Kidney Cancer Biomarker Insights: JAVELIN Renal 101

In the second video of this series, Katy Beckermann, MD, PhD, of Vanderbilt-Ingram Cancer Center, and David Braun, MD, PhD, of Yale School of Medicine, review recent findings from a study on the JAVELIN Renal 101 trial, focusing on the impact of avelumab and axitinib treatment on kidney cancer, as well as highlighting correlations between immune populations, T-cell receptor repertoire, and specific biomarkers like memory B cells and cytokine levels.

View their other comments on Transcriptomic Insights and Integrative Approaches.

—

Dr. Beckermann: You were part of a recent paper just out in Cancer Discovery, based on the JAVELIN Renal 101 trial, involving axitinib and avelumab. It was a significant effort, with more than 886 patients. You not only conducted transcriptomics but also had this nice correlative peripheral blood treatment over time changes.

Could you give us an overview of the big questions going into this trial from a translational correlative perspective, and then highlight some of the major findings?

Dr. Braun: This was the phase 3 study of avelumab plus axitinib, an anti-PD-L1 plus a VEGF TKI, versus a TKI alone. It led to the approval of avelumab plus axitinib in the first-line setting for metastatic clear cell. There was a significant biomarker paper led by Dr. Motzer and colleagues about 4 years ago in Nature Medicine, the first look largely at tumor-intrinsic factors but beginning to consider some host factors as well.

The key findings from that paper showed that total mutation burden, while predictive for anti-PD-L1 response in other tumor types, did not have the same association in kidney cancer. They identified an immune signature that predicted benefit from the IO-containing arm and an angiogenic signature that predicted benefit from the sunitinib arm. These findings underscored that tumor phenotypes make a difference in response.

They also found that individual mutations, particularly double mutants, where 2 mutations in specific genes each affected progression-free survival, stratified patients effectively.

The motivation for this second work was to move beyond tumor-intrinsic factors to consider circulating biomarkers. The lessons learned over the years suggest that effective biomarker discovery will not be be simply picking 1 gene at a time. It requires integrating multiple factors — tumor, host, and peripheral immune system.

The study looked at parameters within the peripheral blood affecting both treatment arms. Longer progression-free survival was associated with lower platelets and lower myeloid populations, particularly granulocytes, and higher lymphocytes. Sunitinib seemed to affect immune cell levels, whereas avelumab plus axitinib maintained more constant levels over time.

The analysis of T-cell receptors within the blood and tumor showed that sunitinib led to higher T-cell counts associated with longer progression-free survival, while avelumab plus axitinib resulted in greater changes in T-cell receptor repertoire and higher clonality. In the tumor, higher T cells were associated with better progression-free survival with immune therapy.

Further analyses integrated tumor genomics, host genetics, circulating biomarkers, and tumor microenvironment. The double mutant population, defined by somatic mutations and germline polymorphisms, had better progression-free survival. Within this group, high memory B cells and low naïve B cells were associated with better outcomes, suggesting a role for B-cell-mediated immunity. The double mutant group also had lower cytokine levels associated with myeloid recruitment and development, resulting in lower myeloid cell levels.

This study is an initial attempt to move away from single-factor analysis towards integrated markers. Understanding these factors together will be crucial for better predictive ability.