Novel Biomarkers in RCC: Imaging and Blood-Based

Rana McKay, MD, University of California, San Diego, and David Braun, MD, PhD, Yale Cancer Center, highlights novel biomarkers that are being developed, including imaging-, pathology-, AI-, and blood-based biomarkers.

Dr. McKay: I’d love to hear your thoughts about other novel biomarkers that are being developed, imaging biomarkers, pathology-based, AI-based biomarkers, etc. Where do blood-based biomarkers stand in kidney cancer?

Dr. Braun: I think those are phenomenal questions, and that’s why, despite the challenges so far, I am incredibly optimistic for the future. I think the hope is the low hanging fruit, right? That we’re going to find our EGFR. We’re going to find the one mutation that’s going to tell us yes or no for a therapy. I think the answer is it’s more complicated than that, and, because of that, we’re going to need more sophisticated approaches, and that’s going to fall into a lot of different buckets. Thinking still about tissue-based approaches, we are just starting to scratch the surface. We’re moving from our one gene at a time or bulk RNA sequence where we put a tumor in a blender, the single-cell approaches, where we actually are teasing out individual cells and finding which populations and interactions might matter. Spatial approaches where we can actually see some cellular neighborhoods that might have an impact. We’re starting to realize those probably do have an impact, things like tertiary lymphoid structures within the tumor. There’s hope still for tissue-based biomarkers.

But then, you’re absolutely right, there’s all of these other dimensions. There’s circulating biomarkers. We know that the circulating cytokine milieu and the peripheral immune system play a critical role, and so understanding those using high-dimensional approaches, flow cytometry, single-cell sequencing, cytokine assays, those are going to be really important. Then, those enable you in some way to capture dynamics, too. While it’s really hard, it’s not impossible. It’s really hard to sample a tumor over and over again. Getting routine sampling of blood is relatively straightforward and it enables you to not just get this static snapshot at one time point, but actually see the evolution of things like in the cytokine milieu, the growth factor milieu, the peripheral immune system, that evolution might be really important for response or not. We’ve seen that in some systems. We’ve seen expansion of certain T-cell populations, the presence of certain inflammatory cytokines really influencing response to immune therapy. So, I think those will be a rich source of biomarkers.

You mentioned imaging as well. I think that’s really nice. I think we’re starting to get the point of molecular imaging. The CA9 molecular imaging was really interesting. We know this PD-L1-based imaging, this CD8-based imaging. So, starting to get where you really have a whole body view of what that looks like and can track the dynamics of that over time is really interesting. But, now we’re at a point where we have all these different areas and this tremendous amount of data. How do we make sense of it?

I was hopeful, maybe naively, that we’d get that one marker that’s going to tell us yes/no, but I think it’s going to be more complicated. I think you’re absolutely right. This is where things like AI-based approaches is going to be important. What are the different features, data sets, that we can get? Tissue-based. Blood-based. Imaging-based. Then, how can we integrate that together and say, “What is the most important thing to decide yes or no for this patient?” It’s probably not going to be necessarily binary, and that’s okay. I think we can deal with complexity. But, I think it’s going to capture each of those data types. The hard work is going to be then combining it together in an intelligent way to make sense of it.

View their other comments on Current Biomarkers, Strategies for Patient Selection, and Novel Therapies With the Most Promise.