Differential Gene Expression in oxaliplatin-resistant versus unteated PANC1 cells

We sought to examine changes in gene expression of PANC1 cells with acquired resistance to oxaliplatin chemotherapy. Cultures of PANC1 cells, an established pancreatic cancer cell line, were exposed to oxaliplatin chemotherapy over multiple passages and acquired a stable drug-resistant phenotype labeled as PANC1OR. Previously reported immunofluorescence and western blots indicate that PANC1OR exhibit increased EMT (increased cytoskeletal vimentin, decreased E-cadherin and fibroblast-like morphology). Here we sought to examine more broadly the differential expression of genes associated with acquired drug resistance in these cells. As reported in Cramer et al Mol Cancer Res (2017) PANC1 cells were grown with increasing concentrations of oxaliplatin (Tocris) in regular complete media over successive passages until a stable proliferative phenotype (PANC1OR) without chemotherapy was observed and maintained following cryopreservation and thawing. Drug resistance was confirmed by comparative dose response and measurement of a statistically significant increase in IC50. RNA was extracted from triplicate cell cultures of PANC1 and PANC1OR cells using the TRIzol reagent (Life Technologies Corporation, Carlsbad, CA). RNAseq was performed by the Center for Personalized Therapy Genomics Core at University of Massachusetts Boston. The quality of the raw fastq files were assessed using FastQC (v.0.11.5). Adaptor sequences, “AGATCGGAAGAGCACACGTCTGAACTCCAGTCA”, and “AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT" were trimmed from the 3’ end of the reads using Cutadapt commandline tool form the Trime Galore package (v.0.4.2). The trimmed reads were mapped against the human reference genome (Ensemble, GRCh38) using STAR/2.5 using default parameters25. The average alignment rate was 96%. The sorted BAM files generated by STAR were used to estimate the transcript abundance per sample using featureCount from the Subread package (v.1.6.2)26. Gene expression analysis was performed using the edgR Bioconductor R package (v.3.24.3)27. The edgeR TMM method (trimmed mean of M values) was applied to the filtered genes utilizing the DGElist(), calcNormFactors(), estimateGLMCommonDisp(), estimateGLMTrendedDisp(), estimateGLMTagwiseDisp() functions. The glmFit and glmLRT functions from edgeR were used to fit a negative binomial generalized log-linear model to the read counts. The expression of the genes was ranked by logFoldChange (logFC) and false discovery rate (FDR). Differentially expressed genes (DEGs) were determined using abs(logFC) > 2 and FDR < 0.01 cutoffs, resulting in 1342 protein coding DEGs.