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- W4308430094 abstract "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." @default.
- W4308430094 created "2022-11-11" @default.
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- W4308430094 date "2022-11-07" @default.
- W4308430094 modified "2023-10-11" @default.
- W4308430094 title "Microfluidics guided by deep learning for cancer immunotherapy screening" @default.
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- W4308430094 doi "https://doi.org/10.1073/pnas.2214569119" @default.
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