26 Applying single cell RNA sequencing of sarcomatoid renal cell carcinoma for the development of a novel transcriptomic biomarker to predict immunotherapy response
Authors: Salgia Mr Nicholas , Aubrecht Mr Wilhelm , Attwood Kristopher , Chow Jacky , Eng Kevin , Wang Jianmin , Xu Bo , Long Mark , Kauffman Eric , Muhitch Jason 
Published: August 2024

Background

Renal cell carcinoma with sarcomatoid features (sRCC) is a unique kidney cancer subtype associated with aggressive biology and poor clinical outcomes. Intriguingly, sRCC has recently exhibited preferential responsiveness to immune checkpoint blockade (ICB) therapies. Within the RCC population, however, there currently is a paucity of biomarkers predictive for ICB response, representing a critical unmet clinical need for optimal therapy selection. Therefore, we sought to derive a transcriptomic signature from sRCC samples encapsulating this paradoxical hyper-aggression and ICB-responsiveness to identify RCC patients most likely to benefit from ICB irrespective of sarcomatoid feature presence.

Methods

Nephrectomy specimens from patients with RCC were processed for single cell RNA sequencing (scRNAseq). Clustering was performed and annotated tumor cells were computationally isolated. Tumor cell counts were aggregated and differential expression between sRCC and non-sRCC cases was performed. Genes significantly upregulated in sRCC were next filtered against differential gene expression data of sRCC vs non-sRCC from three clinical RCC datasets (TCGA, CheckMate, & Javelin101) to identify genes enriched in sRCC across scRNAseq and bulk RNA sequencing. The prognostic and predictive features of this gene signature were then assessed in cohorts of patients with RCC receiving ICB.

Results

In total, 73,123 unique cells from 18 RCC patients (10 sRCC; 8 clear cell RCC) were analyzed by scRNAseq, including 5,386 tumor cells. Differential gene expression of tumor clones revealed 20 genes significantly enriched in sRCC relative to ccRCC. Filtering against public differential gene expression data resulted in 10 genes included within the final Sarcomatoid Signature (SS). SS expression scores for clinical specimens were calculated by single-sample gene set enrichment analysis. SS scores were significantly enriched in sRCC patient tumors across TCGA KIRC, CheckMate, and IMmotion151 datasets (p&lt;0.001 for all cohorts). Within TCGA KIRC, SS scores were significantly increased in patients with nuclear grade 4 (p&lt;0.001) and metastatic (p&lt;0.001) disease, while stratification by median SS score revealed worsened overall (HR=2.19, p&lt;0.001) and disease-free (HR=2.08, p&lt;0.001) survival among SS-high patients. Amongst the CheckMate cohort (including patient samples from the CheckMate-010 & CheckMate-025 trials), SS-high patients experienced improved progression free survival (PFS) when treated with nivolumab relative to everolimus (HR=0.70, p=0.055), whereas no difference was seen between therapies in the SS-low patient population (HR=0.98, p=0.9). In the IMmotion151 trial, which was not utilized for gene filtering and signature refinement and thus represents an independent validation cohort, SS-high patients experienced reduced PFS duration (HR=1.39, p&lt;0.001) irrespective of treatment arm. However, SS-high patients experienced improved objective response rates with the ICB-containing atezolizumab/bevacizumab regimen compared to sunitinib (p&lt;0.001) and relative to SS-low patients receiving atezolizumab/bevacizumab (p=0.028) (Figure 1). Within the SS-high population, patients experienced prolonged PFS with atezolizumab/bevacizumab (HR=0.70, p=0.003) relative to sunitinib whereas no PFS difference between arms was seen in the SS-low population (HR=1.01, p&gt;0.99).

Conclusions

This novel SS derived from scRNAseq demonstrates negative prognostic yet positive predictive value for ICB response. In a domain currently void of efficacious biomarkers, the SS, upon prospective validation, may assist in optimal therapy selection for patients with RCC.