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The role of quantitative mass spectrometry in the discovery of pancreatic cancer biomarkers for translational science.

Ansari, Daniel LU ; Aronsson, Linus LU ; Sasor, Agata ; Welinder, Charlotte LU ; Rezeli, Melinda ; Marko-Varga, György and Andersson, Roland LU (2014) In Journal of Translational Medicine 12(1).
Abstract
In the post-genomic era, it has become evident that genetic changes alone are not sufficient to understand most disease processes including pancreatic cancer. Genome sequencing has revealed a complex set of genetic alterations in pancreatic cancer such as point mutations, chromosomal losses, gene amplifications and telomere shortening that drive cancerous growth through specific signaling pathways. Proteome-based approaches are important complements to genomic data and provide crucial information of the target driver molecules and their post-translational modifications. By applying quantitative mass spectrometry, this is an alternative way to identify biomarkers for early diagnosis and personalized medicine. We review the current... (More)
In the post-genomic era, it has become evident that genetic changes alone are not sufficient to understand most disease processes including pancreatic cancer. Genome sequencing has revealed a complex set of genetic alterations in pancreatic cancer such as point mutations, chromosomal losses, gene amplifications and telomere shortening that drive cancerous growth through specific signaling pathways. Proteome-based approaches are important complements to genomic data and provide crucial information of the target driver molecules and their post-translational modifications. By applying quantitative mass spectrometry, this is an alternative way to identify biomarkers for early diagnosis and personalized medicine. We review the current quantitative mass spectrometric technologies and analyses that have been developed and applied in the last decade in the context of pancreatic cancer. Examples of candidate biomarkers that have been identified from these pancreas studies include among others, asporin, CD9, CXC chemokine ligand 7, fibronectin 1, galectin-1, gelsolin, intercellular adhesion molecule 1, insulin-like growth factor binding protein 2, metalloproteinase inhibitor 1, stromal cell derived factor 4, and transforming growth factor beta-induced protein. Many of these proteins are involved in various steps in pancreatic tumor progression including cell proliferation, adhesion, migration, invasion, metastasis, immune response and angiogenesis. These new protein candidates may provide essential information for the development of protein diagnostics and targeted therapies. We further argue that new strategies must be advanced and established for the integration of proteomic, transcriptomic and genomic data, in order to enhance biomarker translation. Large scale studies with meta data processing will pave the way for novel and unexpected correlations within pancreatic cancer, that will benefit the patient, with targeted treatment. (Less)
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author
; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Journal of Translational Medicine
volume
12
issue
1
article number
87
publisher
BioMed Central (BMC)
external identifiers
  • pmid:24708694
  • wos:000336925200002
  • scopus:84899626810
  • pmid:24708694
ISSN
1479-5876
DOI
10.1186/1479-5876-12-87
project
Pancreatic cancer
language
English
LU publication?
yes
id
5004ef01-877e-4113-8835-e332fbaa75ce (old id 4430698)
alternative location
http://www.ncbi.nlm.nih.gov/pubmed/24708694?dopt=Abstract
date added to LUP
2016-04-01 13:47:36
date last changed
2022-03-21 20:31:25
@article{5004ef01-877e-4113-8835-e332fbaa75ce,
  abstract     = {{In the post-genomic era, it has become evident that genetic changes alone are not sufficient to understand most disease processes including pancreatic cancer. Genome sequencing has revealed a complex set of genetic alterations in pancreatic cancer such as point mutations, chromosomal losses, gene amplifications and telomere shortening that drive cancerous growth through specific signaling pathways. Proteome-based approaches are important complements to genomic data and provide crucial information of the target driver molecules and their post-translational modifications. By applying quantitative mass spectrometry, this is an alternative way to identify biomarkers for early diagnosis and personalized medicine. We review the current quantitative mass spectrometric technologies and analyses that have been developed and applied in the last decade in the context of pancreatic cancer. Examples of candidate biomarkers that have been identified from these pancreas studies include among others, asporin, CD9, CXC chemokine ligand 7, fibronectin 1, galectin-1, gelsolin, intercellular adhesion molecule 1, insulin-like growth factor binding protein 2, metalloproteinase inhibitor 1, stromal cell derived factor 4, and transforming growth factor beta-induced protein. Many of these proteins are involved in various steps in pancreatic tumor progression including cell proliferation, adhesion, migration, invasion, metastasis, immune response and angiogenesis. These new protein candidates may provide essential information for the development of protein diagnostics and targeted therapies. We further argue that new strategies must be advanced and established for the integration of proteomic, transcriptomic and genomic data, in order to enhance biomarker translation. Large scale studies with meta data processing will pave the way for novel and unexpected correlations within pancreatic cancer, that will benefit the patient, with targeted treatment.}},
  author       = {{Ansari, Daniel and Aronsson, Linus and Sasor, Agata and Welinder, Charlotte and Rezeli, Melinda and Marko-Varga, György and Andersson, Roland}},
  issn         = {{1479-5876}},
  language     = {{eng}},
  number       = {{1}},
  publisher    = {{BioMed Central (BMC)}},
  series       = {{Journal of Translational Medicine}},
  title        = {{The role of quantitative mass spectrometry in the discovery of pancreatic cancer biomarkers for translational science.}},
  url          = {{https://lup.lub.lu.se/search/files/3592617/4731525}},
  doi          = {{10.1186/1479-5876-12-87}},
  volume       = {{12}},
  year         = {{2014}},
}