[Show abstract][Hide abstract] ABSTRACT: Response to treatment with selective serotonin reuptake inhibitors (SSRIs) varies considerably between patients. The International SSRI Pharmacogenomics Consortium (ISPC) was formed with the primary goal of identifying genetic variation that may contribute to response to SSRI treatment of major depressive disorder. A genome-wide association study of 4-week treatment outcomes, measured using the 17-item Hamilton Rating Scale for Depression (HRSD-17), was performed using data from 865 subjects from seven sites. The primary outcomes were percent change in HRSD-17 score and response, defined as at least 50% reduction in HRSD-17. Data from two prior studies, the Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomics Study (PGRN-AMPS) and the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study, were used for replication, and a meta-analysis of the three studies was performed (N=2394). Although many top association signals in the ISPC analysis map to interesting candidate genes, none were significant at the genome-wide level and the associations were not replicated using PGRN-AMPS and STAR*D data. Top association results in the meta-analysis of response included single-nucleotide polymorphisms (SNPs) in the HPRTP4 (hypoxanthine phosphoribosyltransferase pseudogene 4)/VSTM5 (V-set and transmembrane domain containing 5) region, which approached genome-wide significance (P=5.03E−08) and SNPs 5' upstream of the neuregulin-1 gene, NRG1 (P=1.20E−06). NRG1 is involved in many aspects of brain development, including neuronal maturation and variations in this gene have been shown to be associated with increased risk for mental disorders, particularly schizophrenia. Replication and functional studies of these findings are warranted.
[Show abstract][Hide abstract] ABSTRACT: Bone fractures are a major consequence of osteoporosis. There is a direct relationship between serum estrogen concentrations and osteoporosis risk. Aromatase inhibitors (AIs) greatly decrease serum estrogen levels in postmenopausal women, and increased incidence of fractures is a side effect of AI therapy. We performed a discovery case-cohort genome-wide association study (GWAS) using samples from 1071 patients, 231 cases and 840 controls, enrolled in the MA.27 breast cancer AI trial to identify genetic factors involved in AI-related fractures, followed by functional genomic validation. Association analyses identified 20 GWAS SNP signals with p<5E-06. After removal of signals in gene deserts and those composed entirely of imputed SNPs, we applied a functional validation "decision cascade" that resulted in validation of the CTSZ-SLMO2-ATP5E, TRAM2-TMEM14A and MAP4K4 genes. These genes all displayed estradiol (E2)-dependent induction in human fetal osteoblasts transfected with estrogen receptor (ER)α and their knockdown altered the expression of known osteoporosis-related genes. These same genes also displayed SNP-dependent variation in E2 induction that paralleled the SNP-dependent induction of known osteoporosis genes such as osteoprotegerin (OPG). In summary, our case-cohort GWAS identified SNPs in or near CTSZ-SLMO2-ATP5E, TRAM2-TMEM14A and MAP4K4 that were associated with risk for bone fracture in ER-positive breast cancer patients treated with AIs. These genes displayed E2-dependent induction; their knockdown altered the expression of genes related to osteoporosis; and they displayed SNP genotype dependent variation in E2 induction. These observations may lead to the identification of novel mechanisms associated with fracture risk in postmenopausal women treated with AIs.
[Show abstract][Hide abstract] ABSTRACT: Aim:
We investigated candidate genes associated with thiopurine metabolism and clinical response in childhood acute lymphoblastic leukemia.
Materials & methods:
We performed genome-wide SNP association studies of 6-thioguanine and 6-mercaptopurine cytotoxicity using lymphoblastoid cell lines. We then genotyped the top SNPs associated with lymphoblastoid cell line cytotoxicity, together with tagSNPs for genes in the 'thiopurine pathway' (686 total SNPs), in DNA from 589 Caucasian UK ALL97 patients. Functional validation studies were performed by siRNA knockdown in cancer cell lines.
SNPs in the thiopurine pathway genes ABCC4, ABCC5, IMPDH1, ITPA, SLC28A3 and XDH, and SNPs located within or near ATP6AP2, FRMD4B, GNG2, KCNMA1 and NME1, were associated with clinical response and measures of thiopurine metabolism. Functional validation showed shifts in cytotoxicity for these genes.
The clinical response to thiopurines may be regulated by variation in known thiopurine pathway genes and additional novel genes outside of the thiopurine pathway.
[Show abstract][Hide abstract] ABSTRACT: Citalopram (CT) and escitalopram (S-CT) are among the most widely prescribed serotonin reuptake inhibitors (SSRIs) used to treat Major Depressive Disorder (MDD). We applied a genome-wide association study (GWAS) to identify genetic factors that contribute to variation in plasma concentrations of CT or S-CT and their metabolites in MDD patients treated with CT or S-CT.
Our GWAS was performed using samples from 435 MDD patients. Linear mixed models were used to account for within-subject correlations of longitudinal measures of plasma drug/metabolite concentrations (4 and 8 weeks after the initiation of drug therapy) and SNPs were modeled as additive allelic effects.
Genome-wide significant associations were observed for S-CT concentration with SNPs in or near the CYP2C19 gene on chromosome 10 (rs1074145, p = 4.07E-09) and with S-DDCT concentration for SNPs near the CYP2D6 locus on chromosome 22 (rs1058172, p = 2.0E-16), supporting the important role of these CYP enzymes in citalopram biotransformation. After adjustment for the effect of CYP2C19 functional alleles, the analyses also identified novel loci that will require future replication and functional validation.
In vitro and in vivo studies have suggested that the biotransformation of CT to monodesmethylcitalopram (DCT) and didesmethylcitalopram (DDCT) are mediated by CYP isozymes. The results of our GWAS performed in MDD patients treated with CT or S-CT have confirmed those observations but also identified novel genomic loci that might play a role in variation in plasma levels of CT or its metabolites during the treatment of MDD patients with these SSRIs. These two authors contributed equally to this manuscript.
British Journal of Clinical Pharmacology 02/2014; 78(2). DOI:10.1111/bcp.12348 · 3.88 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Two cytidine analogues, gemcitabine and cytosine arabinoside (AraC), are widely used in the treatment of a variety of cancers with a large individual variation in response. To identify potential genetic biomarkers associated with response to these two drugs, we used a human lymphoblastoid cell line (LCL) model system with extensive genomic data, including 1.3 million SNPs and 54,000 basal expression probesets to perform genome-wide association studies (GWAS) with gemcitabine and AraC IC50 values.
We identified 11 and 27 SNP loci significantly associated with gemcitabine and AraC IC50 values, respectively. Eleven candidate genes were functionally validated using siRNA knockdown approach in multiple cancer cell lines. We also characterized the potential mechanisms of genes by determining their influence on the activity of 10 cancer-related signaling pathways using reporter gene assays. Most SNPs regulated gene expression in a trans manner, except 7 SNPs in the PIGB gene that were significantly associated with both the expression of PIGB and gemcitabine cytotoxicity.
These results suggest that genetic variation might contribute to drug response via either cis- or trans- regulation of gene expression. GWAS analysis followed by functional pharmacogenomics studies might help identify novel biomarkers contributing to variation in response to these two drugs and enhance our understanding of underlying mechanisms of drug action.
[Show abstract][Hide abstract] ABSTRACT: Homoharringtonine (HHT) has been widely used in China to treat patients with acute and chronic myeloid leukemia for decades. Since response to HHT varies among patients, our study aimed to identify biomarkers that might influence the response to HHT using a panel of various human lymphoblastoid cell lines (LCLs). Genome-wide association (GWA) analysis using single nucleotide polymorphism (SNP) and mRNA expression data was assessed for association with cytotoxicity to HHT in LCLs. Integrated analysis among SNPs, expression, AUC value was also performed to help select candidate genes for further functional characterization. Functional validation of candidate genes was performed using leukemia cell lines (U937, K562). Candidate genes were knocked down using specific siRNA and its response to HHT was assessed using MTS assay. We found that 15 expression probes were associated with HHT AUC with P < 10(-4), and 96 individual probe sets with P < 10(-3). Eighteen SNPs were associated with HHT AUC with P < 10(-5) and 281 SNPs with P < 10(-4). The integrated analysis identified 4 unique SNPs that were associated with both expression and AUC. Functional validation using siRNA knockdown in leukemia cell lines showed that knocking down CCDC88A, CTBP2, SOCS4 genes in U937 and K562 cells significantly altered HHT cytotoxicity. In summary, this study performed with LCLs can help to identify novel biomarker that might contribute to variation in response to HHT therapy.
Frontiers in Genetics 01/2014; 5:465. DOI:10.3389/fgene.2014.00465
[Show abstract][Hide abstract] ABSTRACT: Abstract Integrative genomics has the potential to uncover relevant loci, as clinical outcome and response to chemotherapies are most likely not due to a single gene (or data type) but rather a complex relationship involving genetic variation, mRNA, DNA methylation, and copy number variation. In addition to this complexity, many complex phenotypes are thought to be controlled by the interplay of multiple genes within the same molecular pathway or gene set (GS). To address these two challenges, we propose an integrative gene set analysis approach and apply this strategy to a cisplatin (CDDP) pharmacogenomics study involving lymphoblastoid cell lines for which genome-wide SNP and mRNA expression data was collected. Application of the integrative GS analysis implicated the role of the RNA binding and cytoskeletal part GSs. The genes LMNB1 and CENPF, within the cytoskeletal part GS, were functionally validated with siRNA knockdown experiments, where the knockdown of LMNB1 and CENPF resulted in CDDP resistance in multiple cancer cell lines. This study demonstrates the utility of an integrative GS analysis strategy for detecting novel genes associated with response to cancer therapies, moving closer to tailored therapy decisions cancer patients.
Omics: a journal of integrative biology 11/2013; 18(1). DOI:10.1089/omi.2013.0099 · 2.36 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The mammalian target of rapamycin (mTOR) inhibitors, a set of promising potential anti-cancer agents, has shown response variability among individuals. This study aimed to identify novel biomarkers and mechanisms that might influence the response to Rapamycin and Everolimus. Genome-wide association (GWA) analyses involving single nucleotide polymorphisms (SNPs), mRNA, and microRNAs microarray data were assessed for association with area under the cytotoxicity dose response curve (AUC) of two mTOR inhibitors in 272 human lymphoblastoid cell lines (LCLs). Integrated analysis among SNPs, expression data, microRNA data and AUC values were also performed to help select candidate genes for further functional characterization. Functional validation of candidate genes using siRNA screening in multiple cell lines followed by MTS assays for the two mTOR inhibitors were performed. We found that 16 expression probe sets (genes) that overlapped between the two drugs were associated with AUC values of two mTOR inhibitors. One hundred and twenty seven and one hundred SNPs had P < 10(-4), while 8 and 10 SNPs had P < 10(-5) with Rapamycin and Everolimus AUC, respectively. Functional studies indicated that 13 genes significantly altered cell sensitivity to either one or both drugs in at least one cell line. Additionally, one microRNA, miR-10a, was significantly associated with AUC values for both drugs and was shown to repress expression of genes that were associated with AUC and desensitize cells to both drugs. In summary, this study identified genes and a microRNA that might contribute to response to mTOR inhibitors.
Frontiers in Genetics 08/2013; 4:166. DOI:10.3389/fgene.2013.00166
[Show abstract][Hide abstract] ABSTRACT: The selective estrogen receptor modulators (SERM) tamoxifen and raloxifene can reduce the occurrence of breast cancer in high-risk women by 50%, but this U.S. Food and Drug Administration-approved prevention therapy is not often used. We attempted to identify genetic factors that contribute to variation in SERM breast cancer prevention, using DNA from the NSABP P-1 and P-2 breast cancer prevention trials. An initial discovery genome-wide association study identified common single-nucleotide polymorphisms (SNP) in or near the ZNF423 and CTSO genes that were associated with breast cancer risk during SERM therapy. We then showed that both ZNF423 and CTSO participated in the estrogen-dependent induction of BRCA1 expression, in both cases with SNP-dependent variation in induction. ZNF423 appeared to be an estrogen-inducible BRCA1 transcription factor. The OR for differences in breast cancer risk during SERM therapy for subjects homozygous for both protective or both risk alleles for ZNF423 and CTSO was 5.71.
Cancer Discovery 06/2013; 3(7). DOI:10.1158/2159-8290.CD-13-0038 · 19.45 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: We performed a discovery genome-wide association study to identify genetic factors associated with variation in plasma estradiol (E2) concentrations using DNA from 772 postmenopausal women with estrogen receptor (ER)-positive breast cancer prior to the initiation of aromatase inhibitor therapy. Association analyses showed that the single nucleotide polymorphisms (SNP) (rs1864729) with the lowest P value (P = 3.49E-08), mapped to chromosome 8 near TSPYL5. We also identified 17 imputed SNPs in or near TSPYL5 with P values < 5E-08, one of which, rs2583506, created a functional estrogen response element. We then used a panel of lymphoblastoid cell lines (LCLs) stably transfected with ERα with known genome-wide SNP genotypes to demonstrate that TSPYL5 expression increased after E2 exposure of cells heterozygous for variant TSPYL5 SNP genotypes, but not in those homozygous for wild-type alleles. TSPYL5 knockdown decreased, and overexpression increased aromatase (CYP19A1) expression in MCF-7 cells, LCLs, and adipocytes through the skin/adipose (I.4) promoter. Chromatin immunoprecipitation assay showed that TSPYL5 bound to the CYP19A1 I.4 promoter. A putative TSPYL5 binding motif was identified in 43 genes, and TSPYL5 appeared to function as a transcription factor for most of those genes.In summary, genome-wide significant SNPs in TSPYL5 were associated with elevated plasma E2 in postmenopausal breast cancer patients. SNP rs2583506 created a functional estrogen response element, and LCLs with variant SNP genotypes displayed increased E2-dependent TSPYL5 expression. TSPYL5 induced CYP19A1 expression and that of many other genes. These studies have revealed a novel mechanism for regulating aromatase expression and plasma E2 concentrations in postmenopausal women with ER(+) breast cancer.
[Show abstract][Hide abstract] ABSTRACT: Objectives:
FKBP51 (51 kDa immunophilin) acts as a modulator of the glucocorticoid receptor and a negative regulator of the Akt pathway. Genetic variation in FKBP5 plays a role in antidepressant response. The aim of this study was to comprehensively assess the role of genetic variation in FKBP5, identified by both Sanger and Next Generation DNA resequencing, as well as genome-wide single nucleotide polymorphisms (SNPs) associated with FKBP5 expression in the response to the selective serotonin reuptake inhibitor (SSRI) treatment of major depressive disorder.
We identified 657 SNPs in FKBP5 by Next Generation sequencing of 96 DNA samples from white patients, and 149 SNPs were selected for the genotyping together with 235 SNPs that were trans-associated with variation in FKBP5 expression in lymphoblastoid cells. A total of 529 DNA samples from the Mayo Clinic PGRN-SSRI Pharmacogenomic trial for which genome-wide SNPs had already been obtained were genotyped for these 384 SNPs, and associations with treatment outcomes were determined. The most significant SNPs were genotyped using 96 DNA samples from white non-Hispanic patients of the NIMH-supported Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study to attempt replication, followed by functional genomic studies.
Genotype-phenotype association analysis indicated that rs352428 was associated with both 8-week treatment response in the Mayo study (odds ratio=0.49; P=0.003) and 6-week response in the STAR*D replication study (odds ratio=0.74; P=0.05). The electrophoresis mobility shift assay and the reporter gene assay confirmed the possible role of this SNP in transcription regulation.
This comprehensive FKBP5 sequence study provides insight into the role of common genetic polymorphisms that might influence SSRI treatment outcomes in major depressive disorder patients.
Pharmacogenetics and Genomics 01/2013; 23(3). DOI:10.1097/FPC.0b013e32835dc133 · 3.48 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Background
Taxane is one of the first line treatments of lung cancer. In order to identify novel single nucleotide polymorphisms (SNPs) that might contribute to taxane response, we performed a genome-wide association study (GWAS) for two taxanes, paclitaxel and docetaxel, using 276 lymphoblastoid cell lines (LCLs), followed by genotyping of top candidate SNPs in 874 lung cancer patient samples treated with paclitaxel.
GWAS was performed using 1.3 million SNPs and taxane cytotoxicity IC50 values for 276 LCLs. The association of selected SNPs with overall survival in 76 small or 798 non-small cell lung cancer (SCLC, NSCLC) patients were analyzed by Cox regression model, followed by integrated SNP-microRNA-expression association analysis in LCLs and siRNA screening of candidate genes in SCLC (H196) and NSCLC (A549) cell lines.
147 and 180 SNPs were associated with paclitaxel or docetaxel IC50s with p-values <10-4 in the LCLs, respectively. Genotyping of 153 candidate SNPs in 874 lung cancer patient samples identified 8 SNPs (p-value < 0.05) associated with either SCLC or NSCLC patient overall survival. Knockdown of PIP4K2A, CCT5, CMBL, EXO1, KMO and OPN3, genes within 200 kb up-/downstream of the 3 SNPs that were associated with SCLC overall survival (rs1778335, rs2662411 and rs7519667), significantly desensitized H196 to paclitaxel. SNPs rs2662411 and rs1778335 were associated with mRNA expression of CMBL or PIP4K2A through microRNA (miRNA) hsa-miR-584 or hsa-miR-1468.
GWAS in an LCL model system, joined with clinical translational and functional studies, might help us identify genetic variations associated with overall survival of lung cancer patients treated paclitaxel.
BMC Cancer 09/2012; 12(1). DOI:10.1186/1471-2407-12-422 · 3.36 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: A genome-wide association (GWA) study of treatment outcomes (response and remission) of selective serotonin reuptake inhibitors (SSRIs) was conducted using 529 subjects with major depressive disorder. While no SNP associations reached the genome-wide level of significance, 14 SNPs of interest were identified for functional analysis. The rs11144870 SNP in the riboflavin kinase (RFK) gene on chromosome 9 was associated with 8-week treatment response (odds ratio (OR)=0.42, P=1.04 × 10(-6)). The rs915120 SNP in the G protein-coupled receptor kinase 5 (GRK5) gene on chromosome 10 was associated with 8-week remission (OR=0.50, P=1.15 × 10(-5)). Both SNPs were shown to influence transcription by a reporter gene assay and to alter nuclear protein binding using an electrophoretic mobility shift assay. This report represents an example of joining functional genomics with traditional GWA study results derived from a GWA analysis of SSRI treatment outcomes. The goal of this analytical strategy is to provide insights into the potential relevance of biologically plausible observed associations.The Pharmacogenomics Journal advance online publication, 21 August 2012; doi:10.1038/tpj.2012.32.
The Pharmacogenomics Journal 08/2012; 13(5). DOI:10.1038/tpj.2012.32 · 4.23 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Variation in drug response results from a combination of factors that include differences in gender, ethnicity, and environment, as well as genetic variation that may result in differences in mRNA and protein expression. This article presents two integrative analytic approaches that make use of both genome-wide SNP and mRNA expression data available on the same set of subjects: a step-wise integrative approach and a comprehensive analysis using sparse canonical correlation analysis (SCCA). In addition to applying standard SCCA, we present a novel modification of SCCA which allows different weighting for the various pair-wise relationships in the SCCA. These integrative approaches are illustrated with both simulated data and data from a pharmacogenomic study of the drug gemcitabine. Results from these analyses found little overlap in terms of genes detected, possibly detecting different biological mechanisms. In addition, we found the proposed weighted SCCA to outperform its unweighted counterpart in detecting associations between the genomic features and phenotype. Further research is needed to develop and assess new integrative methods for pharmacogenomic studies, as these types of analyses may uncover novel insights into the relationship between genomic variation and drug response.
Omics: a journal of integrative biology 06/2012; 16(7-8):363-73. DOI:10.1089/omi.2011.0126 · 2.36 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Percent mammographic density adjusted for age and body mass index (BMI) is one of the strongest risk factors for breast cancer and has a heritable component that remains largely unidentified. We performed a three-stage genome-wide association study (GWAS) of percent mammographic density to identify novel genetic loci associated with this trait. In stage 1, we combined three GWASs of percent density comprised of 1241 women from studies at the Mayo Clinic and identified the top 48 loci (99 single nucleotide polymorphisms). We attempted replication of these loci in 7018 women from seven additional studies (stage 2). The meta-analysis of stage 1 and 2 data identified a novel locus, rs1265507 on 12q24, associated with percent density, adjusting for age and BMI (P = 4.43 × 10(-8)). We refined the 12q24 locus with 459 additional variants (stage 3) in a combined analysis of all three stages (n = 10 377) and confirmed that rs1265507 has the strongest association in the 12q24 region (P = 1.03 × 10(-8)). Rs1265507 is located between the genes TBX5 and TBX3, which are members of the phylogenetically conserved T-box gene family and encode transcription factors involved in developmental regulation. Understanding the mechanism underlying this association will provide insight into the genetics of breast tissue composition.
Human Molecular Genetics 04/2012; 21(14):3299-305. DOI:10.1093/hmg/dds158 · 6.39 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Recently, structural variation in the genome has been implicated in many complex diseases. Using genomewide single nucleotide polymorphism (SNP) arrays, researchers are able to investigate the impact not only of SNP variation, but also of copy-number variants (CNVs) on the phenotype. The most common analytic approach involves estimating, at the level of the individual genome, the underlying number of copies present at each location. Once this is completed, tests are performed to determine the association between copy number state and phenotype. An alternative approach is to carry out association testing first, between phenotype and raw intensities from the SNP array at the level of the individual marker, and then aggregate neighboring test results to identify CNVs associated with the phenotype. Here, we explore the strengths and weaknesses of these two approaches using both simulations and real data from a pharmacogenomic study of the chemotherapeutic agent gemcitabine. Our results indicate that pooled marker-level testing is capable of offering a dramatic increase in power (> 12-fold) over CNV-level testing, particularly for small CNVs. However, CNV-level testing is superior when CNVs are large and rare; understanding these tradeoffs is an important consideration in conducting association studies of structural variation.
PLoS ONE 04/2012; 7(4):e34262. DOI:10.1371/journal.pone.0034262 · 3.23 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: We set out to test the hypothesis that pharmacometabolomic data could be efficiently merged with pharmacogenomic data by single-nucleotide polymorphism (SNP) imputation of metabolomic-derived pathway data on a 'scaffolding' of genome-wide association (GWAS) SNP data to broaden and accelerate 'pharmacometabolomics-informed pharmacogenomic' studies by eliminating the need for initial genotyping and by making broader SNP association testing possible.
We previously genotyped 131 tag SNPs for six genes encoding enzymes in the glycine synthesis and degradation pathway using DNA from 529 depressed patients treated with citalopram/escitalopram to pursue a glycine metabolomics 'signal' associated with selective serotonine reuptake inhibitor response. We identified a significant SNP in the glycine dehydrogenase gene. Subsequently, GWAS SNP data were generated for the same patients. In this study, we compared SNP imputation within 200 kb of these same six genes with the results of the previous tag SNP strategy as a rapid strategy for merging pharmacometabolomic and pharmacogenomic data.
Imputed genotype data provided greater coverage and higher resolution than did tag SNP genotyping, with a higher average genotype concordance between genotyped and imputed SNP data for '1000 Genomes' (96.4%) than HapMap 2 (93.2%) imputation. Many low P-value SNPs with novel locations within genes were observed for imputed compared with tag SNPs, thus altering the focus for subsequent functional genomic studies.
These results indicate that the use of GWAS data to impute SNPs for genes in pathways identified by other 'omics' approaches makes it possible to rapidly and cost efficiently identify SNP markers to 'broaden' and accelerate pharmacogenomic studies.
Pharmacogenetics and Genomics 02/2012; 22(4):247-53. DOI:10.1097/FPC.0b013e32835001c9 · 3.48 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Gemcitabine is widely used to treat non-small cell lung cancer (NSCLC). The aim of this study was to assess the pharmacogenomic effects of the entire gemcitabine metabolic pathway, we genotyped single nucleotide polymorphisms (SNPs) within the 17 pathway genes using DNA samples from patients with NSCLC treated with gemcitabine to determine the effect of genetic variants within gemcitabine pathway genes on overall survival (OS) of patients with NSCLC after treatment of gemcitabine.
Eight of the 17 pathway genes were resequenced with DNA samples from Coriell lymphoblastoid cell lines (LCLs) using Sanger sequencing for all exons, exon-intron junctions, and 5'-, 3'-UTRs. A total of 107 tagging SNPs were selected on the basis of the resequencing data for the eight genes and on HapMap data for the remaining nine genes, followed by successful genotyping of 394 NSCLC patient DNA samples. Association of SNPs/haplotypes with OS was performed using the Cox regression model, followed by functional studies performed with LCLs and NSCLC cell lines.
Five SNPs in four genes (CDA, NT5C2, RRM1, and SLC29A1) showed associations with OS of those patients with NSCLC, as well as nine haplotypes in four genes (RRM1, RRM2, SLC28A3, and SLC29A1) with a P value of less than 0.05. Genotype imputation using the LCLs was performed for a region of 200 kb surrounding those SNPs, followed by association studies with gemcitabine cytotoxicity. Functional studies demonstrated that downregulation of SLC29A1, NT5C2, and RRM1 in NSCLC cell lines altered cell susceptibility to gemcitabine.
These studies help in identifying biomarkers to predict gemcitabine response in NSCLC, a step toward the individualized chemotherapy of lung cancer.
Pharmacogenetics and Genomics 12/2011; 22(2):105-16. DOI:10.1097/FPC.0b013e32834dd7e2 · 3.48 Impact Factor