Cancers are diseases of uncontrolled cell growth in which cells acquire mutations that lead to cell proliferation outside of the context of normal tissue development. Molecular advances over the past 30 years have characterized many of the signal transduction pathways and gene transcription networks that are altered during cancer progression. Aberrant regulation of these networks invariably results in gross alterations of the metabolic network. Differences in the metabolism of glucose in tumors compared to that of normal tissue have been noted for over 70 years;yet, the origins, consequences, cancer specificities, and the principles of intervention are poorly understood. Our understanding of cancer cell metabolism is challenged by the enormous complexity of the interaction between metabolic pathways and the genetic aberrations that alter these pathways. Advances will require new technologies and conceptual frameworks, such as high-throughput metabolomics, a technique that aims to quantify within a single measurement, a large number of small-molecules within cells and tissues, and mathematical models that can parse the effects of many simultaneous interactions. Investing such effort has the potential to fundamentally alter our understanding of basic cancer biology and lead to innovative therapies. My proposed research focuses on this central problem of cancer cell growth and development and utilizes the application of computational methods rooted in systems biology in conjunction with the use high-throughput technologies such as mass spectrometry-based metabolomics to understand mechanisms that lead to unregulated growth and altered metabolism in cancer cells and primary tumors. During my postdoctoral work, I discovered two novel metabolic pathways in cells undergoing rapid proliferation and tumor development. These studies combined metabolomics technology with techniques I acquired in my postdoctoral training involving cell biology, biochemistry and genetics. One pathway involves an alternate route of glucose uptake that decouple catabolic glucose metabolism with energy metabolism. The other involves the diversion of glycolytic flux into anabolic metabolism through a glycolytic intermediate. Further genetic studies established that this pathway is selected for in the development of human cancer. I will continue these projects during the remainder of my postdoctoral training in the mentored phase. This work will allow me to establish an independent research program involving using systems biology techniques to investigate define biological problems in understanding the role of glucose metabolism in cancer. My previous training in systems biology and current training in a leading cancer biology and signal transduction lab provides a skill-set that is uniquely suited to approach this problem.

Public Health Relevance

The goal of this proposal is to employ a multidisciplinary approach to understanding and intervening in human cancer. Cancer is the leading source of human mortality in the developed world. Cancer cells have different nutritional requirements from that of their normal counterparts and this proposal aims to further understand of the nature of this altered metabolism.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Transition Award (R00)
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Subcommittee G - Education (NCI)
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Li, Jerry
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Cornell University
Schools of Earth Sciences/Natur
United States
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Chen, Jinyu; Lee, Ho-Jeong; Wu, Xuefeng et al. (2015) Gain of glucose-independent growth upon metastasis of breast cancer cells to the brain. Cancer Res 75:554-65
Gerriets, Valerie A; Kishton, Rigel J; Nichols, Amanda G et al. (2015) Metabolic programming and PDHK1 control CD4+ T cell subsets and inflammation. J Clin Invest 125:194-207
Mehrmohamadi, Mahya; Liu, Xiaojing; Shestov, Alexander A et al. (2014) Characterization of the usage of the serine metabolic network in human cancer. Cell Rep 9:1507-19
Shestov, Alexander A; Liu, Xiaojing; Ser, Zheng et al. (2014) Quantitative determinants of aerobic glycolysis identify flux through the enzyme GAPDH as a limiting step. Elife 3:
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Huang, Lei; Kim, Dongsung; Liu, Xiaojing et al. (2014) Estimating relative changes of metabolic fluxes. PLoS Comput Biol 10:e1003958

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