Bioinformatic analyses identifies novel protein-coding pharmacogenomic markers associated with paclitaxel sensitivity in NCI60 cancer cell lines

<p>Abstract</p> <p>Background</p> <p>Paclitaxel is a microtubule-stabilizing drug that has been commonly used in treating cancer. Due to genetic heterogeneity within patient populations, therapeutic response rates often vary. Here we used the NCI60 panel to identify SNPs associated with paclitaxel sensitivity. Using the panel's GI50 response data available from Developmental Therapeutics Program, cell lines were categorized as either sensitive or resistant. PLINK software was used to perform a genome-wide association analysis of the cellular response to paclitaxel with the panel's SNP-genotype data on the Affymetrix 125 k SNP array. FastSNP software helped predict each SNP's potential impact on their gene product. mRNA expression differences between sensitive and resistant cell lines was examined using data from BioGPS. Using Haploview software, we investigated for haplotypes that were more strongly associated with the cellular response to paclitaxel. Ingenuity Pathway Analysis software helped us understand how our identified genes may alter the cellular response to paclitaxel.</p> <p>Results</p> <p>43 SNPs were found significantly associated (FDR < 0.005) with paclitaxel response, with 10 belonging to protein-coding genes (<it>CFTR</it>, <it>ROBO1</it>, <it>PTPRD</it>, <it>BTBD12</it>, <it>DCT</it>, <it>SNTG1</it>, <it>SGCD</it>, <it>LPHN2</it>, <it>GRIK1</it>, <it>ZNF607</it>). SNPs in <it>GRIK1</it>, <it>DCT</it>, <it>SGCD </it>and <it>CFTR </it>were predicted to be intronic enhancers, altering gene expression, while SNPs in <it>ZNF607 </it>and <it>BTBD12 </it>cause conservative missense mutations. mRNA expression analysis supported these findings as <it>GRIK1</it>, <it>DCT</it>, <it>SNTG1</it>, <it>SGCD </it>and <it>CFTR </it>showed significantly (p < 0.05) increased expression among sensitive cell lines. Haplotypes found in <it>GRIK1, SGCD, ROBO1, LPHN2</it>, and <it>PTPRD </it>were more strongly associated with response than their individual SNPs.</p> <p>Conclusions</p> <p>Our study has taken advantage of available genotypic data and its integration with drug response data obtained from the NCI60 panel. We identified 10 SNPs located within protein-coding genes that were not previously shown to be associated with paclitaxel response. As only five genes showed differential mRNA expression, the remainder would not have been detected solely based on expression data. The identified haplotypes highlight the role of utilizing SNP combinations within genomic loci of interest to improve the risk determination associated with drug response. These genetic variants represent promising biomarkers for predicting paclitaxel response and may play a significant role in the cellular response to paclitaxel.</p

Differential immunoglobulin class-mediated responses to components of the U1 small nuclear ribonucleoprotein particle in systemic lupus erythematosus and mixed connective tissue disease

OBJECTIVE: To determine whether patients with Systemic Lupus Erythematosus (SLE) and Mixed Connective Tissue Disease (MCTD) possess differential IgM-and IgG-specific reactivity against peptides from the U1 small nuclear ribonucleoprotein particle (U1 snRNP). METHODS: The IgM- and IgG-mediated responses against 15 peptides from subunits of the U1 snRNP were assessed by indirect ELISAs in sera from patients with SLE and MCTD and healthy individuals (n = 81, 41 and 31, respectively). Additionally, 42 laboratory tests and 40 clinical symptoms were evaluated to uncover potential differences. Binomial logistic regression analyses (BLR) were performed to construct models to support the independent nature of SLE and MCTD. Receiver Operating Characteristic (ROC) curves corroborated the classification power of the models. RESULTS: We analyzed IgM and IgG anti-U1 snRNP titers to classify SLE and MCTD patients. IgG anti-U1 snRNP reactivity segregates SLE and MCTD from non-disease controls with an accuracy of 94.1% while IgM-specific anti-U1 snRNP responses distinguish SLE from MCTD patients with an accuracy of 71.3%. Comparison of the IgG and IgM anti-U1 snRNP approach with clinical tests used for diagnosing SLE and MCTD revealed that our method is the best classification tool of those analyzed (p ≤ 0.0001). CONCLUSIONS: Our IgM anti-U1 snRNP system along with lab tests and symptoms provide additional molecular and clinical evidence to support the hypothesis that SLE and MCTD may be distinct syndromes