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Annexin A1 expression in a pooled breast cancer series: association with tumor subtypes and prognosis

  • Marcelo Sobral-Leite1, 2,
  • Jelle Wesseling1, 3,
  • Vincent T. H. B. M. Smit4,
  • Heli Nevanlinna5, 6,
  • Martine H. van Miltenburg1,
  • Joyce Sanders1,
  • Ingrid Hofland7,
  • Fiona M. Blows8,
  • Penny Coulson9,
  • Gazinska Patrycja10,
  • Jan H. M. Schellens1, 11,
  • Rainer Fagerholm5, 6,
  • Päivi Heikkilä5, 12,
  • Kristiina Aittomäki5, 13,
  • Carl Blomqvist5, 14,
  • Elena Provenzano15, 16,
  • Hamid Raza Ali15, 17,
  • Jonine Figueroa18,
  • Mark Sherman18, 19,
  • Jolanta Lissowska20,
  • Arto Mannermaa21, 22, 23,
  • Vesa Kataja24, 25,
  • Veli-Matti Kosma21, 22, 23,
  • Jaana M. Hartikainen21, 22, 23,
  • Kelly-Anne Phillips26, 27, 28, 29,
  • kConFab/AOCS Investigators,
  • Fergus J. Couch30,
  • Janet E. Olson31,
  • Celine Vachon31,
  • Daniel Visscher30,
  • Hermann Brenner32, 33, 34,
  • Katja Butterbach32,
  • Volker Arndt32,
  • Bernd Holleczek35,
  • Maartje J. Hooning36,
  • Antoinette Hollestelle36,
  • John W. M. Martens36,
  • Carolien H. M. van Deurzen37,
  • Bob van de Water38,
  • Annegien Broeks7,
  • Jenny Chang-Claude39,
  • Georgia Chenevix-Trench40,
  • Douglas F. Easton8,
  • Paul D. P. Pharoah8,
  • Montserrat García-Closas9, 10,
  • Marjo de Graauw38 and
  • Marjanka K. Schmidt1, 41Email author
BMC Medicine201513:156

https://doi.org/10.1186/s12916-015-0392-6

Received: 18 February 2015

Accepted: 4 June 2015

Published: 2 July 2015

Abstract

Background

Annexin A1 (ANXA1) is a protein related with the carcinogenesis process and metastasis formation in many tumors. However, little is known about the prognostic value of ANXA1 in breast cancer. The purpose of this study is to evaluate the association between ANXA1 expression, BRCA1/2 germline carriership, specific tumor subtypes and survival in breast cancer patients.

Methods

Clinical-pathological information and follow-up data were collected from nine breast cancer studies from the Breast Cancer Association Consortium (BCAC) (n = 5,752) and from one study of familial breast cancer patients with BRCA1/2 mutations (n = 107). ANXA1 expression was scored based on the percentage of immunohistochemical staining in tumor cells. Survival analyses were performed using a multivariable Cox model.

Results

The frequency of ANXA1 positive tumors was higher in familial breast cancer patients with BRCA1/2 mutations than in BCAC patients, with 48.6 % versus 12.4 %, respectively; P <0.0001. ANXA1 was also highly expressed in BCAC tumors that were poorly differentiated, triple negative, EGFR-CK5/6 positive or had developed in patients at a young age. In the first 5 years of follow-up, patients with ANXA1 positive tumors had a worse breast cancer-specific survival (BCSS) than ANXA1 negative (HRadj = 1.35; 95 % CI = 1.05–1.73), but the association weakened after 10 years (HRadj = 1.13; 95 % CI = 0.91–1.40). ANXA1 was a significant independent predictor of survival in HER2+ patients (10-years BCSS: HRadj = 1.70; 95 % CI = 1.17–2.45).

Conclusions

ANXA1 is overexpressed in familial breast cancer patients with BRCA1/2 mutations and correlated with poor prognosis features: triple negative and poorly differentiated tumors. ANXA1 might be a biomarker candidate for breast cancer survival prediction in high risk groups such as HER2+ cases.

Keywords

Breast cancerAnnexin A1BRCA1 and BRCA2 mutations

Background

Breast cancer is a heterogeneous group of pathologic entities with different risk of recurrence and therapy response [1]. In order to improve breast cancer diagnosis and treatment decision, it is necessary to gain a better understanding of the metastatic pathways and etiology.

Annexin A1 (ANXA1) protein binds the cellular membrane phospholipids in a Ca2+ regulated manner and can be phosphorylated on several residues both on the N-terminal functional domain and on the C-terminus core [2] by different proteins, such as the epidermal growth factor receptor (EGFR) [3], insulin receptor (IR) [4], TRPM7 channel kinase 1 (ChaK1) [5], protein kinase C (PKC) and protein kinase A (PKA) [6]. ANXA1 has been found in several tissues and regulates physiological mechanisms such as hormone secretion [7], EGFR degradation [8], membrane transport [9], apoptosis [10] and cell differentiation [11]. As a glucocorticoid-induced molecule, ANXA1 plays an important role in the inflammatory response [12].

ANXA1 expression is related with the carcinogenesis process [1315] and with metastasis formation in many tumors [1618], including breast tumors [1923], where we and others have shown that ANXA1 overexpression is associated with high pathological differentiation grade, lack of hormone receptor expression and a basal-like phenotype [20, 24, 25]. Patients with BRCA1 or BRCA2 (BRCA1/2) germline mutations often present tumors with these characteristics, but until now there are no data in the literature implicating a link between high ANXA1 expression and familial breast cancer. The main focus of this study was to analyze the relationship between high ANXA1 tumor expression with BRCA1/2 germline carriership and survival in breast cancer patients, including those with specific tumor subtypes, using a large dataset of pooled breast cancer series. These analyses allow us to explore the potential of ANXA1 as a marker for breast cancer outcome prediction and treatment response.

Methods

Study populations

The international Breast Cancer Association Consortium (BCAC) comprises a large number of studies investigating the role of common germline genetic variation in breast cancer susceptibility [26]. Nine studies from Europe, North America, New Zealand and Australia contributed with 8,182 cases to this ANXA1 study (Additional file 1: Table S1). All studies were approved by the relevant ethics committees and informed consent was obtained from all participants (Additional file 1: Table S1). Clinical-pathological information and follow-up data were collected by each study individually through medical records, cancer registries and cause of death registries. Data were pooled in the BCAC database according to a data dictionary, and centrally checked for accuracy and consistency. Data included were: age at diagnosis; behavior (in situ or invasive); morphology (ductal, lobular and others); tumor size (≤2 cm, >2 and ≤5 cm, or >5 cm); differentiation grade (1, 2 or 3); lymph node status (negative or positive); and breast cancer treatment (radiotherapy, hormonal therapy and chemotherapy). The most common source of data for ER, PR and HER2 status was from medical records, followed by immunohistochemistry (IHC) performed on tumor tissue microarrays (TMAs) or whole section tumor slides. The subtypes were defined as follows: luminal 1 (ER+ and/or PR+ and HER2-); luminal 2 (ER+ and/or PR+ and HER2+); HER2-like (ER-, PR- and HER2+); and triple negative (ER-, PR- and HER2-). Data on CK5/6 and EGFR tumor status were derived from IHC performed on TMAs or whole sections detailed previously [27]. The p53 staining data (received only from one breast cancer study) and the BRCA1 and BRCA2 status mutation of the BCAC patients were obtained as described previously [28, 29]. A specific cohort of 132 BRCA1/2 mutated (BRCA1|2), familial breast cancer patients (a minimum of three first- or second-degree relatives affected with breast and/or ovarian cancer in a family) were included from the Helsinki University Central Hospital (HUCH) in southern Finland as described previously [30]. In addition, within the BCAC there were a few cases from some studies known to be BRCA1/2 mutated and we excluded these for the analyses comparing BCAC with BRCA1|2 tumors.

ANXA1 staining

Ninety TMA slides from 8,705 patients were received for ANXA1 staining (including 1 to 6 tumor cores per patient). The ANXA1 staining was performed at the Core Facility Molecular Pathology and Biobanking (CFMPB) at the NKI-AVL on a BenchMark ULTRA autostainer (Ventana Medical Systems, Tucson, AZ, USA). Briefly, paraffin sections were heated at 75 °C for 28 min and deparaffinized in the instrument with EZ Prep solution (Ventana Medical Systems). Heat-induced antigen retrieval was carried out using Cell Conditioning 1 (CC1; Ventana Medical Systems) for 64 min at 95 °C. ANXA1 was detected by incubating sections with antibody clone 29/Annexin I (610066; BD Transduction Laboratories, Franklin Lakes, NJ, USA), 1/1500 dilution for 1 h. Specific reactions were detected using ultraView Universal DAB Detection Kit (Ventana Medical Systems) and slides were counterstained with hematoxylin.

The ANXA1 expression in tumor cells was scored only if the spot was evaluable, subjectively defined as approximately more than 30 % of tumor cells present in the spot. ANXA1 is also expressed in stromal cells but this was not included in the evaluation. Three variables were evaluated: intensity of the staining (negative, weak, moderate or strong); cellular location of the staining (cytoplasm and nucleus or only in cytoplasm); and the percentage of stained cells (0 % to 100 %). Scores were categorized as: 0, no expression; 1+, 10 % to 30 % stained; 2+, 40 % to 70 %; and 3+, 80 % or more cells stained (Additional file 2: Figure S1). The intensity of the staining and the cellular location scores did not contribute to the discernment of different groups by ANXA1 stains and therefore were not further used in the analysis (Additional file 3: Figures S2A and S2B).

Evaluation of ANXA1 expression levels were performed by MdG with consultation of three pathologists (JW, JS and VTS). The concordance was around 81.7 % and 92.4 % among all of them, considering 0 or 1+ as negative and 2+ or 3+ as positive, with a kappa value of 0.86, considering positive versus negative cases between MdG and JW. A subset of 452 patients in the previously published ABCS study were rescored for this study [24].

Around 20 % (n = 2,124) of the included patients enrolled in TMA constructions could not be scored due to technical problems (no sample or less than 30 % of tumor cells in the spot), but the clinical-pathological variable distributions did not differ between patients with or without ANXA1 scores (Additional file 4: Table S2). For analyses, we clustered 0 and 1+ groups based on previous experience [24]. Of note, overall survival (OS) and breast cancer-specific survival (BCSS) did not differ between the 0 and 1+ groups nor between the 2+ and 3+ groups (Additional file 3: Figures S2C and S2D).

Statistical analyses

In total, we included 6,177 patients for descriptive analysis. For association and survival analysis, the in situ breast cancer cases were excluded (n = 204). Patients diagnosed with distant metastases at diagnosis of the primary tumor (n = 31) and those who received chemotherapy before the surgery (n = 84) were also excluded (Additional file 5: Figure S3).

In the case of significant associations between ANXA1 expression and a histopathological variable as evaluated by the Chi-square test, the odds ratios (OR) and their respective 95 % confidence intervals (95 % CI) adjusted for independent clinical variables (ORadj) were assessed using logistic regression models. The ANXA1 expression was tested for linear-by-linear associations to calculate trend significances (Ptrend) between tumor subtypes in Fig. 1. The statistical association analyses were conducted using SPSS 20 (SPSS Inc., Chicago, IL, USA).
Fig. 1

ANXA1 expression profile. Percentage of patients with ANXA1 positive tumors according to breast cancer subtypes comparing BCAC (excluding 37 patients with BRCA1 or BRCA2 mutations) versus BRCA1/2 mutation carriers. For the subtype analysis, patients with missing information for ER, PR and/or HER2 were excluded (393 in BCAC and 36 in BRCA1/2 mutation carriers). Luminal 1 subtype was defined as ER+ and/or PR+ and HER2-, and triple negative (TN) was defined as ER-, PR- and HER2-. Numbers of HER2+ were too small in the BRCA1/2 mutation carriers to make a comparison. ANXA1: Annexin A1; BCAC: Breast Cancer Association Consortium; TN: Triple negative

Survival time was calculated from date of diagnosis to date of death/censoring. In order to allow for prevalent cases, time at risk was calculated from date of study entry to date of death/censoring. This generates an unbiased estimate of the hazard ratio (HR) provided the Cox proportional hazards assumption holds [31]. End of follow-up was defined as the date of (breast cancer) death, last follow-up or 10 years, whichever came first. Distant metastasis-free survival (DMFS) analysis was performed as well, with time censored at 5 years. HRs were estimated using Cox regression models, stratified by BCAC studies. Multivariate Cox models were fitted including the variables associated with breast cancer prognosis: age at diagnosis as continuous variable; tumor grade (1, 2 or 3); tumor size (≤2 cm, >2 cm and ≤5 cm, or >5 cm); lymph node status (negative versus positive); ER/PR status (ER and PR negative versus ER or PR positive); and HER2 receptors status (negative versus positive) as categorical covariates. The analyses were performed as a complete case analysis and a secondary analysis was performed including the missing values in the model. ER/PR status was included as a time-varying covariate because of violation of the proportional hazards assumption using the Schoenfeld residuals test in the multivariable model (P <0.0001). Adjustment for chemotherapy and/or hormonal therapy did not significantly change the results and these were not included in the final models. All P values reported are from two-sided tests and the threshold for significance was set at P = 0.05. The survival analyses were performed using STATA version 11.0 (StataCorp, TX, USA).

Results

Analyses included 5,752 patients from BCAC cohorts, including cases not known to be BRCA1/2 carriers, and 107 breast cancer patients from one study of familial breast cancer patients with BRCA1/2 mutations (Table 1; Additional file 4: Table S2; Additional file 5: Figure S3).
Table 1

ANXA1 expression and clinical variables

 

BCAC patients

 

BRCA1|2 mutation carriers

 

ANXA1 negative

ANXA1 positive

ANXA1 negative

ANXA1 positive

n

%

n

%

P valuea

n

%

n

%

P valueb

P valuec

5,040

87.6

712

12.4

 

55

51.4

52

48.6

 

<0.0001

Age of diagnosis

    

<0.0001

    

0.2253

0.0401

 <50 years old

2,462

85.6

413

14.4

 

34

47.2

38

52.8

  

 ≥50 years old

2,578

89.6

299

10.4

21

60.0

14

40.0

 Missing

0

 

0

  

0

 

0

   

BRCA status

    

0.0026

    

0.0583

 

 Non-carrier

858

83.6

168

16.4

       

BRCA1

11

61.1

7

38.9

25

42.4

34

57.6

 

BRCA2

14

73.7

5

26.3

30

62.5

18

37.5

 Missing

3,812

 

430

  

0

 

0

   

Menopausal status

    

0.0330

      

 Pre-

1,556

86.2

250

13.8

       

 Post-

2,258

88.3

298

11.7

    

 Missing

1,226

 

164

        

Morphology

    

<0.0001

    

0.5946

0.0235

 Ductal

3,558

86.2

568

13.8

 

33

50.8

32

49.2

  

 Lobular

710

94.3

43

5.7

11

61.1

7

38.9

 Missing

772

 

101

  

11

 

13

   

Grade

    

<0.0001

    

0.0040

0.1460

 1

1,129

95.0

59

5.0

 

7

77.8

2

22.2

  

 2

2,246

92.8

173

7.2

26

66.7

13

33.3

 3

1,306

75.2

431

24.8

19

35.8

34

64.2

 Missing

359

 

49

  

3

 

3

   

Tumor size

    

<0.0001

    

0.9910

0.7780

 ≤2 cm

2,809

88.8

354

11.2

 

31

51.7

29

48.3

  

 >2 cm and ≤5 cm

1,610

86.5

252

13.5

19

52.8

17

47.2

 >5 cm

109

79.9

29

21.0

2

50.0

2

50

 Missing

512

 

77

 

3

 

4

 

Node status

    

0.2725

      

 Negative

2,669

87.1

395

12.9

 

31

47.0

35

53.0

0.3044

0.1790

 Positive

1,955

88.1

263

11.9

 

22

59.5

15

40.5

  

 Missing

416

 

54

 

2

 

2

 

ER status

    

<0.0001

      

 Negative

977

67.8

465

32.2

 

13

28.3

33

71.7

<0.0001

0.7373

 Positive

3,796

95.0

200

5.0

 

36

75.0

12

25.0

  

 Missing

267

 

47

 

6

 

7

 

PR status

    

<0.0001

      

 Negative

1,435

75.9

455

24.1

 

20

35.1

37

64.9

<0.0001

0.1670

Positive

3,159

94.8

175

5.2

 

27

77.1

8

22.9

  

 Missing

446

 

82

 

8

 

7

 

HER2 status

    

0.1328

      

 Negative

3,484

88.7

442

11.3

 

25

48.1

27

51.9

1.0000

0.0684

 Positive

643

86.8

98

13.2

 

1

50.0

1

50.0

  

 Missing

913

 

172

 

29

 

24

 

EGFR-CK5/6 statusd

    

<0.0001

      

 Negative

3,317

93.8

218

6.2

       

 Positive

541

67.6

259

32.4

       

 Missing

1,182

 

235

     

p53 statuse

    

0.0572

      

 Negative

172

90.1

19

9.9

 

17

45.9

20

54.1

0.0211

0.8116

 Positive

57

80.3

14

19.7

 

3

14.3

18

85.7

  

 Missing

596

 

89

 

35

 

14

 

Adjuvant chemotherapy

    

<0.0001

      

 No

2,593

89.9

282

10.1

       

 Yes

1,585

83.2

320

16.8

       

 Missing

862

 

100

     

Adjuvant hormonal therapy

    

<0.0001

      

 No

2,059

82.5

438

17.5

       

 Yes

2,562

92.6

204

7.4

       

 Missing

419

 

70

       

Distribution of the clinical variables in breast cancer patients according to the ANXA1 expression in all invasive tumors from the BCAC and BRCA1|2 set of patients

ANXA1 annexin A1, BCAC Breast Cancer Association Consortium, EGFR epidermal growth factor receptor

aP value of the comparison between ANXA1 positive and negative patients in the BCAC set

bP value of the comparison between ANXA1 positive and negative patients in the BRCA1|2 mutated set

cP value of the comparison between the two sets of ANXA1 positive patients: BCAC and BRCA1|2 mutated patients

dEGFR-CK5/6 status was defined as positive when CK5/6 and/or EGFR were positive

ein the BCAC set, p53 status information was only available for the ABCS study

ANXA1 expression in breast cancer patients

The distribution of patients and tumor characteristics of BCAC and BRCA1/2 mutated patients is shown in Table 1. Younger patients (<50 years old) had slightly more often ANXA1 positive tumors compared with the older group (≥50 years old), in both subsets of patients. Poorly differentiated (high grade) tumors were associated with ANXA1 positive expression (tumor grade 2 or 3: ORadj = 1.59; 95 % CI = 1.04–2.43); as well as those positive for basal-like markers (EGFR and/or CK5/6 positive) or triple negative (ORadj = 4.21; 95 % CI = 3.22–5.50 and ORadj = 6.01; 95 % CI = 4.61–8.01, respectively) (Additional file 6: Table S3).

ANXA1 expression was higher in the tumors from BRCA1/2 mutated patients compared to BCAC patients overall: 48.6 % versus 12.4 %, respectively; P <0.0001, and within specific breast cancer subtypes (Fig. 1). Although all BRCA1/2 mutated carriers were only from Finland, the proportion of ANXA1 positives in the Finnish BCAC study (HEBCS) was the same compared to that of other BCAC studies (Additional file 1: Table S1A). Triple negative tumors in BRCA1/2 mutated carriers showed a higher ANXA1 expression than triple negative breast cancer patients in the BCAC cohort (84.2 % versus 41.9 %, respectively; P <0.0001). Also, there was a trend for higher ANXA1 expression with a decrease in hormone receptor positivity (i.e. subtypes ranging from luminal to HER2+ to triple negative; Ptrend <0.0001) (Fig. 1). Moreover, the BRCA1 patients presented a slightly higher expression of ANXA1 compared with BRCA2 mutated patients (57.6 % versus 37.5 %, respectively; P = 0.0583) (Table 1). In BRCA1/2 mutated patients, ANXA1 expression was associated with p53 positive status (ORadj = 14.97; 95 % CI = 1.38–163.49; Additional file 6: Table S3).

Survival analysis according to ANXA1 expression

We performed survival analysis of all BCAC patients with follow-up information (follow-up mean: 8.9 years). Patients with ANXA1 positive tumors showed a worse survival than the ANXA1 negative ones, both for OS (Fig. 2a; P = 0.0004) and BCSS (Additional file 7: Figure S4A; P <0.0001). Similar trends of worse survival were seen in the nine separate cohorts, except for MCBCS (data not shown). After adjustment for clinical variables, a significant association between ANXA1 positivity and worse survival was observed only during the first 5 years of follow-up, but not after 10 years of follow-up (5-years BCSS: HRadj = 1.35; 95 % CI = 1.05–1.73 and 10-years BCSS: HRadj = 1.13; 95 % CI = 0.91–1.40; see also Additional file 8: Table S4). The strong association of ANXA1 expression with poorly differentiated grades and triple negative status likely contributed to this lack of association after 5 years. Similar time-dependent differences in survival were observed in lymph node positive patients (Fig. 2d and Additional file 7: Figure S4D), but ANXA1 expression did not influence survival in lymph node negative patients (Fig. 2c and Additional file 7: Figure S4C).
Fig. 2

Survival analysis. Survival curves, crude hazard ratios (HR) and adjusted hazard ratios (HRadj) in patients from BCAC according to ANXA1 expression for overall survival in (a) all BCAC patients, (b) HER2 positive patients, (c) lymph node negative patients and (d) lymph node positive patients. Hazard ratios were adjusted for: age of diagnosis; tumor grade; tumor size; lymph node metastasis; ER/PR status; and HER2 status. ANXA1: Annexin A1; BCAC: Breast Cancer Association Consortium; HR: Hazard ratio

Evaluating the tumor subtypes, ANXA1 high expression was specifically associated with an increased mortality in HER2 positive patients (10-years OS: HRadj = 1.60; 95 % CI = 1.06–2.41 and 10-years BCSS: HRadj = 1.70; 95 % CI = 1.17–2.45; Fig. 2b and Additional file 7: Figure S4B). The P values for interaction between ANXA1 and HER2 in a full model for BCSS or OS, including HER2 positive and negative cases, were 0.136 and 0.140, respectively. In addition, ANXA1 positive cases showed a slightly worse survival in the subgroup of patients older than 49 years old, which seemed to be related to menopausal status (data not shown). For none of the subgroup analyses the DMFS was significantly different between ANXA1 high and low expression groups.

ANXA1 expression and treatment response

In order to explore the value of the ANXA1 expression in therapy resistance, we performed survival analyses in the group of patients who received adjuvant chemotherapy, as currently recommended. Using clinical guidelines [32], the BCAC patients were classified according to the risk of recurrence using classic prognostic factors for determining the chance of distant metastases occurrence. Within the group that received adjuvant chemotherapy, patients with high risk of recurrence (HER2+ and/or pN+) showed a slightly worse OS and BCSS when ANXA1 was positive, compared with the ANXA1 negative ones in the group aged 50–69 years only (BCSS HRadj = 2.02; 95 % CI = 1.21–3.36 and HRadj = 1.41; 95 % CI = 0.80–2.50; Additional file 9: Figure S5).

Part of these patients (24.2 %) received anthracycline-based adjuvant chemotherapy. In the same high risk group aged 50–69 years, the ANXA1 positive cases presented a suggestive worse outcome after anthracycline-based adjuvant chemotherapy (10-years OS: HRadj = 2.48; 95 % CI = 0.82–7.50; Fig. 3 and BCSS: HRadj = 2.96; 95 % CI = 0.92–9.57; Additional file 7: Figure S4F).
Fig. 3

Adjuvant treatment response. Survival curves, crude hazard ratios (HR) and adjusted hazard ratios (HRadj) according to ANXA1 expression in patients from BCAC with high risk of recurrence (HER2+ and/or pN+) who received anthracycline-based adjuvant chemotherapy. Overall survival and disease-free survival in (a, b) patients under 50 years old and (c, d) patients over 49 and under 70 years old. Hazard ratios were adjusted for: age of diagnosis; tumor grade; tumor size; lymph node metastasis; ER/PR status; and HER2 status. Note: low risk of recurrence was defined as: 35 years old or older; lymph node negative; tumor size ≥2 cm with any grade or tumor size ≥1 cm with tumor grade ≥2; high risk of recurrence was defined as: HER2+ and/or lymph node positive. ANXA1: Annexin A1; BCAC: Breast Cancer Association Consortium; HR: Hazard ratio

Discussion

Here, for the first time, the ANXA1 expression in a subset of BRCA1/2 mutated carriers is described. We found a significantly higher expression of ANXA1 in tumors from familial breast cancer patients with BRCA1/2 mutations compared with hospital and population-based breast cancer series.

We also found a higher ANXA1 expression in triple negative patients, confirming previous studies [20, 24, 25]; association with poor differentiation grade is also described in other types of cancers [33, 34]. The association between ANXA1 expression and basal markers (EGFR-CK5/6) as shown in this study was also described in our previous work using a smaller cohort of breast cancer patients [24], suggesting that ANXA1 may play a role in EGFR trafficking [8, 35]. Moreover, the higher frequency of ANXA1 expression in younger patients is not surprising since this group develops more often tumors of poor differentiation grade, triple negative status or with basal marker overexpression [36].

The BRCA1/2 mutated patients belong to a group that already contains a high number of triple negative and basal-like breast cancers [37], but here we showed that triple negative tumors in BRCA1/2 mutated carriers are even more highly expressing ANXA1 than triple negative patients in the BCAC cohort (Fig. 1). Perhaps such differences may involve the p53 expression, which was higher in the ANXA1 positive tumors (Additional file 6: Table S3). The tumor suppressor gene TP53 is more commonly altered in BRCA1/2-related breast cancers, as measured either by IHC or mutation analysis [38]. Indeed, in vitro studies in colon cancer cells suggest the existence of a binding site for p53 in the promoter of the ANXA1 gene, inducing its expression and phosphorylation [39, 40]. Unfortunately, the group of BRCA1/2 mutated carriers presented here is small, indicating that other studies focused in the ANXA1 expression profile in this group of patients are required.

In the survival analysis, ANXA1 positive tumors were independently associated with OS and BCSS in the first 5 years, but not in years 5 to 10. Findings of OS and BCSS were in line, signaling that a significant proportion of the patients with breast cancer died from it and not from other causes, which is expected especially since this series included a large proportion of relatively young breast cancer patients (Additional file 7: Figure S4). We had observed some indication for time dependency of ANXA1. Including ANXA1 time dependency in the model, we even found a suggestion of better survival in the ANXA1 positive group in the 5- to 10-year period of follow-up (BCSS: HRadj = 0.65; 95 % CI = 0.40–1.03). For ANXA1 gene expression, using KM plotter [41], a similar trend was seen with a worse recurrence-free survival in the first 5 years (HR = 1.15; CI = 1.18–10.29; P = 0.03), but not over the whole 10-year period (HR = 1.08; CI = 0.96–1.21; P = 0.21).

In our data, specifically patients with HER2+ tumors and ANXA1, overexpression showed a worse outcome, even after 5 years (10-years BCSS: HRadj = 1.70; 95 % CI = 1.17–2.45). This is in line with the finding from Yom et al., who observed a worse recurrence-free survival for ANXA1 positive cases also in lymph node positive and HER2+ patients [25]. Of note, most of the cases in our study were not treated with trastuzumab due to the period that they were diagnosed. Accordingly, the absolute OS was lower than would be expected after breast cancer treatment nowadays. Even so, the worse relative survival seen in our study is still relevant for HER2+ patients though with some caution because in most countries nowadays HER2+ patients are also treated with trastuzumab. Further research is therefore warranted to investigate the potential of ANXA1 as a predictor of trastuzumab resistance.

Of note, we observed some heterogeneity between the BCAC studies for the percentage of annexin A1 positive tumors (Additional file 1: Table S1A). These did not seem to be fully explained by differences in tumor characteristics (data not shown) and since the staining was done centrally there might have been some influence of the age of the tumor material and/or fixation at the time of embedding.

We also observed that ANXA1 overexpression was associated with worse survival in patients with high risk of recurrence in an age-dependent manner, with worst outcome in premenopausal patients, especially in the group that received anthracycline-based adjuvant chemotherapy. Unfortunately, for many cases, information about the type of chemotherapy was missing. Therefore, this might be due to chance and hypothesizing a mechanism for these findings is difficult. However, Ang et al. suggested that ANXA1 can regulate growth arrest induced by high levels of estrogen [42], which is the typical physiological condition in premenopausal women. To our knowledge there are no other studies that evaluated ANXA1 expression and anthracycline-based chemotherapy resistance. ANXA1 overexpression was associated with cisplatin resistance in lung adenocarcinoma [43], radiotherapy and chemotherapy resistance in nasopharyngeal carcinoma [44], worse chemotherapy response after treatment with docetaxel, cisplatin and 5-fluorouracil in oral squamous cell carcinoma [34], and poor response after neoadjuvant treatment with taxotere and carboplatin in triple negative breast cancer [45].

Although ANXA1 has been described to play a role in metastasis formation in breast cancer [46], the exact mechanism remains unknown. ANXA1 being also expressed in normal myoepithelial cells, the loss of ANXA1 expression in breast carcinomas has been described as a stage of malignant transformation [19, 20, 47]. In breast cancer models, ANXA1 has been shown to modulate cell adhesion and motility [23] by TGFβ-mediated EMT-like switch [24] and by matrix metalloproteinase-9 regulation via NF-κB [21, 48], but another study found conflicting evidence [49]. Moreover, as a glucocorticoid-induced protein, ANXA1 might also be able to provide critical interference in the tumor stroma and its microenvironment cross-talk [17]. Altogether, our findings stress the importance of ANXA1 for prognosis and possibly for therapy resistance in breast cancer. We are also the first to show that there is a link between BRCA1/2 mutations and ANXA1 overexpression.

Conclusion

We conclude here that ANXA1 expression is associated with tumors with selected well-known poor prognosis characteristics (e.g. poor differentiation grade, triple negative, BRCA1/2 mutations). Our survival analysis showed that ANXA1 expression in breast tumors might be a biomarker candidate for breast cancer outcome prediction in high risk groups such as HER2+ cases, playing a complex role in chemotherapy resistance. Further studies are needed to elucidate whether ANXA1 is indeed a prognostic factor or may be used to predict chemotherapy response.

Abbreviations

ANXA1: 

Annexin A1

BCAC: 

Breast Cancer Association Consortium

BCSS: 

Breast cancer-specific survival

CC1: 

Cell Conditioning 1

CFMPB: 

Core Facility Molecular Pathology and Biobanking

ChaK1: 

Channel kinase 1

CI: 

Confidence interval

DMFS: 

Distant metastasis-free survival

EGFR: 

Epidermal growth factor receptor

EMT: 

Epithelial-mesenchymal transition

ER: 

Estrogen receptor

HR: 

Hazard ratio

HUCH: 

Helsinki University Central Hospital

IHC: 

Immunohistochemistry

IR: 

Insulin receptor

OR: 

Odds ratio

OS: 

Overall survival

PKA: 

Protein kinase A

PKC: 

Protein kinase C

PR: 

Progesterone receptor

TMA: 

Tissue microarray

TN: 

Triple negative

Declarations

Acknowledgements

The authors thank all the individuals who took part in these studies and all the researchers, clinicians, technicians and administrative staff who have enabled this work to be carried out: Manjeet Bolla, Jean Wang, Renske Keeman, Renate de Groot, Sandra van de Broek, Sten Cornelissen, Katarzyna Jozwiak, Michael Schaapveld, Hartwig Ziegler, Sonja Wolf, Volker Hermann, Kirsimari Aaltonen, Karl von Smitten, Sofia Khan, Tuomas Heikkinen, Irja Erkkilä, Eija Myöhänen, Helena Kemiläinen, Heather Thorne and Eveline Niedermayr; the KconFab/AOCS gratefully acknowledge the cooperation of the following institutions: New South Wales: John Hunter Hospital, North Shore Private Hospital, Royal Hospital for Women, Royal North Shore Hospital, Royal Prince Alfred Hospital, Westmead Hospital, Clinical Trials NSW; Queensland: Mater Misericordiae Hospital, Royal Brisbane and Women’s Hospital, Townsville Hospital, Wesley Hospital, Queensland Cancer Registry; South Australia: Flinders Medical Centre, Queen Elizabeth II, Royal Adelaide Hospital, South Australian Cancer Registry; Tasmania: Royal Hobart Hospital; Victoria: Freemasons Hospital, Mercy Hospital for Women, Monash Medical Centre, Royal Women’s Hospital; Western Australia: King Edward Memorial Hospital, St John of God Hospitals Subiaco, Sir Charles Gairdner Hospital, Western Australia Research Tissue Network (WARTN), Western Australia Cancer Registry, all the kConFab project managers, study nurses and research assistants, the heads and staff of the Family Cancer Clinics, and the Clinical Follow Up Study; Louise Brinton, Mark Sherman, Neonila Szeszenia-Dabrowska, Beata Peplonska, Witold Zatonski, Pei Chao, Michael Stagner, Petra Bos, Jannet Blom, Ellen Crepin, Elisabeth Huijskens, Annette Heemskerk, the Erasmus MC Family Cancer Clinic, the SEARCH and EPIC teams, the Core Facility Molecular Pathology and Biobanking (CFMPB) of the Netherlands Cancer Institute for their technical support, Rosane Vianna-Jorge, and the many patients and families who contributed to this study.

Financial support

Cancer Research UK (C1287/A10118, C1287/A12014, C490/A10124, C490/A10119 and C490/A16561), the UK National Institute for Health Research Biomedical Research Centre at the University of Cambridge; BIHR Biomedical Research Centre at the University of Cambridge; Dutch Cancer Society (grants NKI 2007–3839, 2009–4363; DDHK 2004–3124, DDHK 2009–4318); Baden Württemberg Ministry of Science, Research and Arts, Helsinki University Central Hospital Research Fund, Academy of Finland (266528), the Finnish Cancer Society, the Nordic Cancer Union and the Sigrid Juselius Foundation, Special Government Funding (EVO) of Kuopio University Hospital grants, Cancer Fund of North Savo, the Finnish Cancer Organizations, Australia National Breast Cancer Foundation, National Health and Medical Research Council (NHMRC), the Queensland Cancer Fund, the Cancer Councils of New South Wales, Victoria, Tasmania and South Australia, the Cancer Foundation of Western Australia, NIH grants (CA128978, CA116167, CA176785), NIH Specialized Program of Research Excellence (SPORE) in Breast Cancer (CA116201), the Breast Cancer Research Foundation, the David F and Margaret T Grohne Family Foundation, the Ting Tsung and Wei Fong Chao Foundation, Intramural Research Funds of the National Cancer Institute, Department of Health and Human Services, USA and CAPES Foundation. KAP is supported by a National Breast Cancer Foundation Practitioner Fellowship.

Authors’ Affiliations

(1)
Division of Molecular Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands
(2)
Programa de Farmacologia, Instituto Nacional do Câncer (INCA), Rio de Janeiro, Brazil
(3)
Division of Diagnostic Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
(4)
Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands
(5)
University of Helsinki, Helsinki, Finland
(6)
Department of Obstetrics and Gynecology, Helsinki University Central Hospital, Helsinki, Finland
(7)
Core Facility Molecular Pathology and Biobanking, Division of Molecular Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands
(8)
Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
(9)
Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK
(10)
Breakthrough Breast Cancer Centre, London, UK
(11)
Department of Pharmacoepidemiology & Clinical Pharmacology, Utrecht Institute of Pharmaceutical Sciences (UIPS), Utrecht, The Netherlands
(12)
Department of Pathology, Helsinki University Central Hospital, Helsinki, Finland
(13)
Department of Clinical Genetics, Helsinki University Central Hospital, Helsinki, Finland
(14)
Department of Oncology, Helsinki University Central Hospital, Helsinki, Finland
(15)
Cancer Research UK Cambridge Institute Oncology, University of Cambridge, Cambridge, UK
(16)
Department of Histopathology, Addenbrooke’s Hospital, Cambridge University Hospital NHS Foundation Trust, Cambridge, UK
(17)
Department of Pathology, University of Cambridge, Cambridge, UK
(18)
Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, USA
(19)
Division of Cancer Prevention, National Cancer Institute, Rockville, USA
(20)
Department of Cancer Epidemiology and Prevention, Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Warsaw, Poland
(21)
School of Medicine, Institute of Clinical Medicine, Pathology and Forensic Medicine, University of Eastern Finland, Kuopio, Finland
(22)
Cancer Center of Eastern Finland, University of Eastern Finland, Kuopio, Finland
(23)
Imaging Center, Department of Clinical Pathology, Kuopio University Hospital, Kuopio, Finland
(24)
Cancer Center, Kuopio University Hospital, Kuopio, Finland
(25)
Jyväskylä Central Hospital, Jyväskylä, Finland
(26)
Division of Cancer Medicine, Peter MacCallum Cancer Centre, Melbourne, Australia
(27)
Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Australia
(28)
Centre for Molecular, Environmental, Genetic and Analytic Epidemiology, School of Population Health, The University of Melbourne, Melbourne, Australia
(29)
Department of Medicine, St Vincent’s Hospital, The University of Melbourne, Melbourne, Australia
(30)
Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, USA
(31)
Department of Health Sciences Research, Mayo Clinic, Rochester, USA
(32)
Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
(33)
German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
(34)
Division of Preventive Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
(35)
Saarland Cancer Registry, Saarbrücken, Germany
(36)
Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
(37)
Department of Pathology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
(38)
Division of Toxicology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
(39)
Division of Cancer Epidemiology, Unit of Genetic Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
(40)
Department of Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Australia
(41)
Division of Psychosocial Research and Epidemiology, Netherlands Cancer Institute, Amsterdam, The Netherlands

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© Sobral-Leite et al. 2015

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/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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