Alexandre Ho-Pun-Cheung

Hervé Bazin

Florence Boissière-Michot

Caroline Mollevi

Joëlle Simony-Lafontaine

Emeline Landas

Jean-Pierre Bleuse

Thierry Chardès

Jean-François Prost

André Pèlegrin

William Jacot

Gérard Mathis

Evelyne Lopez-Crapez

Evelyne Lopez
email: evelyne.crapez@icm.unicancer.fr

Quantification of HER1, HER2 and HER3 by time-resolved Förster resonance energy transfer in FFPE triple-negative breast cancer samples

INTRODUCTION

The term triple-negative breast cancers (TNBC) was first used in 2005 [START_REF] Brenton | Molecular classification and molecular forecasting of breast cancer: ready for clinical application?[END_REF] to describe a subset of tumours characterized by absence or low levels of expression of oestrogen receptor (ER), progesterone receptor (PgR), and human epidermal growth factor receptor 2 (HER2). The lack of ER, PgR, and HER2 overexpression rules out the use of hormonal therapies or anti-HER2 agents in TNBC. For this reason, systemic treatment of TNBC was limited to chemotherapy till the recent introduction of poly-ADP ribose polymerase (PARP) inhibitors for BRCA-deficient tumours [START_REF] Papadimitriou | The role of PARP inhibition in triple-negative breast cancer: Unraveling the wide spectrum of synthetic lethality[END_REF] . Recurrence is more frequent and the 5-year survival rate is lower in patients with TNBC than other breast cancer subtypes (34% versus 20%, and 77% versus 93%, respectively). [START_REF] Dent | Triple-negative breast cancer: clinical features and patterns of recurrence[END_REF][START_REF] Bauer | Descriptive analysis of estrogen receptor (ER)-negative, progesterone receptor (PR)-negative, and HER2-negative invasive breast cancer, the so-called triple-negative phenotype: a population-based study from the California cancer Registry[END_REF] Therefore, due to their poor prognosis and scarcity of targeted therapies, actionable molecular targets need to be identified.

Histologically, most TNBC share common characteristics, and 95% of them are classified as invasive ductal carcinomas. [START_REF] Bianchini | Triple-negative breast cancer: challenges and opportunities of a heterogeneous disease[END_REF] At the molecular level, Lehmann et al., [START_REF] Lehmann | Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies[END_REF] and more recently, Burstein et al. [START_REF] Burstein | Comprehensive genomic analysis identifies novel subtypes and targets of triple-negative breast cancer[END_REF] have described molecular subtypes of TNBC with distinct outcomes and drug sensitivities. [START_REF] Lehmann | Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies[END_REF][START_REF] Burstein | Comprehensive genomic analysis identifies novel subtypes and targets of triple-negative breast cancer[END_REF][START_REF] Masuda | Differential response to neoadjuvant chemotherapy among 7 triple-negative breast cancer molecular subtypes[END_REF] While additional studies are needed to determine whether the different TNBC subtypes can be targeted with specific therapies, these "omics" analyses demonstrated that TNBC should not be considered as a single clinical entity that can be uniformly treated.

Given TNBC molecular heterogeneity, targeting tumour-specific alterations could significantly improve the outcome of the 60-70% of patients with TNBC who do not fully respond to chemotherapy. [START_REF] Bianchini | Triple-negative breast cancer: challenges and opportunities of a heterogeneous disease[END_REF] As dysregulated expression of human epidermal receptor (HER) family members is frequent in breast cancer, and given their crucial role in proliferation, [START_REF] Yarden | Untangling the ErbB signalling network[END_REF] these receptors have been extensively investigated as targets for anticancer therapy, particularly HER1, HER2 and HER3. HER1 overexpression is frequent in TNBC and is associated with poor clinical outcome. [START_REF] Gonzalez-Conchas | Epidermal growth factor receptor overexpression and outcomes in early breast cancer: A systematic review and a meta-analysis[END_REF] Preclinical studies demonstrated the sensitivity of TNBC cell lines to HER1 inhibitors, providing the rationale to test the efficacy of HER1-targeting agents in patients with TNBC. [START_REF] Hoadley | EGFR associated expression profiles vary with breast tumor subtype[END_REF] However, clinical studies remain inconclusive, possibly due to the lack of patient selection, because anti-HER1 targeted therapies were not restricted to patients with HER1overexpressing tumours or patients without KRAS/NRAS mutations. [START_REF] Costa | Targeting Epidermal Growth Factor Receptor in triple negative breast cancer: New discoveries and practical insights for drug development[END_REF] Some recent studies suggested that patients with TNBC, which is, by definition, characterized by absence of HER2 overexpression, could benefit from anti-HER2 therapies, such as trastuzumab. [START_REF] Paik | HER2 status and benefit from adjuvant trastuzumab in breast cancer[END_REF][START_REF] Perez | HER2 and chromosome 17 effect on patient outcome in the N9831 adjuvant trastuzumab trial[END_REF] This issue was specifically addressed by the National Surgical Adjuvant Breast and Bowel Project B-47 (NSABP B-47) trial that was designed to evaluate trastuzumab effect in 3 270 women with breast cancers with low HER2 levels (IHC 1+ or 2+ and/or negative by FISH). [START_REF] Fehrenbacher | OT1-02-07: NSABP B-47: A Randomized Phase III Trial of Adjuvant Therapy Comparing Chemotherapy Alone (Six Cycles of Docetaxel Plus Cyclophosphamide or Four Cycles of Doxorubicin Plus Cyclophosphamide Followed by Weekly Paclitaxel) to Chemotherapy Plus Trastuzumab in Women with Node-Positive or High-Risk Node-Negative HER2-Low Invasive Breast Cancer[END_REF] This randomized trial did not find any improvement in invasive diseasefree survival or overall survival in patients treated with trastuzumab and standard chemotherapy versus chemotherapy alone. [START_REF] Fehrenbacher | Abstract GS1-02: NSABP B-47 (NRG oncology): Phase III randomized trial comparing adjuvant chemotherapy with adriamycin (A) and cyclophosphamide (C) → weekly paclitaxel (WP), or docetaxel (T) and C with or without a year of trastuzumab (H) in women with node-positive or high-risk nodenegative invasive breast cancer (IBC) expressing HER2 staining intensity of IHC 1+ or 2+ with negative FISH (HER2-Low IBC)[END_REF] Nevertheless, a recent study reported that in patients with TNBC, moderate HER2 expression (IHC score of 2+) correlates with relapse, [START_REF] Ilie | HER2 based expression subpopulations in TNBC: pathological aspects and clinical significance[END_REF] suggesting that the clinical significance of different degrees of HER2 expression should be thoroughly explored in TNBC. Finally, the therapeutic potential of anti-HER3 agents for cancer treatment has been less investigated because HER3 kinase domain is defective. However, HER3 expression could be a prognostic marker in TNBC. [START_REF] Bae | HER3 status by immunohistochemistry is correlated with poor prognosis in hormone receptor-negative breast cancer patients[END_REF] IHC has become the gold standard method to assess protein expression in formalin-fixed, paraffin-embedded (FFPE) clinical samples. However, IHC typically relies on chromogenic detection that has a narrow linear dynamic range, thus limiting its ability to generate accurate quantitative results. [START_REF] Goldstein | Members of Ad-Hoc Committee On Immunohistochemistry Standardization. Recommendations for improved standardization of immunohistochemistry[END_REF] Moreover, IHC scoring system is subjective and this can lead to reproducibility issues. [START_REF] Perez | Immunohistochemistry and fluorescence in situ hybridization assessment of HER2 in clinical trials of adjuvant therapy for breast cancer (NCCTG N9831, BCIRG 006, and BCIRG 005)[END_REF][START_REF] Mccullough | Central pathology laboratory review of HER2 and ER in early breast cancer: an ALTTO trial [BIG 2-06/NCCTG N063D (Alliance)] ring study[END_REF] Therefore, to improve the accuracy and reproducibility of HER family member quantification in tumours, we developed a time-resolved Förster resonance energy transfer (TR-FRET) method that allows the quantitative and objective measurements of protein expression levels. This method relies on the energy transfer between two fluorophores, a donor and an acceptor. [START_REF] Mathis | HTRF(R) Technology[END_REF] When the donor (in this case, a lanthanide complex characterized by a long fluorescence lifetime) is excited by an energy source, it transfers its excitation energy to the acceptor only if the two fluorophores are in close proximity. The acceptor will then emit a specific long-lived fluorescence. In our assays, the analytes are detected using two antibodies that bind to two distinct epitopes within the analyte. One antibody is coupled to a donor fluorophore, while the second antibody is coupled to an acceptor fluorophore. When these antibodies bind to specific epitopes on the target protein, the distance between the donor and the acceptor is small enough to allow the energy transfer. The intensity of the acceptor fluorescence signal is proportional to the receptor number in the sample or standard, thereby allowing quantitative measurements. We previously described such assays for the quantification of HER1 and HER2 in tumour cryosections. [START_REF] Ho-Pun-Cheung | Quantification of HER expression and dimerization in patients' tumor samples using time-resolved Förster resonance energy transfer[END_REF][START_REF] Lopez-Crapez | Evaluation of the dimerization profiles of HER tyrosine kinases by time-resolved Förster resonance energy transfer (TR-FRET)[END_REF] As the use of these TR-FRET assays is limited by the need of fresh or freshly frozen tumour tissues, we now developed new TR-FRET assays to quantify HER1, HER2 and HER3 in FFPE tumour samples. In this study, we evaluated HER quantification by TR-FRET assays and assessed the prognostic role of HER1, HER2 and HER3 expression in TNBC.

METHODS

Patients and tissue samples

Tumour samples were selected from the Montpellier Cancer Institute (ICM) biological resource centre. Pathological data including hormone-receptor status, HER2 status, histological type, grade and pTNM were recorded. Clinical data (e.g., age, type of treatment, occurrence and type of relapse) were obtained by review of the medical files. The selected population included patients with non-metastatic TNBC who underwent mastectomy or breast-conservative surgery with negative margins, without previous history of cancer. In total, 51 patients with TNBC treated between 2004 and 2008 were included in this retrospective study. These patients did not receive neoadjuvant treatment, and were treated by postoperative radiotherapy or by radiotherapy with standard chemotherapy (anthracyclines and/or taxanes). All tumours were considered as ER-and PgR-negative and HER2-unamplified. ER and PgR status were assessed by IHC, using the mouse monoclonal antibodies 6F11 (against ER, 1:100 dilution, Leica, Wetzlar, Germany) and PgR636 (against PgR, 1:400 dilution, Dako, Glostrup, Denmark). ER and PgR negativity was defined as <10% of ER and PgR immunoreactivity, irrespective of the staining intensity. Of note, among the 51 ER-negative tumours, 5 included 1-10% of ER-stained tumour cells.

HER2 status was determined by IHC using the anti-cErbB2 polyclonal antiserum (#A0485, 1:800 dilution, Dako). HER2 expression was scored according to the recommendations at the time of diagnosis. [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] HER2 negativity was defined as a score of 0, 1+, or 2+, with normal gene copy number by fluorescent/chromogenic in situ hybridization in tumours with a 2+ score.

Tissue lysate preparation

For each FFPE tumour sample, a 3 µm-thick section was examined histologically after haematoxylin and eosin staining to assess the percentage of tumour cells. Adjacent 5 μm-thick sections were then macrodissected to enrich in tumour cell (>50%) and used for lysate preparation. The number of sections was adjusted to the tumour area (ideally three sections for a tumour with an area of 100 mm² after macrodissection). Sections were placed in a 1.5 ml microcentrifuge tube containing 1 ml of xylene substitute (#A5597, Sigma, Ile d'Abau, France). Paraffin was dissolved by incubation at room temperature (RT) for 5min, and then removed by centrifugation. Pellets were washed to remove residual contaminants (absolute ethanol twice, then 95% ethanol), collected by centrifugation (12 000 RCF) and resuspended in 1X Tris/EDTA pH 9 (Target Retrieval Solution #S2367, Dako). Samples were then heated at 95°C in a Thermomixer (Eppendorf, Hamburg, Germany) for 45min, centrifuged to remove the Tris/EDTA buffer, and resuspended in 250 µl of Lysis Buffer (LB4 #64KL4FDF, Cisbio, Codolet, France). Finally, ice-cold samples were lysed by sonication (80W for 15s) using a Vibra-Cell™ sonicator (Sonics & Materials, Newtown, CT, USA). Lysates were clarified by centrifugation (10 000 RCF) at 4°C for 5min and then transferred to the wells of a microtiter plate.

TR-FRET assays

The TR-FRET assays were carried out in white 384-well small volume high-base microtiter plates (Ref. 784075 Greiner Bio-One, Courtaboeuf, France) in duplicate using Eu 3+ cryptate trisbipyridine (TBP, #62EUSPEA, Cisbio) as donor, and d2 as acceptor, conjugated to the specific antibodies. Antibodies were labelled as previously described. [START_REF] Lopez-Crapez | Evaluation of the dimerization profiles of HER tyrosine kinases by time-resolved Förster resonance energy transfer (TR-FRET)[END_REF] For HER1 quantification, the TR-FRET assay was performed using two monoclonal antibodies from the Total EGFR Cellular Assay Kit (#64NG1PEG, Cisbio). HER2 expression was quantified using Ab-15 (LabVision, Fremont, CA, USA) labelled with TBP, and Ab-8 (LabVision) labelled with d2. HER3 was quantified using the ErbB3 monoclonal antibody 2F12 (Thermo Scientific, Rockford, IL, USA) labelled with TBP, and the ErbB3 monoclonal antibody 2B5 (Thermo Scientific) labelled with d2. The final assay volume was 20 μl and comprised 16 μl of lysate and 4 µl of a solution containing the donor/acceptor antibodies diluted in HTRF cellular kinase detection buffer (Cisbio). Plates were incubated at RT for 20 hours, and read using a Pherastar FS fluorometer (BMG LABTECH, Champigny-sur-Marne, France) with a classical HTRF protocol (excitation at 337 nm, donor and acceptor emission measured respectively at 620 nm and 665 nm, 60 μs delay, 400 μs integration). The TR-FRET signals (deltaF) were calculated as previously described. [START_REF] Albizu | Time-resolved FRET between GPCR ligands reveals oligomers in native tissues[END_REF] For each assay, a standard curve was prepared using human recombinant proteins to convert the TR-FRET signals into receptor number per µl of lysate. Recombinant HER1, HER2 and HER3 were purchased from Origene technologies (Catalog No.: TP710011; TP710032, and TP710089, respectively).

For normalization to DNA concentration, 2 µl of the sample solution used for the TR-FRET assays was transferred into a new microplate well, and 18 µl of a solution containing 1X SYBR Safe DNA Gel Stain (Invitrogen, Carlsbad, CA, USA), 50mM HEPES buffer and 0.1% BSA was added.

After incubation at RT for 2 hours, SYBR Green fluorescence intensity was measured by reading the fluorescence at 520 nm in Fluorescence Polarization mode upon 485 nm excitation using a Pherastar FS fluorimeter. Sample DNA concentrations were interpolated from a standard curve generated using serial dilutions in LB4 of a DNA standard of known concentration (50 µg/ml) prepared from human placental DNA (# D-7011, Sigma). By assuming 6.6 pg DNA per diploid cell, 1 nanogram of DNA was considered to correspond to 151 cells. This allowed converting the number of receptors per ng of DNA into a number of receptors per cell.

Immunohistochemistry assays

IHC analysis was performed on 3 μm-thick FFPE tumour tissue sections. The HER1 and HER3 IHC assays were carried out on adjacent sections that are the mirror image of the ones used for the TR-FRET assays. For HER1 detection, slides were deparaffinized in xylene, hydrated in serial dilutions of alcohol, and then immersed in proteinase K solution (Dako) at RT for 10min. For HER3 detection, section deparaffinization, rehydration and antigen retrieval were performed simultaneously in an automated PT Link module (Dako) using EnVision FLEX Target Retrieval Solution, High pH (Dako), at 95°C for 15min. Endogenous peroxidase activity was blocked by incubation with the EnVision Flex Peroxidase Block (Dako) at RT for 5min. Sections were then incubated with anti-HER1 (mouse monoclonal, clone 31G7, 1:50 dilution, Invitrogen) or anti-HER3 (mouse monoclonal, clone DAK-H3-IC, 1:50 dilution, Dako) antibodies, for 20 and 30min, respectively. For HER3 detection, EnVision FLEX+ Mouse LINKER (Dako) was used to amplify the primary antibody signal. After two rinses in EnVision FLEX wash buffer (Dako), sections were incubated with a horseradish peroxidase-labelled polymer coupled to secondary antibodies (Envision FLEX HRP, Dako) at RT for 20min, followed by incubation with EnVision FLEX Substrate Working Solution containing 3,3'-Diaminobenzidine as chromogen (Dako) at RT for 10min. Sections were counterstained with EnVision Flex Hematoxylin (Dako), rinsed with tap water for 5min, dehydrated, and mounted with a coverslip. A negative control (a sample incubated with non-specific mouse IgG instead of the primary mouse monoclonal antibodies) was included in each IHC batch. Sections were read independently by two trained observers (Boissière-Michot F and Simony-Lafontaine J) blinded to the patients' clinicopathological characteristics. HER1 and HER3 expression levels were scored using the H-Score method [START_REF] Mccarty | Use of a monoclonal antiestrogen receptor antibody in the immunohistochemical evaluation of human tumors[END_REF] in which the membrane staining intensity (no staining = 0, weak staining = 1, moderate staining =2, and intense staining = 3) is multiplied by the percentage of stained tumour cells (from 0 to 100%) to give a score ranging from 0 to 300. H-Scores were averaged between observers, except for conflicting results that were examined conjointly to reach consensus. HER2 expression levels were extracted from the patient medical files, and therefore, they were from IHC assays carried out using sections that were not the mirror image of the ones used for the TR-FRET assays.

Statistical analysis

Continuous variables were described as medians and range, and categorical variables as frequencies and percentages. A non-parametric test for trend was performed to test the TR-FRET expression increase across the three ordered groups of expression determined by IHC (weak/negative, moderate, and high for HER1 and HER3; 0, 1+, and 2+ for HER2). Relapse-free survival (RFS) was defined as the time from the date of surgery to the date of last contact or recurrence (local, regional, or distant), and estimated using the Kaplan Meier method. Univariate and multivariate analyses were performed using a Cox proportional hazard model. Hazard ratios (HR) were given with their 95% confidence interval (95% CI). All statistical analyses were performed with the STATA 13.0 software (StatCorp, College Station, TX).

RESULTS

Patients and clinico-pathological characteristics

The study cohort included 51 women with TNBC. Using 20 November, 2014 as cut-off date, the median follow-up was 6.5 years (95% CI [5.3 -7.8]). Among these patients, 19 (37.2%) had a recurrence that was loco-regional in nine and distant in ten patients. Most of these recurrences (84%) occurred during the first 42 months of follow-up, which is consistent with the previously reported relapse risk temporal distribution [START_REF] Dent | Triple-negative breast cancer: clinical features and patterns of recurrence[END_REF] . The patients' clinico-pathological characteristics are detailed in Table 1.

TR-FRET assay performance

The standard curves for TR-FRET-based quantification of HER expression showed a linear relationship between TR-FRET signal and HER1, HER2 and HER3 concentration for concentration ranges of 0.98-250 ng/ml (R² = 0.995), 0.24-62.5 ng/µl (R² = 0.997) and 0.24-62.5 ng/ml (R² = 0.988), respectively, and tended to saturate at higher levels (Fig. 1). The limit of detection (LOD) was in the range of 0.19-0.60 ng/ml, depending on the assay. The lower limit of quantification (LLOQ) values were 0.98 ng/ml (HER1), 0.48 ng/ml (HER2), and 0.48 ng/ml (HER3). For all assays, the upper limit of quantification (ULOQ) was equal to the concentration of the highest calibration standard within the linear dynamic range of concentrations (HER1: 250 ng/ml; HER2: 62.5 ng/ml; HER3: 62.5 ng/ml).

As the TR-FRET assays rely on antigen-antibody interactions, the specificity of each antibody pair needed to be validated. For that purpose, the ability of the antibody pairs to specifically detect HER1, HER2 or HER3 was tested by using as negative controls the other HER family members. These negative controls (1 000 ng/ml of recombinant proteins) did not generate any TR-FRET signal above the LOD (data not shown).

(3.9%), 37 (72.6%) and 51 (100.0%) tumours, respectively. Moderate expression (between 10 000 and 100 000 receptors per cell) of HER1 and HER2 was observed in 43 (84.3%) and 14 (27.4%) tumours, respectively. High HER1 expression (above 100 000 receptors per cell) was found in six (11.8%) tumours. Not surprisingly, in most tumours, HER1 expression levels were more elevated than those of HER2 and HER3 (96% and 100% of tumours, respectively).

Comparison of the TR-FRET and IHC results (Supplementary Fig. 3) showed a significant trend of increasing HER1 (P < 0.001) and HER2 (P = 0.007) receptor number by TR-FRET across the ordered IHC expression groups. For HER3, no significant trend was found between IHC and TR-FRET expression levels, because of its very low expression.

Correlation with relapse-free survival

The estimated 36-month RFS rate was 76% (95% CI 62-86%). Univariate analysis showed that histological type, histologic grade and pathologic stage (pT) were not correlated with the RFS rate.

Patients with node-positive (pN+) TNBC were more likely to relapse, although this trend did not reach significance (P = 0.056). Analysis of whether the HER1, HER2 or HER3 expression levels could be potential prognostic factors of relapse showed that moderate HER2 expression, assessed by IHC (P = 0.043) or TR-FRET (P = 0.003), was associated with significantly reduced RFS (Table 3 and Fig. 3).

When variables with p <0.1 were entered in multivariate analyses, only HER2 TR-FRET expression was an independent risk factor for tumour recurrence (Table 3).

DISCUSSION

Recently, we described a TR-FRET-based assay for the precise and objective quantitative analysis of HER family member expression in tumour cryosections. [START_REF] Ho-Pun-Cheung | Quantification of HER expression and dimerization in patients' tumor samples using time-resolved Förster resonance energy transfer[END_REF] Here, we successfully applied this TR-FRET technology for the quantification of HER1, HER2 and HER3 in FFPE tumour samples. The use of standard curves generated with recombinant HER1, HER2 and HER3 proteins allowed us to convert TR-FRET signals into number of receptors per µl of lysate. However, to accurately compare protein expression levels between patient samples using a quantitative method, measurements must be normalized relatively to the amount of starting material. Ideally, cell number should be assessed for each sample to derive the amount of proteins per cell. While this approach is feasible for cultured cells and blood samples, it is not applicable to tissue lysates. As DNA concentration is a robust surrogate of cell number in a wide range of cell types, including cancer cell lines, [START_REF] Silva | Measurement of DNA concentration as a normalization strategy for metabolomic data from adherent cell lines[END_REF] we developed a SYBR greenbased quantification assay that measures the total DNA concentration in the same reaction mix used for the TR-FRET assay. Although most cancer cells are not diploid, we assumed that 1 nanogram of DNA was equivalent to 151 cells. 29 For each assay, we then derived the number of receptors per cell from the number of receptors per ng of DNA. It is important to note that several factors could influence the determination of the receptor number per cell. First, chromosomal abnormalities are a hallmark of cancer cells, which means that tumour cells have variable DNA content. In addition, for tissue lysate preparation, samples undergo physical disruption in denaturing conditions that can damage HER protein epitopes. This implies that the measured receptor number is likely to represent an underestimation of the true receptor number. Finally, the extraction and solubilization efficiency might be different between proteins and DNA. For these reasons, the calculated number of receptors per cell may differ from the true number of receptors per cell. Nevertheless, the TR-FRET signal conversion into number of receptors per cell is very convenient because it makes it easier to visualize the receptor expression level (low, moderate, or high) in a tumour. Moreover, the calculated number of receptors per cell was consistent from experiment to experiment, as indicated by the low coefficient of variation (CV <10%) between technical replicates prepared from adjacent, mirror-image FFPE sections. This standardized relative quantification allows comparing the expression levels of different proteins, which is not possible with a semi-quantitative method, such as IHC.

We determined the performance of the new HER1, HER2 and HER3 TR-FRET assays for FFPE tumour samples by determining their LOD (from 0.19 to 0.60 ng/ml) and dynamic range (250-fold linear dynamic range). By comparison, commercially available ELISA kits, such as the Invitrogen human EGFR, ErbB2 and ErbB3 ELISA kits (Catalogue No.: KHR9061; EHERBB2, and EHERBB3, respectively), are more sensitive (<0.1ng/ml), but with smaller dynamic range (less than 100-fold), and require much more biological material (sample volume is usually 100 µl vs 16 µl for our TR-FRET assays). Moreover, these ELISA kits are intended for serum and cell culture supernatants, and may not be suitable for HER1, HER2 or HER3 quantification in FFPE lysates.

When used to quantify HER1, HER2 and HER3 in 51 FFPE TNBC samples, our TR-FRET approach demonstrated a high reproducibility level (median CV of technical replicates ≤8%). This compares quite favourably with CV of ≈ 15% reported for other quantitative methods to measure protein expression in FFPE samples, such as the mTRAQ [START_REF] Desouza | mTRAQbased quantification of potential endometrial carcinoma biomarkers from archived formalin-fixed paraffin-embedded tissues[END_REF] and VeraTag TM proximity-based assays. [START_REF] Larson | Analytical Validation of a Highly Quantitative, Sensitive, Accurate, and Reproducible Assay (HERmark) for the Measurement of HER2 Total Protein and HER2 Homodimers in FFPE Breast Cancer Tumor Specimens[END_REF] We think that due to their precision, sensitivity and objectivity, our TR-FRET assays are an appropriate method for the quantification of HER family members.

The HER family plays a major role in the regulation of cell proliferation, differentiation, and survival (9), and aberrant HER signalling is frequent in breast cancer. To clarify the role of HER1, HER2 and HER3 in TNBC, we assessed the expression of these receptors in our 51 TNBC using IHC and compared the results with those obtained with the TR-FRET assays.

HER1 IHC overexpression has been reported in 13-76% of TNBC [START_REF] Rakha | Prognostic markers in triple-negative breast cancer[END_REF] and is associated with worse disease-free survival. [START_REF] Gonzalez-Conchas | Epidermal growth factor receptor overexpression and outcomes in early breast cancer: A systematic review and a meta-analysis[END_REF] HER1 overexpression frequency depends strongly on the scoring methods and the antibodies used. [START_REF] Nakai | A perspective on anti-EGFR therapies targeting triplenegative breast cancer[END_REF] In our study, we detected high HER1 expression only in 13.8% of TNBC samples by IHC with the anti-HER1 antibody clone 31G7 (Dako), which is widely used to assess HER1 expression in colorectal cancer, and the H-Score scoring method. We obtained similar results with the TR-FRET assay (11.8% of tumours with >100 000 HER1 receptors per cell). However, HER1 number per cell was higher than 10 000 HER1 in most TNBC samples, a value above the level that is usually considered as endogenous/physiological. [START_REF] Zhang | Functional isolation of activated and unilaterally phosphorylated heterodimers of ERBB2 and ERBB3 as scaffolds in ligand-dependent signaling[END_REF][START_REF] Capuani | Quantitative analysis reveals how EGFR activation and downregulation are coupled in normal but not in cancer cells[END_REF] These results nuance rather than contradict the belief that HER1 overexpression is common in TNBC.

Currently, HER2 status is generally determined by IHC. In our series of 51 TNBC, comparison of the TR-FRET and IHC results showed a significant trend of increasing HER2 receptor number by TR-FRET across the HER2 IHC ordered groups, from 0 to 2+. However, our HER2 TR-FRET assay could detect as few as 900 receptors per cell, whereas conventional IHC is typically 20 times less sensitive. [START_REF] Zhang | Functional isolation of activated and unilaterally phosphorylated heterodimers of ERBB2 and ERBB3 as scaffolds in ligand-dependent signaling[END_REF] HER2 expression values varied considerably among TNBC samples, with tumours displaying up to 60 000 receptors per cell. Therefore, in some TNBC, HER2 expression level is not negligible. Of note, the Ab-8 and Ab-15 antibodies used for HER2 TR-FRET quantification recognize epitopes localized in HER2 intracellular domain. This means that our HER2 TR-FRET assay also detects p95HER2, a truncated form of HER2 that lacks the extracellular domain.

HER3 overexpression has been associated with poor outcome in many cancer types. [START_REF] Witton | Expression of the HER1-4 family of receptor tyrosine kinases in breast cancer[END_REF][START_REF] Tanner | ErbB-3 Predicts Survival in Ovarian Cancer[END_REF] In our 51 TNBC samples, 82.4% displayed negative or weak HER3 expression by IHC, and all tested samples had less than 5 500 receptors per cell by TR-FRET analysis. Our results indicate that most TNBC have very low HER3 expression.

Finally, analysis of HER1, HER2 and HER3 expression patterns relative to the patient outcome showed that HER2 level is a potential biomarker of relapse. Indeed, moderate HER2 expression by IHC (score of 1+/2+) and TR-FRET (≥8 500 HER2 per cell) was significantly associated with reduced RFS in our cohort of 51 TNBC. These results are in accordance with previous findings showing that moderate HER2 expression (IHC score of 2+) correlates with relapse in a small series of 47 patients with TNBC. [START_REF] Ilie | HER2 based expression subpopulations in TNBC: pathological aspects and clinical significance[END_REF] This suggests that HER2 could be an actionable target in TNBC with moderate HER2 expression levels.

Two controversial studies published in 2008 [START_REF] Paik | HER2 status and benefit from adjuvant trastuzumab in breast cancer[END_REF] and 2010 [START_REF] Perez | HER2 and chromosome 17 effect on patient outcome in the N9831 adjuvant trastuzumab trial[END_REF] showed that some patients with HER2negative breast tumours could benefit from HER2-targeting agents. Ithimakin et al. suggested that this unexpected finding might be explained by the cancer stem cell (CSC) hypothesis [START_REF] Ithimakin | HER2 drives luminal breast cancer stem cells in the absence of HER2 amplification: implications for efficacy of adjuvant trastuzumab[END_REF] according to which CSCs are implicated in treatment resistance [START_REF] Phillips | The response of CD24(-/low)/CD44+ breast cancerinitiating cells to radiation[END_REF][START_REF] Shafee | Cancer stem cells contribute to cisplatin resistance in Brca1/p53-mediated mouse mammary tumors[END_REF] and tumour recurrence. [START_REF] Li | Intrinsic resistance of tumorigenic breast cancer cells to chemotherapy[END_REF] Specifically, Ithimakin et al. demonstrated that, in some tumours with moderate HER2 levels, HER2 is selectively expressed in the CSC population. Therefore, trastuzumab and other HER2-targeting agents should be effective by targeting HER2-expressing CSCs. Indeed, trastuzumab can reduce the CSC populations of cell lines with moderate HER2 levels by specifically targeting HER2, and when administered early (adjuvant setting), it blocks the growth of tumour xenografts of these cell lines. Conversely, trastuzumab has no effect on cell lines and xenografts with low HER2 expression. [START_REF] Ithimakin | HER2 drives luminal breast cancer stem cells in the absence of HER2 amplification: implications for efficacy of adjuvant trastuzumab[END_REF] However, the recent results of the NSABP B-47 trial undermine this theory by showing that adjuvant trastuzumab in patients with HER2 1+ or 2+ breast cancers by IHC does not improve disease-or relapse-free survival in this population. [START_REF] Fehrenbacher | Abstract GS1-02: NSABP B-47 (NRG oncology): Phase III randomized trial comparing adjuvant chemotherapy with adriamycin (A) and cyclophosphamide (C) → weekly paclitaxel (WP), or docetaxel (T) and C with or without a year of trastuzumab (H) in women with node-positive or high-risk nodenegative invasive breast cancer (IBC) expressing HER2 staining intensity of IHC 1+ or 2+ with negative FISH (HER2-Low IBC)[END_REF] One possible explanation is that moderate HER2 expression is challenging to detect by IHC because of its semi-quantitative nature and its narrow dynamic range. In our study, multivariate analysis demonstrated that HER2 protein expression determined by TR-FRET is a stronger predictor of relapse than the HER2 IHC score. However, these results have some limitations due to the small patient population and the retrospective nature of our study. Prospective studies with larger cohorts of patients are required to confirm the relevance of our results. Currently, IHC is the gold standard method to assess protein expression in patient tissue samples; however, its reproducibility is still an issue, although many improvements have been made in the past several years. [START_REF] Mccullough | Central pathology laboratory review of HER2 and ER in early breast cancer: an ALTTO trial [BIG 2-06/NCCTG N063D (Alliance)] ring study[END_REF] Alternative methods exist for the objective quantification of HER proteins in FFPE samples. First, IHC can be enhanced by using fluorescence microscopy and advanced image analysis algorithms. This approach allows the continuous and quantitative measure of protein expression, but the inherent autofluorescence of FFPE sections may be a source of trouble. [START_REF] O'hurley | Garbage in, garbage out: A critical evaluation of strategies used for validation of immunohistochemical biomarkers[END_REF] Recently, Targeted Mass Spectrometry has been used to assess HER2 expression in FFPE tissue samples. [START_REF] Steiner | Quantification of HER2 by Targeted Mass Spectrometry in Formalin-Fixed Paraffin-Embedded (FFPE) Breast Cancer Tissues[END_REF] This approach is more quantitative than IHC and does not depend on the availability of specific antibodies.

However, the detection of low and moderate levels of proteins remains challenging. Alternatively, Monogram Biosciences proposes the VeraTag™ proximity-based assays [START_REF] Shi | A novel proximity assay for the detection of proteins and protein complexes: quantitation of HER1 and HER2 total protein expression and homodimerization in formalin-fixed, paraffin-embedded cell lines and breast cancer tissue[END_REF][START_REF] Desmedt | Quantitation of HER2 expression or HER2:HER2 dimers and differential survival in a cohort of metastatic breast cancer patients carefully selected for trastuzumab treatment primarily by FISH[END_REF] to quantify HER1, HER2 and HER3 in FFPE samples. The VeraTag™ technology uses a dual antibody format, whereby a fluorescent tag on one anti-HER antibody is released when in close proximity to a second specific antibody conjugated with molecular scissors that are activated upon illumination. The VeraTag™ assays are very sensitive and can detect down to 2 500 receptors per cell. [START_REF] Nuciforo | Quantification of HER family receptors in breast cancer[END_REF] However, access to this technology for research purposes is restricted because samples must be shipped to the Monogram CAP/CLIA certified laboratory in California for the analysis. We believe that our TR-FRET approach is a suitable alternative to these methods, because it overcomes many of their limitations. By using longlived fluorophores, a delay can be introduced between the excitation pulse and the signal measurement window, thus allowing the elimination of short-lived background autofluorescence from FFPE material.

Our assays are quantitative, with high sensitivity, and could be easily performed in any laboratory equipped with a TR-FRET instrument, without much training required. Moreover, the TR-FRET technology could be extended to the quantification of other proteins of interest, such as p95HER2, the relevance of which as a prognostic marker has recently been reported in patients with HER2positive breast cancer treated with trastuzumab. [START_REF] Sperinde | p95HER2 Methionine 611 Carboxy-Terminal Fragment Is Predictive of Trastuzumab Adjuvant Treatment Benefit in the FinHer Trial[END_REF][START_REF] Chumsri | High p95HER2/HER2 Ratio Associated With Poor Outcome in Trastuzumab-Treated HER2-Positive Metastatic Breast Cancer NCCTG N0337 and NCCTG 98-32-52 (Alliance)[END_REF] In summary, the present study demonstrates that our TR-FRET assays can reproducibly quantify the expression of HER family members in FFPE samples with high sensitivity. Moreover, it shows that quantification of HER2 expression by TR-FRET may be useful to predict tumour recurrence in TNBC, although additional studies in a larger population are required to confirm our findings.

FIGURE LEGENDS

Fig. 1 Calibration curve plots showing the limit of detection (LOD), lower limit of quantification (LLOQ), linear dynamic range, and upper limit of quantitation (ULOQ) of the HER1, HER2, and HER3 TR-FRET assays. For each assay, the LOD was calculated as the average signal of 20 replicate negative samples plus three standard deviations. The lower limit of quantification (LLOQ) and the upper limit of quantification (ULOQ) were the lowest and highest standard point that can be measured with <15% coefficient of variance (CV). HER2 expression levels measured by TR-FRET were dichotomized in the low and moderate expression groups according to the cut-off value derived from the ROC curve for predicting disease recurrence or metastases. The optimal cut-off value was 8 500 receptors per cell (sensitivity = 52.63%, specificity = 84.38%).

Fig. 2

 2 Fig. 2 Quantitative measurement of HER1, HER2 and HER3 expression by TR-FRET in 51 TNBC

Fig. 3

 3 Fig. 3 Kaplan-Meier curves for relapse-free survival of 51 patients with TNBC stratified according to

Fig. 1

 1 Fig. 1

Fig. 2

 2 Fig. 2

Fig. 3

 3 Fig. 3

Table 1 .

 1 Patients' characteristics

	Characteristics	Number of patients (%)
	Age, years
	Median	62
	Range	30-89
	TNBC histological type
	Ductal	46 (90.2)
	Other	5 (9.8)
	Histologic grade (SBR)
	I	1 (2.0)
	II	8 (15.7)
	III	42 (82.3)
	pT stage
	T1	14 (27.4)
	T2	33 (64.7)
	T3	3 (5.9)
	T4	1 (2.0)
	pN stage
	N-	25 (49.0)
	N+	26 (51.0)

Table 2 .

 2 HER1, HER2 and HER3 expression in TNBC by IHC

	Number of patients
	(%)

Table 3 .

 3 Univariate and multivariate analyses of prognostic factors related to relapse-free survival

	Univariate analysis	Multivariate analysis

*

Likelihood-ratio test.

Acknowledgements:

We thank Magalie Pedot for her excellent technical assistance. We are grateful to Blandine Massemin for her help in collecting and preparing clinical samples. We also thank Sonia Boulabas for collecting the clinical data.

Funding: This work was supported by the grant AAP13 "Fonds Unique Interministériel" FUI UmAbHER3 F120402M. Authors' contributions: E.C., T.C., J.F., A.P. and G.M. were responsible for the conception and design of the study. J.B. and W.J. enrolled the patients and contributed to the patient data collection. A.H., H.B., F.B, E.L. and J.S. performed the experiments. A.H, C.M, E.C. and F.B. contributed to data analysis and interpretation. A.H. drafted the manuscript and prepared the figures. All authors discussed the results and implications, and critically revised and approved the final manuscript version.

Availability of data and material:

The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.

Precision of the TR-FRET assays

For each of the 51 TNBC samples, two independent technical replicates were prepared using consecutive serial sections from the same FFPE tumour block. Each lysate was independently used for HER1, HER2 and HER3 quantification by TR-FRET. Technical replicate data were normalized and the numbers of receptors per cell were then plotted to assess the precision of each assay. The values of duplicate samples were strongly correlated (HER1: R²=0.986; HER2: R²=0.993; HER3: R² =0.877) (Supplementary Fig. 1). The median coefficient of variation values between technical replicates were 5.3% (HER1), 5.2% (HER2), and 8.2% (HER3). Taken together, these results demonstrate that the TR-FRET assays allow the reliable and precise quantification of HER1, HER2 and HER3 in FFPE tumour samples.

HER1, HER2 and HER3 expression in TNBC

The IHC results are presented in Table 2 and in Supplementary Table 1. Briefly, among the 51 TNBC specimens, only seven (13.8%) displayed high HER1 expression. In most tumours, HER2 could not be detected (score = 0), and only 11 TNBC samples (21.5%) showed weak or moderate HER2 expression (score = 1+ or 2+). Moderate HER3 expression was found in 9 tumours (17.6%), and none showed high HER3 expression. Representative examples of HER3 IHC staining are shown in Supplementary Fig. 2. Although only membrane staining was taken into account in the H-Score, HER3 membrane expression was significantly associated with cytoplasmic expression (P <0.001). Indeed, 22 of the 30 HER3 membrane-positive tumours displayed concomitant cytoplasmic staining, whereas only 2 of the 21 HER3 membrane-negative TNBC samples showed HER3 cytoplasmic expression. HER3 nuclear expression was detected to various extent in 53% of TNBC specimens (27 of 51), without association with membrane or cytoplasmic HER3 expression (data not shown).

The HER1, HER2 and HER3 expression levels measured by TR-FRET are shown in Fig. 2, and data are listed in Supplementary Table 1. The median number of receptors per cell were 34 540 (range 4 452-2 159 308) for HER1, 4 463 (range 945-59 302) for HER2, and 1 532 (323-5 348) for HER3.

Low endogenous HER1, HER2 and HER3 levels (below 10 000 receptors per cell) were observed in 2

ADDITIONAL INFORMATION

Ethics approval and consent to participate: The present study was performed in accordance with the Declaration of Helsinki guidelines, and was approved by the ICM Institutional Review Board (ID number ICM-CRB-2012/04). Samples were provided by ICM biological resource centre (Biobank number BB-0033-00059), following the ethics and legal national dispositions for the patients' information and consent. Considering the retrospective, non-interventional nature of this study, no consent was deemed necessary by the review board.

Consent for publication: Our manuscript does not contain any individual person's data in any form.

Conflict of interest:

The authors declare no conflict of interest.


Michael Kaestner
email: kaestner@univ-montp1.fr

Paul Amadieu

Véronique Bessière

Pascal Dumontier

Terrence Odean

Patrick Sentis

Jacques Teulié

Jakob Thomas

Investors' Misreaction to Unexpected Earnings: Evidence of Simultaneous Overreaction and Underreaction *

Behavioral Finance aims to explain empirical anomalies by introducing investor psychology as a determinant of asset pricing. Two kinds of anomalies, namely underreaction and overreaction, have been established by an impressive record of empirical work. While underreaction defines a slow adjustment of prices to corporate events or announcements, overreaction deals with extreme stock price reactions to previous information or past performance. Theoretical models have shown that both phenomena find potential explanations in cognitive biases, that is, investor irrationality.

This study investigates current and past earnings surprises and subsequent market reaction for listed US companies over the period 1983-1999. The results confirm the existence of post-earnings announcement underreaction and provide new evidence of overreaction to highly unexpected past earnings surprises. Traditionally, unexpected earnings are defined by the difference between the last individual or consensus estimate and the actual, reported earnings per share, standardized by the share price. Other methodologies include scaling by the actual eps or the forecast made. While former is a measure of the market surprise, latter can be considered as the analyst's proportional estimation error. In order to capture the extent to which an earnings surprise is highly unexpected, some of the tests performed in this study use the standard deviation of consensus forecasts as a scaling factor.

The results suggest that investors simultaneously exhibit shortterm underreaction to earnings announcements and long-term overreaction to past highly unexpected earnings. A potential explanation for the reported overreaction phenomenon is the representativeness bias. As I show, the overreaction and the later reversal is stronger for events, which exhibit a long series of similar past earnings surprises.

Introduction

In recent years, Behavioral Finance provided evidence for anomalous stock price behavior and attempted to provide explanations based on not fully rational behavior. Two families of anomalies, namely short-term underreaction and long-term overreaction challenge the traditional view of efficient markets.

This study aims to reconcile short-term underreaction with long-term overreaction in the quarterly earnings announcement framework. Unlike other empirical studies related to the Post-Earnings-Drift-Anomaly, I scale unexpected earnings by the consensus forecast standard deviation. This way, I identify highly unexpected surprises, i.e. important positive and negative surprises, that occur for earnings announcements, characterized by a high degree of consensus among analysts. In such a context, representativeness, a bias leading to overreaction, is highly probable. While the tests confirm the classical PEAD underreaction to current surprises, I also find evidence of investor overreaction to previous unexpected earnings. In line with theoretical and experimental findings, this overreaction phenomenon strengthens with the length of similar past surprises.

The paper is organized as follows: the first section provides a short review of Behavioral Finance literature related to overreaction and underreaction. The data and research methodology is exposed in section 2. Section 3 presents the main results regarding short-term underreaction and long-term overreaction to earnings surprises. Section 4 concludes.

Existing evidence of investor misreaction regarding earnings information

Since the seminal articles by De [START_REF] De Bondt | Does the stock market overreact ?[END_REF] and [START_REF] Jegadeesh | Returns to buying winners and selling losers: Implications for stock market efficiency[END_REF], empirical research in finance has confirmed the existence of two families of pervasive anomalies: short-term underreaction and longterm overreaction. From a theoretical perspective, underreaction defines a situation where individuals fail to react completely and immediately to new information. Overreaction is sometimes mistakenly considered as the opposite phenomenon. Investors excessively react to news, which results in a later reversal. But over-and underreaction do not cancel each other out, for they do not focus on the same underlying information and do not arise in the same context. Underreaction seems to be related to anchoring, and tends to produce effects systematically. Conversely, overreaction may stem from representativeness, which arises only after a series of similar information. 1

The following subsection briefly reviews empirical and theoretical findings

1 "The overreaction evidence shows that over long horizons [. . . ], security prices overreact to consistent patterns of news pointing in the same direction ", Shleifer (2000). related to over-and underreaction.

The overreaction phenomenon

Investors' overreaction to past stock performance seems to be the main conclusion of the seminal De [START_REF] De Bondt | Does the stock market overreact ?[END_REF]'s study. The authors rank all stocks traded on the NYSE by their past three year cumulative return. Subsequent abnormal performance turns out to be higher for prior "losers", that is, stocks having experienced the poorest past performance.

Over the sub-sequent three years, the bottom decile portfolio yields an abnormal return 8% higher than that of the top decile portfolio, the prior winners. This reversal suggests that part of an initial overweighing of negative (positive) stock information, driving prices below (over) their rational levels is subsequently corrected. The overreaction phenomenon over long horizons has been confirmed several times on the stock market [START_REF] De Bondt | Does the stock market overreact ?[END_REF], [START_REF] Chopra | Measuring abnormal performance: Do stocks overreact[END_REF] and more recently [START_REF] Lewellen | Momentum and autocorrelation in stock returns[END_REF]), but also for international stock market indices [START_REF] Chui | Momentum, legal systems and ownership structure: An analysis of asian stock markets[END_REF][START_REF] Bhojraj | Macromomentum: Evidence of predictability in international equity markets[END_REF], the gold market [START_REF] Cutler | Speculative dynamics[END_REF] and the options market [START_REF] Poteshman | Underreaction, overreaction and increasing misreaction to information in the options market[END_REF].

The underreaction phenomenon

Several studies, beginning with [START_REF] Jegadeesh | Returns to buying winners and selling losers: Implications for stock market efficiency[END_REF] have examined stock price underreaction and possible explanations. In their study, stocks are ranked into portfolios based on their prior six month return. Unlike De [START_REF] De Bondt | Does the stock market overreact ?[END_REF]'s finding, prior winners outperform prior losers over the following 6 month period. This "momentum effect" was confirmed by [START_REF] Lee | Do stock prices overreact to earnings news[END_REF] and [START_REF] Jegadeesh | Returns to buying winners and selling losers: Implications for stock market efficiency[END_REF]. [START_REF] Chan | Profitability of momentum strategies in the international equity markets[END_REF] examine 23 international stock market indices and report a short-run momentum. Although some methodological issues were mentioned (momentum larger in small firms [START_REF] Jegadeesh | Returns to buying winners and selling losers: Implications for stock market efficiency[END_REF][START_REF] Grinblatt | The cross section of expected returns and its relation to past returns: New evidence[END_REF] and in growth firms [START_REF] Daniel | Market efficiency in an irrational world[END_REF]), the anomaly has proved robust.

Another large body of literature focuses on stock price underreaction to corporate announcements and events, such as earnings announcements, dividend initiations or omissions and public offerings. Since [START_REF] Ball | An empirical evaluation of accounting income numbers[END_REF], the Post-Earnings-Announcement-Drift (PEAD) has become one of the most famous stock market anomalies. Many theories have been advanced to explain the slow adjustment of stock prices to recent and publicly available earnings information. Among them, the role played by individual investors [START_REF] Hirshleifer | Do individual investors drive post-earnings announcement drift? Dice Center Working Paper Series[END_REF], illiquidity issues (Sadka, 2005) and analyst related issues: low analyst coverage [START_REF] Hong | Bad news travel slowly: Size, analyst coverage and the profitability of momentum strategies[END_REF] or analyst underreaction to extreme bad news [START_REF] Easterwood | Inefficiency in analysts' earnings forecasts: Systematic misreaction or systematic optimism[END_REF], causing market underreaction.

Simultaneous over-and underreaction

Evidence of simultaneous over-and underreaction is found in De [START_REF] De Bondt | Does the stock market overreact ?[END_REF], where along with long-term overreaction, the authors find that the top one-year decile (the winner portfolio) outperforms the bottom decile (the loser portfolio)2 . [START_REF] Cutler | Speculative dynamics[END_REF] finds positive short-lag autocorrelations and negative autocorrelations at horizons of a few years for 13 stock markets. Similar results of momentum and later reversal were reported in [START_REF] Chui | Momentum, legal systems and ownership structure: An analysis of asian stock markets[END_REF] and [START_REF] Bhojraj | Macromomentum: Evidence of predictability in international equity markets[END_REF].

While only relatively few empirical studies reported simultaneous overand underreaction, recent theoretical models integrated both phenomena based on cognitive errors, in order to understand if mispricing may be related to cognitive biases. [START_REF] Daniel | Investor psychology and security market under and overreactions[END_REF] propose a model, where overreaction is due to overconfidence whereas self attribution causes underreaction. In [START_REF] Barberis | A model of investor sentiment[END_REF], conservatism and trend chasing cause respectively underreaction and overreaction by integrating two cognitive heuristics: anchoring and representativeness. In [START_REF] Hong | A unified theory of underraction, momentum trading and overreaction in asset markets[END_REF], investors who focus on fundamentals exhibit underreaction, those using market-related information are responsible for overreaction.

2 Data and methodology

Data

Financial analysts' earnings forecasts and actual earnings were provided by the I/B/E/S summary file. Return data is obtained from the Center of Research in Security Prices (CRSP) for the period from January, 1st, 1983until December, 31st, 1999. For each quarterly earnings announcement made by any company over this period, the consensus earnings estimate from the month preceding the earnings announcement and the actual earnings per share (EPS) are collected. The tests of investors' underreaction to the current earnings surprise involve the current EPS measure (EP S c ) and the latest consensus forecast made in the month preceding the earnings announcement (EST c ). For each earnings announcement, return data for the announcement day and the 60 trading days following the actual announcement date was extracted from CRSP. Quarterly earnings, earnings announcement dates and estimates were not available for all companies in all quarters. Also, a few companies could not be found in CRSP and were deleted. I excluded events with low analyst coverage (less than three individual estimates) and low eps values (below 5 cents). The final sample for the underreaction test consists of 43 680 earnings announcements. To capture investors' overreaction to past earnings surprises, EPS estimates and their actual values for each of the 4 preceding quarters (denoted respectively EST c-• and EP S c-• ) were also obtained. These additional requirements reduce the number of available events for the overreaction tests to 18 385.

Methodology

Computation of unexpected earnings

As will be shown later, detecting overreaction to past earnings surprises requires a measure of the degree of consensus among analysts. For this reason, I use the latest consensus estimate before an announcement instead of the commonly used last individual estimate. For a given quarter q, unexpected earnings (U E q ) equal the difference between actual earnings (EP S q ) and the consensus estimate in the month preceding the actual announcement (EST q ):

U E q = EP S q -EST q (1)

Scaling Unexpected Surprises

For this study, I used two different scaling methods for unexpected earnings.

The first, rather classical, consists of dividing the unexpected earnings by the actual earnings per share. 3 The purpose of this methodology is to equally weight unexpected earnings for portfolio or event studies and allow for crosssectional comparison of all surprise and return variables. I calculate scaled unexpected earnings (SCU E q ) by dividing unexpected earnings (U E q ) by the reported eps (EP S q ):

SCU E q = U E q abs(EP S q ) (2)
The second methodology is used to identify highly unexpected earnings.

I compute standardized unexpected earnings (SU E q ) by dividing the unexpected earnings (U E q ) by the standard deviation of the consensus forecast (σ ESTq ). In this manner, I capture the degree to which analysts (and the market) agree on a given earnings estimate:

SU E q = U E q σ ESTq (3)
When analysts disagree on earnings forecasts, the standard deviation of estimates within a monthly consensus is high. In the extreme case of high uncertainty, that is, low consensus among analysts, the degree to which actual earnings are considered as unexpected (the SU E measure) would be rather low. Conversely, the higher the consensus among analysts, the lower the consensus standard deviation, which results in a higher surprise for a given level of unexpected earnings. It follows that standardized unexpected earnings measure the strength in which the actual earnings differ from their estimate.

Abnormal returns

Daily abnormal returns are computed using a size-adjusted approach: for stock i at time t, the daily abnormal return is defined by the difference between the stock's daily raw return and the equally weighted daily return of the size portfolio, the stock belongs to at the beginning of the year:

4 AR i,t = R i,t -Size t (4)
where R i,t is stock i's daily return at time t and Size t is the equally weighted daily return for the corresponding size portfolio.

Cumulative abnormal returns for stock i in an event window (p, q) are computed as follows:

CAR i (p, q) = q t=p AR i,t (5)
where p and q are the dates relative to the announcement date, denoted as 0.

Portfolio construction and statistical significance of abnormal returns

The tests presented hereafter rely on a sort ranking procedure, where each event is assigned to one of ten portfolios, portfolio 1 displaying the highest positive surprises and portfolio 10 the highest negative surprises. For the tests which focus on overreaction to a series of similar past surprises, we repeat the portfolio formation procedure backwards. The events contained in each portfolio, obtained at the first step, are, in a second step, ranked according to the earnings surprise of the preceding quarter and assigned to one of three portfolios (respectively positive, null and negative surprises). This procedure is repeated up to 4 times, yielding one current earnings surprise and 4 past surprises. This selection-rank methodology allows a progressive portfolio study, where consecutively formed portfolios only differ from their parent portfolio by the most ancient earnings surprise. This methodology allows to focus on the impact of the number of similar past earnings surprises on the market reaction to the most recent earnings announcement.

For most of the tests, a non parametric significance test, initiated by [START_REF] Foster | Earnings releases, anomalies and the behavior of security returns[END_REF] and reviewed by [START_REF] Lyon | Improved methods for tests of long-run abnormal returns[END_REF] 1. For each event in the portfolio, randomly select one event in the parent population.

2. Compute equal weighted CARs for the companion sample.

3. Repeat steps 1 and 2 1500 times and rank the companion sample CARs from the lowest to the highest to obtain the empirical distribution.

This test has several appealing properties. It does not assume normality, it does not assume constant variance of security returns over time and it does not assume cross-sectional independence in the residuals. Moreover, as Lyon, Barber, and Tsai (1999) point out, unlike the conventional t-statistic, in which the null hypothesis is that the mean CAR is zero, the null hypothesis by approximating the empirical distribution is that the mean CAR equals the companion sample mean CAR.

Investors' underreaction to earnings announcements

Numerous empirical studies found significant abnormal returns for periods up to 480 days after earnings announcements. The anomaly, known as the Post-Earnings-Announcement-Drift has shown to be robust and is considered as one of the most challenging evidence against market efficiency.

To confirm the existence of a price drift after earnings announcements, I ranked all events into 10 portfolios, according to its current scaled unexpected earnings (SCU E c ), portfolio 1 containing the highest positive surprises, portfolio 10 the highest negative surprises. The table above displays cumulated abnormal returns for different event windows and 10 portfolios, formed by sort-ranking all events according to their current scaled unexpected earnings. Portfolio 1 contains the highest positive surprises, portfolio 10 the highest negative surprises. SU Ec denotes the average scaled unexpected earnings for a given portfolio. The symbols * , * * , * * * , and * * * * indicate that the measure is significantly higher than, respectively, 90%, 95%, 99%, and 99, 5% of a sample-wide empirical distribution. The symbols , , , and indicate that the measure is significantly lower than, respectively, 90%, 95%, 99%, and 99, 5% of a sample-wide empirical distribution.

The results confirm the existence of a slow price adjustment to unexpected earnings. With a few exceptions, cumulated abnormal returns increase steadily up to 60 days after the earnings announcement. The strategy which consists in buying portfolio 1 and short-selling portfolio 10 yields abnormal returns of 3.89% over the 10 days and 7.41% over the 60 days following the earnings announcement.

The results regarding market underreaction to unexpected earnings I obtained in this study are neither original, nor especially different from those found in the existing literature. What will be shown hereafter (section 4) is that while the use of scaled unexpected surprises (SCU E) shows underreaction to current earnings surprises, the use of standardized unexpected earnings (SU E) uncovers an overreaction phenomenon to past earnings surprises.

Investors' overreaction to past highly unexpected surprises

Investors, who exhibit representativeness, extrapolate their information too far into the future. As, on average, these extreme expectations are not confirmed by actual figures, one could expect subsequent reversals. Especially after highly unexpected surprises, investors are likely to overestimate future earnings surprises. On average, these important surprises should be followed, at the date of subsequent earnings announcement, by a correction of the initial overreaction, that is, by CARs of the opposite sign.

I formed 10 portfolios based on the previous quarter standardized unexpected surprise SU E c-1 . Results are presented in table 2. The table shows cumulated abnormal returns for 10 portfolios formed according to the preceding quarter standardized unexpected earnings (SU E c-1 ) for different event windows. Each window A, B, C et D displays, in braces, the period (start date, end date), the announcement day being denoted day 0.

The symbols * , * * , * * * , and * * * * indicate that the measure is significantly higher than, respectively, 90%, 95%, 99%, and 99, 5% of a sample-wide empirical distribution. The symbols , , , and indicate that the measure is significantly lower than, respectively, 90%, 95%, 99%, and 99, 5% of a sample wide empirical distribution.

The results displayed in table 2 are consistent with the overreaction hypothesis. It seems that investors rely too heavily on the information carried by a prior highly unexpected earnings surprise. After an important positive surprise (SU E c-1 = 0.0513 for portfolio 1) investors are deceived by the current earnings surprise. For this portfolio, cumulated abnormal returns computed over the period following the current earnings announcement are negative, yielding -0.73% for the first two trading days (including the day of announcement) and even -2.37% over the 61 first trading days after the announcement.

For positive and negative surprise portfolios, computed CARs are of the opposite sign of the past earnings surprise, and generally significantly different from the mean value of a randomly generated sample-wide empirical distribution. Thus, a highly unexpected positive (negative) surprise is, on average, followed by a reversal, that is, negative (positive) abnormal returns at the time of the subsequent earnings announcement.

Market reaction to null surprises

The results presented above indicate that an extreme earnings surprise is followed, at the time of the subsequent earnings announcement, by a market reaction of the opposite sign to the initial surprise. These findings suggest the presence of investors' overreaction to earnings surprises. If this overreaction is due to representativeness, then investors would not only extrapolate a given earnings surprise into the future (and end up disappointed when the subsequent actual earnings figures are announced), but also misreact more heavily to a series of similar surprises. Hence, I expect the reversal to be more pronounced for events with long series of good or bad earnings surprises.

To ascertain wether these abnormal returns are due to investors misperception of past earnings surprises and not related to the current unexpected earnings, the following tests focus on a portfolio, composed of recent null surprises.5 If any subset of this sample exhibits CARs significantly different from the parents portfolio, they might be related to previous earnings surprises.

Using a sequential sort-ranking procedure, all null-surprise events (recent-surprise-portfolio 5) are ranked according to the most recent past standardized unexpected earnings (SU E c-1 ) and assigned to one of three equal sized portfolios (positive, null, and negative c -1 surprise portfolios). Each of those portfolios is divided again into three subportfolios, based on the standardized unexpected earnings, that lie two quarters behind (SU E c-2 ). I repeat this procedure to obtain 5 consecutive quarters (SU E c until SU E c-4 ). This procedure identifies events with a series of similar past earnings surprises, while keeping the most recent surprise SU E c close to zero. If the overreaction hypothesis holds, I should find significant abnormal returns of the opposite sign to the initial series of unexpected earnings: the longer the series of past similar surprises, the higher the subsequent reversal. The table shows that cumulated abnormal returns, computed for different event windows, are increasing in the length of a past earnings surprise series. All portfolios are formed sequentially starting from the current null surprise (portfolio 5), then forming, at each step, 3 portfolios based on the preceding standardized unexpected earnings.

Cumulated abnormal returns are computed for the first two trading days including the announcement day (event window A), the period covering the first four trading days (B), 31 trading days (C), and 61 trading days including the earnings announcement.

The symbols * , * * , * * * , and * * * * indicate that the measure is significantly higher than, respectively, 90%, 95%, 99%, and 99, 5% of the empirical distribution generated from the companion portfolio composed of all events, that differ only in the most ancient standardized unexpected earnings. The symbols , , , and indicate that the measure is significantly lower than, respectively, 90%, 95%, 99%, and 99, 5% of the empirical distribution generated from the companion portfolio composed of all events, that differ only in the most ancient standardized unexpected earnings.

As all events display, on average, a current null surprise, there should be no significant market reaction to this announcement. However, extreme past surprise portfolios exhibit a strong correction pattern: cumulated abnormal returns are globally negative for prior positive surprises and globally positive for prior negative surprises. My understanding is that investors overreact to past highly unexpected earnings and only correct their extreme belief at the date of subsequent earnings announcement. Moreover, this correction is stronger for a long series of similar consecutive earnings surprises.

For example, portfolio denoted 5, 1, 1, 1, 1, having experienced a series of four positive past surprises and a current null surprise, displays a negative cumulated abnormal return of -9.80% over the first 31 trading days after the announcement. For the specific event window C, this portfolio significantly outperforms portfolio 5, 1, 1, 1, with only three consecutive positive past earnings surprises and displaying a lower correction (cumulated abnormal returns equal -5.30%). Similar, although less striking results are obtained for prior negative surprises, which are followed by a reversal that strengthens with the length of the series of prior negative surprises.

Recall that those earnings announcements are actually null surprises, that is, the current earnings figures match, on average, analysts' estimates.

Similar results are obtained for portfolios with prior negative surprises; displaying positive abnormal returns after the current null-surprise, increasing in the number of similar past surprises.

These results indicate that the longer the series of similar earnings surprises (standardized unexpected earnings in this test), the stronger the subsequent correction. This evidence is consistent with the idea that representativeness causes investors to overreact more heavily to a series of similar information. As these beliefs are not confirmed by actual earnings figures, the market returns experience a strong reversal. Latter is increasing in the length of the series of similar earnings surprises.

Conclusions

Two families of pervasive anomalies have emerged from empirical work in the field of what is commonly called Behavioral Finance. While underreaction defines a slow adjustment of prices to corporate events or announcements, overreaction deals with extreme stock price reactions to previous information or past performance. Theoretical models have shown that both phenomena find potential explanations in cognitive biases, that is, investor irrationality.

In this study, I confirm the existence of post-earnings announcement underreaction and provide new evidence of overreaction to highly unexpected past earnings surprises. Traditionally, unexpected earnings are defined by the difference between the last individual or consensus estimate and the actual, reported earnings per share, standardized by the share price. Other methodologies include scaling by the actual eps or the forecast made. While former is a measure of the market surprise, latter can be considered as the analyst's proportional estimation error. In order to capture the extent to which an earnings surprise is highly unexpected, some of the tests performed in this study use the standard deviation of consensus forecasts as a scaling factor.

The results suggest that investors simultaneously exhibit short-term underreaction to earnings announcements and long-term overreaction to past highly unexpected earnings. A potential explanation for the reported overreaction phenomenon is the representativeness bias. As I show, the overreaction and the later reversal is stronger for events, which exhibit a long series of similar past earnings surprises.

  is used. It relies on statistical significance levels which are drawn from an empirical sample distribution. Statistical significance is assessed by comparing the observed portfolio cumulated abnormal return (CAR) with the empirical distribution of CARs for a companion sample. The empirical distribution is generated as follows:

Table 1 :

 1 Market reaction to current scaled unexpected earnings (SCU E c )

	Event Windows

Table 1 displays cumulated abnormal returns for different event windows for each portfolio.

Table 2 :

 2 Market Reaction Conditional on the Previous Standardized Unexpected Earnings SU E c-1

			A	B	C	D
	Portfolio SU E c-1	(0;1)	(0;3)	(0;30)	(0;60)
	1	0.0513 -0.73%	-0.95%	-1.44%	-2.37%
	2	0.0178 -0.44%	-0.65%	-0.77%	-1.44%
	3	0.0104 -0.06%	-0.20%	-0.39%	-1.19%
	4	0.0048	0.06%	-0.13%	-0.60%	-1.31%
	5	0.0004	0.05%	0.09%	0.35%	-0.70%
	6 -0.0043 -0.13%	-0.13%	0.03%	-0.54%
	7 -0.0091	0.57% * * * *	0.58% * * * *	0.83% * * *	0.38% *
	8 -0.0163	0.08%	0.25% * *	0.18%	-0.37%
	9 -0.0296	0.49% * * * *	0.61% * * * *	1.26% * * *	0.47% * *
	10 -0.1061	1.11% * * * *	1.17% * * * *	1.76% * * *	1.58% * * * *

Table 3 :

 3 Market Reaction Conditional on Previous Earnings Surprises

		Sample	A	B	C	D
	Portfolio SUE c	Size	(0;1)	( 0;3)	(0;30)	(0;60)
	5,1,1,1,1 0.0026	18	0.23%	1.54%	-9.80%	-5.48%
	5,1,1,1 0.0006	48 -0.23%	-1.28%	-5.30%	-6.04%
	5,1,1 0.0001	160 -1.19%	-2.00%	-3.74%	-4.14%
	5,1 0.0000	478 -0.95%	-1.41%	-1.68%	-1.93%
	5 0.0000	1443 -0.12%	-0.41%	0.34%	-0.84%
	5,3 0.0001	514	0.82% * * * *	0.62% * * * *	1.44% * * * *	0.94% * * * *
	5,3,3 0.0001	167	1.40% * * * *	1.19% * * *	2.07% *	2.23% * *
	5,3,3,3 0.0001	54	1.74% *	0.71%	1.78%	3.96% *
	5,3,3,3,3 0.0000	18	1.16%	0.83%	2.60% *	0.61%

[START_REF] De Bondt | Does the stock market overreact ?[END_REF], table 1, page 801.

Other methodologies use the share price as denominator. Although not reported, similar results were obtained.

The size of a company is calculated at the beginning of each year, by multiplying the share price by the number of shares outstanding. Each stock is then assigned to one of ten size-portfolios.

The use of events with exact matching of estimates and eps values (zero surprise events) was not possible due to IBES split adjustment problems. I thank Jakob Thomas for that helpful remark.

 Sadka, R. (2005). Momentum and post-earnings-announcement drift anomalies: The role of liquidity risk. Working Paper. , A. (2000). Inefficient markets. University Press.

Shleifer