Literature DB >> 26958599

Global proteomic characterization of microdissected estrogen receptor positive breast tumors.

Tommaso De Marchi1, Ning Qing Liu1, Christoph Sting2, Marcel Smid1, Mila Tjoa1, René B H Braakman1, Theo M Luider2, John A Foekens1, John W M Martens3, Arzu Umar1.   

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.

Entities:  

Year:  2015        PMID: 26958599      PMCID: PMC4773412          DOI: 10.1016/j.dib.2015.09.034

Source DB:  PubMed          Journal:  Data Brief        ISSN: 2352-3409


Specifications table

RAW MS Orbitrap XL data MaxQuant “Protein groups.txt” output Cryo-sectioning of breast cancer tissues and collection on polyethylene–naphtalate coated slides Hematoxylin staining and LCM-enrichment Protein digestion (MS Grade Trypsin) LC–MS analysis Label-free quantitation (LFQ) by MaxQuant software

Value of the data

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.

Materials and methods

Sample sets

We collected a total of 112 fresh frozen ER positive breast cancer tissues that displayed a minimum ( ≥) of 40% tumor area and that were collected from patients who received tamoxifen therapy for recurrent disease and no adjuvant hormonal therapy after resection of the primary tumor. Patient groups were defined based on outcome to tamoxifen therapy for recurrent disease: patients who manifested progression of disease within (≤) 6 months after start of therapy were defined as manifesting poor outcome, while the good outcome group comprised patients with disease progression after (>) 6 months. Patient samples in the training set (PXD000484) were collected from Erasmus Medical Center (n=56; 32×good, 24×poor), while the test set (PXD000485) comprised tumors collected from the Netherlands Cancer Institute – Antoni van Leeuwenhoek hospital (n=41) and Radboud University Medical Center (n=15), which comprised 41 good and 15 poor outcome patients, respectively, as previously reported (Ref. [2]). Clinical information for every patient in the training an test sets are reported in Tables S1 and S2, respectively.

Sample preparation

Breast cancer tissue samples were processed according to our previously reported tissue proteomic workflow [4], [5]. Frozen tissue specimens were cut into 8 µm cryo-sections, collected on polyethylene naphtalate coated glass slides, and stained with hematoxylin. From each sample, around 4000 epithelial tumor cells were collected through LCM (corresponding to an area of ~500,000 µm2) and suspended into 20 µl of 0.1% w/v Rapigest/50 mM ammonium bicarbonate solution.

Protein digestion

LCM collected tissues were lysed through sonication at 70% amplitude. Proteins were denatured at 95 °C, reduced with a 100 µM dithiothreithol solution, and alkylated with a 300 mM iodoacetamide solution. MS grade trypsin was added in a 1:4 enzyme–protein ratio and incubated for 4 h at 37 °C. Digested samples were then acidified with trifluoroacetic acid and spun down at 14,000 RPM. Supernatants were collected and transferred to HPLC vials for further MS measurement.

High resolution MS analysis

MS measurements were performed as previously described with on an LTQ Orbitrap XL interfaced with a nano liquid chromatography system (Ultimate 3000, Dionex, Amsterdam, The Netherlands) [2], [5], [6]. Digested proteins were separated on a reverse phase analytical column (PepMap C18, 75 μm ID×50 cm, 3 μm particle size and 100 Å pore size) in a 3 h gradient: 2 h 0–25% mobile phase B (80% acetonitrile and 0.08% formic acid), and 1 h 25–50% mobile phases B and A (2% acetonitrile and 0.1% formic acid in purified water). The top 5 most intense peaks in full scan (from 400 to 1800 Th) were fragmented by collision induced dissociation.

Protein identification and quantitation

Orbitrap.RAW files were analyzed by MaxQuant (v1.2.2.5), using Andromeda for peptide search [3], [7]. UniProt-SwissProt human canonical database (version 2012-09, human canonical proteome; 20,243 identifiers) was used as reference database. For identification, peptide length was set to 7 aminoacids, match between runs was enabled and settings were kept as default. All other settings were set as default. “Protein groups.txt” files were uploaded in ProteomeXchange along with Orbitrap.RAW files.

Financial support

This study was supported by the Dutch Cancer Society (KWF), EMCR2009-4319 and the CTMM-Breast Care project 030-104-06.
Subject areaBiology
More specific subject areaClinical Proteomics
Type of data

RAW MS Orbitrap XL data

MaxQuant “Protein groups.txt” output

How data was acquiredLTQ Orbitrap XL MS interfaced with a reverse phase column (PepMap C18, 75 µm ID x 50 cm, 3 µm particle size, 100 Å pore size).
Data formatRAW;.txt
Experimental factorsAll 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.
Experimental features

Cryo-sectioning of breast cancer tissues and collection on polyethylene–naphtalate coated slides

Hematoxylin staining and LCM-enrichment

Protein digestion (MS Grade Trypsin)

LC–MS analysis

Label-free quantitation (LFQ) by MaxQuant software

Data source locationRotterdam, The Netherlands
Data accessibilityPXD000484:http://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD000484
PXD000485:http://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD000485
  7 in total

1.  Optimized nLC-MS workflow for laser capture microdissected breast cancer tissue.

Authors:  René B H Braakman; Madeleine M A Tilanus-Linthorst; Ning Qing Liu; Christoph Stingl; Lennard J M Dekker; Theo M Luider; John W M Martens; John A Foekens; Arzu Umar
Journal:  J Proteomics       Date:  2012-01-24       Impact factor: 4.044

2.  MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.

Authors:  Jürgen Cox; Matthias Mann
Journal:  Nat Biotechnol       Date:  2008-11-30       Impact factor: 54.908

3.  Andromeda: a peptide search engine integrated into the MaxQuant environment.

Authors:  Jürgen Cox; Nadin Neuhauser; Annette Michalski; Richard A Scheltema; Jesper V Olsen; Matthias Mann
Journal:  J Proteome Res       Date:  2011-02-22       Impact factor: 4.466

Review 4.  Proteomics pipeline for biomarker discovery of laser capture microdissected breast cancer tissue.

Authors:  Ning Qing Liu; René B H Braakman; Christoph Stingl; Theo M Luider; John W M Martens; John A Foekens; Arzu Umar
Journal:  J Mammary Gland Biol Neoplasia       Date:  2012-05-30       Impact factor: 2.673

5.  Comparative proteome analysis revealing an 11-protein signature for aggressive triple-negative breast cancer.

Authors:  Ning Qing Liu; Christoph Stingl; Maxime P Look; Marcel Smid; René B H Braakman; Tommaso De Marchi; Anieta M Sieuwerts; Paul N Span; Fred C G J Sweep; Barbro K Linderholm; Anita Mangia; Angelo Paradiso; Luc Y Dirix; Steven J Van Laere; Theo M Luider; John W M Martens; John A Foekens; Arzu Umar
Journal:  J Natl Cancer Inst       Date:  2014-01-07       Impact factor: 13.506

6.  The PRoteomics IDEntifications (PRIDE) database and associated tools: status in 2013.

Authors:  Juan Antonio Vizcaíno; Richard G Côté; Attila Csordas; José A Dianes; Antonio Fabregat; Joseph M Foster; Johannes Griss; Emanuele Alpi; Melih Birim; Javier Contell; Gavin O'Kelly; Andreas Schoenegger; David Ovelleiro; Yasset Pérez-Riverol; Florian Reisinger; Daniel Ríos; Rui Wang; Henning Hermjakob
Journal:  Nucleic Acids Res       Date:  2012-11-29       Impact factor: 16.971

7.  4-protein signature predicting tamoxifen treatment outcome in recurrent breast cancer.

Authors:  Tommaso De Marchi; Ning Qing Liu; Cristoph Stingl; Mieke A Timmermans; Marcel Smid; Maxime P Look; Mila Tjoa; Rene B H Braakman; Mark Opdam; Sabine C Linn; Fred C G J Sweep; Paul N Span; Mike Kliffen; Theo M Luider; John A Foekens; John W M Martens; Arzu Umar
Journal:  Mol Oncol       Date:  2015-08-07       Impact factor: 6.603

  7 in total

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