Microfluidics guided by deep learning for cancer immunotherapy screening.

Zheng Ao, Hongwei Cai, Zhuhao Wu, Liya Hu, Asael Nunez, Zhuolong Zhou, Hongcheng Liu, Maria Bondesson, Xiongbin Lu, Xin Lu, Ming Dao, Feng Guo

Proceedings of the National Academy of Sciences of the United States of America 2022 Nov 16

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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.

Citation

Zheng Ao, Hongwei Cai, Zhuhao Wu, Liya Hu, Asael Nunez, Zhuolong Zhou, Hongcheng Liu, Maria Bondesson, Xiongbin Lu, Xin Lu, Ming Dao, Feng Guo. Microfluidics guided by deep learning for cancer immunotherapy screening. Proceedings of the National Academy of Sciences of the United States of America. 2022 Nov 16;119(46):e2214569119