Exploiting Novel Somatic Mosaic Models of Clear Cell Renal Cell Carcinoma as Preclinical Platforms for Combinatorial Testing of Therapeutics
Abstract
Kidney cancer is the 13th most common cancer in both men and women and the 16th most common cause of cancer worldwide. Renal cell carcinoma (RCC) is one of the 10 most frequently diagnosed cancers. Approximately 60,000 new cases are diagnosed annually, with most being sporadic. Approximately half of the people diagnosed will die from the disease. Incidence and mortality rates have been increasing in many countries due to lifestyle factors including smoking tobacco and obesity--risk factors for RCC. Given the high rate of smoking tobacco in the military and risk factors associated with service, the incidence of ccRCC in Service Members is manifold higher than the general public. Clear cell renal cell carcinomas (ccRCC) are the most common subtype of RCC, accounting for roughly 70% of adult cases. Patients with ccRCC confined to the kidney receive a nephrectomy or nephron-sparing surgery. However, in the majority of cases, patients with metastases will not survive the disease. We now know an incredible amount about the genetic mutations leading to ccRCC. However, we are lacking representative genetically engineered, preclinical disease models that reflect these combinations of mutations. Specifically, genetically engineered mice are the current standard for human disease modeling. Yet, current mouse models of ccRCC are a mix of mutations that are either not prevalent in ccRCC, or do not reflect the most common mutation signature, specifically, combined loss of the genes VHL, PBRM1, BAP1, and SETD2. Transplant of human tumors into immunodeficient mice is an alternative, but this approach severely inhibits testing of immunotherapies, one of the most exciting therapeutic approaches for ccRCC. Our approach is to generate novel ccRCC models using our new state-of-the-art genetic engineering approaches for tumor modeling in mice, by precisely targeting each of the above mutations simultaneously. Completion of the novel experiments outlined in this proposal will directly address a Fiscal Year 2020 Kidney Cancer Research Program Area of Emphasis: Basic/Translational Science - New Disease Model Systems. Benefits and Risks: Deriving new cancer models can be time-consuming and fraught with challenges. However, our published approach has demonstrated the ability to accurately model multiple tumor types, even when assessing cell-by-cell with human tumors. Further, we can mix and match additional mutations in the future to expand to the less common tumor signature, allowing for precision patient disease avatars. In addition, our group is actively directing several clinical trials in ccRCC. We will thus use these mice as patient avatars for the ongoing clinical trial by employing the same therapeutics as are used in the trial. Using survival analysis, we will assess the predictive nature of these models for therapeutic testing. In addition, using single-cell approaches, we will directly compare the mouse and human tumors on a cell-by-cell basis to assess their fidelity in recapitulating human tumors. By credentialing our models against current frontline and experimental therapies, we have designed our proposal to be of maximal benefit to patients with metastatic kidney cancer. Specifically, if our models are predictive, they can be used to assess the safety and efficacy of combinatorial approaches and novel therapeutic interventions that would take years to decades to similarly complete in the clinical trial setting, all while patients with metastatic ccRCC continue to succumb to the disease. Our proposed models can additionally be used to investigate the mechanisms of ccRCC and derive new targets for therapeutic intervention. Finally, our approach is relatively cheap and simple. We have previously made our tools available to the scientific community (e.g., through open access repositories such as Addgene) and will continue to do so, multiplying the potential for impact in the fight against ccRCC. Ther
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Dec 05, 2021
- Source ID
- W81XWH2110484
Entities
People
- Joshua J. Breunig
Organizations
- Cedars-Sinai Medical Center
- United States Army