Microfluidics guided by deep learning for cancer immunotherapy screening

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...

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Published inProceedings of the National Academy of Sciences - PNAS Vol. 119; no. 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 Journal Article
LanguageEnglish
Published United States National Academy of Sciences 15.11.2022
Subjects
Deep Learning
Early Detection of Cancer
Humans
Immunologic Factors
Immunotherapy - methods
Lymphocytes, Tumor-Infiltrating
Microfluidics - methods
Neoplasms - drug therapy
Physical Sciences
Tumor Microenvironment
deep learning
immune infiltration
drug screening
microfluidics
cancer immunotherapy
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Summary: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.
Bibliography:Author contributions: Z.A., H.C., M.D., and F.G. designed research; Z.A., H.C., Z.W., L.H., A.N., and Z.Z. performed research; H.C., Z.Z., H.L., and Xiongbin Lu. contributed new reagents/analytic tools; Z.A., H.C., L.H., H.L., M.B., Xin Lu., M.D., and F.G. analyzed data; and Z.A., H.C., M.B., Xiongbin Lu, Xin Lu, M.D., and F.G. wrote the paper; and M.D. and F.G., supervised research.
Edited by David Weitz, Harvard University, Cambridge, MA; received August 25, 2022; accepted October 13, 2022
1Z.A. and H.C. contributed equally to this work.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.2214569119