Abstract 4922: Unraveling sarcomatoid features in clear cell renal cell carcinoma with RNA-seq

Abstract Introduction: Sarcomatoid сlear cell renal cell carcinoma (ccRCC) represents a challenging and aggressive subset of kidney cancers with poor prognosis and resistance to conventional treatments. Recent studies have shown the benefit of checkpoint inhibitors (CPIs) for patients with sarcomatoid components; therefore, precise and objective evaluation of sarcomatoid differentiation is essential for CPI selection. Here, we developed a predictive model to identify sarcomatoid features in ccRCC. Methods: The machine learning-based transcriptomic sccRCC model was developed on 512 samples from publicly available datasets: TCGA-KIRC and CPTAC-ccRCC. Samples were excluded with the following criteria: inconsistent with ccRCC, less than 10% sarcomatoid component, or 20% tumor neoplastic cellularity. Sarcomatoid features for each case were assessed by 3 pathologists. The meta-cohort was divided into training (n = 348) and validation (n = 164) cohorts. The model was tested on a publicly available cohort of ccRCC patients from Sato et al. (n = 100). Clinical validation was performed using an additional prospective cohort that received BostonGene’s Tumor PortraitTM test (n = 32) with RNA-seq and whole exome sequencing (WES). The JAVELIN Renal 101 cohort was used to uncover differences in progression-free survival (PFS) associated with a predicted sarcomatoid component in advanced or metastatic ccRCCs treated with a combination of CPIs and tyrosine kinase inhibitors (TKIs). Results: H Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 4922.