Author
Listed:
- Nan Jin
(Chinese Academy of Sciences
University of Chinese Academy of Sciences)
- Aiwei Bi
(Chinese Academy of Sciences
University of Chinese Academy of Sciences)
- Xiaojing Lan
(Chinese Academy of Sciences)
- Jun Xu
(Chinese Academy of Sciences
University of Chinese Academy of Sciences)
- Xiaomin Wang
(Chinese Academy of Sciences
University of Chinese Academy of Sciences)
- Yingluo Liu
(Chinese Academy of Sciences
University of Chinese Academy of Sciences)
- Ting Wang
(Chinese Academy of Sciences
University of Chinese Academy of Sciences)
- Shuai Tang
(Chinese Academy of Sciences)
- Hanlin Zeng
(Chinese Academy of Sciences
University of Chinese Academy of Sciences)
- Ziqi Chen
(Chinese Academy of Sciences
University of Chinese Academy of Sciences)
- Minjia Tan
(University of Chinese Academy of Sciences
Shanghai Institute of Materia Medica, Chinese Academy of Sciences)
- Jing Ai
(Chinese Academy of Sciences
University of Chinese Academy of Sciences)
- Hua Xie
(Chinese Academy of Sciences
University of Chinese Academy of Sciences)
- Tao Zhang
(Chinese Academy of Sciences
University of Chinese Academy of Sciences)
- Dandan Liu
(University of Chinese Academy of Sciences
Shanghai Institute of Materia Medica, Chinese Academy of Sciences)
- Ruimin Huang
(University of Chinese Academy of Sciences
Shanghai Institute of Materia Medica, Chinese Academy of Sciences)
- Yue Song
(Agilent Technologies (China) Co., Ltd.)
- Elaine Lai-Han Leung
(State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology)
- Xiaojun Yao
(State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology)
- Jian Ding
(Chinese Academy of Sciences
University of Chinese Academy of Sciences)
- Meiyu Geng
(Chinese Academy of Sciences
University of Chinese Academy of Sciences)
- Shu-Hai Lin
(Xiamen University)
- Min Huang
(Chinese Academy of Sciences
University of Chinese Academy of Sciences)
Abstract
One of the biggest hurdles for the development of metabolism-targeted therapies is to identify the responsive tumor subsets. However, the metabolic vulnerabilities for most human cancers remain unclear. Establishing the link between metabolic signatures and the oncogenic alterations of receptor tyrosine kinases (RTK), the most well-defined cancer genotypes, may precisely direct metabolic intervention to a broad patient population. By integrating metabolomics and transcriptomics, we herein show that oncogenic RTK activation causes distinct metabolic preference. Specifically, EGFR activation branches glycolysis to the serine synthesis for nucleotide biosynthesis and redox homeostasis, whereas FGFR activation recycles lactate to fuel oxidative phosphorylation for energy generation. Genetic alterations of EGFR and FGFR stratify the responsive tumors to pharmacological inhibitors that target serine synthesis and lactate fluxes, respectively. Together, this study provides the molecular link between cancer genotypes and metabolic dependency, providing basis for patient stratification in metabolism-targeted therapies.
Suggested Citation
Nan Jin & Aiwei Bi & Xiaojing Lan & Jun Xu & Xiaomin Wang & Yingluo Liu & Ting Wang & Shuai Tang & Hanlin Zeng & Ziqi Chen & Minjia Tan & Jing Ai & Hua Xie & Tao Zhang & Dandan Liu & Ruimin Huang & Yu, 2019.
"Identification of metabolic vulnerabilities of receptor tyrosine kinases-driven cancer,"
Nature Communications, Nature, vol. 10(1), pages 1-15, December.
Handle:
RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-10427-2
DOI: 10.1038/s41467-019-10427-2
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Citations
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Cited by:
- Tea Pemovska & Johannes W. Bigenzahn & Ismet Srndic & Alexander Lercher & Andreas Bergthaler & Adrián César-Razquin & Felix Kartnig & Christoph Kornauth & Peter Valent & Philipp B. Staber & Giulio Sup, 2021.
"Metabolic drug survey highlights cancer cell dependencies and vulnerabilities,"
Nature Communications, Nature, vol. 12(1), pages 1-19, December.
- Yuanli Zhen & Kai Liu & Lei Shi & Simran Shah & Qin Xu & Haley Ellis & Eranga R. Balasooriya & Johannes Kreuzer & Robert Morris & Albert S. Baldwin & Dejan Juric & Wilhelm Haas & Nabeel Bardeesy, 2024.
"FGFR inhibition blocks NF-ĸB-dependent glucose metabolism and confers metabolic vulnerabilities in cholangiocarcinoma,"
Nature Communications, Nature, vol. 15(1), pages 1-17, December.
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