Hamdi Jarjanazi

Biological implications of SNPs in signal peptide domains of human proteins

Proteins destined for secretion or membrane compartments possess signal peptides for insertion into the membrane. The signal peptide is therefore critical for localization and function of cell surface receptors and ligands that mediate cell–cell communication. About 4% of all human proteins listed in UniProt database have signal peptide domains in their N terminals. A comprehensive literature survey was performed to retrieve functional and disease associated genetic variants in the signal peptide domains of human proteins. In 21 human proteins we have identified 26 disease associated mutations within their signal peptide domains, 14 mutations of which have been experimentally shown to impair the signal peptide function and thus influence protein transportation. We took advantage of SignalP 3.0 predictions to characterize the signal peptide prediction score differences between the mutant and the wild‐type alleles of each mutation, as well as 189 previously uncharacterized single nucleotide polymorphisms (SNPs) found to be located in the signal peptide domains of 165 human proteins. Comparisons of signal peptide prediction outcomes of mutations and SNPs, have implicated SNPs potentially impacting the signal peptide function, and thus the cellular localization of the human proteins. The majority of the top candidate proteins represented membrane and secreted proteins that are associated with molecular transport, cell signaling and cell to cell interaction processes of the cell. This is the first study that systematically characterizes genetic variation occurring in the signal peptides of all human proteins. This study represents a useful strategy for prioritization of SNPs occurring within the signal peptide domains of human proteins. Functional evaluation of candidates identified herein may reveal effects on major cellular processes including immune cell function, cell recognition and adhesion, and signal transduction. Proteins 2008.

Discovery of genetic profiles impacting response to chemotherapy: application to gemcitabine†

Chemotherapy is a major treatment modality for individuals affected by cancer. Currently, a number of genome‐based technologies are being adopted to identify genes associated with drug response; however, large‐scale genetic association applications are still limited. Here we describe a novel strategy based on the genetic and drug response data of the NCI60 cell lines to discover potential candidate genetic variants associated with variable response to chemotherapy. As an example we have applied this strategy to discover single genetic markers and haplotypes from candidate genes previously implicated in the pharmacobiology of gemcitabine. Single‐marker association analyses have implicated the association of four SNPs within the gene loci of CDC5L, EPC2, POLS, and PARP1. We have also investigated the combined effect of SNPs using haplotype‐based analysis. Accordingly, we have shown modest association of haplotypes in six genes, whereas the most significant associations included a haplotype of the POLS gene. The hypothesis‐generating tool presented in this study can be applied to drugs profiled in the NCI60 cell line screen and provides an effective means for the identification of genes associated with drug response. The results obtained using this novel methodology can be used to better design the clinical trials for effective study of the chemotherapeutic agents and thus provide a basis for individualized chemotherapy. Hum Mutat 29(4), 461–467, 2008.

A whole-genome SNP association study of NCI60 cell line panel indicates a role of Ca2+ signaling in selenium resistance.

Epidemiological studies have suggested an association between selenium intake and protection from a variety of cancer. Considering this clinical importance of selenium, we aimed to identify the genes associated with resistance to selenium treatment. We have applied a previous methodology developed by our group, which is based on the genetic and pharmacological data publicly available for the NCI60 cancer cell line panel. In short, we have categorized the NCI60 cell lines as selenium resistant and sensitive based on their growth inhibition (GI50) data. Then, we have utilized the Affymetrix 125K SNP chip data available and carried out a genome-wide case-control association study for the selenium sensitive and resistant NCI60 cell lines. Our results showed statistically significant association of four SNPs in 5q33–34, 10q11.2, 10q22.3 and 14q13.1 with selenium resistance. These SNPs were located in introns of the genes encoding for a kinase-scaffolding protein (AKAP6), a membrane protein (SGCD), a channel protein (KCNMA1), and a protein kinase (PRKG1). The knock-down of KCNMA1 by siRNA showed increased sensitivity to selenium in both LNCaP and PC3 cell lines. Furthermore, SNP-SNP interaction (epistasis) analysis indicated the interactions of the SNPs in AKAP6 with SGCD as well as SNPs in AKAP6 with KCNMA1 with each other, assuming additive genetic model. These genes were also all involved in the Ca2+ signaling, which has a direct role in induction of apoptosis and induction of apoptosis in tumor cells is consistent with the chemopreventive action of selenium. Once our findings are further validated, this knowledge can be translated into clinics where individuals who can benefit from the chemopreventive characteristics of the selenium supplementation will be easily identified using a simple DNA analysis.

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

BackgroundPaclitaxel 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 panels 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 panels SNP-genotype data on the Affymetrix 125 k SNP array. FastSNP software helped predict each SNPs 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.Results43 SNPs were found significantly associated (FDR < 0.005) with paclitaxel response, with 10 belonging to protein-coding genes (CFTR, ROBO1, PTPRD, BTBD12, DCT, SNTG1, SGCD, LPHN2, GRIK1, ZNF607). SNPs in GRIK1, DCT, SGCD and CFTR were predicted to be intronic enhancers, altering gene expression, while SNPs in ZNF607 and BTBD12 cause conservative missense mutations. mRNA expression analysis supported these findings as GRIK1, DCT, SNTG1, SGCD and CFTR showed significantly (p < 0.05) increased expression among sensitive cell lines. Haplotypes found in GRIK1, SGCD, ROBO1, LPHN2, and PTPRD were more strongly associated with response than their individual SNPs.ConclusionsOur 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.

Functional nsSNPs from carcinogenesis-related genes expressed in breast tissue: Potential breast cancer risk alleles and their distribution across human populations

Although highly penetrant alleles of BRCA1 and BRCA2 have been shown to predispose to breast cancer, the majority of breast cancer cases are assumed to result from the presence of low-moderate penetrant alleles and environmental carcinogens. Non-synonymous single nucleotide polymorphisms (nsSNPs) are hypothesised to contribute to disease susceptibility and approximately 30 per cent of them are predicted to have a biological significance. In this study, we have applied a bioinformatics-based strategy to identify breast cancer-related nsSNPs from 981 carcinogenesis-related genes expressed in breast tissue. Our results revealed a total of 367 validated nsSNPs, 109 (29.7 per cent) of which are predicted to affect the protein function (functional nsSNPs), suggesting that these nsSNPs are likely to influence the development and homeostasis of breast tissue and hence contribute to breast cancer susceptibility. Sixty-seven of the functional nsSNPs presented as commonly occurring nsSNPs (minor allele frequencies ≥ 5 per cent), representing excellent candidates for breast cancer susceptibility. Additionally, a non-uniform distribution of the common functional nsSNPs among different human populations was observed: 15 nsSNPs were reported to be present in all populations analysed, whereas another set of 15 nsSNPs was specific to particular population(s). We propose that the nsSNPs analysed in this study constitute a unique resource of potential genetic factors for breast cancer susceptibility. Furthermore, the variations in functional nsSNP allele frequencies across major population backgrounds may point to the potential variability of the molecular basis of breast cancer predisposition and treatment response among different human populations.

NCI60 Cancer Cell Line Panel Data and RNAi Analysis Help Identify EAF2 as a Modulator of Simvastatin and Lovastatin Response in HCT-116 Cells

Simvastatin and lovastatin are statins traditionally used for lowering serum cholesterol levels. However, there exists evidence indicating their potential chemotherapeutic characteristics in cancer. In this study, we used bioinformatic analysis of publicly available data in order to systematically identify the genes involved in resistance to cytotoxic effects of these two drugs in the NCI60 cell line panel. We used the pharmacological data available for all the NCI60 cell lines to classify simvastatin or lovastatin resistant and sensitive cell lines, respectively. Next, we performed whole-genome single marker case-control association tests for the lovastatin and simvastatin resistant and sensitive cells using their publicly available Affymetrix 125K SNP genomic data. The results were then evaluated using RNAi methodology. After correction of the p-values for multiple testing using False Discovery Rate, our results identified three genes (NRP1, COL13A1, MRPS31) and six genes (EAF2, ANK2, AKAP7, STEAP2, LPIN2, PARVB) associated with resistance to simvastatin and lovastatin, respectively. Functional validation using RNAi confirmed that silencing of EAF2 expression modulated the response of HCT-116 colon cancer cells to both statins. In summary, we have successfully utilized the publicly available data on the NCI60 cell lines to perform whole-genome association studies for simvastatin and lovastatin. Our results indicated genes involved in the cellular response to these statins and siRNA studies confirmed the role of the EAF2 in response to these drugs in HCT-116 colon cancer cells.

Associations of the A66G Methionine Synthase Reductase Polymorphism in Colorectal Cancer: A Systematic Review and Meta-Analysis.

Inconsistency in the reported associations between the A66G polymorphism in the methionine synthase reductase (MTRR) gene and colorectal cancer (CRC) prompted a meta-analysis, so that we could obtain a more precise estimate. Databases searches of the published literature yielded 20 case-control studies from 17 articles (8,371 cases and 12,574 controls). We calculated pooled odds ratios (ORs) and 95% confidence intervals in three genetic comparisons (A allele, G allele, and A/G genotype). We found no evidence of overall associations between MTRR A66G and CRC risk (OR 0.96–1.05, P = 0.12–0.44). This was materially unchanged when reanalyzed without the Hardy-Weinberg equilibrium (HWE)-deviating studies (OR 0.97–1.06, P = 0.11–0.65). In the A allele comparison, however, outlier treatment generated significant protection (OR 0.91, P = 0.01). Combined removal of the outliers and HWE-deviating studies reflected this summary effect (OR 0.90, P = 0.01) as did the pooled OR from high-quality studies (OR 0.90, P = 0.01). Only the Asian subgroup showed significant (both at P = 0.05) A allele (OR 1.13) and A/G genotype (OR 0.88) associations. In conclusion, post-outlier A allele effects were protective. Our study also suggests ethnic-specific associations with Asian susceptibility and protection in the A allele and A/G genotype comparisons, respectively. Folate status showed no association of this polymorphism with CRC.

Evaluating Meta-Analysis Research of Escherichia coli

This chapter summarizes the progress in Escherichia coli research that used the meta-analysis approach. Using systematic searches for E. coli literature, we tracked meta-analysis publications and analyzed them based on a number of parameters. These included subject/topic (epidemiology, clinical/intervention/prevention and environmental), geographical region (the Americas, Europe and Australasia) and clinical syndrome (enteric, renal, and sepsis/meningitis). These parameters were plotted in terms of time span to obtain a sense of dynamic change or its absence through the years since the turn of the twentieth century. In terms of region, topic and syndrome, highest meta-analysis productivity was attributed to the Americas, clinical/intervention/prevention and enteric, all of which took place in the last 5 years (2011–2016). Over the combined time span of 16 years, the Americas significantly dominated meta-analysis outputs when compared to Europe and Australasia (P = 0.003). In conclusion, our findings facilitate awareness of the progress in this field wherein the studied parameters were analyzed for patterns over time and differential rates of publication productivity.

Abstract 1679: Identification of pharmacogenomic markers associated with paclitaxel and carboplatin response in cancer: The cross-talk between agents

Introduction: Recent emerging results from various cancer trials have implicated the critical role of Paclitaxel and Carboplatin combination therapy. Paclitaxel stabilizes microtubules whereas Carboplatin cross-links with DNA; both leading to cellular damage and cell death. Genetic variability among patients has been shown to impact the outcome of chemotherapy, and forms the basis of our study. Here we applied a novel strategy developed by our group, where we have identified genes that may impact the therapeutic response to Paclitaxel and Carboplatin. Materials and Methods: The GI50 response data on the NCI60 panel, available from DTP was obtained and resistant versus sensitive cell line categories were defined using a non-parametric kernel estimation on SAS 9.1. Using the Affymetrix 125k SNP Chip9s genotype data for the cell lines, we carried out a genome-wide association study to identify SNPs associated with response to Paclitaxel and Carboplatin. After correction for multiple testing, significant SNPs were mapped to chromosomal regions using information from dbSNP. Genes with significant SNPs were also investigated for haplotypes using Haploview software and for mRNA expression differences using the Affymetrix U133A Chip data at BioGPS database. Using Ingenuity, we studied the biological associations and pathways between identified genes for both drugs. Results: Our strategy identified 48 SNPs with 13 in protein-coding genes (KIAA0427, ROBO1, BTBD12, CCDC26, SLC2A9, DCT, SNTG1, SGCD, GRIK1, ZNF607, CFTR, LPHN2, PTPRD) for Paclitaxel and 20 SNPs with 7 in protein-coding genes (GRIN2A, CD86, KIAA1239, CTNND2, CMYA5, COL19A1, PTPRD) for Carboplatin. Interestingly, PTPRD was associated with both drugs suggesting some synchrony in their mechanisms. Ingenuity analyses have also shown common biological pathways involved in drug response mainly via β-catenin/p53, and microtubular interactions. Haplotype analyses revealed blocks around the significant SNPs for LPHN2, PTPRD, SLC2A9, GRIK1, ROBO1 and SGCD for Paclitaxel. Differential mRNA expression was found for SGCD and DCT for Paclitaxel. Haplotype and mRNA expression analysis for Carboplatin is still being conducted. Conclusions: The common genes and pathways identified support the interaction of both drugs during combination therapy. As only 2 of the 13 genes for Paclitaxel showed significant changes in mRNA expression, this highlights the importance of looking for variants at the DNA level because mRNA expression alone would not have identified many of these genes. Furthermore, the 6 haplotypes identified so far can better predict of drug response than their respective SNPs, showing the role of SNP-SNP interactions. These genetic variants represent promising biomarkers that can one day be used to predict Paclitaxel and Carboplatin therapeutic response among cancer patients. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 1679.