Section: New Results

Axis 1: Tumor modeling for patient-specific simulations

Lung metastasis

Patient specific simulation of tumor growth, response to the treatment and relapse of a lung metastasis: a clinical case  [10] , [1]

Team participants: Thierry Colin, Julien Jouganous, François Cornelis (Hôpital Pellegrin), Olivier Saut

Other participant: Jean Palussière (Bergonié Institute)

In this work, a parametrization strategy based on reduced order methods is presented for tumor growth PDE models. This is applied to a new simple spatial model for lung metastasis including angiogenesis. The goal is to help clinicians monitoring tumors and eventually predicting its evolution or response to a particular kind of treatment. To illustrate the whole approach, a clinical case including the natural history of the lesion, the response to a chemotherapy and the relapse before a radiofrequency ablation is presented.

Nenuphar

Team participants: Thierry Colin, Julien Jouganous, Marie Martin, Olivier Saut

This work concerns the development of Nenuphar which is a software devoting to the evaluation and the surveillance of the tumor aggressiveness.

Take into account the drug resistance

Modeling and analysis of tumor heterogeneity during treatments resistance: GIST liver metastases case

Team participants: Thierry Colin, François Cornelis, Guillaume Lefebvre, Clair Poignard, Olivier Saut

This works deals with tumor heterogeneity analysis and modeling during treatments resistances. A patient-dependent PDEs model, that takes into account two kinds of treatments, is presented. It qualitatively and quantitatively reproduces the different stage during the tumor growth undergoing treatments. In order to overcome a numerical instability linked to the type of modeling, a new numerical scheme is built. Then, an image synthesis method is developed to enable a better comparison between the numerical results and the clinical data. Finally, a robust criteria that quantifies the tumor heterogeneity from the clinical data and from the synthesis images, is built.

Mathematical study and asymptotic analysis of a model for tumour drug resistance [19]

Team participants: Thierry Colin, Thomas Michel, Clair Poignard

In this work we study a partial differential equations model for tumour growth taking into account drug resistance. It is well known that angiogenesis, the process of creation of new blood vessels from existing ones, is induced by tumour cells to get the amount of nutrients and oxygen needed to continue their proliferation when the tumour has reached a critical size. Angiogenesis is therefore a target for therapy. The model we study takes into account two kinds of treatments: a cytotoxic treatment and a treatment which is both cytotoxic and anti-angiogenic. It is based on mass-balance equations on cells densities coupled with a diffusion equation for the nutrients and oxygen concentration. In a first part we prove that the model is well-posed if the initial tumour is compactly supported in the domain, which is the case for tumour metastases. The proof states that the tumour remains compactly supported in a finite time. In the model, we also consider the presence of a necrotic compartment composed of dead cells. Since some tumours can present necrosis while other do not, we want a model which can reproduce these two different cases. The second part of this work is devoted to an asymptotic analysis which proves that the absence of necrosis is the limit case of our model when the necrosis is immediately evacuated.

Motility phenotype

TMOD−03 * Motility controls growth and progression patterns of glioblastoma multiforme [13]

Team participants: Olivier Saut, Thierry Colin

Other participants: Hassan Fathallah, Elizabeth Scribner

Purpose: Glioblastoma multiforme (GBM) is a malignant brain tumor with poor prognosis and high morbidity due to its invasiveness. Hypoxia-driven motility (HM) and concentration-driven motility (CM) are two mechanisms of GBM invasion in the brain. The use of anti-angiogenic drugs has uncovered new progression patterns of GBM associated with significant differences in overall survival times. Here, we test the hypotheses that the types and rates of GBM motility predict its progression pattern and the patients’ survival times. Methods: We applied a mathematical model of GBM growth and invasion in humans to simulate a clinical trial and study the effects of the rate and mechanism of motility on the patterns of progression and on survival times. Results: The motility phenotype appears to determine the progression pattern as well as the survival time of a patient treated by anti-angiogenesis. Highly-dispersive tumors are associated with the longest survival times (p , 0.001) and with progression by Expanding FLAIR. Moderately-Dispersive tumors are associated with short survival times and with progression by Expanding FLAIR + Necrosis. Tumors with HMare associated with the shortest survival times and with progression by Expanding Necrosis. The survival times of the latter are similar to non-responders. This investigation also uncovered the HM-CM principle: the aggressive HM-dependent phenotype surfaces only when the rate of CM is low in both untreated and bevacizumab-treated GBM. Conclusions: Finding that the motility phenotype is a fundamental property that controls progression and survival times, has biological, clinical and therapeutic implications.