Timothée Duron
email: timothee.duron@univ-pau.fr

Laurent Gallon
email: laurent.gallon@univ-pau.fr

Philippe Aniorte
email: philippe.aniorte@univ-pau.fr

Modeling Learner's Perseverance in Software Engineering Education

Keywords: perseverance modeling, Model Driven Engineering, motivation, student-mouse interaction, adaptive learning

With the arrival of digital tools, education research is evolving thanks to Technology Enhanced Learning Systems. These latter are becoming more and more personalized taking into consideration the student's performance. However, a significant rate of dropout is often observed. In this paper, we address the question: How to keep students engaged in their learning processes ? Our purpose is to model student's perseverance, through student-mouse interactions, in order to adapt his/her curricula depending on his/her motivation. Our solution is based on Model Driven Engineering concept to ensure reusability and automatic computations.

I. Introduction

Educational research is constantly developing, in particular with the integration of new technologies such as digital tools, the Internet, etc [START_REF] Malekzadeh | A review of emotion regulation in intelligent tutoring systems[END_REF]. The French PERSE-VERONS 1 project tries to answer the following question: What impact can numerical tools have on learning and in particular on the perseverance of students? Our team works on modeling perseverance, focusing on the interaction between the student and a digital learning tool. The main goal is to automatically adapt the curricula proposed by this tool taking into consideration the measure of perseverance of learners, in order to keep them engaged. By adapting we mean, choosing correctly the next pedagogical situation. From a Computer Science point of view, the addressed challenge is the modeling of perseverance, based on the use of mouse and keyboard.

The originality of our proposition is that it is based on a multidisciplinarity approach : educational theories (model of curricula), psychological theories (model of perseverance) and Model Driven Engineering concepts (automatic computation of models).

In the following, section II gives a short state of the art about school perseverance in both domains of Education Science/Psychology and Computer Science. Section III follows up with our approach in modeling school perseverance as a Computer Science model, and how it can be used for example in a Technology Enhanced Learning System. Section IV concludes and gives some perspectives.

II. Background and related Works

In this section, we first introduce some theories of Education Science and Psychology, related to perseverance and motivation. This part is essential, because we need to base our works on sound theoretical foundations. We take here the definition of perseverance like being motivated in time [START_REF] Dussarps | Dimension socio-affective et abandon en formation ouverte et à distance[END_REF]. The second part of this section is dedicated to related works in Computer Science.

A. Education Science and Psychology

Several researches have been conducted in the literature to understand the learning process and how to motivate students and continue to attract their attention. We highlight here some of them.

1) The motivational dynamics: according to R. Viau [START_REF] Viau | La motivation en contexte scolaire. Edition du Renouveau Pédagogique[END_REF], the student's perception of a pedagogical activity is one of the most important keys of motivation. Indeed, the motivation and the cognitive engagement depend on the student's perception of the pedagogical activity. Viau describes three kinds of perceptions: Value (what does this activity represent for the student?); Controllability (does the student have a choice whether he do the task or not?); Competence (does the student have the necessary competence to accomplish the activity?). While constructing its pedagogical sequence, the teacher needs to take these three perceptions into account. To do so, Viau [START_REF] Viau | La motivation en contexte scolaire. Edition du Renouveau Pédagogique[END_REF] describes 10 pedagogical criteria to meet an assessment of these perceptions. It permits the teacher to construct an 'a priori' motivating learning activity.

2) The Self Determination Theory (SDT): is based on the innate psychology needs of: autonomy (need of being actor of his/her own life, make his own choice according to his/her preference), competence (need of control and be competent in domain in which he/she was interested in) and relatedness (need of interact with other people). They can be easily linked to the three perceptions of Viau. SDT also integrates the work of Deci & Ryan [START_REF] Deci | Intrinsic motivation and selfdetermination in human behavior[END_REF] on the different kinds of motivation (external, internal etc). It is organized in three levels: global, contextual and situational. Each level describes a different time scale for the evolution of motivation. The global level addresses motivation that comes from the social environment, religion, etc (long time range). For example, if the student's parents are teachers, then the student will have a good impression of school. The contextual level addresses motivation that comes from the context of the student's environment (medium time range). For example, if the student has bad results at school, then he will not be motivated by the different topics taught. The situational level addresses motivation from the current situation in which the student is (short time range). If the student likes one sport in specific, even if he has, in general, bad school results, he will be motivated by course of this specific sport.

3) The Flow theory: Mihály Csíkszentmihályi [START_REF] Novak | Measuring the flow experience among web users[END_REF] describes different psychological states people can be in during an activity (fear, boredom, etc). To summarize this theory, a good balance between the needed skills and the proposed challenges is necessary to keep people motivated during an activity. If challenges are too low, the student may get bored. If challenges are too high, then the student may apprehend and panic. The best state for learning is the FLOW state, in which students are completely involved and absorbed in the activity, with a maximum concentration level.

4) The causal attributions theory: [START_REF] Barbeau | Pour mieux comprendre la réussite et les échecs scolaires[END_REF] proposes to define an 'a posteriori' assessment attribution for a success or a fail at the end of an activity. Indeed, for each real-life situation, we still need to understand why (Why is this happening to me? Why this result? etc.). To answer these questions, we attribute a cause to each event or result of an activity. To characterize this cause, we need to know : Origin: Is it internal or external? Is it our responsibility or not?; level of controllability: Is it controllable or not? Can I have control over it or not?; stability: Is the cause stable in time or not? In a school context, it is important to determine which assignments/attributions a student gives to the result of each learning activity (whether the result is a success or a failure). Bad causal attributions can affect the student's perceptions of his/her skills/competences and impact his/her motivation in future similar activities.

To summarize, in Fig 1 we proposed an integrated view of these previous motivation theories, centred on the pedagogical activity. When a pedagogical activity starts, the student has an initial level of motivation, coming from the structure of the pedagogical activity proposed (Viau theory, didactic, etc), from the context in which the activity is done (SDT theory) and depending on the current psychological state of the student (Flow theory). During the activity, this motivation can change depending on the interactions with other students or the teacher (external regulation), and the strategy he/she applies to reach the activity goal (self-regulation [START_REF] Carre | Traité de psychologie de la motivation[END_REF]). It also depends on the difficulties encountered, and the obtained result (success/failures). All these parameters impact his/her psychological state (Flow theory). Finally, at the end of the activity, the student gives causal attributions to its success/failures, which will have an impact on the initial motivation of the next activity.

In the context of our work, we focus on the Flow theory, which addresses the state of mind and change of motivation of the student during an activity. Our purpose is to give us the capacity to detect those changes at 'runtime', and if necessary get the student back into Flow state.

B. Related Works in Computer Science

In this section, we describe some works related to the evolution, of a student during a pedagogical activity in a Computer Science context, from a psychological point of view. More precisely, we focus on works which assess motivation/perseverance at run-time.

In [START_REF] Sakdavong | Help design for the metacognitive guidance of the learner -a proposition of computer-based system[END_REF], Sakdavong and al. design a real-time helper for an e-learning platform, to assist the learner at the right moment, by analyzing the interaction student-computer. This helper aims to give meta-cognitive guidance normally given by a teacher in a face to face educational situation. For that, they use an individual profile made of cognitive and meta-cognitive characteristics. This profile is assessed and updated throughout the activity. They focus, on the semantic of the actions taken by the student (select this menu, click on this or that button etc) to build their meta-cognitive guidance.

[9] try to measure the personality of people through mouse and keyboard metrics when using a computer. To do so they study some metrics of interest on a rating windows: mouse click, number of backspace and delete keys, number of other keys etc. They demonstrate that it might be possible to determine some personality traits (extroversion, anxiety, etc) from analyzing the mouse and keyboard interactions.

[10] study the variation of metrics (keystroke latency, mouse speeds, mouse inactivity occurrences, mouse inactivity duration, etc) with different stress levels during arithmetic exercises. This paper is very interesting for us, because it gives some results about characterization of stress during a course through his/her interactions with a computer. They demonstrate that stressed people make more mistakes, take more time to answer, and observe an increase of the periods during which the mouse stays unused. On the other hand, mouse speed, click rate and keystroke speed decrease.

[11] uses mouse and keyboard interactions to classify people in three different categories of mood: relax (soft music, very comfortable chair, etc) , neutral (no sound, standard chair, etc) or stress (background noise, chair uncomfortable, etc). Their study confirms the result of [START_REF] Lim | Detecting cognitive stress from keyboard and mouse dynamics during mental arithmetic[END_REF]. They show that simple metrics belongings to the keyboards (average latency by key, average typing speed for every key, number of backspace, number of delete, etc) and mouse (average mouse speed, total time of inactivity, total time of stop using mouse, etc) permit to infer the characterization of user behavior with similar precision compare to other method (bio-metrics, questionnaire...), but with a more simple approach.

All these papers demonstrate that simple metrics from mouse and keyboard uses permit to characterize different psychological states. In our works, our goal is not to precisely characterize each psychological state, but to detect the change of mood at run-time, i.e when the student leaves the flow state. So we are interested in the evolution of mouse and keyboard metrics in runtime, especially when they converge to a characteristic invariant of transient states that announce boredom or stress. This is the originality of our approach compared to works detailed in this section.

III. Our Proposition

A. Main principle based on the Flow theory

As said before, our goal is to take into account the perseverance and the motivation of a student during an activity using a dedicated software.

Our aim is to define a perseverance model, based on metrics arising from mouse and keyboard uses. Basing on the Flow theory, we argue that these metrics can help us detect changes of engagement and motivation of students at run-time. Fig. 2 shows the evolution in time of the student's motivation during a pedagogical activity according to the Flow theory. More precisely, it shows the different states the student can be in. The central state is the good one, in which the student is motivated and committed in time.

States "risk of drop out" are temporary and occur when the student considers that challenges are too ambitious for him/her, or when he thinks that his/her skills are too low to reach the goals. Finally, "drop out" states are bad due to a difficult challenge and a lack of student skills (stress, fear ...), or on the other hand a lack of challenge while the student has many skills (boredom). By using run-time perseverance metrics, we want to detect when the student goes from the flow state into a "risk of drop out" state (P1, P2 on Fig. 2). If detected, we must act to make her/him get back to the flow state by using one of the following strategies:

• S1: Increasing the number of skills implemented by the student with quite the same level of difficulty. For example, we can use a tutorial, a video clip etc. to bring these new skills to the student; • S2: Decreasing the difficulty of the next activity; • S3: Increasing the difficulty of the next activity The choice of the strategy to apply in P1 does not only dependent on run-time perseverance, but also, for example, on the perseverance measured during a larger range of time (contextual level in SDT). So it is important to maintain a portfolio of student's evolution in the curricula, to have future access to this information. B. Software Architecture Fig. 3. Software Architecture Fig. 3 shows the software architecture of our solution. The left side corresponds to the perseverance (psychology), the right side to the curricula (education science and didactic). This architecture is based on an MDE approach (Metamodel/Model) because we need to be able to compute these models automatically at run-time, and MDE is particularly well suited for that. Model of perseverance will define mouse and keyboards metrics used to assess the perseverance state of the student. It also defines the rules (correlation between metrics) that identify "risk of drop out" states. Model of curricula (right side) is a classic tree of pedagogical activity and the pathways deal with prerequisite, difficulty, etc. The Portfolio contains history of past pedagogical situations (derived from the generic curricula model) and associates information to each pedagogical situation (like success, completion time, final motivation etc) and a sub-graph of the generic curricula.

Note that, we focus on educational software based on a blocky2 like programming (like scratch3 , code.org4 , etc). In this kind of interfaces, the keyboard is rarely used, that's why, we will focus only on the mouse metrics in the next section. When assessing mouse and keyboard metrics, we are not observing motivation, but a manifestation of motivation: the behaviour/action. In a more general context, Fenouillet [START_REF] Carre | Traité de psychologie de la motivation[END_REF] decomposes the behaviour into intensity and persistance. In a Computer Science context, based on Fenouillet works, we propose to decompose the learner behaviour, i.e. the use of mouse and keyboard, into intensity and efficiency: Intensity is a quantification of student actions and efficiency measures the enforcement, and the semantic of these actions. Semantic metrics address the meaning of the action. Enforcement metrics address the manner the action is realized.

C. Assessing Perseverance using mouse metrics

In the Intensity group of metrics, we will find quite similar metrics than those given in [START_REF] Lim | Detecting cognitive stress from keyboard and mouse dynamics during mental arithmetic[END_REF] and [START_REF] Ligarreto | Metrics design for keyboard and mouse: Assessing learning levels[END_REF], like mouse speed, number of left clicks, number of mouse pauses, etc. In the Efficiency group, we find enforcement metrics like pause duration, sinuosity of movement, etc. In addition, we also find semantic metrics like the total number of deleted blocks and the current number of isolated blocks.

A first experimentation was made in a class of pupils aged 10 to 11. These metrics was extracted from events found in the log file of the mouse activity on each computer. This file was recorded with a keylogger5 . For metrics using time (average mouse speed, mouse speed variation, etc), the time windows used for computation was chosen to remove the effects of fast variations of mind change. This permits to detect only the significant changes of the psychological state.

We are actually analyzing these metrics. First results are encouraging, and must be corroborated by more larger experimentation.

IV. Conclusion and perspectives

In this paper, we present an approach to assess perseverance from a Computer Science point of view. More precisely, we try to detect motivation changes by assessing mouse metrics. The first experimentation made allows us to define these metrics.

In a second experimentation, we will capture log files of mouse and keyboard activity, screen, web-cam of users, and bio-metrics from heart pulse. Bio-metric data will permit in a first step to detect mood changes [START_REF] Hong | Multi-scale entropy approach to physiological fatigue during long-term web browsing[END_REF], and to characterize points of interest where perseverance metrics evolve. The correlation of these metrics at these points of interest should lead to the definition of characteristic invariant of transients states. Both metrics and characteristic invariant form our perseverance model. The validation will be made in a second time on a larger pool of users, always comparing our results with bio-metrics detection.

At mid term, we also want to determine if the perseverance model is the same for different age of pupils, and see if the difference between student who often use a mouse, and student who are not used to using a mouse is significant.

Fig. 1 .

 1 Fig. 1. Federative view centred on the pedagogical situation

Fig. 2 .

 2 Fig. 2. Evolution of the student perseverance

TABLE I

 I

	Categorization of mouse metrics

This project was supported by French PIA2 E-FRAN program, and granted by Caisse des Dépots et Consignation (CDC) and the Conseil Départemental des Landes.

https://developers.google.com/blockly/

https://scratch.mit.edu/

https://code.org/

https://sourceforge.net/projects/basickeylogger/


Nia Cason
email: niacason@yahoo.co.uk

Corine Astésano
email: corine.astesano@lpl-aix.fr

Daniele Schön
email: daniele.schon@univ-amu.fr

Bridging music and speech rhythm: rhythmic priming and audio-motor training affect

Keywords: Speech, Music, Prosody, Metre, Rhythm A group (Auditory modality exposure group), AM group (Audio-motor training group), Reaction Times

Following findings that musical rhythm enhances the perception of subsequently presented speech, we investigated whether priming for spoken sentences with metrical rhythms can enhance phonological processing -the building blocks of speech -and to what extent this priming effect is sensitive to rhythmic audio-motor training. Participants heard a metrical prime followed by a sentence (with a matching/mismatching prosodic structure in terms of both stress patterns and the number of elements), for which they performed a phoneme detection task. Behavioural (RT) data were collected from two groups of participants: a group who underwent audio-motor training with the musical rhythms (AM group), and a group who did not (A group).

We hypothesised that 1) inducing metrical expectations about sentence prosody through a musical rhythmic prime (i.e. metrical priming) would enhance the phonological processing of sentences when expectations were matched, and 2) that audio-motor training with musical rhythms would enhance this effect. Indeed, our results show that providing a matching rhythmic prime context resulted in faster phoneme detection, thus revealing a crossdomain effect of musical rhythm on the phonological processing. In addition, our results indicate that rhythmic audio-motor training enhances this priming effect. These results have important implications for rhythm-based speech therapies, and suggest that metrical rhythm in music and speech may rely on shared temporal processing brain resources.

Introduction

Though speech and music may seem distinct from one another, both are hierarchicallyorganised, rule-based systems whose processing shares a wide range of similarities [START_REF] Jäncke | The relationship between music and language[END_REF][START_REF] Patel | Why would musical training benefit the neural encoding of speech? The OPERA hypothesis[END_REF]. Here, the current focus is how cross-domain similarities in the processing of "rhythm" can explain the potential for musical rhythm to impact on speech processing.

Rhythm (in both domains) can be broadly defined as the temporal organisation of acoustic events which unfold over time. Two basic properties of rhythm can be said to be its ability to give rise to a sense of "beat" -a series of regular and recurrent psychological events [START_REF] Cooper | The rhythmic structure of music[END_REF] and "meter", which can be described as an emergent temporal structure that results in a hierarchical organisation of salient and less salient events (London, 2012). The present focus is with metre: how musical metre can inform a listener about speech meter, and the consequences of this in terms of speech processing.

Western music typically has a binary, march-like metre (12 12 12) or a ternary, waltzlike metre (123 123 123). The emergence of metre from a sequence of rhythmic sounds is not only automatic (London, 2012), but its perception also said to be present from infanthood [START_REF] Hannon | Infants use meter to categorize rhythms and melodies: Implications for musical structure learning[END_REF]. This preference for perceiving metrical patterns has long been acknowledged: even on hearing an isochronous sequence of identical sounds, we automatically project a metrical structure onto them, engendering a perceptual illusion of weak and strong elements (Bolton, 1894).

Similarly in speech, salient and less salient syllables form the metrical patterning of utterances. Though speech does not possess the same degree of regularity as music [START_REF] Patel | Music, Language and the Brain[END_REF], metrical "rules" allow for a degree of rhythmic predictability in speech; a final stressed syllable marks the end of word groups in French, for example [START_REF] Hirst | A survey of intonation systems[END_REF]. We will refer here to stress as to the relative emphasis that is not uniquely signaled by intensity changes, but also importantly by changes in pitch and vowel duration. There are multiple levels of saliencies in speech rhythm: there are different types of prosodic stress (e.g. lexical stress, pitch stress, emphatic stress), all of which have different functions in speech, as well as different degrees of stress (e.g. primary and secondary lexical stress; high, low or complex pitch stress). As in the music domain, it is the interactions between these multiple levels of stress that allow for the emergence of speech rhythm (Handel, 1989: pp. 383).

In both domains, metre is key in allowing a listener to form predictions about what will happen next. For instance, on hearing a ternary metrical sequence in music (123 123 123), we are able to automatically and implicitly predict that a "123" (weak-weak-strong) pattern will follow. This is also the case in speech, whereby hearing a list of trisyllabic words with a final stress induces expectations for a word with the same metre [START_REF] Pitt | The use of rhythm in attending to speech[END_REF].

Given this, it is perhaps not surprising that attentional accounts have been used to explain predictive mechanisms in both speech [START_REF] Pitt | The use of rhythm in attending to speech[END_REF] and in music [START_REF] Jones | Musical Time in Oxford Handbook of Music Psychology S Hallam, I Cross[END_REF][START_REF] Large | Perceiving temporal regularity in music[END_REF]. In speech, The Attentional Bounce hypothesis states that attention is oriented to syllables which are expected to be stressed [START_REF] Pitt | The use of rhythm in attending to speech[END_REF]. It claims that the position of these stressed syllables can be predicted on the bases of the metrical patterns of speech, and that this is reflected by quicker phoneme detection at attended syllables [START_REF] Pitt | The use of rhythm in attending to speech[END_REF][START_REF] Rothermich | Rhythm"s gonna get you: Regular meter facilitates semantic sentence processing[END_REF]. In music, the Dynamic Attending Theory (DAT) predicts that, on hearing a metrical rhythm (external oscillators), neural rhythms synchronise to it with similar phase and period relations (internal oscillators), and that this coupling dynamically modifies attention in time, with more attentional resources being allocated to strong (predictable) metrical positions [START_REF] Jones | Musical Time in Oxford Handbook of Music Psychology S Hallam, I Cross[END_REF][START_REF] Large | The dynamics of attending: how people track timevarying events[END_REF]London, 2012;[START_REF] Snyder | Gamma-band activity reflects the metric structure of rhythmic tone sequences[END_REF]. As a result, auditory events occurring at these more predictable locations are better processed [START_REF] Barnes | Expectancy, attention, and time[END_REF][START_REF] Ellis | Rhythmic context modulates foreperiod effects[END_REF][START_REF] Jones | Controlled attending as a function of melodic and temporal context[END_REF][START_REF] Jones | Temporal aspects of stimulus-driven attending in dynamic arrays[END_REF]. This effect of auditory rhythmic expectation is also cross-modal, and can enhance the detection of visual targets, too [START_REF] Brochard | Got rhythm…for better and for worse. Crossmodal effects of auditory rhythm on visual word recognition[END_REF][START_REF] Escoffier | Unattended musical beats enhance visual processing[END_REF].

To conclude thus far, metre can be said to dynamically modulate attention in time in such a way that processing efficiency is influenced. This possibly takes place is a similar way in music and speech, as shown by impacts of musical metre on the phonological processing of speech [START_REF] Cason | Rhythmic priming enhances the phonological processing of speech[END_REF]. Should this be the case, expectations induced by musical metre can be hypothesised to impact on the processing of speech metre, and, as a consequence, to impact on the phonological processing of speech. Notably, this prediction is scale free: it considers only metrical relations, which are considered to include "number" expectations (how many elements to expect) and "stress" expectations (what stress patterning to expect), and not the exact durational expectations (i.e. inter onset intervals). This hypothesis (that musical rhythm can have a cross-domain impact on speech processing) has been received convincing support. For instance, temporal alignment between stressed beats and stressed syllables allows for a greater comprehension of speech [START_REF] Gordon | EEG Correlates of Song Prosody: a new look at the relationship between linguistic and musical rhythm[END_REF]. More specifically in relation to the current research question, inducing rhythmic expectations can have also have a cross-domain effect on subsequent speech processing [START_REF] Cason | Rhythmic priming enhances the phonological processing of speech[END_REF]. In that experiment, expectations about beat and metre were induced by a musical rhythmic prime, which was followed by a bi-or trisyllabic pseudoword with a final stressed syllable. Focussing here on the meter aspect, we found electrophysiological (EEG) evidence of an enhanced processing when the speech metre matched the prime metre (for example, when a prime with a ternary metre (123 123 123) was followed by a trisyllabic word with the same ternary metre (123)). These results indicate that musical rhythm impacts on phonological processing (stimuli for this experiment were pseudowords with no semantic meaning) by inducing domain-general metrical expectations.

To build upon these findings, the current study aimed to investigate whether this effect -seen at the level of single pseudowords -would also be present for real sentences.

Because active (audio-motor) engagement with musical rhythm may allow listeners to further internalise musical rhythms which can be shared by speech, a second aim of the present study was to investigate the role of rhythmic sensorimotor training on this crossdomain priming effect. In both speech and music, audio-motor interactions are vitally important for the consolidation of accurate production and the continual monitoring of output [START_REF] Guenther | Cortical interactions underlying the production of speech sounds[END_REF][START_REF] Lappe | Cortical plasticity induced by short-term unimodal and multimodal musical training[END_REF][START_REF] Levelt | Models of word production[END_REF][START_REF] Zatorre | When the brain plays music: auditory-motor interactions in music perception and production[END_REF]. In music, audio-motor training can strongly influence the perception of rhythm [START_REF] Phillips-Silver | Feeling the beat: Movement influences infant rhythm perception[END_REF], 2007;[START_REF] Su | Body movement enhances the extraction of temporal structures in auditory sequences[END_REF] and can enhance listeners" sensitivity to metrical deviants (Geiser, Sandmann, Jancke & Meyer, 2010;[START_REF] Vuust | To musicians, the message is in the meter: pre-attentive neuronal responses to incongruent rhythm are left-lateralized in musicians[END_REF]. Considering this, rhythmic training would intuitively result in a stronger priming effect, through an enhanced metrical sensitivity. From a clinical standpoint, the importance of musical audio-motor training in enhancing speech fluency has not been fully acknowledged.

In therapies such as Rhythmic Speech Cueing [START_REF] Thaut | Neurologic Music Therapy in Speech and Language Rehabilitation[END_REF], for example, it is often the therapist who produces the rhythm on a drum whilst the patient is asked only to produce speech (rather than themselves actively participating in the musical rhythm production). In this case, any further benefits of motor rhythmic engagement are not fully potentiated.

In the present experiment, a musical metrical prime sequence was used to induce metrical expectations about both stress patterns and the number of elements. A subsequentlyheard sentence either conformed to these stress and number expectations -and thus to listeners" metrical expectations -or did not. Considering the impact of rhythmic predictability on processing efficiency, we hypothesised that in conditions where metrical expectations were met, phonological processing of a target sentence would be enhanced. The experiment employed a phoneme detection task to measure phonological processing, a building block of speech perception, with faster reaction times (RTs) indicating a facilitated access to phonological information. To address the second aim of this study, we tested two groups: one group who underwent audio-motor training with the musical rhythms presented in the experiment (Audio-Motor (AM) Group), and another group without this training (Auditory only (A) Group). We hypothesised that the positive effect of metrical priming on phonological processing would be more striking in the AM Group due to the consolidation of metrical representations afforded by audio-motor training.

Materials and Methods

Stimuli

Experimental trials consisted of a rhythmic prime sequence followed by a sentence. We used four rhythmic prime sequences that comprised four experimental blocks (Prime 1, Prime 2, Prime 3, Prime 4), and four sentence types built around these same rhythms (Figure 1). Prime sequences were created in Adobe Audition and differed in number of beats (6 or 7) and in the placement of two stressed events. These rhythmic patterns were composed of percussion sounds, which had a stimulus onset asynchrony of 225 msecs (beat level=450ms).

The first stressed percussion sound of each prime had a rim shot timbre, a duration of 196 ms and an average Root Mean Square (RMS) of -41.25 dB, the second stressed percussion sound of each prime had a snare timbre, a duration of 353 ms and an RMS of -28.88 dB, and unstressed percussion sounds had a closed high hat timbre, a duration of 138 ms and an RMS of -43.63 dB.

Sentence stimuli were constructed around the four prime rhythms, thus using two syllable groups of 3 or 4 syllables (the most common meter in French, whatever the speaking style, [START_REF] Astésano | Rythme et accentuation en français: invariance et variabilité stylistique[END_REF]. 40 sentences were constructed for each of the four prime rhythms, resulting in a total of 160 sentences. The final word of the sentence (in which the phoneme target was present 50% of the time) was selected using Lexique 3 [START_REF] New | Une base de données lexicales du français contemporain sur internet: LEXIQUE™//A lexical database for contemporary french: LEXIQUE™[END_REF]. These sentence-final words were bisyllabic, had a CV/CV structure (at the phonological level), and were balanced for lexical frequency across the four sentence rhythm types. The last syllable of each word (an open CV syllable) contained a target vowel, for instance, as /i/ in /mari/ for 'mari", and the previous consonant macro-class (liquid, nasal, unvoiced fricative, voiced fricative, unvoiced stop and voiced stop) was balanced over the 4 rhythmic conditions.

Sentences were recorded in a soundproof booth by a native French speaker. Sentences were spoken within a carrier sentence simply to control for sentence-ending prosodic effects; the extracted (first) half of the sentence was followed by a carrier (second) half of the sentence which had exactly the same prosodic structure. To present an example, from 'J'ai bu un bon cafe' n'est pas facile a dire ['I drank a good coffee' is not easy to say], we extracted the first part of the sentence (J'ai bu un bon cafe) "x x x X x X"), which has exactly the same prosodic structure as the following (discarded) "n'est pas facile a dire' ("x x x X x X").

Without these carrier sentences, the prosodic structure of the sentences may have been confounded, due to phrase-final falling intonation, a well-established phenomenon in French.

This characteristic sentence-final stress pattern is marked by lengthening, pitch changes and, to a minor extent, intensity. By recording within carrier sentences, we thus eliminated this final stress. In addition, all sentences were presented in all possible conditions across participants (fully counterbalanced design) thus eliminating any possibility that acoustic differences in syllable saliencies could bias results. Determining syllable onsets and offsets of extracted sentences (PRAAT speech segmentation software, Boersma & Weenink, 2012) allowed duration values to be calculated (sentence duration, final syllable duration, and the onset of the final word relative to the sentence onset). These duration values were used to determine when each sentence would be presented following the prime as well as to timelock RT data to the onset of the final (target) vowel. Examples of the 4 primes and corresponding sentences are available as supplementary audio material.

Participants

Behavioural (RT) data were collected from two groups of healthy participants. The "auditory only" group (A Group) was exposed to the rhythmic primes auditorily during the experimental task, whilst the "audio-motor" group (AM Group) underwent an additional period of audiomotor training with the rhythmic primes. In the A Group, 17 participants (7 female) between the ages of 22 and 39 years (mean = 28 years, 4 months) participated in the experiment. In the AM Group, 17 participants (7 female) between the ages of 23 and 42 (mean = 30 years) participated in the experiment. All participants had normal hearing, were non-musicians and were native French speakers. Behavioural data were collected during a RT (phoneme detection) task. Each participant gave informed consent prior to experimentation and was compensated for their time with a gift.

Procedure

For both groups (A and AM), participants sat in front of a computer screen. The volume for auditory stimuli was adjusted to a comfortable level. For the duration of each trial, participants were presented with a target vowel on a computer screen and were asked to decide if it was or was not present in the final syllable of the auditorily-presented sentence, and to make their responses as quickly as possible by pressing either a "yes" button or a "no" button. The hand in which each button was held was balanced across subjects.

After a training session of 12 trials, the experiment began. The experiment consisted of 4 blocks of 40 trials each, with each block dedicated to one of the four primes (order counterbalanced across subjects). For each trial, the target vowel (/a/, /e/, /i/, /y/, /u/ or /o/) was present within the heard sentence 50% of the time. These vowel targets were equally distributed across all conditions for each participant. In order to minimise the effects of beat entrainment, the beat relationship between the prime and the sentence was disrupted: the first stressed syllable of sentences landed 2.5 beats (1125 ms) after the final stressed beat of the prime.

As to the manipulation of interest, sentences could either match or mismatch the metrical structure of the prime in terms of both the number of elements and/or the stress pattern (number and stress are two aspects which contribute to "metre"); "number" refers to the number of the prime"s percussion sounds compared to the number of syllables in the following sentence, and "stress" refers to the patterning of stressed and unstressed percussion sounds compared to the patterning of stressed and unstressed syllables in the following sentence. Overall, Condition 1 presented a matching metrical structure between the prime and the sentence, and Conditions 2, 3 and 4 presented mismatching metrical structures of varying degrees. More specifically, the experimental conditions were as follows: Condition 1) number and stress match; Condition 2) number match, stress mismatch; Condition 3) number mismatch, partial stress match (the first half of the sentence matched the first half of the prime"s stress patterning); Condition 4) number mismatch, stress mismatch (full mismatch).

Conditions 2, 3 and 4 therefore each provided different degrees of a metric mismatch between the prime and sentence (see Figure 2 for an example). The order (and frequency) of these 4 experimental conditions was pseudo-randomised within each experimental block, yielding 40 trials per condition for the whole experiment. Participants heard each sentence only once, though sentences were used in all four conditions across participants (i.e. it was a fully balanced design). Error feedback was received at the end of each experimental block. Both visual (target phoneme) and auditory stimuli were presented using Presentation Software. This protocol was identical for both the A and AM groups. However, the AM group also had a short audio-motor training: they were asked to copy (vocally) the prime rhythm using different sounds to distinguish between strong and weak elements of the rhythmic prime (e.g. "ba" and "ka": a prime of x x X x x X would be repeated as "ba ba KA ba ba KA). For each experimental block (i.e. each prime), participants in the AM Group underwent two periods of this audio-motor training, once before the experimental block (n=10 trials), and once midway through (n=10 trials). This made a total of 20 audio-motor rehearsals per prime, which were recorded using a M-Audio Microtrack 24/96 recorder.

For clarity, what follows is a summary of the experimental protocol: there were 4 prime blocks and 4 experimental conditions. For each experimental trial, participants were to decide if a target vowel (presented on-screen) was present in the final syllable of the sentence as quickly as possible, using a "yes"/"no" button press. We expected to see quicker RTs for metrically matching conditions (Condition 1), and that any differences in the mismatching conditions (Conditions 2, 3 and 4) may depend on the type of mismatch (stress/number/partial stress mismatch). In addition, there were 2 groups; one with audio-motor training (AM group) and one without (A Group), the hypothesis being that audio-motor training would augment the cross-domain priming effect, and also that the ability to reproduce the rhythm of a given prime may be related to the subsequent priming effect.

Data Acquisition and Analyses

Analyses on behavioural RT data were performed for correct trials only. For each participant, outlier responses were removed from data (RTs which were +/-2.5 x s.d. from the mean).

Repeated-measures analysis of variance (RM-ANOVA, mixed factorial design) was used, with the categorical predictor "Group" as a between-subjects factor (2 levels, A Group or AM Group). Within subject factors included "Prime" (4 levels; Prime 1, Prime 2, Prime 3, Prime 4), and "Condition" (4 levels; Condition 1, Condition 2, Condition 3, Condition 4). All pvalues reported below were adjusted using the Greenhouse-Geisser correction for nonsphericity, when appropriate, and Fisher least significant difference (LSD) test was used in post-hoc comparisons. Prime reproduction (for the AM group, during training) were scored as follows: 1 for a correct reproduction (the number/stress pattern of the prime sequence was reproduced without errors), 0.5 for a correct reproduction but with a noticeable hesitation, and 0 for incorrect prime reproduction. Of the 17 participants in the AM Group, we collected recordings from 13 participants. The scores for the first 5 repetitions of each prime were taken. Friedman"s test was used for this reproduction analyses.

[Insert Figure 1 around here] [Insert Figure 2 around here]

Results

All participants were able to perform the task easily (mean percentage of incorrect/missed trials in the experimental blocks = 5.88% for A group, 5.55% for AM group; standard deviation across condition was 0.35).

A three-way RM-ANOVA was run on correct response data with "Group" (A group/AM group) as a between-subjects factor and "Prime" (Prime 1/Prime 2/Prime 3/Prime 4) and Condition (Condition 1/Condition 2/Condition 3/Condition 4) as within-subject factors.

The Effect of a Matching Metrical Prime

To consider first the main effects over both groups, there was a main effect of Condition (F(3, 96)=3.5072, p=.0182). A post-hoc analysis revealed this to be the result of quicker RTs in the matching Condition 1 (mean 521.805 msec) compared to those in Conditions 2 (p=0.004, mean 547.08 msec), Condition 3 (p=0.064, mean 538.157) and Condition 4 (p=0.0077, mean

545.605) (metrically mismatching conditions).

Whilst the triple interaction (Group x Prime x Condition) was not significant, we found there to be a strong trend towards significance in the Group x Condition interaction (F(3, 96)=2.3, p=0.08). Post-hoc comparisons showed that Condition 1 (matching) resulted in shorter RTs compared to all three other conditions (p<0.04, p<0.002, p<0.0002, respectively) in the AM group only (Figure 3).

There was also a Prime x Condition interaction (F(9, 288)=2.342, p=.014), revealing that the main effect of Condition was driven by Prime 1 and Prime 2: a post-hoc analysis revealed there to be an experimental effect of Condition only in Prime 1 and Prime 2 (Figure 4). There was no effect of condition for Prime 3 and Prime 4. For Prime 1, Condition 1 (matching) resulted in significantly shorter RTs compared to Condition 2 (p=0.00004), and Condition 4 (p=0.0036), and was not significantly different from Condition 3 (p=0.618). For Prime 2, Condition 1 (matching) resulted in faster RTs compared to condition 2 (p=0.077), Condition 3 (p=0.04) and Condition 4 (p=0.024). Interestingly, results of the behavioural measures during audio-motor rhythm training showed that (for the AM Group), Primes 1 and 2 were also the easiest to reproduce (mean error rates: Prime 1: 1.875 %,s.d. 4.411;Prime 2: 6.736%,s.d. 11.27;Prime 3: 9.556%,s.d. 24.09;Prime 4: 10.417%,s.d. 13.97), although these differences did not reach statistical significance.

[Insert Figure 3 around here] [Insert Figure 4 around here]

Discussion

The aim of this experiment was two-fold. First, we wanted to extend previous findings that musically-induced rhythmic expectations (rhythmic priming) can impact on speech processing of spoken pseudowords [START_REF] Cason | Rhythmic priming enhances the phonological processing of speech[END_REF]. We did this by testing whether rhythmic primes would allow a listener to form rhythmic expectations about a subsequentlypresented sentence. Importantly, the rhythmic primes induced metrical expectations -the number and stress patterning of elements -rather than durational (IOI) expectations such as those induced by "beat". Second, we wanted to investigate how additional audio-motor training with the musical rhythms influences this cross-domain effect.

We found that, under certain conditions, sentences preceded by a matching metrical prime were processed more efficiently, as seen by quicker RTs to a phoneme detection task.

This effect was more prominent with primes that were more easily reproduced, and when participants were trained to reproduce the prime before the experiment (AM Group). We will first discuss the effect of rhythmic priming on speech processing and will then consider how additional audio-motor training with musical rhythm may enhance the effects of rhythmic priming.

The effect of metrical priming on sentence processing

In the experimental task, the prosodic structure of heard sentences either matched or mismatched a prior metrical prime in terms of both stress patterning and/or number of elements. No relation was made between durational relations, however (i.e. rhythmic primes were isochronous and sentences were spoken naturally, and so were not isochronous). RTs were taken as a measure of sentence processing efficiency, with quicker RTs to the phoneme detection task taken to indicate a facilitated access to phonological information. Here, we will discuss the finding that metrical predictability of sentences, afforded by musical rhythm, can enhance sentence processing.

Metrical expectations about number and stress were induced. The number of prime sounds was mirrored by the number of syllables in the sentence (Condition 1 and 2) or not (Condition 3 and 4). In the case of stress patterning, the weak and strong prime sounds were fully mirrored by the sentence metre (Condition 1), partially mirrored by the sentence metre (Condition 3) or were not mirrored by the sentence metre (Condition 2 and 4). In partially matching stress patterning (Condition 3), the first half of the sentence conformed to listeners" expectations and diverged from metrical expectations at the 6th syllable, whilst in fully mismatching stress patterning (Conditions 2 and 4), the first stressed syllable occurred either one syllable earlier or one syllable later than anticipated and so diverged earlier, at either the 3rd or 4th syllable. These four conditions were chosen in order to give an indication about which aspects of musical metre (stress pattern, number of elements, or both) might contribute most to inducing metrical expectations. We found that a rhythmic sequence which wellprimed for the target sentence (Condition 1) resulted in an enhanced phonological processing of sentences; RTs were significantly quicker than stress mismatching conditions (reaching significance for Conditions 2 and 4, almost significant for the partial stress matching Condition 3). Number and stress are two components thought to comprise metre, and so the finding that partially-matching stress context (Condition 3) resulted in RTs which were more similar to those of the metrically matching condition (Condition 1) could indicate that stress patterning is a more salient metrical cue than number, and that stress expectations which are met in the first half of a sentence may thus also enhance sentence processing somewhat.

The finding that rhythmic predictability can enhance auditory processing is central to the hypotheses presented in this paper. Musical rhythm may induce expectations about "when" events are expected to occur [START_REF] Jones | Dynamic attending and responses to time[END_REF][START_REF] Large | The dynamics of attending: how people track timevarying events[END_REF], as well as "what" events are expected to occur. For instance, on hearing a musical rhythm a listener forms expectations about when in time a future event will occur, and events conforming to these expectations are better perceived in both music [START_REF] Jones | Controlled attending as a function of melodic and temporal context[END_REF][START_REF] Jones | Temporal aspects of stimulus-driven attending in dynamic arrays[END_REF] as well as speech [START_REF] Cason | Rhythmic priming enhances the phonological processing of speech[END_REF]. The current interest was the ability for rhythmic priming to also induce cross-domain expectations about "what" speech events will occur (the sequence of weak and strong elements). We reasoned that hearing musical metrical patterns would have the ability to induce expectations about speech metre, and in isolation from durational/beat expectations -the formation of metrical expectations in speech does not require temporally isochronous stimuli [START_REF] Pitt | The use of rhythm in attending to speech[END_REF]. Noteworthy, however, is Quené & Port"s (2005) findings that whilst temporal regularity in speech (priming for "beat") enhanced phonological processing, inducing metrical expectations did not. This study differs from our own in several ways, however: firstly, their target stimuli were single words whilst our own were sentences, secondly, we disrupted beat relations between the prime and the sentence so as to measure only the effects of metrical expectancies on sentence processing, and lastly, rather than using speech itself to induce metrical expectations we used a musical rhythmic prime.

While compared to music synchronisation is not afforded to the same degree by natural speech, temporal features of speech are nonetheless vital for perception and comprehension: when speech metre is clearly primed by music, different levels of speech processing may be affected more strongly than speech which is primed by regular speech alone. These different levels may include the phonological level [START_REF] Cason | Rhythmic priming enhances the phonological processing of speech[END_REF][START_REF] Pitt | The use of rhythm in attending to speech[END_REF]Quene & Port, 2005), syntactic level (Roncaglia-Denissen, [START_REF] Roncaglia-Denissen | Speech rhythm facilitates syntactic ambiguity resolution: ERP evidence[END_REF][START_REF] Schmidt-Kassow | Event-related brain potentials suggest a late interaction of meter and syntax in the P600[END_REF], and also semantic level of processing [START_REF] Rothermich | Rhythm"s gonna get you: Regular meter facilitates semantic sentence processing[END_REF]. We would therefore expect the results found here, for phonological processing, to apply to these other levels of speech processing, too. Since no participants claimed to be aware of the rhythmic relationship between the primes and sentences, speech metre may well have been implicitly recognised, much like that of music can be (Schultz et al., 2013); further research would be required to determine whether listeners need to be aware of this metrical relationship for it to impact on their speech processing.

A differential effect of the four rhythmic primes

There was a difference between primes in their ability to impact on sentence processing: Prime 1 (x x X x x X) and Prime 2 (x x X x x x X) drove the main effect of rhythmic priming on phonological processing; matching conditions (Condition 1) resulted in quicker RTs for these two primes only. We will now discuss why these two rhythms in particular may have been most successful in priming for speech prosody.

Because the four primes were similar in terms of number of sounds, sound durations and tempo, differences should reflect the presence of a more solidified representation of the rhythmic structures. Primes 1 and 2 together differ from Primes 3 and 4 in their initial rhythmic unit: these "successful" primes begin with a "x x X" unit, whilst "unsuccessful" primes begin with a "x x x X" unit. Bringing the first stress earlier may therefore benefit the induction of metrical expectations. This may be particularly true when using short rhythmical patterns, because one may expect that using longer sequences there would be less metrical ambiguity. There are two non-exclusive possibilities as to why. Considering now Prime 1, it is the only prime with a repeating structure (x x X), whilst the other primes involved no repetition of the same unit. Indeed, the repetition of rhythmic units (in this case, the repetition of "x x X") is favoured in speech motor rhythms [START_REF] Pasdeloup | Figures et fond dans la scène prosodique: leur résistance face aux variations du débit de parole[END_REF]. Listeners" native language (French) may have also played a role in the ability for the rhythmical prime to influence perception; the metric structure of Prime 1 is more representative of speech rhythms typically found in spoken French [START_REF] Wenk | Is French really syllable-timed?[END_REF], and thus a more "familiar" sentence rhythm may have been more prone to disruption in mismatching conditions and/or more prone to facilitating effects in matching conditions.

The effect of audio-motor training on cross-domain speech processing

Since multimodal engagement with musical rhythm enhances rhythm perception [START_REF] Geiser | Refinement of metre perception-training increases hierarchical metre processing[END_REF][START_REF] Manning | Moving to the beat" improves timing perception[END_REF][START_REF] Su | Body movement enhances the extraction of temporal structures in auditory sequences[END_REF], it seems intuitive that such training would also impact on speech processing. As noted, while both groups showed a priming effect for the Prime 1 and 2 rhythms, when considering all four prime rhythms, only the AM group showed an effect.

When considering the fact that additional audio-motor training allowed an effect to be seen for all Primes the question arises as to how audio-motor training can increase the priming effect. Matching metre conditions (Condition 1) resulted in quicker RTs to the phoneme detection task than did mismatching metre conditions. Important to note, however, is that the metrically matching condition (Condition 1) did not seem to enhance processing in the AM Group; rather, mismatching metre conditions (Conditions 2, 3 & 4) resulted in slower RTs. That is to say, primes with an incongruent metrical pattern to the target sentence prosody resulted in a more delayed processing for listeners in the AM Group. Nonetheless, this reveals a cross-domain effect of audio-motor training on sentence perception, perhaps through a reinforcement of metrical representations and consequent increase in sensitivity to metrical patterns. This enhanced sensitivity to the prime could result in a greater conflict in mismatching conditions. A similar detrimental processing when attention is allocated to irrelevant information was shown in a study investigating the effect of a background auditory rhythm on visual word recognition [START_REF] Brochard | Got rhythm…for better and for worse. Crossmodal effects of auditory rhythm on visual word recognition[END_REF]. Though these are in themselves interesting results, perhaps a greater amount of audio-motor training would be required for a more apparent group difference to be seen. A greater effect of audio-motor training might also be observed if participants are made aware of the rhythmic relationship between the prime and the sentence, as they might be in a therapeutic context.

Conclusions

We have replicated previous findings that musical rhythm can induce implicit prosodic expectations, and that this impacts upon the processing of subsequent speech [START_REF] Cason | Rhythmic priming enhances the phonological processing of speech[END_REF]. We have shown this to be the case for real speech (as opposed to pseudowords) and in sentence contexts (as opposed to single words). Moreover, the finding that there is a differential effect of the rhythmical primes used in the experiment suggests that listeners may benefit from shorter, more repetitive rhythmic structures.

Most interestingly, audio-motor training seems to further sensitise listeners to musical rhythm, which in turn increases the potential for musical rhythm to exert an effect on sentence perception. The ability for audio-motor training to amplify the cross-domain effect from music to speech in this way has important implications for using audio-motor musical rhythm engagement in speech rehabilitation contexts, though more research is required to determine how much mismatching music-speech rhythms might interfere with phonological processing, and whether more intense rhythmic training would result in a more potent effect on speech processing.

Perspectives

Whilst the current experiment has considered the effects of musical rhythmic priming on speech perception, we can also predict that speech production would benefit from rhythmic priming, and through two possible mechanisms. Auditory rhythm engages the motor system [START_REF] Chen | Listening to musical rhythms recruits motor regions of the brain[END_REF][START_REF] Grahn | Rhythm and beat perception in motor areas of the brain[END_REF], and can provide a direct template for timely speech initiation and/or production (e.g. [START_REF] Cummins | Rhythmic constraints on stress timing in English[END_REF]. Second, it might be predicted that production may benefit from an enhancement in perception. Several lines of evidence indicate strong links between speech perception and production: speech perception engages areas involved in speech production (review, [START_REF] Galantucci | The motor theory of speech perception reviewed[END_REF][START_REF] Geiser | The neural correlate of speech rhythm as evidenced by metrical speech processing[END_REF][START_REF] Liberman | The motor theory of speech perception revised[END_REF][START_REF] Watkins | Seeing and hearing speech excites the motor system involved in speech production[END_REF] which may reflect how the speech production system (motor) is recruited to understand and predict what might happen next (auditory) [START_REF] Pickering | Do people use language production to make predictions during comprehension?[END_REF]. In this case, an enhancement of perception will necessarily result in an enhanced production, and vice versa. The next step would therefore be to test the effect of rhythmic priming on speech production. This would have more relevance in the context of speech rehabilitation therapies, many of which already recognise musical rhythm as an important fluency-enhancing tool (e.g. [START_REF] Thaut | Neurologic Music Therapy in Speech and Language Rehabilitation[END_REF].

Figure 1 .

 1 Figure 1 Click here to download high resolution image

Figure 2 .

 2 Figure 2. The four experimental conditions. Participants heard the prime followed by a sentence,

Figure 3 :

 3 Figure 3: Results illustrating RTs for each Condition in the two Groups

Figure 4 .

 4 Figure 4. 4a, left: Within Prime 1, Condition 1 (matching condition) resulted in faster RTs in both

Acknowledgements

This research was supported by a grant from EBRAMUS ITN (Europe BRAin and MUSic) (Grant Agreement number 238157). We thank Frederic Anguille, François Guimont, Bastien Intartaglia, Anaïs Llorens and Patrick Marquis for technical support and help with data collection.


Raoudha Doghri
email: raoudha.doghri@gmail.com

Maroua Manai
email: maroua.m@hotmail.com

Pascal Finetti
email: finettip@ipc.unicancer.fr

Maha Driss

Emilie Agavnian
email: agavniane@ipc.unicancer.fr

Marc Lopez
email: marc.lopez@inserm.fr

Meriam Elghardallou
email: meriamelghardallou@yahoo.fr

Emmanuelle Charafe-Jauffret

Mohamed Manai
email: mohamed.manai@fst.rnu.tn

Max Chaffanet
email: chaffanetm@ipc.unicancer.fr

Daniel Birnbaum
email: daniel.birnbaum@inserm.fr

Karima Mrad
email: karimamrad@ymail.com

François Bertucci
email: bertuccif@ipc.unicancer.fr

Stromal Expression of MARCKS Protein in Ovarian Carcinomas Has Unfavorable Prognostic Value

Keywords: epithelial ovarian cancer, immunohistochemistry, MARCKS, prognosis, stroma, survival

Epithelial ovarian cancer (EOC) is the most lethal gynecological cancer. Identification of new therapeutic targets is crucial. MARCKS, myristoylated alanine-rich C-kinase substrate, has been implicated in aggressiveness of several cancers and MARCKS inhibitors are in development. Using immunohistochemistry (IHC), we retrospectively assessed MARCKS expression in epithelial and stromal cells of 118 pre-chemotherapy EOC samples and 40 normal ovarian samples from patients treated at Salah Azaiez Institute. We compared MARCKS expression in normal versus cancer samples, and searched for correlations with clinicopathological features, including overall survival (OS). Seventy-five percent of normal samples showed positive epithelial MARCKS staining versus 50% of tumor samples (p = 6.02 × 10 -3 ). By contrast, stromal MARCKS expression was more frequent in tumor samples (77%) than in normal samples (22%; p = 1.41 × 10 -9 ). There was no correlation between epithelial and stromal IHC MARCKS statutes and prognostic clinicopathological features. Stromal MARCKS expression was correlated with shorter poor OS in uni-and multivariate analyses. Stromal MARCKS overexpression in tumors might contribute to cancer-associated fibroblasts activation and to the poor prognosis of EOC, suggesting a potential therapeutic interest of MARCKS inhibition for targeting the cooperative tumor stroma.

Introduction

Epithelial ovarian cancer (EOC) is the most lethal gynecological cancer because of often late diagnosis and high recurrence rate [START_REF] Bowtell | The genesis and evolution of high-grade serous ovarian cancer[END_REF]. Despite initial chemosensitivity, ovarian cancer is not chemocurable. One reason of the high mortality rate is the lack of effective therapeutic options 2 of 12 in case of chemoresistance. According to the Cancer Registry of Northern Tunisia, EOC is the second gynecological cancer with an incidence rate of 3.9 per 100,000 inhabitants [START_REF] Ferlay | Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012[END_REF]. It is distributed into four major pathological types: "serous" (75%), "endometrioid" (10%), "mucinous" (3%), and "clear cell" (10%), which differ in pathogenesis, molecular alterations, and prognosis [START_REF] Vang | Ovarian low-grade and high-grade serous carcinoma: Pathogenesis, clinicopathologic and molecular biologic features, and diagnostic problems[END_REF][START_REF] Shih | Ovarian tumorigenesis: A proposed model based on morphological and molecular genetic analysis[END_REF]. The classical clinicopathological prognostic factors include patient's age, Fédération Internationale de Gynécologie et d Obstétrique (FIGO) stage, pathological tumor grade and type, and initial surgery results. Optimal debulking surgery and paclitaxel/platinum-based chemotherapy have improved survival [START_REF] Ledermann | ESMO Guidelines Working Group Newly diagnosed and relapsed epithelial ovarian carcinoma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up[END_REF][START_REF] Tothill | Novel molecular subtypes of serous and endometrioid ovarian cancer linked to clinical outcome[END_REF][START_REF]NCCN Clinical Practice Guidelines in Oncology, Ovarian Cancer Including Fallopian Tube Cancer and Primary Peritoneal Cancer, version 2.2015[END_REF]. Nevertheless, the overall 5-year survival rate for patients with EOC is still very low, approximately 30% [START_REF] Liao | Ovarian cancer spheroid cells with stem cell-like properties contribute to tumor generation, metastasis and chemotherapy resistance through hypoxia-resistant metabolism[END_REF][START_REF] Stark | Standard chemotherapy with or without bevacizumab in advanced ovarian cancer: Quality-of-life outcomes from the International Collaboration on Ovarian Neoplasms (ICON7) phase 3 randomised trial[END_REF]. In Tunisia, overall survival is 27% at 5-years for advanced-stage EOC [START_REF] Ben Fatma | Epithelial advanced ovarian carcinoma in the central region of Tunisia: Therapeutic results and prognostic factors on 104 patients[END_REF]. Several molecular subtypes of EOC [START_REF] Arai | Defining a Hodgkin lymphoma population for novel therapeutics after relapse from autologous hematopoietic cell transplant[END_REF][START_REF] Sasse | German Hodgkin Study Group (GHSG) Comparing long-term toxicity and efficacy of combined modality treatment including extended-or involved-field radiotherapy in early-stage Hodgkin's lymphoma[END_REF] and prognostic gene expression signatures [START_REF] Behringer | Gonadal function and fertility in survivors after Hodgkin lymphoma treatment within the German Hodgkin Study Group HD13 to HD15 trials[END_REF][START_REF] Hanly | Measuring the societal burden of cancer: The cost of lost productivity due to premature cancer-related mortality in Europe[END_REF] have been reported, but without clinical application to date.

MARCKS, myristoylated alanine-rich C-kinase substrate, a substrate for protein kinase C, is localized in the plasma membrane and is an actin filament cross-linking protein. Phosphorylation by protein kinase C or binding to calcium-calmodulin inhibits its association with actin and plasma membrane, leading to its presence in the cytoplasm. MARCKS plays an important role in the regulation of cytoskeletal plasticity and especially of actin filaments [START_REF] Ramsden | MARCKS: A case of molecular exaptation?[END_REF][START_REF] Singer | A MARCKS-related peptide blocks mucus hypersecretion in a mouse model of asthma[END_REF]. Ubiquitously expressed in various tissues [START_REF] Ouimet | Localization of the MARCKS (87 kDa) protein, a major specific substrate for protein kinase C, in rat brain[END_REF], it is involved in cell motility, cell adhesion, cytokines secretion [START_REF] Li | MARCKS protein is a key molecule regulating mucin secretion by human airway epithelial cells in vitro[END_REF] phagocytosis, membrane trafficking [START_REF] Allen | A role for MARCKS, the alpha isozyme of protein kinase C and myosin I in zymosan phagocytosis by macrophages[END_REF], and mitogenesis [START_REF] Blackshear | The MARCKS family of cellular protein kinase C substrates[END_REF]. Many studies have shown the implication of MARCKS in cancer aggressiveness, notably metastatic process and therapeutic resistance [START_REF] Chen | PhosphoMARCKS drives motility of mouse melanoma cells[END_REF][START_REF] Hanada | Myristoylated alanine-rich C-kinase substrate as a prognostic biomarker in human primary lung squamous cell carcinoma[END_REF][START_REF] Rombouts | Myristoylated Alanine-Rich protein Kinase C Substrate (MARCKS) expression modulates the metastatic phenotype in human and murine colon carcinoma in vitro and in vivo[END_REF][START_REF] Techasen | Myristoylated alanine-rich C kinase substrate phosphorylation promotes cholangiocarcinoma cell migration and metastasis via the protein kinase C-dependent pathway[END_REF][START_REF] Yang | Targeting phospho-MARCKS overcomes drug-resistance and induces antitumor activity in preclinical models of multiple myeloma[END_REF][START_REF] Yang | MARCKS contributes to stromal cancer-associated fibroblast activation and facilitates ovarian cancer metastasis[END_REF]. A few studies also demonstrated the efficiency of therapeutic inhibition of MARCKS [START_REF] Chen | Targeting myristoylated alanine-rich C kinase substrate phosphorylation site domain in lung cancer. Mechanisms and therapeutic implications[END_REF][START_REF] Chen | A peptide that inhibits function of Myristoylated Alanine-Rich C Kinase Substrate (MARCKS) reduces lung cancer metastasis[END_REF]. A single study of MARCKS expression in EOC has shown that MARCKS is highly expressed in the ovarian stroma, is required for the differentiation and tumor-promoting function of cancer-associated fibroblasts (CAFs) and is associated with poor survival [START_REF] Yang | MARCKS contributes to stromal cancer-associated fibroblast activation and facilitates ovarian cancer metastasis[END_REF].

We have here analyzed MARCKS protein expression in stromal and epithelial cells in tumor samples of patients with EOC treated at Salah Azaiez Institute of Tunis, Tunisia, and searched for correlations with clinicopathological features and survival. We found more frequent stromal MARCKS expression in tumor samples (77%) than in normal samples (22%), and correlation with shorter overall survival in uni-and multivariate analyses, suggesting that MARCKS inhibition might represent a new therapeutic approach in EOC.

Results

Patients' Population and Clinicopathological Features

The clinicopathological features of all samples (N = 118) are shown in Table 1. The patients had a median age of 55 years at diagnosis (range, 27-85). As expected, most of the patients (91%) had an advanced stage III-VI (FIGO), including 21 cases (18%) with metastasis at diagnosis. The most frequent pathological type was serous (90%), followed by clear cell carcinoma (4%), mixed (3.5%) and endometrioid (2.5%). Most of the cases were high-grade (grade 3: 70%). All cases were operated before or after chemotherapy, with a macroscopic tumor residue in 35% of cases. Adjuvant and/or neoadjuvant chemotherapy was delivered to 89 patients.

MARCKS Protein Expression in Epithelial Ovarian Cancer

We first validated the MARCKS antibody using western blot analysis on three breast cancer cell lines with known MARCKS mRNA expression. As shown in Figure S1, the antibody specifically recognized MARCKS protein with a good correlation between protein and mRNA expression levels.

MARCKS protein expression was then measured on the 118 tumor samples and 40 normal samples present on the tissue-microarray (TMA). Examples of staining are shown in Figure 1a and results are summarized in Figure 1b. Using 1% of stained epithelial cells as positivity cut-off, we found that 75% of normal samples showed positive MARCKS immunostaining versus 50% of tumor samples (p = 6.02 × 10 -3 ; Fisher's exact test). Regarding the stroma staining and using the same positivity cut-off (1% of stained stromal cells), 77% of tumor samples showed positive MARCKS immunostaining versus 22% of normal samples (p = 1.41 × 10 -9 ; Fisher's exact test). In tumor samples, the staining was observed mainly in stromal cells, notably fibroblasts (77% positivity) and to a lesser degree in tumor epithelial cells (50% positivity; p = 2.37 × 10 -5 , Fisher's exact test), whereas in normal tissues, it was weakly expressed in stromal cells (22% positivity) and more expressed in epithelial cells (75% positivity; p = 4.85 × 10 -6 , Fisher's exact test).

ng was observed mainly in stromal cells, notably fibroblasts (77% positivity) and t tumor epithelial cells (50% positivity; p = 2.37 × 10 -5 , Fisher's exact test), whereas i was weakly expressed in stromal cells (22% positivity) and more expressed in positivity; p = 4.85 × 10 -6 , Fisher's exact test).

Correlation of MARCKS Protein Expression with Clinicopathological Features

We analyzed correlations between the binary MARCKS IHC status and prognostic clinicopathological features of samples including patients' age, FIGO stage, pathological type and grade, and macroscopic tumor residue after debulking surgery (Table 2). We did not find any correlation between epithelial MARCKS expression and clinicopathological features. Similarly, the stromal IHC status did not correlate with any tested feature. Epithelial and stromal IHC MARCKS status were not correlated.

Correlation of MARCKS Protein Expression with Overall Survival

We then assessed the prognostic value of MARCKS expression for OS in the population of 68 patients treated with surgery and carboplatin-paclitaxel chemotherapy and with available follow-up. Fifteen of 68 patients (22%) died and the 5-year OS was 35% [95CI 0.22-0.56] (Figure 2a).

In univariate analysis (Table 3), high grade (p = 2.16 × 10 -2 , Wald test; HR = 2.51 [95CI, 1.14-5.49]) and positive stromal MARCKS status (p = 3.77 × 10 -2 , Wald test; HR = 2.59 [95CI, 1.06-6.36]) were associated with poor OS, whereas epithelial MARCKS status was not (p = 0.881). In multivariate analysis (Table 3), the grade remained significant (p = 4.82 × 10 -2 , Wald test; HR = 2.21 [95CI, 1.01-4.86]), whereas the stromal MARCKS status tended to remain significant (p = 7.90 × 10 -2 , Wald test; HR = 2.24 [95CI, 0.91-5.53]).The 5-year OS rate was 28% [95CI 0.15-0.52] in the stromal MARCKS-positive group versus 53% [0.27-1] in the stromal MARCKS-negative group (p = 3.13 × 10 -2 , log-rank test; Figure 2b).

Correlation of MARCKS Protein Expression with Overall Survival

We then assessed the prognostic value of MARCKS expression for OS in the population of 68 patients treated with surgery and carboplatin-paclitaxel chemotherapy and with available follow-up. Fifteen of 68 patients (22%) died and the 5-year OS was 35% [95CI 0.22-0.56] (Figure 2a).

Discussion

Despite therapeutic progresses achieved during the last decades, the survival of patients with EOC remains poor, and the identification of new therapeutic targets is crucial. The objective of this study was to evaluate and compare MARCKS protein expression in stromal and epithelial cells in a large retrospective series of 118 EOC samples collected from Tunisian patients and to search for correlations with clinicopathological features. We have shown that MARCKS tumor expression is more frequent in stromal cells than in epithelial cells, and that stromal MARCKS expression is associated with shorter overall survival (OS).

We focused on MARCKS protein expression for several reasons: (i) proven role of MARCKS in cancer progression including metastasis and therapeutic resistance; (ii) ongoing development of MARCKS inhibitors [START_REF] Chen | Targeting myristoylated alanine-rich C kinase substrate phosphorylation site domain in lung cancer. Mechanisms and therapeutic implications[END_REF][START_REF] Chen | A peptide that inhibits function of Myristoylated Alanine-Rich C Kinase Substrate (MARCKS) reduces lung cancer metastasis[END_REF]; (iii) commercial availability of a corresponding monoclonal antibody performing sufficiently well in IHC on paraffin-embedded tissues, as previously reported [START_REF] Yang | MARCKS contributes to stromal cancer-associated fibroblast activation and facilitates ovarian cancer metastasis[END_REF][START_REF] Browne | Global characterization of signalling networks associated with tamoxifen resistance in breast cancer[END_REF]. Before analysis of tissue samples, we revalidated the antibody on cancer cell lines by using western blot analysis.

MARCKS expression in our series of 118 EOC samples and 40 normal ovarian samples was heterogeneous in all samples. We found that 75% of normal samples showed positive epithelial staining versus 50% of tumor samples. Regarding the stroma staining and with the same positivity cut-off, 77% of tumor samples showed positive staining versus 22% of normal samples. Because secretory epithelial cells of the fallopian tubes are the precursors of high-grade serous ovarian tumors, we analyzed MARCKS expression in 27 samples of normal fallopian tube (Figure S2). Interestingly, the results were very similar to those observed in the normal ovarian samples for both epithelial (74% positivity versus 75% in normal ovarian samples) and stromal staining (19% positivity versus 22%). In tumor samples, the staining was observed mainly in stromal cells, notably fibroblasts, and to a lesser degree in tumor epithelial cells, whereas in normal tissues, it was weakly expressed in stromal cells and more expressed in epithelial cells. Thus, MARCKS protein was overexpressed in ovarian tumor stroma as compared to epithelial cells. In the literature, only one study analyzed MARCKS protein expression in epithelial ovarian cancer [START_REF] Yang | MARCKS contributes to stromal cancer-associated fibroblast activation and facilitates ovarian cancer metastasis[END_REF] and found similar results. From in silico analyses of public transcriptional data, the authors first showed that, compared with its expression in the tumor epithelial compartment, MARCKS was specifically expressed in the stromal compartment. In contrast, MARCKS level was reduced in the tumor epithelial cells compared with normal ovary epithelial. Second, using IHC analyses in a small series of 10 normal ovarian tissues and 18 pairs of primary and metastatic tumors from patients with advanced (stages III-IV) serous adenocarcinoma, they showed that MARCKS protein was highly expressed in ovarian tumor stroma versus epithelial cells compartment. Additionally, MARCKS was highly expressed in normal ovary epithelial cells compared with tumor epithelial cells. We confirmed these results in our present larger study. The authors found at the transcriptional level in a series of 3431 ovarian cancer specimens a small correlation between high stroma MARCKS expression and higher FIGO stage. In our series, there was no correlation between stromal or epithelial protein staining and the classical clinicopathological prognostic features, including the FIGO stage. Finally and despite the relatively small size of our series (68 informative cases), we found that stromal MARCKS expression in tumors was associated with shorter OS in univariate analysis (HR = 2.59), and that such correlation tended towards significance in multivariate analysis with a hazard ratio equal to 2.24. Such correlation was reported [START_REF] Yang | MARCKS contributes to stromal cancer-associated fibroblast activation and facilitates ovarian cancer metastasis[END_REF], but at the transcriptional level (but not at the protein level) in terms of progression-free survival and OS. It was suggested that MARCKS stromal overexpression might contribute to cancer-associated fibroblasts activation in EOC and explain their therapeutic resistance and unfavorable prognostic impact. In experimental models, MARCKS overexpression was shown to suppress cellular senescence and boost the activation of AKT/TWIST1 (Protein kinase B/Twist-related protein 1) signaling to sustain the cancer-associated fibroblasts features, thus supporting tumor cells growth and invasion.

Several studies in other types of cancers have shown involvement of MARCKS tumor expression in cancer progression, chemoresistance, and suggested that MARCKS inhibition could be a novel therapeutic approach. In breast cancer, MARCKS was reported to be implicated in tamoxifen-resistant MCF7 (Michigan Cancer Foundation-7) breast cancer cells and its inhibition decreased cell motility. The same study also noted correlations with poor-prognosis features and short OS [START_REF] Browne | Global characterization of signalling networks associated with tamoxifen resistance in breast cancer[END_REF]. More frequent IHC staining for phospho-MARCKS was found in breast cancer than in normal breast tissue and correlated with unfavorable prognostic parameters and metastatic status. Using both in vitro and in vivo models of breast cancer, the p-MARCKS was involved in resistance to paclitaxel treatment, with an increased paclitaxel sensitivity after reduction of p-MARCKS by knockdown or by treatment with MANS (p-MARCKS inhibitor peptide targeting the N-terminal myristoylation site) [START_REF] Chen | Elevated MARCKS phosphorylation contributes to unresponsiveness of breast cancer to paclitaxel treatment[END_REF]. In a prior study, we showed that overexpression of MARCKS was correlated with the inflammatory breast cancer (IBC) phenotype. MARCKS stromal overexpression was more frequent in IBC than in non-IBC, which could sustain more cancer-associated fibroblasts activation in IBC and higher metastatic potential. We also showed the unfavorable prognostic value of MARCKS expression for metastasis-free survival in uni and multivariate analyses [START_REF] Manai | MARCKS protein overexpression in inflammatory breast cancer[END_REF]. MARCKS was found to have an important role in the progression of colorectal cancer and to be implicated in cell motility, invasion and proliferation of colon cancer cells, whereas its inhibition clearly affected these cancer features and reduced the metastatic events [START_REF] Rombouts | Myristoylated Alanine-Rich protein Kinase C Substrate (MARCKS) expression modulates the metastatic phenotype in human and murine colon carcinoma in vitro and in vivo[END_REF]. The metastatic potential of p-MARCKS was shown in melanoma [START_REF] Chen | PhosphoMARCKS drives motility of mouse melanoma cells[END_REF]. In prostate cancer, MARCKS promoted migration and invasion [START_REF] Dorris | MARCKS promotes invasion and is associated with biochemical recurrence in prostate cancer[END_REF]. In cholangiocarcinoma, MARCKS overexpression correlated with poor survival, and experimental models showed the role of MARCKS in the migration of cholangiocarcinoma cells [START_REF] Techasen | Myristoylated alanine-rich C kinase substrate phosphorylation promotes cholangiocarcinoma cell migration and metastasis via the protein kinase C-dependent pathway[END_REF]. In a series of 99 patients with squamous cell lung carcinoma, a correlation was found between protein expression and poor survival [START_REF] Hanada | Myristoylated alanine-rich C-kinase substrate as a prognostic biomarker in human primary lung squamous cell carcinoma[END_REF]. In another study [START_REF] Chen | A peptide that inhibits function of Myristoylated Alanine-Rich C Kinase Substrate (MARCKS) reduces lung cancer metastasis[END_REF], MARCKS, specifically its phosphorylated form, was a key player in potentiating lung cancer cell migration/metastasis, suggesting a potential use of MARCKS-inhibition peptides in the treatment of lung cancer metastasis. Overexpression of p-MARCKS was associated with unfavorable survival in a series of 195 operated lung cancers and targeting MARCKS phosphorylation site domain (PSD) with MPS peptide (MARCKS phosphorylation site domain) suppressed tumor growth, metastasis and increased the sensibility to erlotinib treatment in vivo and in vitro [START_REF] Chen | Targeting myristoylated alanine-rich C kinase substrate phosphorylation site domain in lung cancer. Mechanisms and therapeutic implications[END_REF]. A recent study demonstrated that a MARCKS ED peptide inhibited MARCKS phosphorylation, leading to an increase in sensitivity to radiation therapy [START_REF] Rohrbach | MARCKS phosphorylation is modulated by a peptide mimetic of MARCKS effector domain leading to increased radiation sensitivity in lung cancer cell lines[END_REF]. Finally, in kidney cancer, MARCKS inhibition with MPS peptide synergistically interacted with regorafenib treatment and decreased survival of kidney cancer cells through inactivation of AKT and mTOR (mechanistic target of rapamycin) [START_REF] Chen | Upregulation of MARCKS in kidney cancer and its potential as a therapeutic target[END_REF]. Thus, our observation of association of stromal MARCKS expression in EOC with shorter OS is consistent with the clinical and pre-clinical findings published in ovarian cancer and many other cancers.

Materials and Methods

Patients and Samples

We retrospectively collected pre-chemotherapy diagnostic tumor samples from 118 patients with epithelial ovarian cancer (EOC) treated between 2009 and 2015 at Institute Salah Azaiez of Tunis, Tunisia. These samples represented the operative specimen for women immediately operated or the surgical biopsies for women with non-operable disease thus treated with neo-adjuvant chemotherapy. Main inclusion criteria were pathologically-confirmed EOC, with available formaldehyde-fixed and paraffin-embedded pre-therapeutic tumor samples, available clinicopathological data, and signed informed patient's consent. The control group included 40 normal ovarian tissues from Tunisian women operated for non-tumor ovarian lesions. All samples were spotted onto a tissue microarray before IHC analysis. The study was approved by our institutional ethics committee at Institut Salah Azaiez (N • 1646; 15 September 2016).

Tissue Microarray Construction

One tissue microarray (TMA) was constructed for all 158 cases, as previously described, with slight modifications [START_REF] Ginestier | Distinct and complementary information provided by use of tissue and DNA microarrays in the study of breast tumor markers[END_REF]. For each sample, two representative tumor areas were carefully selected from a hematoxylin-eosin stained section of the donor block. Core cylinders with a diameter of 0.6 mm each were punched from each of these areas and deposited into the recipient paraffin block using a specific arraying device (Alphelys, Plaisir, France). Four-µm sections were made from the resulting TMA block, then used for IHC.

Western Blot Analyses

Before IHC analysis, we validated our MARCKS antibody by using western blot analysis with breast cancer cell lines. Expression was analyzed in three breast cancer cell lines (T47D, SUM149, MDA-MB-231) previously profiled using Affymetrix (Thermo Fisher Scientific, Inc., Rockford, IL, USA) DNA microarrays and for which MARCKS mRNA expression was documented as very low (T47D), moderate (SUM149), and very high (MDA-MB-231). Cells were washed 3 times with ice-cold PBS and then resuspended for 30 min in 750 µL of ice cold lysis buffer containing 50 mM Hepes, pH 7.5, 150 mM NaCl, 1.5 mM MgCl 2 , 1 mM EGTA, 1% Triton X-100, and 10% glycerol. A protease inhibitor mixture (Pierce, Thermo Fisher Scientific, Inc., Rockford, IL, USA) and the phosphotyrosyl phosphatase inhibitor sodium orthovanadate (BioLabs Cambridge, MA, USA) were added as recommended. Lysates were heated in SDS sample buffer (60 mM Tris-HCl, pH 6.7, 3% SDS, 2% (v/v) 2-mercaptoethanol, and 5% glycerol), separated by 10% SDS-PAGE, and transferred to nitrocellulose blotting membrane (Amersham, GE Healthcare Bio-Sciences, Pittsburgh, PA, USA). Membranes were blocked in PBS supplemented with BSA 5% and tween 0.1% for 1h30 and then incubated overnight at 4 • C with indicated antibodies. Visualization was done with ECL (Pierce, Thermo Fisher Scientific, Inc., Rockford, IL, USA).

Immunohistochemistry Analysis

MARCKS protein expression was analyzed on TMA for all 118 EOC and 40 normal ovarian tissues using standard IHC protocols. Paraffin sections were pretreated in PH6 PT Link (Agilent, Santa Clara, CA, USA). We used the rabbit monoclonal antibody, anti-MARCKS (D88D11) XP ® Rabbit mAb#5607, from Cell Signaling Technology (dilutionat1/400) for staining and the Flex system (Dako, Agilent, Santa Clara, CA, USA) using a peroxidase enzyme for antigen revelation. Sections counterstained with hematoxylin were independently evaluated by two experienced ovarian pathologists (RD and MD) using light microscopy. Immunostaining scoring of epithelial cancer cells and of stromal cells was evaluated on the basis of staining intensity and positively stained areas as previously described [START_REF] Manai | MARCKS protein overexpression in inflammatory breast cancer[END_REF]. MARCKS-negative cases were defined by 0% level expression and positive cases by at least 1% stained cells.

Statistical Analysis

Data were summarized by numbers and percentages for categorical variables, and median and range for continuous variables. Correlations between tumor groups and clinicopathological features were analyzed using the t-test or the Fisher's exact test when appropriate. Follow-up was calculated from the date of diagnosis to the date of last news for event-free patients. Overall survival (OS) was calculated from the date of diagnosis until the date of death from any cause. Survival was calculated using the Kaplan-Meier method and curves were compared with the log-rank test. Uniand multivariate prognostic analyses for OS were performed using Cox regression analysis (Wald test). Variables with a p-value ≤ 0.05 in univariate analysis were tested in multivariate analysis. All statistical tests were two-sided at the 5% level of significance. Analyses were done using the survival package (version 2.30) in the R software (version 2.9.1; http://www.cran.r-project.org/). We followed the reporting REcommendations for tumor MARKer prognostic studies (REMARK criteria) [START_REF] Mcshane | Statistics Subcommittee of the NCI-EORTC Working Group on Cancer Diagnostics REporting recommendations for tumour MARKer prognostic studies (REMARK)[END_REF].

Conclusions

We showed MARCKS protein overexpression in the stroma of EOC and its association with shorter OS. The strengths of our study include the number of cases tested with a total of 118 EOC; to our knowledge, it is the first study analyzing specifically MARCKS protein expression in a large series of EOC. Importantly, we highlighted the association of MARCKS protein expression with poor OS in uni-and multivariate analyses, whereas the only other published study in ovarian cancer showed the association of MARCKS overexpression with poor OS only at the mRNA level and without multivariate analysis. Limitations of our study include its retrospective nature and associated biases such as missing data with the absence of survival information and/or of information about important prognostic factors such as macroscopic residual disease after surgery for all patients. Our results suggest that MARCKS overexpression in stromal cells of EOC might contribute to cancer-associated fibroblasts activation and contribute to the poor prognosis of disease. These findings suggest that inhibition of MARCKS could be a new potential therapeutic approach in EOC.

Supplementary Materials:

The following are available online at www.mdpi.com/1422-0067/19/1/41/s1. Acknowledgments: Our work is supported by Inserm, Institut Paoli-Calmettes, and the Ligue Nationale Contre le Cancer. Ma Manai was in part funded by a «projet de recherche mixte franco-tunisien Comité Mixte de Coopération Universitaire (CMCU) PHC Utique». We thank the Institute Salah Azaiez de Tunis, the Ministère d'Enseignement Supérieur de la Recherche Scientifique de Tunisie and ICEP platform.

re 1 .

 1 MARCKS (myristoylated alanine-rich C-kinase substrate) immunostaining in epith an cancer and normal ovarian samples. (a) Representative images (×200) of stromal MAR unohistochemistry (IHC) staining in normal ovary (left top panel: 0% positive cells), and in ary epithelial ovarian cancer (EOC) samples showing different percentages of stained cells s 70%, and 90%; (b) Representative images (×200) of normal ovary with weak expressio CKS in stromal cells but strong expression in epithelial cells (left) and of primary EOC sa strong expression in stromal fibroblasts and weak expression in epithelial tumor cells (righ plots showing the percentage of MARCKS-positive (black) samples and MARCKS-neg te) samples (normal, primary tumors) for the epithelial staining (left) and stromal stai t). The p-values are for the Fischer's exact test.

Figure 1 .

 1 Figure 1. MARCKS (myristoylated alanine-rich C-kinase substrate) immunostaining in epithelial ovarian cancer and normal ovarian samples. (a) Representative images (×200) of stromal MARCKS immunohistochemistry (IHC) staining in normal ovary (left top panel: 0% positive cells), and in three primary epithelial ovarian cancer (EOC) samples showing different percentages of stained cells score: 10%, 70%, and 90%; (b) Representative images (×200) of normal ovary with weak expression of MARCKS in stromal cells but strong expression in epithelial cells (left) and of primary EOC sample with strong expression in stromal fibroblasts and weak expression in epithelial tumor cells (right); (c) Box plots showing the percentage of MARCKS-positive (black) samples and MARCKS-negative (white) samples (normal, primary tumors) for the epithelial staining (left) and stromal staining (right). The p-values are for the Fischer's exact test.

Figure 2 .

 2 Figure 2. Overall survival in patients with epithelial ovarian cancer. (a) Kaplan-Meier OS curve in 68 patients treated with treated with surgery and carboplatin-paclitaxel chemotherapy and with available follow-up; (b) Similar to (a), but according to the stromal IHC MARCKS status. The p-value of log-rank test is indicated.

Figure 2 .

 2 Figure 2. Overall survival in patients with epithelial ovarian cancer. (a) Kaplan-Meier OS curve in 68 patients treated with treated with surgery and carboplatin-paclitaxel chemotherapy and with available follow-up; (b) Similar to (a), but according to the stromal IHC MARCKS status. The p-value of log-rank test is indicated.

Table 1 .

 1 Clinicopathological characteristics of patients.

	Characteristics		N (%)
	Age		55 (27-85)
	Pathological type	clear cell	5 (4%)
		endometrioid	3 (3%)
		mixed	4 (3%)
		serous	106 (90%)
	FIGO Stage	1-2	10 (8%)
		3-4	108 (92%)
	Pathological grade	1	5 (4%)
		2-3	113 (96%)
	Surgery	no	0 (0%)
		yes	118 (100%)
	Adjuvant and/or neo-adjuvant chemotherapy	no	12 (12%)
		yes	89 (88%)
	Macroscopic residual disease after surgery	no	47 (46%)
		yes	55 (54%)
	Deaths *		15 (22%)
	5-year OS *, [CI95]		35% (22-56)
	Median OS, months (range) *		44 (1-172)

* concerns the 68 patients treated with surgery and carboplatin-paclitaxel chemotherapy and with available follow-up. FIGO: Fédération Internationale de Gynécologie et d Obstétrique; OS: overall survival.

Table 2 .

 2 Clinicopathological correlations with MARCKS expression (epithelial and stromal).

			Epithelial MARCKS IHC Status		Stromal MARCKS IHC Status
	Characteristics	N	Negative (N = 59)	Positive (N = 59)	p-Value	N	Negative (N = 27)	Positive (N = 91)	p-Value
	Age	118	54 (30-85)	57 (27-79)	0.85	118	57 (27-85)	55 (30-80)	0.171
	Pathological type				1				0.847
	clear cell	5	2 (3%)	3 (5%)		5	1 (4%)	4 (4%)
	endometrioid	3	2 (3%)	1 (2%)		3	0 (0%)	3 (3%)
	mixed	4	2 (3%)	2 (3%)		4	0 (0%)	4 (4%)
	serous	106	53 (90%)	53 (90%)		106	26 (96%)	80 (88%)
	FIGO Stage				0.743				0.694
	1-2	10	6 (10%)	4 (7%)		10	3 (11%)	7 (8%)
	3-4	108	53 (90%)	55 (93%)		108	24 (89%)	84 (92%)
	Pathological grade				0.226				0.159
	1-2	35	21 (36%)	14 (24%)		35	11 (41%)	24 (26%)
	3	83	38 (64%)	45 (76%)		83	16 (59%)	67 (74%)
	Macroscopic
	residual disease				0.321				0.213
	after surgery
	no	47	21 (40%)	26 (52%)		47	12 (60%)	35 (43%)
	yes	55	31 (60%)	24 (48%)		55	8 (40%)	47 (57%)
	Epithelial MARCKS IHC								0.662
	negative					59	15 (56%)	44 (48%)
	positive					59	12 (44%)	47 (52%)
	Stromal MARCKS IHC				0.662
	negative	27	15 (25%)	12 (20%)
	positive	91	44 (75%)	47 (80%)

Table 3 .

 3 Uni-and multivariate analyses for OS.

	Characteristics		Univariate			Multivariate
			N	HR [95CI]	p-Value	N	HR [95CI]	p-Value
	Age		68	1.02 [0.99-1.05]	0.304
	Pathological type	endometrioid versus clear cell	68	8.61 [0.74-100]	0.132
		mixed versus clear cell		3.00 [0.27-33.7]
		serous versus clear cell		1.60 [0.22-11.9]
	FIGO Stage	3-4 versus 1-2	68	4.18 [0.54-32.1]	0.169
	Pathological grade	3 versus 1-2	68	2.51 [1.14-5.49]	2.16 × 10 -2	68	2.21 [1.01-4.86]	4.82 × 10 -2
	Macroscopic residual disease after surgery	yes versus no	65	1.57 [0.77-3.20]	0.212
	Epithelial MARCKS IHC	positive versus negative	68	0.95 [0.45-1.97]	0.881
	Stromal MARCKS IHC	positive versus negative	68	2.59 [1.06-6.36]	3.77 × 10 -2	68	2.24 [0.91-5.53]	7.90 × 10 -2

Int. J. Mol. Sci. 2018, 19, 41; doi:10.3390/ijms19010041 www.mdpi.com/journal/ijms

Author Contributions: Maroua Manai, Raoudha Doghri, and François Bertucci conceived and designed the experiments; Raoudha Doghri, Maroua Manai, Maha Driss, Emilie Agavnian, Marc Lopez, and Emmanuelle Charafe-Jauffret performed the experiments; Maroua Manai, Pascal Finetti, and François Bertucci analyzed data; Meriam Elghardallou, Mohamed Manai, Max Chaffanet, Daniel Birnbaum, and Karima Mrad revised the manuscript. Maroua Manai, Raoudha Doghri, and François Bertucci wrote the manuscript.

Conflicts of Interest:

The authors declare no conflict of interest.