Contributors: Laboratory of Pathogen and Host Immunity Montpellier (LPHI); Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM); Université de Montpellier (UM); Institut du Cerveau = Paris Brain Institute (ICM); Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière AP-HP; Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS); Institut régional de Cancérologie de Montpellier (ICM); Institut de Recherche en Cancérologie de Montpellier (IRCM - U1194 Inserm - UM); CRLCC Val d'Aurelle - Paul Lamarque-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM); Equipe labellisée Ligue contre le Cancer; Sorbonne Université (SU); Centre for Eye Research Australia (CERA); University of Melbourne-Royal Victorian Eye and Ear Hospital; Algorithms, models and methods for images and signals of the human brain = Algorithmes, modèles et méthodes pour les images et les signaux du cerveau humain ICM Paris (ARAMIS); Inria de Paris; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM); Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière AP-HP; We acknowledge financial support from ITMO Cancer on funds administered by INSERM (project MALMO) and a support by Mr Jean-Paul Baudecroux and The Big Brain Theory Program—Paris Brain Institute (ICM). We are grateful for the technical support of the Montpellier Rio Imaging (MRI) and animal (RAM) facilities. Histological analyses were performed thank to the 'Réseau d'Histologie Expérimentale de Montpellier' (RHEM), a facility supported by the SIRIC Montpellier Cancer (Grant INCa-DGOS-INSERM-ITMO Cancer 18004), the european regional development foundation and the Occitanie region (FEDER-FSE 2014-2020 Languedoc Roussillon), REACT-EU (Recovery Assistance for Cohesion and the Territories of Europe), IBiSA. We express our gratitude to Sophie Baize for her handling of the histological blocks. This work was also publicly funded through ANR (the French National Research Agency) under the 'Investissements d'avenir' programme with the reference ANR-16-IDEX-0006 (Projet I-Site Muse 'MEL-ECO').; ANR-16-IDEX-0006,MUSE,MUSE(2016)
Abstract: International audience ; Abstract Objective . The distribution of hypoxia within tissues plays a critical role in tumor diagnosis and prognosis. Recognizing the significance of tumor oxygenation and hypoxia gradients, we introduce mathematical frameworks grounded in mechanistic modeling approaches for their quantitative assessment within a tumor microenvironment. By utilizing known blood vasculature, we aim to predict hypoxia levels across different tumor types. Approach . Our approach offers a computational method to measure and predict hypoxia using known blood vasculature. By formulating a reaction-diffusion model for oxygen distribution, we derive the corresponding hypoxia profile. Main results . The framework successfully replicates observed inter- and intra-tumor heterogeneity in experimentally obtained hypoxia profiles across various tumor types (breast, ovarian, pancreatic). Additionally, we propose a data-driven method to deduce partial differential equation models with spatially dependent parameters, which allows us to comprehend the variability of hypoxia profiles within tissues. The versatility of our framework lies in capturing diverse and dynamic behaviors of tumor oxygenation, as well as categorizing states of vascularization based on the dynamics of oxygen molecules, as identified by the model parameters. Significance . The proposed data-informed mechanistic method quantitatively assesses hypoxia in the tumor microenvironment by integrating diverse histopathological data and making predictions across different types of data. The framework provides valuable insights from both modeling and biological perspectives, advancing our comprehension of spatio-temporal dynamics of tumor oxygenation.
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