Olivier Brouckaert

Saskia Pintens

Vanya Belle

Sabine Huffel

Edward Camerlynck

Frédéric Amant

Karin Leunen

An Smeets

Patrick Berteloot

Erik Limbergen

A E Saskia

Pintens Ae

Vanya Van

Belle Ae

Sabine Van Huffel

Ae Edward Camerlynck

A E Fre

´de ´ric

Amant Ae

Karin Leunen Ae

A E Patrick

Berteloot Ae

Erik Van

Limbergen Ae

Julie Decock

A E Wouter

Hendrickx Ae

Caroline Weltens

Ae Walter Van Den

Bogaert Ae

Isabelle Vanden

Bempt Ae

Maria Drijkoningen

A E Robert

Paridaens Ae

Hans Wildiers

A E Ignace

Vergote Ae

Marie-Rose Christiaens

Ae Patrick Neven

Á E Camerlynck

Á F Amant

Á K Leunen

Á A Smeets

Á P Berteloot

Á E Van Limbergen

Á J Decock

Á W Hendrickx

Á C Weltens

Á W Van Den Bogaert

Á M Drijkoningen

Á R Paridaens

Á H Wildiers

M.-R Christiaens

Á P Neven

Á I Vanden Bempt

V Van Belle

Á S Van Huffel

Á S Pintens

Á I Vergote

M.-R Christiaens

E Van Limbergen

R Paridaens

Short-term outcome of primary operated early breast cancer by hormone and HER-2 receptors

Keywords: Breast cancer, Disease free survival, Disease free interval, HER-2, Steroid receptors, Estrogen receptor, Progesterone receptor

Introduction

Worldwide, breast cancer is the most common malignancy among women. Its incidence has been increasing over recent decades, with a recent trend toward lower incidence in Western Countries. This trend can partly be explained by primary (SERMs) and secondary prevention (screening mammography) and a decrease in the use of postmenopausal hormone-replacement therapy [START_REF] Jemal | Cancer statistics 2007[END_REF]. However, breast cancer remains the leading cause of death in women between ages 40 and 55 [START_REF] Benshushan | Hormonal manipulations and breast cancer[END_REF]. The large biological heterogeneity of breast cancers is reflected in the large differences in disease outcome between women with this disease. Although prognostic classification systems of operable breast cancers, for example the Nottingham Prognostic Index (NPI) are important determinants of clinical outcome, they remain insufficient [START_REF] Lee | The Nottingham Prognostic Index for invasive carcinoma of the breast[END_REF]. Even women with a similar NPI and with similar histological tumor type experience differences in disease outcome. Age at diagnosis, a healthy lifestyle, co-morbidity, race, previous use of hormone-replacement therapy, and localization of the tumor within the breast are only a limited listing of known prognostic factors of breast cancer outcome other than the commonly used morphological prognostic factors [START_REF] Constanza | Epidemiology and risk factors for breast cancer[END_REF].

Outcome of operable breast cancer is also affected by the presence or absence of hormone receptors and human epidermal growth factor receptor 2 (HER-2) [START_REF] Suen | Prognostic contribution of the HER-2 oncogene overexpression to the Nottingham Prognostic Index in breast cancer[END_REF]. The prognostic value of these predictive biomarkers can be summarized as follows. Women with an ER-positive tumor have a better prognosis than those with an ER-negative tumor, although this is not true for very young women and not after approximately eight years of follow-up (FU) when survival curves of ER-positive and ER-negative cases cross [START_REF] Suen | Prognostic contribution of the HER-2 oncogene overexpression to the Nottingham Prognostic Index in breast cancer[END_REF][START_REF] Pichon | Prognostic value of steroid receptors after long-term follow-up of 2257 operable breast cancers[END_REF][START_REF] Cui | Biology of progesterone receptor loss in breast cancer and its implications for endocrine therapy[END_REF]. The combination of ER with PR further refines the short-term prognostic value of ER. Within ER-positive breast cancers, women with negative PR expression do worse than those expressing PR, but data are only of value for the postmenopausal population [START_REF] Bardou | Progesterone receptor status significantly improves outcome prediction over estrogen receptor status alone for adjuvant endocrine therapy in two large breast cancer databases[END_REF][START_REF] Rakha | Biologic and clinical characteristics of breast cancer with single hormone receptor-positive phenotype[END_REF][START_REF] Mauriac | Predictors of early relapse in postmenopausal women with hormone receptorpositive breast cancer in the BIG 1-98 trial[END_REF][START_REF] Oh | Estrogen-regulated genes predict survival in hormone receptor-positive breast cancers[END_REF]. This is probably related to the fact that a negative PR status stands for alternative signaling through, for example, the epidermal growth factor pathway [START_REF] Arpino | Estrogen receptorpositive, progesterone receptor-negative breast cancer: association with growth factor receptor expression and tamoxifen resistance[END_REF][START_REF] Huang | Association between HER-2/neu and the progesterone receptor in oestrogendependent breast cancer is age-related[END_REF][START_REF] Kim | Progesterone receptor loss correlates with human epidermal growth factor receptor 2 overexpression in estrogen receptor-positive breast cancer[END_REF][START_REF] Clark | Correlations between estrogen receptor, progesterone receptor, and patient characteristics in human breast cancer[END_REF]. ER-positive/PR-negative tumors express higher levels of HER-1 and, especially, HER-2 and have more aggressive features than ER/PRpositive tumors [START_REF] Arpino | Estrogen receptorpositive, progesterone receptor-negative breast cancer: association with growth factor receptor expression and tamoxifen resistance[END_REF][START_REF] Park | EGFR gene and protein expression in breast cancers[END_REF]. Overexpression of receptors for growth factors such as HER-2 has also been shown to play an important prognostic role, independent of lymph node status, tumor size, or ER-status [START_REF] Slamon | Human breast cancer: correlation of relapse and survival with amplification of the HER-2/neu oncogene[END_REF][START_REF] Collett | The prognostic contribution of estrogen and progesterone receptor status to a modified version of the Nottingham Prognostic Index[END_REF][START_REF] Elzagheid | Lymph node status as a guide to selection of available prognostic markers in breast cancer: the clinical practice of the future?[END_REF][START_REF] Peiro | Analysis of HER2 by chromogenic in situ hybridization and immunohistochemistry in lymph node-negative breast carcinoma: prognostic relevance[END_REF][START_REF] Schmidt | Free in PMC Long-term prognostic significance of HER-2/neu in untreated node-negative breast cancer depends on the method of testing[END_REF].

In general, most of these studies were based on the prognostic value of one or two of the hormone receptors but studies focusing on breast cancer prognosis based on the combined expression of ER, PR, and HER-2 are limited. Recently, cDNA microarrays of 496 genes of breast cancer tissue identified different prognostic subgroups based on differences in gene expression profiles [START_REF] Perou | Molecular portraits of human breast tumours[END_REF][START_REF] Sorlie | Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications[END_REF][START_REF] Kapp | Discovery and validation of breast cancer subtypes[END_REF]. This prognostic subgroup classification of breast cancers seems closely, but not completely, related to the immunohistochemical (IHC) classification of tumors according to their combined expression of hormone receptors and HER-2 [START_REF] Perou | Molecular portraits of human breast tumours[END_REF][START_REF] Carey | Race, breast cancer subtypes, and survival in the Carolina Breast Cancer Study[END_REF][START_REF] Francis | Evaluation of oestrogen and progesterone receptor status in HER-2 positive breast carcinomas and correlation with outcome[END_REF]. The partial inconsistency between IHC ''phenotype'' and microarray ''genotype'' is not fully understood and might be partially related to technical issues of receptor determination. Five main prognostic ''genotype'' subgroups are described. The most frequent is the ''luminal A'' subgroup which represents in general all ER-positive and/or PR-positive cases; these tumors are HER-2-negative. The second group are breast cancers in the ''luminal B'' subgroup; they are also ER-positive and/ or PR-positive but with a lower expression of both steroid receptors. The HER-2-overexpressing breast cancers that are ER-positive also belong to this subgroup. The ''normallike'' subgroup is less well defined but seems to include ER-positive cases not showing HER-2 gene amplification. The fourth group consists of ''ER-negative HER-2-overexpressing'' breast cancers, in principle characterized by HER-2 gene amplification with an absence of both ER and PR. The last group is called ''triple negative'' tumors. Although they lack expression of both the female steroid hormone receptors and HER-2, some are also classified as basal like, but such tumors should overexpress HER-1 and/ or express high-molecular-weight cytokeratins CK 5.6, CK 14, and/or CK17. The latter are typically expressed in the basal, myoepithelial cells lining the breast duct. This particular group seems to be better defined as time goes by [START_REF] Perou | Molecular portraits of human breast tumours[END_REF][START_REF] Sorlie | Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications[END_REF][START_REF] Kapp | Discovery and validation of breast cancer subtypes[END_REF].

Because of its high cost, the need for frozen tissue, and the absence of a morphological control, it remains hard to use the molecular classification system in the routine clinical setting. IHC markers for ER, PR, HER-2, and CK 5 and 6 have been successfully verified against the gene expression profiles as such in a short-term disease-free survival study [START_REF] Carey | Race, breast cancer subtypes, and survival in the Carolina Breast Cancer Study[END_REF][START_REF] Francis | Evaluation of oestrogen and progesterone receptor status in HER-2 positive breast carcinomas and correlation with outcome[END_REF]. Francis et al. recently showed Kaplan-Meier (KM) survival curves with a reduced breast cancer-specific survival for HER-2-positive tumors [START_REF] Francis | Evaluation of oestrogen and progesterone receptor status in HER-2 positive breast carcinomas and correlation with outcome[END_REF]. The breast cancer specific survival in these patients correlated with hormone receptor status of the tumors for both HER-2-negative and HER-2-positive tumors. This has been confirmed in various ethnic populations [START_REF] Carey | Race, breast cancer subtypes, and survival in the Carolina Breast Cancer Study[END_REF][START_REF] Francis | Evaluation of oestrogen and progesterone receptor status in HER-2 positive breast carcinomas and correlation with outcome[END_REF][START_REF] Sorlie | Repeated observation of breast tumor subtypes in independent gene expression data sets[END_REF][START_REF] Fan | Concordance among geneexpression-based predictors for breast cancer[END_REF][START_REF] Pusztai | Molecular classification of breast cancer: limitations and potential[END_REF][START_REF] Sorlie | Distinct molecular mechanisms underlying clinically relevant subtypes of breast cancer: gene expression analyses across three different platforms[END_REF]. Therefore, they can be used to estimate the prevalence of the five intrinsic subtypes in epidemiological studies without loss of their prognostic significance [START_REF] Perou | Molecular portraits of human breast tumours[END_REF][START_REF] Carey | Race, breast cancer subtypes, and survival in the Carolina Breast Cancer Study[END_REF][START_REF] Hannemann | Molecular subtypes of breast cancer and amplification of topoisomerase II alpha: predictive role in dose intensive adjuvant chemotherapy[END_REF]. IHC of receptors for female steroid hormones and HER-2 remains the most common practice in the primary evaluation of breast cancer. Although there can be several technical reasons for discordant results between centers, hormone receptor determination has not only become routine because it is easy in use-it also has a morphological control and does have a much lower cost than gene profiling. It is a straightforward technique that uses paraffinembedded tissue and can therefore be used for retrospective analyses [START_REF] Eden | Good Old'' clinical markers have similar power in breast cancer prognosis as microarray gene expression profilers[END_REF].

We here present KM survival curves of primary operated breast cancer patients stratified by the IHC expression of the joint receptor status for ER/PR/HER-2. We also studied the role of joint receptor subgroups, adjusting for adjuvant therapy.

Patients and methods

Cases were selected from our institutional database containing all patients with breast cancer treated at the Multidisciplinary Breast Centre in UZ-Leuven between January 1, 2000 and June 2005. We used our database to retrospectively seek all patients eligible for inclusion. Patients were included after primary surgery for an early invasive breast cancer if clinico-pathological data and updated FU information were available. Women who received preoperative systemic therapy were excluded. In cases of bilateral breast cancer we included the tumor characteristics of the lesion with the highest NPI.

A total of 2,059 patients were included in this study. In 85 patients, information about ER, PR, and/or HER-2 status was incomplete. It was mainly HER-2 status which was missing in patients operated during early 2000. In that year analysis of HER-2 by IHC was introduced into clinical routine in UZ Leuven. After exclusion of the small group of women with an ER-negative/PR-positive tumour, a dataset of 1,958 patients remained (Fig. 1). Patients with incomplete information on tumor variables such as tumor size, grade, or lymph node status were only excluded in function of the analysis performed.

Tumor size was evaluated by the pathologist and the largest diameter from the histopathology report was chosen. If there was more than one lesion, the largest was selected, even if this was biologically (grading, receptor status) not the most aggressive. Tumor grading was performed according to the Ellis and Elston grading system [START_REF] Elston | Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up[END_REF]. Lymph nodes were assessed on at least three sections stained with conventional hematoxylin and eosin (HE). Sentinel lymph nodes and lymph nodes from lobular breast cancers classified as negative on HE were also stained with epithelial markers. NPI was computed from the above mentioned tumour characteristics, using the formula NPI = 0.2 9 T + N (1-3) + G (1-3) where T is the maximum diameter (in cm), N the number of lymph nodes involved (1 = no axillary lymph nodes involved; 2 = 1-3 axillary lymph nodes involved; 3 = more than 3 axillary lymph nodes involved), and G histological grade (1-3: good, moderate, poorly differentiated). The patients are grouped into three prognostic risk categories for relapse according to the NPI results: 1 = low risk, with NPI equal to or less than 3.4; 2 = medium risk, with NPI between 3.4 and 5.4; 3 = high risk, with NPI over 5.4 [START_REF] Blamey | Survival of invasive breast cancer according to the Nottingham Prognostic Index in cases diagnosed in 1990-1999[END_REF]. NPI could not be calculated in cases when no axillary lymph node dissection was performed (n = 31).

For routine clinical use, breast cancer tissue was examined by IHC for the expression of ER, PR, and HER-2 using antibodies NLC-ER-6F11, NCL-PR-312, and CB11, respectively, all from Novocastra Laboratories (Newcastleon-Tyne, UK). Since 2005, highly sensitive rabbit monoclonal antibodies have been in use for the assessment of ER and PR expression (SP1 and SP2 respectively; Labvision, Fremont, CA, USA).

Briefly, 4 lm thick paraffin sections were cut. Heatinduced epitope retrieval was carried out in a calibrated water bath (95-99°C) and antibody complexes were visualized by use of EnVision+ (DakoCytomation, Glostrup, Denmark) and diaminobenzidine. The latter antibodies are highly sensitive and SP1 has recently been proposed as an improved standard for ER IHC assessment in breast cancer [START_REF] Cheang | Immunohistochemical detection using the new rabbit monoclonal antibody SP1 of estrogen receptor in breast cancer is superior to mouse monoclonal antibody 1D5 in predicting survival[END_REF].

Stainings were scored using the semi-quantitative Allred score considering both the proportion of stained tumour cell nuclei (scored on a 0-5 scale) and staining intensity (scored Fig. 1 Flow of patients through the study. Overlap of excluded patients is possible, for example, patients lost to FU could also be those with missing data or those with an ER-PR+ tumor on a 0-3 scale) [START_REF] Mohsin | Progesterone receptor by immunohistochemistry and clinical outcome in breast cancer: a validation study[END_REF][START_REF] Harvey | Estrogen receptor status by immunohistochemistry is superior to the ligand-binding assay for predicting response to adjuvant endocrine therapy in breast cancer[END_REF]. A total sum of more than 2 was considered as positive for ER and PR status. The semiquantitative H-score was used before 2003. This was calculated by summing the products of the percentage of cells stained (0-100%) by staining intensity (0-3+). For example, a specimen with 10% of cells staining 3+, 20% of cells staining 2+, 10% of cells staining 1+, and 50% of cells unstained would have a complete H-score of (3 9 10) + (2 9 20) + (1 9 10) = 80. For steroid receptors, any nuclear staining in invasive tumour cells was considered positive, both using Allred-or H-score.

HER-2 immunostaining was scored according to the standardized HercepTest scoring system in which HER-2 expression is scored on a 0 to 3+ scale. Score 0 represents absence of staining whereas score 3+ indicates strong membranous HER-2 staining. We have previously shown that women with a HER-2/neu DAKO score 3+ were all HER-2 FISH-positive [START_REF] Huang | Association between tumour characteristics and HER-2/neu by immunohistochemistry in 1362 women with primary operable breast cancer[END_REF]. In the case of intermediate scoring (score 2+), underlying HER-2 gene copy number was investigated by fluorescence in situ hybridization or FISH (PathVision; Vysis, Downers Grove, IL, USA) for the presence of HER-2 gene amplification. Only when the latter was present in score 2+ cases was HER-2 status considered positive.

Surgical treatment consisted in wide local excision plus axillary dissection followed by whole breast radiotherapy (RT) plus a boost on the tumour bed, or modified radical mastectomy when breast-conserving surgery was not indicated. In our institution, sentinel node biopsy became standard treatment for cT1N0 patients in June 2003; since then, completion axillary dissection was carried out only in cases of metastatic sentinel node. Chest wall RT following mastectomy was given to patients with T3 or T4 tumors, with positive lymph nodes or with positive tumour section margins. Irradiation to the internal mammary chain was performed only in cases of axillary lymph node involvement or medial tumour sites. This was implemented first in the EORTC trial and subsequently as routine in this subset of patients.

Patients received systemic adjuvant therapy that was considered appropriate for the time of treatment (rule based) and several patients participated in clinical studies with endocrine or chemotherapeutic modalities. All other patients were treated according to international guidelines and consensus conferences (rule based). Endocrine therapy (HT) was prescribed if ER and/or PR expression were present. Although tamoxifen 20 mg/day for five years was the standard HT, many postmenopausal women also received an oral aromatase-inhibitor for a period of five years. Anthracycline-based chemotherapy (CT) was given if patients were classified as intermediate or high risk for relapse but endocrine sensitivity, NPI, and age at diagnosis were also important when deciding upon systemic adjuvant therapy. Only a few patients (n = 10, included in the HERA-trial) were treated with adjuvant trastuzumab, which was, therefore, not taken into account.

Using the electronic patient files, FU status was updated (range 1-2,484 days) for most of the patients. If patients did not have FU in Gasthuisberg, Leuven, treating general physicians were contacted by telephone to obtain the latest FU status. Our objective was to provide FU data for all patients up until June 2006. Only 17 patients defined as ''lost to FU'' had no FU at all and were excluded from this study. We applied the reporting REcommendations for tumour MARKer prognostic studies (REMARK) [START_REF] Mcshane | REporting recommendations for tumor MARKer prognostic studies (REMARK)[END_REF]

Statistical analysis

The clinical and pathologic characteristics of women in this study are reported as frequencies, medians, and ranges. Statistical analysis was performed by ESAT, and SAS 9.1.3 service pack 4 was used to run all statistical analysis.

The primary outcome was disease-free interval (DFI) by joint receptor status and defined as the length of time from the date of surgery to a first predefined breast-cancer event. Events included local (ipsilateral or regional) recurrence of breast cancer, contralateral disease, and distant metastasis. The event time of patients without relapse was censored at last FU or death. We thus evaluated only breast cancerspecific DFI. Overall survival was not calculated. No patients died of breast cancer without first having had a relapse.

We defined the following six subgroups based on joint receptor status and excluding ER-negative/PR-positive tumors: ER + PR + HER-2 -(PPN), ER + PR -HER-2 -(PNN), ER + PR + HER-2 + (PPP), ER -PR -HER-2 -(NNN), ER -PR -HER-2 + (NNP), and ER + PR -HER-2 + (PNP). Median NPI and NPI risk group distribution were calculated for each joint receptor status.

The non-parametric KM method, allowing for right censoring, was used to visualize the survival curves. Different types of right censoring occurring in this study were: patients without relapse at the end of the study time, patients lost to FU, and patients who died a non-breastcancer-related death. The KM method incorporates the censored data by changing the risk set at every event time. Data points having an event or censoring time which is less than the survival time at which point the survival is calculated were excluded from the risk set.

The log-rank test was used for comparison between different survival curves. At each event time a (2 9 2) table is constructed. The recurrence rates between the two groups are compared, conditional on the number of patients at risk in the group. The tables are then combined using the Cochran-Mantel-Haenszel test. If the constructed tables are independent, the log-rank-statistic will have an approximate v 2 distribution with one degree of freedom. Correction for multiple testing was performed using the step-down Bonferroni correction as proposed by Holm et al. [START_REF] Holm | A simple sequentially rejective Bonferroni test procedure[END_REF] and the step-up method introduce by Benjamini and Hochberg [START_REF] Benjamini | Controlling the false discovery rate: a practical and powerful approach to multiple testing[END_REF].

Results

Median FU was 41 months (1-2,484 days). Tables 1 and2 summarize the clinico-pathological features and local and systemic adjuvant therapy of all patients by the predefined joint receptor subtype. In the overall study population (n = 1958), median age at diagnosis was 57 years and most (65.19%) women were in a postmenopausal state. Analysis of receptor-prevalence showed ER staining in 87.47% of all cases, whereas PR and HER-2 were positive in 75.18% and 12.16%, respectively. Half (47.80%) of the patients presented with an intermediate NPI risk at diagnosis (median NPI 4.30; Table 1), most patients were node-negative (61.90%) and grade-1 tumors were not frequently seen (13.79%).

Most women (57.51%) had breast conservative surgery and 84.13% received postoperative RT. Data on adjuvant systemic therapy were complete for all modalities for 1,937 patients; 1,808 (93.34%) did receive it. For HT data were available for 1,948 patients and 1,571 of these (80.23%) received an anti-oestrogen for five years which was tamoxifen, ovarian suppression and tamoxifen, or an oral aromatase inhibitor, but some postmenopausal patients participated in clinical trials comparing tamoxifen with an oral aromatase inhibitor (BIG 1-98, IES, TEAM). Of all patients with complete data on adjuvant systemic therapy 690 patients (38.16%) received adjuvant CT, most likely in an anthracycline-based protocol which in 454 cases (25.11%) was followed by an adjuvant HT (Table 2).

During the study FU, we observed 185 breast-cancerrelated events (9.43%) in our cohort. The worst reported events were an ipsilateral relapse in 17 patients (0.87%), a The KM curves showed, in order from good to worse survival, the following order: PPN, PNN, NNN, PPP, PNP, NNP (Fig. 2 and Table 3). PPN tumours, the most prevalent group (69.36% of all patients), presented with a superior DFI and lowest median NPI compared with other subgroups (Tables 1 and3; Fig. 2). Compared with the overall study population, PPN tumours were also more often low-grade (Table 1). HER-2 overexpression in ER/ PR-positive tumors resulted in a significantly (P \ 0.0001) worse DFI and a higher median NPI (P \ 0.0001 Wilcoxon rank sum test). These PPP tumors constituted a younger age group (P \ 0.0001 Wilcoxon rank sum test) than PPN tumors (median age 50.00 versus 57.00 years). PPP tumors were also of a higher grade (P \ 0.0001 (v 2 )) and were more frequently node-positive (P \ 0.0001 (v 2 )) as was reflected in their median NPI score and NPI risk group distribution. The impact of HER-2 overexpression on short-term DFI was more prominent in lymph nodepositive cases (P = 0.0002 (v 2 )). We noticed this finding in all three ER/PR subgroups, but it was only significant in tumors expressing both ER and PR.

Hormone receptor-positive tumors only staining for ER made up 11.29% of all cases. Their DFI was significantly (P = 0.0082, log-rank test) worse than ER/PR-positive tumors. HER-2-negative tumors within this subgroup (PNN) had a non-significantly better (P = 0.30, log-rank test) DFI (Fig. 2). PNP tumors represented the smallest subgroup (1.63% of all patients) with the smallest median lesion size (22.00 mm).

Taking only ER and PR into account, the 13.53% of breast cancers with a double-negative hormone receptor status had the worst DFI compared with PP and PN tumors. The HER-2-positive subgroup in double hormone receptornegative tumors had an even worse DFI (P = 0.22). ER/ PR-negative tumors were more often high grade than tumors belonging to any other subgroup (P \ 0.0001 (v 2 )). There were no age (P = 0.6324, Wilcoxon rank sum test) or NPI (P = 0.4010, Wilcoxon rank sum test) differences, nor in the proportion of high grade tumors (P = 0.2808 (v 2 )) comparing NNN with NNP lesions. Table 4 presents the calculated P-values for comparison of the survival curves. No comparisons were made with the PNP curve, given the low number of such tumors. The calculated P-values are subject to multiple testing. Therefore the significance level of P = 0.05 is no longer valid. We leave it up to the reader to define a significance level. A possible adaptation for multiple testing (according to Holm) is also given, indicating a significant difference for the first three tests and a non-significant difference for the last five. For more details we refer to the work of Holm and Benjamini [START_REF] Holm | A simple sequentially rejective Bonferroni test procedure[END_REF][START_REF] Benjamini | Controlling the false discovery rate: a practical and powerful approach to multiple testing[END_REF].

Examination of a multivariate Cox model with ER, PR, and HER2 as main effects, all significant in univariate analysis, indicated that the effect of ER is no longer significant after inclusion of PR (Table 5). To adjust for treatment, the variables indicating whether the patient received chemotherapy and hormonal therapy where included in a model with PR and HER2 (Table 6). The effect of PR and HER2 remained significant after adjusting for treatment. ER status was overruled in the multivariate model by PR but remained a significant prognostic factor in interaction with HT. Furthermore, both ER and PR were found to have a significant interaction with CT. Since the interaction effects of ER-CT and PR-CT are strongly correlated, we excluded the ER-CT interaction, knowing that all PR+ tumours in this study are ER+ and most PR-tumours are ER+ (Tables 1, 5 and6).

Discussion

We confirmed that breast cancer subgroups based only on their combined IHC expression of ER, PR, and HER-2 have a prognostic impact on the short-term DFI of primary operable breast cancers. We have also shown that there is no important interaction of currently applied local and systemic therapies on the prognostic importance of these IHC subtypes. In a multivariate model with given therapy, PR and HER2 expression or amplification remained independent prognostic factors. We chose not to include NPI in this model, because the treatment is decided on the value of the Significant interaction effects were found for ER and CT, PR and CT, and ER and HT. Because the ER-CT and PR-CT interaction effects were strongly correlated, we excluded the ER-CT interaction. There is an independent significant statistical effect for PR and HER-2, after adjusting for therapy Prevalence of combined receptor subgroups in our study differs from those reported in the literature. The frequency of HER-2-over-expression (12.16%) is low compared with other series but analysis and definitions used in this series were based on current ASCO guidelines [START_REF] Francis | Evaluation of oestrogen and progesterone receptor status in HER-2 positive breast carcinomas and correlation with outcome[END_REF][START_REF] Wolff | American Society of Clinical Oncology/College of American Pathologists guideline recommendations for human epidermal growth factor receptor 2 testing in breast cancer[END_REF][START_REF] Ciocca | Co-expression of steroid receptors (estrogen receptor alpha and/or progesterone receptors) and Her-2/neu: clinical implications[END_REF][START_REF] Thomas | Prognostic and predictive implications of HER2 status for breast cancer patients[END_REF]. Although reports state that CB11 might not be the best predictive antibody for HER2 testing we were able to clearly show its prognostic importance when overexpressed [45,[START_REF] Kitano | Assessment of a new Anti-HER2 monoclonal antibody, SV2-61gamma: a best concordance with HER2 FISH[END_REF]. Our lower than usually presented proportion of HER-2 overexpression may also be explained by the inclusion of a consecutive series of primary operated breast cancers leaving out locally advanced operable breast cancers. In a recently reported series from Sweden considering 5,043 consecutive cases, 13.3% of the samples were HER-2 amplified [START_REF] Ferno | Reproducibility of HER2-analysis in a national survey performed at 24 routine pathology departments in Sweden[END_REF].

The proportion of breast cancers defined as ER-positive (87.47%) or PR-positive (75.18%) is higher than literaturereported proportions for ER (75%) and PR (55%) [START_REF] Cui | Biology of progesterone receptor loss in breast cancer and its implications for endocrine therapy[END_REF][START_REF] Kim | Progesterone receptor loss correlates with human epidermal growth factor receptor 2 overexpression in estrogen receptor-positive breast cancer[END_REF][START_REF] Cleator | Triple-negative breast cancer: therapeutic options[END_REF][START_REF] Francis | Frequency and reliability of oestrogen receptor, progesterone receptor and HER2 in breast carcinoma determined by immunohistochemistry in Australasia: results of the RCPA quality assurance program[END_REF]. Improved laboratory sensitivity for detecting ER, different quantification techniques, and dichotomous cutoff values for assessing ER and PR account for important interlaboratory variability [START_REF] Mohsin | Progesterone receptor by immunohistochemistry and clinical outcome in breast cancer: a validation study[END_REF][START_REF] Layfield | Assessment of tissue estrogen and progesterone receptor levels: a survey of current practice, techniques, and quantitation methods[END_REF][START_REF] Layfield | Interlaboratory variation in results from immunohistochemical assessment of estrogen receptor status[END_REF]. In our study, all IHC data were interpreted in one institute and validated by one pathologist (MD) which is a strength. The use of very sensitive monoclonal rabbit antibodies urged us to consider any nuclear staining for ER or PR as positive when using this biomarker for prognostic purposes. This approach has recently been described as appropriate for predictive and possibly also for prognostic indications although a quantitative measure for both ER and PR may be better [START_REF] Schnitt | Estrogen receptor testing of breast cancer in current clinical practice: what's the question?[END_REF].

The quantitative instead of qualitative values of steroid receptors may improve them for breast cancer prognosis. However, different antibodies and cut-offs for steroid receptor evaluation made quantification impossible as this study was retrospective. The qualitative approach for steroid receptors made it possible to separate short-term DFI curves for ER/PR-positive, ER-positive/PR-negative, ER-positive/ PR-negative and ER/PR-negative tumors, and this was true within each HER-2 status. Although nobody knows exactly the best cut-off for ER or PR for predictive purposes, Schnitt et al. recently proposed considering ''any nuclear staining'' as a positive predictor for anti-estrogens [START_REF] Schnitt | Estrogen receptor testing of breast cancer in current clinical practice: what's the question?[END_REF].

Rather than only using the joint ER/PR-expression as a prognostic factor, DFI was here presented by six predefined breast cancer subgroups combining ER/PR status with HER-2 status. Our outcome data as presented in Fig. 2 confirmed what others have recently published and clearly separates some prognostic subgroups using only ER, PR, and HER2. We however, presented data from a large number of breast cancer patients, all treated in one center, including almost 2,000 patients; this is, as far as we are aware, the largest series reported in the literature. Another strength of our study is that we did take local and systemic therapy into account, which was not done by others [START_REF] Carey | Race, breast cancer subtypes, and survival in the Carolina Breast Cancer Study[END_REF].

In our series, women with a triple negative tumour presented with a better short-term DFI compared with women with any of the three HER-2-positive subgroups; the literature frequently emphasizes the poor prognosis of this subgroup [START_REF] Cleator | Triple-negative breast cancer: therapeutic options[END_REF][START_REF] Banerjee | Basal-like breast carcinomas: clinical outcome and response to chemotherapy[END_REF]. HER-2 as a poor prognostic marker is already now being affected by adjuvant trastuzumab therapy and triple negative tumors may, anyway, eventually become the subgroup with the worst DFI. Also, patients with a triple negative breast cancer suffer from more limited treatment options and exhibit inherent aggressive tumour characteristics as expressed by their high median NPI score [START_REF] Cleator | Triple-negative breast cancer: therapeutic options[END_REF][START_REF] Francis | Frequency and reliability of oestrogen receptor, progesterone receptor and HER2 in breast carcinoma determined by immunohistochemistry in Australasia: results of the RCPA quality assurance program[END_REF] (Tables 1 and2).

We noticed a worse DFI for HER-2-positive tumours, irrespective of hormone receptor status (Fig. 2). We confirm, as previously reported by others, that the impact of HER-2 overexpression on short-term DFI was more prominent in lymph node-positive cases (P = 0.0002 (v 2 )). We noticed this finding in all three ER/PR subgroups, but it was only significant in tumors expressing both ER and PR. It has, however, been suggested that as more data become available in the literature, and with longer FU, the prognostic role of HER-2 will probably become independent of nodal status [START_REF] Perou | Molecular portraits of human breast tumours[END_REF][START_REF] Francis | Evaluation of oestrogen and progesterone receptor status in HER-2 positive breast carcinomas and correlation with outcome[END_REF][START_REF] Sorlie | Repeated observation of breast tumor subtypes in independent gene expression data sets[END_REF][START_REF] Banerjee | Basal-like breast carcinomas: clinical outcome and response to chemotherapy[END_REF]. A weakness in our study is the median FU period which consisted of 3.5 years only and which allows us only short-term DFI interpretations. Others have referred to ER and PR as only being short-term prognostic factors whereas little data are available on HER-2 as a prognostic factor beyond five years of FU [START_REF] Pichon | Prognostic value of steroid receptors after long-term follow-up of 2257 operable breast cancers[END_REF][START_REF] Ferretti | HER2/neu role in breast cancer: from a prognostic foe to a predictive friend[END_REF][START_REF] Ponzone | Clinical outcome of adjuvant endocrine treatment according to PR and HER-2 status in early breast cancer[END_REF].

Youth is a well recognized risk factor in breast cancer especially for early relapse. We chose not to evaluate the prognostic impact of age at diagnosis in this study. We were able to confirm that women with a triple-positive phenotype were younger than women with any other breast cancer phenotype. Huang et al. have already reported that the inverse association between HER-2 and PR only appears after age 45 [START_REF] Oh | Estrogen-regulated genes predict survival in hormone receptor-positive breast cancers[END_REF]. This not only implies that the PR status cannot be used to predict HER-2 signalling in young breast cancer patients, as opposed to the elder women, but also that women with a triple-positive breast cancer tend to be younger than women with any other breast cancer phenotype (Table 1). Women with a triple-positive breast cancer were also more likely lymph node-positive [START_REF] Van Calster | Axillary lymph node status of operable breast cancers by combined steroid receptor and HER-2 status: triple positive tumours are more likely lymph node positive[END_REF] which reflected their worse DFI outcome compared with any other ER-positive group, although the difference between PPP and PNP was not calculated, as already stated (Fig. 2; Tables 3 and4).

In conclusion, our data and within a short-term FU, clearly confirmed the possibility of separating prognostic subgroups based on joint receptor status for ER, PR, and HER-2. All three receptors remain significant factors in multivariate analysis taking adjuvant therapy into account. While gene expression profiles based on microarray data of over hundreds of genes will probably teach us a lot about the biology, heterogeneity, and prognosis of breast cancer, we emphasize the prognostic value of current routinely used IHC markers in casu ER, PR, and HER-2.

Fig. 2

 2 Fig. 2 KM curve depicting DFI of joint receptor subgroups

Table 1

 1 Descriptive statistics for subgroups and overall study population

	Variables	Combined steroid/HER-2 receptor subgroup				All	P-value
		NNN	NNP	PNN	PNP	PPN	PPP
		n = 174,	n = 91,	n = 189,	n = 32,	n = 1357,	n = 115,	n = 1958,
		8.89%	4.64%	9.65%	1.63%	69.31%	5.87%	100%
	Median age at	55.00	56.00	61.00	57.00	57.00	50.00	57.00	\0.0001
	diagnosis a
	Tumour grade
	Missing	1 (0.57)	1 (1.10)	-	-	1 (0.07)	-	3 (0.15)	\0.0001
	Grade 1	1 (0.57)	1 (1.10)	31 (16.40)	-	233 (17.17)	4 (3.48)	270 (13.79)
	Grade 2	13 (7.47)	12 (13.19)	78 (41.27)	11 (34.38)	753 (55.49)	29 (25.22)	896 (45.76)
	Grade 3	159 (91.38)	77 (84.62)	80 (42.33)	21 (65.63)	370 (27.27)	82 (71.30)	789 (40.30)
	Nodal status
	Missing	5 (2.87)	3 (3.30)	-	2 (6.25)	16 (1.18)	-	26 (1.33)	\0.0007
	Negative	110 (63.22)	48 (52.75)	114 (60.32)	21 (65.63)	867 (63.89)	52 (45.22)	1212 (61.90)
	Positive	59 (33.91)	40 (43.96)	75 (39.68)	9 (28.13)	474 (34.93)	63 (54.78)	720 (36.77)
	pT Stage
	Missing	1 (0.58)	-	-	-	1 (0.07)	-	1 (0.05)	0.1499
	0	-	-	-	-	1 (0.07)	-	1 (0.05)
	1	75 (43.10)	40 (43.96)	93 (49.21)	18 (56.25)	702 (51.73)	48 (41.74)	976 (49.85)
	2	79 (45.40)	42 (46.15)	81 (42.86)	14 (43.75)	528 (38.91)	57 (49.57)	801 (40.91)
	3	20 (11.49)	9 (9.89)	15 (7.94)	-	120 (8.84)	10 (8.70)	174 (8.89)
	4	-	-	-	-	5 (0.37)	-	5 (0.26)
	Median NPI a	4.60	4.87	4.34	4.45	3.90	4.70	4.30	\0.0001
	NPI group
	Missing	6 (3.45)	5 (5.49)	-	2 (6.25)	18 (1.33)	-	31 (1.58)	\0.0001
	\3.4	7 (4.02)	3 (3.30)	57 (30.16)	5 (15.63)	516 (38.03)	13 (11.30)	601 (30.69)
	3.4-5.4	114 (65.52)	56 (61.54)	89 (47.09)	19 (59.38)	598 (44.07)	60 (52.17)	936 (47.80)
	C5.4	47 (27.01)	27 (29.67)	43 (22.75)	6 (18.75)	225 (16.58)	42 (36.52)	390 (19.92)

a Kruskal-Wallis test (Wilcoxon-based test), whereas v 2 test for other analysis contralateral breast cancer in 23 patients (1.17%), and distant metastases in 145 patients (7.39%).

Table 2

 2 Adjuvant therapy for all predefined tumour subgroups Missing patients present missing data (for example, for NNN tumors we have data on HT for all 174 patients, but for CT only for 172 patients. Thus one should read: 70.69% of NNN received CT, 28.16% did not, and the remaining 1.15% (not shown) data are missing). RT, radiotherapy; CT, chemotherapy; HT, endocrine therapy; ME, mastectomy; BCS, breast conservative surgery

	Local and systemic	Subgroup	Combined steroid/HER-2 receptor subgroup			All	P-value
	adjuvant therapy	total %	NNN	NNP	PNN	PNP	PPN	PPP
	Surgery	ME	76 (43.68) 61 (67.03) 84 (44.44) 17 (53.13) 538 (39.59) 53 (46.09) 829 (42.30) \0.0001
		BCS	98 (56.32) 29 (31.87) 105 (55.56) 15 (46.88) 817 (60.12) 62 (53.91) 1126 (57.45)
	Adjuvant RT	Yes	147 (84.48) 71 (78.02) 157 (83.07) 27 (84.38) 1146 (84.45) 100 (86.96) 1648 (84.13) 0.6429
		No	27 (15.52) 19 (20.88) 32 (16.93) 4 (12.50) 203 (14.96) 15 (13.04) 300 (15.31)
	Adjuvant CT	Yes	123 (70.69) 67 (73.63) 59 (31.22) 18 (56.25) 351 (25.87) 72 (62.61) 690 (35.24) \0.0001
		No	49 (28.16) 23 (25.27) 130 (68.78) 13 (40.63) 1001 (73.77) 43 (37.39) 1259 (64.30)
	Adjuvant HT	Yes	10 (5.75)	4 (4.40) 160 (84.66) 24 (75.00) 1267 (93.37) 106 (92.17) 1571 (80.23) \0.0001
		No	164 (94.25) 85 (93.41) 29 (15.34) 7 (21.88)	83 (6.12)	9 (7.83)	377 (19.25)
	Adjuvant CT + HT	Yes	4 (2.30)	3 (3.30)	41 (21.69) 11 (34.38) 331 (24.39) 64 (55.65) 454 (23.19) \0.0001
		No	170 (97.70) 86 (94.51) 148 (78.31) 20 (62.50) 1020 (75.17) 51 (44.35) 1495 (76.35)

Table 3

 3 DFI and mean NPI for subgroups

	Subgroup	DFI (%)	Order	NPI	Order
	NNN	85.65	3	4.95	4
	NNP	75.81	6	5.04	6
	PNN	89.50	2	4.33	2
	PNP	80.42	5	4.61	3
	PPN	93.63	1	4.11	1
	PPP	81.47	4	4.98	5

Table 4

 4 Calculated P-values for comparison of the survival curves

	Test	P-value (log-rank test)	P-value (Holm)
	PPN-NNP	\0.0001	\0.0001
	PPN-NNN	\0.0001	0.0007
	PPN-PPP	0.0001	0.0008
	PPN-PNN	0.0146	0.1022
	PNN-NNP	0.0295	0.1770
	NNN-NNP	0.2240	1.0000
	PNN-PPP	0.2660	1.0000
	PNN-NNN	0.3375	1.0000
	PPP-NNP	0.3383	1.0000
	NNN-PPP	0.8372	1.0000
	The adaptation for multiple testing according to Holm is given

Table 6

 6 Multivariate Cox analyses were performed to check for statistically significant effects of ER, PR, and HER-2, after adjustment for therapy

	Variable	Parameter	Standard	Chi-square	P-value	Hazard ratio	95% Hazard ratio
		estimate	error				confidence limits
	PR	-0.69730	0.23084	9.1248	0.0025	0.498	0.317	0.783
	HER-2	0.52384	0.18718	7.8322	0.0051	1.688	1.170	2.437
	CT	-0.43454	0.24893	3.0473	0.0809	0.648	0.398	1.055
	HT	0.86821	0.46804	3.4409	0.0636	2.383	0.952	5.963
	ER * HT	-1.45379	0.48842	8.8598	0.0029	0.234	0.090	0.609
	PR * CT	0.94133	0.31450	8.9587	0.0028	2.563	1.384	4.748
	ME versus BCS	0.49216	0.15261	10.4004	0.0013	1.636	1.213	2.206

Table 5

 5 Multivariate Cox model including three main effects: ER, PR, and HER2 While gene expression profiles based on microarray data of over hundreds of genes are teaching us a lot about the biology, heterogeneity, and prognosis of breast cancer, we emphasized that early relapse in patients with an operable breast cancer also depends on increased expression and amplification of steroid-receptors and HER-2.

	Variable	Parameter	Standard	Chi-square	P-value	Hazard ratio	95% Hazard ratio
		estimate	error				confidence limits
	ER	-0.22454	0.24463	0.8425	0.3587	0.799	0.495	1.290
	PR	-0.48085	0.20902	5.2925	0.0214	0.618	0.410	0.931
	HER-2	0.66757	0.18488	13.0375	0.0003	1.949	1.357	2.801

Breast Cancer Res Treat (2009) 115:349-358


Simon A Joosse
email: s.joosse@nki.nl

Erik H Beers

Ivon H G Tielen

Hugo Horlings

Johannes L Peterse

Nicoline Hoogerbrugge

Marjolijn J Ligtenberg

F A Lodewyk

Priscilla Wessels

Senno Axwijk

Verhoef

Simon A Joosse Ae

Erik H Van Beers

A E Ivon

H G Tielen

A E Hugo

Horlings Ae

A E Nicoline

Hoogerbrugge Ae

A E Lodewyk

F A Wessels

A E Priscilla

Axwijk Ae

Senno Verhoef

A E Frans

AE B L Hogervorst

Petra M Nederlof
email: p.nederlof@nki.nl

Á E H Van Beers

Á H Horlings

F B L Hogervorst

Á M J Ligtenberg

L F A Wessels

P Axwijk

Á S Verhoef

Prediction of -association in hereditary non-breast carcinomas with array-CGH

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

describe the specific chromosomal aberrations in BRCA1related breast carcinomas. We developed a predictive genetic test for BRCA1-association and show that BRCA1related tumours can still be identified in HBOC families after routine DNA diagnostics.

Keywords

Background

Breast cancer is the most common cancer in developed countries and one of the leading causes of death in women. One out of nine women will be affected by breast cancer [START_REF] Visser | Incidence of cancer in the Netherlands 1999/2000 eleventh report of the Netherlands cancer registry[END_REF][START_REF]Cancer facts and figures[END_REF]. Up to 25% of familial breast cancer cases are explained by mutations in BRCA1 and BRCA2 [START_REF] Szabo | Population genetics of BRCA1 and BRCA2[END_REF][START_REF] Van Der Hout | A DGGE system for comprehensive mutation screening of BRCA1 and BRCA2: application in a Dutch cancer clinic setting[END_REF]. Women carrying a mutation in BRCA1/2 have a lifetime risk of up to 80% of developing breast cancer [START_REF] Easton | Breast and ovarian cancer incidence in BRCA1-mutation carriers. Breast Cancer Linkage Consortium[END_REF][START_REF] Ford | Genetic heterogeneity and penetrance analysis of the BRCA1 and BRCA2 genes in breast cancer families The Breast Cancer Linkage Consortium[END_REF][START_REF] Antoniou | Average risks of breast and ovarian cancer associated with BRCA1 or BRCA2 mutations detected in case series unselected for family history: a combined analysis of 22 studies[END_REF][START_REF] King | Breast and ovarian cancer risks due to inherited mutations in BRCA1 and BRCA2[END_REF].

Identification of such a mutation may not only influence the treatment of a patient or carrier (e.g. chemotherapy, radiation, bilateral prophylactic mastectomy, or salphingooophorectomy [START_REF] Tercyak | Quality of life after contralateral prophylactic mastectomy in newly diagnosed high-risk breast cancer patients who underwent BRCA1/2 gene testing[END_REF][START_REF] Cleator | Triple-negative breast cancer: therapeutic options[END_REF]) and surveillance, but also allows pre-symptomatic mutation screening of family members.

Based on family history and age of onset, breast cancer patients are eligible for DNA screening for pathogenic mutations in BRCA1/2. Diagnostics currently include mutation scanning and sequencing of gene fragments derived from germ line DNA, however, it is possible that part of the mutations still remains undetected [START_REF] Van Der Hout | A DGGE system for comprehensive mutation screening of BRCA1 and BRCA2: application in a Dutch cancer clinic setting[END_REF][START_REF] Ford | Genetic heterogeneity and penetrance analysis of the BRCA1 and BRCA2 genes in breast cancer families The Breast Cancer Linkage Consortium[END_REF][START_REF] Narod | Risk modifiers in carriers of BRCA1 mutations[END_REF][START_REF] Van Der Groep | Distinction between hereditary and sporadic breast cancer on the basis of clinicopathological data[END_REF]. Additionally, the detection of variants of unknown clinical significance complicates counselling and clinical management. Therefore, an additional tool that would indicate BRCA1 or BRCA2 involvement in breast cancer would be an asset to current clinical diagnostics.

Numerous studies show specific genetic characteristics with which tumours can be categorised into subclasses [START_REF] Perou | Molecular portraits of human breast tumours[END_REF]. For hereditary BRCA1-mutated cancer, previous publications from our research group and others show that these tumours develop distinct genetic alterations on which they can be recognised and distinguished from non-hereditary (sporadic) tumours [START_REF] Hedenfalk | Gene-expression profiles in hereditary breast cancer[END_REF][START_REF] Van't Veer | Gene expression profiling of breast cancer accurately predicts clinical outcome of disease[END_REF][START_REF] Wessels | Molecular classification of breast carcinomas by comparative genomic hybridization: a specific somatic genetic profile for BRCA1 tumors[END_REF][START_REF] Jonsson | Distinct genomic profiles in hereditary breast tumors identified by array-based comparative genomic hybridization[END_REF]. Various methods using expression profiling [START_REF] Hedenfalk | Gene-expression profiles in hereditary breast cancer[END_REF][START_REF] Van't Veer | Gene expression profiling of breast cancer accurately predicts clinical outcome of disease[END_REF] or comparative genomic hybridisation (CGH) [START_REF] Wessels | Molecular classification of breast carcinomas by comparative genomic hybridization: a specific somatic genetic profile for BRCA1 tumors[END_REF][START_REF] Jonsson | Distinct genomic profiles in hereditary breast tumors identified by array-based comparative genomic hybridization[END_REF][START_REF] Van Beers | Comparative genomic hybridization profiles in human BRCA1 and BRCA2 breast tumors highlight differential sets of genomic aberrations[END_REF] show specific genetic alterations for these tumour groups. Although analysis of tumour mRNA has led to the identification of many different molecular portraits, freshly frozen tissue is often not available, especially when family screening includes deceased relatives. On the other hand, formalin-fixation and embedding in paraffin is the common procedure used to handle and archive tumour tissue for pathology based diagnosis. We have previously shown that paraffin embedded tumours can be of adequate quality to perform CGH studies [START_REF] Van Beers | A multiplex PCR predictor for aCGH success of FFPE samples[END_REF][START_REF] Joosse | Automated array-CGH optimized for archival formalin-fixed, paraffin-embedded tumor material[END_REF]. The enhanced resolution of a microarray, compared with metaphase CGH [START_REF] Wessels | Molecular classification of breast carcinomas by comparative genomic hybridization: a specific somatic genetic profile for BRCA1 tumors[END_REF], may improve the sensitivity and specificity of the detection of BRCA1-or BRCA2-related tumours using CGH technology. Additionally, it will also provide a better estimate of the location of the chromosomal breakpoints of the genetic aberrations.

To limit the already large number of individuals eligible for DNA-screening, a pre-selection procedure to detect individuals with the highest risk of carrying a mutation is desirable. Prediction models based on family history to calculate the risk for carrying a mutation can be inadequate predictors, e.g. in small families [START_REF] Kang | Evaluation of models to predict BRCA germ-line mutations[END_REF]. An independent test based on tumour characteristics that would indicate involvement of BRCA1 could help to select for those patients who may be offered more extensive mutation analysis. Studies based on clinical assessment and pathological reviewing show the limited sensitivity and specificity of predicting BRCA1-status with the currently available markers [START_REF] Van Der Groep | Distinction between hereditary and sporadic breast cancer on the basis of clinicopathological data[END_REF][START_REF] Lakhani | Prediction of BRCA1 status in patients with breast cancer using estrogen receptor and basal phenotype[END_REF]. Genomic profiling of tumours using comparative genomic hybridisation could also function as a predictive strategy to select patients with a high priori risk of a BRCA1 mutation. However, this approach has not been applied earlier in a diagnostic setting. In general, more BRCA1 mutations are being found in HBOC (Hereditary Breast and Ovarian Cancer) than in HBC (Hereditary Breast Cancer) families, and the former group would therefore be more suited for our study to identify possible missed BRCA1associated tumours for evaluation of current diagnostics.

Patients and methods

Patient selection

This study was performed on primary tumour samples of three breast cancer groups: (1) 34 breast tumours from patients with a confirmed pathogenic BRCA1 germ-line mutation, mean age at diagnosis of 38 years (range: 27-61); (2) 48 sporadic breast tumours, mean age at diagnosis of 45 years (range: 32-60), no family history of breast cancer and selected from the institute's pathology archive matched for p53-status with the BRCA1-associated tumour group (Table 1); (3) 48 tumours from HBOC families (defined as at least two breast and one primary ovarian cancer), that were subjected to routine diagnostic testing [START_REF] Van Der Hout | A DGGE system for comprehensive mutation screening of BRCA1 and BRCA2: application in a Dutch cancer clinic setting[END_REF] and had a negative test result for mutations in both BRCA1 and BRCA2, with a mean age at diagnosis of 48 years (range: 20-61). Patient's characteristics for all three groups are described in Supplementary data 1. All sample material was formalin-fixed, paraffin-embedded (FFPE) tissue and extracted DNA had to be of sufficient quality, which was tested as previously described [START_REF] Van Beers | A multiplex PCR predictor for aCGH success of FFPE samples[END_REF]. All experiments involving human tissues were conducted with the permission of the institute's medical ethical advisory board.

Immunohistochemistry

Presence of ER, PR, ERBB2 (HER2/neu), p53 and CK5/6 was determined by immunohistochemistry staining using the antibodies: estrogen receptor AB-14 clone 1D5 ? 6F11, titre 1:50 (Neomarkers); progesterone receptor clone PR-1 titre 1:400 (Immunologic), c-erbB-2 clone SP3, titre 1:25 (Neomarkers); p53 clone D0-7, titre 1:8000 (Dako); and cytokeratin 5/6 clone D5/16 B4, titre 1:100 (Dako) respectively. If C70% of the tumour cells expressed ER, PR, p53 or CK5/6, the tumour was scored as positive (?) for the corresponding staining, in case B10% of the cells were stained, the tumour was scored as negative (-) and between 10 and 70% the tumour was scored as ± for the corresponding staining. HER2/neu staining was scored positive when a 3? staining was observed, otherwise it was scored negative (only one sporadic case was IHC 2?, and was called negative).

DNA isolation

Tumour DNA was isolated from FFPE tumour tissues as follows. 10 9 10 lm slices containing at least 70% tumour cells were cleared of paraffin (2 9 5 min xylene, 2 9 30 s 100% ethanol, 30 s 90% ethanol, 30 s 70% ethanol, and rinsed with H 2 O), treated with 1 M NaSCN at 37°C overnight, and sections of interest ([70% tumour cells) were scraped in 200 ll buffer ATL (Qiagen, cat. no. 51304). 27 ll of proteinase K (15 lg/ll, Roche, cat. no. 3115879001) was immediately added, as well as at the end of the day, and at the beginning and end of the next day; samples were constantly shaken at 37°C during the time of digestion. The following day, 40 ll RNase A (20 lg/ll, Sigma, cat. no. R5500) was added to the sample, vortexed, and incubated for 2 min at room temperature. 400 ll of buffer AL (Qiagen, cat.no.51304) was added and incubated for 10 min at 70°C. 420 ll of 100% ethanol was added and vortexed. The sample mixture was spun on a spincolumn (Qiagen, cat. no. 51304) for 1 min at 8,000 rpm. The column was sequentially washed with 500 ll of the following reagents and spun for 1 min at 8,000 rpm: AW1, AW2, and twice with 80% ethanol. The column was spun dry for 3 min at 14,000 rpm. The sample was eluted with 50 ll of AE buffer by spinning for 1 min at 8,000 rpm. Reference DNA was isolated from lymphocytes from six apparently healthy women and pooled. Lymphocytes were purified by adding lysis buffer (155 mM NH 4 Cl, 10 mM KHCO 3 , 1 mM EDTA) 49 the blood volume, followed by centrifugation at 3,000 rpm for 10 min at 4°C. The supernatant was removed and the cell pellet re-suspended in lysis buffer 59 the original blood volume. These steps were repeated until all erythrocytes were removed and the supernatant formed a clear solution. 1/10 of the initial blood volume DNAzol (Invitrogen, cat. no. 10503-027) was added to the cell pellet and mixed by pipetting until a clear solution remained. 1/2 of the DNAzol volume 100% ethanol was added, DNA was removed from the solution, washed in 70% ethanol and dissolved in Tris-EDTA buffer. DNA was sonicated until the average length was 300-800 bp.

Array-CGH

As described previously [START_REF] Joosse | Automated array-CGH optimized for archival formalin-fixed, paraffin-embedded tumor material[END_REF], hybridisations were done on microarrays containing 3.5 k BAC/PAC-derived DNA segments covering the whole genome with an average spacing of 1 Mb, obtained from the Welcome Trust Sanger Institute (UK). The whole library was spotted in triplicate on every slide. To prevent slide batch spotting bias, samples were hybridised in randomised order. Data processing of the scanned microarray slide included signal intensity measurement with the ImaGene software program, followed by median pin-tip (c.q. subarray) normalization. Intensity ratios (Cy5/Cy3) were log 2 -transformed and triplicate spot measurements were averaged.

Aberration detection and quantification

To analyse and visualize the chromosomal aberrations, we determined breakpoint locations and estimated copy number levels using the CGH-segmentation algorithm [START_REF] Picard | A statistical approach for array CGH data analysis[END_REF]. Based on the estimated copy number levels, the frequency of gains and losses for all BAC clones was calculated using the fixed log 2 -ratio thresholds of 0.15 and -0.15, respectively. The association of the frequency of a clone being 'gained', 'lost' or 'unchanged' and the two tumour groups was calculated by employing a 3 9 2 Fisher's exact (FE) test. A small P-value corresponds to a significant association between the observed copy number changes and the two groups. This procedure was employed to compare the whole cohort of BRCA1-related tumours to: (1) the whole set of sporadic tumours and (2) to particular subgroups in order to obtain an indication of the aberrations associated with these subgroups. First, subgroups were defined based on IHC-status of ER, PR, HER2/neu or p53 of the tumours (CK5/6 was not used to define a subgroup due to the small number of CK5/6 positive tumours). IHC status can either be positive or negative (IHC-medium (±) samples were assigned to the corresponding IHC-positive group). Hence, four IHC-negative and four IHC-positive subgroups of sporadic tumours were generated. Then the FE test was employed to identify two sets of significant different aberrations. The first set, denoted as 'BRCA1/IHC-negative' includes aberrations that are significantly associated with the class label (BRCA1-related or sporadic) when comparing BRCA1-relateted and IHC-negative sporadic tumours. The second set, denoted as 'BRCA1/IHC-positive', includes aberrations that are significantly associated with the class label when comparing BRCA1-related and IHC-positive sporadic tumours. Since a IHC status (e.g. ER) can strongly be associated with the class label in a dataset contacting the BRCA1-related and IHC-positive sporadic subsets, the aberrations in the 'BRCA1/IHCpositive' can be either BRCA1 associated or IHC associated. Since BRCA1-related tumours are mostly ER, PR and HER2/neu negative, the aberrations in these 'BRCA1/IHCnegative' sets are mainly BRCA1 associated. Therefore, an aberration included in either of these subgroups is specific for the associated IHC-status. An aberration included in both sets is likely to be specific for BRCA1-associated tumours only.

Class prediction: training

To build a class predictor based on log 2 -ratios derived from array-CGH experiments, the shrunken centroids (SC) algorithm was employed [START_REF] Tibshirani | Diagnosis of multiple cancer types by shrunken centroids of gene expression[END_REF] using equal priors, p k = 1/K, where K is the number of classes. We predicted, employing the approach of Dobbin and Simon [START_REF] Dobbin | How large a training set is needed to develop a classifier for microarray data?[END_REF], that in order to detect a standardized fold change of 2.32 (1 copy number change) amongst 3277 BAC clones at an error tolerance of 0.05 for the classifier, the minimal sample size for the training set had to be 26, equally divided over the two classes. The class predictor was built on 18 BRCA1-related and 32 sporadic breast tumours (referred to as the training sets). Since it is known that p53-and ER-status are associated with specific genomic aberrations in breast cancer [START_REF] Loo | Array comparative genomic hybridization analysis of genomic alterations in breast cancer subtypes[END_REF][START_REF] Fridly | Breast tumor copy number aberration phenotypes and genomic instability[END_REF] that could influence the classification process, we stratified for p53-status in both the training sets, and for ER-status in the sporadic training set only.

Class prediction: testing

The class predictor was validated on independent sets of 16 BRCA1-mutated and 16 sporadic tumours (referred to as the validation sets). Classification of a sample using the SC algorithm results in the probability scores between 0 and 1 for each class. The sum of the two probability scores for any sample is always 1; hence, in a two-class problem, the most likely class is the class for which the probability is exceeds 0.5. For legibility we only describe the highest probability. 95% reference intervals were calculated based on the class-probability distribution in the training sets and employed in the validation of the classifier and the classification of the HBOC group. Samples predicted outside the 95% reference intervals were not assigned to a class but scored as ''not classified''.

Methylation detection

Hypermethylation of the BRCA1 promoter for all samples (n = 130) was determined using Methylation MLPA according to the manufacture's protocol (MRC-Holland, ME001). DNA fragments were analysed on a 3730 DNA Analyzer (AB, USA).

Loss of heterozygosity (LOH)

LOH at the BRCA1 locus was determined for the HBOC cases (n = 48) using 5 STR markers: D17S579, D17S588, D17S1322, D17S1323 and THRA1. Primers and the detailed PCR program are described in Supplementary data 2. DNA fragments were analysed on a 3730 DNA Analyzer (AB, USA).

Results

In total, we obtained the array-CGH profiles of 34 BRCA1related, 48 sporadic and 48 HBOC breast tumours. In this report we outline the chromosomal aberrations and their locations, the differences between the tumour groups, and the discriminating power of a class predictor based on our CGH results.

Chromosomal aberrations

We observed significant differences in the spectrum of aberrations with respect to the BRCA1-associated and sporadic breast tumours. The upper panels of Fig. 1 depict the frequency of gain (green) and loss (red) of the BAC clones for the BRCA1-associated and the sporadic breast tumours, respectively. The significances of the group differences are calculated by Fisher's exact test for each clone and are displayed in the bottom panel of Fig. 1.

We also found that BRCA1-related tumours have more copy number alterations (CNAs) compared with the sporadic breast tumours. Table 2 summarizes the most prominent aberrations of both tumour groups. These regions comprise several adjacent clones (at least 10 Mb in size), are aberrant in at least 30% of the tumour cases in one group, or show a significant difference between the tumour groups (average P-value for that region of \0.01, Fisher's exact test). Gain of chromosome 1q and 8q are found in almost half of both the tumour groups which have been reported previously to be common for breast cancer [START_REF] Bergamaschi | Distinct patterns of DNA copy number alteration are associated with different clinicopathological features and gene-expression subtypes of breast cancer[END_REF][START_REF] Chin | Using array-comparative genomic hybridization to define molecular portraits of primary breast cancers[END_REF][START_REF] Ghazani | Genomic alterations in sporadic synchronous primary breast cancer using array and metaphase comparative genomic hybridization[END_REF][START_REF] Vincent-Salomon | Identification of typical medullary breast carcinoma as a genomic sub-group of basal-like carcinomas, a heterogeneous new molecular entity[END_REF]. In total, the BRCA1-associated tumours showed 12 regional ([10 Mb) gains and 11 regional losses that were observed in [30% of the tumours. Using the same criteria, we observed gain in 2 chromosomal regions and 5 regional losses in the sporadic breast tumours.

BRCA1 and sporadic breast tumour class predictor

We used Shrunken Centroids (SC) [START_REF] Tibshirani | Diagnosis of multiple cancer types by shrunken centroids of gene expression[END_REF] to discriminate between germ-line-mutated BRCA1 and sporadic tumours. We randomly selected 18 BRCA1-associated and 32 sporadic tumours for the SC analysis; these tumours are referred to as the training set. Employing leave-one-out cross-validation (LOOCV) on the training set, D = 1.3 was the parameter setting resulting in the smallest number of misclassifications. The training set yielded 191 discriminatory features (Supplementary data 3) which were used in this study for further classification. From these 191 features most were abundant in regions of chromosome 3q22-27 (gain), 5q12-14 (loss), 6p23-22 (gain), 12p13 (gain), The remaining samples were used as external validation for the class predictor. In the validation set, 14/16 samples of the BRCA1-related group were predicted as BRCA1-like and were inside the 95% reference interval, while the remaining two cases were outside the interval but predicted as BRCA1-like, with a lower (\0.8) probability. One of the 16 sporadic breast cancer cases was classified as sporadiclike with a probability of 0.62 whereas all others (n = 15) classified within the 95% reference intervals. These results can be formulated as a total sensitivity of 88% and a specificity of 94% (PPP: 93%, NPP: 88%). Figure 2a depicts the distribution of the classification scores obtained on the training and validation sets; the classification scores for each individual sample are documented in Supplementary data 1.

ER, PR, HER2/neu and p53 specific aberrations

In our tumour groups, 91% of the BRCA1-mutated tumours are ER, PR and HER2/neu-negative (also known as 'triple negative'), while only 19% of the sporadic cases are triplenegative (Supplementary data 1). To investigate the relationship between ER, PR, HER2/neu or p53-status with chromosomal aberrations and thus the possible influence on our class predictor, the BRCA1-associated tumour group was compared to subsets of the sporadic tumours selected by their IHC phenotypes as described in the Methods section ''Aberration detection and quantification''. Chromosomal regions 3q22-3q26, 5p14, 6p22.3 and 14q22 were significantly more often aberrant in BRCA1-associated tumours, independent of the sporadic breast tumours' IHC phenotype (Fig. 3 upper four panels, indicated in blue). BAC clones within these BRCA1-specific regions were also represented in the classifier (Supplementary data 3, Fig. 3 bottom panel). However, the largest part of the loss in chromosome 5q, that was selected by the SC algorithm, appeared to be ER and/or HER2/neu status specific rather than BRCA1-specific as calculated by Fisher's exact test (Fig. 3, indicated in orange).

To evaluate the performance of the classifier features in discriminating BRCA1-related and sporadic tumours and the influence of the IHC profile, we performed hierarchical cluster analysis (complete linkage, Pearson correlation) to the array CGH results of the 34 BRCA1-associated and 48 sporadic breast tumours based on the 191 classifier features. The samples were separated into two large clusters, one containing most of the sporadic breast cancer cases (Fig. 4, left branch), and one containing all the BRCA1associated tumour samples (Fig. 4, right branch). Although, some of the sporadic cases resided together with the BRCA1-associated cases, this could not be explained by association with the ER-or HER2/neu-status (P = 0.24 and P = 0.25, respectively; FE test). Since the basal-like phenotype is very common for BRCA1-associated breast cancer, we investigated whether the sporadic tumours clustering together with the BRCA1-related tumours were also basal-like by performing IHC for CK5/6 (Supplementary data 1). Only, two of the five sporadic breast tumours expressing CK5/6 clustered within the BRCA1- Since ER status is highly correlated with the class label (BRAC1-related or sporadic) we compared the performance of our classifier with a classifier that predicts a tumour to be BRCA1-like when the tumour is ER negative. In the validation set, 15/16 samples of the BRCA1-related tumour group are predicted as BRCA1-like while seven of the 16 sporadic breast cancer cases were classified as sporadiclike. When employing the conventional approach in the SC classifier of assigning all samples as BRCA1-like when the probability of the BRCA1-like class exceeds 0.5 (i.e. not using rejection regions) the SC classifier has a total error rate of 0/32. The ER classifier, when also employing a single ER level cut-off, achieves an error rate of 8/32. Assuming these proportions are samples from a binomial distribution, the 95% confidence intervals for these proportions are [0; 0.1089] for the SC classifier and [0.1146; 0.4340] for the ER classifier, respectively. Employing the McNemar test for matched pairs, we can conclude that the SC classifier has a significantly better performance on the validation set than the ER classifier (P = 0.007).

Taken together, these results suggest that specific chromosomal aberrations are associated with the tumours' IHC-status (Fig. 3), nevertheless, BRCA1-related and sporadic breast tumours can be distinguished using the 191 features selected by the SC algorithm (Figs. 2,4).

Application of the classifier on non-BRCA1/2 families

Forty-eight patients from non-BRCA1/2 HBOC families were selected and analysed using aCGH. We found 2 samples (HBOC34 and HBOC41) to be BRCA1-like (P [ 0.8), 40 samples were predicted as sporadic-like, and 6 samples could not be assigned to a class with sufficient certainty as they were classified outside the 95% reference intervals. Figure 2b shows the distribution of the clinical samples in comparison with the BRCA1-related and sporadic tumours used to build and validate our class predictor.

To find evidence for BRCA1 involvement in the two BRCA1-like breast cancer cases, we first performed LOH analysis of the BRCA1 locus. Loss of the wild type BRCA1 tumour suppressor gene is considered to be required for BRCA1-related tumour development. We observed clear LOH of BRCA1 in samples HBOC34, HBOC41 and HBOC08, as well as allelic imbalance (where one allele is diminished but still present) in HBOC03, HBOC04, HBOC07, HBOC18, HBOC29, HBOC042 and HBOC45. Allelic imbalance can be caused by trisomy of the locos, tumour heterogeneity and limited tumour cell percentage. We next performed additional tests that were not included in the original routine diagnostic setting. As BRCA1 exon 11 was analysed for truncating mutations using the Protein Truncation Test (PTT) [START_REF] Loo | Array comparative genomic hybridization analysis of genomic alterations in breast cancer subtypes[END_REF], we now sequenced exon 11 in DNA isolated from peripheral blood lymphocytes from cases HBOC34 and HBOC41 but found no mutations. The next test was to investigate somatic inactivation of BRCA1 by methylation of the BRCA1 promoter. This was determined for all BRCA1-associated, sporadic and HBOC samples using MLPA-methylation (MRC-Holland, ME001). Case HBOC34, which was classified as BRCA1like, and sporadic tumour C048, which was present in the validation set of the classifier, were the only samples that showed methylation at the BRCA1 promoter (labelled ''M'' in Fig. 2). Case HBOC34 also presented with an ovarian carcinoma that, like the breast tumour, showed methylation at the BRCA1 promoter, interestingly, germ-line DNA of this patient did not show methylation at this site.

Discussion

In this study we show that BRCA1-associated breast tumours possess rearranged genomes with specific genomic aberrations that differ significantly from sporadic breast tumours. Based on array-CGH data, we identified the most significant differences between these two tumour groups and built a class predictor with 88% sensitivity and 94% specificity using the Nearest Shrunken Centroids method [START_REF] Tibshirani | Diagnosis of multiple cancer types by shrunken centroids of gene expression[END_REF]. Compared with the BRCA1-associated tumours, aberrations are less frequent in the sporadic breast tumours. Many of the identified regions specific for the BRCA1related tumours have been reported before [START_REF] Wessels | Molecular classification of breast carcinomas by comparative genomic hybridization: a specific somatic genetic profile for BRCA1 tumors[END_REF][START_REF] Jonsson | Distinct genomic profiles in hereditary breast tumors identified by array-based comparative genomic hybridization[END_REF][START_REF] Van Beers | Comparative genomic hybridization profiles in human BRCA1 and BRCA2 breast tumors highlight differential sets of genomic aberrations[END_REF][START_REF] Tirkkonen | Distinct somatic genetic changes associated with tumor progression in carriers of BRCA1 and BRCA2 germ-line mutations[END_REF][START_REF] Johannsdottir | Chromosome 5 imbalance mapping in breast tumors from BRCA1 and BRCA2 mutation carriers and sporadic breast tumors[END_REF][START_REF] Vincent-Salomon | X inactive-specific transcript RNA coating and genetic instability of the X chromosome in BRCA1 breast tumors[END_REF]. In this study we applied the BRCA1 classifier tool on diagnostic cases in order to investigate the performance of the familial breast cancer routine mutation screening. By doing so, we identified 2 out of 48 tumours as BRCA1-like. Since all tumours were formalin-fixed and paraffinembedded, investigation of mRNA could be problematic [START_REF] Penland | RNA expression analysis of formalinfixed paraffin-embedded tumors[END_REF], and further analyses were performed on genomic DNA from the tumour. Since potentially any (somatic) inactivation of BRCA1 could result in a BRCA1-like phenotype [START_REF] Snell | BRCA1 promoter methylation in peripheral blood DNA of mutation negative familial breast cancer patients with a BRCA1 tumour phenotype[END_REF][START_REF] Esteller | DNA methylation patterns in hereditary human cancers mimic sporadic tumorigenesis[END_REF], we investigated methylation of the BRCA1 promoter. One of the BRCA1-like HBOC cases indeed showed methylation and LOH of the BRCA1 gene, strongly indicating BRCA1 dysfunction in the tumour. Cancer formation due to BRCA1 mutation is generally accompanied by the loss of the wild-type allele, i.e. LOH, which was also found in the second BRCA1-like HBOC tumour. However, no novel or described mutations in the BRCA1 gene were Fig. 3 Influence of IHC-status. The four upper panels depict the significance in differences between the BRCA1-related cohort and the sporadic IHC tumour subgroups calculated by Fisher's exact test. Blue areas are BRCA1-specific aberrations while orange aberrations are specific for the corresponding IHC positive-phenotype, computed as described in the Methods. Grey are the non-significant areas (P [ 0.01). Lower panel shows the 191 shrunken centroids that are used for the classifier identified in this patient after sequencing. This particular patient's family history was atypical from that of an average BRCA1-involved family (breast and ovarian cancer), with incidence of brain cancer, colon cancer, and leukaemia. Additionally, the tumour was ER and PRpositive which is rare for BRCA1-related tumours [START_REF] Lakhani | The pathology of familial breast cancer: predictive value of immunohistochemical markers estrogen receptor, progesterone receptor, HER-2, and p53 in patients with mutations in BRCA1 and BRCA2[END_REF]. This unresolved BRCA1-like case may be analysed more intensely when new techniques and knowledge become available. Another way to predict the involvement of a BRCA1-mutation is to use prediction models based on family characteristics. We have calculated Evans' scores [START_REF] Evans | A new scoring system for the chances of identifying a BRCA1/2 mutation outperforms existing models including BRC-APRO[END_REF] for all possible cases to determine the probability to find a BRCA1 mutation. Both BRCA1-like tumours showed a small probability (20 and 11.8%) which could explain why no germ-line BRCA1 mutations were found in these families but a somatic inactivation of BRCA1 in case HBOC34, consistent with a low Evans' score.

In an earlier study we were able to classify BRCA1associated and control tumours using chromosomal CGH with an accuracy of 84% [START_REF] Wessels | Molecular classification of breast carcinomas by comparative genomic hybridization: a specific somatic genetic profile for BRCA1 tumors[END_REF]. A control group with a relatively large proportion of bilateral tumours was used to mimic the situation in high-risk breast cancer families. The disadvantage of this approach was that it resulted in many 'false' positives in the control group (specificity of 76%); however, many of those were proven to be actual BRCA1associated tumours later on (unpublished results). We now used automated array-CGH, which is a high throughput technique and therefore suitable to be performed in specialized diagnostic laboratories. Additionally, the use of a micro array in this study localises the significant genomic areas with increased chromosomal resolution and may help to develop a test (e.g. PCR based) that can be applied in any routine diagnostic laboratory. In contrast to our previous study that contained a relatively large number of control cases later proven to be true BRCA1-mutated tumours, we now use a random control group of sporadic tumours excluding family history for breast cancer and bilateral breast cancer. Although the differences between chromosomal and array-CGH and patient selections between our previous and current studies are substantial, both loss in 5q and gain in 3q were identified as important discriminatory aberrations, as confirmed by others [START_REF] Jonsson | Distinct genomic profiles in hereditary breast tumors identified by array-based comparative genomic hybridization[END_REF][START_REF] Tirkkonen | Distinct somatic genetic changes associated with tumor progression in carriers of BRCA1 and BRCA2 germ-line mutations[END_REF][START_REF] Johannsdottir | Chromosome 5 imbalance mapping in breast tumors from BRCA1 and BRCA2 mutation carriers and sporadic breast tumors[END_REF].

While exploring chromosomal aberrations it has to be kept in mind that steroid hormone receptor status is strongly correlated with the genomic profile [START_REF] Loo | Array comparative genomic hybridization analysis of genomic alterations in breast cancer subtypes[END_REF][START_REF] Fridly | Breast tumor copy number aberration phenotypes and genomic instability[END_REF]. It has been reported that BRCA1-associated tumours are in general ([90%) ER, PR, and HER2/neu-negative [START_REF] Lakhani | The pathology of familial breast cancer: predictive value of immunohistochemical markers estrogen receptor, progesterone receptor, HER-2, and p53 in patients with mutations in BRCA1 and BRCA2[END_REF]. Since we chose to randomly select breast carcinomas for our control group, these tumours do not all share the triplenegative phenotype of the BRCA1-related tumours. Training on triple-negative breast tumours only could restrict our class predictor to triple-negative carcinomas while our goal was to build a general classification method to classify all non-BRCA1/2 HBOC patients. Also, selection for triplenegative tumours only would increase the likelihood for false positives since a triple negative population is per definition enriched for unidentified BRCA1-associated cases. Instead, we investigated the extent to which specific aberrations could be associated with ER, PR, HER2/neu and p53-status to obtain an indication of their possible influence on our class predictor. Loss of chromosomal region 5q12-14, which is present in our classifier as discriminatory region (Fig. 3), was found to be specific for ER-negative tumours. Although this would suggest selection for the ER-phenotype, no false positives or false negatives were present in the validation sets. The result that all ER-negative (and triple-negative) sporadic tumours were correctly classified as sporadic-like further supports that classification is not based on ER-status alone, but is based on the combination of the BRCA1-specific regions.

Increasing evidence shows that the majority of BRCA1related carcinomas are basal-like tumours with respect to morphology and mRNA expression level [START_REF] Reis-Filho | Triple negative tumours: a critical review[END_REF]. This is also true for the BRCA1-specific aberrations as reported here and elsewhere [16-18, 32, 33] that show many analogies to the breast cancer basal-like subtype [28, this article]. Similarities between these hereditary and sporadic breast cancer groups could be explained by the effect of the same deficient DNA repair pathway (i.e. BRCA1). So far, our Fisher's exact test (Fig. 3), hierarchical clustering (Fig. 4), and classification results (Fig. 2) all indicate differences between the BRCA1-related and the triple-negative (basal-like) sporadic tumours that lie within the 191 discriminatory features.

In the future, it may be possible to include our profiling test in clinical genetic screening programs to select the individual in a high-risk family with the highest prior probability for finding the BRCA1 germ-line mutation, as an alternative or addition to screening of the youngest affected case as is currently done. Furthermore, it could help in decision making and treatment management, also when no BRCA1-like profile is found which would be an (extra) indication to rule out BRCA1 involvement. Moreover, aCGH classification of a tumour with a nucleotide variant of uncertain significance may give extra indications for the significance of the variant [START_REF] Tischkowitz | Pathogenicity of the BRCA1 missense variant M1775 K is determined by the disruption of the BRCT phosphopeptide-binding pocket: a multi-modal approach[END_REF].

Conclusion

Based on aCGH data, we were able to identify BRCA1specific aberrations that were different from those seen in sporadic breast tumours and employed this to build a classpredictor. Although steroid hormone receptor status is strongly associated with genomic instability, this class-predictor distinguished BRCA1-associated tumours from sporadic breast carcinomas with increased accuracy than current screening protocols. We conclude that current BRCA1 mutation screening seems to identify most hereditary BRCA1-associated breast tumours. However, while we could still find BRCA1-related breast tumours in a non-BRCA1/2 tumour group, our array-CGH approach may also be used as an additional tool to identify BRCA1-associated patients or families where the relation to BRCA1 is still unclear.

Fig. 1

 1 Fig. 1 Frequency plots. The top two panels display the frequency of gain (green) and loss (red) in 34 BRCA1-associated and 48 sporadic breast carcinomas based on the estimated copy numbers as described

Fig. 2

 2 Fig. 2 Classification results. Probability scores for the Training and Validation sets of the BRCA1-associated (red) and sporadic (yellow) tumour samples (a). Samples predicted as BRCA1-like are plotted right of the 0.5 probability border and samples predicted as sporadic are plotted left. Dotted lines indicate the 95% reference intervals for

Fig. 4

 4 Fig. 4 Hierarchical clustering. Complete hierarchical clustering of 34 BRCA1-related and 48 sporadic breast carcinomas based on the log 2ratios of the 191 BAC clones (shrunken centroids) from the classifier.

  BRCA1 Á Hereditary breast cancer Á Array CGH Á Classification Á Comparative genomic hybridisation

	Abbreviations
	CGH	Comparative genomic hybridisation
	FE	Fisher's exact
	FFPE	Formalin-fixed, paraffin-embedded
	HBOC Hereditary breast and ovarian cancer
	LOH	Loss of heterozygosity

Table 1

 1 Pathological characteristics of the analysed BRCA1 mutation carriers, sporadic, and HBOC breast carcinomas

		BRCA1	Sporadic	HBOC
	No. analysed	34	48	48
	ER-positive	5.9% (2/34)	54.3% (25/46) 68.9% (31/45)
	PR-positive	5.9% (2/34)	46.8% (22/47) 50.0% (23/46)
	Her2/neu-positive 2.9% (1/34)	40.0% (17/46) 9.8% (4/41)
	p53-positive	44.1% (15/34) 43.5% (20/46) 9.8% (4/41)

Table 2

 2 Chromosomal aberrations

	Chromosome	Aberration	BRCA1	Sporadic	P-value
			percentage	percentage
	1q	Gain	53	47	0.5186
	3q22-25	Gain	46	4	0.0006
	3q26-29	Gain	43	13	0.0327
	4p16-15	Loss	34	19	0.2466
	5p13-5p12	Loss	19	1	0.0031
	5q11-23	Loss	37	6	0.0017
	5q31-35	Loss	24	4	0.0111
	6p	Gain	37	7	0.0010
	6q21-q23	Gain	32	17	0.1493
	7p22-15	Loss	17	0	0.0080
	7q22-36	Gain	37	13	0.0283
	8p23	Loss	31	28	0.5767
	8q	Gain	55	40	0.3306
	9p21-13	Loss	35	28	0.7177
	10p15-14	Gain	57	27	0.0129
	10p14-12	Gain	32	11	0.0248
	11p14-13	Gain	30	13	0.1000
	11q22-25	Loss	13	35	0.1523
	12p13-12	Gain	38	12	0.0165
	12q12-14	Loss	20	0	0.0100
	13q	Loss	36	32	0.2302
	14q22-23	Loss	45	16	0.0568
	14q32	Loss	49	26	0.0942
	15q11-21	Loss	35	13	0.0791
	16q	Loss	10	36	0.0312
	17p	Loss	24	32	0.5579
	17q22-23	Gain	34	26	0.5522
	20q11-12	Loss	26	0	0.0007
	22q	Loss	34	33	0.4091
	Xp22	Loss	31	15	0.1638
	Xq	Loss	40	24	0.1715
	Locations and average frequencies of the most prominent aberrations
	in 34 BRCA1-associated and 48 sporadic breast tumours together with
	the average P-values (FE test) for the significance in aberration dif-
	ference between tumour groups

branch which showed not to be a significant correlation (P = 0.23; FE test).

Breast Cancer Res Treat (2009) 116:479-489

Acknowledgements We like to acknowledge the Central Microarray Facility (NKI) for the production of the microarrays, Aafke Ariaens and Roelof Pruntel for technical support, Sonja Springer for management of material and Juliane Hannemann for critically reading the manuscript. Grant support Dutch Cancer Society/Koningin Wilhelmina Fonds grant NKB_NKI2005-3436.