Open Access

Discovery of genetic biomarkers contributing to variation in drug response of cytidine analogues using human lymphoblastoid cell lines

  • Liang Li1,
  • Brooke L Fridley2, 3,
  • Krishna Kalari2,
  • Nifang Niu1,
  • Gregory Jenkins2,
  • Anthony Batzler2,
  • Ryan P Abo1,
  • Daniel Schaid2 and
  • Liewei Wang2Email author
BMC Genomics201415:93

DOI: 10.1186/1471-2164-15-93

Received: 7 June 2013

Accepted: 10 January 2014

Published: 1 February 2014

Abstract

Background

Two cytidine analogues, gemcitabine and cytosine arabinoside (AraC), are widely used in the treatment of a variety of cancers with a large individual variation in response. To identify potential genetic biomarkers associated with response to these two drugs, we used a human lymphoblastoid cell line (LCL) model system with extensive genomic data, including 1.3 million SNPs and 54,000 basal expression probesets to perform genome-wide association studies (GWAS) with gemcitabine and AraC IC50 values.

Results

We identified 11 and 27 SNP loci significantly associated with gemcitabine and AraC IC50 values, respectively. Eleven candidate genes were functionally validated using siRNA knockdown approach in multiple cancer cell lines. We also characterized the potential mechanisms of genes by determining their influence on the activity of 10 cancer-related signaling pathways using reporter gene assays. Most SNPs regulated gene expression in a trans manner, except 7 SNPs in the PIGB gene that were significantly associated with both the expression of PIGB and gemcitabine cytotoxicity.

Conclusion

These results suggest that genetic variation might contribute to drug response via either cis- or trans- regulation of gene expression. GWAS analysis followed by functional pharmacogenomics studies might help identify novel biomarkers contributing to variation in response to these two drugs and enhance our understanding of underlying mechanisms of drug action.

Keywords

Cytidine analogues Gemcitabine Cytosine arabinoside Lymphoblastoid cell lines Expression array Genome-wide SNPs Genome-wide association study Functional genomics Translational research

Background

Both gemcitabine and AraC are widely used in the treatment of a variety of cancers and both display wide individual variation in drug response [16]. Pharmacogenomic studies have the potential to provide insight into mechanisms underlying individual variation in response to these two drugs [711]. Many previous pharmacogenetic studies focused on the bioactivation and metabolism pathways for cytidine analogues [12, 13]. For example, SNPs in genes encoding ribonucleotide reductase (RRM1) and cytidine deaminase (CDA) were found to be associated with gemcitabine chemosensitivity in the NCI-60 cell lines or with active gemcitabine metabolite plasma levels [1416]. Those findings provided the initial evidence that genetic variation might contribute to variation in cytidine analogue response. We previously used the “Human Variation Panel”, a genomic data-rich lymphoblastoid cell line model system, to identify markers that might contribute to variation in response to these two cytidine analogues [17, 18]. These cell lines have proven to be a powerful tool for both the identification of pharmacogenomic hypotheses and for the pursuit of hypotheses from the clinical GWAS [1921]. However, the earlier studies were performed with less dense SNP coverage, in the present study, we expanded our previous 550 K SNP data to include a total of 1.3 million SNPs obtained with both Illumina and Affymetrix SNP genotyping platforms in an attempt to identify additional genes or SNPs that might be associated with drug response. To follow-up the candidates, we performed functional studies using tumor cell lines in an attempt to determine possible underlying mechanisms that might help us to better understand mechanisms of action for these two drugs. The results of the comprehensive series of experiments described subsequently resulted in the identification of several novel SNPs and genes associated with gemcitabine and AraC drug response in these cell lines. These results could be tested in future clinical studies to determine whether they might help predict response to gemcitabine and AraC.

Methods

Cell lines

One hundred and seventy four human lymphoblastoid cell lines from 60 Caucasian-American (CA), 54 African-American (AA) and 60 Han Chinese-American (HCA) (sample sets HD100CAU, HD100AA, and HD100CHI) subjects were purchased from the Coriell Cell Repository (Camden, NJ). All of these cell lines had been obtained and anonymized by the National Institute of General Medical Sciences prior to deposit, and all subjects had provided written consent for the use of their DNA and cells for experimental purposes. Human SU86 pancreatic cancer cell lines were a gift from Dr. Daniel D. Billadeau (Department of Immunology and Division of Oncology Research, Mayo Clinic College of Medicine). Human breast cancer MDA-MB-231 and leukemia BDCM and THP-1 cell lines were purchased from the American Type Culture Collection (ATCC) (Manassas, VA) and were cultured in DMEM with 1% L-glutamine (Mediatech) supplemented with 10% FBS (Mediatech). Other cell lines were maintained in RPMI medium 1640 with 1% L-glutamine (Mediatech) supplemented with 10% FBS (Mediatech).

Drugs and cell proliferation assays

Gemcitabine was provided by Eli Lilly (Indianapolis, IN). AraC was purchased from Sigma-Aldrich (St. Louis, MO). Cytotoxicity assays with the lymphoblastoid and tumor cell lines were performed with the CellTiter 96® AQueous Non-Radioactive Cell Proliferation Assay (Promega Corporation, Madison, WI) as previously described [17]. IC50 values were calculated using a three or four parameter logistic model with the R package “drc” (http://cran.r-project.org/web/packages/drc/drc.pdf), as described previously [18].

SNP genotyping

In order to validate the imputation results, six top imputed SNPs (rs10447475, rs4621668, rs11215400, rs10926784, rs3196512 and rs7762319) were selected for genotyping using Applied Biosystems TaqMan technology (Life Technologies, Grand Island, NY). One SNP (rs7762319) was not genotyped because the assay failed functional test, and four of the remaining five SNPs were successfully genotyped. Among these four SNPs, rs11215400 was a pre-designed assay, while the remaining three SNPs (rs10447475, rs4621668 and rs10926784) were customized assays designed with Custom TaqMan® Assay Design Tool (Life Technologies, Grand Island, NY). Primer and probe sequences for these assays are available upon request. PCR protocols were followed according to the manufacturer’s guidelines for the 384-well format. PCR amplifications were performed using Applied Biosystems® TaqMan® Genotyping Master Mix with an Applied Biosystems® Veriti® 384-Well Thermal Cycler (Life Technologies, Grand Island, NY), and PCR products were analyzed on an Applied Biosystems 7900HT [22].

Transient transfection and RNA interference

Specific siGENOME siRNA SMARTpool® reagents against a given gene, as well as a negative control, siGENOME Non-Targeting siRNA Pool #2, were purchased from Dharmacon Inc. (Lafayette, CO). Human pancreatic cancer SU86 and breast cancer MDA-MB-231 cell lines were used to perform the siRNA knockdown studies. The lipofectamine RNAiMAX transfection reagent (Invitrogen, Carlsbad, CA) was used for siRNA reverse or forward transfection. Specifically, cells were seeded into 96-well plates and were mixed with siRNA-complex consisting of 20–50 nM of specific siGENOME siRNA SMARTpool or non-targeting negative control (Dharmacon) and the lipofectamine RNAiMAX transfection reagent. The human leukemia cell lines, BDCM and THP-1, were transfected with electroporation using the Nucleofector System with 500 nM of specific or negative siRNA (Lonza Inc., Basel, Switzerland).

Quantitative reverse transcription-PCR (QRT-PCR)

Total RNA was isolated from cultured cells with the Qiagen RNeasy kit (QIAGEN Inc. Valencia, 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 with the Stratagene Mx3005P™ Real-Time PCR detection system (Stratagene). All experiments were performed in triplicate with β-actin as an internal control. Reverse transcribed Universal Human reference RNA (Stratagene) was used to generate a standard curve. Control reactions lacked RNA template.

Caspase-3/7 activity assay

Caspase-3/7 activity was measured with the Caspase-Glo®3/7 Assay kit (Promega). Specifically, siRNA-transfected cells (100 μl) were seeded overnight into 96-well plates at a density of 10,000 cells per well and were then treated with DMSO or increasing concentrations of gemcitabine or AraC for 48 h. 100 μL of Caspase-Glo® 3/7 Reagent was then added to each well, and the cells were incubated at room temperature for 1 h, followed by the measurement of luminescence with a Safire2 microplate reader (Tecan Trading AG, Switzerland). The luminescent signal was proportional to caspase-3/7 activity and was used as a measure of apoptosis. Wells containing only culture medium were used as controls.

Cancer cignal finder 10-pathway reporter array

The Cignal Finder Arrays consist of 10 dual-luciferase reporter assays for ten cancer-related signaling pathways. Each reporter construct is a mixture of an inducible transcription factor (TF) responsive firefly luciferase reporter and a constitutively expressing Renilla construct at a ratio of 40:1, respectively (SABioscience Co., Frederick, MD). Specifically, cells were reversely transfected with 30 nM of specific siRNA pools in 96-well plates using Lipofectamine RNAiMAX reagent (Invitrogen) for 24 h, followed by transfection with 100 ng of each reporter construct. Forty-eight h after the transfection, a dual-luciferase assay was performed with the Dual-Luciferase Reporter Assay System (Promega) in a Safire2 microplate reader (Tecan).

Electrophoresis mobility shift assays (EMSA)

Based on the genome-wide association results, we performed EMSA for SNPs in potential regulatory regions of genes that were associated with the measured phenotypes. Specifically, double-strand probes were 5′-end labeled with biotin and electrophoresis was performed with 5% acrylamide gels, followed by autoradiography. Competition experiments were performed with excess non-labeled probe.

Genome-wide gene expression and SNP analysis

Expression array data were obtained for all 174 lymphoblastoid cell lines (LCLs) as previously described [17]. Illumina HumanHap550K and 510S BeadChips, which assayed 561,298 and 493,750 SNPs, respectively, were used to obtain genome-wide SNP data for these LCLs [23]. Genotyping was performed in the Genotype Shared Resource (GSR) at the Mayo Clinic, Rochester, MN. We also obtained publicly available Affymetrix SNP Array 6.0 Chip SNP data which involved 643,600 SNPs unique to the Affymetrix SNP array for the same cell lines. After quality control (QC), SNPs with call rates <0.95, Hardy-Weinberg Equilibrium (HWE) P values < 0.001, or MAFs <5% were excluded, as were DNA samples with call rates <0.95. A total of 1,348,798 SNPs that passed QC were used to perform the association studies.

Imputation analysis

SNPs not genotyped were imputed across a region 200 kb up or downstream of the selected genes harboring or close to the SNPs associated with drug response in the LCLs. Imputation was performed using Beagle (v3.3.1) [24] with the 11/23/2010 release of the 1000 Genomes project as a reference population [25]. Imputed SNPs with a dosage R2 quality measure of less than 0.3, and SNPs with MAF <0.01 were not included in the analysis. Four of the imputed SNPs were genotyped for validation, the average squared difference between the count of the same allele in the imputed and genotyped version of these SNPs was computed to measure the concordance of the imputed genotype with actual genotype, a smaller difference indicating greater concordance.

Statistical methods

Partial Pearson correlations were used to quantify the association between: SNPs and mRNA expression; SNPs and IC50; and mRNA expression and IC50. IC50 was transformed to remove skewness using a log transformation for gemcitabine and van der Waerden rank transformation for AraC. The adjustment variables in the partial correlation were race and gender if SNPs were not involved; or race, gender and five eigenvectors controlling for population stratification as described previously [23]. These partial correlations were tested using a Wald test, false discovery q-values [26] were also computed for each test.

Results

Genome-wide SNP vs. drug cytotoxicity association study and imputation analysis

Previously, we had performed GWAS using only the 550 k SNP data set for this cell line system [18]. In the current study, we expand the SNPs studies to include additional Illumina SNPs as well as publically available SNP data obtained with Affymetrix 6.0 SNP data for the same cell lines to identify additional novel potential pharmacogenomic biomarkers. As a result, we performed an analysis for the association of 1,348,798 SNPs with IC50 values for gemcitabine and AraC (Figure 1A and 1B). The most significant SNP for gemcitabine was rs1598848 with a P value 7.08 × 10-7 (r = −0.391, MAF = 0.473), while the most significant SNP for AraC was rs4078252 with a P value 1.54 × 10-7 (r = 0.405, MAF = 0.198) (Additional file 1: Table S1A and B). Fourteen SNPs for gemcitabine and 34 for AraC had P values <10-5, and 143 SNPs for gemcitabine and 204 SNPs for AraC had P values <10-4, respectively. One hundred and twenty six SNPs with P < 10-3 were common to both drugs (Additional file 1: Table S1C). To explore ungenotyped SNPs that might be functional, we imputed SNPs surrounding the selected genes (+/−200 kb) harboring or close to the most significant SNP loci using 1000 Genomes data as a reference (Additional file 1: Figure S1A, B, and C). Besides the “observed SNPs” on the genotyping platforms, there were 23 imputed SNPs for gemcitabine and 35 for AraC, respectively, that were also associated with drug response IC50 values (P < 10-4) (Additional file 1: Table S2A and B). In order to determine the accuracy of imputation, we selected 6 imputed SNPs (rs10447475, rs4621668, rs11215400, rs10926784, rs3196512 and rs7762319) that were among the top two SNPs from each gene region associated with drug response IC50s (P < 10-3) to genotype using Taqman assay. Four SNPs ((rs10447475, rs4621668, rs11215400 and rs10926784) were successfully genotyped. The average squared difference between the count of the same allele in the imputed and genotyped version of these 4 SNPs was low ranging from 0.02-0.065 indicating that the concordance was high (Additional file 1: Figure S2).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-15-93/MediaObjects/12864_2013_Article_5753_Fig1_HTML.jpg
Figure 1

GWAS findings. (A). Manhattan plot of 1.3 million SNPs with gemcitabine IC50 values. (B). Manhattan plot of 1.3 million SNPs with gemcitabine IC50 values. SNPs are plotted on the x-axis based on their chromosomal locations. P values of 10-4 are highlighted with a red line.

“Integrated analyses” of SNP loci, basal expression and drug cytotoxicity

We also performed integrated analyses of SNPs, expression array and cytotoxicity data [18, 23]. To do that, we began with SNPs that had P values <10-3. We selected a less stringent P value cutoff to include as many potential candidates as possible for follow-up functional genomic studies. Next, we tested expression probe sets that were associated with these SNPs, followed by association of those expression probe sets with drug IC50 values, ie we performed an “integrated analysis”. In these analyses, we used SNP loci, defined as a region that contained at least 2 SNPs with P < 10-4 or 1 SNP with P < 10-4, plus 3 additional SNPs with P < 10-3 within +/−100 kb surrounding the most significant SNPs. All of the SNPs within each of those loci are listed in Additional file 1: Table S2, which includes genotyped as well as imputed SNPs. We identified 11 loci containing 166 SNPs for gemcitabine and 23 loci with 187 SNPs for AraC that were associated with IC50 values for these two drugs, respectively (Table 1). Four loci containing 4 genes – HLA-DRA, ZNF215, MASS1, and PLD5 – were common to both drugs (Table 1).
Table 1

The top 11 loci for Gemcitabine (A) and the top 27 loci for AraC (B) that were associated with drug response-IC50 values

 Top SNP rsID

 Lowest R

 Lowest P

 Nearby gene

 Chr

 Position

 MAF

Region

Number of SNPs in each locus

(A) Gemcitabine

rs10513968

−0.382

1.04E-06

DOK6

18

65434121

0.445

Intron

9

rs7719624

−0.370

4.22E-06

TGFBI

5

135405465

0.448

Intron

28

rs2274870

0.362

4.33E-06

NIPSNAP3A

9

106555035

0.371

Coding

7

rs316838

−0.345

1.21E-05

PLD5*

1

240387283

0.093

Intron

12

rs2638094

−0.339

1.72E-05

ZNF215*

11

6938064

0.679

Flanking_3UTR

5

rs2290344

0.339

1.75E-05

PIGB

15

53407088

0.253

Coding

9

rs3129890

0.332

2.67E-05

HLA-DRA*

6

32522251

0.340

Flanking_3UTR

23

rs2928445

−0.331

2.76E-05

IPMK

10

58816782

0.267

Flanking_3UTR

23

rs4392391

−0.318

5.78E-05

C3orf23

3

44247970

0.419

Downstream

15

rs12522395

0.318

6.58E-05

MASS1*

5

90246274

0.215

Intron

4

rs13171512

−0.313

7.857E-05

PIK3R1

5

68000787

0.462

Flanking_3UTR

4

(B) AraC

rs10495285

0.393

4.13E-07

KIAA0133 (URB2)

1

227871618

0.171

Downstream

2

rs1450679

0.389

5.20E-07

SMC2L1

9

106066633

0.098

Flanking_3UTR

8

rs6128386

0.375

1.38E-06

LOC149773

20

56626955

0.466

Upstream

4

rs2172820

−0.373

1.63E-06

TUSC3

8

15370039

0.256

Flanking_5UTR

13

rs2857891

−0.372

1.75E-06

ZNF215*

11

6919533

0.101

Intron

6

rs9512755

−0.361

3.67E-06

LNX2

13

27056229

0.055

Flanking_5UTR

4

rs888468

−0.356

5.08E-06

FGF6

12

4403640

0.184

Flanking_3UTR

5

rs2653165

−0.347

9.28E-06

PLD5*

1

240432905

0.207

Intron

7

rs11215416

−0.346

9.57E-06

IGSF4

11

114582537

0.147

Intron

7

rs10060641

0.335

1.90E-05

MASS1*

5

90249006

0.216

Intron

7

rs5989586

0.336

1.94E-05

HDHD1A

23

6757306

0.457

Flanking_3UTR

8

rs216465

−0.330

2.76E-05

GOSR1

17

25880801

0.494

Flanking_3UTR

12

rs856548

−0.326

3.29E-05

TNS3

7

46730993

0.296

Flanking_3UTR

8

rs6689258

0.325

3.40E-05

GPR137B

1

234362702

0.267

Flanking_5UTR

2

rs3794794

−0.325

3.61E-05

CPD

17

25744867

0.477

Intron

10

rs7192

0.322

4.10E-05

HLA-DRA*

6

32519624

0.371

Coding

11

rs4572738

0.322

4.45E-05

CAST1

3

55799452

0.321

Intron

8

rs1433446

0.319

5.05E-05

TMEM83

15

85351874

0.109

Flanking_5UTR

5

rs300962

0.320

5.47E-05

LOC51334

5

119909842

0.269

Intron

15

rs6441911

0.315

6.20E-05

RIS1

3

45322523

0.055

Flanking_5UTR

6

rs1159388

0.314

6.61E-05

DCAMKL1

13

35544706

0.172

Intron

11

rs718979

0.313

6.82E-05

UBE3A

15

23325370

0.394

Flanking_5UTR

6

rs1955412

0.309

8.74E-05

FLRT2

14

85082547

0.21

Intron

11

Each locus contained at least 2 SNPs within 50 kb with P values <10-4. R values represent correlation coefficients for the association. Number of SNPs indicates the number of significantly associated SNPs in the locus. rsID indicates the most significant SNP associated with drug cytotoxicity in the locus. * = common to both gemcitabine and AraC.

The integrated analyses identified 66 SNPs in 6 loci that were associated with gemcitabine IC50 values and the expression of 12 genes represented by 17 probesets. Those 17 probesets were also associated with gemcitabine IC50 values (P < 10-4). The integrated analyses also identified 36 SNPs in 3 loci that were associated with AraC IC50 values and the expression of 9 genes (10 probesets) (Table 2). For gemcitabine, 19 SNPs were within cis-regulatory regions for PIGB or C3orf23. No cis- regulation between SNP and gene expression was identified for AraC. Of interest, SNPs in PIGB were associated with the expression of that gene (lowest P = 5.97 × 10-9) as well as the expression of FKBP5 (lowest P = 2.70 × 10-6), a gene that we previously reported to play an important role in response to gemcitabine and AraC as well as many other chemotherapeutic agents including gemcitabine and AraC [17, 27]. We next moved to further analyses of candidate genes identified during the integrated analysis.
Table 2

Integrated analyses for drug response for either (A) Gemcitabine or (B) AraC

SNP

Probe set

GWAS

rslD

Closest gene

MAF

Chr

Position

Region

Probeset

Gene symbol

Chr

R value (SNP vs IC50)

P value (SNP vs IC50)

R value (SNP vs Exp)

P value (SNP vs Exp)

R value (EXP vs IC50)

P value (EXP vs IC50)

(A) Gemcitabine

rs316871

PLD5

0.097

1

240409507

Intron

225532_at

CABLES1

18

-0.326

4.14E–05

-0.242

6.54E–05

0.347

3.12E–06

rs316823

PLD5

0.093

1

240422651

Intron

225532_at

CABLES1

18

-0.288

2.88E–04

-0.257

2.05E–05

0.347

3.12E–06

rs402098

PLD5

0.093

1

240430321

Intron

225532_at

CABLES1

18

-0.288

2.88E–04

-0.257

2.05E–05

0.347

3.12E–06

rs427498

PLD5

0.087

1

240424078

Intron

225532_at

CABLES1

18

-0.280

4.43E–04

-0.256

2.28E–05

0.347

3.12E–06

rs316823

PLD5

0.093

1

240422651

Intron

225531_at

CABLES1

18

-0.288

2.88E–04

-0.239

7.69E–05

0.321

1.72E–05

rs402098

PLD5

0.093

1

240430321

Intron

225531_at

CABLES1

18

-0.288

2.88E–04

-0.239

7.69E–05

0.321

1.72E-05

rs4392391

C3orf23

0.419

3

44247970

Downstream

1555906_s_at

C3orf23

3

-0.318

5.78E–05

-0.250

3.46E–05

0.263

4.86E–04

rs1565214

C3orf23

0.425

3

44309303

Upstream

1555906_s_at

C3orf23

3

-0.308

1.35E–04

-0.251

4.09E–05

0.263

4.86E–04

rs9809107

C3orf23

0.419

3

44257938

Flanking_5UTR

1555906_s_at

C3orf23

3

-0.301

1.46E–04

-0.242

5.97E–05

0.263

4.86E–04

rs967285

C3orf23

0.422

3

44250280

Downstream

1555906_s_at

C3orf23

3

-0.301

1.48E–04

-0.247

4.36E–05

0.263

4.86E–04

rs9821268

C3orf23

0.413

3

44278128

Upstream

1555906_s_at

C3orf23

3

-0.292

2.43E–04

-0.241

6.29E–05

0.263

4.86E–04

rs1565215

C3orf23

0.418

3

44309103

Upstream

1555906_s_at

C3orf23

3

-0.291

2.64E–04

-0.246

4.62E–05

0.263

4.86E–04

rs10510741

C3orf23

0.445

3

44239198

Flanking_5UTR

1555906_s_at

C3orf23

3

-0.283

3.68E–04

-0.275

4.71E–06

0.263

4.86E–04

rs9852733

C3orf23

0.451

3

44252343

Flanking_5UTR

1555906_s_at

C3orf23

3

-0.279

4.59E–04

-0.268

8.53E–06

0.263

4.86E–04

rs6808448

C3orf23

0.451

3

44218611

Flanking_5UTR

1555906_s_at

C3orf23

3

-0.273

6.19E–04

-0.273

5.38E–06

0.263

4.86E–04

rs9846155

C3orf23

0.456

3

44257844

Flanking_5UTR

1555906_s_at

C3orf23

3

-0.272

6.33E–04

-0.262

1.38E–05

0.263

4.86E–04

rs9284879

C3orf23

0.433

3

44259588

Flanking_5UTR

1555906_s_at

C3orf23

3

-0.264

9.43E–04

-0.253

2.65E–05

0.263

4.86E–04

rs12486452

C3orf23

0.433

3

44299569

Flanking_5UTR

1555906_s_at

C3orf23

3

-0.264

9.43E–04

-0.241

6.45E–05

0.263

4.86E–04

rs12631341

C3orf23

0.433

3

44284272

Flanking_5UTR

1555906_s_at

C3orf23

3

-0.264

9.43E–04

-0.241

6.45E–05

0.263

4.86E–04

rs7631790

C3orf23

0.433

3

44274209

Flanking_5UTR

1555906_s_at

C3orf23

3

-0.264

9.43E–04

-0.241

6.45E–05

0.263

4.86E–04

rs4392391

C3orf23

0.419

3

44247970

Downstream

227978_s_at

ZADH2

18

-0.318

5.78E–05

0.251

3.11E–05

-0.351

2.42E–06

rs1565214

C3orf23

0.425

3

44309303

Upstream

227978_s_at

ZADH2

18

–0.308

1.35E–04

0.246

5.9E–05

-0.351

2.42E–06

rs9809107

C3orf23

0.419

3

44257938

Flanking_5UTR

227978_s_at

ZADH2

18

-0.301

1.46E–04

0.273

5.72E–06

-0.351

2.42E–06

rs967285

C3orf23

0.422

3

44250280

Downstream

227978_s_at

ZADH2

18

-0.301

1.48E–04

0.254

2.58E–05

-0.351

2.42E–06

rs9821268

C3orf23

0.413

3

44278128

Upstream

227978_s_at

ZADH2

18

-0.292

2.43E–04

0.247

4.17E–05

-0.351

2.42E–06

rs1565215

C3orf23

0.418

3

44309103

Upstream

227978_s_at

ZADH2

18

-0.291

2.64E–04

0.250

3.46E–05

-0.351

2.42E–06

rs4682949

C3orf23

0.398

3

44220159

Flanking_5UTR

227978_s_at

ZADH2

18

-0.287

3.01E–04

0.247

4.14E–05

–0.351

2.42E–06

rs10510741

C3orf23

0.445

3

44239198

Flanking_5UTR

227978_s_at

ZADH2

18

-0.283

3.68E–04

0.251

3.03E–05

–0.351

2.42E–06

rs9852733

C3orf23

0.451

3

44252343

Flanking_5UTR

227978_s_at

ZADH2

18

–0.279

4.59E–04

0.239

7.38E–05

–0.351

2.42E–06

rs6808448

C3orf23

0.451

3

44218611

Flanking_5UTR

227978_s_at

ZADH2

18

-0.273

6.19E–04

0.260

1.62E–05

-0.351

2.42E–06

rs9846155

C3orf23

0.456

3

44257844

Flanking_5UTR

227978_s_at

ZADH2

18

-0.272

6.33E–04

0.260

1.58E–05

-0.351

2.42E–06

rs12486452

C3orf23

0.433

3

44299569

Flanking_5UTR

227978_s_at

ZADH2

18

-0.264

9.43E–04

0.239

7.52E–05

-0.351

2.42E–06

rs12631341

C3orf23

0.433

3

44284272

Flanking_5UTR

227978_s_at

ZADH2

18

-0.264

9.43E–04

0.239

7.52E–05

-0.351

2.42E–06

rs7631790

C3orf23

0.433

3

44274209

Flanking_5UTR

227978_s_at

ZADH2

18

-0.264

9.43E–04

0.239

7.52E–05

-0.351

2.42E–06

rs12188464

PIK3RI

0.459

5

67999705

Flanking_5UTR

225935_at

–––

7

-0.309

9.69E–05

0.366

2.47E–06

-0.296

7.99E–05

rs13171512

PIK3RI

0.462

5

68000787

Flanking_5UTR

236170_x_at

–––

7

-0.313

7.86E–05

0.349

7.69E–06

-0.299

6.75E–05

rs13171512

PIK3RI

0.462

5

68000787

Flanking_5UTR

225935_at

–––

7

-0.313

7.86E–05

0.375

1.33E–06

-0.296

7.99E–05

rs7713001

PIK3RI

0.459

5

67999371

Flanking_5UTR

225935_at

–––

7

-0.309

9.69E–05

0.366

2.47E–06

-0.296

7.99E–05

rs12188464

PIK3RI

0459

5

67999705

Flanking_5UTR

201334_s_at

ARHGEF12

11

-0.309

9.69E–05

0.380

9.03E–07

-0.339

5.35E–06

rs13171512

PIK3RI

0.462

5

68000787

Flanking_5UTR

201334_s_at

ARHGEF12

11

-0.313

7.86E–05

0.372

1.59E–06

-0.339

5.35E–06

rs7713001

PIK3RI

0.459

5

67999371

Flanking_5UTR

201334_s_at

ARHGEF12

11

-0.309

9.69E–05

0.380

9.03E–07

-0.339

5.35E–06

rs12188464

PIK3RI

0.459

5

67999705

Flanking_3UTR

238012_at

DPP7

9

-0.309

9.69E–05

0.372

1.63E–06

-0.324

1.45E–05

rs13171512

PIK3RI

0.462

5

68000787

Flanking_3UTR

238012_at

DPP7

9

-0.313

7.86E–05

0.378

1.08E–06

-0.324

1.45E–05

rs7713001

PIK3RI

0.459

5

67999371

Flanking_3UTR

238012_at

DPP7

9

-0.309

9.69E–05

0.372

1.63E–06

-0.324

1.45E–05

rs12188464

PIK3RI

0.459

5

67999705

Flanking_3UTR

225086_at

FAM98B

15

-0.309

9.69E–05

0.345

9.5E–06

-0.352

2.18E–06

rs13171512

PIK3RI

0.462

5

68000787

Flanking_3UTR

225086_at

FAM98B

15

-0.313

7.86E–05

0.354

5.38E–06

-0.352

2.18E–06

rs3135351

HLA–DRA

0.137

6

32500923

Flanking_5UTR

1566082_at

–––

10

0.265

9.48E–04

0.277

4.24E–06

0.320

1.88E–05

rs2472476

NIPSNAP3B

0.389

9

106571777

Intron

200988_s_at

PSME3

17

0.338

1.92E–05

-0.239

7.6E–05

-0.350

.60E–06

rs2928445

IPMK

0.267

10

58816782

Flanking_3UTR

227482_at

ADCK1

14

-0.331

2.76E–05

0.247

4.09E–05

-0.293

9.45E–05

rs12244977

IPMK

0.195

10

58762688

Flanking_3UTR

1569396_at

RAB40C

16

-0.308

1.03E–04

-0.243

5.72E–05

0.303

5.47E–05

rs12256364

IPMK

0.195

10

58765694

Flanking_3UTR

1569396_at

RAB40C

16

-0.308

1.03E–04

-0.243

5.72E–05

0.303

5.47E–05

rs2928445

IPMK

0.267

10

58816782

Flanking_3UTR

226987_at

RBM15B

3

-0.331

2.76E–05

0.263

1.2E–05

-0.354

1.90E–06

rs4774760

PIGB

0.416

15

53376504

upstreaintronm

224856_at

FKBP5

6

0.326

3.63E–05

-0.241

6.7E–05

-0.411

2.15E–08

rs7174876

PIGB

0.416

15

53406853

Upstream

224856_at

FKBP5

6

0.284

4.06E–04

-0.237

9.71E–05

-0.411

2.15E–08

rs2290344

PIGB

0.253

15

53407088

Coding

224856_at

FKBP5

6

0.339

1.75E–05

-0.266

9.87E–06

-0.393

9.66E–08

rs4774760

PIGB

0.416

15

53376504

Upstream

204560_at

FKBP5

6

0.326

3.63E–05

-0.282

2.7E–06

-0.393

9.66E–08

rs12050587

PIGB

0.45

15

53414820

Intron

224856_at

FKBP5

6

0.305

1.23E–04

-0.256

2.27E–05

-0.393

9.66E–08

rs28668016

PIGB

0.365

15

53398725

5UTR

204560_at

FKBP5

6

0.304

1.31E–04

-0.261

1.49E–05

-0.393

9.66E–08

rs11636687

PIGB

0.421

15

53392444

Flanking_5UTR

224856_at

FKBP5

6

0.302

1.53E–04

-0.252

3.34E–05

-0.393

9.66E–08

rs2414409

PIGB

0.448

15

53419009

Intron

204560_at

FKBP5

6

0.299

1.62E–04

-0.264

1.17E–05

-0.393

9.66E–08

rs7183960

PIGB

0.451

15

53409987

Intron

224856_at

FKBP5

6

0.292

2.37E–04

-0.262

1.37E–05

-0.393

9.66E–08

rs7174876

PIGB

0.423

15

53406853

Intron

204560_at

FKBP5

6

0.284

4.06E–04

-0.274

6.06E–06

-0.393

9.66E–08

rs2290344

PIGB

0.253

15

53407088

Coding

224856_at

PIGB

15

0.339

1.75E–05

-0.301

4.81E–07

-0.294

8.98E–05

rs4774760

PIGB

0.416

15

53376504

Upstream

242760_x_at

PIGB

15

0.326

3.63E–05

-0.240

6.85E–05

-0.294

8.98E–05

rs8024695

PIGB

0.285

15

53426597

Intron

242760_x_at

PIGB

15

0.321

5.00E–05

-0.282

2.56E–06

-0.294

8.98E–05

rs28668016

PIGB

0.365

15

53398725

5UTR

242760_x_at

PIGB

15

0.304

1.31E–04

-0.274

5.28E–06

-0.294

8.98E–05

(B) AraC

rs10495285

KIAA0133 (URB2)

0.171

1

227871618

Downstream

219526_at

C14orf169

14

0.393

4.13E–07

-0.240

6.97E–05

-0.318

1.86E–05

rs9861198

Probable leucyl–tRNA synthetase

0.086

3

45310394

Upstream

235936_at

LOC254559

15

0.308

9.07E–05

0.274

5.12E–06

0.294

8.27E–05

rs9816196

U3 small nucleolar RNA

0.055

3

45321382

Upstream

235936_at

LOC254559

15

0.315

6.70E–05

0.242

7.05E–05

0.294

8.27E–05

rs6441911

RIS1

0.055

3

45322523

Flanking_5UTR

235936_at

LOC254559

15

0.315

6.20E–05

0.250

3.26E–05

0.294

8.27E–05

rs9878275

Probable leucyl–tRNA synthetase

0.055

3

45331043

Upstream

235936_at

LOC254559

15

0.311

8.31E–05

0.249

3.79E–05

0.294

8.27E–05

rs9846284

U3 small nucleolar RNA

0.055

3

45331970

Upstream

235936_at

LOC254559

15

0.315

6.20E–05

0.250

3.37E–05

0.294

8.27E–05

rs9850725

LARS2

0.055

3

45332431

Flanking_5UTR

235936_at

LOC254559

15

0.315

6.20E–05

0.250

3.26E–05

0.294

8.27E–05

rs9861198

Probable leucyl–tRNA synthetase

0.086

3

45310394

Upstream

206603_at

SLC2A4

17

0.308

9.07E–05

0.239

7.63E–05

0.345

3.18E–06

rs9816196

U3 small nucleolar RNA

0.055

3

45321382

Upstream

206603_at

SLC2A4

17

0.315

6.70E–05

0.294

1.21E–06

0.345

3.18E–06

rs6441911

RIS1

0.055

3

45322523

Flanking_5UTR

206603_at

SLC2A4

17

0.315

6.20E–05

0.301

4.80E–07

0.345

3.18E–06

rs9878275

Probable leucyl–tRNA synthetase

0.055

3

45331043

Upstream

206603_at

SLC2A4

17

0.311

8.31E–05

0.300

5.67E–07

0.345

3.18E–06

rs9846284

U3 small nucleolar RNA

0.055

3

45331970

Upstream

206603_at

SLC2A4

17

0.315

6.20E–05

0.301

5.03E–07

0.345

3.18E–06

rs9850725

LARS2

0.055

3

45332431

Flanking_5UTR

206603_at

SLC2A4

17

0.315

6.20E–05

0.301

4.80E–07

0.345

3.18E–06

rs9816196

U3 small nucleolar RNA

0.055

3

45321382

Upstream

1553755_at

NXNL1

19

0.315

6.70E–05

0.237

9.98E–05

0.312

2.82E–05

rs6441911

RIS1

0.055

3

45322523

Flanking_5UTR

1553755_at

NXNL1

19

0.315

6.20E–05

0.245

4.73E–05

0.312

2.82E–05

rs9878275

Probable leucyl–tRNA synthetase

0.055

3

45331043

Upstream

1553755_at

NXNL1

19

0.311

8.31E–05

0.244

5.26E–05

0.312

2.82E–05

rs9846284

U3 small nucleolar RNA

0.055

3

45331970

Upstream

1553755_at

NXNL1

19

0.315

6.20E–05

0.245

4.88E–05

0.312

2.82E–05

rs9850725

LARS2

0.055

3

45332431

Flanking_5UTR

1553755_at

NXNL1

19

0.315

6.20E–05

0.245

4.73E–05

0.312

2.82E–05

rs17564430

IGSF4 (CADM1)

0.155

11

114548784

Flanking_3UTR

206571_s_at

MAP4K4

2

-0.337

1.74E–05

0.237

8.48E–05

-0.322

1.51E–05

rs11215406

IGSF4 (CADM1)

0.138

11

Intron114570292

Intron

206571_s_at

MAP4K4

2

-0.335

1.92E–05

0.248

3.93E–05

-0.322

1.51E–05

rs11215406

IGSF4 (CADM1)

0.138

11

114570292

Intron

204201_s_at

PTPN13

4

-0.335

1.92E–05

0.243

5.49E–05

-0.301

5.30E–05

rs17564430

IGSF4 (CADM1)

0.155

11

114548784

Flanking_3UTR

204880_at

MGMT

10

-0.337

1.74E–05

-0.236

9.07E–05

0.332

7.44E–06

rs17564430

IGSF4 (CADM1)

0.155

11

114548784

Flanking_3UTR

1556095_at

UNC13C

15

-0.337

1.74E–05

0.256

2.21E–05

-0.354

1.60E–06

rs17564430

IGSF4 (CADM1)

0.155

11

114548784

Flanking_3UTR

1556096_s_at

UNC13C

15

-0.337

1.74E–05

0.240

7.07E–05

-0.337

5.47E–06

rs17564430

IGSF4 (CADM1)

0.155

11

114548784

Flanking_3UTR

1569969_a_at

UNC13C

15

-0.337

1.74E–05

0.257

1.93E–05

-0.332

7.56E–06

rs11215406

IGSF4 (CADM1)

0.138

11

114570292

Intron

1556095_at

UNC13C

15

-0.335

1.92E–05

0.276

4.27E–06

-0.354

1.60E–06

rs11215406

IGSF4 (CADM1)

0.138

11

114570292

Intron

1556096_s_at

UNC13C

15

-0.335

1.92E–05

0.255

2.29E–05

-0.337

5.47E–06

rs11215406

IGSF4 (CADM1)

0.138

11

114570292

Intron

1569969_a_at

UNC13C

15

-0.335

1.92E–05

0.270

7.08E–06

-0.332

7.56E–06

rs11215416

IGSF4 (CADM1)

0.147

11

114582537

Intron

1556095_at

UNC13C

15

-0.346

9.57E–06

0.260

1.59E–05

-0.354

1.60E–06

rs11215416

IGSF4 (CADM1)

0.147

11

114582537

Intron

1556096_s_at

UNC13C

15

-0.346

9.57E–06

0.242

6.21E–05

-0.337

5.47E–06

rs11215416

IGSF4 (CADM1)

0.147

11

114582537

Intron

1569969_a_at

UNC13C

15

-0.346

9.57E–06

0.258

1.84E–05

-0.332

7.56E–06

rs2008801

IGSF4 (CADM1)

0.126

11

114513385

Flanking_3UTR

225532_at

CABLES1

18

-0.317

5.42E–05

-0.254

2.54E–05

0.271

2.92E–04

rs2507905

IGSF4 (CADM1)

0.126

11

114513815

Downstream

225532_at

CABLES1

18

-0.317

5.42E–05

-0.254

2.54E–05

0.271

2.92E–04

rs7122402

IGSF4 (CADM1)

0.126

11

114521517

Downstream

225532_at

CABLES1

18

-0.317

5.42E–05

-0.261

1.43E–05

0.271

2.92E–04

rs4938179

IGSF4 (CADM1)

0.132

11

114538635

Downstream

225532_at

CABLES1

18

-0.289

2.56E–04

-0.247

4.34E–05

0.271

2.92E–04

rs17564430

IGSF4 (CADM1)

0.155

11

114548784

Flanking_3UTR

225532_at

CABLES1

18

-0.337

1.74E–05

-0.242

6.19E–05

0.271

2.92E–04

“Integrated analyses” with the top expression probe sets that were associated with SNPs within loci with gemcitabine IC50 (SNP vs. Expression P value <10–4, and Expression vs. IC50 P value < 10–4), except for C3orf23 (SNP vs. Expression P value < 10–4, and Expression vs. IC50 P value ≈ 10–4). Eighteen unique probe sets, representing 12 annotated genes that were associated with 66 SNPs within 7 loci and were significantly correlated with gemcitabine IC50 values are listed. (B) “Integrated analyses” for AraC. Eleven unique probe sets, representing 6 annotated genes that were associated with 15 SNPs within 3 loci and were significantly correlated with AraC IC50 values are listed. R values represent correlation coefficients for each association.

Follow-up functional validation of candidate genes in cancer cells

Since the regulation of gene expression is tissue specific [28], we wanted to functionally validate in cancer cell lines candidate genes selected based on our analysis performed with LCLs. The tumor cell lines that we selected were based on the expression of the genes of interest and on the clinical uses of these two drugs. Gemcitabine is used in the treatment of pancreatic cancer but it is also used to treat other solid tumors such as breast cancer, while AraC is first-line therapy for acute myelogenous leukemia (AML). Therefore, we selected one human pancreatic cancer cell line, SU86, one breast cancer cell line, MDA-MB-231 and two leukemia cell lines, BDCM and THP1, to functionally characterize the genes of interest. Twenty-six genes were selected based on a series of criteria including association P value, SNP locus, whether the gene was expressed in LCLs and the biological function of the genes (Table 3 and Figure 2). To determine the functional impact of those genes, we used specific siRNA pools to knockdown the 26 candidate genes, followed by QRT-PCR and MTS cytotoxicity assay. Eleven genes showed an effect on gemcitabine cytotoxicity, 10 on AraC and 5 were common to both drugs. Knockdown of PIGB, ZADH2, PSME3, DOK6, TGFBI, and HLA-DRA in both SU86 and MDA-MB-231 cells significantly desensitized the cells to gemcitabine (Table 3 and Figure 3), consistent with the association study results. Knockdown of C4orf169, TUSC3, LNX2, RIS1, and SMC2 and HLA-DRA in both SU86 and MDA-MB-231 cells significantly desensitized the cells to AraC (Table 3 and Figure 4). Finally, knockdown of HLA-DRA in THP-1 leukemia cells, LNX2 in BDCM cells, and SMC2 and RIS1 in both THP1 and BDCM cells also desensitized the cells to AraC, results that were also consistent with our association results (Figure 4).
Table 3

Functional studies of candidate genes

Gene symbol

Selection strategy

Cytotoxicity validation in cancer cell lines

SNP vs. IC50

Integrated analysis

Both SU86 and MDA231

Either THP-1 or BDCM

SNP Location

P < 10-4

SNP vs. Exp P < 10-4

(cancer cell lines)

(leukemia cell lines)

Exp vs. IC50 P <10-4

PIGB

Intron/coding/5UTR/3UTR

Gem

Gem

Yes

NP

C3orf23

5UTR/upstream

Gem

Gem

No

NP

ZADH2

  

Gem

Yes

NP

ARHGEF12

  

Gem

No

NP

DPP7

  

Gem

No

NP

PSME3

  

Gem

Yes

NP

NIPSNAP3B

Intron/5UTR

Gem

 

No

NP

PIK3R1

Flanking_5UTR/3UTR

Gem

 

No

NP

DOK6

Intron

Gem

 

Yes

NP

TGFBI

Flanking_5UTR

Gem

 

Yes

NP

UNC13C

  

AraC

No

No

C14orf169

  

AraC

Yes

NP

MAP4K4

  

AraC

No

NP

URB2

Downstream

AraC

 

No

No

TUSC3

Intron/5UTR/3UTR

AraC

 

Yes

No

LARS2

5UTR

AraC

 

Yes

No

RIS1 (TMEM158)

5UTR

AraC

 

Yes

Yes

IGSF4 (CADM1)

Intron/downstream/3UTR

AraC

 

No

No

LNX2

Intron/5UTR/3UTR

AraC

 

Yes

No

SMC2

3UTR

AraC

 

Yes

Yes

PLD5

Intron

Both

 

No

NP

GPR98

Intron

Both

 

No

No

HLA-DRA

Intron/3UTR

Both

 

Yes

No

ZNF215

Flanking_5UTR/3UTR/coding

Both

 

No

No

CABLES1

  

Both

No

No

The table lists the top 26 candidate genes selected for siRNA screening for both gemcitabine and AraC, with MTS assay results as well as QRT-PCR assay results when appropriate.

A total of 26 genes were selected for siRNA screening, with 11 genes for gemcitabine, 10 for AraC, and 5 for both. For the validation assays with MTS and QRT-PCR, “Yes” indicates that knockdown of the gene altered drug cytotoxicity, while “No” indicates no alteration. “NP” indicates not performed. The genes that are bolded were functionally validated.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-15-93/MediaObjects/12864_2013_Article_5753_Fig2_HTML.jpg
Figure 2

Schematic diagram of the strategy for candidate gene selection for functional validation. Genome-wide association studies for either gemcitabine or AraC cytotoxicity were performed with 1.3 million SNPs or 54,000 expression probe sets. “Integrated analyses” were performed using SNP “loci” that contained at least 2 SNPs (P < 10-4) or 1 SNP (P < 10-4) plus 3 SNPs (P < 10-3) within 100 kb surrounding the top significant SNPs, 54,000 expression probe sets and IC50 values to identify SNPs associated with drug IC50 values through their influence on gene expression (SNP-Expression P value <10-4, Expression-IC50 P value <10-4). Finally, 26 candidate genes, including 11 for gemcitabine, 10 for AraC, and 5 for both, were selected for functional validation studies that were performed with multiple cancer cell lines.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-15-93/MediaObjects/12864_2013_Article_5753_Fig3_HTML.jpg
Figure 3

Functional characterization of candidate genes. Knockdown of individual genes in cancer cell lines followed by MTS assays to determine the effect of the candidate gene on gemcitabine. Data are shown for 11 validated out of the 26 candidate genes tested in SU86, MDA-MB-231, BDCM, and THP-1 cancer cell lines by MTS assay after siRNA knockdown performed with a pool of 4 specific siRNAs. The drug dose response curves were obtained with the MTS assays. Red lines indicate the negative control siRNA pool, while blue lines indicate data obtained with specific siRNA pool. The x-axis indicates the log10 gemcitabine concentration and the y-axis indicates the proportion of cells surviving after drug exposure. The bar graphs at the bottom show knockdown efficiency tested by QRT-PCR assay using the same transfected cells as those used to perform the MTS assays. The y-axis indicates relative gene expression after siRNA knockdown when compared with negative control siRNA. The experiments were repeated 3 times and the error bar represents SEM. Each of the genes was significantly knocked down when compared to the negsiRNA control, P<0.05.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-15-93/MediaObjects/12864_2013_Article_5753_Fig4_HTML.jpg
Figure 4

Functional characterization of candidate genes. Knockdown of individual genes in cancer cell lines followed by MTS assays to determine the effect of the candidate gene on AraC. Data are shown for 11 validated out of the 26 candidate genes tested in SU86, MDA-MB-231, BDCM, and THP-1 cancer cell lines by MTS assay after siRNA knockdown performed with a pool of 4 specific siRNAs. The drug dose response curves were obtained with the MTS assays. Red lines indicate the negative control siRNA pool, while blue lines indicate data obtained with specific siRNA pool. The x-axis indicates the log10 AraC concentration and the y-axis indicates the proportion of cells surviving after drug exposure. The bar graphs at the bottom show knockdown efficiency tested by QRT-PCR assay using the same transfected cells as those used to perform the MTS assays. The y-axis indicates relative gene expression after siRNA knockdown when compared with negative control siRNA. The experiments were repeated 3 times and the error bar represents SEM. Each of the genes was significantly knocked down when compared to the negsiRNA control, P<0.05.

We next wanted to determine whether the cytotoxic effects of those genes might involve apoptosis. Therefore, we performed caspase-3/7 activity assays after knockdown of the candidate genes in SU86 cells. As shown in Figure 5A and 5B, down-regulation of PIGB, DOK6, TGFBI, ZADH2, PSME3, and HLA-DRA in SU86 cells significantly decreased caspase-3/7 activity after treatment with gemcitabine as compared with negative control siRNA-treated cells. Similar results were also observed for AraC treatment following siRNA knockdown of TUSC3, C14orf169, and HLA-DRA. However, knockdown of LNX2, RIS1, and SMC2 did not alter the cellular caspase-3/7 activity (Table 4), suggesting that a different mechanism was involved.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-15-93/MediaObjects/12864_2013_Article_5753_Fig5_HTML.jpg
Figure 5

Effect of candidate genes on apoptosis in SU86 cells. Apoptosis was measured in SU86 cancer cell line after transfection with a pool of 4 specific siRNAs for 24 h and exposure to gemcitabine (A) or AraC (B) for an additional 48 h, followed by Caspase-3/7 assay. The x-axis indicates the drug dose and the y-axis represents the relative Caspase 3/7 activity normalized to nontreatment control. P values represented the significant difference in the AUC values derived from the curves between the control and specific knockdown.

Table 4

Functional studies of candidate genes

Gene symbol

Caspase-3/7 activity

Cancer-related Cignal Reporter Array

Wnt (TCF/LEF)

Notch (RBP-Jκ)

p53/DNA damage (p53)

TGFβ (SMAD2/3/4)

cell cycle (E2F/DP1)

NFκB (NFκB)

c-Myc (Myc/Max)

Hypoxia (HIF1A)

MAPK /ERK (Elk-1/SRF)

MAPK /JNK (AP-1)

PIGB

Decrease

No

No

No

No

No

Decrease

Decrease

No

Decrease

Decrease

ZADH2

Decrease

No

No

No

No

No

No

Increase

No

No

No

PSME3

Decrease

No

Increase

No

Decrease

No

No

Increase

Decrease

No

No

DOK6

Decrease

No

No

No

No

No

Decrease

No

No

No

Decrease

TGFBI

Decrease

No

No

Increase

No

No

Decrease

Decrease

Decrease

Decrease

Decrease

C14orf169

Decrease

No

Increase

Increase

No

No

Increase

Increase

No

Increase

Increase

TUSC3

Decrease

No

No

No

Decrease

Decrease

No

No

Decrease

No

No

RIS1 (TMEM158)

No

No

No

No

No

No

No

Increase

No

No

No

LNX2

No

No

No

No

No

No

No

No

No

Increase

No

SMC2

No

No

No

No

No

No

No

No

No

No

No

HLA-DRA

Decrease

No

Increase

No

No

No

Increase

No

No

No

No

Functional characterization of 11 genes using the Cancer-related Cignal Reporter Array (SABioscience).

Transcription factors for each signal pathway are listed in parenthesis. “Increase” means at least a 2-fold increase of luciferase activity in cells treated with specific siRNA compared with cells treated with negative control siRNA; while “decrease” indicates at least a 50% decrease of luciferase activity in control cells. “No” means no alteration in luciferase activity after siRNA knockdown.

Finally, we used the Cancer Cignal Finder Array (SABioscience) that consists of 10 dual-luciferase reporter gene assays to determine whether our candidate genes might affect any of the 10 cancer-related signaling pathways in SU86 cells by measuring changes in transcriptional activities of 10 key transcription factors (TF) after knockdown of each candidate gene. We observed changes in transcriptional activity of several TFs after knockdown of specific genes in SU86 cells, suggesting that these genes might be involved in the regulation of a particular cancer-related signaling pathway or pathways that might contribute to resistance to gemcitabine and AraC (Figure 6 and Table 3). For example, knockdown of PIGB resulted in a decrease in transcriptional activity of Elk-1/SRF, AP1, NFκB, and Myc/MAX in SU86 cells, indicating a down-regulation of these signaling pathways. Knockdown of DOK6 dramatically decreased the transcription activities of both NFκB and AP1 in the NFκB and MAPK/JNK pathways, while the activity of the transcription factor Myc/MAX that is involved in the c-Myc pathway was increased significantly after ZADH2 knockdown. However, we did not observe any significant changes after SMC2 knockdown.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-15-93/MediaObjects/12864_2013_Article_5753_Fig6_HTML.jpg
Figure 6

Effect of candidate gene knockdown on 10 cancer related pathways in SU86 cells. The Cancer Cignal Reporter assay was used to determine the effect of candidate gene knockdown on 10 cancer related signaling pathways. Each column indicates relative luciferase activity of the transcription factor-transfected SU86 cells. The x-axis indicates individual TF dependent cancer signaling pathways. * indicates P < 0.05 as compared to control cells, while ** indicates P < 0.01.

Functional characterization of PIGB SNPs

When we performed integrated analysis among SNPs, gene expression and gemcitabine cytotoxicity, we found that the only cis- regulated SNPs mapped to PIGB. Knockdown of PIGB resulted in desensitization of cancer cells to gemcitabine. PIGB contained 7 SNPs that were associated both with gemcitabine response (P < 10-3) and with its own gene expression (P < 10-4) (Table 5). PIGB expression was also significantly correlated with gemcitabine cytotoxicity (P = 8.95 × 10-5 and P = 5.31 × 10-3 for two different probe sets for PIGB mRNA). We also determined LD patterns for those 7 SNPs using HapMap data for each ethnic group. As shown in Figure 7A, LD patterns differed among the three ethnic groups. In both CHB/JPT and CEPH groups, those 7 SNPs were in tight LD, while there was not significant linkage among the SNPs in the YRI population. The top 3 SNPs in PIGB, including rs2290344, a nonsynonymous coding SNP (M161T) in exon 4, rs28668016 in the 5′-UTR, and rs11636687 in the 5′ flanking region (Table 5) were selected for further functional characterization.
Table 5

The top seven SNPs in PIGB that were associated with gemcitabine cytotoxicity and its expression in LCLs by integrated analysis

SNP

Gene

SNP vs. IC50

SNP vs. Expression

Expression vs. IC50

 rsID

Chr

Position

Region

MAF

 Probeset

Gene symbol

 Chr

R value

 P value

 R value

 P value

 R value

 P value

rs2290344

15

53407088

Coding

0.253

205452_at

PIGB

15

0.339

1.75E-05

−0.340

1.06E-08

−0.212

5.31E-03

rs2290344

15

53407088

Coding

0.253

242760_x_at

PIGB

15

0.339

1.75E-05

−0.301

4.81E-07

−0.294

8.98E-05

rs4774760

15

53376504

5′-upstream

0.416

205452_at

PIGB

15

0.326

3.63E-05

−0.283

2.48E-06

−0.212

5.31E-03

rs4774760

15

53376504

5′-upstream

0.416

242760_x_at

PIGB

15

0.326

3.63E-05

−0.240

6.85E-05

−0.294

8.98E-05

rs8024695

15

53426597

Intron

0.285

205452_at

PIGB

15

0.321

5.00E-05

−0.326

4.60E-08

−0.212

5.31E-03

rs8024695

15

53426597

Intron

0.285

242760_x_at

PIGB

15

0.321

5.00E-05

−0.282

2.56E-06

−0.294

8.98E-05

rs12050587

15

53414820

Intron

0.450

205452_at

PIGB

15

0.305

1.23E-04

−0.246

4.54E-05

−0.212

5.31E-03

rs28668016

15

53398725

5′-UTR

0.365

205452_at

PIGB

15

0.304

1.31E-04

−0.346

5.97E-09

−0.212

5.31E-03

rs28668016

15

53398725

5′-UTR

0.365

242760_x_at

PIGB

15

0.304

1.31E-04

−0.274

5.28E-06

−0.294

8.98E-05

rs11636687

15

53392444

5′-upstream

0.421

205452_at

PIGB

15

0.302

1.53E-04

−0.287

2.22E-06

−0.212

5.31E-03

rs7174876

15

53406853

Intron

0.423

205452_at

PIGB

15

0.284

4.06E-04

−0.261

1.64E-05

−0.212

5.31E-03

R values represent correlation coefficients for associations. We performed integrated analysis among the SNPs and mRNA expression of the PIGB gene as well as gemcitabine cytotoxicity. The only cis- associated SNPs with PIGB gene expression were listed.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-15-93/MediaObjects/12864_2013_Article_5753_Fig7_HTML.jpg
Figure 7

Functional characterization of SNPs in the PIGB gene. (A) Patterns of linkage disequilibrium (LD) within ~200 Kb surrounding the rs3797418 SNP among three different ethnic groups using HapMap3 R2 data as a reference. The top 7 SNPs in PIGB are arranged in order from 5′ to 3′, as shown in the gene structure above each plot. Black indicates combinations where R2 = 1 and linkage of disequilibrium (LOD) ≥ 2; light grey, combinations where 0 < R2 < 1 and LOD ≥ 2; white, where R2 = 0 and LOD < 2. (B) Association between SNPs and PIGB expression measured by QRT-PCR assay and microarray using 37 randomly selected LCLs. (C) Effect of the nonsynonymous coding SNP (rs2290344, M161T) on PIGB mRNA expression, protein expression and response to gemcitabine. PIGB mRNA and protein levels were determined in SU86 and MDA-MB-231 cells transfected with either PIGB wild-type or variant constructs with GST-tags. Antibody against GST (Antibody #2622, Cell Signaling Inc.) was used for detection of PIGB expression, and Antibody against MUC1 (VU4H5 Mouse mAb #4538, Cell Signaling Inc.) served as a loading control in Western Blot assay. Gemcitabine cytotoxicity performed with the MTS assay was performed in cells transfected with WT and variant constructs. No differences were observed between WT and variant SNP for any of the phenotypes tested. (D) EMSAs were performed for two regulatory SNPs in PIGB gene. The arrows indicate different binding pattern between WT and variant sequences for rs11636687 and rs28668016.

We first determined PIGB expression levels in 37 LCLs selected on the basis of genotypes for those 3 SNPs using both QRT-PCR assay and expression array data to confirm the association between the SNPs and PIGB expression. Cells carrying the variant alleles showed significantly lower expression levels than did WT cells (Figure 7B). We next determined the functional impact of these 3 SNPs. As shown in Figure 7C, overexpression of a construct for the PIGB coding SNP (rs2290344, M161T) in SU86 and MDA-MB-231 cells did not alter either mRNA or protein levels, nor did it have an effect on gemcitabine cytotoxicity. We then determined whether the two SNPs in regulatory regions, rs11636687 and rs28668016, might have functional impact. We performed electrophoresis mobility shift assays (EMSAs) for these two SNPs to determine whether there might be differences in binding patterns for possible transcription factors. Interestingly, the results from EMSA showed that DNA-protein binding was significantly increased for the probe containing the variant sequences for these two SNPs in both SU86 and MDA-MB-231 cells (Figure 7D). These results suggested that these two SNPs might alter the binding of transcription factors and, as a result, affect PIGB expression level.

Discussion

We previously performed a genome wide SNP association study with 550 K SNPs obtained with Illumina HumanHap550 BeadChips for the same cell lines to identify common polymorphisms that might influence both gene expression and response to these two drugs [18]. In the present study, we expanded the number of SNPs from 550 K to include over 1.3 million SNPs and selected candidate genes for functional follow-up studies based on SNP loci. This dense SNP coverage made it possible to identify many more candidates for functional follow-ups. That enabled us to take a different approach by focusing on “SNP loci” instead of single SNPs. The results listed in Table 3 show that 11 of 26 candidate genes selected in this fashion were validated functionally, while only two other genes from the previous 550 k studies were functionally validated [18].

We also tested the concordance of the results generated with 550 K and 1.3 million SNPs if we had used the same strategy as we did in the current study, i.e. using SNP loci to perform the association studies. The majority of top SNP peaks from the 550 K SNP data for both drugs displayed less significant SNPs for each locus as compared to the 1.3 million SNP data (Additional file 1: Table S3). These observations illustrate the advantage of the present selection strategy for candidate identification, as well as the advantage of using denser SNP coverage.

Of the 26 candidate genes that we identified for further siRNA screening followed by MTS assay, eleven candidate genes, including PIGB, TGFBI, DOK6, PSME3, ZADH2, TUSC3, C14orf169, SMC2, LNX2, RIS1, and HLA-DRA, showed a significant effect on response to gemcitabine and/or AraC in SU86 and/or MDA-MB231 cells. To identify potential pathways with which these genes might be involved, we used a dual luciferase reporter gene assay to assess the impact of these genes on 10 major cancer-related signaling pathways. As shown in Figure 6 and Table 3, except for the SMC2 gene, knockdown of the other 10 genes in SU86 cells significantly altered activities, based on the luciferase assay for at least one of the 10 cancer related signaling pathways. Genes such as TGFB1 showed changes for the most pathways. While TGFB1 has been well studied, genes such as C14orf169, an unknown gene, also showed increased activity in 7 of the 10 pathways.

We also observed that the activities of the Elk-1/SRF, AP1, NFκB, and Myc/MAX pathways were significantly decreased in SU86 cells when PIGB was down-regulated by a specific siRNA. PIGB, a gene of the phosphatidylinositol glycan (PIG) class B, encodes an enzyme involved in the synthesis of a glycosylphosphatidylinositol (GPI) anchor that is a membrane attachment structure for many proteins, including membranous enzymes, receptors, differentiation antigens, and other biologically active proteins [29]. GPI anchoring is essential for the expression of many of those proteins in either biological processes or cancer progression [30, 31]. The PIGB protein is a GPI mannosyltransferase III and is required for the transfer of the third mannose into the core structure of the GPI anchor [29, 32]. Previous studies have demonstrated that other PIG class members, such as PIGU and PIGT, are oncogenes in either human bladder cancer or breast cancer, respectively [33, 34]. Our findings indicate that PIGB is involved in sensitizing cancer cells to both gemcitabine and AraC, suggesting a possible role in oncogenic pathways as well as chemoresistance. The 8 PIGB SNPs were also associated with the expression of FKBP5, a gene that we previously reported to be important for gemcitabine and AraC response [17, 27]. Furthermore, PIGB expression itself is also correlated with FKBP5 gene expression. Although down regulation of PIGB altered FKBP5 mRNA level, overexpression of FKBP5 in PIGB stable knockdown cell lines did not change response to gemcitabine or AraC (Additional file 1: Figure S3). These observations indicate that PIGB influences the cytotoxicity of the two cytidine analogues through mechanisms that differ from FKBP5, in spite of the correlation of their expression levels observed in the LCLs. The exact mechanisms by which PIGB affects gemcitabine and AraC cytotoxicity need to be explored in the course of future experiments.

In addition to the characterization of candidate genes, we also focused on SNPs in the PIGB gene that showed cis-regulation of PIGB expression. SNPs in regulatory regions can influence drug response through an influence on gene expression. During our analysis, we found that most SNP associations with expression were through trans- regulation. The reason that we focused on SNPs in PIGB is because those SNPs displayed cis- regulations of PIGB and knockdown of PIGB showed an effect on cytotoxicity. The EMSA results also demonstrated “shifts” for the variant SNP sequences (Figure 7D), suggesting that PIGB gene expression might be regulated through binding to those transcription factors.

Previous studies demonstrated that one mechanism by which SNPs might influence drug cytotoxicity is through transcription regulation in either a cis- or trans-manner [18, 3537]. In this analysis, we found SNPs that could both have cis or trans relationship. In addition to the SNPs that cis regulate PIGB, we also found that SNPs close to C3orf23 were not only cis-associated with its own gene expression, but also trans-correlated with the expression of ZADH2 which was confirmed to affect drug response of gemcitabine in our functional validation study. How those genetic variations located in the upstream of C3orf23 affect the expression of ZADH2 gene in a trans- manner remains unknown. One mechanism might be that those SNPs nearby C3orf23 could alter DNA sequence binding to transcription factors (TFs), microRNA, or other long non-coding RNA (lnc RNA), thus affect transcriptional regulation of their target genes including ZADH2 gene, which could in turn, affect gemcitabine response.

Conclusions

In summary, this study performed with LCLs followed by functional characterization has enhanced our understanding of the action of gemcitabine and AraC in the therapy of cancer. Although there are limitations associated with the use of LCLs [38, 39], this system has proven to be extremely useful, both to generate pharmacogenomic hypothesis and to test pharmacogenomic signals identified during the clinical GWAS [1921]. Future studies using patient samples will now be required to confirm the candidates identified during this study.

Abbreviations

AraC: 

Cytosine arabinoside

LCL: 

Lymphoblastoid cell line

RRM1: 

Ribonucleotide reductase

CDA: 

Cytidine deaminase

CA: 

Caucasian-American

AA: 

African-American

HCA: 

African-American

EMSA: 

Electrophoresis mobility shift assays

QRT-PCR: 

Quantitative reverse transcription-PCR

QC: 

Quality control

HWE: 

Hardy-Weinberg equilibrium

AML: 

Acute myelogenous leukemia

TF: 

Transcription factors

GWAS: 

Genome-wide association studies

LD: 

Linkage disequilibrium.

Declarations

Acknowledgements

This study was supported in part by U.S. National Institutes of Health research grants K22 CA130828, R01 CA138461, P50 CA102701, U19 GM61388 (The Pharmacogenomics Research Network), ASPET-Astellas Award, a PhRMA Foundation “Center of Excellence in Clinical Pharmacology” Award, and the Gerstner Family Career Development Awards in Individualized Medicine.

Authors’ Affiliations

(1)
Division of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester
(2)
Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic
(3)
Current affiliation: Department of Biostatistics, University of Kansas Medical Center

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