Leveraging tumor organoids to model drug response in clear cell renal cell carcinoma.
Authors: Buffi NicolòMaria , Avolio Pier Paolo , Casale Paolo , Lughezzani Giovanni , Saita Alberto , Hurle Rodolfo , Paciotti Marco , Fasulo Vittorio , Contieri Roberto , Beatrici Edoardo , Koczberska Karina , Piscuoglio Salvatore , Lazzeri Massimo 
Published: February 2025

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Background:
Clear cell renal cell carcinoma (ccRCC) is characterized by a highly immunosuppressive tumor microenvironment that plays a crucial role in promoting tumor progression and conferring resistance to conventional treatments, such as chemotherapy and radiotherapy. Systemic therapies, including tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs), have become central to the management of ccRCC. However, patient responses to these treatments vary significantly. Given the pivotal role of ICIs in ccRCC therapy, recreating the tumor-immune microenvironment in vitro is essential to address the current lack of reliable biomarkers. Patient-derived tumor organoids (PDTOs) offer a highly accurate platform for evaluating drug efficacy tailored to individual patients, significantly enhancing therapeutic decision-making. These models closely replicate the three-dimensional architecture, genetic profile, and behavior of the original tumor tissue. Moreover, PDTOs can be co-cultured with various cell types, such as cancer-associated fibroblasts (CAFs) and immune cells, to simulate the tumor-immune microenvironment. The aim of our study is to set an in vitro drug screening by providing more physiologically relevant PDTOs which could enable researchers to study how therapies, especially TKIs and ICIs, interact with both tumor and immune cells.
Methods:
This is a translational on-going study including patients with diagnosis ccRCC. We successfully established five patient-derived ccRCC PDTO lines from individuals who underwent partial or radical laparoscopic robotic assisted nephrectomy. All the models faithfully recapitulated the original tumors at both the histological and genetic levels, harboring somatic mutations in key genes such as VHL (50%), PBRM1 (30%), SETD2, and BAP1. The PDTOs were treated with standard-of-care compounds, including Cabozantinib, Lenvatinib, and Axitinib, and the resulting dose-response curves observed.
Results:
When co-cultured with autologous cancer-associated fibroblasts (CAFs) and immune cells, and treated with the same compounds, the PDTOs exhibited different response patterns compared to PDTOs alone, indicating that the tumor microenvironment significantly modulates drug response. We tested Ipilimumab, Nivolumab, and Pembrolizumab in our co-culture system, both as monotherapies and in combination. The resulting dose-response curves revealed heterogeneous levels of drug sensitivity and resistance.
Conclusions:
In conclusion, by faithfully replicating the tumor microenvironment and facilitating comprehensive drug screening, this approach aims to identify optimal treatment regimens and uncover mechanisms of resistance. Ultimately, it seeks to create a personalized, predictive model that can enhance clinical decision-making in the management of ccRCC.