Distinct ATOH1 and Neurog3 requirements define tuft cells as a new secretory cell type in the intestinal epithelium

Tuft cells represent a fourth type of intestinal secretory cell that constitutes the primary source of endogenous intestinal opioids and are the only epithelial cell that constitutively express cyclooxygenases

Reconstruction vasculaire 3D et analyse de lames virtuelles H&E dans l'étude du mélanome

International audienceLe mélanome cutané est responsable d'environ 57,000 décès dans le monde. A un stade avancé, le mélanome est traité par la chirurgie et la chimiothérapie. Cependant, même avec des thérapies robustes, le pronostic du mélanome reste souvent mauvais. La complexité du mélanome provient de sa capacité à se reconnecter et à adapter son état métabolique pour garantir sa survie, même lorsque les ressources en nutriments et en oxygène sont épuisées dans le microenvironnement tumoral.Notre hypothèse principale est que la résistance aux chimiothérapies découle d'une série de transitions non génétiques et de changements d'états métaboliques. Pour saisir les changements d'états métaboliques, des images de lames entières (WSI) de tumeurs de mélanome marquées à l'hématoxyline et à l'éosine (H&E) ont été générées en utilisant des modèles de souris PDX (Patient Derived Xenograft). Les échantillons PDX ont subi des coupes en série à 12µm. Deux types d'ensembles de données H&E ont ainsi été étudiées : un sans et un avec traitement.Dans cette étude, nous analysons de deux aspects. Nous présentons notre protocole et la suite algorithmique de reconstruction 3D qui comprend la segmentation 2D (Tableau 1), le recalage d'images de lames virtuelles ainsi que la reconstruction 3D par interpolation linéaire (Figure 1). Ensuite, nous illustrons l'utilisation de la reconstruction 3D pour comprendre les réseaux vasculaires via l'extraction de caractéristiques 3D, telles que la forme et la taille. Nous discutons ensuite de la manière dont ce travail est utilisé pour créer un modèle mathématique de prédiction de l'efficacité du traitement

Introducing [MALMO]: Mathematical approaches to modelling metabolic plasticity and heterogeneity in Melanoma

International audienceA once rare disease, malignant cutaneous melanoma has evolved to become one of the most fatal forms of cancers, accounting for a global death toll of 50,000 every year. While surgical interventions have shown to increase survival rates for patients with early-stage melanoma, these survival rates decrease as the disease progresses to metastases. Surgical interventions alone are no longer curative, and interventions must include chemotherapies to improve line of defense. However, while therapeutic interventions are available, the efficacy of these treatments can be greatly impacted by the complexity of the disease. When introduced to new metastatic sites, the cancer cells are faced with new environmental conditions, in which sometimes nutrients and oxygen are scarcely available. Through a biological phenomenon referred to as metabolic rewiring, the cancer cells adapt to their new, metabolic changes, promoting its own survival and proliferation under all stressful conditions. While the impact of metabolic rewiring and its influence on treatment efficacy has been established, the direct correlation between the two is not completely understood. The MALMO project (Mathematical Approaches to Modelling Metabolic Plasticity and Heterogeneity in Melanoma) aims to bridge the gap in knowledge within this domain by combining mathematical approaches with state-of-the-art artificial intelligence (AI) methods to automate pathology grading systems, conduct quantitative image analysis (including feature extraction of relevant biomarkers), and develop a model to predict the development of heterogeneity in melanoma tumors. The mathematical model – based on partial differential equations (PDE) – is being designed to account for time, space, and biological scales, such as molecular, cellular, and tissular, in order to understand how melanoma behaves in oxygen and nutrient-deprived tumor microenvironments. The mathematical model is augmented with the help of AI, in which features from hematoxylin and eosin (H&E) and cluster of differentiation 31 (CD31)-stained whole slide images (WSI), as well as Hyperion images, are being extracted and evaluated to assist with the modeling component. In this paper, we introduce the MALMO project and demonstrate some preliminary findings using AI. Understanding this rewiring and developing a prediction model will help to further our understanding of melanoma progression. In doing so, we can begin creating tailored therapies which could better target metastatic melanoma and improve the survival rate for these late stage affected patients

Data driven mechanistic modeling of oxygen distribution and hypoxia profile in tumor microenvironment

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Estimating spatial distribution of oxygen and hypoxia in tumor microenvironment: a mechanistic approach

International audienceBeing a hallmark of several solid tumors, hypoxia - a state of reduced level of tissue oxygen tension and a result of aberrant vasculature - leads to several alterations in the tumor microenvironment. Hypoxic regions of neoplasm are prone to be more resistant towards radiation therapy than compared to well oxygenated ones (A. L. Harris 2002). Furthermore, hypoxia and its mediators influence multiple signaling pathways and gene regulation to promote neovascularization, invasion, migration, adhesion, metastasis, and phenotypic switches (D. S. Widmer et al. 2013, A. Tameemi et al., 2019). Hence hypoxia is one of the leading factors which contributes towards intratumor heterogeneity and resistance against treatments, these two features being particularly important and common in many invasive tumors including melanoma (B. Bedogni et al. 2009, D'Aguanno et al. 2021). Estimation of accurate hypoxia profile would be key for better prognosis and design of more efficient treatment approaches. Mathematical modeling has been proven a useful tool to understand and predict such complex dynamics. Several computational and mathematical models have been proposed to describe tissue oxygenation, however the majority of them are restricted to synthetic data and qualitative results, lacking application to and connection with real tumor tissues and experimental results.We propose mechanistic modeling frameworks, which are driven by experimental data, to explain and mimic oxygen-hypoxia dynamics. The data is in the form of tissue scans of Patient Derived Xenograft (PDX) of breast, ovarian and pancreatic as well as human melanoma tumors. These scans of tumor tissue slices are immunohistochemical stained with CD31 -cluster of differentiation 31, marking the presence of endothelial cells- and CAIX- carbonic anhydrase IX, regulated by the hypoxia-inducible factor (HIF) 1, is an intrinsic marker of tumor hypoxia - markers. Keeping the data availability in mind, the distribution of oxygen is described by a reaction-diffusion partial differential equation with the source term incorporating the contribution from blood vessel density (obtained from CD31 staining) for the 2D model and from the vasculature architecture and the geometry of each blood vessel (reconstructed from several 2D tissue slices) for the 3D model. Next, hypoxia is modeled from the obtained oxygen distribution using an algebraic equation. The further steps include estimation of parameters and validation. The obtained parameters demonstrate biological relevance. 3D reconstruction, which is underway, is required for obtaining 3D profiles of oxygen and hypoxia. This requirement leads to another aspect of this work consisting in quantification of the error made when 2D models are used instead of more realistic 3D models. This is important since the 3D reconstruction is not always feasible, especially for patient tissue samples. A framework to quantify this approximation error would be essential for evaluating the hypoxia profile for clinical applications. Future work involves development of a general framework, applicable to most of the solid tumors, to estimate oxygen and hypoxia distribution based on the 3D reconstruction of blood vessels as well as for the 2D case with an error bound due to the approximation

Data driven mechanistic modeling of oxygen distribution and hypoxia profile in tumor microenvironment

International audienc

3D Reconstruction of H&E Whole Slide Images in Melanoma

International audienceCutaneous melanoma is an invasive cancer with a worldwide annual death toll of 57,000 (Arnold et al., JAMA Dermatol 2022). Changes in the tumor microenvironment, including the evolution of the metabolic states, have been suggested to be associated with poor chemotherapeutic response. To elucidate differing metabolic states and their correlation with treatment response, we have developed a vascular-based 3D pipeline for whole slide images (WSI). In this presentation, we share details of the pipeline, provide quantitative results to validate its performance, and share the challenges experienced and the solutions created to manage and process WSIs