Computational protocol: Inhibition of cyclo-oxygenase 2 reduces tumor metastasis and inflammatory signaling during blockade of vascular endothelial growth factor

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[…] HG-U133A GeneChips (Affymetrix, Santa Clara, CA) were used to investigate gene expression in xenografts. cRNA probes were synthesized as recommended by Affymetrix. Briefly, total RNA was isolated in two steps using ToTALLY RNA Total RNA isolation kit (Ambion, Austin, TX) followed by RNeasy (Qiagen, Valencia, CA) purification. Double-stranded cDNA was generated from 5 μg of total RNA using a polydT oligonucleotide that contained a T7 RNA polymerase initiation site and the Superscript Choice System kit (Invitrogen, Carlsbad, CA). Biotinylated cRNA was generated by in vitro transcription using the Bio Array High Yield RNA Transcript Labeling System (Enzo, Farmingdale, NY). cRNA was purified using RNeasy and fragmented according to the Affymetrix protocol, and 15 μg of biotinylated cRNA hybridized to HGU133A microarrays (Affymetrix). Raw CEL files were processed using Bioconductor packages in an R environment []. Briefly, quality controls were performed by inspecting Affymetrix® metrics using the simpleaffy package. Probe level signals were then background-corrected, normalized, and summarized using the GC-RMA function. Differential gene expression was computed using an Empirical Bayesian model implemented in the Limma package (Additional File Table S1). To determine whether the addition of SC236 broadly changed inflammation-related pathways in BV-treated tumors, we used Gene Set Expression Analysis and GenePattern tools (GSEA, Broad Institute, Cambridge, MA). The MSigDB gene set database (5,542 total sets) was queried for gene sets containing inflammation-related pathways. From this, we constructed a 67 gene set matrix. GSEA was used to assess expression of gene sets in this matrix in BV- and BV+SC236-treated samples (N = 3, 4 respectively). To compute gene enrichment, we permuted by gene (as recommended for small sample sizes) 5000 times. We then computed odds ratios for the genesets identified as being significant by GSEA, defined as ratio of odds for the hits before and after the leading edge. In particular, for each geneset we computed the following:oddsRatios =[(Hits/Misses) BeforeTheLeadingEdge]/[(Hits/Misses) AfterTheLeadingEdge]We used the odds ratios to additionally filter the results, as nominal p-value is not informative in this setting, and the NES has a bias towards bigger genesets. […]

Pipeline specifications

Software tools Simpleaffy, GC–RMA, limma, GenePattern
Application Gene expression microarray analysis
Organisms Homo sapiens
Diseases Neoplasms