Switching from the healthy stage to the uncontrolled development of tumors relies on complicated mechanisms and the activation of antagonistic immune responses, that can ultimately favor the tumor growth. We introduce here a mathematical model intended to describe the interactions between the immune system and tumors. The model is based on partial differential equations, describing the displacement of immune cells subjected to both diffusion and chemotactic mechanisms, the strength of which is driven by the development of the tumors. The model takes into account the dual nature of the immune response, with the activation of both antitumor and protumor mechanisms. The competition between these antagonistic effects leads to either equilibrium or escape phases, which reproduces features of tumor development observed in experimental and clinical settings. Next, we consider on numerical grounds the efficacy of treatments: the numerical study brings out interesting hints on immunotherapy strategies, concerning the role of the administered dose, the role of the administration time and the interest in combining treatments acting on different aspects of the immune response. Such mathematical model can shed light on the conditions where the tumor can be maintained in a viable state and also provide useful hints for personalized, efficient, therapeutic strategies, boosting the antitumor immune response, and reducing the protumor actions.
Background: Resistance to PD1/L1 immune checkpoint inhibitors (ICIs) in advanced NSCLC patients is observed in about 80% of individuals with no robust predictive biomarker yet. The PIONeeR trial (NCT03493581) aims to predict such resistances through a comprehensive multiparametric biomarkers analysis. Methodology: Among the >300 advanced NSCLC patients (pts) recruited in PIONeeR, we focused on the first 137 ≥2nd line ECOG PS0-1 pts treated with single-agent nivolumab, pembrolizumab or atezolizumab. Tumor tissue was collected at baseline and pts were re-biopsied at 6 weeks, and blood-sampled every cycle throughout the 24 weeks post C1D1. Response to PD1/L1 ICIs was assessed by RECIST 1.1 every 6 weeks. Immune contexture was characterized in tumor & blood of each pt through FACS for circulating immune cell subtypes quantification and endothelial activation, blood soluble factors dosage, dual- & multiplex IHC/digital pathology to quantify immune cells infiltrating the tumor, WES for TMB & ICI plasma dosage, leading to 331 measured biomarkers in addition to routine clinical parameters. Multivariable (MV) logistic regression was used to examine the association of each biomarker (controlled by sex, age, smoking status, histological type & PDL1+ Tumor Cells) with the risk of Early Progression (EP), i.e. within 3.5 months of treatment. Multivariable Cox regression analysis was conducted for association with PFS and OS. Results: Overall, the 137 pts were mainly male (64%), smokers (92%) and <70yrs (68%). Tumors were mainly non-squamous (79%) with >1% PDL1+ TC in 36% of the cases, and 21% of pts were still on treatment at data cut-off. Archived samples were available for 80% of pts at inclusion and re-biopsy was available in 52.9% of these cases. The median follow up was 19.8 months, 22.5% of pts did not progress at data cut-off while 62% presented EP. Tumor Cytotoxic T-cells density, especially PD1+ were lower in EP (MV OR=0.45, p=0.022); conversely, higher proportions of circulating cytotoxic T-cells and activated T-cells (HLA-DR+) were observed in EP (MV OR=3.8, p<0.001). Among other biomarkers, Tregs (MV OR=0.44, p=0.018), NK cell subsets (MV OR≤0.44, p<0.05), albumin (MV OR=0.4, p<0.01) and PDL1 TC % (MV OR=0.27, p<0.01) were decreased whereas alkaline phosphatase was increased (OR=3, p=0.018). >65% inter-pt variability was observed in plasma exposures for all ICIs, with 8-10% of pts displaying trough levels below the target engagement threshold. Data will be presented through unsupervised clustering algorithms & multi-modal supervised learning methods. Changes after 6 weeks of treatment will be analyzed to further investigate drugs mechanisms of action. Conclusion: The PIONeeR trial provides with the 1st comprehensive biomarkers’ analysis to establish predictive models of resistance in advanced NSCLC pts treated with PD1/L1 ICIs and highlights how tumor and circulating biomarkers are complementary. Citation Format: Laurent Greillier, Florence Monville, Vanina Leca, Frédéric Vely, Stephane Garcia, Joseph Ciccolini, Florence Sabatier, Gilbert Ferrani, Nawel Boudai, Lamia Ghezali, Marcellin Landri, Clémence Marin, Mourad Hamimed, Laurent Arnaud, Melanie Karlsen, Kevin Atsou, Sivan Bokobza, Pauline Fleury, Arnaud Boyer, Clarisse Audigier-Valette, Stéphanie Martinez, Hervé Pegliasco, Patrice Ray, Lionel Falchero, Antoine Serre, Nicolas Cloarec, Louisiane Lebas, Stephane Hominal, Patricia Barre, Sarah Zahi, Ahmed Frikha, Pierre Bory, Maryannick Le Ray, Lilian Laborde, Virginie Martin, Richard Malkoun, Marie Roumieux, Julien Mazieres, Maurice Perol, Eric Vivier, Sebastien Benzekry, Jacques Fieschi, Fabrice Barlesi. Comprehensive biomarkers analysis to explain resistances to PD1-L1 ICIs: The precision immuno-oncology for advanced non-small cell lung cancer (PIONeeR) trial [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB120.
We study a model of cell segregation in a population composed of two cell types. Starting from a model initially proposed in [3], we aim to understand the impact of a cell division process on the system’s segregation abilities. The original model describes a population of spherical cells interacting with their close neighbors by means of a repulsion potential and which centers are subject to Brownian motion. Here, we add a stochastic birth-death process in the agent-based model, that approaches a logistic growth term in the continuum limit. We address the linear stability of the spatially homogeneous steady states of the macroscopic model and obtain a precise criterion for the phase transition, which links the system segregation ability to the model parameters. By comparing the criterion with the one obtained without logistic growth, we show that the system’s segregation ability is the result of a complex interplay between logistic growth, diffusion and mechanical repulsive interactions. Numerical simulations are presented to illustrate the results obtained at the microscopic scale.
In this paper we write, analyze and experimentally compare three different numerical schemes dedicated to the one dimensional barotropic Navier-Stokes equations: a staggered scheme based on the Rusanov one for the inviscid (Euler) system,a staggered pseudo-Lagrangian scheme in which the mesh “follows” the fluid,the Eulerian projection (on a fixed mesh) of the preceding scheme. All these schemes only involve the resolution of linear systems (all the nonlinear terms are solved in an explicit way). We propose numerical illustrations of their behaviors on particular solutions in which the density has discontinuities (hereafter called Hoff solutions). We show that the three schemes seem to converge to the same solutions, and we compare the evolution of the amplitude of the discontinuity of the numerical solution (with the pseudo-Lagrangian scheme) with the one predicted by Hoff and observe a good agreement.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.