Luciano Da Fontoura Costa
email: luciano@ifsc.usp.br

Supervised Image Segmentation by Using Multiset Similarity Neurons

The separation of several regions of interest in an image constitutes an frequent, important, and particularly challenging task in artificial intelligence, typically known as image segmentation. The difficulties found when approaching this problem include but are not limited to the presence of several types of noise, varying focus, shadows, reflections, texture, and transparency. In the present work, this important task is approached by using the real-valued coincidence similarity which, based on multiset concepts, possesses some particularly interesting properties including enhanced sensitivity and selectivity when comparing similar patterns and good robustness to localized features perturbations. More specifically, multiset coincidence neurons and respective networks are combined in simple frameworks for image segmentation. After presenting the basic concepts of image segmentation, multisets and the coincidence similarity, several examples involving synthetic images are presented in order to illustrate the effect of the few involved parameters on the segmentation. The potential of the reported concepts and methods are also illustrated respectively to several real-world gray-level and color images.

Introduction

Intelligent agents -including living beings, humans, and machines -rely critically on receiving information about the surrounding environment, so that proper decisions can be taken respectively to each possible action and procedure to be applied. For instance, a flying flamingo needs to know about the presence of a lake in the landscape before it can land and feed. A good deal of the information about the environment in such tasks presents visual nature, leading to the areas of image analysis as well as natural and artificial vision (e.g. [START_REF] Davies | Machine Vision[END_REF][START_REF] Da | Shape Classification and Analysis: Theory and Practice[END_REF][START_REF] Gonzalez | Digital Image Processing[END_REF][START_REF] Horn | Robot Vision[END_REF]). Basically, vision can be understood as the ability, given a scene, to identify the presence of relevant objects and entities as well as their position and state/properties (e.g. rotated, moving, folded, open, etc.).

It sometimes comes as a surprise to know that vision constitutes one of the most difficult abilities in natural and artificial intelligence. Given the impressive performance of biological vision systems, they have often provided motivation and inspiration for developing concepts and methods in vision research (e.g. [START_REF] Horn | Robot Vision[END_REF][START_REF] Da | Shape Classification and Analysis: Theory and Practice[END_REF]). One of the characteristic of both biological and machine vision system is that they involve several data representations and processing stages, often in modular and pipelined organi-zations (e.g. [START_REF] Zeki | The functional logic of cortical connections[END_REF]).

After a projection of the given scene becomes available as a respective image, one of the first task to be performed consists in separating (often partitioning) the image into several regions of potential interest. These regions may correspond to whole objects in the scene, or parts of them, such as the petals and leaves of a flower plant. The task of separating regions of potential relevance in a given image, often called image segmentation, represents a particularly important and critical step along the pipeline leading from the projected image to its comprehensive understanding.

The special relevance of image segmentation in image analysis, robotics, and vision has motivated a substantial number of related approaches (e.g. [START_REF] Gonzalez | Digital Image Processing[END_REF][START_REF] Da | Shape Classification and Analysis: Theory and Practice[END_REF]) adopting a wide range or concepts and methodologies ranging from statistical mechanics to neuronal networks and datamining (e.g. [START_REF] Haykin | Neural Networks And Learning Machines[END_REF][START_REF] Broomhead | Multivariable functional interpolation and adaptive networks[END_REF][START_REF] Park | Universal approximation using radial-basis-function networks[END_REF]), to name just a few possibilities.

Since its description [START_REF] Da | Further generalizations of the Jaccard index[END_REF][START_REF] Da | On similarity[END_REF][START_REF] Da | Multiset neurons[END_REF], the concept of coincidence similarity has been found to present particularly interesting properties when applied to compare two generic mathematic structures including vectors, matrices and functions. Corresponding to a combination of the realvalued Jaccard and interiority indices, the coincidence index is particularly robust to local perturbations (e.g. an individual component of a vector), has enhanced sensitiv-ity when comparing similar structures, intrinsically implements relative magnitude normalization, and implements substantially strict comparison when compared to other approaches such as the cosine similarity, Euclidean distance, and the Pearson correlation coefficient [START_REF] Da | Further generalizations of the Jaccard index[END_REF][START_REF] Da | On similarity[END_REF][START_REF] Da | Multiset neurons[END_REF]. These interesting properties have allowed the coincidence index to be applied with particularly effectiveness in template matching [START_REF] Da | On similarity[END_REF], translating datasets into respective networks [START_REF] Da | Coincidence complex networks[END_REF], as well as particularly encouraging preliminary results related to the application of multiset neurons to image segmentation [START_REF] Da | Multiset neurons[END_REF]. To investigate in a more systematic manner the potential of multiset similarity concepts and methods constitutes the main objective of the present work.

The reported approach to multiset similarity-based image segmentation involves the consideration of synthetic images in order to allow full control on the respective characteristics as well as their usage as gold standards. Two types of these images, corresponding to graded graylevels as well as a respectively noisy version, are particularly important as they allow effective verification of the effect of the involved parameters in a simultaneous manner respectively to several important image characteristics commonly found in practice. The important issue regarding which features to use for pixels characterization is also approached in detail, as well as the effect of the application of smooth thresholding by using a sigmoid function.

To complement the reported study, the application of the multiset similarity method is also illustrated respectively to the RGB (color) images.

It should be kept in mind that, though the present work takes into account multiset distances, all concepts, methods and resources presented in this article, including the same overall method and evaluating framework, can be immediately applied with other similarity and distances approaches, including Euclidean distance (normalized by magnitude or not), cosine distance, and Pearson correlation coefficient, among a wide range of possibilities (e.g. [START_REF] Vijaymeena | A survey on similarity measures in text mining[END_REF][START_REF] Cha | Comprehensive survey on distance/similarity measures between probability density functions[END_REF]).

We start by presenting some basic concepts on imaging, multisets and similarity, which is followed by the description of the proposed approach for image segmentation by using multiset neurons. Then, we approach the important issue regarding several possible manners to choose the features to characterize the image properties around neighborhood centered at each pixel of interest. The effect of the involved parameters are then investigated respectively to graded templates with uniform and progressive linear noise. Several examples respective to real-world gray-level and color (RGB) images are also provided in order to better illustrate the potential for image segmentation of the proposed concepts and methods.

Basic Concepts

A gray-level image can be represented as an N x × N y matrix A whose each element a i,j , with i = 1, 2, . . . , N x and j = 1, 2, . . . , N y , is proportional to the average luminosity at that respective position. By average it is meant the integration of the light signal in the sensor finite area. An RGB image can be represented by using three matrices with values proportional to the red (R), green (G) and blue (B) components. Each of the elements of a gray level image, or the three respective R, G, and B elements in an RGB image, are said to correspond to one of the image pixels.

Interesting information about the distribution of graylevels of an image can be obtained by considering its respective histogram h() corresponds to the count of gray level pixels contained in N h successive bins of the same size (e.g. [START_REF] Gonzalez | Digital Image Processing[END_REF][START_REF] Da | Shape Classification and Analysis: Theory and Practice[END_REF]). Three separate histograms can also be obtained in the case of RGB images.

Multisets (e.g. [START_REF] Hein | Discrete Mathematics[END_REF][START_REF] Knuth | The Art of Computing[END_REF][START_REF] Blizard | Multiset theory[END_REF][START_REF] Blizard | The development of multiset theory[END_REF][START_REF] Mahalakshmi | Properties of multisets[END_REF][START_REF] Singh | Complementation in multiset theory[END_REF]) constitute generalization of the concept of set, allowing repeated elements. The possible elements of a multiset constitute its support S, and the number of repeated entries of each these elements is said to be its respective multiplicity. For instance, consider two multisets containing characters as follows:

A = {a, a, a, b, c, c} B = {a, a, b, b, d} (1)
The support of these multisets are S A = {a, b, c} and S B = {a, b, d}, and their representations in terms of multiplicities correspond to:

A = {[a, m A (a) = 3] ; [b, m A (b) = 1] ; [c, m A (c) = 2]} B = {[a, m B (a) = 2] ; [b, m B (b) = 2] ; [d, m B (d) = 1]}
The union (intersection) between two multisets A and B can be obtained by taking the maximum (minimum) between the multiplicities of each of the elements in both supports. In the case of the above multisets, we have:

A ∪ B = {[a, 3] ; [b, 2] ; [c, 2] , [d, = 1]} ; S A∪B = {a, b, c, d} A ∩ B = {[a, 2] ; [b, 1]} ; S A∩B = {a, b}
We shall assume henceforth that all multisets in this work have the same support S = {e 1 , e 2 , . . . , e M }, so that the multiplicities of a multiset S can be written as {x 1 , x 2 , . . . , x M }. The union and intersection of two such multisets X and Y can now be written as:

X ∪ Y = {[e i , max {x i , y i }]} X ∩ Y = {[e i , min {x i , y i }]}
The real-valued Jaccard similarity index between two real-valued multisets X and Y can now be expressed as:

J (X, Y ) = |X ∩ Y | |X ∪ Y | (2)
where |A| is the cardinality of A, corresponding to the sum of its multiplicities.

The real-valued interiority between those two multisets can be written as:

I(X, Y ) = |X ∩ Y | max {|X|, |Y |} (3)
The real-valued coincidence index between X and Y corresponds to the product between the respective realvalued Jaccard and coincidence indices [START_REF] Da | Further generalizations of the Jaccard index[END_REF][START_REF] Da | On similarity[END_REF][START_REF] Da | Coincidence complex networks[END_REF], i.e.:

C(X, Y ) = I(X, Y ) J (X, Y ) (4)
It is also possible to introduce a parameter D so that:

C(X, Y ) = I(X, Y ) [J (X, Y )] D (5)
Three parameters can be possibly involved in the multiset similarity comparision: (i) the similarity threshold T (only similarity values larger than T are kept); and (ii) the exponent D, which controls the sensitivity and selectivity of the comparison [START_REF] Da | Multiset neurons[END_REF].

In the next section we describe how the above presented multiset similarity values can be applied for image segmentation, also identifying the main involve parameters and their typical effects.

Multiset Image Segmentation

It is often interesting to approach a problem from the perspective of mathematical modeling. More specifically, the qualitative description and specification of a problem can be translated as completely as possible into mathematical concepts and representations.

In the case of image segmentation, this term has often been employed to convey the idea of separating, from a given image, regions that are intrinsically related to objects of interest, or part of these objects. The problem with this understanding is that it requires the preliminary identification of the objects, which implies some recursive nature to the problem, as segmentation is usually one of the first steps towards object recognition and localization. However, this is no problem in the case of supervised image segmentation, which is the case henceforth assumed in the present work.

In the present work, we shall understand a region to be segmented simply as a portion of the image whose parts share mutually similar properties, typically gray or color levels, though other properties as texture also apply.

Therefore, these regions presents some level of uniformity respectively to the properties of interest. For instance, in the case of gray-level images, a segmented region will correspond to a relatively uniform portion of the image that has some special interest, such as the petals of some specific flower in the image. Oftentimes, the region will correspond to the whole object of interest, but it will often be the case that the objects will need to be constructed by the union (or other set operations) between two or more segmented regions. The multiset approach reported in this work inherently caters for all these possibilities, as one or more neuron can be assigned to recognizing one uniform object or the several parts of heterogeneous objects.

Figure 1 depicts the basic setting underlying the considered application of multiset neurons for segmenting a region of interest in a given image [START_REF] Da | Multiset neurons[END_REF], respectively to three multiset neurons with hard-limiting non-linear outputs.

Figure 1: Overall diagram of the adopted multiset neuron image segmentation approach respectively to Ns = 3 neurons (any number of neurons and layers can be used), one for each sample. Features are extracted from the neighborhood, which can be of varying sizes and shapes, of each of the pixels in the image and fed as input to all the considered multiset neurons. Each of the neurons then compares the received features with the features of its respectively associated prototype p i , and the comparison result is submitted to a threshold T i (or smooth function). The so-obtained outputs are then integrated (blocks '+') and subjected to a final thresholding by T . All pixels are scanned into the multiset neuronal net in order to implement the segmentation.

Features are obtained from the neighborhood of each pixel and scanned into each of the multiset neurons. Several types and shapes of neighborhoods are possible, including the 2 × 2 neighborhood in Figure 1. Observe that each neuron i has its own parameters T i , α i and D i , which may or not be respectively identical among themselves. The features received by each neuron i are then compared, by using multiset similarity, to the features of the respectively associated prototype p i , yielding a comparison result that is respectively threshold by T i , or subjected to another non-linear function such as a sigmoid. The thresholding operation is understood to implement greater or equal to comparison. The outputs of the multiset neurons are then integrated by and submitted to a final threshold T . Outputs equal to 1 are understood to indicate that the respectively pixel does belong to the segmented region. Except where otherwise indicated in this work, we will assume T = 1.

The above description relates to the recognition stage of the image segmentation. However, being a supervised method, it also requires a respective training stage, which happens to be straightforward in the case of the multiset neurons approach. More specifically, it consists simply in selecting in the image, e.g. by mouse clicking, a small number of samples that are then understood as prototypes. The features are extracted from these prototypes exactly in the same manner as during the recognition stage, and supplied to each respective neuron. Therefore, during the training stage multiset neuron is assigned to each of the prototypes.

Though the present work focus on the neuronal architecture illustrated in Figure 1, considering variations such as number of neurons, parameter settings, etc., it is also possible to have alternative architectures involving other types of output integration, as well as multiple layers.

The impressive performance of multiset neuronal networks such as that illustrated in Figure 1 are in striking contrast to their simplicity, which is achieved as a consequence of the interesting characteristics underlying comparisons by using multiset similarities [START_REF] Da | On similarity[END_REF][START_REF] Da | Multiset neurons[END_REF].

One additional interesting feature of multiset image segmentation is that relatively few parameters are involved, therefore requiring respective set up. Table 1 present the involved parameters as well as their meaning and expected effects on the segmentation.

The setting of these parameters will be discussed respectively to the several application examples involving synthetic and real-world images as described along the remainder of this work. However, before that it is interesting to discuss in some detail which types features can be considered for the multiset neuron image segmentation, which is approached in the subsequent section.

Choosing Features

The type of information about each image pixel to be sent to the multiset neurons is critically important for the proper performance, having great potential effect on the results. There is a virtually infinite number of possible manners to select the features characterizing the neighborhoods around each of the pixels in the image.

A first important issue concerns the definition of the neighborhood on itself, which is characterized by its shape and size. Possible choices include squares of w × w size, as well as circular regions with a given radius r. In the present work, we will be constrained to square neighborhoods centered at each pixel, as illustrated in Figure 1.

Figure 2 illustrates some of the many geometrical possibilities for extracting features aimed at representing the properties of the image around each pixel of interest. Basically, we have the possibility to take the whole set of the pixels inside the window as features (a), possibly sorted in order to enhance generalization. This same set of pixels can also be represented in terms of the respective histogram, with each bin corresponding to a features to be fed into the multiset neurons. Another scheme, illustrated in (b), consists in successive layers centered at the reference pixel. The average and standard deviation of the values of these pixels can be used as features. In all other schemes in this figure, a pixel is considered to belong to one of the sectioning regions provided the largest portion of its area is contained into that region. The schemes illustrated at (c) and (d) involve taking statistical properties of the pixels within radial or circular regions, respectively. The combination of these two schemes gives rise to polar coordinates-like regions, as illustrated in (e). Yet another possibility consists of taking parallel rectangular sections along the neighborhood, as shown in (f). spective partitioning, including those illustrated in Figure 2, will have different effects on the segmentation results. A suitable respective choice should take into account the properties of the images, including their graylevels and shapes, the regions to be identified, possible presence of noise, defocusing, distortions, eventual requirements such as rotation invariance, etc. In the present work, we shall be however restricted to the scheme depicted in Figure 2, i.e. considering as features the all pixels within the adopted w × w square regions, sorted in ascending order.

Once the features have been defined as corresponding to a set of pixels chosen as discussed above, it is also possible to normalize them in several ways, including standardization, minmax normalization, and by density (e.g. [START_REF] Da | Revisiting colocalization from the perspective of similarity[END_REF]). It should be observed, however, that each possible type of normalization can strongly influence the results in its own specific ways. For instance, after standardization the features will have zero average and unit standard deviation, therefore eliminating any constant term. The choice of normalization, if any, should be taken while carefully considering the type of images to be segmented, the type regions of interest, interferences (e.g. the presence of noise, saturation, shadows, etc.), as well as the specific parameter configuration and architecture of the adopted multiset neuronal network.

There is another critically important aspect regarding the choice and organization of features, and it has to do with the consideration, or not, of the position of the pixels as features. In case the segmentation is to be applied on the whole image regardless of the position of the pixels and taking into account only the gray-levels, the coordinates of the pixels should not be considered. However, in case we aim at segmenting regions based also on the proximity (similarity) between the constituent pixels, their respective position needs also to be incorporated as additional features.

Gradient Images

In this section we will illustrate and investigate multiset image segmentation respective to gradient images, namely images in which the gray-levels vary linear and progressively along a given direction which will be henceforth referred to the horizontal axis. This type of image is particularly interesting because it underlies many practical situations, in which the regions to be segmented have intrinsic gray level variations. Consequently, gradient images provide a particularly effective manner to systematically study the effects of the parameters involved in multiset neuron image segmentation. Henceforth, all images have gray levels between 0 and 1. Henceforth, all thresholds respective to each multiset neuron will be taken as being identical, being represented as Th, while the final threshold is indicated as T .

Shown in Figure 3 is the result of the application of a multiset neuronal network with N s = 5 neurons, D = 1, w = 0, and T h = 0.95. The image corresponds to a discrete linear gradient of gray-levels without any noise other than the geometric and gray-level discretization intrinsic to any digital image. Each of the four successive images indicate the effect of incorporating four additional sampling points, going from four to sixteen sampling points, each assigned to a respective multiset neuron. The obtained borders are perfect in these situations even if only the gray level at the reference pixel has been supplied as feature to the multiset neurons. Each portion of the image delimitated by the respective borders corresponds to the segmented associated to the respective prototypes.

Figure 4 presents the segmentation results using exactly the same parameter configuration as before, but now the gradient image has been added uniform noise with relative intensity of 0.1. The susceptibility of this configuration can be plain realized from the strongly jagged obtained borders. Observe also that the incorporation of additional samples virtually has no contribution for improving the segmentation results in this particular case. This effect is a direct consequence of the reduced number of features adopted, which consists only of the gray level at each pixel.

One way to cope with the added noise is to take into account more information about the image properties (gray levels) around each prototype, therefore providing a more comprehensive respective characterization of the interest spots. This can be readily achieved by using a larger neighborhood, as illustrated in Figure 5, which adopts a neighborhood of 7×7 pixels, which are sorted and fed into the multiset neurons. Even though all other parameter setting are exactly the same as in the previous example, substantially better results have been obtained thanks to the larger neighborhood. It should be kept in mind that the adoption of a larger neighborhood will imply in interference between the characterization of two distinct prototypes that are too close.

Figure 6 illustrates the effect of adopting an exponent D larger than 1, more specifically D = 4. Larger exponents imply in sharper, more strict comparisons that are both more sensitive and selective [START_REF] Da | Multiset neurons[END_REF]. Relatively to a parameter configuration with smaller D, the respective threshold will have typically need to be reduced when decreasing D. Figure 6 illustrates the adoption of D = 4 and T h = 0.80 (decreased from the previously adopted T = 0.95) while maintaining the neighborhood configuration respectively to the previous example. The adoption of D = 4 can be observed to have improved the segmentation.

Table 2 presents estimations of the jaggedness in the segmentation respectively to the four examples presented and discussed in this section. This estimation involves projecting (adding) all the border pixels (0 or 1) onto the x-axis, yielding a vector s of length N , which is then thresholded by 20, resulting in s T . The maximum possible sum is determined in terms of the length of the vertical size of the image, in this case corresponding to M = 100 pixels. The jaggednees error is then obtained as:

jg = 1 8M N k=1 |M -s T [i]| (6)
Thus, the higher this index is, the more jagged the segmentation tends to be. Observe that this index assumes the geometry and other characteristics of the specific images and examples in this section, not being otherwise suitable.

Noisy Gradient

Having preliminary acquainted with the multiset neuron method for image segmentation respectively to image gradients with uniform noise, we now proceed to the consideration of linearly increasing noise along the gradient images. Figure 7 illustrates one such situation, as well as the respective segmentation by adopting the minimalistic parameter configuration D = 1, w = 0, and T h = 0.95. The results are completely undermined, and even the effect of increasing the number of prototype samples seems to have no effect on improving the results.

Figure 8 presents the segmentation of the same previous image, but now by adopting D = 1, w = 3, and T h = 0.95. The potentially dramatic effect of considering larger neighborhoods can be readily perceived from the obtained segmentation results, which are much more reasonable. In addition, the incorporation of an increasing number of sample on the enhancement of the obtained contours can also be plainly observed in this example. The effect of increasing the size of the neighborhood to w = 5 (meaning a square 11 × 11 neighborhood) is shown in Figure 9, leading to a further improvement in the quality of the obtained segmentation. Now, if we increase D to 4 and adopt T h = 0.80, but reduce w to 3, the obtained results will be as shown in Figure 10, which can be considered to be comparable to those obtained for the previous configuration (Fig. 9).

The results of the segmentation obtained by adopting D = 4, w = 5, and T h = 0.80, shown in Figure 11 can be considered the best among those presented in this section.

As before, we summarize the quality of the segmentations obtained respectively to the several examples and configurations in this section in terms of the jaggedness values shown in Table reftab:results2.

In order to provide some indication of the effect of taking the histogram of the pixels in the neighborhoods instead of sorting them into a feature vector as before, we present in Figure 12 the results of pixels histogramming respectively to D = 4, w = 5, T h = 0.20, and N h = 20.

Though the obtained regions have more similar widths, the quality of the contours is worst than some of the preliminary sorting-based results. It should be observed that the varying widths obtained in the case of sorted features do not represent a major problem, given that the extension of the regions can be conveniently controlled by varying the parameters and/or by combining two or more neurons.

Having familiarized ourselves with the application of the multiset neuron segmentation method and respective parameter configurations while considering synthetic images that allowed a systematic consideration of combinations between gray level and noise distributions, we now proceed to illustrating the potential of the proposed approach respectively to gray-level and RGB real-world images. 7 Real-World Gray-Scale Images

Figure 13 presents the segmentation of an image of a marble stone, represented by a respective gray-level image (a) by adopting D = 5, w = 3, and T h = 0.75, and T = 1. Five samples, identified by red crosses, have been selected as prototypes during the training stages. These prototypes should be marked in terms of their potential for representing the typical properties of the region of interest. It is often useful not to take the prototypes only from similar points, also considering samples indicating possible property variations, so as to enhance the generalization on the segmented patterns.

In the case of the present example, the background (instead of the blobs) was taken as the pattern to be segmented. Observe that, in these cases where the background is taken as the region to be identified, the threshold Th has an opposite influence, in the sense that larger values will imply in smaller blobs (but larger background). Most of the blobs have been identified and delimitated by smooth contours.

Real-World RGB Images

We now proceed to color images in the RGB format. Figure 14 presents the segmentation of a granite stone image by adopting D = 5, w = 3, and T h = 0.55, and T = 1. Despite the intricate nature of the texture in this image, the segmentation approach effectively identified and delimitated each of the blobs in an accurate manner.

Another example, concerning an image of completely distinct nature corresponding to flowers, is shown in Figure 15, respectively to D = 6, w = 3, T h = 0.55 and T = 1. A particularly selective segmentation has been implemented as a consequence of adopting D = 6. This allowed a detailed identification of the regions similar to the prototypes which were set not only on the petals, but also on the cores, with respective effects on the obtained results.

Yet another example, now concerning a landscape image incorporating several types of materials, patterns, textures, and colors, is presented in Figure 16, respectively to the configurationD = 1, w = 1, and T h = 0.65, and T = 1. The objective here was to identify the sky, which contains a mixture of blue and cloud patches. A particularly challenging aspect here is that most of the sky is seen through the intricate ramifications of the trees. Only five samples were chose, as indicated by the red crosses.

The obtained segmentation results can be understood to be particularly impressive, with most of the sky and including its reflection on the stream, being successfully identified by accurate contours.

Concluding Remarks

Image segmentation is a task as recurring as it is challenging in several image-related areas, including visual inspection, natural and artificial vision, robotics, and surveillance, among many others. A good deal of the difficulties in image segmentation stem from a wide range of possible types of variations arising not only intrinsically from the objects of interest, but also including shades, reflections, noise, and transparency. Yet, often the regions of interest still maintain some level of uniformity, in the sense of its parts between similar one another.

The present work presented an supervised approach to image segmentation based on multiset similarities -more specifically the real-valued coincidence similarity -implemented in terms of multiset neurons and respective networks. During the training stage, a multiset neuron is assigned for each sample taken from the image, which is considered as a prototype. The segmentation proper than proceeds by scanning the image pixels to each of the multiset neurons, with features extracted from the surroundings of the pixel fed as input to the neurons, which then performs a respective coincidence similarity comparison with its assigned prototype, yielding as output a value corresponding to the similarity between the prototype and each input features. The output from these neurons can then be combined in several manners, such as adding them in order to achieve better precision and generalization.

Though the above similarity-based framework can be implemented using other metrics and types of comparisons, several interesting properties of the multiset coincidence -including enhanced sensitivity and selectivity while comparing similar patterns, as well as robustness to localized feature perturbations, contribute to achieving more robust, selective and accurate image segmentation results.

The potential for image segmentation of the proposed concepts and methodology, as well as the setting of the few involved parameters, was illustrated in terms of several gray-level and color images, with impressive results. The typically positive effect of incorporating several prototypes has also been illustrated.

The allied to its simplicity, the impressive performance of the proposed multiset neuron approach for image segmentation paves the way to many future developments. These include a more systematic study of the parameters respectively to an even broader set of images. In particular, it would be interesting to investigate further how the setting of the overall parameter T can influence the selectivity and generalization of the segmentation. Other interesting possibilities concerns the consideration of multiple layers of multiset neurons, as well as the development of interactive systems allowing the setting of the parameters to be performed while observing the respective effects on the segmentation. The latter possibility is favored by the fact that the multiset neurons involve only basic, simple set and arithmetic operations. Indeed, they have been shown to be particularly suitable for analog hardware implementation [START_REF] Da | Multiset signal processing and electronics[END_REF], paving the way to real-time streaming image segmentation. It would also be interesting to evaluate the performance of the proposed method in the case one mixed images. Yet another possibility, which is being currently developed [START_REF] Da | Multiset-based image segmentation[END_REF], concerns the development of

Figure 2 :

 2 Figure2: Examples of the several possible schemes for defining the features within the adopted neighborhood of each of the image pixels. The whole set of pixels, or their statistical properties within each cell can be taken as features. It is also possible to have these features sorted so as to remove the dependency on the cell allocation, or normalized in several ways (e.g. minmax and standardization).

Figure 3 :

 3 Figure 3: Segmentation of a gradient image by considering 1 (a), 2 (b), 3 (c) and 4 (d) samples within each gray-level range of interest. The parameters were set as D = 1, w = 0, and T h = 0.95. This minimalistic parametric configuration is enough to ensure exact results throughout the image.

Figure 4 :

 4 Figure 4: Segmentation of a noisy gradient image by considering 1 (a), 2 (b), 3 (c) and 4 (d) samples within each gray-level range of interest. The parameters were set as D = 1, w = 0, and T h = 0.95. The added noise severely disrupted the shape of the boundaries of the segmented regions.

Figure 5 :

 5 Figure 5: Segmentation of a noisy gradient image by considering 1 (a), 2 (b), 3 (c) and 4 (d) samples within each gray-level range of interest. The parameters were set as D = 1, w = 3, and T h = 0.95. By providing a more comprehensive characterization of the image properties around each prototype, the adopted larger neighborhood greatly contributed to improving the quality of the segmentation.

Figure 6 :

 6 Figure 6: Segmentation of a gradient image by considering 1 (a), 2 (b), 3 (c) and 4 (d) samples within each gray-level range of interest. The parameters were set as D = 4, w = 3, and T h = 0.80.

Figure 4 samples 8

 48 Figure

Fig. 3 ; 4 ;Fig. 5 ;Fig. 6 ;Table 2 :

 34562 Fig. 3; D = 1; w = 0; T h = 0.95 0 0 0 0 Fig. 4; D = 1; w = 0; T h = 0.95 1.45 1.66 1.79 1.76 Fig. 5; D = 1; w = 3; T h = 0.95 0.662 0.656 0.533 0.533 Fig. 6; D = 4; w = 3; T h = 0.80 0.413 0.287 0.163 0.163 Table 2: Estimation of the jaggedness of the segmentations respectively to the four examples discussed in this section. The lower the presented values, the smoother the respective segmentation. The best results have been obtained by increasing w and D.

Figure 7 :

 7 Figure 7: Segmentation of a gradient image with progressive noise by considering 1 (a), 2 (b), 3 (c) and 4 (d) samples within each gray-level range of interest. The parameters were set as D = 1, w = 0, and T h = 0.95.

Figure 8 :

 8 Figure 8: Segmentation of a gradient image with progressive noise by considering 1 (a), 2 (b), 3 (c) and 4 (d) samples within each gray-level range of interest. The parameters were set as D = 1, w = 0, and T h = 0.95.

Figure 9 :

 9 Figure 9: Segmentation of a gradient image with progressive noise by considering 1 (a), 2 (b), 3 (c) and 4 (d) samples within each gray-level range of interest. The parameters were set as D = 1, w = 5, and T h = 0.95.

Figure 10 :

 10 Figure 10: Segmentation of a gradient image with progressive noise by considering 1 (a), 2 (b), 3 (c) and 4 (d) samples within each gray-level range of interest. The parameters were set as D = 4, w = 3, and T h = 0.80.

Figure 11 :

 11 Figure 11: Segmentation of a gradient image with progressive noise by considering 1 (a), 2 (b), 3 (c) and 4 (d) samples within each gray-level range of interest. The parameters were set as D = 1, w = 0, and T h = 0.95.

Figure 4 samples 8 samples 12 samples 16 samplesFig. 7 ;Fig. 8 ;Fig. 9 ;Fig. 10 ;Fig. 11 ;Table 3 :

 41678910113 Figure

Figure 12 :

 12 Figure 12: Segmentation of a noise gradient image by considering 1 (a), 2 (b), 3 (c) and 4 (d) samples within each gray-level range of interest by using the histogram with N h = 20 bins of the pixels within a 5 × 5 region around the pixel of interest. The parameters were set as D = 4, w = 5, T h = 0.20, and N h = 20. Regions with more uniform width were obtained as a consequence of the features, which correspond to the histogram counts, have more similar values than the ordered pixel values adopted otherwise in this work. However, more jagged borders were obtained comparatively to the results in Fig. 11.

Figure 13 :

 13 Figure 13: A gray-level image of a marble stone (a) with smooth and subtle patterns segmented (b) by the multiset neuron approach with D = 5, w = 3, T h = 0.75 and T = 1. Most of the blobs in the image have been identified by smooth and relatively accurate contours. The smoothness and accuracy of the segmentation can be controlled by varying D, w and T .

Figure 14 :

 14 Figure 14: An RGB image of a granite stone (a) and its segmentation (b) by the multiset neuron approach with D = 5, w = 3, T h = 0.55 and T = 1. Most of the stone details have been identified by relatively smooth contours. The segmentation allowed impressive performance despite the intricate texture pattern of this image.

Figure 15 :

 15 Figure 15: An RGB image of yellow fowers (a) and its respective segmentation (b) by the multiset neuron approach with D = 6, w = 3, T h = 0.55 and T = 1. Most of the stone details have been identified by relatively smooth contours.

Figure 16 :

 16 Figure16: An RGB image of a complex landscape (a) and its respective segmentation (b) by the multiset neuron approach with D = 6, w = 1, T h = 0.65 and T = 1. The sky has been successfully segmented despite its many variations in colors and texture and the fact that it is been mostly seen through the tree ramifications.

Table 1 :

 1 Needless to say, each choice of neighborhood and re-How many samples are taken into account for the segmentation. Larger values contribute to enhancing accuracy and generalization.Controls the number of features and context to be used in the comparison.Larger values contribute to enhancing the generalization but at the expense of border localization accuracy. The four parameters involved in the multiset neurons and their respective effects on the implemented comparison.

	param.	meaning	controlling effect
	N s	number of samples
	w	window width
	T	similarity threshold	Only coincidence values larger than T are retained. Higher values mean more strict comparison
	D	similarity exponent	Controls the sensitivity ab selectivity of the comparison. Higher values mean more sensitive and selective comparison

Acknowledgments.

Luciano da F. Costa thanks CNPq (grant no. 307085/2018-0) and FAPESP (grant 15/22308-2).

Observation:

As all other preprints by the author, this work is possibly being considered by a scientific journal. It is copyrighted, and respective modification or distribution of any of its parts are not allowed.


Cédric Leroy

Camille Fialin

Audrey Sirvent

Valérie Simon

Serge Urbach

Joël Poncet

Bruno Robert

Patrick Jouin

Serge Roche
email: serge.roche@crbm.cnrs.fr

Quantitative phosphoproteomics reveals a cluster of tyrosine kinases that mediates SRC invasive activity in

Keywords: tyrosine kinase Src, oncogenic signaling, colorectal cancer, phosphoproteomics, invasion

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

INTRODUCTION

The non receptor tyrosine kinase Src plays important roles in cell proliferation, survival and migration induced by extracellular stimuli including growth factors and integrins [START_REF] Thomas | Cellular functions regulated by Src family kinases[END_REF]. It also shows oncogenic activity when deregulated, a situation originally found with v-Src, the transforming product of the avian retrovirus RSV [START_REF] Yeatman | A renaissance for SRC[END_REF]. Since then, Src has been reported deregulated in human cancer [START_REF] Summy | Src family kinases in tumor progression and metastasis[END_REF]. Remarkably, elevated kinase activity has been found in more than 80% of CRC as compared to the normal counterpart and this has been associated with a poor clinical outcome [START_REF] Summy | Src family kinases in tumor progression and metastasis[END_REF]. How Src is deregulated in this cancer has not been clearly established yet. An activating mutation has been reported in a minority of advanced tumors, suggesting the existence of alternative mechanisms for kinase deregulation [START_REF] Irby | Activating SRC mutation in a subset of advanced human colon cancers[END_REF]. Accordingly, kinase activation often correlates with protein overexpression [START_REF] Yeatman | A renaissance for SRC[END_REF]. This molecular event is however not sufficient in a normal cellular context as Src is subjected to strict regulation that keeps the enzyme in an inactive form. Therefore, it has been postulated that Src activation additionally implicates alteration of important Src regulators by genetic or epigenetic mechanisms, including the inhibitory C-terminal Src tyrosine kinase Csk [START_REF] Yeatman | A renaissance for SRC[END_REF].

A large body of evidence points to Src deregulation as an important event for colon tumorigenesis and metastasis [START_REF] Summy | Src family kinases in tumor progression and metastasis[END_REF]. For instance, Src regulates growth, survival and invasion of some CRC cell-lines in vitro [START_REF] Summy | Src family kinases in tumor progression and metastasis[END_REF][START_REF] Emaduddin | Cell growth, global phosphotyrosine elevation, and c-Met phosphorylation through Src family kinases in colorectal cancer cells[END_REF]. Moreover, it contributes to tumor growth, angiogenesis and metastasis in xenografts nude mouse models [START_REF] Summy | Src family kinases in tumor progression and metastasis[END_REF]. Therefore, Src has become an attractive therapeutic target in this cancer and several small inhibitors are under clinical trials.

Intriguingly, these Src oncogenic signaling have been unraveled only partially. While Src interacts with components of the focal contacts and the adherent junctions for cell migration [START_REF] Yeatman | A renaissance for SRC[END_REF], substrates important for cellular growth and invasiveness have been poorly described.

MS-based quantitative phosphoproteomics has been a valuable tool to decipher signaling pathways initiated by a given tyrosine kinase [START_REF] Schmelzle | Phosphoproteomic approaches to elucidate cellular signaling networks[END_REF]. Among these, the Stable Isotope Labeling with Amino acids in Cell culture (SILAC) has been employed for the identification of oncogenic tyrosine kinase signaling such as HER2 [START_REF] Bose | Phosphoproteomic analysis of Her2/neu signaling and inhibition[END_REF] and Bcr-Abl [START_REF] Liang | Quantification of change in phosphorylation of BCR-ABL kinase and its substrates in response to Imatinib treatment in human chronic myelogenous leukemia cells[END_REF]. Here we used this approach to address Src oncogenic signaling in advanced CRC cells. By overexpressing Src in SW620 cells, we identified 136 substrates and/or tyrosine phosphorylation-associated proteins. These include proteins with signaling, cytoskeleton and vesicular trafficking functions, which may be important for the high Src invasiveness observed in these cells.

Additionally, we identified a small cluster of tyrosine kinases that mediates this Src oncogenic signaling, uncovering a tyrosine kinases network important for the induction of cellular invasion. Targeting these tyrosine kinases may therefore be of significant value in advanced CRC therapy. Biochemistry. Immunoprecipitation, Western blotting and siRNA transfections were performed as described in [START_REF] Sirvent | The tyrosine kinase Abl is required for Src-transforming activity in mouse fibroblasts and human breast cancer cells[END_REF]. Imunoprecipitates (ip, when indicated) or 20 to 50 µg of proteins of a whole cell lysate were loaded per lane on 9% SDS-PAGE gels and then transferred onto Immobilon membranes (Milllipore) as previously described. Detection was performed using the ECL system (Amersham). Optimal exposure times of membranes were used and protein expression and phosphorylation was quantified using ImageQuant TL software (Molecular Dynamics), and adjusted for background noise and protein loading.

MATERIAL AND METHODS

Reagents

RESULTS

Src promotes growth and invasiveness of the SW620 CRC cells.

We first transduced wild-type Src retrovirally in the metastatic CRC SW620 cells that exhibit a moderate level of endogenous Src as compared to the metastatic Colo205 cells [START_REF] Dehm | SRC transcriptional activation in a subset of human colon cancer cell lines[END_REF]. A >5 fold increase in Src protein level was obtained (Fig. 1A), which induced a strong increase in tyrosine phosphorylation content (Fig. 2A) together with significant cellular oncogenic activity: increased cell growth was observed both in standard (Fig. 1B) and soft agar conditions (Fig. 1C). Similarly, Src increased tumor growth by 3 fold in mouse xenograft assays (Supplementary Fig. S1). While Src moderately increased cell motility (Fig. 1D, left panel), it induced remarkable cell invasiveness (Fig. 1D, right panel). This cellular activity was still dependent upon Src activity as it was inhibited by the Src-like inhibitor SU6656 [START_REF] Laird | Src family kinase activity is required for signal tranducer and activator of transcription 3 and focal adhesion kinase phosphorylation and vascular endothelial growth factor signaling in vivo and for anchorage-dependent and -independent growth of human tumor cells[END_REF] (Fig. 1D, right panel). This data supports the hypothesis that the induction of the Src level is sufficient to induce its oncogenic activity even at late stage of tumorigenesis Quantitative Src phosphoproteomics in CRC cells.

We next sought Src substrates important for oncogenic signaling by quantitative phosphoproteomics using the SILAC approach. SW620-Src cells were labeled with "heavy" 13 C 6 15 N 4 -Arg-and 13 C 6 15 N 2 -Lys-containing medium while SW620 cells were cultured in "light" normal conditions. Cell lysates were next combined, and phosphotyrosine-containing proteins were purified using anti-phosphotyrosine antibodies. Proteins were separated by SDS/PAGE, digested with trypsin, and subjected to liquid chromatography-tandem MS [START_REF] Amanchy | Phosphoproteome analysis of HeLa cells using stable isotope labeling with amino acids in cell culture (SILAC)[END_REF].

Quantification was made from the relative intensities of Arg/Lys labeled tryptic peptides.

With this approach, we obtained 467 proteins, 136 of which exhibiting increased, and 13 reduced, tyrosine phosphorylation, upon Src expression (Fig. 2B and Supplementary Table S1). The small number of reduced phosphorylations was attributed to the cellular treatment of the tyrosine phosphatases inhibitor orthovanadate performed shortly before lysis in order to increase tyrosine phosphorylation content. Src-induced phosphorylation was next confirmed biochemically for 32/35 of them (Fig. 3 and4A), suggesting that more than 90% of the identified candidates are indeed Src substrates and thus validates the SILAC analysis. The absence of increased tyrosine phosphorylation content for 3 of them may be due to an increased association with Src substrates or/and the inability of the used antibodies to recognize the phosphorylated form of the protein. 41 of identified proteins have signaling, and 31 cytoskeletal functions (supplementary Table S2). Of some note are the CDCP1/PKCδ transmembrane signaling complex involved in survival [START_REF] Uekita | CUB domaincontaining protein 1 is a novel regulator of anoikis resistance in lung adenocarcinoma[END_REF], the E-cadherin/catenins complexes in mesenchyme induction and cell motility (2), and proteins of the ERM family in cell invasion [START_REF] Sahai | Differing modes of tumour cell invasion have distinct requirements for Rho/ROCK signalling and extracellular proteolysis[END_REF]. Additionally, Src targets the Wave2 and the alpha6/beta4 integrin complexes, recently identified as novel players of epithelial cell motility/invasion [START_REF] Iwaya | Correlation between liver metastasis of the colocalization of actin-related protein 2 and 3 complex and WAVE2 in colorectal carcinoma[END_REF][START_REF] Gimona | Assembly and biological role of podosomes and invadopodia[END_REF].

We also noticed a subgroup of signaling proteins that regulate the small GTPases of the Rho family, and which may modulate actinic cytoskeleton occurring during cell invasion [START_REF] Sahai | Differing modes of tumour cell invasion have distinct requirements for Rho/ROCK signalling and extracellular proteolysis[END_REF].

These include the Rho GAP ARAP1, 3 and p200 Rho GAP, and the Rho GEF Vav2 and FGD5, which has not been previously identified as a Src substrate. Unexpectedly, we found a third group of 23 Src targets with vesicular trafficking functions (supplementary Table S2).

This was exemplified with components of the COPII complex of the secretory pathway [START_REF] Sato | Mechanisms of COPII vesicle formation and protein sorting[END_REF] and VHS-containing proteins (Hrs and Tom1L1), involved in transmembrane proteins trafficking/signaling [START_REF] Lohi | VHS domain --a longshoreman of vesicle lines[END_REF].

Src phosphorylates a cluster of tyrosine kinases important for cell invasiveness.

The SILAC analysis confirmed a 6 fold increase in Src expression showing accuracy of the method, but it additionally identified a cluster of 7 protein tyrosine kinases and pseudotyrosine kinases as potential Src substrates (Table 1). This included the receptors Met, EphA2

and EphB2, and the cytoplasmic tyrosine kinases Fak and Syk and pseudokinases SgK223 and SgK269. Increased tyrosine phosphorylation was confirmed for 5 of them biochemically, ie Met, EphA2, Fak, Syk and SgK223 (Fig. 4A), and this correlated with a Src complex formation (not shown). Similar results were obtained from cells that were not treated with the phosphatase inhibitor orthovanadate, indicating that these phosphorylations were specific to Src expression (Supplementary Fig. S2A). We next investigated their role in Src-induced tyrosine phosphorylation. To this end, respective tyrosine kinase was inactivated by stably expressing a shRNA leading to >50% reduction of both mRNA (not shown) and protein levels (Fig. 4B) in SW620-Src cells. We observed that tyrosine phosphorylation was significantly reduced in cells with inactive SgK223 and Syk, and at a lower level in cells with inactive Met.

Accordingly, Src induced Met and Syk activation as shown by the increase level in the phosphorylated and activated form of these kinases (Supplementary Fig. S2B). Therefore Src may use these TyrK activities to induce maximal phosphorylation in these CRC cells.

Surprisingly, Src specific activity was also reduced in these cells with inactive Met, EphA2, Syk and SgK223, suggesting the existence of interplay between Src and these TyrK for signaling (Fig. 4C). The function for these kinases was next evaluated on Src oncogenic activity. We found that Src-induced anchorage-independent cell growth was strongly inhibited in all cell-lines, except for Met (Fig. 4D). However, none of them had a significant role in Src-induced cell proliferation (Supplementary Fig. S3), indicating that these TyrK may impact on survival signaling rather than cell-cycle progression, the later being probably ensured by phosphorylation of direct Src substrates.

The role for these TyrK was next evaluated on Src-induced invasiveness. We found that this cellular activity was strongly inhibited in all cell-lines with down-regulated tyrosine kinases, except Syk (Fig. 5A, left panel). Similar results were obtained from SW620-Src cells that were transiently transfected with specific siRNA targeting Met, EphA2 and Fak, indicating that these inhibitory effects were not due to off-targets and/or long-term depletion of these kinases (Supplementary Fig. S4C andS4D). In contrast, they had a low impact on the residual invasiveness of SW620 cells, confirming that their role is tightly related to Src S3). This compound may therefore target an additional kinase important for cellular proliferation to be identified.

Since very few information is available on SgK223, we wished to address its invasive function in more details. To this end, the rat ortholog Pragmin was reintroduced in SW620-Src cells with reduced Sgk223, which was not targeted with the used shRNA sequence.

Pragmin largely restored Src invasive activity, confirming a specific function for SgK223 in this signaling process (Fig. 5B and5C). Whether this uncovered function was regulated by Src was next investigated. The phosphosite data base http://www.phosphosite.org revealed that SgK223 was frequently phosphorylated on Tyr413. Accordingly, we found that Src induced a robust phosphorylation of Pragmin in these cells, unlike PragminY391F, in which the corresponding Tyr413 present in Pragmin was replaced by Phe (Fig. 5B). At the functional level, PragminY391F had a reduced rescuing effect in these cells (Fig. 5C). This set of data strongly suggested Src invasive signaling implicates phosphorylation of SgK223 on Tyr413 in advanced CRC cells.

Finally, we wished to confirm these data in cells expressing high endogenous Src, with the example of the metastatic CRC cell-line Colo205. As observed with SW620-Src, these cells required a Src-like activity to induce cellular invasion (Fig. 6D). We found that the tyrosine kinases Met, EphA2, Fak, Syk were also phosphorylated in a Src-like dependent manner, as suggested with the Src-like inhibitors SU6656, PP1 and PP2 (Fig. 6A). The variable effect of SU6656 and PP1 on these biochemical events were attributed to their capacity to target distinct pools of Src present in these cells rather than the targeting of additional kinases (not shown). SgK223 protein phosphorylation could not be assessed in these cells. This may probably due to the moderate efficacy of our antibodies and the low level of the expressed protein as observed on its mRNA level (not shown). At the functional level, all these phosphorylated TyrK, but not Syk, were required for cell invasion: downregulation of respective kinases (Fig. 6B) with the same shRNA strategy gave strong inhibitory effects (Fig. 6D). Thus, Src uses a similar TyrK network to induce its invasive activity in Colo205 cells, suggesting the existence of a common TyrK signaling network for oncogenic induction in advanced CRC cells.

DISCUSSION

Here we provide new insights into Src oncogenic activity in CRC. Firstly, our data show that normal Src overexpression can induce high invasiveness of advanced cancer cells in vitro, in close correlation with the proposed role for Src in CRC metastasis [START_REF] Summy | Src family kinases in tumor progression and metastasis[END_REF]. Secondly, applied quantitative SILAC phosphoprotemics uncovered several features of Src signaling in these cells, including phosphorylation/interaction of components of phosphoinositides and Rho GTPases-dependent pathways and regulators of F-actin, microtubule and vesicular trafficking networks. These data also show that Src targets more than a dozen of plasma membrane proteins, several of them having ligand, receptor/co-receptor functions related to cellular growth, survival and adhesion. Therefore, Src may use a wide range of reverse signaling processes important for its oncogenic activity as recently suggested with the Met receptor [START_REF] Emaduddin | Cell growth, global phosphotyrosine elevation, and c-Met phosphorylation through Src family kinases in colorectal cancer cells[END_REF].

Another important insight from this study relies on the interaction of Src with a cluster of tyrosine kinases required for cell invasion. Src may thus require additional tyrosine kinases to phosphorylate components of the invasive pathway. This cluster comprises the receptors Met and EphA2, previously reported interacting with Src [START_REF] Ponzetto | A multifunctional docking site mediates signaling and transformation by the hepatocyte growth factor/scatter factor receptor family[END_REF][START_REF] Fang | Overexpression of EPHA2 receptor destabilizes adherens junctions via a RhoA-dependent mechanism[END_REF] and being implicated in CRC tumorigenesis [START_REF] Birchmeier | Met, metastasis, motility and more[END_REF][START_REF] Merlos-Suarez | Eph-ephrin signalling in adult tissues and cancer[END_REF]. Moreover, our functional studies demonstrate that they mediate Src oncogenic activity in agreement with a reverse signaling process operating in these cells.

It also includes the cytoplasmic tyrosine kinase Fak, which has been largely implicated in Srcinduced cell motility and invasion [START_REF] Mitra | Integrin-regulated FAK-Src signaling in normal and cancer cells[END_REF]. Interestingly, Fak does not impact on the global tyrosine phosphorylation content in contrast to the other members of the cluster, suggesting that either it phosphorylates a discrete group of substrates or it has an adaptor function, as previously reported [START_REF] Mitra | Integrin-regulated FAK-Src signaling in normal and cancer cells[END_REF]. More surprisingly is the identification of Syk as a Src substrate in these cancer cells, which is inconsistent with its tumor suppressor role described in breast cancer [START_REF] Coopman | The Syk tyrosine kinase suppresses malignant growth of human breast cancer cells[END_REF]. Therefore, Syk may have distinct functions dependent on the tumor origin. In supporting this idea, Syk regulates Src-induced tyrosine phosphorylation and anchorageindependent growth of SW620-Src cells. This is further supported with the function recently reported in squamous cell carcinoma of the head and neck [START_REF] Luangdilok | Syk tyrosine kinase is linked to cell motility and progression in squamous cell carcinomas of the head and neck[END_REF]. Finally, we show that Src also uses SgK223 for its invasive activity. SgK223 belongs to the 10 % of catalytic-inactive kinases present in the human kinome ( 27) that nevertheless can have important oncogenic function. This has been first highlighted with the EGF receptor member HER3 in breast cancer. HER3 exhibits a co-receptor function that boosts signaling induced by other receptors of the family [START_REF] Sithanandam | The ERBB3 receptor in cancer and cancer gene therapy[END_REF]. Very few information is however available for SgK223. It belongs to the NFK3 sub-family that also includes SgK269, with unknown functions [START_REF] Manning | The protein kinase complement of the human genome[END_REF]. Interestingly, SgK223 has been recently identified as an important effector of the small GTPase Rnd2 for Rho-dependent F-actin assembly [START_REF] Tanaka | Pragmin, a novel effector of Rnd2 GTPase, stimulates RhoA activity[END_REF], which would fit with its pro-invasive activity in CRC cells. While it does not have apparent intrinsic tyrosine kinase activity (not shown), SgK223 strongly impacts on Src-induced tyrosine phosphorylation in cellulo. We thus hypothesize that it regulates Src phosphorylation events important for Rho-dependent signaling. Overall, these results assign an unanticipated role for SgK223 in Src oncogenic signaling and suggest an involvement of this pseudo-kinase in CRC advanced tumorigenesis.

Finally, our report suggests that Src orchestrates a tyrosine kinases signaling network for invasiveness. This notion has been recently suggested with the example of two recent phosphoproteomic studies revealing the phosphorylation of cluster of the TyrK FAK, EphA2, Met and ACK in Src-transformed mouse fibroblasts [START_REF] Luo | Global impact of oncogenic Src on a phosphotyrosine proteome[END_REF] and the cluster of TyrK including Met and EphA2 in non small cell lung cancer lines expressing activated ROS, ALK, PDGFRα and EGFR [START_REF] Rikova | Global survey of phosphotyrosine signaling identifies oncogenic kinases in lung cancer[END_REF]. Nevertheless, the function for these TyrK networks has not been investigated.

These reports together with our results thus suggest the involvement of a common TyrK signaling network operating during neoplastic transformation initiated by a deregulated TyrK.

Interestingly, our data additionally indicate that Met, Syk and SgK223 may in turn further activate Src, uncovering an interplay between Src and these tyrosine kinases. How Src interacts with these tyrosine kinases is an important issue that can be addressed by phosphoproteomics. Similarly, these results raise the interesting idea that Src deregulation could originate, at least in part, from the complex array of phosphorylated/activated tyrosine kinase receptors recently observed in other solid tumors with important oncogenic activities [START_REF] Stommel | Coactivation of receptor tyrosine kinases affects the response of tumor cells to targeted therapies[END_REF]; thus targeting Src activity could be of therapeutic value in these cancers.

Disclosure of Potential Conflicts of Interest

No potential conflicts of interest were disclosed.

Table1. List of Tyrosine kinases targeted by Src.

.

  Human Src has been subcloned in pMX-pS-CESAR. Construct expressing Pragmin (a gift of Dr Negishi, Kioto University) and PragminY397F that was generated by mutagenesis have been subcloned into pBABE retroviral vectors. pRETRO-SUPER expressing shRNA used in this study were from TRANSAT (Saint-Priest, France). Targeting sequences inserted in shRNA constructs were GACACTCGGTAGTCTATAC (control), ). siRNA targeting sequence were form Darmacon and incuded AATTCTCCGAACCTGTCACGT (control), AAAGATAAACCTCTCATAATG (met), AATGACATGCCGATCTACATG (EphA2) and AAGCTAGTGACGTATGGATGT (Fak).

(

  NewOjective, Woburn, USA) with an outlet diameter of 8 µm. Spectra were recorded using the Analyst QS 1.1 software (Applied Biosystems). Parameters were adjusted as follows: ion spray voltage (IS), 1800 V; curtain gas (CUR), 25; declustering potential (DP), 75 V; focusing potential (FP), 265 V; declustering potential 2 (DP2), 15 V. Spectra were acquired with the instrument operating in the information dependant acquisition mode throughout the HPLC gradient. Every 7 s, the instrument cycled through acquisition of a full-scan spectrum (1 s) and two MS/MS spectra (3 s each). Peptides fragmentation was performed using nitrogen gas on the most abundant doubly or triply charged ions detected in the initial MS scan, with a collision energy profile optimized according to peptide mass (using manufacturer parameters), and an active exclusion time of 0.60 min. All MS/MS spectra were searched against the Homo sapiens entries of either Swiss-Prot or Trembl databases (release 53.0: http://www.expasy.ch), by using the Mascot v 2.1 algorithm (http://www.matrixscience.com). Search parameters were mass accuracy 0.1 Da for MS and MS/MS data ; 1 miscleavage ; variable modifications: oxidized methionine, Phospho (Y), SILAC-labels : Lys-8 and Arg-10. All significant hits (p<0.05) were manually inspected. Quantification was performed by using MSQuant v1.4.1 software developed by Mann and colleagues (http://msquant.sourceforge.net). Data were manually inspected and corrected when necessary. All significant hits were manually inspected.

  activity (FigS4A and B). The absence of Syk function was next confirmed with the orally available Syk catalytic inhibitor BAY 61-3606[START_REF] Yamamoto | The orally available spleen tyrosine kinase inhibitor 2-[7-(3,4-dimethoxyphenyl)imidazo[1,2-c]pyrimidin-5-ylamino]nicotinamide dihydrochloride (BAY 61-3606) blocks antigen-induced airway inflammation in rodents[END_REF], that did not affect invasiveness (Fig.5A, right panel), while reducing Syk tyrosine phosphorylation (Fig. 6C, bottom panel). In contrast, the Met inhibitor SU11274 (19) (Fig. 6C, upper panel) blocked this cellular response (Fig. 5A, right panel), confirming the requirement of Met in this Src oncogenic function. It should be mentioned that BAY 61-3606 strongly inhibited cellular proliferation, in contrast to what observed in cells with reduced Syk (Supplementary Fig.

Figure 1 .

 1 Figure 1. Src promotes cell growth and invasiveness of SW620 cells.

Figure 2 .

 2 Figure 2. Src phosphoproteomic analysis in SW620 cells using SILAC.

Figure 3 .

 3 Figure 3. Validation of Src-induced protein tyrosine phosphorylation by

Figure 4 .Figure 5 .

 45 Figure 4. Src targets a cluster of tyrosine kinases important for increased tyrosine

Figure 6 .

 6 Figure 6. Src targets a similar cluster of tyrosine kinases for the promotion of

Growth of SW620 xenografts in nude mice. In

	and BAY 61-3606 from Sigma Aldrich. Antibodies used in this study were 4G10 (42), pY100 invasion assays were performed in Boyden chambers (BD Bioscience, Le Pont de Claix, 80% B in 15 min (A = 0.1% formic acid, 2% acetonitrile in water ; B = 0.1 % formic acid in
	(CST, Danvers, MA), ARAP3 (33), anti-Calponin (34), anti-p130Cas (Upstate Inc, France) using 50,000 and 100,000 cells respectively, and in the presence of 100 μl of Matrigel acetonitrile) at 300 nl/min was used to elute peptides from the capillary (0.075 mm x 150 mm)
	Charlottesville, USA), anti-α-δ Catenin (BD Biosciences), anti-Cbl (sc-170, Santa Cruz (2 mg/ml) (BD Bioscience) for invasion assays. reverse-phase column (Pepmap ® , Dionex), fitted with an uncoated silica PicoTip Emitter
	Biotech, CA) anti-CHC X22 (Alexis Biochemicals, San Diego, CA), anti-EphA2 (sc-924,
	Santa Cruz Biotech), anti-EphB2 (sc-28980, Santa Cruz Biotech), anti-ezrin (35), anti-Fak vivo experiments were performed in
	(Upstate Inc), anti-pY861Fak, (sc-16663, Santa Cruz Biotech), anti-Hrs A-5 (Alexis compliance with the French guidelines for experimental animal studies (Direction des
	Biochemicals), anti-IRSp53 (36), anti-Met (sc-161, Santa Cruz Biotech), anti-Services Vétérinaires, Ministère de l'Agriculture, agreement no. B 34-172-27) and fulfil the
	pY1234/1235Met (Invitrogen, CA), anti-Moesin was generated against the purified protein as U.K. Coordinating Committee on Cancer Research guidelines for the welfare of animals in
	experimental neoplasia. Swiss nu/nu (nude) mice (Charles River, L'Arbresle, France) were
	injected s.c. with 2.10 6 of SW620 cells infected with the control virus in one flank and with
	the same amount of cells infected with the Src virus on the opposite side. Tumor growth was
	assessed by tumor volume and tumor mass measurement at indicated days.
	Mass Spectrometry analysis. SILAC ( 13 C 6	15 N 4 -Arg-and 13 C 6	15 N 2 -Lys as heavy labeling),
	phosphotyrosine immunoaffinity purification (using a mixture of a mixture of 4G10 and
	pY100 antiboies), and tryptic digests were essentially performed as described in (11) except
	that cells (2.10 8 ) were treated with the tyrosine phosphatases inhibitor orthovanadate (1 mM)
	for 1 h before lysis to increase tyrosine phosporylation content. 50 mM β-octylglucoside was
	also added in the 1% Triton X-100 lysis buffer to increase protein solubilization. Purified
	the generation of stable expressors in a polyclonal background. Src expressors were isolated proteins were next separated on a SDS-PAGE gel. Trypsin-digested samples (1 µl) obtained
	by FACS and shRNA expressing cells by puromycin (1 μg/ml) selection. Standard cell from 44 cut gel slices were next analyzed online using nanoflow HPLC-nano-electrospray
	growth was performed in 2% serum medium and measured by Sulforhodamide B staining ionization on a quadrupole time-of-flight (Q-TOF) mass spectrometer (QSTAR Pulsar-i,
	13 C 6 15 N 4 -Arg and 13 C 6 (Sigma Aldrich). Soft-agar cell growth was performed as in (44). After 18-21 days colonies 15 N 2 -Lys were from Invitrogen (Cergy, France) and Sigma Applied Biosystems, Foster City, CA) coupled with an Ultimate 3000 HPLC (Dionex,
	having >50 cells were scored as positive. Cells were treated every day for standard conditions Amsterdam, Netherland). Desalting and pre-concentration of samples were effectued on-line
	and every week for soft-agar assays, with vehicle or indicated drug. Cell migration and on a Pepmap ® precolumn (0.3 mm x 10 mm). A gradient consisting of 0-40% B in 60 mn,

Aldrich (St Quentin, France) respectively, anti-pTyr column from Upstate Inc (Charlottesville, USA), SU6656 and PP1 and PP2 from Calbiochem (La Jolla, CA), SU11274 in

[START_REF] Andreoli | Ezrin has properties to self-associate at the plasma membrane[END_REF]

, anti-Odin (37), anti-Radixin was generated against the purified protein as in

[START_REF] Andreoli | Ezrin has properties to self-associate at the plasma membrane[END_REF]

, anti-Sam68

[START_REF] Lock | The human p62 cDNA encodes Sam68 and not the RasGAP-associated p62 protein[END_REF]

, anti-SgK223 (

29

) was raised against the GST-Pragmin N-terminus, anti-SgK269 3G6 (Abnova GmbH, Heidelberg, Germany), anti-SHIP1 (39), anti-SHP2 (sc-280, Santa Cruz Biotech), anti-Src family kinases

[START_REF] Roche | DNA synthesis induced by some but not all growth factors requires Src family protein tyrosine kinases[END_REF]

, anti-p85α subunit of PI3K

[START_REF] Roche | The phosphatidylinositol 3-kinase alpha is required for DNA synthesis induced by some, but not all, growth factors[END_REF]

, anti-PLCγ (42), anti-pY418Src (Biosource), anti-Stam1/2 (43), anti-Syk (sc-1240, Santa Cruz Biotech), anti-pY325Syk and anti-pY524/525Syk (CST, Danvers, MA), anti-tubulin

[START_REF] Roche | Requirement of phospholipase C gamma, the tyrosine phosphatase Syp and the adaptor proteins Shc and Nck for PDGF-induced DNA synthesis: evidence for the existence of Rasdependent and Ras-independent pathways[END_REF]

, anti-Vav2 (sc-20803, Santa Cruz Biotech), anti-Wave2 (36), anti-ZO1 (sc-10804, Santa Cruz Biotech), anti-ZO2 (sc-11448, Santa Cruz Biotech).

. Cell infections, growth, migration and invasion. SW620 and Colo205 cells (ATCC, Rockville, MD) were grown, infected and selected as described in

[START_REF] Sirvent | The tyrosine kinase Abl is required for Src-transforming activity in mouse fibroblasts and human breast cancer cells[END_REF]

. This procedure allows

ACKNOWLEDGEMENTS

We thank P. Coopman, P. Mangeat, F. Hollande, C. Gauthier-Rouvière, B. Payrastre, G. Scita, E. Stradal, P. Lock and M. Negishi for various reagents, S. Perrier and M. Brissac for technical assistance and members of the laboratory for helpful discussion. This work was supported by the CNRS, University of Montpellier 1 and 2, Association pour la Recherche contre le Cancer (ARC, n° 4025), lnstitut National du Cancer INCa and the Canceropole GSO. CL is supported by the Ligue Contre de Cancer, LCC, and AS by INCa. SR is an INSERM investigator.