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Antibody microarray-based oncoproteomics

Borrebaeck, Carl LU (2006) In Expert Opinion on Biological Therapy 6(8). p.833-838
Abstract
The driving force behind oncoproteomics is the belief that certain protein signatures or patterns exist that are associated with a particular malignancy. if so, the correlation of clinical parameters with defined protein expression patterns would allow us to predict disease progression and perhaps even postulate improved therapeutic modalities. The technological challenges to achieve these goals are significant, as the human proteome is not defined. No general methodological approach exists today, and human cancer can, furthermore, be divided into several disease subgroups. One potential solution to finding cancer-associated protein signatures is the emerging technology of affinity proteomics. This approach addresses some of the... (More)
The driving force behind oncoproteomics is the belief that certain protein signatures or patterns exist that are associated with a particular malignancy. if so, the correlation of clinical parameters with defined protein expression patterns would allow us to predict disease progression and perhaps even postulate improved therapeutic modalities. The technological challenges to achieve these goals are significant, as the human proteome is not defined. No general methodological approach exists today, and human cancer can, furthermore, be divided into several disease subgroups. One potential solution to finding cancer-associated protein signatures is the emerging technology of affinity proteomics. This approach addresses some of the shortcomings of traditional proteomics and combines it with the power of microarrays. The present review focuses on the role of antibody microarrays in oncoproteomics and its potential to provide a truly proteome-wide analytical approach. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
recombinant, antibody, proteome analysis, microarray, affinity proteomics, cancer
in
Expert Opinion on Biological Therapy
volume
6
issue
8
pages
833 - 838
publisher
Ashley Publications
external identifiers
  • pmid:16856804
  • wos:000239378700010
  • scopus:33746820320
ISSN
1471-2598
DOI
10.1517/14712598.6.8.833
language
English
LU publication?
yes
id
d50ee478-c7b3-417d-b0ef-24d760aadcf3 (old id 399577)
date added to LUP
2016-04-01 11:44:47
date last changed
2022-01-26 17:34:29
@article{d50ee478-c7b3-417d-b0ef-24d760aadcf3,
  abstract     = {{The driving force behind oncoproteomics is the belief that certain protein signatures or patterns exist that are associated with a particular malignancy. if so, the correlation of clinical parameters with defined protein expression patterns would allow us to predict disease progression and perhaps even postulate improved therapeutic modalities. The technological challenges to achieve these goals are significant, as the human proteome is not defined. No general methodological approach exists today, and human cancer can, furthermore, be divided into several disease subgroups. One potential solution to finding cancer-associated protein signatures is the emerging technology of affinity proteomics. This approach addresses some of the shortcomings of traditional proteomics and combines it with the power of microarrays. The present review focuses on the role of antibody microarrays in oncoproteomics and its potential to provide a truly proteome-wide analytical approach.}},
  author       = {{Borrebaeck, Carl}},
  issn         = {{1471-2598}},
  keywords     = {{recombinant; antibody; proteome analysis; microarray; affinity proteomics; cancer}},
  language     = {{eng}},
  number       = {{8}},
  pages        = {{833--838}},
  publisher    = {{Ashley Publications}},
  series       = {{Expert Opinion on Biological Therapy}},
  title        = {{Antibody microarray-based oncoproteomics}},
  url          = {{http://dx.doi.org/10.1517/14712598.6.8.833}},
  doi          = {{10.1517/14712598.6.8.833}},
  volume       = {{6}},
  year         = {{2006}},
}