Masitinib Combined with Standard Gemcitabine Chemotherapy: In Vitro and In Vivo Studies in Human Pancreatic Tumour Cell Lines and Ectopic Mouse Model

International audienceBackground: Tyrosine kinases are attractive targets for pancreatic cancer therapy because several are over-expressed, including PDGFRα/β, FAK, Src and Lyn. A critical role of mast cells in the development of pancreatic cancer has also been reported. Masitinib is a tyrosine kinase inhibitor that selectively targets c-Kit, PDGFRα/β, Lyn, and to a lesser extent the FAK pathway, without inhibiting kinases of known toxicities. Masitinib is particularly efficient in controlling the proliferation, differentiation and degranulation of mast cells. This study evaluates the therapeutic potential of masitinib in pancreatic cancer, as a single agent and in combination with gemcitabine.Methodology/Findings: Proof-of-concept studies were performed in vitro on human pancreatic tumour cell lines and then in vivo using a mouse model of human pancreatic cancer. Molecular mechanisms were investigated via gene expression profiling. Masitinib as a single agent had no significant antiproliferative activity while the masitinib/gemcitabine combination showed synergy in vitro on proliferation of gemcitabine-refractory cell lines Mia Paca2 and Panc1, and to a lesser extent in vivo on Mia Paca2 cell tumour growth. Specifically, masitinib at 10 µM strongly sensitised Mia Paca2 cells to gemcitabine (>400-fold reduction in IC50); and moderately sensitised Panc1 cells (10-fold reduction). Transcriptional analysis identified the Wnt/β-catenin signalling pathway as down-regulated in the cell lines resensitised by the masitinib/gemcitabine combination.Conclusions: These data establish proof-of-concept that masitinib can sensitise gemcitabine-refractory pancreatic cancer cell lines and warrant further in vivo investigation. Indeed, such an effect has been recently observed in a phase 2 clinical study of patients with pancreatic cancer who received a masitinib/gemcitabine combination

Masitinib (AB1010), a Potent and Selective Tyrosine Kinase Inhibitor Targeting KIT

International audienceBackground: The stem cell factor receptor, KIT, is a target for the treatment of cancer, mastocytosis, and inflammatory diseases. Here, we characterise the in vitro and in vivo profiles of masitinib (AB1010), a novel phenylaminothiazole-type tyrosine kinase inhibitor that targets KIT. Methodology/Principal Findings: In vitro, masitinib had greater activity and selectivity against KIT than imatinib, inhibiting recombinant human wild-type KIT with an half inhibitory concentration (IC50) of 200 ± 40 nM and blocking stem cell factor-induced proliferation and KIT tyrosine phosphorylation with an IC50 of 150 ± 80 nM in Ba/F3 cells expressing human or mouse wild-type KIT. Masitinib also potently inhibited recombinant PDGFR and the intracellular kinase Lyn, and to a lesser extent, fibroblast growth factor receptor 3. In contrast, masitinib demonstrated weak inhibition of ABL and c-Fms and was inactive against a variety of other tyrosine and serine/threonine kinases. This highly selective nature of masitinib suggests that it will exhibit a better safety profile than other tyrosine kinase inhibitors; indeed, masitinib-induced cardiotoxicity or genotoxicity has not been observed in animal studies. Molecular modelling and kinetic analysis suggest a different mode of binding than imatinib, and masitinib more strongly inhibited degranulation, cytokine production, and bone marrow mast cell migration than imatinib. Furthermore, masitinib potently inhibited human and murine KIT with activating mutations in the juxtamembrane domain. In vivo, masitinib blocked tumour growth in mice with subcutaneous grafts of Ba/F3 cells expressing a juxtamembrane KIT mutant. Conclusions: Masitinib is a potent and selective tyrosine kinase inhibitor targeting KIT that is active, orally bioavailable in vivo, and has low toxicit

3DMASC: Accessible, explainable 3D point clouds classification. Application to Bi-spectral Topo-bathymetric lidar data

International audienceThree-dimensional data have become increasingly present in earth observation over the last decades. However, many 3D surveys are still underexploited due to the lack of accessible and explainable automatic classification methods, for example, new topo-bathymetric lidar data. In this work, we introduce explainable machine learning for 3D data classification using Multiple Attributes, Scales, and Clouds under 3DMASC, a new workflow. This workflow introduces multi-cloud classification through dual-cloud features, encrypting local spectral and geometrical ratios and differences. 3DMASC uses classical multi-scale descriptors adapted to all types of 3D point clouds and new ones based on their spatial variations. In this paper, we present the performances of 3DMASC for multi-class classification of topo-bathymetric lidar data in coastal and fluvial environments. We show how multivariate and embedded feature selection allows the building of optimized predictor sets of reduced complexity, and we identify features particularly relevant for coastal and riverine scene descriptions. Our results show the importance of dual-cloud features, lidar return-based attributes averaged over specific scales, and of statistics of dimensionality-based and spectral features. Additionally, they indicate that small to medium spherical neighbourhood diameters (<7 m) are sufficient to build effective classifiers, namely when combined with distance-to-ground or distance-to-water-surface features. Without using optional RGB information, and with a maximum of 37 descriptors, we obtain classification accuracies between 91 % for complex multi-class tasks and 98 % for lower-level processing using models trained on less than 2000 samples per class. Comparisons with classical point cloud classification methods show that 3DMASC features have a significantly improved descriptive power. Our contributions are made available through a plugin in the CloudCompare software, allowing non-specialist users to create classifiers for any type of 3D data characterized by 1 or 2 point clouds (airborne or terrestrial lidar, structure from motion), and two labelled topo-bathymetric lidar datasets, available on https://opentopography.org/

3DMASC: accessible, explainable 3D point clouds classification. Application to bi-spectral topo-bathymetric LiDAR data.

International audienceThree-dimensional data have become increasingly present in earth observation over the last decades and, more recently, with the development of accessible 3D sensing technologies. However, many 3D surveys are still underexploited due to the lack of accessible and explainable automatic classification methods. In this work, we introduce explainable machine learning for 3D data classification using Multiple Attributes, Scales, and Clouds under 3DMASC, a new workflow. It handles multiple clouds at once, including or not spectral and multiple returns attributes. Through 3DMASC, we use classical 3D data multi-scale descriptors and new ones based on the spatial variations of geometrical, spectral and height-based features of the local point cloud. We also introduce dual-cloud features, encrypting local spectral and geometrical ratios and differences, which improve the interpretation of multi-cloud surveys. 3DMASC thus offers new possibilities for point cloud classification, namely for the interpretation of bi-spectral lidar data. Here, we experiment on topo-bathymetric lidar data, which are acquired using two lasers at infrared and green wavelengths, and feature two irregular point clouds characterized by different samplings of vegetated and flooded areas, that 3DMASC can harvest. By exploring the contributions of 88 features and 30 scales – including two types of neighborhoods – we identify a core set of features and scales particularly relevant for coastal and riverine scenes description, and give indications on how to build an optimal predictor vector to train 3D data classifiers. Our findings highlight the predominance of lidar return-based attributes over classical features based on dimensionality or eigenvalues, and the significant contribution of spectral information to the detection of more than a dozen of land and sea covers – artificial/vegetated/rocky/bare ground, rocky/sandy seabed, intermediate/high vegetation, buildings, vehicles, power lines. The experimental results show that 3DMASC competes with state-of-the-art methods in terms of classification performances while demanding lower complexity and thus remaining accessible to non-specialist users. Relying on a random forest algorithm, it generalizes and applies quickly to large datasets, and offers the possibility to filter out misclassified points depending on their prediction confidence. Classification accuracies between 91% for complex scene classifications and 98% for lower-level processing are observed, with average prediction confidences above 90% and models relying on less than 2000 samples per class and at most 30 descriptors – including both features and scales. Though dual-cloud features systematically outperform their single cloud equivalents, 3DMASC also performs on single cloud lidar data, or structure from motion point clouds. Our contributions are made available through a self-contained plugin in CloudCompare allowing non-specialist users to create a classifier and apply it, and an opensource labelled dataset of topo-bathymetric data