Modeling the Effects of Stroma on Clear Cell Renal Cell Carcinoma
Abstract
Clear cell renal cell carcinoma (ccRCC) is our 10th most common cancer. Surgical removal of the tumor is the primary treatment for the majority of patients and is curative in over 50% of cases. However, therapeutic options are limited for the large number of patients whose cancer recurs. Chemo- and radiotherapies are ineffective, and the current focus is on inhibitors of specific pathways. In other forms of cancer, the communication between cancer cells and surrounding support tissue known as stroma determines the behavior of cancer cells and their responsiveness to therapies. Because faithful laboratory models of ccRCC are lacking, it has not been possible to critically evaluate the role of the stroma in this type of cancer. We propose to use novel technologies to accurately mimic the tumor environment and to implant laboratory-generated tumors in the cortex of the mouse kidney, the location where human ccRCCs are found. Our goal is to generate faithful, patient-specific replicas of ccRCC with which we can determine the role of stromal cells in promoting aggressiveness of the tumor. In addition to providing an understanding of the role of tumor stroma in determining ccRCC aggressiveness, our work will provide patient-specific tumor models using which drugs can be tested to understand which are most effective. This personalized medicine approach is based on building models using tumor cells derived from surgically removed patient tumors. We also propose to extend this personalized medicine approach to the use of reprogrammed pluripotent stem cells from cancer patients. The proof-of-principle experiment included in our project involves generating the cell of origin for ccRCC from induced pluripotent stem cells with a mutation in a common ccRCC disease gene, VHL. Using this model, we will be able to study early behaviors of these mutant cells in the formation of cancer, which will yield important insights into the origins of ccRCC that may be applicable to disease prevention. This state-of-the-art approach to disease modeling for ccRCC takes advantage of recent years’ progress in understanding how to generate new kidney tissue.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Oct 29, 2018
- Source ID
- W81XWH1810620
Entities
People
- Leif Oxburgh
Organizations
- Maine Medical Center
- United States Army