MS02 - ONCO-1
Ohio Staters Traditions Room (#2120) in The Ohio Union

Techniques and Methods in Modelling Cancer Treatment

Monday, July 17 at 04:00pm

SMB2023 SMB2023 Follow Monday during the "MS02" time block.
Room assignment: Ohio Staters Traditions Room (#2120) in The Ohio Union.
Note: this minisymposia has multiple sessions. The other session is MS01-ONCO-1 (click here).

Share this


Kathleen Wilke, Gibin Powathil


This mini-symposium aims to bring together speakers that will highlight advances in the various mathematical methods and techniques used in the study of cancer treatment. There are numerous aspects to consider in the biological response of a treated tumour within the human body. Here we will highlight many of these factors, and the diverse modelling approaches needed to describe and analyse the motivating complex system. Talks will be of a great interest to a wide range of participants due to the variety of topics such as radiation, targeted therapy, and immunotherapy, and due to the variety of modelling approaches and techniques such as ODES, Fractional-DEs, PDEs, PK-PD, multi scale, and systems modelling.

Annabelle Ballesta

Inserm & Institut Curie (unit 900)
"Quantitative Systems Pharmacology to Personalize Temozolomide-based Drug Combinations against Brain Tumors."
Objectives: Large inter-patient heterogeneity in anticancer drug response highlights the critical need for personalized cancer management which has favored the generation of multi-type individual patient data. However, quantitative systems pharmacology (QSP) approaches handling the complexity of multiple preclinical and clinical data types for designing patient-specific treatments are critically lacking [1-2]. This study aims to design such methodology, to individualize the combination of cytotoxic drugs with targeted molecules, towards a high benefit for patients. Multiple regulatory pathways may be altered initially or activated upon drug exposure in cancer cells, which advocates for the design of combination therapies simultaneously inhibiting multiple targets [3-4]. Such theoretical considerations are backed up by success stories of associating cytotoxic drugs with targeted therapies. The approach was developed here for Glioblastoma multiforme (GBM), the most frequent and aggressive primary brain tumors in adults, which is associated to a median overall survival <18 months despite intensive treatments combining maximal safe neurosurgery, radiotherapy and temozolomide (TMZ)-based chemotherapy. The objective was to develop a QSP pipeline to potentiate TMZ treatment by priming cancer cells with targeted molecules affecting key intracellular functions. Methods: A mathematical model of TMZ cellular pharmacokinetics-pharmacodynamics (PK-PD) based on ordinary differential equations (ODEs) was designed, building on existing works [5]. The model describes key regulatory networks that count among the most deregulated pathways in GBM according to TCGA [6]. Briefly, TMZ is a methylating agent that is spontaneously activated upon a two-step pH-dependent process. Four types of DNA adduct are formed upon TMZ exposure, which are handled either by base excision repair (BER) or by O6-methylguanine-DNA methyltransferase (MGMT). If these initial processes of DNA repair are unsuccessful, DNA single- or double-strand breaks are created, which triggers Homologous Recombination (HR), ATR/Chk1 and p53 activation, cell cycle arrest and possibly apoptosis. TMZ PK-PD model was connected to an ODE-based cell population model that represented cell viability during drug exposure. Model calibration consisted in a modified least square approach ensuring data best-fit under biologically-sound constraints. The minimization task was performed by the Covariance Matrix Evolutionary Strategy (CMAES) algorithm. The same algorithm was used for therapeutic optimization procedures. Results: Parameters of TMZ PK-PD model were estimated in sequential steps involving the use of longitudinal and dose-dependent datasets, informing on the concentrations of TMZ PK, DNA adducts, MGMT, double-stranded breaks, ATR, Chk1 and p53 phosphorylation, and cell death (295 datapoints in total). Most of the datasets were performed in two LN229 glioblastoma human cell lines: the parental TMZ sensitive (MGMT-) and the MGMT-overexpressing TMZ resistant (MGMT+) cells [7-11]. The model was able to faithfully reproduce these multi-type datasets coming from several independent studies. Next, the calibrated model was used as a powerful tool to investigate new therapeutic targets. As a start, we investigated drug combinations involving TMZ and only one targeted inhibitor, which was computationally represented by decreasing the value of the corresponding model parameter. The only strategy leading to a drastic increase of TMZ efficacy in both parental and resistant cell lines consisted in the complete (>90%) inhibition of the BER pathway, prior to TMZ exposure. Such high level of inhibition being challenging to achieve in the clinics, we further explored the combination of TMZ and two inhibitors. This numerical study revealed three possible parameters to be jointly targeted: MGMT protein level, BER activity, and HR activity. The optimal strategy, defined as the one requiring the smaller percentages of inhibition for both targets, was the combined administration of BER and HR inhibitors, prior to TMZ exposure. This therapeutic strategy was investigated experimentally in both LN229 cell lines and led to a drastic increase in TMZ efficacy. The model prediction of cell viability under exposure of TMZ after either BER inhibitor or HR inhibitor only, were also validated. Conclusions: A model of TMZ PK-PD model was carefully calibrated to data and allowed to identify a non-intuitive TMZ-based drug combination leading to a drastic increase of cell death in initially resistant cells. This QSP model is being personalized using multi-omics datasets available in GBM patient-derived cell lines towards the design of patient-specific therapeutic strategies.
Additional authors: Sergio Corridore^1, Maité Verreault^2, Hugo Martin^1, Thibault Delobel^1, Cécile Carrère^3, Ahmed Idbaih^4 1INSERM U900 Institut Curie, Saint Cloud, France ; MINES ParisTech, Paris, France ; PSL Research University, Paris, France. 2Paris Brain Institute, Inserm UMR 1127, Hopital Pitié Salpetrière AP-HP, Paris, France 3Institut Denis Poisson, Université d’Orléans, CNRS, 45100 Orléans, France 4 Sorbonne Université, AP-HP, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, Hôpitaux Universitaires La Pitié Salpêtrière - Charles Foix, DMU Neurosciences, Service de Neurologie 2-Mazarin, F-75013, Paris, France

Kévin Spinicci

Swansea University
"Mathematical modelling of HIF on regulating cancer cells metabolism and migration"
The number of studies on tumour metabolism has increased in the recent years as it appears to differ from normal cells. Effort has been put in order to assess dysregulated mechanisms to design new strategies aiming to target cancer cells specifically. It has been observed that the median oxygen level in tumour is less than 2%. This altered environmental condition leads to an adaptation of the cell energetic metabolism and induces angiogenesis. Furthermore, the literature shows that hypoxic cells are more resistant to radiotherapy and potentially more aggressive. Here, we will present a mathematical model of the Hypoxia Inducible Factor (HIF), the main actor in the cellular response to hypoxia, to study how it drives the cell metabolism [1] and the cell ability to migrate. To that end, we have implemented an agent-based model to simulate tumour growth in an in vitro setting using the PhysiCell software. The model includes ODEs to describe the genetic regulations of metabolic key genes with respect to the effect of HIF on those genes. Cells consumption and secretion are affected by the genetic regulation. The results of the model show the consequences on the Warburg Effect and on cancer cell migration.

Linh Nguyen Phuong

Aix-Marseille University (COMPutational pharmacology and clinical Oncology Team)
"Mechanistic modeling of the longitudinal tumor and biological markers combined with quantitative cell-free DNA"
Early prediction of resistance to immunotherapy is a major challenge in oncology. The ongoing SChISM (Size Cell-fre DNA (cfDNA) Immunotherapies Signature Monitoring) clinical study proposes an innovative approach based on patented cfDNA quantification methods, providing concentration and size profile fluctuations of plasmatic circulating DNA for early therapeutic management of immune checkpoint inhibitors treated patients. The main interest is that such measures can be performed in a less invasive, less expansive way, and especially much earlier than the first imaging evaluation, thanks to liquid biopsies. Five cancer types are investigated: melanoma, head and neck, renal, bladder and lung cancers, with a total of 260 patients at the end of the study, described by their clinical and classical biological data, and cfDNA features, such as concentration, first and second peak of the cfDNA size distribution, and specific size ranges of cfDNA fragments. We developed a mechanistic model of cfDNA joint kinetics with other longitudinal markers and tumor size imaging to help describe and understand the time dynamics of the quantitative profiles of cfDNA over time. The model consists of a dynamical system of differential equations that estimates specifically the component corresponding to cfDNA production by tumor lesions. Subsequently, the model is embedded within a nonlinear mixed-effects statistical framework in order to quantify inter-patient variability, and calibrated on the data. Future perspective will use machine learning models to predict early progression, progression-free survival or overall survival, combining these dynamic parameters and other variables available at baseline.
Additional authors: Linh Nguyen Phuong1, Laurent Greillier1,2, Caroline Gaudy2, Jean-Laurent Deville2, Jean-Charles Garcia3, Frédéric Fina3,4, Sébastien Salas1*, Sébastien Benzekry1* 1 COMPutational pharmacology and clinical Oncology Team, Inria Sophia Antipolis - Méditerranée, Cancer Research Center of Marseille, Inserm, CNRS, Aix Marseille University, Marseille, France; 2 Multidisciplinary Oncology and Therapeutic innovations Department, Assistance Publique - Hôpitaux de Marseille, Aix Marseille University, Marseille, France; 3 Id-Solutions, Grabels, France; 4 ADELIS, Labège, France. * co-authors director.

Heiko Enderling

Moffitt Cancer Center (Department of Integrated Mathematical Oncology)
"Mathematical modeling of cancer radiotherapy"
Radiation therapy is a mainstay of cancer treatment, with more than 50% of all cancer patients receiving radiation at some point of their clinical care. Mathematical modeling has a long history in radiation oncology, and recent modeling approaches saw translation into prospective clinical trials. Here, we will present the different mathematical modeling approaches to simulate radiation response, and their implication on personalizing radiation dose and dose fractionation, towards a novel concept of adaptive radiation therapy. We will focus on head and neck cancer, one of the few cancer types rising in incidence, that is routinely treated with definitive radiation. Using the data of 39 head and neck cancer patients, we develop, calibrate, and validate the model before making predictions on novel therapies.
Additional authors: Mohammad Zahid, Moffitt Cancer Center

#SMB2023 Follow
Annual Meeting for the Society for Mathematical Biology, 2023.