Role of ALK gene and mutations in initiation and progression of neuroblastoma

Introduction Le neuroblastome (NB) est la tumeur maligne solide extra-crânienne la plus fréquente chez l'enfant. Sa présentation clinique est très hétérogène, allant d'une tumeur localisée à une atteinte métastatique sévère. Malgré des traitements agressifs, environ 55% des NB de hauts risques sont actuellement résistants aux thérapies. L'espoir réside dans le développement de traitements ciblant les mécanismes moléculaires responsables du développement et de la progression du NB. Le gène Anaplastic Lymphoma Kinase (ALK) codant pour un récepteur tyrosine kinase a été particulièrement étudié ces dernières années car il est muté, amplifié ou surexprimé dans une majorité des NBs. Le but de ce projet était d'investiguer le rôle de ALK-wt, ainsi que de ces deux plus fréquentes mutations, ALK- F1174L et ALK-R1245Q, dans l'oncogenèse du NB. Le NB étant originaire des cellules de la crête neurale, nous avons analysé le potentiel oncogénique de ces différentes formes de ALK dans des cellules progénitrices de la crête neurale (NCPC). Méthode Des NCPC de souris (JoMal), possédant un c-MycER inductible pour leur maintien en culture in vitro, ont été transduites par un rétrovirus permettant l'expression stable de ALK-wt, ALK-F1174L et ALK-R1245Q. Des tests in vitro ont d'abord été effectués pour tester le système c-MycER, la stabilité de nos cellules transduites, leur phénotype, leur capacité de croissance et leur tumorigénicité. Les cellules transduites ont ensuite été injectées dans des souris immunosupprimées en sous-cutané, puis en orthotopique, c'est-à-dire dans leur glande surrénale, afin de mesurer leur tumorigénicité in vivo. Résultats La transduction et l'expression stable de ALK n'ont pas modifié le phénotype indifférencié des JoMal, ni de manière significative la capacité de croissance des cellules in vitro en absence d'activation de c-MycER. Par contre, lorsque c-MycER est actif, les cellules porteuses des mutations Fl 174L et R1245Q ont montré une meilleure capacité de prolifération et de formation de colonies, par rapport aux JoMal-ALK-wt et aux cellules contrôles en culture 3D dans de la méthylcellulose et dans un test de formation de neurosphères. In vivo, les souris injectées avec les cellules JoMal-ALK- F1174L en sous-cutané ou dans la glande surrénale ont rapidement développé des tumeurs, suivies par le groupe JoMal-ALK-R1245Q et le groupe JoMal-ALK-wt, alors que les groupes de souris contrôles n'ont présenté aucune tumeur. En orthotopique, nous avons obtenu 5/6 tumeurs ALK-F1174L, 7/7 tumeurs ALK-R1245Q et 6/7 tumeurs ALK-wt. Les tumeurs sous-cutanées ne présentaient pas de différences morphologiques et histologiques entre les différents groupes et montraient une histologie compatible avec un NB. Les tumeurs orthotopiques restent encore à analyser. Conclusion Cette étude a permis de démontrer que les mutations activatrices Fl 174L et R1245Q ont des propriétés tumorigéniques in vitro dans des NCPC et in vivo tandis que la forme sauvage de ALK montre une capacité oncogénique uniquement in vivo. Bien que la caractérisation des tumeurs orthotopiques n'a pas encore été effectuée, l'analyse des tumeurs sous-cutanées nous suggère que l'expression de ALK- wt ou muté est suffisante pour induire la formation de NB à partir des cellules progénitrices de la crête neurale. Le gène ALK semble donc jouer un rôle important dans l'oncogénèse du NB, aussi bien par la présence de mutations activatrices que par sa fréquente surexpression

Visualizing Convolutional Networks for MRI-based Diagnosis of Alzheimer's Disease

Visualizing and interpreting convolutional neural networks (CNNs) is an important task to increase trust in automatic medical decision making systems. In this study, we train a 3D CNN to detect Alzheimer's disease based on structural MRI scans of the brain. Then, we apply four different gradient-based and occlusion-based visualization methods that explain the network's classification decisions by highlighting relevant areas in the input image. We compare the methods qualitatively and quantitatively. We find that all four methods focus on brain regions known to be involved in Alzheimer's disease, such as inferior and middle temporal gyrus. While the occlusion-based methods focus more on specific regions, the gradient-based methods pick up distributed relevance patterns. Additionally, we find that the distribution of relevance varies across patients, with some having a stronger focus on the temporal lobe, whereas for others more cortical areas are relevant. In summary, we show that applying different visualization methods is important to understand the decisions of a CNN, a step that is crucial to increase clinical impact and trust in computer-based decision support systems.Comment: MLCN 201

Testing the robustness of attribution methods for convolutional neural networks in MRI-based Alzheimer's disease classification

Attribution methods are an easy to use tool for investigating and validating machine learning models. Multiple methods have been suggested in the literature and it is not yet clear which method is most suitable for a given task. In this study, we tested the robustness of four attribution methods, namely gradient*input, guided backpropagation, layer-wise relevance propagation and occlusion, for the task of Alzheimer's disease classification. We have repeatedly trained a convolutional neural network (CNN) with identical training settings in order to separate structural MRI data of patients with Alzheimer's disease and healthy controls. Afterwards, we produced attribution maps for each subject in the test data and quantitatively compared them across models and attribution methods. We show that visual comparison is not sufficient and that some widely used attribution methods produce highly inconsistent outcomes

A study of the influence of TiO2 addition in Al2O3 coatings sprayed by suspension plasma spray

In this work, five different concentrations of a mixture of TiO2/Al2O3 nanopowders in an alcoholic suspension at 10wt.% solid content were sprayed by Suspension Plasma Spraying on steel discs. The influence of the presence of TiO2 at 0, 13, 40 and 75wt.% in Al2O3 was analysed by studying the properties of the sprayed coatings. Microscopy analysis of the projected coatings revealed a homogeneously distributed microstructure, where the densification of the coating increases with TiO2 content, while the original nanostructure is maintained. A nanoindentation study revealed an increment of nanohardness and elastic modulus due to the densifying effect of TiO2. The addition of significant amounts of TiO2 has been revealed as necessary in order to favour the fusion of Al2O3 in the SPS process

Constant Size Molecular Descriptors For Use With Machine Learning

A set of molecular descriptors whose length is independent of molecular size is developed for machine learning models that target thermodynamic and electronic properties of molecules. These features are evaluated by monitoring performance of kernel ridge regression models on well-studied data sets of small organic molecules. The features include connectivity counts, which require only the bonding pattern of the molecule, and encoded distances, which summarize distances between both bonded and non-bonded atoms and so require the full molecular geometry. In addition to having constant size, these features summarize information regarding the local environment of atoms and bonds, such that models can take advantage of similarities resulting from the presence of similar chemical fragments across molecules. Combining these two types of features leads to models whose performance is comparable to or better than the current state of the art. The features introduced here have the advantage of leading to models that may be trained on smaller molecules and then used successfully on larger molecules.Comment: 18 pages, 5 figure

Prediction of the Atomization Energy of Molecules Using Coulomb Matrix and Atomic Composition in a Bayesian Regularized Neural Networks

Exact calculation of electronic properties of molecules is a fundamental step for intelligent and rational compounds and materials design. The intrinsically graph-like and non-vectorial nature of molecular data generates a unique and challenging machine learning problem. In this paper we embrace a learning from scratch approach where the quantum mechanical electronic properties of molecules are predicted directly from the raw molecular geometry, similar to some recent works. But, unlike these previous endeavors, our study suggests a benefit from combining molecular geometry embedded in the Coulomb matrix with the atomic composition of molecules. Using the new combined features in a Bayesian regularized neural networks, our results improve well-known results from the literature on the QM7 dataset from a mean absolute error of 3.51 kcal/mol down to 3.0 kcal/mol.Comment: Under review ICANN 201

Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier features

One-class support vector machine (OC-SVM) for a long time has been one of the most effective anomaly detection methods and extensively adopted in both research as well as industrial applications. The biggest issue for OC-SVM is yet the capability to operate with large and high-dimensional datasets due to optimization complexity. Those problems might be mitigated via dimensionality reduction techniques such as manifold learning or autoencoder. However, previous work often treats representation learning and anomaly prediction separately. In this paper, we propose autoencoder based one-class support vector machine (AE-1SVM) that brings OC-SVM, with the aid of random Fourier features to approximate the radial basis kernel, into deep learning context by combining it with a representation learning architecture and jointly exploit stochastic gradient descent to obtain end-to-end training. Interestingly, this also opens up the possible use of gradient-based attribution methods to explain the decision making for anomaly detection, which has ever been challenging as a result of the implicit mappings between the input space and the kernel space. To the best of our knowledge, this is the first work to study the interpretability of deep learning in anomaly detection. We evaluate our method on a wide range of unsupervised anomaly detection tasks in which our end-to-end training architecture achieves a performance significantly better than the previous work using separate training.Comment: Accepted at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) 201

Achieving Generalizable Robustness of Deep Neural Networks by Stability Training

We study the recently introduced stability training as a general-purpose method to increase the robustness of deep neural networks against input perturbations. In particular, we explore its use as an alternative to data augmentation and validate its performance against a number of distortion types and transformations including adversarial examples. In our image classification experiments using ImageNet data stability training performs on a par or even outperforms data augmentation for specific transformations, while consistently offering improved robustness against a broader range of distortion strengths and types unseen during training, a considerably smaller hyperparameter dependence and less potentially negative side effects compared to data augmentation.Comment: 18 pages, 25 figures; Camera-ready versio