Recent development in mathematical oncology in Asia and Australia

Tuesday, June 15 at 05:45pm (PDT)
Wednesday, June 16 at 01:45am (BST)
Wednesday, June 16 09:45am (KST)

SMB2021 SMB2021 Follow Tuesday (Wednesday) during the "MS09" time block.
Note: this minisymposia has multiple sessions. The second session is MS15-ONCO (click here).

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Yangjin Kim (Konkuk University, Korea, Republic of), Eunjung Kim (Korea Institute of Science and Technology, Korea)


Cancer is a nonlinear and multiscale system that involves complex interactions between molecules, cells, and tissue. Genetic mutations occurring at a subcellular molecule level could drive functional changes at the cellular level, leading to tissue-level changes. Tissue-level properties produce selection pressure that governs the distribution and growth of cancer cells. In particular, the tumor microenvironments such as extracellular matrix, vascular bed, stromal cells are known to modulate tumour progression. Therefore, a thorough understanding of tumor microenvironment would provide a foundation to generate new strategies in therapeutic drug development. To understand this complex dynamical system, many mathematical models have been developed at a sub-cellular, tissue, and multiscale level. The main aim of this session is to discuss current stages and challenges in cancer modeling in Asia. Specific goals of the session include: (i) to analyze both computational and analytical solutions to various mathematical models of tumor growth and progression, (ii) to discuss how mathematical models can be used to support better clinical decisions, (iii) to present models that have improved our biochemical/biomechanical understanding of the fundamental mechanism that drives tumor progression, (iv) to discuss new treatment strategies guided by mathematical models.

Shinji Nakaoka

(Faculty of Advanced Life Science, Hokkaido University, Japan)
"A computational pseudo-tracking method for cancer progression by microbiome data"
In this presentation, we would like to present recent research progress on applying a pseudotime reconstruction method to microbiome data. Pseudotime reconstruction methods have been originally developed in the field of single-cell RNA-seq analysis. Pseudotime reconstruction is also known as trajectory inference, which utilizes many samples to infer a developmental path such as cell differentiation, from a non-time series dataset. Although the validity of applying pseudotime reconstruction methods to microbiome data is not confirmed, the potential of its usefulness has been demonstrated on some datasets. In our ongoing work, we have been trying to apply a pseudotime reconstruction method to microbiome data obtained from patients who are diagnosed with some cancer. In this presentation, we will report a summary of computational results for the comparison of different pseudotime reconstruction methods to infer a possible trajectory of cancer progression.

Aurelio A. de Los Reyes V

(University of the Philippines Diliman, Philippines)
"Polytherapeutic strategies in cancer treatment"
This study aims to identify strategic infusion protocols of bortezomib, OV and natural killer (NK) cells to minimize cancer cells by utilizing optimal control theory. Three different therapeutic protocols will be presented: (i) periodic bortezomib and single administrations of both OV and NK cells therapy; (ii) alternating sequential combination therapy; and (iii) NK cell depletion and infusion therapy. The first treatment strategy shows that early OV administration followed by well-timed adjuvant NK cell infusion maximizes antitumour efficacy and the second scheme supports timely OV infusion. The last treatment protocol indicates that transient NK cell depletion followed by appropriate NK cell adjuvant therapy yields the maximal benefits. This study could provide potential combination therapies in cancer treatment.

Eunjung Kim

(Korea Institute of Science and Technology, Korea)
"Understanding the potential benefits of adaptive therapy for metastatic melanoma"
Understanding the potential benefits of adaptive therapy for metastatic melanoma Adaptive therapy is an evolution-based treatment approach that aims to maintain tumor volume by employing minimum effective drug doses or timed drug holidays. For successful adaptive therapy outcomes, it is critical to find the optimal timing of treatment switch points. Mathematical models are ideal tools to facilitate adaptive therapy dosing and switch time points. We developed two different mathematical models to examine interactions between drug-sensitive and resistant cells in a tumor. The first model assumes genetically fixed drug-sensitive and resistant populations that compete for limited resources. Resistant cell growth is inhibited by sensitive cells. The second model considers phenotypic switching between drug-sensitive and resistant cells. We calibrated each model to fit melanoma patient biomarker changes over time and predicted patient-specific adaptive therapy schedules. Overall, the models predict that adaptive therapy would have delayed time to progression by 6-25 months compared to continuous therapy with dose rates of 6%-74% relative to continuous therapy. We identified predictive factors driving the clinical time gained by adaptive therapy. The first model predicts 6-20 months gained from continuous therapy when the initial population of sensitive cells is large enough, and when the sensitive cells have a large competitive effect on resistant cells. The second model predicts 20-25 months gained from continuous therapy when the switching rate from resistant to sensitive cells is high and the growth rate of sensitive cells is low. This study highlights that there is a range of potential patient specific benefits of adaptive therapy, depending on the underlying mechanism of resistance, and identifies tumor specific parameters that modulate this benefit.

Masud MA

(Korea Institute of Science and Technology, Korea)
"The impact of spatial heterogeneity on treatment response"
A long-standing practice in cancer treatment is hit hard with maximum tolerated dose to eradicate the tumor. Such continuous therapy, however, selects for resistance cells leading to treatment failure. A different type of treatment strategy, adaptive therapy, has recently shown a degree of success in both preclinical xenograft experiments and clinical trials. Adaptive therapy aims to maintain tumor volume by exploiting the competition between drug-sensitive and resistance cells with minimum effective drug doses or timed drug holidays. To further understand the role of spatial competition between cancer cells, we develop a 2D on-lattice agent-based model. Specifically, we address the role of resistant cell distribution on the treatment outcomes. Our simulations show that the superiority of adaptive strategy over continuous therapy depends on the local competition shaped by the spatial distribution of resistant cells. Cancer cell migration and increased carrying capacity drive a faster tumor progression time under both types of treatment by reducing local competition. The intratumor competition can be modulated by fibroblasts, which produce microenvironmental factors that promote cancer cell growth. Our simulations show that the spatial architecture of fibroblasts modulates treatment outcomes. As proof of concept, we simulate adaptive therapy outcomes on multiple metastatic sites composed of different spatial distributions of fibroblasts and drug resistance cell populations. We predict that spatial distribution of resistance cells and fibroblasts metastatic lesions modulate the benefit of adaptive therapy.

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Virtual conference of the Society for Mathematical Biology, 2021.