Zachary Klaassen: Hi, my name is Zach Klaassen, Urologic Oncologist at the Georgia Cancer Center. And I'm delighted to be joined on UroToday for an ASCO 2025 discussion with Dr. Deepak Kilari, who is at the Medical College of Wisconsin medical oncologist.

Deepak is going to be discussing the efficacy of subsequent treatment after combination therapy in non-clear cell renal cell carcinoma – the ORACLE study. Deepak, thank you so much for the kindness of your time and always joining us on UroToday. Excited to discuss the ORACLE study with you today.

Deepak Kilari: Zach, I really appreciate you guys having me on today, and I'm really excited to present the results of the ORACLE study. And as you can see from the first slide here, this has been a multi-institution endeavor. Without all of these folks on here, we would not be able to do what we've done.

So I have to give a big applause to all the folks that have contributed data from different sites. We have 20 different academic sites collaborating on this trial, and it's been a great opportunity for us to learn more about non-clear cell RCC.

So I'm going to give a brief overview of the ORACLE study. So this is one of the presentations that we had at ASCO, where we were basically looking at what happens when patients receive frontline combination treatments.

So we know that there's been multiple prospective trials looking at different combination therapies for people with non-clear cell RCC. The combination treatments that have been evaluated in a prospective study include IO-IO, IO-VEGF, and VEGF-mTOR. And they're all small studies, but they've all looked at these combinations mostly in a frontline setting.

But what we don't have is what happens when patients progress on these treatments? What subsequent lines of treatment actually are efficacious? Do we have any data on that? And that's what we try to look through the ORACLE database. And that's what we're going to be talking about today.

So the eligibility for this substudy of the ORACLE database was patients had to be older than 18 years. They had to have a diagnosis of non-clear cell RCC with stage IV disease. And they could have had any combination treatment in the past. So they could have had IO-IO, IO-VEGF, VEGF-mTOR, and they should have had at least one dose.

And there are a lot of patients in the ORACLE database that did not get the combination in a frontline setting. They actually got it in a second-line or third-line setting. So we included those patients also, and we tried to see what happens if you even get the combination treatment in a later-line setting, and then get subsequent treatments, what your outcomes are. And one of the main eligibility for this study was that you had to have received subsequent treatment after prior exposure to any combination regimen mentioned above.

As we discussed previously, this was a multicenter retrospective analysis evaluating real-world outcomes. We have 253 patients in the database that actually received combination regimens. Of those patients, only 105 patients received subsequent treatment, again indicating that this is a very aggressive disease and less than half of the patients get subsequent treatment.

And what we looked at is what the baseline and follow-up demographics were, what the clinical treatment and radiographic data was for these patients. And we categorized these patients based on what they received. So we categorized them as whether they received cabozantinib or another VEGF monotherapy, whether they had IO and VEGF combination, or whether they had VEGF and mTOR combination.

So a lot of times, we all know that there's patients that have gotten IO and IO in the frontline setting. And for a long time, we've continued with IO in combination with VEGF until we saw the CONTACT data and we saw TiNivo data. So this is obviously oncologists probably doing the combination of continuing IO despite IO progression because we didn't have that data back then.

So the primary endpoint of the study was objective response rate. And this was assessed by an investigator using RECIST 1.1 principles, as we had really no central review for the study.

The study had several secondary endpoints, including time to treatment failure, which was basically time to discontinuation of both the agents if they were getting two treatments, and disease control rate, which is basically stable disease and objective response rate. And outcomes are reported according to subsequent therapies received and histologic subtype. Kaplan-Meier survival estimates were used for time-to-event endpoints.

So these are the baseline characteristics of the 105 patients included in the study. The median age at diagnosis was 59. A majority of them were male. A majority of them actually had de novo stage IV disease. Only one-third of them did not have a nephrectomy before getting treatment.

And as you can see here, the majority of patients actually had papillary RCC. So 40% had papillary RCC. That was followed by unclassified at 32%, and then followed by chromophobe at 16%. And 15% of these patients actually had a sarcomatoid component. And most of these patients actually had extensive-volume disease, I mean, more than four mets at the time of treatment in the second or in a subsequent-line setting.

So the other thing that we looked at is what their IMDC risk score was at the time of subsequent treatment. As you can see here, almost 75% of patients had intermediate- and poor-risk disease at the time of subsequent treatment.

And a majority of these patients had received the combination therapy in a frontline setting. So 71% received IO-IO, IO-VEGF, or VEGF-mTOR in a frontline setting, but 29% did actually receive these combinations in a second-line or later-line setting.

And as you can see here, the most commonly used combination treatment was a PD-1 and CTLA-4 inhibitor. And if you think about the subsequent treatment, second-line was 71%. So in other words, 71% got combination treatment in a frontline setting and then subsequently moved on to these subsequent treatment. And then there were a few patients that got subsequent treatment in the third-line or a later-line setting.

So in terms of what was received after the combination treatment, it's pretty interesting to see that cabozantinib was the most commonly used treatment for these patients with non-clear cell RCC after combination treatment, with 54%, followed by other VEGF inhibitors like sunitinib, pazopanib, lenvatinib, and axitinib. And there were a couple of patients that actually got a VEGF-mTOR inhibitor; almost 20% got VEGF-mTOR inhibitors, and then another 16% got an anti-PD-1 and a VEGF inhibitor.

So these are the results of the study. And as you can see here, at a brief overlook, there's not that much difference between each of these. So cabozantinib by itself—so what we looked at is just looked at these agents irrespective of the histology, irrespective of the line of treatment.

And as you can see here, the objective response rate with cabozantinib was close to 20% and the disease control rate was close to 50% with cabozantinib. And that was similar across the treatment regimens. There was really no difference between one versus the other, except for maybe anti-PD-1 and a VEGF inhibitor, which seemed to be a little bit higher on brief overlook. But again, I would say that the difference didn't seem that striking when we tried to compare these, acknowledging that they're very small numbers.

Then what we did was we took a deep dive and we said, let's look at outcomes based on histology. And what we did find was that the chromophobe patients actually had a very strong disease control rate with second-line treatment.

And when we did a deeper dive, we actually did find that most of the chromophobe that had a better disease control rate actually were those patients that got an mTOR treatment, again underlying the role of mTOR inhibition in these patients with chromophobe RCC. The numbers are small, so that's the reason why we didn't do a lot of deeper dives at different subsets. But again, these are all thought-provoking things that I think can lead to further trials.

So also, besides looking at objective response rate, we did look at time to treatment failure because we really don't have data for—and this is a surrogate endpoint for progression-free survival. And as you can see here with cabozantinib, the median time to treatment failure was 3.6 months, with a 95% confidence interval being 3.1 to 5.8 months.

And you could argue that, well, cabozantinib did not do as well as other VEGF. But you have to understand that cabozantinib had 56 patients versus other VEGF treatment—only 13 patients had received other VEGF treatments.

What's also really noteworthy here is that the VEGF plus mTOR treatment was given in 19 patients, and the median time to treatment failure was 9.3 months. And when we did a deep dive, we found that most of these patients actually had chromophobe RCC, again highlighting the role of, I think in my opinion, mTOR inhibition in these patients.

We also looked at outcomes based on histology irrespective of what treatment type. And you can see here the chromophobes again seem to be doing much better and probably again driven by mTOR inhibitors. But again, it was 5.5 months for papillary and 4.7 months for unclassified.

So we looked at the Kaplan-Meier curves for PFS. And as you can see here, they seem to be overlapping for the most part, except for the VEGF-mTOR inhibitor. And we talked about why we think that probably is the case in our cohort.

So the summary for our study is that there is data—from our study, there's clearly data suggesting that VEGF-based approaches do have activity after combination therapy in refractory non-clear cell RCC. However, the outcomes are modest and the duration of response is modest as well.

And we really need to focus on developing novel therapeutics for these patients that have progressed on combination therapies, and it's a huge unmet need. While we keep on focusing on clear cell RCC, I think we need to think about how we can optimize patients in a non-clear cell RCC setting as well. So thank you again, Zach. I really appreciate your time.

Zachary Klaassen: Deepak, thank you so much. Great presentation as always, and congratulations to you and your collaborators as a challenging disease space and really getting some insights into that real-world setting.

One thing I noticed from your poster was there's 25% African-Americans in this cohort. Just speak to the generalizability—obviously real-world, so this is not a clinical trial—but the generalizability when we have good representation of the patients that we're actually treating.

Deepak Kilari: That's a great point, Zach. I mean, as you know very well, most trials have had under-representation of minorities. And I think looking at these real-world outcomes actually helps us understand how minorities do on these trials.

We were a little bit surprised to actually see that we had a 25% African-American population. We are looking more deeply into how they performed compared to the rest. But I think it's reassuring that we've had adequate patients, and we will be doing something down the line, looking at under-represented populations and how they do on these combination treatments and subsequent treatments as well.

Zachary Klaassen: That's great. And I think some great insights—specifically with chromophobe seems to be doing a little bit better, as you mentioned, with the combination of VEGF-mTOR. I'm going to put you on the spot a little bit, because papillary is the most common we see in this non-clear cell cohort. In a combination, whether it's second-line or third-line, what in your practice, what are you using for those papillary patients after they've received combination therapy?

Deepak Kilari: So, in my practice in a frontline setting, we typically do len-pem based on the KEYNOTE study. So based on what we saw in the ORACLE study, I think clearly cabozantinib seems to be the most preferred regimen.

Yes, the outcomes are not as great as we would have expected, but again, I think it's a medley of patients with different subtypes of non-clear cell RCC in the cohort. But I would say that there seems to be activity of VEGF-TKIs, especially cabozantinib in this space, and I think it's a very reasonable option to consider cabozantinib in the second or later-line setting if they have not already seen it.

Zachary Klaassen: Yeah, great answer. Probably the most important question—you kind of alluded to it a little bit. This is a big unmet need for metastatic non-clear cell RCC. How does this data from ORACLE perhaps shape future clinical trials? You mentioned new therapeutics, which I totally agree with. But based on the data you have from ORACLE, how should we be thinking about designing the next set of trials?

Deepak Kilari: So great question, Zach, and I'm glad you asked that. I mean, I think most companies are just so focused on the front setting that I think it's important to go back and see if there is efficacy in a prospective setting with all of these things.

I think there are some signals that we could see in our cohort. I mean, we could see that there's probably a better efficacy of mTOR inhibition in chromophobe RCC. So maybe we should try to focus—mostly do a subgroup—I mean, do a further deep dive in our study. But also actually ask ourselves, can we do a genomically directed treatment for these patients?

Yes, it's going to be small numbers, but can we do these studies where we ask ourselves, OK, we're not going to give everybody with, for instance, chromophobe cabozantinib. We're going to try to give them mTOR inhibitors. For papillary, we're going to try to give them VEGF-TKIs. For unclassified, are we going to try to give them immune checkpoint inhibitors, because clearly they seem to have not the best outcomes as well?

So I think this data set can help us work with pharmaceutical companies and the cooperative groups and say, can we just not throw everything at everybody? But can we look at this data set and say, this might be more effective for these patients? We don't have historical controls, to be honest.

But I think we can do trials that are more histology-specified rather than just giving everybody the same thing. And this data from this trial could help us determine what treatment we should be giving each of these subsets.

Zachary Klaassen: Yeah, great point. I think that's an awesome point. The histology deep dive you guys have done could clearly inform future trials—more specific, rather than just grouping them all as non-clear cell RCC. Deepak, always great to chat with you, and thank you again for your time. Anything we haven't hit on, any take-home messages before we wrap up?

Deepak Kilari: Oh, again, thank you so much for having us. And I think we are continuing to build upon the ORACLE database. We're looking at genomics, we're looking at what pathology looks like, and we're trying to confirm pathology centrally. We're doing a lot of great stuff with the ORACLE database, and I hope we continue to evolve and continue to build upon this database and educate all of us.

Zachary Klaassen: Well, it's great work, and not just you, but your collaborators as well. And we'll certainly have you back on to discuss future ORACLE work. Thank you.

Deepak Kilari: Thank you. Thanks for having us.