Development and Validation of an Intratumor Heterogeneity-Based Prognostic Model for Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is characterized by marked intratumor heterogeneity (ITH), which contributes to therapeutic resistance and poor clinical outcomes. We aimed to develop a robust prognostic model for stratifying patients with ccRCC on the basis of ITH.

RNA-seq data from 522 patients with ccRCC in TCGA-KIRC were analyzed using the DEPTH algorithm to quantify ITH, with external validation in the E-MTAB-1980 cohort (N = 101). Differentially expressed genes between high and low DEPTH tumors were identified, and a machine learning framework was applied to develop the ITHscore. The ITHscore was compared with other published signatures in literature for ccRCC.

The random survival forest model on the basis of three genes (UBE2C, MOCOS, and MELTF) was selected to compose the ITHscore, showing high accuracy in the development (5-year AUC = 0.957) and in the validation cohorts (5-year AUC = 0.82). The ITHscore had the best performance across all 45 retrieved signatures in both development and validation data sets. High-ITHscore tumors exhibited immunosuppressive microenvironments and were associated with immune checkpoint blockade (ICB) resistance signatures. The ITHscore was significantly associated with poor overall survival in five distinct tumor types across a meta-analysis of 104 independent data sets comprising 18,004 patients.

We developed and validated the ITHscore, a three-gene expression-based model with superior prognostic performance in ccRCC. The ITHscore reflects key features of aggressiveness in tumor biology, including immune evasion and ICB resistance. Its minimal gene set and consistent performance across data sets support its potential for clinical implementation in ccRCC stratification.