Taking a systems biology approach to model how antigens of different sequences impact T cell biology, Achar, Bourassa, and Rademaker et al. used a robotics platform to dynamically monitor multiple secreted cytokines as a measure of T cell activation, utilizing murine and human antigen:TCR pairs, a CAR, and multiple perturbations. Dimensionality reduction, modeling, and machine learning deduced at least 6 classes of antigens, which varied in signal direction (agonism vs antagonism), strength, and “latent space”, a parameter reflecting antigen quality. CAR-19 insertion into OT-I cells revealed the complex impact of co-delivering TCR and CAR signals.
Contributed by Ed Fritsch
ABSTRACT: Systems immunology lacks a framework with which to derive theoretical understanding from high-dimensional datasets. We combined a robotic platform with machine learning to experimentally measure and theoretically model CD8(+) T cell activation. High-dimensional cytokine dynamics could be compressed onto a low-dimensional latent space in an antigen-specific manner (so-called "antigen encoding"). We used antigen encoding to model and reconstruct patterns of T cell immune activation. The model delineated six classes of antigens eliciting distinct T cell responses. We generalized antigen encoding to multiple immune settings, including drug perturbations and activation of chimeric antigen receptor T cells. Such universal antigen encoding for T cell activation may enable further modeling of immune responses and their rational manipulation to optimize immunotherapies.
Author Info: (1) Immunodynamics Group, Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA. (2) Department of Physics, McGill U
Author Info: (1) Immunodynamics Group, Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA. (2) Department of Physics, McGill University, Montral, Qubec, Canada. (3) Department of Physics, McGill University, Montral, Qubec, Canada. (4) Immunodynamics Group, Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA. (5) Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA. (6) Immunodynamics Group, Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA. (7) Genitourinary Malignancies Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA. (8) Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA. (9) Department of Physics, McGill University, Montral, Qubec, Canada. (10) Immunodynamics Group, Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA.
