I-Corps: Chimeric Antigen Receptor mediated immunosynapse assay

Rutgers, The State University Of New Jersey-Rbhs-New Jersey Med, Newark NJ

Investigators

Abstract

The broader impact / commercial potential of this I-Corps project is to facilitate the discovery and development of new chimeric antigen receptor (CAR)-modified cell therapies as well as improvement of personalized medicine for cancer patients. This technology may allow scientists across all sectors of the cellular therapy industry to discover and develop new and more effective cell therapies. The technology may advance the field of cell therapy by providing an efficient and reproducible way to analyze and predict the efficacy of different CAR-modified cells during research and development. The platform also has the potential to improve patient care by creating a faster and easier route to personalized medicine. Currently, patients eligible to receive cellular therapies undergo multiple rounds of cancer treatment and require swift action to prevent disease progression. This technology may serve as a diagnostic tool for medical oncologists to rapidly determine the most effective cell therapy available for cancer patients, thereby speeding the process of enrolling patients in the right course of treatment. Overall, this technology may impact both the scientific and healthcare communities by expediting cell therapy development and improving patient care. This I-Corps project further develops a diagnostic tool to improve the effectiveness of chimeric antigen receptor (CAR) modified cell therapies. CAR cell therapies use genetically modified donor immune cells to directly recognize and kill cancer cells. Current CAR cell therapies and those in clinical trials have been successful with refractory blood cancers. Many more therapies are in clinical development for both blood and solid cancers. Key to the recognition and killing of cancer cells is the strength of the communication between the CAR-modified cells and the cancer cells, called the immunological/immune synapse. Predicting the efficacy and potency of CAR-modified cells in patients represents one of the key unsolved problems in cell immunotherapy. The quality of immunosynapse correlates well with the efficacy of CAR cell therapies. The technology developed here is a rapid method of analyzing the quality of immunosynapse between CAR-modified cells and cancer cells using machine learning algorithms. This technology may contribute to understanding of the immunosynapse biology and improve clinical translation of new CAR cell therapies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.