Understanding molecular phenotypes in RCC may help drive treatment decisions

In this interview with Urology Times®’ sister site, OncLive®, Benjamin L. Maughan, MD, PharmD, highlights key studies presented at the 2024 ASCO Annual Meeting regarding biomarkers and the evolution of first-line treatments in renal cell carcinoma (RCC). Maughan is an assistant professor in the Division of Medical Oncology at Huntsman Cancer Institute in Salt Lake City, Utah.

Benjamin L. Maughan, MD, PharmD

During the discussion, Maughan references data from the phase 3 KEYNOTE-426 trial (NCT02853331; abstract 4505), which showed that higher T-cell inflamed gene expression profile was significantly positively associated with objective response rate (P < .0001), progression-free survival (PFS; P < .0001), and overall survival (OS; P = .002) for pembrolizumab (Keytruda) plus axitinib (Inlyta), and that angiogenesis had a positive association with clinical outcomes for sunitinib (Sutent).¹

Additionally, findings from the phase 3 CLEAR trial (NCT02811861; abstract 4504) showed a greater clinical benefit of lenvatinib plus pembrolizumab vs sunitinib,² and data from the phase 3 IMmotion010 trial (NCT03024996; abstract 4506) also showed the potential for post-nephrectomy KIM-1 serum levels to act as a circulating protein biomarker for minimal residual disease and disease recurrence.³

What data were you most excited to see presented at ASCO in RCC?

A couple of biomarker studies were presented—abstracts, 4504, 4505, and 4506—from prospective, large phase 3 studies. We have a number of combinations that we can use right now and hopefully work like this will help us better understand what the optimal setting is that one treatment will work in vs another. That’s particularly important because some combinations [examined] used a TKI-based angiogenesis-based therapy and one of the combinations [examined was] pure immunotherapy.

There has been preceding work that has started identifying [certain] molecular categorizations of disease biology, and some of these categories suggest an angiogenesis-driven type disease, whereas others [present more as] an immune-inflamed phenotype. Immunotherapies seem to work better in general for the T-cell immune infiltrating phenotype, whereas the angiogenesis-driven phenotype seems to be less responsive to checkpoint inhibitors—not unresponsive, just less responsive—but more responsive to these VEGF-based therapies. As we continue to do more work in this space, understanding these molecular phenotypes may help us understand who the optimal patients are [to receive] a TKI/IO combination vs an IO/IO combination.

Abstract 4519 is potentially very informative for us because it may take this biomarker work from molecular data to help us understand [if] there are basic things we can look for like an H&E [stained] slide [to identify relevant RCC subtypes]; that would make it much more accessible to pathologists [and] community oncology clinics to help us with this treatment selection process.⁴

What is important for community oncologists to know regarding the RCC field currently?

[First], we are continuing to understand the biology of these diseases, whether it’s clear cell or some of the less common non–clear cell diseases, which is going to help us tremendously with optimizing treatment selection as well as developing better therapeutics. The second thing is we are continuing to move away from the idea that non–clear cell is one [disease], and we’re breaking it up into its very specific disease entities and starting to develop treatments that are very disease specific. That’s a big advancement.

Finally, abstract 4527 highlighted this [at ASCO and] looked at the evolution of treatment patterns over time—there’s still a relatively high proportion of patients who are being treated in the first line with TKI monotherapy.⁵ This doesn’t appear to be as big of a problem as it is [in] prostate cancer, but using combination therapies in the upfront setting leads to much better disease control overall, so that’s something that we can work on improving as an oncology field.

In what cases would patients still receive frontline TKI monotherapy?

We don’t have a lot of information about why certain treatments were chosen vs others [in the data examined in abstract 4527], but we [saw] a decrease over time in TKI monotherapy as an upfront choice. There is an argument to be made for using TKI monotherapy in certain patients, and one setting is in patients with IMDC favorable-risk [disease]. There’s clearly a PFS benefit that’s been shown with combination therapy over TKI monotherapy for those patients, but in terms of OS we have yet to see a very well powered study clearly proving that even in that group, there’s an OS improvement.

The [retrospective study presented at ASCO] looked at treatment patterns over time. [Prior to] 2018, before we had a lot of combination therapies, approximately 79% of patients were treated in the first line with TKI monotherapy. Since then, it decreased to approximately [26%]. If you approximate that across studies, consistent IMDC favorable-risk [disease has been seen in] approximately 20% of patients—that is higher than what you would hope or expect it would be. We still have work to do to try and provide better treatment options for patients, but overall, we are making a lot of progress.

References

1. Rini BI, Plimack ER, Stus V, et al. Biomarker analysis of the phase 3 KEYNOTE-426 study of pembrolizumab (P) plus axitinib (A) versus sunitinib (S) for advanced renal cell carcinoma (RCC). J Clin Oncol. 2024;42(suppl 16):4505. doi:10.1200/JCO.2024.42.16_suppl.4505

2. Motzer RJ, Porta C, Eto M, et al. Biomarker analyses in patients with advanced renal cell carcinoma (aRCC) from the phase 3 CLEAR trial. J Clin Oncol. 2024;42(suppl 16):4504. doi:10.1200/JCO.2024.42.16_suppl.4504

3. Albiges L, Bex A, Suárez C, et al. Circulating kidney injury molecule-1 (KIM-1) biomarker analysis in IMmotion010: a randomized phase 3 study of adjuvant (adj) atezolizumab (atezo) vs placebo (pbo) in patients (pts) with renal cell carcinoma (RCC) at increased risk of recurrence after resection. J Clin Oncol. 2024;42(suppl 16):4506. doi:10.1200/JCO.2024.42.16_suppl.4506

4. Beig N, Nofallah S, McDermott SF, et al. Association of machine learning (ML)–derived histological features with transcriptomic molecular subtypes in advanced renal cell carcinoma (RCC). J Clin Oncol. 2024;42(suppl 16):4519. doi:10.1200/JCO.2024.42.16_suppl.4519

5. Fortuna GG, Jo Y, Chehade CH, et al. Changes in treatment (Rx) patterns and attrition rates in patients (pts) with metastatic clear cell renal cell carcinoma (mccRCC). J Clin Oncol. 2024;42(suppl 16):4527. doi:10.1200/JCO.2024.42.16_suppl.4527