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Clinical Studies

Genetic Variants in Oxidative Stress–Related Genes PredictChemoresistance in Primary Breast Cancer: A ProspectiveObservational Study and Validation

Ke-Da Yu1, A-Ji Huang1, Lei Fan1, Wen-Feng Li2, and Zhi-Ming Shao1

AbstractChemotherapy response in patientswith primary breast cancer is difficult to predict and the role of host genetic

factors has not been thoroughly investigated. We hypothesized that polymorphisms in oxidative stress (OS)-related genes, including estrogen–quinone metabolizing enzymes NQO2 and GSTM1-5, may influence diseaseprogression and treatment response. In this prospective observational study, nineteen polymorphisms taggingknown variations in candidate genes were genotyped and analyzed in 806 patients with primary breast cancer.Three functional polymorphisms, whichwere shown to affect gene expression levels in experiments in vitro and exvivo, modified the effect of chemotherapy on disease-free survival. There were significant interactions betweenchemotherapy and individual polymorphisms or combined genotypes (designated as genetic score). Patientsharboring high genetic score had a 75% reduction in the hazard of disease progression compared with patientswith low genetic score when no chemotherapy was administered (HR ¼ 0.25, 95% CI: 0.10–0.63, P ¼ 0.005);however, they received much less survival benefit from adjuvant chemotherapy compared with patients with lowgenetic score when chemotherapy was administered (HR ¼ 4.60 for interaction, 95% CI: 1.63–13.3, P ¼ 0.004).These findings were validated in another population (n ¼ 339). In conclusion, germline polymorphisms in OS-related genes affect chemotherapy sensitivity in breast cancer patients. Although reducedOS levelsmight preventbreast cancer progression, they probably compromise the effectiveness of adjuvant chemotherapy. Our findingsalso indicate that host-related factors must be considered for individualized chemotherapy. Cancer Res; 72(2);408–19. �2011 AACR.

Introduction

It is well established that estrogens and their metabolitesplay critical roles in breast carcinogenesis. One of the mostimportant mechanisms involved in this process is oxidativestress (OS) generated by estrogen quinones (1). Cells haveapproaches to reduce redundant quinines or semiquinones,such as reducing them by quinone oxidoreductases (NQO) andclearing them by glutathione S-transferases (GST). Of note, theestrogen–quinone metabolizing enzymes are not only specif-ically involved in breast carcinogenesis but are also related tothe detoxification of reactive oxygen species (ROS; ref. 1).

The NQO family consists of 2 members, NAD(P)H:quinoneoxidoreductase (NQO1) and NRH:quinine oxidoreductase(NQO2), both of which catalyze the detoxification of quininesand protect against OS (2). NQO1 was recently identified as astrong prognostic and predictive factor in breast cancer (3).The NQO1-deficient phenotype is a defective anthracyclineresponse. In contrast, the role of NQO2 in breast cancerprognosis, as well as in chemotherapy response, is still unclear.Besides the NQO family, GSTs have been shown to haveimportant roles in protection against ROS by conjugating withglutathione. GST detoxification pathways are active in normalbreast tissue and GSTM and GSTP are the predominantenzymes in the breast (4). The genes of the GSTM family arearranged in a tandem of 50-GSTM4-M2-M1-M5-M3-30 (5).GSTM1 is of particular interest because it possesses a nullpolymorphism that results in a complete absence of GSTM1enzyme activity. Our previous studies concluded that geneticvariants inNQO2 andGSTM1-5 are related to breast cancer riskto different extents (6, 7).

Thus far, genetic alterations in somatic tumor cells havebeen shown to be correlated with prognosis, but the effects ofgenetic variations are less well understood (8–10). We hypoth-esize that genes modifying susceptibility to breast cancer mayalso influence disease progression and treatment response;variations in estrogen–quinonemetabolizing genes involved inOS are good candidates. Notably, the relationship between OS

Authors' Affiliations: 1Department of Breast Surgery, Cancer Center andCancer Institute, Shanghai Medical College, Fudan University, Shanghai,People's Republic of China; and 2Department of Oncology, the AffiliatedHospital ofQingdaoUniversityMedical College,Qingdao,People'sRepub-lic of China

Note: Supplementary data for this article are available at Cancer ResearchOnline (http://cancerres.aacrjournals.org/).

Corresponding Author: Zhi-Ming Shao, Department of Breast Surgery,Cancer Center and Cancer Institute, Shanghai Medical College, FudanUniversity, 399 Ling-Ling Road, Shanghai 200032, People's Republic ofChina. Phone: 86-13611709888; Fax: 86-21-64434556; E-mail:[email protected]

doi: 10.1158/0008-5472.CAN-11-2998

�2011 American Association for Cancer Research.

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and breast cancer prognosis is somewhat complicated. On onehand, increasing evidence has implied that OS may facilitatetumor cell migration, invasion, and metastasis through mul-tiple mechanisms, including activation of the MAPK–HSP27pathway, activation of matrix metalloproteinases, and inhibi-tion of anti-proteases (11). A recent study also uncovered anovel mechanism in which OS could affect tumor microenvi-ronment and increase the migratory properties of stromalfibroblasts, which in turn potentiate breast cancer dissemina-tion (12). On the other hand, most chemotherapy regimensexert their cytotoxic effects through apoptosis which is mainlymediated by ROS and concomitant OS (13, 14), indicating thatincreased OS levels might enhance the effectiveness of adju-vant chemotherapy. Therefore, the influence of variations inOS-related genes on breast cancer progression and prognosismight depend on whether a patient underwent chemotherapyor not.To our knowledge, although adjuvant chemotherapy could

effectively reduce the risk of recurrence and mortality forwomen with operable disease, only a fraction of patientsbenefit from it. It is difficult to predict the response of patientsto chemotherapy. Many somatic factors such as nodal status,tumor size, and expression of hormone receptors andHER2 areroutinely used to determine prognosis and response to specifictherapies (15). However, the roles of host-related factors, forexample, inherited genetic factors, have not been thoroughlyinvestigated. In this study, we investigate whether womenharboring genetic variations in estrogen–quinone metaboliz-ing genes involved in OS experience different disease progres-sion, and we explore the effect of these variations on chemo-therapy response, which is determined by the survival afterdiagnosis of breast cancer in the adjuvant setting.

Materials and Methods

PatientsThis study was approved by the Ethical Committee of the

Shanghai Cancer Center of Fudan University, and each par-ticipant signed an informed consent document. This prospec-tive observational study was initiated in 2004. All patients withmalignant breast cancer tumors who were willing to partici-pate in the study were enrolled. For each participant, clinico-pathologic and treatment data were recorded, disease out-comewas followed up, and a blood sample was collected. FromJanuary 2004 to January 2008, we recruited approximately 1,036unrelated patients with pathologically confirmed primarybreast cancer in the Shanghai Cancer Center. Genotyping ofthe NQO2 and GSTM1-5 genes was done in 2008–2009 (6, 7, 16).Patients selected for the present analysis fulfilled the followinginclusion criteria: (i), female patients diagnosed with unilateralinvasive breast cancer; breast carcinoma in situ (with orwithout microinvasion) were excluded; (ii), pathologic exam-ination of tumor specimens was carried out in the Departmentof Pathology in our hospital; (iii), patients with operable tumorand without any evidence of metastasis at diagnosis; (iv),patients not receiving neoadjuvant systemic therapy (chemo-therapy and/or hormone therapy) or preoperative irradiation;(v), patients harboring a rare 16-bp insertion allele of a triallelic

polymorphism in theNQO2 genewere also excluded because ofthe indefinite function of that allele; (vi), with follow-up datafor at least 3 months; (vii), not treated with anti-HER2 therapy,for example, trastuzumab.

Of the 1,036 unrelated patients who were originally enrolledin the prospective observational study, 38 had bilateral breastcancer, and 158 of the 998 unilateral patients were DCIS. In the840 unilateral patients with invasive cancer, 34 patients werefurther excluded due to not fulfilling other inclusion criteria. Asa result, 806 patients were included in this study as the test set.The preoperative evaluation and examination has beendescribed elsewhere (17). The basic information of patientsis shown in Table 1. Determination of estrogen receptor (ER),progesterone receptor (PR), and HER2 status was done bypathologists in the Department of Pathology in our hospital.Most of, but not all, patients with equal HER2 protein expres-sion (immunohistochemistry 2þ) were also selected to have aFISH test for HER2 gene amplification. This was done accord-ing to established procedures that have been described else-where (18, 19). Because the data for tumor grade were lackingin many cases, we did not include this variable in our analysis.

Postoperative recurrence risk category was mainly deter-mined according to St. Gallen consensus. The choice ofchemotherapy depended on the risk category: patients withmoderate recurrence risk underwent cyclophosphamide,doxorubicin/epirubicin, and 5-Fu (CAF) regimen; patientswithlow risk underwent cyclophosphamide, methotrexate, and5-Fu (CMF) regimen, or AC regimen; and patients with highriskwould receive taxane-containing regimens [AC followedbypaclitaxel (P), or CAF followed by docetaxel (T), or TAC].Approximately 100% and 85% of the patients that were treatedwith chemotherapy received cyclophosphamide-containingand anthracycline-based regimens, respectively. All of thepatients with positive hormone receptor status were recom-mended to take tamoxifen or aromatase inhibitors (only forpostmenopausal women) for 5 years. The strategy of systemictreatments was updated according to the St. Gallen consensus(15, 20, 21).

We also validated the significant polymorphisms from thefirst set of tests in an independent population with mainlyfamilial/early-onset breast cancer cases (ref. 22; Table 1). Since2000, the Shanghai Cancer Hospital has conducted a multi-center hospital-based gene mutation screening project to gaina full understanding of the contribution of germline mutationsof high-penetrance genes to hereditary and early-onset breastcancer in the Han Chinese population (22). Approximately 600cases coming from 5 medical centers in China had beencollected between 2000 and 2008, and an additional 150 newcases were recruited in 2008–2009 in our hospital. The eligi-bility criteria have been described elsewhere. Using early-onset/familial breast cancer patients as a validation set isacceptable because that, first, although the etiology of sporadicand familial/early-onset breast cancers is probably different,the clinical administration of adjuvant chemotherapeutic reg-imen does not differ between these 2 diseases according toeither NCCN breast cancer guideline or St. Gallen early breastcancer consensus (15); second, prognosis of breast cancerseems to be not affected by family history (23); third, evidence

Genetic Variants in OS-Related Genes Predict Chemoresistance

www.aacrjournals.org Cancer Res; 72(2) January 15, 2012 409




has shown that in patients with BCRA1/2-negative familialbreast cancer (in this study, the familial patientswere also non-BRCA1/2 carriers), the objective response rate of neoadjuvantchemotherapy is comparable with that of sporadic breastcancer patients (24, 25). We finished genotyping of NQO2 andGSTM1-5 in approximately 400 unrelated familial and early-onset cases from southeast China (mainly Shanghai City and itssurrounding regions) in 2008–2009 (6, 7, 16). In this study, weselected 339 patients who fulfilled the inclusion criteriadescribed above.

DNA/RNA preparationExtraction, preservation of genomic DNA and mRNA, and

general PCR were done as previously reported (22). We alsocollected 40 pairs of tissue samples including normal breast(>3 cm away from tumor), peritumor (1–2 cm away fromtumor), and cancer tissue; each pair was collected from thesame patient.

Selection of genetic variants and genotypingSelection of genetic variations of NQO2 and GSTM1-5 has

been described elsewhere (6, 7). The 19 polymorphisms ana-lyzed are listed in Table 2. Detailed information of selection ofgenetic variants is shown in Supplementary Materials andMethods. Single-nucleotide polymorphisms (SNP) were geno-typed on the 12-plex SNPstream platform (Beckman CoulterInc.; ref. 26). The genotyping work was carried out by theChinese National Human Genome Center (Shanghai), andthe call rates varied from 93.2% to 99.6%. The genotyping ofthe GSTM1 gene-deletion variant and the I-29/D polymor-phism have been reported elsewhere (6, 16). In the validationstudy, the SNP rs2071002 (þ237A>C) was genotyped using aPCR-RFLP–based assay (6). The samples were assayed in a96-well PCR plate with a positive control consisting of a DNAsample with known heterozygous genotype. Two researchassistants (K-D.Y and L.F) independently examined the gelpictures and repeated the assays if they did not reach a

Table 1. Characteristics of breast cancer patients in 2 sets

Variable Test set(n ¼ 806, %)

Validation set(n ¼ 339, %)

P

Age (continuous) Median (ranges) 50 y (23–87) 43 y (19–85) <0.001b

Follow-up time Median (ranges) 52 mo (3–86) 50 mo (3–86) <0.001b

Age �50 y 49.4 64.3 <0.001>50 y 50.6 35.7Missing data 0.0 0.0

Menopausal status Premenopausal 56.6 71.4 <0.001Postmenopausal 43.4 28.6Missing data 0.0 0.0

Lymph nodes Negative 56.5 56.9 0.612Positive 41.9 39.5Missing data 1.6 3.5

Tumor size �2 cm 53.2 51.3 0.825>2 cm 43.2 40.4Missing data 3.6 8.3

ER Negative 34.1 42.8 0.002Positive 64.1 52.8Missing data 1.7 4.4

PR Negative 41.4 49.9 0.002Positive 56.8 45.4Missing data 1.7 4.7

HER2/neu Negative 75.4 65.5 0.051Positive 22.0 25.7Missing data 2.6 8.8

Chemotherapya No 28.0 27.1 0.756Yes 72.0 72.9Missing data 0.0 0.0

Endocrine therapy No 30.8 35.4 0.034Yes 67.5 57.8Missing data 1.7 6.8

aApproximately 100% and 85% of the patients that were treated with chemotherapy received cyclophosphamide-containing andanthracycline-based regimens, respectively.bCompared by student t test and other P values by Pearson's c2 test.

Yu et al.

Cancer Res; 72(2) January 15, 2012 Cancer Research410




consensus on the genotype. An adequate quantity of restrictionenzyme was used to completely cleave PCR amplicons.Samples with inconsistent outcomes in 2 independent testswere directly sequenced. In addition, 10% of the samples wererandomly selected for repeated RFLP analysis for both of thepolymorphisms, and the results were 100% concordant.

Plasmid constructs, cell culture, transient transfection,and luciferase assaysThe pGL3-Basic reporter vector from Promega was used to

construct luciferase reporter plasmids using standard recom-bination techniques, as previously described (6). Briefly, pro-moter regions of NQO2 (�537 to þ529 bp, the transcriptionalstart site is designated asþ1) containing haplotype I-C and D-C were cloned from individual DNA samples and were thencloned into the pGL3-Basic vector to generate pGL3-I-C andpGL3-D-C constructs, respectively. Wang and colleagues haveindicated that approximately �500 to þ300 bp of NQO2promoter region is sufficient for basal expression of NQO2(27). A site-directed mutagenesis kit (Stratagene) was used togenerate pGL3-I-A and pGL3-D-A plasmids. The abbreviationsI, D, C, and A in above pGL3 constructs denote 29-bp insertionallele of I-29/D polymorphism, 29-bp deletion allele of I-29/Dpolymorphism,þ237C allele of rs2071002, andþ237A allele ofrs2071002, respectively. A human Sp1 expression vector wasconstructed using the pcDNA 3.1 Directional TOPOExpressionKit (Invitrogen). All constructs were verified by direct sequenc-ing before use.A human immortal normal breast epithelial cell line (HBL-

100) was obtained from the American Type Culture Collection(ATCC). Liquid nitrogen stocks were made upon receipt andmaintained until the start of each study. Morphology anddoubling times were also recorded regularly to ensure main-tenance of phenotypes. Cells were used for no more than 3months after being thawed. Thus, the cell line has been testedand authenticated by ATCC and maintained in our laboratoryfor less than 3 months, during which all experiments wereconducted. Cells were grown in complete medium consistingof Dulbecco's modified Eagle's medium supplemented with10% heat-inactivated fetal calf serum in a humidified, 5% CO2

incubator at 37�C. For the luciferase experiments, cells weretransfectedwith 500 ng of plasmidDNA (4 haplotype vectors orpGL3-Basic as a negative control) and cotransfected with 10 ngof pRL-SV40 as a control for transfection efficiency, with orwithout cotransfection of 1.0 mg of the Sp1 expression vector.Transfections were done using Lipofectamine 2000 (Invitro-gen) according to the manufacturer's protocol. Luciferaseactivity was measured on a VeritasTM Microplate lumin-ometer (Turner BioSystems) using the Dual-Luciferase Report-er Assay System Kit (Promega). Each experiment was con-ducted in triplicate at least 3 times. Luciferase units werecalculated using the formula Firefly luciferase units/Renillaluciferase units. Fold increase was reported by defining theactivity of the empty pGL3-Basic vector as one.

Real-time PCRReal-time PCR with SYBR Green fluorescent-based assay

(TaKaRa) was done in a fluorescence temperature cycler

(Opticon, MJ Research) using the standard curves method(28). All samples were done in triplicate at least 3 times.cDNA-specific primers were designed using Primer Premier5.00 (Premier Biosoft International). The following primerswere used for gene expression detection: NQO2 sense,50-GAAACCCACGAAGCCTACA-30, and antisense, 50-CAG-CACCCTATCCATCCAG-30 (153 bp); glyceraldehyde-3-phos-phate dehydrogenase (GAPDH) was used for normalization.

Survival analysis, prognosis modeling, and receiveroperating characteristics curve

The categories analyzed for disease-free survival (DFS)were the first recurrence of disease at a local, regional, ordistant site; the diagnosis of contralateral breast cancer; anddeath from any cause. All of these categories listed abovewere considered DFS events. Patients with study end dateand loss of follow-up were considered to be censored.Survival curves were determined using the Kaplan–Meiermethod and compared by the log-rank test (univariateanalysis). HR for disease progression and 95% CIs werecalculated by the Cox risk proportion model. Multivariateanalysis was carried out using the Cox risk proportion model(method: backward stepwise, likelihood ratio). Timescale offollow-up time used for Cox proportional hazards model is"month."

We used multivariate logistic regression to construct theprediction model for DFS events. The aim of the model was topredict the risk of occurrence of disease events of an individualwoman using individual clinicopathologic data, with or with-out personal genetic information. For a feasible modelingprocedure, we divided the patients into 2 groups. One groupexperienced relapse during the follow-up period, whereas theother group did not. All of the cases selected for modelingshould be followed up for at least 6 months, and a few caseswith insufficient follow-up time were thus excluded from themodeling analysis. Because we observed an interactionbetween chemotherapy and genotypes of estrogen–quinonemetabolizing genes, we carried out modeling using indepen-dent variables [age (years), lymph node status (positive ornegative), tumor size (�2 cm or >2 cm), ER (positive ornegative), PR (positive or negative), HER2 (positive or nega-tive), and endocrine therapy (yes or no), with or withoutgenetic factor] in the nonchemotherapy group. The probabilityof disease progression was estimated by the formula "eL/(1þeL)", in which the value of L was derived by multivariatelogistic regression analysis (method: backward stepwise, like-lihood ratio).

To further evaluate the accuracy of the prediction model,we employed receiver operating characteristics (ROC)curves and calculated the area under the curve (AUC) withits 95%CIs. The ROC curve shows the relation betweensensitivity and false-positive rate (1-specificity) of a giventest across all possible threshold values that define thepositivity of a disease or condition. In ROC analysis, theindependent variable was disease outcome (occurrence ofdisease events or not), and the classification variable isprobability of disease progression, which was calculatedusing the formula eL/(1 þ eL).






Tab

le2.

Impac

tof

geno

types

ofNQO2an

dGSTM

1-5on

disea

seprogn

osis

Ove

rall(n

¼80

6)Noch

emotherap

y(n

¼22

6)Che

motherap

y(n

¼58

0)

Gen

etic

varian

tLo

cation

Gen

otype

nHRa(95%

CI)

Pb

HRc(95%

CI)

Pb

HRc(95%

CI)

Pb

Pa

Pc

Pc

NQO2-rs20

7099

9Promoter

GG

433

Referen

ce0.16

4Referen

ce0.62

5Referen

ce0.07

7AGþA

A36

30.84

(0.57–

1.23

)0.36

51.72

(0.74–

4.01

)0.20

80.69

(0.44–

1.08

)0.10

8NQO2-I-29

/DPromoter

I-29

/I-29

525

Referen

ce0.44

7Referen

ce0.02

4Referen

ce0.64

4I-29

/DþD

/D26

40.87

(0.58–

1.30

)0.49

90.34

(0.12–

0.99

)0.04

91.06

(0.68–

1.66

)0.79

4NQO2-rs20

7100

250-U

TRAA

332

Referen

ce0.51

0Referen

ce0.00

18Referen

ce0.30

8ACþC

C43

40.82

(0.56–

1.20

)0.30

80.24

(0.10–

0.55

)0.00

071.18

(0.75–

1.85

)0.46

9NQO2-rs11

4368

4Exo

n,Phe

47Le

uTT

355

Referen

ce0.00

3Referen

ce0.16

2Referen

ce0.00

9CTþ

CC

444

1.68

(1.14–

2.47

)0.00

91.40

(0.62–

3.18

)0.41

71.75

(1.12–

2.74

)0.01

5NQO2-rs41

4936

7Exo

nSer13

5Ser

CC

540

Referen

ce0.96

8Referen

ce0.06

0Referen

ce0.37

7CTþ

TT25

90.94

(0.63–

1.38

)0.74

00.44

(0.15–

1.30

)0.13

91.10

(0.71–

1.71

)0.66

2NQO2-rs18

8529

8Intron

GG

609

Referen

ce0.41

9Referen

ce0.50

7Referen

ce0.47

9GTþ

TT18

90.86

(0.54–

1.36

)0.52

00.51

(0.16–

1.64

)0.26

10.94

(0.56–

1.57

)0.80

5NQO2-rs95

0191

0Intron

GG

318

Referen

ce0.01

4Referen

ce0.08

8Referen

ce0.06

6CGþC

C43

31.63

(1.09–

2.44

)0.01

61.73

(0.71–

4.21

)0.23

01.58

(1.01–

2.50

)0.04

7GSTM

4-rs54

2370

Promoter

TT41

0Referen

ce0.75

6Referen

ce0.44

0Referen

ce0.95

2CTþ

CC

379

0.92

(0.63–

1.33

)0.65

90.75

(0.33–

1.70

)0.48

71.51

(0.97–

2.35

)0.06

6GSTM

4-rs10

1016

750-U

TRGG

717

Referen

ce0.33

1Referen

ce0.12

6Referen

ce0.98

2CGþC

C38

0.87

(0.32–

2.41

)0.79

5N.A.

0.98

21.16

(0.42–

3.21

)0.77

6GSTM

4-rs56

0018

Intron

TT72

6Referen

ce0.04

8Referen

ce0.41

2Referen

ce0.06

4CTþ

CC

731.93

(1.13–

3.32

)0.01

71.83

(0.61–

5.53

)0.28

42.19

(1.18–

4.04

)0.01

3GSTM

4-rs53

5537

Intron

GG

743

Referen

ce0.05

8Referen

ce0.15

8Referen

ce0.15

6AGþA

A59

0.41

(0.13–

1.28

)0.12

5N.A.

0.97

70.51

(0.16–

1.61

)0.24

7GSTM

2-rs65

5315

Intron

GG

418

Referen

ce0.73

1Referen

ce0.81

4Referen

ce0.84

4AGþA

A37

50.86

(0.59–

1.25

)0.43

90.60

(0.27–

1.34

)0.21

50.97

(0.63–

1.47

)0.88

0GSTM

1-Null/P

rese

ntWho

lege

neNull

456

Referen

ce0.02

3Referen

ce0.00

9Referen

ce0.28

5Prese

nt34

30.60

(0.41–

0.88

)0.00

90.33

(0.14–

0.75

)0.00

80.79

(0.51–

1.22

)0.28

2GSTM

5-rs37

5444

6Promoter

CC

385

Referen

ce0.19

6Referen

ce0.45

7Referen

ce0.33

8ACþA

A40

90.67

(0.46–

0.98

)0.03

90.35

(0.14–

0.85

)0.02

00.75

(0.49–

1.15

)0.18

4GSTM

5-rs49

7077

3Intron

GG

344

Referen

ce0.21

7Referen

ce0.79

7Referen

ce0.21

2CGþC

C42

10.69

(0.47–

1.02

)0.06

00.45

(0.18–

1.12

)0.08

50.70

(0.45–

1.08

)0.10

9GSTM

5-rs11

807

30-U

TRTT

589

Referen

ce0.89

2Referen

ce0.54

4Referen

ce0.52

7CTþ

CC

209

0.83

(0.54–

1.29

)0.42

60.98

(0.40–

2.44

)0.97

10.78

(0.47–

1.28

)0.32

0GSTM

3-rs74

83Exo

n,Val22

4Ile

TT46

2Referen

ce0.27

3Referen

ce0.66

9Referen

ce0.40

1CTþ

CC

337

0.80

(0.55–

1.18

)0.25

80.58

(0.26–

1.27

)0.17

40.84

(0.54–

1.32

)0.45

5

(Con

tinue

don

thefollo

wingpag

e)

Yu et al.





L for the traditional model is:

L traditional ¼ ð�1:74Þ þ ð�1:06Þ � ERþ 1:69�HER2þ 4:04� LN;

L for the combined model is:

L combined ¼ ð�1:15Þ þ ð�1:26Þ � ERþ 2:25�HER2þ 5:85� LNþ ð�2:67Þ �Genetic score

Statistical analysisTests of association were conducted using Pearson c2 test.

Student t test was used to compare continuous variablesbetween 2 groups. A P value of less than 0.05 (2-sided) wasconsidered to be significant. Statistical analysis was done usingStata/SE 10.0 (Stata) and SPSS 12.0 (SPSS).

Results

Association of candidate gene genotypes with DFSWe studied the association of the genotypes of 19 genetic

variants with DFS in the dominant model (major homozygousvs. heterozygousþminor homozygous; Table 2). Analyses of afew variants were unavailable in the recessive model (majorhomozygousþ heterozygous vs. minor homozygous) or in theadditive model because of rare numbers of minor homozygous(data not shown). In the overall population, 4 polymorphisms(2 in NQO2, 1 in GSTM4, and the GSTM1-Null/Present poly-morphism) showed significant associations with DFS in uni-variate analysis. All of these polymorphismswere still related toDFS after adjustment for clinicopathologic factors. However,after conservative Bonferroni correction of multiple compar-isons, none of the polymorphisms remained significant.

Association of genotypewithDFS ismodified by adjuvantchemotherapy

We further investigated the effect of chemotherapy on theassociation between genotypes and DFS (Table 2). Interest-ingly, in patients not treated with chemotherapy, genotypeswith minor alleles of the 3 polymorphisms, NQO2-I-29/D(Fig. 1A), NQO2-rs2071002 (Fig. 1B), and GSTM1-Null/Present(Fig. 1C), showed significantly better DFS than their majorcounterparts, although these associationswere not observed inthe chemotherapy group (Supplementary Fig. S1A–S1C). Incontrast, the variant genotype of NQO2-rs1143684 was signif-icantly correlated with poorer DFS compared with the wild-type genotypes in the chemotherapy group, but this effect wasnot observed in the nonchemotherapy group. After multivar-iate adjustment, all of the 4 polymorphisms still reachedsignificant P values.

Interaction between genetic variations and resistance tochemotherapy

The results presented above strongly suggest the presence ofan interaction. Multivariate analysis of interaction was done in2 steps. In the first step, the Cox regression model includedestablished prognostic factors but not genotypes (see details innote of Table 3). We identified that positive lymph node status

Tab

le2.

Impac

tof

geno

types

ofNQO2an

dGSTM

1-5on

disea

seprogn

osis

(Con

t'd)

Ove

rall(n

¼80

6)Noch

emotherap

y(n

¼22

6)Che

motherap

y(n

¼58

0)

Gen

etic

varian

tLo

cation

Gen

otype

nHRa(95%

CI)

Pb

HRc(95%

CI)

Pb

HRc(95%

CI)

Pb

Pa

Pc

Pc

GSTM

3-rs13

3201

850-U

TRAA

555

Referen

ce0.06

2Referen

ce0.15

8Referen

ce0.25

5ACþC

C22

10.75

(0.48–

1.19

)0.22

60.65

(0.27–

1.58

)0.34

60.74

(0.43–

1.27

)0.28

2GSTM

3-rs49

7073

7Promoter

GG

374

Referen

ce0.92

9Referen

ce0.79

1Referen

ce0.87

3CGþC

C37

60.88

(0.59–

1.30

)0.50

80.62

(0.26–

1.48

)0.28

50.93

(0.59–

1.45

)0.74

0

Abbreviations

:N.S.,no

tsign

ifica

nt;U

TR,u

ntrans

latedregion

;N.A.,no

tap

plicab

le.

Paan

dPc:m

ultiv

ariate

adjusted

bytheCox

riskproportio

nmod

el.

aAdjusted

forag

e(y),lymphno

destatus

(pos

itive

orne

gativ

e),tumor

size

(�2cm

or>2

cm),ER

(pos

itive

orne

gativ

e),PR

(pos

itive

orne

gativ

e),HER2(pos

itive

orne

gativ

e),

chem

othe

rapy

(yes

orno

),an

den

docrinetherap

y(yes

orno

).HRwith

its95

%CIisca

lculated

bytheCox

riskproportio

nmod

el.

bP:u

nadjusted

.cAdjusted

forag

e,lymphno

destatus

,tum

orsize

,ER,P

R,H

ER2,

anden

docrinetherap

y.






(HR¼ 2.85, 95% CI: 1.83–4.46, P < 0.0001), large tumor size (HR¼ 2.36, 95% CI: 1.56–3.57, P < 0.0001), positive ER (HR ¼ 0.40,95% CI: 0.27–0.59, P < 0.0001), positive HER2 (HR ¼ 1.76, 95%:CI: 1.20–2.58, P ¼ 0.004), and using chemotherapy (HR ¼ 0.40,

95% CI: 0.24–0.65, P ¼ 0.0005) were significant independentfactors for DFS after multivariate adjustment. In the secondstep, interaction between each genetic variation and chemo-therapywas investigated, alongwith adjustment for the factors

Table 3. Multivariate Cox model (DFS) including interaction of genotypes with adjuvant chemotherapy

Genetic variant Factorsa P HR (95% CI)

NQO2-I-29/D Genotype (AA vs. Aaþaa) 0.062 0.38 (0.14–1.09)Chemo. (no vs. yes) <0.001 0.30 (0.17–0.51)Interaction, Chemo�Genotype 0.073 2.87 (0.91–9.06)

NQO2-rs2071002 Genotype (AA vs. Aaþaa) 0.003 0.30 (0.14–0.66)Chemo. (no vs. yes) <0.001 0.20 (0.10–0.37)Interaction, Chemo�Genotype 0.003 4.02 (1.61–10.0)

NQO2-rs1143684 Genotype (AA vs. Aaþaa) 0.670 1.21 (0.49–2.98)Chemo. (no vs. yes) <0.001 0.40 (0.24–0.67)Interaction, Chemo�Genotype 0.009 1.68 (1.14–2.48)

NQO2-rs9501910 Genotype (AA vs. Aaþaa) 0.023 1.59 (1.07–2.37)Chemo. (No vs. Yes) 0.002 0.42 (0.25–0.72)Interaction, Chemo�Genotype 0.915 0.95 (0.36–2.49)

GSTM1-Null/present Genotype (AA vs. Aaþaa) 0.001 0.25 (0.10–0.56)Chemo. (no vs. yes) <0.001 0.21 (0.11–0.40)Interaction, Chemo�Genotype 0.015 3.38 (1.27–8.97)

Genetic score Genotype (0–1 vs. 2–3) 0.005 0.25 (0.10–0.63)Chemo. (no vs. yes) <0.001 0.22 (0.12–0.40)Interaction, Chemo�Genotype 0.004 4.60 (1.63–13.3)

NOTE: Multivariate analysis of interaction was done in 2 steps. In the first step, the Cox regression model included establishedprognostic factors (age, lymph node status, tumor size, ER, PR, HER2, chemotherapy, and endocrine therapy) but not genotypes. Thefirst step identified that lymph node status (P < 0.0001), tumor size (P < 0.0001), ER (P < 0.0001), HER2 (P¼ 0.004), and chemotherapy(P¼0.0005)were significant independent factors forDFS.Aswell, age tends tobesignificant (P¼0.08). In the secondstep, interactionsbetween each genetic variant (in the dominant model) and chemotherapy were investigated along with adjustment for those factors(with P < 0.10) identified in the first step. This table shows the results of the second step.Abbreviations: Chemo., chemotherapy; AA, major homozygous; Aa, heterozygous; aa, minor homozygous.aHere, we present only 3 items: genotype (AA vs. Aaþaa), chemotherapy, and the interaction between them. Other parameters(tumor size, lymph node status, ER, and HER2 status) are not shown.

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Follow-up time (mo)

0.0

0.2

0.4

0.6

0.8

1.0

Cu

mu

lati

ve

DF

S

GSTM1-null/present P = 0.009

Null (n = 128)

Present (n = 98)

Null censored

Present censored

806040200

Follow-up time (mo)

0.0

0.2

0.4

0.6

0.8

1.0

Cu

mu

lati

ve

DF

S

NQO2-rs2071002 P = 0.002

AA (n = 97)

AC+CC (n = 116)

AA-censored

AC+CC censored

806040200

Follow-up time (mo)

0.0

0.2

0.4

0.6

0.8

1.0

Cu

mu

lati

ve D

FS

NQO2-I-29/D P = 0.024

I-29/D+D/D (n = 72)

I-29/I-29 (n = 151)

I-29/D+D/D censored

I-29/I-29 censored

A CB

Figure1. Effects of genetic variants onDFSaccording to adjuvant chemotherapy in primary breast cancerwithout adjuvant chemotherapy forNQO2-I-29/D (A),rs2071002 (B), and GSTM1-null/present (C). P value tested by log-rank test.

Yu et al.





identified in the first step (factors with P < 0.10). The inter-action between genotypes and chemotherapy had strongimpacts on DFS (Table 3). For instance, an interaction impliesthat patients with the variant allele of NQO2-rs2071002received only 25% (1/4.02, 4.02 is the HR of interaction betweenchemotherapy and rs2071002 genotype) of the benefit fromadjuvant chemotherapy compared with patients with the wild-type genotypes. Interestingly, NQO2-I-29/D, NQO2-rs2071002,and GSTM1-Null/Present not only interact with chemotherapybut tend to be independent prognostic factors if their inter-actions to chemotherapy are adjusted.

Relationship between host genotypes and ER phenotypeIt is well established that ER-positive tumors tend to be

resistant to chemotherapy (29). It is possible that the observedinteraction between chemotherapy resistance and geneticvariants is caused by a linkage of genetic variants to tumorER phenotype. Therefore, we studied the relationship betweengenotype and tumor phenotype (Supplementary Table S1).However, no significant genotype–phenotype association wasfound for the 4 significant polymorphisms.

Functional polymorphisms alter expression of NQO2We have shown that genetic variations in NQO2 and GSTM1

affect the resistance to chemotherapy. This is biologicallyplausible for GSTM1-Null/Present because the GSTM1-nullgenotype results in GSTM1 enzyme deficiency. However, thefunctional basis ofmultiple variations inNQO2 requires furtherinspection. Among the 4 crude significant polymorphisms,either in the overall population or in subgroups (Table 2), two(NQO2-I-29/D, NQO2-rs2071002) located in the promoter canaffect gene expression, and one located in an exon results in anamino acid change (NQO2-rs1143684). The remaining SNPNQO2-rs9501910, which is located in an intron, has no potentialfunction. However, it has high linkage disequilibrium (LD) toNQO2-rs1143684 with a D' of 0.88 and r2 of 0.77 (Fig. 2A),indicating that it is a linked marker rather than a causal SNP.We previously reported that the I-allele of NQO2-I-29/D intro-duces binding sites for the transcriptional repressor Sp3, andthe C-allele of NQO2-rs2071002 creates a new Sp1 binding site(ref. 6; Fig. 2B). Here, we further show that theD-allele ofNQO2-I-29/D and the C-allele of NQO2-rs2071002 are associated withhigher activity of the NQO2 gene promoter in human normalbreast cells (Fig. 2C) and lead to higher expression of NQO2 innormal breast and peritumoral tissues (Fig. 2D) comparedwiththeir wild-type counterparts. However, NQO2 genotype andNQO2 expression are not correlated in cancer tissue (Fig. 2D).

Validation of interaction between genotypes andresistance to chemotherapyIt is critical to validate ourfindings thatNQO2-I-29/D,NQO2-

rs2071002, and GSTM1-Null/Present are associated with dis-ease progression in the nonchemotherapy group. To validatethe integral effect of multiple genetic variations, we developeda combined "genetic score" by assigning "0" to risk genotypesand "1" to protective genotypes of the 3 variants. Thus, eachpatient had a genetic score ranging from 0 to 3. For conve-nience, we further divided patients into 2 groups (score 0–1,

and score 2–3). In the test set, the original genetic score (with 4categories) had a significant discrimination capability in DFSin the nonchemotherapy group (P ¼ 0.0027, SupplementaryFig. S2A) but not in the chemotherapy group (P ¼ 0.088,Supplementary Fig. S2B). The modified genetic score (with 2categories) displayed similar discrimination capability (Sup-plementary Fig. S2C and S2D). An interaction of genetic scoreand chemotherapy was also observed (P ¼ 0.004, Table 3). Inthe validation set, which consisted of 339 patients mainly withfamilial/early-onset breast cancer, the genetic score tended tobecome a predicator of DFS in the nonchemotherapy group(P ¼ 0.050, Fig. 3A) but not in the chemotherapy group(P ¼ 0.304, Fig. 3B). Similarly, an interaction between geneticscore and chemotherapy was observed with a borderlinesignificance (HR ¼ 2.07, P ¼ 0.045).

Predictive value of polymorphisms in diseaseprogression

The genetic factors that are capable of predicting diseaseprogression could still have no clinical utility unless they canoffer additional information beyond what classic predictorscan already tell us.We evaluated the predictive value of geneticscore in disease progression in patients receiving no chemo-therapy. Genetic score (0–1 vs. 2–3) was added in a traditionalmodel. ROC analysis showed an AUC of 0.70 (95% CI: 0.60–0.80)for the traditional model and 0.78 (95% CI: 0.69–0.86) for thecombined model (Fig. 3C), suggesting that adding geneticfactors to classic factors markedly improved the predictioncapability (P ¼ 0.047 for AUC comparison).

Discussion

In this prospective observational study, we noted thatassociations between DFS and germline polymorphisms inthe estrogen–quinone metabolizing genes involved in OS weremodified by adjuvant chemotherapy. The observed interactionwas successfully validated. Although the genetic variationsassociated with an enhanced ROS-metabolizing capabilitywould reduce the hazard of disease progression amongwomenreceiving no chemotherapy, they would compromise the effi-ciency of adjuvant chemotherapy.

Several prior studies have examined the association betweenbreast cancer disease outcome and genotypes of GSTM1 andNQO2 (30–36). Our study is the only study that has investigatedthe interaction between chemotherapy and genotypes. Ourresults consistently indicate that genetic variants in estrogen–quinone metabolizing genes play protective roles in diseaseprogression if no chemotherapy is administered. However, thiseffect disappears after chemotherapy is administered; in otherwords, patients harboring genotypes related to low ROS levelsreceive limited, if any, benefit from chemotherapy.

The conflicting outcome between the chemotherapy andnonchemotherapy groups is explainable. In general, the pro-tective genotypes that are associated with higher expression orelevated activity of estrogen–quinone metabolizing enzymeswould reduce ROS levels and subsequently inhibit OS-inducedcancer cell proliferation, angiogenesis, and blood supply toresidual cells or tumors in breast cancer patients after surgery






(11). Some may argue that breast tumors have significantlyhigher OS levels than normal tissues and that a moderatedecrease of ROS caused by germline genetic variants mighthave limited effects. This is true for advanced large tumors.However, after surgery, patients have only disseminated cancercells or a tiny residual tumor rather than a large mass. Thesmall quantity of cancer cells is exposed to the normal micro-environment and affected by its surrounding, which consists ofthousands of normal cells. Variability in ROS levels in themicroenvironment caused by germline variations may influ-ence the outcome of dormancy and extinction or proliferationand dissemination of residual cancer cells. Moreover,

decreased ROS can protect normal breast cells from OS-induced DNA damage and reduce genetic instability, resultingin prevention of second primary breast cancers. However,when chemotherapy is administered, the situation is changed.As we know, most chemotherapy regimens exert their cyto-toxic effects by elevating the OS levels within breast carcino-mas, pushingmalignant cells "over the edge" and increasing OSdamage to a level that the cancer cells cannot cope with (14).Although anthracyclines act primarily by interfering withtopoisomerase II activity, some studies indicated that manyactive chemotherapeutic drugs in breast cancer treatment(including anthracyclines and cyclophosphamide) are also

7654321

62

87

81

41

87

80

76

65

49

54

57

80

53

70

64

95

93

88

83

40

Block 1(7 kb)

rs20

7099

9

29bp

-I/D

rs20

7100

2

rs1143684

rs4

14

93

67

rs1

88

52

98

rs95

0191

0

7654321

3

14

11

1

4

11

25

4

22

1

4

16

11

14

11

9

6

77

3

7

1

Block 1(7 kb)

rs20

7099

9

29bp

-I/D

rs20

7100

2

rs1143

684

rs414

9367

rs188

5298

rs95

0191

0

62

87

81

41

87

80

76

65

49

54

57

80

53

70

64

95

93

88

83

40 3

14

11

1

4

11

25

4

22

1

4

16

11

14

11

9

6

77

3

7

1

Luc.

Luc.

-60 to -32 +237

Luc.

Luc.+529–537

pGL3-Basic

pGL3-D-C

pGL3-D-A

pGL3-I-C

Luc. pGL3-I-A

D C

D A

I-29 C

I-29 A

Sp1

Sp3 Sp1

Sp3

I-29/D rs2071002

Luc.

Luc.

Luc.

Luc.

Luc.

D C

D A

I-29 C

I-29 A

B

DC

Baseline Sp1 expr. vector0

10

20

30

40pGL3-D-CpGL3-D-ApGL3-I-CpGL3-I-A

Arb

itra

ry u

nits

I II III I II III I II III0.001

0.01

0.1

1

I, both major homozygous (II and AA)

II, the remaining genotypes except I and III

III, at least one minor homozygous (DD or CC)

Normal tissue Peritumor Cancer

Re

lati

ve

mR

NA

exp

ressio

n o

f N

QO

2

A

Figure 2. Genetic variants in NQO2 and functional investigation. A, pairwise linkage disequilibrium (LD) among selected variants in the NQO2 gene in breastcancer patients. The valuewithin each diamond represents the pairwise correlation between polymorphisms [measured asD' (left) and r2 (right)] definedby thetop left and the top right sidesof thediamond. The red-to-white gradient reflects higher to lowerD' values; theblack-to-whitegradient reflects higher to lower r2

values. Haploview 4.1 software is used to draw the LD plot. B, schematic graphs of luciferase reporter plasmids. NQO2 promoter regions (�537 toþ529 bp,the transcriptional start site is designated as þ1) containing both I-29/D and rs2071002 (þ237A>C) polymorphisms are cloned into a pGL3-Basic reportergene. The I-29 allele of I-29/D introduces transcriptional–repressor Sp3 binding sites, whereas the A allele of rs2071002 abolishes the binding site oftranscriptional–activator Sp1. C, promoter activity in normal HBL-100 breast cells in vitro. Arbitrary units denote fold increase (pGL3-Basic vector isset as one). Significantly lowpromoter activity is observed in the I-A haplotype comparedwith the other 3 haplotype constructs (allP < 0.05). After transfectionof Sp1 expression vector, the I-A haplotype shows a lower activity compared with I-C (P < 0.01), D-A (P < 0.05), and D-C (P < 0.0001). Data representmean values, with error bars showing SE. Comparisons are done by ANOVA analysis and adjusted by the Games–Howell method if equal variances are notassumed. D, box plots of NQO2 mRNA expression according to genotypes and tissue types. We collected 40 pairs of tissue samples including normalbreast (>3 cm away from tumor), peritumor (1–2 cm away from tumor), and cancer tissue from the same patient. The 40 pairs specimens are divided into3 groups according to germline genotypes: group I, both major homozygous (II and AA, n ¼ 15); group III, at least one minor homozygous (DD or CC,n ¼ 7); group II, the remaining genotypes (n ¼ 18). In normal tissue and peritumor tissue, the 3 genotype groups have differential expression of NQO2(P ¼ 0.0001 and P ¼ 0.0003, respectively, by Kruskal–Wallis test); in cancer tissue, genotypes have no effect on gene differential expression (P ¼ 0.752 byKruskal–Wallis test). GAPDH is used for normalization.

Yu et al.





ROS-generating agents (14, 37). Therefore, we might speculatethat when chemotherapy is administered to a patient withdecreased ROS levels, the originally protective effect of ROSmight cause resistance to chemotherapy. The precise mech-anismof interaction betweenROSand chemoresistance in vivo,however, needs further investigation. Another explanation isthat, because both anthracyclines and cyclophosphamide aremetabolized through reactions mediated by GSTMs (13), thepresence of GSTM1 could accelerate the inactivation andmetabolism mechanisms of these therapeutic agents.The interaction between chemotherapy and genotypes of

ROS-reducing genes can account for the conflicting resultsseen in previous studies with regard to the relationshipbetween GSTM1-null genotype and disease progression in anadjuvant setting. For patients receiving no chemotherapy, theGSTM1-present genotype tends to be protective (GSTM1 is oneof the 16 cancer-related geneswithin the 21-geneOncotype-DXassay for predicting recurrence of tamoxifen-treated, node-negative breast cancer; ref. 38). For patients undergoing che-motherapy, GSTM1-present probably has a similar effect toGSTM1-null (30–33). For mixed patients, the results tend to benonsignificant (34, 35). We also noted some SNPs in GSTM4that were associated to different extents with DFS regardless ofwhether chemotherapy was used or not. This issue needsfurther study. Moreover, our data imply that NQO2 has similar

features to GSTM1. It is probable that NQO2 has an intrinsicmetabolizing capability for many chemotherapeutic agents.We also found no change in expression ofNQO2 gene in cancercells with different germline genotypes, which indicates thatmany other factors (e.g., methylation, aberrant regulation)rather than only the regulative effect of polymorphic allelesinfluence NQO2 expression in cancer cells. This observationmay suggest that NQO2 expression levels in healthy tissuesaffect disease progression, perhaps, by influencing tumormicroenvironment.

Our study has several limitations. First, as a prospectiveobservational study, but not a clinical trial, the chemotherapyregimens in our study are not uniform. It is difficult todetermine whether the chemoresistance is specific to a par-ticular agent or to several agents. Second, genetic variants inother OS-related genes as well as combinations of thesegenotypes might provide a more accurate prediction of che-motherapy resistance. Third, because recurrence events aretimedependent, and the time-dependentmodel is complicatedto establish and to utilize, we employed a feasible modelingprocedure in our analysis, which however might compromisethe results.

In summary, our results suggest that although reduced OSlevels might be important in preventing breast cancer progres-sion, they probably compromise the effectiveness of adjuvant

Figure 3. Combined genetic factorsimprove the prediction of diseaseprogression in patients notundergoing chemotherapy. Effect ofgenetic score (0–1 vs. 2–3) on DFS inprimary breast cancer patientstreated without (A) or with (B)adjuvant chemotherapy in a secondpopulation (n¼ 339) are shown, withlog-rankP values of 0.050 and 0.304,respectively. C, ROC curvesassessing the discriminatoryperformance of the combined modeland traditional model for predictionof disease progression in patientsnot undergoing chemotherapy.Variables for regression of thetraditional model include age, lymphnodes status, tumor size, ER, PR,HER2, and endocrine therapy.Genetic score is added in thecombined model. The probability ofdisease events is estimated aseL/(1þ eL), in which L is derived fromlogistic regression analysis.P ¼ 0.047 for AUC comparison.

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0.0

0.2

0.4

0.6

0.8

1.0

Cu

mu

lati

ve D

FS

Genetic score P = 0.050

Score 0-1

Score 2-3

Score 0-1 censored

Score 2-3 censored0.

000.

250.

500.

751.

00

Sen

sitiv

ity

0.00 0.25 0.50 0.75 1.00

1-Specificity

AUC of traditional model: 0.7047AUC of combined model: 0.7792Reference

806040200

Follow-up time (mo)

0.0

0.2

0.4

0.6

0.8

1.0

Cu

mu

lati

ve D

FS

Genetic score P = 0.304

Score 0-1

Score 2-3

Score 0-1 censored

Score 2-3 censored

A B

C






chemotherapy. Thus far, there are few rigorous data in theliterature to support individualized regimens of chemotherapyaccording to host genotypes. The new understanding of inter-actions between chemotherapy resistance and host geneticfactors could impact basic research as well as clinical man-agement, potentially leading to individualized strategies foradjuvant chemotherapy. For instance, for patients with aggres-sive disease and also harboring genotypes associated withhigher expression or enhanced activity of ROS-reducingenzymes, physiciansmay consider choosing chemotherapeuticagents that exert their efforts by non/low OS-mediatedmechanisms such as taxanes and vinca alkaloids, rather thananthracyclines and cyclophosphamide (14, 37). Finally, wepropose that combined germline genotypes of multiple genesinvolved in ROS pathways might achieve a more preciseprediction of disease progression on the basis of classic somat-ic factors.

Disclosure of Potential Conflicts of Interest

No potential conflicts of interest were disclosed.

Grant Support

This research is supported by grants from the National Natural ScienceFoundation of China (30971143, 30972936, 81001169), the Shanghai UnitedDeveloping Technology Project of Municipal Hospitals (SHDC12010116), theKey Clinical Program of the Ministry of Health (2010-2012), the 2009 Youth Fundof Shanghai Public Health Bureau, the 2009 Youth Fund of Shanghai MedicalCollege, and the Shanghai Committee of Science and Technology Fund for 2011Qimingxing Project (for K-D. Yu, 11QA1401400). The funders had no role in studydesign, data collection and analysis, decision to publish, or preparation of themanuscript.

The costs of publication of this article were defrayed in part by the payment ofpage charges. This article must therefore be hereby marked advertisement inaccordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Received September 8, 2011; revised November 16, 2011; accepted November29, 2011; published OnlineFirst December 6, 2011.

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2012;72:408-419. Published OnlineFirst December 6, 2011.Cancer Res Ke-Da Yu, A-Ji Huang, Lei Fan, et al. Observational Study and ValidationChemoresistance in Primary Breast Cancer: A Prospective

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