L James

K M Morgan

T P Taylor

R I Green

Nicholson

S Hiscox
email: hiscoxse1@cf.ac.uk

Ae N J Jordan

Ae C Smith

Ae M James

Ae L Morgan

Ae K M Taylor

Ae T P Green

Ae R I Nicholson

N J Jordan

Á C Smith

Á M James

Á L Morgan

K M Taylor

Á R I Nicholson

Dual targeting of Src and ER prevents acquired antihormone resistance in breast cancer cells

Keywords: Src, ER, Tamoxifen-resistance, Breast cancer

Dual targeting of Src and ER prevents acquired antihormone resistance in breast cancer cells

Introduction

Steroid hormones are central in directing the growth and development of oestrogen receptor (ER) positive breast cancers; as such, endocrine therapies which seek to perturb the steroid hormone environment of the tumour cells can promote extensive remissions in established disease and provide significant benefits in terms of patient survival [START_REF] Clarke | Effects of radiotherapy and of differences in the extent of surgery for early breast cancer on local recurrence and 15-year survival: an overview of the randomised trials[END_REF][START_REF] Nicholson | Growth factor signalling networks in breast cancer and resistance to endocrine agents: new therapeutic strategies[END_REF]. However, despite the undoubted improvements brought by endocrine therapies, in practice their benefits are limited by the capacity of a significant proportion of breast cancer patients that show an initial positive response to ultimately suffer a recurrence due to acquired antioestrogen resistance [START_REF] Clarke | Molecular and pharmacological aspects of antiestrogen resistance[END_REF][START_REF] Clarke | Antiestrogen resistance in breast cancer and the role of estrogen receptor signaling[END_REF][START_REF] Jordan | Studies on the estrogen receptor in breast cancer''-20 years as a target for the treatment and prevention of cancer[END_REF][START_REF] Nicholson | The biology of antihormone failure in breast cancer[END_REF]. In these patients, relapse on such therapies clinically presents as local and/or regional recurrences, frequently with distant metastases [START_REF] Nicolini | Metastatic breast cancer: an updating[END_REF] and the outlook for these patients is poor. Recent in vitro studies have demonstrated that chronic exposure of breast cancer cells to tamoxifen ultimately results in the acquisition of tamoxifen resistance [START_REF] Knowlden | Elevated levels of epidermal growth factor receptor/c-erbB2 heterodimers mediate an autocrine growth regulatory pathway in tamoxifen-resistant MCF-7 cells[END_REF][START_REF] Lykkesfeldt | Altered expression of estrogen-regulated genes in a tamoxifen-resistant and ICI 164, 384 and ICI 182, 780 sensitive human breast cancer cell line, MCF-7/TAMR-1[END_REF] which is accompanied by the gain of aggressive, invasive characteristics [START_REF] Hiscox | Tamoxifen resistance in breast cancer cells is accompanied by an enhanced motile and invasive phenotype: inhibition by gefitinib ('Iressa', ZD1839)[END_REF][START_REF] Hiscox | Elevated Src activity promotes cellular invasion and motility in tamoxifen resistant breast cancer cells[END_REF].

Overexpression of the non-receptor tyrosine kinase, Src, and/or an increase in its kinase activity has been associated with the development and progression of a number of human malignancies including breast cancer [START_REF] Alvarez | The role of Src in solid and hematologic malignancies: development of new-generation Src inhibitors[END_REF][START_REF] Yeatman | A renaissance for SRC[END_REF] where it has been implicated in metastatic progression [START_REF] Rucci | Inhibition of protein kinase c-Src reduces the incidence of breast cancer metastases and increases survival in mice: implications for therapy[END_REF]. Interestingly, it has recently been reported that expression of activated Src is associated with a reduced recurrencefree survival in ductal carcinoma in situ [START_REF] Wilson | Activated c-SRC in ductal carcinoma in situ correlates with high tumour grade, high proliferation and HER2 positivity[END_REF]. Steroidinduced cellular responses involve activation of Src and, although the molecular pathways involved in these responses have yet to be fully defined, can involve growth factor receptor signalling. For example, targeting of the Cterminal region of the ER to the membrane of ER-negative breast cancer cells results in oestrogen-stimulated, c-Srcdependent responses including activation of the epidermal growth factor receptor (EGFR) [START_REF] Razandi | Proximal events in signaling by plasma membrane estrogen receptors[END_REF]. Furthermore, oestrogen stimulation results in the recruitment and activation of Src leading to activation of Shc, MEK/MAPK, and PI3K/ PKB [START_REF] Coleman | Intracellular signaling pathways: nongenomic actions of estrogens and ligand-independent activation of estrogen receptors[END_REF]. Significantly, we have recently identified that Src kinase activity is significantly elevated in tamoxifenresistant breast cancer cells, where it promotes an aggressive, invasive phenotype [START_REF] Hiscox | Elevated Src activity promotes cellular invasion and motility in tamoxifen resistant breast cancer cells[END_REF]. A role for Src kinase in acquired endocrine resistance has also been suggested by others, where it may contribute to cellular growth via regulation of Cas-mediated EGFR signalling [START_REF] Riggins | Physical and functional interactions between Cas and c-Src induce tamoxifen resistance of breast cancer cells through pathways involving epidermal growth factor receptor and signal transducer and activator of transcription 5b[END_REF] or through interplay with focal adhesion kinase (FAK) [START_REF] Planas-Silva | Role of c-Src and focal adhesion kinase in progression and metastasis of estrogen receptor-positive breast cancer[END_REF].

Given the putative role of Src in acquired tamoxifen resistance, we hypothesised that targeting of Src alongside the ER would present a novel strategy to suppress the development of an aggressive endocrine-resistant phenotype. In this report, we demonstrate that combined targeting of the ER and Src kinase using tamoxifen and the novel Src inhibitor, AZD0530 [START_REF] Hennequin | N-(5-chloro-1, 3-benzodioxol-4-yl)-7-[2-(4methylpiperazin-1-yl) ethoxy]-5-(tetrahydro-2H-pyran-4-yloxy) quinazolin-4-amine, a novel, highly selective, orally available, dual-specific c-Src/Abl kinase inhibitor[END_REF] in two separate, ERpositive breast cancer cell lines, greatly enhances the growth inhibitory effects seen with either agent alone, preventing the development of acquired tamoxifen resistance.

Materials and methods

Cell culture

Tamoxifen-responsive MCF7 and T47D breast cancer cells were routinely cultured in home medium (RPMI medium supplemented with 5% foetal calf serum (FCS), 10 IU/ml penicillin, 10 lg/ml streptomycin, 2.5 lg/ml fungizone and 200 mM glutamine). Subsequent treatment of these cells was performed in experimental medium (phenol-red free RPMI containing 5% charcoal-stripped, steroid-depleted FCS with antibiotics and glutamine as above) supplemented with 10 -7 M tamoxifen (4-hydroxy tamoxifen), AZD0530 (0-1 lM) or both agents in combination as indicated. In all cases the tissue culture medium (±tamoxifen/AZD0530) was replaced every 3 days unless otherwise stated. Stock solutions of AZD0530 and tamoxifen were dissolved in DMSO and ethanol respectively prior to diluting in tissue culture medium. Corresponding DMSO/ethanol controls had no effect on the parameters analysed. Treatment of cells with AZD0530 alone produced similar results irrespective of whether cells were cultured in home medium or experimental medium.

Cell growth assays

Cells were grown for 10 days in the presence of tamoxifen (10 -7 M), AZD0530 (0-1 lM) or a combination of both agents. Changes in cell growth were then evaluated by means of trypsin dispersion and subsequent counting using a Coulter Counter. To determine the effects of chronic exposure to these agents on the growth of both cell lines, log-phase cell cultures were either grown in home medium with no treatments or exposed to tamoxifen ± AZD0530 (0-1 lM) in experimental medium with routine passaging once cell confluency had reached *80%. Each passage was subsequently recorded and plotted against time in culture to allow the comparison of the ability of these treatments to maintain their growth suppressive effects.

Measurement of apoptosis

Cells were harvested at various times during long-term culture and apoptotic events analysed using a Vybrant Apoptosis Assay (Molecular Probes, Eugene, OR, USA) as follows: harvested cells were washed and resuspended to 1 9 10 6 cells/ml in PBS prior to the addition of FITClabelled YO-PRO-1 (final concentration of 100 nM) and TRITC-labelled propidium iodide (final concentration of 1.5 nM) for 30 min at 4°C. Cells were immediately read on a FACScan flow cytometer. Negative controls consisted of cells alone without antibody. The mean fluorescence of a minimum of 1 9 10 4 cells was determined for each sample and the experiments were repeated 4 times, each time in duplicate. Data represents mean of three separate experiments, each performed in duplicate.

RT-PCR

Total RNA was extracted from MCF7 and T47D cells using Trizol reagent (Life Technologies) and converted to cDNA using random hexamers and Super-Script II reverse transcriptase (Invitrogen). cDNA was amplified using PCR primer pairs (MWG Biotech) specific for c-myc (Fwd:5 0ccaagctcgtctcagagaag-3 0 , Rev:5 0 -cagcaggatagtccttccga-3 0 ) and cyclin-D1 (Fwd:5 0 -ggatgctggaggtctgcgag-3 0 , Rev:5 0gagaggaagcgtgtgaggcg-3 0 ). All PCR reactions were performed as multiplex reactions with PCR primers specific for b-actin (Fwd:5 0 -ccttcctgggcatggagtcct-3 0 , Rev:5 0 -ggag caatgatcttgatctt-3 0 ) as an internal control. PCR was performed in a semi-quantitative manner using 25 cycles so that products were in the linear range of amplification. PCR products were separated on a 1.0% agarose gel, stained with ethidium bromide and visualized and photographed under ultraviolet light.

Measurement of cellular proliferation

Ki67 antigen was assessed in both cell types following treatment with tamoxifen ± AZD0530 using the MIB-1 anti-Ki67 antibody (Coulter Electronics, Luton, United Kingdom). Briefly, cells were grown and treated in petri dishes containing coverslips as indicated then washed and fixed in formal saline. Primary antibody (MIB-1) was applied to the coverslips after blocking with PBS/Tween and incubation performed for 60 min. After washing in PBS, the secondary antibody (Mouse Envision, DAKO UK Ltd., Ely, Cambridgeshire) was applied to the coverslips for 75 min. After further PBS washing, the chromogen (''SigmaFast'' DAB, Sigma, Poole, UK) was added to the cells for 10 min after which the coverslips were rinsed in distilled water. Samples were counterstained with 20% haematoxylin for 3 min and mounted for examination by light microscopy. Control coverslips (no primary antibody) were checked for non-specific binding before assessing staining intensity in the test samples. The percentage of Ki67-positive cells was estimated after counting at least 1,000 tumour cells.

Cell cycle analysis

Cells were cultured in the presence or absence of tamoxifen, AZD0530 or the combination for up to 8 weeks. For analysis of cell cycle, the cells were harvested by trypsinisation and resuspended at 10 -7 cells/ml before using the Cycletest Plus DNA reagent kit (Becton Dickinson) following the manufacturers instructions to produce uniform suspensions of single nuclei with DNA stained and suitable for reading on a FACScan Flow Cytometer (Becton Dickinson).

Cell migration and invasion assays

Cells were exposed to tamoxifen ± AZD0530 for the times indicated in the figures prior to seeding onto fibronectincoated polycarbonate, microporous membranes (8 lm pore size) at 5 9 10 4 cells/membrane and allowed to migrate to the underside of the membrane for a period of 24 h. Migratory cells were fixed, stained with 0.5% crystal violet and counted. To determine the cells' invasive capacity, cells were seeded onto Matrigel-coated, porous membranes as described previously [START_REF] Hiscox | Tamoxifen resistance in breast cancer cells is accompanied by an enhanced motile and invasive phenotype: inhibition by gefitinib ('Iressa', ZD1839)[END_REF]. After 72 h culture, invaded cells were fixed, stained with DAPI and counted. Cell migration/invasion was quantified as the mean number of cells observed in each of eight random fields of view per sample, in duplicate with each experiment performed a minimum of three times.

Western blotting

Cells were harvested at 2 week intervals during their exposure to tamoxifen ± AZD0530 and lysates resolved by SDS-PAGE using 8% gels. Proteins were immobilised on nitrocellulose membranes and subsequently probed with phospho-specific antibodies that recognised the active forms of Src (Y419) and FAK (Y861, both from Biosource, Nivelles, Belgium). Tyrosine 419 in Src is a major positive-regulatory site, the phosphorylation of which increases Src activity and is widely used as a indicator of changes in Src activity both in vitro and in vivo (for example, see [START_REF] Serrels | Identification of potential biomarkers for measuring inhibition of Src kinase activity in colon cancer cells following treatment with dasatinib[END_REF]. Although other tyrosine residues on FAK are phosphorylated in a Src-dependent manner, phosphorylation of FAK at Y861 was investigated as this is well known to regulate cancer cell migration and to be increased in metastatic breast cancer cells [START_REF] Earley | Phosphorylation of focal adhesion kinase promotes extravasation of breast cancer cells[END_REF][START_REF] Vadlamudi | Heregulin and HER2 signaling selectively activates c-Src phosphorylation at tyrosine 215[END_REF] and thus may be important in regulating the effects of Src kinase in cancer cells. Repeat immunoprobings were performed using pan antibodies to determine total levels of these proteins and with b-actin to correct for loading. Western blots were then scanned to provide data for quantitation, with normalization against actin first. Each experiment was performed at least three times with representative gels shown.

Statistical analysis

Data was analysed using one-way ANOVA with post-hoc analysis in order to examine whether inclusion of AZD0530 (0.1 and 1 lM) significantly affected the observations obtained with tamoxifen alone. Significance was assumed at P \ 0.05.

Results

AZD0530 and tamoxifen together showed improved growth inhibitory effects compared with either agent alone

Treatment of both MCF7 and T47D cells with AZD0530 resulted in a dose-dependent inhibition of Src kinase activity, as shown by a decrease in phosphorylation of Src at Y419 (Fig. 1a) whilst having no effect on the level of Src protein in these cells. Despite both cell type having similar levels of Src kinase, T47D cells exhibited a small but nonsignificant decrease in sensitivity to the Src inhibitor. However, at the highest concentration of AZD0530 (1 lM), a corresponding inhibition of MAPK activity was also observed in both cell types. Treatment of cells with AZD0530 also resulted in a dose-dependent decrease in growth over a period of 10 days (Fig. 1b). A modest suppression of cell growth was seen following culture with tamoxifen alone at this time point (Fig. 1c) with T47D cells appearing more resistant to its growth inhibitory effects, as has been previously reported [START_REF] Al-Dhaheri | Identification of novel proteins induced by estradiol, 4-hydroxytamoxifen and acolbifene in T47D breast cancer cells[END_REF]. Importantly, when AZD0530 (1 lM) and tamoxifen were combined, maximal inhibition of cell growth over this time period was observed (Fig. 1c). Analysis of the growth data by isobologram suggested that the combined effect of the two drugs was additive rather than synergistic (data not shown).

AZD0530 and tamoxifen prevent the emergence of tamoxifen resistance

Since prolonged exposure of breast cancer cell lines to antioestrogens ultimately results in acquired resistance [START_REF] Knowlden | Elevated levels of epidermal growth factor receptor/c-erbB2 heterodimers mediate an autocrine growth regulatory pathway in tamoxifen-resistant MCF-7 cells[END_REF][START_REF] Lykkesfeldt | Altered expression of estrogen-regulated genes in a tamoxifen-resistant and ICI 164, 384 and ICI 182, 780 sensitive human breast cancer cell line, MCF-7/TAMR-1[END_REF], a process in which activated Src has been implicated [START_REF] Hiscox | Elevated Src activity promotes cellular invasion and motility in tamoxifen resistant breast cancer cells[END_REF][START_REF] Riggins | Physical and functional interactions between Cas and c-Src induce tamoxifen resistance of breast cancer cells through pathways involving epidermal growth factor receptor and signal transducer and activator of transcription 5b[END_REF][START_REF] Planas-Silva | Role of c-Src and focal adhesion kinase in progression and metastasis of estrogen receptor-positive breast cancer[END_REF], we wished to determine whether inhibition of Src activity alongside tamoxifen treatment would delay or prevent the development of resistance. Chronic exposure of both MCF7 and T47D cells to tamoxifen resulted in the emergence of a resistant phenotype after an initial period of growth suppression, as demonstrated by the ability of these cells to regain their ability to grow in the presence of the antihormone (Fig. 2). Interestingly, treatment with AZD0530 as a monotherapy also ultimately resulted in cells which were able to grow in the presence of this inhibitor, suggestive of further acquired drug resistance. Significantly, our data revealed that cells exposed to the combination of tamoxifen and AZD0530 did not grow but rather, a gradual loss of cells in culture was observed, with no viable cells visible by approximately 13 weeks (Fig. 2).

Addition of AZD0530 to tamoxifen does not enhance apoptosis

We next investigated whether tamoxifen and AZD0530 combined increased cellular apoptosis. FACS analysis of cells cultured in the presence of tamoxifen ± AZD0530 revealed that combining these agents did not significantly increase the extent of apoptosis seen with either compound as a single agent. (Table 1 shows data from 1 and 8 weeks' continuous culture in the presence of these agents).

AZD0530 and tamoxifen in combination significantly suppress expression of the Ki67 antigen

Since an induction of apoptosis did not appear to play a significant role in AZD0530-induced prevention of acquired tamoxifen resistance, we next evaluated the proliferative capacity of MCF7 and T47D cells cultured in the presence of tamoxifen with and without AZD0530. Analysis of the Ki67 expression, a marker of cellular proliferation, revealed that the combination of AZD0530 and tamoxifen significantly inhibited cellular proliferation in both cell lines, an effect observable as early as 10 days post treatment (Fig. 3a-MCF7 cells, 3b-T47D cells). To address possible mechanisms underlying these proliferation changes, we analysed the expression of a number of cell proliferation regulators which may be modulated in a Src-dependent manner. In agreement with recent reports showing inhibition of key cell cycle control elements following Src inhibition [START_REF] Planas-Silva | Targeting c-Src kinase enhances tamoxifen's inhibitory effect on cell growth by modulating expression of cell cycle and survival proteins[END_REF], we observed that suppression of Src activity in both MCF7 and T47D cells resulted in reduced expression of both cyclin-D1, necessary for the progression of cells from G1 to S phase, and c-Myc, a positive regulator of cellular proliferation [START_REF] Adhikary | Transcriptional regulation and transformation by Myc proteins[END_REF] (Table 2). Moreover, cells treated with the combination of the two agents had significantly lower levels of these genes at times [4 weeks (Table 2). Subsequent analysis of cell cycle progression in cells after 4 weeks treatment revealed that the combination of tamoxifen and AZD0530 together significantly reduced the number cells in S phase cell cycle in both cell lines compared with either treatment alone (Fig. 4).

AZD0530 and tamoxifen together show improved effects against migration and invasion compared with either agent alone

We have previously reported that chronic exposure to tamoxifen results in the acquisition of a highly migratory and invasive phenotype in vitro in which Src activation plays a key role [START_REF] Hiscox | Tamoxifen resistance in breast cancer cells is accompanied by an enhanced motile and invasive phenotype: inhibition by gefitinib ('Iressa', ZD1839)[END_REF][START_REF] Hiscox | Elevated Src activity promotes cellular invasion and motility in tamoxifen resistant breast cancer cells[END_REF]. We thus wished to determine whether inclusion of AZD0530 with tamoxifen during these long-term treatments could prevent this. The migratory and invasive capacity of both MCF7 and T47D cells was assessed every 2 weeks during long-term culture with tamoxifen ± AZD0530. After 10 weeks, the migratory and invasive capacity of MCF7 cells was seen to increase over their basal level (Fig. 5a); interestingly, analysis of lysates obtained from such cells revealed that levels of Src kinase were also elevated at this time point as was the activity of FAK, a Src-associated substrate involved in cellular migration [START_REF] Cox | New concepts regarding focal adhesion kinase promotion of cell migration and proliferation[END_REF] (Fig. 5b). The migratory and invasive capacity of T47D cells at this time point was again elevated following culture with tamoxifen alone, although these levels did not reach significance until C12 weeks, at which time cells demonstrated greatly increased Src and FAK activity (Fig. 5b). Inclusion of AZD0530 with tamoxifen significantly suppressed cell migration and invasion at all time points (Fig. 5a), and consistently inhibited the activity of Src and FAK (Fig. 5b).

Discussion

Endocrine therapy is a major therapeutic option in the management of breast cancer. Tamoxifen is the most widely used anti-oestrogen, with approximately 50% of women having ER-positive breast disease benefiting from this treatment [START_REF] Howell | The use of selective estrogen receptor modulators and selective estrogen receptor down-regulators in breast cancer[END_REF][START_REF] Ring | Mechanisms of tamoxifen resistance[END_REF]. However, acquisition of resistance to such therapies frequently arises and, in such cases, the identification of novel targets that promote a resistant phenotype may themselves prove useful targets through which the development of resistance and the associated tumour re-growth and spread may be delayed or prevented. We and others have recently identified a role for Src kinase in acquired endocrine resistance in vitro [START_REF] Hiscox | Elevated Src activity promotes cellular invasion and motility in tamoxifen resistant breast cancer cells[END_REF][START_REF] Planas-Silva | Role of c-Src and focal adhesion kinase in progression and metastasis of estrogen receptor-positive breast cancer[END_REF], where it may regulate both proliferative and invasive responses in such cells. Data is emerging which reveals Src kinase as a novel tumour target, where its inhibition is effective at suppressing proliferation and/or metastatic events in a wide range of tumour types including CML [START_REF] Golas | SKI-606, a 4-anilino-3-quinolinecarbonitrile dual inhibitor of Src and Abl kinases, is a potent antiproliferative agent against chronic myelogenous leukemia cells in culture and causes regression of K562 xenografts in nude mice[END_REF][START_REF] Tipping | Efficacy of dual-specific Bcr-Abl and Src-family kinase inhibitors in cells sensitive and resistant to imatinib mesylate[END_REF], colon tumours [START_REF] Golas | SKI-606, a Src/Abl inhibitor with in vivo activity in colon tumor xenograft models[END_REF][START_REF] Nam | Src family kinase inhibitor PP2 restores the E-cadherin/catenin cell adhesion system in human cancer cells and reduces cancer metastasis[END_REF] and pancreatic cancer [START_REF] Duxbury | Inhibition of SRC tyrosine kinase impairs inherent and acquired gemcitabine resistance in human pancreatic adenocarcinoma cells[END_REF][START_REF] Yezhelyev | Inhibition of SRC tyrosine kinase as treatment for human pancreatic cancer growing orthotopically in nude mice[END_REF]. Further studies [START_REF] Fiskus | Cotreatment with vorinostat (suberoylanilide hydroxamic acid) enhances activity of dasatinib (BMS-354825) against imatinib mesylate-sensitive or imatinib mesylate-resistant chronic myelogenous leukemia cells[END_REF][START_REF] Pengetnze | Src tyrosine kinase promotes survival and resistance to chemotherapeutics in a mouse ovarian cancer cell line[END_REF] also provide compelling evidence that targeting Src may be of value in combination with existing chemotherapy to provide an augmented antitumour response. In breast cancer cells, Src is implicated as a key element in non-genomic, oestrogen-induced MAPK and MMP activation [START_REF] Razandi | Proximal events in signaling by plasma membrane estrogen receptors[END_REF][START_REF] Belcher | Rapid estrogenic regulation of extracellular signal-regulated kinase 1/2 signaling in cerebellar granule cells involves a G protein-and protein kinase A-dependent mechanism and intracellular activation of protein phosphatase 2A[END_REF][START_REF] Filardo | Estrogen action via the G protein-coupled receptor, GPR30: stimulation of adenylyl cyclase and cAMP-mediated attenuation of the epidermal growth factor receptor-to-MAPK signaling axis[END_REF] and oestrogen-mediated cellular proliferation and cell-cycle progression [START_REF] Castoria | PI3-kinase in concert with Src promotes the S-phase entry of oestradiol-stimulated MCF-7 cells[END_REF][START_REF] Song | Estrogen rapid action via protein complex formation involving ERalpha and Src[END_REF]. Interestingly, tamoxifen treatment itself has been reported to promote activation of intercellular signalling elements, including Src, resulting in cellular migration [START_REF] Acconcia | Estrogen and tamoxifen induce cytoskeletal remodeling and migration in endometrial cancer cells[END_REF] and tamoxifen-mediated adhesion-dependent cell survival occurs through a Src-dependent process [START_REF] Cowell | Tamoxifen treatment promotes phosphorylation of the adhesion molecules, p130Cas/BCAR1, FAK and Src, via an adhesion-dependent pathway[END_REF]. Thus, the ability to inhibit Src activity, alongside targeting of the ER with anti-oestrogens, may enhance the anti-tumour activity seen with each agent alone. Indeed, two recent studies provide compelling evidence to support such a hypothesis, demonstrating the short-term effectiveness of such combinational treatments in preventing the growth of MCF7 cells [START_REF] Planas-Silva | Targeting c-Src kinase enhances tamoxifen's inhibitory effect on cell growth by modulating expression of cell cycle and survival proteins[END_REF][START_REF] Herynk | Cooperative action of tamoxifen and c-Src inhibition in preventing the growth of estrogen receptor-positive human breast cancer cells[END_REF]. However, it is currently unknown whether co-targeting both Src and the ER is effective in other ERpositive cell lines and whether such treatment regimens would be effective in circumventing the phenomena of acquired tamoxifen resistance that occurs following chronic exposure to this agent [START_REF] Knowlden | Elevated levels of epidermal growth factor receptor/c-erbB2 heterodimers mediate an autocrine growth regulatory pathway in tamoxifen-resistant MCF-7 cells[END_REF][START_REF] Badia | Long-term hydroxytamoxifen treatment of an MCF-7-derived breast cancer cell line irreversibly inhibits the expression of estrogenic genes through chromatin remodeling[END_REF][START_REF] Park | Deregulation of estrogen induced telomerase activity in tamoxifen-resistant breast cancer cells[END_REF].

Our data demonstrates that combining tamoxifen with the novel Src/Abl inhibitor, AZD0530 [START_REF] Hennequin | N-(5-chloro-1, 3-benzodioxol-4-yl)-7-[2-(4methylpiperazin-1-yl) ethoxy]-5-(tetrahydro-2H-pyran-4-yloxy) quinazolin-4-amine, a novel, highly selective, orally available, dual-specific c-Src/Abl kinase inhibitor[END_REF], improves the growth-inhibitory response seen with each treatment alone. Moreover, whereas long-term treatment with each agent alone resulted in the acquisition of a resistant state as demonstrated by a regain in growth ability in the presence of the drug, cells exposed to the combination of tamoxifen and AZD0530 did not acquire resistance; rather, complete cell loss was observed following such treatments in two Cells were subsequently analysed for cell cycle phase using a flow cytometer. A significant reduction in cells in S phase was observed in cells cultured in the presence of tamoxifen plus AZD0530 combined. *P \ 0.05 vs. single agent treatment independent ER-positive cell lines (MCF7 and T47D). These data were achieved using clinically relevant concentrations of AZD0530 since data on the pharmacokinetics of AZD0530 suggest average plasma concentrations of 552 ng/ml [range 356-855 ng/ml] are achievable in humans using a single daily dose regimen [START_REF] Gallagher | A phase I multiple ascending dose study to assess the safety, tolerability and pharmacokinetics of AZD0530, a highly selective, orally available, dual-specific Src-Abl kinase inhibitor[END_REF] and are thus comparable with the concentrations used in this study (1 lM = 542 ng/ml).

Although inhibition of Src activity has previously been demonstrated to induce apoptosis in cancer cells through a variety of mechanisms including down regulation of Bcl-2 and Bcl-X L expression [START_REF] Fujimoto | A Src family inhibitor (PP1) potentiates tumor-suppressive effect of connexin 32 gene in renal cancer cells[END_REF], caspase activation [START_REF] Lee | Apoptotic effect of PP2 a Src tyrosine kinase inhibitor, in murine B cell leukemia[END_REF] and decreased AKT phosphorylation [START_REF] Liu | Antiproliferative effects of Src inhibition on medullary thyroid cancer[END_REF], we did not observed a significant increase in apoptotic cells following dual targeting of Src and ER. That apoptosis does not present a mechanism of cell loss induced by combination treatment was also confirmed through investigating presence of poly(ADP-ribose) polymerase (PARP) cleavage products, indicative of caspase activation and apoptosis; no such cleavage of PARP was detectable in the lysates of such cells (S. Hiscox, unpublished observations). Despite no evidence for a role of apoptosis, our data clearly demonstrates that combination treatment reduced the proliferative capacity of these cells, as revealed by a loss of expression of the Ki-67 cellular proliferation. Indeed, cell cultures treated with tamoxifen and AZD0530 combined were almost entirely Ki-67-negative after 4 weeks treatment. Although the ability of Src kinase inhibitors to suppress tumour cell growth are widely reported [START_REF] Mizenina | S-phase inhibition of cell cycle progression by a novel class of pyridopyrimidine tyrosine kinase inhibitors[END_REF][START_REF] Moasser | Inhibition of Src kinases by a selective tyrosine kinase inhibitor causes mitotic arrest[END_REF][START_REF] Wisniewski | Characterization of potent inhibitors of the Bcr-Abl and the c-kit receptor tyrosine kinases[END_REF], cells treated with either tamoxifen or AZD0530 alone still had a significant number of Ki-67positive cells remaining at these time points. Our RT-PCR analysis suggests that these observations might be due to modulation of cyclin-D1 and c-myc, genes which play a central role in the control of cellular proliferation. Such data is not surprising given the reported interplay between Src and cell-cycle-associated genes [START_REF] Frame | Src in cancer: deregulation and consequences for cell behaviour[END_REF][START_REF] Riley | The mechanism of cell cycle regulation by v-Src[END_REF]; indeed, it has been recently reported that pharmacological inhibition of Src activity in MCF7 cells results in suppression of both c-Myc and cyclinD1 [START_REF] Planas-Silva | Role of c-Src and focal adhesion kinase in progression and metastasis of estrogen receptor-positive breast cancer[END_REF]. Our data here demonstrates that prolonged exposure to a Src inhibitor and tamoxifen results in even greater suppression of these elements and interestingly, cell cycle analysis revealed a significant reduction in the numbers of cells in S phase following combination treatment. Given that Ki67 positivity is significantly correlated with cells in S phase [START_REF] Martinez-Arribas | Flow cytometry vs. Ki67 labelling index in breast cancer: a prospective evaluation of 181 cases[END_REF][START_REF] Turner | Are markers of proliferation valuable in the histological assessment of pituitary tumours?[END_REF], these cell cycle observations confirm our Ki67 data and together these observations demonstrate that combined targeting of the ER and Src in ER-positive MCF7 and T47D cells results in a loss of proliferative response.

A significant impact of combining Src inhibition with tamoxifen was the ability to suppress the migratory and invasive characteristics of these cells. MCF7 cells are poorly invasive in vitro, whereas T47D cells have a higher intrinsic invasive capacity. However, despite these differences, the capacity of both cell types to migrate and invade in the absence of any exogenous stimulation in vitro is significantly enhanced following the long-term treatment with antihormones and the acquisition of an endocrineresistant state [START_REF] Hiscox | Elevated Src activity promotes cellular invasion and motility in tamoxifen resistant breast cancer cells[END_REF] and Fig. 5a). Interestingly, T47D models of acquired resistance appeared generally less invasive than did MCF7 cells that were tamoxifen resistant. The acquisition of endocrine resistance is accompanied by altered expression and/or activation of growth factor signalling pathways (reviewed in [START_REF] Nicholson | Growth factor signalling in endocrine and anti-growth factor resistant breast cancer[END_REF]. However, it is likely that models of acquired resistance derived from different breast cancer cell lines (e.g. MCF7, T47D) will differ in the intrinsic expression of growth factor receptors/pathway components; i.e. the dominant signalling pathway in one model may not be the same for another. This can give rise to phenotypic differences between drug-resistant cells that may include their proliferative and invasive capacities. The observations that acquired resistance in vitro is accompanied by an enhanced invasive behaviour may have significant implications in vivo, where the development of aggressive characteristics, even in the absence of cellular proliferation, may favour cell dissemination and tumour spread. Interestingly, we observed that resistance was also acquired to AZD0530 monotherapy upon continued exposure, as was the case with tamoxifen. However, unlike tamoxifen resistant cells, AZD0530 resistant cells had only a modest invasive capacity further implicating Src activity as a key element in the acquisition of an aggressive cell phenotype. Despite Src activity remaining suppressed in AZD0530-resistant cells, little is known concerning the activity of other Src family kinases in such cells. Although AZD0530 is known to inhibit the Src family kinase Yes [START_REF] Hennequin | N-(5-chloro-1, 3-benzodioxol-4-yl)-7-[2-(4methylpiperazin-1-yl) ethoxy]-5-(tetrahydro-2H-pyran-4-yloxy) quinazolin-4-amine, a novel, highly selective, orally available, dual-specific c-Src/Abl kinase inhibitor[END_REF], it remains to be determined whether there is a role for other members of the Src family in AZD0530-resistant cells and their contribution to their proliferative capacity.

Our data shows that the effect of combining AZD0530 and tamoxifen was to produce cells which were unable to migrate or invade in our in vitro assays. We and others have previously demonstrated that Src kinase promotes cellular invasion through a mechanism involving activation of FAK [START_REF] Hiscox | Elevated Src activity promotes cellular invasion and motility in tamoxifen resistant breast cancer cells[END_REF][START_REF] Planas-Silva | Role of c-Src and focal adhesion kinase in progression and metastasis of estrogen receptor-positive breast cancer[END_REF][START_REF] Brunton | Identification of Src-specific phosphorylation site on focal adhesion kinase: dissection of the role of Src SH2 and catalytic functions and their consequences for tumor cell behavior[END_REF]. Importantly, dual treatment of both MCF7 and T47D cells, in addition to suppressing Src activity, also consistently suppressed the activity of the Src-associated molecule FAK a central component of the cells' migratory machinery. Such data may further reflect the potential benefit of combination drug regimens that involve Src inhibitors in suppressing characteristics of tumour behaviour involved in tumour spread.

In summary, these data suggest that integration of Src kinase inhibitors with current anti-oestrogen therapies may present a useful treatment regimen through which subsequent anti-oestrogen resistance development could be prevented.

Fig. 1

 1 Fig.1Western blotting analysis demonstrated that AZD0530 inhibited Src kinase phosphorylation at Y419 in both MCF7 and T47D cells in a dosedependent manner whilst MAPK activity was inhibited at only 1 lM (a). MCF7 and T47D cell growth in response to increasing concentrations of AZD0530 (b) or tamoxifen ± AZD0530 (c) over 10 days was quantified using a Coulter counter. Whereas both AZD0530 and tamoxifen alone inhibited cellular growth in a dose-dependent manner, AZD0530, when combined with tamoxifen, dramatically reduced cellular growth over this time point. P \ 0.02 vs. control; *P \ 0.02 vs. tamoxifen alone at this data point. Data are means of at least three independent experiments

Fig. 3

 3 Fig. 3 Analysis of the cellular proliferation marker, Ki67, revealed that AZD0530 in combination with tamoxifen (10 -7 M) significantly inhibited cellular proliferation in both MCF7 (A) and T47D (B) cells. These events were seen from the earliest time point examined (1 week). Graphs are the mean of three experiments and show counts of Ki-67 stained cells (1,000 cells counted per treatment sample). Scale bar (shown in control sample) represents 20 lm; *P \ 0.02 vs. tamoxifen alone

12 *Fig. 4

 124 Fig.4Cells were maintained in culture in the presence of tamoxifen ('T', 10 -7 M), AZD0530 ('A', 1 lM) or the combination for 4 weeks. Cells were subsequently analysed for cell cycle phase using a flow cytometer. A significant reduction in cells in S phase was observed in cells cultured in the presence of tamoxifen plus AZD0530 combined. *P \ 0.05 vs. single agent treatment

Fig. 5

 5 Fig. 5 Cellular migration and invasion was assessed every two weeks during treatment (a). Dual inhibition of Src kinase and ER promoted a greater inhibition of these characteristics than either agent alone, with the remaining few cells present at the longest time points having little or no intrinsic invasive or migratory capacity. (Data is presented as per-cent of control invasion at that time point, with the dotted line representing the control value (=100%). (b) Western blot analysis of MCF7 and T47D cell samples following continuous exposure to the agents as indicated. Tamoxifen alone had only a modest inhibitory effect on the activity of Src kinase and the Src substrate, FAK, with levels of Src and FAK appearing to increase by week 10 (MCF7 cells) and week 12 (T47D cells). Inclusion of AZD0530 with tamoxifen almost completely inhibited activity of Src and FAK for the duration of the experiment. t = total protein; p = phospho protein (pY419 Src; pY861 FAK). Gels are representative of three separate experiments; accompanying graphs show densitometry data from all experiments, calculated as mean optical density of treated samples as a per-cent of control (untreated) cells normalized against b-actin

Table 1

 1 The extent of apoptosis in cell populations treated with tamoxifen ('Tam' 10 -7 M) ± AZD0530 (1 lM) over 1-8 weeks was determined using FACS analysis

	Fig. 2 MCF7 and T47D cells were exposed to tamoxifen ± AZD0530 (1 lM) with routine passageing as required or until all cells were dead. Graphs are representative of four separate long-term experiments. The number of weeks taken for the cells to acquired resistance, as	Passage number	2 4 6 8 10 12 14 16 18 0	0	2	control + tam + AZD0530 +tam +AZD0530 4 6 8	MCF7 10 12 14 16 18 20 22 24 26	Passage number	10 2 3 4 5 6 7 8 9 0 1	0	2	T47D 8 10 12 14 16 18 20 22 24 26 +tam +AZD0530 control + tam + AZD0530 4 6
	demonstrated by a regain in						Time (weeks)					Time (weeks)
	their growth in the presence of
	inhibitor, was recorded for both								Control	+tam	+AZD0530	+tam +AZD0530
	cell types for each experiment											(1µM)	(1µM)
	and presented in the accompanying table						Mean time to	MCF7	-	13±4	16±3	No resistance developed; all
								resistance (wks)	T47D	-	17±4	12±5	cells dead by week13±3
		Mean fold change in apoptotic cells (vs. control)
		Wk1			Wk8
	MCF7
	Control	1			1
	Tam	0.57 ± 0.23			1.71 ± 0.19
	AZD	1.60 ± 0.40			1.54 ± 0.60
	Tam ± AZD 0.45 ± 0.15			1.96 ± 0.98
	T47D
	Control	1			1
	Tam	1.29 ± 0.09			2.05 ± 0.19
	AZD	1.10 ± 0.12			2.95 ± 0.05
	Tam ± AZD 1.27 ± 0.05			1.84 ± 0.80
	Data is presented as mean fold change in apoptotic cells (±sd) vs.
	untreated control. Treatment with AZD0530 and tamoxifen combined
	did not significantly alter the level of apoptosis seen with tamoxifen
	as a single agent

Table 2

 2 RT-PCR analysis of proliferation-related gene expression was performed with subsequent densitometric analysis of PCR products

		CyclinD1				c-Myc
		AZD0530	Wk1	Wk4	Wk8	AZD0530	Wk1	Wk4	Wk8
	MCF7
	-tam	-	1	1	1	-	1	1	1
		+	0.87 ± 0.03	0.56 ± 0.19	0.68 ± 0.19	+	0.63 ± 0.09	0.49 ± 0.27	0.42 ± 0.04
	+tam	-	1.01 ± 0.18	0.78 ± 0.02	0.88 ± 0.05	-	1.21 ± 0.15	0.42 ± 0.22	0.79 ± 0.01
		+	1.07 ± 0.06	0.41 ± 0.05*	0.50 ± 0.13*	+	0.50 ± 0.14	0.42 ± 0.08	0.21 ± 0.12*
	T47D
	-tam	-	1	1	1	-	1	1	1
		+	0.80 ± 0.18	0.90 ± 0.14	0.69 ± 0.16	+	1.11 ± 0.03	075 ± 0.23	1.01 ± 0.20
	+tam	-	0.75 ± 0.21	1.03 ± 0.07	1.13 ± 0.19	-	0.46 ± 0.18	0.26 ± 0.05	0.52 ± 0.04
		+	0.52 ± 0.09*	0.77 ± 0.39	0.31 ± 0.23*	+	0.70 ± 0.34	0.64 ± 0.11	0.24 ± 0.

Breast Cancer Res Treat (2009) 115:57-67

Acknowledgments

The authors wish to thank Carol Dutkowski, Denise Barrow, Lindy Goddard, Huw Mottram and Lucy Green for their expert contributions to this work.


Majed Bilal

Thierry Ae

A E Moreau
email: moreau@vjf.inserm.fr

Asselain Bernard
email: bernard.asselain@curie.net

Ae

B Majed
email: bmajed@free.fr

Á B Asselain

T Moreau

Overweight, obesity and breast cancer prognosis: optimal body size indicator cut-points

Keywords: Breast cancer, Prognosis, Body size indicators, Cut-points, External validation

Background Evidence from the data provided in numerous published articles indicates that obesity and overweight can have a negative prognosis role in breast cancer. However, different Body Size Indicators (BSI) and cut-points have been employed and may partly explain discrepancies between the findings of various studies. Material and methods 14,709 women were recruited, treated and followed for a first unilateral breast cancer. After randomly splitting the patients' data into two groups, a maximum statistical outcome approach was used to select optimal BSI cut-points from a ''training sample'', when prognosis events were investigated. External validation was then carried out using a ''validation sample'', and agreement between the selected optimal BSI cut-points was assessed. Body Mass Index (BMI), weight (W), Ideal Weight Ratio (IWR) and Body Surface Area (BSA) were used, and were assessed at the time of diagnosis. Results

The selected optimal BSI cut-points were reliable when overall survival, metastasis recurrence and disease free interval events were investigated. The chosen BMI cutpoint values matched the overweight cut-point value given by the World Health Organization. Agreement between defined binary BSI was acceptable; however, it varied from ''fair'' to ''very good''. Analysis of second primary cancer occurrence and contralateral recurrence events was not conclusive. When local and node recurrence events were taken into account, the results were inconsistent and were linked to an unconfirmed relationship between stoutness and these prognosis events. Conclusions Efficient, optimal BSI cut-points indicate a poorer prognosis, illustrated by a shortened overall survival and an increase of metastasis recurrences, from a BMI value of 25 kg/m 2 , a W value of 60 kg, an IWR value of 20% and a BSA value of 1.7 m 2 . Further BSI cut-point investigations are needed, taking into account contralateral recurrence and second primary cancer events.

Introduction

Numerous relevant studies support the negative prognosis role of obesity and/or overweight in female breast cancer [START_REF] Rock | Nutrition and survival after the diagnosis of breast cancer: a review of the evidence[END_REF][START_REF] Goodwin | Body size and breast cancer prognosis: a critical review of the evidence[END_REF][START_REF] Ryu | Is body mass index the prognostic factor in breast cancer? a meta-analysis[END_REF][START_REF] Carmichael | Obesity and breast cancer: a review of the literature[END_REF][START_REF] Majed | Is obesity an independent prognosis factor in woman breast cancer? Breast Cancer Res Treat 6[END_REF]. Nonetheless, significant discrepancies between methodologies and results have been observed, and some findings support the controversy surrounding this topic. One of the limitations of this type of assessment is the use of various stoutness indicators and cut-points in order to define overweight and obesity [START_REF] Rock | Nutrition and survival after the diagnosis of breast cancer: a review of the evidence[END_REF].

The Body Mass Index (BMI) is often used according to the recommendations of the World Health Organization (WHO) [6]. Although the WHO has defined recommended BMI cut-points (18.5; 25 and 30 kg/m 2 ), other values are used in the literature dealing with breast cancer prognosis: the cut-points range extends from 21 to 30 kg/m 2 in studies exploring the influence of excess of weight in the overall survival (the most frequently investigated prognosis event) of women with breast cancer [START_REF] Rock | Nutrition and survival after the diagnosis of breast cancer: a review of the evidence[END_REF]. In these surveys, significant estimated effects vary largely according to the choice of cut-points. Though BMI is known to provide a reliable assessment of the prevalence of obesity and overweight on the scale of a population [6][START_REF] Gray | Use of relative weight and body mass index for the determination of adiposity[END_REF][START_REF] Garrow | Quetelet's index (W/H2) as a measure of fatness[END_REF], it also suffers from some limitations [START_REF] Frankenfield | Limits of body mass index to detect obesity and predict body composition[END_REF]. The same cut-off value may correspond to different degrees of fatness, depending on the studied populations [6,[START_REF] Swinburn | Body composition differences between Polynesians and Caucasians assessed by bioelectrical impedance[END_REF]. Moreover, Stevens et al. [START_REF] Stevens | The effect of decision rules on the choice of a body mass index cutoff for obesity: examples from African American and white women[END_REF] observed that ''BMI cut-offs associated with equivalent risk across ethnic groups differ widely depending on the outcome and the risk estimate''.

Other Body Size Indicators (BSI), obtained from weight (W) and height (H), have also been considered in breast cancer prognosis, including weight itself [START_REF] Boyd | Body weight and prognosis in breast cancer[END_REF][START_REF] Borugian | Waist-to-hip ratio and breast cancer mortality[END_REF][START_REF] Donegan | The association of body weight with recurrent cancer of the breast[END_REF][START_REF] Enger | Body weight correlates with mortality in early-stage breast cancer[END_REF][START_REF] Goodwin | Fasting insulin and outcome in early-stage breast cancer: results of a prospective cohort study[END_REF][START_REF] Hebert | Weight, height and body mass index in the prognosis of breast cancer: early results of a prospective study[END_REF][START_REF] Kyogoku | Survival of breast-cancer patients and body size indicators[END_REF][START_REF] Mohle-Boetani | Body size, reproductive factors, and breast cancer survival[END_REF], Ideal Weight Ratio (IWR) [START_REF] Kyogoku | Survival of breast-cancer patients and body size indicators[END_REF][START_REF] Bastarrachea | Obesity as an adverse prognostic factor for patients receiving adjuvant chemotherapy for breast cancer[END_REF][START_REF] Senie | Obesity at diagnosis of breast carcinoma influences duration of disease-free survival[END_REF][START_REF] Suissa | Body size and breast cancer prognosis: a statistical explanation of the discrepancies[END_REF] and Body Surface Area (BSA) [START_REF] Boyd | Body weight and prognosis in breast cancer[END_REF][START_REF] Kyogoku | Survival of breast-cancer patients and body size indicators[END_REF]. Kyogoku et al. [START_REF] Kyogoku | Survival of breast-cancer patients and body size indicators[END_REF] showed that there are strong quantitative correlations between BSI themselves, whilst significant multivariate breast cancer prognosis effects were observed when BSI were coded as categorical variables. However, Himes et al. [START_REF] Himes | Lack of correspondence among measures identifying the obese[END_REF] observed a lack of correspondence between some measures, commonly used to identify obesity, when the BSI cut-points were defined using the distributions' fifth quintile. This study revealed a potential classification bias, linked to the choice of BSI cutpoints used to define overweight or obesity, whereas strong quantitative correlations were observed between stoutness indicators computed from height and weight.

As a result of the multiplicity of published overweight and obesity definitions [START_REF] Rock | Nutrition and survival after the diagnosis of breast cancer: a review of the evidence[END_REF], whose influence is taken into account in breast cancer prognosis, the reliability of BSI and cut-point choices can be questioned. Furthermore, as excess of weight can have potentially clinical implications linked to nutritional habits [START_REF] Rock | Diet and breast cancer: can dietary factors influence survival?[END_REF], BSI (which can be easily obtained from weight and height) should be preferred to other invasive and costly fatness evaluation tools for the purposes of breast cancer prognosis. In this context, the continuous use of stoutness indicators would not be useful, whereas the use of binary coding to oppose heavier patients to others would indeed be more relevant. In a previous paper [START_REF] Majed | Is obesity an independent prognosis factor in woman breast cancer? Breast Cancer Res Treat 6[END_REF], the authors of the present paper confirmed, using recommended WHO BMI cut-points, the independent role of obesity in the prognosis of female breast cancer. Multivariate analyses revealed relevant overweight and obesity effects related to metastasis recurrences, overall survival and second primary cancer occurrences. Knowing that the choice of BSI cut-points can have an influence on the association between the stoutness and the prognosis of the breast cancer, the purpose of this study was to establish and validate the most appropriate and informative cutpoints for different BSI. In addition, the agreement between selected optimal BSI cut-points was evaluated.

Material and methods

Material

The characteristics of our population are summarized in a previously published paper [START_REF] Majed | Is obesity an independent prognosis factor in woman breast cancer? Breast Cancer Res Treat 6[END_REF]. We selected 14,709 women who were recruited, treated and followed at the Curie Institute (Paris-France) for a first unilateral breast cancer without distant dissemination. The patients were recruited between 1981 and 1999 and were followed prospectively. Less than 15% of the initially eligible patients were excluded because of missing anthropometric data. Height and weight were measured at the time of diagnosis.

Methods

Previous published analyses [START_REF] Majed | Is obesity an independent prognosis factor in woman breast cancer? Breast Cancer Res Treat 6[END_REF] of this population revealed significant obesity prognosis effects using a categorical BMI variable to define overweight and obesity, according to WHO recommendations. In this study, our aim was to define the most suitable BSI cut-points, in terms of their relevance to several prognosis events in breast cancer: local, nodal, contralateral and distant recurrences, overall survival and second primary cancer occurrences. The Disease Free Interval (DFI), including the absence of metastases, nodal or local recurrences, was also explored. We thus computed several BSI from the patients' weight and height: BMI expressed in kg/m 2 , W in kg, IWR 1 À W HÀ100 ð ÞÃ0.9 Ã 100 ! in % and BSA according to the formula given by Gehan and George: 0.0235 Ã H 0:42246 Ã W 0:51456 Â Ã in m 2 (H is in centimeters and W is in kilograms) [START_REF] Kyogoku | Survival of breast-cancer patients and body size indicators[END_REF][START_REF] Gehan | Estimation of human body surface area from height and weight[END_REF].

The search for optimal binary BSI cut-offs was supported by an observed continuous monotonous relationship between BSI and prognosis events in the studied population (data not shown). The range of explored cut-offs for each BSI was based on the observed descriptive statistics of these indicators in the studied population, and the published BSI cut-points employed in breast cancer prognosis studies (Table 1). The optimal BSI cut-points were selected by using a maximum statistical outcome method. Its goal was to select BSI values that best separated low and high risk patients, according to the studied outcome. We used Cox proportional hazards models [START_REF] Therneau | Modeling Survival Data: Extending the Cox Model[END_REF] based on a partial likelihood maximization, for the estimation of parameters, in order to appreciate the univariate prognosis effect of BSI cut-points. We employed the Log Likelihood (LL) and the Akaike Information Criterion (AIC) of the Cox models, which are known to be reliable tools for the selection of variables in a modeling process [START_REF] Collett | Modelling survival data in medical research[END_REF][START_REF] Harrell | Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis[END_REF][START_REF] Vittinghoff | Regression methods in biostatistics: linear, logistic, survival, and repeated measures models[END_REF]. They enable, respectively, nested and non-nested models to be compared. Harrell's C-index [START_REF] Harrell | Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis[END_REF]30], a tool used for the assessment of model discrimination ability, was also used. It represents the probability of concurrence between model predicted values and accurate outcomes. In survival data, it is computed by using all informative pairs of subjects (at least one subject in each given pair is not censored), for which predicted and observed survival times are compared. As presented by Pencina and D'Agostino [START_REF] Pencina | Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation[END_REF], the overall C index is ''the proportion of usable pairs in which the predictions and outcomes are concordant''. A C-index value of 0.5 is considered to result from random predictions, whereas a value of 1 corresponds to perfectly accurate predictions [START_REF] Harrell | Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis[END_REF]; however, poor C-index values can be induced by a high number of censored times (Table 2). After having selected the optimal BSI cut-points for a given prognosis event, we assessed the agreement between the cut-points using the kappa coefficient [START_REF] Viera | Understanding interobserver agreement: the kappa statistic[END_REF]. Continuous correlations between the BSI themselves were assessed using a correlation coefficient [START_REF] Rosner | Fundamentals of biostatistics[END_REF].

The definition of 'best' BSI thresholds according to statistical criteria, has been associated with an inflation of type I error rates, and a tendency towards the overestimation of measured effects [34]. Therefore, by taking advantage of data based on a large population and a considerable number of prognosis events (Table 2), we achieved an external validation strategy [START_REF] Harrell | Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis[END_REF] based on data splitting: we randomly selected 70% of our initial population data, thereby defining a ''training or test sample'' (N = 10,296), to perform ''optimal'' BSI cut-points choices for each prognosis event. The results obtained were then applied to the other patients, i.e. the remaining 30% of our initial population, thereby defining a ''validation sample'' (N = 4413). Whereas univariate results were used to select ''optimal'' BSI thresholds, multivariate adjustments were then computed using the principal breast cancer prognosis factors: age, menopausal status, size of tumor, tumor's Scarf-Bloom-Richardson grade, year of diagnosis (recruitment), expression of estrogen and progesterone receptors by the tumor, local and lymph node invasion, number of invaded nodes after axillary dissection, type of surgical treatment, use of hormonotherapy, chemotherapy and/or radiotherapy. The obesity prognosis effect is known to be linked to breast cancer characteristics at the time of diagnosis [START_REF] Majed | Is obesity an independent prognosis factor in woman breast cancer? Breast Cancer Res Treat 6[END_REF]35], as it may be linked to a delayed diagnosis. Multivariate adjustments were therefore computed. As common breast cancer prognosis factors are not useful for the assessment of the risk of second primary cancers, only age and menopausal status were used as multivariate adjustments for the study of second primary cancer events. Hazard Ratios (HR), corresponding to each binary BSI effect, were computed with 95% confidence intervals (95 CI) and LL ratio tests [START_REF] Collett | Modelling survival data in medical research[END_REF][START_REF] Harrell | Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis[END_REF].

Power tests linked to the adjusted HR corresponding to the validation sample, which represented 30% of the initial population, were estimated in order to improve the interpretation of results. A substantial loss of power was expected. The Freedman formula, used in Cox proportional models, proposed by Rosner [START_REF] Rosner | Fundamentals of biostatistics[END_REF], was used to estimate power test outcomes. The S-plus statistical software was used for data analysis.

Results

High significant correlations were observed between BSI indicators when they were used as continuous variables.

The highest correlations were found between BMI and IWR (the correlation coefficient reached the value of 0.99) and between Weight and BSA (0.98); the lowest correlation coefficient was observed between IWR and BSA (0.74).

From the training sample, the agreement between LL, AIC and C-index best values (obtained from the univariate analysis), enabled optimal BSI cut-points to be selected when all prognosis events were considered. The results in terms of overall survival, metastasis recurrence and DFI events indicated that unadjusted and adjusted HR estimates from the training sample were reproducible in the validation sample (Table 3a). Nonetheless, in the validation sample, adjusted HR were often statistically non-significant and estimated power tests were frequently poor (data not shown). DFI evaluations made with the validation sample presented comparatively less significant results. The Kappa agreement quality [START_REF] Carpenter | Kappa statistic[END_REF] between selected BSI cut-offs varied from ''fair'' to ''very good'' (Table 4). The best kappa coefficients were observed between BMI and IWR (0.90), and between W and BSA (0.84), when overall survival outcome was evaluated. Optimal BMI cut-points, linked to overall survival, metastasis recurrences and DFI, corresponded to the overweight threshold value according to the WHO definition. Concerning second primary cancer and contralateral recurrence outcomes, significant univariate and multivariate effects were observed in the training sample (Table 3b). However, in the case of contralateral recurrences, with the exception of BSA, reduced HR estimations were found in the validation sample. This phenomenon was more pronounced for second primary cancer events, for which the estimated HR were half of those from the training sample. Once again, quality agreements between defined best binary BSI for these outcomes varied from ''fair'' to ''very good''. The best agreements were observed between BMI and IWR (0.92), and between W and BSA (0.90), when contralateral recurrence outcomes were investigated; the BMI optimal cut-point matched the threshold value of overweight defined by the WHO. Concerning the analysis of second primary cancer outcomes, the optimal BMI cut-point was 29 kg/m 2 which closely matched the WHO threshold value for obesity. In this case, the BMI cut-off had a modest (''fair'') kappa coefficient when compared with IWR, whereas W agreed much better with the BSA.

The study of local recurrence events led, in most cases, to non-significant HR from both training and validation samples (Table 3c). HR estimations were often close to the unity, except for the case of BSA: the choice of a 2.2 m 2 BSA cut-off led to 0.6% of the test sample patients being defined as ''stout'', and to fewer uncensored times, whereas other BSI cut-points led to a far greater number of patients being defined as overweight. When node recurrence events were taken into account, few statistically significant results were observed from the training sample (univariate and multivariate tests of W and BSA). HR values estimated from the validation sample were contradictory to those determined using the training sample. Very poor kappa coefficients were found between some optimal binary BSI, when local and nodal recurrences were explored (considering, respectively, BSA and IWR).

Univariate associations between optimal binary BSI and prognostic events were frequently linked to poor C-index values (close to 0.5). Multivariate adjustments improved the C-index values to nearly 0.70-0.75 in almost all cases, except for those characterized by second cancer and contralateral events for which multivariate adjustments very slightly improved this index (Table 5). In the case of second cancer events, C-index values from the training sample were clearly higher than those from the validation sample. Concerning node recurrence events, C-indices values linked to adjusted HR values from the validation sample were clearly higher than those derived from the training sample.

Finally, optimal BMI cut-points were frequently characterized by the best LL, AIC and C-index values, linked to breast cancer prognosis analyses, even though, the other BSI had values very close to those given by the BMI.

Discussion

This study, carried out using a methodology which implements an external validation, based on data splitting, has enabled optimal BSI cut-points to be identified and associated to the prognosis of breast cancer. When compared with other published studies which investigate the prognostic role of obesity and/or overweight [START_REF] Rock | Nutrition and survival after the diagnosis of breast cancer: a review of the evidence[END_REF] in breast cancer, the significant number of events and large population size, reported here, ensure that HR estimations were derived with a reliable effect size beyond the statistical tests significance. Nevertheless, in a simulation study, Faraggi and Simon [34] found that a maximum statistically selected variable cut-point can lead to an overestimated HR. This phenomenon would be related to the HR achieved from our training sample.

Selected optimal BSI cut-points were effective in terms of breast cancer prognosis when overall survival (OS), metastasis recurrence and DFI events were studied. As the BMI selected threshold values were 24 and 25 kg/m 2 , the role of excess weight in breast cancer prognosis starts to become significant at the WHO-defined threshold (cutpoint) for overweight. The W, IWR and BSA cut-offs are [START_REF] Kyogoku | Survival of breast-cancer patients and body size indicators[END_REF] found that there was a 12% difference in survival likelihood between patients with a BMI of 20 kg/m 2 and those with a BMI of 24 kg/m 2 , after a 10-year of follow-up. Kimura et al. [START_REF] Rock | Nutrition and survival after the diagnosis of breast cancer: a review of the evidence[END_REF] defined stout patients as those with a BMI of at least 23 kg/m 2 , and revealed a significantly lower survival probability for stout post-menopausal women after a 10-year follow-up, when compared to leaner patients with a BMI lower than 21 kg/m 2 . Using nonlinear modeling, Goodwin et al. [START_REF] Rock | Nutrition and survival after the diagnosis of breast cancer: a review of the evidence[END_REF] observed degraded disease free survival (DFS) and overall survival outcomes for patients with a BMI of at least 25 kg/m 2 ; nevertheless, these authors also observed poorer outcomes for patients with a BMI of less than 20 kg/m 2 . In the modeling process of our population data, the use of a quadratic BMI term in addition to the linear term, did not suggest any increase of metastasis, DFI or overall survival events among leaner significant relationship between BSA and breast cancer DFS [START_REF] Boyd | Body weight and prognosis in breast cancer[END_REF] or OS [START_REF] Kyogoku | Survival of breast-cancer patients and body size indicators[END_REF]. In these cases, Gehan and George's formula [START_REF] Gehan | Estimation of human body surface area from height and weight[END_REF] was employed and the highest used cut-point value was 1.5 m 2 , with an unadjusted HR of about 1.7, when compared to patients with a BSA of less than 1.4 m 2 [START_REF] Kyogoku | Survival of breast-cancer patients and body size indicators[END_REF]. Concerning second cancer events, the influence of significant overweight was estimated from our population training sample. The selected BMI threshold matches the obesity cut-point definition given by the WHO. Nonetheless, results from the validation sample did not confirm these findings. Dignam et al. [START_REF] Dignam | Obesity, tamoxifen use, and outcomes in women with estrogen receptor-positive early-stage breast cancer[END_REF] observed, from a substantial cohort of 3,385 patients, a significant increase in the occurrence of second cancers and contralateral breast cancers among women with a BMI above 30 kg/m 2 , when compared to those with a BMI of less than 25 kg/m 2 : HR values of up to 1.60 were estimated. In the survey described in the present paper, when optimal BSI cut-offs were implemented, the The C-index from the adjusted HR in the test sample, and the C-index from the unadjusted and adjusted HR in the validation sample were also computed estimated univariate HR for second primary cancers reached a value of 2, whereas adjusted HR values ranged between 1.6 and 1.8, which is close to the HR range estimated given by Dignam et al. Among the patients in the validation sample defined as being stout, few events were noticed (about 20). Together with node recurrence, second primary cancer outcomes are the least observed events (Table 2). Although the C-index values are clearly better, as far as the results from the training sample are concerned (thus supporting HR as an accurate indicator), HR discrepancies in the validation sample did not enable BSI cut-offs to be validated for second primary cancer outcomes. A similar situation was observed concerning the outcome of contralateral recurrences. Nevertheless, the use of the optimal 1.7 m 2 BSA cut-point appears to be efficient, contrary to the use of other selected BSI cut-points are not; however, the former BSA threshold does not produce better outcome statistics (LL, AIC and C-Index). Suitable agreement between binary BSI corresponds to the WHO overweight cut-point definition.

Although obesity is clearly recognized as a risk factor for breast cancer among post-menopausal women [START_REF] Renehan | Body-mass index and incidence of cancer: a systematic review and meta-analysis of prospective observational studies[END_REF], these results do not permit BSI cut-points, derived from contralateral recurrence event analysis, to be validated. Nodal and local recurrence events are rarely specifically studied. Marret et al. [START_REF] Rock | Nutrition and survival after the diagnosis of breast cancer: a review of the evidence[END_REF] found an independent prognostic role for BMI, when used as a continuous variable, by associating it with a reduced risk of local recurrence (HR = 0.92 [0.85-0.99]). In the survey reported here, the investigation of local recurrence events led to a non-significant relationship between overweight and the risk of local recurrence. However, optimal BMI, W and IWR cutoffs enabled a reliable HR to be estimated, with a value a little less than unity (Table 3c). When node recurrence events are considered, HR estimates from the validation sample are contradictory to those derived from the test sample. The IWR cut-point have very poor agreements with the other binary BSI, whereas W and BSA exhibit a ''very good'' agreement coefficient (0.8), better LL, AIC and C-index values, and significant unadjusted and adjusted HR estimates from the training sample. However, adjusted HR estimates from the validation sample had clearly better C-index values. Our interpretation of these contradictory results is that they are not consistent and are linked to an unconfirmed relationship between stoutness and breast cancer local or nodal recurrence. A simulation study has shown that the use of a cut-point, in presence of a continuous link between covariate and event risk, leads to a significant loss of power [34]. The continuous use of BSI in local or nodal recurrence risks was non-significant in our study. In the case of local and nodal recurrences, significant results appear to be related to sampling fluctuations, especially when the studied subpopulations have a small number of uncensored survival times [START_REF] Harrell | Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis[END_REF].

The present study offers new data relevant to the use of optimal cut-points, for several different BSI. This is particularly accurate and contributive for OS, DFI and Metastasis recurrence, the most frequently observed events during breast cancer follow-up. Suggested optimal thresholds for second primary cancers and contralateral recurrence events were not validated, since the outcome of our analysis was not conclusive. While local and nodal recurrence event analyses were irrelevant, the influence of BSI on DFI events is related to the risk of metastasis recurrence. As shown in our results, even though various binary BSI can have equivalent efficiencies, their kappa agreement coefficients vary from ''fair'' to ''very good'' [START_REF] Carpenter | Kappa statistic[END_REF]. In our survey, this finding could not be explained by a kappa paradox [START_REF] Viera | Understanding interobserver agreement: the kappa statistic[END_REF]. Although optimally efficient binary BSI cut-points do not allow exactly the same patients to be identified, they are in substantial agreement. The results of our study can be used to improve the understanding of discrepancies between other studies, arising from the choice of BSI and cut-points.

The analysis methodology described in this study is based on usual statistical modeling tools for the selection of variables (log likelihood, AIC), to check the model's discriminatory ability (C-index) and to validate results (external validation by data splitting). The efficiency of this strategy has already been illustrated [START_REF] Hilsenbeck | Why do so many prognostic factors fail to pan out?[END_REF], and presents the advantage of simplified computations and interpretation of results, in the case of a substantially large population sample. Some authors have suggested selecting optimal cut-points by the use of cross validation procedures [34], bootstrapping computations of an optimal cut-point effect confidence interval [START_REF] Abdolell | Binary partitioning for continuous longitudinal data: categorizing a prognostic variable[END_REF][START_REF] Hollander | Confidence intervals for the effect of a prognostic factor after selection of an 'optimal' cutpoint[END_REF], adjusted significance level tests [34, 42] and multivariate settings [43]. However, loss of power [34,43] and underestimated HR [34] have been reported in the case of the cross validation strategy. When only a limited population size is available, internal validation methods such as re-sampling techniques and bootstrapping [START_REF] Harrell | Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis[END_REF] should obviously be preferred.

From our study, validated BSI cut-points from the investigation of overall survival and metastasis recurrence events support the use of a BMI value of 25 kg/m 2 , a W value of 60 kg, an IWR value of 20% and a BSA value of 1.7 m 2 to identify stout patients with a poorer breast cancer prognosis. These cut-points may be used by health professionals, to conduct nutritional intervention studies for the improvement of patients' breast cancer prognosis.

Additional studies are needed to explore second primary cancer and contralateral recurrence events in order to validate optimal binary BSI cut-points. The improvement of breast cancer survival, over the last decades, should enable these required surveys. Further validation of optimal BSI cut-offs, in relation to OS and metastasis recurrence events among other populations [START_REF] Himes | Lack of correspondence among measures identifying the obese[END_REF], would also be useful.

  patients. With regard to the W cut-off values selected in our survey (60 and 61 kg), various authors have used the same or very similar values to investigate overall breast cancer survival and DFS. Boyd et al. (64 kg cut-off), Donegan et al. (59 kg cut-off, unadjusted HR for DFS:1.4), Haybittle et al. (60 kg cut-off, unadjusted HR for OS: 1.7), Kyogoku et al. (60 kg cut-off, unadjusted HR for OS: 1.5 compared to patients under 45 kg), Mohle-Boetani et al. (64 kg cut-off, adjusted HR for OS: 1.

Table 1

 1

	BSI descriptive statistics and cut-off range explored in our study	BSI	Mean & 95% CI	Quintile 25% Quintile 50% Quintile 75% Explored BSI cut-points range
		BMI (kg/m 2 ) 23.76 [23.69-23.82]	20.94	23.05	25.72	18-40
		IWR (%)	12.44 [12.12-12.76] -1.43	9.26	22.22	5-55
		W (kg)	61.96 [61.78-62.14]	54	60	68	50-85
		BSA (m 2 )	1.68 [1.67-1.68]	1.56	1.66	1.77	1.25-2.25

Table 3

 3 Best BSI cut-offs according to the prognosis events leading to (a) contributive analysis. (b) non-conclusive analysis. (c) non-contributive analysis

	Breast Cancer Res Treat (2009) 115:193-203
		HR (adj)		1*	1.16[1.02-1.32]	1	1.10[0.96-1.25]	1	1.12[0.99-1.27]	1**	1.19[1.06-1.35]		1	1.10[0.97-1.24]	1	1.06[0.93-1.22]	1*	1.16[1.03-1.31]	1*	1.16[1.02-1.32]		1	1.05[0.93-1.18]	1	1.03[0.91-1.17]	1	1.11[1.00-1.24]	1	1.11[0.99-1.24]			1	0.76[0.44-1.31]	1	0.81[0.52-1.27]
		HR		1****	1.38[1.22-1.56]	1****	1.35[1.19-1.53]	1****	1.27[1.13-1.42]	1****	1.31[1.17-1.48]		1**	1.21[1.07-1.36]	1**	1.20[1.06-1.37]	1***	1.23[1.09-1.38]	1***	1.25[1.11-1.42]		1	1.10[0.99-1.23]	1	1.11[0.99-1.25]	1**	1.15[1.04-1.27]	1	1.16[1.04-1.3]			1	0.81[0.47-1.41]	1	0.87[0.56-1.36]
	Validation sample	N and (%) Events		3088(70) 744	1325(30) 391	3173(71.9) 768	1240(28.1) 367	2305(52.2) 555	2108(47.8) 580	2647(60) 635	1766(40) 500		2641(59.8) 655	1772(40.2) 491	3356(76) 846	1057(24) 300	2305(52.2) 560	2108(47.8) 586	3130(70.9) 778	1283(29.1) 368		3088(70) 1009	1325(30) 451	3356(76) 1096	1057(24) 364	2043(46.3) 651	2370(53.7) 809	3130(70.9) 1011	1283(29.1) 449			3934(89.1) 161	479(10.9) 14	3732(84.6) 153	681(15.4) 22
		HR (adj)		1*	1.11[1.02-1.2]	1*	1.11[1.02-1.21]	1**	1.12[1.03-1.21]	1*	1.08[1.00-1.18]		1***	1.15[1.07-1.25]	1**	1.15[1.06-1.26]	1****	1.18[1.09-1.27]	1***	1.18[1.09-1.29]		1*	1.10[1.02-1.18]	1*	1.10[1.02-1.19]	1*	1.09[1.02-1.17]	1**	1.12[1.04-1.21]			1***	1.81[1.37-2.38]	1***	1.58[1.23-2.03]
		HR		1****	1.37[1.26-1.48]	1****	1.39[1.28-1.51]	1****	1.32[1.22-1.43]	1****	1.27[1.17-1.37]		1****	1.29[1.20-1.40]	1****	1.32[1.21-1.44]	1****	1.31[1.21-1.41]	1****	1.29[1.19-1.40]		1****	1.17[1.09-1.26]	1****	1.19[1.1-1.29]	1****	1.16[1.08-1.24]	1****	1.18[1.10-1.27]			1****	2.00[1.52-2.63]	1****	1.75[1.37-2.24]
		Events		1676	882	1719	839	1235	1323	1464	1094		1468	1166	1899	735	1269	1365	1777	857		2327	1089	2527	889	1510	1906	2368	1048			318	62	298	82
	Training sample	N and (%)		7167(69.6)	3129(30.4)	7361(71.5)	2935(28.5)	5454(53)	4842(47)	6227(60.5)	4069(39.5)		6178(60)	4118(40)	7781(75.6)	2515(24.4)	5454(53)	4842(47)	7335(71.2)	2961(28.8)		7167(69.6)	3129(30.4)	7781(75.6)	2515(24.4)	4764(46.3)	5532(53.7)	7335(71.2)	2961(28.8)			9229(89.6)	1067(10.4)	8716(84.7)	1580(15.3)
	Best BSI cut-offs according to prognosis events	a	Overall survival	BMI above 25 kg/m 2 non stout	stout	IWR above 20% non stout	stout	Weight above 61 kg non stout	stout	BSA above 1.7 m 2 non stout	stout	Metastasis	BMI above 24 kg/m 2 non stout	stout	IWR above 23% non stout	stout	Weight above 61 kg non stout	stout	BSA above 1.75 m 2 non stout	stout	DFI	BMI above 25 kg/m 2 non stout	stout	IWR above 23% non stout	stout	Weight above 60 kg non stout	stout	BSA above 1.75 m 2 non stout	stout	b	Second cancer events	BMI above 29 kg/m 2 non stout	stout	IWR above 31% non stout	stout

Table 4

 4 Best body size indicators' cut-offs agreements according to the several prognosis events studied, kappa coefficients (top) and observed agreement (bottom)

	Overall survival events				Metastasis recurrence
		BMI(25)	W(61)	IWR(20)	BSA(1.7)		BMI(24)	W(61)	IWR(23)	BSA(1.75)
	BMI(25)	1	0.59	0.9	0.58	BMI(24)	1	0.68	0.65	0.58
	W(61)	0.8	1	0.51	0.84	W(61)	0.84	1	0.46	0.63
	IWR(20)	0.95	0.56	1	0.49	IWR(23)	0.84	0.74	1	0.54
	BSA(1.7)	0.81	0.92	0.64	1	BSA(1.75)	0.81	0.82	0.82	1
	Second cancer events				Contralateral recurrence
		BMI(29)	W(71)	IWR(31)	BSA(1.85)		BMI(24)	W(64)	IWR(14)	BSA(1.7)
	BMI(29)	1	0.63	0.48	0.6	BMI(24)	1	0.7	0.92	0.6
	W(71)	0.91	1	0.6	0.83	W(64)	0.86	1	0.63	0.9
	IWR(31)	0.84	0.86	1	0.46	IWR(14)	0.96	0.82	1	0.53
	BSA(1.85)	0.91	0.95	0.82	1	BSA(1.7)	0.81	0.95	0.78	1
	Nodes recurrence				Local recurrence
		BMI(21)	W(50)	IWR(55)	BSA(1.5)		BMI(21)	W(51)	IWR(5)	BSA(2.2)
	BMI(21)	1	0.38	0.02	0.37	BMI(21)	1	0.49	0.68	0
	W(50)	0.81	1	0.01	0.8	W(51)	0.84	1	0.3	0
	IWR(55)	0.29	0.12	1	0.01	IWR(5)	0.86	0.7	1	0.01
	BSA(1.5)	0.8	0.97	0.15	1	BSA(2.2)	0.27	0.13	0.41	1

BMI: Body Mass Index in kg/m 2 ; W: Weight in Kg; IWR: Ideal Weight Ratio; BSA: Body Surface Area according to the Gehan & George formula in m 2 . BSI cut-off values are between parentheses

Table 5

 5 Computed Log Likelihood, AIC and C-index enabling the selection of BSI cut-offs selection from unadjusted HR in the test sample

	BSI optimal cut-offs according	Training sample				Validation sample
	to prognosis events	Log-likelihood	AIC	C-index	C-index	C-index	C-index
		(univariate)	(univariate)	(univariate)	(multivariate)	(univariate)	(multivariate)
	Overall survival
	BMI above 25 kg/m 2	-22078.89	44159.78	0.534	0.748	0.535	0.740
	IWR above 20%	-22076.76	44155.52	0.533	0.744	0.531	0.734
	Weight above 61 kg	-22080.84	44163.69	0.534	0.749	0.529	0.743
	BSA GG above 1.7 m 2	-22088.42	44178.84	0.524	0.748	0.531	0.743
	Metastasis recurrence
	BMI above 24 kg/m 2	-23235.28	46472.57	0.533	0.743	0.520	0.741
	IWR above 23%	-23237.05	46476.09	0.528	0.743	0.516	0.740
	Weight above 61 kg	-23233.06	46468.12	0.535	0.744	0.526	0.742
	BSA GG above 1.75 m 2	-23237.98	46477.96	0.521	0.744	0.526	0.739
	Second cancer
	BMI above 29 kg/m 2	-3272.14	6546.27	0.542	0.596	0.506	0.550
	IWR above 31%	-3273.74	6549.48	0.536	0.582	0.504	0.560
	Weight above 71 kg	-3273.11	6548.22	0.545	0.571	0.500	0.538
	BSA GG above 1.85 m 2	-3273.44	6548.89	0.541	0.575	0.505	0.536
	Contralateral recurrence
	BMI above 24 kg/m 2	-5861.78	11725.56	0.526	0.558	0.512	0.532
	IWR above 14%	-5861.83	11725.66	0.519	0.564	0.503	0.513
	Weight above 64 kg	-5862.72	11727.43	0.517	0.554	0.509	0.535
	BSA GG above 1.7 m 2	-5863.18	11728.37	0.513	0.556	0.524	0.532
	Nodes recurrences
	BMI above 21 kg/m 2	-2019.93	4041.86	0.512	0.668	0.514	0.745
	IWR above 55%	-2020.68	4043.35	0.503	0.672	0.501	0.748
	Weight above 50 kg	-2015.62	4033.24	0.539	0.676	0.514	0.757
	BSA GG above 1.5 m 2	-2016.66	4035.33	0.537	0.673	0.511	0.756
	Local recurrence
	BMI above 21 kg/m 2	-12591.74	25185.48	0.511	0.689	0.507	0.688
	IWR above 5%	-12591.80	25185.60	0.512	0.690	0.509	0.689
	Weight above 51 kg	-12592.18	25186.37	0.502	0.685	0.505	0.696
	BSA GG above 2.2 m 2	-12591.81	25185.62	0.503	0.691	0.500	0.695
	DFI
	BMI above 25 kg/m 2	-29919.09	59840.17	0.517	0.703	0.503	0.704
	IWR above 23%	-29918.12	59838.23	0.517	0.702	0.508	0.709
	Weight above 60 kg	-29918.72	59839.43	0.517	0.703	0.517	0.710
	BSA GG above 1.75 m 2	-29918.07	59838.14	0.515	0.701	0.514	0.711

Breast Cancer Res Treat (2009) 115:193-203