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Published on: August 16, 2024
The ExoGAN generative AI framework enables extracellular vesicle-based immunotherapy
Kiley Graim1, Zachary Greenberg1, Tina Salehi Torabi1
1University of Florida.
Neoantigens, specifically tumor antigenic peptides presented by MHC-I, are essential for activating cytolytic CD8+ T cells via T Cell Receptor (TCR) recognition. Neoantigens have drawn growing attention for developing novel cancer immunotherapy approaches. However, current cancer immunotherapy suffers from low and unpredictable response rates due to the heterogeneity and unknown TCR binding properties among human patients. To address this issue, here we introduce the ExoGAN generative AI framework to guide the design of novel tumor antigenic peptides. ExoGAN is a generative adversarial network (GAN) that integrates HLA physiochemistry feature engineering with sequence-level data, used here to design new HLA-A*02:01-targeting peptides. We apply ExoGAN to design human-based neoantigens that bind to specific MHC-Is (HLAs) with improved TCR recognition and T cell activation, training the model on the largest IEDB dataset of HLA peptides. To deliver these ExoGAN-designed peptides to cells for effective T cell activation, we incorporate a membrane surface molecular engineering approach to fully decorate extracellular vesicles (EVs) with ExoGAN-designed peptides in MHC complex proteins. EVs have been recognized as essential immunity mediators, which carry costimulators with antigenic presentation for leading the emerging and advanced cancer immunotherapy, making them ideal for peptide delivery. Computational and experimental validation shown here, demonstrate that the ExoGAN-generated peptide carrying EVs consistently express antigenic presentation to T cell TCRs, resulting in precisely controlled and programmable EV agents for anti-tumor immunity activation. This greater coverage of effective antigens advances mechanistic understanding of neoantigen functionality, despite inconsistent TCR signaling across human patients. Combined with EV immunity activation, our ExoGAN framework is deployable for clinical translation and enabling precision cancer immunotherapy. The increased scientific understanding of neoantigen diversity across tumors, discovered by ExoGAN, will also accelerate the development of new functional immunotherapy agents in activating patient-specific TCRs.
Neoantigens, specifically tumor antigenic peptides presented by MHC-I, are essential for activating cytolytic CD8+ T cells via T Cell Receptor (TCR) recognition. Neoantigens have drawn growing attention for developing novel cancer immunotherapy approaches. However, current cancer immunotherapy suffers from low and unpredictable response rates due to the heterogeneity and unknown TCR binding properties among human patients. To address this issue, here we introduce the ExoGAN generative AI framework to guide the design of novel tumor antigenic peptides. ExoGAN is a generative adversarial network (GAN) that integrates HLA physiochemistry feature engineering with sequence-level data, used here to design new HLA-A*02:01-targeting peptides. We apply ExoGAN to design human-based neoantigens that bind to specific MHC-Is (HLAs) with improved TCR recognition and T cell activation, training the model on the largest IEDB dataset of HLA peptides. To deliver these ExoGAN-designed peptides to cells for effective T cell activation, we incorporate a membrane surface molecular engineering approach to fully decorate extracellular vesicles (EVs) with ExoGAN-designed peptides in MHC complex proteins. EVs have been recognized as essential immunity mediators, which carry costimulators with antigenic presentation for leading the emerging and advanced cancer immunotherapy, making them ideal for peptide delivery. Computational and experimental validation shown here, demonstrate that the ExoGAN-generated peptide carrying EVs consistently express antigenic presentation to T cell TCRs, resulting in precisely controlled and programmable EV agents for anti-tumor immunity activation. This greater coverage of effective antigens advances mechanistic understanding of neoantigen functionality, despite inconsistent TCR signaling across human patients. Combined with EV immunity activation, our ExoGAN framework is deployable for clinical translation and enabling precision cancer immunotherapy. The increased scientific understanding of neoantigen diversity across tumors, discovered by ExoGAN, will also accelerate the development of new functional immunotherapy agents in activating patient-specific TCRs.
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