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  • br Statistical analysis of the

    2020-08-18


    Statistical analysis of the IHC data
    Associations between ERBB2 staining intensity and ER status were assessed by Fisher’s exact test in R 3.0.2.
    Correlation analysis of SWATH-MS and SRM quantitation
    The SRM data were extracted from Supplementary file 3A of the previous publication (Procha´zkova´ et al., 2017) and the data from three technical replicates (injections) were averaged. To compare SWATH-MS and SRM quantitation, comparison based on intensity ratios was applied as follows: Intensity ratios in SWATH-MS dataset were calculated as a ratio between peptide intensity of endog-enous peptide in the sample (I in Data S5) and average intensity of the particular peptide across the dataset (Imean). Intensity ratios in SRM dataset were calculated as a ratio between peptide intensity of endogenous peptide in the sample (ID0) and intensity of global internal standard (ID8). Data analysis and correlation was performed in stringr (version 1.3.0.) and ggplot2 (version 3.0.0.) packages from cran.r-project.org, in R version 3.4.4.
    DATA AND CODE AVAILABILITY
    All MS/MS data files in wiff and mzXML format as well as intermediary and final files of the library building workflow are available at https://db.systemsbiology.net/sbeams/cgi/PeptideAtlas/PASS_View?identifier=PASS00857, the accession number in PeptideAtlas is PASS00857. Breast cancer SWATH assay library has been made available through the SWATHAtlas database (http://www. SWATHAtlas.org). All SWATH-MS raw data and OpenSWATH output files are available at https://db.systemsbiology.net/sbeams/ cgi/PeptideAtlas/PASS_View?identifier=PASS00864, the accession number in PeptideAtlas is PASS00864. OpenSWATH related soft-ware is available on http://www.openswath.org/en/latest/.
    Contents lists available at ScienceDirect
    Journal of Chromatography B
    journal homepage: www.elsevier.com/locate/jchromb
    Breast cancer detection using targeted AM 251 metabolomics T
    Paniz Jasbia,1, Dongfang Wangb,1, Sunny Lihua Chengc, Qiang Feid, Julia Yue Cuic, Li Liue,f, Yiping Weia,g, Daniel Rafteryh, Haiwei Gua, a Arizona Metabolomics Laboratory, College of Health Solutions, Arizona State University, Scottsdale, AZ 85259, USA b Chongqing Blood Center, Chongqing 400015, PR China c Department of Environmental & Occupational Health Sciences, University of Washington, Seattle, WA 98105, USA d College of Chemistry, Jilin University, Changchun, Jilin Province 130061, PR China e Department of Biomedical Informatics, Arizona State University, Tempe, AZ 85259, USA f Department of Neurology, Mayo Clinic, Scottsdale, AZ 85259, USA g Department of Thoracic Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi Province 330006, PR China h Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA 98109, USA
    Keywords:
    Metabolomics
    Breast cancer
    LC-MS/MS
    Targeted detection
    Biomarker discovery 
    Breast cancer (BC) is a major cause of human morbidity and mortality, especially among women. Despite the important role of metabolism in the molecular pathogenesis of cancer, robust metabolic markers to enable enhanced screening and disease monitoring of BC are still critically needed. In this study, a targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) metabolic profiling approach is presented for the identification of metabolic marker candidates that could enable highly sensitive and specific detection of all-stage as well as early-stage BC. In this targeted approach, 105 metabolites from > 35 metabolic pathways of potential biological relevance were reliably detected in 201 plasma samples taken from two groups of subjects (102 BC patients and 99 healthy controls). The results of our general linear model and partial least squares-discriminant analysis (PLS-DA) informed the construction of a novel 6-metabolite panel of potential biomarkers. A receiver operating characteristic (ROC) curve generated based on an improved PLS-DA model showed rela-tively high sensitivity (0.80), specificity (0.75), and area under the receiver-operating characteristic curve (AUROC = 0.89). Similar classification performance of the model was observed for detection of early-stage BC (AUROC = 0.87, sensitivity: 0.86, specificity: 0.75). Bioinformatics analyses revealed significant disturbances in arginine/proline metabolism, tryptophan metabolism, and fatty acid biosynthesis. Our univariate and multi-variate results indicate the effectiveness of this metabolomics approach for all-stage as well as early-stage BC diagnosis; our bioinformatics results indicate affected pathways related to tumor growth, metastasis, and im-mune escape mechanisms. Future studies should validate these results using more samples from different lo-cations.