Open Access

Copy number variation and cytidine analogue cytotoxicity: A genome-wide association approach

  • Krishna R Kalari1,
  • Scott J Hebbring2,
  • High Seng Chai1,
  • Liang Li2,
  • Jean-Pierre A Kocher1,
  • Liewei Wang2 and
  • Richard M Weinshilboum2Email author
BMC Genomics201011:357

DOI: 10.1186/1471-2164-11-357

Received: 28 August 2009

Accepted: 4 June 2010

Published: 4 June 2010

Abstract

Background

The human genome displays extensive copy-number variation (CNV). Recent discoveries have shown that large segments of DNA, ranging in size from hundreds to thousands of nucleotides, are either deleted or duplicated. This CNV may encompass genes, leading to a change in phenotype, including drug response phenotypes. Gemcitabine and 1-β-D-arabinofuranosylcytosine (AraC) are cytidine analogues used to treat a variety of cancers. Previous studies have shown that genetic variation may influence response to these drugs. In the present study, we set out to test the hypothesis that variation in copy number might contribute to variation in cytidine analogue response phenotypes.

Results

We used a cell-based model system consisting of 197 ethnically-defined lymphoblastoid cell lines for which genome-wide SNP data were obtained using Illumina 550 and 650 K SNP arrays to study cytidine analogue cytotoxicity. 775 CNVs with allele frequencies > 1% were identified in 102 regions across the genome. 87/102 of these loci overlapped with previously identified regions of CNV. Association of CNVs with gemcitabine and AraC IC50 values identified 11 regions with permutation p-values < 0.05. Multiplex ligation-dependent probe amplification assays were performed to verify the 11 CNV regions that were associated with this phenotype; with false positive and false negative rates for the in-silico findings of 1.3% and 0.04%, respectively. We also had basal mRNA expression array data for these same 197 cell lines, which allowed us to quantify mRNA expression for 41 probesets in or near the CNV regions identified. We found that 7 of those 41 genes were highly expressed in our lymphoblastoid cell lines, and one of the seven genes (SMYD3) that was significant in the CNV association study was selected for further functional experiments. Those studies showed that knockdown of SMYD3, in pancreatic cancer cell lines increased gemcitabine and AraC resistance during cytotoxicity assay, consistent with the results of the association analysis.

Conclusions

These results suggest that CNVs may play a role in variation in cytidine analogue effect. Therefore, association studies of CNVs with drug response phenotypes in cell-based model systems, when paired with functional characterization, might help to identify CNV that contributes to variation in drug response.

Background

It is known that inherited genomic CNV is linked to risk for human disease and response to treatment. It has also been established for decades that genomic variation, including CNV in germline DNA, can help predict variation in efficacy and/or adverse responses to therapeutic drugs [15]. For example, individuals with multiple copies of the gene encoding the drug metabolizing enzyme CYP2D6 are "ultrarapid" metabolizers as compared to those with CYP2D6 deletions ("poor" metabolizers), and these genotypes are associated with variation in response to a large number of drugs [4, 6]. CNVs within the human genome are not rare events. Redon et. al. [7] identified nearly 1,500 CNV regions scattered throughout the genome in 270 HapMap samples. Those regions comprised approximately 10% of the human genome, encompassing coding and non-coding regions, as compared to the < 1% of the genome that is occupied by SNPs [8]. CNVs appear to be present at lower frequencies than SNPs [9], but this may be due in part to the techniques utilized to identify them. Thus, the prevalence and biological significance of CNVs may be underestimated. As of early 2009, nearly 6,225 CNV loci had been cataloged by the Database of Genomic Variants http://projects.tcag.ca/variation/. In addition, nearly 18% of mRNA species that are genetically regulated through cis effects could be explained by CNVs [10]. Together with SNP genotypes, CNV data can be generated with SNP arrays [7, 9, 1113]. Although these methodologies have limitations [14], CNVs, depending on their size and location, may be just as important for variation in function as are SNPs.

The cytidine analogues, gemcitabine and AraC, show significant therapeutic effect in several types of cancer. Gemcitabine is mainly used to treat solid tumors [15, 16] while AraC is used to treat acute myelogenous leukemia [17]. Clinical response to these two drugs varies widely, and previous studies showed that inheritance can contribute to the variation in response of these two drugs [18]. In this study, we set out to test the hypothesis that CNV might contribute to variation in gemcitabine and AraC response in 197 EBV transformed lymphoblastoid cell lines using SNP data obtained with Illumina 550 and 650 K SNP arrays.

Methods

Genotyping and populations

A subset of the "Human Variation Panel" lymphoblastoid cell lines consisting of 60 Caucasian-American (CA), 54 African-American (AA), and 60 Han Chinese-American (HCA), as well as 23 CEPH Caucasian HapMap EBV transformed cell lines was obtained from the Coriell Cell Repository (Camden, NJ). These cell lines had been obtained from healthy individuals and were anonymized by the National Institute of General Medical Sciences prior to deposit. All of these individuals had provided written consent for the use of their cells and DNA from those cells to be used for experimental purposes. We genotyped the AA DNA from these cell lines using the Illumina Human Hap 650 beadchip (Human660W-Quad v1), and the Illumina Human Hap 550 beadchips were used to genotype the remainder of the samples. All samples were genotyped in the Mayo Clinic Genotyping Core Facility. All but two samples had a call rate greater than 98%, and those two samples, even after repetition, had call rates between 95 and 98%. We assessed LRR standard deviation (SD) for our samples and found that none of the samples had a SD less than 0.21. Quality control (QC) recommendations for the PennCNV or QuantiSNP algorithms suggest using a SD < 0.3 [19]. Since, our LRR standard deviation did not exceed this QC threshold; we also used those two samples in the analysis. For consistency, we did not include the additional 100 K SNPs genotyped for the AA samples in the CNV analysis.

Copy number identification

Bead Studio version 3.1 was used to obtain log R ratios and B allele frequencies for 550,000 SNPs in the 197 samples studied. LogR ratios were generated by comparing our experimental LogR values to Bead Studio's built in multi-ethnic HapMap population. CNV genotyping was performed using an Objective Bayes Hidden-Markov model (QuantiSNP) [20] plug-in within Illumina's Bead Studio interface. QuantiSNP is a statistical algorithm that utilizes joint information with regard to log R ratios and B allele frequencies for quantitative SNP array data analysis that allows for precise discovery and mapping of copy number changes. We used the QuantiSNP parameters recommended by Illumina: expectation maximization = 10, CNV length = 10,000, maximum copy number returned = 4, no GC content normalization, and score threshold = 50. After applying the QuantiSNP algorithm, we exported the CNV values and confidence values for each SNP out of the Bead Studio software. Using our own in-house programs written in R and Perl, we then separated all SNPs associated with CNVs that were observed in two or more samples (frequency > 1%). These thresholds and parameters were set conservatively to accurately identify CNVs under these conditions. This approach should reduce the false positive rate, but at the risk of increasing the false negative rate and missing more common, yet smaller CNVs that are inherently more difficult to detect. After transforming CNV values for each SNP into deletion (CNV value < 2), normal (CNV value = 2) or amplification (CNV value > 2), distinct copy number regions were obtained by merging neighboring SNPs with identical CNVs across samples. A detailed description of the methods used is available in the Additional file 1 Methods Section.

Copy number validation

Eleven copy number regions found to be associated with gemcitabine and AraC IC50 values (p < 0.05) were selected for validation using multiplex ligation-dependent probe amplification (MLPA). Oligonucleotides were preferentially designed based on a successful assay, followed by selection for coding sequences and underlying p-values. M13 sequence was attached to each probe together with a complementary FlexMap100 sequence (Luminex, Austin, TX). Specifically, 80 ng of DNA was denatured at 98°C for 5 minutes, followed by 25°C for 1 minute. In an 8 μL reaction, 80 ng of DNA and 0.3 femtomole/μL of each probe were mixed with 1.5 μL of MLPA buffer (MRC-Holland, Amsterdam, Netherlands). Probes were allowed to hybridize at 60°C for 16-24 hours. Probes were ligated in a reaction containing 25 μl H2O, 3 μL Ligase-65 Buffer A and B (MRC-Holland), and 1 μL Ligase-65 (MRC-Holland) at 54°C for 15 minutes, followed by 98°C for 5 minutes. Each 50 μL PCR reaction consisted of 10 μL of ligated product mixed with 27.5 μL H2O, 5 μL 10 × buffer (Invitrogen, Carlsbad, CA), 1.5 μL 50 mM MgCl2 (Invitrogen), 4 μL 10 mM dNTPs (Applied Biosystems, Foster City, CA), 0.5 μL 10 μM M13 primers, and 1 μL Platinum Taq (Invitrogen). 10 μL of PCR product was then added to 40 μL of bead mix containing 2,000 beads for each FlexMap Microsphere (Luminex) suspended in 1 × TMAC, and the mixture was incubated at 96°C for 2 minutes, followed by 37°C for 60 minutes. Following incubation, 0.2 μL of Streptavidin R-Phycoerythrin Conjugate (Invitrogen) plus 25 μL of 1 × TMAC was added and incubated at room temperature for 30 minutes. Samples were assayed on a LiquiChip 100IS System (Qiagen, Valencia, CA) and results were analyzed with GeneMarker 1.6 software. Of the 11 CNV assayed, one (chr14CNV87:106047919-106066496), did not provide adequate signal intensity for analysis.

MTS assay

AraC was purchased from Sigma-Aldrich (St. Louis, MO) and gemcitabine was provided by Eli Lilly (Indianapolis, IN). Cytotoxicity assays were performed with the CellTiter 96® Aqueous Non-Radioactive Cell Proliferation Assay (Promega Corporation, Madison, WI). The drug concentrations used to perform these experiments were described in detail previously by Li et al., 2008 [18].

Statistical analysis

The cytotoxicity phenotype (IC50) was determined on the basis of the best fitting curve, either 4 parameter logistic, 4 parameter logistic with top = 100%, or 4 parameter logistic with bottom = 0%. The curves were constructed using the dose response curves package in R. The logistic model with the lowest mean square error was used to determine IC50 values for gemcitabine and AraC as described in Li et al. [18]. Drug response phenotypes (IC50 values) for both drugs were adjusted for ethnicity, gender and storage time of the 197 samples using linear regression (natural log transformation applied to IC50 values). In addition, CNV values were adjusted for ethnicity and gender. Linear regression was then used to perform association with adjusted CNV values (residuals from regressing CNV against ethnicity and gender) with adjusted IC50 phenotypes (residuals from regressing log IC50 against ethnicity, gender and storage time). P-values for association were obtained after performing 1000 permutations for both gemcitabine and AraC IC50 values.

Transient transfection and RNA interference

Human MiaPaca-2 pancreatic cancer cells were transfected with siRNA using Lipofectamine RNAMAX (Invitrogen). Specifically, cells were seeded into 96-well plates and were mixed with siRNA-complex containing 50 nM specific or negative control siRNA (Qiagen) and transfection reagent (Invitrogen) in Opti-MEM® I Reduced Serum Media (Invitrogen). Forty eight hours post-transfection, cells were harvested for cell-based assays. SMYD3 siRNA and negative control siRNA were purchased from Qiagen and were used as suggested by the manufacturer.

Sequences for siRNA against SMYD3 were: Sense strand: GGC GAU CAU AAG CAG CAA UdTdT CGA UUA UAA UAA AUU CAA CdTdT

Antisense strand: AUU GCU GCU UAU GAU CGC CdTdT UUU GAA UUU AUU AUA AUC GdTdG

Sequences for negative control siRNA were:

Sense strand: UUC UCC GAA CGU GUC ACG UdTdT

Antisense strand: ACG UGA CAC GUU CGG AGA AdTdT

Real-time quantitative reverse transcription-PCR

Total RNA was isolated from cultured cells with the Qiagen RNeasy kit (Qiagen), followed by QRT-PCR performed with the 1-step, Brilliant II SYBR Green QRT-PCR master mix kit (Stratagene, La Jolla, 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 in triplicate with β-actin as an internal control. Control reactions lacked RNA template.

Results

CNV identification

We used the QuantiSNP parameters recommended by Illumina for copy number identification. The QuantiSNP algorithm in Illumina provided CNV values and confidence values for each SNP and sample. After pre-processing the data, we had 73,738 SNPs with CNV values other than "normal" (CNV value = 2). 1,674 SNPs were retained in the analysis after excluding SNPs that did not display variation in at least two samples (minor allele frequency > 1%). We then applied a simple segregation algorithm as described in Additional file 1 Methods and identified 775 CNVs at 102 loci using the 197 DNA samples obtained from 3 ethnic groups. Figure 1 shows the CNV call results using CNV region display in Bead Studio Software for 15 samples selected randomly from among the 197 samples assayed. From the randomly selected data displayed in Figure 1, it is clear that specific CNV regions could be associated with multiple DNA samples. Copy number loci or regions can also have multiple forms of variation, probably as a result of different breakpoints. The mean and median CNV frequencies per sample were 3.9 and 4.0, respectively, with a maximum value of 10. The 102 CNV loci identified represented 7.8 Mb of sequence, with an average length of 77 kb and a median of 20 kb (90 bp to 1.7 Mb) (Table 1). Twenty five CNVs identified in this study were observed in all three ethnic groups; 45 CNVs were found in 2 ethnicities; and 32 CNVs were observed in only one ethnic group (Table 1). No loci below 1% copy number frequency were reported in this study.
Table 1

CNV regions identified in 197 DNA samples.

CNV_ID

Start

Stop

Size

AA

CA

HCA

Combined CNV Frequency

chr1CNV1

12789177

12834675

45498

0.036

0.024

0.050

0.035

chr1CNV2

94906770

94925850

19080

0.091

0.000

0.000

0.025

chr1CNV3

105966892

106000090

33198

0.055

0.000

0.017

0.020

chr1CNV4

147306690

147414362

107672

0.036

0.012

0.000

0.015

chr1CNV5

187665261

187809352

144091

0.000

0.000

0.083

0.025

chr1CNV6

195092486

195160949

68463

0.073

0.000

0.083

0.045

chr1CNV7

243707190

243713984

6794

0.000

0.036

0.000

0.015

chr2CNV8

41092376

41099005

6629

0.273

0.024

0.017

0.091

chr2CNV9

57281457

57295357

13900

0.000

0.036

0.000

0.015

chr2CNV10

89397452

89877778

480326

0.036

0.060

0.017

0.040

chr2CNV11

110228954

110315618

86664

0.036

0.012

0.050

0.030

chr2CNV12

184808081

184866619

58538

0.127

0.012

0.000

0.040

chr2CNV13

242566407

242653950

87543

0.000

0.084

0.000

0.035

chr3CNV14

6194326

6211038

16712

0.073

0.000

0.000

0.020

chr3CNV15

53003415

53010084

6669

0.000

0.048

0.017

0.025

chr3CNV16

65168286

65187636

19350

0.018

0.120

0.117

0.091

chr3CNV17

75535790

75610832

75042

0.000

0.036

0.000

0.015

chr3CNV18

101837214

101854561

17347

0.000

0.036

0.000

0.015

chr3CNV19

152997395

153028291

30896

0.000

0.036

0.000

0.015

chr3CNV20

163614102

163617940

3838

0.018

0.060

0.000

0.030

chr3CNV21

163701543

163710564

9021

0.000

0.012

0.283

0.091

chr3CNV22

166750382

166766442

16060

0.036

0.012

0.000

0.015

chr3CNV23

177371924

177397828

25904

0.000

0.072

0.000

0.030

chr3CNV24

189070613

189088009

17396

0.000

0.012

0.033

0.015

chr3CNV25

192548615

192550982

2367

0.000

0.120

0.000

0.051

chr4CNV26

64386888

64391664

4776

0.000

0.024

0.033

0.020

chr4CNV27

64780488

64795145

14657

0.000

0.000

0.050

0.015

chr4CNV28

88405746

88445557

39811

0.055

0.000

0.000

0.015

chr4CNV29

104433739

104454829

21090

0.091

0.000

0.000

0.025

chr4CNV30

161277505

161290832

13327

0.000

0.060

0.000

0.025

chr4CNV31

162083578

162175279

91701

0.000

0.012

0.083

0.030

chr5CNV32

9955403

9976731

21328

0.036

0.036

0.000

0.025

chr5CNV33

97075236

97107276

32040

0.036

0.036

0.000

0.025

chr5CNV34

117418457

117420311

1854

0.109

0.060

0.000

0.056

chr5CNV35

120337992

120440119

102127

0.055

0.000

0.000

0.015

chr5CNV36

160474752

160479606

4854

0.000

0.000

0.050

0.015

chr2CNV37

67076651

67104015

27364

0.018

0.181

0.000

0.081

chr6CNV38

79031111

79086086

54975

0.255

0.410

0.083

0.268

chr6CNV39

93632051

93634213

2162

0.073

0.000

0.017

0.025

chr6CNV40

167619368

167688151

68783

0.055

0.000

0.000

0.015

chr7CNV41

64334979

64553672

218693

0.000

0.024

0.033

0.020

chr7CNV42

76038186

76394983

356797

0.073

0.036

0.033

0.045

chr7CNV43

81761377

81761849

472

0.000

0.000

0.050

0.015

chr7CNV44

89187436

89247424

59988

0.036

0.012

0.000

0.015

chr7CNV45

141420759

141433796

13037

0.036

0.060

0.067

0.056

chr8CNV46

3775146

3776955

1809

0.055

0.036

0.050

0.045

chr8CNV47

3987675

3990899

3224

0.073

0.000

0.000

0.020

chr8CNV48

5583294

5591685

8391

0.018

0.036

0.000

0.020

chr8CNV49

13643904

13652680

8776

0.036

0.012

0.000

0.015

chr8CNV50

13657871

13680110

22239

0.182

0.012

0.000

0.056

chr8CNV51

16307052

16309085

2033

0.018

0.048

0.000

0.025

chr8CNV52

72378670

72378984

314

0.000

0.036

0.000

0.015

chr8CNV53

137757412

137919630

162218

0.018

0.036

0.000

0.020

chr9CNV54

507715

513495

5780

0.055

0.000

0.000

0.015

chr9CNV55

581094

597738

16644

0.073

0.000

0.000

0.020

chr9CNV56

5376384

5382199

5815

0.109

0.000

0.000

0.030

chr9CNV57

9782326

9786116

3790

0.000

0.000

0.050

0.015

chr9CNV58

11941204

12175185

233981

0.018

0.000

0.067

0.025

chr10CNV59

20891019

20894574

3555

0.000

0.036

0.033

0.025

chr10CNV60

45489854

47173619

1683765

0.200

0.157

0.050

0.136

chr10CNV61

58186118

58188682

2564

0.036

0.024

0.000

0.020

chr10CNV62

58583657

58603555

19898

0.000

0.000

0.067

0.020

chr10CNV63

122759910

122774261

14351

0.127

0.000

0.000

0.035

chr10CNV64

135116379

135219995

103616

0.073

0.060

0.017

0.051

chr11CNV65

5858528

5889688

31160

0.000

0.000

0.050

0.015

chr11CNV66

21145662

21170916

25254

0.109

0.012

0.000

0.035

chr11CNV67

25574425

25580720

6295

0.036

0.012

0.000

0.015

chr11CNV68

25662734

25676867

14133

0.127

0.012

0.000

0.040

chr11CNV69

55139733

55201444

61711

0.036

0.120

0.267

0.141

chr11CNV70

55217364

55346872

129508

0.036

0.000

0.000

0.010

chr11CNV71

81182373

81192815

10442

0.018

0.120

0.117

0.091

chr11CNV72

99154024

99156143

2119

0.000

0.012

0.050

0.020

chr11CNV73

132499814

132509446

9632

0.000

0.036

0.000

0.015

chr11CNV74

134154053

134211153

57100

0.018

0.036

0.000

0.020

chr12CNV75

7888157

7982106

93949

0.018

0.000

0.050

0.020

chr12CNV76

19364102

19442103

78001

0.000

0.024

0.017

0.015

chr12CNV77

31180151

31298076

117925

0.036

0.060

0.017

0.040

chr12CNV78

62269256

62415375

146119

0.036

0.012

0.033

0.025

chr12CNV79

69162075

69162165

90

0.018

0.024

0.033

0.025

chr12CNV80

126056007

126056525

518

0.055

0.000

0.017

0.020

chr12CNV81

127794683

127830402

35719

0.000

0.012

0.017

0.010

chr12CNV82

130297674

130314013

16339

0.000

0.024

0.017

0.015

chr12CNV83

130368913

130378451

9538

0.036

0.024

0.033

0.030

chr14CNV84

43576901

43584372

7471

0.000

0.000

0.133

0.040

chr14CNV85

85358666

85376726

18060

0.018

0.024

0.000

0.015

chr14CNV86

85540054

85557089

17035

0.000

0.036

0.000

0.015

chr14CNV87

105997070

106237639

240569

0.073

0.157

0.067

0.106

chr15CNV88

30298847

30301633

2786

0.073

0.012

0.033

0.035

chr15CNV89

32530025

32587887

57862

0.127

0.072

0.067

0.086

chr15CNV90

32724681

32757729

33048

0.000

0.036

0.000

0.015

chr17CNV91

6047837

6061766

13929

0.036

0.012

0.000

0.015

chr17CNV92

14988424

14998870

10446

0.000

0.000

0.117

0.035

chr18CNV93

1915033

1964966

49933

0.018

0.036

0.000

0.020

chr18CNV94

64898548

64905367

6819

0.018

0.036

0.333

0.121

chr18CNV95

65360121

65362926

2805

0.000

0.036

0.017

0.020

chr19CNV96

15641747

15690364

48617

0.091

0.000

0.000

0.025

chr19CNV97

20423788

20473895

50107

0.109

0.084

0.033

0.076

chr19CNV98

48066441

48387680

321239

0.127

0.084

0.133

0.111

chr20CNV99

14729882

14770129

40247

0.036

0.036

0.000

0.025

chr22CNV100

17270615

17376565

105950

0.036

0.000

0.033

0.020

chr22CNV101

20718332

21554058

835726

0.218

0.253

0.167

0.217

chr22CNV102

23994408

24239811

245403

0.055

0.060

0.033

0.051

  

Total

7805201

    
  

Average

76522

    
  

Median

19624

    
  

Minimum

90

    

The 102 CNV regions identified using the 197 DNA samples obtained from 3 ethnic groups.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-11-357/MediaObjects/12864_2009_Article_2951_Fig1_HTML.jpg
Figure 1

Visualization of copy number regions identified in 15 randomly selected samples using Bead studio software. Randomly selected individual samples are listed on the X-axis and chromosomes on the Y-axis. Each colored bar represents one CNV call. Colors indicate copy number; where dark red indicates copy # 0, dark orange indicates copy # 1, dark blue indicates copy # 3, blue violet indicates copy # 4+. The thickness of the band indicates the length of the CNV region.

We compared our loci to those in the structural variation table in the University of California Santa Cruz's (UCSC) database http://genome.ucsc.edu. Figure 2 shows an example on chromosome 22. Eighty-seven of the 102 loci that we identified overlapped with previously characterized CNVs, and 51/102 had been identified in more than one study. Of the 15 loci that had not been characterized previously, no obvious differences in size or prevalence were observed, suggesting that these are likely to be true CNVs and not the result of systematic error. In Figure 2 we have superimposed our CNV values for a portion of chromosome 22 over UCSC database data. Divergence from the baseline indicates regions of CNV, while amplitude represents prevalence. Figure 2 represents the overlap of our data (Illumina 550 + 650 K, Illumina 550 K, Illumina 650 K) at the top with previous reports at the bottom [7, 1113, 2125]. Lack of technical bias between Illumina 550 K and 650 K data is also shown since only the overlapping SNP set for the two platforms was used (Figure 2).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-11-357/MediaObjects/12864_2009_Article_2951_Fig2_HTML.jpg
Figure 2

Comparison of chromosome 22 CNV regions identified using our 197 cell line samples compared with the results of previous studies in the UCSC genome browser. The Illumina 550 + 650 K (all samples combined), Illumina 550 K (CA, CEPH, HCA populations) and Illumina 650 K (AA samples) results in the diagram are from the present study, where spikes in the data indicate changes in CNV values. The "RefSeq Genes" row shows the locations of known genes in the human genome. In the "Structural Variation" tracks, green color indicates duplications, red indicates deletions, blue indicates both deletion and duplication, black represents an inversion and gray could be a gain or loss. "Conrad Dels" in the diagram are deletions detected by the analysis of SNP genotypes using the HapMap Phase I data, release 16c.1, CEU and YRI samples [11]. "Hinds Dels" are deletions observed during haploid hybridization analysis in 24 unrelated individuals from the Polymorphism Discovery Resource, selected for a SNP LD study [12]. "Iafrate CNVs" are from BAC microarray analysis of a population of 55 individuals [21]. "Locke CNVs" are CNV regions identified using array CGH in 269 HapMap individuals [22]. "McCarroll Dels" are deletions from genotype analysis, performed with HapMap Phase I data, release 16a [13]. "Redon CNVs" are from SNP and BAC microarray analysis of HapMap Phase II data [7]. "Sebat CNVs" represents oligonucleotide microarray analysis performed with a population of 20 normal individuals [7]. "Sharp CNVs" represents putative CNV regions detected by BAC microarray analysis in a population of 47 individuals [24]. The "Tuzun Fosmids" row consists of fosmid mapping sites detected by mapping paired-end sequences from a human fosmid DNA library [25].

We also had Affymetrix U133 Plus 2.0 expression array data for the same 197 lymphoblastoid cell lines in which we assayed CNV [18], which made it possible for us to quantify the expression of genes linked to CNVs. Forty one expression array probesets mapped close to (within 500 kb) or within the 102 CNV regions that we identified. Of those 41 probesets, only 7 were expressed (17% (7/41) when compared to the 28% of the 54,000 probesets across the entire genome that were expressed) in the lymphoblastoid cell lines, with an average expression value above "100" using GCRMA normalization data (Additional file 1 Table S1).

Gemcitabine and AraC IC50 value associations with CNVs

To identify gene(s) that might contribute to variation in cytidine analog-induced cytotoxicity, we next analyzed associations between CNVs and IC50 values for gemcitabine and AraC. We had previously performed gemcitabine and AraC cytotoxicity studies using the same cell lines, as described previously [18]. IC50 values for both drugs were used as phenotypes for the association studies, and the analysis was adjusted for race and gender. The association studies with gemcitabine and AraC IC50 value phenotypes resulted in the identification of 5 and 6 CNV regions, respectively, that showed associations with p-values < 0.05 after 1000 permutations. Although these two drugs are similar in structure, we did not observe any common CNV regions that were significantly associated with IC50 values for both gemcitabine and AraC. The annotation and association results for gemcitabine and AraC are listed in Tables 2 and 3, respectively.
Table 2

Significant associations between gemcitabine IC50 values and CNV regions.

CNV ID

Permutation P-value

Chromosome: Region

Number of SNPs

Length(bp)

SNP start-SNP end

Nearest Gene(s)

chr9CNV58

0.027

chr9:12005741-12098916

23

93176

rs10809674-rs12351590

TYRP1

chr1CNV5

0.031

chr1:187795066-187809352

4

14287

rs382645-rs269747

FAM5C

chr14CNV87

0.036

chr14:106047919-106066496

2

18578

rs4562969-rs10151262

ADAM6

chr11CNV74

0.042

chr11:134154053-134211153

25

57101

rs1289444-rs2155304

B3GAT1

chr11CNV65

0.043

chr11:5858528-5889688

12

31161

rs1377518-rs1453428

OR52E4

Association of CNV regions with the gemcitabine phenotype.

CNV validation using MLPA

To experimentally validate CNVs that were significantly associated with drug cytotoxicity, we tested the 11 CNVs with permutated p-values for association that were less than 0.05 using a high-throughput method designed to quantify genomic content, multiplex ligation-dependent probe amplification (MLPA). We were unable to amplify one CNV (chr14CNV87:106047919-106066496). When we compared MLPA to CNV values on a per-sample basis, 173/197 samples matched our original QuantiSNP CNV calls. Therefore, our original analysis had a zero false positive rate for all but two regions (1.23%), and the false negative rates ranged from 0% to 65% for the 10 regions that could be amplified (Additional file 1 Table S2). The chr2CNV10 CNV, as shown in Table S2, had an exceptionally high false negative rate, and we cannot rule out the possibility that a SNP beneath the MLPA probe might be responsible.

Functional characterization

To further characterize CNV regions, we identified genes within 500 Kb of the 11 regions that were associated with gemcitabine or AraC IC50 values. This relatively large region was chosen because previous studies have shown that cis-acting regulators can act over megabase distances [26, 27]. Two of the 5 regions that were significantly associated with gemcitabine cytotoxicity contained a gene within 500 Kb of the CNV. Both regions were on chromosome 11. One chromosome 11 region, 134154053-134158019, had 25 SNPs associated with the CNV. The nearest gene, B3GAT1, was 372016 bp distant from the CNV (Table 2). The second chromosome 11 region, 5858528-5889688, had 12 SNPs associated with the CNV and the OR52E4 gene overlapped this region.

In the case of AraC, one region (divided into 3 sub-regions, as shown in Table 3) associated with AraC cytotoxicity was located on chromosome 22, with the nearest gene LRP5L (low density lipoprotein receptor-related protein 5-like), more than 3486 bases distant. Genes associated with chromosome 2 and 12 regions were NPHP1 [nephronophthisis 1 (juvenile)], PLEKHA5 (pleckstrin homology domain containing, family A member 5) and GPR133 (G protein-coupled receptor 133), respectively. NPHP1 and PLEKHA5 overlapped the CNV regions, whereas GPR133 was 179 Kb away from the CNV region associated with AraC. The region located on chromosome 1 overlapped the KIF26B gene and was 295 Kb away from a gene encoding a histone methyltransferase, SMYD3.
Table 3

Significant associations between AraC IC50 values and CNV regions.

CNV ID

Permutation P-value

Chromosome: Region

Number of SNPs

Length (bp)

SNP start-SNP end

Nearest Gene(s)

chr22CNV102

0.013

chr22:24092010-24128856

5

36847

rs713878-rs84486

LRP5L

 

0.028

chr22:23999581-24091936

14

92356

rs6004527-rs713847

LRP5L

 

0.028

chr22:24135224-24239811

30

104588

rs13057190-rs2780695

LRP5L

chr2CNV10

0.016

chr2:89714801-89874746

9

159946

rs2847840-rs842164

FLJ40330

chr12CNV76

0.020

chr12:19364102-19442103

16

78002

rs12825616-rs2565666

PLEKHA5

chr1CNV7

0.035

chr1:243707190-243713984

6

6795

rs10737772-rs12121903

SMYD3,KIF26B

chr2CNV11

0.044

chr2:110243431-110315618

11

72188

rs3789735-rs17463266

NPHP1

chr12CNV83

0.047

chr12:130368913-130378451

5

9539

rs12319995-rs4759915

GPR133

Association of CNV regions with the AraC phenotype.

Since we had Affymetrix U133 Plus 2.0 mRNA expression array data for the same lymphoblastoid cell lines in which we had assayed CNV [18]; we determined average expression levels for all genes associated with the CNVs. Although there were no probesets associated with ORE52E4, we found that the average mRNA expression levels after GCRMA normalization for probesets linked with B3GAT1, TYRP1, FAM5C, ADAM6, LRP5L, PLEKHA5, KIF26B, NPHP1, FLJ40330, and GPR133 were less than 10, suggesting either low or no expression of these genes in lymphoblastoid cells. Therefore, no further analysis was conducted with these candidates. However, one of the genes associated with AraC IC50 had an average expression of 247 (SMYD3) in our cell lines and the expression of this gene was also associated with AraC IC50, with a p-value of 0.0027. The Chr1CNV7 association did not pass Bonferroni correction. However, the association study was a "discovery" study - to be followed by functional genomics validation. Hence, we selected the SMYD3 candidate gene based on expression and possible biological relevance to cancer.

SMYD3 functional validation

It is known that knockdown of SMYD3 inhibits cervical carcinoma cell growth and invasion [28] and that mutations in the 5'-flanking region of SMYD3 may represent a risk factor for human cancer [29]. It is also known that SMYD3 plays crucial roles in HeLa cell proliferation and migration/invasion, so it has been suggested that it may be a useful therapeutic target in human cervical carcinomas [30]. As shown in Table 3, a CNV region located on chromosome 1 close to the SMYD3 gene (chr1CNV7) is associated with AraC IC50 value, with a permutation p-value of 0.035. The chr1CNV7 deletion occurred in 3 samples, all from Caucasian subjects, so we also tested the association in only this ethnic group. Likelihood ratio testing of linear regression of AraC log IC50 values against gender and storage time, with or without the relevant CNV values, gave a p-value of 0.019. In addition, analysis of IC50 values with the chr1CNV7 region showed that deletion of this CNV region was associated with an increase in the IC50 value for AraC (Table 4). To confirm results obtained from the association study, we also performed specific siRNA knockdown of the SMYD3 gene in human MIApaca-2 pancreatic cancer cells, followed by cytotoxicity studies. Down regulation of SMYD3 mRNA by siRNA desensitized the pancreatic cancer cells to AraC (P-value= 0.0011) when compared with cells transfected with negative control siRNA (Figure 3), a directional change consistent with the results of our CNV association study. Although, SMYD3 did not show a significant association with gemcitabine cytotoxicity during the association study, we also performed functional studies with that drug. We found that knockdown of the SMYD3 gene also made MIApaca-2 cells more resistant to gemcitabine (P-value = 0.0002) as shown in Figure 3. The Chr1CNV7 copy number was associated with gemcitabine IC50 values, (r-value = -0.01 and p-value = 0.804). While the p-value of association was insignificant, the directionality of the association was consistent with the results of the knockdown studies. In summary, knockdown of SMYD3, followed by cytotoxicity studies with both drugs, showed significant deviations, but the deviation was small for AraC when compared to that after gemcitabine treatment (Figure 3).
Table 4

AraC IC50 value mean, median and standard deviations for copy number values = 1 (deletion) and copy number value = 2 (normal).

 

AraC IC50 values (μmol)

CNV Value

Mean

Median

Std Dev

1

1.177

1.092

0.486

2

-0.017

-0.180

0.998

Summary of IC50 values for copy number = 1 and copy number = 2.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-11-357/MediaObjects/12864_2009_Article_2951_Fig3_HTML.jpg
Figure 3

Functional characterization for the SMYD3 candidate gene with specific siRNA knockdown. (A) Knockdown of the SMYD3 gene in human MIApaca-2 pancreatic cancer cells resulted in increased resistance to both AraC and gemcitabine as determined by MTS assay. SEM values for 3 independent experiments were so small that they are contained within the symbols. (B) Quantitative RT-PCR for SMYD3 in MIApaca-2 cells. Error bars represent SEM values for three independent experiments.

Finally, since the CNV close to the SMYD3 gene was significantly associated with IC50, and since the functional validation studies of SMYD3 agreed with the association study results, we expected that variation of mRNA expression for SMYD3 in the cell lines might be significantly correlated with IC50. SMYD3 mRNA expression was significantly associated with IC50 value, with a p-value of 0.028.

Discussion

CNV can occur as a result of genomic rearrangements like deletion, duplication, inversion, and translocation. Features such as the presence of repetitive elements, size of the sequences, GC content, similarity and distance between the sequences play a critical role in determining susceptibility of regions to these rearrangement events [31]. Many methods have been used successfully to identify CNV regions across the genome. The high density of data from SNP platforms such as those of supplied by Illumina or Affymetrix has not only allowed us to perform genome-wide association studies to identify genotypes that are associated with a phenotype, but have also made it possible to quantify SNP alleles (log R ratios and B allele frequencies) for CNV. These log R ratios and B allele frequencies can be used to discover CNV by applying computational algorithms. However, despite advances in computational methods, the identification of intermediate sized CNVs (50 bp to 50 Kb) remains a challenge; since detection of CNVs is based on the density and spacing of probes on the platform.

In this study, we have used 550,000 SNP markers (Illumina 550 K Bead chip) to discover CNV in 197 human lymphoblastoid cell lines obtained from ethnically diverse populations. The average distance between markers on the Illumina 550 K chip is 5.8 kb, and the average size of CNVs identified in our cell lines was more than 76,000 bp, indicating that smaller CNV may be underrepresented in our study. The Illumina 550 K SNP chip was designed, in part, to interrogate gene rich regions [32]; which is an advantage with regard to a lower probability of our missing a gene-related CNV. We identified 775 CNVs in 102 regions with minor allele frequencies > 1% (Table 1). Variables such as array, coverage, intensity and CNV calling algorithms may all give different CNV calls. Therefore, we used previous copy number findings represented in the structural variation table in the UCSC database http://genome.ucsc.edu to compare with our CNVs and found that the vast majority of variant loci (87 of 102) were found in other publications, and 51/102 were represented in multiple studies. Although we did find agreement for many of our CNVs with previously reported variants, there were other CNV regions previously reported that were not identified in our study. This could be due to our stringent criteria for CNVs. It also could be due to the different platforms, methods and study populations used in different studies. In addition rare events are usually not reported.

It is known that variation in response to chemotherapy results from many factors, including gender, race, environmental factors and DNA sequence variation. DNA sequence variation may include both SNPs and CNV. Therefore, the presence of CNV is an important factor that may contribute to variation in response to chemotherapy. Specifically, the existence of CNV within or near a gene might result in differences in mRNA and protein expression. To identify possible pharmacogenomic candidate genes that might be affected by CNV, we tested the association of CNV with a drug response phenotype (IC50) for gemcitabine and AraC using a 197 lymphoblastoid cell line-based model system designed to make it possible to study common human genetic variation. Although tumor genome is critical for understanding response to therapy and disease pathophysiology, the germline genome is also critical, especially for drug response phenotypes. Obviously, we understand that these lymphoblastoid cell lines were EBV transformed from normal individuals, and that they were neither collected from cancer patients nor tumor tissues. Hence, we might miss some candidate genes that may be specific to cancer. However, lymphoblastoid cell lines have been shown by several groups, including ours, to be useful for identifying candidate genes or genetic variation associated with drug-induced cytotoxicity [18, 3337]. Therefore, in this study we also used these lymphoblastoid cell lines to study the possible contribution of CNVs to variation in drug response. To begin the process of understanding how variation in copy number might affect drug response phenotypes for gemcitabine and AraC, we correlated 775 CNVs with IC50 values in 197 lymphoblastoid cell lines. 11/102 regions were associated with gemcitabine and AraC IC50 values (Tables 2 and 3). We then performed MLPA to compare with and to validate the in-silico QuantiSNP CNV calls.

Since we had Affymetrix U133 Plus 2.0 expression array data for the same lymphoblastoid cell lines [18]; we determined expression levels for genes surrounding the 11 CNV regions. The B3GAT1, LRP5L, PLEKHA5, KIF26B, NPHP1, TYRP1, FAM5C, ADAM6, FLJ40330, and GPR133 genes had low expression. Only one CNV on chromosome 1 (chr1CNV7) had a gene (SMYD3) in close proximity that displayed high expression in the lymphoblastoid cell lines.

SMYD3, found on the q arm of chromosome 1, encodes an alternatively spliced transcript for 369 or 428 amino acids protein. Hamamoto et. al. first described SMYD3's histone methyltransferase activity with specificity for di- and tri- methylation of lys4 on Histone 3. SMYD3's histone methyltransferase activity results in transcription induction for at least 60 targets across the genome [38]. Enhanced expression of SMYD3 has been observed in numerous tumors including colorectal, hepatocellular [38] and breast cancer [39]. Overexpression experiments of SMYD3 have repeatedly shown to increase the rate of cell proliferation [3840], while knockdown experiments result in decrease cell proliferation and cell migration while increasing apoptosis [28, 41]. Our studies indicated the association of the chr1CNV7 with AraC cytotoxicity as well as correlation with SMYD3 expression. Functional validations of our results were performed with knockdown of the SMYD3 gene in pancreatic cancer cell lines. Knockdown made the cells more resistant to AraC, confirming the association study results, and also made them resistant to gemcitabine. Our results suggest that joining association studies with functional validation experiments may help to identify biomarkers for disease or response to therapy.

Conclusions

We took the advantage of genome-wide SNP data obtained with 550 K Illumina Bead Chips to identify CNV regions across the genome in 197 lymphoblastoid cell lines. Association studies with gemcitabine and AraC cytotoxicity phenotypes identified CNV regions that might be associated with cytotoxicity for these two drugs. In this study we investigated the role of CNVs together with expression of neighboring genes (B3GAT1, LRP5L, PLEKHA5, KIF26B, TYRP1, FAM5C, ADAM6, FLJ40330, NPHP1, OR52E4, GPR133 and SMYD3) with drug response phenotypes. Analysis in lymphoblastoid cell lines and functional validation in cancer cell lines suggest the probable role of SMYD3 to AraC and gemcitabine drug response phenotype. The current study provides additional information with regard to the contribution of CNVs to variation in drug response for two important antineoplastic drugs and indicates that the assay of CNV should be included in pharmacogenomic studies.

Declarations

Acknowledgements

This work was supported in part by National Institutes of Health (NIH) grants U01 GM6133 (The Pharmacogenetics Research Network) (SH, KRK, JPK, LW and RW), R01 CA138416 (LW), K22 CA130828 (LW) and R01 CA136780 (LL and RMW)

Authors’ Affiliations

(1)
Division of Biostatistics and Informatics, Department of Health Sciences Research, Mayo Clinic
(2)
Division of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic

References

  1. Aklillu E, Persson I, Bertilsson L, Johansson I, Rodrigues F, Ingelman-Sundberg M: Frequent distribution of ultrarapid metabolizers of debrisoquine in an ethiopian population carrying duplicated and multiduplicated functional CYP2D6 alleles. J Pharmacol Exp Ther. 1996, 278 (1): 441-446.PubMedGoogle Scholar
  2. Bertilsson L, Dahl ML, Sjoqvist F, Aberg-Wistedt A, Humble M, Johansson I, Lundqvist E, Ingelman-Sundberg M: Molecular basis for rational megaprescribing in ultrarapid hydroxylators of debrisoquine. Lancet. 1993, 341 (8836): 63-10.1016/0140-6736(93)92546-6.PubMedView ArticleGoogle Scholar
  3. Dahl ML, Johansson I, Bertilsson L, Ingelman-Sundberg M, Sjoqvist F: Ultrarapid hydroxylation of debrisoquine in a Swedish population. Analysis of the molecular genetic basis. J Pharmacol Exp Ther. 1995, 274 (1): 516-520.PubMedGoogle Scholar
  4. Johansson I, Lundqvist E, Bertilsson L, Dahl ML, Sjoqvist F, Ingelman-Sundberg M: Inherited amplification of an active gene in the cytochrome P450 CYP2D locus as a cause of ultrarapid metabolism of debrisoquine. Proc Natl Acad Sci USA. 1993, 90 (24): 11825-11829. 10.1073/pnas.90.24.11825.PubMed CentralPubMedView ArticleGoogle Scholar
  5. Roden DM, Altman RB, Benowitz NL, Flockhart DA, Giacomini KM, Johnson JA, Krauss RM, McLeod HL, Ratain MJ, Relling MV: Pharmacogenomics: challenges and opportunities. Ann Intern Med. 2006, 145 (10): 749-757.PubMedView ArticleGoogle Scholar
  6. Dalén P, Dahl ML, Bernal Ruiz ML, Nordin J, Bertilsson L: 10-Hydroxylation of nortriptyline in white persons with 0, 1, 2, 3, and 13 functional CYP2D6 genes. Clin Pharmacol Ther. 1998, 63 (4): 444-452. 10.1016/S0009-9236(98)90040-6.PubMedView ArticleGoogle Scholar
  7. Redon R, Ishikawa S, Fitch KR, Feuk L, Perry GH, Andrews TD, Fiegler H, Shapero MH, Carson AR, Chen W: Global variation in copy number in the human genome. Nature. 2006, 444 (7118): 444-454. 10.1038/nature05329.PubMed CentralPubMedView ArticleGoogle Scholar
  8. Frazer KA, Ballinger DG, Cox DR, Hinds DA, Stuve LL, Gibbs RA, Belmont JW, Boudreau A, Hardenbol P, Leal SM: A second generation human haplotype map of over 3.1 million SNPs. Nature. 2007, 449 (7164): 851-861. 10.1038/nature06258.PubMedView ArticleGoogle Scholar
  9. Jakobsson M, Scholz SW, Scheet P, Gibbs JR, VanLiere JM, Fung HC, Szpiech ZA, Degnan JH, Wang K, Guerreiro R: Genotype, haplotype and copy-number variation in worldwide human populations. Nature. 2008, 451 (7181): 998-1003. 10.1038/nature06742.PubMedView ArticleGoogle Scholar
  10. Stranger BE, Forrest MS, Dunning M, Ingle CE, Beazley C, Thorne N, Redon R, Bird CP, de Grassi A, Lee C: Relative impact of nucleotide and copy number variation on gene expression phenotypes. Science. 2007, 315 (5813): 848-853. 10.1126/science.1136678.PubMed CentralPubMedView ArticleGoogle Scholar
  11. Conrad DF, Andrews TD, Carter NP, Hurles ME, Pritchard JK: A high-resolution survey of deletion polymorphism in the human genome. Nat Genet. 2006, 38 (1): 75-81. 10.1038/ng1697.PubMedView ArticleGoogle Scholar
  12. Hinds DA, Kloek AP, Jen M, Chen X, Frazer KA: Common deletions and SNPs are in linkage disequilibrium in the human genome. Nat Genet. 2006, 38 (1): 82-85. 10.1038/ng1695.PubMedView ArticleGoogle Scholar
  13. McCarroll SA, Hadnott TN, Perry GH, Sabeti PC, Zody MC, Barrett JC, Dallaire S, Gabriel SB, Lee C, Daly MJ: Common deletion polymorphisms in the human genome. Nat Genet. 2006, 38 (1): 86-92. 10.1038/ng1696.PubMedView ArticleGoogle Scholar
  14. Carter NP: Methods and strategies for analyzing copy number variation using DNA microarrays. Nat Genet. 2007, 39 (7 Suppl): S16-21. 10.1038/ng2028.PubMed CentralPubMedView ArticleGoogle Scholar
  15. Kern W, Estey EH: High-dose cytosine arabinoside in the treatment of acute myeloid leukemia: Review of three randomized trials. Cancer. 2006, 107 (1): 116-124. 10.1002/cncr.21543.PubMedView ArticleGoogle Scholar
  16. Kindler HL: In focus: advanced pancreatic cancer. Clin Adv Hematol Oncol. 2005, 3 (5): 420-422.PubMedGoogle Scholar
  17. Wiley JS, Taupin J, Jamieson GP, Snook M, Sawyer WH, Finch LR: Cytosine arabinoside transport and metabolism in acute leukemias and T cell lymphoblastic lymphoma. J Clin Invest. 1985, 75 (2): 632-642. 10.1172/JCI111741.PubMed CentralPubMedView ArticleGoogle Scholar
  18. Li L, Fridley B, Kalari K, Jenkins G, Batzler A, Safgren S, Hildebrandt M, Ames M, Schaid D, Wang L: Gemcitabine and cytosine arabinoside cytotoxicity: association with lymphoblastoid cell expression. Cancer Res. 2008, 68 (17): 7050-7058. 10.1158/0008-5472.CAN-08-0405.PubMed CentralPubMedView ArticleGoogle Scholar
  19. Wang K, Li M, Hadley D, Liu R, Glessner J, Grant SF, Hakonarson H, Bucan M: PennCNV: an integrated hidden Markov model designed for high-resolution copy number variation detection in whole-genome SNP genotyping data. Genome Res. 2007, 17 (11): 1665-1674. 10.1101/gr.6861907.PubMed CentralPubMedView ArticleGoogle Scholar
  20. Colella S, Yau C, Taylor JM, Mirza G, Butler H, Clouston P, Bassett AS, Seller A, Holmes CC, Ragoussis J: QuantiSNP: an Objective Bayes Hidden-Markov Model to detect and accurately map copy number variation using SNP genotyping data. Nucleic Acids Res. 2007, 35 (6): 2013-2025. 10.1093/nar/gkm076.PubMed CentralPubMedView ArticleGoogle Scholar
  21. Iafrate AJ, Feuk L, Rivera MN, Listewnik ML, Donahoe PK, Qi Y, Scherer SW, Lee C: Detection of large-scale variation in the human genome. Nat Genet. 2004, 36 (9): 949-951. 10.1038/ng1416.PubMedView ArticleGoogle Scholar
  22. Locke DP, Sharp AJ, McCarroll SA, McGrath SD, Newman TL, Cheng Z, Schwartz S, Albertson DG, Pinkel D, Altshuler DM: Linkage disequilibrium and heritability of copy-number polymorphisms within duplicated regions of the human genome. Am J Hum Genet. 2006, 79 (2): 275-290. 10.1086/505653.PubMed CentralPubMedView ArticleGoogle Scholar
  23. Sebat J, Lakshmi B, Troge J, Alexander J, Young J, Lundin P, Månér S, Massa H, Walker M, Chi M: Large-scale copy number polymorphism in the human genome. Science. 2004, 305 (5683): 525-528. 10.1126/science.1098918.PubMedView ArticleGoogle Scholar
  24. Sharp AJ, Locke DP, McGrath SD, Cheng Z, Bailey JA, Vallente RU, Pertz LM, Clark RA, Schwartz S, Segraves R: Segmental duplications and copy-number variation in the human genome. Am J Hum Genet. 2005, 77 (1): 78-88. 10.1086/431652.PubMed CentralPubMedView ArticleGoogle Scholar
  25. Tuzun E, Sharp AJ, Bailey JA, Kaul R, Morrison VA, Pertz LM, Haugen E, Hayden H, Albertson D, Pinkel D: Fine-scale structural variation of the human genome. Nat Genet. 2005, 37 (7): 727-732. 10.1038/ng1562.PubMedView ArticleGoogle Scholar
  26. Nobrega MA, Ovcharenko I, Afzal V, Rubin EM: Scanning human gene deserts for long-range enhancers. Science. 2003, 302 (5644): 413-10.1126/science.1088328.PubMedView ArticleGoogle Scholar
  27. Lettice LA, Horikoshi T, Heaney SJ, van Baren MJ, van der Linde HC, Breedveld GJ, Joosse M, Akarsu N, Oostra BA, Endo N: Disruption of a long-range cis-acting regulator for Shh causes preaxial polydactyly. Proc Natl Acad Sci USA. 2002, 99 (11): 7548-7553. 10.1073/pnas.112212199.PubMed CentralPubMedView ArticleGoogle Scholar
  28. Wang SZ, Luo XG, Shen J, Zou JN, Lu YH, Xi T: Knockdown of SMYD3 by RNA interference inhibits cervical carcinoma cell growth and invasion in vitro. BMB Rep. 2008, 41 (4): 294-299.PubMedView ArticleGoogle Scholar
  29. Tsuge M, Hamamoto R, Silva FP, Ohnishi Y, Chayama K, Kamatani N, Furukawa Y, Nakamura Y: A variable number of tandem repeats polymorphism in an E2F-1 binding element in the 5' flanking region of SMYD3 is a risk factor for human cancers. Nat Genet. 2005, 37 (10): 1104-1107. 10.1038/ng1638.PubMedView ArticleGoogle Scholar
  30. Silva FP, Hamamoto R, Kunizaki M, Tsuge M, Nakamura Y, Furukawa Y: Enhanced methyltransferase activity of SMYD3 by the cleavage of its N-terminal region in human cancer cells. Oncogene. 2008, 27 (19): 2686-2692. 10.1038/sj.onc.1210929.PubMedView ArticleGoogle Scholar
  31. Kalari KR, Casavant TL, Scheetz TE: A knowledge-based approach to predict intragenic deletions or duplications. Bioinformatics. 2008, 24 (18): 1975-1979. 10.1093/bioinformatics/btn370.PubMedView ArticleGoogle Scholar
  32. Evans DM, C BJ, Cardon LR: To what extent do scans of non-synonymous SNPs complement denser genome-wide association studies?. Eur J Hum Genet. 2008, 16 (6): 718-723. 10.1038/sj.ejhg.5202011.PubMedView ArticleGoogle Scholar
  33. Duan S, Bleibel WK, Huang RS, Shukla SJ, Wu X, Badner JA, Dolan ME: Mapping genes that contribute to daunorubicin-induced cytotoxicity. Cancer Res. 2007, 67 (11): 5425-5433. 10.1158/0008-5472.CAN-06-4431.PubMed CentralPubMedView ArticleGoogle Scholar
  34. Hebbring SJ, Adjei AA, Baer JL, Jenkins GD, Zhang J, Cunningham JM, Schaid DJ, Weinshilboum RM, Thibodeau SN: Human SULT1A1 gene: copy number differences and functional implications. Hum Mol Genet. 2007, 16 (5): 463-470. 10.1093/hmg/ddl468.PubMedView ArticleGoogle Scholar
  35. Huang RS, Duan S, Kistner EO, Bleibel WK, Delaney SM, Fackenthal DL, Das S, Dolan ME: Genetic variants contributing to daunorubicin-induced cytotoxicity. Cancer Res. 2008, 68 (9): 3161-3168. 10.1158/0008-5472.CAN-07-6381.PubMed CentralPubMedView ArticleGoogle Scholar
  36. Moyer AM, Salavaggione OE, Hebbring SJ, Moon I, Hildebrandt MA, Eckloff BW, Schaid DJ, Wieben ED, Weinshilboum RM: Glutathione S-transferase T1 and M1: gene sequence variation and functional genomics. Clin Cancer Res. 2007, 13 (23): 7207-7216. 10.1158/1078-0432.CCR-07-0635.PubMedView ArticleGoogle Scholar
  37. Welsh M, Mangravite L, Medina MW, K T, Zhang W, Huang RS, McLeod H, Dolan ME: Pharmacogenomic discovery using cell-based models. Pharmacol Rev. 2009, 61 (4): 413-429. 10.1124/pr.109.001461.PubMed CentralPubMedView ArticleGoogle Scholar
  38. Hamamoto R, Furukawa Y, Morita M, Iimura Y, Silva FP, Li M, Yagyu R, Nakamura Y: SMYD3 encodes a histone methyltransferase involved in the proliferation of cancer cells. Nat Cell Biol. 2004, 6 (8): 731-740. 10.1038/ncb1151.PubMedView ArticleGoogle Scholar
  39. Hamamoto R, Silva FP, Tsuge M, Nishidate T, Katagiri T, Nakamura Y, Furukawa Y: Enhanced SMYD3 expression is essential for the growth of breast cancer cells. Cancer Sci. 2006, 97 (2): 113-118. 10.1111/j.1349-7006.2006.00146.x.PubMedView ArticleGoogle Scholar
  40. Luo XG, Xi T, Guo S, Liu ZP, Wang N, Jiang Y, Zhang TC: Effects of SMYD3 overexpression on transformation, serum dependence, and apoptosis sensitivity in NIH3T3 cells. UBMB Life. 2009, 61 (6): 679-684. 10.1002/iub.216.View ArticleGoogle Scholar
  41. Zou JN, Wang SZ, Yang JS, Luo XG, Xie JH, Xi T: Knockdown of SMYD3 by RNA interference down-regulates c-Met expression and inhibits cells migration and invasion induced by HGF. Cancer Letters. 2009, 280 (1): 78-85. 10.1016/j.canlet.2009.02.015.PubMedView ArticleGoogle Scholar

Copyright

© Kalari et al; licensee BioMed Central Ltd. 2010

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.

Advertisement