Microfluidics guided by deep learning for cancer immunotherapy screening

• Published in: Proceedings of the National Academy of Sciences - PNAS 2022-11, Vol.119 (46), p.e2214569119
• Main Authors: Ao, Zheng, Cai, Hongwei, Wu, Zhuhao, Hu, Liya, Nunez, Asael, Zhou, Zhuolong, Liu, Hongcheng, Bondesson, Maria, Lu, Xiongbin, Lu, Xin, Dao, Ming, Guo, Feng
• Format: Article
• Language: English
• Subjects: Deep Learning; Early Detection of Cancer; Humans; Immunologic Factors; Immunotherapy - methods; Lymphocytes, Tumor-Infiltrating; Microfluidics - methods; Neoplasms - drug therapy; Physical Sciences; Tumor Microenvironment
• ISSN: 0027-8424; 1091-6490
• DOI: 10.1073/pnas.2214569119

Immunocyte infiltration and cytotoxicity play critical roles in both inflammation and immunotherapy. However, current cancer immunotherapy screening methods overlook the capacity of the T cells to penetrate the tumor stroma, thereby significantly limiting the development of effective treatments for solid tumors. Here, we present an automated high-throughput microfluidic platform for simultaneous tracking of the dynamics of T cell infiltration and cytotoxicity within the 3D tumor cultures with a tunable stromal makeup. By recourse to a clinical tumor-infiltrating lymphocyte (TIL) score analyzer, which is based on a clinical data-driven deep learning method, our platform can evaluate the efficacy of each treatment based on the scoring of T cell infiltration patterns. By screening a drug library using this technology, we identified an epigenetic drug (lysine-specific histone demethylase 1 inhibitor, LSD1i) that effectively promoted T cell tumor infiltration and enhanced treatment efficacy in combination with an immune checkpoint inhibitor (anti-PD1) in vivo. We demonstrated an automated system and strategy for screening immunocyte-solid tumor interactions, enabling the discovery of immuno- and combination therapies.