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Global proteomic characterization of microdissected estrogen receptor positive breast tumors

De Marchi, Tommaso LU ; Liu, Ning Qing; Sting, Christoph; Smid, Marcel; Tjoa, Mila; Braakman, René B H; Luider, Theo M.; Foekens, John A.; Martens, John W. M. and Umar, Arzu (2015) In Data in Brief 5. p.399-402
Abstract

We here describe two proteomic datasets deposited in ProteomeXchange via PRIDE partner repository [1] with dataset identifiers PXD000484 (defined as "training") and PXD000485 (defined as "test") that have been used for the development of a tamoxifen outcome predictive signature [2]. Both datasets comprised 56 fresh frozen estrogen receptor (ER) positive primary breast tumor specimens derived from patients who received tamoxifen as first line therapy for recurrent disease. Patient groups were defined based on time to progression (TTP) after start of tamoxifen therapy (6 months cutoff): 32 good and 24 poor treatment outcome patients were comprised in the training set, respectively. The test set included 41 good and 15 poor treatment... (More)

We here describe two proteomic datasets deposited in ProteomeXchange via PRIDE partner repository [1] with dataset identifiers PXD000484 (defined as "training") and PXD000485 (defined as "test") that have been used for the development of a tamoxifen outcome predictive signature [2]. Both datasets comprised 56 fresh frozen estrogen receptor (ER) positive primary breast tumor specimens derived from patients who received tamoxifen as first line therapy for recurrent disease. Patient groups were defined based on time to progression (TTP) after start of tamoxifen therapy (6 months cutoff): 32 good and 24 poor treatment outcome patients were comprised in the training set, respectively. The test set included 41 good and 15 poor treatment outcome patients. All specimens were subjected to laser capture microdissection (LCM) to enrich for epithelial tumor cells prior to high resolution mass spectrometric (MS) analysis. Protein identification and label-free quantification (LFQ) were performed with MaxQuant software package [3]. A total of 3109 and 4061 proteins were identified and quantified in the training and test set, respectively. We here present the first public proteomic dataset analyzing ER positive recurrent breast cancer by LCM coupled to high resolution MS.

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author
publishing date
type
Contribution to journal
publication status
published
keywords
Journal Article
in
Data in Brief
volume
5
pages
4 pages
publisher
Elsevier
external identifiers
  • scopus:84944039712
ISSN
2352-3409
DOI
10.1016/j.dib.2015.09.034
language
English
LU publication?
no
id
18dd8786-1ddf-4c0e-a4e1-a1aeb9440b13
date added to LUP
2017-06-27 14:31:06
date last changed
2017-07-02 05:03:59
@article{18dd8786-1ddf-4c0e-a4e1-a1aeb9440b13,
  abstract     = {<p>We here describe two proteomic datasets deposited in ProteomeXchange via PRIDE partner repository [1] with dataset identifiers PXD000484 (defined as "training") and PXD000485 (defined as "test") that have been used for the development of a tamoxifen outcome predictive signature [2]. Both datasets comprised 56 fresh frozen estrogen receptor (ER) positive primary breast tumor specimens derived from patients who received tamoxifen as first line therapy for recurrent disease. Patient groups were defined based on time to progression (TTP) after start of tamoxifen therapy (6 months cutoff): 32 good and 24 poor treatment outcome patients were comprised in the training set, respectively. The test set included 41 good and 15 poor treatment outcome patients. All specimens were subjected to laser capture microdissection (LCM) to enrich for epithelial tumor cells prior to high resolution mass spectrometric (MS) analysis. Protein identification and label-free quantification (LFQ) were performed with MaxQuant software package [3]. A total of 3109 and 4061 proteins were identified and quantified in the training and test set, respectively. We here present the first public proteomic dataset analyzing ER positive recurrent breast cancer by LCM coupled to high resolution MS.</p>},
  author       = {De Marchi, Tommaso and Liu, Ning Qing and Sting, Christoph and Smid, Marcel and Tjoa, Mila and Braakman, René B H and Luider, Theo M. and Foekens, John A. and Martens, John W. M. and Umar, Arzu},
  issn         = {2352-3409},
  keyword      = {Journal Article},
  language     = {eng},
  pages        = {399--402},
  publisher    = {Elsevier},
  series       = {Data in Brief},
  title        = {Global proteomic characterization of microdissected estrogen receptor positive breast tumors},
  url          = {http://dx.doi.org/10.1016/j.dib.2015.09.034},
  volume       = {5},
  year         = {2015},
}