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Computational structural optimization enhances IL13Rα2 - B7-H3 tandem CAR T cells to overcome
Michaela M Meehl1, Kalyan Immadisetty2, Vikas D Trivedi3
1Department of Bone Marrow Transplantation and Cellular Therapy (BMTCT), St. Jude Children's Research Hospital, Memphis, TN 38105, USA; College of Graduate Health Sciences, University of Tennessee Health Science Center, Memphis, TN 38163, USA.
Abstract:
Chimeric antigen receptor (CAR) T cell therapy is a highly effective treatment for multiple malignancies. However, one limitation is tumor antigen-heterogeneity and downregulation, which allows tumor cells to evade conventional, monospecific CAR T cells. One approach to overcome this tumor escape is by utilizing a tandem CAR recognizing two antigens. However, tandem CAR constructs often require optimization to achieve cell surface expression and function. Herein, we describe our process of designing an IL-13Rα2-B7-H3 tandem CAR. Interestingly, our original tandem CAR failed to express on the cell surface, leading to a systematic evaluation of 24 tandem constructs varying in their scFv positioning, linkers, and specific amino acids. We identified a "trouble region" in the CAR and optimized it using computational approaches, rescuing surface expression and improving function compared with monospecific CAR T cells. Further, the optimized tandem CAR T cells more effectively eliminated tumors than monospecific CAR T cells in vivo. Our study demonstrates the successful application of structure-guided computational strategies to restore surface expression and antitumor efficacy of an IL13Rα2 - B7-H3 tandem CAR. Our study also highlights the necessity of computational methods to guide the design of synthetic proteins, and that these methods can increase CAR T cell efficacy.
Insights
Designing a dual-targeting chimeric antigen receptor (CAR) T cell therapy using computational methods improved its ability to fight tumors. This optimized tandem CAR T cell therapy overcomes tumor escape mechanisms for better cancer treatment.
Area of Science:
- Immunotherapy
- Cancer Biology
- Computational Biology
Background:
- Chimeric antigen receptor (CAR) T cell therapy shows efficacy against malignancies.
- Tumor antigen heterogeneity and downregulation enable cancer cells to evade monospecific CAR T cells.
- Tandem CARs targeting multiple antigens offer a strategy to overcome tumor escape.
Purpose of the Study:
- To design and optimize an IL-13Rα2-B7-H3 tandem CAR T cell therapy.
- To address challenges in tandem CAR surface expression and function.
- To enhance antitumor efficacy against tumors exhibiting antigen heterogeneity.
Main Methods:
- Systematic evaluation of 24 tandem CAR constructs with variations in scFv positioning, linkers, and amino acids.
- Identification and optimization of a critical "trouble region" using computational approaches.
- In vivo efficacy studies comparing optimized tandem CAR T cells with monospecific CAR T cells.
Main Results:
- The original tandem CAR construct failed to express on the cell surface.
- Optimization using computational strategies rescued CAR surface expression and improved function.
- Optimized tandem CAR T cells demonstrated superior tumor elimination compared to monospecific CAR T cells in vivo.
Conclusions:
- Structure-guided computational strategies can restore surface expression and enhance antitumor efficacy of tandem CAR T cells.
- Computational methods are essential for designing synthetic proteins like CARs.
- Optimized tandem CAR T cell therapy offers a promising approach to overcome tumor escape and improve cancer treatment outcomes.
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