Krishnan Patel: Thanks. Pleasure to be here.
Zachary Klaassen: So before we get into the study design and what you guys looked at, why is there a need to risk stratify these patients for treatment intensification versus not?
Krishnan Patel: I think most oncologists who are seeing these patients in their clinic, probably one of the first things they do is they pull up the NCCN guidelines and they try to figure out, is this patient low risk, intermediate risk, and high risk? And roughly speaking, the way the literature has borne out is that we deliver treatment intensity proportional to the risk. That has historically been based on clinical factors alone, PSA, Gleason score, and T-stage historically by digital rectal exam, but now increasingly more often MRI. But we have all these new tools available, these next-generation biomarkers, and they can help us actually discriminate risk a little bit better. But it's oftentimes a little bit unclear how to use those tools in modern practice.
Zachary Klaassen: Sure. That's a great background. You guys took a pool of data from previous trials, and this is patients basically figuring out who we need to get abiraterone intensification to. So maybe just set up the study design. I know we're going to take a slide that works through your algorithm, so just walk us through some of that.
Krishnan Patel: Yeah. So this study really began with a clinical need. In our clinics, every day, we're seeing patients with these clinical risk factors, as well as these next-generation biomarkers, like Decipher. And so, we just really wanted to understand, hey, how can we use this to understand who we should give abiraterone to?
Zachary Klaassen: Sure.
Krishnan Patel: And so, what we did was we took a combined cohort of patients from RTOG, NRG RTOG 9202, 9413, 9902, and 0521, and we profiled their genomic classifier score, and that has been previously published. And we tried to use these scores, in addition to their clinical data and their outcomes, to help us understand which of these patients had a similar prognosis to those who were in STAMPEDE, the control arm of STAMPEDE. And that helped us get an understanding for who might be a good candidate for abiraterone using these new tools.
Zachary Klaassen: Right. That's great. And so, maybe just as part of that, you give a couple of analyses you guys looked at, so maybe walk us through that and what the key findings were.
Krishnan Patel: Yeah. So we broke the study into three separate parts and we looked at them sequentially. The first part was that we wanted to understand if Decipher could improve the prognosis over clinical risk variables alone, and this is widely known, but we wanted to just verify that in this pooled cohort, and we did find that on the endpoints of MFS, OS, and cumulative incidence of distant metastasis. The second part was that we wanted to understand, does this incongruence, clinical risk stratification and biomarker risk stratification, happen often or is it a rare event? Because let's say it only happened 1% of the time, then it's probably something that we don't really need to study.
Zachary Klaassen: Sure.
Krishnan Patel: So we found that it happened about 25% of the time, in about a quarter of the patients, and we felt that that was not only congruent with our clinical experience.
Zachary Klaassen: Yeah.
Krishnan Patel: But we also felt that that's meaningful to investigate further. So then, the third part of the study, as you alluded to, we're going to show for the audience a slide.
Zachary Klaassen: Yeah.
Krishnan Patel: The third part of the study was to help us develop... We tried to make a tree-based model to develop a simple algorithm that clinicians could print out and put in their cubicle or in their office to just quickly figure out, does this patient have a prognosis which would maybe merit abiraterone intensification?
Zachary Klaassen: Awesome. That's a great summary. I think you guys have done this, radiation oncologists, a great job of not only doing these trials over the past couple of decades, but taking this trial data, using things like Decipher to help us risk stratify these patients. Where do you see this going forward, and how do we take this to the clinic in terms of counseling patients about abiraterone intensification?
Krishnan Patel: That's a really great question. I think that currently, at the current time, it's a little bit like the Wild West out there.
Zachary Klaassen: Yeah.
Krishnan Patel: As I talk to a lot of physicians, some people stick only to clinical risk stratification, some people only use genomic classifiers.
Zachary Klaassen: Right.
Krishnan Patel: Some people only use other next-generation biomarkers. And I think this study can help us integrate at least the clinical and genomic biomarker data at the current time and it can be employed in clinical practice today.
Zachary Klaassen: Yeah.
Krishnan Patel: As you know, NRG-GU009, which is a trial which has finished accrual, but is currently awaiting maturation, will help us better understand the role for the Decipher biomarker in clinical practice. So until the results of GU009 are known, I think this can stand in for just the routine clinician.
Zachary Klaassen: Yeah. That's well said. Krishnan, congratulations on the great work on the oral presentation at ASCO 2026, and thanks for joining us on UroToday.
Krishnan Patel: Thank you so much for having me.