Using 3D Invasion properties of RCC Cell Lines In Vitro to predict their Metastatic Potential In Vivo

Read the full article See related articles

Listed in

This article is not in any list yet, why not save it to one of your lists.
Log in to save this article

Abstract

Renal cell carcinoma (RCC) exhibits significant heterogeneity, making it challenging to predict tumor aggressiveness and therapeutic response. To improve prognostic accuracy and develop tailored treatment strategies, it is crucial to mimic both cancer cells and their microenvironment in vitro . Using a combination of in vitro and in vivo models, we investigated the invasive properties of three RCC cell lines—RCC10, RCC7 and 786-O— that displayed distinct signaling profiles, combining EMT characteristics and upregulation of key metastatic markers. Our findings revealed that RCC7 and 786-O exhibited greater metastatic potential than RCC10, as demonstrated by increased extravasation in zebrafish embryos and higher lung metastases in the chorioallantoic membrane (CAM) and mice models. Comparative pathway analysis indicated that RCC7 displays partial epithelial-mesenchymal transition (pEMT) characteristics and upregulates key metastatic markers. Furthermore, our 3D spheroid invasion model as well as our patient-derived RCC tumoroid system predicted accurately their metastatic behavior, closely mirroring their aggressiveness in vivo . Thus, these 3D models might be predictive of tumor outcome, underscoring their utility as reliable predictive tools for RCC progression and therapeutic response.

Novelty and Impact

Non-uniform distribution of genetic and phenotypic subpopulations within RCC tumors causes many tumors of similar histological grade to have vastly different metastatic potential. We show that 3D spheroids and RCC patient-derived tumoroid models more accurately reflect in vivo invasive behavior than traditional 2D assays, providing powerful predictive tools for RCC aggressiveness and metastatic disease. These findings have significant implications for precision oncology, enabling better preclinical evaluation of the metastatic risk to the patient.

Article activity feed