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  • br In a separate series of experiments

    2020-07-28


    In a separate series of experiments, huTGO or huFGO lines were grown in a 48-well plate and treated with a combination of epirubicin/oxaliplatin/5-FU at IC50 con-centrations calculated for each organoid line. Organoids were then dissociated to single AZD8931 by using Accutase for 10–15 minutes at 37 C at 0, 24, 48, 72, and 96 hours after drug treatment. Organoids treated with vehicle were har-vested at the same time points. Cell viability was then assayed by flow cytometry using the LIVE/DEAD Viability/ Cytoxicity Kit (ThermoFisher Scientific; L3224). The % dead cells was calculated on the basis of the ability of ethidium homodimer-1 (ex/em approximately 495/635 nm) to enter the cells with damaged membranes. All calculations were normalized to the number % live/dead cells in vehicle controls. Samples were run on the CANTO 3 and analyzed by FlowJo software.
    RNA Sequencing
    RNA was isolated from patient tumor tissue, gastric organoids using TRIzol (Molecular Research Center Inc, Cincinnati, OH) according to the manufacturer’s instructions. RNA-seq data were aligned to the reference human genome (hg19), and expression levels of all genes were quantified by  Cellular and Molecular Gastroenterology and Hepatology Vol. 7, No. 1
    using the standard Bioconductor workflow.31 The differential expression analysis between sample types was performed on the basis of the negative-binomial statistical model of read counts as implemented in the DESeq Bioconductor pack-age.32,33 The differential expression analysis between sample types was performed on the basis of the negative-binomial statistical model of read counts as implemented in the edgeR Bioconductor package.34 A two-factor generalized linear model was used to identify genes differentially expressed between 2 groups of samples, TGO and cancer tissues samples vs two-dimensional cultures, adjusted for the patient effect. The comparison was made. False discovery rates were calculated,33 and genes with false discovery rates <0.1 were considered statistically significant. Cluster anal-ysis of differentially expressed genes was performed by using Bayesian infinite mixture model-based clustering35 of the normalized log-2 rpkm gene expression profiles after adjusting for the patient effect. The enrichment analysis of the clusters of differentially expressed genes was performed by using the CLEAN package.36
    Quantitative Real-Time Polymerase Chain Reaction
    Total RNA was isolated from tissue, organoids, or cell lines by using TRIzol according to manufacturer’s protocol (Life Technologies). The High Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA) was used for cDNA synthesis of RNA following the recommended protocol. For each sample, 60 ng RNA was reverse tran-scribed to yield approximately 2 mg total cDNA that was then used for the real-time polymerase chain reaction. Pre-designed real-time polymerase chain reaction assays were purchased for the following genes (Thermo Fisher, Applied
    Fold change was calculated as the following: (Ct–Ct high) ¼ n target, 2ntarget/2nHPRT ¼ fold change where Ct ¼ threshold
    cycle. The results were expressed as average fold change in gene expression relative to control, with GAPDH used as an internal control according to Livak and Schmittgen.37
    2019 Human-Derived Gastric Cancer Organoids 183
    Statistical Analyses
    The significance of the results was tested by two-way analysis of variance or Student t test by using commer-cially available software (GraphPad Prism). A P value <.05 was considered significant.
    References
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    5. Yano T, Doi T, Ohtsu A, Boku N, Hashizume K, Nakanishi M, Ochiai A. Comparison of HER2 gene amplification assessed by fluorescence in situ hybridi-zation and HER2 protein expression assessed by immunohistochemistry in gastric cancer. Oncol Rep 2006;15:65–71.
    6. Abrahao-Machado LF, Jacome AA, Wohnrath DR, dos Santos JS, Carneseca EC, Fregnani JH, Scapulatempo-Neto C. HER2 in gastric cancer: comparative analysis of three different antibodies using whole-tissue sections and tissue microarrays. World J Gastroenterol 2013; 19:6438–6446.
    7. Domcke S, Sinha R, Levine DA, Sander C, Schultz N. Evaluating cell lines as tumour models by comparison of genomic profiles. Nat Commun 2013;4:2126.
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    9. Gillet JP, Calcagno AM, Varma S, Marino M, Green LJ, Vora MI, Patel C, Orina JN, Eliseeva TA, Singal V, Padmanabhan R, Davidson B, Ganapathi R, Sood AK, Rueda BR, Ambudkar SV, Gottesman MM. Redefining the relevance of established cancer cell lines to the study of mechanisms of clinical anti-cancer drug resistance. Proc Natl Acad Sci U S A 2011;108:18708–18713.