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1. Supplementary Figure 1. Illustration of tubes area detection as rectangles based on the bright field channel. 2. Supplementary Figure 2. Diarrhea severity during combination treatment with lumretuzumab (RO5479599), pertuzumab and paclitaxel in patients. 3. Full Unedited western blots of figures 2c and 2d

Combination of targeted therapies is expected to provide superior efficacy in the treatment of cancer either by enhanced antitumor activity or by preventing or delaying the development of resistance. Common challenges in developing combination therapies include the potential of additive and aggravated toxicities associated with pharmacologically related adverse effects. We have recently reported that combination of anti-HER2 and anti-HER3 antibodies, pertuzumab and lumretuzumab, along with paclitaxel chemotherapy in metastatic breast cancer, resulted in a high incidence of diarrhea that ultimately limited further clinical development of this combination. Here, we further dissected the diarrhea profile of the various patient dose cohorts and carried out in vitro investigations in human colon cell lines and explants to decipher the contribution and the mechanism of anti-HER2/3 therapeutic antibodies to intestinal epithelium malfunction. Our clinical investigations in patients revealed that while dose reduction of lumretuzumab, omission of pertuzumab loading dose, and introduction of a prophylactic antidiarrheal treatment reduced most severe adverse events, patients still suffered from persistent diarrhea during the treatment. Our in vitro investigations showed that pertuzumab and lumretuzumab combination treatment resulted in upregulation of chloride channel activity without indication of intestinal barrier disruption. Overall, our findings provide a mechanistic rationale to explore alternative of conventional antigut motility using medication targeting chloride channel activity to mitigate diarrhea of HER combination therapies. Mol Cancer Ther; 17(7); 1464–74. ©2018 AACR.

Identification of novel antibiotics remains a major challenge for drug discovery. The present study explores use of phenotypic readouts beyond classical antibacterial growth inhibition adopting a combined multiparametric high content screening and genomic approach. Deployment of the semi-automated bacterial phenotypic fingerprint (BPF) profiling platform in conjunction with a machine learning-powered dataset analysis, effectively allowed us to narrow down, compare and predict compound mode of action (MoA). The method identifies weak antibacterial hits allowing full exploitation of low potency hits frequently discovered by routine antibacterial screening. We demonstrate that BPF classification tool can be successfully used to guide chemical structure activity relationship optimization, enabling antibiotic development and that this approach can be fruitfully applied across species. The BPF classification tool could be potentially applied in primary screening, effectively enabling identification of novel antibacterial compound hits and differentiating their MoA, hence widening the known antibacterial chemical space of existing pharmaceutical compound libraries. More generally, beyond the specific objective of the present work, the proposed approach could be profitably applied to a broader range of diseases amenable to phenotypic drug discovery., Scientific Reports, 9 (1), ISSN:2045-2322

Background: To evaluate the performance of a machine-learning (ML) algorithm to detect and classify choroidal neovascularization (CNV), secondary to age-related macular degeneration (AMD) on spectral-domain optical coherence tomography (SD-OCT) images. Methods: Baseline fluorescein angiography (FA) and SD-OCT images from 1037 treatment-naive study eyes and 531 fellow eyes, without advanced AMD from the phase 3 HARBOR trial (NCT00891735), were used to develop, train, and cross-validate an ML pipeline combining deep-learning–based segmentation of SD-OCT B-scans and CNV classification, based on features derived from the segmentations, in a five-fold setting. FA classification of the CNV phenotypes from HARBOR was used for generating the ground truth for model development. SD-OCT scans from the phase 2 AVENUE trial (NCT02484690) were used to externally validate the ML model. Results: The ML algorithm discriminated CNV absence from CNV presence, with a very high accuracy (area under the receiver operating characteristic [AUROC] = 0.99), and classified occult versus predominantly classic CNV types, per FA assessment, with a high accuracy (AUROC = 0.91) on HARBOR SD-OCT images. Minimally classic CNV was discriminated with significantly lower performance. Occult and predominantly classic CNV types could be discriminated with AUROC = 0.88 on baseline SD-OCT images of 165 study eyes, with CNV from AVENUE. Conclusions: Our ML model was able to detect CNV presence and CNV subtypes on SD-OCT images with high accuracy in patients with neovascular AMD.

Background: In some people with Parkinson’s disease (PD), a-synuclein (αSyn) accumulation may begin in the enteric nervous system (ENS) decades before development of brain pathology and disease diagnosis. Objective: To determine how different types and severity of intestinal inflammation could trigger αSyn accumulation in the ENS and the subsequent development of αSyn brain pathology. Methods: We assessed the effects of modulating short- and long-term experimental colitis on αSyn accumulation in the gut of αSyn transgenic and wild type mice by immunostaining and gene expression analysis. To determine the long-term effect on the brain, we induced dextran sulfate sodium (DSS) colitis in young αSyn transgenic mice and aged them under normal conditions up to 9 or 21 months before tissue analyses. Results: A single strong or sustained mild DSS colitis triggered αSyn accumulation in the submucosal plexus of wild type and αSyn transgenic mice, while short-term mild DSS colitis or inflammation induced by lipopolysaccharide did not have such an effect. Genetic and pharmacological modulation of macrophage-associated pathways modulated the severity of enteric αSyn. Remarkably, experimental colitis at three months of age exacerbated the accumulation of aggregated phospho-Serine 129 αSyn in the midbrain (including the substantia nigra), in 21- but not 9-month-old αSyn transgenic mice. This increase in midbrain αSyn accumulation is accompanied by the loss of tyrosine hydroxylase-immunoreactive nigral neurons. Conclusions: Our data suggest that specific types and severity of intestinal inflammation, mediated by monocyte/macrophage signaling, could play a critical role in the initiation and progression of PD., Free Neuropathology, Bd. 2 (2021)

Combination of targeted therapies is expected to provide superior efficacy in the treatment of cancer either by enhanced antitumor activity or by preventing or delaying the development of resistance. Common challenges in developing combination therapies include the potential of additive and aggravated toxicities associated with pharmacologically related adverse effects. We have recently reported that combination of anti-HER2 and anti-HER3 antibodies, pertuzumab and lumretuzumab, along with paclitaxel chemotherapy in metastatic breast cancer, resulted in a high incidence of diarrhea that ultimately limited further clinical development of this combination. Here, we further dissected the diarrhea profile of the various patient dose cohorts and carried out in vitro investigations in human colon cell lines and explants to decipher the contribution and the mechanism of anti-HER2/3 therapeutic antibodies to intestinal epithelium malfunction. Our clinical investigations in patients revealed that while dose reduction of lumretuzumab, omission of pertuzumab loading dose, and introduction of a prophylactic antidiarrheal treatment reduced most severe adverse events, patients still suffered from persistent diarrhea during the treatment. Our in vitro investigations showed that pertuzumab and lumretuzumab combination treatment resulted in upregulation of chloride channel activity without indication of intestinal barrier disruption. Overall, our findings provide a mechanistic rationale to explore alternative of conventional antigut motility using medication targeting chloride channel activity to mitigate diarrhea of HER combination therapies. Mol Cancer Ther; 17(7); 1464–74. ©2018 AACR.

The global burden of diabetic retinopathy (DR) continues to worsen and DR remains a leading cause of vision loss worldwide. Here, we describe an algorithm to predict DR progression by means of deep learning (DL), using as input color fundus photographs (CFPs) acquired at a single visit from a patient with DR. The proposed DL models were designed to predict future DR progression, defined as 2-step worsening on the Early Treatment Diabetic Retinopathy Diabetic Retinopathy Severity Scale, and were trained against DR severity scores assessed after 6, 12, and 24 months from the baseline visit by masked, well-trained, human reading center graders. The performance of one of these models (prediction at month 12) resulted in an area under the curve equal to 0.79. Interestingly, our results highlight the importance of the predictive signal located in the peripheral retinal fields, not routinely collected for DR assessments, and the importance of microvascular abnormalities. Our findings show the feasibility of predicting future DR progression by leveraging CFPs of a patient acquired at a single visit. Upon further development on larger and more diverse datasets, such an algorithm could enable early diagnosis and referral to a retina specialist for more frequent monitoring and even consideration of early intervention. Moreover, it could also improve patient recruitment for clinical trials targeting DR.

Purpose To develop deep learning (DL) models for the automatic detection of optical coherence tomography (OCT) measures of diabetic macular thickening (MT) from color fundus photographs (CFPs). Methods Retrospective analysis on 17,997 CFPs and their associated OCT measurements from the phase 3 RIDE/RISE diabetic macular edema (DME) studies. DL with transfer-learning cascade was applied on CFPs to predict time-domain OCT (TD-OCT)-equivalent measures of MT, including central subfield thickness (CST) and central foveal thickness (CFT). MT was defined by using two OCT cutoff points: 250 μm and 400 μm. A DL regression model was developed to directly quantify the actual CFT and CST from CFPs. Results The best DL model was able to predict CST ≥ 250 μm and CFT ≥ 250 μm with an area under the curve (AUC) of 0.97 (95% confidence interval [CI], 0.89-1.00) and 0.91 (95% CI, 0.76-0.99), respectively. To predict CST ≥ 400 μm and CFT ≥ 400 μm, the best DL model had an AUC of 0.94 (95% CI, 0.82-1.00) and 0.96 (95% CI, 0.88-1.00), respectively. The best deep convolutional neural network regression model to quantify CST and CFT had an R2 of 0.74 (95% CI, 0.49-0.91) and 0.54 (95% CI, 0.20-0.87), respectively. The performance of the DL models declined when the CFPs were of poor quality or contained laser scars. Conclusions DL is capable of predicting key quantitative TD-OCT measurements related to MT from CFPs. The DL models presented here could enhance the efficiency of DME diagnosis in tele-ophthalmology programs, promoting better visual outcomes. Future research is needed to validate DL algorithms for MT in the real-world.

NeuriteTracker is a computer vision approach used to analyze neuronal morphodynamics and to examine spatiotemporal Rho GTPase signaling networks regulating neurite outgrowth., Rho guanosine triphosphatases (GTPases) control the cytoskeletal dynamics that power neurite outgrowth. This process consists of dynamic neurite initiation, elongation, retraction, and branching cycles that are likely to be regulated by specific spatiotemporal signaling networks, which cannot be resolved with static, steady-state assays. We present NeuriteTracker, a computer-vision approach to automatically segment and track neuronal morphodynamics in time-lapse datasets. Feature extraction then quantifies dynamic neurite outgrowth phenotypes. We identify a set of stereotypic neurite outgrowth morphodynamic behaviors in a cultured neuronal cell system. Systematic RNA interference perturbation of a Rho GTPase interactome consisting of 219 proteins reveals a limited set of morphodynamic phenotypes. As proof of concept, we show that loss of function of two distinct RhoA-specific GTPase-activating proteins (GAPs) leads to opposite neurite outgrowth phenotypes. Imaging of RhoA activation dynamics indicates that both GAPs regulate different spatiotemporal Rho GTPase pools, with distinct functions. Our results provide a starting point to dissect spatiotemporal Rho GTPase signaling networks that regulate neurite outgrowth.

Today, novel therapeutics are identified in an environment which is intrinsically different from the clinical context in which they are ultimately evaluated. Using molecular phenotyping and an in vitro model of diabetic cardiomyopathy, we show that by quantifying pathway reporter gene expression, molecular phenotyping can cluster compounds based on pathway profiles and dissect associations between pathway activities and disease phenotypes simultaneously. Molecular phenotyping was applicable to compounds with a range of binding specificities and triaged false positives derived from high-content screening assays. The technique identified a class of calcium-signaling modulators that can reverse disease-regulated pathways and phenotypes, which was validated by structurally distinct compounds of relevant classes. Our results advocate for application of molecular phenotyping in early drug discovery, promoting biological relevance as a key selection criterion early in the drug development cascade.

We propose a novel Bayesian approach to automated delineation of curvilinear structures that form complex and potentially loopy networks. By representing the image data as a graph of potential paths, we first show how to weight these paths using discriminatively-trained classifiers that are both robust and generic enough to be applied to very different imaging modalities. We then present an Integer Programming approach to finding the optimal subset of paths, subject to structural and topological constraints that eliminate implausible solutions. Unlike earlier approaches that assume a tree topology for the networks, ours explicitly models the fact that the networks may contain loops, and can reconstruct both cyclic and acyclic ones. We demonstrate the effectiveness of our approach on a variety of challenging datasets including aerial images of road networks and micrographs of neural arbors, and show that it outperforms state-of-the-art techniques.

We propose a new approach to detecting irregular curvilinear structures in noisy image stacks. In contrast to earlier approaches that rely on circular models of the cross-sections, ours allows for the arbitrarily-shaped ones that are prevalent in biological imagery. This is achieved by maximizing the image gradient flux along multiple directions and radii, instead of only two with a unique radius as is usually done. This yields a more complex optimization problem for which we propose a computationally efficient solution. We demonstrate the effectiveness of our approach on a wide range of challenging gray scale and color datasets and show that it outperforms existing techniques, especially on very irregular structures.

We present a fully automatic method to track and quantify the morphodynamics of differentiating neurons in fluorescence time-lapse datasets. Previous high-throughput studies have been limited to static analysis or simple behavior. Our approach opens the door to rich dynamic analysis of complex cellular behavior in high-throughput time-lapse data. It is capable of robustly detecting, tracking, and segmenting all the components of the neuron including the nucleus, soma, neurites, and filopodia. It was designed to be efficient enough to handle the massive amount of data from a high-throughput screen. Each image is processed in approximately two seconds on a notebook computer. To validate the approach, we applied our method to over 500 neuronal differentiation videos from a small-scale RNAi screen. Our fully automated analysis of over 7,000 neurons quantifies and confirms with strong statistical significance static and dynamic behaviors that had been previously observed by biologists, but never measured.

This paper introduces a subgradient descent algorithm to compute a Riemannian metric that minimizes an energy involving geodesic distances. The heart of the method is the Subgradient Marching Algorithm to compute the derivative of the geodesic distance with respect to the metric. The geodesic distance being a concave function of the metric, this algorithm computes an element of the subgradient in O(N (2) log(N)) operations on a discrete grid of N points. It performs a front propagation that computes a subgradient of a discrete geodesic distance. We show applications to landscape modeling and to traffic congestion. Both applications require the maximization of geodesic distances under convex constraints, and are solved by subgradient descent computed with our Subgradient Marching. We also show application to the inversion of travel time tomography, where the recovered metric is the local minimum of a non-convex variational problem involving geodesic distances.

International audience; This short paper describes our contribution in the research aimed at model based vessel segmentation on CTA. Although each partner was involved in a main subject among what follows, the contribution is a joint effort of all the partners, as a result of regular visits in France and Israel, as well as between partners in each country. The French Hospital Partner in Lyon provided a large set of CTA studies, including sets with two studies performed on each patient and about 20 studies suitable for work on other aspects of cardiac vessel segmentation.

We present a semi-automatic algorithm for Carotid lumen segmentation on CTA images. Our method is based on a variant of the minimal path method that models the vessel as a centerline and boundary. This is done by adding one dimension for the local radius around the centerline. The crucial step of our method is the definition of the local metrics to minimize. We have chosen to exploit the tubular structure of the vessels one wants to extract to built an anisotropic metric giving higher speed on the center of the vessels and also when the minimal path tangent is coherent with the vessel’s direction. Due to carotid stenosis or occlusions on the provided data, segmentation is refined using a region-based level sets.

International audience; Starting from a continuous congested traffic framework recently introduced in [Carlier, Jimenez, Santambrogio, 2008], we present a consistent numerical scheme to compute equilibrium metrics. We show that equilibrium metric is the solution of a variational problem involving geodesic distances. Our discretization scheme is based on the Fast Marching Method. Convergence is proved via a $\Gamma$-convergence result and numerical results are given.

In this paper, we present a new approach for segmentation of tubular structures in 2D images providing minimal interaction. The main objective is to extract centerlines and boundaries of the vessels at the same time. The first step is to represent the trajectory of the vessel not as a 2D curve but to go up a dimension and represent the entire vessel as a 3D curve, where each point represents a 2D disc (two coordinates for the center point and one for the radius). The 2D vessel structure is then obtained as the envelope of the family of discs traversed along this 3D curve. Since this 2D shape is defined simply from a 3D curve, we are able to fully exploit minimal path techniques to obtain globally minimizing trajectories between two or more user supplied points using front propagation. The main contribution of our approach consists on building a multi-resolution metric that guides the propagation in this 3D space. We have chosen to exploit the tubular structure of the vessels one wants to extract to built an anisotropic metric giving higher speed on the center of the vessels and also when the minimal path tangent is coherent with the vessel's direction. This measure is required to be robust against the disturbance introduced by noise or adjacent structures with intensity similar to the target vessel. Indeed, if we examine the flux of the projected image gradient along a given direction on a circle of a given radius (or scale), one can prove that this flux is maximal at the center of the vessel, in its direction and with its exact radius. This approach is called optimally oriented flux. Combining anisotropic minimal paths techniques and optimally oriented flux we obtain promising results on noisy synthetic and real data.

We propose a novel approach to automated delineation of linear structures that form complex and potentially loopy networks. This is in contrast to earlier approaches that usually assume a tree topology for the networks. At the heart of our method is an Integer Programming formulation that allows us to find the global optimum of an objective function designed to allow cycles but penalize spurious junctions and early terminations. We demonstrate that it outperforms state-of-the-art techniques on a wide range of datasets.

This document describes an ITK implementation of an interactive method for tracing curvilinear structures. The basic tools provided in this framework are an oriented flux-based tubularity measure and a geodesic path tracer that uses the fast marching algorithm. The framework is efficient and requires minimal user interaction to trace curvilinear structures such as vessels and neurites in 2D images and 3D image stacks.

In this paper we empirically analyze the importance of sparsifying representations for classification purposes. We focus on those obtained by convolving images with linear filters, which can be either hand designed or learned, and perform extensive experiments on two important Computer Vision problems, image categorization and pixel classification. To this end, we adopt a simple modular architecture that encompasses many recently proposed models. The key outcome of our investigations is that enforcing sparsity constraints on features extracted in a convolutional architecture does not improve classification performance, whereas it does so when redundancy is artificially introduced. This is very relevant for practical purposes, since it implies that the expensive run-time optimization required to sparsify the representation is not always justified, and therefore that computational costs can be drastically reduced.

Although tracing linear structures in 2D images and 3D image stacks has received much attention over the years, full automation remains elusive. In this paper, we formulate the delineation problem as one of solving a Quadratic Mixed Integer Program (Q-MIP) in a graph of potential paths, which can be done optimally up to a very small tolerance. We further propose a novel approach to weighting these paths, which results in a Q-MIP solution that accurately matches the ground truth. We demonstrate that our approach outperforms a state-of-the-art technique based on the k-Minimum Spanning Tree formulation on a 2D dataset of aerial images and a 3D dataset of confocal microscopy stacks.