Dataset Information

Proteomic portrait of human breast cancer progression identifies novel prognostic markers

ABSTRACT: Triplicate analysis of eleven breast cancer cell lines, each separated to 12 offgel fractions. Proteomes were quantified relative to heavy SILAC-labeled MCF7 cells that served as a spike-in standard. In this study, we used system-wide analysis of breast cancer proteomes to identify proteins that are associated with the progression of ER(-) tumors. Our two-step approach included an initial deep analysis of cultured cells that were obtained from tumors of defined breast cancer stages, followed by a validation set using human breast tumors. Using high-resolution mass spectrometry and quantification by Stable Isotope Labeling with Amino Acids in Cell Culture (SILAC), we identified 8,750 proteins and quantified 7,800 of them. A stage-specific signature was extracted and validated by mass spectrometry and immunohistochemistry on tissue microarrays. Data analysis: Raw MS files from the LTQ-Orbitrap were analyzed by MaxQuant (version 1.1.1.9). MS/MS spectra were searched against the decoy IPI-human database version 3.68 containing both forward and reverse protein sequences by the Andromeda search engine. For identification, the false discovery rate (FDR) was set to 0.01 on the protein and on the peptide levels.

REANALYSED by: PAe004809

INSTRUMENT(S): LTQ Orbitrap

ORGANISM(S): Homo Sapiens (human)

SUBMITTER: Mario Oroshi

PROVIDER: PXD000309 | Pride | 2013-07-02

REPOSITORIES: Pride

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Publications

Proteomic portrait of human breast cancer progression identifies novel prognostic markers.

Geiger Tamar T Madden Stephen F SF Gallagher William M WM Cox Juergen J Mann Matthias M

Cancer research 20120313 9

Breast cancer is the second leading cause of cancer death for women in the United States. Of the different subtypes, estrogen receptor-negative (ER(-)) tumors, which are ErbB2+ or triple-negative, carry a relatively poor prognosis. In this study, we used system-wide analysis of breast cancer proteomes to identify proteins that are associated with the progression of ER(-) tumors. Our two-step approach included an initial deep analysis of cultured cells that were obtained from tumors of defined br ...[more]

PMID: 22414580

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Project description:Recently, significant progress has been made in characterizing and sequencing the genomic alterations in statistically robust numbers of samples from several types of cancer. For example, The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and other similar efforts are identifying genomic alterations associated with specific cancers (e.g., copy number aberrations, rearrangements, point mutations, epigenomic changes, etc.). The availability of these multi-dimensional data to the scientific community sets the stage for the development of new molecularly targeted cancer interventions. Understanding the comprehensive functional changes in cancer proteomes arising from genomic alterations and other factors is the next logical step in the development of high-value candidate protein biomarkers. Hence, proteomics can greatly advance the understanding of molecular mechanisms of disease pathology via the analysis of changes in protein expression, their modifications and variations, as well as protein-protein interaction, signaling pathways and networks responsible for cellular functions such as apoptosis and oncogenesis. Realizing this great potential, the NCI launched the second phase of the CPTC initiative in September 2011. Renamed the Clinical Proteomic Tumor Analysis Consortium, CPTAC is beginning to leverage its analytical outputs from Phase I to define cancer proteomes on genomically-characterized biospecimens. The purpose of this integrative approach is to provide the broad scientific community with knowledge that links genotype to proteotype and ultimately phenotype. The data contained in this dataset are derived from samples designed to confirm CPTAC findings from the TCGA samples. These confirmatory samples contain breast, ovarian, colon, and lung tumors collected via a protocol optimized for proteomics. Specifically, ischemic time of the sample was controlled and restricted to less than 30 minutes. ACGT, Inc. produced whole exome, mRNAseq, and miRNAseq for these samples. Corresponding proteomic data are available at: <a href="https://cptac-data-portal.georgetown.edu/cptacPublic/">https://cptac-data-portal.georgetown.edu/cptacPublic/</a> The study design was to profile colon, breast, ovarian, and lung tumors both genomically and proteomically. Germline DNA was obtained from blood. Normal control samples for proteomics varied by organ site: adjacent colon tissue for colon cases, contralateral breast tissue for some breast cases, and Fallopian tube fimbria for some ovarian cases. Lung cases had no normal control for proteomic analysis. All cancer samples were derived from primary and untreated tumors.

Dataset Information

Proteomic portrait of human breast cancer progression identifies novel prognostic markers

Publications

Proteomic portrait of human breast cancer progression identifies novel prognostic markers.

Similar Datasets

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