Commentary

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Dr Kashima on T-Cell Phenotypes Associated With ICI Response/Resistance in RCC

Soki Kashima, MD, PhD, discusses a study investigating T-cell phenotypes associated with response or resistance to checkpoint inhibitors in RCC.

Soki Kashima, MD, PhD, postdoctoral associate, Department of Internal Medicine, Yale School of Medicine; urologic surgeon, physican-scientist, Akita University School of Medicine, discusses findings from a single-cell RNA sequencing analysis investigating T-cell phenotypes associated with response or resistance to immune checkpoint inhibitors (ICIs) in tumor samples from patients with renal cell carcinoma (RCC).

Investigators hypothesized that comprehensive characterization of the tumor microenvironment would reveal mechanisms of response or resistance to anti–PD-1 ICIs in RCC. This study collected 70 samples from 63 patients with clear cell (n = 59), chromophobe (n = 5), papillary (n = 1), and other (n = 5) RCC subtypes. Patients had received prior treatment with dual ICI (n = 37), an ICI plus a non-ICI agent (n = 12), a VEGF TKI (n = 9), and chemotherapy (n = 1). Eleven patients had received no prior treatment. Among the tumor samples collected for this analysis, 48 were naive to treatment at the time of collection, and 22 were treatment exposed at the time of collection. Best tumor responses to prior treatment included a complete response or partial response in 22 patients and progressive disease in 33 patients.

Investigators performed single-cell RNA sequencing (scRNAcseq) on all samples and created a transcriptomics atlas in RCC. Among CD8-positive T cells, heterogeneity was observed in exhausted T cells expressing TIM-3 and PD-1. The innate-like/tissue-resident program was upregulated in tumor samples that did not respond to ICIs. Conversely, the stress response program was upregulated in tumor samples that responded to ICIs. These findings indicate that exhausted T-cell phenotypes may correlate with the efficacy of ICIs in RCC. 

Although investigators are still assessing the clinical applications of these results, Kashima says that the scRNAseq data from this study cohort may help them identify a biomarker in the RCC tumor microenvironment of responders or nonresponders.

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