genetic variation predicting cisplatin cytotoxicity …...2011/07/20 · genetic variation...

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Genetic variation predicting cisplatin cytotoxicity associated with overall

survival in lung cancer patients receiving platinum-based chemotherapy †, ‡

Xiang-Lin Tan1,2, Ann M. Moyer1,4, Brooke L. Fridley3, Daniel J. Schaid3, Nifang Niu1, Anthony

J. Batzler3, Gregory D. Jenkins3, Ryan P. Abo1, Liang Li1, Julie M. Cunningham4, Zhifu Sun2,

Ping Yang2, Liewei Wang1,*

1Division of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental

Therapeutics, 2Division of Epidemiology, Department of Health Sciences Research, 3Division of

Biomedical Statistics and Informatics, Department of Health Sciences Research, 4Department of

Laboratory Medicine and Pathology, Mayo Clinic College of Medicine, Rochester, MN, 55905,

United States of America

Running title: Genetic variation and platinum response in lung cancer

Key words: Lung cancer, cisplatin, pharmacogenomics, lymphoblastoid cell lines, GWAS

† Sources of Support: This study was supported by U.S. National Institutes of Health research

grants, R01 CA80127 (to PY), R01 CA84354 (to PY), K22 CA130828 (to LW), R01 CA138461

(to LW), R25T CA92049 (to XT) and U19 GM61388 (The Pharmacogenomics Research

Network), Mayo Foundation funds (to PY), ASPET-Astellas Award (to LW) and a PhRMA

Foundation “Center of Excellence in Clinical Pharmacology” Award (to LW)

‡ Author disclosures: no conflicts of interest.

Research. on May 20, 2020. © 2011 American Association for Cancerclincancerres.aacrjournals.org Downloaded from

Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on July 20, 2011; DOI: 10.1158/1078-0432.CCR-11-1133

http://clincancerres.aacrjournals.org/

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* To whom correspondence and requests for reprints should be addressed: Liewei Wang M.D.,

Ph.D., Division of Clinical Pharmacology, Department of Molecular Pharmacology and

Experimental Therapeutics, Mayo Clinic, 200 First Street SW, Rochester, MN, U.S.A. 55905

Email: [email protected] or [email protected] ; Telephone: 507-284-5264; Fax:

507-284-4455

5Abbreviations used: GWAS, genome-wide association study; LCLs, lymphoblastoid cell lines;

SCLC, small cell lung cancer, NSCLC, non-small cell lung cancer; OS, overall survival; SNPs,

single-nucleotide polymorphisms; AA, African-American; CA, Caucasian-American; HCA, Han

Chinese-American; DMSO, dimethyl sulfoxide; HWE, Hardy-Weinberg Equilibrium; MAF,

minor allele frequency; HR, hazard ratio




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Translational Relevance

Chemotherapy with platinum-based regimens is the standard of care for both small cell and non-

small cell lung cancer, but large inter-individual variations are observed in platinum efficacy and

toxicity. However, reliable genetic biomarkers for the prediction of response to platinum-based

therapy are still not well established. Our study identified several genetic variants that are

potentially associated with the efficacy of platinum-based chemotherapy in patients with lung

cancer by using genome-wide analysis of data generated with lymphoblastoid cell lines followed

by genotyping studies using DNA samples from lung cancer patients, and functional validation

in lung cancer cells. These results provide important insight into mechanisms that might

contribute to variation in response to platinum-based therapy of lung cancer.




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Abstract

Purpose: Inherited variability in the prognosis of lung cancer patients treated with platinum-

based chemotherapy has been widely investigated. However, the overall contribution of genetic

variation to platinum response is not well established. To identify novel candidate SNPs/genes,

we performed a genome-wide association study (GWAS) for cisplatin cytotoxicity using

lymphoblastoid cell lines (LCLs), followed by an association study of selected SNPs from the

GWAS with overall survival (OS) in lung cancer patients.

Experimental Design: GWAS for cisplatin were performed with 283 ethnically diverse LCLs.

168 top SNPs were genotyped in 222 small cell and 961 non-small cell lung cancer (SCLC,

NSCLC) patients treated with platinum-based therapy. Association of the SNPs with OS was

determined using the Cox regression model. Selected candidate genes were functionally

validated by siRNA knockdown in human lung cancer cells.

Results: Among 157 successfully genotyped SNPs, 9 and 10 SNPs were top SNPs associated

with OS for patients with NSCLC and SCLC, respectively, although they were not significant

after adjusting for multiple testing. Fifteen genes, including 7 located within 200 kb up or

downstream of the four top SNPs and 8 genes for which expression was correlated with three

SNPs in LCLs were selected for siRNA screening. Knockdown of DAPK3 and METTL6, for

which expression levels were correlated with the rs11169748 and rs2440915 SNPs, significantly

decreased cisplatin sensitivity in lung cancer cells.

Conclusions: This series of clinical and complementary laboratory-based functional studies

identified several candidate genes/SNPs that might help predict treatment outcomes for

platinum-based therapy of lung cancer.




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Introduction

Platinum compounds are the major chemotherapeutic agents used to treat both small cell

and non-small cell lung cancer (SCLC and NSCLC), with platinum compounds as the base for

combination chemotherapy. Meta-analyses from randomized trials of cisplatin-based

chemotherapy versus best supportive care have shown that cisplatin-based chemotherapy was

associated with a modest improvement in overall survival (OS) (1). Unfortunately, although

these agents have shown success in treating lung cancer, the use of platinum-based

chemotherapy is limited by the development of chemoresistance and toxicity. In addition,

response rates vary greatly among individuals for any given cisplatin-based chemotherapy

regimen. Factors such as disease stage, tumor histology, sex, tobacco exposure and patient age

may influence platinum-based chemotherapy outcomes. Although tumor DNA is important for

drug response, previous studies have shown clearly that germline genetic variations also play an

important role in determining treatment outcome (2-4). Therefore, identification and

characterization of the role of genetic variation in differential response to platinum may help

optimize individual treatment plans and improve platinum treatment outcomes.

Chemoresistance and toxicity associated with platinum-based therapy are complex

phenotypes that may involve many processes (5). These processes include alterations in drug

influx or efflux, detoxification through glutathione conjugation, DNA repair capacity and other

cellular pathways required for proper response to DNA damage (6). The development of

chemoresistance to platinum agents can be due to germline genetic variation or acquired

mutation through their effects on mRNA and protein levels in key pharmacokinetic or

pharmacodynamic pathways. Candidate gene and/or candidate pathway approaches indicated

that single nucleotide polymorphisms (SNPs) in genes responsible for drug influx and efflux (7),




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metabolism and detoxification (8-10), and DNA damage repair pathways (2-4, 11-17), were

associated with treatment outcome of lung cancer patients. However, the overall contribution of

these genetic biomarkers in the prediction of response to platinum-based therapy is still not well

established, suggesting that additional genes might be involved.

Therefore, in the current study, we took a genome-wide approach using 283

lymphoblastoid cell lines (LCLs) to identify SNPs/genes that might contribute to variation in

cisplatin cytotoxicity as represented by IC50 values. This step was followed by genotyping top

candidate SNPs from the LCL GWAS studies using 1183 DNA samples from lung cancer

patients treated with platinum-based therapy to identify SNPs that might be associated with OS.

Finally, functional studies of candidate genes identified during the association studies were

performed using siRNA knockdown in human lung cancer cells.

Materials and Methods

Cell lines

This study was reviewed and approved by the Mayo Clinic Institutional Review Board.

Human Variation Panel LCLs from sample sets HD100AA, HD100CAU, HD100CHI,

corresponding to 100 African-American (AA), 100 Caucasian-American (CA), and 100 Han

Chinese-American (HCA) unrelated subjects, were obtained from the Coriell Cell Repository

(Camden, NJ). The National Institute of General Medical Sciences had obtained and

anonymized these cell lines before deposit, and all subjects had provided written informed

consent for the use of their samples for research purposes. Human NSCLC cell lines H1437,

H1299 and the SCLC cell line H196 were obtained from the American Type Culture Collection

(Manassas, VA). LCLs were cultured in RPMI 1640 medium (Mediatech, Manassas, VA)




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supplemented with 15% FBS (Mediatech). H1437, H1299 and H196 cell lines were cultured in

RPMI 1640 medium containing 10% FBS.

Patient samples

The study group included 1183 Caucasian patients with pathologically confirmed primary

lung cancer, 222 SCLC and 961 NSCLC, who were treated with platinum-based chemotherapy

at the Mayo Clinic (Rochester, MN) between 1997 and 2008. Details with regard to clinical

characteristics of patients, patient enrollment, diagnosis, and data collection procedures were

described previously (18-20). Briefly, each patient was identified through the Mayo Clinic

pathologic database. After written informed consent had been obtained, a blood sample was

collected. The characteristics of patients, including demographics, lung cancer pathology,

anatomic site, and types and timing of treatment and chemotherapeutic agents, were abstracted

from patient medical records by a trained nurse. Vital status and cause of death were determined

by reviewing the Mayo Clinic registration database and medical records, correspondence from

patients' next-of-kin, death certificates, obituary documents, the Mayo Clinic Tumor Registry,

and the Social Security Death Index website. Clinical staging and recurrence or progression

information was determined by results from available chest radiography, computerized

tomography, bone scans, positron emission tomography scans, and magnetic response imaging.

All patients were actively followed up during the initial six months after diagnosis, with

subsequent annual follow-up by mailed questionnaires and annual verification of the patients’

vital status.




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Cisplatin cytotoxicity assay

Cisplatin was obtained from Sigma-Aldrich (St. Louis, MO) and was dissolved in dimethyl

sulfoxide (DMSO) immediately before use. Cells (4 ×104 cells/well) were plated into 96-well

plates, and incubated with cisplatin for 72 h in eight concentrations ranging from 0.1 to 80

μmol/L. DMSO alone was used as a control. Cisplatin cytotoxicity was evaluated by

determining the concentration of cisplatin required to inhibit growth and/or survival by 50%

(IC50) using the CellTiter 96@ Aqueous Non-Radioactive Cell Proliferation Assay (Promega,

Madison, WI). Experiments were performed successfully for 283 LCLs (91 AA, 96 CA, and 96

HCA).

Genome-wide SNP and expression arrays for LCLs

The genotyping and expression array data for all LCLs were described previously (21-23)

and are publically available from NCBI Gene Expression Omnibus

(http://www.ncbi.nlm.nih.gov/geo) under SuperSeries accession No. GSE24277 and accession

No. GSE23120. Briefly, Illumina HumanHap 550K and 510S BeadChips were used for

genotyping of DNA samples from all of the LCLs in the Genotype Shared Resource at Mayo

Clinic. Publicly available Affymetrix SNP Array 6.0 Chip SNP data were also obtained for the

same cell lines. SNPs that deviated from Hardy-Weinberg Equilibrium (HWE) (24) based on

minimum P value from an exact test for HWE (25) and the stratified test for HWE (26) (P <

0.001); SNPs with call rates < 95%; or SNPs with minor allele frequency (MAF) < 5% were

removed from the analysis (27, 28). As a result, 1,348,798 SNPs that passed these quality

control measures were included in the GWA analyses of SNP vs. IC50 and SNP vs. expression.




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SNP selection and genotyping in lung cancer patients

SNP selection was based on P values obtained during the analysis of genome-wide SNP vs.

IC50, and SNP vs. expression, as well as expression vs. IC50 using LCL data. We selected all

SNPs with P < 10-5 from the SNP vs. IC50 analysis. Additionally, we defined a SNP peak as a

locus that contained at least two SNPs associated with cisplatin IC50 with P <10-4. SNPs within

the SNP peaks were used to perform an association study with 54,613 expression probesets to

identify SNPs that were associated with gene expression (P < 10-4). Finally an association study

was performed with gene expression and cisplatin IC50 to identify SNPs that might be associated

with cisplatin IC50 through an influence on gene expression. Therefore, we also selected SNPs

with SNP vs. expression: P value < 10-4 and expression vs. IC50: P value < 10-4 to genotype the

patient samples (Supplementary Table 1).

A total of 168 top hit SNPs were selected and genotyped in 1183 lung cancer patients who

received platinum-based chemotherapy. Genotyping was performed in the Mayo Clinic

Genomics Shared Resource using a custom-designed Illumina GoldenGate panel. Quality

control tests of the genotyping results were performed by assessing concordance among three

control DNA samples that were present in duplicate, SNP call rates, sample call rates, MAF of

SNPs or departure of SNP genotypes from HWE. SNPs were excluded if they failed genotyping,

or displayed ambiguous clustering monomorphic genotyping, MAFs of <0.01, or significant

departures from HWE (P < 0.001). SNPs having call rates > 95% but which passed all other

quality control tests were included in the analysis. Of the SNPs with genotyping data, 157 SNPs

passed the quality control tests and were included in the analyses.




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Functional validation by siRNA knockdown

Human NSCLC cell lines, H1437, H1299 and the SCLC cell line, H196, were used in

siRNA screening studies. siRNAs for the candidate genes and non-targeting negative control

siRNA pool were purchased from Dharmacon (Hiden, Germany). Specifically, approximately

3,000-4,000 cells were seeded into 96-well plates, mixed with an siRNA-mixture consisting of

25 nmol/L of specific or negative control siRNAs and the LipofectamineTM RNAiMAX reagent

(Invitrogen, Carlsbad, CA). Twenty-four hours after transfection, the cells were treated with

vehicle or increasing concentrations of cisplatin for an additional 72 h, followed by MTS assay

using the CellTiter 96@ Aqueous Non-Radioactive Cell Proliferation Assay.

Knockdown efficiency determination by real-time RT-PCR and Western blot analysis

Total RNA was isolated from cultured cells transfected with controls or specific siRNAs

with the Mini RNA isolation kit (ZYMO Research, Orange, CA), followed by qRT-PCR

performed with the 1-step, Brilliant SYBR Green qRT-PCR master mix kit (Stratagene, La Jolla,

CA). Specifically, primers purchased from QIAGEN were used to perform qRT-PCR using the

ABI StepOne™ Real-Time PCR System (Applied Biosystems). Western Blots were performed

for DAPK3, METTL6 and RUFY1 using goat polyclonal antibodies purchased from Santa Cruz

biotechnology, Inc. All experiments were performed in triplicate with β-actin as an internal

control.

Statistical analysis

A detailed description for GWAS of LCLs has been described elsewhere (21-23). Briefly,

the cisplatin cytotoxicity IC50 phenotype was calculated for each cell line based on a logistic




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dose-response model using the R package ‘drc’ (http://cran.r-project.org/doc/packages/ drc.pdf).

Logarithm transformed IC50 values were then compared between genders, batches of samples

based on time since purchase, and between the CA race and all other samples using independent

samples t-tests. An overall comparison of transformed IC50 values among ethnic groups was

performed using an F-test based on ANOVA. Because we used LCLs from multiple races/ethnic

groups, population stratification was adjusted using the method developed by Price et al. (29),

which uses an eigen analysis for detecting and adjusting. SNPs. The log transformed IC50

values and GCRMA normalized expression data were adjusted for race using the five

eigenvectors. The GWA analysis of the association of SNP vs. IC50 or SNP vs. expression was

performed with Pearson correlations using adjusted SNP, IC50 and expression values for each

individual. For the siRNA knockdown experiments, group mean values of IC50 were compared

using Student's t test.

The overall survival time was used as the primary endpoint, defined as the time from lung

cancer diagnosis to either death or the last known date alive. Patients known to be alive were

censored at the time of last contact. All the patients included in the analysis were Caucasians.

To test for the effect of SNP on OS, we used the Cox regression model that included the effects

of a SNP genotype dosage (count of minor alleles). A total of 157 SNPs were included in this

analysis, and the association was performed for NSCLC and SCLC separately because of

significant differences between the two diseases. To correct for multiple testing of the 157 SNPs

assayed in the initial experiment, the Bonferroni corrected P value threshold of 0.0003 was used

to determine statistically significant associations. To determine whether associations with SNPs

should be adjusted for the clinical covariates of age at diagnosis, gender, smoking status, disease

stage, and treatment, backward selection was done. The disease stage was included in the final




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multivariate Cox-regression model since it was significantly associated with OS of the lung

cancer patients. The disease stage was divided into five categories: small cell lung cancer with

limited versus extensive stages; NSCLC with stages I and II, versus III, versus IV. We used 0.05

as a cutoff for P values (not adjusted for multiple testing) to select SNPs/genes for further

functional validation.

Results

Correlation analysis of genome-wide SNP vs cisplatin IC50 in LCLs

Cytotoxicity assays were performed for cisplatin to determine the range of variation in drug

response, and IC50 was used as a phenotype to indicate the drug sensitivity for each cell line.

The unadjusted average IC50 value for cisplatin in these 283 cell lines was 1.79 ± 1.64 (mean ±

SD) µmol/L. Gender had no significant effect on IC50 values (P = 0.15). Caucasian Americans

(CAs) were more sensitive to cisplatin compared to the other two races (AAs and HCAs) (P =

0.0001). Time since the Coriell Institute acquired the cell lines had no significant effect on

cisplatin cytotoxicity (P = 0.64).

A total of 1,348,789 SNPS were used in the genome-wide SNP analysis for 283 cell lines.

Figure 1A depicts the association results graphically. 364 SNPs were associated with cisplatin

IC50 with P values < 10-4, and 50 SNPs had P values < 10-5. None of the top 50 SNPs were

within the coding region of a gene. Twenty-three, 16 and 11 SNPs were in introns, 5’-upstream

or 3’-downstream flanking regions of genes, respectively. The top three SNPs from the GWAS

were rs6633130 (P = 1.66 × 10-7), rs6946197 (P = 1.99 × 10-7) and rs12304656 (P = 3.64 × 10-7)

at loci containing the PPEF1 (intron), LOC100128030 (3’downstream) and STYK1 (intron)

genes, respectively.




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Survival analysis for the selected SNPs and lung cancer patient samples

A total of 168 SNPs were selected and genotyped to determine whether any of those SNPs

might be associated with patient OS. Table 1 lists basic demographic and clinic data with regard

to the patients studied. Of these patients, 656 (55.5%) were male and 527 (44.5%) were female,

with a median age of 63.0 years. Most patients had a history of smoking, with 47.3% (n = 560)

being former smokers and 36.5% (n = 431) current smokers. 550 (46.5%) tumors were classified

as adenocarcinoma, 194 (16.4%) as squamous cell carcinoma and 222 (18.8%) as small cell

carcinoma. 881 (74.5%) patients received platinum drugs in combination of surgery and/or

radiation, and 302 (25.5%) patients received only platinum drugs.

Only disease stage was included in the analysis as a confounder among all the others tested

including age at diagnosis, gender and smoking status. Nine and 10 different SNPs were

associated with OS for NSCLC and SCLC patients, respectively (P < 0.05) (Table 2 and

Supplemental Fig. 1). However none of the SNPs were statistically significant after correcting

for multiple testing. The most significant SNPs for NSCLC and SCLC, rs1287276 (located

within an intron of UGT3A2 on chromosome 5) and rs12669805 (located 3’-downstream of the

LOC100128030 pseudogene on chromosome 7) were associated with OS in NSCLC (P =

0.0004) and SCLC (P = 0.0021), respectively. A hazard ratio > 1 indicated that patients carrying

the minor allele had worse survival. These SNPs would also be expected to be associated with

higher IC50 values in LCLs, and the cells carrying these SNPs would be predicted to be more

resistant to cisplatin. Therefore, 12 out of the 19 SNPs showed concordant association directions

between the two results (Table 2), when we compared the association direction of SNPs with

clinical OS with that of the same SNP with cisplatin IC50 values in LCLs.




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Functional characterization of candidate gene

Although none of the SNPs studied in the patient samples reached statistical significance

after multiple testing, to further pursue the possibility that the locus harboring these SNPs might

include functional genes, we took advantage of the expression data we had for the cell lines (21-

23) to help interpret SNP function and guide the selection of genes for siRNA screening. The

overall selection strategy is depicted graphically in Figure 1B. First, we focused on genes

located within 200 kb up or downstream of the 12 SNPs which showed a concordant association

with the IC50 phenotype in LCLs and the OS phenotype in patients. 39 genes were found to be

located within 200 kb up or downstream of these SNPs, and 7 out of the 39 genes close to 4

SNPs had the expression levels > 50 in LCLs after GCRMA normalization (Fig. 1B). Next,

since SNPs might influence cisplatin response through the regulation of gene expression by

either cis- or trans-regulation, we also performed association analysis using the genotyping data

for these 12 SNPs and basal expression array data (54,613 expression probesets) available for all

of the LCLs. Eight of 12 SNPs were associated with the expression of 212 probesets (154

unique genes) using a cutoff P value <10-4, and 4 of 12 SNPs were associated with mRNA

expression levels of 54 genes with P value <10-5 (Fig. 1B). No cis-regulation was identified.

We selected these 54 genes plus 2 genes with multiple probesets associated with the SNPs to

check expression levels in LCLs. However, only 8 of these genes had the expression levels > 50

after GCRMA normalization. Finally, 15 genes, including 7 genes located within 200 kb up or

downstream of 4 SNPs (rs11169748, rs2440915, rs5952066 and rs7620841) and 8 genes for

which expression was correlated with three SNPs (rs11169748, rs2440915 and rs5952066) were

selected for siRNA screening (Fig. 1B).




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We performed knockdown experiments for these 15 genes in the NSCLC cell lines H1437

and H1299 and in the SCLC cell line H196. Knockdown of DAPK3 and METTL6 significantly

decreased cisplatin sensitivity in all three lung cancer cell lines, while knockdown of RUFY1 did

not significantly alter cisplatin cytotoxicity (Fig. 2). The rs11169748 and rs2440915 SNPs that

were associated with lung cancer OS were also correlated with cisplatin IC50 values in LCLs. In

addition, these two SNPs were also associated with expression of DAPK3 and METTL6 in LCLs

(Fig. 3). However only DAPK3 expression level was associated with cisplatin IC50 (P = 0.03) in

LCLs, while METTL6 was not (P = 0.32). To obtain ungenotyped SNPs, we imputed SNPs that

were not on our genotyping platform and that were located within 200 kb up or downstream of

these two SNPs (rs11169748 and rs2440915), using MACH 1.0 (30), with 1000 Genome Project

data (31) as the reference panel. We observed no stronger association with cisplatin IC50 for the

imputed SNPs, compared to the observed SNPs (Fig. 3E and F).

Discussion

In the current study, we took advantage of the extensive genomic data that we have already

obtained for 283 human LCLs, together with DNA samples obtained from lung cancer patients

treated with platinum-based therapy to determine whether SNPs identified during a GWAS

performed with LCLs might contribute to our understanding of the drug actions of platinum

compounds and the response to platinum compounds in the treatment of lung cancer. Therefore,

we first conducted a GWAS for cisplatin in 283 LCLs to identify top candidate SNPs that were

associated with platinum cytotoxicity in vitro (Fig. 1 and Supplemental Table 1). We then

performed an association study with selected top candidate SNPs using DNA samples obtained

from NSCLC and SCLC patients (Table 2), followed by functional validation of candidate genes




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using human lung cancer cell lines (Fig. 2 and Table 3). We found that 19 SNPs were associated

with OS with P values <0.05 of either NSCLC or SCLC patients (Table 2), and that knockdown

of DAPK3 and METTL6, for which expression levels were correlated with the rs11169748 and

rs2440915 SNPs (Fig. 3), significantly decreased cisplatin sensitivity in the lung cancer cell lines

H1437, H1299 and H196 (Fig. 2).

Previous candidate gene and candidate pathway-based pharmacogenomic analyses have

shown that genetic variation may play a role in determining response to platinum-based

chemotherapy. Much of the focus has been on SNPs within the DNA damage and repair

pathway (2-4, 11-17, 32). However, those results remain controversial. For example, Zhou et al.

reported that the ERCC1 8092C>A variant allele was associated with a 50% increased risk of

death in Stage III (A+B) and IV NSCLC patients (32), while Wu et al. observed that the variant

A allele was significantly associated with a shorter OS (HR = 0.68, 95%CI. 0.48-0.95) (3).

These previous studies indicate that the effects of each individual SNP are modest and suggest

that additional SNPs/genes and/or other possible genetic mechanisms such as CpG methylation

or copy number variation might influence response to platinum-based therapy.

With advances in genotyping and gene expression technologies, several studies have taken

an unbiased approach towards understanding genetic factors influencing response to platinum-

based chemotherapy. In LCLs obtained from pedigrees of Centr d’Etudes du polymorphism

Human (CEPH) individuals of European background, it was estimated that 30-40% of the

variation in cisplatin-induced cytotoxicity was due to heritable factors (33, 34). Using this cell

line model system, those investigators subsequently identified several genetic variants that

contributed to cisplatin and carboplatin cytotoxicity (24, 34-36). However, the results of those

studies have not been replicated in cancer patients treated with platinum-based




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chemotherapeutics. Our genome-wide pharmacogenomics analysis did not confirm those

variants associated with response to cisplatin (24). The difference could be due to differences

between populations from which these LCLs are derived. In Huang’s study, the LCLs derived

from CEU, European descent and YRI, African descent while our LCLs derived from CA, AA

and HCA. It could also be due to confounding factors associated with the use of LCLs, such as

cell growth rate and ATP levels (37). Therefore, we performed siRNA screening for selected

candidate genes to pursue the underlying biology. Several previous studies have suggested an

important contribution of trans-acting variants to individual variation in human gene expression

(38, 39). Among the genes tested, knockdown of DAPK3 and METTL6, for which expression

levels correlated with the rs11169748 and rs2440915 SNPs, significantly decreased cisplatin

sensitivity in three lung cancer cell lines, although METTL6 expression levels were not

significantly associated with cisplatin IC50 in LCLs, suggesting a potential difference between

LCLs and lung cancer cell lines.

DAPK3, also known as zipper interacting protein kinase (ZIPK) or DAPK like kinase

(DLK), is a member of the DAPK family. DAPK was initially identified as being encoded by a

gene whose reduced expression, mediated by antisense cDNA transfection, protected HeLa cells

from gamma interferon-induced cell death. DAPK3 contains several putative nuclear localization

signal sequences and a leucine zipper domain, required for homo-oligomerization, interaction

with other leucine zipper-containing proteins and for its death-promoting effects (40, 41).

DAPK3 has been shown to play distinct roles in tumor suppression and the regulation of

apoptosis (42-44). We have shown that knockdown of DAPK3 significantly decreased cisplatin

sensitivity in lung cancer cell lines and might be related to OS of NSCLC patients treated with

platinum-based therapy.




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METTL6 belongs to the methyltransferase superfamily. It has been suggested that

chemoresistance and toxicity associated with platinum compound treatment might be mediated

through methylation-dependent gene silencing. For example, methylation of FANCF, a gene

associated with Fanconi anemia, confers cisplatin sensitivity in ovarian cancer cell lines (45).

Additionally, Ramirez et al. observed that 14-3-3ơ, a cell cycle checkpoint gene, is methylated in

one third of NSCLC patients and that methylation is related to better median OS for these

patients (46). It is possible that METTL6 might influence DNA methylation, and further

influence response to platinum-based therapy.

In summary, we have identified several genetic variants that are potentially associated with

the efficacy of platinum-based chemotherapy in patients with lung cancer by using genome-wide

interrogation of LCLs together with genotyping studies using DNA samples from lung cancer

patients, followed by functional validation in lung cancer cells. Our results provide important

insight into novel genes and mechanisms that may contribute to variation in response to platinum

drug therapy. Most of our patients with advanced NSCLC were treated with cisplatin

combination therapy, often with the addition of other chemotherapeutics, radiation or surgery.

Even though the SNPs that we tested were selected based on their association with cisplatin

cytotoxicity in LCLs, we can not exclude the possibility of potential interaction of genetic

variation with other treatments and their effect on cisplatin treatment outcome in patients with

lung cancer. Therefore, it is particularly important that our functional studies confirmed the

functional effects of these genes in cisplatin response in vitro. Obviously, additional replication

studies in independent sample sets of patients with lung cancer treated with platinum-based

chemotherapy would be required to further confirm these findings.




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Acknowledgements

We thank Luanne Wussow for her assistance with the preparation of this manuscript. This study

was supported by U.S. National Institutes of Health research grants, R01 CA80127 (to PY), R01

CA84354 (to PY), K22 CA130828 (to LW), R01 CA138461 (to LW), R25T CA92049 (to XT)

and U19 GM61388 (The Pharmacogenomics Research Network), Mayo Foundation funds (to

PY), an ASPET-Astellas Award (to LW) and a PhRMA Foundation “Center of Excellence in

Clinical Pharmacology” Award (to LW).




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26

Figure Legends

Figure 1. Genome-wide SNP association with cisplatin IC50 values (A) and the strategies

used to select candidate genes for siRNA validation (B). Genome-wide SNP association

analysis was performed with cisplatin IC50 as the drug-response phenotype (n = 283). The y-

axis represents the -log10(P value) for association of SNPs and IC50 values. SNPs are plotted on

the x-axis on the basis of their chromosomal locations. The cut-off P value of 10-5 is highlighted

with a black line. Detailed strategies for the selection of candidate genes have been described in

the text. OS, overall survival; LCLs, lymphoblastoid cell lines.

Figure 2. Functional characterizations of candidate genes with siRNA knockdown of

DAPK3 and METTL6. Knockdown of DAPK3 and METTL6 in human NSCLC cell lines,

H1437 (A), H1299 (B) and the SCLC cell line, H196 (C), showed increased resistance to

cisplatin. A control was also included in which knockdown of RUFY1 did not significant change

the response to cisplatin as determined by MTS assay. Quantitative RT-PCR (D) and western

blot (E) were performed to determine the knockdown efficiency for the three genes. RT-PCR

results are expressed as % of control. Error bars represent standard error of mean (SEM) values

for 3 independent experiments.

Figure 3. Association between the rs1169748 and rs2440915 SNPs and cisplatin IC50 values

and expression of DAPK3 and METTL6 in LCLs, as well as imputation analyses for these

two SNPs. Genotypes for each SNP are plotted against cisplatin IC50 values as well as gene

expression levels of DAPK3 and METTL6, respectively. (A and C) SNP rs1169748 association

with cisplatin IC50 values and DAPK3 expression; (B and D) SNP rs2440915 association with




27

cisplatin IC50 values and METTL6 expression; (E and F) imputation analysis for the rs1169748

and rs2440915 SNPs. Black circles indicate SNPs observed by genotyping, while black triangles

indicate imputed SNPs. The y-axis represents –log10(P value) for the association of each SNP

with cisplatin IC50, and the x-axis represents the chromosome location of the SNPs. No stronger

association with cisplatin IC50 were observed for the imputed SNPs, compared to the genotyped

SNPs. AA, African Americans; CA, Caucasian Americans; and HCA, Han-Chinese Americans.




-Log

10(p

val

ue)

Chromosome location

1

6

5

4

3

2

1

0

2 3 4 5 6 7 8 9 10 11 13 15

X o

r Y17 19 22

19 SNPs associated with OS in clinical study (P < 0.05)

Gene expression level >50 in LCLs (7 genes)

Gene expression level >50 in LCLs (8 genes)

15 candidate genes selected for functional validation

Genes within 200 kb up or downstream of the 12 SNPs (39 genes)

SNP vs. expression in LCLs with P <10-5

or P <10-4 with ≥ 2 probe sets (56 genes)

12 SNPs concordantly associated with IC50 and OS phenotype

A

B




A B C

DAPK352kDaMETTL630kDaRUFY180kDaβ-actin43kDa

Specific siRNA Negative siRNAE

H1437H1299

H196H1437

H1299H196

-0.5 0.0 0.5 1.0 1.5 2.00.00.20.40.60.81.01.2

DAPK3 siRNAIC50(95%CI): 16.0(15.3-16.6)

Negative siRNAIC50(95%CI): 12.3(11.0-13.8)

Cisplatin concentration, Log (μmol/L)

Cell s

urviv

al

-0.5 0.0 0.5 1.0 1.5 2.00.00.20.40.60.81.01.2

METTL6 siRNAIC50 (95%CI): 14.9(13.5-16.5)


Cell s

urviv

al

-0.5 0.0 0.5 1.0 1.5 2.0-0.10.10.30.50.70.91.1

DAPK3 siRNAIC50(95%CI): 26.7(23.8-29.8)



Cell s

urviv

al

-0.5 0.0 0.5 1.0 1.5 2.0-0.10.10.30.50.70.91.1

METTL6 siRNAIC50(95%CI): 33.9(26.3-43.7)


Cell s

urviv

al

-0.5 0.0 0.5 1.0 1.5 2.0-0.10.10.30.50.70.91.1

RUFY1 siRNAIC50(95%CI): 15.9(13.7-18.5)

Cisplatin concentration, Log (µmol/L)

Cel

l sur

vival

-0.5 0.0 0.5 1.0 1.5 2.00.00.20.40.60.81.01.2

DAPK3 siRNAIC50(95%CI): 8.1(7.4-8.8)



Cell s

urviv

al

-0.5 0.0 0.5 1.0 1.5 2.00.00

0.25

0.50

0.75

1.00METTL6 siRNAIC50(95%CI): 8.4(7.9-9.0)


Cell s

urviv

al-0.5 0.0 0.5 1.0 1.5 2.0

0.0

0.2

0.4

0.6

0.8

1.0RUFY1 siRNAIC50(95%CI): 6.0(5.6-6.4)

Cisplatin concentration, Log (µmol/L)Ce

ll sur

vival

-0.5 0.0 0.5 1.0 1.5 2.00.0

0.2

0.4

0.6

0.8

1.0

RUFY1 siRNAIC50(95%CI): 11.6(10.9-12.5)


Cell s

urviv

al

0.0

0.2

0.4

0.6

0.8

1.0

1.2

NegativesiRNA

DAPK3siRNA

METTL6siRNA

RUFY1siRNA

Rela

tive

mRN

A ex

pres

sion

H1437 H1299 H196

D




A B

C D

E F

Adj

uste

d IC

502.0

1.5

1.0

0.5

0.0

-0.5

-1.0

Adj

uste

d IC

50

2.01.51.00.50.0

-0.5-1.0

Adj

uste

d DAPK3

mR

NA

exp

ress

ion

(Log

) 15

10

5

0

Rs1169748 AA AG GG AA AG GG AA AG GG AA AG GGNumber 63 25 3 94 2 0 94 2 0 251 29 3Ethnicity AA CA HCA Total

Adj

uste

d METTL6

mR

NA

exp

ress

ion

(Log

)

121086420



Imputed SNPGenotyped SNP

-Log

10(P

)

5

4

3

2

1

0

5

4

3

2

1

0

-Log

10(P

)

49700 49800 49900 50000Position (kb)

61200 61300 61400 615500Position (kb)

Imputed SNPGenotyped SNPrs1169748 rs2440915




Characteristics of diagnosis and treatment Values and percentages

Mean (SD) 62.3 (10.34)

Median (range) 63.0 (27.0-88.0)

Female 527 (44.5%)

Male 656 (55.5%)

Never 192 (16.2%)

Former smokers 560 (47.3%)

Current smokers 431 (36.5%)

Unknown

SCLC: LIMITED 129 (10.9%)

EXTENSIVE 93 (7.9%)

NSCLC: I 123 (10.4%)

II 82 (6.9%)

III 403 (34.1%)

IV 353 (29.8%)

Adenocarcinoma 550 (46.5%)

Squamous cell carcinoma 194 (16.4%)

Small cell carcinoma 222 (18.8%)

Large cell carcinoma 39 (3.3%)

Mixed and unspecified NSCLC 167 (14.1%)

Carcinoid or salivary gland tumors 11 (0.9%)

Nongradable or unknown 80 (6.8%)

Well differentiated 91 (7.7%)

Histologic cell type

Tumor differentiation grade

Table 1. Description of 1183 patients who were diagnosed with primary lung cancer and

received platinum-based chemotherapy.

Age at diagnosis

Gender, n (%)

Cigarette smoking status

Lung cancer stage a




Moderately differentiated 392 (33.1%)

Poor/undifferentiated 620 (52.4)

Only platinum drugs 302 (25.5%)

Surgery and platinum drugs 192 (16.2%)

Radiation and platinum drugs 455 (38.5%)

Surgery, radiation, and platinum drugs 234 (19.8%)a Stage for NSCLC is described on the basis of the tumor-node-metastasis

classification. The small cell lung cancer stage is described as suggested by the

American Cancer Society as either “EXTENSIVE” or “LIMITED.”

Treatment modality




MAF R valuea

P value MAF HR (95% CI) b

P value

rs1287276 5 36083458 0.242 -0.247 4.72E-05 0.095 1.36 (1.15-1.61) 0.0004

rs9405302 c

6 8228049 0.394 0.267 1.08E-05 0.211 1.22 (1.08-1.37) 0.001

rs8064630 17 3789240 0.152 0.251 3.61E-05 0.187 0.82 (0.71-0.94) 0.004

rs5970160 c

23 150844654 0.17 0.259 2.25E-05 0.126 1.17 (1.05-1.31) 0.006

rs1946518 11 111540668 0.384 0.249 5.55E-05 0.396 0.87 (0.78-0.96) 0.008

rs2440915 c

10 61343778 0.058 0.249 4.10E-05 0.029 1.41 (1.08-1.83) 0.012

rs5952066 c

23 146692840 0.178 0.242 6.92E-05 0.2 1.14 (1.03-1.26) 0.014

rs901893 c

5 8228049 0.353 0.239 8.53E-05 0.337 1.14 (1.02-1.27) 0.018

rs11169748 c 12 49865438 0.062 0.237 9.60E-05 0.011 1.75 (1.03-2.97) 0.039

rs12669805 c

7 79509055 0.188 0.253 3.32E-05 0.024 3.42 (1.56-7.48) 0.002

rs6092092 c

20 53291685 0.076 0.249 4.28E-05 0.006 7.74 (1.85-32.30) 0.005

rs2833537 21 32135333 0.074 0.254 3.03E-05 0.036 0.40 (0.20-0.79) 0.008

rs7620841 c

3 187425014 0.06 0.242 6.77E-05 0.052 1.80 (1.12-2.89) 0.015

rs1921626 c

2 77025574 0.205 0.238 9.00E-05 0.084 1.57 (1.09-2.26) 0.015

rs17043088 12 76103718 0.104 -0.238 8.88E-05 0.026 2.21 (1.16-4.22) 0.016

rs753921 c

4 35579894 0.231 0.239 8.69E-05 0.274 1.31 (1.05-1.64) 0.018

rs7618373 3 177819713 0.446 -0.255 3.17E-05 0.378 1.27 (1.02-1.57) 0.03

rs5916072 23 5406196 0.343 0.247 5.04E-05 0.442 0.84 (0.70-1.00) 0.046

rs10517364 c

4 35562853 0.246 0.258 2.12E-05 0.31 1.23 (1.00-1.52) 0.05

Table 2. The 19 SNPs with the lowest unadjusted P values that were associated with the overall survival of lung

cancer patients treated with platinum compounds.

SNP Chr. SNP position

LCL GWAS

a R values represent correlation coefficients for the associations between SNPs and cisplatin IC50.

b HR, hazard ratios; if HR > 1, patients carrying the minor allele had worse overall survival.

c SNP showed a concordant association with the IC50 and OS phenotype.

Clinical Study

Among NSCLC patients

Among SCLC patients




Gene SNP Distance from the

SNP (< 200 kb)

Expression associated

with the SNP in LCLs

R value P value

DAZAP2 rs11169748 Yes NAa NA

TFCP2 rs11169748 Yes NA NA

LETMD1 rs11169748 Yes NA NA

GALNT6 rs11169748 Yes NA NA

DAPK3 rs11169748 Yes 0.293 1.05E-06

EML3 rs11169748 Yes 0.31 2.22E-07

LRIG1 rs11169748 Yes 0.273 5.66E-06

NENF rs11169748 Yes 0.292 1.15E-06

RUFY1 rs11169748 Yes 0.301 4.92E-07

CCDC6 rs2440915 Yes NA NA

METTL6 rs2440915 Yes 0.267 9.64E-06

FMR1 rs5952066 Yes NA NA

ELOVL5 rs5952066 Yes 0.278 3.75E-06

METTL9 rs5952066 0.250b 3.57E-05

0.261 1.53E-05

ETV5 rs7620841 Yes NA NA

Table 3. Fifteen candidate genes selected for functional validation.

a The gene is located within 200 kb up or downstream of the SNP, and no cis-regulation was identified.

NA, no association between the gene and SNP.b Two probesets were associated with this SNP.

Basis for selection SNP vs. expression in LCLs

Yes




Published OnlineFirst July 20, 2011.Clin Cancer Res Xiang-Lin Tan, Ann M. Moyer, Brooke L. Fridley, et al. platinum-based chemotherapywith overall survival in lung cancer patients receiving Genetic variation predicting cisplatin cytotoxicity associated

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