Use of Cell Lines in the Investigation of Pharmacogenetic Loci

Star allele search: a pharmacogenetic annotation database and user-friendly search tool of publicly available 1000 Genomes Project biospecimens

Here we describe a new public pharmacogenetic (PGx) annotation database of a large (n = 3,202) and diverse biospecimen collection of 1000 Genomes Project cell lines and DNAs. The database is searchable with a user friendly, web-based tool ( www.coriell.org/StarAllele/Search ). This resource leverages existing whole genome sequencing data and PharmVar annotations to characterize *alleles for each biospecimen in the collection. This new tool is designed to facilitate in vitro functional characterization of *allele haplotypes and diplotypes as well as support clinical PGx assay development, validation, and implementation.

Identifying genetic modulators of statin response using subject-derived lymphoblastoid cell lines

Although statins (3-hydroxy-3-methylglutaryl-CoA reductase inhibitors) have proven effective in reducing plasma low-density lipoprotein levels and risk of cardiovascular disease, their lipid lowering efficacy is highly variable among individuals. Furthermore, statin treatment carries a small but significant risk of adverse effects, most notably myopathy and new onset diabetes. Hence, identification of biomarkers for predicting patients who would most likely benefit from statin treatment without incurring increased risk of adverse effects can have a significant public health impact. In this review, we discuss the rationale for the use of subject-derived lymphoblastoid cell lines in studies of statin pharmacogenomics and describe a variety of approaches we have employed to identify novel genetic markers associated with interindividual variation in statin response.

Genomic and Phenomic Research in the 21st Century

The field of human genomics has changed dramatically over time. Initial genomic studies were predominantly restricted to rare disorders in small families. Over the past decade, researchers changed course from family-based studies and instead focused on common diseases and traits in populations of unrelated individuals. With further advancements in biobanking, computer science, electronic health record (EHR) data, and more affordable high-throughput genomics, we are experiencing a new paradigm in human genomic research. Rapidly changing technologies and resources now make it possible to study thousands of diseases simultaneously at the genomic level. This review will focus on these advancements as scientists begin to incorporate phenome-wide strategies in human genomic research to understand the etiology of human diseases and develop new drugs to treat them.

Polymorphic variation in TPMT is the principal determinant of TPMT phenotype: A meta-analysis of three genome-wide association studies

Thiopurine-related hematotoxicity in pediatric acute lymphoblastic leukemia (ALL) and inflammatory bowel diseases has been linked to genetically defined variability in thiopurine S-methyltransferase (TPMT) activity. While gene testing of TPMT is being clinically implemented, it is unclear if additional genetic variation influences TPMT activity with consequences for thiopurine-related toxicity. To examine this possibility, we performed a genome-wide association study (GWAS) of red blood cell TPMT activity in 844 Estonian individuals and 245 pediatric ALL cases. Additionally, we correlated genome-wide genotypes to human hepatic TPMT activity in 123 samples. Only genetic variants mapping to chromosome 6, including the TPMT gene region, were significantly associated with TPMT activity (P < 5.0 × 10^-8 ) in each of the three GWAS and a joint meta-analysis of 1,212 cases (top hit P = 1.2 × 10^-72 ). This finding is consistent with TPMT genotype being the primary determinant of TPMT activity, reinforcing the rationale for genetic testing of TPMT alleles in routine clinical practice to individualize mercaptopurine dosage.

Proteomics in the Assessment of the Therapeutic Response of Antineoplastic Drugs: Strategies and Practical Applications

Uncovering unknown pathological mechanisms and body response to applied medication are the driving forces toward personalized medicine. In this post-genomic era, all eyes are turned to the proteomics field, searching for answers and explanations by investigating the gene end point functional units-proteins and their proteoforms. The development of cutting-edge mass spectrometric technologies and bioinformatics tools have allowed the life-science community to discover disease-specific proteins as biomarkers, which are often concealed by high sample complexity and dynamic range of abundance. Currently, there are several proteomics-based approaches to investigate the proteome. This chapter focuses on gold standard proteomics strategies and related issues toward candidate biomarker discovery, which may have diagnostic/prognostic as well as mechanistic utility in cancer drug resistance.

Genome-wide analysis links NFATC2 with asparaginase hypersensitivity

Asparaginase is used to treat acute lymphoblastic leukemia (ALL); however, hypersensitivity reactions can lead to suboptimal asparaginase exposure. Our objective was to use a genome-wide approach to identify loci associated with asparaginase hypersensitivity in children with ALL enrolled on St. Jude Children's Research Hospital (SJCRH) protocols Total XIIIA (n = 154), Total XV (n = 498), and Total XVI (n = 271), or Children's Oncology Group protocols POG 9906 (n = 222) and AALL0232 (n = 2163). Germline DNA was genotyped using the Affymetrix 500K, Affymetrix 6.0, or the Illumina Exome BeadChip array. In multivariate logistic regression, the intronic rs6021191 variant in nuclear factor of activated T cells 2 (NFATC2) had the strongest association with hypersensitivity (P = 4.1 × 10(-8); odds ratio [OR] = 3.11). RNA-seq data available from 65 SJCRH ALL tumor samples and 52 Yoruba HapMap samples showed that samples carrying the rs6021191 variant had higher NFATC2 expression compared with noncarriers (P = 1.1 × 10(-3) and 0.03, respectively). The top ranked nonsynonymous polymorphism was rs17885382 in HLA-DRB1 (P = 3.2 × 10(-6); OR = 1.63), which is in near complete linkage disequilibrium with the HLA-DRB1*07:01 allele we previously observed in a candidate gene study. The strongest risk factors for asparaginase allergy are variants within genes regulating the immune response.

On the use of pharmacogenetics in cancer treatment and clinical trials

There are an increasing number of studies devoted to the identification of associations between anticancer drug efficacy and toxicity and common polymorphisms present in the patients' genome. However, many articles presenting the results of such studies do not bring the simple and necessary background information allowing the evaluation of the relevance of the study, its significance and its potential importance for patients' treatment. This position paper first addresses clinical oncologists with the aim of giving them the basic knowledge on pharmacogenetics and on the potential use of gene polymorphisms as predictive biomarkers in routine and clinical research. A secondary objective is to give molecular biologists some recommendations on how to conceive protocols and how to publish their results when they develop pharmacogenetic studies appended to clinical trials or with autonomous goals.

In vitro and in vivo mouse models for pharmacogenetic studies

The identification of causative genes underlying biomedically relevant phenotypes, particularly complex multigenic traits, is of vital interest to modern medicine. Using genome-wide association analysis, many studies have successfully identified thousands of loci (called quantitative trait loci or QTL), some of these associating with drug response phenotypes. However, the determination and validation of putative genes has been much more challenging. The actions of drugs, both efficacious and deleterious, are complex phenotypes that are controlled or influenced in part by genetic mechanisms.Investigation for genetic correlates of complex traits and pharmacogenetic traits is often difficult to perform in human studies due to cost, availability of relevant sample population, and limited ability to control for environmental effects. These challenges can be circumvented with the use of mouse models for pharmacogenetic studies. In addition, the mouse can be treated at sub- and supratherapeutic doses and subjected to invasive procedures, which can facilitate measures of drug response phenotypes, making identification of pharmacogenetically relevant genes more feasible. The availability of multiple mouse genetic and phenotypic resources is an additional benefit to using the mouse for pharmacogenetic studies.Here, we describe the contribution of animal models, specifically the mouse, towards the field of pharmacogenetics. In this chapter, we describe different mouse models, including the knockout mouse, recombinant mouse inbred strains, in vitro mouse cell-based assays, as well as novel experimental approaches like the Collaborative Cross recombinant mouse inbred panel, which can be applied to preclinical pharmacogenetics research. These approaches can be used to assess drug response phenotypes that are difficult to model in humans, thereby facilitating drug discovery, development, and application.

Pharmacogenetics of antidepressant drugs: an update after almost 20 years of research

Major depressive disorder (MDD) is an emergent cause of personal and socio-economic burden, both for the high prevalence of the disorder and the unsatisfying response rate of the available antidepressant treatments. No reliable predictor of treatment efficacy and tolerance in the single patient is available, thus drug choice is based on a trial and error principle with poor clinical efficiency. Among modulators of treatment outcome, genetic polymorphisms are thought to explain a significant share of the inter-individual variability. The present review collected the main pharmacogenetic findings primarily about antidepressant response and secondly about antidepressant induced side effects, and discussed the main strengths and limits of both candidate and genome-wide association studies and the most promising methodological opportunities and challenges of the field. Despite clinical applications of antidepressant pharmacogenetics are not available yet, previous findings suggest that genotyping may be applied in the clinical practice. In order to reach this objective, further rigorous pharmacogenetic studies (adequate sample size, study of better defined clinical subtypes of MDD, adequate covering of the genetic variability), their combination with the results obtained through complementary methodologies (e.g., pathway analysis, epigenetics, transcriptomics, and proteomics), and finally cost-effectiveness trials are required.

Insulin-like growth factor 1 (IGF-1) expression is up-regulated in lymphoblastoid cell lines of lithium responsive bipolar disorder patients

Bipolar disorder (BD) is a debilitating psychiatric disease characterized by alternating episodes of mania and depression. Among mood stabilizers, lithium is the mainstay for the treatment of BD, with approximately one-third of patients showing remission from episode recurrence. While there is evidence suggesting genetic load for lithium response in BD, its molecular underpinnings are still not completely understood. To identify genes potentially involved in (or correlated with) lithium response, we carried out a genome-wide expression analysis on lymphoblastoid cell lines (LCLs) from 10 BD patients responders (R) and 10 non-responders (NR) to lithium. We compared expression levels of the two groups and tested whether in vitro lithium treatment had different effects in LCLs of R compared to NR. At basal, 2060 genes were differentially expressed between R and NR while no genes were differentially regulated by lithium in the two groups. After pathway analysis based on the 2060 genes, 9 genes were selected for validation with qRT-PCR. Eight genes were validated in the same sample of LCLs while only insulin-like growth factor 1 (IGF-1) was significantly over-expressed in R compared to NR in the same sample as well as in an independent sample comprised of 6 R and 6 NR (sample 1, fold change=1.94; p=0.005; sample 2, fold change=2.21; p=0.005). IGF-1 was also significantly over-expressed in R but not in NR when compared to a sample of non-psychiatric controls. Our findings suggest that IGF-1 may be involved in lithium response, supporting further investigation on its potential as a biomarker.

Pharmacogenomics discovery and implementation in genome-wide association studies era

Clinical response to therapeutic treatments often varies among individual patients, ranging from beneficial effect to even fatal adverse reaction. Pharmacogenomics holds the promise of personalized medicine through elucidating genetic determinants responsible for pharmacological outcomes (e.g., cytotoxicities to anticancer drugs) and therefore guide the prescription decision prior to drug treatment. Besides traditional candidate gene-based approaches, technical advances have begun to allow application of whole-genome approaches to pharmacogenomic discovery. In particular, comprehensive understanding of human genetic variation provides the basis for applying GWAS (genome-wide association studies) in pharmacogenomic research to identify genomic loci associated with pharmacological phenotypes (e.g., individual dose requirement for warfarin). We therefore briefly reviewed the background for pharmacogenetic/pharmacogenomic research with statins and warfarin as examples for the GWAS discovery and their clinical implementation. In conclusion, with some challenges, whole-genome approaches such as GWAS have allowed unprecedented progress in identifying genetic variants associated with pharmacological phenotypes, as well as provided foundation for the next wave of pharmacogenomic discovery utilizing sequencing-based approaches. Furthermore, investigation of the complex interactions among genetic and epigenetic factors on the whole-genome scale will become the post-GWAS research focus for pharmacologic complex traits.

Integrating heterogeneous high-throughput data for meta-dimensional pharmacogenomics and disease-related studies

The current paradigm of human genetics research is to analyze variation of a single data type (i.e., DNA sequence or RNA levels) to detect genes and pathways that underlie complex traits such as disease state or drug response. While these studies have detected thousands of variations that associate with hundreds of complex phenotypes, much of the estimated heritability, or trait variability due to genetic factors, remain unexplained. We may be able to account for a portion of the missing heritability if we incorporate a systems biology approach into these analyses. Rapid technological advances will make it possible for scientists to explore this hypothesis via the generation of high-throughput omics data - transcriptomic, proteomic and methylomic to name a few. Analyzing this 'meta-dimensional' data will require clever statistical techniques that allow for the integration of qualitative and quantitative predictor variables. For this article, we examine two major categories of approaches for integrated data analysis, give examples of their use in experimental and in silico datasets, and assess the limitations of each method.

Genome-wide miRNA expression profiling of human lymphoblastoid cell lines identifies tentative SSRI antidepressant response biomarkers

Aim: Over 30% of patients with major depression do not respond well to first-line treatment with selective serotonin reuptake inhibitors (SSRIs). Using genome-wide expression profiling of human lymphoblastoid cell lines (LCLs) CHL1 was identified as a tentative SSRI sensitivity biomarker. This study reports on miRNAs implicated in SSRI sensitivity of LCLs. Methods: Eighty LCLs were screened from healthy adult female individuals for growth inhibition by paroxetine. Eight LCLs exhibiting high or low sensitivities to paroxetine were chosen for genome-wide expression profiling with miRNA microarrays. Results: The miRNA miR-151-3p had 6.7-fold higher basal expression in paroxetine-sensitive LCLs. This corresponds with lower expression of CHL1, a target of miR-151-3p. The additional miRNAs miR-212, miR-132, miR-30b*, let-7b and let-7c also differed by >1.5-fold (p < 0.05) between the two LCL groups. Conclusion: The potential value of these miRNAs as tentative SSRI response biomarkers awaits validation with lymphocyte samples of major depression patients.

Original submitted 28 March 2012; Revision submitted 21 May 2012

Bioinformatic Resources of microRNA Sequences, Gene Targets, and Genetic Variation

Variation in quantitative gene expression has been observed in natural populations and associated with various complex traits/phenotypes such as risks for common diseases and drug response. MicroRNAs (miRNAs), a family of small, non-coding RNA molecules, have been demonstrated to be an important class of gene regulators that mostly downregulate gene expression at the post-transcriptional level. A comprehensive and reliable catalogue of miRNAs and miRNA gene targets is critical to understanding the gene regulatory networks. Though experimental approaches have been used to identify many miRNAs and their gene targets, due to cost and efficiency, currently miRNA and target identification still largely relies on computational algorithms. We reviewed several widely used bioinformatic resources of miRNA sequences and gene targets that take advantage of the unique characteristics of miRNA–mRNA interactions, experimental validation, as well as the integration of sequence-based evidence and microarray expression data. Furthermore, given the importance of miRNAs in regulating gene expression, elucidating expression quantitative trait loci involved with miRSNPs or miR-polymorphisms will help improve our understanding of complex traits. We reviewed the available resources of miRNA genetic variation, and the current progress (e.g., the 1000 Genomes Project) in detailing the genetic variation in miRNA-related single nucleotide polymorphisms (SNPs). We also provided our perspectives of the potential impact of next-generation sequencing on the research of miRNAs, gene targets, and miRSNPs. These bioinformatic resources may help interpret experimental and association study results, thus enhancing our knowledge of the dynamic gene regulatory networks and the physiological pathways for complex traits/phenotypes. Prospectively, these bioinformatic resources of miRNAs will need to address the challenges raised by the application of next-generation sequencing in miRNA research.

Genome-wide identification of possible methylation markers chemosensitive to targeted regimens in colorectal cancers

PURPOSE

Few efficient methylation markers of chemosensitivity have been discovered. The genome-wide analysis of methylation markers is needed to identify chemosensitive candidates to targeted therapy.

METHODS

This study describes a two-step process to select chemosensitive candidates of methylation genes. A genome-wide screening of methylation genes was performed using a Beadarray and an in vitro chemosensitivity assay of 119 colorectal cancers (CRCs). Ten candidate genes identified during the initial screening were verified by biological utility assessment using cell viability assays of transfected CRC cells.

RESULTS

Five methylation genes related to sensitivity to bevacizumab regimens (RASSF1, MMP25, KCNQ1, ESR1, and GALR2) or cetuximab regimens (SCL18A2, GPX7, NID2, IGFBP3, and ALX4) were chosen during the first step. A viability assay revealed that GALR2-overexpressing HCT116 cells were significantly more chemosensitive to bevacizumab regimens than control cells (P = 0.022 and 0.019 for bevacizumab with FOLFIRI and FOLFOX, respectively), concurrently verified on a caspase-3 activity assay. GPX7- or ALX4-overexpressed RKO cells were significantly less viable to cetuximab regimens compared to control cells (GPX7: P = 0.027 each for cetuximab with FOLFIRI and FOLFOX; ALX4: P = 0.049 and 0.003 for cetuximab with FOLFIRI and FOLFOX, respectively), but caspase-3 activity was not prominent in GPX7-overexpressed RKO cells.

CONCLUSIONS

Two novel genes, GALR2 and ALX4, have been identified as chemosensitive methylation candidates to bevacizumab and cetuximab regimens, respectively. As our study did not include a clinical association study, the two candidates should be validated in large clinical cohorts, hopefully predicting responsive patients to targeted regimens.

Variability in transport and biotransformation of cytarabine is associated with its toxicity in peripheral blood mononuclear cells

AIM

To adopt an individualized approach to assess cytarabine (ara-C) hematotoxicity, we studied the relationship between pharmacogenetic variability in the cytidine deaminase gene (CDA) and ara-C toxicity in native peripheral blood mononuclear cells from 100 healthy volunteers.

MATERIALS & METHODS

Peripheral blood mononuclear cells were incubated for 48 h with 3 µM ara-C, and cell viability was analyzed by flow cytometry with and without the addition of an equilibrative nucleoside transporter transport inhibitor. CDA promoter and exonic variants were genotyped to derive haplotypes for the CDA gene.

RESULTS

Significant between-subject variability was observed in ara-C toxicity (21-fold with 40.1% coefficient of variation compared with 1.2-fold within-subject variability [9.6% coefficient of variation]). Inhibition of hENT1 reversed ara-C cytotoxicity. The linked CDA promoter variants -451C>T, -92A>G, -31Del and the exonic 79A>C variant were associated with ara-C toxicity (p < 0.05). CDA*2A haplotype was associated with ara-C toxicity (p = 0.03).

CONCLUSION

Genetic polymorphisms within CDA may be risk factors for ara-C-induced hematotoxicity. Original submitted 6 October 2010; Revision submitted 29 November 2010.

Population differences in microRNA expression and biological implications

Population differences observed for complex traits may be attributed to the combined effect of socioeconomic, environmental, genetic and epigenetic factors. To better understand population differences in complex traits, genome-wide genetic and gene expression differences among ethnic populations have been studied. Here we set out to evaluate population differences in small non-coding RNAs through an evaluation of microRNA (miRNA) baseline expression in HapMap lymphoblastoid cell lines (LCLs) derived from 53 CEU (Utah residents with northern and western European ancestry) and 54 YRI (African from Ibadan, Nigeria). Using the Exiqon miRCURYTM LNA arrays, we found that 16% of all miRNAs evaluated in our study differ significantly between these 2 ethnic groups (pBonferroni corrected< 0.05). Furthermore, we explored the potential biological function of these observed differentially expressed miRNAs by comprehensively examining their effect on the transcriptome and their relationship with cellular sensitivity drug phenotypes. After multiple testing adjustment (false discovery rate (FDR)< 0.1), we found that 55% and 88% of the differentially expressed miRNAs were significantly and inversely correlated with an mRNA expression phenotype in the CEU and YRI samples, respectively. Interestingly, a substantial proportion (64%) of these miRNAs correlated with cellular sensitivity to chemotherapeutic agents (FDR< 0.05). Lastly, upon performing a genome-wide association study between SNPs and miRNA expression, we identified a large number of SNPs exhibiting different allele frequencies that affect the expression of these differentially expressed miRNAs, suggesting the role of genetic variants in mediating the observed population differences.

Genome-wide expression profiling of human lymphoblastoid cell lines identifies CHL1 as a putative SSRI antidepressant response biomarker

AIMS

Selective serotonin reuptake inhibitors (SSRIs) are the most commonly used class of antidepressants for treating major depression. However, approximately 30% of patients do not respond sufficiently to first-line antidepressant drug treatment and require alternative therapeutics. Genome-wide studies searching for SSRI response DNA biomarkers or studies of candidate serotonin-related genes so far have given inconclusive or contradictory results. Here, we present an alternative transcriptome-based genome-wide approach for searching antidepressant drug-response biomarkers by using drug-effect phenotypes in human lymphoblastoid cell lines (LCLs).

MATERIALS & METHODS

We screened 80 LCLs from healthy adult female individuals for growth inhibition by paroxetine. A total of 14 LCLs with reproducible high and low sensitivities to paroxetine (seven from each phenotypic group) were chosen for genome-wide expression profiling with commercial microarrays.

RESULTS

The most notable genome-wide transcriptome difference between LCLs displaying high versus low paroxetine sensitivities was a 6.3-fold lower (p = 0.0000256) basal expression of CHL1, a gene coding for a neuronal cell adhesion protein implicated in correct thalamocortical circuitry, schizophrenia and autism. The microarray findings were confirmed by real-time PCR (36-fold lower CHL1 expression levels in the high paroxetine sensitivity group). Several additional genes implicated in synaptogenesis or in psychiatric disorders, including ARRB1, CCL5, DDX60, DDX60L, ENDOD1, ENPP2, FLT1, GABRA4, GAP43, MCTP2 and SPRY2, also differed by more than 1.5-fold and a p-value of less than 0.005 between the two paroxetine sensitivity groups, as confirmed by real-time PCR experiments.

CONCLUSION

Genome-wide transcriptional profiling of in vitro phenotyped LCLs identified CHL1 and additional genes implicated in synaptogenesis and brain circuitry as putative SSRI response biomarkers. This method might be used as a preliminary tool for searching for potential depression treatment biomarkers.

Human lymphoblastoid cell lines: a goldmine for the biobankomics era

Biobanking became a necessity for translating genetic discoveries into clinical practice. Approaches to personalized medicine require a new model system for functional and pharmacogenomic studies of a variety of accumulating genetic variations, as well as new research environments such as biobankomics. Human lymphoblastoid cell lines (LCLs) will provide a valuable tool to meet such new demands in the biobankomics era. The National Biobank of Korea (NBK), which is leading the Korea Biobank Project, has a large collection of LCLs derived mostly from population-based cohort samples. Using a special long-term subculture collection of NBK LCLs, biological characteristics of early passage LCLs and terminally immortalized LCLs have been investigated to promote the utilization of LCLs and provide well quality-controlled LCLs for genetic and pharmacogenomic studies. As LCLs have been successfully phenotyped for cytotoxicity in response to various stimulators, including chemotherapeutic agents, environmental chemicals and irradiation, the utility of LCLs will increase in the future. Here, we discuss current and future applications of NBK LCLs for the biobankomics era.

Bioinformatics challenges for personalized medicine

MOTIVATION

Widespread availability of low-cost, full genome sequencing will introduce new challenges for bioinformatics.

RESULTS

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.

CONTACT

russ.altman@stanford.edu

Integrating microRNAs into a system biology approach to acute lung injury

Acute lung injury (ALI), including the ventilator-induced lung injury (VILI) and the more severe acute respiratory distress syndrome (ARDS), are common and complex inflammatory lung diseases potentially affected by various genetic and nongenetic factors. Using the candidate gene approach, genetic variants associated with immune response and inflammatory pathways have been identified and implicated in ALI. Because gene expression is an intermediate phenotype that resides between the DNA sequence variation and the higher level cellular or whole-body phenotypes, the illustration of gene expression regulatory networks potentially could enhance understanding of disease susceptibility and the development of inflammatory lung syndromes. MicroRNAs (miRNAs) have emerged as a novel class of gene regulators that play critical roles in complex diseases including ALI. Comparisons of global miRNA profiles in animal models of ALI and VILI identified several miRNAs (eg, miR-146a and miR-155) previously implicated in immune response and inflammatory pathways. Therefore, via regulation of target genes in these biological processes and pathways, miRNAs potentially contribute to the development of ALI. Although this line of inquiry exists at a nascent stage, miRNAs have the potential to be critical components of a comprehensive model for inflammatory lung disease built by a systems biology approach that integrates genetic, genomic, proteomic, epigenetic as well as environmental stimuli information. Given their particularly recognized role in regulation of immune and inflammatory responses, miRNAs also serve as novel therapeutic targets and biomarkers for ALI/ARDS or VILI, thus facilitating the realization of personalized medicine for individuals with acute inflammatory lung disease.

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

BACKGROUND

Paclitaxel is a microtubule-stabilizing drug that has been commonly used in treating cancer. Due to genetic heterogeneity within patient populations, therapeutic response rates often vary. Here we used the NCI60 panel to identify SNPs associated with paclitaxel sensitivity. Using the panel's GI50 response data available from Developmental Therapeutics Program, cell lines were categorized as either sensitive or resistant. PLINK software was used to perform a genome-wide association analysis of the cellular response to paclitaxel with the panel's SNP-genotype data on the Affymetrix 125 k SNP array. FastSNP software helped predict each SNP's potential impact on their gene product. mRNA expression differences between sensitive and resistant cell lines was examined using data from BioGPS. Using Haploview software, we investigated for haplotypes that were more strongly associated with the cellular response to paclitaxel. Ingenuity Pathway Analysis software helped us understand how our identified genes may alter the cellular response to paclitaxel.

RESULTS

43 SNPs were found significantly associated (FDR<0.005) with paclitaxel response, with 10 belonging to protein-coding genes (CFTR, ROBO1, PTPRD, BTBD12, DCT, SNTG1, SGCD, LPHN2, GRIK1, ZNF607). SNPs in GRIK1, DCT, SGCD and CFTR were predicted to be intronic enhancers, altering gene expression, while SNPs in ZNF607 and BTBD12 cause conservative missense mutations. mRNA expression analysis supported these findings as GRIK1, DCT, SNTG1, SGCD and CFTR showed significantly (p<0.05) increased expression among sensitive cell lines. Haplotypes found in GRIK1, SGCD, ROBO1, LPHN2, and PTPRD were more strongly associated with response than their individual SNPs.

CONCLUSIONS

Our study has taken advantage of available genotypic data and its integration with drug response data obtained from the NCI60 panel. We identified 10 SNPs located within protein-coding genes that were not previously shown to be associated with paclitaxel response. As only five genes showed differential mRNA expression, the remainder would not have been detected solely based on expression data. The identified haplotypes highlight the role of utilizing SNP combinations within genomic loci of interest to improve the risk determination associated with drug response. These genetic variants represent promising biomarkers for predicting paclitaxel response and may play a significant role in the cellular response to paclitaxel.

Novel chemosensitive single-nucleotide polymorphism markers to targeted regimens in metastatic colorectal cancer

PURPOSE

Methods for predicting individual responsiveness to targeted chemotherapy are urgently needed, considering the frequent resistance and extremely high cost.

EXPERIMENTAL DESIGN

A chemosensitive single-nucleotide polymorphism (SNP) discovery schema is presented that utilizes (i) genome-wide SNP screening with a human SNP array and an in vitro chemosensitivity assay in 118 colorectal cancers, (ii) clinical association analysis in the other 98 patients who had received chemotherapy for metastatic cancer, and (iii) biological utility assessment using cell viability assays of transfected colorectal cancer (CRC) cells.

RESULTS

Nine SNPs related to bevacizumab and cetuximab regimen sensitivity were chosen during screening. Overall responses for bevacizumab regimens revealed that patients carrying the TT genotype at ANXA11 rs1049550 or at least one G allele at LINS1 rs11247226 seemed greater chemosensitive than those carrying at least one C allele or the AA genotype, respectively (P < 0.05). For cetuximab regimens, patients carrying the GG genotype at DFNB31 rs2274159 or LIFR rs3729740 seemed greater chemosensitive than those carrying at least one A allele (P = 0.025 and P = 0.07). Cytotoxicity analyses showed that all RKO and HCT116 CRC clones transfected with the G allele at LIFR rs3729740 and the C allele at ISX rs361863 were more sensitive to cetuximab regimens than those with the A and T allele, respectively (P ≤ 0.001-0.024).

CONCLUSIONS

Chemosensitive SNP markers were identified using a novel three-step process. The candidate marker LIFR rs3729740 and possibly ISX rs361863 will hopefully predict responsive patients to cetuximab regimens, although further validation is needed in large cohorts.

The emerging role of microRNAs in drug responses

In recent years, pharmacogenomic research has begun to integrate genetics, gene expression and pharmacological phenotypes. MicroRNAs (miRNAs), 21- to 25-nucleotide, non-coding RNAs that are present in almost all metazoan genomes, are a class of gene regulators that downregulate gene expression at the post-transcriptional level. Experimental evidence for the role of miRNAs in regulating pharmacology-related genes and drug responses is increasing. Given the universal roles of miRNAs in various diseases, including cancer, miRNAs (eg, chemotherapy) are anticipated to have potential therapeutic effects in various diseases. The incorporation of miRNAs into pharmacogenomic research could provide improved insight into drug responses. However, more studies are necessary to evaluate the effects of these molecules in patients.

Exprtarget: an integrative approach to predicting human microRNA targets

Variation in gene expression has been observed in natural populations and associated with complex traits or phenotypes such as disease susceptibility and drug response. Gene expression itself is controlled by various genetic and non-genetic factors. The binding of a class of small RNA molecules, microRNAs (miRNAs), to mRNA transcript targets has recently been demonstrated to be an important mechanism of gene regulation. Because individual miRNAs may regulate the expression of multiple gene targets, a comprehensive and reliable catalogue of miRNA-regulated targets is critical to understanding gene regulatory networks. Though experimental approaches have been used to identify many miRNA targets, due to cost and efficiency, current miRNA target identification still relies largely on computational algorithms that aim to take advantage of different biochemical/thermodynamic properties of the sequences of miRNAs and their gene targets. A novel approach, ExprTarget, therefore, is proposed here to integrate some of the most frequently invoked methods (miRanda, PicTar, TargetScan) as well as the genome-wide HapMap miRNA and mRNA expression datasets generated in our laboratory. To our knowledge, this dataset constitutes the first miRNA expression profiling in the HapMap lymphoblastoid cell lines. We conducted diagnostic tests of the existing computational solutions using the experimentally supported targets in TarBase as gold standard. To gain insight into the biases that arise from such an analysis, we investigated the effect of the choice of gold standard on the evaluation of the various computational tools. We analyzed the performance of ExprTarget using both ROC curve analysis and cross-validation. We show that ExprTarget greatly improves miRNA target prediction relative to the individual prediction algorithms in terms of sensitivity and specificity. We also developed an online database, ExprTargetDB, of human miRNA targets predicted by our approach that integrates gene expression profiling into a broader framework involving important features of miRNA target site predictions.

Impact of the 1000 genomes project on the next wave of pharmacogenomic discovery

The 1000 Genomes Project aims to provide detailed genetic variation data on over 1000 genomes from worldwide populations using the next-generation sequencing technologies. Some of the samples utilized for the 1000 Genomes Project are the International HapMap samples that are composed of lymphoblastoid cell lines derived from individuals of different world populations. These same samples have been used in pharmacogenomic discovery and validation. For example, a cell-based, genome-wide approach using the HapMap samples has been used to identify pharmacogenomic loci associated with chemotherapeutic-induced cytotoxicity with the goal to identify genetic markers for clinical evaluation. Although the coverage of the current HapMap data is generally high, the detailed map of human genetic variation promised by the 1000 Genomes Project will allow a more in-depth analysis of the contribution of genetic variation to drug response. Future studies utilizing this new resource may greatly enhance our understanding of the genetic basis of drug response and other complex traits (e.g., gene expression), therefore, help advance personalized medicine.