Rapid Profiling of Tumor‐Immune Interaction Using Acoustically Assembled Patient‐Derived Cell Clusters. Issue 22 (25th May 2022) - Ao, Zheng; Wu, Zhuhao; Cai, Hongwei; Hu, Liya; Li, Xiang; Kaurich, Connor; Chang, Jackson; Gu, Mingxia; Cheng, Liang; Lu, Xin; Guo, Feng
Abstract: Tumor microenvironment crosstalk, in particular interactions between cancer cells, T cells, and myeloid‐derived suppressor cells (MDSCs), mediates tumor initiation, progression, and response to treatment. However, current patient‐derived models such as tumor organoids and 2D cultures lack some essential niche cell types (e.g., MDSCs) and fail to model complex tumor‐immune interactions. Here, the authors present the novel acoustically assembled patient‐derived cell clusters (APCCs) that can preserve original tumor/immune cell compositions, model their interactions in 3D microenvironments, and test the treatment responses of primary tumors in a rapid, scalable, and user‐friendly manner. By incorporating a large array of 3D acoustic trappings within the extracellular matrix, hundreds of APCCs can be assembled within a petri dish within 2 min. Moreover, the APCCs can preserve sensitive and short‐lived (≈1 to 2‐day lifespan in vivo) tumor‐induced MDSCs and model their dynamic suppression of T cell tumor toxicity for up to 24 h. Finally, using the APCCs, the authors succesully model the combinational therapeutic effect of a multi‐kinase inhibitor targeting MDSCs (cabozantinib) and an anti‐PD‐1 immune checkpoint inhibitor (pembrolizumab). The novel APCCs may hold promising potential in predicting treatment response for personalized cancer adjuvant therapy as well as screening novel cancer immunotherapy and combinational therapy. Abstract : Acoustically assembled patient‐derived cell clusters for the rapid profiling of complicated tumor microenvironment crosstalk are reported. This method is demonstrated to successfully model interactions among cancer cells, T cells, and short‐lived myeloid‐derived suppressor cells within 24 h. This method holds promising potential in predicting treatment response for personalized cancer adjuvant therapy and screening novel cancer treatments.