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  • br Wojnar A Kobierzycki C Krolicka A Pula

    2020-08-18


    Wojnar, A., Kobierzycki, C., Krolicka, A., Pula, B., Podhorska-Okolow, M., and Dziegiel, P. (2010). Correlation of Ki-67 and MCM-2 proliferative marker α-Linolenic Acid with grade of histological malignancy (G) in ductal breast cancers. Folia Histochem. Cytobiol. 48, 442–446.
    STAR+METHODS
    KEY RESOURCES TABLE
    REAGENT or RESOURCE SOURCE IDENTIFIER
    Antibodies
    anti-HER2 antibody
    Leica Biosystems
    anti-HER2 antibody
    Leica Biosystems
    antibody diluent
    Agilent/DakoCytomation
    biotinylated anti-mouse secondary antibody
    Vector-Elite, Vector Laboratories
    biotinylated anti-rabbit secondary antibody
    Vector-Elite, Vector Laboratories
    peroxidase ABC reagent
    Vector-Elite, Vector Laboratories
    Chemicals, Peptides, and Recombinant Proteins
    trypsin
    SCIEX
    guanidine hydrochloride
    Sigma-Aldrich
    Sigma-Aldrich
    tris(2-carboxyethyl)phosphine
    Sigma-Aldrich
    S-methyl methanethiosulfonate
    Sigma-Aldrich
    urea
    Sigma-Aldrich
    ammonium formate
    Sigma-Aldrich
    Critical Commercial Assays
    iRT-kit
    Biognosys
    RC-DC Protein Assay
    Bio-Rad
    Agilent
    Deposited Data
    SWATH assay library
    this paper http://www.SWATHAtlas.org Human breast
    mass spectrometry SWATH data
    this paper https://db.systemsbiology.net/sbeams/cgi/
    PeptideAtlas/PASS_View?identifier=PASS00864
    mass spectrometry spectral library data
    this paper https://db.systemsbiology.net/sbeams/cgi/
    PeptideAtlas/PASS_View?identifier=PASS00857
    microarray mRNA breast cancer data
    RNA-Seq TCGA breast cancer data
    (Cancer Genome Atlas, 2012; https://portal.gdc.cancer.gov/projects/TCGA-BRCA
    survival analysis data
    cancer=breast
    Oligonucleotides
    p53 sequencing sense primer
    EastPort
    50 TCCCCTCCCATGTGCTCAAGACTG 30
    p53 sequencing antisense primer
    EastPort
    50 GGAGCCCCGGGACAAAGCAAATGG 30
    Software and Algorithms
    (Choi et al., 2014) http://bioconductor.org/packages/release/bioc/
    html/MSstats.html
    GSEA
    Broad Institute, Inc.
    http://software.broadinstitute.org/gsea/login.jsp
    ctree()
    index.html
    limma
    (Ritchie et al., 2015) http://bioconductor.org/packages/release/bioc/
    html/limma.html
    edgeR
    (Robinson et al., 2010; https://bioconductor.org/packages/release/bioc/
    html/edgeR.html
    OpenMS tool
    https://www.openms.de/
    (Continued on next page)
    Continued
    REAGENT or RESOURCE SOURCE
    IDENTIFIER
    (Deutsch et al., 2010) https://sourceforge.net/projects/sashimi/files/
    OpenSWATH tool
    http://www.openswath.org/en/latest/
    Other
    HILIC Kinetex column
    Phenomenex
    Sartorius
    C18 MicroSpin Columns for peptide desalting
    The Nest Group
    HercepTestTM Interpretation Manual
    Agilent/Dako Cytomation https://www.agilent.com/cs/library/usermanuals/
    public/28630_herceptest_interpretation_manual-
    breast_ihc_row.pdf
    LEAD CONTACT AND MATERIALS AVAILABILITY
    Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Dr. Pavel Bouchal (e-mail: [email protected]).
    EXPERIMENTAL MODEL AND SUBJECT DETAILS
    Study design
    The objective of the study was to compare classification of breast cancer tissues based on proteotypes obtained using a novel next generation proteomics approach, SWATH-MS, with clinically used subtypes classified by immunohistological markers and grade. To avoid lymph node status as confounding factor in tumor classification into subtypes, we decided for the same representation of lymph node positive and lymph node negative tumors in the sample set. A secondary aim was to compare SWATH-MS data with previous measurement using discovery proteomics via data dependent analysis (DDA) in the same sample set. To this end we designed a retrospective pilot discovery study on a cohort of well characterized breast tumor samples available from Masaryk Memorial Cancer Institute (MMCI) (Bouchal et al., 2015). The samples were analyzed by SWATH-MS and the findings confirmed both by immunochemical validation and by meta-analysis of corresponding mRNA levels in independent publicly available sets of patients.
    Clinical tissue samples
    Informed patient consent forms and the use of collected tissues for targeted proteomics analysis were approved by the Ethics com-mittee of the Masaryk Memorial Cancer Institute (MMCI). Breast cancer tissue samples were frozen in liquid nitrogen within 20 min of surgical removal and stored at 180 C in the tissue bank at MMCI. A set of 96 preoperatively untreated women’s breast carcinomas of 11-20 mm maximum diameter (pT1c) was selected. The set consisted of 48 ER+, PR+, HER2-, grade 1 tumors (luminal A (LA) subtype); 16 ER+, PR+/ , HER2-, grade 3 tumors (luminal B (LB) subtype); 8 ER+, PR+/ , HER2+, grade 3 tumors (luminal B-like HER2 positive (LBH) subtype); and 16 ER-, PR-, HER2-, grade 3 tumors (triple negative (TN) subtype). Half of the tumors in each group was lymph node positive and half was lymph node negative at the time of diagnosis. Full details are available in Data S1A and S1B. The cases were reviewed by involved pathologist (Rudolf Nenutil) before entering the study. The tumors were classified and reviewed using FFPE blocks, taken in parallel with the native deeply frozen samples used for proteomics. The samples with very low cellularity of invasive tumor component (e.g., below 20%), and/or dominant in situ component and/or apparent clonal morphological hetero-geneity were not used. As the dataset attempted to represent the main phenotypes, the cases were of variable malignancy and different cellularity. On average, the low grade tumors are inherently of lower cellularity compared to high grade ones. Based on the results, additional independent set of 78 grade 2 and 3 breast tumors was used for IHC validation of ERBB2 protein levels in HER2+, ER+ (n = 41) versus HER2+, ER- (n = 37) tumors (Data S1C). Sample sets used for meta-analysis of mRNA levels are described in Statistical Analysis section.