Identification of Genetic Variants Contributing to Cisplatin-Induced Cytotoxicity by Use of a Genomewide Approach
2007; Elsevier BV; Volume: 81; Issue: 3 Linguagem: Inglês
10.1086/519850
ISSN1537-6605
AutoresR. Stephanie Huang, Shiwei Duan, Sunita J. Shukla, Emily O. Kistner, Tyson A. Clark, Tina X. Chen, Anthony Schweitzer, John E. Blume, M. Eileen Dolan,
Tópico(s)DNA Repair Mechanisms
ResumoCisplatin, 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. 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. Cisplatin, a platinating agent, is commonly used to treat head and neck, testicular, lung, and gynecological cancers.1Chaney S Campbell S Bassett E Wu Y Recognition and processing of cisplatin- and oxaliplatin-DNA adducts.Crit Rev Oncol Hematol. 2005; 53: 3-11Abstract Full Text Full Text PDF PubMed Scopus (273) Google Scholar, 2Siddik Z Cisplatin: mode of cytotoxic action and molecular basis of resistance.Oncogene. 2003; 22: 7265-7279Crossref PubMed Scopus (2396) Google Scholar, 3Wang D Lippard S Cellular processing of platinum anticancer drugs.Nat Rev Drug Discov. 2005; 4: 307-320Crossref PubMed Scopus (2742) Google Scholar It has been shown that cisplatin exerts its antitumor activity by binding preferentially to the nucleophillic positions on guanine and adenine of DNA, resulting in the formation of intra- and interstrand crosslinks. Eventually, crosslinks lead to DNA-strand breaks and, ultimately, to cell death.4Zorbas H Keppler B Cisplatin damage: are DNA repair proteins saviors or traitors to the cell?.Chembiochem. 2005; 6: 1157-1166Crossref PubMed Scopus (84) Google Scholar, 5Decatris MP Sundar S O'Byrne KJ Platinum-based chemotherapy in metastatic breast cancer: the Leicester (U.K.) experience.Clin Oncol R Coll Radiol. 2005; 17: 249-257Abstract Full Text Full Text PDF PubMed Scopus (9) Google Scholar Despite its wide usage, dose-limiting toxicities—in particular, nephrotoxicity6Daugaard G Cisplatin nephrotoxicity: experimental and clinical studies.Dan Med Bull. 1990; 37: 1-12PubMed Google Scholar and neurotoxicity7Verstappen C Heimans J Hoekman K Postma T Neurotoxic complications of chemotherapy in patients with cancer: clinical signs and optimal management.Drugs. 2003; 63: 1549-1563Crossref PubMed Scopus (310) Google Scholar—have hindered the utility of this agent. In addition, treatment-induced ototoxicity can result in dose reduction or discontinuation of cisplatin treatment.8Rybak L Kelly T Ototoxicity: bioprotective mechanisms.Curr Opin Otolaryngol Head Neck Surg. 2003; 11: 328-333Crossref PubMed Scopus (62) Google Scholar The incidences of cisplatin treatment–induced toxicities are highly variable and are associated with cumulative treatments or dose intensities.7Verstappen C Heimans J Hoekman K Postma T Neurotoxic complications of chemotherapy in patients with cancer: clinical signs and optimal management.Drugs. 2003; 63: 1549-1563Crossref PubMed Scopus (310) Google Scholar High levels of drug efflux transporters, detoxifiers, and DNA-repair proteins and a low Bax:Bcl-2 ratio have all been suggested to play a role in cisplatin resistance.9Masters J Koberle B Curing metastatic cancer: lessons from testicular germ-cell tumours.Nat Rev Cancer. 2003; 3: 517-525Crossref PubMed Scopus (174) Google Scholar Genetic variants in candidate genes have demonstrated an association with clinical response to or toxicity from cisplatin. For example, two common SNPs of ERCC1 are correlated with an increased risk of toxicity and with the survival of cisplatin-treated patients with non–small-cell lung cancer.10Zhou W Gurubhagavatula S Liu G Park S Neuberg D Wain J Lynch T Su L Christiani D Excision repair cross-complementation group 1 polymorphism predicts overall survival in advanced non-small cell lung cancer patients treated with platinum-based chemotherapy.Clin Cancer Res. 2004; 10: 4939-4943Crossref PubMed Scopus (267) Google Scholar, 11Suk R Gurubhagavatula S Park S Zhou W Su L Lynch T Wain J Neuberg D Liu G Christiani D Polymorphisms in ERCC1 and grade 3 or 4 toxicity in non-small cell lung cancer patients.Clin Cancer Res. 2005; 11: 1534-1538Crossref PubMed Scopus (104) Google Scholar Polymorphisms in cytokine-promoter genes (e.g., TNF, IL1, IL6) have been suggested to be associated with toxicities induced by treatment with 5-fluorouracil and cisplatin.12Sakamoto K Oka M Yoshino S Hazama S Abe T Okayama N Hinoda Y Relation between cytokine promoter gene polymorphisms and toxicity of 5-fluorouracil plus cisplatin chemotherapy.Oncol Rep. 2006; 16: 381-387PubMed Google Scholar Glutathione S-transferase genetic polymorphisms have also been associated with treatment outcomes of paclitaxel- and cisplatin-based chemotherapy.13Medeiros R Pereira D Afonso N Palmeira C Faleiro C Afonso-Lopes C Freitas-Silva M Vasconcelos A Costa S Osorio T et al.Platinum/paclitaxel-based chemotherapy in advanced ovarian carcinoma: glutathione S-transferase genetic polymorphisms as predictive biomarkers of disease outcome.Int J Clin Oncol. 2003; 8: 156-161Crossref PubMed Scopus (64) Google Scholar An illustration of candidate genes involved in the mechanism of cisplatin activity can be found at the PharmGKB Web site. Although the study of candidate genes and pathways has increased our understanding of the mechanism of action of platinating agents, our understanding of genetic variants important in determining a patient's likelihood of response or toxicity is extremely limited. Thus, the development of comprehensive, unbiased models is critical to the identification of genetic variants and genes contributing to interindividual variation in drug effect. Genomewide approaches open up the possibility of identifying genetic and/or expression signatures that can be evaluated in clinical trials, for validation. Previously, we used Epstein-Barr virus (EBV)–transformed B-lymphoblastoid cell lines (LCLs) derived from healthy individuals within 10 large CEPH pedigrees and demonstrated that 38%–47% of human variation in susceptibility to cisplatin-induced cytotoxicity is due to genetic components.14Dolan ME Newbold KG Nagasubramanian R Wu X Ratain MJ Cook Jr, EH Badner JA Heritability and linkage analysis of sensitivity to cisplatin-induced cytotoxicity.Cancer Res. 2004; 64: 4353-4356Crossref PubMed Scopus (99) Google Scholar To better elucidate the genetic variants important in cisplatin-induced cytotoxicity, we employed a genomewide association study, using the International HapMap cell lines derived from trios of northern and western European and Yoruban populations. These well-genotyped samples provide an extremely rich data set for genotype–drug effect correlations.15The International HapMap Consortium A haplotype map of the human genome.Nature. 2005; 437: 1299-1320Crossref PubMed Scopus (4512) Google Scholar We performed gene-expression analysis on these HapMap cell lines, using the Affymetrix GeneChip Human Exon 1.0 ST Array, and phenotyped the samples for susceptibility to cisplatin-induced cytotoxicity. The focus of this article is the description of genetic variants in two populations that contribute, through variation in gene expression, to cisplatin-induced cytotoxicity. To this end, we designed a three-way model, correlating genotype, gene expression, and cytotoxicity data, to identify potentially functional SNPs and/or haplotypes associated with cisplatin-induced cytotoxicity. Cell lines derived from individuals of European and African descent allow us to define a set of genetic variants unique to and common among the populations. The long-term goal is to identify, through a genetic signature, patients at risk for adverse events associated with these agents. EBV-transformed LCLs derived from 30 CEPH trios (i.e., mother, father, and child) collected from Utah residents with northern and western European ancestry (CEU [HAPMAPPT01]) and from 30 trios collected from Yoruba in Ibadan, Nigeria (YRI [HAPMAPPT03]), were purchased from the Coriell Institute for Medical Research. Cell lines were maintained and were diluted as described elsewhere.16Huang RS Kistner EO Bleibel WK Shukla SJ Dolan ME Effect of population and gender on chemotherapeutic agent-induced cytotoxicity.Mol Cancer Ther. 2007; 6: 31-36Crossref PubMed Scopus (62) Google Scholar Cisplatin and dimethyl sulfoxide (DMSO) were purchased from Sigma-Aldrich. Cell-growth inhibition was evaluated at concentrations of 0, 0.5, 1, 2.5, 5, 10, 20, 40, and 80 μM of cisplatin. Cisplatin was dissolved in DMSO immediately before use. DMSO concentrations did not exceed 0.1% in the cells. The cytotoxic effect of cisplatin was determined using the nontoxic colorimetric-based assay alamarBlue, as described elsewhere.16Huang RS Kistner EO Bleibel WK Shukla SJ Dolan ME Effect of population and gender on chemotherapeutic agent-induced cytotoxicity.Mol Cancer Ther. 2007; 6: 31-36Crossref PubMed Scopus (62) Google Scholar The concentration required to inhibit 50% of cell growth (IC50) was determined by curve fitting the percentage of cell survival against the concentration of cisplatin. SNP genotypes were downloaded from the International HapMap database (release 21). To perform a high-quality genomewide association study, we employed several data filters. To reduce possible genotyping errors, we excluded 100,536 and 138,533 SNPs with Mendelian allele-transmission errors in 22 autosomes in the 30 CEU and 30 YRI HapMap trios, respectively. To exclude the extreme outliers and to increase the power of the association studies within our limited number of samples, we included only the SNPs that met the criteria of having three genotypes and containing a minimum of two counts for each genotype in the unrelated individuals of each population. To obtain functionally relevant SNPs, we further filtered the SNPs by their location. Only SNPs located in genes or within 10 kb up- or downstream of a gene were included. Thus, our final data set consisted of 387,417 very informative SNPs covering 22,667 well-annotated genes. All 175 IC50 values (from 86 CEU and 89 YRI) were log2 transformed to obtain normally distributed data. The quantitative transmission/disequilibrium test (QTDT) was performed to identify any genotype-cytotoxicity associations, with the use of QTDT software.17Abecasis G Cardon L Cookson W A general test of association for quantitative traits in nuclear families.Am J Hum Genet. 2000; 66: 279-292Abstract Full Text Full Text PDF PubMed Scopus (944) Google Scholar Because of the possible heterogeneity between and within populations, we performed association studies in these two ethnic groups separately, using sex as a covariate, and together, using sex and race as covariates. P≤.0001 was considered statistically significant. RNA from 87 CEU and 89 YRI cell lines was extracted after four dilutions, by use of RNeasy Plus Mini Kits (QIAGEN). RNA quality was assessed using the RNA 6000 Nano Assay (Agilent Technologies). For each cell line, ribosomal RNA was depleted from 1 μg of total RNA by use of the RiboMinus Human/Mouse Transcriptome Isolation Kit (Invitrogen). cDNA was generated using the GeneChip WT cDNA Synthesis and Amplification Kit (Affymetrix), per the manufacturer's instructions. cDNA was fragmented and end labeled using the GeneChip WT Terminal Labeling Kit (Affymetrix). Approximately 5.5 μg of labeled DNA target was hybridized to the Affymetrix GeneChip Human Exon 1.0 ST Array at 45°C for 16 h, per the manufacturer's recommendation Affymetrix Web site for additional information). Hybridized arrays were washed and stained on a GeneChip Fluidics Station 450 and were scanned on a GCS3000 Scanner (Affymetrix). Resulting probe-signal intensities were sketch-quantile normalized using a subset of the 1.4 million probe sets. Gene-expression levels were summarized using the robust multiarray average (RMA). A constant of 16 was added for variance stabilization, and summarized signals were log2 transformed.18Irizarry RA Hobbs B Collin F Beazer-Barclay YD Antonellis KJ Scherf U Speed TP Exploration, normalization, and summaries of high density oligonucleotide array probe level data.Biostatistics. 2003; 4: 249-264Crossref PubMed Scopus (8012) Google Scholar This was done with signals generated on a core set of well-annotated exons (∼200,000) within the Affymetrix Exon Array Computational Tool (ExACT) software package. To prevent confounding interpretations of gene-expression variation, we removed data from exons for which probe sets contained two or more probes harboring SNPs, before summarizing expression. All raw exon-array data have been deposited into Gene Expression Omnibus (GEO) (accession number GSE7761). A second QTDT test that integrated mRNA gene expression and significant SNPs found in the genotype and cytotoxicity association analysis was performed to identify possible association with gene expression. Significant SNPs generated from the genotype-cytotoxicity association in CEU, YRI, or combined populations were tested for their association with gene expression in the same population. Genes with average intensity >5 from Affymetrix GeneChip Human Exon 1.0 ST Array analysis were considered expressed and were included in this association analysis. The QTDT test was performed using gene-expression analysis in CEU and YRI populations separately and combined, with sex and race as covariates in the combined samples. We examined not only the cis-acting gene, defined as gene expression associated with SNP(s) within 5 Mb on the same chromosome, but also the trans-acting gene, defined as gene expression associated with SNP(s) on different chromosome(s) or >5 Mb away on the same chromosome. A Bonferroni correction (P<.05) that used a number of transcript clusters in the analysis was used to adjust raw P values after QTDT analysis. To examine the relationship between gene expression and sensitivity to cisplatin, we constructed a general linear model with log2-transformed cisplatin IC50 as the dependent variable and RMA-summarized log2-transformed gene-expression level and an indicator for sex as the independent variables. The dependent variable was transformed to satisfy the assumption of normality. Trios were treated as units of analysis, and members of different families were considered independent. The covariance structure within a trio was modeled using a Toeplitz structure with two diagonal bands, such that the trios were ordered father, then offspring, and then mother. With this covariance structure, mother and father IC50 values were independent, but the offspring's value was allowed to covary with both the father's and mother's values. If a SNP was significantly associated with cisplatin IC50 and the same SNP was significantly associated with gene expression, then the above approach was used to test whether gene expression significantly predicted IC50. In the CEU population, 4 transcript clusters were tested for their expression correlation with cisplatin IC50, whereas 19 transcript clusters were tested in the YRI population, and 19 transcript clusters were tested in the combined CEU and YRI populations. With the combined approach, predictors of population and sex were included in the model. Sex was also tested in the separate CEU and YRI populations as a predictor of cisplatin IC50. P<.05 was considered statistically significant. The model was programmed using the PROC MIXED procedure in SAS/STAT software version 9.1.19SAS Institute (1997) SAS/STAT software release 9.1. Cary, NCGoogle Scholar The REPEATED statement was used to model the Toeplitz covariance structure. The linkage disequilibrium (LD) of significant SNPs within each population was evaluated using Haploview version 3.32. To examine the overall genetic contributions to sensitivity of cisplatin, additional general linear models were constructed with transformed cisplatin IC50 as the dependent variable. The independent variables included all the significant SNP genotypes (with assumption of an additive genetic effect) that were selected using the three-way model in the combined populations and in the two populations independently. These SNP genotypes were significantly associated with cisplatin IC50 through their effect on gene expression. For the model of combined populations, indicators of race and sex were also included as predictors. Trios were analyzed as independent units. The covariance was modeled as described above. Models were reduced using a backward-elimination approach. SNPs included in each of the final models were statistically significant at the α=.05 level. By use of the final model, predicted transformed IC50 values were computed. For the unrelated individuals (parents from the trios and, separately, offspring from the trios), R2 was estimated between observed IC50 and the predicted IC50 from the final model. Lastly, a weighted average of the two R2 estimates was computed to quantify the amount of variation in cisplatin IC50 explained by the selected SNP genotypes. Alternative methods were considered to evaluate the endpoints of the analytical experiments with use of a different initial step but with the same statistical cutoffs. The first alternative approach involved analyzing the SNP genotype and gene-expression association, testing the association of the significant SNPs with cisplatin IC50, and then performing linear regression between gene expression and cisplatin IC50. The second alternative approach involved evaluating the correlation between gene expression and cisplatin IC50, followed by analyzing the SNPs associated with gene expression and testing the association between those SNPs and cisplatin IC50. Elsewhere, we reported the median IC50 as 5.1 μM and 6.3 μM for cell lines derived from CEU (n=86) and YRI (n=89) trios, respectively, after exposure to increasing concentrations of cisplatin (0.5–80 μM) for 48 h.16Huang RS Kistner EO Bleibel WK Shukla SJ Dolan ME Effect of population and gender on chemotherapeutic agent-induced cytotoxicity.Mol Cancer Ther. 2007; 6: 31-36Crossref PubMed Scopus (62) Google Scholar Interindividual variation in the IC50 was 17-fold for CEU and 49-fold for YRI.16Huang RS Kistner EO Bleibel WK Shukla SJ Dolan ME Effect of population and gender on chemotherapeutic agent-induced cytotoxicity.Mol Cancer Ther. 2007; 6: 31-36Crossref PubMed Scopus (62) Google Scholar Using 387,417 SNPs representing 22,667 genes (∼85% of genes in the entire genome), we evaluated whether genetic variation was associated with sensitivity to cisplatin by use of the IC50 value. An arbitrary P value threshold (P≤.0001) resulted in the identification of 96, 57, and 138 SNPs significantly associated with cisplatin IC50 in the combined, CEU, and YRI populations, respectively (table 1). These SNPs were located in or within 10 kb up- or downstream of 67, 36, and 88 genes, respectively.Table 1Significant Results from the Three-Way Model with Combined, CEU, and YRI PopulationsNo. of SNPs (No. of Genes)Approach and StepsCombined PopulationsCEUYRICurrent: SNP associated with cisplatin IC50aP≤.0001.96 (67)57 (36)138 (88) SNP associated with gene expressionbBonferroni-corrected P<.05.8 (22)3 (4)11 (25) Gene expression correlated with cisplatin IC50cP<.05.6 (8)2 (2)10 (17)Alternative 1: SNP associated with gene expressionbBonferroni-corrected P<.05.20,440 (8,451)16,284 (5,922)23,787 (9,059) SNP associated with cisplatin IC50aP≤.0001.8 (22)3 (4)11 (25) Gene expression correlated with cisplatin IC50cP<.05.6 (8)2 (2)10 (17)Alternative 2: Gene expression correlated with cisplatin IC50cP<.05.NA (2,934)NA (1,770)NA (1,882) SNP associated with gene expressionbBonferroni-corrected P<.05.16,129 (2,378)11,576 (1,311)21,456 (1,522) SNP associated with cisplatin IC50aP≤.0001.22dThe additional SNPs generated from this approach are rs10825264, rs10894795, rs12049577, rs12278731, rs13278343, rs2484665, rs3123678, rs3886003, rs6436716, rs6552924, rs7013683, rs7699288, rs773921, rs7795668, rs7825213, and rs979532. (19)8eThe additional SNPs generated from this approach are rs10898290, rs1556223, rs1953951, rs1975092, rs2111890, and rs2276607. (8)36fThe additional SNPs generated from this approach are rs1004407, rs10053097, rs10221083, rs10431791, rs12499960, rs1291362, rs17740395, rs1889785, rs2017791, rs4474730, rs6043976, rs6043979, rs6043981, rs6043984, rs6043986, rs6974263, rs7226876, rs8045919, rs8051159, rs850920, rs940795, rs9455158, rs981890, rs9821880, rs9881766, and rs9882242. (24)Note.—NA = not applicable.a P≤.0001.b Bonferroni-corrected P<.05.c P<.05.d The additional SNPs generated from this approach are rs10825264, rs10894795, rs12049577, rs12278731, rs13278343, rs2484665, rs3123678, rs3886003, rs6436716, rs6552924, rs7013683, rs7699288, rs773921, rs7795668, rs7825213, and rs979532.e The additional SNPs generated from this approach are rs10898290, rs1556223, rs1953951, rs1975092, rs2111890, and rs2276607.f The additional SNPs generated from this approach are rs1004407, rs10053097, rs10221083, rs10431791, rs12499960, rs1291362, rs17740395, rs1889785, rs2017791, rs4474730, rs6043976, rs6043979, rs6043981, rs6043984, rs6043986, rs6974263, rs7226876, rs8045919, rs8051159, rs850920, rs940795, rs9455158, rs981890, rs9821880, rs9881766, and rs9882242. Open table in a new tab Note.— NA = not applicable. We generated expression data on 176 LCLs (87 CEU and 89 YRI), using the Affymetrix GeneChip Human Exon 1.0 ST Array (an exon array). A total of 14,722 transcript clusters with a mean log2-transformed gene-expression intensity of >5, indicating expression in both CEU and YRI samples, were included in the analysis. The QTDT association analysis was conducted between gene expression and the SNPs that were significantly associated with cisplatin IC50. We found 2 cis- and 32 trans-acting relationships in the combined populations, 1 cis- and 3 trans-acting relationships in CEU, and 2 cis- and 36 trans-acting relationships in YRI (Bonferroni-corrected P<.05). Among all observed cis- and trans-acting relationships, some SNPs were significantly associated with more than one gene expression, and some gene expressions were associated with more than one SNP. Therefore, the final cis- and trans-acting relationships were represented by 8 SNPs that were significantly associated with 22 gene expressions in the combined population, by 3 SNPs that were significantly associated with 4 gene expressions in CEU, and by 11 SNPs that were significantly associated with 25 gene expressions in YRI (table 1 and the tab-delimited ASCII file, which can be imported into a spreadsheet, of [data set 1 [online only]]] [data set 1]]). We examined the correlation between gene expression and cisplatin IC50, using a general linear model that was constructed to reflect the trio relationships in our data. Since some genes shared the same transcript cluster identification numbers (IDs) on the exon array, the expression of 19 transcript clusters (representing 22 genes identified above) were evaluated in the combined population. Eight genes had significant correlation with cisplatin IC50 (P<.05) (table 1). In the same manner, we found 2 and 17 genes whose expression significantly correlated with cisplatin IC50 in the CEU and YRI populations, respectively (P<.05) (table 1, current approach). A summary of SNPs that were found to be significantly associated with cisplatin IC50 through gene-expression analysis of the CEU, YRI, and combined populations is shown in table 2.Table 2SNPs Associated with Cisplatin IC50 through Gene-Expression Analysis of the CEU, YRI, and Combined PopulationsHost GeneTarget GenePSNPChromosomeSNP LocationNameTranscript Cluster IDNameChromosome LocationGenotype and IC50Genotype and ExpressionGene Expression and IC50Populationrs164994210IntronNRG33448088BHLHB312p11.23-p12.1.00007.000003.0038237CEUrs75509181PromoterLOC6448522790062FLJ320284q31.3.00008.000003.0204CEUrs475114310IntronEBF33867247DBP19q13.3.00006.0000001.0342YRIrs23056383IntronNBEAL22461531IRF2BP21q42.3.0001.0000002.0187YRIrs651267020IntronPARD6B2873785ALDH7A15q31.00003.0000003.0019804YRIrs1227873111IntronGALNTL43705491FAM57A17p13.3.00004.0000005.000198YRIrs998886811IntronGALNTL43705491FAM57A17p13.3.00004.0000005.000198YRIrs93519615IntronATP8B44013434TAF9LXq13.1-q21.1.0001.0000007.0008489YRIrs1227873111IntronGALNTL42489228WDR542p13.1.00004.000001.0143YRIrs25877082IntronTMEM372946319HIST1H4D6p21.3.00004.000001.0007479YRIrs998886811IntronGALNTL42489228WDR542p13.1.00004.000001.0143YRIrs1123683611TailLRRC323995804FLJ4385516p11.2.0001.000002.000005933YRIrs1123683611TailLRRC323995804SLC6A8Xq28.0001.000002.000005933YRIrs1227873111IntronGALNTL42673312PFKFB43p22-p21.00004.000003.0004802YRIrs1227873111IntronGALNTL43622386GATM15q21.1.00004.000003.0104YRIrs1227873111IntronGALNTL43965751HDAC1022q13.31.00004.000003.00002793YRIrs1227873111IntronGALNTL43965751MAPK1222q13.33.00004.000003.00002793YRIrs1227873111IntronGALNTL42339786KIAA17991p31.3.00004.000003.0026355YRIrs1227873111IntronGALNTL42339786PGM11p31.00004.000003.0026355YRIrs998886811IntronGALNTL43622386GATM15q21.1.00004.000003.0104YRIrs998886811IntronGALNTL42339786KIAA17991p31.3.00004.000003.0026355YRIrs998886811IntronGALNTL42339786PGM11p31.00004.000003.0026355YRIrs998886811IntronGALNTL43965751HDAC1022q13.31.00004.000003.00002793YRIrs998886811IntronGALNTL43965751MAPK1222q13.33.00004.000003.00002793YRIrs998886811IntronGALNTL42673312PFKFB43p22-p21.00004.000003.0004802YRIrs653757110PromoterC10orf642999516STK17A7p12-p14.00007.000002.0183372YRIrs37321032IntronPQLC33761451HOXB917q21.3.0001.000003.00008451YRIrs4569985IntronFCHSD13645565WDR5816p13.3.00004.0000002.0006867Combinedrs1736835IntronFCHSD13418007SHMT212q12-q14.0001.0000006.0001657Combinedrs1736835IntronFCHSD13645565WDR5816p13.3.0001.0000009.0006867Combinedrs809464718IntronMYO5B3115504MYC8q24.12-q24.13.00002.000001.0000000000454Combinedrs4569985IntronFCHSD13418007SHMT212q12-q14.00004.000001.0001657Combinedrs4569985IntronFCHSD13251393DDIT410pter-q26.12.00004.000002.00009656Combinedrs4569985IntronFCHSD12454444NEK21q32.2-q41.00004.000002.000008268Combinedrs15663474IntronSORBS23887117PPGB20q13.1.00006.000002.0044Combinedrs21362411PromoterCDCA13850445CDKN2D19p13.00002.000003.0157Combinedrs4569985IntronFCHSD13317868FRAG111p15.5.00004.000003.0425Combinedrs724467918IntronMYO5B3115504MYC8q24.12-q24.13.00005.000003.0000000000454Combined Open table in a new tab Alternative methods were considered to evaluate the endpoints of the analytical experiments with use of a different initial step but with the same statistical cutoff. If the initial step is the analysis of the SNP genotype and gene-expression association followed by tests of association of the significant SNPs with cisplatin IC50 and linear-regression analysis between gene expression and cisplatin IC50 (table 1, alternative approach 1), we find identical results. If, however, the initial step is an analysis of gene expression and cisplatin IC50 followed by analysis of the SNP associated with gene expression and then association analysis of those SNPs with cisplatin IC50 (table 1, alternative approach 2), the same genetic variants with additional variants are identified. Additional SNPs found through alternative approach 2 are indicated in table 1. When the results generated from association tests between genotype, cisplatin IC50, and gene expression—as well as the linear-regression results between gene expression and cisplatin IC50—were combined, we identif
Referência(s)