Rapid Profiling of Tumor-Immune Interaction Using Acoustically Assembled Patient-Derived Cell Clusters

Zheng Ao, Zhuhao Wu, Hongwei Cai, Liya Hu, Xiang Li, Connor Kaurich, Jackson Chang, Mingxia Gu, Liang Cheng, Xin Lu, Feng Guo

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

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.

Original languageEnglish
Article number2201478
JournalAdvanced Science
Volume9
Issue number22
DOIs
StatePublished - 5 Aug 2022
Externally publishedYes

Keywords

  • acoustofluidics
  • cancer immunity
  • cell clusters
  • myeloid-derived suppressor cells
  • patient-derived tumor model

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