A reduced Gompertz model for predicting tumor age using a population approach

Tumor growth curves are classically modeled by ordinary differential equations. In analyzing the Gompertz model several studies have reported a striking correlation between the two parameters of the model. We analyzed tumor growth kinetics within the statistical framework of nonlinear mixed-effects (population approach). This allowed for the simultaneous modeling of tumor dynamics and inter-animal variability. Experimental data comprised three animal models of breast and lung cancers, with 843 measurements in 94 animals. Candidate models of tumor growth included the Exponential, Logistic and Gompertz. The Exponential and-more notably-Logistic models failed to describe the experimental data whereas the Gompertz model generated very good fits. The population-level correlation between the Gompertz parameters was further confirmed in our analysis (R 2 > 0.96 in all groups). Combining this structural correlation with rigorous population parameter estimation, we propose a novel reduced Gompertz function consisting of a single individual parameter. Leveraging the population approach using bayesian inference, we estimated the time of tumor initiation using three late measurement timepoints. The reduced Gompertz model was found to exhibit the best results, with drastic improvements when using bayesian inference as compared to likelihood maximization alone, for both accuracy and precision. Specifically, mean accuracy was 12.1% versus 74.1% and mean precision was 15.2 days versus 186 days, for the breast cancer cell line. These results offer promising clinical perspectives for the personalized prediction of tumor age from limited data at diagnosis. In turn, such predictions could be helpful for assessing the extent of invisible metastasis at the time of diagnosis. Author summary Mathematical models for tumor growth kinetics have been widely used since several decades but mostly fitted to individual or average growth curves. Here we compared three classical models (Exponential, Logistic and Gompertz) using a population approach, which accounts for inter-animal variability. The Exponential and the Logistic models failed to fit the experimental data while the Gompertz model showed excellent descriptive power. Moreover, the strong correlation between the two parameters of the Gompertz equation motivated a simplification of the model, the reduced Gompertz model, with a single individual parameter and equal descriptive power. Combining the mixed-effects approach with Bayesian inference, we predicted the age of individual tumors with only few late measurements. Thanks to its simplicity, the reduced Gompertz model showed superior predictive power. Although our method remains to be extended to clinical data, these results are promising for the personalized estimation of the age of a tumor from limited measurements at diagnosis. Such predictions could contribute to the development of computational models for metastasis

A reduced Gompertz model for predicting tumor age using a population approach

Tumor growth curves are classically modeled by ordinary differential equations. In analyzing the Gompertz model several studies have reported a striking correlation between the two parameters of the model. We analyzed tumor growth kinetics within the statistical framework of nonlinear mixed-effects (population approach). This allowed for the simultaneous modeling of tumor dynamics and inter-animal variability. Experimental data comprised three animal models of breast and lung cancers, with 843 measurements in 94 animals. Candidate models of tumor growth included the Exponential, Logistic and Gompertz. The Exponential and-more notably-Logistic models failed to describe the experimental data whereas the Gompertz model generated very good fits. The population-level correlation between the Gompertz parameters was further confirmed in our analysis (R 2 > 0.96 in all groups). Combining this structural correlation with rigorous population parameter estimation, we propose a novel reduced Gompertz function consisting of a single individual parameter. Leveraging the population approach using bayesian inference, we estimated the time of tumor initiation using three late measurement timepoints. The reduced Gompertz model was found to exhibit the best results, with drastic improvements when using bayesian inference as compared to likelihood maximization alone, for both accuracy and precision. Specifically, mean accuracy was 12.1% versus 74.1% and mean precision was 15.2 days versus 186 days, for the breast cancer cell line. These results offer promising clinical perspectives for the personalized prediction of tumor age from limited data at diagnosis. In turn, such predictions could be helpful for assessing the extent of invisible metastasis at the time of diagnosis. Author summary Mathematical models for tumor growth kinetics have been widely used since several decades but mostly fitted to individual or average growth curves. Here we compared three classical models (Exponential, Logistic and Gompertz) using a population approach, which accounts for inter-animal variability. The Exponential and the Logistic models failed to fit the experimental data while the Gompertz model showed excellent descriptive power. Moreover, the strong correlation between the two parameters of the Gompertz equation motivated a simplification of the model, the reduced Gompertz model, with a single individual parameter and equal descriptive power. Combining the mixed-effects approach with Bayesian inference, we predicted the age of individual tumors with only few late measurements. Thanks to its simplicity, the reduced Gompertz model showed superior predictive power. Although our method remains to be extended to clinical data, these results are promising for the personalized estimation of the age of a tumor from limited measurements at diagnosis. Such predictions could contribute to the development of computational models for metastasis

Immediate vs. deferred switching from a boosted protease inhibitor (PI/r) based regimen to a Dolutegravir (DTG) based regimen in virologically suppressed patients with high cardiovascular risk or Age ≥50 years: final 96 weeks results of NEAT 022 study

Background Both immediate and deferred switching from a ritonavir-boosted protease inhibitor (PI/r)–based regimen to a dolutegravir (DTG)–based regimen may improve lipid profile. Methods European Network for AIDS Treatment 022 Study (NEAT022) is a European, open-label, randomized trial. Human immunodeficiency virus (HIV)–infected adults aged ≥50 years or with a Framingham score ≥10% were eligible if HIV RNA was <50 copies/mL. Patients were randomized to switch from PI/r to DTG immediately (DTG-I) or to deferred switch at week 48 (DTG-D). Week 96 endpoints were proportion of patients with HIV RNA <50 copies/mL, percentage change of lipid fractions, and adverse events (AEs). Results Four hundred fifteen patients were randomized: 205 to DTG-I and 210 DTG-D. The primary objective of noninferiority at week 48 was met. At week 96, treatment success rate was 92.2% in the DTG-I arm and 87% in the DTG-D arm (difference, 5.2% [95% confidence interval, –.6% to 11%]). There were 5 virological failures in the DTG-I arm and 5 (1 while on PI/r and 4 after switching to DTG) in the DTG-D arm without selection of resistance mutations. There was no significant difference in terms of grade 3 or 4 AEs or treatment-modifying AEs. Total cholesterol and other lipid fractions (except high-density lipoprotein) significantly (P < .001) improved both after immediate and deferred switching to DTG overall and regardless of baseline PI/r strata. Conclusions Both immediate and deferred switching from a PI/r to a DTG regimen in virologically suppressed HIV-infected patients ≥50 years old or with a Framingham score ≥10% was highly efficacious and well tolerated, and improved the lipid profile

Implication of small GTPases Rho in endothelial response to high dose of ionizing radiation.

Microvasculature plays an important role in normal and tumoral tissue responses to high dose of irradiation (IR) as endothelial cells apoptotic death is a pre-requisite to deleterious effets of IR on surrounding tissues. Molecular mechanisms involved in this apoptotic pathway, despite being clearly independant of DNA damage, are still poorly understood. Small GTPases of the Rho family are crucial membrane-linked signalling proteins involved in many cellular functions, especially in actin cytoskeleton organisation but also in control of migration, proliferation and cell death. Their involvement in cellular response to ionizing radiation remains unclear, particularly in the endothelial compartment. Our study aims at studying 1) the regulation of activity of RhoA and Rac1, two main small Rho G proteins expressed in endothelial cells and 2) the possible role of these proteins in endothelial cellular functions critically affected by ionizing radiation like cytoskeleton reorganisation, cell death and migration. Using the microvascular endothelial cell line HMEC1 irradiated at 15 Gy, we show a rapid activation of RhoA concomitantly to an inactivation of Rac1. Analysis of actin cytoskeleton by confocal microscopy in HMEC1 cells indicate that 15 Gy-irradiation induces deep reorganisation of HMEC1 cell morphology, characterized by induction of stress fibers and decrease of lamellipodia, structures of polymerized actin respectively induced by RhoA and Rac1. We are currenly investigating the role of RhoA and Rac1 in induction of apoptotic cell death and in regulation of migration in 15 Gy-irradiated HMEC1, by the use of pharmacological specific inhibitors (Y-27632 for the RhoA pathway and NSC23766 for Rac1) and by invalidation of RhoA and Rac1 expression by stable RNA interference. Identifying Rho proteins as potential actors in endothelium damage to IR will permit a better understanding of molecular pathways involved and may lead to development of new strategies to modulate radiosensitization of this cellular compartment

Séquences IRM « SWAN, SWI et VenoBOLD » exploitant le phénomène de susceptibilité magnétique : principes techniques et applications cliniques

International audienc

Neutralization escape in kidney transplant recipients with persistent BK polyomavirus replication

International audienc