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 ()
Additional contact information
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
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.nature.com/articles/s41467-019-11808-3 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-11808-3

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-019-11808-3

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().