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Hardie RA, van Dam E, Cowley M, et al. Mitochondrial mutations and metabolic adaptation in pancreatic cancer. Cancer Metab. 2017;5:2doi: 10.1186/s40170-017-0164-1.
Hardie, R. A., van Dam, E., Cowley, M., Han, T. L., Balaban, S., Pajic, M., Pinese, M., Iconomou, M., Shearer, R. F., McKenna, J., Miller, D., Waddell, N., Pearson, J. V., Grimmond, S. M., Sazanov, L., Biankin, A. V., Villas-Boas, S., Hoy, A. J., Turner, N., & Saunders, D. N. (2017). Mitochondrial mutations and metabolic adaptation in pancreatic cancer. Cancer & metabolism, 52. https://doi.org/10.1186/s40170-017-0164-1
Hardie, Rae-Anne, et al. "Mitochondrial mutations and metabolic adaptation in pancreatic cancer." Cancer & metabolism vol. 5 (2017): 2. doi: https://doi.org/10.1186/s40170-017-0164-1
Hardie RA, van Dam E, Cowley M, Han TL, Balaban S, Pajic M, Pinese M, Iconomou M, Shearer RF, McKenna J, Miller D, Waddell N, Pearson JV, Grimmond SM, Sazanov L, Biankin AV, Villas-Boas S, Hoy AJ, Turner N, Saunders DN. Mitochondrial mutations and metabolic adaptation in pancreatic cancer. Cancer Metab. 2017 Jan 30;5:2. doi: 10.1186/s40170-017-0164-1. eCollection 2017. PMID: 28163917; PMCID: PMC5282905.
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Cancer Metab. 2017 Jan 30;5:2. doi: 10.1186/s40170-017-0164-1. eCollection 2017.

Mitochondrial mutations and metabolic adaptation in pancreatic cancer.

Cancer & metabolism

Rae-Anne Hardie, Ellen van Dam, Mark Cowley, Ting-Li Han, Seher Balaban, Marina Pajic, Mark Pinese, Mary Iconomou, Robert F Shearer, Jessie McKenna, David Miller, Nicola Waddell, John V Pearson, Sean M Grimmond, Leonid Sazanov, Andrew V Biankin, Silas Villas-Boas, Andrew J Hoy, Nigel Turner, Darren N Saunders

Affiliations

  1. The Kinghorn Cancer Centre, Garvan Institute of Medical Research, Darlinghurst, NSW 2010 Australia.
  2. St Vincent's Clinical School, University of New South Wales, Sydney, NSW Australia.
  3. School of Biological Sciences, University of Auckland, Auckland, 1142 New Zealand.
  4. Discipline of Physiology, School of Medical Sciences and Bosch Institute, University of Sydney, Sydney, NSW 2006 Australia.
  5. Centre for Medical Genomics, Institute for Molecular Bioscience, University of Queensland, St. Lucia, QLD 4072 Australia.
  6. Mitochondrial Biology Unit, Wellcome Trust, Cambridge, CB2 0XY UK.
  7. Wolfson Wohl Cancer Research Centre, Institute of Cancer Sciences, University of Glasgow, Glasgow, UK.
  8. Boden Institute of Obesity, Nutrition, Exercise and Eating Disorders, University of Sydney, Sydney, NSW 2006 Australia.
  9. School of Medical Sciences, University of New South Wales, Sydney, NSW 2052 Australia.

PMID: 28163917 PMCID: PMC5282905 DOI: 10.1186/s40170-017-0164-1

Abstract

BACKGROUND: Pancreatic cancer has a five-year survival rate of ~8%, with characteristic molecular heterogeneity and restricted treatment options. Targeting metabolism has emerged as a potentially effective therapeutic strategy for cancers such as pancreatic cancer, which are driven by genetic alterations that are not tractable drug targets. Although somatic mitochondrial genome (mtDNA) mutations have been observed in various tumors types, understanding of metabolic genotype-phenotype relationships is limited.

METHODS: We deployed an integrated approach combining genomics, metabolomics, and phenotypic analysis on a unique cohort of patient-derived pancreatic cancer cell lines (PDCLs). Genome analysis was performed via targeted sequencing of the mitochondrial genome (mtDNA) and nuclear genes encoding mitochondrial components and metabolic genes. Phenotypic characterization of PDCLs included measurement of cellular oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) using a Seahorse XF extracellular flux analyser, targeted metabolomics and pathway profiling, and radiolabelled glutamine tracing.

RESULTS: We identified 24 somatic mutations in the mtDNA of 12 patient-derived pancreatic cancer cell lines (PDCLs). A further 18 mutations were identified in a targeted study of ~1000 nuclear genes important for mitochondrial function and metabolism. Comparison with reference datasets indicated a strong selection bias for non-synonymous mutants with predicted functional effects. Phenotypic analysis showed metabolic changes consistent with mitochondrial dysfunction, including reduced oxygen consumption and increased glycolysis. Metabolomics and radiolabeled substrate tracing indicated the initiation of reductive glutamine metabolism and lipid synthesis in tumours.

CONCLUSIONS: The heterogeneous genomic landscape of pancreatic tumours may converge on a common metabolic phenotype, with individual tumours adapting to increased anabolic demands via different genetic mechanisms. Targeting resulting metabolic phenotypes may be a productive therapeutic strategy.

Keywords: Genome; Glutamine; Lipid; Metabolomics; Mitochondria; Pancreas

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