581 Multi-dimensional Synergy of Combinations (MuSYC) Algorithm Optimizes Combinatorial STING and TLR Adjuvant Cancer Vaccines

BackgroundOptimized cancer vaccine’s T cell priming potential can promote their translation in the current clinical climate of immune checkpoint inhibitor approval for many cancers.MethodsTo rigorously optimize adjuvant combinations that would effectuate an improved in vivo anti-tumor response, we u...

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Bibliographic Details
Published in:Journal for immunotherapy of cancer 2021-11, Vol.9 (Suppl 2), p.A611-A611
Main Authors: Taylor, David, Korrer, Michael
Format: Article
Language:English
Subjects:
Algorithms
Cancer
Cancer vaccines
Immunotherapy
Regular and Young Investigator Award Abstracts
Vaccines
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Summary:BackgroundOptimized cancer vaccine’s T cell priming potential can promote their translation in the current clinical climate of immune checkpoint inhibitor approval for many cancers.MethodsTo rigorously optimize adjuvant combinations that would effectuate an improved in vivo anti-tumor response, we utilized a novel algorithm, the multi-dimensional synergy of combinations (MuSYC), to maximize efficacy and minimize dosing for various classes of adjuvant combinations (Figure 1).ResultsIn-vitro, the MuSYC algorithm characterized the combination of R848 (TLR7/8 adjuvant) and STING agonist as synergistically efficacious and potent in activating murine bone marrow-derived dendritic cells (mBMDCs) and human monocytic cell line THP-1. These two selected adjuvants were then used to generate a MuSYC-derived optimized combination strategy for optimal in vivo priming. Finally, using B16 melanoma and MOC1 head and neck models, MuSYC-optimized cancer vaccines had the best anti-tumor response associated with increased tumor-infiltrating lymphocytes and changes in myeloid infiltration.Abstract 581 Figure 1ConclusionsCumulatively, we believe our MuSYC-centered approach will optimize translatable adjuvant combinations to improve cancer immunotherapy.
ISSN:2051-1426
DOI:10.1136/jitc-2021-SITC2021.581