Decoding the mechanisms of chimeric antigen receptor (CAR) T cell-mediated killing of tumors: insights from granzyme and Fas inhibition.

Chimeric antigen receptor (CAR) T cell show promise in cancer treatments, but their mechanism of action is not well understood. Decoding the mechanisms used by individual T cells can help improve the efficacy of T cells while also identifying mechanisms of T cell failure leading to tumor escape. Here, we used a suite of assays including dynamic single-cell imaging of cell-cell interactions, dynamic imaging of fluorescent reporters to directly track cytotoxin activity in tumor cells, and scRNA-seq on patient infusion products to investigate the cytotoxic mechanisms used by individual CAR T cells in killing tumor cells. We show that surprisingly, overexpression of the Granzyme B (GZMB) inhibitor, protease inhibitor-9 (PI9), does not alter the cytotoxicity mediated by CD19-specific CAR T cells against either the leukemic cell line, NALM6; or the ovarian cancer cell line, SkOV3-CD19. We designed and validated reporters to directly assay T cell delivered GZMB activity in tumor cells and confirmed that while PI9 overexpression inhibits GZMB activity at the molecular level, this is not sufficient to impact the kinetics or magnitude of killing mediated by the CAR T cells. Altering cytotoxicity mediated by CAR T cells required combined inhibition of multiple pathways that are tumor cell specific: (a) B-cell lines like NALM6, Raji and Daudi were sensitive to combined GZMB and granzyme A (GZMA) inhibition; whereas (b) solid tumor targets like SkOV3-CD19 and A375-CD19 (melanoma) were sensitive to combined GZMB and Fas ligand inhibition. We realized the translational relevance of these findings by examining the scRNA-seq profiles of Tisa-cel and Axi-cel infusion products and show a significant correlation between GZMB and GZMA expression at the single-cell level in a T cell subset-dependent manner. Our findings highlight the importance of the redundancy in killing mechanisms of CAR T cells and how this redundancy is important for efficacious T cells.

Automated detection of apoptotic bodies and cells in label-free time-lapse high-throughput video microscopy using deep convolutional neural networks.

MOTIVATION: Reliable label-free methods are needed for detecting and profiling apoptotic events in time-lapse cell-cell interaction assays. Prior studies relied on fluorescent markers of apoptosis, e.g. Annexin-V, that provide an inconsistent and late indication of apoptotic onset for human melanoma cells. Our motivation is to improve the detection of apoptosis by directly detecting apoptotic bodies in a label-free manner. RESULTS: Our trained ResNet50 network identified nanowells containing apoptotic bodies with 92% accuracy and predicted the onset of apoptosis with an error of one frame (5 min/frame). Our apoptotic body segmentation yielded an IoU accuracy of 75%, allowing associative identification of apoptotic cells. Our method detected apoptosis events, 70% of which were not detected by Annexin-V staining. AVAILABILITY AND IMPLEMENTATION: Open-source code and sample data provided at https://github.com/kwu14victor/ApoBDproject.