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

Author

Listed:
  • Ziming Li

    (Shanghai Jiao Tong University)

  • Zhuo Wang

    (Fudan University)

  • Yin Tang

    (Shanghai Jiao Tong University
    Institute for Systems Biology)

  • 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
    University of California
    University of California)

  • Qihui Shi

    (Fudan University
    Fudan University
    Fudan University)

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.

Suggested Citation

  • 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 & Qihui Shi, 2019. "Liquid biopsy-based single-cell metabolic phenotyping of lung cancer patients for informative diagnostics," Nature Communications, Nature, vol. 10(1), pages 1-16, December.
  • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-11808-3
    DOI: 10.1038/s41467-019-11808-3
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    Cited by:

    1. Alexander Coulton & Jun Murai & Danwen Qian & Krupa Thakkar & Claire E. Lewis & Kevin Litchfield, 2024. "Using a pan-cancer atlas to investigate tumour associated macrophages as regulators of immunotherapy response," Nature Communications, Nature, vol. 15(1), pages 1-14, December.

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