15-Gene Signature for Predicting Clear Cell Renal Cell Carcinoma Risk

In this interview, Daniel Joyce, MD, from Vanderbilt University, and Simpa Salami, MD, MPH, from the University of Michigan, discuss Dr. Salami’s groundbreaking research on a 15-gene prognostic signature for clear cell renal cell carcinoma. The conversation highlights the potential clinical applications of the gene signature in predicting treatment response and guiding active surveillance.

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Dr. Joyce: Hi, everyone. My name’s Dan Joyce. I’m a urologic oncologist at Vanderbilt University, and I have the distinct pleasure of being joined by Simpa Salami here today. He is a urologic oncologist at the University of Michigan and is well known for his work with genomics and biomarkers and was recently appointed as the highest honor the AUA has to give. And that’s the Gold Cystoscope Award for his work in this space. So Dr. Salami, thank you so much for being here and talking with us.

Dr. Salami: Thanks, Dr. Joyce, I appreciate your kind words. Glad to be with you today.

Dr. Joyce: I first want to congratulate you for this work that was published in JCO Precision Oncology. This is exciting stuff. You guys have found a 15-gene prognostic signature for clear cell renal cell carcinoma, which is a space where we’re often dependent on clinical pathologic features and not necessarily genomics and gene signatures. So tell me a little bit about what inspired you to pursue this work and a little bit of background on, as you were thinking about the questions you wanted to answer and how to pursue this, what informed that decision.

Dr. Salami: Yes. As you know, we have a number of biomarkers for risk stratifying patients with prostate cancer and other cancers, but in kidney cancer, we really do not have anything currently available clinically to risk stratify patients. And as you mentioned, right now, we rely on a number of clinical pathologic factors, like what the stage of the tumor is or what aggressive pathologic features there might be in the cancer, to really risk stratify patients with kidney cancer. So we wanted to develop a biomarker to risk stratify patients with this disease. It is well known that kidney cancer is one of the common cancers that we diagnose. About 82,000 new cases are diagnosed annually, and the challenge is that, even in patients with cancers that appear to be confined to the kidney, stage one or stage two kidney cancer, about 10% of those patients still die within five years of diagnosis. So we really need to figure out what patients with kidney cancer will behave in an aggressive way versus in a non-aggressive way, so that we can potentially intensify treatment of those cancer cases and minimize the risk of dying from those cancers.

Dr. Joyce: And so, you looked at patients with that clinically localized disease, T1 to T3, and wanted to look at the different genomics that might inform or predict or prognosticate, I should say, their future course, so recurrence and cancer specific survival. That’s a huge task. That’s a huge effort to develop this. Tell us a little bit about how you went about it, maybe in terms of somebody who’s maybe not an expert like you in genomics, like myself.

Dr. Salami: So what we did was to assemble a code of about a hundred patients at the end of the day, 91 had sufficient data for analysis. What we did was to assemble patients who had undergone nephrectomy or removal of the kidney for kidney cancer. And then, they were followed up for a period of time and were monitored by CT scans, chest x-rays, to make sure that, when they do develop recurrence or metastasis, that we picked that up. So what we did was to assemble a hundred patients, about 50 patients with recurrence of cancer during follow-up, the other 50 without recurrence as controls.

And then, we went back, retrieved their specimens that were archived, and extracted RNA from the cancer tissue. What we did was to do next-generation sequencing. Essentially, what this means is that we’re looking at the RNA assessing over 13,000 genes in the cancer itself. We then went on to compare the RNA expression in those who develop recurrence versus those who did not develop recurrence and essentially asking the questions, are there molecular differences between these two groups of patients that we can use to determine who will have an aggressive cancer versus not?

Dr. Joyce: And based on what you found with this 15-gene score, what does that tell us about the biology of clear cell renal cell carcinoma? Are there insights you gained from that, that say, hmm, this is confirming a lot of what we know based on how RCC responds to systemic treatments, let’s say? Or is there something new here that we didn’t know that might help develop newer treatments from a different mechanism?

Dr. Salami: So maybe a little bit more about how we ended up with the 15-gene score. So what we did was to use a number of sophisticated analytic tools, like what we call differential expression analysis. Essentially, saying what gene is up, what gene is down, in those who develop recurrence versus those we did not develop recurrence. And after a number of complex analysis, we arrived at a 15-gene signature, essentially, a signature that encompasses 15 genes that were altered in aggressive kidney cancer. And we did what we call a multivariable analysis. What that means is what we know previously, we want to make sure that we take those into account, like the age of the patient, the stage of the cancer, the sex of the patient, the grade of the cancer, adjust for that, and ask the question, “Does this 15-gene score add any additional value? Does it provide any additional information?”

We did that in our internal discovery cohort, and what we found was that, yes, the 15-gene signature is associated with having recurrence or dying from kidney cancer. When we stratify patients into higher versus low risk, those who were stratified by this score to be higher risk had a higher risk of developing recurrence. They have a higher risk of dying from kidney cancer. And then, we went on to test the performance of this signature in two large cohorts. We used The Cancer Genome Atlas, or TCGA, that had about 380 patients.

We also assembled another group of patients, a total of 379 patients. And we found that, yes, this gene signature does add additional value, in addition to what we already know. Now, can this potentially help us to determine who needs immunotherapy or what treatment a patient might be responsive to? That’s an excellent question that we’re still investigating, but based on initial analysis that we performed in this two different validation cohorts, what we found was that patients who are classified as higher risk may not be that responsive to immunotherapy, but we do need additional cohort of patients who are treated with immunotherapy, for example, to determine if this score will predict those who will respond versus not.

Dr. Joyce: Got it. One other thing I really liked what you did is you looked at these other prognostic scores that have been done based on clinical pathologic features and compared this 15-gene score to that and really remarkable findings that this outperformed those, even accounting for those other clinical pathologic features that are included in some of those other scores. Why do you think that is? Do you think it’s this genomics that really matters more than T stage or high grade, low grade features of the tumor? Is there something about these genes that make them more prognostic than what we’ve already known in this space?

Dr. Salami: I think that, when we look at a pathologist specimen, let’s say we look at the kidney cancer under the microscope, we look at how aggressively cancer cells are like grade. We look at how proliferative or how fast the cancer cells may be dividing. There’s a lot of information that we don’t see with our naked eyes or even under the microscope, that we’re not measuring, and these are the kind of differences or molecular features that, during next-generation sequencing, can pick up and provide additional value to what we already know.

Dr. Joyce: Now, in the paper, you mentioned that perhaps a liquid biopsy version of this might be doable in the future. Do you think that’s possible with these genes? Are they detectable in blood or urine? And how do you see that moving forward and exploring that in the future?

Dr. Salami: I think the first step will be to, first of all, see what molecules we can extract from liquid biopsy specimen, blood or urine. Currently, we are focusing on blood. We are isolating circulating tumor cells, and then, the next step will be to see if we can do the same set of experiments that we did in tissue, that is RNA next-generation sequencing of the circulating tumor cells. And then, next to that will be to measure the expression of this 15 genes. And then, of course, it’s going to require a large cohort of patients with long-term follow-up to figure out if a signature developed in liquid biopsy specimens can actually provide very similar information or not.

Dr. Joyce: And then, along those lines, in this study, obviously, you had the whole path specimen from surgical resection to look at. What about biopsy? Can this be done on biopsy? Is it reliable on biopsy, do you think? Because that would be huge for us to guide basically active surveillance of these tumors based on what the genomics show or the genes show.

Dr. Salami: Excellent question. That’s one of the potential clinical application of this gene signature is trying to risk stratify patients who undergo biopsy. Based on our experience with prostate cancer, we’re able to do this very similar experiments in prostate cancer biopsy specimens. I think that we’ll be able to do that. And currently, we are assembling an active surveillance cohort, where we would measure this 15-gene signature. But based on our experimental parameters, we believe that this will be able to work in biopsy specimens.

Dr. Joyce: It’s incredibly exciting, especially in a space where now we have adjuvant pembrolizumab, where we’re still trying to figure out, who are the best patients that we send to our medical oncology colleagues to get that started? Or in some of these patients, is it better to wait and then hit them with doublet therapy? There’s so much unknown in this space, and a genomic prognostic biomarker could be hugely valuable. So really exciting work. One of the criticisms that I always hear with biomarkers, and you see it in the prostate cancer space for sure, is “Okay, we have biomarkers, but nobody’s using them.” And so, what do you see? Do you foresee similar sort of obstacles in this space? Or is this something that you feel like won’t meet as much resistance, as far as adoption? And is this something that can be upscaled and adopted relatively easy? Or are there things we need to do in order to make that easier for clinicians to get ahold of?

Dr. Salami: Dr. Joyce, you highlight a number of challenges that we encounter in the biomarker space, and in, I think it was, 2022, I wrote an opinion piece in European Urology Focus, where I argue that biomarkers should become part of our management strategies for urologic cancers, but I also highlighted reasons why they have not been. And one of them you mentioned is, sometimes, they actually do not provide actionable information. Like when I show you a biomarker, the question you would ask me or I should be asking is, “How would this change what I already do?” So it has to provide actionable information, and then, two, it has to be high throughput. It should be a test that can easily be done at scale to meet the needs of all of our patients. It should be readily available, should be at reduced costs. Many of these biomarkers are so expensive. Until recently, some patients will have to pay out of pocket for tests that cost for as much as $3000 to $5,000. And there has to be insurance coverage available.

Now, do I think it’s going to be an easy road for the 15-gene score to make it into clinical practice? No, but we’re taking specific steps to address a number of these limitations. One is, for example, we’re trying to convert the 15-gene score from a next-generation sequencing test, which does very well, into an open area platform. This is a platform that will be a little bit cheaper. It will be highly reproducible. And potentially, because it’s going to be at reduced cost, potentially, you’ll be able to get coverage for it. And then, the other piece is providing actionable information, meaning how do you use the test result that you get? Some of the areas that we’re looking at, specifically testing the gene signature, one we already discussed, is in the active surveillance setting, meaning you do a biopsy in a patient with a renal mass, can you classify the patient into low versus high risk?

And then, those who are higher risk, you treat. Those who are low risk, you don’t treat. We need to be able to generate data to support that, and I believe that, if you demonstrate how a test will change management, then patients and physicians are more likely to order the test or the areas that this test can potentially be useful will be in determining, how often should you get imaging after you do nephrectomy, for example? It’s like maybe some people get imaging as frequently as every three to six months, but if I told you that a patient is low risk of recurrence, then why do imaging every three to six months? The other area would be, who should get additional treatment after surgery? I think generating data in each of these different spaces to inform how a test like this can be used will be necessary to really get it from the lab to clinic.

Dr. Joyce: Yeah, that’s a great point too. You mentioned the cost up front of a biomarker like this, but there’s also the cost of numerous CT scans, follow-ups, trips, time off from work, going to those clinic visits. There’s some financial toxicity there to the patient, that could potentially be avoided with something like this.

Dr. Salami: Yeah.

Dr. Joyce: Well, Dr. Salami, thank you so much. This is incredibly exciting work. Congratulations. I can’t thank you enough for spending the time to speak with us about this, and I look forward to seeing what’s coming next.

Dr. Salami: Thanks, Dr. Joyce. I appreciate the time.