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Expression profiling to predict outcome in breast cancer: the influence of sample selection

Gruvberger, Sofia LU ; Ringnér, Markus LU ; Edén, Patrik LU ; Borg, Åke LU ; Fernö, Mårten LU ; Peterson, Carsten LU and Meltzer, Paul S (2003) In Breast Cancer Research 5(1). p.23-26
Abstract
Gene expression profiling of tumors using DNA microarrays is a promising method for predicting prognosis and treatment response in cancer patients. It was recently reported that expression profiles of sporadic breast cancers could be used to predict disease recurrence better than currently available clinical and histopathological prognostic factors. Having observed an overlap in those data between the genes that predict outcome and those that predict estrogen receptor- status, we examined their predictive power in an independent data set. We conclude that it may be important to define prognostic expression profiles separately for estrogen receptor--positive and estrogen receptor--negative tumors.
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Breast Cancer Research
volume
5
issue
1
pages
23 - 26
publisher
BioMed Central
external identifiers
  • wos:000180046700007
  • pmid:12559041
  • scopus:0012333539
ISSN
1465-5411
DOI
10.1186/bcr548
language
English
LU publication?
yes
id
b7b7790f-2677-427d-abbb-f9f9bc471918 (old id 131208)
alternative location
http://www.ncbi.nlm.nih.gov/sites/entrez?cmd=Retrieve&db=PubMed&list_uids=12559041&dopt=Abstract
date added to LUP
2007-07-18 16:18:09
date last changed
2017-01-01 04:38:50
@article{b7b7790f-2677-427d-abbb-f9f9bc471918,
  abstract     = {Gene expression profiling of tumors using DNA microarrays is a promising method for predicting prognosis and treatment response in cancer patients. It was recently reported that expression profiles of sporadic breast cancers could be used to predict disease recurrence better than currently available clinical and histopathological prognostic factors. Having observed an overlap in those data between the genes that predict outcome and those that predict estrogen receptor- status, we examined their predictive power in an independent data set. We conclude that it may be important to define prognostic expression profiles separately for estrogen receptor--positive and estrogen receptor--negative tumors.},
  author       = {Gruvberger, Sofia and Ringnér, Markus and Edén, Patrik and Borg, Åke and Fernö, Mårten and Peterson, Carsten and Meltzer, Paul S},
  issn         = {1465-5411},
  language     = {eng},
  number       = {1},
  pages        = {23--26},
  publisher    = {BioMed Central},
  series       = {Breast Cancer Research},
  title        = {Expression profiling to predict outcome in breast cancer: the influence of sample selection},
  url          = {http://dx.doi.org/10.1186/bcr548},
  volume       = {5},
  year         = {2003},
}