Thomas Colleu

Jian-Kun Shen

Bogdan J Matuszewski

Lik-Kwan Shark
email: lshark@uclan.ac.uk

Claude Cariou

Feature-Based Deformable Image Registration with RANSAC Based Search Correspondence

The paper presents an algorithm for deformable image registration based on point features extracted from input images using the Harris corner detector. The correspondence between the points extracted from the different images is established using RANdom SAmple Consensus (RANSAC) method with the affine and perspective global transformation used to model the deformations. The initial correspondence is established using an enumerative search with rotation invariant cross-correlation measure. Based on the estimated corresponding set of points the further refinement of the displacement field is achieved through application of the multilevel B-spline technique applied to the overlapping image area. The performance of this method is demonstrated using few pairs of remote sensing images. The method is also evaluated against the global polynomial transformation and the registration results are compared with the results achieved using ENVI software.

Introduction

Image registration is the process of overlaying two or more images of the same scene taken at different times, from different viewpoints, and/or by different sensors. It geometrically aligns two images: the reference and sensed images. Typical applications of image registration lie in the field of medicine (monitoring tumor growth), computer vision (automatic quality control), and remote sensing (multispectral monitoring, integrating information into geographic information system (GIS)). The basic methods used for registering two images are now shortly explained. The reader is referred to [START_REF] Zitova | Image registration methods: a survey[END_REF] for a comprehensive survey. Two methods are usually considered for image registration algorithms: intensity-based methods and feature-based methods. The first one looks at the structure of the image (via correlation metrics, fourier properties ...) whereas the second one is based on matching features like lines, curves, points. The use of feature-based methods is recommended if the images contain enough distinctive and easily detectable objects. This is usually the case of applications in remote sensing and computer vision where images contain a lot of details (towns, rivers, roads...). On the other hand, medical images are not so rich in such details and thus area-based methods are usually employed here. In this paper, a feature-based registration is used and tested with remote sensed images. This method usually consists of the following four steps:

• Feature detection: Salient and distinctive objects (closed-boundary regions, edges, contours, line intersections, corners, etc.) are manually or, preferably, automatically detected. For further processing, these features can be represented by their point representatives (centres of gravity, line endings, distinctive points), which are called control points in the literature. • Feature matching: In this step, the correspondence between the features detected in the sensed image and those detected in the reference image is established.

Various feature descriptors and similarity measures along with spatial relationships among the features are used for that purpose. as translation, rotation, scale, etc. On the other hand, a local transform model is needed when the images differ locally. A typical example is the use of airborne camera because there are a lot of local deformations in the acquired images due to the movement of the plane. This local transformation is also referred to as deformable registration. Several methods based on features and deformable registrations have already been studied. A lot of the contributions lie in the field of medical images. Thus, in [START_REF] Fornefett | Radial Basis Functions with Compact Support for Elastic Registration of Medical Images[END_REF], radial basis functions with compact support are used for deformable registration of medical images; In [START_REF] Rohr | Project IMAGINE: Landmark-Based Elastic Registration and Biomechanical Brain Modelling[END_REF], a landmark-based registration technique makes use of thin plate splines for medical image registration and biomechanical brain modeling. Fewer publications can be found in the field of remote sensing. Yet, the paper [START_REF] Larry | Image registration using multiquadratics functions, the finite element method, bivariate mapping polynomials and thin plate spline[END_REF], written within the national center for geographic information and analysis, presents different image registration techniques for remote sensing applications such as multiquadratics functions, the finite element method, bivariate mapping polynomials, and thin plate spline. Finally, [START_REF] Szeliski | Spline-Based Image Registration[END_REF] describes an image registration method based on splines. In the algorithm proposed here, the feature detection step is achieved with the well-known Harris corner detector. Then, an initial correspondence is given by enumerative search with rotation invariant cross-correlation. The final correspondence is obtained using RANdom SAmple Consensus (RANSAC) method which, also estimates the deformations with affine or perspective global transformation. Based on the estimated corresponding set of points, the further refinement of the displacement field is achieved through application of a local transformation using multilevel B-spline approximation. This paper is organized as follows. Section 2 describes the algorithm in details. Section 3 presents three different experiments: It first compares the performances of the affine transformation and the perspective one, then evaluates the method against the global polynomial, and finally compares the results with those achieved using ENVI software. Conclusion and further work are discussed in section 4.

Methods

Feature detection and initial correspondence estimation

In order to find the same objects independently in both images, a detector with some invariant properties is needed. The Harris Corner Detector (see [START_REF] Harris | A combined corner and edge detector[END_REF]) possesses strong invariance to rotation, illumination variation and image noise. It is based on the local similarity measure of a signal that gives the local changes of the signal thanks to a window shifted by a small amount in different directions. Figure 1 shows examples of shifted windows. On 1a, an edge is detected because changes appear in one direction only. On 1b, a corner is detected because significant changes appear in all directions. After the corners have been extracted, the next task is to find which corner in the sensed image corresponds to which corner in the reference image. To do this, circular normalized cross-correlation coefficient is used. The idea is to measure the local intensity similarity between each corner in both images thanks to the computation of the normalized cross-correlation coefficient defined as:

where A and B are the n-by-n window surrounding a corner in reference and sensed images respectively and are the mean values of A and B. Then, the correspondence between two corners is established only if their correlation coefficient is greater than a fixed threshold. Moreover, in order to improve the quality of match, the correlation is computed in polar coordinates where the angle that gives the maximum correlation is selected. Finally, a faster implementation of the circular correlation is achieved by using the 1-D Discrete Fourier Transform (DFT) during the computation of the coefficient. The correspondence found is invariant to rotation only, this is a fast method but it doesn't guarantee that all the correspondences are correct.

Transform model estimation and RANSAC algorithm

At this stage, the wrong correspondences are identified and a global transform model (affine or perspective) is estimated. The use of a global model here is justified by its shape preserving property. Indeed, each image contains an overlapping area, i.e. a region which is common to both images, and a non-overlapping area whose shape must be preserved as well as possible since no information about the displacement is available in this area. Therefore, the global model provides a rough alignment and a good preservation of the non-overlapping areas. The refinement of the alignment will be achieved with a local transformation in the next stage.

Affine transformation and perspective transformation

An affine transformation is defined by the equation:

In order to find the 6 unknowns, at least 3 data points are necessary. These unknowns are then approximated in the least-squares sense.

A perspective transformation, when defined in homogeneous coordinates, is defined by the equation:

The non-homogeneous coordinates are computed as following:

Since the equation is unique up to a scale factor, the problem is reduced to 8 unknowns and, therefore, the minimum number of data points necessary to solve the system of equations is 4. The explanations of how to compute the parameters of a perspective transformation are given in [START_REF] Hartley | Multiple View Geometry in Computer Vision[END_REF].

RANSAC algorithm

As explained in the previous section, the circular crosscorrelation method doesn't guarantee that all the correspondences are correct, introducing outliers. A point is considered outlier if it follows a different model than the one estimated. These outliers can severely disturb the estimated transformation, and consequently must be identified. RANSAC algorithm handles this problem by introducing a classification of the data into inliers (valid points) and outliers while estimating the optimal transformation for the inliers. A threshold t is used, it insures that none of the inliers deviates from the model by more than t. The procedure, described in [START_REF] Hartley | Multiple View Geometry in Computer Vision[END_REF], can be summarized as following:

1. Select as small data subset as possible. Basically, the number of data points necessary to solve the leastsquares approximation is used. The subset must be selected randomly among the correspondence points. 2. Calculate the transformation model using the selected subset. Use the least-squares approximation here. 3. Transform all the other correspondence points with the calculated transformation and classify them into inliers and outliers using a threshold t. 4. Remember the transformation if the number of inliers is big enough. 5. Repeat this process a specified number of times. 6. When the iterations are finished, re-estimate the model using the inliers. This step provides the optimal model wrt the inliers found previously. 7. Re-classify the inliers as in step 3 with threshold t .

Indeed, after step 6, additional points may be classified as inliers. 8. Repeat step 6 and 7 until the number of inliers converges.

Since the right number of inliers needed and the number of iterations are usually not known. An adaptive method is used that relieves the user from initialising those two parameters. The reader is referred to [START_REF] Hartley | Multiple View Geometry in Computer Vision[END_REF] for more details about this method.

Finally, the important parameter to be initialized is the distance threshold t for which a point is declared inlier or outlier. Without knowledge of the distance of the outliers, the only way to determine this parameter is by experimentation.

Figure 2 shows how the threshold is determined during the process. The graph gives the number of inliers as a function of the value of the threshold. As the threshold increases, the number of correspondence points within the threshold distance also increases and so does the number of inliers.

One can easily observe a step behaviour on the graph, these steps enhance the fact that a big increase of the threshold is needed before a new inlier is found and it therefore gives the value when outliers begin to be considered as inliers. In conclusion, the user should select the threshold just before the first step appears as shown by the point in the graph.

Obviously, this method is subjective since few outliers could be close to the distance threshold, making the choice harder for the user. In that case, the best choice is to underestimate the number of inliers so that to make sure that no outliers would be present.

Deformable registration using multilevel B-Splines

As the image is scanned during the flight, it is highly probable that the deformation differs from the start of the scan to the end, resulting in local deformations throughout

Definition

A B-spline function is a sum of piecewise polynomials called B-spline basis functions. Its support is minimal with respect to a given degree. More precisely, a p-th degree Bspline curve is defined by: where u is a parameter that depends on the position x of the data point; P i are the control points, and N i,p (u) are the p-th B-spline basis functions defined on the nonperiodic (and in general nonuniform) knot vector Usually, a=0 and b=1.

Properties

• Local modification: moving P i changes C(u) only in the interval [u i , u i+p+1 ). This follows from the fact that N i,p (u)=0 for u∉[u i , u i+p+1 ).

• Moving along the curve from u=0 to u=1, the N i,p (u) functions act like switches; as u moves past a knot, one N i,p (u) (and hence the corresponding P i ) switches off, and the next one switches on. • The continuity and differentiability of C(u) follow from that of the N i,p (u) (since C(u) is just a linear combination of the N i,p (u)). Thus, C(u) is infinitely differentiable in the interior of knots intervals, and it is at least p-k times continuously differentiable at a knot of multiplicity k.

Implementation

The paper [START_REF] Lee | Scattered data interpolation with multilevel B-splines[END_REF] explains how to approximate scattered data with B-splines. It follows a simple process that, first, fixes the position of the control points P i as a grid that overlaps all over the image, and then, parameterizes the data points according to their positions in the grid. The method uses only uniform B-splines of degree 3, and the only user-defined parameter is the size of the grid. Nevertheless, it is sufficient to achieve a good trade-off between shape smoothness and accuracy of the approximation. Indeed, a coarse grid provides a rough approximation but smooth throughout the image, whereas a fine grid approximates better each data points, but is also more local. In the paper, a hierarchy of control grids is used to generate a sequence of B-spline functions whose sum results in a smooth and accurate approximation. This is called multilevel B-spline approximation. In conclusion, the user just has to choose the size of the coarsest and finest grid that will determine the locality of the transformation and its accuracy.

Polynomial model

A polynomial model has also been tested for the refinement of the displacement field.

Definition

A polynomial involves a sum of powers in one or more variables multiplied by coefficients. A bi-variate polynomial of degree n (specified by the user) is used in the program: A polynomial tra models thanks to its degree and number of unknowns. Moreover, the interesting property is the smoothness because polynomials are infinitely differentiable. But in return, polynomials higher than the first degree may result in unwanted stretching or squeezing due to oscillations between the data points. This is known as the Runge's phenomenon.

Before the estimation of t correspondence points have been added in the nonoverlapping area. Since the polynomial transformation is a global one, these points aim at constraining the transformation in the region where no information about the displacement is available. In the following, these points will be called "fixing points". The polynomial model is then

Garstang set

Affine Perspective Perspective wo sets of images have been used for testing the algorithm: Caton set and Garstang set. The evaluation of on can be evaluated with the alignment error.

f the deformation between these sets. Indeed, the local deformations are much stronger in Garstang set T the registration's quality is achieved with the root mean square (RMS) of the alignment error between the correspondence points, and with the observation of the registered image in RGB color.space where the reference image is in red and the transformed sensed image is in green.

Affine vs perspective

This section aims at comparing affine transformation and perspective transformation in order to determine which is the most appropriate for the global mapping. The most important criterion at this stage is that the right number of inliers is reached. Indeed, the alignment error for those inliers would be reduced in the following process. Thus, the threshold t is determined such that the right number of inliers is obtained, and then, the quality of the transformati Tables 1 and2 allow comparing both transformations for the images Caton and Garstang respectively. These two tables show that the performances of affine and perspective transformation are very similar. This observation suggests that the right global transformation model is probably close to affine. Another interesting result is the difference of the alignment error between the two sets. This is due to a difference o than in Caton set. Figure 3 shows a part of the image Garstang registered with perspective transformation. The correspondence points emphasize the misalignment, the black points are the reference ones and the white points are the sensed which have to be transformed (in the color version of the paper, the black points are blue and the white points are yellow).

Clearly, the points don't overlap. This region will have to be corrected with the deformable registration using B-splines.

Although the difference between the two transformations is very small, perspective transformation is used in the algorithm because it theoretically can model more complex deforma han the ne transfo ion.

Perspective + polynomial vs perspective

T , the B-splines tions t affi rmat

+ Bsplines

his part aims at correcting local misalignment that the previous transformation couldn't reduce. The method that uses polynomial transformation with "Fixing points" in the non-overlapping areas is compared with B-spline approximation. A first remark is that the method using polynomial is not easy to apply because of its big distortions in the non-overlapping areas if the degree of the polynomial is too high. Hence, for each new image, the user must determine the right polynomial degree. This distortion problem doesn't occur with B-spline approximation because the correction is only local. Table 3 gives the results. In any case the alignment error is much smaller than the previous results, but the quality of B-spline approximation appears clearly superior for Garstang images. Indeed have reduced the error by 93%, whereas the polynomial transformation has reduced the error by 1.6% only. Figure 4 shows the resulting image after registration with the B-splines. The correspondence points now overlap, and much less misalignment is visible which means that the local correction has been efficient in this region of the image. However, figure 5 shows that the correspondence points are not regularly spread along the images and therefore, the local correction is limited to small regions only.

Comparison with ENVI Software

ENVI (Environment for Visualizing Images) is an image processing system specialized for satellite and aircraft remote sensing data. A registration method is available; it warps the image with a polynomial transformation of a First, the software needs the correspondence points. ENVI proposes automatic feature detection, but the mismatching with the set Caton is too high, so the correspondence points obtained with the method presented in this paper are used. Then, the polynomial transformation of ENVI is compared visually with the algorithm presented in this paper. Figure 6,7 and 8 show the results for Caton images. The deformable registration appears more efficient thanks to its local corrections. It was expected since the correction used by ENVI is affine in this case, thus giving the same result as in section 3.1 when using affine transformation. The image 8, gives the registered image with a polynomial of degree 2.

Although the local misalignment is better corrected, a big deformation appears in the bottom right of the image showing that this transformation cannot be used for registering Caton images.

Conclusions and future works

A deformable registration method based on features is proposed here. First the features are detected thanks to the Harris corner detector and the initial correspondence is established through a rotation invariant cross-correlation measure. Then, RANdom SAmple Consensus method is applied in order to find the final correspondence and estimate a perspective transformation in the same time.

Finally, multilevel B-spline approximation enables reducing the local misalignment. The performances of perspective transformation have been compared with the affine transformation and it was shown that both transformations give very similar results. Then, two other models were tested as local transformations: polynomial transformation with fixing points in the non-overlapping areas, and Bspline approximation. The B-spline method appeared to be more efficient in terms of alignment error and also in the preservation of the shape in the non-overlapping areas. The improvement achieved with the final algorithm is important when the real model presents the same kind of transformation (i.e. a global transformation close to homography, and some small local transformation) which is exactly the case in the example of Caton images. On the other hand, if the real model has a lot of local transformations that avoids finding a global one, then the first step of the mapping gives big misalignment and few inliers, involving a weak correction of the local transformations in the second step. This is the case in the example of Garstang images. Although the algorithm was designed to be as automatic as possible, some parameters must be initialized by the user. The most sensitive parameter is the threshold t used in RANSAC, and on which depend the number of inliers and the alignment error.

For this parameter, the algorithm displays the graphs showing the number of inliers as a function of the threshold. This graph should help the user to find the value for t by localizing where the first outliers appear.

In the future, a modification in the feature detection and feature matching parts could improve the performances of the algorithm. Indeed, if more inliers are found, a better correction can be performed. To do so, an affine invariant feature detector combined with affine invariant matching could increase the number of inliers while reducing the number of outliers. Moreover, the algorithm should be tested with more images in order to know if the method proposed here can correct those images as well as the Caton images, or if it gives unsufficient correction like in the Garstang example.

Summary of the parameters' values

Figure 1 :

 1 Figure 1: Examples of shifted windows

Figure 2 :

 2 Figure 2: Determination of the threshold t.

  he polynomial's parameters, more estimated in the least-squares sense.

Figure 3 :

 3 Figure 3: Perspect ransformatio ive t n applied on Garstang set of images.

Figure 4 :Figure 5 :Table 3 :

 453 Figure 4: Deformable registration applied on Garstang set of images

Figure 6 :Figure 7 :

 67 Figure 6: Deformable registration applied on Caton set of images

Figure 8 :

 8 Figure 8: Polynomial registration with ENVI applied on Caton set of images

•

  Transform model estimation: The type and parameters of the so-called mapping functions are estimated. It must align the sensed image with the reference image. The parameters of the mapping functions are computed by means of the established feature correspondence. Image values at non-integer coordinates are computed by the appropriate interpolation technique. The type of transform model must be chosen according to the context in which the images where acquired. It can be a global transform model or a local one. The global transform model can be used if the images only differ by a global transformation such

• Image re-sampling and transformation: The sensed image is transformed by means of the mapping functions.

Table 1 :

 1 Caton images, comparison of the performances of affine transformation and perspective transformation.

	Caton set	Affine	Perspective
	numInliers	56	56
	t	10	10
	alignError	3.87	3.32

Table 2 :

 2 Garstang images, comparison of the performances of Affine transformation and perspective transformation.

Table 5 :

 5 Summary of the parameters

Acknowledgment

This work was supported by the PIMHAI project run under the INTERREG IIIB programme.


An De Weer

Joni Van Der Meulen

Pieter Rondou

Tom Taghon

Torsten Konrad

Katleen De Preter

Pieter Mestdagh

Tom Van Maerken

Nadine Van Roy

Marta Jeison

De Weer

An Pieter

; Poppe Bruce

Speleman Frank
email: franki.speleman@ugent.be

Prof. dr F Speleman

Nurten Yigit

Justine Nuytens

Els De Smet

EVI1 mediated

locus are recurrent genetic events in myeloid leukemia and are associated with poor prognosis. In this study, we assessed the role of EVI1 in the transcriptional regulation of microRNAs (miRNAs) involved in the leukemic phenotype. For this, we profiled expression of 366 miRNAs in 38 EVI1 rearranged patient samples, normal bone marrow controls and EVI1 knock down/re-expression models. Cross-comparison of these miRNA expression profiling data showed that EVI1 rearranged leukemias are characterized by down regulation of miR-449a. Reconstitution of miR-449a expression in EVI1 rearranged cell line models induced apoptosis resulting in a strong decrease in cell viability. These effects might be mediated in part by miR-449a regulation of NOTCH1 and BCL2 which are shown here to be bona fide miR-449a targets. Finally, we confirmed that miR-449a repression is mediated through direct promoter occupation of the EVI1 transcriptional repressor.

In conclusion, this study reveals miR-449a as a crucial direct target of EVI1 involved in the pathogenesis of EVI1 rearranged leukemias and unravels NOTCH1 and BCL2 as important novel targets of miR-449a. This EVI1-miR-449a-NOTCH1/BCL2 regulatory axis might open new possibilities for the development of therapeutic strategies in this poor prognostic leukemia subgroup. gene on chromosome band 3q26.2 are a recurrent finding in malignant myeloid disorders [START_REF] Nucifora | The EVI1 gene in myeloid leukemia[END_REF]. EVI1 gene rearrangements (translocations and inversions) account for approximately 5% of cytogenetic abnormalities in acute myeloid leukemias (AML), myelodysplastic syndromes (MDS) and chronic myeloid leukemias in blast crisis (CML-BC) [START_REF] Buonamici | The role of EVI1 in normal and leukemic cells[END_REF]. EVI1 transcriptional activation has also been reported in approximately 8 -15% of AML patients without chromosomal defects affecting the EVI1 locus [START_REF] Haas | Expression and prognostic significance of different mRNA 5'-end variants of the oncogene EVI1 in 266 patients with de novo AML: EVI1 and MDS1/EVI1 overexpression both predict short remission duration[END_REF][START_REF] Lugthart | High EVI1 levels predict adverse outcome in acute myeloid leukemia: prevalence of EVI1 overexpression and chromosome 3q26 abnormalities underestimated[END_REF].

KEYWORDS

The EVI1 gene encodes a zinc finger transcription factor that mainly acts as a repressor of gene expression, but relatively few data are available on the downstream targets and pathways involved in EVI1 pathogenesis [START_REF] Qiu | Regulation of the calreticulin gene by GATA6 and Evi-1 transcription factors[END_REF][START_REF] Spensberger | A novel interaction between the proto-oncogene Evi1 and histone methyltransferases, SUV39H1 and G9a[END_REF][START_REF] Takahashi | The human promyelocytic leukemia zinc finger gene is regulated by the Evi-1 oncoprotein and a novel guanine-rich site binding protein[END_REF][START_REF] Yatsula | Identification of binding sites of EVI1 in mammalian cells[END_REF][START_REF] Yuasa | Oncogenic transcription factor Evi1 regulates hematopoietic stem cell proliferation through GATA-2 expression[END_REF]. As survival of patients with EVI1 overexpressing leukemias is poor (Barjesteh van Waalwijk van [START_REF] Van Waalwijk Van Doorn-Khosrovani | High EVI1 expression predicts poor survival in acute myeloid leukemia: a study of 319 de novo AML patients[END_REF][START_REF] Haas | Expression and prognostic significance of different mRNA 5'-end variants of the oncogene EVI1 in 266 patients with de novo AML: EVI1 and MDS1/EVI1 overexpression both predict short remission duration[END_REF][START_REF] Lugthart | High EVI1 levels predict adverse outcome in acute myeloid leukemia: prevalence of EVI1 overexpression and chromosome 3q26 abnormalities underestimated[END_REF], new insights into the molecular pathogenesis of these leukemias are needed for rational development of targeted therapies.

MicroRNAs (MiRNAs) are 19-24 nucleotide long RNA molecules that can interfere with gene expression through degradation of the mRNA or inhibition of the translational machinery [START_REF] Medina | microRNAs and cancer: an overview[END_REF]. Recently, miRNA deregulation has been identified as a major contributor to cancer initiation and progression [START_REF] Deng | Mechanisms of microRNA deregulation in human cancer[END_REF]. In particular, miRNA genes have been shown to be directly regulated by activated oncogenes such as the miR-17-92 cluster which is up regulated by MYC/MYCN [START_REF] Bray | Widespread dysregulation of MiRNAs by MYCN amplification and chromosomal imbalances in neuroblastoma: association of miRNA expression with survival[END_REF][START_REF] Mendell | miRiad roles for the miR-17-92 cluster in development and disease[END_REF]. In view of these observations, we hypothesized that the malignant phenotype of EVI1 overexpressing leukemic cells might be, at least in part, explained through deregulation of miRNAs controlling the expression of genes critically involved in oncogenesis. To test this hypothesis, we analysed the expression of 366 miRNAs in a large set of bone marrow samples from patients with EVI1 rearrangements and crossed this data with miRNA profiling of two functional EVI1 knockdown models.

METHODS

Patients and cell lines

Bone marrow samples from 27 AML patients, 6 MDS patients and 5 CML patients were collected in a multi-centre setting. Patients were included in the study after confirmation Two EVI1 rearranged cell lines (Kasumi-3 and UCSD-AML1) were used to develop the knockdown models and cultured as previously described [START_REF] Asou | Establishment of an undifferentiated leukemia cell line (Kasumi-3) with t(3;7)(q27;q22) and activation of the EVI1 gene[END_REF][START_REF] Oval | Characterization of a factordependent acute leukemia cell line with translocation (3;3)(q21;q26)[END_REF]. The Kasumi-3 cell line was purchased from ATCC (Middlesex, UK; ATCC#CRL-2725) and the UCSD-AML1 was a generous gift from dr. Bernard O. (Paris, France). For modulation of miRNA expression, two additional AML cell lines, ME-1 [START_REF] Yanagisawa | Establishment and characterization of a new human leukemia cell line derived from M4E0[END_REF] and NB-4 [START_REF] Lanotte | NB4, a maturation inducible cell line with t(15;17) marker isolated from a human acute promyelocytic leukemia (M3)[END_REF], without EVI1 rearrangement or EVI1 overexpression were included. Furthermore, the two AML cell lines TF-1 (EVI1 overexpressing) and MOLM-13 (EVI1 negative) were used for chromatin immunoprecipitation (ChIP) experiments and were maintained in RPMI-1640, supplemented with 1% penicillin-streptomycin, and 20% FBS (GIBCO-BRL, Grand Island, NY, USA) and additionally 5ng/ml GM-CSF (Promocell, UK) for TF-1. The HEK293T cell line was maintained in RPMI-1640 supplemented with 10% fetal bovine serum, Glutamine (2 mM) and penicillin (100 U/ml)-streptomycin (100 µg/ml). Patient and EVI1 rearranged cell line characteristics and karyotypes are listed in Supplementary Table 1.

In order to validate the differentially expressed miRNAs, a tetracycline regulable U937 model system (U937T EVI1-HA) was used and propagated in RPMI-1640 medium supplemented with 10% fetal bovine serum, 0.5 µg/ml puromycin, 1 µg/ml tetracycline, and 500 µg/ml hygromycin [START_REF] Konrad | Inducible expression of EVI1 in human myeloid cells causes phenotypes consistent with its role in myelodysplastic syndromes[END_REF].

Knock down models

An EVI1 knockdown model was developed in two EVI1 rearranged cell lines, Kasumi-3 and UCSD-AML1, by electroporation of two EVI1 targeting small interfering RNAs (siRNA) (Invitrogen, Belgium). To evaluate the role of NOTCH1 and BCL2 in EVI1 pathogenesis, knockdown models of each gene were created in Kasumi-3 and UCSD-AML1 using a combination of four NOTCH1 specific siRNAs (Dharmacon, USA) or four BCL2 specific siRNAs (Dharmacon, USA), respectively. The siRNA sequences are listed in Supplementary The Kasumi-3 cells were electroporated at 250 V, 1000 µF and the UCSD-AML1 cells at 300 V, 1000 µF (exponential decay pulse) (Genepulser II, Bio-Rad, USA), respectively, using 100 nM of the EVI1, NOTCH1 or BCL2 siRNAs.

The cell lines were also electroporated with a scrambled control siRNA duplex (Invitrogen, Belgium or Dharmacon, USA) in order to account for any effect of the electroporation itself and for possible off-target effects. The cells were subsequently evaluated for knockdown at the mRNA level by RT-qPCR as previously described [START_REF] Poppe | EVI1 is consistently expressed as principal transcript in common and rare recurrent 3q26 rearrangements[END_REF][START_REF] Vandesompele | Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes[END_REF]. The reference genes RPL13A and YWHAZ were used for normalization after selection using the geNorm software [START_REF] Vandesompele | Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes[END_REF]. The qbasePLUS software version 1.2 (Biogazelle, Belgium) was used for the calculations of the RT-qPCR results [START_REF] Hellemans | qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data[END_REF]. The primer sequences are listed in Supplementary Table 3. Knock down of EVI1, NOTCH1 and BCL2 on the protein level was evaluated by Western blotting. Protein isolation was performed with the Nuclear protein Extraction Kit (Pierce, Belgium). Protein concentrations were determined using the Bradford reagent (Bio-Rad, Belgium). For Western blotting, 20 µg of protein was loaded onto a 7.5% (EVI1) or a 10% (NOTCH1 and BCL2) pre-cast gel (Bio-Rad, Belgium) and the Western blot procedure was performed according to the manufacturer's descriptions using a monoclonal EVI1 antibody (1/2000, #2265, Cell Signalling Technologies, The Netherlands), polyclonal NOTCH1 or BCL2 antibodies (1/1000, 2421S and 2876; Cell Signalling Technologies, The Netherlands) and an α-tubulin antibody (1/5000, Sigma-Aldrich, Belgium) [START_REF] Girish | Affordable image analysis using NIH Image/ImageJ[END_REF].

MicroRNA expression profiling

The Trizol reagent (Invitrogen, Belgium) was used to extract total RNA from total bone marrow samples or bone marrow leukocytes for all patients and normal bone marrow samples. All RNA fractions from cell lines were isolated with the miRNeasy mini kit (Qiagen, Belgium). MiRNA expression profiling was performed using high-throughput stem-loop RT-qPCR as previously described [START_REF] Mestdagh | High-throughput stem-loop RT-qPCR miRNA expression profiling using minute amounts of input RNA[END_REF], Mestdagh, et al 2009).

To identify differentially expressed miRNAs between the EVI1 siRNA treated fraction and controls fraction, we used fold change analysis (cut-off level of 1.5, p<0.05) [START_REF] Chang | Widespread microRNA repression by Myc contributes to tumorigenesis[END_REF][START_REF] Hu | MicroRNA expression and regulation in mouse uterus during embryo implantation[END_REF][START_REF] Ohlsson Teague | MicroRNAregulated pathways associated with endometriosis[END_REF][START_REF] Pradervand | Impact of normalization on miRNA microarray expression profiling[END_REF][START_REF] Tzur | MicroRNA expression patterns and function in endodermal differentiation of human embryonic stem cells[END_REF][START_REF] Wang | Ethanol exposure induces differential microRNA and target gene expression and teratogenic effects which can be suppressed by folic acid supplementation[END_REF] [START_REF] Breitling | Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments[END_REF].

Modulation of microRNA expression

To evaluate the phenotypic role of specific differentially expressed miRNAs, Kasumi-3, UCSD-AML1, NB-4 and ME-1 cells (2 x 10 6 ) were electroporated with 100 nM of the precursor miR-449a (PM11127; Applied Biosystems, Belgium) or 100 nM of the anti-miRNAs miR-213 and miR-107 (AM10381 and AM10056; Applied Biosystems, Belgium), according to the above described electroporation conditions for Kasumi-3 and UCSD-AML1. The NB-4 and ME-1 cells were electroporated at 300 V, 1000 µF (exponential decay pulse). As a control, cells were electroporated with 100 nM of a scrambled precursor or a scrambled anti-miRNA (Applied Biosystems, Belgium), respectively.

Cell viability and apoptosis assays

Upon electroporation of cells, cell viability and apoptosis were determined using the CellTiter-Glo assay (Promega, Belgium) and Caspase-Glo 3/7 assay (Promega, Belgium), respectively, according to the manufacturer's descriptions. Effects on apoptosis were confirmed using flow cytometry with AnnexinV staining (Bender Medsystems, Belgium).

Chromatin immunoprecipitation (ChIP)

ChIP was carried out on two AML cell lines, the TF-1 (EVI1 over-expressing) and MOLM-13 (EVI1 negative). ChIP samples were prepared using 10 7 cells of each cell line per condition. Chromatin was fragmented with a Bioruptor (Diagenode, Belgium) for 30 minutes (30 seconds pulses, 30 seconds pauses). ChIP was carried out according to the manufacturer's protocol (High Cell ChIP kit, Diagenode, Belgium, Catalog # kch-mahigh-G16) using anti-EVI1 (Cell Signalling Technology, Catalog #2593) or an equal amount of IgG isotype as negative control (Cell Signaling Technology, Catalog #2729) or anti-RNA Polymerase II (Millipore, as positive control. Real-time PCR was used to amplify immunoprecipitated DNA using Power Sybr Green (Applied Biosystems) and a 7500 real-time PCR instrument (Applied biosystems). PCR results were calculated using the ∆∆Ct method. The amount of immunoprecipitated DNA in each experiment is represented as signal relative to the amount of input and was calculated with the quantitative real-time PCR. Primers used for the ChIP experiment covered the potential 5'GAAACAAGACATGCCAACCA-3') and regions covering previously described direct EVI1 target genes PBX1 [START_REF] Shimabe | Pbx1 is a downstream target of Evi-1 in hematopoietic stem/progenitors and leukemic cells[END_REF], FAM83B and CRHBP [START_REF] Lugthart | Aberrant DNA hypermethylation signature in acute myeloid leukemia directed by EVI1[END_REF] as positive controls.

Luciferase reporter experiments

Sixty-three basepair fragments containing the predicted target sites of miR-449a in the NOTCH1 and BCL2 3' untranslated regions (UTR), as well as mutant target sites with 3 point mutations, were cloned into the NotI and XhoI cloning sites of the PsiCheck-2 vector (Promega, Belgium). For this purpose, synthetic oligonucleotides (Supplementary Table 4) were annealed for 3 min at 90°C and 1h at 37°C. Ligation into the PsiCheck-2 vector was performed using a T4 ligase (Promega, Belgium). Subsequently, competent E. coli cells were transformed with the NOTCH1 and BCL2 predicted binding site constructs and DNA was isolated using the High Pure Plasmid isolation kit (Roche, Belgium).

HEK293 cells (80 000 cells) were transfected with 200 ng of the NOTCH1 or BCL2

3'UTR PsiCheck-2 constructs in combination with 100 ng of the precursor miR-449a

(Applied Biosystems, Belgium) using the DharmaFECT Duo transfection reagent (Dharmacon, USA). After 24h incubation, luciferase activity was assayed using the Dual-Glo luciferase assay system (Promega, Belgium) on a FLUOstar OPTIMA (BMG labtechnologies GmbH, Germany).

Target gene expression analysis

To investigate the expression of candidate miR-449a target genes NOTCH1 and BCL2, RT-qPCR on Kasumi-3 and UCSD-AML1 cells treated with a precursor miR-449a or with a scrambled precursor miRNA was performed as previously described [START_REF] Poppe | EVI1 is consistently expressed as principal transcript in common and rare recurrent 3q26 rearrangements[END_REF][START_REF] Vandesompele | Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes[END_REF]. The reference genes RPL13A, GAPDH and YWHAZ were used for normalization as described above, and the qBasePlus software version 1.2 (Biogazelle, Belgium) was used for the calculations [START_REF] Hellemans | qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data[END_REF]. Primer sequences for all tested genes are listed in Supplementary Table 3. Effects of precursor miR-449a electroporation on NOTCH1 and BCL2 protein levels was measured using Western blotting as described above. For the identification of differentially expressed miRNAs, we used fold change analysis (cut-off level of 1.5, p<0.05) [START_REF] Chang | Widespread microRNA repression by Myc contributes to tumorigenesis[END_REF][START_REF] Hu | MicroRNA expression and regulation in mouse uterus during embryo implantation[END_REF][START_REF] Ohlsson Teague | MicroRNAregulated pathways associated with endometriosis[END_REF][START_REF] Pradervand | Impact of normalization on miRNA microarray expression profiling[END_REF][START_REF] Tzur | MicroRNA expression patterns and function in endodermal differentiation of human embryonic stem cells[END_REF][START_REF] Wang | Ethanol exposure induces differential microRNA and target gene expression and teratogenic effects which can be suppressed by folic acid supplementation[END_REF] and the Rank Product algorithm [START_REF] Breitling | Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments[END_REF]. Statistical analysis of differences in cell survival/apoptosis was calculated by the Student's t-test. Values were considered statistically significant at p < 0.05.

Results

Identification of EVI1 regulated microRNAs

In order to discover possible miRNAs that participate in EVI1 mediated transformation, we performed miRNA expression analysis in EVI1 knock down models and crossed these data with miRNA profiles of 38 EVI1 rearranged patient samples and 6 normal bone barrow samples.

RNAi mediated knock down of EVI1 was performed in the EVI1 overexpressing cell lines Kasumi-3 and UCSD-AML1 and EVI1 knock down was confirmed on mRNA and protein level (Supp Fig 1A,B). Differentially expressed miRNAs were identified according to a fold change analysis with a cut-off of 1.5 (p<0.05) (Supp Table 5).

Next, rank product analysis (p<0.05) was used to compare miRNA profiles of EVI1 rearranged samples and 6 normal bone marrow samples, which lead to the identification of 25 down regulated and 24 up regulated miRNAs in EVI1 samples compared to normal bone marrow (Supp Table 6).

Three down regulated (miR-190, miR-215 and miR-449a) and 3 up regulated miRNAs (miR-213, miR-187 and miR-107) were identified as differentially expressed in both the in vitro knock down models and the primary patient samples. Of these overlapping miRNAs, three (miR-449a, miR-213 and miR-107) were selected for further functional analysis, based on a known role in tumorigenesis [START_REF] Noonan | miR-449a targets HDAC-1 and induces growth arrest in prostate cancer[END_REF][START_REF] Roldo | MicroRNA expression abnormalities in pancreatic endocrine and acinar tumors are associated with distinctive pathologic features and clinical behavior[END_REF][START_REF] Wong | Mature miR-184 as Potential Oncogenic microRNA of Squamous Cell Carcinoma of Tongue[END_REF]. Figure 1 represents the overall strategy for miRNA selection used in this study. Figure 2 shows the expression of miR-449a, miR-213 and miR-107 in primary leukemia samples, normal bone marrow and the EVI1 knock down models. Taken together, the above findings suggest that low miR-449a expression and high miR-213/miR-107 expression are critical for the survival of EVI1 deregulated cell lines. Given the fact that the most prominent functional effects were achieved upon modulation of miR-449, we decided to select this particular miRNA for further analysis.

Modulation of miR-449a, miR-213 and miR-107 expression decreases cell viability and increases apoptosis of EVI1 overexpressing leukemic cells

MiR-449a is down regulated in a regulable EVI1 overexpression model

To further confirm EVI1 mediated transcriptional regulation of miR-449a, we used a tetracycline inducible U937 EVI1 overexpression model [START_REF] Konrad | Inducible expression of EVI1 in human myeloid cells causes phenotypes consistent with its role in myelodysplastic syndromes[END_REF]

NOTCH1 and BCL2 are direct miR-449a targets

In order to identify miR-449a target genes, we interrogated 4 different miRNA target prediction algorithms (DIANA-microT, miRanda, PicTar, Targetscan), which rendered a list of 1607 candidate genes. This list was narrowed down to 15 genes by selecting for those genes predicted by at least 2 or more algorithms and those genes with known functions in cell viability, cell cycle regulation, apoptosis or differentiation (Supp Table 7).

Next, RT-qPCR expression analysis of these candidate genes revealed decreased NOTCH1 and BCL2 mRNA levels in Kasumi-3 and UCSD-AML1 cells following treatment with a miR-449a precursor, as compared to control cells. This effect was not observed using housekeeping genes, ruling out possible broad off-target effects (Fig switching on EVI1 expression, suggesting a direct regulation. In concordance with these data, we confirmed that miR-449a is a direct transcriptional target of EVI1.

Other evidence in the literature highlights the importance of miR-449a in cancer.

Recently, the miR-449a promoter was shown to be hypermethylated in sarcoma [START_REF] Yang | miR-449a and miR-449b are direct transcriptional targets of E2F1 and negatively regulate pRb-E2F1 activity through a feedback loop by targeting CDK6 and CDC25A[END_REF]. MiR-449a was also described as down regulated in prostate cancer and reexpression studies of miR-449a in prostate cancer cells resulted in cell cycle arrest and apoptosis [START_REF] Noonan | miR-449a targets HDAC-1 and induces growth arrest in prostate cancer[END_REF], similar to the results obtained in this study. In addition, a recent study showed that the tumor suppressor function of miR-449a/b might in part be mediated through regulation of Rb/E2F1 activity and subsequently miR-449a/b inhibits CDK6 and CDC25A, resulting in a negative feedback loop. Furthermore, the expression of miR-449a/b was shown to be epigenetically silenced through histone H3 Lys27

trimethylation. This would suggest that escape from Rb/E2F1 regulation through an aberrant epigenetic event contributes to E2F1 deregulation and unrestricted proliferation in human cancer [START_REF] Noonan | miR-449a causes Rbdependent cell cycle arrest and senescence in prostate cancer cells[END_REF], Yang, et al 2009).

In this study, we show for the first time that BCL2 and NOTCH1 are direct miR-449a targets. BCL2 is an anti-apoptotic gene capable of antagonizing the p53 pathway, and has been described as over expressed in different types of lymphoma and AML (Nagy, et al 2003, Thomadaki and[START_REF] Thomadaki | BCL2 family of apoptosis-related genes: functions and clinical implications in cancer[END_REF] Interestingly, as EVI1 is associated with a stem cell phenotype [START_REF] Goyama | Evi-1 is a critical regulator for hematopoietic stem cells and transformed leukemic cells[END_REF][START_REF] Takeshita | AML1-Evi-1 specifically transforms hematopoietic stem cells through fusion of the entire Evi-1 sequence to AML1[END_REF], regulation of NOTCH1, which is required for self-renewal, through an EVI1-miR-449a axis, could lead to survival of leukemic stem cells which are resistant to chemotherapeutics [START_REF] Misaghian | Targeting the leukemic stem cell: the Holy Grail of leukemia therapy[END_REF]. Therefore, targeting NOTCH1 might aid in eliminating those cells that are likely to cause disease relapse [START_REF] Drewa | Is the cancer stem cell population "a player" in multi-drug resistance?[END_REF].

In conclusion, our study uncovered miR-449a as a crucial direct target of EVI1 involved in the pathogenesis of EVI1 rearranged leukemias and unravels NOTCH1 and BCL2 as important novel targets of miR-449a.

EVI1 overexpression model

Expression of miR-449a (A) and NOTCH1 and BCL2 (B) in a tetracycline regulable EVI1 overexpression model system was determined using RT-qPCR. U937T EVI1-HA cells were transferred to media without tetracycline and RNA was extracted at the indicated time points thereafter. The expression of miR-449a, NOTCH1 or BCL2 in cells transduced with an empty vector was set to 100%. Each RT-qPCR reaction was performed in duplicate for all conditions in a single experiment. A) Kasumi-3 cells were electroporated with the precursor miR-449a and the mRNA expression of NOTCH1, BCL2, YWHAZ, RPL13A and GAPDH was determined using RT-qPCR. For each time point, expression of these genes in a control fraction treated with a scrambled precursor was set to 100%. Each RT-qPCR reaction was performed in duplicate for all conditions in a single experiment. B) Kasumi-3 was electroporated with the precursor miR-449a and the protein levels of NOTCH1 and BCL2 were evaluated using Western blotting. As a loading control, the blots were stained with an α-tubulin antibody.

C) The NOTCH1 and BCL2 3'UTR miR-449a putative target sites (www.targetscan.org). D) Luciferase activity upon transfection of HEK293 cells with a vector containing the wild-type 3'UTR NOTCH1 miR-449a binding site or a vector containing the wild-type BCL2 miR-449a binding site in combination with the precursor miR-449a. Also the luciferase activity of the mutated constructs in combination with the precursor miR-449a was determined. Luciferase activity of cells transfected with the Deleted: All RT-qPCR measurements were performed in duplicate.

Deleted: Measurements were performed in duplicate. or a polyclonal BCL2 antibody was performed. As a loading control, the blots were treated with an α-tubulin antibody. 46,XY,t(2;5)(p13;q33),t(3;7)(q26;q22),del(5)(q15),-8,del(9)(q32),add(12)(p11),add(16)(q13),+mar [20] 45,XX,t(3;3)(q21;q26),-7,t(2;22)(p13;q12) [20] 1 AML = acute myeloid leukemia, MDS = myelodysplastic syndrome and CML-BC = chronic myeloid leukemia in blast crisis 2 The chromosomal aberration implicating the EVI1 locus is indicated with bold formatting. Karyotype nomenclature according to ISCN 2009.

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

F

NOTCH1 F TCGATTATGTACTTTTATTTTACACAGAAACACTGCCTTTTTATTTATATGTACTGTTTTATC NOTCH1 R GGCCGATAAAACAGTACATATAAATAAAAAGGCAGTGTTTCTGTGTAAAATAAAAGTACATAA BCL2 F TCGACAGGCAAAACGTCGAATCAGCTATTTACTGCCAAAGGGAAATATCATTTATTTTTTACA BCL2 R GGCCTGTAAAAAATAAATGATATTTCCCTTTGGCAGTAAATAGCTGATTCGACGTTTTGCCTG NOTCH1 mut F TCGATTATGTACTTTTATTTTACACAGAAACGCCGTCTTTTTATTTATATGTACTGTTTTATC NOTCH1 mut R GGCCGATAAAACAGTACATATAAATAAAAAGACGGCGTTTCTGTGTAAAATAAAAGTACATAA BCL2 mut F TCGACAGGCAAAACGTCGAATCAGCTATTTAATACTAAAGGGAAATATCATTTATTTTTTACA BCL2 mut R GGCCTGTAAAAAATAAATGATATTTCCCTTTAGTATTAAATAGCTGATTCGACGTTTTGCCTG 1 F = forward,

  the Ecotropic Viral Integration site 1 (EVI1)

  the Ecotropic Viral Integration site 1 (EVI1)

  by karyotyping and Fluorescence In Situ Hybridization (FISH) (RP11-362K14, RP11-82C9 and RP11-694D5) and confirmation of EVI1 ectopic expression with reverse transcription quantitative PCR (RT-qPCR) (De Weer, et al 2008). Six normal bone marrow samples from patients without a hematological malignancy were also included in the study. The study was approved by the ethical committee of the Ghent University Hospital (2003/273).

  evaluate a potential functional role of these miRNAs in EVI1 overexpressing leukemic cells, we modulated the expression levels of miR-449a with a precursor miRNA or miR-213 and miR-107 with anti-miRNAs and measured the effects on cell viability and apoptosis.Electroporation of anti-miRNAs directed against miR-213 and miR-107 as single agents resulted in a significant decrease in cell viability in the Kasumi-3 cell line (Fig3A).Interestingly, the largest impact on cell viability was noticed after administration of the precursor miR-449a, with a decrease of 95% for Kasumi-3 at the 96h time point(Fig 3A).Electroporation of Kasumi-3 with the precursor miR-449a and anti-miRNAs miR-213 and miR-107 resulted in an increase in caspase-3 and/or caspase-7 activity, 48h after electroporation, indicative of induction of apoptosis. Again, the largest effects were seen after modulation of miR-449a expression (Fig3B). Similar results both for cell viability as well as induction of apoptosis were obtained for the UCSD-AML1 cell line (Fig3C, D).To confirm the specificity of the observed effects on cell viability and apoptosis in EVI1 deregulated cells, modulation of the expression levels of miR-449a, miR-213 and miR-107 were also performed in two cell lines without EVI1 rearrangement, namely NB-4 and ME-1. MiR-449a, miR-213 or miR-107 were expressed at similar levels in these two cell lines as compared to normal bone marrow samples (data not shown). For these cell lines, no significant effects were observed on cell viability or apoptosis upon reexpression of miR-449a or inhibition of miR-213 and miR-107 (Supp Fig 2A-D).

  . Using this model, miR-449a was shown to be rapidly down regulated following overexpression of the EVI1 oncogene (Fig 4A). The results from this validated EVI1 regulable model system are in accordance with our previous observations in the EVI1 knock down cell line models and EVI1 rearranged patient samples, thus further supporting the robustness of miR-449a repression in EVI1 overexpressing leukemic cells. Deleted: (data not shown) is a direct target of the transcriptional repressor EVI1 The short time interval between EVI1 upregulation and subsequent miR-449a downregulation in the U937 EVI1 overexpression model (Fig 4A) is suggestive for a direct regulation of transcription of the miR-449a gene by the EVI1 transcription factor.To test this hypothesis, we identified a potential EVI1 binding site 4875 bp upstream of the miR-449a transcription start site using MatInspector 37(Fig 5A) and performed EVI1 ChIP analysis. Despite extensive experimental efforts, we were not able to optimize the ChIP protocol in Kasumi-3 or UCSD-AML1 cell lines, and we therefore performed ChIP assays in the EVI1 overexpressing cell line TF-1 and the EVI1 negative MOLM-13 cell line as negative control. In this analysis, we showed specific DNA binding of EVI1 to the promoter of miR-449a similar to previously described direct EVI1 target genes PBX1[START_REF] Shimabe | Pbx1 is a downstream target of Evi-1 in hematopoietic stem/progenitors and leukemic cells[END_REF], FAM83B and CRHBP(Lugthart, et al 2011) (Fig 5B).

  6A). NOTCH1 and BCL2 modulation upon miR-449a re-expression was also confirmed on the protein levels(Fig 6B).Next, we used luciferase assays to confirm that NOTCH1 and BCL2 are bona fide miR-449a targets. The decrease in luciferase activity was rescued by mutation of 3 critical bases in the predicted binding sites for miR-449a in the 3' UTR of NOTCH1 and BCL2(Fig 6C, D). Finally, we used the tetracycline regulable U937 EVI1 overexpression model[START_REF] Konrad | Inducible expression of EVI1 in human myeloid cells causes phenotypes consistent with its role in myelodysplastic syndromes[END_REF] to show additional evidence of increased NOTCH1 and BCL2 expression upon EVI1 overexpression (Fig 4B), as expected from miR-449a repression in this model (Fig 4A).To evaluate the role of NOTCH1 and BCL2 in relation to the observed phenotypic effects upon modulation of miR-449a expression, we performed knock down of NOTCH1 and BCL2 by use of siRNAs in Kasumi-3 and UCSD-AML1 and evaluated the effects on cell viability and apoptosis. Knock down of NOTCH1 and BCL2 was confirmed at the mRNA level using RT-qPCR and at the protein level using Western blotting. A decrease in cell viability and an increase in caspase-3 and/or caspase-7 activity, indicative of apoptosis, was observed in Kasumi-3 following NOTCH1 and BCL2 knock down, respectively. The most prominent effects were observed for BCL2 knock down (Figure7A-B). Similar results were obtained for UCSD-AML1 (data not shown). Taken together, these results suggest that the observed effects on cell viability and apoptosis by miR-449a re-expression are at least in part due to suppression of the expression of its target genes NOTCH1 and BCL2.DiscussionLeukemias over expressing the EVI1 oncogene are responsible for ≥10% of all myeloid leukemias and are characterized by a poor prognosis. Recently, it was shown that transcription factors can directly regulate the expression of miRNAs, as well as of mRNAs, adding another dimension to the transcriptional control of downstream pathways[START_REF] Schulte | MYCN regulates oncogenic MicroRNAs in neuroblastoma[END_REF]. As miRNAs are involved in processes such as proliferation, apoptosis and differentiation[START_REF] Medina | microRNAs and cancer: an overview[END_REF], pathways that are deregulated in cancer, identification of miRNAs involved in EVI1 pathogenesis could offer novel therapeutic strategies and further insight into leukemic initiation and progression.In this study, we identified a distinct series of up-and down regulated miRNAs following expression profiling of 366 miRNAs using stem loop RT-qPCR of 38 EVI1 rearranged and overexpressing patient samples, normal bone marrow samples, and two EVI1 cell line knock down model systems. Upon modulation of three selected miRNAs (miR-449a, miR-213 and miR-107), a decrease in cell viability and an increase in apoptosis was observed, with the most prominent effects observed after precursor miR-449a electroporation. For the latter miRNA, we further validated its expression in relation to EVI1 expression levels using a tetracycline regulable EVI1 overexpression model system. MiR-449a expression was indeed suppressed in less than 12 hours after

  . The NOTCH1 gene, which is part of a type 1 transmembrane protein family, is involved in regulation of self-renewal, apoptosis and differentiation. Up regulation of NOTCH1 has already been described in T-cell acute lymphoblastic leukemia (T-ALL), where NOTCH1 activating mutations occur in about 50% of the patients[START_REF] Palomero | Oncogenic NOTCH1 control of MYC and PI3K: challenges and opportunities for anti-NOTCH1 therapy in T-cell acute lymphoblastic leukemias and lymphomas[END_REF]. Interestingly, the C. elegans EVI1 homolog EGL-43 is known to act downstream of NOTCH signalling in cell fate specification[START_REF] Hwang | C. elegans EVI1 proto-oncogene, EGL-43, is necessary for Notch-mediated cell fate specification and regulates cell invasion[END_REF]. These results indicate that the relationship between NOTCH1 and EVI1 might be highly context dependent, differing from normal developing cells to cancer cells.An important failsafe program against tumorigenesis is provided by the gatekeeper protein p53, and it is generally accepted that this barrier must be overcome during the ontogenesis of a tumour. Interestingly, both NOTCH1[START_REF] Beverly | Suppression of p53 by Notch in lymphomagenesis: implications for initiation and regression[END_REF][START_REF] Rosati | Constitutively activated Notch signaling is involved in survival and apoptosis resistance of B-CLL cells[END_REF][START_REF] Secchiero | Nutlin-3 up-regulates the expression of Notch1 in both myeloid and lymphoid leukemic cells, as part of a negative feedback antiapoptotic mechanism[END_REF] and BCL2[START_REF] Thomadaki | BCL2 family of apoptosis-related genes: functions and clinical implications in cancer[END_REF] have been implicated in suppression of p53 activity in leukemic cells. Therefore, our findings position repression of miR-449a as a means by which EVI1 leukemic cells circumvent the p53 anti-tumour barrier and it is tempting to speculate that this suppression of the p53 pathway relies on up regulation of both BCL2 and NOTCH1 expression.

Figure 1 .

 1 Figure 1. Overall strategy for microRNA selection. The expression of 384 small RNAs was determined for 38 EVI1 rearranged patient samples, 6 normal bone marrow samples and EVI1 knock down model systems of the EVI1 rearranged cell lines Kasumi-3 and UCSD-AML1. Analysis of the microRNA profiles lead to the identification of 25 downregulated and 24 upregulated microRNAs in EVI1 samples compared to normal bone marrow (p<0.05). Three downregulated (miR-190, miR-215 and miR-449a) and 3upregulated microRNAs (miR-213, miR-187 and miR-107) were identified as differentially expressed in both the cell line models and patient samples. Of these overlapping microRNAs, three (miR-449a, miR-213 and miR-107) were selected for further functional analysis, based on a known role in tumorigenesis(miR-213 and miR- 107) or homology to microRNAs with an established role in cancer (miR-449a). MiR-449a was selected for further analysis, as the most prominent functional effects were achieved upon modulation of this microRNA.

Figure 2 .

 2 Figure 2. MiR-449a, miR-213 and miR-107 expression in EVI1 rearranged patient samples and EVI1 knockdown models Expression levels of A) miR-449a, B) miR-213 and C) miR-107, in 38 EVI1 deregulated patient samples, 6 normal bone marrow samples and two EVI1 rearranged cell lines Kasumi-3 and UCSD-AML1 (with or without EVI1 siRNA electroporation, triplicate) as determined with RT-qPCR. Red bars indicate the median expression.

Figure 3 .Figure 4 .

 34 Figure 3. MiR-449a re-expression or inhibition of miR-213 or miR-107 results in decreased cell viability and increased apoptosis in the Kasumi-3 and UCSD-AML1 cell lines Kasumi-3 (A,B) or UCSD-AML1 (C,D) cells were electroporated with precursor miR-449a, anti-miR-213 or anti-miR-107 and effects on cell viability (A+C) and caspase-3 and caspase-7 activity (B+D) were measured 96h (cell viability) or 48h (apoptosis) after electroporation. Measurements were performed in duplicate for each condition within one biological experiment. Viability or caspase-3 and caspase-7 activity of cells treated with a scrambled precursor or anti-miRNA was set to 100% or 1, respectively.

Figure 5 .

 5 Figure 5. MiR-449a is a direct target of EVI1 A) Schematic representation of the predicted EVI1 binding site in the promoter region of miR-449a. B) Specific DNA binding of EVI1 to the promoter region -4875 bp of the transcription start site of miR-449a was detected by ChIP. Real time PCR was performed on fragmented chromatin precipitated by anti-EVI1 antibody (black bars) or normal Rabbit IgG (grey bars) from an EVI1 positive (EVI1 + ) TF-1 cell line and the EVI1 negative (EVI1 -) MOLM-13 cell line. Primers were designed to amplify the predicted EVI1 binding site in the promoter region miR449. Primers directed to the promoter region of the EVI1 positive target genes PBX1, FAM83B and CRHBP were used as positives controls. The ratio of immunoprecipitated DNA versus input material (in %) is shown.

Figure 6 .

 6 Figure 6. NOTCH1 and BCL2 are direct targets of miR-449a

  set to 1 and data were normalized accordingly. The luciferase experiment shown is representative for two independent biological assays; in each experiment, luciferase measurements were performed in duplicate for each condition.

Figure 7 .Figure 2 .Figure 3 .Figure 6 .

 7236 Figure 7. Knockdown of NOTCH1 or BCL2 results in decreased cell viability and increased apoptosis Kasumi-3 was electroporated with NOTCH1 or BCL2 siRNAs and A) cell viability or B)caspase-3 and caspase-7 activity was measured 96h (cell viability) or 48h (apoptosis) after electroporation. Viability or caspase-3 and caspase-7 activity of cells treated with a scrambled siRNA was set to 100% or 1, respectively. Measurements were performed in duplicate for each condition within one biological experiment.

  R = reverse and mut = mutated F o r P e e r R e v i e w

Table 2 .

 2

	F o
	r
	P
	e e r
	R e v i e w
	6

Table 1 .

 1 Patient and cell line characteristics: diagnosis, age at diagnosis and karyotype

	Supplementary Case Diagnosis 1	Age at diagnosis	Karyotype 2
	1	AML	64	45,XY,inv(3)(q21q26),-7[13]
	2	AML	69	45,XX,inv(3)(q21q26),del(5)(q22q32),-7[14]
	3	AML
			51	46,XY,inv(3)(q21q26)[7]/
				46,XY[8]
	4	MDS	71	45,XX,t(3;8)(q26;q23),-7[8]
	5	MDS	66	46,XY,inv(3)(q21q26)[8]/
				46,XY[2]
	6	AML	72	43,XY,inv(2)(p25q34),inv(3)(q21q26),add(10)(p15),t(11;16)(q10;p10),-17,-18,-21[15]
	7	AML	56	45,X,add(x)(p22),t(1;3)(p32;p13),inv(3)(q21q26),-5,del(11)(p14),+mar[15]
	8	AML	70	46,XY,t(3;6)(q26;q25),del(5)(q22q34)[16]
	9	AML	48	46,XX,inv(3)(q21q26)[4]/
				46,XX[6]
	10	AML	30	46,XX,t(3;12)(q26;p13),del(7)(q11q35)[13]/
				46,XX[3]
	11	AML	52	46,XY,t(2;3)(p22;q26)[15]
	12	AML	66	45,XY,inv(3)(q21q26),-7[20]
	13	CML-BC	79	46,XY,t(9;22)(q34;q11)[5]/
				46,sl,t(3;21)(q26;q22)[10]
	14	CML-BC	36	46,XY,t(9;22)(q34;q11)[6]/
				46,sl,inv(3)(q21q26)[24]
	15	AML	45	48,XY,add(3)(q26),+8,del(8)(q24),t(9;22)(q34;q11),+16,add(16)(p13)[9]/
				54,sl,+4,+del(8)(q24),+10,+12,+20,+der(22)t(9;22)[21]
	16	MDS	71	45,XX,inv(3)(q21q26),-7[10]

Table 2 .

 2 EVI1, NOTCH1 and BCL2 siRNA sequences

	Supplementary siRNA name	Sequence 5' -3'
	EVI1 siRNA 1	AUUGAAGCCAGAUUCUGAAGAGGC
	EVI1 siRNA 2	UUUCGAGGCUCAGUCAGCUUUGUCC
	NOTCH1 siRNA 1 GCGACAAGGUGUUGACGUU
	NOTCH1 siRNA 2 GAUGCGAGAUCGACGUCAA
	NOTCH1 siRNA 3 GGACAUCACGGAUCAUAUG
	NOTCH1 siRNA 4 GAACGGGGCUAACAAAGAU
	BCL2 siRNA 1	GGGAGAACAGGGUACGAUA
	BCL2 siRNA 2	GAAGUACAUCCAUUAUAAG
	BCL2 siRNA 3	GGAGGAUUGUGGCCUUCUU
	BCL2 siRNA 4	UCGCCGAGAUGUCCAGCCA
		o
		r
		P
		e e r
		R e v i e w

Table 4 .

 4 Oligonucleotide sequences comprising predicted microRNA binding sites in 3'UTR of target genes

	Oligonucleotide name 1	Sequence 5' -3' 2

Table 5 .

 5 Up-and downregulated microRNAs in two EVI1 knockdown model systems using a combination of two EVI1 siRNAs

	Supplementary

DOWN regulated microRNAs after siRNA treatment UP regulated microRNAs after siRNA treatment

  Bold formatting indicates microRNAs that were also identified as being differentially expressed in EVI1 deregulated patient samples compared to normal bone marrow.

	hsa-mir-493-3p	1.8232	hsa-mir-302a	2.5379	hsa-mir-519d	1.7690	hsa-mir-520a	1.6558	hsa-mir-139	2.1097	hsa-mir-525	1.9323
	hsa-mir-525	1.7266	hsa-mir-183	2.1515	hsa-mir-302c	1.7458	hsa-mir-518c*	1.6455	hsa-mir-449a	1.8212	hsa-mir-409-5p	1.9118
	hsa-mir-220	1.6575	hsa-mir-107	2.1472	hsa-mir-323	1.7211	hsa-mir-558	1.5679	hsa-mir-657	1.7214	hsa-mir-449a	1.7933
	Kasumi-3 24h time point MicroRNA ID 1 change 2 Fold hsa-mir-96 1.6345 hsa-mir-520c 1.6711 hsa-mir-148a 1.9461 hsa-mir-521 1.7831 Supplementary 12h time point MicroRNA ID 1 Fold change 2 hsa-mir-652 1.5121 hsa-mir-302d 1.5323 hsa-mir-409-5p 1.6232 hsa-mir-373* 1.5547	48h time point MicroRNA ID 1 change 2 Fold hsa-mir-600 1.5646 hsa-mir-20a 1.5545	Kasumi-3 24h time point change 2 MicroRNA ID 1 12h time point MicroRNA ID 1 Fold Fold change 2 hsa-mir-137 1.5434 hsa-mir-182* 1.6082	MicroRNA ID 1 hsa-mir-558 hsa-mir-182* 48h time point change 2 1.5788 Fold 1.6428 hsa-mir-519e* 1.7480 hsa-mir-520a 1.6991
	hsa-let-7f	4.0971	hsa-mir-213	4.3519	hsa-mir-220	3.7943	hsa-mir-141	4.2972	hsa-mir-139	7.5819	hsa-mir-410 hsa-mir-23a	6.6164 1.5121
	hsa-mir-544	2.8722	hsa-mir-107	4.1011	hsa-mir-500	3.4824	hsa-mir-23b	3.9814	hsa-mir-31	3.5201	hsa-mir-449a	3.7619
	hsa-mir-139 p<0.05	2.6045	hsa-mir-204	3.6594	hsa-mir-514	3.3594	hsa-mir-432	3.7962	hsa-mir-520d*	2.4293	hsa-mir-521	2.6209
	hsa-mir-432*	2.5093	hsa-mir-508	3.4388	hsa-mir-517*	2.9009	hsa-mir-501	3.5560	hsa-mir-551a	2.3453	hsa-mir-33	2.3291
	hsa-mir-187	2.2751	hsa-let-7f	3.3384	hsa-mir-302d	2.8016	hsa-mir-335	3.3066	hsa-mir-521	2.2647	hsa-mir-215	2.2260
	hsa-mir-526b* hsa-mir-213 hsa-mir-107 hsa-mir-514 hsa-mir-520b hsa-mir-539 hsa-mir-656 hsa-mir-302c hsa-mir-632 12h time point 2.0241 2.0230 2.0210 2.0010 1.9017 1.7411 1.7188 1.7093 1.5419	hsa-mir-509 hsa-mir-199b hsa-mir-514 hsa-mir-383 hsa-mir-505 hsa-mir-539 hsa-mir-503 hsa-mir-187 hsa-mir-153 hsa-mir-107 hsa-mir-9* hsa-mir-429 hsa-mir-374 hsa-mir-7 hsa-mir-511 UCSD-AML1 3.1167 3.0701 2.9235 2.5816 2.5519 2.4426 2.3200 2.2423 2.1006 2.0105 1.8857 1.7556 1.6690 1.6458 1.6211 24h time point F o hsa-mir-107 hsa-mir-31 hsa-mir-213 hsa-mir-497 hsa-let-7f hsa-mir-187 hsa-mir-501 hsa-mir-503 hsa-mir-573 hsa-let-7e hsa-mir-30e-3p hsa-mir-520h hsa-mir-326 hsa-mir-580 hsa-mir-509 48h time point 2.5760 2.3323 2.0765 2.0351 2.0250 2.0140 1.7811 1.7569 1.7494 1.6809 1.6481 1.5520 1.5351 1.5342 1.5327 r P F o r P e e r R e v i e w hsa-mir-215 3.2656 hsa-mir-215 2.1589 hsa-mir-184 hsa-mir-502 3.0249 hsa-mir-449a 2.1246 hsa-mir-520g hsa-mir-616 2.7049 hsa-mir-656 1.9532 hsa-mir-147 hsa-mir-503 2.5061 hsa-mir-184 1.8250 hsa-mir-135b hsa-mir-135b 2.4798 hsa-mir-302d 1.6844 hsa-mir-651 hsa-mir-379 2.3692 hsa-mir-432* 1.6026 hsa-mir-507 hsa-mir-573 2.3425 hsa-mir-526b* 1.5285 hsa-mir-302a* hsa-mir-511 2.2638 hsa-mir-204 hsa-mir-216 2.1783 hsa-mir-449a 2.1773 hsa-mir-410 2.0212 hsa-mir-23a 1.9675 hsa-mir-181a 1.9423 hsa-mir-9* 1.9218 hsa-mir-662 1.7580 hsa-mir-449b 1.7091 hsa-mir-34b 1.6528 hsa-mir-148a 1.6388 hsa-mir-452* 1.6163 hsa-mir-100 1.5418 hsa-mir-184 1.5227 UCSD-AML1 12h time point 24h time point 48h time point 1.9912 1.9140 1.8868 1.8526 1.7909 1.7821 1.6546 1.5740 e e r
	MicroRNA ID 1 hsa-mir-107 hsa-mir-7 hsa-mir-187 hsa-mir-503	Fold change 2 3.4511 2.5165 2.4155 1.8455	MicroRNA ID 1 hsa-mir-7 hsa-mir-627 hsa-mir-147 hsa-mir-520a	Fold change 2 13.3526 3.4528 2.9738 2.7131	MicroRNA ID 1 hsa-mir-7 hsa-mir-107 hsa-mir-220 hsa-mir-373*	Fold change 2 10.4877 2.7899 1.9857 1.8556 R MicroRNA ID 1 hsa-mir-190 hsa-mir-139 hsa-mir-216 hsa-mir-449a e v i Fold change 2 MicroRNA ID 1 3.7895 hsa-mir-489 2.5465 hsa-mir-662 1.7575 hsa-mir-190 1.7357 hsa-mir-138 e w	Fold change 2 4.1702 3.4666 3.4624 2.2722	MicroRNA ID 1 hsa-mir-190 hsa-mir-139 hsa-mir-662 hsa-mir-622	Fold change 2 2.5778 2.4896 2.0114 1.9575

Table 6 .

 6 Up-and downregulated microRNAs in EVI1 rearranged patients compared to normal bone marrow Bold formatting indicates microRNAs that were also identified as being differentially expressed in EVI1 overexpressing cell lines. 2 Based upon the UCSC genome browser (http://genome.ucsc.edu/cgi-bin/hgGateway)

		DOWN regulated microRNAs
	MicroRNA ID 1	Adjusted P-value	Fold change	Chromosomal position 2
	hsa-mir-627	< 0.0001	2.9174	15q15.1
	hsa-mir-376a	< 0.0001	8.4942	14q32.31
	hsa-mir-377	< 0.0001	14.8259	14q32.31
	hsa-mir-182	< 0.0001	21.6981	7q32.2
	hsa-mir-96	0.0004	23.2055	7q32.2
	hsa-mir-302c	0.0004	25.0552	4q25
	hsa-mir-373	0.0021	29.3798	19q13.41
	hsa-mir-486	0.004	37.0995	8p11.21
	hsa-mir-451	0.0093	39.1498	17q11.2
	hsa-mir-519b	0.0086	42.764	19q13.41
	hsa-mir-453	0.0084	42.9166	14q32.31
	hsa-mir-190	0.0083	43.2435	15q22.2
	hsa-mir-210	0.0098	43.7469	11p15.5
	hsa-mir-519e	0.0095	44.7324	19q13.41
	hsa-mir-555	0.0104	44.8523	1q22
	hsa-mir-215	0.0162	45.9622	1q41
	hsa-mir-411	0.0309	48.7512	14q32.31
	hsa-mir-449b	0.0351	53.0335	5q11.2
	hsa-mir-192	0.0333	54.2642	11q13.1
	hsa-mir-449a	0.037	54.3671	5q11.2
	hsa-mir-18b	0.0469	55.3124	xq26.2
	hsa-mir-518c	0.0458	57.3651	19q13.41
	hsa-mir-382	0.0471	57.9604	14q32.31
	hsa-mir-566	0.0471	58.2334	3p21.31
	hsa-mir-193b	0.0462	58.3512	16p13.12
		UP regulated microRNAs
	MicroRNA ID 1	Adjusted P-value	Fold change	Chromosomal position 2
	hsa-mir-213	< 0.0001	7.6312	1q31.3
	hsa-mir-34a	< 0.0001	21.2414	1p36.23
	hsa-mir-372	< 0.0001	22.5682	19q13.41
	hsa-mir-202	< 0.0001	23.1649	10q26.3
	hsa-let-7e	< 0.0001	25.1082	19q13.33
	hsa-mir-369-3p	< 0.0001	30.8797	Xq28
	hsa-mir-107	< 0.0001	31.0029	10q23.31
	hsa-mir-187	< 0.0001	37.0609	18q12.2
	hsa-mir-208	0.0009	39.0362	14q11.2
	hsa-mir-133a	0.0008	39.0706	18q11.2
	hsa-mir-519e	0.0009	41.9395	19q13.41
	hsa-mir-496	0.0017	43.7542	14q32.31
	hsa-mir-128b	0.005	47.5518	3p22.3
	hsa-mir-33	0.0118	51.3368	22q13.2
	hsa-mir-517	0.0119	51.5086	19q13.41
	hsa-mir-223	0.0225	54.8395	Xq12
	hsa-mir-23b	0.0242	55.6051	9q22.32
	hsa-mir-196b	0.0241	55.9266	7p15.2

British Journal of Haematology

Oligonucleotide comprises Not1 or Xho1 restriction site (Italics), 26 nucleotides left from target sequence, target sequence according to TargetScanHuman 5.1 (www.targetscan.org) (underlined, point mutations indicated in bold formatting) and 26 nucleotides right from target sequence

Acknowledgements

for extensive technical assistance with cell culture. A. De Weer was the recipient of a predoctoral BOF grant (Bijzonder Onderzoeksfonds UGent, grant nr. 01D28905) and P. Rondou of a postdoctoral BOF grant (nr. 01P14010). B. Poppe, P. Vandenberghe and B. Verhasselt are senior clinical investigators of FWO-Vlaanderen. P. Van Vlierberghe and T. Taghon are postdoctoral fellows and J. Van der Meulen a predoctoral fellow of FWO-Vlaanderen. P. Van Vlierberghe is a fellow Scholar from the American Society of Haematology (ASH). This study was supported by the FWO-Vlaanderen, grant no. G.0106.05, by GOA-UGent, grant no. 12051203, and by the Austrian Science Foundation, grant no. P20920. This text presents research results of the Belgian Program of Interuniversity Poles of Attraction initiated by the Belgian State, Prime Minister's Office, Science Policy Programming. The scientific responsibility is assumed by the authors.

Supplementary files

Legends to supplementary figures

Supplementary Figure 1. Introduction of EVI1 siRNAs results in efficient EVI1

knockdown on mRNA and protein level Two EVI1 overexpressing cell lines Kasumi-3 and UCSD-AML1 were electroporated with a combination of two EVI1 targeting siRNAs and RT-qPCR and Western blotting were performed. A) RT-qPCR with EVI1 specific primers (measurements were performed in duplicate). The expression of EVI1 mRNA in cells treated with a scrambled siRNA duplex (NTC) was set to 100%. B) Western blotting with a monoclonal EVI1 antibody. The 145 kDa protein band corresponding to the full length EVI1 protein is displayed. As a loading control, the blots were treated with an α-tubulin antibody.

Supplementary

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