Comparative single-cell transcriptomic profiling of patient-derived renal carcinoma cells in cellular and animal models of kidney cancer

Clear-cell renal cell carcinoma (ccRCC) is the most common form of kidney cancer, which is often resistant to conventional cancer therapies including chemotherapy and radiation therapy. Targeted treatments including immunotherapies and small molecule inhibitors have been recently developed with positive outcomes. However, variations in the patient response and resistance to therapies suggest that models that better recapitulate the pathogenesis and metastatic mechanisms of ccRCC are required to improve our understanding and subsequent management of ccRCC. Here, we examined the transcriptional landscapes of in vitro as well as orthotopic and metastatic NOD/SCID-γ mouse models of ccRCC using the RCC243 cell line established from the primary tumour of a patient that was diagnosed with metastatic ccRCC. Notably, RCC243 cells developed metastatic tumours using assays in mouse models, and they retained their clear-cell morphology irrespective of model type but exhibited differential gene expression profiles between models of RCC243, highlighting the impact of the cellular environment and the stage of disease progression. Furthermore, we identified prognostic markers that were conserved between RCC243 models and patient ccRCC tumour datasets and showed that genes that were upregulated in the metastatic RCC243 model were associated with worse prognosis in patients. Overall, this study presents a novel metastatic model of ccRCC, highlights the role of the cellular environment on the transcriptome, and demonstrates the importance of the choice of an experimental model. RCC243/RCC243-tdTomato were used to generate cell-line derived xenograft models in both the orthotopic (renal capsule) setting and metastatic (heterotopic) setting. The RCC243 patient-derived xenograft model was also generated in the orthotopic setting. The human tumour cells were isolated after tumour excision using magnetic-activated cell sorting (MACS) and then sent for single-cell RNA-sequencing.