Monitoring CAR T Cell Therapeutic Response and Toxicity with Real-time, Label-free Cell Quantification
- Institution
- Stanford University, CA
- Principal investigator
- Jennifer A. Dionne
- Funding
- $170K
- Duration
- 2025–2027
Moonbase score
Still being scored
Abstract
Chimeric antigen receptor (CAR) T cell therapy has emerged as a transformative immunotherapy for cancer, autoimmune diseases, and transplantation. In this treatment, a patient’s immune cells are isolated and engineered to express a synthetic CAR on their surface to direct T cell reactivity towards diseased cells. Patient CAR T cell quantity throughout the therapeutic process is a contributing indicator of the efficacy of immune response as well as an immediate sign of toxicity through complications such as cytokine release syndrome and immune effector cell-associated neurotoxicity syndrome. However, current methods for quantification are costly in both time and money, precluding point-of-care clinical support. There is a critical need for real-time, low-cost CAR T cell quantification. We propose a rapid, label-free, and cost-effective technique for CAR T cell counting in patient blood using surface enhanced Raman spectroscopy (SERS) and machine learning (ML). Aim 1 entails optimizing a SERS approach to collect Raman spectra from immune cells. This includes the development of methods in Raman spectroscopy and sample preparation, and plasmonic gold nanorod fabrication and surface modification. In Aim 2 we will develop and optimize ML infrastructure to classify and quantify engineered T cell spectra from that of other cells. Finally, in Aim 3 we will digitize patient blood samples mixed with nanorods into single T cell droplets, from which we extract optical signals for each cell using Raman spectroscopy and use ML to quantify CAR T cells. By conducting this procedure on patient blood over the course of CAR treatment, we will generate efficient cell quantification data that can be correlated to therapeutic impact with significant time and financial advantage over previous methods. Our proposal combines innovations in cancer biology, immunology, nanophotonics, and machine learning to develop a technique that will benefit researchers, clinicians, and patients for safer, more effective CAR-therapies. The successful completion of this project will lead to further efficient label-free methodologies, for example in cell receptor expression level quantification, strengthening the addition of this technique to the immunologist’s toolbox. Both the specific aims of this project as well as its future potential strongly align with the agency’s mission. This research will allow for the advancement of fundamental knowledge of CAR T cells and the downstream impacts of CAR expression on immune cells. Additionally, its application in patient care has the critical potential to benefit CAR therapy development and minimize fatal complications, aiding in enhancing health and reducing illness.
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