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Liquid biopsy-based single-cell metabolic phenotyping of lung cancer patients for informative diagnostics

Ziming Li, Zhuo Wang, Yin Tang, Xiang Lu, Jie Chen, Yu Dong, Baojun Wu, Chunying Wang, Liu Yang, Zhili Guo, Min Xue, Shun Lu (), Wei Wei () and Qihui Shi ()
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Ziming Li: Shanghai Jiao Tong University
Zhuo Wang: Fudan University
Yin Tang: Shanghai Jiao Tong University
Xiang Lu: University of California
Jie Chen: Shanghai Jiao Tong University
Yu Dong: Shanghai Jiao Tong University
Baojun Wu: Shanghai Jiao Tong University
Chunying Wang: Shanghai Jiao Tong University
Liu Yang: Shanghai Jiao Tong University
Zhili Guo: University of California
Min Xue: University of California
Shun Lu: Shanghai Jiao Tong University
Wei Wei: Institute for Systems Biology
Qihui Shi: Fudan University

Nature Communications, 2019, vol. 10, issue 1, 1-16

Abstract: Abstract Accurate prediction of chemo- or targeted therapy responses for patients with similar driver oncogenes through a simple and least-invasive assay represents an unmet need in the clinical diagnosis of non-small cell lung cancer. Using a single-cell on-chip metabolic cytometry and fluorescent metabolic probes, we show metabolic phenotyping on the rare disseminated tumor cells in pleural effusions across a panel of 32 lung adenocarcinoma patients. Our results reveal extensive metabolic heterogeneity of tumor cells that differentially engage in glycolysis and mitochondrial oxidation. The cell number ratio of the two metabolic phenotypes is found to be predictive for patient therapy response, physiological performance, and survival. Transcriptome analysis reveals that the glycolytic phenotype is associated with mesenchymal-like cell state with elevated expression of the resistant-leading receptor tyrosine kinase AXL and immune checkpoint ligands. Drug targeting AXL induces a significant cell killing in the glycolytic cells without affecting the cells with active mitochondrial oxidation.

Date: 2019
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DOI: 10.1038/s41467-019-11808-3

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