Charlotte Emlinger

Karine Latouche

When quality management helps agri-food firms to export

Keywords: Non-tariff-Measures, quality management, export competitiveness. Classification JEL: F14

This article deals with the effects of firms' quality policies on export performance. We rely on the presence of quality management personnel to assess the level of commitment of firms on issues related to reliability and safety of products, using French administrative employee-firm-level data. We merge these data with French customs data providing the value and quantity of exports, for each firm, by product and destination. We show that firms with quality management employees have a better markets penetration and export higher volumes, especially on markets with high standards requirement (higher number of sanitary and phytosanitary or technical measures). Overall, our paper highlights the role of "quality investment" of agri-food firms in export performance, underlining that product quality is not limited to product differentiation perceived by final consumers. Product traceability and reliability is an essential factor in firms' competitiveness, especially in the perspective of the global value chains.

Introduction

From the Bovine Spongiform Encephalopathy outbreak in the 90's to the milk powder scandal in China in 2008, food safety incidents have led to an increasing demand for traceability and safety of food products, from both consumers and distributors. Governments have answered to this demand by strengthening public regulation on their territory (see for example the European Community Regulation 178/2002 on food traceability) or by increasing the number of Sanitary and Phytosanitary measures (SPS) and Technical barriers to trade (TBT) at the border. The private sector also implemented several certifications to facilitate the standardization of safety and traceability (HACCP, ISO standards) or to manage the buyer-supplier relationship (IFS, BRC retailers standards).

Therefore, the ability to make safe products, to ensure traceability and to have it recognized through certification appears to be an essential element of the international competitiveness of agri-food companies. Even if these characteristics are not systematically observed by final consumers, they may help to access to markets with high levels of sanitary requirements or enable to benefit from international retailer networks.

In this paper, we investigate the effects of firms' commitment to traceability and food safety on export performance. We rely on the presence of quality management and control personnel in agrifood firms to proxy their level of commitment to product reliability and safety issues. Using French administrative employee-firm-level data merged with French customs data, we measure the effect of firms' quality management system on the intensive and extensive margins of trade.

Focusing on a single country's exports allows us to control for consumers' preferences for the different exporting countries. We compare exports of firms with quality management personnel with exports of firms without this category of employees to a given destination, product (defined within 6 digit code of the Harmonized System) and year. Using firm fixed effect, we also perform a within-firm estimation, which allows us to see the effect of quality personnel hiring in a given firm on trade. We furthermore explore whether these effects vary according to destination countries, depending on the level of Non Tariff Measures (NTMs) applied at their border.

Quality management and control personnel are in charge of the firm's products quality. They ensure that goods are safe, reliable and durable, that they meet customers' expectation and that they follow regulatory requirements. According to the size and the organization of the firm, the activities of the employees in charge of quality management may be manifold. They define the quality criteria to fulfill safety and regulatory requirements and design the quality protocols.

They ensure that these protocols are followed by conducting inspections and testing at the different stages of the production, processing and distribution. They also have the responsibilities to create quality documentation along the chain of production, allowing to track and trace products through traceability systems. Finally, they report feedback from customers and analyse safety issues by improving processes. Quality managers are therefore major players in the firm's quality policy, both by preventing failure and by copying with those that do occur. They are key in the adoption of standards and in their maintenance. Working both with the firms' other employees and with external partners such as suppliers, customers, health inspectors or customs, they can lead to better export performance through four channels. First, the increase of product safety allowed by the implementation of quality systems limits consumers' exposure to potentially hazardous foods, and reduces recalls and consumers complains. Second, quality control and management may increase the effectiveness of operation and the efficiency of supply chain, through a better optimization of the processes, inducing higher productivity and a reduction of losses or products deterioration. Third, quality specialists help to comply sanitary, phyto-sanitary or technical legal requirements in destination markets, both by applying the necessary procedures, and by being able to provide evidence of compliance with the measures. Finally, better procedures and traceability can increase firms' reputation among buyers, whether they are intermediaries, retailers or final consumers. This confidence can come from a simple perception or from different certifications (such as HACCP, IFS or ISO), whose adoption is facilitated by the presence of qualified and specialized personnel. The firms' commitment in traceability and food safety may thus lead to a differentiation of products for the buyers' point of view. Of course, firms do not need to have a dedicated staff on quality management to be able to meet standards, implement traceability systems and have certifications. We can however expect that firms hiring specialized employees have stronger policy and capability on these issues, and consequently better export performance.

The effect of firms' quality control and management on trade has not been studied in the literature to our knowledge. This paper is however linked to the literature on traceability and quality in the food supply chain. A first strand of this literature investigates the effect of traceability and quality management systems on firms competitiveness. [START_REF] Alfaro | Traceability as a strategic tool to improve inventory management: A case study in the food industry[END_REF] use a case study in the Spanish vegetable industry and show that traceability systems do not only guarantee food safety, but also give quantitative and qualitative advantages along the different stage of the supply chain, which has also been highlighted by [START_REF] Epelbaum | The technological evolution of food traceability systems and their impact on firm sustainable performance: A RBV approach[END_REF] and [START_REF] Aiello | The expected value of the traceability information[END_REF]. A second strand of literature focuses on the valuation of food traceability systems by consumers, assessing their willingness to pay through choice experiment. [START_REF] Gao | Effects of label information on consumer willingnesstopay for food attributes[END_REF], [START_REF] Gracia | Are local and organic claims complements or substitutes? a consumer preferences study for eggs[END_REF][START_REF] Liu | Consumers' valuation for food traceability in china: Does trust matter?[END_REF], among others, highlight the heterogeneity of the willingness to pay for traceability information and quality certification among consumers. In this paper, we focus on the impact of quality management on firms' export value, trade unit values and quality, to assess the effect of firms' quality commitments on international competitiveness.

Our paper also participates to the literature on standards and their effect on trade. An abundant empirical trade literature deals with NTMs which are public regulations applied at the border and withinof each countries. These works provide mixed evidence on NTMs effect on trade (see [START_REF] Santeramo | The effects of non-tariff measures on agrifood trade: A review and meta-analysis of empirical evidence[END_REF] and [START_REF] Disdier | A Practical Guide to the Economic Analysis of Non-Tariff Measures[END_REF] for a summary). [START_REF] Peterson | Evaluating the trade restrictiveness of phytosanitary measures on u.s. fresh fruit and vegetable imports[END_REF][START_REF] Fontagné | Product standards and margins of trade: Firm-level evidence[END_REF] and [START_REF] Murina | Trading with conditions: The effect of sanitary and phytosanitary measures on the agricultural exports from low-income countries[END_REF] show that US and European SPS measures act as a trade barriers. This is confirmed by [START_REF] Grundke | Hidden protectionism? evidence from non-tariff barriers to trade in the united states[END_REF], demonstrating that enforcement of US standards induce a counter-cyclical, hidden protectionist effect. On the other hand, [START_REF] Crivelli | The impact of sanitary and phytosanitary measures on market entry and trade flows[END_REF] and de [START_REF] De Frahan | Harmonisation of food regulations and trade in the single market: evidence from disaggregated data[END_REF] suggest that NTMs can positively impact trade. [START_REF] Xiong | Disentangling Demand-Enhancing And Trade-Cost Effects Of Maximum Residue Regulations[END_REF] confirm the ambiguous effect of NTMs on trade by showing that NTMs both enhance import demand and reduce foreign exporters' supply. Few papers deals with private standards. [START_REF] Anders | Standards as barriers versus standards as catalysts: Assessing the impact of HACCP implementation on u.s. seafood imports[END_REF] show that HACCP requirement for seafood products in the USA acts as a trade catalyst for developed and big exporters and as a trade barrier for developing and small exporters. In this paper, we do not focus on the impact of standards on trade but show how quality management help firms to export on markets with NTMs.

Finally, our work contributes to the extensive literature on quality and trade. Among others, [START_REF] Verhoogen | Trade, Quality Upgrading, and Wage Inequality in the Mexican Manufacturing Sector[END_REF], [START_REF] Manova | Export Prices Across Firms and Destinations[END_REF] and [START_REF] Baldwin | Zeros, Quality, and Space: Trade Theory and Trade Evidence[END_REF] show that quality is an important component of a firm's export success. This outcome is confirmed for the agri-food sector by [START_REF] Crozet | Quality Sorting and Trade: Firm-level Evidence for French Wine[END_REF] and [START_REF] Curzi | Export behavior of Italian food firms: Does product quality matter?[END_REF] who demonstrate that quality increases both the probability of market entry and the amount of exported values.

Quality may also change the geography of trade as underlined by [START_REF] Fan | On the relationship between quality and productivity: Evidence from china's accession to the wto[END_REF] who show that quality upgrading leads Chinese firms to reorient their exports to high income countries.

The majority of empirical studies on trade uses indirect measures to proxy a product's quality. [START_REF] Hummels | Shipping the Good Apples Out? An Empirical Confirmation of the Alchian-Allen Conjecture[END_REF], [START_REF] Schott | Across-Product Versus Within-Product Specialization in International Trade[END_REF], [START_REF] Schott | The relative sophistication of Chinese exports[END_REF] and [START_REF] Hummels | The Variety and Quality of a Nation's Exports[END_REF] use trade unit values, assuming that higher prices correspond to higher quality of goods. [START_REF] Khandelwal | The Long and Short (of) Quality Ladders[END_REF] (developed in [START_REF] Amiti | Import Competition and Quality Upgrading[END_REF]) proposes an indirect measure of quality derived from econometric estimation and widely used in literature in recent years.

Few studies use direct quality measures to assess the quality of products. A notable exception is [START_REF] Crozet | Quality Sorting and Trade: Firm-level Evidence for French Wine[END_REF] and [START_REF] Chen | Quality, trade, and exchange rate pass-through[END_REF], who use quality ranking by experts and show that quality increases firm-level price, probability of market entry and export values. [START_REF] Hansman | Vertical integration, supplier behavior, and quality upgrading among exporters[END_REF] rely on a direct measure of fish quality in the Peruvian fishmeal industry to assess the impact of organizational structure on quality upgrading. R&D and innovation are also used as a proxy for quality of products at the firm level, as in [START_REF] Curzi | Export behavior of Italian food firms: Does product quality matter?[END_REF] or [START_REF] Kugler | Prices, plant size, and product quality[END_REF]. Our approach differs as we consider another dimension of quality differentiation, which is the safety and traceability of products. This component of the goods' quality may not be directly observable by final consumers but is essential, especially to reach countries with stricter sanitary standards.

We show that quality control and management personnel help firms to export more in value and quantity, but that this effect is limited to destination-products where Non Tariff measures are implemented. This outcome suggests that firms hiring employees specialized in quality management and control are more able to meet the standards on foreign markets than other firms.

The paper is organized as follows. In a first section, we describe the firm-employee data we rely on to assess the firm's commitment in traceability and safety and the trade data we use. We provide some stylized facts on quality management and French firms exports. In section 2 we present our empirical strategy and discuss our results. Section 3 concludes.

Data and descriptive Evidence

Quality control and management

To study the impact of firms' commitment to product reliability and safety issues on trade we rely on French firm-level data on trade and employment from 2009 to 2019. We assess the quality control and management commitment of firms using information from the DADS Poste dataset. This later is the compilation of annual compulsory declarations by all private French companies (except self-employed) and provides for each firm the number of working hours, the total salary and the number of employee, by occupation.

From 2009 to 2018, 13,285,709 firms appear in the DADS Poste data, identified by a unique identifier SIREN. We restrict our sample to the 82,516 French agrifood firms (which corresponds to the code 10 in the APE classification of firm's activities). This excludes non only services and manufacturing firms from our sample, but also wholesalers and retailers who can sell agrifood products but do not produce them. We only keep firms that that have operated continuously from 2009 to 2019 and end up with a dataset of 79,359 firms for the whole period.

The occupation of each worker in the company is defined in the DADS database according to a 4-digit classification. Quality control and management employees correspond to two occupations according to the DADS categories: quality control manager and engineer (387d)

and quality control technician for the processing industries (475b). Figure 1 displays the number of agrifood firms from 2009 to 2019 with and without quality control and management employees. It appears that 7% of agrifood companies employ workers in positions dedicated to quality control and management, which is much higher than the 0.5% obtained if we consider all the firms present in the DADS .

Trade

The information on quality control and management from the DADS poste are merged with data on French firm-level trade on the same period. These data come from the French customs and provided for each firm the quantity and value of export, by destination, product (at the 6digits level of the Harmonised System) and year. Only 6% of our agrifood firms of the DADS dataset export so when we reduce our sample to exporting firms, our sample has only 2,501 firms in 2015.

Specification

Our empirical analysis assesses the impact of quality control and management employees on firm's export performance. We investigate both the extensive margin of trade (probability of export of the firm f of product k to destination j the year t) and the intensive margin of trade with its different components, the exported value, the exported quantity and the unit value. We also test whether quality control and management allow firms to have a higher perceived quality as compared to other firms. We estimate the following equation:

Expfjkt = α1Qualityft+ξjkt+υf +εfjkt (1)
where Qualityft is a dummy indicating whether firm f has quality management the year t, υf a set of firm's characteristics variables (such as productivity and size) and ξjkt a destinationproduct-time fixed effect which control for characteristics of the market of country j and good k the year t. These fixed effects allow us to compare firms with and without quality control and management personnel on a given market (product-destinationyear), controlling for firms characteristics.

In the intensive margin estimations the dependent variables Expfjkt is lvfjkt the logarithm of the export values of firm f to destination j for the product k at time t, or lqfjkt the logarithm of export quantities of f to j for k at t. In the extensive margin estimation, the dependant variable Expfjkt is Xfjkt, a binary variable equal to 1 if a flow exist for the firm f and the product k for the destination j at time t.

The firm's decision to invest in quality management staff may be driven by different factors :

the need to differentiate products in a very competitive market, previous safety or sanitary issues, evolution of domestic regulation, changes in products portfolio... The will to expand activities worldwide or to export to a specific destination could also be one of the reason why a given firm would hire some employees dedicated to sanitary and traceability, which raises an endogeneity issue in our estimation. To deal with this potential reverse causality, we use an instrumental approach, using the share of firms in the same sector (APE) and departement with quality management personnel as first IV, and the share of exported products with NTM on the European market, considering that a firm will be more likely to hire quality management staff if she's producing (exporting) products subject of regulation on the European (therefore the French) market. As the endogeneous variable is a dummy variable, we use a two stage approach.

First we predict ft Quality from a linear probability model including our two instruments. This prediction is used as the instrument in the 2SLS estimation.

Result: effect of quality management on exports

Table 1 lists estimations of equation 1 on the value of trade. In columns (1) to (3) we compare firms with quality management employees with firms without this kind of staff, for a given market product-destination-year. The variable Qualityft exhibits a positive and significant coefficient in column (1), suggesting that firms with quality control and management personnel export more in value than other firms. This result remains when we restrict our sample to firms which do not change status between 2009 and 2019 (i.e. that have or don't have quality management for the whole period, without any change) in column (2). The coefficient is still significant and positive, in column (3) using an instrumental variable approach 1 . Results confirm the endogeneity of the Qualityft variable. Accounting for endogeneity increases the effect of Qualityft on the exported value.

1 the value of the Kleibergen and Paap Wald F statistic of the first order estimation (test for weak identification) suggests that our instrument is not weak. The validity of our instruments is also supported by the rejection of underidentification (a Kleibergen-Paap rk LM statistic significant at 1 percent).

In columns ( 4) and ( 5), we add a firm fixed effect which allows us to control for all non observable and time invariant firm characteristics. The identification of the effect of quality management is thus within time for a given firm. The coefficient relative to Qualityft is still positive and significant, meaning that a firm hiring quality management personnel (switching to Qualityft=0 to Qualityft=1) will increase its value of trade. As previously, 2SLS and OLS differ in terms of magnitude, which is explained by the fact that the two estimators do not estimate the effect of quality exactly on the same population. Table 2 replicates the estimation of table 1 on the probability of trade. We observe the same pattern than previously, with a positive and significant coefficient in all columns, showing that quality control and management increase not only the intensive margin of trade, but also the extensive margin. Firms with quality personnel not only have higher exported trade values, they also have an higher probability to export. Firms hiring quality personnel also increase their market access. In table 3, we interact the variable Qualityft with the logarithm of the total salary paid by the firm to quality control and management employees. Results in columns ( 1) and (3) on the intensive and extensive margin of trade shows that having quality staff increases the trade value, volume and probability, but above a certain threshold of salary. Above this threshold, the higher the "investment in quality", the higher is the positive impact on trade. Coefficients of columns ( 2) and ( 4), where we restrict our sample to firms with quality management staff, confirm that the amount paid for quality management personnel increases both trade values and probability of trade.

Result: quality management and Non Tariff Measures

In table 4, we estimate the impact of quality control and management on trade, distinguishing destination-product pairs with Non-tariff-Measures from those without, using two interacted variables Qualityft×NTMjk and Qualityft×noNTMjk with NTMjk a dummy variable equal to one whether the destination j implement a NTM for the product k, and zero otherwise, and noNTMjk a dummy variable equal to one whether the destination j does not implement a NTM for the product k, and zero otherwise. Data on the presence of NTM come from the UNCTAD database, cover 80 countries and do not have time dimension.

Our results in column (1) show that the positive impact of quality control and management employees on trade revealed in the previous section only occurs when the exporter is facing a NTM, as only Qualityft×NTMjk displays a positive and significant coefficient. This outcome argues in favor of a compliance effect induced by quality control and management staff. Having personnel dedicated to these particular issue helps firms to export more in value to destination with higher requirements in terms of safety and traceability.

In columns ( 2) and (3), we distinguish EU destination from non EU destination as NTM applied by EU countries are the same than those who are applied at the French border, therefore, on the domestic market. We could thus expect NTM on European destinations to have a different impact than on non EU destinations. Our results confirm this intuition as the positive impact of quality management is only significant on non European markets with NTM (column 3). The "investment in quality management" does not seems to positively impact trade towards other European countries with the same sanitary requirement than in France. On the contrary, we observe a negative coefficient on the variable Qualityft for products without any NTM on the European market in column (2). Columns (4) to (6) of table 4 confirm the previous results using the total amount paid to quality management staff instead of a dummy. The pattern is slightly different than on the intensive margin. Having employees dedicated to safety and traceability issues help firms to reach destinations with NTM, and European countries in general but have a negative impact on the probability of trade on other non-EU destination. It suggests that firms with quality management staff concentrate their export to

Conclusion

In this article, we discuss the effects of the "investment in the quality" of agri-food firms on their export performance, underlining that product quality is not limited to product differentiation perceived by final consumers. Product traceability and reliability is an essential factor in firms' competitiveness, especially in the perspective of the global value chains.

Other avenues of research may be considered to continue to work on these issues. The analysis of the effects of certain standards such as the ISO 26000 standard on Social

Responsibility, which brings a new, more social and environmental dimension, could be particularly interesting to understand differentiation strategies of firms on international markets.

Figure 1 :

 1 Figure 1: Number of firms

Figure 3

 3 Figure3displays the total French export made by agrifood companies, distinguishing trade made by firms with quality management and control employees and others. It appears that firms with quality personnel export more that the firms without quality control and management since they account for more than 95% of trade, whereas they only represent half of the exporting firms as seen above. This observation is confirmed by figure 4 which shows the kernel density of the export values and quantity by firm, destination, product and year. Firms with quality control and management personnel generate more flows with higher values or quantities than the other firms.

Figure 2 :

 2 Figure 2: Number of exporting firms

Figure

  Figure 4: Number of exporting firms

Table 1 :

 1 Effect of quality management on the intensive trade margin

	ln

* p < 0.10, ** p < 0.05, *** p < 0.01

Table 2 :

 2 Effect of quality management on the extensive trade margin

	ln vfjkt

Table 3 :

 3 Effect of quality management expenditures on the trade margins

		ln vfjkt	Xfjkt
		(1)	(2)	(3)	(4)
		all firms	Qualityft=1	all firms	Qualityft=1
	Qualityft	-0.266***		-0.003*
		(0.070)		(0.002)
	Qualityft×Salaryft	0.027***	0.054***	0.000**	0.001***
		(0.006)	(0.010)	(0.000)	(0.000)
	productivityft	0.030***	0.042**	0.002***	0.001***
		(0.010)	(0.017)	(0.000)	(0.000)
	Size 2ft	0.026	-0.137*	0.006***	0.009***
		(0.025)	(0.070)	(0.000)	(0.001)
	Size 3ft	0.119***	-0.055	0.012***	0.015***
		(0.033)	(0.079)	(0.001)	(0.001)
	Size 4ft	0.176***	0.014	0.015***	0.018***
		(0.042)	(0.089)	(0.001)	(0.002)
	N	593,758	335,380	11,127,997	5,376,025
	r2	0.4829	0.4937	0.1707	0.1916
	destination-product-year	yes	yes	yes	yes
	firm	yes	yes	yes	yes
	Notes: All continuous variables are in logarithm.
	Robust standard errors clustered by firm-year in parentheses.
	* p < 0.10, ** p < 0.05, *** p < 0.01

Table 4 :

 4 Effect of quality management on the intensive trade margin with NTM All continuous variables are in logarithm. Robust standard errors clustered by firm-year in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01

				ln vfjkt
		(1)	(2)	(3)	(4)	(5)	(6)
		All	EUft=1	EUft=0	All	EUft=1	EUft=0
			all firms	all firms	Qualityft=1	Qualityft=1	Qualityft=1
	Qualityft×NTMjk	0.026**	0.022	0.042**
		(0.013)	(0.015)	(0.021)
	Qualityft×noNTMjk	-0.058	-0.141*	0.061
		(0.056)	(0.076)	(0.076)
	productivityft	0.029*** 0.055***	0.001	0.042**	0.090***	0.010
		(0.010)	(0.012)	(0.017)	(0.019)	(0.022)	(0.032)
	Size 2ft	0.040	0.077**	0.024	-0.139*	-0.128	-0.034
		(0.027)	(0.034)	(0.042)	(0.076)	(0.089)	(0.139)
	Size 3ft	0.124*** 0.194***	0.016	-0.062	0.043	-0.183
		(0.035)	(0.041)	(0.059)	(0.085)	(0.097)	(0.158)
	Size 4ft	0.214*** 0.393***	-0.023	0.051	0.280**	-0.326*
		(0.044)	(0.053)	(0.073)	(0.094)	(0.110)	(0.172)
	Qualityft×Salaryft×NTMjk				0.064***	0.064***	0.026
					(0.011)	(0.012)	(0.022)
	Qualityft×Salaryft×NoNTMjk				0.006	0.029	-0.004
					(0.033)	(0.043)	(0.051)
	N	541,567	358,155	183,189	300,998	206,039	94,875
	r2	0.49	0.53	0.47	0.50	0.53	0.49
	destination-product-year	yes	yes	yes	yes	yes	yes
	firm	yes	yes	yes	yes	yes	yes
	Notes:

  Table 5 follows the same structure than table 4 but focuses on the extensive margin of trade.

markets with NTM as they have a comparative advantage on these markets with their higher safety and sanitary quality, except on the European countries which have the same requirements than the domestic market.


Babar Aziz
email: babar.aziz@ensea.fr

Inbar Fijalkow
email: inbar.fijalkow@ensea.fr

Myriam Ariaudo
email: myriam.ariaudo@ensea.fr

JOINT ESTIMATION OF CHANNEL AND CARRIER FREQUENCY OFFSET FROM THE EMITTER, IN AN UPLINK OFDMA SYSTEM

Keywords: Carrier frequency offset, OFDMA uplink transmission, channel estimation, carrier frequency offset estimation

In this paper, we propose a joint estimation method of carrier frequency offsets (CFO) and channel impulse responses (CIR) for an uplink OFDMA system. We consider the CFOs come into play from the emitters due to frequency mismatch between the terminals and base station oscillators. We also take into account the cyclic prefix to derive a new model of the received signal.

INTRODUCTION

The new mobile communication systems such as Wifi, WiMAX and LTE (Long Term Evolution) have to offer high data rates both in the uplink and downlink directions in order to fulfill the needs of the innovative multimedia applications [START_REF] Molisch | Wideband Wireless Digital Communication[END_REF][START_REF] Huang | Timing and frequency synchronization for OFDM based cooperative systems[END_REF]. OFDMA is coming forth as the favored downlink transmission scheme for these systems because it is highly robust in frequency selective radio channels and also provides a good system flexibility. However, questions are still raised about the use of OFDMA in the uplink direction. A well known problem of OFDMA in the uplink is its sensitivity to carrier frequency offsets (CFOs). This paper concentrates on carrier frequency synchronization error while perfect time synchronization is assumed. The frequency mismatch may result from the user terminals oscillators mismatches with the base station oscillator. This phenomenon is all the more important than the terminals must be cheap. The different CFOs can not be canceled at the base station, resulting in severe interference, see [START_REF] Aziz | Intercarrier interference in uplink ofdma systems with carrier frequency offset[END_REF], which degrades the system performance. The channel estimation for OFDM systems has been well studied, e.g. [START_REF] Jaap Van De Beek | On channel estimation in ofdm systems[END_REF][START_REF] Moose | A technique for orthogonal frequency division multiplexing frequency offset correction[END_REF]. However when uplink OFDMA transmission is considered, an efficient joint estimation of the channel impulse responses and CFOs is required in the presence of frequency mismatch. A few authors have addressed this issue in uplink OFDMA system [START_REF] Morelli | Timing and frequency synchronization for the uplink of an ofdma system[END_REF][START_REF] Van De Beek | A time and frequency synchronization scheme for multiuser ofdm[END_REF]. However most of the presented algorithms consider the estimation of the CFO alone without considering the channel estimation. Some studies have been presented in [START_REF] Pun | Maximum-likelihood synchronization and channel estimation for ofdma uplink transmissions[END_REF][START_REF] Haring | Estimation algorithms of multiple channels and carrier frequency offsets in application to multiuser ofdm systems[END_REF]. In [START_REF] Pun | Maximum-likelihood synchronization and channel estimation for ofdma uplink transmissions[END_REF], joint CFO and channel estimation is done using the maximum-likelihood approach but it proves to be highly complex. In [START_REF] Haring | Estimation algorithms of multiple channels and carrier frequency offsets in application to multiuser ofdm systems[END_REF], authors have presented the estimator based on a polynomial approximation for the CFO estimation which tends to reduce the complexity one faces in the grid search algorithms. However all the authors while doing the joint estimation for the uplink OFDMA transmission assume that the CFO is applied to the signal after the channel convolution i.e. at the receiver. Unlike them, we consider a signal model considering that CFO comes strictly from the mismatch of the emitter oscillator with the base station, i.e. before the channel convolution. Therefore the CFO comes into action from the transmitted signal presented at the output of the emitter. Based on this new transmission model we show that our proposed algorithm further simplifies the CFO estimation compared to the approach provided in [START_REF] Haring | Estimation algorithms of multiple channels and carrier frequency offsets in application to multiuser ofdm systems[END_REF]. In section 2, the considered system model is presented along with an analytical signal model. In section 3, the proposed joint estimation algorithm is presented followed by the numerical results in Section 4.

UPLINK SYSTEM MODEL: CFO FROM EMITTER MODEL

In this section, we present the uplink OFDMA system under consideration. The available bandwidth B is divided into N p subcarriers. N u is the total number of users in the system. The radio channel model is assumed to be frequency selective. The last L cp modulation symbols in the OFDM symbol are repeated after the inverse DFT to form the cyclic prefix. The signal at the output of the transmitter of user u is given by

x (u) (t) = Re{e j2π(fc+δf (u) c )t Np-1 k=0 a (u) k p (u) (t - kT N p )} (1)
where f c is the carrier frequency, p (u) (t) is the impulse response of the low pass filter at the transmitter and T is the symbol period of an OFDMA symbol. δf

(u) c
represents the CFO of user u with respect to f c . The CFO appears in the transmitted signal because we are considering the case where the user u terminal is not perfectly synchronized to the carrier frequency of the base station due to the components misbehavior. Note that a (u) k are the symbols at the IDFT output. Signals from different users x (u) (t) pass through individual convolutive multipath radio channels. The received signal at the base station is the sum of the signals from all users. The received discrete-time baseband signal r i at the base station is given by

r i = Nu u=1 x (u) i e j2π Np (L h +i-1)δf (u) * h (u) i + n i (2)
where * stands for convolution.

r i = Nu u=1 L h -1 l=0 x (u) i-l e j2π Np (L h +i-l-1)δf (u) h (u) l + n i (3) where x (u) i
is the i th sample of the transmitted signal of user u and h

(u) l
denotes the l th sample of the impulse response of the channel for user u, including the transmitter and receiver filters, n i is the additive white Gaussian noise. L h is the channel length which is assumed to be less than the cyclic prefix length i.e. L h < L cp . Note that δf (u) is the normalized CFO of user u and is given by

δf (u) = δf (u) c T
Since we wanted to perform channel and CFO estimation, we carefully worked on eq.( 3) to separate the different contributions. By collecting the N p samples and removing the cyclic prefix as in [START_REF] Aziz | Intercarrier interference in uplink ofdma systems with carrier frequency offset[END_REF], we have derived the following new expression:

r = Nu u=1 (δ (u) X (u) )h (u) + n ( 4
)
where r is an N p × 1 vector containing the samples of the received signal and n is the N p × 1 noise vector with noise samples. The (δ (u) X (u) ) product, in eq.( 4), is shown below in matrix form, where the index u is removed for simplicity,

= δ Lcp-1 2 δ 1 δ 0 δ Lcp δ 2 δ 1 . . . . . . δ Np+Lcp-2 δ N p -1 δ ... ... . . .           x o x -1 Lcp -1 x 1 x o x -1 Lcp-2 x 1 . . . . . . . . . x Np-1          ... x ... x . . . x  Np-L cp                     ...
The N p × L cp X has the samples of the transmitted signal along with the cyclic prefix of length L cp . The N p × L cp matrix δ (u) is the shift matrix of user u containing its shift coefficients. The represents the Hadamard product. Hadamard product comes in because the input data is multiplied with the CFO at the emitter of the desynchronized user u. Note that δ k (u) = e j2πkδf (u) /Np , with k = 0, ..., N p + L cp -2, are the shift coefficients of user u.

PROPOSED ESTIMATION ALGORITHM

Given the model in eq.( 4), we consider the joint maximum likelihood estimation of the channel and the CFO. Assuming the noise n is uncorrelated and complex Gaussian with zero mean and variance σ 2 n , and the different users data are independent from each other, the ML estimate of the channel with respect to the CFO is given by

h (u) = [(δ (u) X (u) ) H (δ (u) X (u) )] -1 (δ (u) X (u) ) H r (5)
The CFO estimate,δ f (u) , should be obtained by inserting h

(u)
in to the log-likelihood function as follows

δ f = arg max δf [J M L (δf )] (6)
where the cost function is given by

JML(δf ) = r H (δ (u) X (u) )[(δ (u) X (u) ) H (δ (u) X (u) )] -1 (δ (u) X (u) ) H r (7)
The cost function in eq.( 7) is very complex to maximize because of the presence of the matrix inversion. By following the approach of [START_REF] Haring | Estimation algorithms of multiple channels and carrier frequency offsets in application to multiuser ofdm systems[END_REF] and choosing orthogonal training sequences like CHU-Codes [START_REF] Chu | Polyphase codes with good periodic correlation properties (corresp.)[END_REF], the cost function in eq.( 7) can be simplified as

J M L (δf ) ≈ r H (δ (u) X (u) )(δ (u) X (u) ) H r (8)
We propose to further simplify the above equation to

J(δf (u) ) = r H (X (u) δ (u) her )r (9)
with

X (u) = X (u) X (u)H and δ (u)
her is a hermitian matrix given by

          δ 0(u ) δ -1( u ) δ -2(u ) -N p +1(u ) δ 1(u ) δ 0( u ) δ -1(u ) δ -N p +2(u ) . . . . . . . . . . . . δ -1(u ) δ Np-1(u ) 1(u ) δ 0( u )           . . . δ . . . δ . . . . . . δ (u) her =
The high complexity of the grid search algorithms has led researchers to look for alternative ways to find estimation algorithms with low complexity to solve eq.( 6). An alternative class of estimator proposed in [START_REF] Haring | Estimation algorithms of multiple channels and carrier frequency offsets in application to multiuser ofdm systems[END_REF][START_REF] Abdel-Samad | Fine frequency offset estimation for frequency-selective channels[END_REF] uses polynomial approximations. For example in [START_REF] Abdel-Samad | Fine frequency offset estimation for frequency-selective channels[END_REF] authors have applied polynomial approximation to the cost function while in [START_REF] Haring | Estimation algorithms of multiple channels and carrier frequency offsets in application to multiuser ofdm systems[END_REF] the polynomial approximation of the shift matrix is applied. We propose to apply the polynomial approximation to the δ (u) her matrix rather than the shift matrix δ (u) for user u. We found that the resulting estimator is simpler. We propose to approximate δ

(u)
her as the first term of the following expression,

δ (u) her = ∞ m=0 ∂ m (δ (u) her ) ∂δf (u)m (δf (u) -δf (u) o ) m m! (10) = ∞ m=0 D m (δf (u) -δf (u) o ) m m! ( 11
)
where the matrix D m is given by

D m           (0) m (-1) m (-2) m . . . (-N p + 1) m (1) m (0) m (-1) m . . . (-N p + 2) m (2) m (1) m (0) m . . . . . . . . . . . . . . . (N p -2) m (N p -1) m (N p -2) m . . . (0) m           δ | δf =δfo j2π N p m her =
Now eq.( 9) becomes

J(δf (u) ) = r H (X (u) ∞ m=0 D m (δf (u) -δf (u) o ) m m! )r (12) = ∞ m=0 (δf (u) -δf (u) o ) m m! R r H (X (u) D m )r (13)
For a polynomial of degree M expansion

J(δf (u) ) ≈ J our (δf (u) )
with

J our (δf (u) ) = M m=0 (δf (u) -δf (u) o ) m m! R r H (X (u) D m )r (14)
To find the frequency estimate, the first derivative of eq.( 14) is set to zero

∂J our (δf (u) ) ∂δf = M -1 m=0 (δf (u) -δf (u) o ) m m! d (u) m+1 (15)
where the co-efficients d

(u)
m+1 are given by

d (u) m+1 = R r H (X (u) D m+1 )r (16)
From eq.( 16) it can be seen that the co-efficients of the first derivative of the cost function J our (δf (u) ) are much simpler compared to those in [START_REF] Haring | Estimation algorithms of multiple channels and carrier frequency offsets in application to multiuser ofdm systems[END_REF]. For example, for a polynomial of degree M = 2, the CFO estimator is given by

δ f (u) = δf (u) o + -R r H (X (u) D 1 )r R r H (X (u) D 2 )r (17)
where δf

(u) o
is the initial guess of the CFO in the system.

NUMERICAL RESULTS

This section presents the simulations that are performed in order to validate our proposed estimation method. We assume that there are two users in the system, N u = 2. The main simulation parameters considered are the total number of the available subcarriers N p = 128 and the length of the cyclic prefix is L cp = N p /8. The channel is implemented with random impulse responses with Rayleigh fading coefficients and the length of the channel is equal to the length of the cyclic prefix L h = L cp . The orthogonal training sequences are generated using [START_REF] Chu | Polyphase codes with good periodic correlation properties (corresp.)[END_REF]. Initially the CFO experienced by both the users is taken as constant.

In Figure 1, we have plotted the mean and the variance of the δ f (u) estimate about the true δf (u) value for iterations over different channel realizations. We can see that the esti- mated value approaches the true value with increase in SNR value. Thus for small values of the normalized CFO i.e. less than 0.1, smaller values of M are sufficient to provide a closed form solution.

In Figure 2, we have plotted the mean and variance of the δ f (u) estimate for a higher value of CFO δf (u) = 0.3. We used two polynomials of degree M = 2 and M = 5. The initial guess was taken equal to zero i.e δ f For higher values of the CFO the M = 2 estimator does not perform well because of the increased distance from the origin and the estimator does not converge unlike for M = 5 which provides better results. So it can be seen that the degree of the polynomial determines the quality of the estimator. Our proposed model reduces the computational steps required in the calculation of the coefficients d m+1 even more compared to the estimator method proposed in [START_REF] Haring | Estimation algorithms of multiple channels and carrier frequency offsets in application to multiuser ofdm systems[END_REF]. Calculation of d m+1 involves simple Hadamard product which can be easily implemented. This difference comes into play because the estimator approach in [START_REF] Haring | Estimation algorithms of multiple channels and carrier frequency offsets in application to multiuser ofdm systems[END_REF] considers the CFO at the receiver only. Figure 3 shows the results for the mean squared error (MSE) values of the CFO along with the MSE values of estimated channel coefficients plotted against the SNR values for our proposed estimation method. Results in Figure 3 are computed with M = 3. It is assumed that no initial knowledge of CFO is available i.e. δf (u) o = 0. The CFO values of the two users are simulated as independent uniformly distributed random variables with a maximum possible value of δf max = 0.2 i.e. for any user u the CFO is δf (u) ≤ δf max .

CONCLUSION

In this paper, we have derived a new received signal model taking into account the CFO at the user terminal and before the multipath channel. We proposed a method for the joint estimation of the channel impulse response and the carrier frequency offsets for an uplink OFDMA system. Thanks to our signal model, we estimate the CFOs using a polynomial approximation. The CFO estimates are then used to estimate the channel using the maximum-likelihood approach.

Fig. 1 .

 1 Fig. 1. Mean and variance of the CFO, δ f (u) vs. SNR. 0.05 is the true value of the shift for user 1, with M = 2, Np = 128, Lcp = 16 and Nu = 2.

Fig. 2 .

 2 Fig. 2. and variance of the CFO, δ f (u) vs. SNR. 0.3 is the true value of the shift for user 1, with M = 2, M = 5, Np = 128, Lcp = 16 and Nu = 2.

Fig. 3 .

 3 Fig. 3. MSE of h (u) l and MSE of δ f (u) vs the SNR(dB) for our proposed method with M = 3, Np = 128, Lcp = 16 and Nu = 2 with independent uniformly distributed CFOs.

This work is supported by the French IdF Region DIM "Logiciels et Systmes Complexes" Project DESAP.


PhD Sirwan Hadad
email: s.hadad@nhs.net

MD Takayuki Iwamoto
email: tiwamoto@mdanderson.org

Lee Jordan
email: lee.jordan@nhs.net

PhD Colin Purdie
email: colin.purdie@nhs.net

PhD Susan Bray
email: s.e.bray@dundee.ac.uk

Lee Baker
email: l.baker@dundee.ac.uk

MSc Gera Jellema
email: gera.jellema@almacgroup.com

Steve Deharo
email: steve.deharo@almacgroup.com

D Grahame Hardie

Lajos Pusztai
email: lpusztai@mdanderson.org

Lee Jordan Mrcpath

Steve Deharo Msc

PhD Grahame Hardie
email: d.g.hardie@dundee.ac.uk

Lajos Pusztai Dphil

MD Stacy Moulder-Thompson

John A Dewar Frcr

Alastair M Thompson Frcsed

Professor A M Thompson
email: a.m.thompson@dundee.ac.uk

Evidence for biological effects of metformin in operable breast cancer: a pre-operative, window-of-opportunity, randomized trial

Keywords: Breast cancer, metformin, ki67, transcriptome analyses, clinical trial

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Introduction

Clinical observations have highlighted metformin as a potential anti-cancer therapy. Diabetics treated with metformin have a 23% reduced risk of cancer, including breast cancer, compared to those on sulfonylureas [START_REF] Evans | Metformin and reduced risk of cancer in diabetic patients[END_REF] and a lower cancer-related mortality at 36 months [START_REF] Monami | Sulphonylureas and cancer: a case-control study[END_REF]. Coincidental use of metformin in diabetics undergoing neoadjuvant chemotherapy for breast cancer resulted in a higher pathological complete response (24%), compared to diabetics not on metformin (8%) or non-diabetic patients (16%) [START_REF] Jiralerspong | Metformin and pathologic complete responses to neoadjuvant chemotherapy in diabetic patients with breast cancer[END_REF].

Metformin activates AMP-activated protein kinase (AMPK) [START_REF] Zhou | Role of AMPactivated protein kinase in mechanism of metformin action[END_REF] by inhibiting mitochondrial ATP production and thus increasing cellular ADP:ATP and AMP:ATP ratios [START_REF] Hawley | Use of cells expressing gamma subunit variants to identify diverse mechanisms of AMPK activation[END_REF]. AMPK is involved in regulating energy balance at the cellular and whole body levels [START_REF] Hardie | AMP-activated/SNF1 protein kinases: conserved guardians of cellular energy[END_REF][START_REF] Kahn | AMP-activated protein kinase: ancient energy gauge provides clues to modern understanding of metabolism[END_REF] and is activated by stresses that deplete ATP, including glucose deprivation [START_REF] Salt | AMP-activated protein kinase is activated by low glucose in cell lines derived from pancreatic beta cells, and may regulate insulin release[END_REF][START_REF] Marsin | Phosphorylation and activation of heart PFK-2 by AMPK has a role in the stimulation of glycolysis during ischaemia[END_REF] and hypoxia. Activation of the AMPK pathway by metformin in breast cancer cell lines reduces cellular proliferation, and is associated with reduced activation of mTOR and S6 kinase [START_REF] Zakikhani | Metformin Is an AMP Kinase-Dependent Growth Inhibitor for Breast Cancer Cells[END_REF]. AMPK activation may be down-regulated in untreated primary breast cancer [START_REF] Hadad | Targeting AMPK: a new therapeutic opportunity in breast cancer[END_REF] suggesting reactivation of the AMPK pathway may inhibit tumours, sparing normal tissues which already have an active AMPK pathway. Treatment with metformin induces G0/G1 cell cycle arrest and inhibits cell proliferation independently of estrogen receptor (ER) and HER2 in pre-clinical models [START_REF] Zhuang | Cell cycle arrest in Metformin treated breast cancer cells involves activation of AMPK, downregulation of cyclin D1, and requires p27Kip1 or p21Cip1[END_REF]. The anti-tumour effect of metformin may also be enhanced by an anti-hyperglycemic effect through suppression of hepatic gluconeogenesis, reducing the growth-promoting and mitogenic effects of circulating insulin [START_REF] Van Der Burg | Mitogenic stimulation of human breast cancer cells in a growth factor-defined medium: synergistic action of insulin and estrogen[END_REF].

The mechanisms of action of metformin in primary breast cancer merit further investigation [START_REF] Hadad | Histological evaluation of AMPK signalling in primary breast cancer[END_REF] and are the subject of on going clinical trials [START_REF] Goodwin | Evaluation of metformin in early breast cancer: a modification of the traditional paradigm for clinical testing of anti-cancer agents[END_REF]. Given the known mechanisms of action of metformin, it was hypothesized that treatment with metformin would reduce tumour proliferation as detected with immunohistochemistry using a preoperative "window of opportunity" trial design. The effects of metformin on breast cancer biomarkers in vivo, on serum insulin and clinical side effects in women with primary, previously untreated breast cancer were also examined.

Methods

Patients

Between July 2007 and August 2009, 55 women with newly diagnosed stage I or II primary breast cancer, >1cm in size, bone marrow function within normal limits, liver and renal function tests within 1.5 times normal and with a life expectancy of at least 6 months were recruited (Figure 1). Trial patients had no history of diabetes (and no evidence of diabetes on blood biochemistry), no concomitant anti-cancer treatment or investigational drug within one month of study participation. Tayside Local Research Ethics Committee (07/S1402/19), Medicines and Healthcare products Regulatory Agency (MHRA) approval and written informed consent from all participants were obtained.

Randomization and masking

The study comprised two cohorts (Figure 1). The initial 8 patients were evaluated as a pre-planned, non-randomized, feasibility cohort A within the study. The remaining 47 patients were randomized 1:1 in an unblinded treatment vs. non-treatment trial design. Patients in both cohorts were treated with the same dosing schedule of oral metformin: 500mg daily for one week, increased to 1g twice daily until surgery 1 further week later. Patients and clinical trial staff were not blinded to metformin use. All tumor samples analyzed were obtained by 14 gauge core needle biopsy at the given time points described below. At the time of surgical resection, core biopsies were obtained after the induction of general anesthesia, just prior to tumour resection. All specimens were processed identically in the same laboratory by researchers blinded to the treatment assignment. Samples were anonymized by trial number and remained blinded until laboratory and statistical analyses were complete. Core biopsies were formalin-fixed for 12 hours at room temperature, processed in a single pathology laboratory and paraffin embedded (formalin-fixed paraffin-embedded, FFPE) before molecular analysis and immunohistochemistry.

Patients treated in the pilot Cohort A (n=8) had tumor core biopsies at three time points: diagnostic cores at clinical presentation; one week later (internal control sample) and following oral metformin. The third core biopsy was taken at the time of surgical resection, 2 weeks after starting metformin. Cohort B patients (n=47) were randomized using a randomization table (www.Randomization.com) to receive metformin or no medication. Core biopsies were taken at two time points: diagnosis and at the time of surgery 2 weeks later. The feasibility cohort suggested a power to reject the null hypothesis that the population means of the metformin and control groups were equal with a power of 80% and a type 1 error probability of 0.05.

Immunohistochemistry

Immunohistochemical was performed on 4μm sections of FFPE tissues using standard methodologies. The primary antibodies comprised NCL-L-Ki67-MM1 (Anti-Ki67, monoclonal antibody, Leica Microsystems), sc-20793 (anti PDE3B (H-330), polyclonal, Santa Cruz Biotechnology), DO1 (anti-p53 monoclonal antibody [START_REF] Vojtesek | An immunochemical analysis of the human nuclear phosphoprotein p53. New monoclonal antibodies and epitope mapping using recombinant p53[END_REF]), estrogen receptor alpha (ER) antibody 6F11 (1:200; Novocastra Laboratories Ltd), progesterone receptor (PR) antibody clone 16 (1:800; Novocastra Laboratories Ltd) and anti-HER2 antibody (CB11; Novocastra Laboratories Ltd). Negative controls (lacking primary antibody) were performed for all staining runs.

Immunohistochemistry was conducted blinded to the clinical data and scored by a single specialist breast pathologist (LJ). Following light microscopy review, slides were scanned into a virtual microscopy format using an Aperio ScanScope XT TM (Aperio Technologies, Vista, Ca., USA) at the x40 objective utilizing standard compression methodology.

The Ki67 index (percentage of nuclear positive cells) per invasive tumor was calculated using manual annotation of the virtual microscopy slide by means of a Wacom Bamboo Pen & Touch tablet device (Wacom Corporation, Saitama Japan) within the WebScope environment (version 10.2.0.2319) of the Aperio Spectrum Plus system version 10.2.2.2317. The annotations were assessed by the Aperio IHC nuclear Algorithm version 10. Only invasive tumor cells were assessed; great care was taken to exclude normal epithelial, in situ epithelial, stromal and inflammatory elements. A mean 5600 nuclei (range 601-39,788) per invasive tumor was assessed to obtain the Ki67 index. A minimum 1000 invasive tumor cells were examined except for one pretreatment and one post-treatment core (601 and 825 cells respectively). PDE3B (cytoplasmic) and D01 (nuclear) staining were scored utilizing the "QuickScore" method (intensity and proportion of invasive tumor cells stained) [START_REF] Detre | A "quickscore" method for immunohistochemical semiquantitation: validation for oestrogen receptor in breast carcinomas[END_REF]. SSR3 and GINS2 immunohistochemistry was unsuccessful despite extensive work with commercially available antibodies. ER and PR scoring used the Allred method in line with UK National Health Service (NHS) Breast Screening Program reporting recommendations [START_REF] Harvey | Estrogen receptor status by immunohistochemistry is superior to the ligand-binding assay for predicting response to adjuvant endocrine therapy in breast cancer[END_REF]. HER2 scoring was assessed as previously described [START_REF] Purdie | HER2 assessment on core biopsy specimens using monoclonal antibody CB11 accurately determines HER2 status in breast carcinoma[END_REF].

Insulin measurements

For cohort B patients, non-fasting venous blood was withdrawn, as per protocol, for insulin level determinations at diagnostic biopsy and at surgery following induction of anesthesia. At diagnostic biopsy, performed at the diagnostic breast clinic, the patients had not been fasted; at surgery, all patients had been fasted for 8 hours and were receiving intravenous fluids, including 5% glucose. Insulin was measured by the Siemens Immulite 2000 insulin assay in an accredited NHS clinical biochemistry laboratory and pair-wise comparison performed using a two sample t-test.

Statistical analyses

For Ki67 analysis, the mean change in Ki67 was calculated between each time point; the Anderson-Darling test was used to test for normal distribution and parametric (paired ttest) or non-parametric (Wilcoxon matched pairs signed rank test) statistics applied. Insulin levels in cohort B patients were compared at diagnosis with insulin levels following metformin or after no medication using a two sample t-test. A probability of <5% was considered significant.

RNA Microarray

For RNA microarray analysis, samples from all cohort A patients (n=8), cohort B treatment group patients who continued metformin to surgery (n=14), and 4/22 randomly selected patients from the cohort B control group were successfully analyzed (Figure 2). RNA from the remaining cohort B control samples were not examined due to cost constraints. Tumor confirmation on a Haematoxylin and Eosin slide was confirmed by LJ. RNA extraction and Breast Cancer Disease-Specific Array (DSA) gene expression profiling were performed by ALMAC Diagnostics Group (Craigavon, UK).

RNA was isolated from FFPE tissue section "curls" in RNase-free tubes, the quality of the RNA samples and single stranded cDNA evaluated by spectrophotometric and bioanalyzer methodologies (Agilent Technologies UK Limited, Stockport, UK). One sample from a cohort A patient failed to meet the minimum RNA concentration, so all samples from this patient were excluded. Arrays were washed, stained and scanned in accordance with the Affymetrix GeneChip® Expression Analysis technical manual (Affymetrix UK Ltd., Buckinghamshire, UK).

Data analysis

Two samples were excluded following initial profile quality assessment based on standard Affymetrix array quality metrics and sample intensity distributions.

Basic Analysis

The data was corrected for background noise, summarized and normalized using RMA in Partek® Genomics Suite TM software, 6.5 beta © 2009 (Partek Inc., St. Louis, MO, USA). Principle component analysis (PCA) revealed that the main variance associated with the first principle component was array quality. An additional transformation based in singular value decomposition was performed to remove this technical variation. The data was subsequently log2 transformed.

Differential Gene Selection:

Reliably detected genes were selected by removing the probe sets with a variance below the mean global variance. The genes were then filtered based on fold change (> 1.3 for less stringent and 1.5 for stringent selection) to select the differentially expressed probe sets between the experimental group (after metformin) and the baseline group (before metformin). A student's t-test without multiple testing correction was performed and significant genes (p-value < 0.05 for less stringent and p-value < 0.005 for stringent selection) selected for further analysis.

Ingenuity Pathway Analysis (IPA)

Ingenuity Pathway Analysis (IPA) analysis mapped genes differentially expressed between baseline and follow-up biopsies to biological pathways using the standard commercial software (IPA, http://www.ingenuity.com) Gene Set Analysis (GSA) Gene Set Analysis (GSA) examined whether members of a particular biological pathway occur toward the top or the bottom of a rank-ordered gene list including all gene expression measurements ranked by differential expression between baseline and metformin treatment. This analysis takes into account information from members of a pathway that would not make it to the top most differentially expressed gene list (used for the IPA analysis above). GSA was performed using the BRB Array Tools software package (http://linus.nci.nih.gov/BRB-ArrayTools.html , US NCI Biometrics Branch) for 2,987 gene sets collectively representing most known biological and metabolic pathways in Gene Ontology (GO, http://www.geneontology.org). To be included, a GO gene set required a minimum of 10 and a maximum of 200 genes. Significance was estimated with a permutation test (n=1000). The null hypothesis was that the average degree of differential expression of members of a given gene set between the baseline and second biopsy was the same as expected from a random permutation of biopsy labels. IPA software was used to generate pathway figures for the significant gene sets.

Results

Clinical effects of metformin

Fifty-five non-diabetic women consented to the trial (Figure 1). Despite the careful design of a feasibility cohort and then a randomized trial, there was withdrawal of patients due to gastrointestinal toxicity and loss of samples during tissue acquisition or processing. Thus the numbers in some groups are less than originally designed and the conclusions need to be interpreted with caution.

In cohort A, 8 patients had core biopsy at three time points; the mean duration of metformin use was 14 days (range 13-18 days) and no patient withdrew. In cohort B, 47 patients had core biopsy at two time points; 17 women completed the metformin course (mean 15.9; range 13-21 days) but a further 7 patients withdrew due to gastrointestinal symptoms including diarrhea, bloating, and/or nausea; 1/23 (4.5%) not receiving metformin withdrew (no reason given). Symptoms resolved completely after stopping metformin. No patients had a notable change in tumor size on examination during the 2-3 weeks of metformin therapy.

Ki67

The mean percentage of cells staining for Ki67 fell after metformin in cohort A by a mean 5% (p=0.041, paired t-test) and cohort B by a mean 3.4% (p=0.027, Wilcoxon matched pairs signed rank test), but remained stable in control patients (Table 1; Figure 3). In pilot cohort A, core biopsies were successfully examined at all three time points (diagnosis, pre-metformin and post-metformin) in 7/8 patients. In the remaining patient there was no cancer in the intermediate, pre-treatment, core. In six patients Ki67 fell significantly with metformin treatment (Figure 3a). The mean percentage of cells stained for Ki67 increased between baseline and interval core samples, from 9.2% to 10.8% (not statistically significant). After initiation of the metformin course the mean percentage dropped by 5.1%, from 10.8% to 5.7% (p=0.041, paired t-test). The fall in Ki67 was evident in all but one patient between diagnostic and post-metformin cores, and similarly between interval and post-metformin cores.

In cohort B (Table 1), mean Ki67 and individual Ki67 values fell in all but 2 patients following metformin (Figure 3b) while the mean Ki67 remained stable (but varied for individual control patients between the paired biopsies) in the control group (Figure 3c).

Effects of metformin on serum insulin levels

Non-fasting paired baseline obtained at the time of diagnosis and, for comparison, preoperative (fasting) insulin levels were successfully obtained in 15/17 of the cohort B metformin patients who completed therapy and in 20/22 of the controls. There was no significant change in serum insulin level in the metformin treated patients (Table 2: p=0.663, two sample t-test; Figure 4a) but a statistically significant increase in serum insulin in the control group (Table 2: p=0.044, two sample t-test; Figure 4b).

Gene array data

Clinical and pathology data for patients who received metformin and underwent successful array analysis are shown in Table 3. In cohort A (n=8) there was a loss of patients due to loss due to lack of tumour in one tissue sample (despite guided targeting)(n=1) and failure in the microarray processing (n=1) resulting in cohort A consisting of 6 complete sets of triplicate samples. For the cohort B metformin treated patients (n= 24), loss of paired pre treatment and post metformin samples was due to the patient ceasing metformin (n=7) or no tumour/processing failure (n=3 ) leaving 14 patients with paired microarray data. For the control group in cohort B, only 4 patients underwent paired microarray for financial reasons. Using the differential gene selection and multiple test correction filters, 63 genes for cohort A (p-value < 0.01) and 145 genes for cohort B (p-value < 0.005) were considered differentially expressed as a result of metformin treatment (supplementary Tables 1a and1b). On matching the stringent gene lists (p< 0.005 for inclusion) of cohort A and cohort B, Signal Sequence Receptor gamma (SSR3) was significantly down regulated by metformin in both (Table 4). Using the less stringent gene lists for cohort A (n=1360 genes) and cohort B (n=1044 genes), phosphodiesterase 3B, cGMP-inhibited (PDE3B), tumor protein p53 (TP53) transcript variant 5 mRNA and chromosome 16 clone CTD-2542L18 complete sequence (GINS2) were down regulated in both.

Immunohistochemistry validation for PDEB3 and p53

As for Ki67, core biopsies were successfully examined with PDEB3 (cytoplasmic staining only) and p53 (nuclear staining alone) at all three time points in 7/8 patients (as for Ki67) (Table 5). A fall in PDE3B staining post-metformin was confirmed in 3/7; for p53, little change was observed. These findings are consistent with and support the observations made on the mRNA expression platform.

Ingenuity Pathway Analysis

Three hundred and ninety-six probe sets were differentially expressed in pre-and postmetformin biopsies (p ≤ 0.01) (Table 6), 259 over-expressed and 137 under-expressed in the post-metformin biopsy (Supplementary Table 1). We mapped these 396 genes into biological pathways; the most significant top 10 cellular pathways whose members were altered with metformin treatment are shown on Figure 5. The most prominent pathway was Tumour Necrosis Factor Receptor 1 (TNFR1) signaling (Supplementary Figure A-1). The corresponding network figures for metformin regulated pathways (Supplementary Figures A 1-10) illustrate genes over-or under-expressed in the post metformin biopsy (Table 7) and their position in the networks. Eight of the top 10 pathways in the metformin treatment group showed no significant alteration in the control group (Figure 5).

Gene Set Analysis (GSA) results

Ninety-four of the 2987 gene sets examined were affected by metformin treatment (p < 0.01, Efron-Tibshirani test) including 34 that were over-expressed and 60 that were under-expressed in the post-metformin samples (Supplementary Table 2). To plot the biological network location of these genes we used IPA software, the genes identified through GSA as significantly altered by metformin therapy were mapped onto IPA networks (Figure 6; Supplementary Figures B 1 -10).

Discussion

This two-stage window-of-opportunity trial has highlighted biomarker and clinical effects of metformin in women with early breast cancer. Such preoperative, biopsy-based clinical studies give the opportunity to elucidate the biological effects of drugs on human tumors in vivo, but have the potential caveats, as here, of small patient numbers, insufficient sample material and patient withdrawal due to drug toxicity. Ki67 is a predictive marker for clinical or pathological response in the neoadjuvant setting (reviewed in [START_REF] Yerushalmi | Ki67 in breast cancer: prognostic and predictive potential[END_REF]) suggesting the significant reduction in cell proliferation identified with metformin in this study indicates clinical efficacy. In cohort A we attempted to take into account any effects of core biopsy itself changing Ki67; while there may be differences in Ki67 between sequential samples of the same tumour without any additional intervention, the additional influence of metformin was clear (Figure 3). The wide range of Ki67 values [START_REF] De Azambuja | Ki-67 as prognostic marker in early breast cancer: a meta-analysis of published studies involving 12,155 patients[END_REF] and the ER, PR and HER2 immunohistochemistry in the present study suggest that there was a different mix of luminal A (Ki67<13% [START_REF] Cheang | Ki67 index, HER2 status, and prognosis of patients with luminal B breast cancer[END_REF]) and luminal B (Ki67>13% [START_REF] Cheang | Ki67 index, HER2 status, and prognosis of patients with luminal B breast cancer[END_REF]) tumors in the patient groups, possibly contributing to the differences in mean tumour Ki67. The effects of metformin on luminal A & luminal B tumors have not been examined separately because the trial was not stratified by luminal status and the numbers are small. Three complementary analytical techniques were used to analyze transcriptome profiling data with subsequent immunohistochemical confirmation of selected genes [START_REF] Vendrell | A candidate molecular signature associated with tamoxifen failure in primary breast cancer[END_REF]. This revealed several consistent and biologically plausible observations despite asymmetry in the timing of the metformin (day 0 v day 14) and control (day 0 v day 7 or day 0 v day 14) biopsies, and unequal sample sizes of the various cohorts. Due to the small sample size inherent to window-of-opportunity studies, the results are susceptible to false discovery and apparent differences between baseline and metformin-treated cohorts could also be due to false negative findings; therefore, further independent validation will be required.

The role and regulation of PDE3B (phosphodiesterase 3B, cGMP-inhibited) has been extensively studied in the regulation of glucose and lipid metabolism, in insulin secretion and in adipocyte functions [START_REF] Choi | Alterations in regulation of energy homeostasis in cyclic nucleotide phosphodiesterase 3B-null mice[END_REF][START_REF] Harndahl | Beta-cell-targeted overexpression of phosphodiesterase 3B in mice causes impaired insulin secretion, glucose intolerance, and deranged islet morphology[END_REF][START_REF] Zmuda-Trzebiatowska | Role of PDE3B in insulin-induced glucose uptake, GLUT-4 translocation and lipogenesis in primary rat adipocytes[END_REF]. Although malignant beast epithelium was not micro-dissected free of stroma and thus the combined effect of metformin on both malignant and stromal cells (including adipocytes) was examined, in 3/8 cancers a fall in PDE3B protein expression was confirmed by semi-quantitative immunohistochemistry. This apparent effect of metformin on PDE3B at the mRNA and protein level requires confirmation in larger studies. Down-regulation of PDE3B by metformin in breast cancer could alter the phosphorylation and activation of AMPK with consequent physiological effects. p53 is a key gene which regulates cellular commitment to DNA replication and cell division [START_REF] Vogelstein | Surfing the p53 network[END_REF]. Treatment with metformin selectively inhibits growth of p53-deficient colon cancer cells in vivo [START_REF] Buzzai | Systemic treatment with the antidiabetic drug metformin selectively impairs p53deficient tumor cell growth[END_REF]; while inhibition of breast cancer cell proliferation in vitro may be independent of p53 status [START_REF] Zhuang | Cell cycle arrest in Metformin treated breast cancer cells involves activation of AMPK, downregulation of cyclin D1, and requires p27Kip1 or p21Cip1[END_REF]. The down regulation of p53 mRNA expression in vivo identified in response to metformin may reflect reduced DNA replication and cell division in primary breast cancers in keeping with the Ki67 data. The signal sequence receptor (SSR), a glycosylated endoplasmic reticulum (ER) membrane receptor associated with protein translocation across the ER membrane [www.ncbi.nlm.nih.gov/refseq], and GINS2 have not been previously linked to breast cancer.

The exploratory analyses suggesting effects of metformin on key cellular pathways is in keeping with the prominence of transforming growth factor beta (TGFB) up-regulation (identified in the Tumour Necrosis Factor Receptor 1 (TNFR1) signaling pathway) for the inhibition of breast cancer [START_REF] Henry | Endoglin expression in breast tumor cells suppresses invasion and metastasis and correlates with improved clinical outcome[END_REF] and conversely the failure of the TGFB pathways in the resistance of breast cancer to tamoxifen [START_REF] Lucke | Inhibiting mutations in the transforming growth factor beta type 2 receptor in recurrent human breast cancer[END_REF] . This suggests potential synergism between metformin and established breast cancer therapies. Similarly, Cdc42 down regulation, G1/S cell cycle checkpoint regulation, and under expression of p53 signaling appear compatible with the Ki67 data and transcriptome analyses. The mTOR and AMPK pathways also confirmed reduced expression in vitro with metformin [START_REF] Zhou | Role of AMPactivated protein kinase in mechanism of metformin action[END_REF][START_REF] Hawley | Use of cells expressing gamma subunit variants to identify diverse mechanisms of AMPK activation[END_REF][START_REF] Zakikhani | Metformin Is an AMP Kinase-Dependent Growth Inhibitor for Breast Cancer Cells[END_REF], and along with metabolism-related genes over-expressed post-metformin merit future exploration. However, the small numbers of patients and the mixture of breast cancer subtypes require confirmation of the networks and genes highlighted by the ingenuity pathway analysis and gene set analysis in future trials with metformin. Some known effects of metformin, including activation of AMPK and ACC [START_REF] Zakikhani | Metformin Is an AMP Kinase-Dependent Growth Inhibitor for Breast Cancer Cells[END_REF][START_REF] Hadad | Targeting AMPK: a new therapeutic opportunity in breast cancer[END_REF], were not demonstrable in vivo at the mRNA level, reflecting the principle regulation of these proteins by phosphorylation rather than by transcription. It is proposed to examine AMPK phosphorylation and downstream MTOR signaling as a separate sub study in these patients. Other factors may also influence the response to metformin, including the expression of the organic cation transporter 1 (OCT1), which is required for rapid cellular uptake of metformin [START_REF] Hawley | Use of cells expressing gamma subunit variants to identify diverse mechanisms of AMPK activation[END_REF].

The insulin levels were measured non-fasting which, with hindsight, limits the interpretation of the results. Initial blood sampling was at the time of clinical diagnosis and diagnostic/research core biopsy, and thus patients were not fasted. In the case of the pre-operative (second) samples, although taken at surgery and patients had been fasted; patients had intravenous fluids including glucose during anaesthesia. These limitations applied to both groups of patients and the rise in insulin levels seen in the control group could be interpreted as a response to fasting and then a glucose infusion. This change was not seen in the metformin group consistent with the metformin inhibiting gluconeogenesis in response to fasting and hence reducing the insulin response. This study confirms an effect of metformin on circulating insulin levels in non-diabetic patients; however, fasting insulin and glucose measurements at all time points should be considered for future trials of metformin in non-diabetic women since this study is too small to establish safety definitively. The gastrointestinal side effects of metformin were also notable and led to withdrawal of over 20% of women receiving metformin within this trial. This intolerance may be attributed to the rapid increase in dose required by this study design in order to reach levels expected to affect the breast cancer prior to surgical resection [START_REF] Zakikhani | Metformin Is an AMP Kinase-Dependent Growth Inhibitor for Breast Cancer Cells[END_REF]. Future trials using metformin may achieve greater adherence to therapy by increasing metformin exposure more gradually, or by using lower doses of metformin [START_REF] Goodwin | Evaluation of metformin in early breast cancer: a modification of the traditional paradigm for clinical testing of anti-cancer agents[END_REF]. This trial has demonstrated clear effects of metformin in women with primary breast cancer including the down-regulation of tumour proliferation and down-regulation of PDE3B, a critical regulator of cAMP, which affects the phosphorylation and activation of AMPK. These are all consistent with the effects of metformin seen in breast cancer cell lines [START_REF] Van Der Burg | Mitogenic stimulation of human breast cancer cells in a growth factor-defined medium: synergistic action of insulin and estrogen[END_REF]. Such biomarkers of metformin effect in vivo may be clinically useful in trials using biguanides in the adjuvant, neoadjuvant and advanced disease settings. All genes that belonged to GO gene sets that were significantly affected by metformin by gene set analysis were loaded to Ingenuity Pathway Analysis. The 20 most significant pathways are shown. The bars indicate -log p-values calculated by Fisher's exact test, threshold set at p=0.05. The line indicates the percent of genes that map to a given pathway from the trial list. Panel A shows the results for the gene sets that were upregulated following metformin treatment and panel B shows the results for the gene sets that were down-regulated by metformin exposure

Figure legends Figure 1 (

 legends1 Figure legends

Figure 2 :Figure 3 (Figure 4 :Figure 5 .

 2345 Figure 2: Modified Trial diagram illustrating the source of the samples successfully analysed for transcriptome. * is for metformin treated samples, # for untreated samples

Figure 6 .

 6 Figure 6. Biological functions defined by GO categories that were affected by metformin treatment. All genes that belonged to GO gene sets that were significantly affected by metformin by gene set analysis were loaded to Ingenuity Pathway Analysis. The 20 most significant pathways are shown. The bars indicate -log p-values calculated by Fisher's exact test, threshold set at p=0.05. The line indicates the percent of genes that map to a given pathway from the trial list. Panel A shows the results for the gene sets that were upregulated following metformin treatment and panel B shows the results for the gene sets that were down-regulated by metformin exposure

Figure 2 Figure 3 Ki67 5 A

 235 Figure 2

  -1 Mediated Inhibition of RXR Function G-Protein Coupled Receptor Signaling Human Embryonic Stem Cell Pluripotency FXR/RXR Activation

Table 2 :

 2 Baseline and preoperative insulin levels (* two sample t-test).

	Group	n. Insulin	Mean SE	St	Median Min Max P*
			Mean	Dev
	Metformin 15 Baseline 15.73 3.77	14.71 10.00	1.00 48.00 0.663
		Preop	16.60 3.22	12.48 15.00	1.00 42.00
	Control	20 Baseline 16.05 2.40	10.75 14.00	1.00 39.00 0.044
		Preop	29.80 6.01	26.88 22.50	1.00 89.00

Table 4 :

 4 Differentially expressed genes by metformin in both cohort A and cohort B.

	* Student's t-test.
	Gene	Description	Cohort Fold	P*
	name			change
	SSR3	Signal sequence receptor gamma (translocon-	A	-1.60	0.009
		associated protein gamma)	B	-1.35	0.007
	PDE3B	Phosphodiesterase 3B cGMP-inhibited	A	-1.88	0.007
		(PDE3B) mRNA.	B	-1.99	0.089
	TP53	Tumor protein p53 (TP53) transcript variant 5	A	-1.79	0.062
		mRNA.	B	-3.23	0.012
	GINS2	Chromosome 16 clone CTD-2542L18 complete	A	-1.96	0.092
		sequence.	B	-4.57	0.003

Acknowledgements

The authors are particularly grateful to the patients who were willing to give written informed consent in support of this clinical trial and thank the Tayside Multidisciplinary breast team ( Dougal Adamson, Douglas Brown, Emad Elsedawy, Andrew Lee, Denis Mclean, Marta Reis and Valerie Walker) for supporting patient recruitment and facilitating accrual of clinical materials and clinical data to this study.

The trial was conducted through funding from Breast Cancer Research (Scotland), Tenovus Tayside, The Anonymous Trust and Cancer Research-UK.

Declaration of interest

G Jellema and S Deharo are employees of Almac Dignostics, manufacturer of the Breast Specific Array utlised in the clinical trial for transcriptome analyses.

Supplementary Figure legends

Supplementary Figure A. Gene networks revealed by differentially expressed genes using t-test These figures were generated using Ingenuity Pathway Analysis tool loading the genes that were identified as significantly over or under expressed between baseline and followup biopsies by t-test. (p ≤ 0.01). The top 10 networks are shown, red=genes overexpressed post-metformin and green=under-expressed post-metformin. White= pathway member not present in the gene list of interest.

Supplementary Figure B Gene networks revealed by GSA for metformin effect These 20 figures were generated using Ingenuity Pathway Analysis loading genes that were significant from the Gene Set Analysis. The genes from Supplementary Table 2 were used and genes were included if parametric p value ≤ 0.1 (red=genes overexpressed post-metformin, green=under expressed post-metformin, grey=no significant change (p ≤ 0.1), white= pathway member not present in the gene list of interest).