Kidney Cancer Models for Pre-Clinical Drug Discovery: Challenges and Opportunities

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.

Microphysiological systems as models for immunologically ‘cold’ tumors

“…This results in metabolic reprogramming toward aerobic glycolysis and the secretion of factors, including VEGFA, that foster angiogenesis. While clear cell renal cell carcinoma (ccRCC) has traditionally been labeled as a ‘cold’ tumor, it does exhibit some degree of response to immune checkpoint inhibitors (ICIs), and current standard care involves the combination of ICIs and TKIs like pembrolizumab and axitinib ( Pohl et al, 2022 ). However, a notable proportion of patients experience disease progression shortly after initiating systemic treatment.…”

“…A contributing factor to therapeutic failure is the distinct immunological characteristics of ccRCC, including a high number of tumor-infiltrating T cells, which paradoxically correlates with a poor prognosis (Pohl et al, 2022). Interestingly, immune-excluded ccRCC tumors, unlike counterparts in other (Heidegger et al, 2019).…”

Comprehensive prognostic assessment in kidney cancer: A multidimensional approach analyzing Fufang Sanling granules, genetic variants, and immune infiltration

“…Current prognostic models, while useful, are not infallible 19,20 . They often fail to capture the nuances of the individual patient's disease course, leading to a one‐size‐fits‐all approach that may not be optimal 21,22 . Thus, there is an evident need for personalized prognostic models that can more accurately predict disease outcomes, facilitating tailored treatment strategies and improving survival rates.…”

Digital Spatial Profiling Identifies the Tumor Periphery as a Highly Active Biological Niche in Clear Cell Renal Cell Carcinoma

“…This tumor entity is characterized by a high level of intratumoral heterogeneity (ITH), genomic instability and therapy resistance [1][2][3]. It is conceivable that this triad has a major impact on patient survival [4][5][6].…”