Mathematical approaches to advance clinical studies in oncology

Monday, June 14 at 11:30am (PDT)
Monday, June 14 at 07:30pm (BST)
Tuesday, June 15 03:30am (KST)

SMB2021 SMB2021 Follow Monday (Tuesday) during the "MS02" time block.
Note: this minisymposia has multiple sessions. The second session is MS01-ONCO (click here).

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Heyrim Cho (University of California Riverside, USA), Russell Rockne (City of Hope Comprehensive Cancer Center, USA)


In this session, we bring together researchers in mathematical oncology to discuss methodologies for translational research in oncology. The advance of clinical data acquisition technologies, such as genome sequencers and new imaging techniques, are providing new opportunities in mathematical oncology. Mathematical modeling and quantitative framework can effectively leverage clinical data to describe various aspects of cancer progression and drug response. Moreover, models calibrated to individual patient data can predict treatment outcome and help design personalized treatment regarding the drug combination, dosage, and scheduling to improve treatment outcome. Here we highlight several applications of mathematical modeling to clinical oncology, including analysis of clinical data and clinical trials designed with mathematical modeling.

Jacob Scott

(Cleveland Clinic, USA)
"Evolutionary Control on Game Landscapes"
Control of evolving populations has recently been postulated using control methods inspired by quantum computing and stochastic thermodynamics. These methods, which are essentially extensions of classical population genetics, require genotype-phenotype maps in the form of fitness seascapes, which are mapping from changes in drug dose to fitness in a combinatorially complete genotype space. These models rarely consider the interaction between individual types in heterogeneous populations (clonal interference) and are therefore of limited practical applicability. In this talk we will present a simplified deterministic (ODE) model of evolution on a landscape that includes these interactions (game landscape), show how the interactions can themselves drastically change the evolutionary dynamics, and sketch a path forward to evolutionary control.

Kristin Swanson

(Mayo Clinic, USA)
"Sex, Drugs and Radiomics of Brain Cancer"
Abstract to be determined. Please check back later.

Sebastien Benzekry

(INRIA, France)
"Quantitative modeling of metastasis: cancer at the organism scale"
In the majority of solid cancers, secondary tumors (metastases) are the main cause of death. Determining the burden of invisible metastases at diagnosis is a crucial challenge in the clinic, as it would allow personalization of therapeutic intervention, e.g. in the perioperative setting. I will present research efforts towards the establishment of such a predictive computational tools of metastatic development, with emphasis on the quantitative calibration of models to empirical data (experimental and clinical). The general framework is based on a physiologically-structured partial differential equation for the time dynamics of a population of metastases. Results will be presented in two clinical settings: brain metastasis from non-small cell lung cancer and early-stage breast cancer. In the first application, comparison of models relying on different biological hypotheses about dissemination and growth indicated periods of dormancy of the order of several months. In the second application, a combination of machine learning techniques and mixed-effects statistical modeling methods was used for individualized predictions of the model parameters from data available at diagnosis. In turn, this allowed patient-specific prediction of the time to metastatic relapse. Together, these results represent a step towards the integration of mathematical modeling as a predictive tool for personalized oncology.

Chengyue Wu

(University of Texas at Austin, USA)
"Towards patient-specific prediction of breast cancer response to neoadjuvant therapy"
Neoadjuvant therapy (NAT) has become the standard-of-care treatment for breast cancers. However, more than 50% of patients undergoing the standard NAT regimen show residual tumors which are associated with metastasis and recurrence. Patient-tailored treatment has been proposed to improve individual response. But with multiple factors to consider, including dose, schedule, and drug combinations, personalization of therapeutic regimens is a complex task which cannot be solved by population-based clinical trials. To address this problem, we develop a clinical-computational framework to systematically evaluate the response of breast cancer patients to different therapeutic regimens. Specifically, we employ quantitative MRI to measure tissue geometries and properties such as vessel permeability and drug diffusivity. Constrained by the patient-specific data, we establish a model consisting of an advection-diffusion equation for flow and drug transport, and a phase-filed equation for tumor growth and response. For each patient, we simulate a group of practical therapeutic regimens by varying administration schedules and doses, and drug combinations. The outcome of each regimen is assessed by the computed tumor cellularity and off-target ratio (accumulative drug outside tumor to that within tumor) at the end of treatment. Preliminary results indicate that the approach has the potential to personally optimize breast cancer NAT.

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Virtual conference of the Society for Mathematical Biology, 2021.