Differentiating Proteomic Biomarkers in Breast Cancer by Laser Capture Microdissection and MALDI MS

Abstract

We assessed proteomic patterns in breast cancer using MALDI MS and laser capture microdissected cells. Protein and peptide expression in invasive mammary carcinoma versus normal mammary epithelium and estrogen-receptor positive versus estrogen-receptor negative tumors were compared. Biomarker candidates were identified by statistical analysis and classifiers were developed and validated in blinded test sets. Several of the m/z features used in the classifiers were identified by LC-MS/MS and two were confirmed by immunohistochemistry. (2) reproducibility analyses, (3) interinstitutional sample variability, (4) variability of samples with age, (5) complete list of features defined for the IMC vs NME comparison, (6) subset of features used for the IMC vs NME classifier, (7) IMC vs NME classification LOOCV error depending on selection of k-value for the KNN algorithm, (8) complete list of features defined for the ER+ vs ER− comparison, (9) subset of features used for the ER+ vs ER− classifier, (10) ER+ vs ER− classification LOOCV error depending on selection of k-value for the KNN algorithm, (11) detailed protein identification data. This material is available free of charge via the Internet at Keywords

Enhanced Interferon Signaling Pathway in Oral Cancer Revealed by Quantitative Proteome Analysis of Microdissected Specimens Using 16O/18O Labeling and Integrated Two-dimensional LC-ESI-MALDI Tandem MS

“…Laser capture microdissection is often used in conjunction with MS-based protein identification technology to assist with the discovery of tumor-associated molecules in tissue specimens containing various types of cells (27,31,(61)(62)(63)(64). However, relatively few studies have used these techniques to identify OSCC/HNSCC-associated proteins in tissue specimens from patients with OSCC/HNSCC (15,17,29).…”

Clinical proteomics in breast cancer: a review

“…Using LCM, Umar et al [24] detected 9 differentially expressed tryptic peptides (not structurally identified) following analysis of stromal and tumour cells collected from five tissue specimens. In addition, Sanders et al [23] identified ubiquitin and S100-A8 to be decreased in tumour (n = 122) compared to normal tissue (n = 167), whereas S100-A6 was found increased. Their split-sample approach allowed a successful within-study validation of the three potential markers [23].…”

“…In addition, Sanders et al [23] identified ubiquitin and S100-A8 to be decreased in tumour (n = 122) compared to normal tissue (n = 167), whereas S100-A6 was found increased. Their split-sample approach allowed a successful within-study validation of the three potential markers [23]. As both ubiquitin and S100-A6 were also found to decrease in lysates of human breast cancer cell lines following chemotherapy induced apoptosis [25], these proteins may provide insight into the pathogenesis of breast cancer upon further investigation.…”

“…This can be reduced by laser capture microdissection (LCM), enabling selective capture of a specific subset of cells [22]. Following microdissection, captured cells can be mounted directly on a MALDI target, thereby preserving their spatial conformation for imaging MS [23,24]. Using LCM, Umar et al [24] detected 9 differentially expressed tryptic peptides (not structurally identified) following analysis of stromal and tumour cells collected from five tissue specimens.…”

Tissue Proteomics for the Next Decade? Towards a Molecular Dimension in Histology

“…The microdissected tissues are dropped on a MALDI target and directly covered by the MALDI matrix (PalmerToy et al, 2000;Xu et al, 2002). This approach was already used in order to classify breast cancer tumor types (Sanders et al, 2008), identify intestinal neoplasia protein biomarkers (Xu et al, 2009), and to determine differential profiles in glomerulosclerosis (Xu et al, 2005).…”