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Mitochondrial mutations and metabolic adaptation in pancreatic cancer

Overview of attention for article published in Cancer & Metabolism, January 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#20 of 231)
  • High Attention Score compared to outputs of the same age (90th percentile)

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29 X users
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2 Google+ users

Citations

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89 Mendeley
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Title
Mitochondrial mutations and metabolic adaptation in pancreatic cancer
Published in
Cancer & Metabolism, January 2017
DOI 10.1186/s40170-017-0164-1
Pubmed ID
Authors

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, Australian Pancreatic Cancer Genome Initiative, Leonid Sazanov, Andrew V. Biankin, Silas Villas-Boas, Andrew J. Hoy, Nigel Turner, Darren N. Saunders

Abstract

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. 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. 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. 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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 29 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 89 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 89 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 18%
Student > Bachelor 16 18%
Researcher 10 11%
Student > Master 10 11%
Student > Postgraduate 5 6%
Other 12 13%
Unknown 20 22%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 34 38%
Agricultural and Biological Sciences 8 9%
Pharmacology, Toxicology and Pharmaceutical Science 7 8%
Medicine and Dentistry 7 8%
Chemistry 4 4%
Other 8 9%
Unknown 21 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 19. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 February 2018.
All research outputs
#1,979,718
of 25,775,807 outputs
Outputs from Cancer & Metabolism
#20
of 231 outputs
Outputs of similar age
#40,102
of 426,451 outputs
Outputs of similar age from Cancer & Metabolism
#2
of 4 outputs
Altmetric has tracked 25,775,807 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 231 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.7. This one has done particularly well, scoring higher than 91% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 426,451 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.