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A pathology atlas of the human cancer transcriptome

Uhlen, Mathias; Zhang, Cheng Jiao; Lee, Sunjae; Sjöstedt, Evelina; Fagerberg, Linn; Bidkhori, Gholamreza; Benfeitas, Rui; Arif, Muhammad; Liu, Zhengtao and Edfors, Fredrik, et al. (2017) In Science 357(6352).
Abstract

Cancer is one of the leading causes of death, and there is great interest in understanding the underlying molecular mechanisms involved in the pathogenesis and progression of individual tumors. We used systems-level approaches to analyze the genome-wide transcriptome of the protein-coding genes of 17 major cancer types with respect to clinical outcome. A general pattern emerged: Shorter patient survival was associated with up-regulation of genes involved in cell growth and with down-regulation of genes involved in cellular differentiation. Using genome-scale metabolic models, we show that cancer patients have widespread metabolic heterogeneity, highlighting the need for precise and personalized medicine for cancer treatment. All data... (More)

Cancer is one of the leading causes of death, and there is great interest in understanding the underlying molecular mechanisms involved in the pathogenesis and progression of individual tumors. We used systems-level approaches to analyze the genome-wide transcriptome of the protein-coding genes of 17 major cancer types with respect to clinical outcome. A general pattern emerged: Shorter patient survival was associated with up-regulation of genes involved in cell growth and with down-regulation of genes involved in cellular differentiation. Using genome-scale metabolic models, we show that cancer patients have widespread metabolic heterogeneity, highlighting the need for precise and personalized medicine for cancer treatment. All data are presented in an interactive open-access database (www.proteinatlas.org/pathology) to allow genome-wide exploration of the impact of individual proteins on clinical outcomes.

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Science
volume
357
issue
6352
publisher
The American Association for the Advancement of Science
external identifiers
  • scopus:85028362951
  • wos:000407793600028
ISSN
0036-8075
DOI
10.1126/science.aan2507
language
English
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yes
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e28d0acd-6006-4078-a10f-f879d43e0a2c
date added to LUP
2017-10-02 10:51:05
date last changed
2018-05-20 04:37:37
@article{e28d0acd-6006-4078-a10f-f879d43e0a2c,
  abstract     = {<p>Cancer is one of the leading causes of death, and there is great interest in understanding the underlying molecular mechanisms involved in the pathogenesis and progression of individual tumors. We used systems-level approaches to analyze the genome-wide transcriptome of the protein-coding genes of 17 major cancer types with respect to clinical outcome. A general pattern emerged: Shorter patient survival was associated with up-regulation of genes involved in cell growth and with down-regulation of genes involved in cellular differentiation. Using genome-scale metabolic models, we show that cancer patients have widespread metabolic heterogeneity, highlighting the need for precise and personalized medicine for cancer treatment. All data are presented in an interactive open-access database (www.proteinatlas.org/pathology) to allow genome-wide exploration of the impact of individual proteins on clinical outcomes.</p>},
  articleno    = {eaan2507},
  author       = {Uhlen, Mathias and Zhang, Cheng Jiao and Lee, Sunjae and Sjöstedt, Evelina and Fagerberg, Linn and Bidkhori, Gholamreza and Benfeitas, Rui and Arif, Muhammad and Liu, Zhengtao and Edfors, Fredrik and Sanli, Kemal and von Feilitzen, Kalle and Oksvold, Per and Lundberg, Emma and Hober, Sophia and Nilsson, Peter and Mattsson, Johanna Sm and Schwenk, Jochen M. and Brunnström, Hans and Glimelius, Bengt and Sjöblom, Tobias and Edqvist, Per-Henrik and Djureinovic, Dijana and Micke, Patrick and Lindskog, Cecilia and Mardinoglu, Adil and Ponten, Fredrik},
  issn         = {0036-8075},
  language     = {eng},
  month        = {08},
  number       = {6352},
  publisher    = {The American Association for the Advancement of Science},
  series       = {Science},
  title        = {A pathology atlas of the human cancer transcriptome},
  url          = {http://dx.doi.org/10.1126/science.aan2507},
  volume       = {357},
  year         = {2017},
}