Computational protocol: Secretome proteomics reveals candidate non-invasive biomarkers of BRCA1 deficiency in breast cancer

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Protocol publication

[…] For each protein, spectral counts were normalized for the total spectral counts in each sample. The normalized spectral counts were used for further comparative analyses. Differences in relative protein abundance, expressed as normalized spectral count, between BRCA1-deficient and -proficient GEMMs were analyzed using the beta binomial test [–]. P < 0.05 was considered statistically significant. Hierarchical unsupervised clustering was carried out on all identified proteins. Associations between immunohistochemistry (IHC) expression of proteins were tested by Pearson's Chi-square test. Multiple logistic regression was used to estimate the predictive effects of candidate biomarkers for identifying mutation carriers while adjusting for confounding factors. All statistical analyses were carried out with SPSS and R.The clinical relevance of protein candidates was evaluated by analyzing the gene expression dataset containing mRNA expression of BRCA1/2-related and sporadic breast carcinomas as published by Jönnson et al. []. As clustering parameters for the gene expression dataset, we used a Spearman rank correlation in combination with the Ward's distance. mRNA levels of candidate proteins between BRCA1/2-deficient breast cancer and sporadic breast cancer were evaluated with Mann-Whitney U test using P < 0.1 as an arbitrary cut-off. To identify putative blood-based markers, protein candidates were evaluated in different human plasma proteome databases [, ].Ingenuity pathway analysis software (Ingenuity Systems) and DAVID tool [] were used to analyze biological functions and deregulated pathways in BRCA1-deficient breast cancer. SignalP and SecretomeP were used to predict proteins potentially undergoing classical and non-classical secretion [, ]. Cytoscape (version 2.7.2) [] and STRING tool (version 9.0) [] were used to analyze protein interactions as well as for network analysis. Cytoscape ClusterViz plugin was employed to identify clusters of biological networks []. […]

Pipeline specifications

Software tools IPA, DAVID, SignalP, SecretomeP
Application Protein sequence analysis
Organisms Mus musculus, Homo sapiens
Diseases Breast Neoplasms, Deficiency Diseases, Neoplasms, Protein Deficiency