Modeling Spatial Ecology in Clear Cell Renal Cell Carcinoma Model: A Novel Tool to Support Drug Sequencing Decisions
Abstract
Approximately a third of the 64,000 patients with newly diagnosed kidney cancer present with metastatic disease. A significant proportion of those with locally advanced disease experience disease recurrence or progression after surgical therapy, necessitating systemic treatment. Most of these patients have clear cell renal cell carcinoma (ccRCC). The availability of systemic agents for the treatment of metastatic ccRCC has evolved tremendously over the past decade. Despite the availability of over 20 Food and Drug Administration-approved drugs for metastatic ccRCC, clinicians face difficulty in treating patients, as studies are lacking that make direct comparisons between most of these agents, and previous studies have failed to show improved benefit among biomarker-selected patient populations. Our proposed study would be a novel investigation into the spatial ecology of ccRCC tumors. Tumors such as ccRCC have classically been defined as having intratumoral heterogeneity, but study of these cellular subpopulations, especially non-tumors cells, has thus far been limited. Many of these interactions can be perceived as either synergistic or antagonistic, especially those that produce a phenotypic change in the tumor-stromal microenvironment (TSM). The importance of these clonal interactions in ccRCC has yet to be explored. We hypothesize that the stromal architecture in ccRCC can predict response to the most common classes of systemic treatment, targeted therapy and immunotherapy. This gap in knowledge of the ccRCC tumor-stromal microenvironment could reveal important insights into pathophysiology and treatment management. Drugs used in metastatic renal cell carcinoma (mRCC), including tyrosine kinase inhibitors (TKIs) sunitinib and pazopanib, have some unique mechanisms of action but also share the characteristic of inhibiting tumor growth through interactions with tumor stroma, i.e., fibroblast and epithelial cells. Smaller studies have looked at the possible development of TKI resistance through cancer-associated fibroblasts (CAFs), yet surprisingly little has been done to investigate the roles of stromal cells and tissue architecture (spatial ecology) in the dynamic process of ccRCC progression and possible resistance. Immunotherapy drugs used in mRCC like nivolumab and IL-2 also have mechanisms of action that include interfacing with the TSM. Studies in other solid tumors like glioblastomas and breast cancer have given insight into possible clinical and therapeutic associations of CAFs and their spatial proximities to tumor cells. It is likely that evaluating and characterizing the tumor stromal architecture will give impactful clinical insights into RCC evolution and treatment response. These results could also provide invaluable information for the development of novel in silico models that would help to elucidate important microenvironmental barriers to effective cancer treatment and suggest how to overcome them. New tools are needed to help understand many of the complex interactions in the TSM. In recent years, mathematical modeling has demonstrated promising results in elucidating important dynamics in cancer research. These models can allow researchers to simulate microenvironmental effects that are difficult or costly to emulate in a biological system, i.e., oxidative stress or chemokines. These models also allow for expansion of time scales (days, years) that are not possible using traditional in vivo experiments. Our study looks to develop a framework using in silico models that help to characterize the TSM in ccRCC. These models could then be built upon in future studies to become more complex or specific and help researchers in the study of ccRCC.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Nov 19, 2019
- Source ID
- W81XWH1910655
Entities
People
- Brandon Manley
Organizations
- H. Lee Moffitt Cancer Center & Research Institute
- United States Army