Renal Cell Carcinoma Podcast

Biomarkers and Renal Cell Carcinoma

Sumanta Pal, MD; Rana McKay, MD

Disclosures

December 08, 2022

This transcript has been edited for clarity.

Sumanta Pal, MD: Welcome to Medscape InDiscussion. My name is Monty Pal, and I'm a genitourinary medical oncologist at the City of Hope Comprehensive Cancer Center in Los Angeles. You're in for a real treat today. We're going to be meeting with Dr Rana McKay who's an associate professor at the University of California San Diego. I got to know Dr McKay when she was finishing her training at the Dana-Farber Cancer Institute, working under some of the greats in kidney cancer. And she's really forged a phenomenal program in kidney cancer down at UCSD. Rana, welcome to the program.

Rana McKay, MD: Monty, thank you so much for having me. It's a pleasure to be here today.

Pal: I gave the listeners a little bit of a preview regarding how you got into the field, but I'd love to hear in your own words what really inspired you and drove you toward a career in kidney cancer.

McKay: Thank you so much for asking that. As a first-year fellow at Dana-Farber, as in most fellowship programs, you circulate through all the different medical oncology clinics. I came into the fellowship thinking I was going to do hematologic malignancies. And after rotating through the genitourinary clinic, I fell in love with the diseases, the people, the patients, and the science. As you think of the spectrum of the different kinds of genitourinary malignancies, from prostate cancer with its long natural disease history and hormone-targeted therapy, to kidney cancer where we don't even use chemo but immunotherapies — then it was targeted therapies — I really gravitated toward genitourinary and the people that are in the field. I think mentorship is huge. And being around people who absolutely love what they do is contagious. That is fuel for success in the future.

Pal: That's so well said. You don't have to confirm this rumor, but I heard from someone who runs the Dana-Farber genitourinary program who told me you wrote 17 clinical trials as a fellow. That number, for some reason, has always stuck in my mind.

McKay: I don't know if it was 17, but it was a lot. Something I learned during fellowship that sticks with me here is that all of the clinical questions that drive research come back to the clinic. You know, Toni [Choueiri] and I were in clinic together. We were seeing a ton of patients who had bone metastases who were all doing so poorly. And we thought, why are these patients doing poorly? How can we investigate this more? Is this just something we're seeing in our clinic or is this a real thing? So for me, it always comes back to the clinic, identifying what those unmet needs are and what those questions are because that's where you're creative in your research, thoughts, and questions and where the field needs to go. Without having a foot in the clinic, my palette would be empty. I would have no colors on my palette. I would not be able to do anything without being able to see patients, care for them, and see their trials and tribulations to identify that we've got to make “X” better.

Pal: That makes perfect sense. And I always think about some of these issues you've posed and what you've done clinically to address them. For instance, in the context of bone metastases, it's so cool that you devised this trial looking at radium 223, which is something we've come to use in prostate cancer in the context of renal cell carcinoma (RCC). I think about the really cutting edge work you're doing in terms of radiating the primary tumor in RCC — something I think is entirely novel and that certainly needs more data behind it. And you're getting us there. The thing I wanted to focus on today as I was thinking about the program was your work in biomarkers. You've been at the forefront of a lot of what's been happening in the field as it pertains to biomarkers. And today I thought we might take a far-ranging approach and review some of the highlights as they pertain to kidney cancer. There's been a bunch of really interesting developments over the past couple of years, and one that you've actually been quite involved in, if I'm correct. That's circulating tumor cells (CTCs) in RCC. Can you tell us a bit more about that?

McKay: Absolutely. This is one of those long-standing collaborations we built with the Lang Lab at the University of Wisconsin and their circulating biomarker core. CTC capture has been very different than for other tumors like breast and prostate cancer. So a lot of the CTC capture approaches use a cell surface epithelial cell adhesion molecule (EpCAM)–based methodology, but RCC doesn't necessarily express EpCAM. Historically, studies trying to evaluate CTCs in RCC have been quite limited, and also the different platforms have had limitations in RCC. I remember when Josh Lang and I first met a long while ago when I was a fellow putting together various clinical trials and embedding the biomarker work into those trials. I was flabbergasted by the technology that they had to be able to evaluate CTCs. They have this chip, and the chip is separated by different wells. The wells are separated by oil, and there's different compartments that are water based and oil based. And that's how they're able to keep the compartments separated on this chip. They grab the CTCs, they capture them with not necessarily EpCAM-based methodologies but targeting CA9, CA12, and cytokeratins that are a little bit more unique to RCC, and they're bound to magnetic beads. And then there's a magnet that carries the CTCs through these oil/water chambers, and that's how they're able to isolate the CTCs, stain the CTCs looking at cell surface markers, break down the cells, lyse the cells, and isolate nucleic acid. I mean, it's pretty remarkable, but it's all simple magnets, physics, oil, and water. They took this process from being a manual process to scaling it up and being able to do it as an automated process. And in some of these very early clinical trials we wrote up — one of the first ones was the OMNIVORE study, which looked at an adaptive immune-oncology (IO) strategy in RCC — we integrated CTC collection throughout the duration of the trial at all imaging timepoints and treatment transition timepoints. And in addition, we had a prospective cohort of patients getting systemic therapy for advanced disease whom we routinely collected specimens on at the start of treatment during treatment — some of it IO-based, some of it non–IO-based. So this is, to my knowledge, one of the first studies looking at longitudinal CTC collection in metastatic RCC. We had over 450 samples collected from 104 patients from the prospective cohort and also the phase 2 response-adaptive IO trial. In essence, what we demonstrated was that the CTC enumeration trajectory was the highest quartile change. If you look at CTCs over time and look at the growth of CTCs during somebody's treatment trajectory, the more rapid kind of increase in CTCs makes sense correlated with overall survival. And throughout treatment, we looked at this ratio for the first time. I think it's being described as diametric — thinking about the diametric immunologic roles of HLA1 and PD-L1. We had this ratio called the HP ratio of HLA1 to PD-L1 and demonstrated that the higher the ratio as patients went through their course of RCC correlated with worse overall survival. This is just the beginning of these data, which were largely around enumeration in the context of people who have advanced disease. And what does the CTC profile look like? We found that lower CTCs correlate with points in time where people were responding to therapy, and higher CTC counts correlate with when people were not responding. Some of these things are intuitive. But I think the next step is molecular profiling of the CTCs; looking at DNA alterations, looking at RNA sequencing from the CTC data and trying to understand biomarkers that could potentially predict for response or lack thereof to various therapies. I think this is the first foray, and the next step is delving deeper into the molecular biomarkers.

Pal: I love this whole concept because it really gives you the metastatic potential of individual cell types, doesn't it? If you see something floating around in circulation, that's the molecular subtype that's going to escape and cause problems for the patient. You know, there's two broad ways I see this field of blood-based diagnostics heading. One is with the CTCs where you obviously have the benefit of this deep individual cell characterization. But then at the other extreme, there's detecting minimal residual disease with small bits of DNA. So let me ask you, if you had to pull out your crystal ball for a second, which do you think ultimately down the line is going to have clinical utility for us as practitioners seeing kidney cancer patients? Or is it both?

McKay: I do think it's probably going to be both. The field of biomarkers has just been exploding across pan-cancer but also in RCC. I think that one of the biggest unmet needs in the clinic is postnephrectomy. Who has disease that is going to recur? How can I better define whose disease is going to recur? What can I use to help predict that? And then now that we have adjuvant immunotherapy options, it's not just whose disease is going to recur, but who's going to respond better to therapy. And can we use a blood-based biomarker to help guide that decision? There's been very exciting work in bladder cancer from the IMvigor trial. Thinking about how we can apply this whole concept of minimal residual disease to RCC, the best biomarker that has the highest sensitivity and specificity in this context is going to be key. I don't think we're there yet, but now is the time to start to build those data repositories, begin to correlate with outcomes, and try to identify a marker that could predict who has disease that's going to recur and potentially predict who's going to respond to a specific therapy. The counter of that, and something we've struggled with in the metastatic setting, has been identifying who's going to be an extreme responder or extreme non-responder to IO therapy. Right now in the frontline setting, we have IO/IO and IO/VEGF. And when you're making that decision in the clinic, I always have this discussion with patients around. Are you going to play the short game or the long game? With IO/VEGF, you're kind of playing that short game — great responses, great progression-free survival, but you don't really have that long-term durability data. With IO/IO, one in five people are going to not respond at all, but there can be that tail on the curve where you're kind of playing the long game. If I had a biomarker that could tell me who that one of five is, it could help me identify what's the Achilles heel to the IO/IO regimen and vice versa. If I had a biomarker that could tell me who's going to be at the extremes, that would dramatically impact how we practice. I think some biomarkers are being developed. I think probably the closest we've gotten has been the IMmotion150 and 151 molecular gene signatures. But those are specifically looking at atezolizumab plus bevacizumab (atezo/bev). And what's the benefit of atezo/bev? Understanding if these signatures are actually going to be applicable to the CheckMate 214 data, for example, or other IO/VEGF combinations is going to be critically relevant and important. Can they be applied to predict response to a pure IO regimen, and can they help us discriminate who that one in five is? I don't know the answer to that, but these are where the unmet needs are in the clinic.

Pal: You took us to what I was hoping would be our next topic, which is some of these molecular signatures derived for prospective studies. You touched on IMmotion150 and 151. Maybe you can give us a little more insight into what these studies showed from a genomic perspective.

McKay: Stepping back, we saw the first dataset in the McDermott paper that was published in Nature Medicine in 2018. That was from IMmotion150. That was a phase 2 study that looked at atezo/bev and atezo in patients with advanced RCC. And I think in that context, they really discriminated out three subsets, an angiogenic-high group of patients who had a T-effector–high signature and patients who had this immunosuppressive gene signature. In the context of the larger phase 3 trial where patients were randomized to atezo/bev vs sunitinib, they were able to expand these signatures into seven clusters. And based off of these seven clusters, they looked at progression-free survival with atezo/bev and the response to atezo/bev vs sunitinib. And there were two signatures that were more angiogenic. There were two signatures that were more T-effector proliferative, and then some signatures that seemed to be correlated with really poor outcomes but didn't necessarily seem to be predictive of response in one way or another. I think these data were very exciting because it was the first time we had had these gene signatures aligning nicely with response to a tyrosine kinase inhibitor–based strategy or an IO/VEGF based strategy. I think the next question is if we can design a trial where we use these signatures as integral biomarkers to help with therapy allocation. But you can imagine a trial like that would be a somewhat resource-intensive study. I know that Dr Brian Rini has done a great deal of work in this space. I think his trial is about to launch, or it may already be opened, called OPTIC, and it is beginning to look at this. To really answer the question of if a therapy is appropriate, you would take a group of patients who express that integral biomarker, and then you would randomize them against a therapy to see if, in these patients who have that biomarker, therapy Y is better than therapy X. But a trial like this is incredibly resource intensive. We, as a community, need to align to develop the studies that can help us answer these key questions and bring biomarkers into the foray and daily routine clinical practice.

Pal: Yeah, it begs this almost political question, which is, where do we plant our resources in kidney cancer, and do we go for the emergence of triplets or quadruplets and so forth as we plan out our next big phase 3 studies? Or do we pull back and see if we can optimize therapy with biomarker-based studies? Any thoughts? Is there room for us to do both?

McKay: I think we sort of have to do both. You know, there's a lot of unmet needs in the clinic. As I step back and think about triplet therapy, maybe a biomarker could be developed to identify who are going to be the people that warrant triplet therapy vs who aren't. COSMIC-313 is about to read out, so hopefully this is soon. That's going to be a big question — who do I give the therapy to? Potentially biomarkers could help with this. It's not just about biomarkers, but that potentially these biomarkers could also help us with novel therapeutics. We've been talking a lot about biomarkers over the last decade and beyond. And I think, clinically, there is not a single biomarker we use in RCC to help guide therapy selection in any way, shape, or form. We still have a long way to go, but we've made tremendous progress. As a field, because molecular sequencing has now gotten cheaper to do, the technologies have gotten quicker, so it's a lot faster to do. These tests are almost at our fingertips. The opportunity is now to think about applying them in the context of a trial to potentially inform practice.

Pal: I really like that idea and completely agree. I think if we are going to go to triplets and quadruplets like they have, for instance, in the myeloma world, it would be fantastic to start investing resources in parallel and really understanding who needs that more aggressive therapy. I think it's a brilliant point. And actually, you mentioned the ease with which we're doing genomic profiling now. One technology that really seems to be emerging is single-cell sequencing. I guess we're not quite at the point where I can go into my Epic system and order single-cell sequencing yet. But there's some really nice papers that do address this concept. Tell me about what some of the single-cell sequencing data in kidney cancer have done for us in terms of our understanding of the disease.

McKay: I think it's pretty remarkable. The amount of data from single-cell sequencing that you get is just tremendous. And we may even begin to realize that while we're looking at these signatures and they're applying to one tumor — maybe within that specific tumor that any one given patient may have, it's almost like a gumball machine you can imagine. You know, one of my mentors, Phil Kantoff, described prostate cancer to me as a gumball machine. And I use this analogy all the time. But you can envision that it's the same sort of thing. Within a tumor, it's incredibly heterogeneous and there's different cells. And within that cell it may even be very heterogeneous. Some of the leading work has been done by David Braun and Cathy Wu's group. And now David is actually launching his lab at Yale, and he's sort of an immunology guru. They've recently published this tremendous work looking at the tumor immune microenvironment across the trajectory of disease stages in clear cell RCC. So, they were looking at early, locally advanced and then metastatic disease to try to understand what's happening with the immune cell populations across the trajectory of the disease. Within the myeloid compartment, they identified that proinflammatory macrophages decreased as you got more advanced disease, and the suppressive M2 macrophages increased as you got more advanced disease. When we're looking at the terminally exhausted CD8 T cells, again, they seem to be higher in the more advanced disease. So this trajectory toward more immune dysfunction as the disease is more advanced is really what they were able to identify and describe from very robust single-cell RNA and T-cell receptor sequencing. This is important as we're thinking about when the most optimal time is to introduce a checkpoint inhibitor. Is the most optimal time in the adjuvant setting where the immune milieu may be more favorable, or is the more optimal time in the very refractory setting? I think the goal is that we will hope to learn more from these studies. And the more we know, the more I feel like we don't know because RCC is just so complex and heterogeneous.

Pal: I love that gumball analogy. You can almost take it back to your initial discussion around CTCs and RCC. Maybe the gumball that escapes out of the machine is that malignant genotype that is driving metastasis. Very, very interesting. I would like to close this podcast by getting a little bit of insight, Rana, on what you would share to this up-and-coming generation of young investigators who are thinking about plotting out a path in RCC investigation. Academic medicine is tough. What are you telling folks who are getting into the field now?

McKay: Very great question. First, like and love what you do, and be around people who like and love what they do because that is really important to success. This is a marathon. This is a career you will stay in for decades, and you have to love it. Mentorship is huge and also identifying mentors you can look up to and say that this is where I would want to be in 10 years or in 20 years. I think mentorship is critical for any fellow or junior faculty member to help understand what their goals are, and what the research projects are that refine their goals. Mentorship takes a lot of work and investment, if you will. So being able to carve out time to sit down strategically with your mentor, whether it be on an every-other-week basis or monthly basis, to go through your vision and your short-term, midterm, and long-term goals. And to state the things you have going on in each one of these niches. And what your strategy is to move each one of these things forward. That is a recipe for success in the future. But to really love what you do is key.

Pal: Love what you do. Great, great words to close out this program. Rana, thank you so much for joining us. Fantastic insights on kidney cancer and life as a kidney cancer investigator. Hope you'll join us again soon.

McKay: Thank you, Monty.

Resources

Longitudinal Molecular Profiling of Circulating Tumor Cells in Metastatic Renal Cell Carcinoma

The Detection of EpCAM+ and EpCAM­ Circulating Tumor Cells

Optimized Management of Nivolumab and Ipilimumab in Advanced Renal Cell Carcinoma: A Response-Based Phase II Study (OMNIVORE)

Longitudinal Molecular Profiling of Circulating Tumor Cells in Metastatic Renal Cell Carcinoma

ctDNA Guiding Adjuvant Immunotherapy in Urothelial Carcinoma

Clinical Activity and Molecular Correlates of Response to Atezolizumab Alone or in Combination With Bevacizumab Versus Sunitinib in Renal Cell Carcinoma

Atezolizumab Plus Bevacizumab Versus Sunitinib in Patients With Previously Untreated Metastatic Renal Cell Carcinoma (IMmotion151): A Multicentre, Open-Label, Phase 3, Randomised Controlled Trial

Nivolumab Plus Ipilimumab Versus Sunitinib in Advanced Renal-Cell Carcinoma

Clinical Activity and Molecular Correlates of Response to Atezolizumab Alone or in Combination With Bevacizumab Versus Sunitinib in Renal Cell Carcinoma

Genetic Testing to Select Therapy for the Treatment of Advanced or Metastatic Kidney Cancer, OPTIC RCC Study

Study of Cabozantinib in Combination With Nivolumab and Ipilimumab in Patients With Previously Untreated Advanced or Metastatic Renal Cell Carcinoma (COSMIC-313)

Single-Cell RNA Sequencing of Human Kidney

Progressive Immune Dysfunction With Advancing Disease Stage in Renal Cell Carcinoma

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