An AI-assisted integrated, scalable, single-cell phenomic-transcriptomic platform to elucidate intratumor heterogeneity against immune response.

We present a novel framework combining single-cell phenotypic data with single-cell transcriptomic analysis to identify factors underpinning heterogeneity in antitumor immune response. We developed a pairwise, tumor-immune discretized interaction assay between natural killer (NK-92MI) cells and patient-derived head and neck squamous cell carcinoma (HNSCC) cell lines on a microfluidic cell-trapping platform. Furthermore we generated a deep-learning computer vision algorithm that is capable of automating the acquisition and analysis of a large, live-cell imaging data set (>1 million) of paired tumor-immune interactions spanning a time course of 24 h across multiple HNSCC lines (n = 10). Finally, we combined the response data measured by Kaplan-Meier survival analysis against NK-mediated killing with downstream single-cell transcriptomic analysis to interrogate molecular signatures associated with NK-effector response. As proof-of-concept for the proposed framework, we efficiently identified MHC class I-driven cytotoxic resistance as a key mechanism for immune evasion in nonresponders, while enhanced expression of cell adhesion molecules was found to be correlated with sensitivity against NK-mediated cytotoxicity. We conclude that this integrated, data-driven phenotypic approach holds tremendous promise in advancing the rapid identification of new mechanisms and therapeutic targets related to immune evasion and response.

A 3D microfluidic model for preclinical evaluation of TCR-engineered T cells against solid tumors.

The tumor microenvironment imposes physical and functional constraints on the antitumor efficacy of adoptive T cell immunotherapy. Preclinical testing of different T cell preparations can help in the selection of efficient immune therapies, but in vivo models are expensive and cumbersome to develop, while classical in vitro 2D models cannot recapitulate the spatiotemporal dynamics experienced by T cells targeting cancer. Here, we describe an easily customizable 3D model, in which the tumor microenvironment conditions are modulated and the functionality of different T cell preparations is tested. We incorporate human cancer hepatocytes as a single cell or as tumor cell aggregates in a 3D collagen gel region of a microfluidic device. Human T cells engineered to express tumor-specific T cell receptors (TCR-T cells) are then added in adjacent channels. The TCR-T cells' ability to migrate and kill the tumor target and the profile of soluble factors were investigated under conditions of varying oxygen levels and in the presence of inflammatory cytokines. We show that only the 3D model detects the effect that oxygen levels and the inflammatory environment impose on engineered TCR-T cell function, and we also used the 3D microdevice to analyze the TCR-T cell efficacy in an immunosuppressive scenario. Hence, we show that our microdevice platform enables us to decipher the factors that can alter T cell function in 3D and can serve as a preclinical assay to tailor the most efficient immunotherapy configuration for a specific therapeutic goal.