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.

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.…”