Identification of Molecular Subtypes of Clear-Cell Renal Cell Carcinoma in Patient-Derived Xenografts Using Multi-Omics

Background/Objectives: Clear-cell renal cell carcinoma (ccRCC) is a heterogenous disease that can be classified into multiple molecular subtypes with differential prognosis and sensitivities to treatments based on their genomic, transcriptomic, proteomic, and metabolic profiles. Patient-derived xenografts (PDXs) are high-fidelity cancer models because they maintain similar genotypes and immunohistologic phenotypes to the parental tumors and respond to standard-of-care therapies as expected. However, whether the molecular subtypes identified in ccRCC patient samples are preserved in PDX models is not clear. Our objective is to compare the transcriptional and proteomic profiles of our PDX models to those of ccRCC patients and identify both similarities and distinctions between molecular profiles of PDX subtypes and corresponding ccRCC patient subtypes, so that proper PDX subtypes can be used when investigating the corresponding ccRCC patient subtypes. Methods: To match PDXs to the human ccRCC molecular subtypes, we compared the transcriptomic and proteomic profiles of five ccRCC PDX models established in our lab to those of the human ccRCC molecular subtypes reported by our group, as well as other groups, using hierarchical analysis, Principal Component Analysis (PCA), and Permutation Correlation Analysis. The enrichment of key molecular pathways in PDXs and ccRCC subtypes was determined using Gene Set Enrichment Analysis. Results: We found that each PDX resembles one of the molecular subtypes closely at both transcript and protein levels. In addition, PDXs representing different molecular subtypes show unique metabolic characteristics. Moreover, molecular subtypes of PDXs correlated with ccRCC patient subtypes in key pathway activities implicated in ccRCC progression and therapy resistance. Conclusions: Our results suggest that PDX subtypes should be used when investigating the molecular mechanism of cancer progression and therapy resistance for corresponding ccRCC patient subtypes. This "matching" strategy will greatly facilitate the clinical translation of positive findings into the optimal management of ccRCC patients.