predictive value of xrcc1 gene polymorphisms on platinum-based chemotherapy in advanced non-small...

2012;18:3972-3981. Published OnlineFirst June 15, 2012.Clin Cancer Res Junjie Wu, Jie Liu, Yuhao Zhou, et al. Cancer Patients: A Systematic Review and Meta-analysis

Small Cell Lung−Platinum-Based Chemotherapy in Advanced Non Gene Polymorphisms onXRCC1Predictive Value of

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Predictive Biomarkers and Personalized Medicine

Predictive Value of XRCC1 Gene Polymorphisms onPlatinum-Based Chemotherapy in Advanced Non–SmallCell Lung Cancer Patients: A Systematic Reviewand Meta-analysis

Junjie Wu1,3, Jie Liu1, Yuhao Zhou5, Jun Ying7, Houdong Zou6, Shicheng Guo1, Lei Wang4,Naiqing Zhao2, Jianjun Hu3, Daru Lu1, Li Jin1, Qiang Li3, and Jiu-Cun Wang1

AbstractPurpose: Published data have shown conflicting results about the relationship between X-ray repair

cross-complementing group 1 (XRCC1) gene polymorphisms (Arg399Gln and Arg194Trp) and clinical

outcome of platinum-based chemotherapy in patients with advanced non–small cell lung cancer (NSCLC).

A meta-analysis is needed to provide a systematic review of the published findings.

Experimental Design: We conducted a systematic review and meta-analysis to evaluate the predictive

value of XRCC1 gene polymorphisms on clinical outcome up to October 1, 2010. The quality of each study

was scored on the basis of predefined criteria.

Results:A total of 13 eligible follow-up studiesmet all the inclusion criteria. TheXRCC1194Trp allelewas

found to be significantly associated with a favorable response rate relative to 194Arg [Trp vs. Arg: OR, 1.88;

95% confidence interval (CI), 1.48–2.38]. XRCC1399Gln was less favorably associated with both response

rate (Gln vs. Arg: OR, 0.67; 95% CI, 0.52–0.87) and overall survival (Gln vs. Arg: HR, 1.30; 95% CI, 1.04–

1.63) than 399Arg in analyses using all available studies; but these associations became insignificant when

only high-quality studies were used.

Conclusion: These findings suggest a predictive role forXRCC1 genepolymorphisms in clinical outcome.

However, the role of 399Gln could be considered controversial because its impact on clinical outcome was

insignificant in high-quality studies. These findings show the importance of establishing suitable criteria,

including genetic epidemiologic, phenotypic, and clinical criteria, to improvequality control of studydesign

andmethods in pharmacogenomic studies related to XRCC1 gene polymorphism. Clin Cancer Res; 18(14);

3972–81. �2012 AACR.

IntroductionCurrently, themain conventional treatment for advanced

non–small cell lung cancer (NSCLC) is platinum-based

chemotherapy (Pt-chemo), which involves co-administra-tion of a platinum-containing drug and another cytotoxicagent (1). Despite improvements made to this treatmentover the past 2 decades, the response rates of chemotherapyregimens remains only 30% to 50% (2) and the 5-yearsurvival rate for NSCLC is still less than 15% (3).

X-ray repair cross-complementing group 1 (XRCC1) pro-tein plays a key role in base excision repair (BER). It serves asa scaffold protein in both single-strand break repair andbase excision repair activities (4). The amount of XRCC1transcription has shown a significant correlation with cis-platin resistance amongNSCLC cell lines (5). Another studyrevealed that XRCC1 protein could bind to platinum-con-taining DNA duplexes (6). These studies imply that XRCC1contributes to the repair of platinum-induced DNA dam-age. Arg399Gln and Arg194Trp are 2 common polymorph-isms in XRCC1. In erythrocytes, human placental aflatoxinB1 (AFB1-DNA) adducts have been shown to respond toenvironmental insults, and somatic glycophorin A (GPA)variants have been shown to respond to smoking. It hasbeen reported that the 399Gln allele is significantly

Authors' Affiliations: 1National Ministry of Education Key Laboratory ofContemporary Anthropology and State Key Laboratory of Genetic Engi-neering, School of Life Sciences, 2School of Public Health, Fudan Univer-sity; Departments of 3Pneumology and 4Cardiothoracic Surgery, ChanghaiHospital of Shanghai, 5Department of Health Statistics, 6Institute ofMilitaryHealth Service Management, Second Military Medical University, Shang-hai, China; and 7Department of Environmental Health, University of Cin-cinnati College of Medicine, Cincinnati, Ohio

Note: Supplementary data for this article are available at Clinical CancerResearch Online (http://clincancerres.aacrjournals.org/).

Corresponding Authors: Qiang Li, Department of Pneumology, Chan-ghai Hospital, the Second Military Medical University, Shanghai200433, China. Phone: 86-21-81873231; Fax: 86-21-51190920; E-mail:[email protected]; and Jiu-Cun Wang, National Ministry of Edu-cation Key Laboratory of Contemporary Anthropology, School of LifeSciences, Fudan University, Shanghai 200433, China. Phone: 86-21-55665499; Fax: 86-21-556648845; E-mail: [email protected]

doi: 10.1158/1078-0432.CCR-11-1531

�2012 American Association for Cancer Research.

ClinicalCancer

Research

Clin Cancer Res; 18(14) July 15, 20123972





associated with higher levels of this type of genotoxicdamage (AFB1-DNA adducts and GPA variants), suggestingthat the Arg399Gln amino acid variant may alter the phe-notype of the XRCC1 protein, causing deficient DNA repair(7). XRCC1 codon 194 was shown to be a significantpredictor of progression-free survival. In a cohort of 229patients with NSCLCs who received radiotherapy, thepatients with haplotype pairs other than the homozygousTGG haplotype (194Trp-280Arg-399Arg) pairs survivedsignificantly longer than those with the homozygous TGGhaplotype pairs (8).The relationship between XRCC1 gene polymorphisms

(Arg399Gln and Arg194Trp) and clinical outcome(response rate and overall survival) in Pt-chemo foradvanced NSCLCs has been investigated extensively. How-ever to date, the evidence has been conflicting. In this study,we conducted a meta-analysis using all eligible studies toevaluate the association between the XRCC1 gene poly-morphisms and outcome in Pt-chemo for advancedNSCLCs.

Materials and MethodsData sources, search strategy, and selection of studiesEfforts were made to collect all published studies

related to the effects of XRCC1 gene polymorphisms onPt-chemo for advanced NSCLCs from various sources.Published articles were searched using databases(PubMed, Embase, and CNKI) up to October 1, 2010.Keywords such as "lung" or "pulmonary" and "cancer" or"carcinoma" and "XRCC1" or "X-ray cross-complement-ing group 1" or "base excision repair" or "BER" and"pharmacogenomics" or "polymorphism" or "variation"

were used in the searching process. Collected studies wereprescreened, and a study was excluded under either of thefollowing circumstances: (i) the study did not report anyclinical outcome; (ii) the clinical outcome reported in thestudy was either not specific to polymorphism or couldnot be attributed to a specific polymorphism; and (iii) theprincipal investigator declined or was unable to providerelevant information upon request. If the same researchgroup published multiple articles on the topic, we select-ed the article that used the most samples, used the mostrecent polymorphism data, or provided the most detailedinformation on each gene polymorphism (9, 10).

Quality assessmentThequality of each studywas also independently assessed

by 2 authors (Y. Zhou and H. Zou) using a predefined scale(Table 1). Our quality scoring criteria were followed fromother studies (11–13). The quality score of a given study(QSS) was determined using pharmacogenetic considera-tions and the following 6 factors: genotyping methods,platinum combinations, evaluation criteria, cancer stages,and sample size. With respect to genotyping methods, both

Translational RelevanceIn this report, we found that XRCC1 polymorphisms

in Arg194Trp could predict the clinical outcome ofplatinum-based chemotherapy for advanced non–smallcell lung cancer (NSCLC). The results, however, werebased upon studies using solely Chinese populations.The role of Arg399Gln was controversial, and itsrelationship to the response rate was found to be insig-nificant in high-quality studies, especially those usinghigh-quality genotyping methods. The importance ofconducting high-quality trials was confirmed in a studyof the effects of XRCC1 gene polymorphisms on clinicaloutcome in platinum-based chemotherapy for NSCLCs.Our study suggests that more studies using high-qualitygenotyping methods may be needed to confirm thepredictive roles of XRCC1 polymorphisms in differentpopulations. Our findings also show the importance ofestablishing suitable criteria, including genetic epidemi-ologic, phenotypic, and clinical criteria. These criteriamay help standardize the design of pharmacogenomicstudies and so aid in cancer research.

Table 1. Scale for quality assessment

Criteria Item Score

Evaluation criteriaa

WHO/RECIST 3Not detailed 0

Platinum combinationsa,b

One kind of platinum combinations 3TAX/TXT, DOC, GEM, or NVB 2Not detailed or other regimens 1

Stagea,b

Detailed 3Not detailed 0

Survivala

Original data 3Estimation of the log HR and variancefrom the Kaplan–Meier curves

1

Genotypinga,b

3D DNA microarray 3TaqMan 3PCR-RFLPc 2

Total sample sizea,b

�150 3>100 but <150 2�100 1

Abbreviations: 3D, 3-dimensional; RECIST, Response Eval-uation Criteria in Solid Tumors; WHO, World HealthOrganization.aCriteria for response rate.bCriteria for overall survival.cPCR-RFLP as genotyping method was categorized intosequencing or no sequencing.

XRCC1 Gene Polymorphisms and Platinum-Based Chemotherapy

www.aacrjournals.org Clin Cancer Res; 18(14) July 15, 2012 3973





DNA microarray and TaqMan were considered to be ofhigher quality than PCR-restriction fragment length poly-morphism (RFLP; refs. 14–17). PCR-RFLP was further cat-egorized intomethods that had been verified by sequencingand those that had not. For platinum combinations, weconsidered chemotherapy regimens, such as vinorelbine,gemzar, paclitaxel, and docetaxel combinedwith platinums(cisplatin or carboplatin) to be clinically equivalent (18–21). Newer agents such as vinorelbine (Navelbine) andpaclitaxel (Taxol) with cisplatin were considered as a moreeffective treatment of advanced disease than older regimensconsisting of cisplatin and a vinca alkaloid or a podophyl-lotoxin (22). The use of radiotherapy was also evaluatedcarefully during the rating of QSS (23). Total score rangedfrom 0 (worst) to 15 (best). A final QSS score was assignedto each study after consensus was reached between

reviewers. A study was considered low (or high) quality ifQSS < 10 (or �10).

Statistical analysisA total of 6 genetic models, with 3 main models (M1,

allele comparison, A vs. a; M2, recessive model, AA vs. Aaþaa; orM3, dominantmodel, AAþAa vs. aa) and 3models ofmultiple pairwise comparisons (M4, AA vs. aa;M5, Aa vs. aa;or M6, AA vs. Aa) were considered in this meta-analysis.Models M1 to M3 were considered primary genetic modelsof interest (24, 25). The ORs with 95% confidence intervals(CI) were estimated for response rate. The odds of responserate were defined as the ratio of complete or partial responseagainst stable or progressive disease. HRs and 95% CIswere estimated for 5-year survival, directly from the rawdata (26, 27), or indirectly from the Kaplan–Meier curve of

1,215 Potentially relevant articles

1,175 Excluded

129 Review

81 Other tumors

965 Other reasons

40 Evaluated articles in detail

17 Excluded

6 Abstract

4 Duplicate studies

7

23 Full text article analysis

13 Articles included in

meta-analysis

10 Excluded

4 SCLC

or including a non–platinum-based chemotherapy

6

Inclusion of

Insufficient information

Lung cancer susceptibility or radiotherapy

Figure 1. Literature search andselection of included studies.

Wu et al.

Clin Cancer Res; 18(14) July 15, 2012 Clinical Cancer Research3974





Tab

le2.

Cha

racteristic

sof

eligible

stud

iesco

nsidered

inthereport

QSS

Arg19

4Trp

Arg39

9Gln

Autho

r(Yea

r)Ethnicity

Sub

jects

Agemed

ian

(min–max

)Eva

luation

criterion

Metho

ds

IIIA,n

(%)IIIB,n

(%)IV,n

(%)

Others,

n(%

)Res

pons

erate

Ove

rall

survival

TrpTrp

TrpArg

ArgArg

Alle

licfreq

uenc

y%

(Trp)

GlnGln

GlnArg

ArgArg

Alle

licfreq

uenc

y%

(Gln)

Ref.

Gurub

haga

vatula

(200

4)

Cau

casian

s10

358

(32–

77)

—PCR-R

FLP

26(25.0)

30(29.0)

47(46.0)

——

9—

——

—10

b42

b51

b30

.10b

(23)

Wan

g(200

4)Chine

se10

556

(30–

74)

WHO

PCR-R

FLP

—17

(16.2)

41(39.0)

47(44.8)

9—

——

——

2/8a

9/33

a22

/31a

29.52a

(39)

DeLa

sPe~ n

as

(200

6)

Cau

casian

s13

562

(31–

81)

—Ta

qMan

—23

(17.0)

112(83.0)

——

14—

——

—18

b63

b49

b38

.08b

(27)

Yua

n(200

6)Chine

se20

056

(30–

74)

WHO

PCR-R

FLP

—49

(24.5)

151(75.5)

—13

—10

/13a

38/46a

24/69a

32.5

a—

——

—(38)

Gao

(200

6)Chine

se57

59(38–

77)

WHO

PCR-R

FLP

22(38.6)

34(59.6)

—12

—2/2a

12/11a

5/25

a27

.19a

0/3a

8/15

a11

/20a

25.44a

(36)

Son

g(200

7)Chine

se97

56(30–

68)

WHO

PCR-R

FLP

—38

(39.2)

59(60.8)

—11

——

——

—1/4a

11/29a

18/34a

25.77a

(10)

Son

g(200

7)Chine

se16

656

(30–

68)

WHO

PCR-R

FLP

—68

(41)

98(59.0)

—13

—4/12

a34

/32a

14/70a

29.52a

——

——

(9)

Kalikak

i(20

09)

Cau

casian

s11

961

(39–

85)

RECIST

PCR-R

FLP

6(5.0)

34(28.6)

79(66.4)

—11

9—

——

—10

b76

b11

/21a,3

3b40

.34b

(28)

Sun

(200

9)Chine

se87

59(34–

79)

WHO

3DDNA

microarray

——

87(100

.0)—

12—

5/6a

18/19a

8/31

a33

.91a

1/3a

8/22

a14

/39a

21.84a

(35)

Yao

(200

9)Chine

se10

861

(39–

79)

WHO

PCR-R

FLP

—37

(34.3)

71(65.7)

—12

12—

——

—9/48

a,6

0b12

/28a,

43b

1/4a,5

b75

.49a,

75.46b

(26)

Hon

g(200

9)Chine

se16

461

(27–

84)

WHO

PCR-R

FLP

100(61.3)

63(38.7)

14—

7/11

a31

/42a

19/54a

33.23a

3/10

a28

/53a

26/44a

32.62a

(34)

Yua

n(201

0)Chine

se19

956

(29–

74)

—PCR-R

FLP

43(21.6)

—15

6(78.4)

——

11—

——

—20

b74

b10

5b28

.64b

(29)

Qian(201

0)Chine

se10

761

(�55

),

46(<55

)

—PCR-R

FLP

—45

(42.1)

62(57.9)

—8

——

——

—2/6a

14/26a

32/27a

26.17a

(37)

Abbreviations

:3D,3

-dim

ension

al;R

ECIST,

Res

pon

seEva

luationCriteria

inSolid

Tumors;

WHO,W

orld

Hea

lthOrgan

ization.

aNum

berof

patientsforresp

onse

rate;infron

tof

obliq

uelineis

good

resp

onder

andbeh

indob

lique

lineis

poo

rresp

onder.

bNum

ber

ofpatientsforov

eralls

urviva

l.







Table 3. Analysis of the association between XRCC1Arg194Trp and response rate in main models

M1: Trp vs. Arg M2: TrpTrp vs. TrpArg þ ArgArg M3: TrpTrp þ TrpArg vs. ArgArg

Study groupsNo. studies(refs.)a

Random-effect;OR (95% CI) P I2 PQ

bRandom-effect;OR (95% CI) P I2 PQ

bRandom-effect;OR (95% CI) P I2 PQ

b

Overall 5 (9, 34–36, 38) 1.88 (1.48–2.38) <0.0001 0% 0.683 1.26 (0.75–2.09) 0.380 0% 0.848 2.91 (2.07–4.08) <0.0001 0% 0.419

Genotyping method

3D DNA microarray 1 (35) 2.15 (1.12–4.12) 0.020 — — 1.60 (0.45–1.75) 0.470 — — 3.56 (1.36–9.33) 0.010 — —

PCR-RFLP 4 (9, 34, 36, 38) 1.84 (1.42–2.37) <0.0001 0% 0.55 1.20 (0.69–2.09) 0.580 0% 0.75 2.87 (1.91–4.33) <0.0001 19% 0.300

PCR-RFLP

Sequencing 1 (34) 1.53 (0.95–2.46) 0.08 — — 1.22 (0.45–3.35) 0.7 — — 2.04 (1.04–3.98) 0.04 — —

No sequencing 3 (9, 36, 38) 1.98 (1.46–2.68) <0.0001 0% 0.53 1.19 (0.61–2.32) 0.61 0% 0.55 3.31 (2.03–5.39) <0.001 0.17 0.3

Tumor stage

Main of stage III 1 (34) 1.53 (0.95–2.46) 0.081 — — 1.22 (0.45–3.35) 0.697 — — 2.04 (1.04–3.98) 0.037 — —

Main of stage IV 4 (9, 35, 36, 38) 2.01 (1.53–2.65) <0.0001 0% 0.721 1.27 (0.70–2.29) 0.430 0% 0.712 3.29 (2.22–4.87) <0.0001 0% 0.486

Abbreviation: 3D, 3-dimensional.aThe detailed references are given in Table 2.bP value of heterogeneity.

Gao (2006) 2.96 (1.26–6.97) 7.7

Hong (2009) 1.53 (0.95–2.46) 25.0

Song (2007) 2.08 (1.27–3.41) 23.2

Sun (2009) 2.15 (1.12–4.12) 13.4

Yuan (2006) 1.72 (1.12–2.65) 30.6

Overall Overall

1.88 (1.48–2.38) 100.0

OR

0.3 1 5 15

Study OR (95% CI) Weight (%)

Study OR (95% CI) Weight (%)

Heterogeneity: χ2 = 2.29, df = 4 (P = 0.683); I2 = 0.0%

Test for overall effect: Z = 5.18 (P < 0.0001)

A

B

favoring 194Arg favoring 194Trp

favoring 399Arg favoring 399Gln

HighHigh quality uality

Gao (2006) 0.70 ( 0.28–1.77) 7.7

Song (2007) 0.73 ( 0.35–1.49) 12.7

Sun (2009) 0.99 ( 0.44–2.24) 10.0

Yao (2009) 0.62 ( 0.30–1.30) 12.3

Subtotal 0.77 ( 0.57–1.05) 70.3

LowLow quality uality

Qian (2010) 0.49 ( 0.26–0.92) 16.1

Wang (2004) 0.48 ( 0.24–0.96) 13.6

Subtotal 0.48 ( 0.30–0.77) 29.7

Overall

OR

0.3 1 5 15

Hong (2009) 0.82 ( 0.50–1.34) 27.6

0.67 ( 0.52–0.87) 100.0

Heterogeneity: χ2 = 0.83, df = 4 (P = 0.935); I 2 = 0.0%

Test for overall effect: Z = 1.65 (P = 0.098)





Figure 2. Forest plots forallele contrasts of XRCC1polymorphisms and clinicaloutcome in chemotherapystratified by study quality levels. A,ORs (and its 95% CI) of responserate between 194Trp and 194Arg.An OR > 1 (or <1) indicates that the194Trp is more (or less) likely toshow response than 194Arg. All 5studies were considered highquality in the analyses. B, ORs (andits 95% CI) of response ratebetween 399Gln and 194Arg. AnOR > 1 (or <1) indicates that the399Gln is more (or less) likely toshow response than 194Arg.

Wu et al.






an article (23, 28–30). Survival probabilities were tran-scribed using Grafula 3, version 2.10. For each trail (i) ineach time interval (t), the effective number of patients at risk[i.e., Ri(t)] and of deaths [i.e., Di(t)] for every arm (AAþ Aaand aAþ aa, respectively)were calculated using Ri(t) andDi(t) for different genotypes (AA, Aa, and aa).Data analyses were conducted as follows. First, Hardy–

Weinberg equilibrium (HWE) was assessed using a good-ness-of-fit test (c2 or the Fisher exact tests). Second,pooled allele frequencies for each ethnicity were thenestimated using a random-effects model with the inversevariance method based on the overall population datafrom each study (13). Third, the clinical outcome wasestimated, specifically the OR for response rate and theHR for overall survival using random-effect models withMantel–Haenszel statistics (31, 32). The heterogeneitybetween studies was investigated visually by scatter plotand estimated by a I2-statistic following the c2 test (33).The same statistical methods were applied in subanalysesusing stratified patient populations. All analyses wereconducted using STATA (version 10.0) except for thepooling of allele frequency, for which MetaAnalyst Ver-sion Beta 2.2 (Tufts Medical Center, Boston, MA) wasused. P values less than 0.05 were considered statisticallysignificant.

ResultsOverall 1,215 studies were selected during the first step

of systematic literature review, and a further review ofthe searched trials excluded 1,175 studies, including 129review articles, 81 studies on other tumors, and 965studies for other reasons. The remaining 23 studieswere identified through detailed assessment. In the end,

13 follow-up studies were considered to meet all ininclusion criteria (Fig. 1). These were included in finalanalyses (Fig. 1; refs. 9, 10, 23, 26–29, 34–39). Theyincluded 1,647 individuals. The baseline characteristicsof the included studies are given in Table 2. Three of thesestudies were conducted on Caucasian patients, and 10were conducted on Chinese patients. Six were publishedin English-language journals (23, 26–29, 35). Seven werepublished in Chinese-language journals (9, 10, 34, 36–39). The sample size of each report ranged from 57 to 200individuals. The quality score for studies of genetic con-trasts between Arg194Trp and Arg399Gln and thepatient’s response rate ranged from 12 to 14 and 8 to14, respectively, with 100% (5 of 5) and 75% (6 of 8) ofthe trials classified as high-quality. Studies of the contrastsbetween Arg399Gln and overall survival ranged from 9to 14 in quality score, with 60% (3 of 5) classified as high-quality. A total of 11 studies were reported using PCR-RFLP genotyping methods. Genotypes were verified bysequencing in all samples in one study (34), partiallyverified in 20% of samples in one study (28), and notverified in the rest of the studies. All of these studies usedsamples of peripheral blood.

Allele frequenciesTable 2 shows the distribution of XRCC1 genotypes with

respect to response rate and overall survival rate. It alsoshows the distribution of the XRCC1 allele frequencies.Using the frequencies of XRCC1 genotypes, all populationswere found to be inHWE except the one studied by Kalikaki(P ¼ 0.00028; ref. 28). This HWE-deviant population eval-uated by Kalikaki was not excluded from the study becauseno genotyping error was detected by PCR-RFLP combinedwith sequencing (28).

Figure 2. (Continued ) C, HRs(and its 95% CI) of 5-year survivalbetween 399Gln and 194Arg. An HR> 1 (or <1) indicates that the 399Gln isless (or more) likely to show survivalthan 194Arg.

C

favoring 399Arg favoring 399Gln0.5 1 2 5

High quality High quality

0.90 ( 0.66–1.23) 20.1

Yao (2009) 1.42 ( 1.06–1.89) 21.1

Yuan (2010) 1.73 ( 1.19–2.52) 17.0

Subtotal 1.29 ( 0.89–1.87) 58.2

Low quality Low quality

1.62 ( 1.15–2.28) 18.4

1.13 ( 0.88–1.45) 23.4

Subtotal 1.32 ( 0.93–1.88) 41.8

Overall 1.30 ( 1.04–1.63) 100.0

Study HR (95% CI) Weight (%)

Gurubhagavatula (2004)

De Las Peñas (2006)

Kalikaki (2009)







HR







The pooled frequency for 194Trp with respect toresponse rate was 31.72% (29.28%–34.25%) and thatfor 399Gln was 34.05% (22.72%–47.55%) in Chinesepatients. Heterogeneity was only observed in the pooledfrequency for 399Gln with respect to response rate instudies of Chinese patients (P < 0.0001). For Caucasianpatients, only the frequencies for 399Gln allele wereavailable for analysis, and the frequency in response ratewas 40.68% (34.59%–47.06%). The frequency in overallsurvival was 36.28% (30.62%–42.34%), which droppedto 34.19% after excluding the Kalikaki study amongCaucasian patients. No heterogeneity was found in thepooled frequencies in Caucasians.

Gene effectsData concerning the predictive value of XRCC1

Arg194Trp with respect to the sensitivity of lung cancerto platinum-based treatment were available in 5 trials(9, 34–36, 38). These covered 674 individuals. Table 3 andSupplementary Table S1 show that the 194Trp allele("increasing" allele) wasmore closely associatedwith betterresponse rates than the 194Arg allele. This indicates that the194Trp allele may be indicative of better response rates toplatinum-based treatment than the 194Arg allele (Fig. 2A).No significant heterogeneity was detected among the pre-dictive values from the 5 Chinese studies (Table 3 andSupplementary Table S1).

Data concerning the predictive value of the 399Gln allelewith respect to the resistance of lung cancer to platinum-based treatment were available from 8 studies covering atotal of 837 individuals (10, 26, 28, 34–37, 39). Table 4 andSupplementary Table S2 show an association between the399Gln allele ("increasing" allele) and response rate relativeto the 399Arg allele. Results suggest that the 399Gln alleleis associated with a poorer response rate to platinum-basedtreatment than the 399Arg allele (Fig. 2B). There was noevidence of heterogeneity with respect to predictive value(Table 4 and Supplementary Table S2).

Five studies covering a total of 659 patients for HRs wererecorded (23, 26–29). There was a statistically significantdifference between the association between the 399Glnallele and lower rates of 5-year survival and that betweenthe 399Arg allele and higher rates of survival. This suggeststhat the 399Gln allele is more closely associated withshorter survival time than the 399Arg allele (Fig. 2C).Significant heterogeneity was detected when these 5 studieswere combined. It was resolved by stratification analysiswith respect to the degree of PCR-RFLP or QSS. Similarresults were also obtained in anM1model when the studieswere stratified with respect to whether or not the PCR-RFLPmethod was used (Table 4 and Supplementary Table S3).

Subgroup analyses were conducted with respect toresponse rate (Tables 3 and 4 and Supplementary TablesS1 and S2). It was noted that the relationship between399Gln and response rate disappeared after the low-qualitystudies were excluded (Fig. 2B). This effect was especiallynoticeable in studies with low-quality genotyping methods(i.e., PCR-RFLP) andunsequenced subgroups. Similar results

were observed in patients from populations where stage IIItumors were common. The pooled ORs for 399Gln weresignificant in Chinese but not in Caucasian individuals. ThepooledHR for399Glnwas also found tobe insignificantwithrespect to overall survival, after studies that had used low-quality genotyping methods were excluded (Fig. 2C). Nosignificant differences were identified with respect to anyassociation between clinical outcome and other subgroups(Tables 3 and 4 and Supplementary Tables S1–S3).

DiscussionOur results showed that XRCC1 194Trp allele was pos-

itively associated with the response rate relative to 194Arg,and XRCC1 399Gln allele was negatively associated withboth the response rate and overall survival relative to399Arg. A previous study showed XRCC1 Arg399Gln to beassociated with the clinical outcome of chemotherapy inpatients with lung cancer (40). These findings, however,could be confounded by including both patients withNSCLCs and small cell lung cancer (SCLC) in the analysis,indicating that the study was biased and the results over-estimated. NSCLCs and SCLCs are different in terms ofdoubling time, metastasis, sensitivity to initial chemother-apy, and survival rate (1, 41). Our study using patients withNSCLCs only showed that the overall survival was unaf-fected by XRCC1 Arg399Gln with a relative risk of death(95% CI) of 0.85 (0.26–2.73).

Heterogeneity was detected in the analysis of XRCC1Arg399Gln to overall survival. It was also noted in the alleleanalysis. One study showed a Gln frequency of 75.46%,much higher than the range of 21.84% to 40.34% seen inother studies (26). In addition, the population evaluated inone study did not show HWE (28). The existence of het-erogeneity indicated variability, which may have beencaused by different characteristics, such as ethnicity, cancerstage, or method of genotyping used among patient popu-lations. Hence, stratified analyses of subpopulations areneeded to reduce such variability.

It is not uncommon that quality of studies (or trials) mayvary in meta-analyses of genetic association studies ingenetic epidemiology (11, 42, 43). In this article, we eval-uated each study using a QSS and provided subanalyses onhigh-quality studies. Both response to chemotherapy andoverall survival showed insignificant associations toXRCC1Arg399Gln, after excluding a few lower quality trials. Ourfindings suggest that the role of XRCC1 Arg399Gln inclinical outcomes might need to be investigated morecarefully in future studies incorporating more criteria inthe design and experimentation to ensure a more accurateand robust conclusion.

The overall survival was associated with the polymorph-isms in stage III but not in stage IV (23, 27). Such phenom-ena could be interpreted using double-edged sword theory(44, 45). However, overactive nucleotide excision repairand DNA replication systems may achieve clinically rele-vant outcomes, causing both favorable prognosis and drugresistance. Some researchers have found this to be

Wu et al.






Tab

le4.

Ana

lysisof

theas

sociationbetwee

nXRCC1Arg39

9Gln

andresp

onse

rate

andov

eralls

urviva

linmainmod

els

M1:G

lnvs

.Arg

M2:G

lnGln

vs.G

lnArg

þArgArg

M3:G

lnGln

þGlnArg

vs.A

rgArg

Studygroup

sNo.s

tudies(refs.)a

Ran

dom-effec

t(95%

CI)

PI2

PQb

Ran

dom-effec

t(95%

CI)

PI2

PQb

Ran

dom-effec

t(95%

CI)

PI2

PQb

Res

pon

serate

(OR)

Ove

rall

7(10,

26,34

–37

,39

)c0.67

(0.52–

0.87

)0.00

20%

0.74

0.49

(0.27–

0.88

)0.01

70%

0.99

70.67

(0.49–

0.92

)0.01

30%

0.70

4Pop

ulation

Cau

casian

s0c

——

——

——

——

0.83

(0.35–

1.96

)0.66

7—

—

Chine

se7(10,

26,34

–37

,39

)0.67

(0.52–

0.87

)0.00

20%

0.74

0.49

(0.27–

0.88

)0.01

70%

0.99

70.65

(0.46–

0.91

)0.01

20%

0.62

7QSS �10

5(10,

26,34

–36

)c0.77

(0.57–

1.05

)0.09

80%

0.94

0.50

(0.25–

1.00

)0.04

90%

0.97

40.83

(0.57–

1.21

)0.32

70%

0.99

6<1

02(37,

39)

0.48

(0.30–

0.77

)0.00

20%

0.97

0.45

(0.14–

1.41

)0.17

0%0.80

10.40

(0.22–

0.72

)0.00

20%

0.85

4Gen

otyp

ingmetho

d3D

DNAmicroarray

1(35)

0.99

(0.44–

2.24

)0.98

——

0.92

(0.09–

9.36

)0.95

——

1.00

(0.38–

2.66

)1

——

PCR-R

FLP

6(10,

26,34

,36–

37,3

9)c

0.64

(0.49–

0.84

)0.00

10%

0.77

0.47

(0.25–

0.86

)0.01

0%1

0.64

(0.46–

0.89

)0.00

80%

0.69

PCR-R

FLP

Seq

uenc

ing

1(34)

c0.82

(0.50–

1.34

)0.43

——

0.54

(0.14–

2.04

)0.36

——

0.83

(0.49–

1.39

)0.48

0%0.99

Nose

que

ncing

5(10,

26,36

–37

,39

)0.58

(0.42–

0.80

)9E

-04

0%0.88

0.45

(0.23–

0.89

)0.02

0%0.99

0.53

(0.34–

0.82

)0.00

40%

0.7

Tumor

stag

ed6(10,

26,34

–37

)c0.71

(0.54–

0.93

)0.01

50%

0.78

0.48

(0.26–

0.91

)0.02

40%

0.98

90.73

(0.52–

1.03

)0.07

10%

0.84

7Mainof

stag

eIII

1(34)

0.82

(0.50–

1.34

)0.43

1—

—0.54

(0.14–

2.04

)0.36

3—

—0.83

(0.44–

1.59

)0.58

——

Mainof

stag

eIV

5(10,

26,35

–37

)c0.66

(0.47–

0.92

)0.01

50%

0.75

0.47

(0.23–

0.96

)0.03

80%

0.96

80.70

(0.47–

1.04

)0.07

50%

0.78

1Ove

rallsu

rvival

(HR)

Ove

rall

5(23,

26–29

)1.30

(1.04–

1.63

)0.02

262

%0.03

2.68

(1.23–

5.82

)0.01

392

%<0

.000

11.09

(0.86–

1.37

)0.48

214

%0.32

6Pop

ulation

Cau

casian

s3(23,

27,28

)1.17

(0.86–

1.58

)0.30

967

%0.05

3.65

(1.36–

9.80

)0.01

89%

<0.000

11.10

(0.73–

1.65

)0.64

553

%0.12

Chine

se2(26,

29)

1.52

(1.21–

1.92

)<0

.000

10%

0.41

1.67

(1.20–

2.33

)0.00

30%

0.85

61.08

(0.78–

1.48

)0.65

70%

0.52

9QSS �10

3(26,

27,29

)1.29

(0.89–

1.87

)0.17

574

%0.02

1.60

(1.18–

2.16

)0.00

20%

0.82

20.95

(0.73–

1.24

)0.71

23%

0.35

8<1

02(23,

28)

1.32

(0.93–

1.88

)0.11

664

%0.1

6.49

(5.07–

8.32

)<0

.000

10%

0.51

1.36

(0.96–

1.94

)0.08

70%

0.87

2Gen

otyp

ingmetho

dTa

qMan

1(27)

0.90

(0.66–

1.23

)0.51

4—

—1.32

(0.65–

2.66

)0.44

3—

—0.75

(0.48–

1.17

)0.20

6—

—

PCR-R

FLP

4(23,

26,28

–29

)1.41

(1.16–

1.71

)0.00

137

%0.19

3.16

(1.35–

7.44

)0.00

893

%<0

.000

11.20

(0.94–

1.52

)0.13

90%

0.71

5PCR-R

FLP

Seq

uenc

ing

1(28)

1.13

(0.88–

1.45

)0.33

3—

—6.68

(5.14–

8.68

)<0

.000

1—

—1.32

(0.79–

2.20

)0.28

3—

—

Nose

que

ncing

3(23,

26,29

)1.55

(1.28–

1.88

)<0

.000

10%

0.69

2.31

(1.22–

4.37

)0.01

70%

0.03

51.16

(0.89–

1.52

)0.26

90%

0.55

7Stage Mainof

stag

eIII

1(23)

1.62

(1.15–

2.28

)0.00

6—

—5.08

(2.35–

10.99)

<0.000

1—

—1.40

(0.86–

2.29

)0.18

——

Mainof

stag

eIV

4(26–

29)

1.24

(0.96–

1.59

)0.09

564

%0.04

2.31

(0.92–

5.78

)0.07

494

%<0

.000

11.02

(0.80–

1.31

)0.86

410

%0.34

4

Abbreviation:

3D,3

-dim

ension

al.

aTh

edetailedreferenc

esaregive

nin

table

2.bPva

lueof

heteroge

neity

.cM

3includ

edan

addition

alstud

y[th

eworkof

Kalikak

iand

colleag

ues(re

f.28

)]in

analyses

.dTh

ereis

nodetailedinform

ationof

tumor

stag

ein

onestud

y[th

eworkof

Wan

gan

dco

lleag

ues(re

f.39

)]an

dthestud

ywas

exclud

ed.







reminiscent of the Roman god of entrances and exits, Janus,who is usually depicted with 2 faces pointing in oppositedirections (3). In our study, we found 399Gln to be neg-atively related to response to chemotherapy in patients whoweremostly in stage IV, but not in patients whoweremostlyin stage III. This suggests that 399Gln might have a distinctassociation with drug resistance in patients at differentstages of the disease. The association between 399Gln andoverall survival was found to be insignificant even afterstratification of cancer stages. This was due to a lack oforiginal staging data in each paper recruited into this meta-analysis. For this reason, the conclusions drawn in thismeta-analysis about the tumor stage subgroup should beweighed with caution. We also noted different outcomeswith respect to 399Gln, response rate, and ethnicity.

The study has some limitations. In some of the subana-lyses on stratified groups of patients, therewas only one trialavailable and hence the variability across trials could not beassessed. Some of the findings in subgroups may have beenundervalued because of the smaller sample size available foranalyses. Among all 13 trials used in meta-analysis, only 3were conducted upon Caucasian populations. None of theCaucasian trials were available for estimation of theresponse rate using the allelemodel (M1). Due to the natureof meta-analysis, the accuracy of inference and statisticalpower were usually limited because analyses could only beconducted on secondary data, other than the original datacollected directly from individual patients. In addition, thequality of trials could not be controlled directly by research-ers conducting the meta-analysis.

SummaryThese findings show a predictive role for XRCC1 gene

polymorphisms in clinical outcome. However, the pre-dictive role of 399Gln with respect to clinical outcomewas here found to differ by study quality, suggesting thatconsistent study quality is important and can be assessedusing the criteria established in our meta-analysis. Thesecriteria include genetic epidemiologic, phenotypic, and

clinical variables. To our knowledge, this quality assess-ment system was designed for the first time in pharma-cogenomic studies. Its feasibility was confirmed in ourstudy. These criteria will help standardize study designand may affect future pharmacogenomic studies in thefield of cancer research. More larger studies on patients ofdifferent ethnicities, especially studies stratified for inter-actions between tumor stage, genotyping method, andclinical outcome, should be conducted to confirm thepredictive roles of XRCC1 Arg399Gln and Arg194Trp.Functional studies may help validate the effects of thesepolymorphisms in Pt-chemo.

Disclosure of Potential Conflicts of InterestNo potential conflicts of interest were disclosed.

AcknowledgmentsThe authors thank Dr. Rafael Rosell from the Institut Catal�a Oncologia,

Badalona, Barcelona, Spain; Mar��a S�anchez Ronco from Departamento deMedicina Preventivay Salud P�ublica, Universidad Aut�onoma de Madrid,Madrid, Spain; andDr. Bo Shen andDr. ChengyunYao from theDepartmentof Chemotherapy, Jiangsu Cancer Hospital and Research Institute, NanjingMedical University, Nanjing, Jiangsu, China, for their kindness in providingus with original data. They also thank Jia He and Yingyi Qin from theDepartment of Health Statistics, Second Military Medical University, Shang-hai, China, for their helpful statistical advice and the processing of digitalgraphics; Wengsheng Guo from Division of Biostatistics, University ofPennsylvania School of Medicine, Philadelphia, PA; Lili Yan from the StateKey Laboratory of Genetic Engineering, Institute of Genetics, School of LifeScience, Fudan University, Shanghai, China; and Dr. Bobing Chen from theDepartment of Hematology, Huashan Hospital, Fudan University, for theirhelpful statistical advice.

Grant SupportThis studywas supported by grants from theNational Science Foundation

of China, grant numbers 30971594 and 30890034; the Science and Tech-nology Committee of Shanghai Municipality, grant number 09XD1400200;and the Major National Science and Technology Program of China, grantnumber 2008ZX10002-002.

The costs of publication of this article were defrayed in part by thepayment of page charges. This article must therefore be hereby markedadvertisement in accordance with 18 U.S.C. Section 1734 solely to indicatethis fact.

Received June 17, 2011; revised May 9, 2012; accepted May 14, 2012;published OnlineFirst June 15, 2012.

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predictive value of xrcc1 gene polymorphisms on platinum-based chemotherapy in advanced non-small...

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