iTRAQ proteomics of sentinel lymph nodes for identification of extracellular matrix proteins to flag metastasis in early breast cancer

Abstract

Patients with early breast cancer are affected by metastasis to axillary lymph nodes. Metastasis to these nodes is crucial for staging and quality of surgery. Sentinel Lymph Node Biopsy that is currently used to assess lymph node metastasis is not effective. This necessitates identification of biomarkers that can flag metastasis. Early stage breast cancer patients were recruited. Surgical resection of breast was followed by identification of sentinel lymph nodes. Fresh frozen section biopsy was used to assign metastatic and non-metastatic sentinel lymph nodes. Discovery phase included iTRAQ proteomics coupled with mass spectrometric analysis to identify differentially expressed proteins. Data is available via ProteomeXchange with identifier PXD027668. Validation was done by bioinformatic analysis and ELISA. There were 2398 unique protein groups and 109 differentially expressed proteins comparing metastatic and non-metastatic lymph nodes. Forty nine proteins were up-regulated, and sixty proteins that were down regulated in metastatic group. Bioinformatic analysis showed ECM-receptor interaction pathways to be implicated in lymph node metastasis. ELISA confirmed up-regulation of ECM proteins in metastatic lymph nodes. ECM proteins have requisite parameters to be developed as a diagnostic tool to assess status of sentinel lymph nodes to guide surgical intervention in early breast cancer.

Realm of proteomics in breast cancer management and drug repurposing to alleviate intricacies of treatment

“…To validate, George et al displayed that the proteome change in the breast microenvironment can lead to BC diagnosis through NAF [110]. Furthermore, with the help of iTRAQ-based comparative tissue analysis, extracellular matrix (ECM)-regulating proteins responsible for cell proliferation, migration, and LN metastasis have been disclosed (Table 1) [111,112]. Therefore, the diagnosis of BC at its early metastasis stage can restrict the spreading of cancer to the distant organ by improving the predictive value of the disease progression.…”

Recent Contributions of Mass Spectrometry-Based “Omics” in the Studies of Breast Cancer

“…After applying specific search parameters, including the database and search algorithm, it imports MS data files into the platform and performs data preprocessing and quality control steps. It performs the database search by matching experimental MS/MS spectra against theoretical spectra and utilizes postprocessing tools for peptide and protein identification and filtering. ,,,,,,,,, Quantification methods based on the experimental design can be applied. Spectronaut is another software tool developed by Biognosys for targeted proteomics data analysis.…”

“…A large number of statistical tools are used for these omics studies, which are listed by the names. Pathway analysis, clustering analysis (e.g., Hierarchical Clustering, K-means), network analysis (e.g., protein-protein interaction networks), functional annotation and gene ontology analysis, pathway enrichment analysis (e.g., GO enrichment, KEGG pathway enrichment), , Bayesian analysis, cox proportional-hazards model, nonparametric statistics (e.g., Wilcoxon Rank-Sum Test), multivariate analysis ,, (e.g., partial least squares, principal component regression), weighted gene co-expression network analysis (WGCNA), gene set variation analysis (GSVA), time-series analysis, copy number variation analysis (CNV), functional regulatory network inference co-expression analysis, gene regulatory network inference, functional genomic screening, comparative genomics analysis, allelic specific expression analysis (ASE), network topology analysis, survival regression analysis (e.g., Cox PH model), metagenomic functional annotation, time-series omics analysis, and hidden Markov models (HMMs) in genomics and oroteomics. For machine learning-based omics analysis a variety of machine learning techniques, e.g., random forest, support vector machines, and feature selection, are also employed. , Bioinformatics software is used in conjunction with the variety of recent MS-based techniques, and applications in breast cancer have been outlined in various reviews published recently.…”

“…109 differentially expressed proteins could be classified as metastasic against nonmetastatic nodes. iTRAC-based labeling in mass spectrometry analysis was used, and ECM proteins were found to flag metastasis in the nodes …”

“…Numerous studies that attempted to derive various biomarkers are integrated under different sections of the review. Such publications of biomarkers are discussed which include proteome markers of drug resistance, identifiers of radiotherapy response, metabolic tissue markers for disease for screening and diagnosis, phosphoproteome-based signaling cascade identifiers for an aggressive disease type, biomarkers from archival samples for prediction of overall survival, protein markers of benign mammogram findings, androgen receptors as diagnosis markers, serum metabolomic fingerprinting, novel volatile organic compounds (VOCs) in urine as biomarkers for diagnosis, , overall survival predictors and immune phenotype related biomarkers, lymph node markers to flag metastasis, interactive marginal tissue biomarkers regulating neoplasm, markers for diagnosis through rapid lipid profiling, endocrine therapy resistance identifiers like aromatase inhibitor resistance (promoted by a RNA DIO3OS), glycan-profile-based markers, specific progression markers, plasma proteome-based prognostic markers, epithelial-mesenchymal transition (EMT) markers, extracellular matrix (ECM) markers like cytokeratin-19, E-cadherin gene expression as metastasis indicators, predictive biomarkers for immunotherapy, retrospective investigation panels based on drug responses and metastasis, tumor microenvironment related target markers implicated in evasion of immunity and drug evasion strategies, extracellular vesicle-based biomarkers for expansion of liquid biopsies, drug resistance linked to tissue environment like hypoxia in BC tumors, proteome signatures of drug resistance in BC cells, biomarkers of invasive ductal carcinoma as novel phenotypes and better classifiers of disease, heterogeneity panel identifiers in subtypes, additional markers for disease subtype conforming to conventional subtypes, glycan biomarkers, and progression identifiers through the salivary proteome . Cell lines are probed for drug-resistant proteomes, and potential drug responsive proteins are identified .…”