Atef Ben Youssef

Thomas Hueber

Pierre Badin
email: pierre.badin@gipsa-lab.grenoble-inp.fr

Gérard Bailly
email: gerard.bailly@gipsa-lab.grenoble-inp.fr

Toward a multi-speaker visual articulatory feedback system

Keywords: Acoustic-articulatory inversion, ElectroMagnetic Articulography (EMA), Hidden Markov Model (HMM), Minimum Generation Error (MGE), Speaker adaptation, Maximum Likelihood Linear Regression (MLLR)

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Systems of visual articulatory feedback aim at providing the speaker with visual information about his/her own articulation. Several studies show that this kind of system can be useful for both speech therapy and Computer Aided Pronunciation Training (CAPT) [START_REF] Badin | Visual articulatory feedback for phonetic correction in second language learning[END_REF]. The visual articulatory feedback system developed at GIPSA-lab is based on a 3D talking head used in an augmented speech scenario, i.e. it displays all speech articulators including usually non visible articulators such as the tongue. In this system, the talking head is animated automatically from the audio speech signal, using acoustic-toarticulatory inversion. For this purpose, we developed different inversion methods based on the joint modeling of acoustic and articulatory data (acquired by electromagnetic articulography -EMA), using statistical models such as Gaussian Mixture Models (GMM) and Hidden Markov Models (HMM) [START_REF] Ben Youssef | Acoustic-toarticulatory inversion in speech based on statistical models[END_REF].

The use of supervised machine learning techniques for acoustic-to-articulatory inversion has already been proposed in the literature. HMMs trained on parallel acoustic and articulatory speech data segmented at the phonetic level, have been used in [START_REF] Hiroya | Estimation of articulatory movements from speech acoustics using an HMM-based speech production model[END_REF], [START_REF] Zhang | Acoustic-articulatory modeling with the trajectory HMM[END_REF], [START_REF] Ling | An Analysis of HMM-based prediction of articulatory movements[END_REF] and [START_REF] Ben Youssef | Acoustic-toarticulatory inversion in speech based on statistical models[END_REF]. GMMs have been used in [START_REF] Ben Youssef | Acoustic-toarticulatory inversion in speech based on statistical models[END_REF] and [START_REF] Toda | Statistical mapping between articulatory movements and acoustic spectrum using a Gaussian mixture model[END_REF]. Artificial neural network (ANN) and Support Vector Machines (SVM) have been used respectively in [START_REF] Richmond | Trajectory mixture density networks with multiple mixtures for acoustic-articulatory inversion[END_REF] and [START_REF] Toutios | A support vector approach to the acoustic-to-articulatory mapping[END_REF] However, these studies do not allow to conclude on the optimal inversion method since data, speakers and languages are not comparable.

Since our goal is to provide "any" speaker with visual articulatory feedback, the inversion system need to be robust and easy to adapt. With that in mind, we present here recent developments on our HMM-based inversion system that have lead to significant improvements. Among them, we introduce a new training procedure based on the optimization of the Minimum Generation criterion (MGE). We also present a first approach to address the speaker adaptation problem. Speaker adaptation can be done in both acoustic and articulatory domains. In the present work, we propose to adapt our multimodal HMM models in the acoustic domain by using maximum likelihood linear regression.

This article is organized as follows. The HMM-based acoustic-to-articulatory inversion system is described in Section 2. The proposed speaker adaptation technique is presented in Section 3. The methodology used for evaluation and experimental results are presented in section 4. Conclusions and perspectives are presented in the last section.

HMM-based speech inversion system

Baseline system

In the proposed HMM-based mapping approach (previously published by Ben Youssef et al. in [START_REF] Ben Youssef | Acoustic-toarticulatory inversion in speech based on statistical models[END_REF]), the sequence of articulatory vectors, predicted from the given sequence of acoustic vectors x, is defined as

{ } ) ( max arg ˆx y p y y = with p(y | x) = p(y | λ, q)P(λ, q | x) (1)
where λ represents the parameters set of the HMM and q the HMM state sequence. By applying the Bayes rule, we obtain

p(y | x) = p(y | λ, q) p(x | λ, q)P(λ ) (2)
As shown in Equation 2, the HMM-based mapping can be achieved by a recognition stage followed by a synthesis stage, which means: (1) finding the most likely phonetic and state sequence for a given source vector (and a set of a priori information provided by a statistical language model), and (2) inferring the target vector from the decoded state sequence.

In the training stage, a left-to-right, 3-state multi-stream HMM is trained on articulatory-acoustic data for each phonetic class. The first stream is dedicated to the modeling of acoustic feature; the second stream is used to model the articulatory features. For each stream, the emission probability density of each state is modeled by a GMM with diagonal covariance matrix. HMM were initialised and trained by the Baum Welch algorithm based on the Maximum Likelihood (ML) criterion.

Due to coarticulatory effects, it is unlikely that a single context-independent HMM could optimally represent any given allophone. Contexts were therefore grouped in context classes for both vowels and consonants separately. Based on the matrix of Mahalanobis distances of the coils coordinates between the centre frame of each pair of phoneme means, hierarchical clustering generated six coherent classes for vowels (/a ɛ ɛ/, /ø oe oẽ/, /e i/, /o ɔ ɑ̃ ɔ/, /y/, and /u/), and ten coherent classes for consonants (/p b m/, /t d n/, /s z/, /ʃ ʒ/, /k g/, /f v/, /l/, /ʁ/, /j ɥ/, and /w/). Context-dependent HMMs were then trained, using various contextual schemes: phonemes without context (no-ctx), with left (L-ctx) or right context (ctx-R), and with both left and right contexts (L-ctx-R). A tree-based state-tying strategy based on the Minimum Description Length (MDL) criterion, was adopted to address the problem of data sparsity (biphones or triphones having only a few occurrences in the training dataset).

Each resulting multi-stream HMM were then split into two distinct HMMs: an "acoustic HMM" and an "articulatory HMM". Acoustic HMMs were finally refined by increasing incrementally the number of Gaussian mixture components.

The prediction of the sequence of articulatory feature vectors, for a given test sequence of acoustic feature vectors, was achieved in two stages. First, phonetic and state decoding was performed by the Viterbi algorithm using the acoustic HMMs. A bigram phonetic language model trained on one year of the newspaper "Le Monde" (year 2003) was used (thus, the recognised phoneme sequences respect French phonotactics). Second, given the predicted sequence of phones and the decoded HMM state sequence, the target vector sequence was inferred by the maximum-likelihood parameter generation algorithm (MLPG) [START_REF] Tokuda | Speech parameter generation algorithms for HMM-based speech synthesis[END_REF], using the articulatory HMMs.

Minimum Generation Error (MGE) criterion

In this paper, we introduce a new training procedure, based on the MGE approach (Minimum Generation Error) initially proposed by Wu et al. in [START_REF] Wu | Minimum generation error criterion for tree-based clustering of context dependent HMMs[END_REF] for HMM-based text-to-speech synthesis. We propose to adapt this technique to the acousticto-articulatory mapping problem. The training procedure is performed as follows. The parameters of single Gaussian articulatory HMMs are first estimated by maximising the likelihood of the model given the training data (using the standard Baum-Welch algorithm). Then, the articulatory trajectories which maximize the likelihood of the current set of articulatory HMMs are generated using the MLPG algorithm. The state sequence used to drive this intermediate synthesis stage is obtained by forced-alignment of the acoustic data at the phonetic level. The generation error is defined as the Euclidean distance between the generated and the measured articulatory trajectories. Given this error, the parameter of the articulatory HMMs (mean and variance) are finally updated using the equations detailed in [START_REF] Wu | Minimum generation error criterion for tree-based clustering of context dependent HMMs[END_REF]. In our implementation, this procedure is iterated 5 times.

Speaker adaptation

Compared to other approaches (based on ANNs or GMMs for instance), the mapping between acoustic and articulatory modalities is not performed at the feature level, but at the phonetic level. Based on this consideration, we investigated the possibility to perform the inversion by directly decoding the new speaker's speech at this level. Because the accuracy of the inversion process depends strongly on the performance of this decoding stage, it is crucial to adapt the reference speaker models (i.e. the speaker used to build the original speech inversion system). This additional stage makes the models of the reference speaker compatible with the new speaker's voice, but also with a different acoustic environment.

To build the adaptation database, the new speaker is asked to utter a corpus of adaptation sentences. The adaptation procedure is performed as follows. First, the speech signal is automatically segmented at the phonetic level using forcedalignment and the acoustic models trained on the reference subject. Second, Maximum Likelihood Linear Regression (MLLR) technique is used to adapt each acoustic HMMs. MLLR estimates linear transformations for models parameters to maximise the likelihood of the adaptation data [START_REF] Leggetter | Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models[END_REF].

Evaluation

Databases

The database used in this study (and also in our previous study [START_REF] Ling | An Analysis of HMM-based prediction of articulatory movements[END_REF]) consists of two repetitions of 224 VCVs (where C is one of the 16 French consonants and V is one of 14 French oral and nasal vowels), two repetitions of 109 pairs of CVC real French words, and 88 sentences, uttered by a male native French speaker (referred to as the reference speaker PB) (approximately 5100 phones). Articulatory movements were recorded synchronously with the audio signal using the Carstens 2D EMA system (AG200). Six coils were used to measure articulators kinetics: a jaw coil was attached to the lower incisors, whereas three coils were attached to the tongue tip, the tongue middle, and the tongue back; upper and lower lip coils were attached to the boundaries between the vermilion and the skin in the midsagittal plane. Two coils were used for head alignment. The audio-speech signal was recorded at a sampling frequency of 22 kHz and was parameterized by 13 MFCC (Blackman window, 25 frame length, 10 ms frame shift). EMA coordinates were recorded at 500 Hz, low-pass filtered at 20 Hz in order to reduce noise, and down sampled to 100 Hz to fit the analysis rate of the acoustic signal. The database, which consists of approximately 17 minutes of speech, long pauses being excluded, was labelled at the phonetic level (using a force-alignement procedure and a manual check).

In order to evaluate the proposed speaker adaptation technique, audio database were recorded from three native French speakers: male speaker TH recorded the same speech material as the reference speaker PB; another male speaker GB and female speaker AC recorded a different corpus, consisting of 240 sentences initially designed for speech synthesis purpose.

Evaluation

The accuracy of the inversion was measured in different ways. First, we calculated the root mean square (RMS) error between the measured and the estimated EMA parameters, such as:

RMS = 1 D 1 T ( ŷt -y t ) 2 t = 1 T ∑ d = 1 D ∑ ( 3
)
where T is the number of frames in the test set, D is the number of EMA parameters (12 in this study), ŷt and y t are respectively the estimated and the measured position of the d th EMA parameters at time t. A different formulation of the RMS error, in which the RMS is averaged over all the features, can be found in the literature (as in [START_REF] Hiroya | Estimation of articulatory movements from speech acoustics using an HMM-based speech production model[END_REF], [START_REF] Zhang | Acoustic-articulatory modeling with the trajectory HMM[END_REF], [START_REF] Ling | An Analysis of HMM-based prediction of articulatory movements[END_REF] or [START_REF] Toda | Statistical mapping between articulatory movements and acoustic spectrum using a Gaussian mixture model[END_REF]). This RMS is called here µRMS and is defined as:

µRMS = 1 D 1 T ( ŷt -y t ) 2 t = 1 T ∑ d = 1 D ∑ (4)
We also calculated the "Pearson Product-Moment Correlation Coefficient" (PMCC) which measures the level of amplitude similarity and synchrony of the trajectories. Finally, we calculated the "recognition accuracy" to assess specifically the phonetic decoding stage.

A 5-fold cross-validation procedure was used for the evaluation: the database was split into 5 partitions approximately homogeneous from the point of view of phone distribution. Each partition was used once as the test set while the other 4 partitions composed the training set. RMS, µRMS PMCC, and recognition rates were averaged over the five test partitions.

In order to estimate the contribution of the new MGEbased training procedure, it is needed to evaluate the synthesis stage independently from the recognition stage. In that purpose, we simulated a "perfect recognition" by aligning the original phonetic labels on the acoustic waveform. Table 1a shows that using MGE decreases the RMSE by an amount between 0.08 mm and 0.26 mm, depending on the context type.

Table 1.b displays the performances of the inversion from audio signal alone. Best results were obtained with contextdependant models including information about the right phonetic context (ctx-R). In this case, µRMSE was found to be 1.48 mm (RMSE = 1.53 mm, PMCC = 0.93, recognition accuracy = 86.20%).

Articulatory recognition

Since no articulatory data were acquired for 3 of the 4 speakers used in this study, it is impossible to determine the RMSE between the measured and the predicted articulatory trajectories. Therefore, we have based the evaluation on the automatic "articulatory recognition" of the predicted trajectories. In that purpose, we have trained an HMM-based phonetic decoder on the articulatory data of the reference speaker PB.

Contrarily to the acoustic recognition stage which determines phonemes, this articulatory recognition procedure was designed to recognise phoneme classes (groups of phonemes). As the context classes, described in section 2.1, were established based on articulatory distances, they take naturally into account the fact that all features are not exhaustively present in the EMA data (voicing cannot be measured; no velum coil was available in our recording setup). Therefore, these 16 context classes have been used as phoneme classes for the articulatory recognition. Note that phonemes differing only by voicing or velum position are grouped in the same classes (e.g. /p b m/, /t d n/, /k g/, etc.). In addition, two extra phoneme classes were used: one for the schwa and the short pause, and the other for the long pause at the boundaries of sentences. Finally, these 18 articulatory phoneme classes were used to train and to recognize the articulatory trajectories.

The HMM-based articulatory recognition system was built using a procedure similar to the one described in section 2.1. The performance of this system was evaluated on the articulatory data of the reference speaker PB, using the same 5-fold cross-validation procedure that the one described previously. Best performance was obtained using context dependent model (with right context) and 8 Gaussians per state. In this case, the recognition accuracy was found to be 84.84 %. These articulatory HMMs are used to evaluate the articulatory trajectories generated from the acoustic signal of any new speaker.

Evaluation of the predicted articulatory trajectories of the reference speaker

In order to establish a baseline for the assessment of inversion of new speakers by automatic articulatory recognition, we have computed the articulatory recognition rates for the original speaker. It was also deemed important to decide which data should be used for training this reference articulatory recognition system: (1) original articulatory trajectories, (2) articulatory trajectories recovered by inversion from the audio signal alone, or (3) articulatory trajectories recovered by inversion from both audio signal and labels (perfect acoustic recognition). All combinations were finally evaluated, for cxt-R contexts, using 5-fold cross validation for each combination, as can be seen in Table 2. Interestingly, we observed that recognition rate were always higher for synthesised trajectories than for measured ones, whatever the training corpus: this might be ascribed to the fact that synthesised articulatory trajectories are more lawful or regular than measured ones, since they are produced by models that constitute simplified representations of data, good though they can be. We observe also that rates are higher for models trained on measured data (except in the case of testing and training with data obtained by perfect recognition). It was therefore decided to use models trained on measured data. Table 2. "Phoneme class" articulatory recognition accuracy (with 5-fold cross-validation) for speaker PB using ctx-R; measured EMA [START_REF] Badin | Visual articulatory feedback for phonetic correction in second language learning[END_REF], EMA synthesized from audio only [START_REF] Ben Youssef | Acoustic-toarticulatory inversion in speech based on statistical models[END_REF], and from both audio and labels (perfect recognition) [START_REF] Hiroya | Estimation of articulatory movements from speech acoustics using an HMM-based speech production model[END_REF].

(1) (2) (3)

Evaluation of the predicted articulatory trajectories of new speakers

The acoustic adaptation technique described at section 3 was applied to the acoustic HMMs trained on 4/5 of the original speaker's corpus using 4/5 of the new speaker's corpus; the remaining 1/5 of the new speaker's corpus was used to test both acoustic recognition and articulatory recognition. Note that in order to avoid the complexity and possible overtraining that may occur when using 5-fold cross-validation for both the reference articulatory training and the new speaker adaptation, all the test have been applied using the first 4/5 of the corpus for training or adaptation and the last 1/5 for testing. For subject TH, the sentences used for the adaptation were the same as those used for training the initial acoustic HMMs on PB.

L-ctx-R

Table 3 shows the various acoustic recognition rates and articulatory recognition rates of the inversed trajectories. We observe that subject TH has performances very close to those of reference PB; this could be explained by the fact that his corpus was recorded in an imitation mode: he imitated each sentence after being prompted by the audio recording from PB, which would favour similar dynamics. Oppositely, the worst performances are obtained for female speaker AC, both at acoustic and articulatory levels, which may be ascribed to the sex difference, and the difference in size and content of the corpus -allowing only 192 adaptation sentences. Intermediary results are obtained for speaker GB, with the intriguing degradation of the articulatory score compared to the fairly good acoustic one. However, a more thorough analysis of the acoustic recognition has shown that the accuracy rates for the set of 631 allophones in right context (ctx-R) were much lower than for the 36 French phonemes for this speaker (see Table 3). This was confirmed by the observation of the detailed recognition rates for vowels which showed some confusion between different contexts.

Conclusions and perspectives

This paper presents latest developments on our HMM-based acoustic-to-articulatory inversion system that we develop for a "visual articulatory feedback" system. The introduction of a new training procedure based on the optimization of the Minimum Generation Error (MGE) criterion has lead to significant improvements (about 10%). As a first step toward a multi-speaker system, we also investigated the use of a MLLR model adaptation technique. The quality of the articulatory trajectories was evaluated by measuring the performance of an "articulatory HMM-based phonetic decoder". Recognition accuracies range between 56.8 % and 82.2 % for three speakers, compared to 83.7 % for the original speaker, demonstrating the interest of the method.

The next step of our development will be to test more speakers, and to study more explicitly the influence of the nature and size of the adaptation corpus. It will also be of great importance to investigate adaptation methods in the case of non-native speaker adaptation (e.g. [START_REF] Ohkawa | A speaker adaptation method for non-native speech using learners' native utterances for computerassisted language learning systems[END_REF]).

Finally, in the framework of Computer Aided Pronunciation Training (CAPT), we aim to use this speaker adaptation approach in our visual articulatory feedback system, based on acoustic-to-articulatory speech inversion.

Context « ctx-R » Test Train

	(1)	84.84	84.56	88.39
	(2)	58.12	79.25	83.78
	(3)	57.06	84.44	90.04

Table 1 .

 1 µRMSE, RMSE (mm) and PMCC for the HMM-based inversion: (a) inversion from audio and labels input (perfect recognition); (b) inversion from audio only.

			no-ctx			L-ctx			ctx-R
		µRMSE RMSE PMCC µRMSE RMSE PMCC µRMSE RMSE PMCC µRMSE RMSE PMCC
	MLE	1,80	1,87	0,89	1,49	1,54	0,93	1,50	1,55	0,93	1,39	1,44	0,94
	MGE	1,55	1,61	0,92	1,34	1,38	0,94	1,35	1,39	0,94	1,31	1,34	0,94
	MLE	1,88	1,96	0,78	1,64	1,70	0,91	1,59	1,65	0,92	1,63	1,69	0,91
	MGE	1,71	1,78	0,90	1,54	1,60	0,92	1,48	1,53	0,93	1,57	1,63	0,92

Table 3 .

 3 Acoustic and articulatory recognition accuracy for all the speakers, using 1/5 of the corpus for testing. Phonemes 85.92 83.77 79.12 62.81 Articulation 83.70 82.23 69.46 56.77 Acoust. Allophones 79.88 76.53 66.77 48.01

	Accuracy PB	TH	GB	AC
	Acoust.

Acknowledgements

The authors would like to acknowledge Christophe Savariaux for its involvement in the recording of EMA data. This work was partially supported by the French ANR-08-EMER-001-02 project ARTIS.


Vincent Lorant
email: vincent.lorant@uclouvain.be.

Raj Bhopal

Ethnicity, socio-economic status and health research: Insights from and implications of Charles Tilly's theory of Durable Inequality Submitted for the 'theory and methods' section of JECH by

Keywords: Ethnic Groups, Migration, Public Policy, Health Inequality, Word count: 3,223 sociology, ethnic groups, socio-economic factors, health status disparities

Background. Ethnic inequalities in health status and health care remain substantial in Europe and addressing them is becoming a priority. However, the best way to respond to such a challenge is, as yet, unclear. The research community is grappling with the contribution of socio-economic discrimination to ethnic inequalities. Methods. We present a new theoretical analysis, based on the landmark work of Charles Tilly on 'Durable Inequality' and we apply it to the public health goal of reducing ethnic health inequalities. Results. Tilly claims that, for organisational reasons, ethnic categories and socio-economic categories are tied together. The theory of Durable Inequality claims that the matching of ethnic categories with socio-economic categories helps to enforce exploitation, leading to durable inequalities. We present the theory, and focus on its main components (categories, exploitation, opportunity hoarding, emulation and adaptation) and discuss the implications for health inequalities by ethnic group. In essence, the theory leads to four recommendations for the study of ethnic health inequalities: (1) to investigate organisational processes that create ethnic health inequalities ; (2) to investigate the role of networks and ties on health behaviours, health care use and their psychological factors; (3) to define ethnicity through flexible, multidimensional binary categories, which should vary according to context; (4) to assess cumulative inequality within a domain, across domains and across generations. Conclusions. Our paper, to our knowledge, is the first attempt to analyse Tilly's theory in relation to ethnicity and health and opens up a debate on refining the implications of these ideas prior to empirical testing.

INTRODUCTION

Ethnic inequalities in health and health care are an important and growing topic of European policy, research and practice. Europe accounts for a quarter of international migration, and in 2005 migrants accounted for 7.6% of its population. [1] Migration is one driver in the creation of multi-ethnic societies, where migrant and ethnic minority groups face important health risks in communicable and non-communicable diseases. [START_REF] Bhopal | Chronic diseases in Europe's migrant and ethnic minorities: challenges, solutions and a vision[END_REF] The key focus that calls for action is inequality in health status and health care, and especially the concern that some of this inequality arises from discriminationboth in the health sector and in wider society (e.g., employment, housing etc.) It is for this reason that academics, practitioners and policy-makers are increasingly turning their attention to ethnic group inequalities. Ethnicity and socio-economic status have long been seen as two important strands of inequality. Yet the ways in which these interact and perhaps magnify inequalities have been somewhat resistant to academic understanding. The report of the UK's Acheson Inquiry into Inequalities in Health had a section on ethnicity that suggested that socioeconomic status contributed to these inequalities. [START_REF] Gordon | Inequalities in health : the evidence presented to the Independent Inquiry into Inequalities in Health[END_REF] However, some landmark studies on social inequalities in health have not recognized the role of ethnicity. [START_REF] Mackenbach | Reducing inequalities in health : a european perpective[END_REF][START_REF] Siegrist | Social inequalities in health new evidence and policy implications[END_REF] The recent Eurothine project, for example, had little to say on ethnic inequalities in health. [6] Similarly, the WHO Commission on Social Determinants of Health, led by Marmot, has been criticized for paying little attention to ethnic group inequalities. [START_REF] Salway | Fair Society, Healthy Lives: a missed opportunity to address ethnic inequalities in health[END_REF] This is odd, as ethnicity has long been recognized as a social status category that is strongly linked to many indicators of deprivation. [START_REF] Krieger | Embodying inequality: A review of concepts, measures, and methods for studying health consequences of discrimination[END_REF][START_REF] Krieger | Stormy weather: Race, gene expression, and the science of health disparities[END_REF][START_REF] Williams | Race and health: Basic questions, emerging directions[END_REF] Interpreting data is made more difficult by a lack of theory on the relationship between ethnicity and socio-economic status. The analysis of ethnic inequalities in health cannot be left to statistical analysis, but requires an understanding of the relationship between ethnicity, socio-economic status and health. Social theory may help to improve our understanding of how ethnicity and socio-economic status interact in producing health inequalities and help to improve the way we analyse these relationships.

In this article, we present Charles Tilly's 'Durable Inequality Theory' (hereafter, DIT) and discuss its implications. [START_REF] Tilly | Durable inequality[END_REF] Tilly's work may provide an explanation of why ethnicity and socio-economic status interact with one another and lead to structural inequalities. Although Tilly's thesis was not directly concerned with health and health care, we consider that it nevertheless gives insight that merits discussion beyond sociology. We briefly review the topic of ethnic and socio-economic inequalities in health, present DIT in further detail and discuss its implications for health studies and practices. To our knowledge, this is the first attempt to translate DIT into the ethnicity and health domain. Our analysis will be carried out mainly from the perspective of Europe, where migration is one important source of diversity.

ETHNICITY, SOCIO-ECONOMIC STATUS AND HEALTH

One common finding in ethnicity and health studies is the important influence of socio-economic status (hereafter, SES) differences in the relationship between ethnicity and health. Most of the previous reviews concluded that socio-economic status is lower in most ethnic minorities and that this contributes significantly to ethnic inequalities in health. This contribution depends on the national context. [START_REF] Krieger | Embodying inequality: A review of concepts, measures, and methods for studying health consequences of discrimination[END_REF][12][START_REF] Lillieblanton | Racial differences in health: Not just black and white, but shades of gray[END_REF][START_REF] Nazroo | The structuring of ethnic inequalities in health: Economic position, racial discrimination, and racism[END_REF] In Europe, ethnic differences in self-rated health were removed by controlling for SES for most ethnic groups living in the UK [START_REF] Nazroo | The structuring of ethnic inequalities in health: Economic position, racial discrimination, and racism[END_REF][START_REF] Chandola | Ethnic and class differences in health in relation to British South Asians: using the new National Statistics Socio-Economic Classification[END_REF], for Turks/Moroccans living in Belgium [START_REF] Lorant | Contextual factors and immigrants' health status: double jeopardy[END_REF] and for Roma living in Hungary. [START_REF] Voko | Does socioeconomic status fully mediate the effect of ethnicity on the health of Roma people in Hungary?[END_REF] They were reduced for Roma living in Slovakia [START_REF] Kolarcik | To what extent does socioeconomic status explain differences in health between Roma and non-Roma adolescents in Slovakia?[END_REF] and for most non-European ethnic minority groups living in Sweden. [START_REF] Wiking | Ethnicity, acculturation, and self reported health. A population based study among immigrants from Poland, Turkey, and Iran in Sweden[END_REF][START_REF] Lindstrom | Ethnic differences in self reported health in Malmo in southern Sweden[END_REF] However, socio-economic status had virtually no effect on ethnic health inequalities in Spain. [START_REF] Borrell | Immigration and self-reported health status by social class and gender: the importance of material deprivation, work organisation and household labour[END_REF] The contribution of socio-economic status to ethnic disparities in health varies between groups. Among ethnic groups living in Sweden, the risk of poor health among Polish migrants was unaffected by their lower SES in comparison with Arabic-speakers or Iranians. [START_REF] Wiking | Ethnicity, acculturation, and self reported health. A population based study among immigrants from Poland, Turkey, and Iran in Sweden[END_REF][START_REF] Lindstrom | Ethnic differences in self reported health in Malmo in southern Sweden[END_REF] These kind of observations raise interesting questions: e.g. why is ethnicity associated with lower SES, and why is this association stronger for some ethnic minority groups than for others; why, for a given ethnic group, is the association context-dependent, being stronger in some countries than in others? Many researchers have treated SES as a confounder of the ethnicity-health relationship. Such 'confounding' has been tackled by controlling ethnic differences in health status by SES. However, this adjustment is made on a component of the causal pathway between ethnicity and the risk of poor health. [START_REF] Kaufman | Socioeconomic status and health in blacks and whites: The problem of residual confounding and the resiliency of race[END_REF] The nature of the interaction between SES, ethnicity and health in such analyses, therefore, remains opaque. So far, discrimination has been an important conceptual vehicle for explaining ethnic inequalities in health and the role of socio-economic differences. [START_REF] Krieger | Embodying inequality: A review of concepts, measures, and methods for studying health consequences of discrimination[END_REF][START_REF] Williams | Race and health: Basic questions, emerging directions[END_REF][START_REF] Blank | Measuring racial discrimination[END_REF]25] An important review article showed a positive association between self-reported discrimination and poor health, particularly for mental health and health behaviour. [26] Research has evidenced significant discrimination regarding both mental and physical health care. [START_REF] Mcguire | Testing for statistical discrimination by race/ethnicity in panel data for depression treatment in primary care[END_REF][START_REF] Schulman | The effect of race and sex on physicians' recommendations for cardiac catheterization[END_REF].

If we want to tackle discrimination we need to understand why it is occurring.

Psychologists have explained the occurrence of discriminatory beliefs by intrapsychic factors such as racial animus or group closure. [START_REF] Pager | The sociology of discrimination: Racial discrimination in employment, housing, credit, and consumer markets[END_REF] Recent European surveys show that racial or ethnic stereotyping is still rife, with around half of Europeans believing that employers should give priority to the non-immigrant population when jobs are scarce. [START_REF] Arts | European values at the turn of the millennium[END_REF] However, the contribution of discriminatory beliefs to discrimination in practice is not clear: experimental studies show that people do not rebuff overt acts of racism in the way they anticipate they would, implying that beliefs seem not to be the main driver of discrimination . [31] This is an empirical issue that requires sustained inter-disciplinary qualitative and quantitative research.

Ethnicity may expose some groups to low SES because of discriminatory practices, partly founded on racism or on values prejudicing minority groups. These values are then often explained by historical and cultural factors, with all the risk of circularity. In Tilly's view these values just relabel the phenomenon instead of explaining it (Tilly, p.21) and he emphasizes the role that institutions such as families, schools and companies play in producing and maintaining the links between low SES and ethnicity. [25]. It is within this context that DIT might offer valuable insights: inequality is not so much the result of discrimination as of organizations installing categorical inequality in order to facilitate organisational functioning.

CHARLES TILLY'S DURABLE INEQUALITY THEORY

Charles Tilly (1929Tilly ( -2008) ) was the Joseph Buttenwieser Professor of Social Science at Columbia University. During his career he worked on several topics ranging from historical sociology to political science. In providing a summary of Charles Tilly's DIT theory, we risk both omission and simplification. We do not aim to summarize Tilly's entire output. Rather, we seek out those elements of his work that are instructive for understanding the links between SES and ethnic inequalities in health.

Our account relies on his book, Durable Inequality [START_REF] Tilly | Durable inequality[END_REF] and on more recent discussions by commentators [START_REF] Diani | The relational element in Charles Tilly's recent (and not so recent) work[END_REF][START_REF] Wright | Metatheoretical foundations of Charles Tilly's Durable Inequality[END_REF] and Tilly himself. [START_REF] Tilly | Relational studies of inequality[END_REF] DIT had two primary and related scientific ambitions. Firstly, Tilly sought to provide a unified framework to explain all forms of durable inequality. [START_REF] Grusky | Durable inequality[END_REF] This contrasts with the current tendency of inequality studies to split into many disconnected subsegments (e.g. gender studies, ethnic studies and socio-economic studies) or disconnected domains (e.g. wages, health, nutrition and information), each with ad hoc explanations. Because public health research is keen on investigating inequality across segments and across domains, DIT provides it with a unique theoretical perspective.

Secondly, Tilly argued against an individualistic perspective in which inequalities are explained by decisions linked to individual features such as motivations, attitudes etc. Instead, Tilly claimed that relational concepts are required to understand inequalities: concepts that tie or separate individuals or groups (e.g. hierarchy, organisation or pairs of categories).

The theory is based on the following proposition: "People who create or sustain categorical inequality by means of the four basic mechanisms (exploitation, opportunity hoarding, emulation and adaptation -see below) rarely set out to manufacture inequality as such. Instead, they solve other organizational problems by establishing categorically unequal access to valued outcomes" (p.11). Essentially, inequality emerges as an artefact of particular strategies of organizations aiming to secure and enhance access to resources. [START_REF] Wright | Metatheoretical foundations of Charles Tilly's Durable Inequality[END_REF] The key components of DIT are categories, exploitation, opportunity hoarding, emulation and adaptation.

A category is a group of people sharing a boundary that distinguishes them from, and relates them to, a group of people excluded by such a boundary. These categories often have binary and unequal relationships: women/men, black/white, employer/employee, citizen/non-citizen, physician/nurse, Muslim/non-Muslim, etc.

Preferment of categories (i.e. social class) instead of continua (income), according to DIT, is because categories play an important role in the way organizations work: recruitment, job and task assignation, rewards, promotion, sanctions, on-the-job friendships etc. In addition, according to DIT, categories also allow for group closure (enacted rituals) and account for much of inequality in reward (income, nutrition etc.).

DIT claims that organizations installing categorical inequalities are able to facilitate their own operations and deliver a greater return to their dominant members.

The emergence of inequality is explained by exploitation and opportunity hoarding.

Exploitation occurs when an elite group enlists a subordinate category to the production of economic value while at the same time excluding it from the full added value of its efforts. Opportunity hoarding is a strategy in which, mostly, subordinate groups seek to monopolize a resource. Evidence of opportunity hoarding among ethnic minority groups is rife among labour market studies [START_REF] Portes | The economic sociology of immigration essays on networks, ethnicity, and entrepreneurship[END_REF] and in Europe is illustrated by, for instance, the high concentration of Filipino nurses in Austria, Ecuadorian cleaning ladies in Madrid, Congolese priests in Belgium or South Asian optometrists in the UK.

Because inequalities created by exploitation and opportunity hoarding run the risk of being contested and becoming unstable, they will be more accepted and durable when internal categorical inequalities are matched to external categorical inequalities.

Internal categories are those created by the organization (such as students/professors or line staff/manager) while external categories are those imported by the organizations (such as men/women or white/non-white). The DIT core thesis is that matching interior categories with exterior categories reinforces the inequality inside the organization and makes inequality durable. Two factors explain why matching occurs. First, it facilitates both exploitation and opportunity hoarding. Second, it reduces the cost of maintaining such inequality. Indeed inequality without matching "requires the expenditures of resources on socialization and commitment while remaining vulnerable to subversion by coalitions based on external categories" (pg 78). For example, as ethnic groups are external categories, the matching of such categories with internal categories (worker/clerk, manager/employee etc.) will lower the cost of enforcing inequalities and will make them more stable: e.g. the wage gap between manager and employee will be more easily enforced and less contested if it matches external categories such as white or non-white. Ethnic inequalities and socioeconomic inequalities are co-constructed because they facilitate the installation and persistence of inequalities. According to the matching hypothesis, ethnic health inequalities may be greater in economic niches or in companies with stronger matching of occupational categories and ethnic categories.

Emulation and adaptation are two further mechanisms that stabilize and perpetuate these inequalities. Emulation is the copying of established organisational models from one setting to another, for example, when women are more likely to work as the secretary of a white male manager in business settings, this may be emulated in the public sector. Adaptation is a routine that facilitates social interaction, such as the tea break, peers lunching together, jokes and stories; these interactions ensure the normalisation of structural inequalities within day-to-day discourse. The social group formed by adaptation acts to exclude other categories of people by, for example, making them feel uncomfortable, by conversations that are not pertinent, or disrespectful, to the excluded, e.g. chatting about drinking alcohol and partying, in the presence of Muslims who are forbidden to consume alcohol by their religion.

IMPLICATIONS OF DIT FOR HEALTH RESEARCH

DIT has important implications for the way we analyse ethnic inequalities in health, particularly on research design, classification of ethnicity, selection of explanatory factors and data analysis (table 1). We focus on the implication of DIT for ethnic health inequalities.

Research design.

DIT suggests that research on inequalities should move from the individual perspective to the relational perspective. One implication for ethnic health inequalities would be to increase the use of social network analysis, which pays attention to ties and how they cluster along categories. Increasingly, public health and epidemiology are looking at networks and peer-effects, even for non-communicable disease risk factors [37] and health behaviours [START_REF] Christakis | The collective dynamics of smoking in a large social network[END_REF][START_REF] Ali | Social network effects in alcohol consumption among adolescents[END_REF]. Networks could become a key component in analysing ethnic inequalities in health behaviours and help-seeking, which so far have been mainly addressed through individual-level cross-sectional surveys. Some ethnic minority groups are known to have many strong ties within their community but relatively few and weak ties with other communities; that kind of social network pattern may play an important role in health behaviours such as smoking [START_REF] Bush | Understanding influences on smoking in Bangladeshi and Pakistani adults: community based, qualitative study[END_REF]. Secondly, multidisciplinary, longitudinal perspectives would help to better understand the interdependence of decisions at different life-stages and in different domains. Ethnic inequalities in health are the result of cumulative processes both within a domain (e.g. health care coverage, access, use, quality and outcome of health care) and across domains (i.e. education, employment and health care) [START_REF] Blank | Measuring racial discrimination[END_REF].

Recent reviews of ethnic inequalities in health care, for example, mostly rely on crosssectional surveys, thereby prioritizing the prevalent individual perspective and overlooking the role of cumulative discrimination over time in leading to adverse outcomes that cannot be captured in cross-sectional data [START_REF] Norredam | Migrants' utilization of somatic healthcare services in Europe--a systematic review[END_REF]. Finally, because a central claim of DIT is that inequality is constructed within organizations, ethnic health inequalities should be increasingly investigated from that perspective: the persisting lack of ethnic diversity within the highest cadres in medicine[42], the role of health services in improving cultural competence [START_REF] Anderson | Culturally competent healthcare systems. A systematic review[END_REF], and different patterns of health care use according to ethnicity [START_REF] Lanting | Ethnic Differences in Mortality, End-Stage Complications, and Quality of Care Among Diabetic Patients[END_REF][START_REF] Stronks | Immigrants in the Netherlands: equal access for equal needs?[END_REF][START_REF] Onega | Race versus place of service in mortality among Medicare beneficiaries with cancer[END_REF][START_REF] Romero-Ortuño | Access to health care for illegal immigrants in the EU: should we be concerned ?[END_REF] are some examples of topics that could be looked at from the perspective of the role of organizations in ethnic health inequalities. One way to achieve such a perspective is through comparative case-study or cross-national comparative study. While these are already topics of interest in public health research, they receive little attention compared to studies of disease patterns and risk factors. DIT encourages us to strike a better balance.

The categorical approach of DIT also has important implications for the classification of ethnicity. DIT suggests that ethnicity categories should be defined along boundaries. This leads to two implications. First, the boundary defines the pair of categories according to exclusion from valued resources. This is consistent with postmodernist theory that takes a non-essentialist view on ethnicity and is also keen to define these terms relationally, "us" and "them". [START_REF] Pfeffer | Theories in health care and research -Theories of race, ethnicity and culture[END_REF] The second implication is that there are many possible boundaries relevant for the domain of ethnicity: Black/White, Citizen/Foreigner, Documented/Undocumented, Migrant/Non-migrant, Englishspeaking/non-English Speaking, Muslim/Non-Muslim, Born in the UK/Born abroad, etc. Because these boundaries overlap only partially (for example, South Asians do not all have the same religious affiliation), it could explain the heterogeneous health status risks between ethnic minority groups. It is acknowledged that socio-economic position is multidimensional and should not be limited to, say, occupation. Following DIT, the same may apply for ethnicity. The implication for health studies would be that ethnic health inequalities may be explained by the exclusion from resources that the categories define. For example, English language proficiency has been shown to partly explain racial inequalities in mental health services use in the US: 32% of bilingual Latinos who speak English received mental health services, as against 8% for Latinos who do not speak English [START_REF] Sentell | Access to mental health treatment by English language proficiency and race/ethnicity[END_REF]. According to DIT we need flexible classifications that reflect how categories influence access to resources. Currently, there is no clarity on directions in this field in Europe: some favour national census type classification of self-reported ethnicity, others country of birth, and some argue that ethnic group classification is fraught with difficulty, if not actually futile. Overall, the public health research field is moving to relatively fixed classifications. DIT makes us re-think this.

Explanatory factors, according to DIT, are to be found at the relational level: mistaken beliefs about differences between categories play little part in the creation of ethnic health inequality and beliefs change as a consequence of shifting forms of exploitation and opportunity hoarding. [START_REF] Tilly | Relational studies of inequality[END_REF] This has important implications. DIT may help us to understand why behavioural and psychological factors are not randomly distributed between ethnic groups. For example, happiness, an important individual psychological resource for health and help-seeking, depends on individual interconnectedness, so that happiness can be seen as collective resource [START_REF] Fowler | Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the Framingham Heart Study[END_REF]. Second, information about access to and availability of health and health care, is highly dependent on social ties. Recent research shows that successful searches for information involve more professional ties than family ties and that these professional ties play a greater role in distant search than in close search [START_REF] Dodds | An experimental study of search in global social networks[END_REF]. This would help to shed light on the link between the concentration of some ethnic groups in specific occupational or educational niches, a mechanism known as opportunity hoarding under DIT, and the lack of circulation of important health and health care information, as evidenced in ethnic inequalities in cancer mortality [START_REF] Onega | Race versus place of service in mortality among Medicare beneficiaries with cancer[END_REF]. As acknowledged by Tilly himself, difference in health behaviours or access to health-enhancing resources should be related to difference in early categorizing and organisational exposure such as educational or job segregation. [START_REF] Tilly | Relational studies of inequality[END_REF] According to DIT, it is the matching of internal and external categories that makes income/status inequalities durable. Accordingly, one consequence of DIT for data analysis is that the practice of controlling for socio-economic status is inadequate for understanding racial/ethnic inequalities in health. If ethnic groups provide effective categories for organisational functioning, this will influence the many socio-economic attributes that matter for rewards and economic performance. Socio-economic status is, therefore, on the causal pathway for ethnicity and health. Controlling for SES leads to misrepresentation of the true effect of ethnicity on health. Studies of ethnicity and health should attempt to understand multiple vulnerability and the extent to which ethnicity combines with other categories affecting health. [START_REF] Williams | Race and health: Basic questions, emerging directions[END_REF] For example, one qualitative research project has shown that socioeconomic position has different (economic and labour) implications for ethnic minority groups than for ethnic majority groups. [START_REF] Smith | Ethnicity, health and the meaning of socioeconomic position[END_REF] Another empirical illustration of this is the evidence that higher educational status has a diminishing effect on self-rated health among African-Americans compared with whites. [START_REF] Farmer | Are racial disparities in health conditional on socioeconomic status?[END_REF] The interest lies in how ethnicity and SES combine to produce ethnic inequalities in health and how to avoid these. [START_REF] Krieger | Embodying inequality: A review of concepts, measures, and methods for studying health consequences of discrimination[END_REF][START_REF] Krieger | Stormy weather: Race, gene expression, and the science of health disparities[END_REF][START_REF] Williams | Race and health: Basic questions, emerging directions[END_REF][START_REF] Blank | Measuring racial discrimination[END_REF][START_REF] Smedley | Unequal treatment : confronting racial and ethnic disparities in health care[END_REF]

Conclusions

DIT is an important sociological theory with potentially significant implications for studies on ethnicity and inequality, and possibly health inequality in general. Its main advantage, as Tilly himself claimed, is that it can be put to empirical test. We are not aware of studies designed to do this in the ethnicity field, but awareness of these ideas may lead others holding such data to examine them. Equally, researchers may be encouraged to set up new studies that permit tests of DIT. In addition, DIT provides a promising avenue for reducing ethnic inequalities in health, without relying on the somewhat unrealistic expectation, at least in the short term, of first changing beliefs.

This is, of course, a strong assumption and will be hotly contested, but it provides research on ethnicity and health with new and much needed perspectives. In public health the most effective interventions are usually systems-based e.g. laws, national policies, strategies etc. The Tilly theory is in line with this perspective.

Earlier, we raised three key questions: first, why is non-white ethnicity usually associated with lower SES? The Tilly theory indicates that this occurs because organizations match their internal structures to external structures, permitting exploitation. Second, why is this association stronger for some ethnic minority groups than for others? Tilly provides two possible explanations: the four inequality mechanisms (exploitation, opportunity hoarding, emulation and adaptation) may combine and yield different outcomes in the different ethnic minority groups; and the multiple categorical boundaries may lead to heterogeneous risks of poor health.

Lastly, why, for a given ethnic group, is the association stronger in some countries than in others? Several country-level features may influence the matching process: regulation of the labour market; the ability of some ethnic groups to hoard an economic niche in one country but not in an another; the importance of the exploitation for the organization's survival in -for example -some extremely open or dependent economies; the role of public policy (schools, housing, health care etc.) in mixing or separating ethnic groups. While we recognize that these are not definitive answers, we believe they show how DIT leads to a fresh mode of analysis.

This introduction to the relevance of the theory is, we hope, merely a prelude to rigorous and detailed theoretical debate and analysis by a multidisciplinary public health research workforce. Following that, we can hope to devise empirical tests.

While we have taken the example of ethnicity, the theory has wider implications within public health, across the various inequality strands.

  studies to analyse the interdependence of decisions at different life-stages and across different domains Comparative studies of how organisational practices match ethnicity/race and socio-economic status and lead to structural differences in SES and friendships Describe the role of ties in health behaviours and their psychological risk factors Describe the organizational processes that account for the selection of some EMG in specific health care niches Data analysis Matching ethnicity with varying socioeconomic status is the major driver of inequalities The higher the level of matching, the higher the inequality Controlling the ethnicity-health relationship for SES is not appropriate. Focus on the interaction between ethnicity and SES in health and health care Assess cumulative inequality within a domain, across domains and across generations

Acknowledgements:

We are very grateful for the valuable comments of participants in the Utrecht HOME meeting, held on 17 February 2009, as well as of members of the Edinburgh Ethnicity and Health Research Group at Edinburgh University. Special thanks to David Ingleby, Martin Pickersgill and Marie Verhoeven for their valuable comments. Karien Stronks, Mark Johnson, and an anonymous referee provided peer review that helped us to substantially improve the paper. We thank Anne Houghton for secretarial assistance in preparing this paper.

Funding:

This work has been made possible thanks to a grant from the COST Action HOME (n° STSM ISO603-3388) as well as a grant from the Fonds de la recherche scientifique Médicale, which made possible a scientific mission at the Public Health Section of Edinburgh University.

Summary Boxes

What is already known?

• Socio-economic status influences the relationship between ethnicity and health.

• However, the nature of this interaction remains opaque because of a lack of theoretical work.

What this study adds?

• Charles Tilly's theory of Durable Inequality explains why, for organisational reasons, ethnicity and socio-economic status are linked and affect health.

• The paper opens a debate on the potential value of this sociological theory in the social epidemiology of ethnicity and health.

Competing interests: none

The Corresponding Author has the right to grant on behalf of all authors and does grant on behalf of all authors, an exclusive licence (or non exclusive for government employees) on a worldwide basis to the BMJ Publishing Group Ltd to permit this article (if accepted) to be published in JECH and any other BMJPGL products and sublicences such use and exploit all subsidiary rights, as set out in our licence (http://jech.bmj.com/site/about/licence.pdf).


M.D Stephan Schwarz
email: stephan.schwarz@uk-erlangen.de

Clemens Stiegler

Maximilian Mueller

M.D Tobias Ettl

Ph.D Gero Brockhoff

M.D Johannes Zenk

M.D Abbas Agaimy

Maximilian Müller

Salivary Gland Mucoepidermoid Carcinoma is a Clinically, Morphologically and Genetically Heterogeneous Entity. A Clinicopathologic Study of 40 Cases with Emphasis on Grading, Histological Variants and Presence of the t(11;19) Translocation

Keywords: mucoepidermoid carcinoma, variant, t(11, 19), salivary gland, grading Mucoepidermoid carcinoma, variant, t(11, 19), salivary gland, grading

INTRODUCTION

Mucoepidermoid carcinoma (MEC) is the most common primary carcinoma of major and minor salivary glands accounting for about one third of all salivary carcinomas. [START_REF] Goode | Mucoepidermoid carcinoma[END_REF][START_REF] Eveson | Salivary gland tumours. A review of 2410 cases with particular reference to histological types, site, age and sex distribution[END_REF] MECs are composed of three cell types in varying proportion: mucous cells, epidermoid (squamoid) cells and undifferentiated small cells (intermediate cells). Any of these cell types may dominate the histological picture in a given tumor, thus posing great diagnostic difficulties. [START_REF] Goode | Mucoepidermoid carcinoma[END_REF] Traditionally, special stains like Meyer´s mucicarmine and periodic acid-Schiff (PAS) are used for detection of sparse mucous cells. Currently, immunohistochemistry proved also of value in recognizing foci of intermediate and epidermoid cells and, in particular, in differentiating MECs from other salivary gland carcinomas that they may closely mimic. [START_REF] De Araujo | Application of immunohistochemistry to the diagnosis of salivary gland tumors[END_REF][START_REF] Rizkalla | Necrotizing sialometaplasia versus invasive carcinoma of the head and neck: the use of myoepithelial markers and keratin subtypes as an adjunct to diagnosis[END_REF] Owing to the highly variable biological behavior of MEC, a variety of prognostic factors have been evaluated on previous large series to predict aggressive tumor behavior and hence the necessity of more aggressive treatment. [START_REF] Ettl | Overexpression of EGFR and absence of C-KIT expression correlate with poor prognosis in salivary gland carcinomas[END_REF][START_REF] Subramaniam | Molecular characterization of dedifferentiated mucoepidermoid carcinoma of the trachea using laser microdissection-based TP53 mutation analysis[END_REF] Of these, histological grading has received special attention and several grading systems have been proposed. [START_REF] Auclair | Mucoepidermoid carcinoma of intraoral salivary glands. Evaluation and application of grading criteria in 143 cases[END_REF][START_REF] Goode | Mucoepidermoid carcinoma of the major salivary glands: clinical and histopathologic analysis of 234 cases with evaluation of grading criteria[END_REF][START_REF] Luna | Salivary mucoepidermoid carcinoma: revisited[END_REF] While the difference in behavior of grade 1 and grade 3 MEC was universally confirmed in different studies, the results with regard to intermediategrade MEC have been inconsistent and varied from low-grade behavior [START_REF] Nance | Treatment and survival outcomes based on histologic grading in patients with head and neck mucoepidermoid carcinoma[END_REF] to an aggressive course comparable to high-grade tumors [START_REF] Aro | Management and outcome of patients with mucoepidermoid carcinoma of major salivary gland origin: a single institution's 30-year experience[END_REF] or something in between. [START_REF] Auclair | Mucoepidermoid carcinoma of intraoral salivary glands. Evaluation and application of grading criteria in 143 cases[END_REF] The Armed Forces Institute of Pathology (AFIP) system showed a higher reproducibility and was considered valid in the current World Health Organization (WHO) classification of head and neck tumors. [START_REF] Goode | Mucoepidermoid carcinoma[END_REF][START_REF] Auclair | Mucoepidermoid carcinoma of intraoral salivary glands. Evaluation and application of grading criteria in 143 cases[END_REF][START_REF] Goode | Mucoepidermoid carcinoma of the major salivary glands: clinical and histopathologic analysis of 234 cases with evaluation of grading criteria[END_REF] However, Brandwein et al. suggested that the AFIP grading system may downgrade some MEC. [START_REF] Brandwein | Mucoepidermoid carcinoma: a clinicopathologic study of 80 patients with special reference to histological grading[END_REF] They proposed considering histological features including infiltration pattern at the invasive front, vascular invasion and bony infiltration in the assessment of tumor grade.

The translocation t (11;19) has been detected in approximately half of MECs in several studies. [START_REF] Nakayama | Clinicopathological significance of the CRTC3-MAML2 fusion transcript in mucoepidermoid carcinoma[END_REF][START_REF] Id | t(11;19) translocation and CRTC1-MAML2 fusion oncogene in mucoepidermoid carcinoma[END_REF][START_REF] Behboudi | Molecular classification of mucoepidermoid carcinomas-prognostic significance of the MECT1-MAML2 fusion oncogene[END_REF] However, data on the detection of this molecular alteration in Warthin tumor have been conflicting in different studies. [START_REF] Martins | A study of MECT1-MAML2 in mucoepidermoid carcinoma and Warthin's tumor of salivary glands[END_REF][START_REF] Bullerdiek | Translocation t(11;19)(q21;p13.1) as the sole chromosome abnormality in a cystadenolymphoma (Warthin's tumor) of the parotid gland[END_REF][START_REF] Tirado | CRTC1/MAML2 fusion transcript in high grade mucoepidermoid carcinomas of salivary and thyroid glands and Warthin's tumors: implications for histogenesis and biologic behavior[END_REF] Presence of t (11;19) was found to correlate with a better outcome in MEC, [START_REF] Nakayama | Clinicopathological significance of the CRTC3-MAML2 fusion transcript in mucoepidermoid carcinoma[END_REF][START_REF] Okabe | MECT1-MAML2 fusion transcript defines a favorable subset of mucoepidermoid carcinoma[END_REF][START_REF] Miyabe | Prognostic significance of p27Kip1, Ki-67, and CRTC1-MAML2 fusion transcript in mucoepidermoid carcinoma: a molecular and clinicopathologic study of 101 cases[END_REF] but the explanation for this is still lacking. To our knowledge, the correlation of well defined histological variants with WHO grading, patient outcome and the presence or absence of the translocation t (11;19) has not been evaluated previously. This was the objective of this study.

MATERIAL AND METHODS

Patients and tumor samples

Fourty MECs were retrieved from a consecutive collection of well characterized primary salivary gland carcinomas of both major and minor salivary glands (total n=290) with complete follow-up treated at the Universities of Erlangen and Regensburg, Germany, or at the City Hospital of Nuremberg, Germany, between 1990 and 2007. Clinical information was obtained from the clinical tumor registries of

Tissue microarray construction

A tissue microarray (TMA) was constructed from formalin-fixed paraffin-embedded tissue blocks of these 40 patients, and of additional 250 patients with salivary gland carcinomas others than MEC as well as of 60 cases of normal salivary gland tissue, as previously described. [START_REF] Schwarz | Losses of 3p14 and 9p21 as shown by fluorescence in situ hybridization are early events in tumorigenesis of oral squamous cell carcinoma and already occur in simple keratosis[END_REF][START_REF] Milanes-Yearsley | Tissue micro-array: a cost and time-effective method for correlative studies by regional and national cancer study groups[END_REF] One tissue cylinder with a diameter of 2.0 mm was punched from morphologically representative tissue areas of each donor tissue block and brought into a recipient paraffin block using a homemade semiautomated tissue arrayer. TMA sections were mounted on charged slides (SuperFrost TM Plus; Menzel GmbH, Braunschweig, Germany). H&E-stained TMA sections were used for reference histology.

Fluorescence in situ hybridization

Fluorescence in situ hybridization (FISH) was performed on 5 µm sections of the TMAs using commercially available, directly labeled DNA break-apart probes to detect the translocation t(11;19) (ZytoVision Ltd., Bremerhaven, Germany). The probes identifiy two locus specific sequences on chromosomes 11 (orange/green) spanning the break point at 11q21 and resulting in a yellow appearing fusion signal.

A translocation was evident if the fusion signal was split into an orange and a green signal. TMA sections were dewaxed for 40 min in an incubator at 72 ºC and for 2 × 10 min in xylene. After being rehydrated in a graded ethanol series and rinsed in distilled water, slides were placed in 0.01 M sodium citrate and steamed for 40 min in a water bath. Cell structures were digested in 0.1% pepsin (Sigma) and 0.01 M HCL for 10 min at 37 ºC. After washing in 2 × standard sodium citrate (SSC) (1×SSC is 150 mM sodium chloride and 15 mM sodium citrate, pH 7) and water, slides were dehydrated in graded alcohols and air-dried. 10 mL of the DNA probe set were applied to the TMA area of each section. Sections were cover-slipped and the edges sealed with rubber cement. For co-denaturation of the probe and target DNA, slides were placed on a hotplate preheated to 73 ºC for 5 min and transferred to a warmed hybridization chamber at 37 ºC overnight. After hybridization, the rubber cement was removed and the slides were immersed in 4×SSC + 0.3% Igepal (Serva), 2×SSC, and 1×SSC for 10 min at 50 ºC, respectively.

The slides were rinsed briefly in Millipore water and air-dried. Nuclei were counterstained with anti-fading DAPI (4´,6-diamidino-2-phenylindole) Vectashield (Vector Laboratories, Burlingame, CA, USA) and were analyzed by epifluorescence microscopy.

Microscopy, FISH scoring, and digital imaging slides were imaged with an Axio Imager Z.1 (Zeiss, Göttingen, Germany) equipped with specific filter sets for DAPI (excitation 365 ± 20 nm, emission 450 ± 25 nm; Zeiss), green (excitation 500 ± 10 nm, emission 535 ± 15 nm), and red fluorescence (excitation 545 ± 15 nm, emission 610 ± 35 nm; AHF, Tübingen, Germany). Fluorescence images were obtained with a Plan-Apochromat lens (63×, 1.4) and recorded with a charge-coupled device (CCD) camera AxioCam MRm (Zeiss). FISH scoring was performed by counting fluorescence signals in 50 malignant, non-overlapping cell nuclei for each case by two independent interpreters (CS, MM). A tumor was considered positive if > 50% of the cells harbored the translocation.

Statistical analyses

All clinicopathologic and cytogenetic data were analyzed with SPSS for Windows, version 15.0 (SPSS, Erkrath, Germany). Relationships between dichotomized histological or cytogenetic markers and clinicopathologic factors were examined using Fisher's exact probability test (significant association with p < 0.05). Overall survival (= primary outcome measure) was calculated as the time from the date of diagnosis to death from any cause or the date the patient was last known to be alive.

Patients lost to follow-up were treated as censored cases based on the date they were last known to be alive. Survival curves were generated by using the Kaplan-Meier method, and log-rank tests compared the distributions between groups. For multivariate analysis a Cox proportional hazards model was used in a backwards, stepwise elimination approach. At each step the least significant factor with p > 0.10 was eliminated with reassessment of each factor in the model at each step. A limit for the factors to be included was set at 5%. The clinicopathologic parameters are summarized in Tables 1 and2. Using the current WHO criteria for grading salivary gland MEC, [START_REF] Goode | Mucoepidermoid carcinoma[END_REF] 27 cases were low grade (G1), 3 cases were intermediate grade (G2) and 10 cases were high grade (G3). Classical MEC was significantly associated with age ≤ 60 years (p = <0.001), grade 1 (p = <0.001) and presence of the translocation t(11;19) (p = 0.003).

RESULTS

F

Mean follow up was 64.5 months (range 1.6-224 months). Ten patients (25%) died of their tumors at a mean interval of 25.2 months (range 1.6-70.9 months).

Histological features of MEC

Classical MEC

Twenty-three tumors fulfilled the above criteria of classical MEC. These revealed variable cystic areas; a prominent cystic component (>20%) was seen in 7 tumors. Mucous cells were prominent in each low-power field and gland-like spaces or aggregates of contiguous mucous cells were well discernible even at low power magnification (Fig. 1A). Focal unusual features in classical MEC included minor components with microvesicular cytoplasm suggesting abortive sebaceous differentiation (two cases; Fig. 1B), focal syncytial overgrowth of spindled intermediate cells reminiscent of myoepithelial differentiation but lacking expression of p63 and CK5 (expressing CK7 diffusely) (Fig. 1C-D) and circumscribed tumor nests composed of peripherally located polygonal myoepithelial-like clear cells punctuated by centrally located small microcystic glands mimicking epithelialmyoepitheial carcinoma (EMC) (Fig. 1E-F). However, the lumina were lined by flattened epithelial cells and lacked the well discernible glands of EMC. Furthermore and interestingly, immunohistochemistry showed the reverse of EMC with CK5 being expressed in the luminal cells and CK7 in the outer layer, thus adopting an "inverted EMC pattern" (Fig. 1E-F). A common finding in several cases was the presence of atypical looking preexisting salivary duct showing both bland-looking ciliated epithelium and crypt-like mucous cell hyperplasia resulting in atypical budding of mucous cell-rich ductules and small solid nests of intermediate cells (Fig. 1G-H).

These changes were often associated with an avascular papillary epithelial tufting.

Squamoid (epidermoid) MEC

Squamoid tumors (n=9) showed variable degree of squamoid differentiation with horn cysts and sharp intercellular junction, but prominent intercellular bridges as seen in pattern. Another pattern seen in this group was the presence of differentiated sqamoid sheets with numerous microcystic lumina filled with blue mucin and imparting a pseudocribriform pattern to the tumor (Fig. 2A-B). One tumor classified into this subtype showed solid sheets of medium-sized to relatively small basophilic cells with histological and immunohistochemical features of intermediate cells with minor component of mucous cells (Fig. 2C-D). This tumor would be better categorized as variant MEC of intermediate cell type, but no more cases were found to warrant an additional subtype and the case was hence discussed under the subheading of squamoid MEC.

Eosinophilic (oncocytoid) MEC

This represented the second most common type of variant MEC (n=5). Tumors in this group showed a predominance of medium-sized to large cells with deeply eosinophilic cytoplasm lacking equivocal squamoid differentiation (Fig. 3A-B). Tumor cells revealed either a prominent glandular differentiation that closely resembled mature striated ducts (Fig. 3C) or they were arranged into solid sheets and possessed prominent centrally located nuclei, thus strikingly mimicking an oncocytic neoplasm (Fig. 3D). Occasionally, both features were seen side-by-side. In addition, all tumors showed either minor classical MEC areas, focal epidermoid/squamoid differentiation or scattered mucous cells, thus establishing the diagnosis of MEC (Fig. 3E-F). The tumor illustrated in Fig. 3A was composed of large atypical cystic glands lined by oncocytic partially ciliated cells with occasional papillary pattern reminiscent of Warthin tumor. Single striated duct-like glands seemed to bud off the cyst.

However, mucous cells and other typical features were evident in small areas of the tumor. Furthermore, a lymphoid component was lacking and, instead, a hypervascularized stroma was seen.

Clear cell MEC

Although focal clear cell areas were quite common in MEC in general, the clear cell variant was the least common variant in our study (n=3). Clear cell variants were composed of solid sheets of medium-sized polygonal cells with cytoplasmic clearing, usually with peripheral cytoplasmic retraction with occasional lightly eosinophilic staining tumor cell groups (Fig. 4A-B). The appearance of some of these tumors was reminiscent of the chromophobe renal cell carcinoma (Fig. 4C). The typical nesting and hyaline bands of clear cell carcinoma, not otherwise specified (NOS), was absent. Unequivocal mucous cells or minor foci of typical MEC were seen, thus establishing the diagnosis (Fig. 4B,D).

Distribution and prognostic role of the translocation t(11;19) in MEC

The t (11;19) was detected in 25 of the 40 MEC (62.5%). This corresponded to 19/23 classical MEC (82.6%) and 6/17 variant MEC (35%). Among the different variant MEC types, the clear cell variant showed the highest, and the squamoid variant the lowest positivity rate (100% versus 11%, respectively). The eosinophilic variant showed an intermediate value (40%). All of the non-MEC salivary carcinomas were translocation-negative. The closer association of the translocation with the classical type of MEC was significant (p=0.003) as was the negative association with the squamoid variant (p=0.001). As given in Table 2 the translocation significantly associated with low grade tumors (p=0.01), but not with age, stage, presence of residual tumor, sex and localization. Although not significant, the occurrence of t(11;19) was a factor of better overall survival (p=0.098).

Associations of the histological variants with clinicopathologic parameters

The different variants of MEC have been correlated with clinical parameters (age and tumor stage) as well as with tumor grade. The classical type predominated in younger patients with a mean age of 42 years (range 11-80 years, p<0.001) whereas the squamoid variant usually occurred in the elderly with a mean age of 68 years (range 37-94 years, p=0.03). MECs of classical type were typically low grade (G1, p<0.001), the squamoid variant was of intermediate or high grade (p<0.001).

Squamoid variant tended to occur more frequently in the major glands (parotid and submandibular gland) than in the minor glands of the oral cavity (p=0.055). An association of the variants with sex and stage could not be demonstrated.

Prognostic significance of clinicopathologic parameters in MEC

To examine the prognostic significance of important clinicopathologic parameters in The univariate Kaplan-Meier analysis was complemented by a multivariate Cox regression to examine the independence of the prognostically relevant clinicopathologic parameters. As the tumor variant was strongly associated with tumor grade (Table 1), the Cox regression analysis could not include both parameters. Thus, two analyses were performed: one with grade (grade 1 versus others) and one with variant (squamoid versus others). The other parameters to include into the analysis were those which proved to be of prognostic significance in univariate Kaplan-Meier analysis (Figure 5): age, stage and residual tumor. The Cox regression model (Table 3) was adjusted within two reverse steps in order to exclude a non significant parameter (stage). High age, higher grade, presence of residual tumor and squamoid variant remained significant prognostic factors with hazard ratios between 4.7 and 18.0.

DISCUSSION

In this study, we analyzed the full histological spectrum of MEC in relation to tumor site, WHO-grade, stage, survival and the presence of the translocation t (11;19) in 40 well characterized MECs of major and minor salivary glands. Our findings underscore the diverse morphology of MEC which results from the striking variability in the proportion of the three cell types comprising the tumor. In its classical form, MEC shows a comparable proportion of three or of at least two cell types, hence the diagnosis of this classical variant is straight forward and usually poses no difficulty.

However, a single cell type may constitute the bulk of the tumor, thus giving rise to monophasic variants of MEC, the diagnosis and true categorization of them may be challenging, even for those with great experience in salivary gland pathology. We 7 MECs of the uterine cervix, but in none of 14 conventional cervical adenosquamous carcinomas. [START_REF] Lennerz | Mucoepidermoid carcinoma of the cervix: another tumor with the t(11;19)-associated CRTC1-MAML2 gene fusion[END_REF] However, sensitivity of t (11;19) for squamoid MEC is quite low in our study (11%). Immunohistochemistry showed consistent expression of CK5 in squamoid MEC as well as in high grade MEC [START_REF] Lueck | High levels of expression of cytokeratin 5 are strongly correlated with poor survival in higher grades of mucoepidermoid carcinoma[END_REF] . One tumor in this series showed a predominance of small blue-staining CK5-negative intermediate cells, suggesting the existence of an intermediate cell variant, but the clinical significance of this remains to be further studied in larger series. We discussed this single case within the spectrum of the squamoid variant.

Eosinophilic MEC (also known as oncocytoid MEC) [START_REF] Brannon | Oncocytic mucoepidermoid carcinoma of parotid gland origin[END_REF][START_REF] Weinreb | Oncocytic mucoepidermoid carcinoma: clinicopathologic description in a series of 12 cases[END_REF] shows increased cytoplasmic eosinophilia irrespective of growth pattern but some cases may appear oncocytoid as a result of prominent striated duct-like differentiation. [START_REF] Foschini | Low-grade mucoepidermoid carcinoma of salivary glands: characteristic immunohistochemical profile and evidence of striated duct differentiation[END_REF] The latter pattern is exceedingly rare and may mimic MEC arising in Warthin tumor. [START_REF] Williamson | Mucoepidermoid carcinoma involving Warthin tumor. A report of five cases and review of the literature[END_REF] Although eosinophilic MEC may show a prominent papillary and cystic pattern, the two-layered pattern of oncocytic cells with centrally located nuclei and a prominent lymphoid stroma typical of Warthin tumor are lacking in de novo eosinophilic MEC. Furthermore, MEC ex Warthin tumor is usually of the classical type. [START_REF] Williamson | Mucoepidermoid carcinoma involving Warthin tumor. A report of five cases and review of the literature[END_REF] On the other hand, eosinophilic MEC with papillary pattern and tumor-associated lymphoid proliferation has to be distinguished from benign Warthin tumor with squamous and/or mucous metaplasia and from so-called Warthin adenocarcinoma. [START_REF] Bell | Warthin adenocarcinoma: analysis of 2 cases of a distinct salivary neoplasm[END_REF] The clear cell variant was generally reminiscent of the clear cell carcinoma, NOS. However, on careful evaluation, a variable, but generally minor population of intermediate cells or a minute focus of classical MEC may be detected. The t (11;19) proved helpful and sensitive in the current study in detecting this variant (3 of 3), but the small case number does not allow for solid conclusions. Of interest, focal clear cell change associated with a biphasic pattern very reminiscent of EMC was seen in one classical MEC. Immunohistochemistry revealed however an "inverted EMC pattern" with the luminal CK5 positivity and CK7 expression in the outer layer.

Awareness of this finding, whether in classical or in clear cell MEC, should help to avoid misinterpretation as EMC.

Our results showed a high reproducibility of the current WHO grading [START_REF] Goode | Mucoepidermoid carcinoma[END_REF] which is based on the AFIP criteria. [START_REF] Auclair | Mucoepidermoid carcinoma of intraoral salivary glands. Evaluation and application of grading criteria in 143 cases[END_REF][START_REF] Goode | Mucoepidermoid carcinoma of the major salivary glands: clinical and histopathologic analysis of 234 cases with evaluation of grading criteria[END_REF] However, due to the relatively limited case number we could not assess, whether the WHO system downgrades MEC. In line with the observation of Brandwein et al., [START_REF] Brandwein | Mucoepidermoid carcinoma: a clinicopathologic study of 80 patients with special reference to histological grading[END_REF] dominant histological pattern/ features of tumors correlated well with the tumor grade and outcome, thus suggesting that defining histological criteria should be included in the scoring/ grading of MEC. Non-classical variants tend to be of higher WHO grade than classical MEC, the latter were predominantly grade 1 tumors (96%) compared to only 29% grade 1 variant MEC. A comparable statistical significance value (p<0.001) was demonstrated for WHO grade 1 and classical variant in the association analysis indicating that recognition of the classical variant implies a grade 1 tumor.

One interesting finding in this study was the occurrence of atypical crypt-like mucous cell hyperplasia and foci of intermediate-like cells associated with residual salivary duct in several cases of classical MEC. This finding probably represents a putative precursor lesion, but reactive changes induced by the tumor as well as secondary colonization of preexisting ducts by tumor cells can not be reliably excluded.

However, in the experience of the authors, this finding is not common in specimens harboring other-type salivary gland carcinomas, suggesting a precursor change specifically associated with mucoepidermoid neoplasms and arguing against a that these might represent precursor lesions for MEC. [START_REF] Williamson | Mucoepidermoid carcinoma involving Warthin tumor. A report of five cases and review of the literature[END_REF] In summary, we analyzed 40 salivary MEC, demonstrating a high histological variation and showing that histological variant correlates well with the WHO tumor grade, biological behavior and presence of t (11;19). Future studies on larger series are needed to further verify the value of histological variant in supplementing tumor grading and predicting treatment failure.

  and Regensburg as well as from the salivary gland carcinoma registry of Erlangen. The registries and the related translational research activities are covered by ethical vota of the medical faculties of the Universities of Regensburg and Erlangen-Nuremberg.Histology and classificationOn the basis of Hematoxylin and Eosin (H&E) and PAS stained slides from at least subtotally embedded tumors all of the 290 tumors have been independently reviewed by two pathologists experienced in salivary gland tumor pathology (S. S. & A. A.) without knowledge of initial histological diagnosis, FISH results or clinical follow-up. A few cases (n=4) were either found to represent other types of salivary gland carcinomas or they were lacking unequivocal MEC areas, hence they were excluded.An agreement was achieved in 40 cases which represented the cohort of the current study.MECs were diagnosed according to the well established WHO criteria.[START_REF] Goode | Mucoepidermoid carcinoma[END_REF] Furthermore, tumors have been categorized as either classical MEC or variant (non-classical) MEC. Classical MECs were tri-or biphasic neoplasms composed of well recognizable mucous cells (prominent single goblet cells or contiguous goblet cells forming mucinous gland-like or cystic spaces) in addition to variable proportions of epidermoid (squamoid) and intermediate cells. In contrast to true squamous cells, epidermoid cells lacked intercellular bridges and intracellular keratinization. Intermediate cells were small undifferentiated cells with round nuclei and scant basophilic cytoplasm. In difficult cases additional immunohistochemical staining for low (CK7) 21 or high molecular weight (CK5) cytokeratins was used to identify mucous, intermediate and epidermoid differentiation, respectively. CK5 antibody was purchased from Zytomed systems Ltd., Berlin, Germany, CK7 antibody from DCS Innovative Diagnostik-Systeme Dr. Christian Sartori Ltd., Hamburg, Germany. Immunostaining was performed on 5 µm sections according to the manufacturer's instructions. Generally, all three components were well recognizable in each lowpower field in classical MECs. Biphasic tumors were considered classical if a prominent mucous component (≥50%) was present beside another one (either intermediate or epidermoid cell component). The presence or absence of a prominent cystic component did not influence this classification. The presence of a focus of classical MEC in an otherwise variant MEC was judged according to the extent of components. Tumors with ≥ 80% variant histology were considered variant MECs in this study. Applying the WHO criteria 1 all tumors have been graded independently by the two pathologists, thereby showing a complete reproducibility in all cases.

  squamous cell carcinoma were lacking. Occasional cytoplasmic vacuoles, possibly representing abortive glandular differentiation, were seen. A few tumors revealed a focal or prominent acantholytic (pseudoglandular or pseudovascular)

  with age >60 years (p=0.001), high UICC stage (II, III, IV vs. I; p=0.031), residual tumor after primary surgery (p<0.001) and higher tumor grade (G2 and G3 vs. G1; p=0.001). Sex and localization did not significantly influence survival. The analysis of known clinicopathologic parameters was supplemented by a Kaplan-Meier analysis of the aforementioned histological variants of MEC. Here, the classical type of MEC was associated with best prognosis, whereas the non-classical MEC variants were significant negative prognosticators (p=0.002).

  subclassified variant MEC into squamoid, eosinophilic and clear cell subtypes based on the presence of >80% variant pattern in the tumor. The squamoid subtype was the most common variant MEC in this study and implied commonly a high-grade tumor with significantly adverse outcome. The diagnosis of this variant may be facilitated by detection of minor foci of classical MEC through excessive tumor sampling and demonstration of isolated mucous cells or intracellular mucin assisted by special stains (mucicarmine, PAS). Also, the presence of a population of smaller cells lacking evident squamoid differentiation (intermediate cells) represents an important clue to diagnosis. Immunohistochemistry may help to recognize such minor foci that would remain undetected on initial H&E evaluation. Adenosquamous carcinoma, which is actually not included into the WHO classification of salivary gland tumors, is an important differential diagnosis of the squamoid MEC. Demonstration of unequivocal mucous cells and/or intermediate cells on H&E, mucin stains or by immunohistochemistry represents the main discriminator. In this setting, the t(11;19) may help to confirm the diagnosis of MEC in equivocal cases. Lennerz et al. detected the t(11;19)-related rearrangements in 7 of

  lesion, but this merits future studies. Williamson et al. described similar foci of squamous and mucous cell metaplasia associated with cytological atypia within oncocytic epithelium of Warthin tumor harboring MEC and speculated

Figure 1

 1 Figure 1 Variations of classical (low-grade) mucoepidermoid carcinoma with unusual features. A Classical and partially cystic MEC composed of mucous, epidermoid and intermediate cells. B MEC with focal sebaceouslike differentiation characterized by polygonal cells with microvesicular cytoplasm. C MEC exhibiting partial overgrowth of spindle shaped intermediate cells mimicking myoepithelial differentiation but negative for myoepithelial markers (p63, not shown) and CK5 (D). E Classical MEC (G1) with focal clear cell change of intermediate cells, negative for CK5 (F), but positive for CK7 (not shown), generating an inverted epithelialmyoepithelial pattern. G Classical MEC entrapping a salivary duct with mucous metaplasia, possibly representing a precursor lesion of MEC. H

Figure 2

 2 Figure 2 Mucoepidermoid carcinoma of squamoid type. A High grade MEC (G3) with mucus filled microcystic spaces and sheets of intermediate cells differentiating to mature squamoid epithelia forming horn pearls. B Strong expression of CK5 in the intermediate/squamoid cells. This tumor was t(11;19) negative. C High grade MEC (G3) predominantly composed of solid sheets of intermediate cells with interspersed single mucous cells. Partial expression of CK5 (D) and negativity of CK7 (not shown) indicate immature/intermediate cells. This tumor was t(11;19) positive (not shown).

Figure 3

 3 Figure 3 Mucoepidermoid carcinoma of eosinophilic (oncocytoid) type / with striated duct differentiation. A,B Cyst wall of a partially cystic MEC of eosinophilic type (G1) resembling a Warthin tumor with mucous metaplasia and without lymphoid stroma. The neoplastic tubules were strikingly similar to striated ducts of normal salivary gland. Translocation t(11;19) was negative. C,D Another t(11;19)-positive low-grade MEC with striated duct differentiation (C) and solid cell sheets with characteristic oncocytic appearance (D). E Eosinophilic MEC with predominant sclerosis (sclerosing type eosinophilic MEC, G2). The tumor was composed of solid sheets of CK7-positive (F) intermediate cells with interspersed mucous cells and focal positivity for CK5 (not shown). This tumor was t(11;19)-negative.

Figure 4

 4 Figure 4 Mucoepidermoid carcinoma of clear cell type. A low-grade MEC predominantly composed of solid sheets of clear cells with inconspicuous nuclei and "chromophobe" appearance. B a small focus with classical morphology detected on further sampling. C FISH using break-apart probes demonstrated translocation t(11;19) indicated by split orange and green signals. D High grade clear cell MEC with higher nuclear pleomorphism and focal well differentiated areas (E). F FISH demonstrated polysomy 11 and bi-or tri-allelic translocation t(11;19).

  Variations of classical (low-grade) mucoepidermoid carcinoma with unusual features. A Classical and partially cystic MEC composed of mucous, epidermoid and intermediate cells. B MEC with focal sebaceous-like differentiation characterized by polygonal cells with microvesicular cytoplasm. C MEC exhibiting partial overgrowth of spindle shaped intermediate cells mimicking myoepithelial differentiation but negative for myoepithelial markers (p63, not shown) and CK5 (D). E Classical MEC (G1) with focal clear cell change of intermediate cells, negative for CK5 (F), but positive for CK7 (not shown), generating an inverted epithelial-myoepithelial pattern. G Classical MEC entrapping a salivary duct with mucous metaplasia, possibly representing a precursor lesion of MEC. H Higher magnification demonstrating a proliferation of intermediate cells within the duct lumen on the right side and a focal eosinophilic change of epidermoid tumor cells on the left side. 650x987mm (79 x 79 DPI) Mucoepidermoid carcinoma of squamoid type. A High grade MEC (G3) with mucus filled microcystic spaces and sheets of intermediate cells differentiating to mature squamoid epithelia forming horn pearls. B Strong expression of CK5 in the intermediate/squamoid cells. This tumor was t(11;19) negative. C High grade MEC (G3) predominantly composed of solid sheets of intermediate cells with interspersed single mucous cells. Partial expression of CK5 (D) and negativity of CK7 (not shown) indicate immature/intermediate cells. This tumor was t(11;19) positive (not shown). 647x487mm (96 x 96 DPI) Mucoepidermoid carcinoma of eosinophilic (oncocytoid) type / with striated duct differentiation. A,B Cyst wall of a partially cystic MEC of eosinophilic type (G1) resembling a Warthin tumor with mucous metaplasia and without lymphoid stroma. The neoplastic tubules were strikingly similar to striated ducts of normal salivary gland. Translocation t(11;19) was negative. C,D Another t(11;19)positive low-grade MEC with striated duct differentiation (C) and solid cell sheets with characteristic oncocytic appearance (D). E Eosinophilic MEC with predominant sclerosis (sclerosing type eosinophilic MEC, G2). The tumor was composed of solid sheets of CK7-positive (F) intermediate cells with interspersed mucous cells and focal positivity for CK5 (not shown). This tumor was t(11;19)-negative. 647x736mm (96 x 96 DPI) Mucoepidermoid carcinoma of clear cell type. A low-grade MEC predominantly composed of solid sheets of clear cells with inconspicuous nuclei and "chromophobe" appearance. B a small focus with classical morphology detected on further sampling. C FISH using break-apart probes demonstrated translocation t(11;19) indicated by split orange and green signals. D High grade clear cell MEC with higher nuclear pleomorphism and focal well differentiated areas (E). F FISH demonstrated polysomy 11 and bi-or tri-allelic translocation t(11;19). 736x647mm (96 x 96 DPI)

2 F o r P e e r R e v i e w

 2 vs. minor glands b stage I vs. stages II, III and IV c grade 1 vs. grades 2 and 3 d residual tumor 0 vs. residual tumor 1 and

Table 1

 1 ). Clinicopathologic parameters related to the four subtypes of MEC (Significance of associations was evaluated by Fisher exact test).

	Figure 5 Univariate Kaplan-Meier analyses of clinicopathologic parameters (sex,
	age, localization, type, UICC stage, residual tumor classification, grade,
	and translocation t(11;19) regarding overall survival (in months). P values
	according to log rank statistics.
	F o r
	P
	e
	e r
	R
	e
	v i e
	w
	18

Table 2

 2 Clinicopathologic parameters related to presence or absence of t(11;19).Significance of association was evaluated by Fisher exact test.

Table 3

 3 Univariate and multivariate Cox regression with reverse stepwise selection.In the upper half age, stage, grade, and residual tumor are included, in the lower half grade is replaced by tumor type (squamoid vs. others).

Table 1 :

 1 Clinicopathologic parameters related to the four subtypes of MEC (Significance of associations was evaluated by Fisher exact test).

					Fisher	Fisher
	classical	clear cell	eosinophilic	squamoid	exact Test	exact Test
	type	variant	variant	variant	classical vs.	squamoid
	(n=23)	(n=3)	(n=5)	(n=9)	others	vs. others

d n.s.

Table 2 :

 2 Clinicopathologic parameters related to presence or absence of t(11;19). Significance of association was evaluated by Fisher exact test.

	t(11;19)	t(11;19)	Fisher
	negative	positive	exact Test

Table 3 .

 3 Univariate and multivariate Cox regression with reverse stepwise selection. In the upper half age, stage, grade, and residual tumor are included, in the lower half grade is replaced by tumor type (squamoid vs. others).

	univariate	multivariate
		step 1	step 2	hazard ratio 95% confidence interval

Published on behalf of the British Division of the International Academy of Pathology Histopathology

ACKNLOWLEDGEMENT

The authors thank Prof Dr Peter Wünsch of the Nuremberg City Hospital for generously supplying paraffin blocks of his institution's MECs. They thank Mrs Marietta Bock and Mr Rudolf Jung for excellent technical assistance. They also thank Dr Sabrina Petsch, Tumor Center Erlangen-Nuremberg, and Dr Monika Klinkhammer-Schalke, Tumor Center Regensburg for yielding the clinical follow up data of the patients to the authors.

Published on behalf of the British Division of the International Academy of Pathology Histopathology