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Genetic association with overall survival of taxane-treated lung cancer patients - a genome-wide association study in human lymphoblastoid cell lines followed by a clinical association study

  • Nifang Niu1,
  • Daniel J Schaid2,
  • Ryan P Abo3,
  • Krishna Kalari2,
  • Brooke L Fridley2,
  • Qiping Feng4,
  • Gregory Jenkins2,
  • Anthony Batzler2,
  • Abra G Brisbin2,
  • Julie M Cunningham5,
  • Liang Li1,
  • Zhifu Sun6,
  • Ping Yang6 and
  • Liewei Wang1Email author
BMC Cancer201212:422

DOI: 10.1186/1471-2407-12-422

Received: 10 April 2012

Accepted: 30 June 2012

Published: 24 September 2012

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.

Methods

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.

Results

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.

Conclusions

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.

Keywords

Taxane Genome-wide association Lymphoblastoid cell line Lung cancer Overall survival

Background

Taxanes are an important class of chemotherapeutic agents that disrupt the dynamics of microtubules by enhancing tubulin assembly and inhibiting depolymerisation [1]. Two taxanes, paclitaxel (Taxol®) and docetaxel (Taxotere®), are widely used for a broad spectrum of cancers, including lung cancer, one of the most common cancer types and the leading cause of cancer mortality in the US in 2012 [2, 3]. However, as a first-line therapy for non-small cell lung cancer (NSCLC) and a second-line therapy for small cell lung cancer (SCLC) [4, 5], large inter-individual variations have been observed in response to taxane therapy, in both efficacy and toxicity. One major side effect of taxanes, especially paclitaxel, is peripheral neuropathy, which limits dose escalation for optimal treatment with taxanes in the clinic [6]. Response rates for a single treatment with paclitaxel in patients with advanced NSCLC or extensive stage of SCLC are 24% and 34%, respectively [7]. Overall response rates for taxane-platinum combination treatment were 17-32%, and the incidence of grade 3/4 peripheral neuropathy was 1-13% in advanced NSCLC [8].

A great deal of effort has been devoted to the identification of biomarkers for response to these agents. Genetic polymorphisms in CYP3A4, ABCB1, ERCC1, ERCC2, and XPD1 were found to be associated with inter-individual differences in taxane response in NSCLC patients [911], while other variants in CYP2C8, CYP3A5 and ABCB1 were related to variability in taxane-mediated neurotoxicity [12, 13]. These observations may relate to the effect of genetic polymorphisms on the alteration of either taxane pharmacokinetic or pharmacodynamic profiles through influence on gene expression or enzyme activities [14, 15]. In addition, a genome-wide linkage study using 427 lymphoblastoid cell lines (LCLs) from 38 Centre d’Etude du Polymorphisme Humain (CEPH) reference pedigrees identified two loci, 5q11-21 and 9q13-22, associated with docetaxel-induced cytotoxicity [16]. Another study using breast cancer cell lines showed that increasing ABCC3 expression was highly associated with paclitaxel resistance [17]. Recently, a clinical GWAS with 1040 patients treated with paclitaxel identified 3 SNPs located in the EPHA5, FGD4 and NDRG1 genes that were associated with peripheral neuropathy [18]. All of these results suggest that genetic variation plays an important role in inter-individual variation in taxane response.

In the present study, we tested the hypothesis that genetic variation may contribute to inter-individual variation in overall survival of lung cancer patients treated with paclitaxel-based therapy. As a first step to identify additional novel quantitative trait loci (QTL) contributing to taxane response, we performed pharmacogenomic studies with both paclitaxel and docetaxel using a genome-wide association (GWA) approach with 276 LCLs, a cell line model system that has been used successfully in many previous pharmacogenomic studies to identify genetic variation related to drug or radiation response phenotypes [1921]. We then genotyped 874 Caucasian lung cancer patients (76 SCLC and 798 NSCLC) for the 170 most significant candidate SNPs identified during the association studies with the 276 LCLs. Eight SNPs were found to be consistently associated with both paclitaxel IC50 in LCLs and overall survival in SCLC or NSCLC patients. Finally, 11 candidate genes, located within 200 kb up-/downstream of those 8 SNPs, were subjected to functional validation in lung cancer cell lines by using siRNA screening and MTS assays (Figure 1). In addition, we also performed SNP-expression association analysis and integrated SNP-miRNA-expression association analysis using those 8 SNPs, expression of 11 candidate genes and 226 miRNAs from LCLs.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2407-12-422/MediaObjects/12885_2012_Article_3659_Fig1_HTML.jpg
Figure 1

Schematic diagram of the experimental strategy. Genome-wide association studies were performed for paclitaxel or docetaxel IC50 using 1.3 million SNPs. 147 SNPs were associated with paclitaxel IC50 with p-values < 10-4 and 76 SNPs were associated with IC50 values for both taxanes with p-values < 10-3. Those SNPs were genotyped in 874 lung cancer patients treated with paclitaxel-based chemotherapy. 8 SNPs were found to be consistently associated with both paclitaxel IC50 in LCLs and lung cancer overall survival. Eleven genes which were close to those 8 SNPs were functionally validated using lung cancer cell lines.

Methods

Cell lines

As described in our previous publication [21], EBV-transformed LCLs from 96 African-American (AA), 96 Caucasian-American (CA), and 96 Han Chinese-American (HCA) unrelated subjects (sample sets HD100AA, HD100CAU, HD100CHI) were purchased from the Coriell Cell Repository (Camden, NJ). These samples had been anonymized by NIGMS, and all subjects had provided written consent for their experimental use. This study was reviewed and approved by Mayo Clinic Institutional Review Board. Human SCLC cell line H196 and NSCLC cell line A549 were obtained from the American Type Culture Collection (Manassas, VA). LCLs were cultured in RPMI 1640 medium (Mediatech, Manassas, VA) supplemented with 15% heat-inactivated Fetal Bovine Serum (FBS) (Mediatech). H196 and A549 cell lines were cultured in RPMI 1640 medium containing 10% FBS.

Lung cancer patient samples

A total of 874 lung cancer patients treated with taxane-based therapy, including 76 SCLC and 798 NSCLC, were identified and enrolled between 1997 and 2008 at the Mayo Clinic (Rochester, MN). Details regarding clinical characteristics of these patients, patient enrollment, and data collection procedures were described previously [22, 23]. Briefly, each case was identified through the Mayo Clinic pathologic diagnostic (Co-Path) system. After obtaining written informed consent, blood samples were collected from patients. The characteristics of patients were abstracted from the medical record, including demographics, lung cancer pathology, anatomic site, and types and timing of treatment and chemotherapeutic agents. The clinical staging and recurrence or progression data were determined by results from available chest radiography, computerized tomography, bone scans, position emission tomography scans, and magnetic resonance 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. These research protocols were also approved by the Mayo Clinic Institutional Review Board.

In addition to paclitaxel, many patients were also treated with radiation therapy and/or surgery, as well as 4 other classes of anticancer drugs: platinum agents, gemcitabine, EGFR inhibitors and etoposide. Detailed information is listed in Table 1.
Table 1

Clinical characteristics of 874 lung cancer patients treated with paclitaxel-based chemotherapy

Description of patients who received taxane-based chemotherapy

 

Total (N = 874)

Age at Diagnosis

 

 Mean (SD)

61.8 (10.4)

 Median (range)

63.0 (34.0-86.0)

Gender

 

 Female

392 (44.9%)

 Male

482 (55.1%)

Cigarette smoking status

 

 Never smokers

158 (18.1%)

 Former smokers

428 (49%)

 Current smokers

273 (31.2%)

 Some smokers

15 (1.7%)

Stage

 

 SCLC

 

 Limited

37 (4.2%)

 Extensive

39 (4.5%)

 NSCLC

 

 Stage I and II

153 (17.5%)

 Stage III

328 (37.5%)

 Stage IV

317 (36.3%)

Histologic cell type

 

 Adenocarcinoma/bronchioloalveolar carcinoma

465 (53.2%)

 Squamous cell carcinoma

162 (18.5%)

 Small cell carcinoma

76 (8.5%)

 Large cell carcinoma

28 (3.2%)

 Mixed and unspecified NSCLC

131 (15.0%)

 Others

12 (1.4%)

Tumor differentiation grade

 

 Well differentiated

76 (8.7%)

 Moderately differentiated

332 (38.0%)

 Poor/undifferentiated

395 (45.2%)

 Nongradable or unknown

71 (8.1%)

Surgery, chemotherapy, radiation

 

 Chemotherapy

238 (27.2%)

 Surgery & chemotherapy

157 (18%)

 Radiation & chemotherapy

311 (35.6%)

 Surgery & radiation & chemotherapy

168 (19.2%)

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.” Abbreviations: SCLC, small cell lung cancer, NSCLC, non-small cell lung cancer.

Cell proliferation assay

Paclitaxel and docetaxel were purchased from Sigma-Aldrich (Milwaukee, WI). Drugs were dissolved in DMSO and aliquots of stock solutions were frozen at −80°C. Cell proliferation assays were performed in triplicate at each drug concentration. Specifically, 90 μl of cells (5 × 105 cells/ml) were plated into each well of 96-well plates (Corning, Lowell, MA) [19] and were treated with 10 μl of paclitaxel or docetaxel at final concentrations of 0, 0.1, 1, 10, 15, 20, 100, 1000, 5000 nmol/L for paclitaxel and 0, 0.1, 1, 5, 7.5, 15, 100, 1000, 10000 nmol/L for docetaxel. 72 hours later, 20 μl of CellTiter 96 AQueous Non-Radioactive Cell Proliferation Assay solution (Promega Corporation, Madison, WI) were added to each well and incubated for an additional 3 hours. Plates were then read in a Safire2 microplate reader (Tecan AG, Switzerland). Experiments were successfully performed for 276 LCLs (93 AA, 87 CA and 96 HCA). The cytotoxicity assays for the lung cancer cell lines were conducted in a similar fashion except paclitaxel was added after the cells were incubated overnight. The final concentrations of paclitaxel were 0, 0.1, 1, 10, 25, 50, 100, 1000, 5000 nmol/L.

Genome-wide SNPs in LCLs

Illumina HumanHap 550 K and 510S BeadArrays, containing 561,298 and 493,750 SNPs respectively, were used to genotype DNA samples from the LCLs in the Genotype Shared Resource (GSR) at Mayo Clinic, Rochester, MN. Publicly available Affymetrix SNP Array 6.0 Chip SNP data were also obtained for the same cell lines, which assayed 643,600 SNPs not covered on the Illumina BeadChips. The genotyping data were used in our previous studies [20, 21] and are public available from NCBI Gene Expression Omnibus under SuperSeries accession No. GSE24277. SNPs that deviated from Hardy-Weinberg Equilibrium (HWE) based on the minimum p-value from an exact test for HWE [24] and the stratified test for HWE [25] (p-values < 0.001); SNPs with call rates < 95%; or SNPs with minor allele frequencies (MAFs) < 5% were removed from the analysis.

Expression array assays in LCLs

Total RNA was extracted from each of the cell lines using Qiagen RNeasy Mini kits (QIAGEN, Inc.). RNA quality was tested using an Agilent 2100 Bioanalyzer, followed by hybridization to Affymetrix U133 Plus 2.0 Gene-Chips. The expression array data was used in our previous studies [1921] and is public available from NCBI Gene Expression Omnibus under SuperSeries accession no. GSE24277 and accession No. GSE23120.

MiRNA array assays in LCLs

Total RNA including miRNA from each LCL was extracted using mirVanaTM miRNA isolation kit (Ambion, Austin, TX). RNA quality was measured using RiboGreen® RNA Quantitation Kit (Molecular Probes, Eugene, OR) in an Agilent 2100 Bioanalyzer. Like described before [26], miRNA array assay was performed using Illumina’s human miRNA BeadArray according to the workflow on Illumina website. Briefly, total RNA were polyadenylated and converted to cDNA using a biotinylated oligo-dT primer with a universal PCR sequenced at its 5' end, followed by the annealing and extension of miRNA-specific oligonucleotide pool (MSO), which consists of a universal PCR priming site at the 5' end, an address sequence complementary to a capture sequence on the BeadArray and a microRNA-specific sequence at the 3' end. Then cDNA was amplified and subsequently hybridized to Illumina Sentrix Array Matrix (SAM)-Bead microarray chips. The SAMs were imaged using an Illumina BeadArray Reader, and microarray data were processed and analyzed using Illumina BeadStudio version 3.1.1. Probe-samples with a signal that was significantly higher than the background detection level were retained (at a significance level of 0.01). Probes with missingness ≥ 80% and individuals with missingness ≥ 50% were removed. The log2 expression levels were adjusted for an observed plate effect; there was no evidence of differential expression by ethnicity. Probes which had expression levels with standard deviation < 0.40 were deemed insufficiently variable to be informative, and potentially reflected only a background level of intensity. These were removed, leaving a final set of 226 probes and 282 individuals.

Genotyping in lung cancer patients

The 170 top SNPs selected from our taxane GWAS in LCLs were used to genotype 874 lung cancer patient DNA samples using a custom-designed Illumina Golden Gate platform at Mayo Clinic, Rochester, MN. The concordance rate among three genomic control DNA samples (CEPH family trio, Coriell Institute) present in duplicate on each 96 well plate was 100%. After removing the subjects with call rates < 90%, SNPs with call rates < 95% and monomorphic SNPs, 153 SNPs (90%) were used for the analysis.

Transient transfection and RNA interference

siRNA pools for candidate genes and negative control were purchased from Dharmacon (Chicago, IL). Reverse transfection of siRNA was performed in 96-well plates with a mixture of either non-small cell lung cancer, A549 cells, or small cell lung cancer, H196 cells and 0.3 μL of lipofectamineTM RNAi-MAX reagent (Invitrogen), as well as 30 nmol/L siRNA pools.

Real-time quantitative reverse transcription-PCR (qRT-PCR)

Total RNA was isolated from cultured cells transfected with negative control or specific siRNA pools using Quick-RNATM MiniPrep kit (Zymo Research, Orange, CA), followed by qRT-PCR performed with the Power SYBR® Green RNA-to-CT TM 1-Step Kit (AB Foster CA). Specifically, primers purchased from QIAGEN were used to perform qRT-PCR using the Stratagene Mx3005P Real-Time PCR detection system (Stratagene). All experiments were performed with beta-actin as an internal control.

Statistical methods

Genome-wide analysis in LCLs

The taxane cytotoxicity phenotype IC50, indicating the drug concentration which inhibits half of maximal cell growth, was calculated based on the Brain–Cousen model [27, 28] using the R package “drc” for each individual cell line for each drug separately. As described previously [21], prior to association the SNPs and IC50 values, the Van der Waerden (rank) transformed IC50 and SNPs were adjusted for gender, race and population stratification. To perform the SNP and mRNA gene expression associations, the mRNA expression array data were normalized using GCRMA, log2 transformed, and adjusted for gender, race, population stratification, and batch effect [29]. For miRNA and mRNA gene expression analyses, the normalized, log2 transformed mRNA expression array data were only adjusted for gender, race, and batch, while the miRNA expression array data were transformed using a Van der Waerden (rank) transformation and adjusted for gender, race and batch. The miRNA and SNP associations used genotype and Van der Waerden (rank) transformed miRNA expression data that were both adjusted for gender, race and population stratification.

To quantify the association of the adjusted IC50 phenotype with genome-wide SNPs (imputed and genotyped), Pearson correlations were calculated with adjusted SNPs. Likewise, the associations of SNPs with mRNA expression, SNPs with miRNA expression, as well as miRNA with mRNA expression were quantified by Pearson correlations using adjusted SNPs, mRNA and miRNA expression. SNPs in regions of interest were imputed with MACH v1.0 [30] using the HapMap Release 22 (Phase II) phased haplotype data as the reference. Specifically, SNPs for AA were imputed using both CEU (Utah residents with Northern and Western European ancestry from the CEPH collection) and YRI (Yoruba in Ibadan, Nigeria) data, SNPs for CA were imputed based on CEU data, and SNPs for HCA were imputed based on the CHB (Han Chinese in Beijing, China) and JPT (Japanese in Tokyo, Japan) data.

Clinical lung cancer patient analysis

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. To test for the effect of SNP genotypes on overall survival, we used the Cox regression model that included the effects of a SNP genotype dosage (count of minor allele). A total of 153 SNPs were included in this analysis, and the association was performed for NSCLC and SCLC separately because of the significant differences between the two diseases. To correct for multiple testing of the 153 SNPs in the two lung cancer subsets (a total of 306 tests), a Bonferroni corrected p-value threshold of 0.0001 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 performed. The disease stage was included in the final multivariate Cox-regression model as it was significantly associated with the overall survival of lung cancer patients. The disease stage was divided into five categories: small cell lung cancer with stages limited versus extensive; NSCLC with stages I + II, versus III versus IV. Since the effect of the SNPs on overall survival might be influenced by histologic subtypes among NSCLC patients, the association of three major histologic cell types (Adenocarcinoma/bronchioloalveolar carcinoma, squamous cell carcinoma and mixed and unspecified NSCLC) with overall survival was also tested with adjustment of disease stage and no significant association was found (p-value = 0.86). We used 0.05 as a cutoff for p-values (not adjusted for multiple testing) to select SNPs/genes for further functional validation.

Results

Paclitaxel and docetaxel cytotoxicity in LCLs

As both taxanes are used in clinical practice and share common mechanisms of action, cytotoxicity assays were performed for both drugs to determine the range of variation in individual drug response. We used IC50 as a phenotype to indicate the drug sensitivity for each cell line. The range of IC50 values for paclitaxel and docetaxel were 3.98-21.36 nmol/L and 1.54-13.32 nmol/L, respectively, and the median values were 9.35 nmol/L and 4.29 nmol/L. There was no evidence of differences in IC50 between genders (p = 0.82, 0.71) or races (p = 0.45, 0.14) in the paclitaxel and docetaxel experiments, respectively.

Genome-wide SNP associations with IC50 values for two taxanes

As described previously [21], after the quality control of all SNPs genotyped with the Illumina HumanHap 550 K, 510S BeadChips and Affymetrix SNP Array 6.0 Chip, approximately 1.3 million SNPs were used for the association analyses between genome-wide SNPs and IC50 values for paclitaxel and docetaxel to identify SNPs that might contribute to variation in drug cytotoxicity phenotypes. As shown in Figure 2A-B and Additional file 1: Tables S1–S2, none of the SNPs remained significant after Bonferroni correction. The most significant SNPs associated with paclitaxel or docetaxel IC50, rs10521792 and rs6044112, had p-values of 2.04 × 10-7 and 6.90 × 10-7, respectively. The rs10521792 SNP is 300 kb upstream from the 5-end of the FGF13 gene and the rs6044112 SNP is within an intron of C20orf23. For paclitaxel, 147 SNPs within or near 88 unique genes had p-values < 10-4 for association with IC50, while docetaxel had 180 SNPs within 102 unique genes meeting these criteria. One thousand and fifteen and 1736 SNPs had association p-values < 10-3 for paclitaxel and docetaxel, respectively (Additional file 1: Tables S1–S2, Figure 2C). As paclitaxel and docetaxel belong to the same class of antimicrotubule agents, we also compared the set of SNPs with p-value < 10-3 between these two drugs, of which 76 SNPs in 55 genes were in common between the top set of SNPs for both drugs (Additional file 1: Table S3, Figure 2C).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2407-12-422/MediaObjects/12885_2012_Article_3659_Fig2_HTML.jpg
Figure 2

1.3 million genome-wide SNPs associations with taxane IC50s in LCLs. (A and B) Genome-wide SNP association with paclitaxel (A) or docetaxel IC50 values (B). The y-axis represents -log10(p-values) for the association of each SNP with paclitaxel/docetaxel IC50 values. SNPs are plotted on the x-axis based on their chromosomal locations. A p-value of 10-4 is highlighted with a red line. (C) The number of overlapping SNPs associated with IC50s for both paclitaxel and docetaxel with p-value < 10-3.

Association study for lung cancer patients treated with taxane-based therapy

Taxanes are one of the most commonly used chemotherapeutic agents in the treatment of lung cancer patients, either alone or in combination with other anticancer drugs. We wanted to determine whether the top candidate SNPs identified during our GWAS using the cell line model system might be associated with overall survival of patients treated with taxanes. We took advantage of 874 germline DNA samples collected from lung cancer patients treated with paclitaxel at Mayo Clinic, including 76 SCLC and 798 NSCLC with well characterized phenotypes, to test this hypothesis. Detailed patient characteristics are described in Table 1.

Since almost all of the 874 lung cancer patient included in the study were treated with paclitaxel, we selected the top SNPs identified from our paclitaxel GWAS in LCLs for genotyping in those 874 lung cancer patients, including 147 SNPs associated with paclitaxel IC50 with p-value < 10-4 and 76 “overlapping SNPs” associated with both taxane IC50s with p-value <10-3 (Figure 1). After removing SNPs with lower Illumina design scores and SNPs with absolute linkage disequilibrium (r2 = 1), 170 SNPs were genotyped using the Illumina Golden Gate platform. For quality control, we excluded SNPs with low call rate and monomorphic SNPs, which resulted in 153 SNPs being analyzed (Additional file 1: Table S4). As shown in Table 2, the Cox regression analysis indicated that 11 SNPs were associated with SCLC or NSCLC overall survival with p-value < 0.05, although none of them were statistically significant after Bonferroni correction. The most significant SNPs, rs1106697 and rs11079337, were associated with SCLC or NSCLC overall survival with p-values of 0.007. SNP rs1106697, which was located on chromosome 7 and with a MAF of 0.106, was associated with overall survival in both NSCLC (p = 0.016, HR = 1.237) and SCLC (p = 0.007, HR = 1.875) patients. The hazard ratio (HR) > 1 means that patients carrying the minor allele had poor survival. In other words, the same SNP would be expected to be associated with higher IC50 values in our LCLs, and the cells carrying this SNP would be expected to be more resistant to paclitaxel. Therefore, we compared the association direction for clinical overall survival with that of the LCL results. We found that 8 out of 11 SNPs showed concordant association directions between the two phenotypes (Table 2).
Table 2

8 SNPs associated with paclitaxel response in both LCLs and lung cancer patients

SNP

Lung cancer patients

LCL

Chr

Position

Gene symbol

Location

Location relative to gene (bp)

 

NSCLC

SCLC

MAF

Paclitaxel

Docetaxel

MAF

     
 

P value

HR

P value

HR

 

P value

R value

P value

R value

      

rs7519667

---

---

0.028

0.543

0.178

2.71E-05

−0.261

---

---

0.308

1

239,951,930

WDR64

intron

−1,292

rs10193067

---

---

0.039

0.372

0.043

3.61E-05

0.254

---

---

0.085

2

52,543,917

ASB3

flanking_3UTR

−1,206,705

rs2700868

0.028

1.193

---

---

0.157

6.56E-05

0.248

---

---

0.260

3

183,922,829

ATP11B

upstream

71,156

rs2662411

---

---

0.039

0.666

0.417

6.36E-05

−0.246

---

---

0.333

5

10,186,704

FAM173B

downstream

92,734

rs1106697

0.016

1.237

0.007

1.875

0.106

7.00E-05

0.245

---

---

0.092

7

155,365,705

SHH

upstream

67,977

rs1778335

---

---

0.019

1.602

0.308

6.04E-05

0.248

---

---

0.211

10

22,972,154

PIP4K2A

intron

0

rs11629576

---

---

0.011

0.637

0.447

6.-0577E

0.245

---

---

0.466

15

76,294,371

ACSBG1

intron

−6,872

rs11079337

0.007

1.168

---

---

0.366

5.10E-04

−0.215

7.85E-04

−0.208

0.338

17

53,517,495

DYNLL2

intron

−1,452

rs17079623

0.016

1.234

---

---

0.122

6.94E-04

0.210

3.32E-04

0.222

0.190

18

64,544,220

TXNDC10

flanking_5UTR

−10,887

rs7260598

0.020

0.821

---

---

0.162

6.27E-06

−0.277

2.46E-05

−0.259

0.165

19

24,014,626

ZNF254

upstream

47,190

rs17304569

0.019

0.818

---

---

0.161

7.76E-05

−0.243

6.74E-05

−0.245

0.163

19

24,032,745

ZNF254

upstream

29,071

11 of 153 SNPs were associated with SCLC or NSCLC overall survival with p-value < 0.05. The hazard ratio (HR) > 1 means that patients carrying the minor allele had poor survival. In other words, the same SNP would be expected to be associated with higher IC50 values in our LCLs, i.e., r-value > 0. After comparing the association direction for clinical overall survival with that of the LCL results, 8 out of 11 SNPs showed concordant association directions between the two phenotypes. The 3 SNPs showed opposite association directions were rs10193067, rs11629576 and rs11079337.

Follow-up analyses

Imputation analysis in LCLs

In order to identify the causal SNPs or additional SNPs that were in strong linkage with the causal SNPs contributing to paclitaxel response, we imputed SNPs based on HapMap data using the genotyping results of LCLs for a region containing 200 kb up-/downstream of the 8 SNPs that were consistently associated with both paclitaxel IC50 in LCLs and overall survival of lung cancer patients. As shown in Additional file 1: Figure S1, imputed SNPs were found to have p-values of association with paclitaxel IC50 < 10-3 in the region 200 kb up-/downstream of SNPs rs1778335, rs2662411, rs7260598, rs17304569 and rs7519667. However, none of the imputed SNPs showed a stronger association with paclitaxel IC50 than did the observed SNPs.

SNP-expression association analysis in LCLs

SNPs might influence paclitaxel response through the regulation of gene expression in a cis-regulation manner. Therefore, we limited our SNP-Expression analyses to 11 genes which encompassed the 8 SNPs of interest noted previously by being within 200 kb of the location of the genes. We further excluded expression probes that were deemed not to be expressed, defined as an expression level of less than 50. None of the SNPs which had p-values for association with paclitaxel IC50 of < 10-3 were found to be associated with this set (possibly cis regulating) of expression probes (data not show).

Integrated SNP-miRNA-mRNA expression association analysis in LCLs

In addition to the direct effect of SNP on gene expression, SNP might alter gene expression through influence on miRNA expression [31]. We further performed the integrated SNP-miRNA-expression association analysis using these 8 SNPs, expression of 11 genes and 226 microRNAs. SNP rs2662411, close to gene CMBL, was associated with miRNA expression of hsa-miR-584 with p-value = 3.05 × 10-5 (r-value = 0.254). The hsa-miR-584 was also associated with CMBL mRNA levels with p-value = 7.46 × 10-4 (r-value = 0.301). Similarly, SNP rs1778335, close to gene PIP4K2A, was associated with the expression of hsa-miR-1468 with p-value = 1.57 × 10-3 (r-value = 0.194), and this microRNA was associated with mRNA expression of PIP4K2A with p-value = 8.24 × 10-3 (r-value = −0.267).

SiRNA screening in lung cancer cell lines

As shown in Table 1, except for paclitaxel, these 874 lung cancer patients were also treated with one or several of the following drugs: platinum compounds, gemcitabine, EGFR inhibitors or etoposide. Although the genotyped SNPs were selected based on their association with taxane IC50 values in LCLs, the SNP effects on lung cancer overall survival might be influenced by other treatments. To further validate the association results, we also investigated mechanisms by which those 8 SNPs might have an effect on paclitaxel response. One of the mechanisms by which SNPs might affect phenotypes is through their influences on transcription regulation in either a cis or a trans- manner. Unfortunately, we did not have enough power to assess the trans-regulation. Although none of the 8 SNPs showed a significant cis effect, there could also be other SNPs in LD with these 8 SNPs that we did not genotype or SNPs with low allele frequencies that could. Therefore, we tested the possible effect of the 11 genes close to those 8 SNPs on drug response by performing knockdown experiments in a SCLC cell line, H196, and a NSCLC cell line, A549, to determine if changing gene expression could influence paclitaxel-induced cytotoxicity. As shown in Figure 3 and Table 3, MTS assay indicated that knockdown of PIP4K2A, CCT5, CMBL, EXO1, KMO and OPN3, which were close to SNPs rs1778335, rs2662411 and rs7519667, significantly desensitized paclitaxel-induced cytotoxicity in the SCLC cell line H196, and those 3 SNPs were also associated with SCLC overall survival with p-value < 0.05 (Table 2). In addition, in the NSCLC cell line, A549, knockdown of the genes, CHML and KMO, which were close to rs7519667, also had a significant effect on paclitaxel cytotoxicity.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2407-12-422/MediaObjects/12885_2012_Article_3659_Fig3_HTML.jpg
Figure 3

siRNA screening of candidate genes by MTS assay in lung cancer cell lines. Data are shown for 7 of the 11 candidate genes that were functionally validated in a SCLC cell line, H196, and an NSCLC cell line, A549, by MTS assay after knockdown with specific siRNA pools. (Red) Data for negative control siRNA; (blue) data for specific siRNAs. “Significance” was defined as a gene with a significant change in apparent area under the curve (AUC) in comparison with control siRNA as indicated by the p-values. For those genes with significant change in paclitaxel response, at least three independent experiments were performed in triplicate. Error bar represents standard error of the mean (SEM) for all of the experiments. (A) Candidate gene symbols. (B) qRT-PCR. The y-axis indicates relative gene expression after siRNA knockdown when compared with negative control siRNA. (C) MTS assays. The x-axis indicates the log transformed paclitaxel dose, and the y-axis indicates proportion survival after drug treatment.

Table 3

siRNA screening of 11 candidate genes by MTS assay

SNP

Basis for selection

 

MTS assay

 

SNP vs paclitaxel and/or docetaxel IC50 in LCLs

SNP vs survival in SCLC patients

SNP vs survival in NSCLC patients

Gene symbol

SCLC

NSCLC

 

(p < 10-3)

(p < 0.05)

(p < 0.05)

 

H196

A549

rs7519667

Yes

Yes

 

CHML

---

Yes

    

EXO1

Yes

---

    

KMO

Yes

Yes

    

OPN3

Yes

---

rs2700868

Yes

 

Yes

ATP11B

---

---

rs2662411

Yes

Yes

 

CCT5

Yes

---

    

CMBL

Yes

---

rs1106697

Yes

Yes

Yes

RBM33

---

---

rs1778335

Yes

Yes

 

PIP4K2A

Yes

---

rs17079623

Yes

 

Yes

TMX3

---

---

rs17304569 & rs7260598

Yes

 

Yes

ZNF254

---

---

“Yes” under “Basis for selection” indicates individual candidate genes with the p-value listed. For the MTS assay, “Yes” indicates that knockdown of the gene altered taxane cytotoxicity when compared with control siRNA. All of the experiments with significant changes were performed in triplicate and were replicated at least three times.

Discussion

Taxanes, including paclitaxel and docetaxel, are microtubule-stabilizing anticancer agents commonly used in the treatment of SCLC and NSCLC. Large inter-individual variation in taxane response has been observed in lung cancer patients in both efficacy and toxicities associated with taxane, such as peripheral neuropathy [32, 33]. This large variation is caused by many different factors, including: tumor genetics, host genetics as well as the microenvironment [34]. Many previous studies have demonstrated that germline genetic polymorphisms can play a significant role in individual variability in taxane-induced efficacy and toxicity [913, 1518].

In order to understand biological mechanisms underlying the variation in response to taxane and to identify novel biomarkers which might be helpful for individualized taxane chemotherapy, we performed pharmacogenomic studies of paclitaxel and docetaxel in 276 LCLs, followed by association studies of candidate SNPs identified during the analysis in LCLs using DNA samples from NSCLC and SCLC patients treated with paclitaxel. We then performed functional studies of candidate genes by siRNA knockdown in lung cancer cell lines. In this study, we mainly focused on genes that might influence the mechanism of drug action, i.e., pharmacodynamics, as genes involved in taxane pharmacokinetic pathways, such as CYP2C8, CYP3A4/A5, which have been well studied in previous taxane pharmacogenomic studies, were not highly expressed in our LCLs.

Genome-wide analyses were performed using 1.3 million genome-wide SNPs and paclitaxel or docetaxel IC50 values for 276 LCLs. The analyses resulted in the identification of a series of candidate SNPs that were associated with cytotoxicity phenotypes for two taxanes (Figure 2 and Additional file 1: Tables S1–S3). Although none of the SNPs maintained statistical significance after Bonferroni correction, 147 and 180 SNPs had p-values for association with paclitaxel or docetaxel IC50 of < 10-4, and 76 SNPs overlapped between the two taxanes with p-values < 10-3. A previous GWAS from an ongoing phase III clinical trial, CALGB 40101, identified 3 top SNPs located in the EPHA5, FGD4 and NDRG1 genes that were associated with paclitaxel-induced peripheral neuropathy, although none reached genome-wide significance [18]. In our study, 3 SNPs located 200-300 kb upstream of EPHA5 genes and 1 SNP ~14 kb upstream of NDRG1 were also found to be associated with paclitaxel IC50 values with p-value < 10-3 in LCLs, but not with docetaxel IC50. Therefore, these SNPs were not included in the subsequent genotyping study with lung cancer patient samples based on our selection criteria.

Like all model systems, the LCL model system has limitations. The variation of taxane response in LCLs might be influenced by EBV transformation-induced cellular changes and non-genetic factors such as cell growth rate or baseline ATP levels [3537]. To further test whether any of these candidate SNPs might be associated with overall survival for lung cancer patients treated with taxanes, we genotyped the 147 top SNPs associated with paclitaxel IC50 and 76 SNPs which overlapped between the two taxanes in LCLs using DNA samples from 76 SCLC and 798 NSCLC patients after paclitaxel-based chemotherapy. In this study, instead of specific taxane response outcomes, overall survival was used as the clinical phenotype. Therefore, the association results could be affected by many other factors, such as histology, stage, performance and treatment. In order to adjust for those confounding factors, Cox regression analysis was performed to test the effect of clinical covariates on overall survival, including age at diagnosis, gender, smoking status, disease stage, and treatment. As a result, disease stage was included in the final multivariate Cox-regression model as it was significantly associated with the overall survival of lung cancer patients. That association study identified 8 SNPs that were associated with SCLC or NSCLC overall survival with p-values < 0.05, although none of the SNPs were statistically significant after Bonferroni correction (Table 2). The statistical power for association with overall survival of SCLC patients was low. Therefore, we did functional studies by using siRNA knockdown, followed by MTS assays, in a SCLC cell line, H196, and a NSCLC cell line, A549, for 11 candidate genes chosen based on their proximity to the 8 SNPs and their expression levels in LCLs. Knockdown of PIP4K2A, CCT5, CMBL, EXO1, KMO and OPN3, 6 genes that were close to the 3 SNPs (rs1778335, rs2662411 and rs7519667) associated with SCLC overall survival, significantly desensitized H196 cells to paclitaxel (Figure 3). Knockdown of CHML and KMO, two genes that were close to rs7519667, also had a significant effect on paclitaxel response in A549 cells (Figure 3). Integrated SNP-miRNA-mRNA expression association analysis indicated that SNPs rs2662411 and rs1778335 were associated with mRNA expression of CMBL or PIP4K2A through hsa-miR-584 or hsa-miR-1468. In our study, SNP rs2662411 was associated with higher miRNA expression of hsa-miR-584 (r-value = 0.254, p-value = 3.05 × 10-5), which was associated with higher mRNA expression of CMBL (r-value = 0.301, p-value = 7.46 × 10-4); in SCLC cell line, knockdown of CMBL caused paclitaxel resistance; those results were consistent with the association of SNP rs2662411 with lower paclitaxel IC50 in LCLs (r-value = −0.245, p-value = 6.36 × 10-5) and better overall survival in SCLC patients (HR = 0.666, p-value = 0.039) (Table 1). Similarly, in LCLs SNP rs1778335 was associated with higher expression of hsa-miR-1468 (r-value = 0.194, p-value = 1.57 × 10-3), which was associated with lower expression of PIP4K2A (r-value = −0.267, p-value = 8.24 × 10-3); in SCLC cell line knockdown of PIP4K2A resulted in paclitaxel resistance; those results were consistent with the association of SNP rs1778335 with higher paclitaxel IC50 in LCLs (r-value = 0.248, p-value = 6.04 × 10-5) and worse overall survival in SCLC patients (HR = 1.602, p-value = 0.019). However, no corresponding miRNA binding sites was found in either CMBL or PIP4K2A from microRNA public database, future experiment will be performed to validate these results.

Previous studies indicated that an individual miRNA could affect expression of multiple genes and an individual mRNA might also be regulated by multiple miRNAs, which was mainly through miRNA targeting 3 untranslated region (3-UTR) of mRNA [38]. SNPs located in the miRNA-coding genes or miRNA-binding site of mRNA could influence the pathogenesis of disease or drug response through affecting the biogenesis of miRNA or binding of miRNA with mRNA [31, 39]. SNP 829C > T in the 3UTR of dihydrofolate reductase (DHFR), which was located in the miR24 microRNA binding site, has been reported that it caused the loss of miR24 function and resulted in DHFR overexpression and methotrexate (MTX) resistance [40]. CMBL gene encoded carboxymethylenebutenolidase homolog (CMBL), which was a cysteine hydrolase of the dienelactone hydrolase family and was involved in the metabolism of prodrug olmesartan medoxomil [41]. The homology of CMBL protein among human, mouse and rat were more than 80%. In human CMBL was widely expressed in many tissues, especially in liver and intestine [41]. A proteomic study by Yang et al. found that CMBL was an H2AX-interacting protein [42], which suggested that CMBL might be involved in cellular responses to DNA damage and DNA repair.

PIP4K2A gene encoded phosphatidylinositol-5-phosphate 4-kinase, type II, alpha (PtdIns5P 4-kinase α). As a major type of type II PtdIns5P 4-kinases, it was involved in the conversion of phophatidylinositol-5-phosphate (PtdIns5P) into phosphatidylinosital-4,5-bisphosphate [PtdIns(4,5)P 2[43]. Since the cellular level of phophatidylinositol-4-phosphate (PtdIns4P), which was another source to form PtdIns(4,5)P 2, was approximately ten times higher than that of PtdIns5P, the major function of type II PtdIns5P 4-kinases was most probably to regulate the level of PtdIns5P[44, 45]. There were three mammalian isoforms for type II PtdIns5P 4-kinases: α, β and γ [46]. PtdIns5P 4-kinase α was located in both cytoplasm and nucleus, and could form homodimer or heterodimer with PtdIns5P 4-kinase β or γ [45, 47]. In vitro assays indicated that PtdIns5P 4-kinase α had the highest enzyme activity [43], and knockdown of PtdIns5P 4-kinase α significantly enhanced the tyrosine-kinase regulated PtdIns5P production [48]. Although no obvious phenotype was found for knockout of PtdIns5P 4-kinase α, the double knockout of PtdIns5P 4-kinase α and β was found to be embryonic lethal [43]. Several previous studies also demonstrated that PtdIns5P was a second messenger in cellular signaling, PtdIns5P could activate PI 3-kinase/Akt pathway [49] and protect Akt from dephosphorylation through inhibition of PP2A phosphatise [50], and in nucleus PtdIns5P could bind to inhibitor of growth protein-2 (ING2) and regulate p53-mediated response to DNA damage [51]. However, the mechanisms of those two genes involved in paclitaxel response still remain unknown, further mechanistic studies will be required.

For the other four genes (CCT5, EXO1, KMO and OPN3) that were close to the 2 SNPs (rs2662411 and rs7519667), our association analyses and knockdown experiments suggested a potential function in paclitaxel response. However, no significant cis relationship was found by either SNP-expression association analysis or integrated SNP-miRNA-mRNA expression association analysis. One possibility is that there might be rare variants in LD with those 2 SNPs that might be the causal SNPs regulating gene expression. In addition, the effect of SNP on gene expression was tissue specific. Therefore, future deep resequencing of these regions might help to identify rare variants to test this hypothesis, and we also need to perform the SNP-expression association analysis using lung cancer tissue samples.

Conclusions

In summary, our GWAS in LCLs, together with translational studies with DNA samples from lung cancer patients, followed by functional studies in lung cancer cell lines showed that 6 genes, PIP4K2A, CCT5, CMBL, EXO1, KMO and OPN3, genes that are close to 3 SNPs associated with SCLC overall survival (rs1778335, rs2662411 and rs7519667), significantly altered paclitaxel cytotoxicity in the SCLC cell line, H196. SNPs rs2662411 and rs1778335 might regulate mRNA expression of CMBL and PIP4K2A through influence on miRNA expression of hsa-miR-584 or hsa-miR-1468. These results provide additional insight into genes that may contribute to variation in response to taxanes and genetic variations that may be associated with overall survival of paclitaxel-treated lung cancer patients. We acknowledge that the patient population used in the association study is heterogenous and that our phenotype, overall survival, could be influenced by multiple factors other than the treatment. Although we adjusted for all the known factors during the association studies, we cannot exclude the possibility that the genetic variations identified might be prognostic factors rather than taxane predictive factors. Further confirmation of these findings using specific taxane response outcome in additional homogeneous patient cohorts would seem to be warranted.

Abbreviations

SNPs: 

Single nucleotide polymorphisms

GWAS: 

Genome-wide association study

LCLs: 

Lymphoblastoid cell lines

SCLC: 

Small cell lung cancer

NSCLC: 

Non-small cell lung cancer

miRNA: 

microRNA

CEPH: 

Centre d’Etude du Polymorhpisme Humain

QTL: 

Quantitative trait loci

GWA: 

Genome-wide association

AA: 

African-American

CA: 

Caucasian-American

HCA: 

Han Chinese-American

FBS: 

Fetal Bovine Serum

GSR: 

Genotype Shared Resource

HWE: 

Hardy-Weinberg Equilibrium

MAFs: 

Minor allele frequencies

MSO: 

miRNA-specific oligonucleotide pool

SAM: 

Sentrix Array Matrix

qRT-PCR: 

Real-time Quantitative Reverse Transcription-PCR

CEU: 

Utah residents with Northern and Western European ancestry from the CEPH collection

YRI: 

Yoruba in Ibadan, Nigeria

CHB: 

Han Chinese in Beijing, China

JPT: 

Japanese in Tokyo, Japan

HR: 

Hazard ratio

3’-UTR: 

3’ untranslated region

DHFR: 

Dihydrofolate reductase

MTX: 

Methotrexate

CMBL: 

Carboxymethylenebutenolidase homolog

PtdIns5P 4-kinase α: 

Phosphatidylinositol-5-phosphate 4-kinase, type II, alpha

PtdIns5P: 

Phophatidylinositol-5-phosphate

PtdIns(4: 

5)P2: Phosphatidylinosital-4,5-bisphosphate

PtdIns4P: 

Phophatidylinositol-4-phosphate

ING2: 

Inhibitor of growth protein-2

AUC: 

Area under the curve

SEM: 

Standard error of the mean.

Declarations

Acknowledgements

This work was supported by NIH grants K22 CA130828, R01 CA138461, U19 GM61388 (The Pharmacogenomics Research Network); R01 CA80127, R01 CA84354 and R01 CA105857. The Mayo Clinic Genotyping Shared Resource is supported by CA15083 (Mayo Comprehensive Cancer Center).

Authors’ Affiliations

(1)
Division of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic
(2)
Division of Biostatistics and Informatics, Department of Health Sciences Research, Mayo Clinic
(3)
Department of Biology, Massachusetts Institute of Technology
(4)
Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center
(5)
Department of Laboratory Medicine and Pathology, Mayo Clinic
(6)
Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic

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  52. Pre-publication history

    1. The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2407/12/422/prepub

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© Niu et al.; licensee BioMed Central Ltd. 2012

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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