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Updated: Sep 21, 2025
A Syngeneic Mouse Model of Metastatic Renal Cell Carcinoma for Quantitative and Longitudinal Assessment of Preclinical Therapies
Published on: April 12, 2017
Kidney Cancer Models for Pre-Clinical Drug Discovery: Challenges and Opportunities
Laura Pohl1, Jana Friedhoff1, Christina Jurcic1
1Molecular Urooncology, Department of Urology, University Hospital Heidelberg, Heidelberg, Germany.
Abstract:
Renal cell carcinoma (RCC) is among the most lethal urological malignancies once metastatic. The introduction of immune checkpoint inhibitors has revolutionized the therapeutic landscape of metastatic RCC, nevertheless, a significant proportion of patients will experience disease progression. Novel treatment options are therefore still needed and in vitro and in vivo model systems are crucial to ultimately improve disease control. At the same time, RCC is characterized by a number of molecular and functional peculiarities that have the potential to limit the utility of pre-clinical model systems. This includes not only the well-known genomic intratumoral heterogeneity (ITH) of RCC but also a remarkable functional ITH that can be shaped by influences of the tumor microenvironment. Importantly, RCC is among the tumor entities, in which a high number of intratumoral cytotoxic T cells is associated with a poor prognosis. In fact, many of these T cells are exhausted, which represents a major challenge for modeling tumor-immune cell interactions. Lastly, pre-clinical drug development commonly relies on using phenotypic screening of 2D or 3D RCC cell culture models, however, the problem of "reverse engineering" can prevent the identification of the precise mode of action of drug candidates thus impeding their translation to the clinic. In conclusion, a holistic approach to model the complex "ecosystem RCC" will likely require not only a combination of model systems but also an integration of concepts and methods using artificial intelligence to further improve pre-clinical drug discovery.
Insights
Novel treatments for metastatic renal cell carcinoma (RCC) are needed due to immune checkpoint inhibitor resistance. Advanced model systems and artificial intelligence are crucial for improving pre-clinical drug discovery in RCC.
Area of Science:
- Oncology
- Urological Malignancies
- Cancer Immunology
Background:
- Metastatic renal cell carcinoma (RCC) remains a lethal malignancy despite advances in immunotherapy.
- A significant patient subset experiences disease progression, necessitating novel therapeutic strategies.
- Pre-clinical models are vital for developing new treatments but face challenges due to RCC's unique biological characteristics.
Purpose of the Study:
- To highlight the limitations of current pre-clinical model systems for renal cell carcinoma (RCC).
- To discuss the impact of intratumoral heterogeneity (ITH) and the tumor microenvironment on RCC progression and treatment response.
- To emphasize the need for advanced, integrated approaches, including artificial intelligence, for effective pre-clinical drug discovery in RCC.
Main Methods:
- Review of current understanding of renal cell carcinoma (RCC) biology, including genomic and functional intratumoral heterogeneity (ITH).
- Analysis of the tumor microenvironment's influence on RCC, particularly the role of exhausted T cells.
- Discussion of challenges in pre-clinical drug development, such as phenotypic screening limitations and the "reverse engineering" problem.
Main Results:
- Renal cell carcinoma (RCC) exhibits significant genomic and functional intratumoral heterogeneity (ITH).
- High intratumoral cytotoxic T cell infiltration in RCC is paradoxically associated with poor prognosis due to T cell exhaustion.
- Current 2D and 3D cell culture models face limitations in accurately reflecting RCC complexity and in identifying drug mechanisms of action.
Conclusions:
- A combination of diverse pre-clinical model systems is required to accurately model the complex RCC "ecosystem".
- Integrating artificial intelligence (AI) with advanced model systems can overcome current limitations in pre-clinical drug discovery for RCC.
- A holistic approach is essential to improve the translation of pre-clinical findings to effective clinical treatments for renal cell carcinoma (RCC).
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