CAR T Cell Optimization at the Level of Receptor Signaling
Using a proteomic biosignature to predict CAR responsiveness and safety
Technology Overview
Dr. Stephen E.P. Smith
Currently, chimeric antigen receptor (CAR) T cells engineered to target cancer cells are tested for potential effectiveness using a standard set of preclinical in vitro cytotoxicity assays. Promising candidates from in vitro testing then undergo testing in animal models. These tests measure downstream effects of the signal transduction pathways, including cytokine production and target killing, that are activated by CAR stimulation. Even CAR T cells that appear to be safe and effective therapeutic candidates based on these assays, though, can be ineffective in practice or cause unpredictable toxic side effects in patients such as cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS). This unpredictability means that many CAR T cell trials fail for unknown reasons, at a cost of many millions of dollars.
To solve this challenge, neuroimmunologist Stephen E.P. Smith, PhD, studies receptor responses at the level of receptor stimulation. As a postdoctoral scholar, he developed a quantitative multiplex immunoprecipitation (QMI) assay in which proteins in complexes are co-immunoprecipitated from CAR T cells and quantified using labeled antibody probes detected by flow cytometry. Within hours, this reporter assay can quantify hundreds of dynamic protein-protein interactions, including interactions that occur during T-cell receptor stimulation.
Using QMI, the Smith Lab can provide a detailed analysis of the intracellular signalosome that forms upon stimulation of a CAR T cell. Even small changes or disruptions to complexes can be detected. Studies from the Smith Lab show that QMI can detect meaningful differences in engineered CAR T cells, and that variations in protein-protein interaction patterns can provide a valuable measure of potential CAR T-cell effectiveness and safety. Together, this information can help predict which CAR constructs are likely to cause CRS, for example.
As such, predictive data from the QMI analysis could offer insights about how to refine engineered T-cell receptors for desired antigen-directed responsiveness with minimal adverse effects. Using this knowledge, CAR T-cell manufacturing protocols could measure, and eventually control, biological variation by QMI-based monitoring of signalosome activation — a personalized “biosignature” for each batch of CAR T cells. This QMI technology platform could inform preclinical decision-making about which CAR products to move forward into clinical trials, which would be a major cost-saving step for companies and reduce the risk of neurotoxic side effects for patients.
Dr. Smith has created a company, Q-Immune, to further explore applications for QMI coupled with machine learning to sensitively quantify dynamic signaling networks that are predictive of a therapy’s safety and efficacy. His lab has completed three proof-of-concept studies, which associated the efficacy and toxicity of different CAR products with the products’ protein-protein signatures.
Use of Q-Immune’s predictive algorithm can de-risk and accelerate the development of high-impact cellular medicines. The aim of this approach is to transform CAR T-cell therapy from a last resort to a more predictable and safe treatment.
Dr. Smith has extensive experience in neuroimmunotherapy, proteomics, QMI, and other technology focused on deciphering the language of the cell. His approach is to determine the mechanistic basis of how the CAR redirects endogenous signaling pathways, and to develop approaches that optimize the cellular response through optimization of protein interaction networks.
He is also interested in industry partnerships that could provide CAR T-cell products and clinical data to further refine the QMI experimental assay and increase its utility in mapping and quantifying signal transduction responses for more rational CAR T-cell design. As QMI and predictive modeling continue to improve, the testing framework could be scaled up relatively quickly to increase capacity.
Stage of Development
- Pre-clinical in vitro
- Pre-clinical in vivo
- Preclinical ex vivo
- Licensing agreements
Partnering Opportunities
- Clinical trials
- Collaborative research and development
- Sponsored research agreement
- Consultation agreement
- Investigator-initiated clinical trials
- Drug development opportunity
- Tissue sample access
- Cell line access
Publications
Ritmeester-Loy SA, Draper IH, Bueter EC, … Smith SEP. Differential protein-protein interactions underlie signaling mediated by the TCR and a 4-1BB domain–containing CAR. Sci Signal. 2024;17(826):eadd4671.
Neier SC, Ferrer A, Wilton KM, Smith SEP, et al. The early proximal αβ TCR signalosome specifies thymic selection outcome through a quantitative protein interaction network. Sci Immunol. 2019;4(32):eaal2201.
Wehle DT, Bass CS, Sulc J, Mirzaa G, Smith SEP. Protein interaction network analysis of mTOR signaling reveals modular organization. J Biol Chem. 2023;299(11):105271.
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