Predicting a Response to a Cell-Based Immune Modulating Cancer Therapy
Categories: “Cancer Therapeutics“
Reference #: 2023-016
OTC Contact: Ruchika Nijhara, Ph.D., MBA, CLP (Directory Information | Send a Message)
Georgetown researchers have designed a novel and innovative that has the potential to serve as a prognostic indicator for CAR-T therapy efficacy in patients. The method uses primary tumor biopsies and CAR- T cells in the zebrafish xenograft model to predict which patients will respond to such therapy before the therapy.
While chimeric antigen receptor T (CAR-T) cells have emerged as a promising immune therapy for treating multiple types of cancer, over 50% of patients still do not respond to treatment and eventually relapse within six months. Clinicians currently have no way to test the efficacy of CAR-T therapy before prescribing it. Given that the treatment costs about $500,000 a patient and is limited by manufacturing capacity, having a reliable model that can provide prognostic information to clinicians is a game changer in human cancer treatments. CAR-T cell therapy has shown considerable promise for hematologic malignancies. Great efforts are being made to ensure CAR-T cells’ high effectiveness in patients with no other treatment option. Testing CAR-T strategies in mice is expensive, laborious, and slow.
- A prognostic method to predict patient responsiveness to CAR-T therapy
- Potential use for preclinical evaluation of novel CAR designs containing a small compound-based ON or OFF switch
- Cost-efficient and fast assay
- Overcome limitations of xenogeneic graft-versus-host disease (xeno GVHD)
- High throughput (400 embryos)
- Small tissue requirement (~100 cells)
- Unparalleled in-vivo imaging
- Patient-Specific Analysis
- Allows the screening for small compounds, acting synergistically with CAR T cells
Stage of Development
Studies are being conducted to develop the imaging modality (fluorescence) and the software image package to analyze fluorescence differences over time.
Provisional application filed.