The ORACLE Study: The Efficacy of Combination Systemic Therapies in Patients with Non-Clear Cell Renal Cell Carcinoma - Deepak Kilari
January 3, 2023
Deepak Kilari, MD, Associate Professor, Department of Medicine, Froedtert Cancer Center, Medical College of Wisconsin, Milwaukee, WI
Pedro C. Barata, MD, MSc, Associate Professor of Medicine at Case Western University and the Director of Clinical Genitourinary Medical Oncology Research Program at University Hospitals Seidman Cancer Center, Cleveland, Ohio
Pedro Barata: Hello, my name is Pedro Barata. I'm a GU medical oncologist at Tulane Medical School in New Orleans. It's my true pleasure to be joined by a friend and colleague, Dr. Kilari. Dr. Deepak Kilari from Medical College of Wisconsin. Welcome.
Deepak Kilari: Thank you Pedro, for having me.
Pedro Barata: Absolutely. So we just came out of ASCO 22 here in Chicago. It was, I think, a very good meeting. A lot of interesting work presented in the GU tumors. One of the great work that was presented here is actually your study, which represents a big effort among other colleagues and friends across United States, investigated the role of IO-based combos in patients with advanced non-clear cell renal cell carcinoma. Can you tell us a little bit about how did you put that study together and how you started with that?
Deepak Kilari: Sure.
Pedro Barata: Because it's a great idea.
Deepak Kilari: We all have a couple of non-clear cell patients that we see per day and per year, between 10 to 20 or so. And we always are in a data void zone. In other words, most of the clinical trials that we have are from 2013. Obviously, there's some clinical trials that Dr. Powell did, and there's other trial retrospective studies, but in non-clear cell cancer, we always operate in a data-free zone, or data-void zone most of the times.
We had a chromophobe renal cell cancer patient that typically we've been giving Everolimus. Then Dr. Hutson looked at Len plus Everolimus in patients with chromophobe RCC, but it was all comers. And there were a few patients in that that actually had a very good response with Len plus Everolimus. So we had a thought, okay, can we pull up data from more institutions to see actually if this translates into more meaningful outcomes in a broader patient population?
And when we had looked around and we sort of felt that there was a very few people with chromophobe RCC that were treated across institutions, and very few people with Len plus Everolimus treated with chromophobe RCC.
So then we sort of said, "Why not look at all combinations? Why just look at one combination of Len Everolimus?" So, what we did was we initially started off with five institutions, and we looked at combination of either a PD 1 or a PD L1 inhibitor with a VEG-F inhibitor, a PD 1 and a CTLA 4 inhibitor, or a VEG-F and an MTOR inhibitor.
So, we took these three combinations and we said, "Let's look at the outcomes of patients, real world outcomes of patients that have gotten these combinations either in our first line or later line setting." And with five institutions we had numbers, but obviously, we realized that the bigger the number, the better we can actually deep dive into the data and actually make more meaningful outcomes.
So, what started off as a five-institution data set quickly became a data set for 15 institutions. We currently have 15 institutions participating in it. All academic institutions. We have over 200 patients with metastatic or locally advanced non-clear cell RCC in the database. And we're looking at outcomes with different combinations in either a first-line or second-line setting. And basically looking at their genomics, looking at their radiomics, and basically looking at what are the outcomes in the real world setting.
Pedro Barata: That's fantastic. What a great effort, because I think you raise a really important point, which is the lack of data for patients with non-clear cell histologies. Right? They are very difficult to treat, as we both know. And so, it's so important because your study has the uniqueness of allowing us to see different cords considering different types of combination regimens, as you said. IOIO or IO VEG-F or even different combos.
So I guess, can you highlight for us, what did you find as main results, perhaps in the different histologies in clear cell? Can you highlight the data for patients treated, for example, with Ipi/Nivo? I'm assuming you did have patients in there versus patients who got an IOTKI that is also standard of care for clear cell. Can you highlight for us what your findings were?
Deepak Kilari: Sure. So for ASCO, we included 128 patients. Patients were stratified based on what they had received and were stratified based on if they got their treatment in a first-line setting or in a second-line or a later-line setting. We stratified patients also based on histology. And again, given small numbers, we can't draw conclusions, but as this data set expands, we hope that we can draw conclusions.
One thing that comes to my mind when I look at that data set is collecting duct patients that don't have any ... What you call responses with VEG-F inhibitors, whereas we've seen outcome responses with IOIO. And I mean, that was like, we had probably 20 patients or so in the cohort, and clearly, IOIO seemed to have some efficacy for patients with collecting duct RCC.
Similarly, for chromophobe we saw that even though they were small numbers, VEG-F plus [inaudible 00:05:21] seemed to have the most benefit. The response rate was higher if these agents were given in a first-line setting. But there were still responses even in a second-line or a third-line setting. So I think that's very important, because when we see patients, we say, "Okay, if we don't get it in our first line, we probably won't do it in a second line." But again, our dataset shows that there are still responses with these combinations in a second or later line setting.
Pedro Barata: That's very, very interesting. Thank you so much. And I think you kind of start talking a little bit about what I predict might be next steps because it sounds like even though you already have a very good number of patients with non-clear cell histologies, including this study that reflects what doctors out there are doing in practice, they are using combination regimens for these patient population. Can you talk to us a little bit about what next steps are and perhaps timeline? What should we expect to see? First a larger cohort being published, and then what are you thinking as what the spinoff can you get out of the study? Because it sounds like there's a number of opportunities. You can explore the biomarker, you can explore different lines of treatment for advanced disease. Can you tell us a little bit about that?
Deepak Kilari: Great question. Thank you for that. So what we do want to do is, as you pointed out, we want to expand this to more institutions numbers. Clearly, we bring in a lot of benefit. So we are expanding it to five more institutions across the country and we hope that that will make the dataset more robust with more patients. We do plan to update this database on a yearly basis. There's a lot of new combinations coming out, a lot of new treatments coming out. I mean, so we feel like this database is obviously going to be updated on a every six month to a year basis. And results will be constantly presented.
In terms of what the next steps are besides adding numbers, I think there's a lot of correlative studies that we could do. All the institutions are interested in participating in the correlative studies, including genomics, transcriptomics, radiomics, looking at how can we predict outcomes based on all of these things. And we hope that this will be the largest non-clear cell database in the country. And eventually, we want to make this international as well, that way we have international collaborators putting in, and getting data. The good thing about the study is any investigator on this, any subsite can actually propose the study. And if they have a question, they can ask the query to the database and we can go back and look at the database and come back and say, "Hey, this is what the outcomes are based on your question."
Pedro Barata: Dr. Kilari, I want to congratulate you for the amazing effort that you put into this and the model that you're using seems to make a lot of sense. And we're looking forward to get more data out of that database. And so again, thank you for that, and congratulations, because this is going to be very helpful for practicing oncologists out there that need data to support their treatment decisions. Thank you very much. And again, congratulations for your work at ASCO, presenting at ASCO 22. Thank you.
Deepak Kilari: Thank you for having me.