partial response, 2 showing a minor response and 12 maintaining stable disease. The median duration of targeted therapy was 16 months. These findings demonstrate the efficacy of targeted therapy in this population.

Regarding toxicity from targeted therapy in this population, the most common adverse event was grade 1 to 2 skin toxicity observed in 52% of patients. Severe toxicity occurred in 36% of patients treated with trametinib including grade 3 skin toxicity, mucositis and hematuria. Additionally, grade 3 gastrointestinal toxicity was reported. Interestingly, all three patients who experienced grade 3 gastrointestinal toxicity had diencephalic syndrome at the time of targeted therapy initiation. The author also noted cases of disease progression during treatment breaks. However, tumor response was restored in all affected patients upon resumption of targeted therapy. The two-year progression-free survival rate was 59%.

In conclusion, Dr. Papusha states the unique characteristics of infantile midline low grade glioma, including the high prevalence of diencephalic syndrome and resistance to chemotherapy. The study contributes valuable information on the targetable mutation profile in this population and provides further evidence supporting the use of targeted therapy while emphasizing the need for low monitoring of severe adverse events. As the author notes, important questions remain regarding the long term side effects of kinase inhibitors in infants and children as well as optimal duration of therapy.

Thank you for listening to JCO Precision Oncology Article Insights and please tune in for the next topic. Don't forget to give us a rating or review and be sure to subscribe so you never miss an episode. You can find all ASCO shows at asco.org/podcasts.

The purpose of this podcast is to educate and to inform. This is not a substitute for professional medical care and is not intended for use in the diagnosis or treatment of individual conditions.

Guests on this podcast express their own opinions, experience and conclusions. Guest statements on the podcast do not express the opinions of ASCO. The mention of any product, service, organization, activity or therapy should not be construed as an ASCO endorsement.

JCO PO author Dr. Hatim Husain at University of California San Diego, shares insights into his JCO PO article, “Adagrasib Treatment After Sotorasib-Related Hepatotoxicity in Patients With KRASG12C-Mutated Non–Small Cell Lung Cancer: A Case Series and Literature Review”, one of the top downloaded articles of 2024. Host Dr. Rafeh Naqash and Dr. Husain discuss how to utilize real-world and clinical trial data to discern the safety of adagrasib (another KRASG12C inhibitor), following sotorasib discontinuation due to hepatotoxicity.

TRANSCRIPT

Dr. Rafeh Naqash: Hello and welcome to JCO Precision Oncology Conversations where we bring you engaging conversations with authors of clinically relevant and highly significant JCOPO articles. I'm your host, Dr. Rafeh Naqash, Podcast Editor for JCO Precision Oncology and Assistant Professor at the OU Stephenson Cancer Center. 

Today, I'm very excited to be joined by Dr. Hatim Hussain, Professor of Medicine at the University of California, San Diego, and author of the JCO Precision Oncology article, “Adagrasib Treatment After Sotorasib-Related Hepatotoxicity in Patients With KRAS-G12C-Mutated Non-Small Cell Lung Cancer: A Case Series and Literature Review.” This was one of the top downloaded articles of 2024. And the other interesting thing is we generally don't do podcasts for case reports or case series, so this is one of the very few that we have selected for the podcast. 

And at the time of the recording, our guest disclosures will be linked in the transcript. 

Dr. Hussain, welcome to our podcast and thank you for joining us today.

Dr. Hatim Husain: Thank you Dr. Naqash. Such a pleasure to be here and to speak with you all.

Dr. Rafeh Naqash: And for the sake of this podcast, we'll refer to each other using our first names. So again, as I mentioned earlier that this is one of the very few case reports that we have selected for podcasts in JCOPO and the intention was very deliberate because it caters to something that is emerging where we are trying to treat more KRAS mutant patients with different KRAS inhibitors. And you tried to address one very unique aspect of it in this article which pertains to toxicity, especially hepatotoxicity. So for the sake of our listeners who tend to be community oncologists, trainees, academic faculty, can you tell us what are KRAS inhibitors? What is KRAS-G12C? And how do some of these approved KRAS inhibitors try to inhibit KRAS-G12C?

Dr. Hatim Husain: Sure. For a long time actually we've not had a selective way to inhibit mutant KRAS. And over the last several years actually now, we've seen some dramatic advances here, particularly with the FDA approval of some of the selective inhibitors against the G12C variant. So KRAS-G12C is an isoform of KRAS that is most common in lung cancer and in fact actually is a transversion mutation in the KRAS gene that is a product of the carcinogen of tobacco. And in fact, the incidence of KRAS-G12C in lung cancer, it's quite astounding where as many KRAS-G12C patients there are, there can be, as you know, more than EGFR patients in certain populations and cohorts. The medicines sotorasib and adagrasib were rationally designed to be selective KRAS-G12C inhibitors. And the way that they do this is that they lock the KRAS protein in the OFF state. KRAS is a protein that oscillates between an ON and an OFF state and by virtue of locking the protein in an OFF state, it has shown inhibition of downstream signaling and mitigation of tumor growth and, in fact, tumor cell death.

Dr. Rafeh Naqash: I absolutely love the way you describe the ON and OFF state, the oscillation where the ON is bound to the GTP and the OFF is bound to the GDP. The two KRAS inhibitors as currently FDA approved, as you mentioned, are RAS OFF inhibitors and they're emerging KRAS inhibitors that are RAS ON. So now, as we have known from previous data related to immunotherapy and EGFR TKIs such as osimirtinib where toxicity tends to be a compounded effect when you have osimertinib given within a certain timeline of previous checkpoint therapy, we've seen that in the clinic as the data for these KRAS inhibitors is emerging, you talk about some very interesting aspects and data about what has been published so far with regards to prior use of immunotherapy or chemo immunotherapy and the subsequent use of KRAS inhibitors. Could you elaborate upon that?

Dr. Hatim Husain: Sure. So for this population of patients, the first line approved strategy is a strategy that most cases will incorporate immune therapy and chemotherapy. Immune therapy can have some important responses for patients with KRAS-G12C. This may be due to the fact that KRAS-G12C patients may have a higher incidence of prior smoking, perhaps higher mutation burdens in some patients, and perhaps immunogenicity is defined in that context. So the standard of care in the first line currently includes immune therapy or immune therapy and chemotherapy. Where the current FDA approvals for selective G12C inhibitors are are after the first line of therapy. There are a number of trials exploring these medicines in the first line to see if they may be incorporated into a future treatment paradigm.

Dr. Rafeh Naqash: Thank you for that explanation. Now, going to what you published in this manuscript, can you help us understand the context of why you looked at this? Even though the data just comprises a case series of a handful of patients, but the observations are very interesting and these are real world scenarios where we often tend to be in situations where an individual has had toxicity on a certain drug and may have some response to that drug, but at the same time, the toxicity is challenging. And then you try to debate whether another drug in the same class might be beneficial without those toxicities. So you've tried to address that to some extent using this data set. So can you elaborate upon the question, the methodology, what you tried to look at, and important observations that you have?

Dr. Hatim Husain: Yes, our paper was actually inspired by one of my patients. My patient was a patient who had received chemotherapy and immune therapy and actually in the past, even, you know, additional lines of immune therapies, it was really coming to the edge of where standard treatments would exist. It was right at the same time that these selective inhibitors had been approved and the patient had received sotorasib. And what was remarkable was, when given sotorasib, patient had a very high peak and spike in the transaminases. And we would do different trials of strategies around dose, around interruptions. And it was becoming quite difficult, actually, for the patient to proceed with additional therapy. It was around similar times, actually, and I do want to make a note that the patient was progressing, driven in large fact by the fact that we've had to interrupt the medicine. So we feel and believe that the patient had had inadequate dosing because of the level of toxicity that the patient was having with transaminase increase. So it was around the same time that adagrasib was first commercially available that we were at that point, and we did a trial of adagrasib post-sotorasib, largely driven by necessity, without having additional options to provide this patient in our environment. What was remarkable was when the patient received the adagrasib, there were no spikes in transaminases similar to what we had seen before. And that really led us thinking and to say, “Is this adverse event of transaminase increase or hepatotoxicity, is this a class effect with KRAS-G12C inhibitors, or is it more nuanced than that? Are there different, perhaps, mechanisms by which the medicines may work that may more or less differentially contribute to this adverse event?” And so that inspired us to kind of do a larger analysis, kind of really reach out to a larger network of physicians to gather insights and to gather responses in patients who had had a serial approach of sotorasib and then adagrasib. 

What we found in this process was, in fact, actually there were many more cases of patients who resembled my patient, where the sequence of sotorasib going to adagrasib may have demonstrated differential contribution of hepatotoxicity in that context. And that really motivated us to put the publication together to due diligence, and in the publication spend a lot of time to kind of outline each patient case in detail around metrics surrounding time from last immune therapy, the number of days on sotorasib, the best response to sotorasib, the interval between sotorasib and adagrasib, the duration of adagrasib and then the grade of hepatotoxicity seen in each of the contexts, and particularly kind of the adagrasib and patient disease status as well. We were quite inspired by the effort to try to, if we do not have randomized data in comparison of one medicine to another, which we do not at this juncture, we do not have a randomized analysis to really diligently and rigorously compare the rates of AEs across each medicine, and even in sequence, we do not have that with immune therapy. But what we felt was trying to get more analysis of this sequential approach of, if patients had received a medicine, had to be taken off because of toxicity and then actually tried on a new medicine, what were those rates? We felt like that was at least some information to try to get at this question.

Dr. Rafeh Naqash: And you bring forward a very important point, which is, a lot of times in the real world setting we don't have cross trial comparisons that can be fully applicable, or we don't have trials that compare two drugs of the same class with respect to the AE profile or efficacy. And observations like the one that you described that led to this study are extremely critical in trying to help answer these questions. 

From a data standpoint, and you allude to it to some extent in your manuscript, the trials that are trying to address combination of KRAS-G12C with immunotherapy, especially sotorasib or adagrasib, can you elaborate on that data, what has been published so far and summarize it for our listeners?

Dr. Hatim Husain: So there is data from clinical trials looking at patients actually who have received concomitant immune therapy and sotorasib. What was seen in this, in a real world analysis, was that some patients actually who had received sotorasib within a close proximity of immune therapy, as well as a larger study actually which showed in combination there were higher rates of hepatotoxicity in that context. In fact, there were rates of grade 3 hepatotoxicity. And I think built upon that data there's a recognition in the field that we have to be very diligent in terms of even the clinical trial designs in how to understand the pairing between immune therapy and selective G12C inhibitors. There are many trials that are ongoing, one of the studies that is ongoing is known as the KRYSTAL-7 study, which is evaluating adagrasib in combination with pembrolizumab in the first line. And we await more information on that strategy as well. In the context of sotorasib, because of some of the trials that have shown higher rates of hepatotoxicity, there are some additional trials now looking at sotorasib in combination with chemotherapy, and those also have some information that have been reported as well.

Dr. Rafeh Naqash: From a drug development standpoint, as you mentioned, there's always a tendency to combine something with something else. And in my practice, and I'm sure in your practice too, when we do early phase trials, many trials are still focused on choosing the maximum tolerated dose, which may be something that we need to gradually move away from as we try to implement these combinations of multiple antibodies plus some of these target agents from maybe the biological optimal dose rather than the maximal tolerant dose is a better way to look at the drugs, the pharmacokinetic profile, and then see what is likely the safest combination with the most appropriate target engagement. Do you have any thoughts on that or insights on that from a drug development perspective?

Dr. Hatim Husain: It's a wonderful question and I think it is a very insightful question and understanding of where we are in space right now. And I agree with you that historically, cancer drug development was really hinged upon medicines that perhaps required higher doses to see a benefit or to inch out kind of marginal increases upon where we were at. Now, in combination with medicines that have non-overlapping mechanisms of action, the concept is: Can there actually be more synergy across an approach using combinatorial strategies rather than just additive effects? And I think that in some cases this is being studied with immune therapy, in some cases actually even in the context of other novel mechanisms for cancer therapy. I think that in my practice, I will really try to see how a patient at an approved dose will respond. But definitely I'm open to the concept that there may be a dose that doesn't have to be the maximally tolerated dose, but rather the dose that responses can be seen and perhaps actually at a lower dose than what drives many toxicities.

Dr. Rafeh Naqash: I often describe this to my patients as individual patient dose optimization outside of a clinical trial, where I'm sure you've probably done this, where in older adults maybe a lower dose of osimertinib is tolerated better, or a lower dose of sotorasib or adagrasib for that matter, tolerated better with perhaps a similar level of efficacy, since we don't have comparisons between doses and efficacy so far. 

So I think in the bigger picture, as we discussed in a nutshell, what I would really like the listeners to understand is as we try to move towards this field of precision medicine targeting more and more of the undruggable genes, there's bound to be a certain level of toxicity patterns that we'll start observing. So I think these real world scenarios which may not be addressed using clinical trials because it is in the real world setting where you cycle one treatment after another after another, which may or may not be allowed in most trials and the real world setting can inform, in certain cases, subsequent trial designs. So I think the most important message, at least that I took from your manuscript, was that these real world observations can make a huge difference and inform practice, even though the data sets may be small. Of course, you want to validate some of these findings in a bigger, broader setting, but proof of concept is there. And I think next time I see an individual in my clinic where I see better toxicity, I'll definitely try to talk to them about subsequent treatment with another KRAS inhibitor, maybe adagrasib or something else, if and when appropriate. 

Do you have any closing thoughts on some of these things that we discussed?

Dr. Hatim Husain: I just want to leave the audience actually with this concept that sometimes we group targeted therapy side effects as being class effects unanimously. And I do think actually that each inhibitor may have different off target effects on where medicine may act. We don't truly understand the mechanism of hepatotoxicity in the context of selective KRAS-G12C inhibitors. One of the hypotheses may be due to off target cysteine reactivity in the numerous off target binding sites that certain medicines may have over others. And just even qualitatively which off target binding sites there may be, and how that may lead to either immunogenic responses and other organs or such. So I do think that we do need more research to understand the mechanism. But I think where we are at right now in this space is not assuming that all medicines are going to have the exact same toxicity. I think especially when patients may not have other options, this is something to consider as well.

Dr. Rafeh Naqash: Thank you so much. Now, outside of the scientific insights, Hatim, I know you a little bit from before. And knowing the kind of work that you've done in precision medicine, I'm really interested to know about where you started, how you started, how things have been, and what kind of advice you have for junior faculty fellows who are interested in this field of precision medicine that is becoming more and more exciting as we progress in the oncology space.

Dr. Hatim Husain: Thank you, Rafeh. I will say, actually as a medical student, I was actually very interested in oncology, partly because it was then and still remains one disease or a constellation of diseases that just has such a high psychological burden on patients. And through the experiences I've had, I really can understand and relate with that concept. I did my medical school at Northwestern, residency at the University of Southern California, and then my oncology fellowship at Johns Hopkins University. 

And now I've been on faculty at University of California, San Diego, for about 12 years now. It's been a great experience paralleled with the fact that during these last 12 years, I've really seen how the developments in precision oncology, both targeted therapy as well as immune therapy, have really blossomed and unfolded. A large area of my research in my career has kind of focused on cancer genome and integration of novel technologies to really see how they may have clinical application. When I was in my fellowship and as a young faculty, the liquid biopsy was actually coming into development. And this was hinged upon information that had come forward in the prenatal space where some patients actually who were undergoing prenatal testing during pregnancy were found to have complex karyotypes and genomic alterations and then retrospectively found to have cancer. 

And doing my fellowship at Johns Hopkins, some of the pioneers in liquid biopsy were my mentors and really kind of instilled in me that passion for really thinking through how cancer genomics can be integrated through time. And some of the research that I have been doing has been looking at clonal evolution of cancer, how cancer is changing over time, and how we can think through the right surveillance strategies to really understand how that change is occurring. The dynamics of ctDNA in retrospective cohorts have been studied and shown that, you know, there can be associations between progression-free survival and other clinical endpoints. The current paper that we are speaking about parallels that in a certain way where, rather than say, looking at clonal evolution and say, the efficacy answer of sotorasib first and then adagrasib and how frequently can adagrasib salvage patients, this looks at it from a different angle around toxicity. And I think that is a key point because, at my core, I really do enjoy the clinical aspect of complex decision making on behalf of patients weighing efficacy and toxicity that they may have as they try to get the best quality of life through this journey.

Dr. Rafeh Naqash: Thank you again, Hatim, for all those insights, both from the scientific perspective as well as personal perspective. We appreciate that you chose JCOPO as the destination for your work. 

And thank you for listening to JCO Precision Oncology Conversations. Don't forget to give us a rating or review and be sure to subscribe so you never miss an episode. You can find all ASCO shows at asco.org/podcasts.

The purpose of this podcast is to educate and to inform. This is not a substitute for professional medical care and is not intended for use in the diagnosis or treatment of individual conditions. 

Guests on this podcast express their own opinions, experience, and conclusions. Guest statements on the podcast do not express the opinions of ASCO. The mention of any product, service, organization, activity, or therapy should not be construed as an ASCO endorsement.

Dr. Hatem Husain Disclosures

Consulting or Advisory Role: AstraZeneca, Foundation Medicine, Janssen, NeoGenomics Laboratories, Mirati

Speakers' Bureau: AstraZeneca, Janssen

Institution Research Funding: Pfizer, Bristol-Myers Squibb, Regeneron, Lilly

Travel, Accommodations, Expenses: AstraZeneca, Janssen, Foundation Medicine

In this JCO PO Article Insights episode, Harold Nathan Tan summarizes findings from the JCO PO article, “Circulating Tumor DNA as a Prognostic Biomarker for Recurrence in Patients With Locoregional Esophagogastric Cancers With a Pathologic Complete Response.”

TRANSCRIPT

Harold Nathan Tan: Welcome to JCO Precision Oncology Article Insights where we explore cutting-edge discoveries in the world of cancer treatment and research. I'm Harold Nathan Tan, your host for today's episode.

Let's dive into a fascinating study published in JCO Precision Oncology entitled, “Circulating Tumor DNA as a Prognostic Biomarker for Recurrence in Patients With Locoregional Esophagogastric Cancers With a Pathologic Complete Response.” This study led by Dr. Eric Michael Lander and colleagues examines a critical question: Can circulating tumor DNA help predict recurrence in patients with esophagogastric cancer who have achieved a favorable pathologic response after treatment? Esophagogastric cancer ranks as the seventh leading cause of cancer-related deaths worldwide. Despite aggressive treatment including neoadjuvant therapy followed by surgery, recurrence remains a grim reality for many patients. Interestingly, even those who achieve a pathologic complete response face a recurrence risk of up to 25%. This highlights a need for better tools to identify high-risk patients post-treatment. Circulating tumor DNA, or ctDNA for short, is emerging as a powerful biomarker in oncology. This minimally invasive blood-based test detects fragments of tumor DNA in the bloodstream, potentially signaling molecular residual disease before any radiographic evidence of recurrence appears.

In this study, researchers focused on patients with locoregional esophagogastric cancer who had undergone neoadjuvant therapy followed by surgery, achieving either a complete or near complete pathologic response. Blood samples were collected postoperatively within a 16-week molecular residual disease window and during routine surveillance. The aim is to determine whether ctDNA positivity correlates with recurrence-free survival. The study analyzed 309 plasma samples from 42 patients across 11 institutions. Detectable ctDNA within the 16-week postoperative window was associated with a significantly higher recurrence risk. Among those with detectable ctDNA, 67% experienced recurrence compared to only 15% for those with undetectable ctDNA. This corresponds to a hazard ratio of 6.2, an alarming figure that underscores the potential for ctDNA as a prognostic tool. But the story doesn't end there. Postoperative surveillance ctDNA testing more than 16 weeks after surgery also proved to be a powerful prognostic indicator. Every patient with detectable ctDNA during surveillance eventually experienced recurrence, while only 7.4% of those with undetectable ctDNA relapse. These findings suggest that ctDNA testing could provide a critical lead time, enabling earlier interventions and personalized treatment strategies.

Now let's talk about the clinical implications. Currently, patients who achieve a pathologic complete response often aren't considered for adjuvant therapies as the absence of visible disease is taken as a sign of remission. However, this study challenges that assumption. By integrating ctDNA testing into routine post-treatment surveillance, clinicians could identify high-risk patients who might benefit from additional therapy even when traditional imaging shows no signs of recurrence. This brings us to the bigger picture. Esophagogastric cancer treatment is evolving rapidly, with trials like CheckMate 577 and ESOPEC offering new insights into perioperative strategies. However, this study highlights a critical gap, the need for personalized, biomarker-driven approaches in the adjuvant setting. ctDNA could fill that gap, offering a non-invasive, dynamic way to monitor patients and guide clinical decisions.

Of course, no study is without its limitations. The authors acknowledge the relatively small sample size and the retrospective nature of their analysis. They also note the variability in ctDNA testing and imaging schedules across institutions. However, the robust association between ctDNA positivity and recurrence-free survival makes a compelling case for further research in larger prospective cohorts.

Looking ahead, what's the next step? The authors call for prospective validation of ctDNA as a prognostic tool, emphasizing its potential to refine risk stratification and optimize treatment strategies. Imagine a future where a simple blood test could dictate not only the need for additional therapies, but also the timing and type of intervention.

As we wrap up, let's reflect on the broader impact of the study. By integrating ctDNA into routine cancer care, we could move closer to a world where treatments are not just effective, but also precisely tailored to each patient's unique biology and disease dynamics.

Thank you for tuning into JCO Precision Oncology Article Insights. Don't forget to subscribe and join us next time as we explore more groundbreaking research shaping the future of oncology. Until then, stay informed and stay inspired.

The purpose of this podcast is to educate and to inform. This is not a substitute for professional medical care and is not intended for use in the diagnosis or treatment of individual conditions.

Guests on this podcast express their own opinions, experience, and conclusions. Guest statements on the podcast do not express the opinions of ASCO. The mention of any product, service, organization, activity, or therapy should not be construed as an ASCO endorsement.

JCO PO author Dr. David R. Gandara at UC Davis Comprehensive Cancer Center, shares insights into his JCO PO article, “Plasma Proteome–Based Test for First-Line Treatment Selection in Metastatic Non–Small Cell Lung Cancer,” one of the Top Articles of 2024. Host Dr. Rafeh Naqash and Dr. Gandara discuss how the PROphet® blood test supports first-line immunotherapy treatment decisions for metastatic NSCLC patients.

TRANSCRIPT 

Dr. Rafeh Naqash: Hello and welcome to JCO Precision Oncology Conversations where we bring you engaging conversations with authors of clinically relevant and highly significant JCOPO articles. I'm your host, Dr. Rafeh Naqash, Podcast Editor for JCO Precision Oncology and Assistant Professor at the OU Health Stephenson Cancer Center at the University of Oklahoma. 

Today, we are absolutely thrilled to be joined by Dr. David R. Gandara, Professor of Medicine Emeritus, Co-Director of the Center for Experimental Therapeutics and Cancer and Senior Advisor to the Director at UC Davis Comprehensive Cancer Center and also the senior author of the JCO Precision Oncology article entitled “Plasma Proteome–Based Test for First-Line Treatment Selection in Metastatic Non–Small Cell Lung Cancer.” This was one of the top performing articles of 2024, which is one of the reasons why we wanted to bring it in for a podcast discussion.

At the time of this recording, our guest’s disclosures will be linked in the transcript. 

David, it is an absolute pleasure to have you today. For somebody like you who's led the field of lung cancer over the years, I'm really excited that you are going to be talking to us about this very interesting article, especially given that I think you're one of the big proponents of liquid biopsies and plasma-based testing. So, for the sake of our listeners - which comprises of academic oncologists, community oncologists, trainees - could you tell us where the biomarker landscape for non-small cell lung cancer is currently, and then we can try to take a deeper dive into this article.

Dr. David Gandar: Okay. Well, thank you, Rafeh. It's a pleasure to be with you here today. And I think the current landscape for biomarkers for immunotherapy in non-small cell lung cancer is a mess. There's no better way to describe it. That makes this paper describing a new plasma proteomic assay even more important. So I'll just give you a perspective. There are 14 trials, phase three trials, that were done in first line non-small cell lung cancer advanced stage of immunotherapy versus chemotherapy and some other aspects, although they vary tremendously. Some of them were checkpoint monotherapy, some combined with chemotherapy, some combined with CTLA-4 inhibitors and so forth. 12 out of the 14 were positive, 12 got FDA approval. So there are 12 different options that an oncologist could use. Some of them were squamous cell only, some non-squamous, some used PD-L1 as a biomarker driven part of the study. Some used TMB, tumor mutational burden, some were agnostic. So when you put all of this together, an oncologist can pick and choose among all these various regimens. And by and large, it's PD-L1 that is the therapeutic decision maker. 

ASCO actually, I think, has done the very best job of making a guideline, and it's, as you well know, called a living guideline, it's dynamic. And it is much easier to interpret, for me and I think for oncologists, than some of the other guidelines. It's got a green light and a red light, it may be kind of orange. And so the green light means this is a strong recommendation by the guideline committee. The orange means it's weak. For this purpose, non-small cell lung cancer, advanced stage, only a very few of the recommendations were green. It's mainly monotherapy and patients with cancers with a PD-L1 over 50%. In our surveys, at our meetings, less than 50% of oncologists in the United States are following these guidelines. Why? Because they don't trust the biomarker. And TMB has the same sort of limitations. They're not bad biomarkers, they're incomplete. They're only looking at a part of the story. So that means we need a new biomarker. And this is one that, I think, the data are quite impressive and we'll discuss it more.

Dr. Rafeh Naqash: Absolutely. Like you said, abundance of many therapy options, but not necessarily everything works the same in different subsets of PD-L1 positivity or different subsets of patients with different levels of tumor burden. And like you said, again, difficulty in trying to identify the right biomarker. And that's a nice segue to this PROphet test that you guys ran. So can you tell us a little bit about the plasma proteomic assay? Because to the best of my knowledge, there's not a lot of validated plasma proteomic assays. A lot has been done on the tumor tissue side as far as biomarkers are concerned, but not much on the blood side, except for maybe ctDNA MRD testing. So what was the background for trying to develop a plasma-based proteomic test? And then how did this idea of testing it in the lung cancer setting come into play? And then we can go into the patient population specifics, the cohort that you guys have.

Dr. David Gandara: Okay. Well, of course there's a company behind this assay, it's called OncoHost, and I'm a consultant for them. And they came to me two years ago and they said, “We have something different from anyone else.” And they explained the science to me, as well as some other lung cancer experts here in the United States. I'm not a proteomic expert, of course, but they developed an AI machine learning platform to assess plasma proteins in normal people and in people with cancer, and specifically then in people with non-small cell lung cancer. They identified over 7,000 proteins that had cancer implications for therapy, for resistance, for prognosis, etc., and they categorized them based on the literature, TCGA data, etc., and used this machine learning process to figure out which proteins might be most specific for non-small cell lung cancer. And that's where they started. And so out of that 7,000 proteins, where they've identified which ones are angiogenic, which ones are involved with EMT or cell cycle or whatever it might be, they distilled it down to 388 proteins which they thought were worth testing in non-small cell lung cancer. And that's when I became involved. 

They had a retrospective cohort of patients that had been treated with various immunotherapies. They looked at the analytic validation first, then applied it to this cohort. It looked good. Then they had a very large cohort, which they split, as you usually do with an assay, into a test set and then a validation set. For the test set, they wanted something more than a response. They wanted some indicator of long term benefit because that's where immunotherapy differentiates itself from chemotherapy and even targeted therapy. And so they picked PFS at 12 months. And I became involved at that point and it looked really good. I mean, if you look at the figures in the manuscript, the AUC is superb about their prediction and then what actually happened in the patient. And then in this paper, we applied it to a validation set of over 500 patients in a prospective trial, not randomized, it's called an observational trial. The investigator got to pick what they thought was the best therapy for that patient. And then in a blinded fashion, the proteomic assay experts did the analysis and applied it to the group. 

And so what that means is some of the patients got chemotherapy alone, some got checkpoint immunotherapy monotherapy, some got in combination with chemotherapy. None of the patients in this study got a CTLA-4 inhibitor. That work is ongoing now. But what the study showed was that this assay can be used together with PD-L1 as what I would call a composite biomarker. You take the two together and it informs the oncologist about the meaning of that PD-L1. I'll give you an example. If that patient has a PD-L1 over 50% in their cancer and yet the PROphet test is negative, meaning less than 5 - it's a 0 to 10 scale - that patient for survival is better served by getting chemotherapy and immunotherapy. However, if the PROphet test is positive and the PD-L1 is over 50%, then the survival curves really look equivalent. As I said earlier, even in that group of patients, a lot of oncologists are reluctant to give them monotherapy. So if you have a test and the same sort of example is true for PD-L1 0, that you can differentiate. So this can really help inform the oncologist about what direction to go. And of course then you use your clinical judgment, you look at what you think of as the aggressiveness of the tumor or their liver metastases, etc. So again, that's how this test is being used for non-small cell lung cancer. And maybe I'll stop there and then I'll come back and add some other points.

Dr. Rafeh Naqash: I definitely like your analogy of this therapy de-escalation strategy. Like you mentioned for PD-L1 high where the PROphet test is negative, then perhaps you could just go with immunotherapy alone. In fact, interestingly enough, I was invited to a talk at SITC a couple of weeks back and this exact figure that you're referring to was one of the figures in my slide deck. And it happened by chance that I realized that we were doing a podcast on the same paper today. 

So I guess from a provocative question standpoint, when you look at the PD-L1 high cohort in the subset where you didn't see a survival difference for chemo plus immunotherapy versus immunotherapy alone, do you think any element of that could have been influenced by the degree of PD-L1 positivity above 50%? Meaning could there have been a cohort that is, let's say PD-L1 75 and above, and that kind of skews the data because I know you've published on this yourself also where the higher the PD-L1 above 50%, like 90% PD-L1 positivity survival curves are much better than 50% to 89%. So could that have somehow played a role?

Dr. David Gandara: The first thing to say is that PD-L1 and the PROphet score, there's very little overlap. I know that sounds surprising, but it's also true for tumor mutational burden. There's very little overlap. They're measuring different things. The PD-L1 is measuring a specific regulatory protein that is applicable to some patients, but not all. That's why even in almost all of the studies, people with PD-L1 0 could still have some survival benefit. But in this case they're independent. And not in this paper, but in other work done by this group, the PROphet group, they've shown that the PROphet score does not seem to correlate with super high PD-L1. So it's not like the cemiplimab data where if you have a PD-L1 of greater than 90%, then of course the patient does spectacularly with monotherapy. The other thing that's important here is they had a group of around a little less than 100 patients that got chemotherapy alone. The PROphet score is agnostic to chemotherapy. And so that means that you're not just looking at some prognostic factor. It's actually clinical utility on a predictive basis.

Dr. Rafeh Naqash: I think those are very important points. I was on a podcast a couple of days back. I think there's a theme these days we're trying to do for JCO Precision Oncology, we're trying to do a few biomarker based podcasts, and the most recent one that we did was using a tissue transcriptome with ctDNA MRD and you mentioned the composite of the PD-L1 and the PROphet test and they use a composite of the tissue transcriptome. I believe they called it the VIGex test as well as MRD ctDNA. And when your ctDNA was negative at, I believe, the three month mark, those individuals had the highest inflamed VIGex test or highest infiltration of T cells, STING pathway, etc. So are there any thoughts of trying to add or correlate tissue based biomarkers or ctDNA based correlations as a further validation in this research with the company?

Dr. David Gandara: Right. So there are many things that are being looked at, various composites looking at the commutations that might affect the efficacy of immunotherapy and how they correlate with profit positivity or negativity. And I'll just give the examples of STK11 and KEAP1. As you know, there's some controversy about whether these are for immunotherapy, whether they're more prognostic or predictive. I'm one of the co-authors among many in the recently published Nature paper by Dr. Skoulidis and the group at MD Anderson which report that for KEAP1 positive especially, but also SDK11 mutated getting immunotherapy, that that's where the CTLA-4 inhibitors actually play the greatest role. So realizing that this is still controversial, there are preliminary data, not published yet, that'll be presented at an upcoming meeting, looking at many of these other aspects, P53, SCK11, KEAP1, other aspects, TMB, that's actually already published, I think in one of their papers. So yes, there's lots of opportunities. 

The other cool thing is that this isn't a test, it's a platform. And so that means that the OncoHost scientists have already said, “What if we look at this test, the assay in a group of patients with small cell lung cancer?” And so I just presented this as a poster at the world conference in San Diego. And it turns out if you look at the biology of small cell, where neither PD-L1 nor TMB seem to be very important, if you look at the biology of small cell and you form an assay, it only shares 44 proteins out of the 388 with non-small cell. It's a different biology. And when we applied that to a group of patients with small cell lung cancer, again it had really pretty impressive results, although still a fairly small number of patients. So we have a big phase three study that we're doing with a pharmaceutical company developing immunotherapy where we are prospectively placing the PROphet test in a small cell trial. 

The platform can also be altered for other cancer types. And at AACR, Dr. Jarushka Naidoo presented really impressive data that you can modify the proteins and you can predict immunotherapy side effects. So this is not like a company that says, “We have one test that's great for everything.” You know how some companies say, “Our test, you can use it for everything.” This company is saying we can alter the protein structures using AI machine learning assisted process to do it and we can have a very informed assay in different tumor types and different situations. So to me, it's really exciting.

Dr. Rafeh Naqash: Definitely to me, I think, combining the AI machine learning aspect with the possibility of finding or trying to find a composite biomarker using less invasive approaches such as plasma or blood, definitely checks a lot of boxes. And as you mentioned, trying to get it to prospective trials as an integral biomarker perhaps would be likely the next step. And hopefully we see some interesting, exciting results where we can try to match or stratify patients into optimal combination therapies based on this test. 

So now to the next aspect of this discussion, David, which I'm really excited about. You've been a leader and a mentor to many. You've led ISLC and several other corporate group organizations, et cetera. Can you tell us, for the sake of all the listeners, junior investigators, trainees, what being a mentor has meant for you? How your career has started many years back and how it's evolved? And what are some of the things that you want to tell people for a successful and a more exciting career as you've led over the years?

Dr. David Gandara: Well, thank you for the question. Mentoring is a very important part of my own career. I didn't have an institutional mentor when I was a junior investigator, but I had a lot of senior collaborators, very famous people that kind of took me under their wing and guided me. And I thought when I basically establish myself, I want to give back by being a mentor to other people. And you wouldn't believe the number of people that I'm even mentoring today. And some of them are not medical oncologists, they're surgeons, they're radiation oncologists, they're basic scientists. Because you don't have to be an expert in that person's field to be a mentor. It helps, but in other words, you can guide somebody in what are the decision making processes in your career. When is it time to move from this institution onward because you can't grow in the institution you're in, either because it's too big or it's too small? So I established a leadership academy in the Southwest Oncology Group, SWOG. I've led many mentoring courses, for instance, for ISLC, now for International Society Liquid Biopsy, where I'm the executive committee liaison for what's called The Young Committee. So ISLB Society, totally devoted to liquid biopsy, six years old now, we have a Young Committee that has a budget. They develop projects, they publish articles on their own, they do podcasts. So what I'm saying is those are all things that I think opens up opportunities. They're not waiting behind senior people, they are leading themselves. 

We just, at our International Lung Cancer Congress, reestablished a fellows program where a group of fellows are invited to that Huntington beach meeting. It's now in its 25th year and we spend a day and a half with them, mentoring them on career building. I'll just give you my first, I have the “Letterman Top 10”. So my first recommendation is if all you have is lemons, make lemonade. And what I'm meaning is find what you can do at your institution if you're a junior person, what you can claim to be your own and make the very best of it. But then as you get further along in my recommendations, one of them is learn when to say ‘no’. Because as a junior investigator the biggest threat to your career is saying ‘yes’ to everybody and then you become overwhelmed and you can't concentrate. So I'll stop there. But anyway, yes, mentoring is a big part of my life.

Dr. Rafeh Naqash: Well, thank you, David. This is definitely something that I'm going to try to apply to my career as well. And this has been an absolute pleasure, especially with all the insights that you provided, not just on the scientific side but also on the personal career side and the mentorship side. And hopefully we'll see more of this work that you and other investigators have led and collaborated on. perhaps more interesting plasma based biomarkers. And hopefully some of that work will find its home in JCO Precision Oncology. Thank you again for joining us today.

Dr. David Gandara: My pleasure.

Dr. Rafeh Naqash: And thank you for listening to JCO Precision Oncology Conversations. Don't forget to give us a rating or review and be sure to subscribe so you never miss an episode. You can find all ASCO shows at asco.org/podcasts.

The purpose of this podcast is to educate and to inform. This is not a substitute for professional medical care and is not intended for use in the diagnosis or treatment of individual conditions. 

Guests on this podcast express their own opinions, experience and conclusions. Guest statements on the podcast do not express the opinions of ASCO. The mention of any product, service organization, activity or therapy should not be construed as an ASCO endorsement.

Dr. David Gandara Disclosures:

Consulting or Advisory Role Company: Henlius USA, Foundation Medicine, Janssen Pharma, Merck & Co, Mirati Therapeutics, Regeneron, AstraZeneca, Guardant Health, Genentech, Exact Sciences 

Research Funding Company: Amgen, Genentech, Astex Pharma

JCO PO authors Dr. Philippe Bedard (Staff Medical Oncologist at Princess Margaret Cancer Centre and Professor of Medicine at University of Toronto) and Dr. Alberto Hernando Calvo (Medical Oncologist at Vall d´Hebron University Hospital) share insights into their JCO PO article, “Combined Transcriptome and Circulating Tumor DNA Longitudinal Biomarker Analysis Associates With Clinical Outcomes in Advanced Solid Tumors Treated With Pembrolizumab,” one of the top downloaded articles of 2024. Host Dr. Rafeh Naqash and Drs. Bedard and Hernando Calvo discuss how combined transcriptome and ctDNA longitudinal analysis associates with pembrolizumab outcomes.

TRANSCRIPT

Dr. Rafeh Naqash: Hello and welcome to JCO Precision Oncology Conversations where we bring you engaging conversations with authors of clinically relevant and highly significant JCO PO articles. I'm your host, Dr. Rafeh Naqash, podcast editor for JCO Precision Oncology and Assistant Professor at the OU Health Stephenson Cancer Center at the University of Oklahoma. 

Today we are excited to be joined by Dr. Philippe Bedard, Staff Medical Oncologist at the Princess Margaret Cancer Center and Professor of Medicine at the University of Toronto, as well as by Dr. Alberto Hernando-Calvo, Medical Oncologist at the Vall d'Hebron University Hospital, both authors of the JCO Precision Oncology article titled, “Combined Transcriptome and Circulating Tumor DNA Longitudinal Biomarker Analysis Associates With Clinical Outcomes in Advanced Solid Tumors Treated With Pembrolizumab.” 

Thank you for joining us today. Phil and Alberto.

Dr. Alberto Hernando-Calvo: Thank you.

Dr. Philippe Bedard: Great to be with you. Thanks for having us. 

Dr. Rafeh Naqash: One of the reasons we do this podcast, as some of the listeners who listen to this podcast regularly may know, is to bring in novel approaches and try to understand how the field is moving towards a space where we are understanding biomarkers better. So your manuscript that was published in JCO Precision Oncology fulfills many of those criteria. And interestingly enough, I was at a conference at the Society for Immunotherapy of Cancer last month earlier in November and a lot of excitement at SITC was revolving around novel transcriptomic biomarkers, proteomic biomarkers or imaging based biomarkers. So could you tell us a little bit about why you started looking at biomarkers? This is an extremely competitive field. Why did you think that looking at the transcriptome is somewhat different from or more interesting from tumor mutational burden PDL-1 than other biomarkers that we currently use? And that question is for you Alberto to start off. 

Dr. Alberto Hernando-Calvo: So I think gene expression profiles may have a predictive performance as compared to already existing biomarkers and this was one of the points that we describe in our manuscript. The gene expression signature that we developed back in 2019 at Vall d'Hebron Institute of Oncology was initially developed based on over 45 different tumor types and tested in over 1000 patients treated with antiPD-1 and anti PDL-1. And back then and in this manuscript, we proved that for instance the gene expression signature VIGex that we developed has a potential complementary role to other predictive biomarkers. In this case, we observe this predictive power with ctDNA dynamics and we then see a correlation with other existing biomarkers such as tumor mutational burden. So I don't think we need to use one or the other, but rather they may have additive predictive power. So we need to better individualize predictive biomarkers based on tumor types and select the best combination possible to improve the performance. 

Dr. Rafeh Naqash: I completely agree that one size does not fit all, especially in the landscape of immunotherapy. From your perspective, when you developed the original signature, how did you choose what genes to look at? I looked at the manuscript, on the methodology side, some of the signatures are pro-inflammatory STING interferon gamma based, so how did you try to identify that these are the 7 to 10 or whatever number of signatures on the transcriptome side? And then why did you try to combine it with ctDNA based changes? 

Dr. Alberto Hernando-Calvo: Back in our initial manuscript, published in Med from Cell Press, we developed the VIGex gene expression signature, as I mentioned, with taking into consideration over 1000 tumor samples from FFPE that we can consider real world samples because they are from real patients coming from the clinic notes as part of real investigational protocol doing or performing biopsies on patients. We did observe after doing a VIGex research and doing different tests, we eventually collected these 12 different genes. Because there is a combination of both genes involved in the interferon gamma pathway, we have genes associated with Tregs as well as T cell memory cells. So it’s not only looking at genes that are associated with T cell activation or CD8+ T cell infiltration, but also looking at genes that may be overactivated, overexpressed, an immunosuppressive tumor microenvironment. So it was both selecting genes, the minimum number of genes to do it more scalable and having the minimum dataset of genes and including in the signature genes that are already at targets for immune sequent inhibitors or are being tested in immunotherapy combinations. 

Dr. Rafeh Naqash: Thank you. And Phil, for the sake of our listeners, could you elaborate upon this aspect of using ctDNA? So this was tumor-informed ctDNA from what I understood in the manuscript. You guys basically try to use it to understand changes in the ctDNA with treatment and then try to combine it with the transcriptome signature. How did the idea come up initially and how did you plan on combining this with an RNA-based signature? Because I have seen manuscripts and other data where people are either using one or the other, but not necessarily both together. So how did you guys come up with that idea?

Dr. Philippe Bedard: Well, we thought that this was a great opportunity to look at the combination of the transcriptome as well as the ctDNA dynamics because we had run an investigator-initiated phase 2 clinical trial called INSPIRE at our institution at Princess Margaret from 2016 to 2018, where patients across five different tumor groups received single agent pembrolizumab. And we really did a deep dive on these patients where there were tumor biopsies before and while on treatment. We did exome sequencing, we did RNA sequencing to capture the transcriptome. And in a prior analysis, we had partnered with Natera to look at their Signatera assay, which is a bespoke ctDNA assay, to look at ctDNA dynamics using this test and the association with response outcomes as well as survival outcomes. So we thought that this was a really unique data set to try and address the question of whether or not there was complementarity in terms of looking at the transcriptome and transcriptome signatures of IO benefit together with the ctDNA dynamics.

Dr. Rafeh Naqash: From a patient treatment standpoint, it sounded like you mostly tried to include individuals who were treated with pembrolizumab. Did this not include individuals who were treated with chemoimmunotherapy or chemotherapy with pembrolizumab? Just pembrolizumab alone? And if that's the case, some of the tumor types there included, from what I remember, ovarian cancer and some other unusual cancers that don't necessarily have approvals for single agent pembrolizumab, but perhaps in the TMB-high setting. So can you elaborate on the patient selection there for the study? 

Dr. Philippe Bedard: Yeah, that's a great question. So at the time that the study was designed in 2015, this was really the early days of immune checkpoint inhibitor therapy, so we didn't have the approvals that we have now in specific tumor types for immunotherapy and chemotherapy combinations. So when the study was designed as an investigator initiated clinical trial, the idea was really to capture patients across different tumor types - so head and neck squamous cell carcinoma, malignant melanoma, ovarian cancer, triple negative breast cancer, and a kind of mixed histology solid tumor cohort, where we knew that there were some patients who were going to be immunotherapy responsive, where there was already approvals or evidence of single agent activity, and others where the responses were more anecdotal, to try and understand in a phase 2 clinical trial with kind of a deep dive, which patients benefited from treatment and which didn't.

Dr. Rafeh Naqash: Interesting approach. Going to the results, Alberto, could you help us understand some of the important findings from these data? Because there's different sections of how you tried to look at the response rates, the survival, looking at the immune deconvolution, if you could explain that.

Dr. Alberto Hernando-Calvo: So the first thing that we tried was to further confirm the external validation of this immune gene expression signature, VIGex in the INSPIRE asset. So what we observed at VIGex-Hot, the category defined by VIGex-Hot tumor microenvironment, was associated with better progression free survival. After including that in a multivariable analysis adjusted by other biomarkers such as TMB, PDL-1 or tumor type, this was also confirmed for overall survival. So then the next step was to really try to hypothesize if the addition of ctDNA dynamics, taking into consideration the ctDNA quantification at baseline as compared to cycle three, if those dynamics could further improve the predictive performance of VIGex categories taken in the baseline samples. What we did observe was that, for instance, VIGex-Hot tumors in baseline tumor samples that were having a ctDNA decrease, as I mentioned before on cycle three assessment as compared to baseline, were having both better progression free survival and better prognosis overall. Another important finding was the evaluation of response rate across tumor types considering both biomarkers. I would say the most important finding is that when we were considering a cold tumor microenvironment in baseline samples before pembrolizumab initiation plus an increase in ctDNA values, what we observed is that those patients were having a 0% response rate. So this may help as a future strategy either for intensification of immunotherapy regimens in a more individualized way or for an early stop to immunotherapy and try to avoid financial toxicities as well as toxicities for our patients.

Dr. Rafeh Naqash: From the data that you showed, it seems that there was a strong correlation, as you sort of mentioned, between individuals that had ctDNA clearance and baseline immune pro-inflammatory signatures. So do you really need the transcriptome signature or could the ctDNA just serve as an easy quick surrogate? Because from a cost standpoint, doing whole transcriptome sequencing or more RNA sequencing or tissue standpoint, where tissue is often limited, can become a big issue. So do you think that validation of this may perhaps more revolve around using ctDNA as an easier metric or surrogate? Or am I overestimating the utility of ctDNA?

Dr. Philippe Bedard: I think it's a really good question. In our data set which was relatively small, there were 10 patients who had ctDNA clearance, meaning ctDNA that was positive at baseline was not detected. And so 9 out of those 10 patients, as you alluded to, were VIGex-Hot. So the question is a good one, could you do the same with just ctDNA clearance alone, particularly in identifying these patients who really do well, who have long term disease control on immunotherapy? I think it's a tough question to answer because the field is also changing in terms of sensitivity of detection of ctDNA tests. So we know now that there are newer generations of tests which can detect even at logs down in terms of allele variants in the circulation. So I think we need more data to address the question. I think it is important as to what is the best test, what is the endpoint that we should be using from a drug development point of view in terms of really trying to push and understand which treatment regimens are the most effective and have early readouts in terms of activity. Because we all recognize in the clinic that radiographic response doesn't tell the whole story, especially early radiographic assessments using RECIST or other criteria that we apply in clinical trials.

Dr. Rafeh Naqash: From a clinical trial standpoint, we often talk about validation of these studies. You may have heard of other tests where, for example, the NCI iMatch, which is incorporating transcriptome sequencing based approach to stratify patients as an integral biomarker for treatment stratification. Is that something that you guys are thinking of using, this approach where individuals who are signature highly inflamed perhaps get lesser therapies or there's a de-intensification of some sort similar to what people are trying to do with ctDNA-based approaches?

Dr. Philippe Bedard: I think that's a great question. I think it makes a lot of sense. And certainly, with the new wave antibody drug conjugates in terms of identifying patients who have expression of targets for antibody drug conjugates, that's very attractive as an approach because we don't necessarily have IHC markers for all of the different targets of antibody drug conjugates. We don't necessarily have IHC markers to completely understand different contributions to the tumor microenvironment and whether or not tumors are inflamed. But it's also a challenging approach too because RNA-seq currently is not a routine clinical test. Sometimes there are issues, particularly in patients who have stored specimens that are formalin-fixed and paraffin-embedded in terms of the quality of the RNA for RNA sequencing. And it's not always feasible to get pre-treatment biopsies and turn them around in an approach. So I think it is an attractive approach for clinical trials, but it's a hypothesis that needs to be tested. It's not something that is ready for clinical prime time today in 2024.

Dr. Rafeh Naqash: One of the other interesting observations that I came across in your manuscript was that tumor mutational burden, interestingly, did not correlate with signature high tumors. What is the explanation for that? Because generally you would expect a TMB high to perhaps also have an immune gene high signature. Could it have something to do with the tumor types because there was a heterogeneous mixture of tumor type? Or I'm not sure. What else could you possibly think of that you didn't see those correlations or just sample size limitations?

Dr. Alberto Hernando-Calvo: Yes. So our findings are consistent with prior data suggesting for instance T cell inflamed gene expression profile was also not correlated with tumor mutational burden and both biomarkers in a prior publication. So to have additive predictive performance for identifying patients most likely to benefit from anti PD-1 regimen, so we somehow were expecting this observation, the fact that both biomarkers are not very correlated.

Dr. Rafeh Naqash: So given the proof of concept findings from your study, Phil, what is the next interesting step that you guys are thinking of to expand this? Would you think that a nivolumab-ipilimumab treated cohort would have similar findings? Or is this a treatment specific single agent immunotherapy specific correlation that you found versus something else that you may find in a nivo-ipi cohort or a doublet immune checkpoint cohort? 

Dr. Philippe Bedard: The findings are really hypothesis generating. They require additional validation. And you're quite right, there may be nuances in terms of specific tumor types, combinations with other immunotherapy or combinations with chemotherapy or other agents. So I think it would be great if there are other data sets that are collecting this type of information that have ctDNA dynamics and also have transcriptome and potentially exome or genome analysis to look at these types of questions because the field is moving quickly and we really need more data sets in order to understand some of the nuances and greater numbers to validate the signals that we see.

Dr. Rafeh Naqash: And one thing, as you said, the field is definitely moving very quickly. I was meeting with a company an hour back and they have an imaging-based approach using fresh tissue to look at pharmacodynamic biomarkers. And I used to work in the NCI with a group that was very interested and they developed an immuno-oncology pharmacodynamic panel that has been used and published in a few clinical trials where they did phosphorylation status. So the final theme that comes out of most of these research based studies that are being done is that one size does not fit all. But the question that comes to my mind is how many things do you necessarily need to combine to get to a predictive biomarker that is useful, that is patient centric, and that perhaps is able to identify the right therapy for the right patient. What is your take on that, Phil? 

Dr. Philippe Bedard: Yeah, that's a great question too. The challenge is it depends on the context in terms of what degree of positive predictive value do you need as well as the negative predictive value to drive clinical decisions. So I think in certain situations where you don't have other approved treatment options and with a therapy that is potentially low toxicity and low financial toxicity, then I think the bar is very high in terms of being able to really confidently identify that patients aren't going to benefit. I think the nuance and the challenge becomes when you move into earlier lines of therapy, or when you talk about combinations of agents, or trying to understand within the context of other available options, particularly with treatments that have significant side effect profiles as well as financial risks, then it becomes a much more nuanced question and you really need comparative studies to understand how it fits versus the existing treatment paradigm. So I'm not really answering your question with a specific number because I think it's hard to give you a number. Some of that we also need input from patients in terms of what kind of level of validation do you need and what kind of level of discrimination do you need in order to drive decisions that are meaningful for them.

Dr. Rafeh Naqash: Definitely early days, as you pointed out. More and more work in this field will hopefully lead us in the direction that we all want to go in. 

Now, going to a different aspect of this podcast, which is trying to understand the trajectories for both of you, Phil and Alberto. And as you mentioned, this project seemed to have started in 2015. So I'm guessing there's a history there between Princess Margaret and Vall d'Hebron. Could you highlight that a little bit? And then perhaps, Alberto, after that you could tell us a little bit about your career when you worked at Princess Margaret as a fellow and then now back at Vall d'Hebron. Phil, you as well.

Dr. Philippe Bedard: So absolutely. We have a long history of collaborating with Vall d'Hebron in Barcelona. It's really a great cancer institution with a lot of like minded individuals. We have a formal partnership and we have a lot of informal links in terms of scientists and clinicians who we work with and who we collaborate with on early phase clinical trials, as well as through different investigator networks and other translational projects. So this was really how this collaboration came about and we were fortunate to have Alberto, who came to work with us for two years and brought this great idea of looking at this signature they had developed at Vall d'Hebron in their phase one group and applying it to a data set that we had through the INSPIRE clinical trial. 

Dr. Rafeh Naqash: Sounds like a very successful academia-academic collaboration, which is very nice to see. So, Alberto, could you tell us a little bit about your career trajectory and how you ended up at Princess Margaret and then back at Vall d'Hebron and what you do currently?

Dr. Alberto Hernando-Calvo: Yes. So I did my oncology residency at Vall d'Hebron in Barcelona, Spain. Then I decided to further specialize in early drug development as well as head and neck cancer oncology. So I decided to pursue a clinical research fellowship under the supervision of Phil Bedard, among others. And so we decided to further validate the signature that we had developed both in the cancer genomic lab at Vall d'Hebron Institute of Oncology and the phase one unit at Vall d'Hebron, and apply the signature that have been originally tested in patients receiving anti PD-1 or anti PDL-1 combinations in early phase clinical trials. In the phase 2 clinical trial of INSPIRE, where we also had ctDNA dynamics and allowed us to test both biomarkers and see that additive predictive power when we were using both. That was one of my research topics under the mentorship of Dr. Bedard and my fellowship at Princess Margaret. And this was one of the manuscripts describing all the findings of this collaboration between Vall d'Hebron and Princess Margaret Cancer Center.

Dr. Rafeh Naqash: And then, Phil, if you could highlight some of the things that you've done over the course of your career and perhaps some advice for early career junior investigators and trainees. 

Dr. Philippe Bedard: I finished my oncology, medical oncology training at the University of Toronto in 2008. And then I did a breast cancer fellowship in Brussels at Breast International Group. At the time, I was really intrigued because it was really kind of the early days of microarray and RNA signatures in terms of expressing signatures were being used as part of a clinical trial that BIG was running called the MINDACT Study. And so when I finished my fellowship, I came back to Princess Margaret, started on staff. I've been here now for 15 years. I was fortunate to work with the phase 1 group and kind of my career has sort of morphed in terms of early drug development as well as genomics. I've been involved with the American Association for Cancer Research project GENIE, where I'm the current chair. This is really an international data sharing project with panel based sequencing, which both Princess Margaret and Vall d'Hebron have contributed to. And I’ve been fortunate to work with a number of really talented early career investigators like Alberto, who spend time with us in our drug development program and launched transitional research projects that leverage some existing data sets at their own institutions and also bring together with different research groups at our institution to lead to publications like this one.

Dr. Rafeh Naqash: Thank you so much. This was very exciting. Phil and Albert, thanks for joining us today and thank you for allowing us to discuss your interesting manuscript and hopefully we'll see more of this biomarker work from you guys in the near future, perhaps published in JCO Precision Oncology.  

And thank you for listening to JCO Precision Oncology Conversations. Don't forget to give us a rating or review and be sure to subscribe so you never miss an episode. You can find all ASCO shows at asco.org/podcasts.

The purpose of this podcast is to educate and to inform. This is not a substitute for professional medical care and is not intended for use in the diagnosis or treatment of individual conditions. 

Guests on this podcast express their own opinions, experience and conclusions. Guest statements on the podcast do not express the opinions of ASCO. The mention of any product, service, organization, activity, or therapy should not be construed as an ASCO endorsement.