Global proteomic characterization of microdissected estrogen receptor positive breast tumors

Tommaso De Marchi,Ning Qing Liu,René B.H Braakman,Theo M Luider,John W.M Martens,Christoph Sting,Mila Tjoa,Arzu Umar,John A Foekens,Marcel Smid

doi:10.1016/j.dib.2015.09.034

Tommaso De Marchi, Ning Qing Liu + Show 8 more

Open Access

https://doi.org/10.1016/j.dib.2015.09.034

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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 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.

Highlights

We here describe two proteomic datasets deposited in ProteomeXchange via PRIDE partner repository [1] with dataset identifiers PXD000484 and PXD000485 that have been used for the development of a tamoxifen outcome predictive signature [2]
RAW;.txt All estrogen receptor (ER) positive fresh frozen breast cancer tissues were subjected to laser capture microdissection (LCM) to enrich for epithelial tumor cells prior to protein digestion, which enabled analysis of highly pure subpopulations of breast cancer cells
First public proteomics datasets of LCM derived ER positive primary tumor cells analyzed by high resolution mass spectrometric (MS)

Summary

Data accessibility

MaxQuant “Protein groups.txt” output LTQ Orbitrap XL MS interfaced with a reverse phase column (PepMap C18, 75 mm ID x 50 cm, 3 mm particle size, 100 Âe pore size). RAW;.txt All ER positive fresh frozen breast cancer tissues were subjected to LCM to enrich for epithelial tumor cells prior to protein digestion, which enabled analysis of highly pure subpopulations of breast cancer cells. Label-free quantitation (LFQ) by MaxQuant software Rotterdam, The Netherlands. First public proteomics datasets of LCM derived ER positive primary tumor cells analyzed by high resolution MS. Characterization of proteomic changes related to resistance to first line tamoxifen therapy. Quantification of 3109 and 4061 unique proteins in training and test sets, respectively

Sample sets

Sample preparation

Protein digestion

High resolution MS analysis

Findings

Protein identification and quantitation