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Int. J. Cancer: 121, 1036–1046 (2007) ' 2007 Wiley-Liss, Inc.

A gene expression signature associated with metastatic cells in effusions of breast carcinoma patients Virginie N. Dupont1, David Gentien2, Marine Oberkampf1, Yann De Rycke3 and Nathalie Blin1* 1 UMR144 CNRS, Research Division, Institut Curie, Paris, France 2 Translational Department, Medical Division, Institut Curie, Paris, France 3 Biostatistics Department, Medical Division, Institut Curie, Paris, France

Malignant effusion in invasive breast carcinoma is associated with poor prognosis. To decipher molecular events leading to metastasis and to identify reliable markers for targeted therapies are of crucial need. Therefore, we have used cDNA microarrays to delineate molecular signatures associated with metastasis and relapse in breast carcinoma effusions. Taking advantage of an immunomagnetic method, we have purified to homogeneity EpCAM-positive cells from 34 malignant effusions. Immunopurified cells represented as much as 10% of the whole cell fraction and their epithelial and carcinoma features were confirmed by immunofluorescence labeling. Gene expression profiles of 19 immunopurified effusion samples, were analyzed using human pan-genomic microarrays, and compared with those of 4 corresponding primary tumors, 8 breast carcinoma effusion-derived cell lines, and 4 healthy mammary tissues. Principal component and multiple clustering analyses of microarray data, clearly identified distinctive molecular portraits corresponding to the 4 categories of specimens. Of uppermost interest, effusion samples were arranged in 2 subsets on the basis of their gene expression patterns. The first subset partly shares a gene expression signature with the different cell lines, and overexpresses CD24, CD44 and epithelial cytokeratins 8,18,19. The second subset overexpresses markers related to aggressive invasive carcinoma (uPA receptor, S100A4, vimentin, CXCR4). These findings demonstrate the importance of using pure cell fractions to accurately decipher in silico gene expression of clinical specimens. Further studies will lead to the identification of genes of oustanding importance to diagnose malignant effusion, predict survival and tailor appropriate therapies to the metastatic effusion disease in breast carcinoma patients. ' 2007 Wiley-Liss, Inc. Key words: breast carcinoma; effusion; immunomagnetic purification; microarray; gene expression profiling

Breast cancer is the most common malignancy and second leading cause of cancer death in women.1 Metastatic relapse is often a final and fatal step in the progression of solid malignancies. Therefore, to decipher key genes and mechanisms supporting metastatic behavior of human breast cells is of crucial need. Microarray technology enables to monitor RNA expression of thousands of genes at once, thus providing a powerful tool to address the complexity of the human cancer genome. Indeed, gene expression profiling has led to important insights into the classification of breast cancers, by revealing biologically and clinically relevant subtypes of tumors that were previously indistinguishable with conventional approaches. Recent developments in microarray technologies appear promising for translation of molecular biology discoveries to clinic. Comprehensive gene expression profiles have enable to predict long-term outcome of individual breast cancer patients2,3 and to define prognostic signatures to classify patients.4,5 Predictive markers responsible for clinical outcome in treated patients6,7 and pattern of resistances8–10 have been further explored by gene expression profiling. Metastatic tumor samples are difficult to obtain and rarely biopsied or stored in tissue banks. Therefore, little is known about the molecular basis of the metastatic disease. Breast cancer relapses in the form of serous malignant effusions for about one half of patients.11,12 Clinical samples may be available from palliative treatment of breast cancer patients, allowing the characterization of Publication of the International Union Against Cancer

metastatic cells in effusions. Recent studies have described genotypic13 and phenotypic14,15 alterations for some metastasis-associated and regulatory molecules in breast primary tumors and pleural effusions. Large discrepancies between cells at both sites were found, underlying the dynamic and adaptative molecular process along cancer progression. Therefore, therapies based on marker expression in primary tumors may be misleading and provide inaccurate data, making thus little sense in terms of patient benefit. Novel markers specific for metastatic effusion cells shall be discovered. First of all, they will help cytopathologists in differentiating breast from ovarian carcinoma and from malignant mesothelioma at diagnosis.16 In addition, powerful and independent prognostic or predictive markers will help the clinician in therapeutic managing and in monitoring response or resistance to treatment. Metastatic spread in serosal cavities affects the pleura or peritoneum, and less often the pericardia. In some cases, the presence of malignant cells in effusions is the first indication of breast cancer. However, the difficulty to morphologically distinguish tumoral cells from reactive mesothelial cells stimulated by injury or treatment is often a source of diagnostic errors (reviewed in Refs. 17,18). To this purpose, immunocytochemical techniques using an accurate panel of markers have been developed to identify carcinoma cells in serous effusions.19 The utility of the MOC31 antibody has been demonstrated in several studies.20–23 An accurate characterization of the phenotype and/or genotype of metastatic cells in breast cancer effusions requires to purify them. We and others have set up a sensitive efficient and reproducible immunopurification method, using MOC31-coated magnetic beads which specifically recognize the epithelial cell adhesion molecule (EpCAM) antigen.24–26 In this study, 50 breast cancer patient effusions were collected and clinical records analyzed as regards to patient history, as well as primary tumor and metastatic disease features. We took advantage of our immunopurification expertise to successfully isolate carcinoma cells in 34 out of 50 effusion samples. The homogeneity of immunopurified cells was verified by phenotypic characterization. Using a human pan-genomic microarray, expression profiles from 19 out of 34 effusion specimens were compared with those of 4 corresponding primary tumors, 8 breast carcinoma effusion-derived cell lines and 4 healthy mammary tissues. Principal component and unsupervised hierarchical clustering of microarray data clearly delineate the 4 different categories of biological specimens and evidence 2 different subsets within the patient effusions. Markers associated with each subset are discussed as regards to their epithelial or basal nature, and the 2 categories of patients are analyzed as regards to their clinical features. This article contains supplementary material available via the Internet at http://www.interscience.wiley.com/jpages/0020-7136/suppmat Grant sponsors: Institut Curie, Centre National de la Recherche Scientifique (Paris, France). *Correspondence to: Institut Curie, 26 rue d’Ulm, 75248 Paris cedex 05, France. Fax: 133-1-42-34-63-19. E-mail: [email protected] Received 27 December 2006; Accepted after revision 14 March 2007 DOI 10.1002/ijc.22775 Published online 20 April 2007 in Wiley InterScience (www.interscience. wiley.com).

GENE EXPRESSION PROFILING OF EFFUSION CELLS

Material and methods Patients and samples Fifty effusion specimens were obtained from 27 breast carcinoma patients treated at the Medical Division of the Institut Curie. All samples were collected from patients with their informed consent. All patients were treated anonymously during the study. Clinical, histological and biological parameters providing informations on the primary tumor and metastatic disease were obtained from patient medical files subsequently to experimental assays. All patients had previous history of invasive ductal or lobular breast carcinoma. Samples were collected on the occasion of palliative treatment at disease reccurence. For 10 patients, 2–4 successive samples were obtained over a 3-month period, while one patient had 9 independent punctures. Effusion sites were pleura for 15 and peritoneum for 12 patients. Patient age ranged from 38 to 83 years, with a mean age of 59 6 11 years (median, 59). To preserve their integrity, effusion samples were collected in citrate buffer (22 g/l trisodium citrate, 8 g/l citric acid, and 24.5 g/l dextrose, pH 7.2) and processed immediately. For each specimen, a cytological diagnosis was performed from a 10-ml aliquot, as part of a routine. After a 1200g centrifugation for 5 min at 4°C, pelleted cells were smeared on slides and their morphology was analyzed by cytopathologists following a May-Gr€unwald Giemsa and/or Papanicolaou staining. Primary tumor specimens were obtained from the Institut Curie tumor bank, for 4 patients whose effusions were collected. Following primary tumor resection, tissue aliquots were snap-frozen and stored at 280°C. From pathologic examination of haematoxylinand eosin-stained adjacent sections, tissue aliquots were selected and their tumor cell content was estimated as ranging from 50 to 100%. Fresh mammary gland fragments (about 10–20 mg) were collected from 4 healthy patients, undergoing reduction mammaplasty. Specimens were macrodissected, chopped in small fragments, and stored in CO2-independent medium (Invitrogen) for no more than 2 hr. In the following, clinical specimens are mentioned as, C30 for patient C and effusion sample number 30, C-TP for patient C primary tumor and TS1 for healthy mammary tissue Number 1. Clinical specimen processing Following a 1200g centrifugation for 5 min at 4°C, effusion cells were collected in 1% bovine serum albumin (BSA) in phosphate-buffered saline (PBS) (cell buffer A). Nucleated alive cells were counted in a 3% acetic acid trypan blue solution. For immunopurification and immunofluorescence experiments, cells may be stored in cell buffer A overnight at 4°C. For RNA extraction, cells were immediately immunopurified and may be kept in Trizol reagent (Invitrogen) for days at 280°C. Remaining cells were stored in dimethyl-sulfoxide/fetal calf serum (DMSO/FCS) (vol/ vol: 10/90) at 280°C. Within 54% of effusion samples, large cell aggregates were found. They were either processed for dissociation by using 0.35 U/ml collagenase A (Roche Diagnostic) in CO2-independent medium for 90 min at 37°C under stirring, or stored in DMSO/FCS at 280°C. The mean volume of samples was of 2.1 6 1.4 l (median, 1.8 l), and usually lower in the pleural than in the peritoneal cavities (means of 1.0 and 3.0 l, respectively). Total effusion cell numbers ranged from 1 3 106 to 3,300 3 106 (mean, 385 3 106, and median, 150 3 106), with respective means of 505 3 106 and 260 3 106 for pleural and peritoneal samples. Thus, it appears that the cell density is 5.8-fold higher in pleura than in peritoneum. Primary tumor were snap-frozen in liquid nitrogen, submerged in Trizol reagent and immediately processed for RNA extraction. Healthy mammary gland fragments kept in CO2-independent medium were collagenase-dissociated as previously described and further processed in Trizol reagent.

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Immunopurification of effusion samples Immunomagnetic purification was conducted as previously described.26 Briefly, 20 3 106 to 50 3 106 total cells were incubated with MOC31 (Dako) antibody-coated M450 Dynabeads (Dynal), in 2 ml cell buffer A, for 30 min at 4°C under rotation. From calibration experiments, we evaluated to 4 3 105 the amount of Dynabeads to be used per 106 total cells in effusion. After 3 washes in ice-cold buffer, the resulting magnetic cell pellet was collected. Immunopurified cells, rosetted with beads, were resuspended in a 50-ll cell buffer A volume. They were either counted using light-microscopy, or immediately processed for immunofluorescence labeling or RNA extraction. Forty samples out of 50 independent effusion specimens were processed for immunopurification. Four of them had no EpCAMpositive cells and 2 of them had compact clusters of non-dissociated cells. As a result, 34 effusion specimens were further analyzed. Immunofluorescence labeling Following immunomagnetic purification, cells were fixed in 1 ml 4% paraformaldehyde 0.2% sucrose PBS for 5 min at 4°C under rotation. After washing in 0.1% BSA PBS (cell buffer B), cells were incubated, for 90 min at 4°C under rotation, with the primary antibody diluted according to the manufacturer instructions or at a concentration of 2 lg/ml. Antibodies used targeted the CK8, CK18 and CK19 (clone A45-B/B3, ChromaVision), CD24 (clone ML5, BD PharMingen), CD44 (clone G44-26, BD PharMingen) and ErbB2 (clone TAB250, Zymed). Labeling was elicited with Alexa-Fluor 488 (green) conjugated anti-mouse antibody (Molecular Probes), diluted 1/1000 in cell buffer B, for 20 min at 4°C under rotation. After final washes, cells were observed in fluorescence mounting medium (Dako). Images were collected using fluorescence microscopy coupled with digital camera (Leica). For each sample, a negative control using the sole secondary antibody was carried out. Cell lines MCF7, T47D, SKBR3, MDA-MB-134-VI (MDA134), MDAMB-231 (MDA231), MDA-MB453 (MDA453), MDA-MB468 (MDA468) and CAMA1 cell lines, originate from the American Type Culture Collection (ATCC, http://www.atcc.org). Except for MDA453 that come out from a pericardial effusion, cell lines all derive from pleural effusions of breast carcinoma patients. Breast cancer cell lines have been used as in vitro model systems to study most aspects of cancer biology. Therefore, they have been extensively characterized (reviewed in Ref. 27). This panel of cell lines was chosen for being representative of the phenotypic heterogeneity in breast cancer. MCF7, T47D, MDA134 and CAMA1 are estrogen receptor-positive cells. SKBR3 and MDA453 overexpress the ErbB2 oncoprotein, while MDA468 and MDA134 overexpress the epidermal growth factor (EGF) and fibroblast growth factor (FGF) receptors, respectively. MDA231, which is estrogen receptor- and ErbB2-negative, is a highly invasive cell line. All cell lines were grown with appropriate media according to ATCC, at 37°C in 5% CO2 and saturated humidity atmosphere. They were used in experiments after being cultured for at least 2 weeks, and collected when reaching preconfluence. Two independently grown MCF7 cell batches were assigned as biological replicates (b1 and b2) in microarray experiments. RNA extraction and quality control The RNA purification method used was previously assessed and chosen among two others for its efficiency in extracting high-quality total RNA from 0.1 3 106 to 1 3 106 cells from clinical samples.28 Under rigorous RNase-free conditions, cell lines (1 3 106 to 4 3 106 cells), immunopurified effusion cells, liquid nitrogen ground tumor fragments and freshly dissociated mammary gland

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tissues were submerged into 1.5-ml Trizol reagent. To increase the extraction yield, samples were passed through a 20G gauge syringe and Pellet Paint (Novagen) was added as a visible dyelabeled carrier during the precipitation step. Extracted RNA were resuspended in RNase-free water (Ambion), denaturated for 10 min at 65°C, aliquoted and stored at 280°C. Total RNA concentration and purity were determined using a ND-1000 spectrophotometer (Nanodrop Technologies) and RNA integrity was monitored by the means of the RNA 6000 Nano LabChip kit combined with the 2100 Bioanalyzer (Agilent Technologies). Total RNA was extracted from 25 immunopurified effusion samples selected for their high EpCAM-positive over total cell content (mean, 16%). High quality RNA was successfully extracted from 19 out of 25 effusion samples that originate from 14 independent patients. A mean of 12 6 6 lg total RNA per million immunopurified cells was extracted. This yield has to be compared to the mean of 14 6 4 lg obtained for the 8 effusion-derived cell lines. The 4 primary tumor and 4 healthy mammary samples provided 3–73 lg total RNA that is 0.3–5 lg RNA per mg tissue. Although difficult to handle, clinical specimen RNAs were of high quality (28S/18S and 260/280 nm mean ratios, RNA size) (Supplementary data S1). RNA processing and microarray hybridization All steps were performed according to the Affymetrix procedure, detailed in the Genechip1 Expression Analysis Technical Manual [http://www.affymetrix.com/support/technical/manual/ expression_manual.affx]. Experimental technical replicates (r1 and r2) of the effusion specimen G26 were included from the first step in order to evaluate the reproducibility of the whole RNA processing procedure. These replicates r1 and r2 led to similar results from step to step and their signal intensities on microarray were highly correlated (r2, 0.97). The 2 biological replicates b1 and b2 led also to high reproducibility in terms of hybridization signals (r2, 0.96) (Supplementary data S1). The total RNA extracts were reverse transcribed according to the Affymetrix-modified SuperScriptTM II procedure (Invitrogen). After synthesis of double-stranded cDNA and purification using the Genechip1 Sample Cleanup Module, an in vitro transcription reaction was conducted overnight, using the Genechip1 IVT Labeling Kit. The quantity and the quality (size >1 kb) of cRNA produced were monitored using the Nanodrop and Agilent technologies (Supplementary data S1). Fragmentation was performed on 20 lg of cRNA from each sample and microarray hybridization was performed with 10 lg fragmented cRNA, using the Genechip1 Fluidics Station 400. Microarray fluorescence signals were measured with the GeneChip1 Scanner 3000 (1.56 lm resolution). Hybridization replicates, introduced on independent microarrays, provided similar signals. The Genechip1 Human Genome U133 Plus 2.0 (HG-U133 Plus 2.0) array is a high-density (11 lm), 25-mer oligonucleotide array, synthezised using light-directed combinatorial chemistry. It includes 54,675 probe sets that are used to analyze the expression level of more than 47,000 transcripts and variants, including 38,500 well-characterized human genes. Its estimated sensitivity is of 1 copy per 200,000 transcripts. Bioinformatic data analysis The identification and quantification of the hybridization signals were performed using the GeneChip1 Operating Software v1.1 (Affymetrix). The generated *.CEL files contain a captured image of the scanned array and calculations of the raw intensities (80% of the normalized signal distribution) for all probe sets. A probe set consists of a series of probe pairs and represents an expressed transcript. After removal of microarray internal controls, 54,613 probe sets out of 54,675 were analyzed. Robust Multi-array Analysis (RMA) was developed to analyze high-density oligonucleotide array data from the Affymetrix

GeneChip1 system.29 Recently, gcRMA has been shown to further improve the accuracy of RMA for the analysis of Affymetrix HG-U133 Plus 2.0 array data. Using disease profiling data, gcRMA was proven to outperform30 and was therefore chosen in this study. Expression map from unsupervised and supervised analyses were displayed using the Treeview software v1.031 [http://prdownloads. sourceforge.net/jtreeview/TreeView-1.0.12-osx.zip?download]. The Amadea Studio (Isoft) v4.5.2 and the Cluster31 v2.11 softwares were used for bioinformatics analyses. Biostatistic data analysis Principal Component Analysis (PCA) was used to visualize how the different clinical sample sets were close to each other regarding their gene expression.32 To this purpose, the whole 54,613 probe sets were included in the analysis. A statistical method allowing to identify significant genes has been described as Significance Analysis of Microarrays (SAM).33 The percentage of potentially significant genes identified by chance is the false discovery rate (FDR). In this study, highly stringent conditions were applied (FDR of 1%). A hierarchical clustering was performed on the selection of probe sets obtained from the SAM method. Unsupervised analysis was performed to investigate the relationships between patterns of genes and categories of samples (primary tumors, effusions, healthy tissues and cell lines). Classifiers of 4,811, 1,849 and 1,595 probe sets, respectively, were able to discriminate between the 4 biological categories, the 2 effusion subsets and the effusions towards their corresponding primary tumors. Supervised analysis was performed to identify genes able to discriminate between well-defined biological and clinical features of patients. In this case, a mild stringency was applied (FDR of 5%). The R software v1.9.1 was used [http://www.r-project.org/] for biostatistic analyses. Gene ontology data The gene list includes Affymetrix probe set_ID, as well as Unigene and Genbank database accession numbers to identify transcripts [http://www.ncbi.nlm.nih.gov/]. Chromosomal location of probe sets are also given according to LocusLink_ID. All genes were annotated according to known functions using the Gene Ontology (GO) Consortium categories [http://www.geneontology. org/], that are biological process, cellular component and molecular function.34 Onto-Express is one of the annotation databases integrated in Onto-Tools,35,36 one can find at [http://vortex.cs.wayne.edu/ Projects.html]. Onto-Express was used to determine whether clusters of genes with similar expression profiles were enriched in specific GO functional categories. Onto-Express calculated the expected number of occurrences of each functional category in each cluster. The probability that each functional category was over-represented in a cluster was derived using a binomial model and the p-values were corrected for multiplicity using the Bonferroni method. Only significant biological processes (corrected p-value < 0.05) were taken into account. Functional profiles for each of the GO categories were built as defined by Proteome [http://www.incyte.com/sequence/proteome] and an additional profile was built for chromosomal location. Results and discussion Clinical features of patients with malignant effusion Patient history and histoclinical parameters refering to primary tumors, metastases and malignant effusions were collected for a cohort of 27 patients (Table I). The patient median age at diagnosis of primary tumor was of 50 years. Primary tumor histological types distributed in 73%

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GENE EXPRESSION PROFILING OF EFFUSION CELLS TABLE I – PATIENT, PRIMARY TUMOR, METASTASIS AND MALIGNANT EFFUSION CHARACTERISTICS Patient (ratios)

Patients Age at tumor diagnosis (years) Age at effusion diagnosis (years) Primary tumors Histological type Ductal Lobular Ductal and lobular Nd1 SBR2 grade I II III Nd AJCC3 stage I II III IV Nd ER4 and PR status 15 0 Nd ErbB2 status 1 0 Nd Treatment Hormonotherapy Chemotherapy Hormono- and Chemotherapy Radiotherapy 1/23 None Nd Metastases Delay between primary tumor and 1rst metastasis (months) No delay Delay inferior to 5 years Delay superior to 5 years Nd Number of sites 1 2 3

(%)

Age or delay (median)

26/27

50

27/27

56

16/22 5/22 1/22 5/27

73 23 4

3/19 11/19 5/19 8/27

16 58 26

2/19 9/19 3/19 5/19 8/27

11 47 16 26

18/23 5/23 4/27

78 22

1/11 10/11 16/27

9 91

3/23 15/2 1/23 4.5 3/23 4/27

13 65 4.5 13 386

4/26 12/26 10/26 1/27

15.5 46 38.5

2/27 8/27 9/27

7.5 30 33

Patient (ratios)

4 6/27 5 2/27 Location Pleura 14/27 Liver 12/27 Bone 12/27 Peritoneum 11/27 Lung 7/27 Lymph nodes 6/27 Brain 3/27 Ovary 3/27 Thorax 3/27 Eye 2/27 Breast 1/27 Bone marrow 1/27 Pancreas 1/27 Skin 1/27 Malignant effusions Delay between 1rst metastasis and 1rst effusion (months) No delay 9/26 Delay inferior to 1 year 7/26 Delay superior to 1 year 10/26 Nd 1/27 Punctures per patient (within 4 months) 1 16/27 2 7/27 3 1/27 4 2/27 9 1/27 Location Pleura 15/27 21/507 Peritoneum 12/27 29/507 Treatment Hormonotherapy 5/27 Chemotherapy 19/27 Hormono- and 1/27 Chemotherapy None 2/27 Delay between 1rst effusion and death (months) After one year follow up Survival 5/24 Death 19/24 Nd 3/27

(%)

Age or delay (median)

22 7.5 52 44 44 41 26 22 11 11 11 7.5 4 4 4 4 5.56 35 27 38 59 26 4 7 4 56 427 44 587 19 70 4 7

177

21 79

1 Nd, not determined.–2SBR, Scarff Bloom Richardson.–3AJCC, American Joint Committee on Cancer.–4ER, estrogen receptor; PR, progesterone receptor.–51, positive; 0, negative.–6Median calculated according to Kaplan-Meier plots.–7Ratios and percentages relative to samples.

ductal and 23% lobular carcinomas. The SBR grading and the staging, according to the revised AJCC system for breast carcinoma,37 were well-distributed as shown in Table I. Among patients whose informations were available, 26% had a grade III tumor as well as clinically detectable metastases at diagnosis. Biochemical parameters, such as the presence of estrogen and progesterone receptors or of the ErbB2 oncoprotein, were positive for 78 and 9% of the patients, respectively. This patient group is fully representative of the overall breast carcinoma population and usual histoclinical parameters determined at tumor diagnosis are not relevant enough to distinguish patients who will subsequently develop effusions. Although 87% of patients analyzed underwent treatment, all of them developed a macrometastatic disease in one or multiple sites, after a median period of 3 years and 2 months (Table I). Pleura, liver, bone and peritoneum were the privileged sites of relapse for these patients. For 9 of them (35%), the metastatic disease was

first diagnosed with the appearance of a malignant effusion. For the others, the median delay for the occurrence of a malignant effusion after diagnosis of a solid metastasis was of 5.5 months. Among the patient cohort, 15 had pleural and 12 had peritoneal effusions and the data set show that malignant effusions occurring in the peritoneal cavity are more often proned to recurrence. The median delay between malignant effusion diagnosis and death was of 17 months for our patient cohort, as compared to 12 months for a similar case cohort reported 20 years ago.38 From this observation, it appears that diagnosis and therapeutic improvements in breast cancer disease have step back metastatic relapse delay and patient fatal issue. For the patients who developed effusions in association with other metastatic sites as a signature for advanced and systemic disease, only 21% of them survived to their disease after 1-year follow up. Thus, clinical effusion in breast cancer is the signature of a severe metastatic relapse mainly associated with

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a fatal issue. These data demonstrate the crucial need to find new therapeutic approaches and therefore to accurately decipher the biology of malignant effusion cells in breast carcinoma patients. Purification of carcinoma cells from malignant effusions To this purpose, we have taken advantage of an immunopurification method, previously developed for the micrometastatic disease25 and based on the recognition of EpCAM-expressing cells. We chose the EpCAM-specific MOC-31 antibody for its ability to distinguish between mesothelioma and metastatic carcinoma cells in body fluids. A recent study demonstrated membranous reactivity for MOC-31 in 100% of 86 metastatic adenocarcinoma specimens, whereas 16 of 17 mesothelioma samples were found negative.39 MOC-31 was also reported as part of a three-antibody immunohistochemical panel, which provided over 96% sensitivity and specificity for distinguishing epithelioid mesothelioma from adenocarcinoma.40 Fifty independent effusion specimens were collected from 27 different breast carcinoma patients (Supplementary data S2). Forty samples were processed for MOC-31 specific immunomagnetic cell sorting and had their EpCAM-positive cell content enumerated. In agreement with breast malignant effusion diagnosis,41 90% of patients had EpCAM-positive cells detected using this immunopurification. In 4 out of 40 (10%) patients, no carcinoma cell was detected, suggesting that their serous fluids had an etiology different from breast carcinoma when diagnosed. Compact clusters of EpCAM-positive cells were observed for 2 samples (H19 and A34). The remaining 34 samples had 10.3% of EpCAMpositive cells as a mean (median, 9.6 million), while one of them (F45) contained 100% of EpCAM-positive cells. The peritoneum metastatic site appears as a privileged compartment for carcinoma cells, with 16.8% EpCAM-positive cells against 4.2% in pleura. Detection of EpCAM-positive cells using immunopurification and cytomorphological observation of tumor cells have been compared (Supplementary data S2). Results obtained with both methods were qualitatively consistent for about 73% of the samples. Quantitative discrepancies may be explained by the presence of aggregates in 54% of the samples. These aggregates, which mainly consisted of carcinoma cells, were usually removed before to smear the sample on cytological slides, while they were dissociated and specifically enriched during the cell sorting process. Therefore, 32% of the EpCAM-positive effusions were not detected as tumor cell-containing samples using cytology. These data point out the usefulness of the immunomagnetic enrichment method to accurately detect malignant effusions in breast carcinoma patients. To further characterize the phenotype of the immunopurified cell fraction, we have used a panel of antibodies specific for carcinoma cells (Fig. 1). Ninety two percent (12/13) of the tested samples were labeled with a pan-cytokeratin antibody (CK8, CK18 and CK19) often used to characterize micrometastatic cells in breast carcinoma patients. An homogenous immunofluorescence labeling was found for 100% (5/5 and 6/6, respectively) of the assayed effusions evaluated for CD24 and CD44. CD24 is an adhesive cell surface glycoprotein expressed in about 80% of invasive breast carcinoma42 and considered to play an important role in tumor progression and metastasis. A recent study demonstrated that CD24 immunolabeling is significantly higher in breast carcinoma than in non-tumorous breast tissues and seem to predict malignant transformation.43 CD44 immunoreactivity was found associated with 21 of 92 (22.82%) serous effusions from metastatic adenocarcinoma.44 In addition, CD44 expression seem to favor the tumorigenic potential45 and the distant metastasis formation in breast cancer tissues.46 The ErbB2 oncoprotein, commonly overexpressed in breast carcinoma, is labeled at high level in 61% (8/ 13) of the tested effusion samples. Confirming our data, a higher incidence of ErbB2 overexpression in breast malignant effusions as compared to primary tumor was reported in 2 independent immunocytochemical studies, with respectively 51% and 56% of

FIGURE 1 – Immunofluorescence staining of immunopurified cells from effusion samples. Total effusion cells from breast carcinoma patients (N41 and A13), were immunopurified using magnetic Dynabeads coated with the MOC31 anti-EpCAM antibody. Resulting cells were labeled using none (a, b), CD24 (c, d), CD44 (e, f) and ErbB2 (g, h), -directed antibodies, in combination with the AlexaFluor488coupled (green) anti-mouse antibody. Magnetic beads appear fluorescent due to reactivity of the MOC31 monoclonal antibody with the anti-mouse secondary antibody. The TIFF immages of the visible (a, c, e, g) and fluorescence (b, d, f, h) records are presented. Microscopy magnification 3400.

positive specimens.47,48 Moreover, ErbB2-specific ELISA staining of effusion cells showed that ErbB2 is a potential tumor marker for the diagnosis of pleural effusion in lung adenocarcinoma.49 Upregulation of ErbB2 in effusion cells from breast carcinoma patients has been also reported.15 Our results show that EpCAM-positive effusion cells immunopurified from whole human specimens express known carcinoma markers and represent a valuable source of human metastatic cells for further characterization at the molecular level. This assay is appropriate for subsequent gene expression profiling, since comparative microarray experiments performed on MCF7 cells, selected or not with EpCAM immuno-beads, did not evidence alterations in gene expression patterns.50 As a whole, 19 effusion samples originating from 14 independent breast carcinoma patients, fulfilled robust quantitative and qualitative requirements to perform subsequent microarray analyses. Gene expression patterns for effusions, primary tumors, healthy tissues and cell lines Thirty independent samples were selected among pathological specimens (14 breast carcinoma patient effusions or EP and 4 of

GENE EXPRESSION PROFILING OF EFFUSION CELLS

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FIGURE 2 – Principal component analysis using 54,613 probe sets in microarray data. Effusion (diamond-shaped), mammary primary tumor (triangle), healthy mammary tissue (square) samples, and effusion-derived cell lines (filled circle), are plotted in 3D, along to the first (X), second (Y) and third (Z) principal components. The EP1 group (A13 B39 C30 F45 N41 W35 Z50), and the EP2a (D27 E16 U25), EP2b (G26 H20 J44 R37) sub-groups, are circled in orange, blue and purple, respectively.

their corresponding primary tumor or TP) and reference tissues (4 healthy patient mammary gland tissues or TS and 8 breast carcinoma effusion-derived cell lines). Effusion cells were immunopurified on the basis of EpCAM-expression, while the whole tumor and healthy tissues were used. Reverse-transcribed extracted RNA (for quantity and quality control, see the Material and methods section) was hybridized on human pan-genomic HGU133 Plus 2.0 microarrays and data were analyzed using sophisticated biostatistic tools. As a first line of evidence, the 2 technical and biological replicates, as well as 5 independent effusion samples collected from a single patient (Patient A), clustered together with a high degree of relatedness, showing that they are more similar to one another than to other samples (data not shown). This warrants the reproducibility of the whole experimental procedure, as well as of the chosen algorithms and biostatistic methods. Furthermore, correlations between gene expression data and protein expression levels, as measured by the means of immunofluorescence, were examined. Within several independent specimens, correlations for the CK8, CK18 and CK19 were obvious in 40% of samples, for CD24 in 60%, for CD44 in 33% and for ErbB2 in 86%. The discrepancy between gene and protein expression data resulted mainly (90%) from the presence of transcripts and the absence of protein signals. The unsupervised PCA of the whole 54,613 probe sets showed a global expression pattern clearly dividing samples into 3 distinct clinical categories (EP, TP, TS) in addition to the cell line group (Fig. 2). A three-dimensional scan of the PCA plot elicited that the first component (X) splits the effusions in 2 groups, EP1 (A13 B39 C30 F45 N41 W35 Z50) and EP2 (D27 E16 G26 H20 J44 R37 U25). EP1 is closer to the cell line group, while EP2 is further subdivided along the first and second components, in 2 subgroups, EP2a (D27 E16 U25) and EP2b (G26 H20 J44 R37). It is noteworthy that the third component separates the 3 types of biological

FIGURE 3 – Multi-class significance analysis of microarrays performed on 30 independent biological samples. The matrix exhibits the clustering of 4,811 probe sets in rows and the relatedness of specimens in columns. The expression level for each gene in a single sample is depicted relative to its median level across all samples. A red and green color scale details expression levels apart the median. Above the matrix, the horizontal bar demarcates the 4 sets of samples: cell lines, effusion (EP1 and EP2), mammary primary tumor (TP) and healthy mammary gland (TS) specimens. The dendrogram elicits overall similarities in gene expression profiles among specimens. Clusters A to G delineate specific groups of genes, differentiating the 4 sets of samples (see the Results and discussion section for details).

samples, which are healthy tissues, tumor specimens and effusion cells (clinical samples and cell lines). To understand the molecular basis for this grouping, gene expression profiling were examined using multi-class SAM for the most variable genes (24,472 probe sets, 45% of total). SAM performed on gene expression profiles of the 30 independent samples led to a classifier (FDR, 1&) based on 4,811 different probe sets (Fig. 3). Gene clustering revealed 4 independent groups of expressed genes (cell lines, effusions, primary tumors and healthy tissues), confirming the classification elicited by PCA. Based on expression pattern and dendrogram branching, the same 2 groups of clinical effusions, EP1 and EP2, were evidenced. EP1 has a high degree of relatedness with the effusion-derived cell line group, whereas EP2 is more related to the physiopathological sam-

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FIGURE 4 – Multi-class significance analysis of microarrays performed on clinical effusion specimens. The matrix exhibits the clustering of 1,849 probe sets in rows and the relatedness of effusion samples in columns. Above the matrix, the horizontal colored bar demarcates the 3 subgroups of samples: EP1 (orange), EP2a (blue), EP2b (purple) specimens. The dendrogram elicits overall similarities in gene expression profiles, and defines 2 main branches, EP1 and EP2, in the effusion group. Dendrograms of the gene Cluster 1 and Cluster 2 are represented in the left margin. Some genes, specifically upregulated in the EP2 group, are highlighted and mentioned in the right margin.

ples (TS and TP). The matrix analysis depicted several clusters of genes, two of them being specific for effusion cell lines (cluster A and cluster B), one being common to EP2, TP and TS (cluster C), one being more specific for EP2 (cluster D), one being expressed only in TP (cluster E) and two which are common to TS and TP (cluster F and cluster G). Molecular signatures in the malignant effusion disease To decipher the molecular signature underlying the EP1 and EP2 group classification, we performed SAM on the most variable genes (20,919 probe sets, 38% of total) of clinical effusion samples (Fig. 4). Signal intensities of the resulting classifier (1,849 probe sets; FDR, 1%) confirmed the existence of 2 subgroups, EP2a and EP2b, sample D27 appearing as an outlier. Closer inspection of the data matrix evidenced 2 main gene clusters (cluster 1 and cluster 2), that differentiate EP1 from EP2. Clusters 1 (r2, 0.77) and cluster 2 (r2, 0.77) encompass 519 (28% of the classifier) and 924 probe sets (50% of the classifier), respectively.

Cluster 1 is upregulated in EP1 versus EP2, wheras Cluster 2 is upregulated in EP2 versus EP1. To gain clues to the function of the prevailing genes expressed in the EP1 and EP2 effusion groups, the GO terms for these genes were analyzed for non-random functional distribution using OntoExpress.35,36 The major and statistically significant biological processes, molecular functions and chromosomal locations for each group were listed in Supplementary data S3. Out of the 519 probe sets from Cluster 1, 8% of them were classified in the biological process termed regulation of transcription, while 3.3% were present in the cell adhesion and in the small GTPase mediated signal transduction categories. The main molecular functions implicated were protein binding (7.4%) and receptor activity (5.8%). Regarding the chromosomal location of the effusion classifier probe sets, regions associated with regulation of transcription mainly involved Chromosome 1, which is the site for numbers of genomic alterations in breast carcinoma, as well as Chromosomes 6 and 12. Genes upregulated in Cluster 1 are mostly involved in the negative regulation of transcription and of proliferation, whereas those upregulated in Cluster 2 participate in the positive regulation of transcription, leading to growth factor activity and cell growth or/and maintenance. Moreover, Cluster 2 genes are mainly associated with immune response, signal transduction and cell adhesion biological processes. A thorough analysis of the differentially expressed genes from Cluster 1 and Cluster 2 was further conducted using Onto-Express. Genes upregulated in EP1 versus EP2 (cluster 1). Among those genes, some encode proteins of epithelial origin, involved in cytoskeleton (CK8, CK18, CK19, epiplakin 1, and microtubuleassociated Protein 7), and cell-cell adhesion (occludin, plakophillin 4); other genes are implicated in cell survival and cell proliferation signaling, such as the transcription regulator Smad 1 (bone morphogenetic protein signaling pathway) and PRKC (repressor of WT1, upregulated during apoptosis), or such as the dual specificity phosphatase 16 and protein phosphatase 1H, which negatively regulate the MAP kinase pathway, or such as the RAS-like estrogen-regulated growth inhibitor; some genes may participate in tumor progression by encoding proteins, like the cell cycle tumor protein D52 and the breast cancer membrane Proteins 11 and 101. In agreement with previous data,15 the epithelial-specific Ets1 transcription factor was upregulated, as was the gene encoding the oncostatin M receptor. This is in agreement with the observation that oncostatin M, although able to inhibit the proliferation of breast cancer cells in vitro, is involved in the development of a metastatic phenotype in vivo.51 Genes upregulated in EP2 versus EP1 (cluster 2). Most of these genes are also found associated with the immune response, like genes implicated in cytokine biosynthesis or lymphocyte and neutrophil chemotaxis. Within the cell adhesion ontological category, numbers of genes encoding proteins involved in heterophilic cell adhesion (C-type lectin super family, galectin 9, versican), as well as in homophilic and heterophilic leukocyte cell adhesion were overexpressed. Several probe sets represent transcripts of the urokinase plasminogen activator (uPA) receptor, whose overexpression has been associated with poor prognosis in breast carcinoma patients.52 The metastasis-associated protein S100A4 was revealed by several probe sets, as depicted in Figure 4. Genes encoding for MMP14 and integrin 9 were overexpressed. Integrin 9 is a receptor for tenascin C, whose elevated expression is predictive of increased risk and rate of death in breast carcinoma patients.53 These 2 proteins have been reported as downregulated in the nm23-mediated metastasis inhibition mechanism.54 Thus and expectedly, we found the nm23 metastasis suppressor gene downregulated in the effusion group. In addition, expression of versican, associated with relapse in breast carcinoma patients,53 was also increased. While the cytoskeleton intermediate filament keratins were absent in EP2, the gene encoding the mesenchymal vimentin protein was selectively expressed to high level. Vimentin expression is a rather rare finding in invasive breast carcinoma

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and usually arise with high tumor invasiveness and chemoresistance. Its expression has been reported to correlate positively with the expression of ErbB255 and estrogen receptors,56 as it is the case for the E16 sample. In addition, the reported positive correlation in the expression of vimentin and CK5 was supported by the coordinated upregulation of their genes for the E16, R37 and G26 samples. Finally, several probe sets representing transcripts of the neuropilin 2, characterized as a receptor for the vascular endothelial growth factor implicated in in vivo angiogenesis57 were upregulated. The microarray analysis performed herein, evidenced 2 different molecular signatures defining 2 types of patients, that may characterize 2 independent effusion diseases. The EP1 group is characterized, (i) by the upregulation of genes encoding the epithelial CK8 CK18 CK19 proteins, as well as genes implicated in the negative regulation of transcription and of cell proliferation, and (ii) by the expression of genes coding for the CD24 and CD44 glycoproteins, but not for the CXCR4 chemokine receptor. The EP2 group is characterized, (i) by the upregulation of genes encoding the mesenchymal vimentin and to some extent the CK5 protein, as well as genes implicated in the positive regulation of transcription and of cell proliferation, and (ii) by the expression of genes coding for the CXCR4 chemokine receptor, but not for the CD24 and CD44 glycoproteins. In addition, in the EP2 group, the S100A4 metastatic protein and the uPA receptor were upregulated, while the metastasis suppressor gene encoding for nm23 was downregulated. On the basis of these data, we propose that the EP2 effusion group represents a more dedifferentiated and aggressive form of metastatic disease than the EP1 effusion group. Indeed, invasiveness and the capacity of tumor cells to form distant metastases are important cell features associated with a poor prognosis in breast cancer patients. CD24, CK8 and CK19 proteins were found to be preferentially expressed by non-invasive cells, whereas vimentin was confirmed as characteristic of invasive cells. CD24 mRNA was found to be absent or weakly expressed in 75% of the invasive cell lines as compared to 23% of the non-invasive cell lines.58 Vimentin expression is a rather rare finding in invasive breast carcinoma and is currently explained by 2 different theories, one praising direct histogenetic derivation from myoepithelial cells and the other one arguing for epithelial-mesenchymal transition reflecting the end-stage of breast cancer dedifferentiation. In agreement with our data, vimentin expression was shown to correlate positively with the expression of CK5 and EGF receptors, and negatively with that of CK8 CK18 and of the estrogen receptor.56 Therefore, an alternative hypothesis was recently reported in that vimentin-expressing breast carcinoma may derive from breast progenitor cells with bilinear (glandular and myoepithelial) differentiation potential. In agreement with our data, a recent immunohistochemical study of 150 breast cancer cases, described S100A4 overexpression as correlated with that of vimentin and of EGF receptors.59 In addition, authors claimed that EGF receptors may represent indicators of high-grade breast carcinoma groups, often containing carcinoma with mesenchymal and/or myoepithelial differentiation. We have identified a plethorus of cytokines and chemokines overexpressed in the EP2 group. Among those, is the CXCR4 receptor, which is indeed associated, with its ligand SDF1, for the dissemination of non-small-cell lung carcinoma cells into the pleural cavity.60 In agreement with previous observations, the autocrine SDF1/CXCR4 signaling pathway is reported to confer to breast cancer cell lines an aggressive behavior, including increasing invasiveness, migration and growth.61 Correlation of gene expression with tumor progression To bring new insights into the molecular pathways implicated in breast carcinoma progression, SAM clustering was performed on 4 effusion samples and their corresponding primary tumors. The 1,595 probe sets signature (FDR, 1%) is shown in Figure 5.

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FIGURE 5 – Multi-class significance analysis of microarrays performed on 4 tumors and their corresponding effusion specimens. The matrix exhibits the clustering of 1,595 probe sets in rows and the relatedness of pathological samples from patients C, G, H, J in columns. Above the matrix, the horizontal colored bar demarcates the EP1 (orange) and EP2b (purple) effusion and primary tumor (TP) (black) specimens. The dendrogram elicits overall similarities in gene expression profiles between effusion samples on one hand and the corresponding primary tumors on the other hand. Dendrograms of the gene Cluster 3 and Cluster 4 are represented in the left margin.

The effusion and primary tumor tissues were clearly distributed apart the 2 branching in the dendrogram. Within effusions, the 3 samples belonging to the EP2b subgroup clustered together. Several clusters of genes were evidenced on the data matrix, but only those with homogeneous gene expression among the 4 effusion samples were analyzed. A potential complication in the application of the microarray technology to whole tumor samples is the presence of variable numbers of non-carcinoma cells such as stromal, endothelial and inflammatory cells, that are intricated in the tumoral mass. Among the selected fragments analyzed, tumor cell contents were estimated, as of 50, 80, 90 and 100% for H- G- C- and J-TP, respectively. Cluster 3 (r2, 0.88) encompasses 97 probe sets (6% of the classifier) that were upregulated in the H, G and C primary tumors, but that were not or weakly expressed in the J-TP sample. This cluster of genes, indeed corresponds to tumor infiltration with stromal

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cells and includes the periostin, transgelin, lumican and decorin matrix proteins that are synthezised by fibroblasts, also the collagen IV and nidogen 2 components of the basement membrane developing underneath the epithelia, as well as the fibrillar collagen I polymerizing in primary tumor associated with increased metastasis and the thrombospondin 4 expressed by endothelial cells. Cluster 4 (r2, 0.89), with 345 probe sets (22% of the classifier), encompasses the major part of genes highly upregulated in effusions and downregulated in the corresponding primary tumors. Among these gene products were the breast cancer metastasis suppressor 1 and the uPA receptor. The neuropilin 1 gene was present and its upregulation was reported to parallel invasive behavior62 and to display opposing autocrine loops regulating chemotaxis of breast carcinoma cells.63 Another relevant gene elicited encodes for the CXCR4 receptor, which is a key mediator of breast cancer cell chemotaxis and cell migration towards privileged metastatic sites.64 As previously discussed, the expression pattern of effusionderived cell lines was closer to effusion samples than to other mammary tissues, whereas gene expression was progressively modified during tumorigenesis (healthy mammary gland to primary tumor) and tumor progression to metastasis (primary tumor to effusion). We found a greater degree of relatedness between primary tumors of different patients, than between a patient effusion sample and its archived primary tumor tissue. This may rely on the impact of the microenvironment, especially for serous metastases as opposed to solid tumors.65 The differences between cell and molecular phenotypes at primary tumor and at effusion sites, argue in favor of studying this secondary site, in order to make an appropriate decision for treatment of patients developing malignant effusions. However, very few studies have dealt with the predictive value of biological markers in effusions as compared to data available for solid tumors and most of cancer-associated molecules have no predictive value at this metastatic site. The Ets-1 transcription factor that activates metastasis-associated molecules was found selectively upregulated in the EP1 effusion group. To date, it is the only marker reported to have a predictive role in both effusions and solid tumors.66 Correlation of gene expression with patient clinical features Unsupervised clustering analysis is a good discovery tool to find similarities in gene expression patterns. To further inquire into the clinical relevance of expression profiling, supervised SAM (FDR, 5%) were conducted using all the filtered effusion probe sets (20,919 probe sets, 38% of total) that were interrogated by the Genechip1 array. A series of informative clinical criteria available from patient medical records were chosen to classify patients within 2 groups. The histological (ductal versus lobular, Grade II versus Grade III) and staging (Stages I and II versus Stages III and IV) parameters, as well as the hormonal receptor

(positive versus negative) status, determined at primary tumor diagnosis, evidenced no differentiating gene. These results are indicative of the large heterogeneity among tumors of the analyzed patients developing metastatic effusions. We sought for molecular signatures of metastatic relapse, effusion occurrence and patient survival. The median delay between tumor diagnosis and the first metastasis (38 months), or the first metastasis and the first effusion (5.5 months), or effusion occurrence and patient death (17 months), were calculated according to the Kaplan-Meier estimator. Supervised SAM, conducted on the sets of patients apart this median, did not reveal any statistically significant changes in gene expression. Similarly, SAM supervised by the type of chemotherapeutic treatment administered at the time of effusion puncture (antimetabolic versus antimitotic) showed that none of the probe sets were expressed differentially at a significant level of 5%. To search for the metastatic site impact, we further examined gene expression variations among pleural and peritoneal effusions. No differentiating gene was evidenced. This result support the idea that gene expression profiles are more similar at 2 different metastatic sites than at primary tumor and metastatic sites. Conclusion This study provides a broad overview of malignant effusion disease in breast carcinoma patients, from clinic to molecular profiling, by the way of cytology. We have established this study as a framework to analyze longitudinally tumor progression (from primary tumor to metastatic disease), as well as horizontally by comparative analysis of expression profiling in malignant effusions. We have demonstrated the ability and usefulness to immunopurify cells from fresh clinical specimens and to get comprehensive gene expression profiling using accurately designed and reliable in silico experiments. We have evidenced 2 molecular signatures that may be characteristic of 2 different effusion diseases. The study of a larger cohort of patients and the validation of expression data, shall lead to the identification of genes of outstanding value to diagnose malignant effusion, to predict survival and to tailor appropriate therapies towards metastatic effusion cells in breast carcinoma patients. Acknowledgements Special thanks to patients for giving their consent to participate in this study. Authors are grateful to Dr. Veronique Girre from the Medical Oncology Department, Mr. Jean-Philippe Meyniel and Mrs. Eleonore Gravier from the Translational Department and Mrs. Doris Hagmann, Dr. Solange Merle, Dr. Fabien Reyal, Mr. Andre Nicolas from the Tumor Biology Department of the Medical Division at Institut Curie. Special thanks to Dr. Jacqueline Jouanneau for her interest and help in writing the manuscript.

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