GSE172325
Behavioral-transcriptomic landscape of engineered T cells targeting human cancer organoids
Cellular immunotherapies are rapidly gaining clinical importance, yet predictive platforms for modeling their mode of action are lacking. Here, we unravel the behavioral and underlying molecular mechanisms that govern breast cancer targeting by an emerging cancer metabolome-sensing immunotherapy: ?? T cells engineered to express a V?9/V?2 T cell receptor (TEGs). Using a dynamic immuno-organoid 3D imaging-transcriptomics platform, BEHAV3D, we first demonstrate that TEGs kill organoids of multiple breast cancer subtypes, yet with varying sensitivity between individual patient-derived organoids (PDOs) and even among individual PDOs derived from the same patient. Furthermore, live-tracking of over 120,000 TEGs revealed a diverse behavioral landscape and identified a 'super-engager' cluster with serial killing capability. Inference of single-cell behavior with transcriptomics unraveled gene signatures linked to specific behaviors. The most notable gene signature identified 'super engager' killer TEGs and contained multiple genes previously unstudied in T cells. Furthermore, guided by a dynamic type 1 interferon (IFN-I) signaling module specifically induced by the highest TEG-sensitive organoid culture, we show that IFN-I can prime resistant organoids for¬ TEG-mediated killing. Thus, BEHAV3D characterizes behavioral-phenotypic heterogeneity of cellular immunotherapies and holds promise for improving solid tumor-targeting in a patient-specific manner. Overall design: Patient derived breast cancer organoids, that are known to have different responsiveness to the same engeneered T cells (TEGs) (Inez Johanna  et al 2019, J Immunother Cancer) were subjected to RNA seq. TEGs were isolated at different stages of targeting from the co-culture with 2 types of breast cancer organoids and subjected to scRNAseq.