Open Access

Genetic risk variants associated with in situ breast cancer

  • Daniele Campa1,
  • Myrto Barrdahl1,
  • Mia M. Gaudet2,
  • Amanda Black3,
  • Stephen J. Chanock3, 4,
  • W. Ryan Diver2,
  • Susan M. Gapstur2,
  • Christopher Haiman5,
  • Susan Hankinson6, 7, 8,
  • Aditi Hazra8, 9, 10,
  • Brian Henderson5,
  • Robert N. Hoover3,
  • David J. Hunter8,
  • Amit D. Joshi8,
  • Peter Kraft8,
  • Loic Le Marchand11,
  • Sara Lindström8,
  • Walter Willett12,
  • Ruth C. Travis13,
  • Pilar Amiano14, 15,
  • Afshan Siddiq16,
  • Dimitrios Trichopoulos8, 17, 18,
  • Malin Sund19,
  • Anne Tjønneland20,
  • Elisabete Weiderpass21, 22, 23, 24,
  • Petra H. Peeters25,
  • Salvatore Panico26,
  • Laure Dossus27, 28, 29,
  • Regina G. Ziegler3,
  • Federico Canzian30 and
  • Rudolf Kaaks1Email author
Contributed equally
Breast Cancer Research201517:82

https://doi.org/10.1186/s13058-015-0596-x

Received: 8 September 2014

Accepted: 4 June 2015

Published: 13 June 2015

Abstract

Introduction

Breast cancer in situ (BCIS) diagnoses, a precursor lesion for invasive breast cancer, comprise about 20 % of all breast cancers (BC) in countries with screening programs. Family history of BC is considered one of the strongest risk factors for BCIS.

Methods

To evaluate the association of BC susceptibility loci with BCIS risk, we genotyped 39 single nucleotide polymorphisms (SNPs), associated with risk of invasive BC, in 1317 BCIS cases, 10,645 invasive BC cases, and 14,006 healthy controls in the National Cancer Institute’s Breast and Prostate Cancer Cohort Consortium (BPC3). Using unconditional logistic regression models adjusted for age and study, we estimated the association of SNPs with BCIS using two different comparison groups: healthy controls and invasive BC subjects to investigate whether BCIS and BC share a common genetic profile.

Results

We found that five SNPs (CDKN2BAS-rs1011970, FGFR2-rs3750817, FGFR2-rs2981582, TNRC9-rs3803662, 5p12-rs10941679) were significantly associated with BCIS risk (P value adjusted for multiple comparisons <0.0016). Comparing invasive BC and BCIS, the largest difference was for CDKN2BAS-rs1011970, which showed a positive association with BCIS (OR = 1.24, 95 % CI: 1.11–1.38, P = 1.27 x 10−4) and no association with invasive BC (OR = 1.03, 95 % CI: 0.99–1.07, P = 0.06), with a P value for case-case comparison of 0.006. Subgroup analyses investigating associations with ductal carcinoma in situ (DCIS) found similar associations, albeit less significant (OR = 1.25, 95 % CI: 1.09–1.42, P = 1.07 x 10−3). Additional risk analyses showed significant associations with invasive disease at the 0.05 level for 28 of the alleles and the OR estimates were consistent with those reported by other studies.

Conclusions

Our study adds to the knowledge that several of the known BC susceptibility loci are risk factors for both BCIS and invasive BC, with the possible exception of rs1011970, a putatively functional SNP situated in the CDKN2BAS gene that may be a specific BCIS susceptibility locus.

Introduction

Breast cancer in situ (BCIS) is a preinvasive breast cancer (BC) with the potential to transform into an invasive tumor within a time period that could vary between a few years to decades [1]. Only a subset of BCIS evolves into the invasive stage, and not all invasive cancers arise from BCIS [24]. Which factors influence the progression of BCIS to invasive BC is still unclear [2, 5, 6]. BCIS was rarely diagnosed before mass screening for BC, but since the introduction of screening they comprise about 20 % of all diagnosed BC [7, 8].

Ductal carcinoma in situ (DCIS) is the most common form of noninvasive BC. It is characterized by malignant epithelial cells inside the milk ducts of the breast. DCIS is known to be a different entity from lobular carcinoma in situ (LCIS), which is characterized by proliferation of malignant cells in the lobules of the breast [9] and is more frequently associated to lobular invasive BC than to ductal invasive BC. DCIS is generally considered a precursor lesion of invasive BC; however, a direct causality has not been firmly established because it is not possible to verify that the removal of DCIS decreases the risk of developing the invasive disease [3, 10].

BCIS is largely understudied and its etiology is poorly understood compared to invasive BC. Family history of BC is considered one of the strongest risk factors [11, 12], clearly stressing the importance of the genetic background. However, only a small number of studies have investigated the genetic risk factors specific for BCIS [13, 14] or DCIS [15, 16]. Genome-wide association studies (GWAS) including both invasive and BCIS cases tend to find similar associations between the two diseases but no specific loci have been identified for BCIS [1719]. Findings from the Million Women Study indicated that 2p-rs4666451 may be differentially associated with invasive BC and BCIS [13], while Milne and colleagues identified the association of 5p12-rs10941679 with lower-grade BC as well as with DCIS, but not with high-grade BC [15].

With the aim of verifying whether susceptibility SNPs identified through GWAS on invasive BC are also relevant for BCIS, we selected 39 single nucleotide polymorphisms (SNPs) previously shown to be associated with invasive BC, and performed an association study on 1317 BCIS cases and 14,006 controls in the context of the US National Cancer Institute’s Breast and Prostate Cancer Cohort Consortium (BPC3). In addition, we compared the association in BCIS with 10,645 invasive BC cases to investigate whether the two types of disease share a common genetic profile or not.

Methods

Study population

The National Cancer Institute’s Breast and Prostate Cancer Cohort Consortium (BPC3) has been described extensively elsewhere [20]. Briefly, it consists of large, well-established cohorts assembled in Europe, Australia and the United States that have both DNA samples and extensive questionnaire information collected at baseline. Cases were women who had been diagnosed with BCIS or invasive BC after enrolment in one of the BPC3 cohorts. This study included 10,645 invasive BC cases, 1317 BCIS cases and 14,006 controls. Of the 1317 BCIS cases included in this study, 71 % had information on tumor histology. Out of these, 85 % had DCIS and 15 % had LCIS. Controls were healthy women selected from each cohort. Relevant institutional review boards from each cohort approved the project and informed consent was obtained from all participants. The names of all approving Institutional Review Boards can be found in the Acknowledgements section.

SNP selection and genotyping

The SNPs included in this analysis were reported to show a statistically significant association with invasive BC risk (P <5 × 10−7) in at least one published study. For eight SNPs whose assays did not work satisfactorily we selected a surrogate in complete linkage disequilibrium (r2 = 1 in HapMap Caucasian in Europe (CEU)). In particular, for the following SNPs we have genotyped either the original SNP or the surrogate: rs4415084 (surrogate rs920329), rs9344191 (surrogate rs9449341), rs1250003 (surrogate rs704010), rs999737 (surrogate rs10483813), rs2284378 (surrogates rs8119937 and rs6059651), rs2180341 (surrogate rs9398840), rs311499 (surrogate rs311498,) and rs1917063 (surrogate rs9344208).

Genotyping was performed using TaqMan assays (Applied Biosystems, Foster City, CA, USA), as specified by the producer. Genotyping of the cases and controls was performed in four laboratories (the German Cancer Research Center (DKFZ), the University of Southern California, the US National Cancer Institute (NCI), and Harvard School of Public Health). Additional information on the genotyping techniques is given elsewhere [21]. Laboratory personnel were blinded to whether the subjects were cases or controls. Duplicate samples (approximately 8 %) were also included.

Data filtering and statistical analysis

Concordance of the duplicate samples was evaluated and found to be greater than 99.99 % for each SNP. Each SNP was tested for Hardy-Weinberg equilibrium in the controls by study. We investigated the association between genetic variants and BCIS risk by fitting an unconditional logistic regression model, adjusted for age at recruitment and cohort (defined as study phase in NHS). Since there were only 19 BCIS patients in the European Prospective Investigation into Cancer (EPIC) we did not adjust the BCIS risk models for country. Instead, we performed sensitivity analyses, excluding EPIC. The genotypes were treated as nominal variables, comparing heterozygotes and minor allele homozygotes to the reference group major allele homozygotes. For the same reason, we did not adjust the risk models for ethnicity but performed sensitivity analyses excluding non-Caucasians.

To test if there were differences in the genetic susceptibility for the two diseases, we performed case-case analyses and subgroup analyses, matching distinct controls to BCIS cases and invasive cases, respectively. The matching factors were age at baseline, menopausal status at baseline and cohort. The same type of case-case analyses were carried out comparing allele distributions between invasive BC and DCIS cases. Furthermore, we investigated the specific associations of the alleles with DCIS.

The significance threshold was adjusted, taking into account the large number of tests carried out. Since some of the SNPs map to the same regions and might be in linkage disequilibrium, for each locus we calculated the effective number of independent SNPs, the number of effectively independent variables (Meff), using the SNP Spectral Decomposition approach (simpleM method) (13). The study-wise Meff obtained was 31 and the adjusted threshold for significance was 0.05/(31) = 0.0016. All statistical tests were two-sided and all statistical analyses were performed with SAS software version 9.2 (SAS Institute, Inc., Cary, NC, USA).

Bioinformatic analysis

We used several bioinformatic tools to assess possible functional relevance for the SNP-BCIS associations. RegulomeDB [22] and HaploReg v2B [23] were used to identify the regulatory potential of the region nearby the SNP. The GENe Expression VARiation database (Genevar) [24] was used to identify potential associations between the SNP and expression levels of nearby genes expression quantitative trait loci (eQTL).

Results

In this study, we investigated the possible effect of 39 SNPs associated with invasive BC on the susceptibility of BCIS using 1317 BCIS cases and 14,006 healthy controls in the framework of BPC3. The relevant characteristics of the study population are presented in Table 1. The vast majority (69 %) of the study participants were postmenopausal and of European ancestry.
Table 1

Characteristics of the study subjects (BCIS and controls)

 

CPS-II

EPIC

MEC

NHS

PLCO

Total

 

Controls

Cases

Controls

Cases

Controls

Cases

Controls

Cases

Controls

Cases

Controls

Cases

Number

3048

569

4745

19

1724

74

3630

489

859

166

14,006

1317

Ductal

 

297 (52 %)

 

14 (74 %)

   

367 (75 %)

 

114 (69 %)

 

792 (62 %)

Lobular

 

42 (8 %)

 

2 (10 %)

   

82 (17 %)

 

15 (9 %)

 

141 (11 %)

Unknown/other

 

230 (40 %)

 

3 (16 %)

 

74 (100 %)

 

40 (8 %)

 

37 (22 %)

 

384 (29 %)

White

3048

569

4745

19

574

15

3605

467

859

166

12,831

1236

Hispanic

.

.

.

.

292

10

2

.

.

.

294

10

African American

.

.

.

.

230

9

7

11

.

.

237

20

Asian

.

.

.

.

379

23

7

6

.

.

386

29

Hawaiian

.

.

.

.

249

17

.

.

.

.

249

17

Other

.

.

.

.

.

.

9

5

.

.

9

5

Age at diagnosis/recruitment, mean (sd)

61.9 (6.2)

68.81 (6.87)

54.0 (8.0)

61.16 (7.32)

57.0 (8.4)

62.86 (8.00)

57.1 (10.7)

59.04 (10.2)

62.3 (5.0)

66.13 (5.54)

57.4 (8.9)

64.41 (9.31)

ER positive

.

151

.

4

.

10

.

175

.

32

.

372

ER negative

.

22

.

.

.

2

.

35

.

9

.

68

ER not classified

.

396

.

15

.

58

.

26

.

.

.

495

ER not classified

.

.

.

.

.

4

.

253

.

125

.

382

BMI (kg/m2), mean (sd)

25.60 (4.93)

25.50 (4.82)

25.44 (4.31)

23.47 (3.57)

26.85 (6.16)

27.54 (5.68)

25.85 (5.20)

25.61 (5.12)

27.08 (5.38)

27.76 (5.47)

25.90 (5.05)

25.91 (5.12)

Height (m), mean (sd)

1.64 (0.063)

1.64 (0.065)

1.62 (0.066)

1.61 (0.054)

1.61 (0.070)

1.59 (0.069)

1.64 (0.061)

1.64 (0.064)

1.63 (0.063)

1.63 (0.067)

1.63 (0.066)

1.64 (0.066)

Premenopausal

108

34

1134

3

357

14

1046

172

.

.

2645

223

Postmenopausal

2902

527

2883

13

1307

56

2473

305

852

165

10,417

1066

Perimenopausal

38

8

728

3

60

4

111

12

7

1

944

28

CPS-II Cancer Prevention Study II, EPIC European Prospective Investigation into Cancer, MEC Multiethnic Cohort, NHS Nurses’ Health Study, PLCO Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial, sd standard deviation, ER estrogen receptor, BMI body mass index

We removed subjects from the NHS cohort for the analysis of ZMIZ1-rs1045485 and 11q13-rs614367 since the genotype distribution showed departure from the Hardy-Weinberg equilibrium among the controls (P = 8.4 × 10−4 and P = 6 × 10−4, respectively) in this cohort. All other SNPs were in Hardy-Weinberg equilibrium (P >0.05). The results of the sensitivity analyses showed that the exclusion of EPIC and non-Caucasian subjects did not affect the results (data not shown).

SNP associations comparing BCIS with controls

We found significant associations (at the conventional 0.05 level) between 14 SNPs and risk of BCIS, with P values ranging from 0.041 (GMBE2-rs311499) to 3.0 x 10−6 (FGFR2-rs2981582) (Table 2). When accounting for multiple testing (P <0.0016), five SNPs (CDKN2BAS-rs1011970, FGFR2-rs3750817, FGFR2-rs2981582, TNRC9-rs3803662, 5p12-rs10941679) showed a statistically significant association with BCIS. Another variant (ZNF365-rs10995190) was very close to this significance threshold (P = 0.0019). None of the SNPs associated exclusively with estrogen receptor negative (ER-) BC (C19Orf62-rs8170, RALY-rs2284378, USHBP1-rs12982178 and TERT-rs10069690) or with both ER- and estrogen receptor positive (ER+) (6q14-rs13437553, 6q14-rs9344191, 6q14-rs17530068 and 20q11-rs4911414) in the literature showed an association with BCIS in this study, even at the 0.05 level.
Table 2

Association between the selected SNPs and risk of developing breast cancer in situ

SNP

Gene

Allelesa

Cases

Controls

OR (95 % CI)

Ptrend

Reference

MM

Mm

mmb

MM

Mm

mmb

rs11249433

NOTCH2

T

G

412

588

228

4757

5523

1892

1.10 (1.01-1.20)

0.022993

[34, 35]

rs10931936

CASP8

G

T

595

479

82

5705

4499

840

0.99 (0.90-1.09)

0.876814

[36]

rs1045485

CASP8

G

G

629

163

15

6653

1839

133

0.94 (0.80-1.11)

0.481823

[37]

rs13387042

Intergenic

A

G

369

590

258

3452

5942

2847

0.88 (0.81-0.96)

0.004138

[18]

rs4973768

SLC4A7

G

T

300

617

307

3482

6000

2728

1.07 (0.98-1.17)

0.11062

[38]

rs4415084c

Intergenic

G

T

384

620

218

4133

5847

2217

1.11 (1.01-1.21)

0.023783

[19]

rs10941679

Intergenic

A

G

610

478

88

6626

4601

854

1.18 (1.07-1.30)

0.001069

[19]

rs10069690

TERT

G

T

665

467

87

6199

4136

774

1.03 (0.93-1.13)

0.573721

[39]

rs889312

MAP3K1

A

G

603

506

130

6113

5020

1135

1.16 (1.06-1.27)

0.001841

[17]

rs17530068

Intergenic

T

G

727

425

86

6642

4137

648

1.01 (0.91-1.11)

0.879429

[35]

rs13437553

Intergenic

T

G

340

181

41

4628

2761

414

1.00 (0.86-1.15)

0.953341

[35]

rs1917063d

Intergenic

G

T

741

424

74

6933

3949

571

1.03 (0.94-1.14)

0.502161

[35]

rs9344191e

Intergenic

T

G

680

447

100

6365

4280

735

1.04 (0.95-1.15)

0.40587

[35]

rs2180341f

RNF146

A

G

685

458

81

6395

4084

650

1.06 (0.96-1.17)

0.250858

[40]

rs3757318

Intergenic

G

A

1019

197

8

9641

1631

54

1.19 (1.02-1.39)

0.02862

[26]

rs9383938

Intergenic

G

T

1013

212

12

9530

1820

85

1.13 (0.97-1.30)

0.108581

[35, 41]

rs2046210

Intergenic

G

T

501

565

163

5216

5494

1535

1.09 (0.99-1.19)

0.071176

[42, 43]

rs13281615

Intergenic

A

G

419

582

210

4068

5818

2232

1.00 (0.92-1.10)

0.915006

[38]

rs1562430

Intergenic

T

G

419

595

222

3821

5594

2023

1.00 (0.92-1.09)

0.992865

[26]

rs1011970

CDKN2BAS

G

T

793

396

42

7977

3099

319

1.24 (1.11-1.38)

0.000127

[44]

rs865686

Intergenic

T

G

481

599

157

4511

5257

1673

0.96 (0.88-1.04)

0.328473

[44]

rs2380205

Intergenic

G

T

402

597

239

3502

5637

2272

0.98 (0.90-1.06)

0.579359

[44]

rs10995190

ZNF365

G

A

943

277

18

8224

2923

238

0.82 (0.72-0.93)

0.001998

[44, 45]

rs16917302

ZNF365

A

G

1006

220

12

9313

2041

102

1.01 (0.88-1.17)

0.849328

[45, 46]

rs1250003g

ZMIZ1

A

G

444

567

227

4369

5309

1742

1.13 (1.04-1.24)

0.004096

[44, 47]

rs3750817

FGFR2

G

T

503

552

178

3989

5362

1804

0.86 (0.79-0.94)

0.00101

[48]

rs2981582

FGFR2

G

T

385

608

241

4591

5793

1847

1.23 (1.13-1.34)

0.00000283

[38]

rs3817198

LSP1

T

G

550

540

138

5807

5185

1178

1.03 (0.94-1.13)

0.467045

[17]

rs909116

LSP1

T

G

357

608

269

3125

5656

2640

0.96 (0.88-1.04)

0.309715

[26]

rs614367

Intergenic

G

T

548

188

19

5783

1909

186

1.04 (0.89-1.21)

0.63419

[49]

rs999737h

RAD51L1

G

T

751

418

58

6575

3927

656

0.89 (0.80-0.99)

0.025235

[34]

rs3803662

TNRC9

G

T

572

514

116

6132

4896

1070

1.20 (1.09-1.32)

0.00015

[17, 18]

rs2075555

COL1A1

G

A

939

265

13

8348

2582

211

0.88 (0.77-1.01)

0.062916

[50]

rs6504950

COX11

G

A

653

499

85

6586

4772

911

0.96 (0.88-1.06)

0.444627

[38]

rs12982178

USHBP1

T

G

790

391

58

7458

3649

476

1.02 (0.92-1.14)

0.667534

[35]

rs8170

C19Orf62

G

A

816

372

50

7699

3446

420

1.02 (0.91-1.13)

0.736771

[35]

rs2284378i

RALY

G

T

504

455

107

4955

4625

1079

0.95 (0.86-1.05)

0.298392

[35]

rs4911414

Intergenic

G

T

549

545

135

5083

5000

1295

0.95 (0.87-1.04)

0.26966

[35]

rs311499j

GMEB2

G

T

1049

169

14

9878

1491

68

1.17 (1.00-1.37)

0.04566

 

SNP single nucleotide polymorphism, OR, odds ratio, CI confidence interval

aThe first allele is the major, the second is the minor allele

bM = Major allele; m = minor allele

c5p12-rs4415084 or surrogate 5p12-rs920329

d6q14-rs1917063 or surrogate 6q14-rs9344208

e6q14-rs9344191 or surrogate 6q14-rs9449341

f ECHDC1R, NF146-rs2180341 or surrogate ECHDC1R, NF146-rs9398840

g ZMIZ1-rs1250003 or surrogate ZMIZ1-rs704010

h RAD51L1-rs999737 or surrogate RAD51L1-rs10483813

i RALY-rs2284378 or surrogate RALY-rs6059651, RALY-rs8119937

j GMEB2-rs311499 or surrogate GMEB2-rs311498

SNP associations comparing DCIS with controls

By utilizing information on tumor histology we selected the DCIS cases and investigated the associations between the alleles and risk. Of the five SNPs significantly associated with BCIS, two (CDKN2BAS-rs1011970, TNRC9-rs3803662) showed a statistically significant association with DCIS (Table S1 in Additional file 1).

SNP associations comparing BCIS with invasive BC

Using case-case analyses to explore possible heterogeneity of associations of the SNPs with the risk of BCIS compared to invasive BC, we found no significant differences in the distribution of the genotypes of the selected SNPs by outcome (Table 3). The strongest difference was observed for CDKN2BAS-rs1011970, although it was not statistically significant considering multiple testing (P value for case-case comparison = 0.006), suggesting a stronger association of CDKN2BAS-rs1011970 with BCIS than with invasive BC. We also performed a subgroup analysis (BCIS vs. invasive) using matched controls in order to more clearly observe the direction of the associations between the selected SNPs and the risk of the two diseases. These latter analyses confirmed that CDKN2BAS-rs1011970 had a preferential association with BCIS compared to invasive BC, however, in both cases the minor T allele was associated with increased risk (Table S2 in Additional file 2).
Table 3

Case-case analysis between invasive breast cancer and breast cancer in situ

SNP

Gene

Allelesa

Invasive breast cancer

Breast cancer in situ

OR (95 % CI)

Ptrend

MM

Mm

mmb

MM

Mm

mmb

rs11249433

NOTCH2

T

G

2569

3884

1474

412

588

228

1.03 (0.94-1.13)

4,87E-01

rs10931936

CASP8

G

T

4470

3697

775

595

479

82

1.06 (0.96-1.18)

2,50E-01

rs1045485

CASP8

G

G

4570

1293

102

629

163

15

1.09 (0.92-1.28)

3,23E-01

rs13387042

Intergenic

A

G

2432

3707

1750

369

590

258

0.95 (0.88-1.04)

2,96E-01

rs4973768

SLC4A7

G

T

1976

4013

1932

300

617

307

0.97 (0.89-1.06)

4,86E-01

rs4415084c

Intergenic

G

T

2559

3863

1437

384

620

218

0.99 (0.91-1.08)

8,66E-01

rs10941679

Intergenic

A

G

4193

3143

605

610

478

88

0.99 (0.89-1.09)

8,19E-01

rs10069690

TERT

G

T

4243

3076

549

665

467

87

1.01 (0.91-1.11)

9,01E-01

rs889312

MAP3K1

A

G

3848

3306

729

603

506

130

0.96 (0.87-1.06)

4,01E-01

rs17530068

Intergenic

T

G

5171

3453

582

727

425

86

1.05 (0.95-1.17)

3,16E-01

rs13437553

Intergenic

T

G

3582

2288

361

340

181

41

1.05 (0.90-1.22)

5,60E-01

rs1917063d

Intergenic

G

T

5433

3301

497

741

424

74

1.02 (0.92-1.13)

7,26E-01

rs9344191e

Intergenic

T

G

4972

3566

645

680

447

100

1.01 (0.92-1.12)

8,36E-01

rs2180341f

RNF146

A

G

4623

2823

479

685

458

81

0.94 (0.85-1.04)

2,35E-01

rs3757318

Intergenic

G

A

7679

1443

66

1019

197

8

1.01 (0.86-1.18)

9,46E-01

rs9383938

Intergenic

G

T

7563

1568

104

1013

212

12

1.01 (0.87-1.17)

8,87E-01

rs2046210

Intergenic

G

T

3207

3633

1069

501

565

163

1.00 (0.91-1.10)

9,69E-01

rs13281615

Intergenic

A

G

2544

3773

1455

419

582

210

1.07 (0.98-1.17)

1,46E-01

rs1562430

Intergenic

T

G

3392

4347

1496

419

595

222

0.93 (0.85-1.02)

1,12E-01

rs1011970

CDKN2BAS

G

T

6327

2623

258

793

396

42

0.85 (0.76-0.96)

6,50E-03

rs865686

Intergenic

T

G

3847

4247

1125

481

599

157

0.93 (0.85-1.02)

1,47E-01

rs2380205

Intergenic

G

T

2961

4505

1742

402

597

239

0.99 (0.91-1.08)

8,03E-01

rs10995190

ZNF365

G

A

6818

2172

172

943

277

18

1.07 (0.94-1.22)

3,28E-01

rs16917302

ZNF365

A

G

7599

1574

86

1006

220

12

0.97 (0.84-1.13)

7,02E-01

rs1250003g

ZMIZ1

A

G

3395

4394

1432

444

567

227

0.93 (0.85-1.02)

1,20E-01

rs3750817

FGFR2

G

T

3146

3615

1063

503

552

178

1.01 (0.92-1.10)

8,82E-01

rs2981582

FGFR2

G

T

2469

3868

1546

385

608

241

1.00 (0.91-1.09)

9,66E-01

rs3817198

LSP1

T

G

3657

3387

821

550

540

138

0.97 (0.88-1.06)

4,67E-01

rs909116

LSP1

T

G

2610

4586

2040

357

608

269

1.02 (0.94-1.12)

6,31E-01

rs614367

Intergenic

G

T

5119

1937

226

548

188

19

1.14 (0.98-1.33)

9,15E-02

rs999737h

RAD51L1

G

T

4829

2702

401

751

418

58

1.04 (0.93-1.15)

5,22E-01

rs3803662

TNRC9

G

T

3655

3328

797

572

514

116

1.02 (0.92-1.12)

7,25E-01

rs2075555

COL1A1

G

A

5851

1856

165

939

265

13

1.18 (1.03-1.35)

1,41E-02

rs6504950

COX11

G

A

4296

3104

547

653

499

85

0.97 (0.88-1.07)

5,34E-01

rs12982178

USHBP1

T

G

6028

2990

327

790

391

58

0.95 (0.86-1.06)

4,04E-01

rs8170

C19Orf62

G

A

6237

2816

290

816

372

50

0.96 (0.85-1.07)

4,36E-01

rs2284378i

RALY

G

T

4080

3624

899

504

455

107

1.01 (0.92-1.12)

7,95E-01

rs4911414

Intergenic

G

T

4177

3954

1048

549

545

135

1.02 (0.93-1.11)

7,34E-01

rs311499j

GMEB2

G

T

7987

1162

66

1049

169

14

0.87 (0.74-1.03)

1,03E-01

SNP single nucleotide polymorphism, OR, odds ratio, CI confidence interval

aThe first allele is the major, the second is the minor allele

bM = Major allele; m = minor allele

c5p12-rs4415084 or surrogate 5p12-rs920329

d6q14-rs1917063 or surrogate 6q14-rs9344208

e6q14-rs9344191 or surrogate 6q14-rs9449341

f ECHDC1R, NF146-rs2180341 or surrogate ECHDC1R, NF146-rs9398840

g ZMIZ1-rs1250003 or surrogate ZMIZ1-rs704010

h RAD51L1-rs999737 or surrogate RAD51L1-rs10483813

i RALY-rs2284378 or surrogate RALY-rs6059651, RALY-rs8119937

j GMEB2-rs311499 or surrogate GMEB2-rs311498

When comparing invasive BC to DCIS, we observed that CDKN2BAS-rs1011970 showed the most promising, albeit nonsignificant association (P value for DCIS vs. BC case-case comparison = 0.0206, Table S3 in Additional file 3). We also noticed a stronger association of CDKN2BAS-rs1011970 with DCIS compared to invasive BC in the subgroup analyses (Table S4 in Additional file 4).

Additionally we also performed an association study considering only invasive BC and we found significant associations at the conventional 0.05 for 28 loci (P values ranging from 0.0387 to 2.27 × 10–16) (Table S2 in Additional file 2).

Possible functional effects

For CDKN2BAS-rs1011970, HaploReg showed that the G to T nucleotide change of the SNP may alter the binding site for three transcription factors: FOXO4, TFC12 and p300. The Regulome DB had no data for this SNP and Genevar showed that the T allele is associated with decreased CDKN2BA gene expression (P = 0.002).

Discussion

With the aim of better understanding the relationship of the genetic background with BCIS, we analyzed the associations of 39 previously identified BC susceptibility SNPs with BCIS risk compared to normal controls and invasive BC cases. Our general observation, as noted by others [13, 16], is that BCIS and invasive BC seem to share the same genetic risk factors. This is also supported by the fact that for the five alleles that were significantly associated (P <0.0016) with BCIS risk the odds ratio (OR) for BCIS risk was on the same side of 1 as the OR for invasive disease. This was true also for all the 14 alleles that were nominally (P <0.05) associated with BCIS risk with the exception of GMEB2-rs311499. However, none of the established ER- specific BC susceptibility loci were associated with BCIS risk in our study. This is not surprising because it is likely that most of the BCIS cases in our study might be ER+ (the information on this variable is extremely sparse in BPC3) and suggests that, from a genetic point of view, ER+ and ER- tumors have different risk factors even for the first stages of carcinogenesis. However, it is difficult to draw a definitive conclusion without more complete ER status data in BPC3.

When conducting case-case analysis, we observed a difference in the association of CDKN2BAS-rs1011970 with invasive BC and BCIS, suggesting an association with BCIS only, although this difference was not statistically significant after adjusting for multiple comparisons (P = 0.006). The association between rs1011970 and BC risk (OR = 1.20) was reported by Turnbull using a large GWAS conducted in European studies and was replicated in the Breast Cancer Association Consortium (BCAC; OR = 1.09) [25, 26]. The lack of association between this SNP and risk of invasive BC in our study does not appear to be due to a lack of statistical power, since with 10,645 invasive BC cases and 14,006 controls we had more than 80 % power to detect an OR of 1.1 or greater, while the ORs reported by Turnbull for this polymorphism ranged from 1.19 to 1.45, depending on the type of statistical model used. However, the results reported by Turnbull originate from cases with a family history of invasive BC, which might explain the contradictory results. These could also arise due to differing adjustments in the statistical models, different screening programs or ways of diagnosing BCIS, or by chance. Additionally, the results from Turnbull and colleagues arise from a case-control study while ours are from a prospective cohort and it has been observed that there might be discrepancies between the two study designs [27]. We found significant associations at the conventional 0.05 level with invasive BC risk for 28 of the loci. For all of these SNPs, the directions of the associations were consistent with those reported in the literature [25, 28].

From a biological point of view the association between rs1011970 and BCIS is intriguing since the SNP lies on 9p21, in an intron of the CDKN2B antisense (CDKN2B-AS1) gene, whose sequence overlaps with that of CDKN2B and flanks CDKN2A. These two genes encode cyclin-dependent kinase inhibitors and are frequently mutated, deleted or hypermethylated in several cancer types, including BC [2932].

HaploReg showed that the G to T nucleotide change of rs1011970 altered the binding ability of three important cell cycle regulators (FOXO4, TFC12 and p300), possibly altering CDKN2B regulation. This hypothesis is corroborated by Genevar, which showed that the T allele was associated with a decreased gene expression. These data are consistent with the observation of an increased BC risk associated with the minor allele. The CDKN2B gene regulates cell growth and inhibits cell cycle G1 progression. The malfunctioning of this checkpoint might be particularly important in the initiation of the tumor. CDKN2B has been repeatedly found to be hypermethylated – a sign that the gene has been shut down, in benign lesions of the breast and in BCIS [30, 31], indicating its involvement in the early phases of carcinogenesis. Furthermore, Worsham and colleagues found that CDKN2B was crucial for initiating immortalization events but less important for progression to malignancy [33]. Taken together, these results suggest an involvement of the gene in early BC carcinogenesis and are consistent with our findings that the association of the SNP with BC overall could be due to its association with development of early-stage tumors, including BCIS, through the downregulation of the CDKN2B gene.

A limitation of this report is the fact that since the study focuses on the 39 SNPs associated with risk of invasive BC, there may be other SNPs specific for BCIS that could not be identified with this approach.

Conclusions

In conclusion, our findings further support that the genetic variants associated with risk of BCIS and invasive BC largely overlap, with the possible exception of rs1011970, a putatively functionally relevant SNP situated in the CDKN2BAS gene that may be a specific BCIS locus. The discovery of a specific locus for BCIS may improve our understanding on both invasive and noninvasive BC susceptibility. However, our results for rs1011970 do not meet the criteria of statistical significance imposed by the number of tests and therefore could still reflect a chance finding.

Notes

Abbreviations

BC: 

breast cancer

BCAC: 

Breast Cancer Association Consortium

BCIS: 

breast cancer in situ

BMI: 

body mass index

BPC3: 

National Cancer Institute’s Breast and Prostate Cancer Cohort Consortium

C19Orf62

chromosome 19 open reading frame 62

CASP8

caspase 8, apoptosis-related cysteine peptidase

CDKN2A

cyclin-dependent kinase inhibitor 2A

CDKN2B

cyclin-dependent kinase inhibitor 2B

CDKN2BAS

CDKN2B antisense RNA 1

CEU: 

Caucasian in Europe

CI: 

confidence interval

COL1A1

collagen, type I, alpha 1

COX11

COX11 cytochrome c oxidase copper chaperone

CPS-II: 

Cancer Prevention Study II

DCIS: 

ductal carcinoma in situ

DKFZ: 

German Cancer Research Center

EPIC: 

European Prospective Investigation into Cancer

eQTL: 

expression quantitative trait loci

ER-: 

estrogen receptor negative

ER+: 

estrogen receptor positive

FGFR2

fibroblast growth factor receptor 2

FOXO4

forkhead box O4

GMEB2

glucocorticoid modulatory element-binding protein 2

GWAS: 

genome-wide association studies

LCIS: 

lobular carcinoma in situ

LSP1

lymphocyte-specific protein 1

MAP3K1

mitogen-activated protein kinase kinase kinase 1

MEC: 

Multiethnic Cohort

Meff

number of effectively independent variables

NCI: 

National Cancer Institute

NHS: 

Nurses’ Health Study

NOTCH2

neurogenic locus notch homolog protein 2

OR: 

odds ratio

PLCO: 

Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial

RAD51L1

RAD51 homolog 2

RALY

RALY heterogeneous nuclear ribonucleoprotein

RNF146

ring finger protein 146

SLC4A7

solute carrier family 4, sodium bicarbonate cotransporter, member 7

SNP: 

single nucleotide polymorphism

TERT

telomerase reverse transcriptase

TFC12

transcription factor 12

TNRC9

OX high mobility group box family member 3

USHBP1

Usher syndrome 1C binding protein 1

ZMIZ1

zinc finger, MIZ-type containing 1

ZNF365

zinc finger protein 365

Declarations

Acknowledgments

The Greece EPIC center has been supported by the Hellenic Health Foundation.

The BPC3 project was approved by the ethics committee of the International Agency for Research on Cancer (IARC) for the EPIC cohort, by the Emory University Institutional Review Board for the CPS-II cohort, by the Institutional Review Board of the University of Hawaii and University of Southern California for the MEC cohort, by the ethics committee of the Brigham and Women’s Hospital for the NHS cohort and the NCI Institutional Review Board for the PLCO cohort.

The authors would like to pay tribute to our deceased colleague Dimitrios Trichopoulos, who will be missed.

Authors’ Affiliations

(1)
Division of Cancer Epidemiology, German Cancer Research Center (DKFZ)
(2)
Epidemiology Research Program, American Cancer Society
(3)
Division of Cancer Epidemiology and Genetics, National Cancer Institute
(4)
Core Genotyping Facility, Frederick National Laboratory for Cancer Research
(5)
Department of Preventive Medicine, Keck School of Medicine, University of Southern California
(6)
Department of Epidemiology, University of Massachusetts-Amherst School of Public Health and Health Sciences
(7)
Cancer Research Center, Brigham and Women’s Hospital
(8)
Department of Epidemiology, Harvard School of Public Health
(9)
Department of Medicine, Harvard Medical School
(10)
Department of Medicine, Channing Division of Network Medicine, Brigham and Women’s Hospital
(11)
Cancer Research Center of Hawaii, University of Hawaii
(12)
Department of Nutrition, Harvard School of Public Health
(13)
Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford
(14)
Public Health Division of Gipuzkoa, BIODonostia Research Institute, Basque Health Department
(15)
CIBER of Epidemiology and Public Health (CIBERESP)
(16)
School of Public Health, Imperial College
(17)
Bureau of Epidemiologic Research, Academy of Athens
(18)
Hellenic Health Foundation
(19)
Department of Surgical and Perioperative Sciences, Umeå University
(20)
Danish Cancer Society Research Center
(21)
Department of Community Medicine, Faculty of Health Sciences, University of Tromsø, The Arctic University of Norway
(22)
Cancer Registry of Norway
(23)
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet
(24)
Department of Genetic Epidemiology, Folkhälsan Research Center
(25)
Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center
(26)
Dipartimento di Medicina Clinica e Chirurgia Federico II University
(27)
Inserm, Centre for research in Epidemiology and Population Health (CESP), U1018, Nutrition, Hormones and Women’s Health Team
(28)
University Paris Sud, UMRS 1018
(29)
IGR
(30)
Genomic Epidemiology Group, German Cancer Research Center (DKFZ)

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© Campa et al. 2015

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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