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PACdb: A database for cell-based pharmacogenomics
Abstract
We have developed Pharmacogenomics And Cell database (PACdb), a results database that makes available relationships between single nucleotide polymorphisms, gene expression, and cellular sensitivity to various drugs in cell-based models to help determine genetic variants associated with drug response. The current version also supports summary analysis on differentially expressed genes between the HapMap samples of European and African ancestry, as well as queries for summary information of correlations between gene expression and pharmacological phenotypes. At present, data generated on the following anticancer agents are included: carboplatin, cisplatin, etoposide, daunorubicin, and cytarabine (Ara-C). The database is also available to assist in the investigation of the effects of potential confounding variables (e.g. cell proliferation rate) in lymphoblastoid cell lines. PACdb will be regularly updated to include more drugs and new datasets (e.g. baseline microRNA levels). PACdb will be linked into PharmGKB to benefit the next wave of pharmacogenetic and pharmacogenomic discovery.
... In a lymphoblastoid cell line model system, Gamazon et al. previously investigated relationships between genetic variants, gene expression, and cytotoxicity to daunorubicin 16 . Querying this dataset, a significant correlation between PLCE1:rs932764 and daunorubicin response (expressed as IC 50 ) was observed (P = 0.0080), as well as four other proxy variants in PLCE1 (rs10786152, rs2901761, rs731141, and rs9663362). ...
... rs10786152 was also predicted by SNiPA to be located in putative regulatory region defined by open chromatin and HaploReg predicted that this same variant was associated with enhancer histone marks in skeletal muscle myoblasts. The Gamazon et al. dataset also demonstrated a significant correlation between ATP2B1 gene expression and daunorubicin IC 50 values in lymphoblastoid cell lines (P = 0.0079) 16 . Significant cis-eQTL relationships 17 in whole blood for two other genes located near ATP2B1 on chr12q21 -GALNT4 and POC1B -were identified for the significant directly genotyped variant in ATP2B1, rs17249754, and nine other proxy variants. ...
... A total of 95 variants were identified and inputted into HaploReg v2 49 and SNiPA 50 for functional predictions. The PACdb database was queried to identify potential correlations between the variant or host gene identified in the association analysis and anthracycline-induced cytotoxicity in lymphoblastoid cell lines 16 . ...
Anthracycline-based chemotherapy is associated with dose-dependent, irreversible damage to the heart. Childhood cancer survivors with hypertension after anthracycline exposure are at increased risk of cardiotoxicity, leading to the hypothesis that genetic susceptibility loci for hypertension may serve as predictors for development of late cardiotoxicity. Therefore, we determined the association between 12 GWAS-identified hypertension-susceptibility loci and cardiotoxicity in a cohort of long-term childhood cancer survivors (N = 108) who received anthracyclines and were screened for cardiac function via echocardiograms. Hypertension-susceptibility alleles of PLCE1:rs9327264 and ATP2B1:rs17249754 were significantly associated with cardiotoxicity risk conferring a protective effect with a 64% (95% CI: 0.18–0.76, P = 0.0068) and 74% (95% CI: 0.07–0.96, P = 0.040) reduction in risk, respectively. In RNAseq experiments of human induced pluripotent stem cell (iPSC) derived cardiomyocytes treated with doxorubicin, both PLCE1 and ATP2B1 displayed anthracycline-dependent gene expression profiles. In silico functional assessment further supported this relationship - rs9327264 in PLCE1 (P = 0.0080) and ATP2B1 expression (P = 0.0079) were both significantly associated with daunorubicin IC50 values in a panel of lymphoblastoid cell lines. Our findings demonstrate that the hypertension-susceptibility variants in PLCE1 and ATP2B1 confer a protective effect on risk of developing anthracycline-related cardiotoxicity, and functional analyses suggest that these genes are influenced by exposure to anthracyclines.
... Developed in response to these limitations, a new in vitro assay system utilizing dose-response (DR) profiles of immortalized lymphoblastoid cell lines http://www.biodatamining.org/content/5/1/21 (LCLs) allows for rapid experimental testing of multiple agents using virtually unlimited sample sizes, for either linkage or association at the genome-wide level [3][4][5][6][7][8][9][10][11][12][13]. A more thorough discussion of the practical benefits for using LCLs in pharmacogenomics research can be found in recent review articles [1,2]. ...
... For instance, if a certain polymorphism affects the expression of an enzyme involved with inactivating a drug target, what kinds of differences in the DR profiles for LCL cytotoxicity will this polymorphism produce? Many previous studies have fit a non-linear equation (often without testing the appropriateness of this model) to each DR curve, choosing one parameter from this fit, and perform association using this parameter estimate [3][4][5][6][7][8][9][10][11][12][13]. As mentioned above, the "true" differences in the DR profiles between phenotypes may not be captured by this parameter. ...
... are due entirely to a simple summary measure. These ideas are illustrated in Figures 8 and 9, where data are generated for large (12) vector responses, correlations are low (ρ = 0.25) and differences in the DR curves between genotypes can be summarized well using simple summary measures. In this case, the MANOVA method does not perform optimally, even though data are generated using a multivariate normal model. ...
Background
The large sample sizes, freedom of ethical restrictions and ease of repeated measurements make cytotoxicity assays of immortalized lymphoblastoid cell lines a powerful new in vitro method in pharmacogenomics research. However, previous studies may have over‐simplified the complex differences in dose‐response profiles between genotypes, resulting in a loss of power.
Methods
The current study investigates four previously studied methods, plus one new method based on a multivariate analysis of variance (MANOVA) design. A simulation study was performed using differences in cancer drug response between genotypes for biologically meaningful loci. These loci also showed significance in separate genome‐wide association studies. This manuscript builds upon a previous study, where differences in dose‐response curves between genotypes were constructed using the hill slope equation.
Conclusion
Overall, MANOVA was found to be the most powerful method for detecting real signals, and was also the most robust method for detection using alternatives generated with the previous simulation study. This method is also attractive because test statistics follow their expected distributions under the null hypothesis for both simulated and real data. The success of this method inspired the creation of the software program MAGWAS. MAGWAS is a computationally efficient, user‐friendly, open source software tool that works on most platforms and performs GWASs for individuals having multivariate responses using standard file formats.
... Drug concentration required to inhibit 50% of cellular growth is defined as IC 50 and was used to characterize individual cellular sensitivity to platinum agents. The cellular sensitivity to carboplatin and cisplatin in various HapMap LCLs including those studied here was reported elsewhere (14,18) and all data as well as genomewide SNP association results with these data have been made publicly available (19). ...
... The ExprTarget database (20), which was constructed to integrate various bioinformatic prediction tools along with our Exiqon and exon array experimental data, was queried to identify the negative correlation relationships between the miRNAs and mRNAs of interest [both 0.05 (for screening) and 10 À4 (false discovery rate (FDR) < 0.05, for multiple testing correction; ref. 21) cut off were examined]. Furthermore, the relationship between mRNA expression levels and sensitivity to carboplatin or cisplatin were queried through PACdb (19), another publicly available database we constructed to host results of SNP associations with chemotherapeutic agent sensitivity or with gene expression. Linear regression analysis was carried out between miRNA expression and log 2 -transformed carboplatin or cisplatin IC 50 with P < 0.05 as cutoff. ...
... To replicate these findings, we used 58 unrelated Hap-Map CEU III samples, for which the rs1649942 genotype and data on cellular sensitivity to the platinum agents are publicly available (18,19). Indeed, the SNP was found to be significantly or suggestively associated with platinum sensitivity in these replication samples (P ¼ 0.03 and 0.07 for carboplatin and cisplatin IC 50 , respectively). ...
Platinum agents are the backbone of cancer chemotherapy. Recently, we identified and replicated the role of a single nucleotide polymorphism (SNP, rs1649942) in predicting platinum sensitivity both in vitro and in vivo. Using the CEU samples from the International HapMap Project, we found the same SNP to be a master regulator of multiple gene expression phenotypes, prompting us to investigate whether rs1649942-mediated regulation of miRNAs may in part contribute to variation in platinum sensitivity. To these ends, 60 unrelated HapMap CEU I/II samples were used for our discovery-phase study using high-throughput genome-wide miRNA and gene expression profiling. Examining the relationships among rs1649942, its gene expression targets, genome-wide miRNA expression, and cellular sensitivity to carboplatin and cisplatin, we identified 2 platinum-associated miRNAs (miR-193b* and miR-320) that inhibit the expression of 5 platinum-associated genes (CRIM1, IFIT2, OAS1, KCNMA1, and GRAMD1B). We further replicated the relationship between the expression of miR-193b*, CRIM1, IFIT2, KCNMA1, and GRAMD1B, and platinum sensitivity in a separate HapMap CEU III dataset. We then showed that overexpression of miR-193b* in a randomly selected HapMap cell line results in resistance to both carboplatin and cisplatin. This relationship was also found in 7 ovarian cancer cell lines from NCI60 dataset and confirmed in an OVCAR-3 that overexpression of miR-193b* leads to increased resistance to carboplatin. Our findings highlight a potential mechanism of action for a previously observed genotype-survival outcome association. Further examination of miR-193b* in platinum sensitivity in ovarian cancer is warranted. Mol Cancer Ther; 11(9); 2054-61. ©2012 AACR.
... Demonstration of the fpp-value approach was achieved using the p-values of SNPs, gene expression, and their interaction in relation to the phenotype of the cytotoxicity induced by an anticancer drug (cisplatin). Cisplatin-induced cytotoxicity was determined using half-inhibition of cell growth/survival (IC 50 ) after treatment with the drug (Gamazon et al., 2010a). For the evaluation of the fpp-value method, sensitivity and specificity were measured using hypothetical true-positive genes, given the absence of a gold-standard gene set for cisplatin-related cytotoxicity. ...
... To address this challenge, we measured the sensitivity/specificity of the fppvalue approach using hypothetical true-positive genes, given the absence of a standard set of genes for drug-induced cytotoxicity. The hypothetical true genes were compiled considering their biological relevance for our target phenotypes: inhibition of cell growth/survival after treatment with cisplatin (Gamazon et al., 2010a). Existing bioinformatics resources were used for the compilation of these hypothetical true genes, which included the Database of Essential Genes (DEG 5.0) (Zhang and Lin, 2009), for human genes that are essential for cell survival, the Kyoto Encyclopedia of Genes and Genomes (KEGG) (Kane-hisa and Goto, 2000), for the cytochrome P450 and membrane transport ABC pathways, and the Pharmacogenetics Knowledge Base (PharmGKB) (Hewett et al., 2002), for cisplatin-and taxane-related pathways regarding cisplatin-related metabolic features (Crul et al., 2002). ...
... 1A and 1B, p-values of SNPs and gene expression in target traits and drug-induced cytotoxicity should be prepared. The existing PACdb database (Gamazon et al., 2010a) fulfilled these requirements by providing the p-values of SNPs and gene expression patterns according to cisplatininduced cytotoxicity in two different populations, CEU and YRI, of the HapMap project. In addition, p-values of interactions between SNPs and gene expression patterns were also prepared using the SCAN public database (Gamazon et al., 2010b). ...
The identification of true causal loci to unravel the statistical evidence of genotype-phenotype correlations and the biological relevance of selected single-nucleotide polymorphisms (SNPs) is a challenging issue in genome-wide association studies (GWAS). Here, we introduced a novel method for the prioritization of SNPs based on p-values from GWAS. The method uses functional evidence from populations, including phenotype-associated gene expressions. Based on the concept of genetic interactions, such as perturbation of gene expression by genetic variation, phenotype and gene expression related SNPs were prioritized by adjusting the p-values of SNPs. We applied our method to GWAS data related to drug-induced cytotoxicity. Then, we prioritized loci that potentially play a role in druginduced cytotoxicity. By generating an interaction model, our approach allowed us not only to identify causal loci, but also to find intermediate nodes that regulate the flow of information among causal loci, perturbed gene expression, and resulting phenotypic variation.
... The commercial availability of these cell lines and the rich genetic information publicly available have enabled a large number of researchers to adopt them as in vitro models for the study of genotype-phenotype relationships in human cells [4]. Consistent with this trend, a vast amount of phenotypic data such as gene expression levels, drug response, and radiation response have been made publicly available5678. Furthermore, an enormous amount of genotype-phenotype relationships have been generated [4,9– 11]. ...
... Furthermore, an enormous amount of genotype-phenotype relationships have been generated [4,9– 11]. Our group has therefore constructed a database, PACdb [6], a public central repository of pharmacology-related phenotypes, to host these integrative results obtained in HapMap LCLs. ...
... Existing data would make up the prior distribution for the intrinsic growth rates and the addition of new data would generate posterior distributions, presumably more concentrated on the true intrinsic growth rates. We plan to regularly update the HapMap LCL intrinsic growth rate phenotype data and make them widely available to the research community through PACdb [6]. We found significant in vitro population differences in cellular growth rate in the HapMap populations included in our study. ...
The International HapMap project has made publicly available extensive genotypic data on a number of lymphoblastoid cell lines (LCLs). Building on this resource, many research groups have generated a large amount of phenotypic data on these cell lines to facilitate genetic studies of disease risk or drug response. However, one problem that may reduce the usefulness of these resources is the biological noise inherent to cellular phenotypes. We developed a novel method, termed Mixed Effects Model Averaging (MEM), which pools data from multiple sources and generates an intrinsic cellular growth rate phenotype. This intrinsic growth rate was estimated for each of over 500 HapMap cell lines. We then examined the association of this intrinsic growth rate with gene expression levels and found that almost 30% (2,967 out of 10,748) of the genes tested were significant with FDR less than 10%. We probed further to demonstrate evidence of a genetic effect on intrinsic growth rate by determining a significant enrichment in growth-associated genes among genes targeted by top growth-associated SNPs (as eQTLs). The estimated intrinsic growth rate as well as the strength of the association with genetic variants and gene expression traits are made publicly available through a cell-based pharmacogenomics database, PACdb. This resource should enable researchers to explore the mediating effects of proliferation rate on other phenotypes.
... In response to these limitations, a novel in vitro assay system has emerged as a promising new approach for gene mapping in pharmacogenomics cancer therapy (Watters et al., 2004). This in vitro system relies on cytotoxicity assays of immortalized lymphoblastoid cell lines (LCLs) to measure dose-response phenotypes of individual cell lines (Dolan et al., 2004;Watters et al., 2004;Huang et al., 2007;Bleibel et al., 2009;Duan et al., 2009;Peters et al., 2009Peters et al., , 2011aGamazon et al., 2010;Stark et al., 2010;Watson et al., 2011a,b). While the direct translational relevance of these assays is not fully understood, LCL-based assay systems can be used to measure interindividual response to cytotoxic drugs, and to assess and map the genetic components that explain this variability (Zhang et al., 2008;Welsh et al., 2009). ...
... Previous studies that have performed gene mapping have employed simple association methods, using summary measures from non-linear modeling of the dose-response curves (Dolan et al., 2004;Watters et al., 2004;Huang et al., 2007;Bleibel et al., 2009;Duan et al., 2009;Peters et al., 2009Peters et al., , 2011aGamazon et al., 2010;Stark et al., 2010;Watson et al., 2011a,b). These studies have summarized dose-response with either IC50 values (the interpolated dose at which 50% of cells have been killed), or the hill slope (the slope of the non-linear model) estimated for each individual (Beam and Motsinger-Reif, 2010). ...
... Previously, investigators of LCL cytotoxicity data have used two primary methods in searching for meaningful single nucleotide polymorphisms (SNPs; Dolan et al., 2004;Watters et al., 2004;Huang et al., 2007;Bleibel et al., 2009;Duan et al., 2009;Peters et al., 2009Peters et al., , 2011aGamazon et al., 2010;Stark et al., 2010;Watson et al., 2011a,b). These two methods summarize the dose-response curve by the IC50 and Slope parameters estimated by the bestfit hill slope curve. ...
Cytotoxicity assays of immortalized lymphoblastoid cell lines (LCLs) represent a promising new in vitro approach in pharmacogenomics research. However, previous studies employing LCLs in gene mapping have used simple association methods, which may not adequately capture the true differences in non-linear response profiles between genotypes. Two common approaches summarize each dose-response curve with either the IC50 or the slope parameter estimates from a hill slope fit and treat these estimates as the response in a linear model. The current study investigates these two methods, as well as four novel methods, and compares their power to detect differences between the response profiles of genotypes under a variety of different alternatives. The four novel methods include two methods that summarize each dose-response by its area under the curve, one method based off of an analysis of variance (ANOVA) design, and one method that compares hill slope fits for all individuals of each genotype. The power of each method was found to depend not only on the choice of alternative, but also on the choice for the set of dosages used in cytotoxicity measurements. The ANOVA-based method was found to be the most robust across alternatives and dosage sets for power in detecting differences between genotypes.
... The primary reason for the choice of such a discovery model is the readily available genomic, transcriptomic and miRNA expression data along with a wide range of phenotypic information (e.g. intrinsic cellular growth rate, cellular sensitivity to different drugs) in these cell lines (11)(12)(13)(14). ...
... Data on sensitivity to chemotherapeutic drugs carboplatin, cisplatin, daunorubicin and etoposide were queried using a publicly available pharmacogenomics resource we developed (www.PACdb.org) (14). ...
Given the fundamental roles of microRNAs (miRNAs) in physiological, developmental, and pathologic processes, we hypothesized that genes involved in miRNA biogenesis contribute to human complex traits. For 13 such genes, we evaluated the relationship between transcription and 2 classes of complex traits, namely cellular growth and sensitivity to various chemotherapeutic agents in a set of lymphoblastoid cell lines. We found a highly significant correlation between protein argonaute 2 (AGO2) expression and cellular growth rate (Bonferroni-adjusted P < 0.05), and report additional miRNA biogenesis genes with suggestive associations with either cellular growth rate or chemotherapeutic sensitivity. AGO2 expression was found to be correlated with multiple drug sensitivity phenotypes. Furthermore, small interfering RNA knockdown of AGO2 resulted in cellular growth inhibition in an ovarian cancer cell line (OVCAR-3), supporting the role of this miRNA biogenesis gene in cell proliferation in cancer cells. Expression quantitative trait loci mapping indicated that genetic variation (in the form of both single-nucleotide polymorphisms and copy number variations) that may regulate the expression of AGO2 can have downstream effects on cellular growth-dependent complex phenotypes.
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... We thus compiled a list of such pharmacogenetic associations from PharmGKB (http://www.pharmgkb.org) [28] and from our own curation [29]. These clinical annotations are classified according to the strength of evidence for the association [28]. ...
... Level 3 falls short of level 2 criteria due to sample size or p-value, or because the evidence is based on in vitro/pharmacokinetic (PK) support only. Consistent with this evidence-based annotation, we incorporated published results from genomewide association studies of a wide array of chemotherapeutic agents, as cataloged in a public resource PACdb [29] we created. In total, 480 SNPs from all three levels were included. ...
Background
Recent studies have illuminated the diversity of roles for microRNAs in cellular, developmental, and pathophysiological processes. The study of microRNAs in human liver tissue promises to clarify the therapeutic and diagnostic value of this important regulatory mechanism of gene expression.
Results
We conducted genome-wide profiling of microRNA expression in liver and performed an integrative analysis with previously collected genotype and transcriptome data. We report here that the Very Important Pharmacogenes (VIP Genes), comprising of genes of particular relevance for pharmacogenomics, are under substantial microRNA regulatory effect in the liver. We set out to elucidate the genetic basis of microRNA expression variation in liver and mapped microRNA expression to genomic loci as microRNA expression quantitative trait loci (miR-eQTLs). We identified common variants that attain genome-wide significant association (p < 10-10) with microRNA expression. We also found that the miR-eQTLs are significantly more likely to predict mRNA levels at a range of p-value thresholds than a random set of allele frequency matched SNPs, showing the functional effect of these loci on the transcriptome. Finally, we show that a large number of miR-eQTLs overlap with SNPs reproducibly associated with complex traits from the NHGRI repository of published genome-wide association studies as well as variants from a comprehensive catalog of manually curated pharmacogenetic associations.
Conclusion
Our study provides important insights into the genomic architecture of gene regulation in a vital human organ, with important implications for our understanding of disease pathogenesis, therapeutic outcome, and other complex human phenotypes.
... PACdb is a cell line dataset (lymphoblastoid cell lines; LCLs) collecting pharmacology associated databases, including genetic expression, genotypes and pharmacological data. ( 45 ) SCAN database is created to store, collect, annotate and demonstrate the connection link between gene and gene expression pro les. Additionally, SCAN is a highly extensive genomic and genetic dataset encompassing CNV and SNP annotations. ...
The Coronavirus Disease 2019 (COVID-19), an unpredictable and highly catastrophic epidemic, resulted in high morbidity and mortality rate in humans. There are now a great number of medications that are undergoing clinical trials despite the lack of well-established evidence about their safety or toxicity profile. The results of such drug therapies are usually hard to anticipate and they might vary anywhere from being useful to having no positive effect at all or having serious unintended side effects. In contrast to the traditional "one size fits all" methodology of administering therapies and drug regimens, the field of pharmacogenomics primarily aims to find the specialised drug treatment strategy and drug-dosage regime that will prove to be particularly beneficial for each individual patient. The application of pharmacogenomics might make it possible to individualise these medications, thus increasing both their efficiency and their safety status. Moreover, large inter-individual variations in the effectiveness and toxicity of many pharmaceuticals have been related to genetic mutations/polymorphism in drug transporters, metabolic enzymes, cellular receptors as well as other multiple drug targets. This chapter highlights the relevance of pharmacogenomics research in the identification of genotypic and phenotypic correlations as well as selection of drug therapies, which may surely help in the management of Covid-19.
... The results from these studies on cytarabine and from other LCL-based pharmacogenomics research, are contained within the Pharmacogenomics, And Cell database (PACdb, www.pacdb.org (accessed on 13 January 2021)) which provides a unique resource to the researchers [164]. Investigators can look for specific genes and SNPs of interest to determine if they have been found to associate with a particular drug phenotype. ...
Simple Summary
In this review, the authors propose a crosswise examination of cytarabine-related issues ranging from the spectrum of clinical activity and severe toxicities, through updated cellular pharmacology and drug formulations, to the genetic variants associated with drug-induced phenotypes. Cytarabine (cytosine arabinoside; Ara-C) in multiagent chemotherapy regimens is often used for leukemia or lymphoma treatments, as well as neoplastic meningitis. Chemotherapy regimens can induce a suboptimal clinical outcome in a fraction of patients. The individual variability in clinical response to Leukemia & Lymphoma treatments among patients appears to be associated with intracellular accumulation of Ara-CTP due to genetic variants related to metabolic enzymes. The review provides exhaustive information on the effects of Ara-C-based therapies, the adverse drug reaction will also be provided including bone pain, ocular toxicity (corneal pain, keratoconjunctivitis, and blurred vision), maculopapular rash, and occasional chest pain. Evidence for predicting the response to cytarabine-based treatments will be highlighted, pointing at their significant impact on the routine management of blood cancers.
Abstract
Cytarabine is a pyrimidine nucleoside analog, commonly used in multiagent chemotherapy regimens for the treatment of leukemia and lymphoma, as well as for neoplastic meningitis. Ara-C-based chemotherapy regimens can induce a suboptimal clinical outcome in a fraction of patients. Several studies suggest that the individual variability in clinical response to Leukemia & Lymphoma treatments among patients, underlying either Ara-C mechanism resistance or toxicity, appears to be associated with the intracellular accumulation and retention of Ara-CTP due to genetic variants related to metabolic enzymes. Herein, we reported (a) the latest Pharmacogenomics biomarkers associated with the response to cytarabine and (b) the new drug formulations with optimized pharmacokinetics. The purpose of this review is to provide readers with detailed and comprehensive information on the effects of Ara-C-based therapies, from biological to clinical practice, maintaining high the interest of both researcher and clinical hematologist. This review could help clinicians in predicting the response to cytarabine-based treatments.
... It is a large-scale genetic and genomic database containing SNP and copy number variation annotations along with a web interface, a set of methods and algorithms, and some data mining tools [34]. PACdb is a pharmacogenetics-cell line database collecting pharmacology-related information including genotypes, gene expression, and pharmacological data gathered via lymphoblastoid cell lines [35]. ...
The outbreak of Coronavirus disease 2019 (COVID-19) has evolved into an emergent global pandemic. Many drugs without established efficacy are being used to treat COVID-19 patients either as an offlabel/compassionate use or as a clinical trial. Although drug repurposing is an attractive approach with reduced time and cost, there is a need to make predictions on success before the start of therapy. For the optimum use of these repurposed drugs, many factors should be considered such as drug–gene or dug–drug interactions, drug toxicity, and patient co-morbidity. There is limited data on the pharmacogenomics of these agents and this may constitute an obstacle for successful COVID-19 therapy. This article reviewed the available human genome interactions with some promising repurposed drugs for COVID-19 management. These drugs include chloroquine (CQ), hydroxychloroquine (HCQ), azithromycin, lopinavir/ritonavir (LPV/r), atazanavir (ATV), favipiravir (FVP), nevirapine (NVP), efavirenz (EFV), oseltamivir, remdesivir, anakinra, tocilizumab (TCZ), eculizumab, heme oxygenase 1 (HO-1) regulators, renin–angiotensin–aldosterone system (RAAS) inhibitors, ivermectin, and nitazoxanide. Drug-gene variant pairs that may alter the therapeutic outcomes in COVID-19 patients are presented. The major drug variant pairs that associated with variations in clinical efficacy include CQ/HCQ (CYP2C8, CYP2D6, ACE2, and HO-1); azithromycin (ABCB1); LPV/r (SLCO1B1, ABCB1, ABCC2 and CYP3A); NVP (ABCC10); oseltamivir (CES1 and ABCB1); remdesivir (CYP2C8, CYP2D6, CYP3A4, and OATP1B1); anakinra (IL-1a); and TCZ (IL6R and FCGR3A). The major drug variant pairs that associated with variations in adverse effects include CQ/HCQ (G6PD; hemolysis and ABCA4; retinopathy), ATV (MDR1 and UGT1A1*28; hyperbilirubinemia; and APOA5; dyslipidemia), NVP (HLA-DRB1*01, HLA-B*3505 and CYP2B6; skin rash and MDR1; hepatotoxicity), and EFV (CYP2B6; depression and suicidal tendencies).
... pacdb.org/) (Gamazon et al., 2010) along with SCAN (SNP and CNV Annotation Database, http://www.scandb.org) (Gamazon, Huang, & Cox, 2013) which integrate gene expression and genotypes (HapMap SNPs) with PGx phenotypes using lymphoblastoid cell lines of European and African ancestry. ...
Pre-mRNA splicing, an essential step in eukaryotic gene expression, relies on recognition of short sequences on the primary transcript intron ends and takes place along transcription by RNA polymerase II. Exonic and intronic auxiliary elements may modify the strength of exon definition and intron recognition. Splicing DNA variants (SV) have been associated with human genetic diseases at canonical intron sites, as well as exonic substitutions putatively classified as nonsense, missense or synonymous variants. Their effects on mRNA may be modulated by cryptic splice sites associated to the SV allele, comprehending exon skipping or shortening, and partial or complete intron retention. As splicing mRNA outputs result from combinatorial effects of both intrinsic and extrinsic factors, in vitro functional assays supported by computational analyses are recommended to assist SV pathogenicity assessment for human Mendelian inheritance diseases. The increasing use of next-generating sequencing (NGS) targeting full genomic gene sequence has raised awareness of the relevance of deep intronic SV in genetic diseases and inclusion of pseudo-exons into mRNA. Finally, we take advantage of recent advances in sequencing and computational technologies to analyze alternative splicing in cancer. We explore the Catalog of Somatic Mutations in Cancer (COSMIC) to describe the proportion of splice-site mutations in cis and trans regulatory elements. Genomic data from large cohorts of different cancer types are increasingly available, in addition to repositories of normal and somatic genetic variations. These are likely to bring new insights to understanding the genetic control of alternative splicing by mapping splicing quantitative trait loci in tumors.
... pacdb.org/) (Gamazon et al., 2010) along with SCAN (SNP and CNV Annotation Database, http://www.scandb.org) (Gamazon, Huang, & Cox, 2013) which integrate gene expression and genotypes (HapMap SNPs) with PGx phenotypes using lymphoblastoid cell lines of European and African ancestry. ...
The current excitement for affordable genomics technologies and national precision medicine initiatives marks a turning point in worldwide healthcare practices. The last decade of global population sequencing efforts has defined the enormous extent of genetic variation in the human population resulting in insights into differential disease burden and response to therapy within and between populations. Population-scale pharmacogenomics helps to provide insights into the choice of optimal therapies and an opportunity to estimate, predict and minimize adverse events. Such an approach can potentially empower countries to formulate national selection and dosing policies for therapeutic agents thereby promoting public health with precision. We review the breadth and depth of worldwide population-scale sequencing efforts and its implications for the implementation of clinical pharmacogenetics toward making precision medicine a reality.
... Tens or even hundreds of thousands of individual cell lines have been banked internationally, from a variety of ethnic backgrounds and disease states [6][7][8]. LCLs have been used for an enormous multitude of genotype-phenotype studies involving small to very large human cohorts, spanning disease-related gene discovery, genome-wide association, and pharmacogenomic studies, among many others [9][10][11][12][13][14][15][16][17][18][19]. Whereas the use of isolated biomolecules from these cell lines has been of great advantage, few studies have ever explored the use of LCLs in more in-depth cell biology-based experiments, despite many potential benefits, e.g., using patient-derived material to understand the impact of genetic mutation on gene function, to study the molecular and cellular mechanisms of disease, and to develop personalised medicines. ...
Lymphoblastoid cell lines (LCLs) have been by far the most prevalent cell type used to study the genetics underlying normal and disease-relevant human phenotypic variation, across personal to epidemiological scales. In contrast, only few studies have explored the use of LCLs in functional genomics and mechanistic studies. Two major reasons are technical, as (1) interrogating the sub-cellular spatial information of LCLs is challenged by their non-adherent nature, and (2) LCLs are refractory to gene transfection. Methodological details relating to techniques that overcome these limitations are scarce, largely inadequate (without additional knowledge and expertise), and optimisation has never been described. Here we compare, optimise, and convey such methods in-depth. We provide a robust method to adhere LCLs to coverslips, which maintained cellular integrity, morphology, and permitted visualisation of sub-cellular structures and protein localisation. Next, we developed the use of lentiviral-based gene delivery to LCLs. Through empirical and combinatorial testing of multiple transduction conditions, we improved transduction efficiency from 3% up to 48%. Furthermore, we established strategies to purify transduced cells, to achieve sustainable cultures containing >85% transduced cells. Collectively, our methodologies provide a vital resource that enables the use of LCLs in functional cell and molecular biology experiments. Potential applications include the characterisation of genetic variants of unknown significance, the interrogation of cellular disease pathways and mechanisms, and high-throughput discovery of genetic modifiers of disease states among others.
... There are knowledge databases such as PharmGKB that stores the genetic, phenotypic and clinical information collected from various pharmacogenetic studies [3]. Pharmacogenomic related data is also available in various other open data repositories [4] such as DrugBank [5], CTD [6], Reactome [7,8], KEGG [9,10], STITCH [11], PACdb [12], dbGaP [13] IGVdb [14], PGP [15] etc. ...
... Given that miR-135b and miR-196b were upregulated following short-term exposure to DNA-damaging agents, we then searched Pharmacogenomics and Cell database (PACdb) (http://www.pacdb.org/) [42], which integrates for Caucasians from Utah, USA (CEU) or Yoruba people from Ibadan, Nigeria (YRI) using a default cutoff of p < 0.05. We also found that non-metastatic cells 4 (NME4) out of 12 common predicted targets for miR-196b was associated with IC 50 of etoposide for YRI. ...
The acquisition of resistance to anticancer drugs is widely viewed as a key obstacle to successful cancer therapy. However, detailed knowledge of the initial molecular events in the response of cancer cells to these chemotherapeutic and stress responses, and how these lead to the development of chemoresistance, remains incompletely understood. Using microRNA array and washout and rechallenge experiments, we found that short term treatment of leukemia cells with etoposide led a few days later to transient resistance that was associated with a corresponding transient increase in expression of ABCB1 mRNA, as well as microRNA (miR)-135b and miR-196b. This phenomenon was associated with short-term exposure to genotoxic agents, such as etoposide, topotecan, doxorubicin and ionizing radiation, but not agents that do not directly damage DNA. Further, this appeared to be histiotype-specific, and was seen in leukemic cells, but not in cell lines derived from solid tumors. Treatment of leukemic cells with either 5-aza-deoxycytidine or tricostatin A produced similar increased expression of ABCB1, miR-135b, and miR-196b, suggesting a role for epigenetic regulation of this phenomenon. Bioinformatics analyses revealed that CACNA1E, ARHGEF2, PTK2, SIAH1, ARHGAP6, and NME4 may be involved in the initial events in the development of drug resistance following the upregulation of ABCB1, miR-135b and miR-196b. In summary, we report herein that short-term exposure of cells to DNA damaging agents leads to transient drug resistance, which is associated with elevations in ABCB1, miR-135b and miR-196b, and suggests novel components that may be involved in the development of anticancer drug resistance.
... Data sets and resources available on pharmacogenomics are scattered in various databases and online resources and most of these databases are interlinked based on the information they carry. Some of these databases include PharmGKB [5], DrugBank [6], CTD [7], Reactome [8,9], KEGG [10,11], STITCH [12], PACdb [13], dbGaP [14] IGVdb, PGP [15]. Brief explanation of the databases are given in the following section and also tabulated in table I. ...
The current challenge in drug discovery lies in developing personalized drugs as individual genetic makeup respond differently to a particular drug. There are enough evidences of adverse drug reactions because of genetic response towards drugs in drug therapy. The study of these relations between the human genomics and pharmacogenetics emerged into Pharmacogenomics. There are many publicly accessible pharmacogenomic data repositories having large, rapidly changing and complex data. These databases provide information about the drugs, their adverse reactions, chemical formula, information about metabolic pathways, drug targets, disease for which a particular drug is used etc. None of the existing pharmacogenomic databases carry the complete integrated information and hence there is a need to develop a database which integrates data from all the widely used databases.
... PharmGED (Zheng et al., 2007), e-PKgene (Hachad et al., 2011), ou encore PACdb (Gamazon et al., 2010). ...
Response to drug treatment can be highly variable between individuals, both in terms of therapeutic effect (efficacy) and of adverse reactions (toxicity).Genetic factors affecting drug pharmacodynamics and pharmacokinetics play a major role in this inter-individual variability. Some of these factors are heterogeneously distributed among human populations. Local adaptation of populations to their environment partly explained those differences. Indeed,during human evolution, populations had to cope with changes in their chemical environment that triggered selective pressures on genes involved in xenobiotic response. Those genes are the same ones that influence drug response today.The tremendous recent advances in genotyping and sequencing technologies now provide access to the genome-wide patterns of genetic variation in a growing number of human populations, facilitating our understanding of the genetic mechanisms underlying complex traits such as drug response. Population genetic tools allow the identification of variants showing an unusual pattern of genetic differentiation among human populations and the determination of the role played by natural selection in shaping the atypical patterns observed.In this thesis, we have applied these tools on both SNP-chip genotyping data and Next Generation Sequencing data to analyze the genetic differentiation patterns of human populations for genes involved in drug response. We show that a nearly complete selective sweep in East Asia in the genomic region of the VKORC1 gene is responsible for an heterogeneous distribution of theVKORC1 functional variant and can explain the inter-population genetic differences in response to oral anti-vitamin K anticoagulants. Extending the analysis to all major pharmacogenes, we have identified new variants of potential relevance to pharmacogenetics which could explain inter-population and inter-individual differences in drug response. Finally, by a comprehensive analysis of the NAT2 gene, we evidence a homogenizing selection process targeting a functional variant associated with a very slow acetylation phenotype. These results emphasize the crucial role of natural selection in the inter-population and inter-individual drug response variability.They also illustrate the relevance of population genetics studies for a better understanding of the genetic component underlying drug response and complex traits.
... PACdb is a pharmacogenetics-cell line database collecting pharmacology-related information including genotypes, gene expression, and pharmacological data gathered via lymphoblastoid cell lines (LCL) [14]. Data in PACdb includes microarray expression data from 90 Yoruba in Ibadan, Nigeria (YRI) and 90 Utah residents with ancestry from northern and western Europe (CEU) LCLs, and some microRNA data from 60 YRI and 60 CEU. ...
Pharmacogenomics is the study of the impact of genetic variations or genotypes of individuals on their drug response or drug metabolism. Compared to traditional genomics research, pharmacogenomic research is more closely related to clinical practice. Pharmacogenomic discoveries may effectively assist clinicians and healthcare providers in determining the right drugs and proper dose for each patient, which can help avoid side effects or adverse reactions, and improve the drug therapy. Currently, pharmacogenomic approaches have proven their utility when it comes to the use of cardiovascular drugs, antineoplastic drugs, aromatase inhibitors, and agents used for infectious diseases. The rapid innovation in sequencing technology and genome-wide association studies has led to the development of numerous data resources and dramatically changed the landscape of pharmacogenomic research. Here we describe some of these web resources along with their names, web links, main contents, and our ratings.
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... It represents one of the most up to date sources of human genetic variation as relevant to drug response. There are a number of databases accumulating pharmacogenomic information, including PharmaADME [72,73], the human cytochrome P450 (CYP) allele nomenclature website [72,74], the human arylamine N-acetyltransferase (NAT) gene nomenclature website [72,75], Pharmacogenetics of Membrane Transporters (PMT) database [72,76], Transporter Database (TP-search) [72,77], the UDP-glucuronosyltransferase (UGT) Allele Nomenclature Page [72,78], and PACdb [79,80], among others. The information compiled by these and other sources is anticipated to play an ever growing role in guiding patient care in conjunction with personal sequencing. ...
The potential for personalized sequencing to individually optimize medical treatment in diseases such as cancer and for pharmacogenomic application is just beginning to be realized, and the utility of sequencing healthy individuals for managing health is also being explored. The data produced requires additional advancements in interpretation of variants of unknown significance to maximize clinical benefit. Nevertheless, personalized sequencing, only recently applied to clinical medicine, has already been broadly applied to the discovery and study of disease. It is poised to enable the earlier and more accurate diagnosis of disease risk and occurrence, guide prevention and individualized intervention as well as facilitate monitoring of healthy and treated patients, and play a role in the prevention and recurrence of future disease. This article documents the advancing capacity of personalized sequencing, reviews its impact on disease-oriented scientific discovery and anticipates its role in the future of medicine.
... PACdb (Gamazon et al. 2010 , http://www.pacdb. org/index.html ...
The post-HapMap era has seen a spurt in the resources available for genome analysis and their interpretation along with databases and consortiums working on personal genomes. The advent of next-generation sequencing has provided an impetus to personal genomics while at the same time giving an indirect boost to concepts such as pharmacogenomics. Modern-day pharmacogenomics (or PGx) has evolved from the mundane – highlighting variations in common genes for drugs used the most, to the correlative study of the influence of a single gene on multiple drugs or combinatorial drugs on other drugs used concomitantly and so on. The vast outburst in data has led to a simultaneous growth in the databases and consortiums working on PGx as well as curating the data generated. This review covers those online resources and tools which are instrumental to the interpretation of personal genomes and contain pharmacogenetically relevant data. Further, it has been divided into five main categories depending upon the content and utility of the resource into (1) pharmacogenomics databases, (2) variation databases, (3) tools/resources for analysing PGx data, (4) community efforts and consortia and (5) standards for data representation.
... In addition to quantitative expansion of the database, efforts are being undertaken to include the information available on biochemical pathways involved in the drug-microbiome interactions from the SEED and KEGG (Kanehisa et al., 2012) databases. Future plans include directly linking current interactions to existing pharmacogenomics databases [e.g., PharmGKB (Owen et al., 2008), CTD (Davis et al., 2008), and PACdb (Gamazon et al., 2010)] and to human microbiome sequence databases Huang et al., 2014). Given the recency of the subject and the short time elapsed since the Pharmacomicrobiomics portal was publicly released, it remains difficult to evaluate the usability, usage, and usefulness of this web resource. ...
Abstract The Human Microbiome Project (HMP) is a global initiative undertaken to identify and characterize the collection of human-associated microorganisms at multiple anatomic sites (skin, mouth, nose, colon, vagina), and to determine how intra-individual and inter-individual alterations in the microbiome influence human health, immunity, and different disease states. In this review article, we summarize the key findings and applications of the HMP that may impact pharmacology and personalized therapeutics. We propose a microbiome cloud model, reflecting the temporal and spatial uncertainty of defining an individual's microbiome composition, with examples of how intra-individual variations (such as age and mode of delivery) shape the microbiome structure. Additionally, we discuss how this microbiome cloud concept explains the difficulty to define a core human microbiome and to classify individuals according to their biome types. Detailed examples are presented on microbiome changes related to colorectal cancer, antibiotic administration, and pharmacomicrobiomics, or drug-microbiome interactions, highlighting how an improved understanding of the human microbiome, and alterations thereof, may lead to the development of novel therapeutic agents, the modification of antibiotic policies and implementation, and improved health outcomes. Finally, the prospects of a collaborative computational microbiome research initiative in Africa are discussed.
... IC 50 data was reported elsewhere [14,19] and data have been deposited into www.PACdb.org [20]. ...
Using genome-wide genetic, gene expression, and microRNA expression (miRNA) data, we developed an integrative approach to investigate the genetic and epigenetic basis of chemotherapeutic sensitivity.
Using a sequential multi-stage framework, we identified genes and miRNAs whose expression correlated with platinum sensitivity, mapped these to genomic loci as quantitative trait loci (QTLs), and evaluated the associations between these QTLs and platinum sensitivity.
A permutation analysis showed that top findings from our approach have a much lower false discovery rate compared to those from a traditional GWAS of drug sensitivity. Our approach identified five SNPs associated with 10 miRNAs and the expression level of 15 genes, all of which were associated with carboplatin sensitivity. Of particular interest was one SNP (rs11138019), which was associated with the expression of both miR-30d and the gene ABCD2, which were themselves correlated with both carboplatin and cisplatin drug-specific phenotype in the HapMap samples. Functional study found that knocking down ABCD2 in vitro led to increased apoptosis in ovarian cancer cell line SKOV3 after cisplatin treatment. Over-expression of miR-30d in vitro caused a decrease in ABCD2 expression, suggesting a functional relationship between the two.
We developed an integrative approach to the investigation of the genetic and epigenetic basis of human complex traits. Our approach outperformed standard GWAS and provided hints at potential biological function. The relationships between ABCD2 and miR-30d, and ABCD2 and platin sensitivity were experimentally validated, suggesting a functional role of ABCD2 and miR-30d in sensitivity to platinating agents.
... Studies on the role of genetic variation in cis-regulatory regions in determining inter-individual splicing differences, including tissue-specific variability (GTex Consortium 2013), are poised to advance our understanding of genome function, disease pathogenesis and therapeutic applications. at the same time, uncovering the mechanistic basis for disease associations, and increasingly pharmacogenomic trait associations (Gamazon et al. 2010;Thorn et al. 2005), emerging from genome-wide association studies is likely to benefit from studies of the effect of genetic polymorphisms regulating differential transcript isoform expression as eQTLs (Coulombe-Huntington et al. 2009). variation in alternative splicing is highly heritable, with family-based linkage analysis demonstrating that transcript isoforms of a variety of genes undergo Mendelian inheritance and segregation (Kwan et al. 2007). ...
Alternative splicing is a major cellular mechanism in metazoans for generating proteomic diversity. A large proportion of protein-coding genes in multicellular organisms undergo alternative splicing, and in humans, it has been estimated that nearly 90 % of protein-coding genes-much larger than expected-are subject to alternative splicing. Genomic analyses of alternative splicing have illuminated its universal role in shaping the evolution of genomes, in the control of developmental processes, and in the dynamic regulation of the transcriptome to influence phenotype. Disruption of the splicing machinery has been found to drive pathophysiology, and indeed reprogramming of aberrant splicing can provide novel approaches to the development of molecular therapy. This review focuses on the recent progress in our understanding of alternative splicing brought about by the unprecedented explosive growth of genomic data and highlights the relevance of human splicing variation on disease and therapy.
... In this chapter, we describe the feature set of a bioinformatics resource that has proven useful for studies in systems-based pharmacogenomics [6,7]. We motivate SCAN's particular variant annotation approach to the prioritization of results from the flood of data from GWA studies. ...
Genome-wide association (GWA) studies have identified thousands of genetic variants that contribute to disease and pharmacologic traits. More recently, high-throughput sequencing studies promise to provide a more complete catalog of genetic variants with roles in human phenotypic variation. Yet, characterizing the influence of functional variants on genes, RNAs, proteins, and ultimately disease or pharmacologic traits is a critical challenge for a vast majority of the implicated susceptibility loci. Here we describe SCAN, a bioinformatics resource we have developed to elucidate the functional consequences of genetic variants identified by genome-wide scans. In particular, this public resource implements a systems biology approach to pharmacogenomic discovery.
... •PacDB (Pharmacogenetics and Cell Database):
http://www.pacdb.org[63] ...
The influence of resident gut microbes on xenobiotic metabolism has been investigated at different levels throughout the past five decades. However, with the advance in sequencing and pyrotagging technologies, addressing the influence of microbes on xenobiotics had to evolve from assessing direct metabolic effects on toxins and botanicals by conventional culture-based techniques to elucidating the role of community composition on drugs metabolic profiles through DNA sequence-based phylogeny and metagenomics. Following the completion of the Human Genome Project, the rapid, substantial growth of the Human Microbiome Project (HMP) opens new horizons for studying how microbiome compositional and functional variations affect drug action, fate, and toxicity (pharmacomicrobiomics), notably in the human gut. The HMP continues to characterize the microbial communities associated with the human gut, determine whether there is a common gut microbiome profile shared among healthy humans, and investigate the effect of its alterations on health. Here, we offer a glimpse into the known effects of the gut microbiota on xenobiotic metabolism, with emphasis on cases where microbiome variations lead to different therapeutic outcomes. We discuss a few examples representing how the microbiome interacts with human metabolic enzymes in the liver and intestine. In addition, we attempt to envisage a roadmap for the future implications of the HMP on therapeutics and personalized medicine.
... PACdb [24] is a large-scale, publicly available genomic database, which to date holds the results of our SNPbased GWAS on the following chemotherapeutic agents: carboplatin, cisplatin, etoposide, daunorubicin, and cytarabine. PACdb implements a structured repository for incorporating other datasets, including information on other drugs, gene expression profiling, and cellular phenotypes. ...
BACKGROUND:
Recent studies have investigated the contribution of copy number variants (CNVs) to disease susceptibility in a multitude of complex disorders, including systemic lupus erythematosus, Crohn's disease, and various neurodevelopmental disorders. Relatively few CNV studies, however, have been conducted on pharmacologic phenotypes even though these structural variants are likely to play an important role. We developed a genome-wide method to identify CNVs that contribute to heterogeneity in drug response, focusing on drugs that are widely used in anticancer treatment regimens.
RESULTS:
We conducted a comprehensive genome-wide study of CNVs from population-scale array-based and sequencing-based surveys by analyzing their effect on cellular sensitivity to platinating agents and topoisomerase II inhibitors. We identified extensive CNV regions associated with cellular sensitivity to functionally diverse chemotherapeutics, supporting the hypothesis that variation in copy number contributes to variation in drug response. Interestingly, although single nucleotide polymorphisms (SNPs) tag some of the CNVs associated with drug sensitivity, several of the most significant CNV-drug associations are independent of SNPs; consequently, they represent genetic variations that have not been previously interrogated by SNP studies of pharmacologic phenotypes.
CONCLUSIONS:
Our findings demonstrate that pharmacogenomic studies may greatly benefit from the study of CNVs as expression quantitative trait loci, thus contributing broadly to our understanding of the complex traits genetics of CNVs. We also extend our PACdb resource, a database that makes available to the scientific community relationships between genetic variation, gene expression, and sensitivity to various drugs in cell-based models.
... We generated 10,000 sets of 181 genes drawn randomly from the genome and counted the number of genes associated with log 2 -transformed paclitaxel AUC (p<0.05) in each set, generating the null distribution. In order to quantify the level of SLC gene enrichment among our top genes, we compared the number of significant SLC genes (p<0.05) and the empirically generated distribution, as previously described [29]. eQTL association with SLC genes and cytotoxicity eQTLs associated with the expression of enriched SLC genes were identified using the online SCAN database (http://www.scandb.org) ...
The clinical use of paclitaxel is limited by variable responses and the potential for significant toxicity. To date, studies of associations between variants in candidate genes and paclitaxel effects have yielded conflicting results. We aimed to evaluate the relationships between global gene expression and paclitaxel sensitivity.
We utilized well-genotyped lymphoblastoid cell lines derived from the International HapMap Project to evaluate the relationships between cellular susceptibility to paclitaxel and global gene expression. Cells were exposed to varying concentrations of paclitaxel to evaluate paclitaxel-induced cytotoxicity and apoptosis. Among the top genes, we identified solute carrier (SLC) genes associated with paclitaxel sensitivity and narrowed down the list to those that had single nucleotide polymorphisms associated with both the expression level of the SLC gene and also with paclitaxel sensitivity. We performed an independent validation in an independent set of cell lines and also conducted functional studies using RNA interference.
Of all genes associated with paclitaxel-induced cytotoxicity at P less than 0.05 (1713 genes), there was a significant enrichment in SLC genes (31 genes). A subset of SLC genes, namely SLC31A2, SLC43A1, SLC35A5, and SLC41A2, was associated with paclitaxel sensitivity and had regulating single nucleotide polymorphisms that were also associated with paclitaxel-induced cytotoxicity. Multivariate modeling demonstrated that those four SLC genes together explained 20% of the observed variability in paclitaxel susceptibility. Using RNA interference, we demonstrated increased paclitaxel susceptibility with knockdown of three SLC genes, SLC31A2, SLC35A5, and SLC41A2.
Our findings are novel and lend further support to the role of transporters, specifically solute carriers, in mediating cellular susceptibility to paclitaxel.
... However, more specific interactions involving drugs or chemicals have received a great deal of recent attention and have led to databases cataloging evidence for such interactions, such as the ToxPO database [54] and, importantly, the Pharmacogenetics Knowledge Base or 'PharmGKB' [55]. Many studies and databases providing context-specific functional annotations of genetic variants are not necessarily population-based, but may leverage specific laboratory or model-organism based functional assays [56]. ...
Advances in DNA sequencing technologies have made it possible to rapidly, accurately and affordably sequence entire individual human genomes. As impressive as this ability seems, however, it will not likely amount to much if one cannot extract meaningful information from individual sequence data. Annotating variations within individual genomes and providing information about their biological or phenotypic impact will thus be crucially important in moving individual sequencing projects forward, especially in the context of the clinical use of sequence information. In this paper we consider the various ways in which one might annotate individual sequence variations and point out limitations in the available methods for doing so. It is arguable that, in the foreseeable future, DNA sequencing of individual genomes will become routine for clinical, research, forensic, and personal purposes. We therefore also consider directions and areas for further research in annotating genomic variants.
... Bioinformatics also translates discoveries to the clinic by disseminating discoveries through curated, searchable databases like PharmGKB, dbGaP, PacDB and FDA AERS (Gamazon et al., 2010;Mailman et al., 2007;Thorn et al., 2010). A major bottleneck for these databases is manual curation of the data. ...
Widespread availability of low-cost, full genome sequencing will introduce new challenges for bioinformatics.
This review outlines recent developments in sequencing technologies and genome analysis methods for application in personalized medicine. New methods are needed in four areas to realize the potential of personalized medicine: (i) processing large-scale robust genomic data; (ii) interpreting the functional effect and the impact of genomic variation; (iii) integrating systems data to relate complex genetic interactions with phenotypes; and (iv) translating these discoveries into medical practice.
russ.altman@stanford.edu
... In silico evaluation of microRNA binding sites in genes that affect drug response revealed miR-133 and miR-137 as potential regulators of vitamin K epoxide reductase complex subunit 1 expression, and miR-22 as a potential regulator of methylene tetrahydrofolate reductase expression (Shomron, 2010). With the emergence of databases for cell-based pharmacogenomics, such as PACdb (Gamazon et al., 2010), studies on the effects of drugs on microRNAs and their predicted effectors will likely play a key role in the future of individualized medicine. ...
MicroRNAs are members of the non-protein-coding family of RNAs. They serve as regulators of gene expression by modulating the translation and/or stability of messenger RNA targets. The discovery of microRNAs has revolutionized the field of cell biology, and has permanently altered the prevailing view of a linear relationship between gene and protein expression. The increased complexity of gene regulation is both exciting and daunting, as emerging evidence supports a pervasive role for microRNAs in virtually every cellular process. This review briefly describes microRNA processing and formation of RNA-induced silencing complexes, with a focus on the role of RNA binding proteins in this process. We also discuss mechanisms for microRNA-mediated regulation of translation, particularly in dendritic spine formation and function, and the role of microRNAs in synaptic plasticity. We then discuss the evidence for altered microRNA function in cognitive brain disorders, and the effect of gene mutations revealed by single nucleotide polymorphism analysis on altered microRNA function and human disease. Further, we present evidence that altered microRNA expression in circulating fluids such as plasma/serum can correlate with, and serve as, novel diagnostic biomarkers of human disease.
... the exon array data on the 176 CEU and YRI HapMap samples from our previous studies of gene expression [8] and transcript isoform variation [9]. We also made available results, using these gene expression data, from our eQTL studies (SCAN database at http://www.scandb.org/) [29] and various pharmacogenomic studies (PACdb at http:// www.pacdb.org/) [30]. This new resource of a comprehensive catalogue of SNP-containing probesets and transcript clusters on the exon array can thus help researchers interpret and evaluate findings based on this platform, facilitate future data re-analysis efforts, and benefit the design of follow-up experiments. The same approach can also be applied to othe ...
Microarray gene expression data has been used in genome-wide association studies to allow researchers to study gene regulation as well as other complex phenotypes including disease risks and drug response. To reach scientifically sound conclusions from these studies, however, it is necessary to get reliable summarization of gene expression intensities. Among various factors that could affect expression profiling using a microarray platform, single nucleotide polymorphisms (SNPs) in target mRNA may lead to reduced signal intensity measurements and result in spurious results. The recently released 1000 Genomes Project dataset provides an opportunity to evaluate the distribution of both known and novel SNPs in the International HapMap Project lymphoblastoid cell lines (LCLs). We mapped the 1000 Genomes Project genotypic data to the Affymetrix GeneChip Human Exon 1.0ST array (exon array), which had been used in our previous studies and for which gene expression data had been made publicly available. We also evaluated the potential impact of these SNPs on the differentially spliced probesets we had identified previously. Though the 1000 Genomes Project data allowed a comprehensive survey of the SNPs in this particular array, the same approach can certainly be applied to other microarray platforms. Furthermore, we present a detailed catalogue of SNP-containing probesets (exon-level) and transcript clusters (gene-level), which can be considered in evaluating findings using the exon array as well as benefit the design of follow-up experiments and data re-analysis.
The recent outbreak of coronavirus disease 2019 (COVID-19) impacted the entire human population. This viral disease caused much morbidity and mortality as cancer has done over the years. The SARS-CoV-2 infection starts with the interaction of spike protein (S) and host cell surface receptor angiotensin-converting enzyme 2 (ACE2) to internalise the virus which is facilitated by transmembrane serine protease 2 (TMPRSS2). Comorbidity includes cancer associated with the severity of the infection, which may cause multiple organ failures and deaths. Individuals in vulnerable populations, patients with metabolic disorders, cancer and others are considered at a high risk of developing severe COVID-19 outcomes. However, cancer and COVID-19 have similar pathophysiological events like cytokine storm, increase oxidative stress and compromised redox. Moreover, the clinical relevance of cancer to COVID-19 is based on cytokines, type I interferons (IFN-I), androgen receptors and immune checkpoint signalling. Over the years, multiple studies have identified a diversity of molecular devices deployed by every known virus family to hijack, control or impair p53 functions. Furthermore, the hallmarks of cancer and COVID-19 have provided a useful conceptual framework for understanding their complex biology. COVID-19 pandemic imposed significant challenges for clinicians, especially for oncologists in diagnosis and therapy. Oncologists must carefully determine viral exposure, the need for ventilation, to continue treatment or provide a particular therapy is a major clinical challenge. This chapter critically reviews the synergistic mechanism of COVID-19 and cancer which can provide a lead to tackle current conceptual and clinical challenges.
Clinical response to therapeutic treatments may vary among patients. An individual's response to prescribed medicines is likely to be a complex trait that is influenced by a variety of genetic and nongenetic (e.g., environment, diet) factors. Pharmacogenomics holds the promise for personalized medicine based on a patient's genetic makeup by investigating the relationships between genetic variation and therapeutic phenotypes. In particular, human genetic variation provides the basis for dissecting the genetic architecture of complex traits including drug response. We introduce the general strategies and data analysis approaches utilized in the current pharmacogenomic research and development. The traditional candidate gene approach aims at pharmacology-related genes with a priori knowledge. In contrast, the advances in genome-wide profiling technologies open the possibility of genome-wide scans for genetic determinants responsible for drug response. In addition, several important bioinformatic resources for pharmacogenomic research are also introduced.
Functional annotation of genetic variants including single nucleotide polymorphisms (SNPs) and copy number variations (CNV) promises to greatly improve our understanding of human complex traits. Previous transcriptomic studies involving individuals from different global populations have investigated the genetic architecture of gene expression variation by mapping expression quantitative trait loci (eQTL). Functional interpretation of genome-wide association studies (GWAS) has identified enrichment of eQTL in top signals from GWAS of human complex traits. The SCAN (SNP and CNV Annotation) database was developed as a web-based resource of genetical genomic studies including eQTL detected in the HapMap lymphoblastoid cell line samples derived from apparently healthy individuals of European and African ancestry. Considering the critical roles of epigenetic gene regulation, cytosine modification quantitative trait loci (mQTL) are expected to add a crucial layer of annotation to existing functional genomic information. Here, we describe the new features of the SCAN database that integrate comprehensive mQTL mapping results generated in the HapMap CEU (Caucasian residents from Utah, USA) and YRI (Yoruba people from Ibadan, Nigeria) LCL samples and demonstrate the utility of the enhanced functional annotation system. Database URL: http://www.scandb.org/.
© The Author(s) 2015. Published by Oxford University Press.
Many genetic variants associated with human disease have been found to be associated with alterations in mRNA expression. Although it is commonly assumed that mRNA expression changes will lead to consequent changes in protein levels, methodological challenges have limited our ability to test the degree to which this assumption holds true. Here, we further developed the micro-western array approach and globally examined relationships between human genetic variation and cellular protein levels. We collected more than 250,000 protein level measurements comprising 441 transcription factor and signaling protein isoforms across 68 Yoruba (YRI) HapMap lymphoblastoid cell lines (LCLs) and identified 12 cis and 160 trans protein level QTLs (pQTLs) at a false discovery rate (FDR) of 20%. Whereas up to two thirds of cis mRNA expression QTLs (eQTLs) were also pQTLs, many pQTLs were not associated with mRNA expression. Notably, we replicated and functionally validated a trans pQTL relationship between the KARS lysyl-tRNA synthetase locus and levels of the DIDO1 protein. This study demonstrates proof of concept in applying an antibody-based microarray approach to iteratively measure the levels of human proteins and relate these levels to human genome variation and other genomic data sets. Our results suggest that protein-based mechanisms might functionally buffer genetic alterations that influence mRNA expression levels and that pQTLs might contribute phenotypic diversity to a human population independently of influences on mRNA expression.
Personalized medicine has the promise to tailor medical care based on the patient's genetic make-up and clinical variables such as gender, race and exposure to environmental stimuli. Recent progress in pharmacogenetic and pharmacogenomic studies has suggested that drug response to therapeutic treatments is likely a complex trait influenced by a variety of genetic and non-genetic factors. Identifying molecular targets (e.g., genetic variants) delineating the genetic basis of drug response could help understand the complex nature of drug response. The last decade has witnessed significant advances in genome-wide profiling technologies for genetic/epigenetic variations and gene expression. As an unbiased, cell-based model for pharmacogenomic discovery, a tremendous resource of whole-genome molecular targets has been accumulated for the HapMap lymphoblastoid cell lines (LCLs) during the past decade. The current progress, particularly in cancer pharmacogenomics, using the LCL model was reviewed to illustrate the potential impact of systems biology approaches on pharmacogenomic discovery.
The human microbiota directly and indirectly impacts drug pharmacokinetics and pharmacodynamics, thus affecting treatment outcome and subsequently human health. The Human Microbiome Project (HMP) revived interest in the role of human microbiota in health and disease. Yet, no repository of reported drug-microbe interactions is publicly available, and no attempts have been made to link those interactions to the human microbiome in a structured way. To begin addressing the need for such a crucial and timely resource, we analyzed published experimental data to extract drug-microbe interactions so as to enable the application of emerging HMP knowledge in postgenomics personalized medicine. We hereby report the creation of the PharmacoMicrobiomics Database, which aims to collect, classify, and cross-reference known drug-microbiome interactions and categorize them according to body site and microbial taxonomy. The database is integrated into a web portal that includes a search engine, through which students and scholars can locate drug-microbiome interaction of interest, compiled from and connected to public databases, such as PubMed, PubChem, and Comparative Toxicogenomics. Making these data available is a significant first step towards the prediction of interactions between drugs with similar chemical properties and microbes with similar metabolic abilities. Currently, the PharmacoMicrobiomics Database contains drug-microbiome interactions for more than 60 drugs curated from over 100 research and review articles. Further developments will include the automation of data updating, classification based on drug classes and biochemical pathways, and the participation of the community into data curation and analysis. This work provides a timely and much needed pioneering resource to the global open science community and usefully builds bridges between the rapidly growing fields of pharmacogenomics and human microbiome research. Database URL: http://www.pharmacomicrobiomics.org; Source Code: http://sourceforge.net/projects/pharmacomicro.
Genome-wide approaches in pharmacogenomics: heritability estimation and pharmacoethnicity as primary challenges The ultimate aim of pharmacogenomics is the identification of genetic variants that underlie interindividual variation in drug response. The furious pace of technological advances [1], such as DNA sequencing and genotyping technolo-gies [2], has begun to enhance our understand-ing of human diversity in pharmacologic traits. Meanwhile, methodological advances, such as the genome-wide association study (GWAS) approach, have facilitated the discovery of genetic variation with considerable clinical rele-vance [3]. The sine qua non of the rapidly develop-ing field of pharmaco genomics is the translation of genomic inform ation from these convergent developments into individualized patient care. Exciting promises of the new science abound, including the possibilities of optimized drug therapy, adverse-effect risk prediction, and improved drug discovery and development. The purpose of this article is to explore two of the major challenges to pharmaco genomic progress despite the substantial advances in genome-wide approaches that have already been made. The central premise is also the 'gap' in pharmacogenomic studies The central premise of every pharmacogenomic study is that the pharmacologic trait under investigation is under appreciable genetic con-trol. Indeed, studies in search of pharmacogenes are, in many instances, conducted without the prerequisite studies to confirm the heritability of the trait. The premise is implicit and, to a large extent, untested. The challenges of quantifying the heritabil-ity of a pharmacologic trait are well recognized. Although twin and family studies have for a long time been part of the human geneticist's standard toolkit to quantify the relative con-tribution of the genetic component to human
The amazing successes in cure rates for children with cancer over the last century have come in large part from identifying clinical, genetic, and molecular variables associated with response to therapy in large cooperative clinical trials and stratifying therapies according to the predicted risk of relapse. There is an expanding interest in identifying germline genomic variants, as opposed to genetic variants within the tumor, that are associated with susceptibility to toxicity and for risk of relapse. This review highlights the most important germline pharmacogenetic and pharmacogenomic studies in pediatric oncology. Incorporating germline genomics into risk-adapted therapies will likely lead to safer and more effective treatments for children with cancer.
The ability to predict how an individual patient will respond to a particular treatment is the ambitious goal of personalized medicine. The genetic make up of an individual has been shown to play a role in drug response. For pharmacogenomic studies, human lymphoblastoid cell lines (LCLs) comprise a useful model system for identifying genetic variants associated with pharmacologic phenotypes. The availability of extensive genotype data for many panels of LCLs derived from individuals of diverse ancestry allows for the study of genetic variants contributing to interethnic and interindividual variation in susceptibility to drugs. Many genome-wide association studies for drug-induced phenotypes have been performed in LCLs, often incorporating gene-expression data. LCLs are also being used in follow-up studies to clinical findings to determine how an associated variant functions to affect phenotype. This review describes the most recent pharmacogenomic findings made in LCLs, including the translation of some findings to clinical cohorts.
Pemetrexed, approved for the treatment of non-small cell lung cancer and malignant mesothelioma, has adverse effects including
neutropenia, leucopenia, thrombocytopenia, anemia, fatigue and nausea. The results we report here represent the first genome-wide
study aimed at identifying genetic predictors of pemetrexed response. We utilized expression quantitative trait loci (eQTLs)
mapping combined with drug-induced cytotoxicity data to gain mechanistic insights into the observed genetic associations with
pemetrexed susceptibility. We found that CTTN and ZMAT3 expression signature explained >30% of the pemetrexed susceptibility phenotype variation for pemetrexed in the discovery
population. Replication using PCR and a semi-high-throughput, scalable assay system confirmed the initial discovery results
in an independent set of samples derived from the same ancestry. Furthermore, functional validation in both germline and tumor
cells demonstrates a decrease in cell survival following knockdown of CTTN or ZMAT3. In addition to our particular findings on genetic and gene expression predictors of susceptibility phenotype for pemetrexed,
the work presented here will be valuable to the robust discovery and validation of genetic determinants and gene expression
signatures of various chemotherapeutic susceptibilities.
Pharmacogenomics has employed candidate gene studies and, more recently, genome-wide association studies (GWAS) in efforts to identify loci associated with drug response and/or toxicity. The advantage of GWAS is the simultaneous, unbiased testing of millions of SNPs; the challenge is that functional information is absent for the vast majority of loci that are implicated. In the present study, we systematically evaluated SNPs associated with chemotherapeutic agent-induced cytotoxicity for six different anticancer agents and evaluated whether these SNPs were disproportionately likely to be within a functional class such as coding (consisting of missense, nonsense, or frameshift polymorphisms), noncoding (such as 3'UTRs or splice sites), or expression quantitative trait loci (eQTLs; indicating that a SNP genotype is associated with the transcript abundance level of a gene). We found that the chemotherapeutic drug susceptibility-associated SNPs are more likely to be eQTLs, and, in fact, more likely to be associated with the transcriptional expression level of multiple genes (n > or = 10) as potential master regulators, than a random set of SNPs in the genome, conditional on minor allele frequency. Furthermore, we observed that this enrichment compared with random expectation is not present for other traditionally important coding and noncoding SNP functional categories. This research therefore has significant implications as a general approach for the identification of genetic predictors of drug response and provides important insights into the likely function of SNPs identified in GWAS analysis of pharmacologic studies.
In addition to the differences between populations in transcriptional and translational regulation of genes, alternative pre-mRNA
splicing (AS) is also likely to play an important role in regulating gene expression and generating variation in mRNA and
protein isoforms. Recently, the genetic contribution to transcript isoform variation has been reported in individuals of recent
European descent. We report here results of an investigation of the differences in AS patterns between human populations.
AS patterns in 176 HapMap lymphoblastoid cell lines derived from individuals of European and African ancestry were evaluated
using the Affymetrix GeneChip® Human Exon 1.0 ST Array. A variety of biological processes such as response to stimulus and transcription were found to be
enriched among the differentially spliced genes. The differentially spliced genes also include some involved in human diseases
that have different prevalence or susceptibility between populations. The genetic contribution to the population differences
in transcript isoform variation was then evaluated by a genome-wide association using the HapMap genotypic data on single
nucleotide polymorphisms (SNPs). The results suggest that local and distant genetic variants account for a substantial fraction
of the observed transcript isoform variation between human populations. Our findings provide new insights into the complexity
of the human genome as well as the health disparities between the two populations.
Etoposide is routinely used in combination-based chemotherapy for testicular cancer and small-cell lung cancer; however, myelosuppression,
therapy-related leukemia and neurotoxicity limit its utility. To determine the genetic contribution to cellular sensitivity
to etoposide, we evaluated cell growth inhibition in Centre d’ Etude du Polymorphisme Humain lymphoblastoid cell lines from
24 multi-generational pedigrees (321 samples) following treatment with 0.02–2.5μM etoposide for 72h. Heritability analysis
showed that genetic variation contributes significantly to the cytotoxic phenotypes (h
2=0.17–0.25, P=4.9×10−5–7.3×10−3). Whole genome linkage scans uncovered 8 regions with peak LOD scores ranging from 1.57 to 2.55, with the most significant
signals being found on chromosome 5 (LOD=2.55) and chromosome 6 (LOD=2.52). Linkage-directed association was performed
on a subset of HapMap samples within the pedigrees to find 22 SNPs significantly associated with etoposide cytotoxicity at
one or more treatment concentrations. UVRAG, a DNA repair gene, SEMA5A, SLC7A6 and PRMT7 are implicated from these unbiased studies. Our findings suggest that susceptibility to etoposide-induced cytotoxicity is
heritable and using an integrated genomics approach we identified both genomic regions and SNPs associated with the cytotoxic
phenotypes.
Genome-wide association studies (GWAS) generate relationships between hundreds of thousands of single nucleotide polymorphisms (SNPs) and complex phenotypes. The contribution of the traditionally overlooked copy number variations (CNVs) to complex traits is also being actively studied. To facilitate the interpretation of the data and the designing of follow-up experimental validations, we have developed a database that enables the sensible prioritization of these variants by combining several approaches, involving not only publicly available physical and functional annotations but also multilocus linkage disequilibrium (LD) annotations as well as annotations of expression quantitative trait loci (eQTLs).
For each SNP, the SCAN database provides: (i) summary information from eQTL mapping of HapMap SNPs to gene expression (evaluated by the Affymetrix exon array) in the full set of HapMap CEU (Caucasians from UT, USA) and YRI (Yoruba people from Ibadan, Nigeria) samples; (ii) LD information, in the case of a HapMap SNP, including what genes have variation in strong LD (pairwise or multilocus LD) with the variant and how well the SNP is covered by different high-throughput platforms; (iii) summary information available from public databases (e.g. physical and functional annotations); and (iv) summary information from other GWAS. For each gene, SCAN provides annotations on: (i) eQTLs for the gene (both local and distant SNPs) and (ii) the coverage of all variants in the HapMap at that gene on each high-throughput platform. For each genomic region, SCAN provides annotations on: (i) physical and functional annotations of all SNPs, genes and known CNVs within the region and (ii) all genes regulated by the eQTLs within the region.
http://www.scandb.org.
Supplementary data are available at Bioinformatics online.
Cytarabine arabinoside (ara-C) is an antimetabolite used to treat hematologic malignancies. Resistance is a common reason for treatment failure with adverse side effects contributing to morbidity and mortality. Identification of genetic factors important in susceptibility to ara-C cytotoxicity may allow for individualization of treatment. We used an unbiased whole-genome approach using lymphoblastoid cell lines derived from persons of European (CEU) or African (YRI) ancestry to identify these genetic factors. We interrogated more than 2 million single nucleotide polymorphisms (SNPs) for association with susceptibility to ara-C and narrowed our focus by concentrating on SNPs that affected gene expression. We identified a unique pharmacogenetic signature consisting of 4 SNPs explaining 51% of the variability in sensitivity to ara-C among the CEU and 5 SNPs explaining 58% of the variation among the YRI. Population-specific signatures were secondary to either (1) polymorphic SNPs in one population but monomorphic in the other, or (2) significant associations of SNPs with cytotoxicity or gene expression in one population but not the other. We validated the gene expression-cytotoxicity relationship for a subset of genes in a separate group of lymphoblastoid cell lines. These unique genetic signatures comprise novel genes that can now be studied further in functional studies.
To gain a better understanding of the genetic variants associated with carboplatin-induced cytotoxicity in individuals of African descent, we present a step-wise approach integrating genotypes, gene expression, and sensitivity of HapMap cell lines to carboplatin. Cell lines derived from 30 trios of African descent (YRI) were used to develop a preclinical model to identify genetic variants and gene expression that contribute to carboplatin-induced cytotoxicity. Cytotoxicity was determined as cell growth inhibition at increasing concentrations of carboplatin for 72 h. Gene expression of 89 HapMap YRI cell lines was determined using the Affymetrix GeneChip Human Exon 1.0 ST Array. Single nucleotide polymorphism genotype and the percent survival at different treatment concentrations along with carboplatin IC50 were linked through whole genome association. A second association test was done between single nucleotide polymorphism genotype and gene expression, and linear regression was then used to capture those genes whose expression correlated to drug sensitivity phenotypes. This approach allows us to identify genetic variants that significantly associate with sensitivity to the cytotoxic effects of carboplatin through their effect on gene expression. We found a gene (GPC5) whose expression is important in all carboplatin treatment concentrations as well as many genes unique to either low (e.g., MAPK1) or high (e.g., BRAF, MYC, and BCL2L1) concentrations of drug. Our whole genome approach enables us to evaluate the contribution of genetic and gene expression variation to a wide range of cellular phenotypes. The identification of concentration specific genetic signatures allows for potential integration of pharmacokinetics, pharmacodynamics, and pharmacogenetics in tailoring chemotherapy.
Large interindividual variance is observed in both response and toxicity associated with chemotherapy. Our goal is to identify factors that contribute to chemotherapy-induced toxicity. To this end, we used EBV-transformed B-lymphoblastoid HapMap cell lines derived from 30 Yoruban trios (African descent) and 30 Centre d' Etude du Polymorphisme Humain (CEPH) trios (European descent) to evaluate population- and gender-specific differences in cytotoxicity of carboplatin, cisplatin, daunorubicin, and etoposide using a high-throughput, short-term cytotoxicity assay. The IC(50) was compared for population- and gender-specific differences for the four drugs. We observed large interindividual variance in IC(50) values for carboplatin, cisplatin, daunorubicin, and etoposide for both Yoruban and CEPH populations (range from 8- to 433-fold). Statistically significant differences in carboplatin and daunorubicin IC(50) were shown when comparing Yoruban cell lines (n = 89) to CEPH cell lines (n = 87; P = 0.002 and P = 0.029, respectively). This population difference in treatment induced cytotoxicity was not seen for either cisplatin or etoposide. In the Yoruban population, cell lines derived from females were less sensitive to platinating agents than males [median carboplatin IC(50), 29.1 versus 24.6 micromol/L (P = 0.012); median cisplatin IC(50), 7.0 versus 6.0 micromol/L (P = 0.020) in female and male, respectively]. This difference was not observed in the CEPH population. These results show that population and gender may affect risk for toxicities associated with certain chemotherapeutic agents.
Large interindividual variance has been observed in sensitivity to drugs. To comprehensively decipher the genetic contribution to these variations in drug susceptibility, we present a genome-wide model using human lymphoblastoid cell lines from the International HapMap consortium, of which extensive genotypic information is available, to identify genetic variants that contribute to chemotherapeutic agent-induced cytotoxicity. Our model integrated genotype, gene expression, and sensitivity of HapMap cell lines to drugs. Cell lines derived from 30 trios of European descent (Center d'Etude du Polymorphisme Humain population) and 30 trios of African descent (Yoruban population) were used. Cell growth inhibition at increasing concentrations of etoposide for 72 h was determined by using alamarBlue assay. Gene expression on 176 HapMap cell lines (87 Center d'Etude du Polymorphisme Humain population and 89 Yoruban population) was determined by using the Affymetrix GeneChip Human Exon 1.0ST Array. We evaluated associations between genotype and cytotoxicity, genotype and gene expression and correlated gene expression of the identified candidates with cytotoxicity. The analysis identified 63 genetic variants that contribute to etoposide-induced toxicity through their effect on gene expression. These include genes that may play a role in cancer (AGPAT2, IL1B, and WNT5B) and genes not yet known to be associated with sensitivity to etoposide. This unbiased method can be used to elucidate genetic variants contributing to a wide range of cellular phenotypes induced by chemotherapeutic agents.
• HapMap
• pharmacogenomics
• toxicity
• whole-genome association
We describe the Phase II HapMap, which characterizes over 3.1 million human single nucleotide polymorphisms (SNPs) genotyped in 270 individuals from four geographically diverse populations and includes 25-35% of common SNP variation in the populations surveyed. The map is estimated to capture untyped common variation with an average maximum r2 of between 0.9 and 0.96 depending on population. We demonstrate that the current generation of commercial genome-wide genotyping products captures common Phase II SNPs with an average maximum r2 of up to 0.8 in African and up to 0.95 in non-African populations, and that potential gains in power in association studies can be obtained through imputation. These data also reveal novel aspects of the structure of linkage disequilibrium. We show that 10-30% of pairs of individuals within a population share at least one region of extended genetic identity arising from recent ancestry and that up to 1% of all common variants are untaggable, primarily because they lie within recombination hotspots. We show that recombination rates vary systematically around genes and between genes of different function. Finally, we demonstrate increased differentiation at non-synonymous, compared to synonymous, SNPs, resulting from systematic differences in the strength or efficacy of natural selection between populations.
Ovarian carcinomas with mutations in the tumour suppressor BRCA2 are particularly sensitive to platinum compounds. However, such carcinomas ultimately develop cisplatin resistance. The mechanism of that resistance is largely unknown. Here we show that acquired resistance to cisplatin can be mediated by secondary intragenic mutations in BRCA2 that restore the wild-type BRCA2 reading frame. First, in a cisplatin-resistant BRCA2-mutated breast-cancer cell line, HCC1428, a secondary genetic change in BRCA2 rescued BRCA2 function. Second, cisplatin selection of a BRCA2-mutated pancreatic cancer cell line, Capan-1 (refs 3, 4), led to five different secondary mutations that restored the wild-type BRCA2 reading frame. All clones with secondary mutations were resistant both to cisplatin and to a poly(ADP-ribose) polymerase (PARP) inhibitor (AG14361). Finally, we evaluated recurrent cancers from patients whose primary BRCA2-mutated ovarian carcinomas were treated with cisplatin. The recurrent tumour that acquired cisplatin resistance had undergone reversion of its BRCA2 mutation. Our results suggest that secondary mutations that restore the wild-type BRCA2 reading frame may be a major clinical mediator of acquired resistance to platinum-based chemotherapy.
Identifying heritable genetic variants responsible for chemotherapeutic toxicities has been challenging due in part to its multigenic nature. To date, there is a paucity of data on genetic variants associated with patients experiencing severe myelosuppression or cardiac toxicity following treatment with daunorubicin. We present a genome-wide model using International HapMap cell lines that integrate genotype and gene expression to identify genetic variants that contribute to daunorubicin-induced cytotoxicity. A cell growth inhibition assay was used to measure variations in the cytotoxicity of daunorubicin. Gene expression was determined using the Affymetrix GeneChip Human Exon 1.0ST Array. Using sequential analysis, we evaluated the associations between genotype and cytotoxicity, those significant genotypes with gene expression and correlated gene expression of the identified candidates with cytotoxicity. A total of 26, 9, and 18 genetic variants were identified to contribute to daunorubicin-induced cytotoxicity through their effect on 16, 9, and 36 gene expressions in the combined, Centre d' Etude du Polymorphisme Humain (CEPH), and Yoruban populations, respectively. Using 50 non-HapMap CEPH cell lines, single nucleotide polymorphisms generated through our model predicted 29% of the overall variation in daunorubicin sensitivity and the expression of CYP1B1 was significantly correlated with sensitivity to daunorubicin. In the CEPH validation set, rs120525235 and rs3750518 were significant predictors of transformed daunorubicin IC(50) (P = 0.005 and P = 0.0008, respectively), and rs1551315 trends toward significance (P = 0.089). This unbiased method can be used to elucidate genetic variants contributing to a wide range of cellular phenotypes.
There is extensive natural variation in human gene expression. As quantitative phenotypes, expression levels of genes are heritable. Genetic linkage and association mapping have identified cis- and trans-acting DNA variants that influence expression levels of human genes. New insights into human gene regulation are emerging from genetic analyses of gene expression in cells at rest and following exposure to stimuli. The integration of these genetic mapping results with data from co-expression networks is leading to a better understanding of how expression levels of individual genes are regulated and how genes interact with each other. These findings are important for basic understanding of gene regulation and of diseases that result from disruption of normal gene regulation.
High-resolution mapping is an important step in the identification of complex disease genes. In outbred populations, linkage disequilibrium is expected to operate over short distances and could provide a powerful fine-mapping tool. Here we build on recently developed methods for linkage-disequilibrium mapping of quantitative traits to construct a general approach that can accommodate nuclear families of any size, with or without parental information. Variance components are used to construct a test that utilizes information from all available offspring but that is not biased in the presence of linkage or familiality. A permutation test is described for situations in which maximum-likelihood estimates of the variance components are biased. Simulation studies are used to investigate power and error rates of this approach and to highlight situations in which violations of multivariate normality assumptions warrant the permutation test. The relationship between power and the level of linkage disequilibrium for this test suggests that the method is well suited to the analysis of dense maps. The relationship between power and family structure is investigated, and these results are applicable to study design in complex disease, especially for late-onset conditions for which parents are usually not available. When parental genotypes are available, power does not depend greatly on the number of offspring in each family. Power decreases when parental genotypes are not available, but the loss in power is negligible when four or more offspring per family are genotyped. Finally, it is shown that, when siblings are available, the total number of genotypes required in order to achieve comparable power is smaller if parents are not genotyped.
With the increase in genomewide experiments and the sequencing of multiple genomes, the analysis of large data sets has become commonplace in biology. It is often the case that thousands of features in a genomewide data set are tested against some null hypothesis, where a number of features are expected to be significant. Here we propose an approach to measuring statistical significance in these genomewide studies based on the concept of the false discovery rate. This approach offers a sensible balance between the number of true and false positives that is automatically calibrated and easily interpreted. In doing so, a measure of statistical significance called the q value is associated with each tested feature. The q value is similar to the well known p value, except it is a measure of significance in terms of the false discovery rate rather than the false positive rate. Our approach avoids a flood of false positive results, while offering a more liberal criterion than what has been used in genome scans for linkage.
Cisplatin, a platinating agent commonly used to treat several cancers, is associated with nephrotoxicity, neurotoxicity, and ototoxicity, which has hindered its utility. To gain a better understanding of the genetic variants associated with cisplatin-induced toxicity, we present a stepwise approach integrating genotypes, gene expression, and sensitivity of HapMap cell lines to cisplatin. Cell lines derived from 30 trios of European descent (CEU) and 30 trios of African descent (YRI) were used to develop a preclinical model to identify genetic variants and gene expression that contribute to cisplatin-induced cytotoxicity in two different populations. Cytotoxicity was determined as cell-growth inhibition at increasing concentrations of cisplatin for 48 h. Gene expression in 176 HapMap cell lines (87 CEU and 89 YRI) was determined using the Affymetrix GeneChip Human Exon 1.0 ST Array. We identified six, two, and nine representative SNPs that contribute to cisplatin-induced cytotoxicity through their effects on 8, 2, and 16 gene expressions in the combined, Centre d'Etude du Polymorphisme Humain (CEPH), and Yoruban populations, respectively. These genetic variants contribute to 27%, 29%, and 45% of the overall variation in cell sensitivity to cisplatin in the combined, CEPH, and Yoruban populations, respectively. Our whole-genome approach can be used to elucidate the expression of quantitative trait loci contributing to a wide range of cellular phenotypes.
Gene expression is a complex quantitative trait partially regulated by genetic variation in DNA sequence. Population differences in gene expression could contribute to some of the observed differences in susceptibility to common diseases and response to drug treatments. We characterized gene expression in the full set of HapMap lymphoblastoid cell lines derived from individuals of European and African ancestry for 9156 transcript clusters (gene-level) evaluated with the Affymetrix GeneChip Human Exon 1.0 ST Array. Gene expression was found to differ significantly between these samples for 383 transcript clusters. Biological processes including ribosome biogenesis and antimicrobial humoral response were found to be enriched in these differential genes, suggesting their possible roles in contributing to the population differences at a higher level than that of mRNA expression and in response to environmental information. Genome-wide association studies for local or distant genetic variants that correlate with the differentially expressed genes enabled identification of significant associations with one or more single-nucleotide polymorphisms (SNPs), consistent with the hypothesis that genetic factors and not simply population identity or other characteristics (age of cell lines, length of culture, etc.) contribute to differences in gene expression in these samples. Our results provide a comprehensive view of the genes differentially expressed between populations and the enriched biological processes involved in these genes. We also provide an evaluation of the contributions of genetic variation and nongenetic factors to the population differences in gene expression.
With the increase in genome-wide experiments and the sequencing of multiple genomes, the analysis of large data sets has become commonplace in biology. It is often the case that thousands of features in a genome-wide data set are tested against some null hypothesis, where many features are expected to be significant. Here we propose an approach to statistical significance in the analysis of genome-wide data sets, based on the concept of the false discovery rate. This approach o#ers a sensible balance between the number of true findings and the number of false positives that is automatically calibrated and easily interpreted. In doing so, a measure of statistical significance called the q-value is associated with each tested feature in addition to the traditional p-value. Our approach avoids a flood of false positive results, while o#ering a more liberal criterion than what has been used in genome scans for linkage.
Secondary mutations as a mechanism of cisplatin resistance in BRCA2-mediated cancers
- W Sakai
- Em Swisher
- By Karlan
- Mk Agarwal
- J Higgins
- C Friedman
Sakai W, Swisher EM, Karlan BY, Agarwal MK, Higgins J, Friedman C, et al. Secondary
mutations as a mechanism of cisplatin resistance in BRCA2-mediated cancers. Nature
2008;451:1116–1120. [PubMed: 18264087]
Secondary mutations as a mechanism of cisplatin resistance in BRCA2-mediated cancers
- Sakai