Francesco Foscarin
email: francesco.foscarin@cnam.fr

Raphaël Fournier-S'niehotta

Florent Jacquemard
email: florent.jacquemard@inria.fr

A diff procedure for music score files Computation and visualization of the differences between two music score files

Comparing music score files is an important task for many activities such as collaborative score editing, version control and evaluation of optical music recognition (OMR) or music transcription. Following the Unix diff model for text files, we propose an original procedure for computing the differences between two score files, typically in XML format. It performs a comparison of scores at the notation (graphical) level, based on a new intermediate tree representation of the music notation content of a score and a combination of sequence-and tree-edit distances. We also propose a tool to visualize the differences between two scores side-by-side, using the music notation engraving library Verovio, and we employ it to test the procedure on an OMR dataset.

INTRODUCTION

Algorithms and tools for comparing text files have been around for more than forty years (Unix diff [START_REF] Wayne | An algorithm for differential file comparison[END_REF], [START_REF] Heckel | A technique for isolating differences between files[END_REF]). Their purpose is to identify differences between two text files relatively similar (typically two versions of the same file), at the granularity level of lines with the Longest Common Subsequence algorithm (LCS), or at the granularity level of characters, with edit distances. They output, in a normalized format, a list of differences between the two files that corresponds to our intuitive notion of difference. This list is called a patch or an edit script and, combined with either one of the two files, enables the reconstruction of the other file. Another application is to merge two files containing independent changes into a single file. Those tools are widely applied to text based documents nowadays, for software development, collaborative edition and version control systems.

Similarly to text files, comparing music scores is relevant for several applications. At a global level, it helps to define metrics capable of grasping the distance between two scores, for example to evaluate the quality of a transcription process (see [START_REF] Cogliati | A Metric for Music Notation Transcription Accuracy[END_REF][START_REF] Mcleod | Evaluating Automatic Polyphonic Music Transcription[END_REF] for recent proposals). At a detailed level, it is very valuable for musicologist and developers of version control systems to get precise clues on the locations of the differences between scores (e.g., between two editions of the same score). One difficulty that immediately arises for defining a diff tool for music scores is that, due to the nature/complexity of the music language, a music score contains multiple levels [START_REF] Calvo | End-to-End Neural Optical Music Recognition of Monophonic Scores[END_REF][START_REF] Foscarin | Gioqoso, an online Quality Assessment Tool for Music Notation[END_REF] that can be compared.

Most of the literature so far focuses on the musical content level, i.e. the sequence of events in the score that describe the intended production of sounds, independently from any encoding or rendering concern. The comparison is made with some extended edit distance [START_REF] Allali | Toward a general framework for polyphonic comparison[END_REF][START_REF] Cogliati | A Metric for Music Notation Transcription Accuracy[END_REF][START_REF] Lemström | String Matching Techniques for Music Retrieval[END_REF][START_REF] Mcleod | Evaluating Automatic Polyphonic Music Transcription[END_REF][START_REF] Mongeau | Comparison of musical sequences[END_REF], or employing tree models [START_REF] José F Bernabeu | Melodic identification using probabilistic tree automata[END_REF][START_REF] Rizo | Symbolic music comparison with tree data structures[END_REF]. In the present paper, instead, we focus on the musical notation level, i.e. how the music is intended to be displayed in the score. In particular we consider 7 elements: note-heads (and rest type), beams, tuplets, ties, dots, note position and accidentals that can be found in scores along with musical content (Figure 1).

The authors of [START_REF] Antila | A hierarchic diff algorithm for collaborative music document editing[END_REF], following the work of [START_REF] Knopke | Towards musicdiff: A foundation for improved optical music recognition using multiple recognizers[END_REF], observe that it does not make sense to apply directly the standard Unix diff utility to XML score files. A possible solution is to extract a linear representation of the graphical content [START_REF] Calvo | End-to-End Neural Optical Music Recognition of Monophonic Scores[END_REF], but motivating by the hierarchical structure of note beaming and tuplet grouping we chose to follow another approach and compare scores in terms of hierarchical structure, by using a tree-edit distance based on tree nodes operations, as proposed by [START_REF] Zhang | Simple fast algorithms for the editing distance between trees and related problems[END_REF] or [START_REF] Cobena | Detecting Changes in XML Documents[END_REF].

In this paper we propose a complete methodology for score comparison at music notation level, articulated in four main contributions: (i) the definition of an original tree-based representation of the music notation content of a bar, (ii) a new edit distance algorithm yielding a list of differences between two scores, (iii) a graphical tool to represent two scores side-by-side with coloured annotations and (iv) a visual evaluation on a OMR dataset.

The tree-based representation of the music notation content of a bar (Section 3.1), acts as a canonical model for disambiguation of the rhythmic content of score formats. For instance, in the case of the XML/MEI format, one score can have many different presentations that lead to the same graphical output, e.g., by using or not a nested structure for beaming, interchanging the order of elements, etc. The situation is similar for MusicXML and other formats. Moreover, using an intermediate representation allows to decouple our approach from the exact file format considered. Anyway for practical reason explained in Section 4 we chose to work mainly with scores in XML/MEI format [START_REF] Roland | The music encoding initiative (mei)[END_REF].

The new edit distance algorithm is based on our original treestructured representation and yield a list of differences between two scores first at the bar level (Section 3.2) and then at a finer granularity level (Section 3.3), similarly to a text diff tool that first finds the different lines and then explores the differences between the words of each line. The distance is the size of the smallest list of modifications required to transform one score into the other, and it reflects visual differences between two scores.

The graphical tool (Section 4) represent two scores side-by-side and shows coloured annotations, highlighting the notation differences between the two scores, in a similar fashion as visual diff tools for text. While it is currently based on our algorithm only, it may be extended to present other metrics and should also be easy to integrate into other programs.

The entire workflow is applied to a dataset of scores produced by OMR and allows to easily spot the mistakes made by the transcription algorithm comparing to the manually annotated version (Figure 2). Related Work. Our aim is to create for music notation a tool similar to the diff program by Douglas McIlroy [START_REF] James | An algorithm for differential le comparison[END_REF], which compares texts. Besides its practical usage, the latter has motivated theoretical studies which led to efficient algorithms for edit distances computation (e.g., [START_REF] Myers | AnO (ND) difference algorithm and its variations[END_REF][START_REF] Ukkonen | Algorithms for approximate string matching[END_REF]). We draw from this work the process of computing the differences with dynamic programming. Comparing music scores has been the object of previous work with different objectives: evaluation of OMR and automatic music transcription (AMT), collaborative score editing, etc., leading to different approaches to the problem.

Knopke and Byrd [START_REF] Knopke | Towards musicdiff: A foundation for improved optical music recognition using multiple recognizers[END_REF] pioneered with their "work towards a musicdiff program", focusing on comparing several OMR outputs. They show that a traditional XML diff tool cannot be used as is on MusicXML, and categorize the comparison difficulties. They also propose a rudimentary visualisation tool. Our workflow is similar to [START_REF] Knopke | Towards musicdiff: A foundation for improved optical music recognition using multiple recognizers[END_REF] although our objectives differ: we build a tool for users, and put more focus on the second step, with a dedicated representation and tree-edit distance computation procedure.

Recently, the subject has been studied in the MIR context of transcription evaluation. Cogliati [START_REF] Cogliati | A Metric for Music Notation Transcription Accuracy[END_REF] present an edit distance similar to the Levenshtein distance, aiming at exhaustive research incorporating the characteristics of music notation into their metric. They set up a very sound evaluation process involving human experts, which highlights how algorithms are biased towards one category of differences or another. Our objectives are different since we are mostly interested in computing and displaying the whole list differences between scores, and not only evaluating an edit distance.

McLeod and Steedman [START_REF] Mcleod | Evaluating Automatic Polyphonic Music Transcription[END_REF] improve slightly their work towards the goal of a joint metric for AMT performance, but they still do not take into account typesetting differences.

Aiming at improving collaborative editing of scores, [START_REF] Antila | A hierarchic diff algorithm for collaborative music document editing[END_REF] introduce the hierarchic paradigm that we followed, and worked with the Zhang-Shasha tree-edit distance [START_REF] Zhang | Simple fast algorithms for the editing distance between trees and related problems[END_REF]. They bring advances from theoretical computer science (e.g., [START_REF] Bille | A survey on tree edit distance and related problems[END_REF]) into the music notation community. They present one example on an MEI-encoded file, showing that writing a diff tool for XML music scores should be liable to the problems identified by [START_REF] Knopke | Towards musicdiff: A foundation for improved optical music recognition using multiple recognizers[END_REF] (i.e. XML coding style differences). We introduce an original tree-based representation to go beyond the problem.

Our last contribution, the graphical tool to visualize differences between scores, is inspired by similar tools for texts. They are now ubiquitous, either being standalone dedicated programs like Meld 1 (cross-platform) or FileMerge/opendiff (MacOS), or integrated into IDEs (Eclipse, IntelliJ). To the best of our knowledge, our work is the first proposition in the context of music scores.

MUSIC CONTENT DIFF

In this first section we consider a simple comparison between two monophonic scores at music content level. Intuitively, its purpose is to be able to compare scores according to the way they sound. We use a intermediate score representation similar to a piano-roll, with no overlapping notes. This approach is not original, but it gives a good baseline to introduce our music notation comparison in Section 3.

Lossy Linear Score Representation

We assume given an XML score, composed of a single monophonic part. From the score we extract a sequence of couples ⟨pitch, duration⟩, where the pitch is a MIDI value in [0, 128] (128 added to represent rests) and duration is expressed in fractions of beats.

This representation in sequences captures the musical content information of a music score disregarding other music notation elements, such as metric cues indicated by grouping events with beams, ties, dots or chords, and information about pitch spelling. Other representations (e.g. **kern [START_REF] Huron | Humdrum and Kern: Selective feature encoding[END_REF]) exist with the objective of encoding the musical content, but they also embed elements of music notation. In order to clearly separate the two levels we decided to use a simpler representation.

String Edit Distance

We compare two parts from two different scores by applying the Levenshtein edit distance to extracted sequences. It is based on the three following edit operations [START_REF] Robert | The string-to-string correction problem[END_REF] on the above triplets (n represent a triple as above, and ε denotes the empty sequence):

ε → n insertion of a triplet, n → ε deletion of a triplet, n → n ′ substitution of a triplet by another. Every such operation α → α ′ is associated a cost value δ (α, α ′ ).
We assume that δ (α, α) = 0 for all α, and triangle inequality. The cost of an edition sequence is the sum of the costs of all operations involved in the sequence. The edit distance D(s, s ′ ) between two sequences of triplets s and s ′ is the minimal cost of an edition sequence transforming s into s ′ . It can be computed using the following dynamic programming equations, where |s | denotes the length of s, and n.s represents a sequence made of triple n followed

D(ε, ε) = 0 D(ε, n ′ .s ′ ) = D(ε, s ′ ) + δ (ε → n ′ ) (ins) D(n.s, ε) = D(s, ε) + δ (n → ε) (del) D(n.s, n ′ .s ′ ) = min        D(s, s ′ ) + δ (n → n ′ ) (subst) D(n.s, s ′ ) + δ (ε → n ′ ) (ins) D(s, n ′ .s ′ ) + δ (n → ε) (del)

MUSIC NOTATION DIFF

We now present our main contribution, which is based on another representation, more structured, whose purpose is to enable a comparison of scores according to the way they look. This representation (Section 3.1) is based on tree structures akin to the rhythm-trees for rhythm notation [START_REF] Agon | Representation and rendering of rhythm structures[END_REF][START_REF] Foscarin | A Parse-based Framework for Coupled Rhythm Quantization and Score Structuring[END_REF][START_REF] Rizo | Symbolic music comparison with tree data structures[END_REF], but more tightly related to the music notation information, in order to have an unique representation of the graphical content of the XML scores.

We consider an XML score as a nested structure: on the top level we have a list of score parts (usually one for each instrument), each part is a list of bars, each bar is a list of voices and each voice as a list of general notes (notes, rests or chords) that occurs sequentially without overlapping. For the sake of presentation, we will initially focus on scores with a single part, where each bar contains a single voice (Sections 3.1-3.4). We then explain in Section 3.5 how our technique generalizes to polyphonic scores with multiple voices and parts.

The first step is to transform every score into a sequence of trees, one tree (more precisely, one pair of trees, as detailed below) for each bar. Then we proceed in two stages: processing of unaligned bars, using purposely designed treeedit distances algorithms that descends into the bar representations in order to identify differences.

Tree-Based Representation

We present an abstract model of the music notation content of bars in music scores. It is designed as an intermediate structure for our algorithms of diff computation, and it is not meant to be a format for data exchange. It can be imported from and exported to XML (see Section 4). This model is based on the following two types of tree representations, corresponding to two aspects of music notation.

Beaming Trees.

A beaming tree represents the notes in a bar and the beams and ties between them. In particular, we encode in the leaves the information about the note-heads, dots and ties; the remaining information (i.e. beams and flags) are encoded in the tree structure (Figure 3). Every leaf of a beaming tree represent an event: a note, a chord or a rest. In the XML representation, a chord can be considered in a single voice, if all the notes of the chord have the same duration. Each leaf is labeled by a triple containing the following information (see Figure 3 for an illustration):

• pitches: a sequence where each element contains a pitch position (letter + octave number for notes or R for rests), an alteration value among 'none' and [ , Z , ^, \ , ] and a tie flag indicating if the corresponding note is tied to an element of (a) [START_REF] José F Bernabeu | Melodic identification using probabilistic tree automata[END_REF] 1 the previous event in the tree (following a depth first traversal). The pitch position represent the vertical position on the score, not the sounding pitch name, in order for it to be independent from the clefs and transposing instruments; we set it as the pitch name with a G-clef by implementation choice. The sequence is ordered from the lowest to the highest pitch; in case of a single note or a rest (not a chord), pitches is a singleton sequence; • note-head: a value representing a fraction of whole note, e.g., 1, 2, 4, represent respectively ¯, ˘" , and ˇ" for a note, and and < , = , > for a rest;

6 1 1 N 4 N 4 N 4 N 4 N 4 N 4 (b) 6 4 1 0 1 1 N 4 1 1 R 4 1 1 R 4 1 1 N 4 N 4 N 4 (c) 8 3 1 1 N 4 N 4 1 N 4 N 4 1 N 4 N 4 (d) 8 3 1 1 N 4 0 R 4 0 R 4 1 N 4 1 N 4 N 4
• dots: a natural number in [0, 3] that specify the number of dots.

We define the size ∥pitches∥ of a sequence pitches as the sum of the sizes of all its elements, where the size ∥pitch∥ of an element pitch is the number of graphical items it contains, e.g., a note D4 \ with a tie will have size of 3, while a rest R will have size of 1.

In the internal nodes, we use the two symbols ′ 0 ′ and ′ 1 ′ for the encoding of beams; intuitively a label ′ 1 ′ indicates that there is a beam between the notes in the subtrees under the node. More precisely, let the weight of a node n be the number of nodes labelled ′ 1 ′ on the path from n to the root of the tree (including n itself). Let n and n ′ be events in two successive leaves and ℓ be their leastcommon-ancestor in the tree (it is always defined and unique). The number of beams between n and n ′ is either weiдht(ℓ), if in the two paths ℓ -→ n and ℓ -→ n ′ all inner nodes are labeled by ′ 1 ′ , or it is 0 otherwise.

N 4 N 4 1 N 4 N 4 N 4 • 3 N 4 N 4 N 4 N 4 N 4 3 N 4 N 4 N 4 0 N 2 N 2 N 4 N 4 N 4 3 N 2 N 2 3 N 4 N 4 N 4 0 N 2 N 2 N 4 N 4 1 N 4 N 4 N 4 N 2 N 2 3 N 4 N 4 3 N 4 N 4 N 4

Tuplet Trees.

The tuplet trees, illustrated on Figure 4, represent the locations of the numbers and brackets explicitly defining in a score the nature of the tuplets. Every inner node of a tuplet tree is either unlabeled (this is denoted with a label • in figures), or labeled by a pair of integers i : j, with i, j ≥ 2, meaning 'i elements in the time of j', or by a single integer i if j can be omitted [START_REF] Gould | Behind Bars: The Definitive Guide to Music Notation[END_REF]. The leaves are labeled like for the beaming trees: Every tuplet tree is associated to a companion beaming tree with the same sequence of leaves (the difference between them is only in the structure).

Longest Common Subsequences of Parts

Assume that we are given two parts extracted from two different scores. We represent each part by a sequence of pairs p = ⟨bt, tt⟩, one pair per bar, where bt is a beaming tree and tt is a companion tuplet tree. Similarly to the line-by-line comparison procedures for text files [START_REF] Wayne | An algorithm for differential file comparison[END_REF], and considering a bar as the analogue of a line in a text file, we shall align identical bars from the two parts, by computing their longest common subsequence (LCS), whose size is defined by the following recursive Dynamic Programming equations (with similar notations as in Section 2.2):

LCS(ε, s ′ ) = LCS(s, ε) = 0 LCS(p.s, p ′ .s ′ ) = 1 + LCS(s, s ′ ) if p ≡ p ′ = max ( LCS(p.s, s ′ ), LCS(s, p ′ .s ′ ) )
otherwise.

In the second line, p ≡ p ′ means that the trees in pairs p = ⟨bt, tt⟩ and p ′ = ⟨bt ′ , tt ′ ⟩ are identical, i.e. that bt = bt ′ and tt = tt ′ . According to the representation presented in Section 3.1, it means the corresponding bars can be considered identical.

The above equation can be adapted in order to compute a maximal alignment of identical pairs between the two sequences. Formally, denoting the two sequences by s = p 1 , . . . , p m and s ′ = p ′ 1 , . . . , p ′ n , the alignment is made of two strictly increasing sequences of indices of same length, (i 0 = 0, i 1 , . . . , i k ) and

0 b 0 p i 1 b 1 p i 2 … b k p i k b k+1 0 b ′ 0 p ′ i ′ 1 b ′ 1 p ′ i ′ 2 … b ′ k p ′ i ′ k b ′ k+1
(i ′ 0 = 0, i 1 , . . . , i ′ k ) such that for all 0 ≤ j < k, (i) p i j+1 ≡ p ′ i ′ j+1
, and

(ii) the pairs in the respective sub-sequences b j = p i j +1 , . . . , p i j+1 -1 and b ′ j = p ′ i j +1 , . . . , p ′ i j+1 -1 are pairwise distinct (each of these sequences might be empty if i j+1 = i j + 1 or i ′ j+1 = i ′ j + 1). We call each pair ⟨b j , b ′ j ⟩ as above a difference block (see Figure 5) and the objective of the next two subsections is to perform a fine comparison between blocks.

Inside-the-bars Comparison

In the following, for a tree t (either a beaming tree or tuplet tree), ∥t ∥ denotes the sum of the sizes of labels of all the nodes n in t (size of labels is 1 by default, ∥n∥ for a leaf as defined in Section 3.1.1, 0 for unlabeled nodes). For a sequence s = t 1 , . . . , t k of trees, ∥s ∥ = ∑ k i=1 ∥t i ∥.

Comparison of Beaming Trees.

The following recursive equations define an edit distance (denoted by B) between sequences of beaming trees. The distance between two trees is defined by the particular case of singleton sequences. In these equations, t .s and t ′ .s ′ denote two beaming tree sequences, with t = a(s 0 ) and t ′ = a ′ (s ′ 0 ), where s 0 and s ′ 0 might be empty. We call a tree atomic if it is composed of a single leaf node (i.e. it represents an event). We call a tree t = a(s 0 ) unary if s 0 is a singleton sequence (i.e. the root has a single child).

B(ε, s

′ ) = ∥s ′ ∥ B(s, ε) = ∥s ∥ B(t .s, t ′ .s ′ ) = min                                    B(s, t ′ .s ′ ) + ∥t ∥ (del-tree) B(t .s, s ′ ) + ∥t ′ ∥ (ins-tree) B(s 0 .s, t ′ .s ′ ) + 1 (del-node) if t non-atomic, t ′ atomic, or t unary, t ′ not unary B(t .s, s ′ 0 .s ′ ) + 1 (ins-node) if t atomic, t ′ non-atomic, or t not unary, t ′ unary B(s 0 .s, s ′ 0 .s ′ ) + δ (a, a ′ ) (descend) if t, t ′ non-atomic B(s, s ′ ) + A(t, t ′ ) (leaf) if t, t ′ atomic where δ (a, a ′ ) is a defined by δ (a, a ′ ) = 0 if a = a ′ ,
and δ (a, a ′ ) = 1 otherwise. The cases (del-tree) and (ins-tree) correspond to the deletion or insert of a whole subtree. The cases (del-node) and (ins-node) with one unary tree correspond to a difference of one beam between the trees. The other cases descend down to leaves and then compare atomic trees t and t ′ with the distance A(t, t ′ ) defined as follows (p, n, d stand respectively for the attributes pitches, noteheads, dots of Section 3.1):

A(t, t ′ ) = D ( p(t), p(t ′ ) ) + δ (n(t), n(t ′ )) + |d(t) -d(t ′ )|
where the first literal D ( pitches(t), pitches(t ′ ) ) is the Levenshtein distance defined in Section 2.2, using the following operations costs:

δ (ε → pitch) = δ (pitch → ε) = ∥pitch∥ δ (pitch → pitch ′ ) = δ (n, n ′ ) + δ (alt, alt ′ ) + δ (tie, tie ′ )
where n, n ′ , alt, alt ′ and tie, tie ′ are the respective name, alteration and tie values of pitch and pitch ′ .

Comparison of Tuplet Trees.

Tuplet trees have a simpler structure than beaming trees (e.g., no unary nodes). We compare them with the classical Zhang-Sasha equations [START_REF] Zhang | Simple fast algorithms for the editing distance between trees and related problems[END_REF] for computing a tree edit distance (denoted by T), i.e. the smallest number of node-edit operations to transform a tuplet tree into another (instead of the algorithm of Section 3.3.1 which is specific to beaming trees).

T(ε, s ′ ) = ∥s ′ ∥ T(s, ε) = ∥s ∥ T(t .s, t ′ .s ′ ) = min        T(s 0 .s, t ′ .s ′ ) + 1 (del-node) T(t .s, s ′ 0 .s ′ ) + 1 (ins-node) T(s, s ′ ) + T(s 0 , s ′ 0 ) + δ (a, a ′ ) (subst-node)
where δ (a, a ′ ) is as in Section 3.3.1 for inner nodes and δ (a, a ′ ) = A(a, a ′ ) when a and a ′ are leaf labels.

Comparison of Difference Blocks

We compute the distance between two difference blocks b, b ′ using the following recursive equations, similar to the equations of Section 2.2. We recall that a block is a sequence of pairs (bar representations) of the form p = ⟨bt, tt⟩, where bt and tt are respectively a beaming tree and a tuplet tree.

D(ε, b ′ ) = ∥b ′ ∥ D(b, ε) = ∥b ∥ D(p.b, p ′ .b ′ ) = min        D(b, b ′ ) + ∆(p, p ′ ) (edit-bar) D(p.b, b ′ ) + ∥p ′ ∥ (ins-bar) D(b, p ′ .b ′ ) + ∥p∥ (del-bar)
where, for p = ⟨bt, tt⟩ and p ′ = ⟨bt ′ , tt ′ ⟩:

∆(p, p ′ ) = B(bt, bt ′ ) + T(tt, tt ′ ) -corr(p, p ′ ) (1)
is the edit distance between two bar representations; corr(p, p ′ ) is a correction of T(tt, tt ′ ) to avoid to count the same differences twice (see Section 4). Finally, the distance between two parts is the sum of distances between all the difference blocks.

Multiple Voices and Parts

Recall that we consider a model of scores made of a list of parts (usually one part for each instrument), where each part is a list of bars, each bar is a list of voices, and each voice is a list of general notes (notes, rests or chords).

The algorithms described in the previous sections deal with the comparison of single-part and single-voice scores. The generalization to polyphonic scores with multiple-voices and multiple-parts is implemented as follows. multiple-voices: when comparing two bars we consider all couples made of one voice of the first bar and one voice of the second one, we run the algorithm of Sections 3.3 and 3.4 on each couple independently and then take the couples that yield with minimal distance. If the number of voices in the two bars is different, we return the corresponding voice-insertion and voice-deletion. multiple-parts: we first map the parts in the first score into parts of the second one using the metadata like part or instrument names. We run then our algorithms on each couple of mapped parts independently. In case of parts of score 1 not present in score 2 or conversely, we return the eventual part-insertion and part-deletion. An alternative would be to apply a procedure similar to the above one for multiple voices (coupling parts and taking the ones with smallest distance) but it would be very costly. Indeed, the number of parts can be high (e.g., in the case of orchestral scores), compared to the number of voices in a bar (typically 2 or 3 voices). Moreover, comparing 2 parts require to compare all the data inside these parts (bars, voices etc).

IMPLEMENTATION & EXPERIMENTS

The construction of the models of Sections 2.1 and 3.1 from MEI scores, as well as the implementation of the algorithms proposed, have been performed in Python3, on the top of the Music21 toolkit [START_REF] Scott | music21: A toolkit for computer-aided musicology and symbolic music data[END_REF].

The computations of the string edit distances of Sections 2.2, 3.2 (LCS) and 3.4 are performed with an iterative computation and run in time O(m * n), where m, n is the length of the two sequences. The computation of the tree-edit distances of Section 3.3, is implemented by recursive functions, with a worse case time complexity in O(n 4 ). We use a table of solutions in order to avoid to recompute the function on the same inputs and hence reduce the computation time. Nevertheless, efficiency is not a crucial issue in this case, because the difference blocks are typically small (e.g., in OMR evaluation), or such that b = ε or b ′ = ε (insertion or deletion of bars).

A difference in our score is modeled as a triple (operation, t, t ′ ) where operation is a string identifying the performed operation (e.g. ins-node), and t, t ′ are the subtrees transformed by that operation (or ε is case of insertion/deletion). In the implementation, the above recursive equations are actually extended in order to compute, beside the edit distance, a list of differences. It means that the functions B, T etc. return pairs made of a cost value along with a diff list. To that respect, the operator + in the recursive equations makes the sum of the cost values and the concatenation of diff lists, and the min selects the pair with the smallest cost value. The function corr(p, p ′ ) in Section 3.4 uses the difference lists returned by B and T, in order to detect intersection that may occur, in particular for leaves. These intersections are removed (operatorin (1)) both from the cost value and the diff list. Thus, we avoid accounting twice for the same difference (e.g., a pitch modification must be counted only once, even if it is present in the result of both B and T). The elements of diff lists are pairs of references to elements in the two scores, plus an identifier of the edit operation. To have an unique reference for the elements in the scores, we use the unique id associated to each element in the MEI format of music scores. We store those ids in the leaves of our tree representation, and they are very useful later in order to display the differences between scores. The XML ids are an important feature, not present in the MusicXML format, and the main reason we always work with MEI score files in input (a MusicXML score can be transformed into a MEI score as a preprocessing).

After computing the edit distances and list of differences between two given scores, we display, using Verovio [START_REF] Pugin | Verovio: A library for Engraving MEI Music Notation into SVG[END_REF], the two scores side-by-side, highlighting the differences.

We run experiments in order to test the usability of our workflow for two scenarios: collaborative edition of scores, and OMR evaluation. For the first use case, we considered made-up scores with some typical modifications (bar insertions, note pitch modifications, etc.) in a context of versioning. For the second scenario, we used a corpus produced by the Bibliothèque nationale de France (BNF) composed of 21 ouvertures of Jean-Philippe Rameau, each with an OMRized version (from manuscripts using PhotoScore2 ) and its manual correction, that we compare with our tool 3 .

Our algorithms has shown promising results in both cases, highlighting very fine differences in the scores (e.g., beams and dots), as illustrated on Figure 6. The evaluation of OMR results were not possible for many scores, due to quality issues in the XML files [START_REF] Foscarin | Gioqoso, an online Quality Assessment Tool for Music Notation[END_REF], e.g., a tie between two non-consecutive notes, or syntax errors in the XML file, such as an opened beam not closed. It should be noted that our tree-based model can only handle correctly encoded scores, and that such faulty notations will result in an error.

CONCLUSION

We have presented a procedures to compare XML scores at music notation level that is based on a tree intermediate representation of bars and on a combination of string edit distance and tree edit distance.

In contrast with most approaches in the state of the art, our methodology produces a list of differences that can be highlighted directly on the scores, and alleviates the user from the tedious task of manual scores comparison. Our algorithms gives also a similarity metrics, but this metric is only used to compute the list of differences and it is not useful per se. For this reason, comparison to other approaches (e.g., techniques for melodic similarity) would not be relevant.

Experiments show that our approach allows to correctly displays very fine differences in the scores. Technical improvements are still needed in other stages (XML parsing, error detection and correction, complex score visualization) to improve usability.

We leave for future work other graphical aspects that are not considered in the actual implementation, such as stem directions, clefs, slurs, expression markings, ornaments and lyrics. We also plan to investigate on a more complete treatment of multi instruments polyphonic scores, that will correctly handle staves swapping and multi-staves voices.

Figure 1 :

 1 Figure 1: Three staves highlighting the differences between comparison at a music content and a music notation level, in the case of simple monophonic excerpts. The staves (1,2) have the same music content but differ in their music notation. The staves (1,3) differ only in one note (a longer C at the end of the first bar) from one music content point of view, but they are very different in term of music notation.

  1 http://meldmerge.org/

Figure 2 :

 2 Figure 2: The score diff graphical tool with two scores side-to-side. The differences are annotated with colors: (red) deletions, (yellow) insertion, (green) modifications. In this example it is used to visually evaluate the result of an OMR transcription.

( 1 )

 1 comparison at the bar level (Section 3.2), with an alignment of identical bars (i.e. they have the same trees), with a Longest-Common-Subsequence (LCS) algorithm. (2) comparison inside-the-bar (Sections 3.3, 3.4): a more in-depth

Figure 3 :

 3 Figure 3: Examples of beaming trees. To simplify the presentation we use a pitch-less notation with notes and rests denoted resp. by N , R. Rests with 2 flags are positioned in the tree like notes with 2 flags, while notes beamed together are represented by a different tree structure.

0 N 4 N 4 N 4 1

 0441

Figure 4 :

 4 Figure 4: Beaming and tuplet tree for nested triplets (in pitch-less representation). In the first bar, the sequence of the first three notes is labeled by 3 (with a bracket), the sequence of the last three notes is also labeled by 3 (no bracket needed because these three notes are beamed, according to the beaming tree), and the two middle notes do not have a tuplet label.

Figure 5 :

 5 Figure 5: LCS and difference blocks.

Figure 6 :

 6 Figure 6: Visualization of differences between two bars computed by the algorithms of Section 3. The upper example (1) is taken from Figure 4 and manually modified to create some differences, while the lower example is a real-life example from an incorrect OMR score import, compared to the correct score.

https://www.neuratron.com/photoscore.htm

see our supplementary material page at https://anonymoususer12042019.github.io/ for details.


Nicolas Jonckheere
email: nicolas.jonckheere@inserm.fr

Isabelle Van Seuningen
email: isabelle.vanseuningen@inserm.fr

Integrative analysis of The Cancer Genome Atlas and Cancer Cell Lines Encyclopedia large-scale genomic databases: MUC4/MUC16/MUC20 signature is associated with poor survival in human carcinomas

Keywords: MUC4, TCGA, CCLE, patient survival, biomarker

Background: MUC4 is a membrane-bound mucin that promotes carcinogenetic progression and is often proposed as a promising biomarker for various carcinomas. In this manuscript, we analyzed large scale genomic datasets in order to evaluate MUC4 expression, identify genes that are correlated with MUC4 and propose new signatures as a prognostic marker of epithelial cancers.

Methods Using cBioportal or SurvExpress tools, we studied MUC4 expression in large-scale genomic public datasets of human cancer (The cancer genome atlas, TCGA) and Cancer Cell Line Encyclopedia (CCLE).

Results:

We identified 187 co-expressed genes for which the expression is correlated with MUC4 expression. Gene ontology analysis showed they are notably involved in cell adhesion, cell-cell junctions, glycosylation and cell signaling. In addition, we showed that MUC4 expression is correlated with MUC16 and MUC20, two other membrane-bound mucins. We showed that MUC4 expression is associated with a poorer overall survival in TCGA cancers with different localizations including pancreatic cancer, bladder cancer, colon cancer, lung adenocarcinoma, lung squamous adenocarcinoma, skin cancer and stomach cancer. We showed that the combination of MUC4, MUC16 and MUC20 signature is associated with statistically significant reduced overall survival and increased hazard ratio in pancreatic, colon and stomach cancer.

Conclusions: Altogether, this study provides the link between (i) MUC4 expression and clinical outcome in cancer and (ii) MUC4 expression and correlated genes involved in cell adhesion, cell-cell junctions, glycosylation and cell signaling. We propose the MUC4/MUC16/MUC20 high signature as a marker of poor prognostic for pancreatic, colon and stomach cancers.

Background

The cancer genome atlas (TCGA) was developed by National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI) in order to provide comprehensive mapping of the key genomic changes that occur during carcinogenesis. Datasets of more than 11,000 patients of 33 different types of tumors are publically available. In parallel, Cancer Cell Line Encyclopedia (CCLE), a large-scale genomic dataset of human cancer cell lines, was generated by the Broad Institute and Novartis in order to reflect the genomic diversity of human cancers and provide complete preclinical datasets for mutation, copy number variation and mRNA expression studies [START_REF] Barretina | The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity[END_REF]. In order to analyse this kind of large scale datasets, several useful online tools have been created. cBioportal is an open-access database analysis tool developed at the Memorial Sloan-Kettering Cancer Centre (MSKCC) to analyze large-scale cancer genomics data sets [START_REF] Cerami | The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data[END_REF][START_REF] Gao | Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal[END_REF]. SurvExpress is another online tool for biomarker validation using 225 datasets available and therefore provide key information linking gene expression and the impact on cancer outcome [START_REF] Aguirre-Gamboa | SurvExpress: an online biomarker validation tool and database for cancer gene expression data using survival analysis[END_REF].

Mucins are large high molecular weight glycoproteins that are classified in two sub groups: (i) the secreted mucins that are responsible of rheologic properties of mucus and (ii) the membrane-bound mucins that include MUC4, MUC16 and MUC20 [START_REF] Corfield | Mucins: a biologically relevant glycan barrier in mucosal protection[END_REF][START_REF] Dekker | The MUC family: an obituary[END_REF]. MUC4 was first discovered in our laboratory 25 years ago from a tracheobronchial cDNA library [START_REF] Porchet | Molecular cloning and chromosomal localization of a novel human tracheo-bronchial mucin cDNA containing tandemly repeated sequences of 48 base pairs[END_REF]. MUC4 is characterized by a long hyper-glycosylated extracellular domain, Epidermal Growth Factor (EGF)-like domains, a hydrophobic transmembrane domain, and a short cytoplasmic tail. MUC4 also contains NIDO, AMOP and vWF-D domains [START_REF] Jonckheere | Membrane-bound mucin modular domains: from structure to function[END_REF]. A direct interaction between MUC4 and its membrane partner, the oncogenic receptor ErbB2, alters downstream signaling pathways [START_REF] Jonckheere | The mucin MUC4 and its membrane partner ErbB2 regulate biological properties of human CAPAN-2 pancreatic cancer cells via different signalling pathways[END_REF]. MUC4 is expressed at the surface of epithelial cells from gastrointestinal and respiratory tracts [START_REF] Jonckheere | Mucins and pancreatic cancer[END_REF] and has been studied in various cancers where it is generally overexpressed and described as an oncomucin and has been proposed as an attractive prognostic tumor biomarker. Its biological role has been mainly evaluated in pancreatic, ovarian, esophagus and lung cancers [START_REF] Jonckheere | The mucin MUC4 and its membrane partner ErbB2 regulate biological properties of human CAPAN-2 pancreatic cancer cells via different signalling pathways[END_REF][START_REF] Bruyere | The MUC4 membranebound mucin regulates esophageal cancer cell proliferation and migration properties: Implication for S100A4 protein[END_REF][START_REF] Skrypek | The MUC4 mucin mediates gemcitabine resistance of human pancreatic cancer cells via the Concentrative Nucleoside Transporter family[END_REF][START_REF] Bafna | Pancreatic cancer cells resistance to gemcitabine: the role of MUC4 mucin[END_REF][START_REF] Kaur | Mucins in pancreatic cancer and its microenvironment[END_REF]. Other membrane-bound mucins MUC16 and MUC20 share some functional features but evolved from distinct ancestors [START_REF] Duraisamy | Distinct evolution of the human carcinomaassociated transmembrane mucins, MUC1, MUC4 AND MUC16[END_REF]. MUC20 gene is located on the chromosomic region 3q29 close to MUC4. MUC16 also known as the CA125 antigen is a routinely used serum marker for the diagnosis of ovarian cancer [START_REF] Bafna | Membrane-bound mucins: the mechanistic basis for alterations in the growth and survival of cancer cells[END_REF]. Both mucins favor tumor aggressiveness and are associated with poor overall survival and could be proposed as prognosis factors [START_REF] Bafna | Membrane-bound mucins: the mechanistic basis for alterations in the growth and survival of cancer cells[END_REF][START_REF] Chen | MUC20 overexpression predicts poor prognosis and enhances EGF-induced malignant phenotypes via activation of the EGFR-STAT3 pathway in endometrial cancer[END_REF][START_REF] Xiao | Role of MUC20 overexpression as a predictor of recurrence and poor outcome in colorectal cancer[END_REF].

In this manuscript, we have used the online tools cBioportal, DAVID6.8 and SurvExpress in order to (i) evaluate MUC4 expression in various carcinomas, (ii) identify genes that are correlated with MUC4 and evaluate their roles and (iii) propose MUC4/MUC16/MUC20 combination as a prognostic marker of pancreatic, colon and stomach cancers.

Material and methods

Expression analysis from public datasets

MUC4 z-score expressions were extracted from databases available at cBioPortal for Cancer Genomics [START_REF] Cerami | The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data[END_REF][START_REF] Gao | Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal[END_REF]. This portal stores expression data and clinical attributes. The z-score for MUC4 mRNA expression is determined for each sample by comparing mRNA expression to the distribution in a reference population harboring typical expression for the gene. The query "MUC4" was realized in CCLE (881 samples, Broad Institute, Novartis Institutes for Biomedical Research) [START_REF] Barretina | The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity[END_REF] and in all TCGA datasets available (13 489 human samples, TCGA Research Network (http://cancergenome.nih.gov/)). The mRNA expression from selected data was plotted in relation to the clinical attribute (tumor type and histology) in each sample.

MUC4 expression was analyzed in normal tissues by using the Genome Tissue Expression (GTEX) tool [START_REF] Lonsdale | The Genotype-Tissue Expression (GTEx) project[END_REF][START_REF] Ardlie | Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans[END_REF]. Data were extracted from GTEX portal on 06/29/17 (dbGaP accession phs000424.v6.p1) using the 4585 Entrez gene ID.

DAVID6.8 identification and gene ontology of genes correlated with MUC4

We established a list of 187 genes that are correlated with MUC4 expression in CCLE dataset out of 16208 genes analyzed with cBioportal tool on co-expression tab. These genes harbor a correlation with both Pearson's and Spearman's higher than 0.3 or lower than -0.3.

Functional annotation and ontology clustering of the complete list of genes were performed using David Functional Annotation Tool (https://david.ncifcrf.gov/) and Homo sapiens background [START_REF] Huang Da | Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources[END_REF][START_REF] Huang Da | Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists[END_REF]. Enrichment scores of ontology clusters are provided by the online tool.

Interaction of proteins correlated with MUC4 was determined using String 10 tool (https://string-db.org/) [START_REF] Szklarczyk | The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible[END_REF]. Edges represent protein-protein associations such as known interactions (from curated databases or experimentally determined), predicted interactions (from gene neighborhood, gene fusion or co-occurrence), text-mining, co-expression or protein homology. The network was divided in 3 clusters based on k-means clustering.

Methylation and copy number analysis

Using https://portals.broadinstitute.org/ccle, we extracted mRNA expression of MUC4, methylation score (Reduced Representation Bisulfite Sequencing: RRBS) and copy number variations of the genes of interest. The mRNA expression of MUC4 was plotted in relation to log2 copy number or RRBS score.

SurvExpress survival analysis

Survival analysis was performed using the SurvExpress online tool available in bioinformatica.mty.itesm.mx/SurvExpress (Aguire Gamboa PLos One 2013). We used the optimized algorithm that generates risk group by sorting prognostic index (higher value of MUC4 for higher risk) and split the two cohorts where the p-value is minimal. Hazard ratio [95% confidence interval (CI)] was also evaluated. The tool also provided a box plot of genes expression and the corresponding p value testing the differences.

Gene Expression Omnibus microarray

GSE28735 and GSE16515 pancreatic cancer microarrays were analysed from the NCBI Gene Expression Omnibus (GEO) database (http://www.ncbi.nml.nih.gov/geo/). GSE28735 is a dataset containing 45 normal pancreas (adjacent non tumoral, ANT) and 45 tumor (T) tissues from PDAC cases. GSE16515 contains 52 samples (16 had both tumor and normal expression data, and 20 only had tumor data. Data were analysed using GEO2R software.

The dataset GSE28735 used Affymetrix GeneChip Human Gene 1.0 ST array. The dataset GSE16515 used the Affymetrix Human Genome U133 Plus 2.0 Array. GSE13507 contains 165 bladder cancer and 58 ANT samples. GSE30219 contains 14 normal lung, 85 adenocarcinomas and 61 squamous cancer samples. GSE40967 contains 566 colorectal cancers and 19 normal mucosae. GSE27342 contains 80 tumors and 80 paired ANT tissues. GSE4587 contains 2 normal, 2 melanomas and 2 metastatic melanomas. GSE14407 contains 12 ovarian adenocarcinomas and 12 normal ovary samples.

Statistical analysis

For MUC4 expression analysis, paired and unpaired t test statistical analyses were performed using the Graphpad Prism 6.0 software (Graphpad softwares Inc., La Jolla, CA, USA). P<0.05 was considered as statistically significant. Receiving operator characteristic

Results

MUC4 expression analysis in databases

MUC4 expression was analyzed from databases available at cBioPortal for Cancer Genomics [START_REF] Cerami | The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data[END_REF][START_REF] Gao | Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal[END_REF]. We queried for MUC4 mRNA expression in the 881 samples from CCLE [START_REF] Barretina | The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity[END_REF] (Figure 1). The oncoprint showed that MUC4 was altered in 195 samples out of 881 (22%).

188 were amplification (n=120) or mRNA upregulation (n=88) (Supplemental Figure 1).

Results were sorted depending on the tumor type. We mainly observed an important z-score expression of MUC4 in carcinoma samples (n= 538 samples, p=0.001) (Figure 2A). MUC4 Expression scores were subsequently sorted depending of the organ (Figure 2B). As expected, pancreatic cancer cell lines harbor the highest MUC4 expression (n=35, z-score=2.166, p=0,0006 against theoretical control median=0). Other cell lines from different tissues (lung NSC, esophagus, bile duct, stomach, upperdigestive, colorectal, ovary, and urinary tract) showed statistically significant alteration. We also performed a similar analysis on 13 489 human samples retrieved from TCGA by using the cBioportal platform. An important MUC4 expression z-score was observed in bladder urothelial carcinoma, cervical squamous cell carcinoma/endocervical adenocarcinoma, colorectal carcinoma, esophageal carcinoma, head and neck squamous cell carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, ovarian serous cystadenocarcinoma, pancreatic adenocarcinoma, prostate adenocarcinoma, stomach adenocarcinoma and uterine corpus endometrial carcinoma (Figure 3). Expression of MUC4 in normal tissues was analyzed using the GTEX project tool, MUC4 was expressed in lung, testis, small intestine, terminal, ileum, prostate, vagina, minor salivary gland and esophagus mucosa and transverse colon (supplemental figure2).

Altogether, this shows that MUC4 high expression is observed in carcinoma and notably in pancreatic cancer.

MUC4 co-regulated genes

Using the co-expression tool on expression data extracted from the 881 samples of CCLE [START_REF] Barretina | The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity[END_REF], we obtained a list of genes that are co-expressed with MUC4. Genes that harbor a correlation with both Pearson's and Spearman's higher than 0.3 or lower than -0.3 were selected. 187 genes are positively (n=178) or negatively (n=9) correlated with MUC4 expression. The better correlated genes were Adhesion G Protein-Coupled Receptor F1 (ADGRF1, Pearson's correlation=0.56) and Lipocalin2 (LCN2, Pearson's correlation=0.54) (table 1). We also observed that expression of other membrane bound mucins MUC16 and MUC20 are positively correlated with MUC4. Correlation between MUC16 and MUC20 was also observed (not shown). Only few genes were negatively correlated such as ZEB1 transcription factor or ST3 Beta-Galactoside Alpha-2,3-Sialyltransferase 2 (ST3GAL2) (table 2).

Functional Annotation of the complete list of genes and ontology clustering were performed using David Functional Annotation Tool. The gene clustering analysis is presented in table 3.

The complete gene ontologies that are statistically significant are provided in supplemental table 1. We observed the highest enrichment scores in gene clusters involved in cell adhesion (7.08) and tight junction (5.44) (table 3). Notably, we observed the correlation of expression of MUC4 with genes encoding integrins (ITGB4 and ITGB6) and cadherin-type proteins such as CDH1, CDH3, Desmocollin 2 (DSC2 Additionally, we analyzed protein-protein interactions of differentially expressed proteins with MUC4 with the String 10 tool. We showed that MUC4 is directly related with CEACAM5, CEACAM6, MUC16, MUC20 and glycosylation enzymes (ST3GAL2, B3GNT3, B3GNT5 and GALNT3) (Supplemental Figure 3). Altogether, we have identified genes with expression correlated with MUC4 involved notably in cell adhesion, cell-cell junctions, glycosylation and cell signaling. In order to understand the association between the observed aberrant expression of MUC4 and other molecular events, we explored the correlation between MUC4 expression in CCLE and DNA methylation (RRBS) of the top genes correlated with MUC4.

We observed that MUC4 expression is negatively correlated with the methylation score of 16 out of 20 of the top genes (LCN2, MUC20, STEAP4, WFDC2, GJB3, SH2D3A, RNF39, PRSS22, HS3ST1, GPR87, TACST2, FAM83A, LAMC2, B3GNT3, CLDN7) (Figure 4)

suggesting that the association of MUC4 and the correlated genes could be mediated by methylation regulation. Only ADGRF1 RBBS is not correlated with MUC4 mRNA level.

MUC16, SCEL and C1ORF116 scores were not available. Additionally we also evaluated the copy number variation association of the top genes with MUC4 expression. We only observed a weak amplification of MUC20 copy number (Pearson's correlation = 0.13) and a weak deletion of MUC16 copy number (Pearson's correlation = -0.14) suggesting that the relationship between MUC4 expression and copy number variation of top genes is unlikely (Supplemental Figure 4).

MUC4 and patient survival

To establish a correlation between MUC4 expression and patient survival, we have compared survival analysis and hazard ratio in population designated as MUC4 high risk and low risk in every organ from TCGA datasets (table 4). We have used SurvExpress optimized algorithm that generates risk group by sorting prognostic index (higher value of MUC4 for higher risk). The algorithm splits the population where the p-value testing the difference of MUC4 expression is minimal [START_REF] Aguirre-Gamboa | SurvExpress: an online biomarker validation tool and database for cancer gene expression data using survival analysis[END_REF]. Pancreatic cancer presented the most important hazard ratio for MUC4 (HR= 3.94 [CI, 1.81-8.61] p=0.0005756) (Figure 5A). MUC4 high risk was also significantly associated with survival in bladder cancer (HR= 1.48), colon cancer (HR= 2.1), lung adenocarcinoma (HR= 1.7), lung squamous carcinoma (HR= 1.69), ovarian cancer (HR= 1.33), skin cancer (HR= 1.87) and stomach cancer (HR= 1.58) (Figure 5A). Acute myeloid leukemia (HR= 1.59) and liver cancer (HR= 1.4) almost reach statistical significance.

Other datasets did not show any statistically significant differences.

A significant reduction in patient's survival was observed in bladder cancer (p=0.01135), colon cancer (p=.00891), lung adenocarcinoma (p=0.008187), lung squamous carcinoma (p=0.03586), ovarian cancer (p=0.0186), pancreatic cancer (p=0.000219), skin cancer (p=0.02384) and stomach cancer (p=0.04751) as illustrated in Kaplan Meier curves (Figure 5B). Strikingly, pancreatic median survival was 593 days in MUC4 high cohort (n=149) whereas the 50% survival was not reached in MUC4 low cohort (n=27). In lung squamous carcinoma, the median survival of MUC4 high cohort (n=116) was 1067 days whereas MUC4 low cohort (n=59) presented a 2170 days median survival. It is interesting to note that the algorithm splits the population in two parts that were characterized as the most different regarding MUC4 expression. Therefore, there are a modest number of MUC4 low PDAC or lung adenocarcinoma patients and a low number of MUC4 high colon or stomach cancer patients. A similar survival analysis was performed on pancreatic cancer by dividing the patient population in two equal parts (88 vs 88), MUC4 high harbored a decreased survival that was close to statistical significance (p=0.06784) (not shown). Therefore, MUC4 expression is associated with a poorer overall survival in different cancers including pancreatic cancer.

We also compared the survival and hazard ratio, in the same cancers whose survival is associated with MUC4 (bladder cancer, colon cancer, lung adenocarcinoma, lung squamous carcinoma, ovarian cancer, pancreatic cancer, skin cancer and stomach cancer), according to gene signatures corresponding to the five first gene ontology term from supplemental table

1 (GO 0031424: keratinization, GO 0007155: cell adhesion, GO 0019897: extrinsic component of plasma membrane, GO 0016323 : basolateral plasma membrane and GO 0016324: apical plasma membrane) (Figure 6A, supplemental table 2). These gene signatures were all significantly associated with survival in the TCGA dataset tested. The "keratinization" (GO 0031424) and "cell adhesion" (GO 0007155) signature are associated with HR comprised between 1.65 and 3.76 and between 2.15 and 3.23, respectively. The GO 0019897 signature is associated with weaker HR (1.55-2.30). "basolateral" (GO 0016323)

and "apical plasma membrane" (GO 0016324) signatures harbors more increased HR (2.21-4.5 and 1.77-4.42, respectively) in these datasets.

We performed a similar analysis according to the top genes (ADGRF1, LCN2, MUC20, C1ORF116, SCEL, STEAP4) that harbored Pearson's correlation with MUC4 superior to 0.5 (Figure 6B, supplemental table 3). This signature is associated with survival in all TCGA dataset tested (HR comprised between 1.91 and 8.77). Notably, pancreatic cancer harbored the strongest association with survival according to this signature (HR=8.77 [CI, 2.15-35.83]).

Overall, these bigger signatures harbored higher hazard ratio compared to MUC4 alone.

MUC4, MUC16 and MUC20 signature in cancer

Mucins have been proposed as potential biomarkers for carcinoma. Notably, previous work suggested that combination of mucins expression may be useful for early detection and evaluation of malignancy of pancreatobiliary neoplasms [START_REF] Yonezawa | Mucins in human neoplasms: clinical pathology, gene expression and diagnostic application[END_REF]. Moreover, MUC16/CA125

antigen is an already routinely used serum marker for the diagnosis of ovarian cancer [START_REF] Bafna | Membrane-bound mucins: the mechanistic basis for alterations in the growth and survival of cancer cells[END_REF].

Therefore, we decided to intentionally focus on the two other membrane bound mucins MUC16 and MUC20 that were correlated with expression of MUC4. We analyzed the survival curves of the high risk group (MUC4/MUC16/MUC20 high , n= 159) and low risk group (MUC4/MUC16/MUC20 low , n=17) from the pancreas TCGA dataset. The MUC4/MUC16/MUC20 high risk group was associated with an increased hazard ratio (HR=6.5

[2.04-20.78], p=0.001582) and a shorter overall survival (p=0.0003088) (Figure 7A). Median survival was similar as in MUC4 high cohort (593 days). The MUC4/MUC16/MUC20 high group harbored a statistically significant increase of MUC4, MUC16 and MUC20 expression (Figure 7B). We also analyzed overall survival in every other PDAC database available in Surexpress. We show that MUC4 high group was associated with a statistically significant reduced overall survival and increased hazard ratio in both ICGC and Stratford (GSE21501) cohorts (Figure 7C). In Zhang cohort (GSE28735), MUC4 high group was associated with a reduced overall survival that was close to statistical significance (p=0.08971). In other organs, the MUC4/MUC16/MUC20 high group was associated with an increased hazard ratio and reduced overall survival in bladder cancer, colon cancer, lung adenocarcinoma, lung squamous adenocarcinoma, skin cancer, stomach cancer (supplemental figure 5A). Notably, the MUC4/MUC16/MUC20 high group in colon cancer (HR=2.26 [1.51-3.4]) showed a median survival of 1741 days whereas the low risk group did not reach the 50% survival. Similarly, the MUC4/MUC16/MUC20 high group in stomach cancer showed a median survival of 762 days whereas the low risk had a median survival of 1811 days. No significant difference was observed for ovarian cancer (p=0.2081). Moreover, a reduced overall survival was observed in liver cancer (p=0.04789) and acute myeloid leukemia (AML) (p=0.02577) (supplemental Figure 5B) in which we did not show any statistical difference when sorting the patients for MUC4 alone. Overall, we observed that MUC4/MUC16/MUC20 signature harbored an increased hazard ratio compared with MUC4 alone for pancreatic cancer and to a lower extent in bladder cancer, colon cancer, lung squamous cancer and stomach cancer.

We analyzed MUC4, MUC16 and MUC20 expression in pancreatic tumor (T) and paired adjacent non tumoral tissues (ANT) from GSE28735 (Figure 6) and GSE16515 (not shown) datasets [START_REF] Pei | FKBP51 affects cancer cell response to chemotherapy by negatively regulating Akt[END_REF][START_REF] Zhang | DPEP1 inhibits tumor cell invasiveness, enhances chemosensitivity and predicts clinical outcome in pancreatic ductal adenocarcinoma[END_REF]. We confirmed MUC4 overexpression in tumor tissues (p<0.0001). MUC16

and MUC20 mRNA level were also increased (p<0.0001 and p=0.0062) in tumor samples (Figure 8A). As previously observed in CCLE dataset, MUC4 expression was correlated with MUC16 (p=0.0006) and MUC20 (p=0.0621) in GSE28735 (Supplemental Figure 6). We also analyzed MUC4, MUC16 and MUC20 expression in datasets of other cancers (supplemental Figure 7). MUC4 expression is increased in bladder cancer vs ANT (GSE13507, p<0.01).

MUC20 is increased in lung adenocarcinoma vs normal samples (GSE30219, p<0.05).

MUC4 and MUC20 expression is increased in colorectal cancer vs normal mucosae (GSE40967, p<0.01). MUC16 and MUC20 relative expression is increased in ovarian adenocarcinoma (GSE14407, p<0.01 and p<0.05 respectively). ROC curves of MUC4, MUC16, MUC20 and MUC4+MUC16+MUC20 combination were established using GSE28735 dataset. The combination of MUC4+MUC16+MUC20 produced a high specificity of 97.78% (88.23-99.94) and a mild sensitivity of 55.56% (40-70.36) (likelihood ratio = 25) (Figure 8B). Similar results were obtained for GSE16515 with 93.75% specificity and 69.44% sensitivity (LR+=11.11) (not shown). MUC16 AUROC was similar to that of MUC4+MUC16+MUC20 in GSE28735 dataset but harbors a lower specificity/sensitivity in GSE16515.

Altogether, this suggests that MUC4/MUC16/MUC20 high signature would be useful in stratification of patients with worst prognosis in several carcinoma and notably pancreatic, stomach and colon cancers.

Discussion

The TCGA and the CCLE have provided a tremendous amount of publicly available data combining gene expression information related to clinical outcome. Web-based tools allow the scientific community to perform powerful large scale genomic analysis and propose new biomarkers or new therapeutic targets. In the present report, we analyzed MUC4 expression systematically in all organs and confirmed its aberrant expression in associated carcinoma.

We identified 187 genes for which the expression is correlated with MUC4 expression. These genes are involved in cell adhesion, cell-cell junctions, glycosylation and cell signaling.

MUC4 was also correlated with MUC16 and MUC20 membrane bound mucins. This combination is associated with a poorer overall survival in different cancers including pancreatic, colon and stomach cancers suggesting MUC4/MUC16/MUC20 as a poor prognostic signature for these cancers.

Previous works have showed that MUC4 is altered in normal, premalignant and malignant epithelia of the digestive tract [START_REF] Jonckheere | The membrane-bound mucins: From cell signalling to transcriptional regulation and expression in epithelial cancers[END_REF]. The mechanisms underlying this alteration of expression are diverse and involve regulators such as growth factors, cytokines, demethylation of promoters and miRNA [START_REF] Andrianifahanana | IFNgamma-induced expression of MUC4 in pancreatic cancer cells is mediated by STAT-1 upregulation: a novel mechanism for IFN-gamma response[END_REF][START_REF] Jonckheere | A role for human MUC4 mucin gene, the ErbB2 ligand, as a target of TGF-beta in pancreatic carcinogenesis[END_REF][START_REF] Vincent | Epigenetic regulation of the human mucin gene MUC4 in epithelial cancer cell lines involves both DNA methylation and histone modifications mediated by DNA methyltransferases and histone deacetylases[END_REF][START_REF] Yamada | Promoter CpG methylation in cancer cells contributes to the regulation of MUC4[END_REF][START_REF] Lahdaoui | miR-219-1-3p is a negative regulator of the mucin MUC4 expression and is a tumor suppressor in pancreatic cancer[END_REF]. In the present manuscript we also observe that MUC4 gene is amplified in 13% of cancer cell lines. We also found a mild correlation between alteration of MUC4 copy number and MUC4 expression suggesting that gene amplification could also mediate this MUC4 aberrant expression. This kind of regulation is scarcely described in the literature. In TCGA, We confirmed that MUC4 expression was observed mainly in human carcinomas including bladder, cervix, head and neck, lung, ovarian, pancreatic, prostate, stomach carcinomas. For most of these organs, MUC4 high expression was associated with a poorer overall survival. MUC4 is one of the most differentially expressed genes in pancreatic cancer that are thought to be potential clinical targets [START_REF] Iacobuzio-Donahue | Highly expressed genes in pancreatic ductal adenocarcinomas: a comprehensive characterization and comparison of the transcription profiles obtained from three major technologies[END_REF]. Recently, a metaanalysis based on 1900 patients from 18 studies showed that MUC4 overexpression was associated with tumor stage, tumor invasion and lymph node metastasis [START_REF] Huang | Clinicopathological and prognostic significance of MUC4 expression in cancers: evidence from meta-analysis[END_REF]. A worse overall survival was observed in MUC4-overexpressing patients with biliary tract carcinoma (HR 2.41), pancreatic cancer (HR 2.01), and colorectal cancer (HR 1.73). Using the TCGA cohorts, we extended this finding on lung adenocarcinoma, lung squamous carcinoma, ovarian cancer, skin cancer and stomach cancer. The authors noted that a limit of this metaanalysis was insufficient statistical power of some eligible studies. The large scale genomic approach of TCGA helps us to overcome this limitation. Based on available TCGA datasets, mucin mutation map was generated by cBioPortal Mutation Mapper [START_REF] King | Genomic alterations in mucins across cancers[END_REF]. MUC4 mutations were notably observed in Kidney Clear Cell Renal Carcinoma (20-45%) and were correlated with survival outcomes. Rare mutations were described in the main overexpressing model that is pancreatic cancer. Because of the very large size of MUC4 gene, probability of acquiring mutation could be increased. MUC4 belongs to the most mutated genes upon stress exposure such as nicotine treatment or aging [START_REF] Bavarva | Nicotine and oxidative stress induced exomic variations are concordant and overrepresented in cancer-associated genes[END_REF][START_REF] Bavarva | The dynamic exome: acquired variants as individuals age[END_REF]. The enrichment of mutation of MUC4 could be related with the fact that the first risk factor of kidney cancer is smoking [START_REF] Hunt | Renal cel l carcinoma in relation to cigarette smoking: meta-analysis of 24 studies[END_REF] and that kidney cancer diagnosis is occurring at elder ages (65 years) [START_REF] Hayat | Cancer statistics, trends, and multiple primary cancer analyses from the Surveillance, Epidemiology, and End Results (SEER) Program[END_REF]. Pancreatic cancer shares these characteristics but harbors a very rare mutation occurrence (3%)

suggesting that aging could be specific of cancers such as kidney or lung and that overexpression is more important for other cancers. So far, functional consequences of MUC4 mutation remain to be elucidated.

We and others have investigated MUC4 biological roles in various cancers such as pancreatic, ovarian, esophagus and lung cancers. MUC4 was shown to promote aggressiveness of tumors as it induces proliferation, migration, invasion, EMT, cell stemness and chemoresistance [START_REF] Jonckheere | The mucin MUC4 and its membrane partner ErbB2 regulate biological properties of human CAPAN-2 pancreatic cancer cells via different signalling pathways[END_REF][START_REF] Bruyere | The MUC4 membranebound mucin regulates esophageal cancer cell proliferation and migration properties: Implication for S100A4 protein[END_REF][START_REF] Skrypek | The MUC4 mucin mediates gemcitabine resistance of human pancreatic cancer cells via the Concentrative Nucleoside Transporter family[END_REF][START_REF] Bafna | Pancreatic cancer cells resistance to gemcitabine: the role of MUC4 mucin[END_REF][START_REF] Kaur | Mucins in pancreatic cancer and its microenvironment[END_REF]. In the present work, we showed that MUC4 expression was correlated with genes, such as integrins cadherin-type proteins, involved in cell adhesion and cell-cell junctions. As a membrane-bound mucin, MUC4 is thought to act on cell-cell and cell-MEC interaction. Because of its huge extracellular domain that profoundly modifies steric hindrance, MUC4 may alter migration, invasion and adherence properties [START_REF] Hollingsworth | Mucins in cancer: protection and control of the cell surface[END_REF]. Rat homologue of MUC4, sialomucin complex (SMC), overexpression leads to suppression of cell adhesion [START_REF] Komatsu | Potentiation of metastasis by cell surface sialomucin complex (rat MUC4), a multifunctional anti -adhesive glycoprotein[END_REF]. Notably, MUC4 overexpression disrupts the adherens junctions and leads to partial delocalization of E-cadherin to the apical surface of the cell causing loss of cell polarity [START_REF] Pino | Membrane mucin Muc4 induces density-dependent changes in ERK activation in mammary epithelial and tumor cells: role in reversal of contact inhibition[END_REF]. Moreover, interactions between MUC4 glycans and galectin-3 were shown to also mediate docking of circulating tumor cells to the surface of endothelial cells [START_REF] Senapati | Novel INTeraction of MUC4 and galectin: potential pathobiological implications for metastasis in lethal pancreatic cancer[END_REF]. The alteration of cell adhesion induced by MUC4 is one of the first steps toward the metastatic process. MUC4 expression was also correlated with several genes encoding glycosylation enzymes or glycoproteins. This essential set of genes is involved in a wide set of cellular function including cell adhesion, barrier role, interaction with selection of endothelial cells or regulation of cell signaling [START_REF] Corfield | Mucins: a biologically relevant glycan barrier in mucosal protection[END_REF][START_REF] Pinho | Glycosylation in cancer: mechanisms and clinical implications[END_REF]. The glycan-associated antigens are commonly associated with patient survival of gastrointestinal cancer [START_REF] Baldus | Biochemistry and pathological importance of mucin-associated antigens in gastrointestinal neoplasia[END_REF]. Alteration of MUC4 glycosylation is proposed to play a substantial role in binding properties mediated by the extracellular subunit of MUC4 and the NIDO domain [START_REF] Hanson | Functional Consequences of Differential O-glycosylation of MUC1, MUC4, and MUC16 (Downstream Effects on Signaling)[END_REF]. One should note that the expression of these genes is correlated with MUC4. However, a direct regulatory mechanism remains to be demonstrated in future studies.

In order to regulate these major biological properties, MUC4 has been commonly associated with cell signaling alteration and notably MAPK, NF-kB, or FAK signaling pathways.

Interestingly, we observed that MUC4 expression is highly correlated with proteins containing Src Homology 2 (SH2) domain or Src Homology 3 (SH3) domains. Intracellular adaptor signaling proteins family is characterized by one SH2 and at least one SH3 domain and is crucial for effective integrating of intracellular and extracellular stimuli [START_REF] Reebye | A perspective on non-catalytic Src homology (SH) adaptor signalling proteins[END_REF].

It is interesting to note that MUC4 expression is not correlated with MUC1 that is a major membrane-bound mucin commonly overexpressed in cancer [START_REF] Kufe | Functional targeting of the MUC1 oncogene in human cancers[END_REF][START_REF] Kufe | Mucins in cancer: function, prognosis and therapy[END_REF]. In the US, it was estimated that 900 000 cancers, out of 1 400 000, harbor overexpression of MUC1 highlighting its attractiveness as a therapeutic target. This could be explained by different regulatory mechanisms such as different signaling pathways or different miRNA regulating the two mucins.

MUC16 is the peptide part to the CA125 serum marker for ovarian cancer [START_REF] Yin | Molecular cloning of the CA125 ovarian cancer antigen: identification as a new mucin, MUC16[END_REF]. MUC16 is a very large mucin (22 000 amino acid (aa)) that is heavily glycosylated and facilitates ovarian cancer. MUC20 is a small mucin (500 aa) mostly expressed in renal proximal tube and that is deregulated in several cancers such as colorectal or ovarian cancers where it favors aggressiveness [START_REF] Chen | MUC20 overexpression predicts poor prognosis and enhances EGF-induced malignant phenotypes via activation of the EGFR-STAT3 pathway in endometrial cancer[END_REF][START_REF] Xiao | Role of MUC20 overexpression as a predictor of recurrence and poor outcome in colorectal cancer[END_REF]. MUC16/CA125 is routinely used in clinics unlike MUC4 and MUC20. In the present manuscript, we showed that expression of MUC16 and MUC20 are positively correlated with MUC4 and that the MUC4/MUC16/MUC20 high combinatory expression is associated with an increased hazard ratio and reduced overall survival suggesting a potential for this signature as a prognostic marker for several carcinomas and notably pancreatic, stomach and colon cancer. Biomarkers for pancreatic cancer are needed for detection and evaluation of response to therapy [START_REF] Kleeff | Pancreatic cancer[END_REF]. Unfortunately, the marker currently used (CA19.9) lacks sensitivity or specificity to be used in cancer diagnosis. Similarly established biomarker with adequate sensitivity and specificity are lacking for gastric cancer [START_REF] Ajani | Gastric adenocarcinoma[END_REF]. The need of biomarkers is less urgent for colorectal cancer since several predictive/prognostic/diagnostic biomarkers have been described [START_REF] Kuipers | Colorectal cancer[END_REF].

The present work highlights the relationship between MUC4/MUC16/MUC20 expression and overall survival. This signature could be proposed as a prognostic marker. Moreover, MUC4

is expressed in the earliest stage (PanIN1A) of pancreatic cancer but is not specific enough.

The potential of the combination MUC4/MUC16/MUC20 as a diagnosis marker is not known and remains to be investigated in the future. Moreover, development of unsupervised algorithm will allow the identification of new non intentional bigger signatures leading to better prognostic and predictive performances. Genome wide computational unsupervised procedure from discovery dataset will help to determine hypothesis signature. The signature will be subsequently validated on a number of independents datasets. Thus, multi-platform analysis using TCGA datasets helped to characterize the complex molecular landscape of PDAC [START_REF] Tcga-Network | Integrated Genomic Characterization of Pancreatic Ductal Adenocarcinoma[END_REF]. Another meta-analysis approach based on PC datasets allowed the identification of a 5 genes classifier signature (TMPRSS4, AHNAK2, POSTN, ECT2, SERPINB5) with 95% sensitivity and 89% specificity in discriminating PDAC from non-tumor samples [START_REF] Bhasin | Meta-analysis of transcriptome data identifies a novel 5-gene pancreatic adenocarcinoma classifier[END_REF].

Interestingly, TMPRSS4 and SERPINB5 are two genes belonging to the gene list correlated with MUC4 expression.

Conclusion

We analyzed MUC4 expression systematically in all organs in TCGA and CCLE large scale databases and confirmed its aberrant expression in associated carcinoma and the MUC4 impact on patient's survival. Moreover, 187 genes (involved in cell adhesion, cell-cell junctions, glycosylation and cell signaling) were correlated with MUC4. Among them, MUC16

and MUC20 membrane bound mucins and their combination MUC4/MUC16/MUC20 is associated with a poorer overall survival in different cancers including pancreatic, colon and stomach cancers suggesting MUC4/MUC16/MUC20 as a poor prognostic signature for these cancers. This potential as new biomarkers remains to be investigated in the future.

Declarations section:

Ethics Authors' Contributions NJ conceived and designed the analysis. NJ analyzed the data. NJ and IVS wrote and edited the paper.

  Abbreviations:

(

  ROC) curves and areas under ROC (AUROC) were evaluated by comparing tumor and ANT values. cBioportal provided Pearson and Spearman tests were performed to analyze correlation of other genes, RRBS score and log2 copy number with MUC4 expression. DAVID tool provided p value of each ontology enrichment score. SurvExpress tool provided statistical analysis of hazard ratio and overall survival. A Log rank testing evaluated the equality of survival curves between the high and low risk groups.

Figure 1 :

 1 Figure 1: Strategy of analysis of genes correlated with MUC4 expression in Cancer Cell Line Encyclopedia. (A) Flowchart of MUC4 analysis. MUC4 mRNA expression z-

Figure 2 :

 2 Figure 2: MUC4 expression in Cancer Cell Line Encyclopedia. MUC4 mRNA expression

Figure 3 :

 3 Figure 3: MUC4 expression in cancer samples from TCGA. MUC4 mRNA expression z-

Figure 4 :

 4 Figure 4: Correlation of MUC4 expression and methylation of genes correlated with

Figure 6 :

 6 Figure 6: Hazard ratio of signatures defined by gene ontology terms and top-genes

Figure 7 :

 7 Figure 7: MUC4/MUC16/MUC20 expression is associated with reduced overall survival

Figure 8 :

 8 Figure 8: Expression and ROC curves of the MUC4/MUC16/MUC20 signature in a pancreatic adenocarcinoma dataset. (A) MUC4, MUC16 and MUC20 mRNA expression

  Figure 1

Figure 1

 1 Figure 1 Click here to download Figure figure 1 flowchart.pdf

Figure 2

 2 Figure 2

Figure 3

 3 Figure 3 Click here to download Figure Figure 3 TCGA NEW.pdf

Figure 4

 4 Figure 4

Figure 4

 4 Figure 4 Click here to download Figure Figure 4 methylation R1ok.pdf

Figure 5 Figure 8

 58 Figure 5 Click here to download Figure figure 5 KM + hazard ratio NEW.pdf

MUC4 expression is associated with reduced overall survival of carcinoma.

  (A) Hazard ratio was calculated in population designated as MUC4 high risk and low risk (higher value of MUC4 for higher risk) by SurvExpress optimized algorithm in every cancer from TCGA datasets. (B) Overall survival values of MUC4 high and low risk groups in bladder cancer, colon cancer, lung adenocarcinoma, lung squamous carcinoma, ovarian cancer, skin cancer, stomach cancer, available in TCGA datasets. The numbers below horizontal axis represent the number of individuals not presenting the event of MUC4 high and low risk group along time.

	STEAP4, SCEL, WFDC2, GJB3, SH2D3A, RNF39, PRSS22, HS3ST1, GPR87, TACST2,
	MUC16,	FAM83A,	LAMC2,	B3GNT3,	CLDN7	were	extracted	using
	https://portals.broadinstitute.org/ccle
	Figure 5:
	Representation Bisulfite Sequencing: RRBS) of ADGRF1, LCN2, MUC20, C1ORF116,

The top genes were defined as genes harboring Pearson's correlation higher than 0.5 with MUC4 expression. MUC4 mRNA expression and methylation score (Reduced

Table 1 : List of mRNA positively correlated with MUC4. Data

 1

	were retrieved from 881

Table 2 : List of mRNA negatively correlated with MUC4

 2

. Data were retrieved from 881 samples of Cancer Cell Line Encyclopedia (Novartis/Broad, Nature 2012). Correlation analysis was performed using cBioPortal.org online tool. 9 genes presented a Pearson's correlation lower than -0.3.

Table 3 : Gene ontology clustering on genes correlated with MUC4 expression. Gene

 3

	list was retrieved from 881 samples of Cancer Cell Line Encyclopedia (Baretina, Nature
	2012). 187 genes that are positively (n=178) or negatively (n=9) correlated with MUC4
	expression were selected. Functional Annotation and gene clustering were performed using
	David Functional Annotation Tool.

Table 4 : Hazard-ratio and survival analysis of high and low risk in TCGA tumor databases

 4

. Hazard ratio and p-value were determined using SurvExpress tool. Risk group were determined using the optimization algorithm (maximize) from the ordered prognostic index (higher values of MUC4 expression for higher risk).

Table 1 : List of mRNA positively correlated with MUC4.

 1 Data were retrieved from 881 samples of Cancer Cell Line Encyclopedia (Novartis/Broad, Nature 2012). Correlation analysis was performed using cBioPortal.org online tool. 178 genes presented a Pearson's correlation higher than 0.3.

	Correlated gene	cytoband	Pearson's correlation	Spearman's correlation
	ADGRF1	6p12.3	0.56	0.40
	LCN2	9q34	0.54	0.41
	MUC20	3q29	0.54	0.42
	C1ORF116	1q32.1	0.52	0.47
	SCEL	13q22	0.52	0.43
	STEAP4	7q21.12	0.51	0.35
	WFDC2	20q13.12	0.48	0.31
	GJB3	1p34	0.48	0.35
	SH2D3A	19p13.3	0.48	0.45
	RNF39	6p21.3	0.47	0.35
	PRSS22	16p13.3	0.47	0.41
	HS3ST1	4p16	0.46	0.35
	GPR87	3q24	0.46	0.35
	TACSTD2	1p32	0.46	0.41
	MUC16	19p13.2	0.46	0.37
	FAM83A	8q24.13	0.45	0.34
	LAMC2	1q25-q31	0.45	0.32
	B3GNT3	19p13.1	0.45	0.40
	CLDN7	17p13.1	0.45	0.44
	ELF3	1q32.2	0.44	0.44
	MIR205HG	1q32.2	0.44	0.37
	PPL	16p13.3	0.44	0.40
	MPZL2	11q24	0.44	0.43
	TMPRSS4	11q23.3	0.44	0.46
	C6ORF132	6p21.1	0.43	0.36
	FGFBP1	4p15.32	0.43	0.38
	IRF6	1q32.3-	0.43	0.44
		q41
	LAMB3	1q32	0.43	0.31
	CDH3	16q22.1	0.43	0.41
	SPINT1	15q15.1	0.43	0.42
	EHF	11p12	0.43	0.41
	CYSRT1	9q34.3	0.42	0.33
	MACC1	7p21.1	0.42	0.38
	MST1R	3p21.3	0.42	0.41
	SERPINB5	18q21.33	0.42	0.39
	TMEM30B	14q23.1	0.42	0.40
	CLDN4	7q11.23	0.41	0.37
	LIPH	3q27	0.41	0.36
	ALS2CL	3p21.31	0.41	0.37
	ITGB6	2q24.2	0.41	0.37

table 1

 1 Click here to download Tabletable1.docx

	RAB25	1q22	0.41	0.41
	CNKSR1	1p36.11	0.41	0.43
	TSPAN1	1p34.1	0.41	0.36
	CEACAM6	19q13.2	0.41	0.37
	KLK10	19q13	0.41	0.37
	UCA1	19p13.12	0.41	0.32
	CXCL16	17p13	0.41	0.35
	ELMO3	16q22.1	0.41	0.44
	PRSS8	16p11.2	0.41	0.42
	ST14	11q24-	0.41	0.40
		q25
	TRIM29	11q23.3	0.41	0.37
	GRHL2	8q22.3	0.40	0.40
	PTK6	20q13.3	0.40	0.34
	FLJ23867	1q25.2	0.40	0.31
	TMC4	19q13.42	0.40	0.38
	CDH1	16q22.1	0.40	0.39
	SDR16C5	8q12.1	0.39	0.35
	S100A14	1q21.3	0.39	0.38
	GJB5	1p35.1	0.39	0.33
	JUP	17q21	0.39	0.40
	TMC5	16p12.3	0.39	0.42
	SCGB1A1	11q12.3	0.39	0.34
	MROH6	8q24.3	0.38	0.39
	MAL2	8q23	0.38	0.41
	ESRP1	8q22.1	0.38	0.42
	GALNT3	2q24-q31	0.38	0.38
	CBLC	19q13.2	0.38	0.40
	FUT3	19p13.3	0.38	0.42
	PKP3	11p15	0.38	0.39
	EPHA1	7q34	0.37	0.39
	AGR2	7p21.3	0.37	0.33
	CDS1	4q21.23	0.37	0.37
	S100P	4p16	0.37	0.36
	ARL14	3q25.33	0.37	0.33
	KRTCAP3	2p23.3	0.37	0.41
	BIK	22q13.31	0.37	0.38
	SFN	1p36.11	0.37	0.41
	TMEM125	1p34.2	0.37	0.44
	C19ORF33	19q13.2	0.37	0.35
	LSR	19q13.12	0.37	0.41
	MISP	19p13.3	0.37	0.39
	ESRP2	16q22.1	0.37	0.39
	PAK6	15q14	0.37	0.37
	KRT4	12q13.13	0.37	0.32

Table 2 : List of mRNA negatively correlated with MUC4.

 2 Data were retrieved from 881 samples of Cancer Cell Line Encyclopedia (Novartis/Broad, Nature 2012). Correlation analysis was performed using cBioPortal.org online tool. 9 genes presented a Pearson's correlation lower than -0.3.

	Correlated gene	cytoband	Pearson's correlation	Spearman's correlation
	SLC35B4	7q33	-0.30	-0.32
	IFFO1	12p13.3	-0.30	-0.36
	TTC28	22q12.1	-0.31	-0.33
	VKORC1	16p11.2	-0.31	-0.35
	DIXDC1	11q23.1	-0.31	-0.31
	ATP8B2	1q21.3	-0.32	-0.33
	ST3GAL2	16q22.1	-0.32	-0.31
	ZEB1	10p11.2	-0.33	-0.35
	MTFR1L	1p36.11	-0.34	-0.35

Table 3 : Gene ontology clustering on genes correlated with MUC4 expression.

 3 Gene list was retrieved from 881 samples of Cancer Cell Line Encyclopedia (Baretina, Nature 2012). 187 genes that are positively (n=178) or negatively (n=9) correlated with MUC4 expression were selected. Functional Annotation and gene clustering were performed using David Functional Annotation Tool (https://david.ncifcrf.gov/).

	Enrichment	Gene Ontology terms and annotations	Count	P value
	score
	7.08	Cell-cell adherens junction	18	1.4E-8
		cadherin binding involved in cell-cell adhesion	17	2.0E-8
		cell-cell adhesion	14	2.2E-6
	5.44	Tight junction	10	6.6E-8
		bicellular tight junction	10	1.4E-6
		Tight junction	9	8.1E-6
		bicellular tight junction assembly	5	2.4E-4
	4.67	Pleckstrin homology-like domain	17	2.6E-6
		Pleckstrin homology domain	13	9.3E-6
		domain:PH	11	8.0E-5
		PH	12	1.1E-4
	3.35	SH2 domain	8	9.1E-5
		domain:SH2	7	2.3E-4
		SH2 domain	7	3.9E-4
		SH2	6	4.8E-3
	3.34	Glycoprotein	64	6.0E-5
		glycosylation site:N-linked (GlcNAc...)	61	1.1E-4
		disulfide bond	44	6.4E-4
		signal peptide	48	9.7E-4
		Disulfide bond	48	9.8E-4
		Signal	54	2.2E-3
	2.76	topological domain:Cytoplasmic	53	8.1E-5
		Membrane	91	1.6E-4
		transmembrane region	66	8.5E-4
		topological domain:Extracellular	42	9.2E-4
		Transmembrane helix	66	7.2E-3
		Transmembrane	66	7.7E-3
		integral component of membrane	59	8.4E-2
	2.6	domain:SH3	9	1.9E-4
		SH3 domain	9	6.5E-4
		Src homology-3 domain	8	4.4E-3
		SH3	6	6.9E-2
	2.48	signal peptide	48	9.7E-4
		Secreted	31	2.0E-3
		extracellular region	25	1.9E-2
	2.43	establishment of protein localization to plasma	6	4.9E-5
		membrane	5	3.0E-3
		cell adhesion molecule binding	4	3.5E-1
		actin cytoskeleton
	2.32	extracellular matrix organization	10	1.2E-4
		Epidermolysis bullosa, junctional, non-Herlitz type	3	2.8E-4
		Epidermolysis bullosa	4	2.8E-4
		hemidesmosome assembly	3	5.7E-3

table 3

 3 Click here to download Tabletable3.docx

		ECM-receptor interaction	4	2.9E-2
		Focal adhesion	5	7.2E-2
		PI3K-Akt signaling pathway	4	5.0E-1
	2.19	Serine protease	8	2.5E-4
		Peptidase S1, trypsin family, active site	7	3.9E-4
		domain:Peptidase S1	7	4.7E-4
		active site:Charge relay system	9	5.3E-4
		Peptidase S1	7	9.1E-4
		Trypsin-like cysteine/serine peptidase domain	7	1.3E-3
		Tryp_SPc	7	1.6E-3
		extrinsic component of plasma membrane	4	1.7E-3
		Peptidase S1A, chymotrypsin-type	6	4.1E-3
		serine-type endopeptidase activity	8	1.2E-2
		serine-type peptidase activity	4	2.3E-2
		Protease	8	2.0E-1
		Zymogen	4	2.9E-1
		proteolysis	7	3.5E-1
		Hydrolase	13	8.1E-1
	1.74	CP2 transcription factor	3	1.3E-3
		region of interest:Transcription activation	3	3.5E-3
		chromatin DNA binding	3	1.1E-1
		sequence-specific DNA binding	8	2.3E-1
	1.69	O-glycan processing	6	2.7E-4
		Glycosphingolipid biosynthesis -lacto and neolacto	4	9.8E-4
		series
		protein glycosylation	6	4.7E-3
		Glycosyltransferase	7	1.8E-2
		topological domain:Lumenal	10	2.1E-2
		Golgi cisterna membrane	4	3.6E-2
		Signal-anchor	9	4.8E-2
		Golgi apparatus	12	1.0E-1
		Golgi membrane	9	2.0E-1
		Metabolic pathways	9	7.5E-1
	1.51	Rho guanyl-nucleotide exchange factor activity	5	6.4E-3
		regulation of Rho protein signal transduction	5	7.6E-3
		Dbl homology (DH) domain	4	2.9E-2
		domain:DH	3	1.3E-1
		RhoGEF	3	1.6E-1

Table 4 : Hazard-ratio and survival analysis of high and low risk in TCGA tumor databases.

 4 Hazard ratio and p-value were determined using SurvExpress tool (http://bioinformatica.mty.itesm.mx/SurvExpress). Risk groups were determined using the optimization algorithm (maximize) from the ordered prognostic index (higher values of MUC4 expression for higher risk).

	Database	N; low vs	Hazard ratio	P value
		risk group	[95% CI]
	Bladder -BLCA-TCGA-Bladder	N=388; 251	1.48 [1.09 ; 2]	p=0.01191
	Urothelial Carcinoma-July 2016	vs 137
	Breast -BRCA-TCGA Breast invasive	N=962; 831	1.06 [0.67 ;	p=0.8038
	carcinoma -July 2016	vs 131	1.67]
	Cervical -CESC-TCGA Cervical	N=191; 147	1.55 [0.76	p=0.2275
	squamous cell carcinoma and	vs 44	; 3.17]
	endocervical adenocarcinoma July 2016
	Colon -COADREAD -TCGA Colon and	N=466; 417	2.1 [1.19 ; 3.71] p=0.01061
	Rectum adenocarcinoma June 2016	vs 49
	Esophagus -ESCA -TCGA Esophageal	N=184; 137	0.68 [0.4 ; 1.15] p=0.1468
	carcinoma June 2016	vs 47
	Head-Neck -HNSC -TCGA Head and	N=502; 107	1.26 [0.88 ;	p=0.204
	Neck squamous cell carcinoma June 2016	vs 395	1.78]
	Hematologic -Acute Myeloid Leukemia	N=168; 146	1.59 [0.97 ;	p=0.06818
	TCGA	vs 22	2.62],
	Kidney -KIPAN -TCGA Kidney PAN	N=792; 555	0.94 [0.7 ; 1.26] p=0.6711
	cancer TCGA June 2016	vs 237
	Kidney -KIRC -TCGA -Kidney renal	N=415; 256	0.98 [0.7 ; 1.37] p=0.9115
	clear cell carcinoma	vs 159
	Kidney -KIRP -TCGA Kidney renal	N=278; 248	1.24 [0.52 ;	p=0.6322
	papillary cell carcinoma June 2016	vs 30	2.94]
	Liver -TCGA-Liver-Cancer	N=304; 137	1.4 [0.97 ; 2.03] p=0.07012
		vs 167
	Lung ADK-LUAD -TCGA -Lung	N=475; 410	1.7 [1.14 ; 2.52] p=0.008963
	adenocarcinoma June 2016	vs 65
	Lung Squamous-LUSC -TCGA -Lung	N=175; 59	1.69 [1.03 ;	p=0.03798
	squamous cell carcinoma June 2016	vs 116	2.78],
	Ovarian -Ovarian serous	N=578; 390	1.33 [1.05 ;	p=0.01908
	cystadenocarcinoma TCGA	vs 188	1.69]
	Pancreatic -PAAD -TCGA -Pancreatic	N=176; 27	3.94 [1.81 ;	p=0.0005756
	adenocarcinoma	vs 149	8.61]
	Prostate -PRAD -TCGA -Prostate	N=497; 328	1.99 [0.57 ;	p=0.2793
	adenocarcinoma June 2016	vs 169	6.88],
	Skin -SKCM-TCGA Skin Cutaneous	N=334; 312	1.87 [1.08 ;	p=0.0262
	Melanoma July 2016	vs 23	3.23]
	Stomach -STAD -TCGA -Stomach	N=352; 306	1.58 [1 ; 2.51],	p=0.04958
	adenocarcinoma June 2016	vs 46
	Testis -TGCT -TCGA -Testicular Germ	N=133; 93	5.56 [0.57 ;	p=0.1407
	Cell Tumors	vs 40	54.52]
	Thymus -THYM -TCGA -Thymoma	N=118; 90	1.92 [0.48 ;	P=0.3588
	June 2016	vs 28	7.77]
	Thyroid -THCA -TCGA -Thyroid	N=247; 45	1.98 [0.69 ;	p=0.2019
	carcinoma -June 2016	vs 202	5.64],

cBioPortal.org: Cancer Cell Line Encyclopedia (Novartis/Broad, Nature 2012) n=881 samples

								Query: MUC4
	MUC4, mRNA Expression z-Scores (microarray)	List of genes correlated with MUC4 expression
	-Cancer type								n=178 positively regulated, table 1
	-Histology								n=9 negatively regulated, table 2
									Gene ontology annotation
									suppl. table 1
									Gene ontology clustering
									table 3
	3	4	5	6	7	8	9	10 11 12

Acknowledgements: We are grateful to M. Foster and A. Turner for helpful contributions and Dr B Neve, Dr A. Vincent, Dr R. Vasseur (Inserm UMR-S1172, Lille) for their critical reading of the manuscript.

Supplemental material legends

Supplemental Figure 1: MUC4 Oncoprint in Cancer Cell Line Encyclopedia. MUC4 alterations were explored in Cancer Cell Line Encyclopedia dataset using cBioPortal webtool.

The oncoprint represents the amplification, deletion, up regulation or in frame mutation.

Supplemental

Supplemental table 1: Ontology of genes correlated with MUC4 expression. Gene list

was retrieved from 881 samples of Cancer Cell Line Encyclopedia (Novartis/Broad, Nature 2012). 187 genes that are positively (n=178) or negatively (n=9) correlated with MUC4 expression were selected. Functional Annotation was performed using David Functional Annotation Tool.

Supplemental table 2: Hazard-ratio and survival analysis of most significant genes clustered in GO term associated with MUC4 expression in TCGA tumor databases.

Hazard ratio and p-value were determined using SurvExpress tool (http://bioinformatica.mty.itesm.mx/SurvExpress). Risk groups were sorted depending on the major GO term GO 0031424, GO 00071555, GO 0019897, GO 0016323 and GO 0016324 using the optimization algorithm (maximize) from the ordered prognostic.

Supplemental table 3: Hazard-ratio and survival analysis of top genes associated with

MUC4 expression in TCGA tumor databases. Hazard ratio and p-value were determined using SurvExpress tool (http://bioinformatica.mty.itesm.mx/SurvExpress). Risk groups were defined using the optimization algorithm (maximize) from the ordered prognostic. Selected genes (ADGRF1, LCN2, MUC20, C1ORF116, SCEL, STEAP4) harbored Pearson's correlation with MUC4 > 0.5.


Autophagy is a physiological process of cellular component recycling promoting cell survival and homeostasis under unfavorable conditions. Pancreatic ductal adenocarcinoma (90-95% of pancreatic cancer) has been associated with multiple metabolic alterations including autophagy. Because of its supporting role in tumor growth, inhibition of autophagy has been proposed as a treatment of pancreatic cancer [START_REF] Yang | Autophagy Sustains Pancreatic Cancer Growth through Both Cell-Autonomous and Nonautonomous Mechanisms[END_REF][START_REF] Boone | Autophagy Inhibition in Pancreatic Adenocarcinoma[END_REF]. Autophagy process is regulated by autophagy-related proteins (ATGs) necessary for the autophagosome formation and subsequent delivering of cytoplasmic content for degradation by lysozomes. Among these proteins, Atg5 is a key player for autophagy onset but little is known about its role in tumorigenesis (3).

Ryan's laboratory previously deciphered the roles of Atg5/7 in pancreatic carcinogenesis and notably showed the importance of p53 status (4). They observed that mice lacking Atg5 accumulate low-grade pancreatic intraepithelial neoplasia (PanIN) lesions and that PDAC progression is impaired. Bardhesy's laboratory demonstrated the induction of an autophagy-lysosome gene program regulating metabolic reprogramming in pancreatic adenocarcinoma highlighting the potential of therapeutic lysozome targeting using chloroquinine [START_REF] Perera | Transcriptional control of autophagy-lysosome function drives pancreatic cancer metabolism[END_REF].

In the later elegant study, Görgülü and colleagues investigated the dosage sensitive effect of autophagy loss [START_REF] Gorgulu | Levels of the Autophagy-Related 5 Protein Affect Progression and Metastasis of Pancreatic Tumors in Mice[END_REF]. Using GFP-LC3 reporter mice, autophagy was confirmed to occur at every step from acinar-to-ductal metaplasias (ADM), PanINs, pancreatic tumors to metastasis. The authors crossed the prototype mice model expressing oncogenic mutated LstopL-KrasG12D in pancreatic cells (deriving from exocrine Ptf1 cell ligneage) with mice carrying conditional disruption of Atg5 (Atg5flox/flox) and compared homozygous (called A5;Kras) and heterozygous (called A5+/-;Kras) with mice harbouring mutated KrasG12D (littermates control). Isolated primary tumor cells were characterized regarding their transcriptome or metabolome and more targeting features such as intracellular calcium, activity of extracellular cathepsin, and cell migration/invasion.

As expected, homozygous A5/Kras did not develop any advanced tumor. This was consistent with previous observations (4). Strikingly, monoallelic Atg5 loss (A5+/-;Kras mice), expressing haploinsufficient Atg5 levels, did not have an intermediate phenotype but harboured an increased burden of tumors and metastases than control mice (Figure 1A). In vitro culture of A5+/-;Kras cells showed that monoallelic Atg5 loss led to anoikis resistance, increased migration, invasion and cell spreading.

Similar observations were independently obtained using Atg5-ShRNA Kras cell lines harbouring 58% or 94% Atg5 relative levels (mimicking A5+/-;Kras and A5;Kras cells, respectively).

Transcriptomic analysis showed that monoallelic Atg5 loss induced a transcriptional reprogramming with increased expression of genes involved in metabolism, immunity, development, and vesicular trafficking/homeostasis and decreased of adhesion and cell cycle associated cellular functions. The A5+/-;Kras pancreas displayed a profound metabolic reprogramming. Indeed, the authors observed elevated amount of oxidative and cell stress associated metabolites. The mitochondrial function and morphology were altered with global decrease of glycolysis, oxidative capacity, acidification rate and ATP turnover in A5+/-cells compared with Kras controls. Cytoplasmic Ca2+ amplitude response and s100a4 (Ca2+ binding protein) were increased in A5+/-cells as well as L-and Dcathepsin activities required for spreading and invasive capacities.

Görgülü and colleagues also investigated the consequence of mono-allelic Atg5 loss on macrophage-mediated inflammation. Cytokine pattern of A5+/-cells was altered with the up-regulation of sets of cytokines involved in macrophage chemoattraction, M2-differentiation and tumor progression/metastasis and decrease of M1 related cytokines. Therefore, A5+/-;Kras cells switched the differentiation pattern from M1 macrophage toward the M2-subtype. M2 macrophages are tumor-associated macrophages (TAM) that contribute to immunosuppressive microenvironment and favor tumor aggressiveness and promote tumor metastasis [START_REF] Najafi | Tumor microenvironment: Interactions and therapy[END_REF].

Finally, analysis of three independent patient's cohorts (from Munich, Heidelberg and Berlin, Germany) showed a tendency of poorer survival in the group expressing moderate/low ATG5 level compared to high expressing group. Using Survexpress tool (8), we observed an opposite trend when comparing high and low expressing Atg5 patients from PAAD-TCGA (p=0.08; n=176) and PACA-AU-ICGC (p=0.03; n=189) datasets (Figure 1B). In this kind of analysis, it would be interesting to stratify patient with low and moderate autophagy features.

Altogether, this interesting manuscript highlights the critical need of a complete blockage of the autophagy for clinical trials targeting autophagy. Chloroquine (CQ) is approved by the US Food and Drug Administration, FDA and widely employed for the prophylactic malaria medication. The therapeutic potential of chloroquine/Nivaquin has been tested for cancers. Several studies have been conducted for pancreatic adenocarcinoma (Table 1). In regards of the recent findings, the residual activity of autophagy should be evaluated in order to avoid the induction of protumoral immunosuppressive environment described by Algül's laboratory [START_REF] Gorgulu | Levels of the Autophagy-Related 5 Protein Affect Progression and Metastasis of Pancreatic Tumors in Mice[END_REF].

Fine-tuning autophagy

  in pancreatic adenocarcinoma: Full blockage is required. Nicolas Jonckheere and Isabelle Van Seuningen Univ. Lille, Inserm, CHU Lille, UMR-S 1172 -JPARC -Jean-Pierre Aubert Research Center, Team "Mucins, epithelial differentiation and carcinogenesis", F-59000 Lille, France Correspondence: nicolas.jonckheere@inserm.fr Phone: +33 3 20 29 88 65, Fax: +33 3 20 53 85 62 Editorial -Invited by Hailing Lian, science editor from Annals of Translational Medicine Editorial on the article "Levels of the Autophagy-Related 5 Protein Affect Progression and Metastasis of Pancreatic Tumors in Mice" Görgülü et al., Gastroenterology 2019

Acknowledgments: Our work is supported by Inserm and CNRS and grants from la Ligue Nationale Contre le Cancer (Equipe Labellisée Ligue 2010, IVS; Ligue comité 59, 62 IVS, Ligue comité 62, 80 NJ) and by SIRIC ONCOLille, Grant INCaDGOS-Inserm 6041 (NJ, IVS) and by a grant from "Contrat de Plan Etat Région" CPER Cancer 2007-2013 (IVS).


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