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Immune checkpoint inhibitors can stimulate antitumor immunity but can also induce toxicities termed immune-related adverse events (irAEs). Colitis is a common and severe irAE that can lead to treatment discontinuation. Mechanistic understanding of gut irAEs has been hampered because robust colitis is not observed in laboratory mice treated with checkpoint inhibitors. We report here that this limitation can be overcome by using mice harboring the microbiota of wild-caught mice, which develop overt colitis following treatment with anti-CTLA-4 antibodies. Intestinal inflammation is driven by unrestrained activation of IFNγ-producing CD4⁺T cells and depletion of peripherally induced regulatory T cells through Fcγ receptor signaling. Accordingly, anti-CTLA-4 nanobodies that lack an Fc domain can promote antitumor responses without triggering colitis. This work suggests a strategy for mitigating gut irAEs while preserving antitumor stimulating effects of CTLA-4 blockade. 

Abstract Multispectral optoacoustic tomography (MSOT) is a beneficial technique for diagnosing and analyzing biological samples since it provides meticulous details in anatomy and physiology. However, acquiring high through‐plane resolution volumetric MSOT is time‐consuming. Here, we propose a deep learning model based on hybrid recurrent and convolutional neural networks to generate sequential cross‐sectional images for an MSOT system. This system provides three modalities (MSOT, ultrasound, and optoacoustic imaging of a specific exogenous contrast agent) in a single scan. This study used ICG‐conjugated nanoworms particles (NWs‐ICG) as the contrast agent. Instead of acquiring seven images with a step size of 0.1 mm, we can receive two images with a step size of 0.6 mm as input for the proposed deep learning model. The deep learning model can generate five other images with a step size of 0.1 mm between these two input images meaning we can reduce acquisition time by approximately 71%.