Renal Cell Carcinoma Podcast

How Single-Cell Sequencing Can Help in Renal Cell Carcinoma

Sumanta Pal, MD; David A. Braun, MD, PhD


May 03, 2023

This transcript has been edited for clarity. For more episodes, download the Medscape app or subscribe to the podcast on Apple Podcasts, Spotify, or your preferred podcast provider.

Sumanta Pal, MD: Hi. I'm Dr Monty Pal, and I'm a medical oncologist at the City of Hope Comprehensive Cancer Center in Los Angeles. Welcome to season two of Medscape's InDiscussion series on renal cell carcinoma (RCC). Today we'll discuss single-cell sequencing and how it can help us in RCC. First, let me introduce my guest, Dr David Braun. David is the Goodman and Gilman Yale scholar, assistant professor of medicine, and a principal investigator in molecular and cellular oncology at Yale Cancer Center. David, thank you so much for doing this program with us.

David A. Braun, MD, PhD: It's such a pleasure. I'm really excited to have the chance to chat.

Pal: I have to tell you that we all look to you — no pressure — as being one of the leaders who will establish what we can ultimately do with biomarkers in RCC. Tell us the current state of the art. Are we using biomarkers to allocate therapy at this point?

Braun: That is a great question. It is really fortunate that our work has been largely collaborative work. I've been involved in a reasonable amount of it, but being incredibly well supported by diverse teams has been helpful. With all of these efforts, we're making progress. My short answer is that I don't think we're there yet. We're not at a place where we have a clinically actionable biomarker where we can take an individual tool, whether it's an immunohistochemistry tool, a genomic tool, or an RNA sequencing tool, and say it should be the deciding factor whether a patient should get therapy X or therapy Y. It's what everyone's looking for on the clinical side, myself included. I'd love to have a test that would tell me, yes, this patient is highly likely to respond to this drug, or we need stay away from that drug. We're making progress and we're learning a lot, but we're just not quite there yet.

Pal: Tell me about this single-cell approach. As you've already alluded to, we've looked at slides, we've done some basic genomics, and so forth. How does that single-cell sequencing principle help us?

Braun: It's a great question. I would frame it that single-cell sequencing is addressing complexity head on. A lot of the initial efforts in biomarkers have focused on trying to find that single thing that's going to be predictive. We learn from the past. We learn from lung cancer. We have an EGFR mutation, and that is a great biomarker for whether a patient is going to respond to an EGFR-targeting therapy. We've looked extensively on the genomic front. There's been a lot of efforts in immunohistochemistry — things like PD-L1 and beyond — and certainly on the transcriptomic front, as well. We haven't quite gotten there yet. I think it is because of this notion of complexity. I use this idea of the Anna Karenina principle. When everything goes right, there's probably a common path by which everything goes right. But when things go wrong and when there's resistance, there's probably lots of different ways for that resistance to happen. There's lots of different potential paths to resistance. The Anna Karenina principle is that happy families are the same, but all unhappy families are unique in how they're unhappy, and that really applies here. When we have this complex situation, how do we begin to address it? That's where single-cell sequencing comes in. Rather than taking what is a very complex tumor microenvironment with lots of different types of cells, T cells, myeloid cells, B cells, tumor cells, endothelial cells, and dozens of cell types and mushing it all together, putting it in a blender, and trying to figure out what it all means, it allows you to pick out individual cells at a time and ask directed questions. What are the individual cells or cell communications? How are the cells talking to one another that might impact response and resistance? It's the ability to address that complexity head on that's hopefully going to allow us to make advances toward biomarkers.

Pal: That's interesting. With the single-cell technology, let's talk about how we can utilize it in the context of RCC specifically. Here's a place where you've really helped us out in the community. Describe some of the stuff you've done with single-cell sequencing and RCC.

Braun: Absolutely. Let's begin with the principle that the tumor isn't a single entity. You might think the tumor is a mass of a lot of different tumor cells, but it's really an ecosystem by itself that has all these different types of cells — immune cells, stromal cells, and tumor cells — talking to one another. How can we understand that in a basic biological way, we can understand the disease, understand how it progresses and becomes more advanced, and then ultimately understand how it might respond or not respond to certain types of therapy? Some of our initial work tried to begin to answer this question. We look at kidney cancer, particularly clear cell kidney cancer, and how it advances and moves from a nonmalignant normal kidney to become early-stage kidney cancer, locally advanced kidney cancer, and metastatic kidney cancer. We know there are genetic changes that happen in the tumor. What is happening in that immune microenvironment? What is happening in that ecosystem? This is a question that single-cell RNA sequencing was poised to allow us to answer. That was one of our first studies — looking at how the immune system evolves with advancing disease stage. We found that there were a couple of different branches to it, and that the immune system really does change and become progressively more dysfunctional. Those soldiers of the immune system, the CD8 T cells that are responsible for antitumor efficacy become progressively dysfunctional. This is a state called exhaustion where they're not able to do the things they normally do, such as make cytokines, proliferate, and kill tumor cells, but this doesn't happen in isolation. Other cells within that tumor microenvironment change too — particularly the myeloid cells. They change state from a more proinflammatory state that might be helpful in controlling the tumor to a state that's anti-inflammatory that suppresses immune responses. It's not one thing that's happening but this orchestrated act where two branches of the immune system are becoming more dysfunctional. They're not independent. They are talking to each other. Those highly dysfunctional or exhausted T cells are talking to those myeloid cells. Those myeloid cells are talking right back. It is what we call the circuit because there is a bidirectional communication. As we begin to identify the points and wires of that circuit, the hope is that we can begin to rationally cut those wires. Rather than empirically trying two therapies together, which obviously has a lot of merit, we can begin to go about this in a much more rational fashion. If we know the sources of the dysfunction, can we go in with a targeted approach and say these are the particular immune interactions we have to cut in order to improve responses? That was some of our initial work. Now we've we built off of this to look at lots of other things — variant histologies, response resistance to immune therapies, and a lot of really exciting developments.

Pal: Let's talk about that because we often want to pitch this work in a manner that helps us align certain therapies. What have you learned about the changes that occur in single-cell sequencing data as you apply immune therapies? Is there an evolution you've observed so far?

Braun: Absolutely. The work on this is still in its infancy. There are studies where it's a few patients here and a few patients there. There is very sophisticated anecdotal evidence that certain processes might be happening. We also have to go about this in a much more systematic way. There is this phenomenal work that's been led by investigators like Eli Van Allen at Dana-Farber and Ari Hakimi at Memorial Sloan-Kettering that has tried to approach some of these questions. What is the impact of therapy? We know there are changes. We know there's certain inhibitory checkpoints beyond PD1 that increase. I think that's important to know. We know that those changes are happening in T cells but also myeloid cells. The next step is the evolution of moving beyond this awareness of the changes that are happening to actually pinpoint the specific changes and what's functionally relevant. Which changes matter for inhibiting an immune response and which should we go after therapeutically? That's where we need a more systematic effort. That's something we've also been trying to tackle through a lot of partnerships from clinical trial leads who have been running fantastic trials of immune therapy in kidney cancer. Can we build in biopsies before therapy, during therapy, and after therapy and systematically look at that evolution? That's the work we've begun now.

Pal: Very interesting. One of the questions that comes to mind is scalability. And one of the themes that's come up as you discussed the work you've done to date is the complexity of it. I agree with you 100%. Sequencing every single cell and trying to amalgamate data into a way that's clinically meaningful is going to be challenging. Do you think we're going to get to a point where we can ultimately do single-cell sequencing across hundreds of patients in a big phase 3 clinical trial?

Braun: It's a great question and one that comes up a lot. I know enough at this point to never say never because technologies evolve and change — even things that seem incredibly challenging to do. Could we have envisioned 15 years ago sequencing individual patients' tumors or designing a custom vaccine? That would have been unheard of 15 years ago. That's something that's doable today. Lots of patients get somatic mutation testing, and we even see efforts to personalize vaccine products. You're absolutely right that this is a clinical test where a patient will have single-cell RNA sequencing. If I was a betting person, this wouldn't be on the horizon. I don't think that's what's going to happen. How is it useful if it's not something that's a clinical test? What it allows us to do is explore the complexity but learn the important lessons we can then apply to our much more conventional technologies. For example, in the work we did, we looked at the entire tumor microenvironment and the incredibly complex interactions of all these different cell types with dozens or hundreds of different interactions. We were able to narrow it down to a couple of cells and how they might interact. You can move from something that's really complicated like single-cell RNA sequencing to immunofluorescence or, even in some cases, immunohistochemistry to understand the specific cell populations that might be important — maybe two or three that form a neighborhood together — and focus on that. These efforts will hopefully go toward using single-cell RNA sequencing as a discovery tool and going deep on a few patients to really understand. By learning the lesson, you can then apply it more broadly using conventional tools.

Pal: It makes a lot of sense to use this to hone in on the specific elements driving tumor biology and then testing them more broadly. I can see that making its way to some of these big phase 3 clinical trials, and maybe in the short term that's something we can do right away. What do you think the applicability of this is across rare histologies? I am very interested in diseases like papillary and chromophobe RCC. Do you think we're going to get different cell sequencing results out of those rare histologies compared to clear cell RCC?

Braun: Absolutely. This is where single-cell sequencing might shine because it's the idea that you can go into a ton of depth on an individual patient, and what better cases to use than these uncommon histologies like papillary, chromophobe, or translocation RCC where we're never going to collect hundreds or thousands of samples for patients. But we can learn a lot from the dozens or even single numbers of patients so we can say how their tumor cells and their microenvironment might be similar or different from clear cell RCC. We know there's a clinical need in that, therapeutically, these cancers don't all respond to immune checkpoint blockade in the same way. We know that certain histologies might not have reasonable responses in large phase 3 trials but at least they do in a smaller phase 2 trial. Unfortunately, there are types of RCC that seem like they're less responsive, like chromophobe RCC. For example, what is it about the immune microenvironment in chromophobe RCC that might be so different from clear cell RCC? Also papillary and unclassified RCCs do have some level of response to immune checkpoint blockade. I think single-cell RNA sequencing is a powerful tool to be able to learn about this.

Pal: You've got the wheels turning here, and I'm thinking about what you said earlier about going back and looking at immunofluorescence targeting specific cell populations. So many patients who walk into our clinics right now are getting genomic sequencing, whether it's a hotspot test or whole-exome sequencing, depending on the platform. Some are getting RNA sequencing and what have you, but in sort of a generalized fashion. Is it possible that at some point in time, these single-cell profiles will coalesce into a more readily identifiable genomic signature? For instance, we talk about our standard, more common molecular markers in kidney cancer — PBRM1 and VHL mutations, etc. What are your thoughts there? Could we ultimately scale back to a genomic profile that might be more clinically relevant using single-cell data?

Braun: That's a great question. My hope is that it will become much more clinically applicable and usable in a clinical setting. Do I think it'll ultimately come down to the point where our current approach of hotspot testing or even whole-exome sequencing for somatic mutations will come back to this to be informative? I'm a little more skeptical of that. The main reason is that we've looked at this — and I say we as a community, right? We've now sequenced not only hundreds but thousands of patients — for example, the MSK-IMPACT effort sequencing thousands of patients — and if we could find that one gene that could tell us yes or no for response, we would have found it by now. I hope I'm wrong, but that's where my gut is. My suspicion is that it's going to be more complex than this. It's not going to come down to just a single gene. This doesn't mean it's not going to be something that's clinically actionable. We know of other examples such as expression-based profiling that's used, for instance, in breast cancer to help stratify people for their risk and assign if patients get adjuvant chemotherapy or not. Those sorts of tools are not reliant on just mutation of one gene. They're more complex in that they might rely on the expression of a dozen or two dozen different genes, but this is something that's clinically feasible. This is what I hoped we would get to. Single-cell RNA sequencing is able to understand the complexity and boil things down to what's important. What's important is not going to come down to a single gene test. It's going to come down to a combination of factors that won't be one factor but not a hundred or 1000 either — something that's tractable that we can use in the clinic.

Pal: Terrific. What you're saying makes perfect sense. We've gotten behind this question together as a community for so many years now. If there was a genomic signature that was tethered to response, I think we would have found it.

Braun: Yes.

Pal: Maybe you're right. It does get me thinking about all of these novel approaches. For instance, molecular imaging, and so forth, to trace the path of CD8 cells and some of the other immune cell subsets you've identified in your work. There might be some promise there, don't you think?

Braun: I think that stuff is incredibly promising. I'm excited about single-cell technologies, but we have to be cognizant of the limitations, too. We're getting a single sample from a particular site of disease at one point in time. Is that the same as what it is going to be tomorrow or a week from now or a month from now? Probably not. It's probably going to be a little different. Is that the same as a different region of the same tumor, a different tumor site altogether, or a different metastatic site? No. There's probably heterogeneity there, as well. With these molecular imaging approaches, we can really capture the what's happening with the whole body and the dynamics over time. We have a lot to learn, and how we're going to process the data and learn from it is still up in the air and to be determined. But it has a lot of advantages in that domain.

Pal: There is another place I'm thinking about applicability of single-cell sequencing — I've seen bits and pieces in the published literature to date about the microbiome and the role of single-cell sequencing there. Right now, with many of the techniques we're using like 16S sequencing, shotgun sequencing, and metagenomic sequencing, we're getting this diffuse snapshot. But again, there's a setting where I really feel single-cell sequencing may pay off in spades, don't you?

Braun: Absolutely. Technology has been growing and developing. The actual technology for the ability to do single-cell RNA sequencing has been based on eukaryotic cells and on mammalian cells and being able to understand the biology of mouse cells, or in our case, human cells. It just hasn't been optimized for using different bacteria and getting single-cell sequencing of this. That's changing. There's tremendous work coming out of largely the infectious disease field but also places like the Broad Institute that are adapting a lot of these single-cell platforms to look at things like bacteria. You're absolutely right, the idea is to take a really complicated situation like the microbiome and break that down into individual cells and understand the heterogeneity there. And also to determine which of the particular components that might be contributing would bring that part of the field forward. You've obviously been such a leader in this and in bringing it into the clinic to understand the particular bacteria that might be impacted by single-cell RNA sequencing, which is a really nice case for the use of this, too.

Pal: Our listeners can't see my face but you can. I'm lost in thought right now about the different ways we can use these technologies to move the field forward. This is incredibly inspirational. Before we wrap up, you've got to tell me, David, how did you get into this? What was your motivation to get into RCC, and how would you propose a path forward for folks who are also interested in carving a niche in the field?

Braun: It's interesting. Part of it was scientific interest and clinical interest. Part of it was serendipity — the people I met. That was a huge part. When I say the people, it was a mix of the patients I encountered and the things that stuck with me. Then also, of course, the mentors I had. I've been so incredibly fortunate to have had tremendous experiences in all of these domains. I had an interest as a physician scientist in immunotherapy and applying immunotherapy to the treatment of solid tumors. For me, it has been so transformative because you're changing the paradigm — the idea of potentially being able to not just treat patients with advanced disease but give them long-term survival. We hate to use the term, but even to give them a cure in some cases. That's so promising. There's so much work that needs to be done. When I was a first-year fellow in 2015, it was right at the cusp of kidney cancer. That's when the CheckMate 025 trial came out — that first immune checkpoint agent in kidney cancer. It was a transformative moment. Then, as I was going through my first-year clinics and rotating through all the different disease sites, I came to my genitourinary clinic and was paired with one individual who was this incredibly dynamic person — not only in the way he thought about the disease but in the way he interacted with patients on a human level. It was really inspirational. This person was Tony Choueiri, and he was a tremendous mentor. He has this infectious enthusiasm. I learned the clinical and the science side of kidney cancer but also the importance of maintaining that human connection. It's hard to meet Tony and interact with him for a while and not fall in love with kidney cancer. He's just so passionate about it. It was this intersection of scientific interests and the people I've met over the years that forged my path in kidney cancer.

Pal: I've heard of this Tony guy. I've heard he's something special.

Braun: He's usually low profile. He doesn't like to get out there. But he really is wonderful.

Pal: I'm a big Tony Choueiri fan, as you know, and I can't agree with you more. He's had such a transformative impact on so many careers. This was a fantastic conversation. Thank you so much, David. We had some great takeaways around single-cell sequencing. We've talked about its role and evolving roles as a biomarker in RCC, but we've also catapulted the conversation around how it might apply to many other domains. I want to thank the listeners for tuning in today. Please take a moment to download the Medscape app to listen and subscribe to this podcast series on RCC. This is Monty Pal for InDiscussion.

Listen to additional seasons of this podcast.


Single-Cell RNA Sequencing Technologies and Applications: A Brief Overview

Progressive Immune Dysfunction With Advancing Disease Stage in Renal Cell Carcinoma

Immune Checkpoint Inhibitors in Non-Conventional Histologies of Renal-Cell Carcinoma

Prevalence and Landscape of Actionable Genomic Alterations in Renal Cell Carcinoma

Molecular Profiling in Early ER+ Breast Cancer to Aid Systemic Therapy Decisions

Dysbiosis of a Microbiota-Immune Metasystem in Critical Illness Is Associated With Nosocomial Infections

A Single-Cell Atlas Reveals Shared and Distinct Immune Responses and Metabolic Profiles in SARS-CoV-2 and HIV-1 Infections

Revealing the Vectors of Cellular Identity With Single-Cell Genomics

Nivolumab Versus Everolimus in Patients With Advanced Renal Cell Carcinoma: Updated Results With Long-term Follow-up of the Randomized, Open-Label, Phase 3 CheckMate 025 Trial

Follow Medscape on Facebook, Twitter, Instagram, and YouTube


Comments on Medscape are moderated and should be professional in tone and on topic. You must declare any conflicts of interest related to your comments and responses. Please see our Commenting Guide for further information. We reserve the right to remove posts at our sole discretion.