Michaël Sdika

Combining Atlas Based Segmentation and Intensity Classification with Nearest Neighbor Transform and Accuracy Weighted Vote

Keywords: atlas based segmentation, intensity classification, distance transform, nearest neighbor transform, multi atlas segmentation, weighted vote, accuracy map, cortical segmentation

In this paper, different methods to improve atlas based segmentation are presented. The first technique is a new mapping of the labels of an atlas consistent with a given intensity classification segmentation. This new mapping combines the two segmentations using the nearest neighbor transform and is especially effective for complex and folded regions like the cortex where the registration is difficult. Then, in a multi atlas context, an original weighting is introduced to combine the segmentation of several atlases using a voting procedure. This weighting is derived from statistical classification theory and is computed offline using the atlases as a training dataset. Concretely, the accuracy map of each atlas is computed and the vote is weighted by the accuracy of the atlases. Numerical experiments have been performed on publicly available in vivo datasets and show that, when used together, the two techniques provide an important improvement of the segmentation accuracy.

Introduction

Atlas based segmentation is now an established technique in medical image processing. It involves using a non-rigid registration algorithm to find a geometric transformation from the subject image to a pre-labeled atlas. The transformation is then used to map the preexistent segmentation of the atlas on the subject [START_REF] Miller | Mathematical textbook of deformable neuroanatomies[END_REF][3] [START_REF] Warfield | Nonlinear Registration and Template Driven Segmentation[END_REF]. When the images to be segmented are all the same up to a spatial deformation, this approach allows to accurately segment all the structures of the subject image in a single pass. Segmentation errors can occur if the registration is trapped in a local minimum. Typically, registration will fail when the structures to match are complex, like in the cortical area of human brain image, or when the contrast-to-noise ratio is low.

To improve the result of atlas based segmentation, one can use an atlas set instead of a single atlas; one can also try to combine the result with other segmentation methods. In this paper, a multi atlas method and a multi segmentation method are proposed and it is shown that, when used together, these methods produce a substantial improvement over state of the art techniques.

Atlas based segmentation is often combined with an intensity classification method. Pixels are, with these methods, clustered based on their intensity. Classes can be found directly on the histogram [START_REF] Otsu | A threshold selection method from gray-level histograms[END_REF] but models can also be much more complex to Email addresses: michael.sdika@univmed.fr (Michaël Sdika) URL: http://crmbm.timone.univ-mrs.fr (Michaël Sdika) 1 This work is supported by CNRS (UMR 6612) and Institut Universitaire de France account for dependencies between pixels and bias field inhomogeneities [START_REF] Wells | Adaptive segmentation of MRI data[END_REF] [START_REF] Zhang | Segmentation of brain MR images through a hidden Markov random field model and the expectation maximization algorithm[END_REF]. Atlases can also be used in this framework to provide spatial priors to the classification [START_REF] Van Leemput | Automated model-based tissue classification of MR images of the brain[END_REF] [START_REF] Pohl | Incorporating nonrigid registration into expectation maximization algorithm to segment MR images[END_REF]. Typical use of these intensity classification methods includes segmentation of brain images in white matter (WM), grey matter (GM) and cerebro-spinal fluid (CSF). In [START_REF] Fischl | Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain[END_REF], the intensity classification is performed using a more complete atlas: the atlas not only contains information about the probability of a class at a given pixel but also about the intensity distribution given the label and about the distribution of the class of its neighbors. The same procedure has also been applied to surface parcellation in [START_REF] Fischl | Automatically parcellating the human cerebral cortex[END_REF]. More recently, registration and intensity classification have been described by a joint model as in [START_REF] Wyatt | Map MRF joint segmentation and registration of medical images[END_REF], [START_REF] Pohl | A Bayesian model for joint segmentation and registration[END_REF] or [START_REF] Yeo | Effects of registration regularization and atlas sharpness on segmentation accuracy[END_REF]. Solutions to these models are found by iteratively performing registration and segmentation, each step using the output of the previous one. The registration is improved by the information provided by the intensity classification, and spatial priors provided by the registration are used for intensity classification. The atlas based segmentation can also be combined with the results of intensity classification by subdividing the labels mapped from the atlas with the different classes of the tissues classification. This technique was introduced in [START_REF] Maes | Quantification of cerebral grey and white matter asymmetry from MRI[END_REF] for the separation of left and right tissues segmentation. It works well when the structures of the atlas are correcty matched. In [START_REF] Collins | Ani-mal+insect: Improved cortical structure segmentation[END_REF], an atlas based segmentation coherent with an intensity classification method is proposed. The atlas provides some maps of the most probable label given the tissue type (WM, GM or CSF) and the labeling is made after intensity classification using the label map corresponding to the tissue. This technique has been specifically designed for the cortical region where the registration is likely to fail but where intensity classification performs well.

To improve atlas based segmentation, some authors have also proposed to use a set of atlases instead of a single atlas. Results are then combined to create the best agreement segmentation. In [START_REF] Warfield | Validation of image segmentation and expert quality with an expectation-maximization algorithm[END_REF] an EM based procedure is proposed to find the best agreement between the binary segmentations of a group of experts. This method has been extended in [START_REF] Rohlfing | Performancebased classifier combination in atlas-based image segmentation using expectation-maximization parameter estimation[END_REF] to multi class segmentation and applied to the problem of atlas based segmentation with multiple atlases. The best agreement can also be found by shape based averaging as in [START_REF] Rohlfing | Shape-based averaging[END_REF]. For each label, distance maps to the segmentation of each atlas are averaged. The output label is chosen to minimize the mean distance maps. The use of a voting procedure has been investigated in [START_REF] Rohlfing | Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains[END_REF] [START_REF] Rohlfing | Multi-classifier framework for atlasbased image segmentation[END_REF] or [START_REF] Heckemann | Automatic anatomical brain MRI segmentation combining label propagation and decision fusion[END_REF] and although simple, this method produces good results. In [START_REF] Artaechevarria | Combination strategies in multi-atlas image segmentation: application to brain MR data[END_REF] and [START_REF] Isgum | Multi-atlas-based segmentation with local decision fusion -application to cardiac and aortic segmentation in CT scans[END_REF], a pixelwise weight is given to the vote of each atlas. Weights are a function of the registration residual in [START_REF] Isgum | Multi-atlas-based segmentation with local decision fusion -application to cardiac and aortic segmentation in CT scans[END_REF] and a distance (e.g. normalized cross correlation or mutual information) between the neighborhood of the pixel in the subject and the atlas in [START_REF] Artaechevarria | Combination strategies in multi-atlas image segmentation: application to brain MR data[END_REF].

The present paper contains several contributions. First we propose a mapping of the atlas labels consistent with the intensity classification segmentation of the subject and show how it can be used to segment cortical structures. For the multi atlas part, the concept of accuracy map of an atlas is introduced, an estimator of this quantity is proposed as well as its use as an effective weight in a voting procedure. This new weighting is particularly efficient when different methods are used to label an image and it is shown that, together, the two techniques produce a substantial improvement of the segmentation. For each method, numerical experiments have been conducted on in vivo data.

Combining Atlas Based Segmentation and Intensity classification with Nearest Neighbor Transform

In this section, it is assumed that a single atlas (a grey level image and its manual labeling) is available. An intensity classification method is also available to segment any grey level image into different classes. The objective is to make the atlas based segmentation of a new subject consistent with its intensity classification as in the ANIMAL+INSECT method described in [START_REF] Collins | Ani-mal+insect: Improved cortical structure segmentation[END_REF]. However, our method does not require the use of a probabilistic atlas. Using intensity classification will be especially useful in cortical region where local minima of the registration induce frequent defect of the atlas based segmentation (see example on Fig 1). To be clear in our presentation, label or labeling will be used for the atlas based segmentation and class or classification will be used for the intensity classification segmentation.

Standard Atlas based Labeling

Let I a be the grey level image of the atlas and L a be the image of the labels of interest. Now, let I s be the grey level image of the subject to be labeled and T , the transformation from the subject to the atlas found by the registration. The classic way to label the subject image is simply to map the label image L a with the transformation:

L s (x) = L a (T (x)).
(1)

Atlas Labeling using the Nearest Neighbor Transform

Let's assume now that both I a and I s have been segmented into C a and C s using the intensity classification method. The labeling proposed here assigns to each pixel of the subject the label of the closest atlas point of the same class. As a consequence, the subject have a labeling coherent with the classification: GM pixels are mapped to GM pixels... To compute the closest point, the Euclidean distance in the subject coordinate system is used.

In concrete terms, the labels and the classes of the atlas are first mapped on the subject:

L a s (x) = L a (T (x)), C a s (x) = C a (T (x)),
then, for each class, the nearest neighbor transform to the current atlas class is computed:

p(x, c) = argmin C a s (y)=c d(x, y), (2)
where d is the Euclidean distance. Finally, the label assigned to a pixel x is the label of its nearest neighbor in its class:

L s (x) = L a s (p(x, C s (x))).

Improving the Robustness

An important requirement of the new labeling is that borders of L a and C a match exactly when they are supposed to. Indeed, as the border pixels are also the nearest neighbors, any mismatch between the two segmentations will have a strong influence on the final labeling. Ideally, L a and C a have been produced together with, for example, C a used as starting point of the manual process to create L a .

In practice, such a C a map is not always available or may not be reproduced by the automatic intensity classification that will be used on the subject. A solution is to create it by running the intensity classification on the atlas image. In this case, as L a and C a have been produced independently, their borders will mismatch. The labeling can be significantly improved if a binary morphological erosion is applied to each class of the atlas before computing the nearest neighbor transform. The erosion will remove the border of the current class and make the nearest neighbor of each point clearly inside the class. The nearest neighbor computation (eqn. 2) is just replaced by:

p e (x, c) = argmin y∈C e,a s,c d(x, y),
where C e,a s,c is the result of an erosion of the C a s = c mask. 2

Multi Atlas Segmentation with Accuracy Weighted Vote

In this section, the concept of accuracy map of an atlas is introduced. A practical estimator for accuracy maps and their use as an effective weighting in a voting procedure are also presented.

The accuracy of a classifier is the likelihood that the output of the classifier is correct. Here, each pixel of the atlas is seen as a classifier. So the value of the accuracy map at pixel x is the probability that pixels segmented with x will be correctly labeled.

Note that, here, atlas refers to the grey level image, its segmentation and the labeling method used to segment other images. If the standard and the nearest neighbor transform based labeling are used, the number of atlases and accuracy maps will be twice the number of pre-segmented images.

Examples of accuracy maps of an atlas with the two labelings are shown on Fig. 2. As expected, segmentation with nearest neighbor transform based labeling is better on cortical regions whereas standard labeling is better on deep grey structures. This result is a direct translation to the new labeling of the behavior of the intensity classification algorithm. Indeed, the typical three classes Gaussian mixture model used is not really suited due to the intensity difference between deep grey and cortical grey matter, and consequently deep grey matter are problematic for theses methods. For example, parts of the thalamus has an intensity similar to white matter and consequently, are often classified as such.

An Estimator for the Accuracy

The accuracy map of an atlas is estimated here using the remaining pre-segmented images as a training dataset. Goodness of segmentation maps are built using each of the other images. These maps are then averaged in the atlas coordinate system. Formally, our dataset is composed of N a images (I i ) i along with their pre-existing segmentations (L i ) i . To estimate the accuracy map of the first atlas, the (non-rigid) transformation T i from I i to I 1 is computed and used to produce Li , the automatic segmentation of the i th images by the first atlas. The goodness map is then computed as follows:

g i (x) = 1 if L i (x) = Li (x) 0 otherwise.
The accuracy map q 1 of the first atlas is then estimated by averaging the goodness maps in its own coordinate system:

q 1 (x) = 1 N a -1 i 1 T -1 i (g i (x)). ( 3
)
This procedure is then repeated to compute the accuracy map of each atlas.

Combining Results with Accuracy Weighted Vote

When a set of atlases is available for the segmentation, voting is a simple and efficient way to combine the result of the segmentations. Each atlas is used to segment the subject, then, the final label at a given pixel is the most frequent one. In basic implementation of voting procedure, each atlas has an equal vote. Consequently, the vote does not account for spatial systematic error that an atlas can produce. We propose to weight the vote of each atlas with its accuracy map. When the label image of an atlas is mapped in the subject reference, the accuracy map is mapped as well and used as weight in the vote. Thus more weight is given to more accurate pixels.

Formally, the label chosen by the vote procedure is:

argmax l L n (T n (x))=l w n (x),
where l index the labels, L n is the ground truth segmentation of the n th atlas and T n the transformation from the subject to the n th atlas. When the vote is unweighted, the w n coefficient is constant (w n (x) = 1). For accuracy weighted vote, w n is the corresponding accuracy of the n th atlas at this location: w n (x) = T n (q n (x)).

Implementation Details

Any non-rigid registration method can be used to compute the transformation from the subject image to the atlas. However, a topology preserving registration would ensure that structures of the atlas are not lost when mapped on the subject. For the experiment of this paper, the method described in [START_REF] Sdika | A fast nonrigid image registration with constraints on the Jacobian using large scale constrained optimization[END_REF] has been used. The transformation is modeled by cubic B-spline (with a node spacing of 6 pixels) and non invertibility is penalized by enforcing positivity of the Jacobian on the pixels and penalizing negative Jacobian between the pixels. A least square cost function as been used within a multi-resolution framework.

Invertible transformations are also preferable for the computation of accuracy maps with formula 3. The inverse of a transformation T is computed using a Levenberg-Marquard routine on the following problem:

T -1 (x) = argmin y x -T (y) 2 2 .
The distance and nearest neighbor transform have been computed in linear time using the algorithm of [START_REF] Felzenszwalb | Distance transforms of sampled functions[END_REF].

As it is the case for registration, any method can be used for the intensity classification. For our experiments, the method of [START_REF] Zhang | Segmentation of brain MR images through a hidden Markov random field model and the expectation maximization algorithm[END_REF] has been used. It alternatively estimates the tissue class label of pixels (assumed to be the realizations of a Markov random field), the parameters of the Gaussian mixture modeling the intensity distribution and the bias field inhomogeneities.

The resampling of label images mapped through a geometric transformation has been done using partial volume interpolation (PVI) (see [START_REF] Maes | Multimodality image registration by maximization of mutual information[END_REF]). To label a continuous point, its 8-nearest neighbors vote for their own label with a weight taken as their coefficient in a trilinear interpolation procedure. The label with the highest vote is assigned to the point. In our experience, this resampling technique provides better results than nearest neighbor interpolation. Mapping of goodness of segmentation maps and accuracy maps, have been done using trilinear interpolation to ensure that their values remain in [0, 1].

Numerical Experiments

Numerical experiments have been performed using two publicly available in vivo datasets. The NA0 dataset provided by the Non-Rigid Image Registration Evaluation Project2 (NIREP) consists of 16 3D MR images of healthy human brains. On each image, the cortex has been segmented into 32 grey matter regions of interest using a combination of manual and automated procedures. The IBSR dataset provided by the Center for Morphometric Analysis at Massachusetts General Hospital3 contains 18 images of healthy brains and the corresponding segmentation of the whole brain into 32 structures. Example images from these two dataset are shown on figure 3.

The relative overlap (RO) and the mean accuracy (MA) has been used to assess the quality of an automated segmentation L a when a ground truth L g is available. The RO of the structure s is given by the ratio of the intersection of the two segmentations to their union and the MA is the percentage of correctly segmented pixel in the volume of interest (the whole brain): MA(L g , L a ) = 100

RO(L s g , L s a ) = 100 V L s g ∩ L s a V L s g ∪ L s a , (
V L g = L a V L g .
For each experiment, different methods are compared using a leave one out procedure. Each image is used as a subject with a ground truth segmentation and automatically labeled using the others as atlases. The average RO or MA is collected for each subject and finally, mean and standard deviation over the different subjects are reported.

Cortical Segmentation using Nearest Neighbor Transform Labeling

The NA0 database has been used to evaluate the nearest neighbor transform based labeling. The leave one out procedure is used to compare the three labelings presented in section 2. Results are reported on Fig. 5(a) for the standard labeling (std), the nearest neighbor transform based labeling of section 2.2 (nn) and the nearest neighbor transform with erosion labeling of section 2.3 (nne). The structures of interest are all in the cortex, in the left part of the brain (similar results are obtained for the right hemisphere). The full names of all cortical structures are given in appendix B.

As shown in Fig. 5(a), nn and nne provide a substantial improvement over standard labeling for all the structures. Indeed, registration is likely to fail in cortical region where structures are complex and folded. It can also be seen that nne provide a further improvement over nn for most of the structures.

Figure 4 displays the results of the three labelings in a region where the registration fell into a local minimum. The folding of the cortex has not been correctly recovered by the registration leading to the failure of std. With nn, the labeling respects the tissue classification computed on the subject image and the result is coherent with the topology of the brain as seen by the intensity classification. However, the segmentation produced with nn is somewhat rough and seems inaccurate at the border of the structures. The nne labeling preserves the advantages observed with nn but the segmentation is cleaner and smoother.

During the registration, constraints are imposed to be able to invert the transformation. One can wonder if these constraints are beneficial or not to the to the labeling of cortical structures. The same experiments has been done a second time with an unconstrained registration and the RO difference between constrained and unconstrained registration is reported on Fig. 5(b), for each structure and each labeling (difference is positive when constrained registration is better). One can see that, for the std labeling, unconstrained registration is preferable for the segmentation of cortical structures. However, with nn, the difference is less pronounced and with nne, no difference is visible. When used in an accuracy weighted vote context, only constrained registration can be used for the computation of the accuracy maps as the inverse of the transformation is needed. Note that this imposes the use of a constrained registration during the labeling itself for a good representativity of the accuracy maps.

Multiple Atlas Segmentation with Accuracy Weighted Vote

The accuracy weighted vote has been evaluated on the IBSR dataset. std and nne labeling (or both) are used with a basic majority vote (bv) or the proposed accuracy weighted vote (awv). These methods are evaluated and tested using a leave one out procedure for different numbers of atlases. Care has been taken not to use the ground truth labels of an atlas when it is used as the subject. In particular, when a set of N atlases is used to label a subject, the corresponding accuracy maps are built from these N atlases exclusively. 5: Results of the leave one out evaluation for cortical segmentation with the standard labeling (std), the nearest neighbor transform based labeling (nn) and the nearest neighbor transform with erosion labeling (nne). Labels abbreviations are given in appendix B (a) relative overlap on each structure with constrained registration and the three labelings, (b) the difference of relative overlap between constrained and unconstrained registration (difference is positive when constrained registration is better).

On Fig. 6 are displayed the RO for the cerebral cortex and the caudate as well as the mean RO over all the structures and the MA in the brain. On Fig. 7, the mean and standard deviation of the RO are reported for each structure when 3 or 16 atlas are used.

One can first see on all graphs of Fig. 6 that for a given labeling, the mean RO is higher with awv than with bv. On Fig. 7, when both labelings are used, this result is confirmed for all structures with the exception of CuWM when 3 atlases vote.

As the intensity classification used performs poorly on deep grey structures, segmentation with nne alone gives bad results on the caudate (Fig. 6(b)). For this structure, all five other methods perform better, especially when awv is used. On the contrary, for the left cerebral cortex (Fig. 6(a)), nne performs better than std and vote with both labeling provide a further important improvement over nne. Note that for std +nne, a high number of atlases is required by bv to reach the performance that awv achieve with few atlases. One can also see that, whatever the number of atlases, about 15 points improvement is achieved when both labelings are combined with awv compared to a standard atlas based segmentation with a majority vote.

If the average over all the structures is considered (Fig. 6(c)), the methods can be classified in three groups. nne with either bv or awv which perform poorly. Then come std with either vote and std +nne with basic vote. This group achieves an improvement of about 6 points over nne. The best results are obtained by accuracy weighted vote with both labelings for which the mean RO is 4 points higher than the second group. Indeed, as shown in Fig. 7 nne is better than std only on few structures: CuCo, CuWM, CaCo and CaWM. Most of the time, std +nne is better or as good as the best of std and nne with either vote technique. For ventricular structures (LatVe, 3Ve and 4Ve) bv does not perform as well as awv to the addition of the poorer nne segmentation in the vote.

On the mean accuracy graph (Fig. performs better on more structures than nne but the few structures for which nne is better have larger volumes. However, in agreement with RO measures, awv always performs better than bv. One can finally see that for all the measures we used, accuracy weighted vote using the two labelings together is always the best method and clearly outperforms the other methods.

Discussion

In this article, several contributions to atlas based segmentation have been proposed. First, a new labeling technique to combine the output of atlas based segmentation with intensity classification segmentation has been presented. Each pixel is labeled by the closest point of the atlas belonging to the same tissue class. As a consequence, labels are assigned coherently with the intensity classification. This labeling is beneficial for highly folded structures such as the cortex, decently segmented by intensity classification, but where the registration is difficult to achieve. For example, it can be used to segment substructures of the cortex such as Broadmann areas.

Contributions have also been made to multi-atlases segmentation. The concept of accuracy map of an atlas has been introduced to visualize where an atlas performs well. The accuracy map of each atlas is computed offline using the other atlases as a training dataset. The best agreement segmentation of different atlases is then chosen by a vote of each atlas weighted by its accuracy. The principle of this accuracy weighted vote (awv), is to give more weight where an atlas "usually" perform well. This weighting is especially useful when the new labeling (nne) is used in conjunction with the standard atlas based labeling (std). Roughly, nne is better on cortex and std is better on deep grey structures and awv will automatically discover and utilize this information when combining several atlases for segmentation. As a consequence, when std and nne are combined using accuracy weighted vote, a substantial improvement is achieved over a vote with the best of the two labelings. The local weighting proposed in [START_REF] Artaechevarria | Combination strategies in multi-atlas image segmentation: application to brain MR data[END_REF] or [START_REF] Isgum | Multi-atlas-based segmentation with local decision fusion -application to cardiac and aortic segmentation in CT scans[END_REF] are both based on local difference between the subject and the atlas. The aim is to weight the vote by a measure of the confidence one can have in the registration of a given pair atlas/subject. The local weighting proposed in the current paper is derived from statistical arguments and the vote is weighted by a measure of the confidence in the atlas itself (and its associated mapping scheme). Future work may include a combination of the two approaches.

It is now established that atlas based segmentation can be improved by using multiple atlas combined by a vote fusion rule. In [START_REF] Rohlfing | Multi-classifier framework for atlasbased image segmentation[END_REF], it is proposed to let the registration parameters vary instead of using new atlases. They found that better results are obtained when several atlases is used. The methods proposed in the current paper allow to let the mapping scheme vary (as well as the atlases). Nearest neighbors based mapping make the mapping scheme change by changing a third party segmentation. The accuracy weighted vote find the best combination of the different mapping schemes used.

In the current paper, only the standard labeling and the neighbors labeling with the method of [START_REF] Zhang | Segmentation of brain MR images through a hidden Markov random field model and the expectation maximization algorithm[END_REF] has been used. No atlas is used for the intensity classification of a new subject. As the registration between the subject and the atlases is performed anyway, more information can be accumulated in the atlas and used during the intensity classification as in [START_REF] Pohl | Incorporating nonrigid registration into expectation maximization algorithm to segment MR images[END_REF] or [START_REF] Fischl | Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain[END_REF]. Note that these methods can be used together with the classification of [START_REF] Zhang | Segmentation of brain MR images through a hidden Markov random field model and the expectation maximization algorithm[END_REF] and not as a replacement. In this case, all theses intensity classifications and the standard labeling would be combined by the accuracy weighted vote.

In term of computation time, the additional cost of nne is the cost of the intensity classification. Indeed, the computation of the nearest neighbors transform for few tissues classes is negligible compared to the registration. In the multi atlases perspective, the most time consuming part of the process is also the non-rigid registration to each atlas. If computation time is a limiting constraint, the vote can be performed with a reduced number of atlases. The complete dataset can however be used offline to learn the accuracy map of each atlas. Note that, atlas selection strategies, as described for example in [START_REF] Rohlfing | Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains[END_REF] or [START_REF] Aljabar | Classifier selection strategies for label fusion using large atlas databases[END_REF], can be used to select a subset of the dataset. In this case, accuracy maps can be part of a new criterion in the selection procedure. Indeed, atlases with high accuracy are, by definition, likely to perform better during the segmentation.

A. Abbreviations for the IBSR database

CaWM

Figure 1 :

 1 Figure 1: A brain image (a), its automatic intensity classification (b), the manual segmentation of the cortex (c), automatic delineation of the cortex with standard atlas based segmentation (d). A local minimum in the registration makes the segmentation (d) fail while the intensity classification (b) is good for this part of the brain.

Figure 2 :Figure 3 :

 23 Figure 2: An atlas (a), the contour of its manual segmentation (b), its accuracy maps for the standard labeling (c), and for the nearest neighbor transform based labeling (d). Bright areas of the accuracy maps are regions where the atlas performs well.

  a) Subject Image (b) Ground Truth (c) std (d) Intensity Classification (e) nn (f) nne

Figure 4 :

 4 Figure 4: Results of the cortical segmentation with the standard atlas based labeling (std), the nearest neighbor transform based labeling (nn) and the nearest neighbor transform with erosion labeling (nne).

  RO Differences between constrained and unconstrained registration

Figure

  Figure5: Results of the leave one out evaluation for cortical segmentation with the standard labeling (std), the nearest neighbor transform based labeling (nn) and the nearest neighbor transform with erosion labeling (nne). Labels abbreviations are given in appendix B (a) relative overlap on each structure with constrained registration and the three labelings, (b) the difference of relative overlap between constrained and unconstrained registration (difference is positive when constrained registration is better).

Figure 6 :

 6 Figure 6: Mean RO or MA as a function of the number of atlases. The graphs are plotted for the left cerebral cortex RO (a), the left caudate RO(b), the average RO over all the structures (c) and a mean accuracy in the brain (d). Each graph shows the results for combinations of basic vote (bv) and accuracy weighted vote (awv) with std, nne or both labelings.

Figure 7 :

 7 Figure7: RO mean and standard deviation for all left hemisphere structures. std is the standard labeling, nne is the new labeling. bv is the basic majority vote, awv is the new accuracy weighted vote. Results are given for a vote with 3 atlases (a) or 16 atlases (b). Labels abbreviations are given in appendix A

  6(d)), the ranking of nne and std is exchanged compared to the mean RO. Indeed, std

		90								76
										74
		85								72
	relative overlap	75 80					std / bv std / awv		relative overlap	64 66 68 70 62					std / bv std / awv
							nne / bv nne / awv			60					nne / bv nne / awv
		70				std+nne / bv std+nne / awv			58 56				std+nne / bv std+nne / awv
		2	4	6	8	10	12	14	16	2	4	6	8	10	12	14	16
				(a) Cerebral Cortex RO							(b) Caudate RO
		68								94
		66								92
		64
	relative overlap	54 56 58 60 62					std / bv std / awv		mean accuracy	86 88 90 84					std / bv std / awv
		50 52				nne / bv nne / awv std+nne / bv			82				nne / bv nne / awv std+nne / bv
		48				std+nne / awv			80				std+nne / awv
		2	4	6	8	10	12	14	16	2	4	6	8	10	12	14	16
					(c) Average RO						(d) MA in the brain

Preprint accepted in Medical Image AnalysisDecember 11, 2009

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email: martin.hagedorn@u-bordeaux.fr

Animalerie A2

Background. Diffuse Midline Glioma, H3K27M-mutant (DMG) is a rare, highly aggressive pediatric tumor affecting the brainstem, and is one of the deadliest cancers. Currently available treatment options such as chemotherapy and radiotherapy do only modestly prolong survival. In this pathology, H3K27 mutations deregulate Polycomb Repressive Complex 2 (PRC2), including enzymatic activity of EZH2, which is therefore under investigation as a therapeutic target. Methods. We used a chemical EZH2 inhibitor, GSK126, small interfering RNAs, and a CRISPR/Cas9 knockout approaches in a series of DMG tumor cell lines to investigate metabolic treatment responses by proteomic analysis. A combination strategy was elaborated and studied in primary and established DMG cells, spheroid 3D cultures, and in vivo in a chick chorio-allantoic membrane DMG assay and an orthotopic intracranial DMG mouse model. Results. GSK126 shows significant (P < .05-.001) inhibitory effects in in vitro cell proliferation assays and induces apoptosis. Chemical targeting of EZH2 induced expression of proteins implicated in cholesterol metabolism. Lowdose GSK126 treatment together with statins revealed strong growth inhibition in combinatorial treatments, but not in single treatments, both in DMG cells in vitro, in DMG spheroid cultures, and in chick and mouse in vivo models (P < .05). All statistical tests were two-sided. Conclusions. Our results reveal an unexpected GSK126-inducible sensitivity to cholesterol biosynthesis inhibitors in highly aggressive pediatric glioma that warrants further evaluation as treatment strategy. This combinatorial therapy should have few side effects because of the low doses used to achieve significant anti-tumor activity.

Key Points

• Diffuse midline glioma are sensitive to EZH2 inhibitor GSK126.

• Low-dose GSK126 induces sensitivity of tumor cells to statins.

• GSK126 with Atorvastatin reduces tumor cell growth at low micro molar doses.

An EZH2 blocker sensitizes histone mutated diffuse midline glioma to cholesterol metabolism inhibitors through an off-target effect

Chemical Inhibitors

EZH2 inhibitor GSK126, Atorvastatin, ACSS2 inhibitor, and Terbinafine have been used (Selleckchemicals, Houston, USA).

Western Blots

Blots were performed using standard techniques. Details are in the Supplementary Methods section.

Identifying novel treatment concepts for incurable cancers remains a major challenge in medicine. Cancer-type specific drugs or so called "precision medicine" are very costly and are available only for some adult cancers. Research for therapies tailored for specific pediatric tumors is still rare, [START_REF] Forrest | Precision medicine in pediatric oncology[END_REF] therefore therapy options are often limited to chemotherapy and radiotherapy with severe side effects.

Drugs targeting proteins regulating epigenetic processes (epi-drugs) have gained increasing attention as treatment for hematological and solid malignancies, including DMG. [START_REF] Zhang | Combination of EZH2 inhibitor and BET inhibitor for treatment of diffuse intrinsic pontine glioma[END_REF][START_REF] Mohammad | EZH2 is a potential therapeutic target for H3K27M-mutant pediatric gliomas[END_REF][START_REF] Capdevielle | HDAC inhibition induces expression of scaffolding proteins critical for tumor progression in pediatric glioma: focus on EBP50 and IRSp53[END_REF] One key element in organization of the genome are histones and several histone mutations have been discovered that deeply influence transcriptional activity. [START_REF] Maze | Every amino acid matters: essential contributions of histone variants to mammalian development and disease[END_REF] For example, reduction of histone methylation by Lysine to Methionine mutations at specific histones (K-to-M at H3K36, H3F3A, or HIST1H3B) has profound effects on cell growth through transcriptional modifications, a phenomenon conserved in all living systems, from plants [START_REF] Sanders | Histone lysine-to-methionine mutations reduce histone methylation and cause developmental pleiotropy[END_REF] to eukaryotic cell, including cancer cells. [START_REF] Lowe | Histone H3 mutations: an updated view of their role in chromatin deregulation and cancer[END_REF] Histone mutations alter functioning of effector protein complexes such as PRC2, which includes the methyltransferase EZH2.

Abnormal epigenetic regulation in DMG tumors are a novel therapeutic target, with possibility for pharmacologic intervention to normalize gene expression, most likely by lowering oncogenic protein levels or augmenting tumor suppressors. We have shown that the pan-HDAC inhibitor panobinostat profoundly changes protein expression profiles, leading to overexpression of only two proteins, IRSp53 and EBP50. These two proteins seem to be markers of resistance, since they are required for DMG cell growth. [START_REF] Capdevielle | HDAC inhibition induces expression of scaffolding proteins critical for tumor progression in pediatric glioma: focus on EBP50 and IRSp53[END_REF] Interfering with histone methylation, acetylation, or activity of BET domain proteins such as BRD4 are therefore promising strategies against DMG. Several of them are actually studied in clinical DMG trials such as panobinostat, vorinostat, or valproate (for review see [START_REF] Hashizume | Epigenetic targeted therapy for diffuse intrinsic pontine glioma[END_REF] )

Analyzing reaction to treatment on a cellular level using high throughput approaches such as microarrays and proteomics combined with diverse artificial intelligence techniques has widely broadened our general understanding of drug mechanisms of action [START_REF] Frejno | Proteome activity landscapes of tumor cell lines determine drug responses[END_REF] and can give information on novel potential targets of existing drugs. [START_REF] Nicora | Integrated multiomics analyses in oncology: a review of machine learning methods and tools[END_REF] In this paper, we analyze the role of cholesterol biosynthesis-related proteins which we found significantly upregulated in DMG cells by GSK126 treatment. We, therefore, hypothesized that EZH2 inhibition by GSK126 renders DMG glioma cells dependent on cholesterol biosynthesis, thus giving the possibility to explore statins or other lipid reducing drugs as treatment option in this pathology. Drug repurposing has already provided novel perspectives for various diseases, including cancer. [START_REF] Pushpakom | Drug repurposing: progress, challenges and recommendations[END_REF][START_REF] Sleire | Drug repurposing in cancer[END_REF] We provide evidence that simultaneous use of low micromolar doses of GSK126 together with statins, especially Atorvastatin, has significant anti-tumor effects in highly malignant glioma in vitro and in vivo, thereby opening a novel treatment perspective for this disease with dismal prognosis.

Methods

Microarray Analysis

The GSE50021 expression profile (35 DMG samples, 10 normal brain samples), was extracted from the Gene Expression Omnibus database. Raw reads were quantile normalized and log2 transformed. Expression values identified by ILMN_1652913 probe for EZH2 were extracted and analyzed using GraphPad Prism.

DMG Cell Lines and Primary BXdmg1 Cells

DMG cell lines used in this study were NEM157i, NEM157i-VEGF, NEM163i, SU-DIPG-IVi, SU-DIPG-IVi-Luc and freshly isolated cells from a biopsy termed BXdmg1. Origin and lentiviral modification procedures (including immortalization), as well as culture conditions, have been described elsewhere [START_REF] Capdevielle | HDAC inhibition induces expression of scaffolding proteins critical for tumor progression in pediatric glioma: focus on EBP50 and IRSp53[END_REF] and in Supplementary Data. BXdmg1 cells are available from the corresponding author depending on reasonable requests.

Importance of the Study

The here described experimental therapy for diffuse midline glioma is a novel form of anticancer treatment where one drug which targets EZH2 (GSK126) and other not identified targets renders tumor cells dependent of cholesterol biosynthesis at a low micromolar dose. The combination of the EZH2 targeting drug with a statin, in this case Atorvastatin, which is a well characterized drug, have strong anticancer effects at low micromolar doses and is therefore an ideal combination for pediatric patients. This combinatory therapy will most probably have very few toxicities compared to standard chemotherapy, it is patented and clinical trials, also in adult common cancer types are planned.

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Histone Extraction

Histone extraction was performed according to manufacturer's instructions (Histone Extraction Kit-ab113476, Abcam, Paris, France).

Proliferation Assays

Cell growth was measured with the In vitro Toxicology Assay kit (Sulforhodamine B, Sigma Aldrich, Saint Quentin Fallavier, France) according to manufacturer's instructions. Cells were plated at a density of 2000 cells per well in 96-well plates in triplicates. Absorbance was measured at 565 nm using the CLARIOstar multiplate reader (BMG Labtech, Champigny-sur-Marne, France) at indicated time points.

Apoptosis Assays

After exposure to drugs at indicated concentrations and times, cells were labeled with Annexin V-PE (BD Biosciences, Le Pont de Claix, France) and 7-AminoActinomycin D (7-AAD) (BD Bioscience) and analyzed using Flow Cytometry as described. [START_REF] Capdevielle | HDAC inhibition induces expression of scaffolding proteins critical for tumor progression in pediatric glioma: focus on EBP50 and IRSp53[END_REF] siRNA and CRISPR/Cas9-Mediated EZH2 Targeting Small interfering RNAs (siRNAs) targeting EZH2 were purchased from Eurofins (Ebersberg, Germany). Transfection protocol is detailed in Supplementary Methods.

For CRISPR/Cas9 mediated EZH2 KO, details are described in the Supplementary Methods.

Label-Free Quantitative Proteomics

Three independent biological replicates of NEM157i, NEM163i, and SU-DIPG-IVi cell lines were performed to compare the following conditions: biological effects of GSK126 versus control and siRNA-EZH2 versus siRNA control. The steps of sample preparation and protein digestion by trypsin were performed as previously described [START_REF] Capdevielle | HDAC inhibition induces expression of scaffolding proteins critical for tumor progression in pediatric glioma: focus on EBP50 and IRSp53[END_REF] and in the Supplementary data section. Proteomic data are available at http://www.proteomexchange.org, ID PXD017525.

Migration Assays

About 2 x 10 4 cells/well were placed in a 96-well and incubated for 24h. An IncuCyte WoundMaker (96pin woundmaking tool) was used to make scratches (Supplementary Methods).

DMG Spheroid Cultures

Standard culture methods for spheroid cultures were used. Details are in the Supplementary Methods section. The DMG NEM157i/NEM157i-VEGF CAM model has been described before. [START_REF] Capdevielle | HDAC inhibition induces expression of scaffolding proteins critical for tumor progression in pediatric glioma: focus on EBP50 and IRSp53[END_REF] A murine orthotopic model (SU-DIPG-IVi-Luc in NOD/ LtSz-scid IL2R gamma mice) was developed based on the work of Mohammed et al. [START_REF] Mohammad | EZH2 is a potential therapeutic target for H3K27M-mutant pediatric gliomas[END_REF] Additional details are in the Supplementary Methods.

DMG In Vivo Models

Statistical Analysis

Statistical analyses were standard procedures and are detailed in Supplementary Data.

All tests were two-sided.

Results

EZH2 Gene and Protein Expression in DMG Samples and Cell Lines

Transcripts of EZH2 are significantly over expressed in DMG samples compared to normal brain (Figure 1A; [START_REF] Buczkowicz | Genomic analysis of diffuse intrinsic pontine gliomas identifies three molecular subgroups and recurrent activating ACVR1 mutations[END_REF] ). Mean EZH2 over expression was not very elevated compared to controls due to high expression variability between the two groups, but a core set of samples regrouped around the median EZH2 expression and differences are still significant (Figure 1A, left plot, boxed frame). Cell lines used in this study show all expression of EZH2 protein as revealed by Western Blot (Figure 1B). Significant growth inhibition of GSK126 was observed at doses above 6 μM and was total at higher doses above 25 μM (Figure 1C). Growth inhibition was accompanied by an increase in tumor cell apoptosis, at similar doses when cell proliferation was strongly reduced (Figure 1D). Typical H3K27me3 reduction after GSK126 treatment is revealed by Western Blot in two DMG cell lines (Figure 1E).

Proteomic Analysis of EZH2 Impairment by GSK126 or siRNA

Volcano plot expression comparison of the two EZH2 inhibition approaches already revealed profound differences (Supplementary Figure 1). Whereas chemical inhibition upregulated 55 proteins in at least two out of three cell lines (Supplementary Figure 2A, left panel), only one protein was upregulated in the same cells after EZH2 knock down (Supplementary Figure 2A, right panel). These data point to profound biological differences caused by these two methods which cannot be explained by simple EZH2 function interference.

We submitted 55 upregulated proteins to gene ontology tools (PANTHER) to identify biological processes enriched in GSK126 treated cells. Importantly, a very significant enrichment (up to >100 times) of genes involved in the cholesterol biosynthesis pathway was evidenced (Supplementary Figure 3). In fact, 11 out of the 55 proteins upregulated by the treatment play key roles in lipid metabolic processes

C T R D M S O 1 µ M 3 µ M 6 µ M 1 2 µ M 2 5 µ M 5 0 µ M N T C T R D M S O G S K 1 2 6 6 µ M G S K 1 2 6 1 0 µ M G S K 1 2 6 1 2 µ M 3 *** Proliferation NEM157i GSK126 Proliferation SU-DIPG-IVi GSK126
1 0 3 N T C T R D M S O 1 µ M 3 µ M 6 µ M 1 2 µ M 2 5 µ M

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(Supplementary Figure 2B). These results suggest that profound metabolic changes have occurred in GSK126-treated DMG cells.

Inhibition of EZH2 Expression by siRNA and CRISPR/Cas9 and Growth Consequences in DMG Cell Lines

To get a more complete understanding of EZH2 role in DMG cell growth we reasoned that knock down or genetic deletion of EZH2 could produce similar results. However, use of a very efficient siRNA which reduced EZH2 protein levels almost to nothing in two DMG cell lines, produced no effects on DMG cell growth 3 or 4 days after transfection. Cell lines devoid of EZH2 continued to proliferated normally, thereby confirming that EZH2 is not required for DMG cell proliferation (Supplementary Figure 5).

Growth inhibition of GSK126 was observed after 24h or 48h in EZH2 KO cells, with IC50s comparable to normal cells controls (under 10 μM, Supplementary Figure 6A-C). Growth promoting or inhibiting roles of EZH2 have already been a debate in other type of cancers and may be due to compensation effects of EZH1. [START_REF] Hock | A complex Polycomb issue: the two faces of EZH2 in cancer[END_REF]

Effects of ACSS2 Inhibitor, Atorvastatin, and Terbinafine on DMG Cells Alone or in Combination with GSK126

In order to interpret these changes, we hypothesized that several key enzymes involved in the biosynthesis of cholesterol became important to GSK126-treated DMG cells. These enzymes are known for a long time to be implicated in diseases related to lipid metabolisms. [START_REF] Cerqueira | Cholesterol biosynthesis: a mechanistic overview[END_REF] For each cholesterol biosynthesis inhibitor tested, no effects have been observed on two different tumor cell lines alone (Supplementary Figure 4).

However, when co-treated with low doses of GSK126 (4 μM) which do not affect DMG cell growth (Figure 1C), significant growth inhibition occurs for all three inhibitors at doses from 1 to 5 μM (Figure 2A-C).

Validation of DMG Cell Line Data on Freshly Isolated DMG Cells

Cell lines that are immortalized or cultured for a certain time can behave differently as the original cells. We therefore isolated cells from a DMG biopsy for functional analysis. Bright field (BF) microscopy shows a homogenous, spindle-formed cell population (Figure 3A, and insert). H&E staining of BXdmg1 cells revealed atypical eosinophilic cells with irregular anisocaryotic cell nuclei with very high mitotic activity, almost all tumor cells express the Ki-67 antigen (Figure 3A). Tumor cells were also strongly positive for the H3K27M mutation and negative for H3K27me3 trimethylation (Figure 3A). Cellular, molecular and genetic characterization of the biopsy where BXdmg1 cells originated from are summarized in Supplementary Figure 7, notably confirming the c83A>T; pK28M mutation in histone H3F3A, leading to the driver oncogenic event, which is conserved in BXdmg1 cells.

GSK126 treatment reduced H3K27me3 trimethylation in a dose-dependent manner as revealed by Western blots of histone-purified protein extracts (Figure 3A, lower right panel). BXdmg1 cells are sensible to growth inhibition by GSK126 (Figure 3B) with an IC50 around 10 μM (Figure 3C), comparable to the other DMG lines. BXdmg1 cell growth is not affected by three cholesterol biosynthesis inhibitors, up to 10 μM (Figure 3D). Combining GSK126 and Atorvastatin showed significant growth inhibition at low doses of 3 and 5 μM for each inhibitor, this effect was masked, as expected, at higher doses because of already established cytotoxicity of GSK126 alone (Figure 3E).

Effects of Atorvastatin, GSK126, and Combination on Cell Migration

We also investigated the influence of the drugs on DMG cell migration using an automated cell scratcher with Incucyte. Cell movement was measured from 24h to 48h after confluence and initiation of wound. Initial denuded area is very clean and marked by a frame of the same size for illustration in all conditions (Figure 4A-C). Percent of denuded area covered by migrating DMG cells NEM157i, NEM163i, and primary BXdmg1 cells after 24h was reported by the Incucyte software and is displayed per condition in the right graphs (Figure 4A-C). For all cells tested, a significant inhibition of migration activity (approximately 2-times) was observed for the combo treatment (P < .001) compared to single treatments or solvent control. In NEM163i and BXdmg1 cells, migration was also slowed by Atorvastatin compared to DMSO (P < .05 and P < .001), but cells exposed to combo treatment still had fewer area covered by migrating cells compared to Atorvastatin alone (P < .001). This anti-migratory effect of Atorvastatin is in line with previous results obtained in adult glioma cells. [START_REF] Oliveira | Atorvastatin promotes cytotoxicity and reduces migration and proliferation of human A172 glioma cells[END_REF]

Inhibition of Tumor Cell Spheroid Formation by Atorvastatin/GSK126 Combination

In an attempt to further investigate cell movements and adhesion phenomena in our cells, we used a protocol that allows spheroid formation of DMG cells and analyzed cell movements and aggregation with an Incucyte imaging system. Tumor spheroids are considered as a more realistic culture system than classical 2D models, adding 3D complexity closer to in vivo growth conditions. [START_REF] Santini | Three-dimensional spheroid model in tumor biology[END_REF] Using three different DMG cell lines, including our primary BXdmg1 cells, we could reliably generate tumor neurospheres with all lines (Figure 5A,B,D). Round-shaped spheres formed rapidly within 24 h, albeit with slightly different sizes and sometimes differences in border shape (clear delimitated vs. irregular).

However, cells exposed to combo treatment almost never formed spheroids compared to single GSK126 treatment (P < .001, for all cells, Figure 5C, E, Supplementary Figure 9). In some GSK126-treated cultures, spheroid formation was affected, especially in the primary cells (Figure 5D,E). We developed an ortothopic brainstem glioma model in newborn immunocompromised mice based on the work of Grasso et al.. [START_REF] Grasso | Functionally defined therapeutic targets in diffuse intrinsic pontine glioma[END_REF] SU-DIPG-IVi-Luc cells where generated using lentiviral transfection procedures, as described ( 4 and Supplementary Methods). In a first experiment, we tested efficacy of GSK126 treatment in this model and found significant growth reduction at an i.p. dose of 10mg/ kg evidenced by bioluminescence (P = .009) in treated mice (n = 23) compared to solvent controls (n = 18, Figure 6A).

For the Atorvastatin/GSK126 combo treatment experiment, we reduced GSK126 dose to 6mg/kg to avoid growth inhibition. After four treatments, a significantly greater tumor growth inhibition of the combo occurred (n = 13) compared to Atorvastatin (n = 13), GSK126 (n = 17) (P < .05) as well as to DMSO controls (P < .01, n = 13, Figure 6B). No significant differences have been found between controls or single treatments. All bioluminescence images of animals used in this study are shown (Figure 6A,B). Combo treatment of GSK126 and Atorvastatin (Ator) shows stronger growth inhibition than GSK126 or Atorvastatin alone after 72h. Combo effect is visible at low doses and is lost at higher doses of GSK126 because of its cytotoxicity alone at these concentrations. One way ANOVA (n = 3, P < .0001), Bonferroni's multiple comparisons post-test. ns, nonsignificant; *P < .05; **P < .01; ***P < .001. Further information about molecular features of BXdmg1 biopsy is presented in Supplementary Figure 7.

Rahal et al. Cholesterol dependency in diffuse midline glioma

Atorvastatin/GSK126 combo treatment also exhibits better anti-tumor effects in a chick CAM DMG model we recently developed. [START_REF] Capdevielle | HDAC inhibition induces expression of scaffolding proteins critical for tumor progression in pediatric glioma: focus on EBP50 and IRSp53[END_REF] In this short-term model, based on a previously established adult glioma CAM model, [START_REF] Hagedorn | Accessing key steps of human tumor progression in vivo by using an avian embryo model[END_REF] drugs can be directly applied on the tumor and growth monitored by biomicroscopy. Phenotypic characterization of drug action can be made by classifying the degree of tumor vascularization into high or low/moderate (Figure 6C, right panel).

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Degree of vascularization should be interpreted as capacity of tumor cells to interact with the host tissue, an indirect indicator of tumor cell aggressiveness. Combo treated experimental tumors showed reduced vascularization compared to Atorvastatin (P = .036), GSK126 (P = .0248), and DMSO controls (P = .03, Figure 6C, left graph).

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EZH2 is under investigation as a potential target in cancer. [START_REF] Kim | Targeting EZH2 in cancer[END_REF] However, EZH2 implication in DMG pathogenesis is still controversial. Mohammed et al. have shown EZH2 dependency of DMG cells in vitro and in vivo, [START_REF] Mohammad | EZH2 is a potential therapeutic target for H3K27M-mutant pediatric gliomas[END_REF] whereas Wiese et al. could not evidence growth inhibition of DMG cells using two chemical EZH2 inhibitors. [START_REF] Wiese | No significant cytotoxic effect of the EZH2 inhibitor tazemetostat (EPZ-6438) on pediatric glioma cells with wildtype histone 3 or mutated histone 3.3[END_REF] In the Mohammed study, the authors used first genetic engineered mouse cell lines and then a couple of H3-mutated patient lines, but not the same as in the Wiese study, except for the H3 wildtype line SF188. Whereas the first study is more focused on molecular mechanisms, the second one correlated expression levels of EZH2 to survival of patients and found no difference between high and low EZH2 expressors. So, the different results could be explained by the different approaches and cell lines used. It is likely that not all DIPG or other glioma cells do exhibit the same sensitivity to EZH2 inhibitors.

Our results suggest that EZH2 protein is not required for DMG cell growth. Neither a very efficient siRNA against EZH2 nor CRISPR/Cas9-mediated EZH2 KO in DMG cells (NEM157i and SU-DIPG-IVi) shows different proliferation patterns compared to empty control vectors (Supplementary Figure 5). Intriguingly, both knock-down and KO cells still remained highly sensitive to the EZH2 inhibitor GSK126, with an IC50 comparable to wild-type DMG cells (Supplementary Figure 6). These results strongly suggest that GSK126 affects a yet unknown growth promoting target (or targets) in DMG cells.

In an attempt to shed light on the molecular changes occurring in DMG cells treated with GSK126, we undertook a systemic proteomic approach using three different cell lines. 55 proteins were induced by GSK126 treatment with a significant enrichment of lipid and cholesterol synthesis proteins (Supplementary 2, 3).

Enzymes implicated in cholesterol synthesis have been described as potential targets in adult glioma [START_REF] Ahmad | Cholesterol metabolism: a potential therapeutic target in glioblastoma[END_REF] and especially Atorvastatin has been shown to be cytotoxic in A172 human glioma cells. [START_REF] Oliveira | Atorvastatin promotes cytotoxicity and reduces migration and proliferation of human A172 glioma cells[END_REF] A recent publication by a Chinese group did find similar results as ours after treating head and neck squamous cell carcinoma with EZH2. They also observed a strong induction of cholesterol pathway genes and combination of pathway inhibitors with EZH2 augmented cytotoxic effects in these cancer cells. [START_REF] Xu | Targeting epigenetic modulation of cholesterol synthesis as a therapeutic strategy for head and neck squamous cell carcinoma[END_REF] Recently, lanosterol synthase has been identified as a target of the menin inhibitor MI-2. Blocking this enzyme impaired biosynthesis of cholesterol in DMG, leading to glioma cell death. [START_REF] Phillips | Target identification reveals lanosterol synthase as a vulnerability in glioma[END_REF] Deciphering the complex interactions between cancer and lipid metabolism is clearly important to elaborate novel therapeutic strategies for cancer treatment (for review see [START_REF] Grunt | Interacting cancer machineries: cell signaling, lipid metabolism, and epigenetics[END_REF] ).

Atorvastatin also has anti-angiogenic and anti-invasive activity [START_REF] Yongjun | Atorvastatin suppresses glioma invasion and migration by reducing microglial MT1-MMP expression[END_REF][START_REF] Bayat | The anti-angiogenic effect of atorvastatin in glioblastoma spheroids tumor cultured in fibrin gel: in 3D in vitro model[END_REF] which is in line with our CAM experiment where Atovarstatin/GSK126-treated tumors appear less vascularized and our migration and spheroid experiments which reveal significant reduction of cell motility (Figures 4,5 and Supplementary Figure 9). These biological effects have also been shown for GSK126 in other solid tumors. [START_REF] Chen | The novel EZH2 inhibitor, GSK126, suppresses cell migration and angiogenesis via down-regulating VEGF-A[END_REF] We did not observe any growth inhibition of Atorvastatin alone, up to 10 μM, whereas in combination with 4 μM GSK126 (which has no inhibitory effect at that dose) significant inhibition was achieved between 1-5 μM. Similar results have been obtained with ACCS2 inhibitor and Terbinafine (Figure 2).

Combinatorial therapies are generally more effective than single treatments in complex malignant disease such as adult or pediatric high-grade glioma. [START_REF] Zhang | Combination of EZH2 inhibitor and BET inhibitor for treatment of diffuse intrinsic pontine glioma[END_REF][START_REF] Capdevielle | HDAC inhibition induces expression of scaffolding proteins critical for tumor progression in pediatric glioma: focus on EBP50 and IRSp53[END_REF][START_REF] Bhat | Dopamine receptor antagonists, radiation, and cholesterol biosynthesis in mouse models of glioblastoma[END_REF][START_REF] Tapia-Pérez | Simultaneous administration of statins and pioglitazone limits tumor growth in a rat model of malignant glioma[END_REF][START_REF] Peng | Atorvastatin augments temozolomide's efficacy in glioblastoma via prenylation-dependent inhibition of Ras signaling[END_REF] In adult glioma, combined treatment with a dopamine receptor antagonist, quetiapine, and radiotherapy, also induces genes in the cholesterin biosynthesis pathway thereby rendering glioma cells vulnerable to atorvastatin. [START_REF] Bhat | Dopamine receptor antagonists, radiation, and cholesterol biosynthesis in mouse models of glioblastoma[END_REF] The combination of a statin and GSK126 seems to be a promising approach, affecting biological processes critical for tumor progression [START_REF] Hanahan | The hallmarks of cancer[END_REF] at micromolar doses at which either drug alone does not alter cell growth.

Even though significant tumor growth inhibition by GSK126 alone, and importantly, at lower doses in combination with Atorvastatin, could be evidenced in our mouse model, a more complex situation may be present in human patients. EZH2 inhibitors are prone to be excluded from penetration to the brain through specific transport efflux mechanisms implicating glycoprotein (P-gp/ABCB1) and breast cancer resistance protein (BCRP/ABCG2), [START_REF] Zhang | ABCB1 and ABCG2 restrict the brain penetration of a panel of novel EZH2-Inhibitors[END_REF] but these inhibitors can achieve therapeutic concentrations in the brainstem when CED (Convection-enhanced delivery) is applied. [START_REF] Sasaki | Convection-enhanced delivery of enhancer of zeste homolog-2 (EZH2) inhibitor for the treatment of diffuse intrinsic pontine glioma[END_REF] Atorvastatin crosses the Blood-brain barrier (BBB), even though at lower levels compared to other statins such as simvastatin. [START_REF] Sierra | Statins as neuroprotectants: a comparative in vitro study of lipophilicity, blood-brain-barrier penetration, lowering of brain cholesterol, and decrease of neuron cell death[END_REF] Given our results with ACCS2 inhibitor and Terbinafine (Figure 2) which have not been tested in vivo, it is likely that brain-penetrating cholesterol biosynthesis inhibitors can be identified which can achieve therapeutic brainstem levels together with GSK126 applied by CED.

Our results did not answer all questions regarding the unexpected efficacy of the combinatory therapy, notably on cell migration and adhesion. Both processes affect cytoskeleton modifications, for example, Atorvastatin has been shown to negatively regulate cell adhesion in patients with myocardial injury [START_REF] Patti | Protection from procedural myocardial injury by atorvastatin is associated with lower levels of adhesion molecules after percutaneous coronary intervention: results from the ARMYDA-CAMs (Atorvastatin for Reduction of MYocardial Damage during Angioplasty-Cell Adhesion Molecules) substudy[END_REF] and other statins have anti-migratory and anti-adhesive effects in vitro in melanoma cells through suppression of Rho/ROCK signaling. [START_REF] Kidera | Reduction of lung metastasis, cell invasion, and adhesion in mouse melanoma by statin-induced blockade of the Rho/Rho-associated coiled-coil-containing protein kinase pathway[END_REF] Epi-drugs work best in combination with other inhibitors, including chemotherapy, underlining their potential to sensitize cells to combo therapies. [START_REF] Morel | Combining epigenetic drugs with other therapies for solid tumours -past lessons and future promise[END_REF] Atorvastatin (Lipitor®) is well tolerated in children from 6 years on [START_REF] Langslet | 3-year study of atorvastatin in children and adolescents with heterozygous familial hypercholesterolemia[END_REF] and EZH2 inhibitors are currently being evaluated in clinical trials, including pediatric malignancies. Given the consistent and encouraging results presented in this paper for experimental DMG treatment, further investigation using this combination therapy should be performed, hopefully leading to clinical trials to eventually ameliorate prognosis of this dismal disease.

  Downloaded from https://academic.oup.com/noa/article/4/1/vdac018/6540481 by Universite de Bordeaux user on 17

Figure 1 .

 1 Figure 1. DMG cells are sensitive to GSK126 at low doses evidenced also by H3K27me3 trimethylation. (A) EZH2 transcripts are significantly overexpressed in a series of DMG biopsies (N = 35) compared to normal brain (N = 10), microarray data retrieved from GSE50021 [12]. Dashed square delimits samples regrouped around median expression. Parametric t test between DMG biopsies versus normal brains B) Patient-derived cell lines express EZH2 protein detected by Western Blot. (C-D) GSK126 strongly inhibits proliferation of NEM157i and SU-DIPG-IVi DMG cells after 72h and induces tumor cell death after 5 days of treatment. One way ANOVA (n > 3, P < .0001), Bonferroni's multiple comparisons post-test. (E) H3K27me3 trimethylation is reduced a dose-dependent manner in GSK126 treated NEM157i and SU-DIPG-IVi DMG cells. One blot representative of 3 independent experiments. *P < .05; **P < .01; ***P < .001.

  Downloaded from https://academic.oup.com/noa/article/4/1/vdac018/6540481 by Universite de Bordeaux user on 17 March 2022 GSK126 Effects on Orthotopic SU-DIPG-IVi-Luc Implanted Tumor Cells in NOD/LtSz-scid IL2R Gamma (NSG) Mice

Figure 2 .Figure 3 .

 23 Figure 2. Synergistic effects of the combinatorial therapy of GSK126 and inhibitors of cholesterol biosynthesis pathway enzymes on DMG cells. (A-C) Cell growth assays of NEM157i and SU-DIPG-IVi cells (as indicated) exposed to 4 µM of GSK126, a dose which has no growth inhibitory effects on DMG cells (open circles), and to increasing doses of ACSS2 inhibitor A), Atorvastatin B) or Terbinafine C). One way ANOVA (n = 3, P < .0001), Bonferroni's multiple comparisons post-test. Bi-therapy treatment shows significant proliferation inhibition after 72h starting at low micro molar doses of statins which show no effects alone (see Figure 4 sup). Solvant: DMSO (control, CTR), **P < .01; ***P < .001. Downloaded from https://academic.oup.com/noa/article/4/1/vdac018/6540481 by Universite de Bordeaux user on 17 March 2022

Figure 4 .

 4 Figure 4. Effects of GSK126 and Atorvastatin on DMG cell migration. (A-C) Cell migration impairment in the combo treatment revealed by a wound scratch assay in the NEM157i (A) and NEM163i (B) cells and in the BXdmg1 primary cells after 24 h (C). One way ANOVA (n = 3, P < .0001), Bonferroni's multiple comparisons post-test. ns, nonsignificant; *P < .05; **P < .01; ***P < .001.

Figure 5 .

 5 Figure 5. Synergistic effects of GSK126 and inhibitors of cholesterol biosynthesis pathway enzymes on DMG spheroid formation. (A, B, D) Phase contrast micrographs of representative spheroids derived from indicated DMG glioma cells after exposure to indicated inhibitors for 24 h (C, E) Statistical analysis of treatment effects on the formation of spheroids derived from the DMG cell line shown above the corresponding graph. Phenotypic criterions were spheroid formed or not formed (dispersed cells). Fisher' Exact test was used to compare relevant treatments and the combination (combo). GSK126 dose was 20 µM for NEM157i and 15 µM for the others. ***P < .001. Time-lapse videos of inhibitor effects are demonstrated in Supplementary Figure 9 and associated videos.

Figure 6 .

 6 Figure 6. Synergistic effect GSK126 and inhibitors of cholesterol biosynthesis pathway enzymes on DMG tumor development in mice. (A) Orthotopic tumor growth inhibition of SU-DIPG-IVi-Luc cells (expressing the Luciferase) implanted in the brainstem of NOD/LtSz-scid IL2R gamma (NSG) mice after GSK126 intraperitoneal treatments. Tumor growth inhibition is demonstrated for all mice by bioluminescence live imaging and statistical analysis after the indicated treatments (solvent control: DMSO; GSK126). Color bar indicates level of expression, blue values equal low and red values high expression. Parametric t Student test (DMSO: n = 18; GSK126: n = 23) (B) The same approach is performed at a noncytotoxic dose of GSK126 in combination with a noncytotoxic dose of Atorvastatin. The combination of both drugs shows significant growth inhibitory effects, whereas single treatments are not effective. Bars indicate median plus range. One way ANOVA (DMSO: n = 13; Atorvastatin: n = 13; GSK126: n = 17; Combo: n = 13; P < .0001), Bonferroni's multiple comparisons post-test. (C) Similar tumor growth inhibitory results were obtained using a chick CAM DMG model where a less angiogenic phenotype of implanted tumors was observed in the combo treatment, whereas single treatments had no anti-angiogenic effect Two-sided Fisher's exact test. ns, nonsignificant; *P < .05; **P < .01.

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Rahal et al. Cholesterol dependency in diffuse midline glioma

Acknowledgments

We thank Dr. Kasia Hooks for precious help with data processing from the GSE source and helpful discussions. We also thank Malak Alannan for help with CAM experiments.

Funding

FR was financed by the Ministry of Interior and Municipalities, Lebanon and by the Eva pour la Vie foundation (EPLV, https:// www.evapourlavie.com). Groupama Foundation (https://www. groupama.com/en/our-commitment/patronage/groupamafoundation/ and EPLV financed CC and JC. Additional support came from the University of Bordeaux (MM), La Ligue Contre le Cancer (GCF and MH, National contract), INSERM and Univ Bordeaux (general funding) and following private foundations: EPLV, Aidons Marina (http://aidonsmarina.com), Cassandra Contre la Leucémie (https://www.associationcassandra.org), ESCAPE, Spheres (http://asso-spheres.com) and Ami de Marius.

Supplementary Material

Supplementary material is available at Neuro-Oncology Advances online.

Keywords atorvastatin | cholesterol metabolism | DMG | diffuse midline glioma | EZH2 | enhancer Of Zeste homolog 2 | H3K27M-mutant | GSK126

Conflict of interest statement. The authors declare no conflicts of interest.

Authorship statement. FR, CC, MM and JC performed in vitro and in vivo experiments and analyzed data. BR and JY performed and supervised in vivo experiments. JWD performed, supervised and analyzed proteomic experiments. DC supervised clinical biopsy sample processing and genetic analysis. VJ, EG, GC and ChC provided biopsy material and access to clinical data. CFG provided funding, access to laboratory infrastructures and cell culture techniques (spheroids). MH provided funding, conceived the study, supervised students or engineers (FR, CC, MM, JC), analyzed data and wrote the paper.