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

Abstract

Abstract 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.

Keywords

• computer vision
• head and neck cancer
• immunotherapy
• microfluidics
• precision oncology
• single‐cell transcriptomics