Abstract SY02-01: Understanding metabolic phenotypes in human tumors
Authors: Deberardinis Ralph J. 
Published: July 2017

Abstract
Metabolic reprogramming—the regulated alteration of flux through metabolic pathways in cancer cells—is an essential component of malignancy (1). In particular, pathways involved in bioenergetics, anabolism, and redox homeostasis are commonly altered by cancer-promoting mutations in oncogenes and tumor suppressor genes, and these metabolic activities support the enhanced survival and growth of cultured cancer cells in vitro (2). The relevance of these reprogrammed pathways to bona fide human cancer is less well established, however, and the knowledge gap between cultured cell models and tumors has created a bottleneck in translating metabolic reprogramming into new approaches for clinical imaging and cancer therapy. Thus, a major current challenge is to develop approaches that can characterize metabolic activity in intact tumors, particularly from human subjects.
The metabolic phenotypes of malignant cells within human tumors result from the integrated effects of a large number of intrinsic (e.g., genetic) and extrinsic (e.g., environmental) factors. The complexity of these influences results in a great deal of metabolic heterogeneity among tumors and even within different regions of the same tumor (3, 4). A complete understanding of cancer metabolism will demand approaches that can report the effects of both intrinsic and extrinsic influences on metabolic pathway preferences within tumors. Medical imaging techniques such as multiparametric magnetic resonance imaging (mpMRI) and positron emission tomography (PET) provide information about various aspects of tumor biology predicted to influence metabolism. Intraoperative infusions of isotope-labeled nutrients such as 13C-glucose provide a means to assess metabolic fluxes within living tumors in patients and mouse models and to compare these activities to adjacent benign tissue (5-8). I discuss approaches to understand metabolic phenotypes in human non–small cell lung cancer (NSCLC). We use a multidisciplinary clinical protocol designed to integrate presurgical imaging, including mpMRI and PET, with intraoperative infusions of isotope-labeled nutrients so that we can register specific metabolic fluxes to specific regions of these biologically heterogeneous tumors (4). This approach has allowed us to characterize fuel utilization within human lung tumors and to identify some of the factors that influence fuel preferences. I also discuss the unexpected contribution of alternative fuels other than glucose to energy metabolism in human NSCLC tissue.
Supported by grants to R.J.D. from the National Cancer Institute (2 R01 CA157996-06), the V Foundation (Translational Research Award), and the Howard Hughes Medical Institute (Faculty Scholars Program).
References
1. Ward PS, Thompson CB. Metabolic reprogramming: a cancer hallmark even Warburg did not anticipate. Cancer Cell 2012;21(3):297-308. doi: 10.1016/j.ccr.2012.02.014. PubMed PMID: 22439925; PMCID: PMC3311998.
2. DeBerardinis RJ, Chandel NS. Fundamentals of cancer metabolism. Sci Adv 2016;2(5):e1600200. doi: 10.1126/sciadv.1600200. PubMed PMID: 27386546; PMCID: PMC4928883.
3. Yuneva MO, Fan TW, Allen TD, Higashi RM, Ferraris DV, Tsukamoto T, et al. The metabolic profile of tumors depends on both the responsible genetic lesion and tissue type. Cell Metab 2012;15(2):157-70. doi: 10.1016/j.cmet.2011.12.015. PubMed PMID: 22326218; PMCID: PMC3282107.
4. Hensley CT, Faubert B, Yuan Q, Lev-Cohain N, Jin E, Kim J, et al. Metabolic heterogeneity in human lung tumors. Cell 2016;164(4):681-94. doi: 10.1016/j.cell.2015.12.034. PubMed PMID: 26853473; PMCID: PMC4752889.
5. Fan TW, Lane AN, Higashi RM, Farag MA, Gao H, Bousamra M, et al. Altered regulation of metabolic pathways in human lung cancer discerned by (13)C stable isotope-resolved metabolomics (SIRM). Mol Cancer 2009;8:41. doi: 10.1186/1476-4598-8-41. PubMed PMID: 19558692; PMCID: PMC2717907.
6. Maher EA, Marin-Valencia I, Bachoo RM, Mashimo T, Raisanen J, Hatanpaa KJ, et al. Metabolism of [U-13 C]glucose in human brain tumors in vivo. NMR Biomed 2012;25(11):1234-44. doi: 10.1002/nbm.2794. PubMed PMID: 22419606; PMCID: PMC3406255.
7. Marin-Valencia I, Yang C, Mashimo T, Cho S, Baek H, Yang XL, et al. Analysis of tumor metabolism reveals mitochondrial glucose oxidation in genetically diverse human glioblastomas in the mouse brain in vivo. Cell Metab 2012;15(6):827-37. doi: 10.1016/j.cmet.2012.05.001. PubMed PMID: 22682223; PMCID: PMC3372870.
8. Mashimo T, Pichumani K, Vemireddy V, Hatanpaa KJ, Singh DK, Sirasanagandla S, et al. Acetate is a bioenergetic substrate for human glioblastoma and brain metastases. Cell 2014;159(7):1603-14. PubMed PMID: Medline:25525878.
Citation Format: Ralph J. Deberardinis. Understanding metabolic phenotypes in human tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr SY02-01. doi:10.1158/1538-7445.AM2017-SY02-01