This week in MathOnco 322

Bayesian methods, spatio-temporal and multi-scale models, intermittent therapy, and more.

“This week in Mathematical Oncology” — Feb 27, 2025

> mathematical-oncology.org

From the editor:

Another week, another great collection of papers! This week we have various topics represented like bayesian methods, spatio-temporal and multi-scale models, intermittent therapy, and more.

Enjoy,

Jeffrey West
jeffrey.west@moffitt.org

1. BESTDR: Bayesian quantification of mechanism-specific drug response in cell culture
   Thomas O. McDonald, Simone Bruno, James P. Roney, Ioannis K. Zervantonakis, Franziska Michor

2. Spatio-temporal dynamics of M1 and M2 macrophages in a multiphase model of tumor growth
   Ioannis Lampropoulos, Panayotis Kevrekidis, Christos Zois, Helen Byrne, Michail Kavousanakis

3. Modeling serum M-protein response for early detection of biochemical relapse in myeloma patients treated with bortezomib, lenalidomide and dexamethasone
   Yuki Otani, Yunqi Zhao, Guanyu Wang, Richard Labotka, Mark Rogge, Neeraj Gupta, Majid Vakilynejad, Dean Bottino, Yusuke Tanigawara

4. Integrating multiscale mathematical modeling and multidimensional data reveals the effects of epigenetic instability on acquired drug resistance in cancer
   Shun Wang, Jinzhi Lei, Xiufen Zou, Suoqin Jin

5. A Simple Framework for Agent-Based Modeling with Extracellular Matrix
   John Metzcar, Ben S. Duggan, Brandon Fischer, Matthew Murphy, Randy Heiland, Paul Macklin

6. Dynamic Behaviors of a Periodic System with Threshold Policy-Guided Periodic and Intermittent Therapy of Tumor
   Biao Tang, Yanni Xiao, Jianhong Wu

7. Characterization of tumour heterogeneity through segmentation-free representation learning on multiplexed imaging data
   Jimin Tan, Hortense Le, Jiehui Deng, Yingzhuo Liu, …, Yi Ban, Benjamin G. Neel, Aristotelis Tsirigos, David Fenyö

1. Lineage plasticity of the integrated stress response is a hallmark of cancer evolution
   Shiqi Diao, Jia Yi Zou, Shuo Wang, Nour Ghaddar, …, Nahum Sonenberg, John Le Quesne, Tuomas Tammela, Antonis E. Koromilas

2. Conditional Success of Adaptive Therapy: The Role of Treatment-Holiday Thresholds Revealed by Mathematical Modeling
   Lanfei Sun, Haifeng Zhang, Kai Kang, Xiaoxin Wang, Leyi Zhang, Yanan Cai, Changjing Zhuge, Lei Zhang

3. Emergent Tissue Shapes from the Regulatory Feedback between Morphogens and Cell Growth

   Bivash Kaity, Daniel Lobo

4. Metamats: A mechanistic software for the simulation, inference and prediction of clinical metastasis
   Célestin Bigarré, Alice Daumas, Laurent Greillier, Xavier Muracciole, Laeticia Padovani, Sébastien Benzekry

1. Refining biomarker design in light of cancer evolution

   Nature Cancer Research Briefing

2. Davide Ambrosi

   Math Oncology Interviews by Thomas Hillen (YouTube)

The newsletter now has a dedicated homepage where we post the cover artwork for each issue. We encourage submissions that coincide with the release of a recent paper from your group. This week’s artwork:

Based on the paper: Diffusion-limited cytokine signaling in T cell populations published in iScience

Artist: Kevin Thurley (www.thurleylab.org)

Caption: Effective immune-cell responses depend on collective decision-making mediated by diffusible intercellular signaling proteins. Here, we designed a three-dimensional modeling framework to systematically investigate the origin and consequences of spatially inhomogeneous signaling in T cell populations. We found that such inhomogeneities are critical for effective cell-cell signaling, and they do not arise by diffusion and uptake alone, but rather depend on properties of the cell population such as an all-or-none behavior of cytokine secreting cells. Furthermore, we assessed the regulatory properties of negative and positive feedback, and we analyzed tissue architectures such as clustering of secreting cells or “vacuum cleaner” cells such as regulatory T cells. Overall, our simulations highlight the complex dynamics imposed by cell-cell signaling with diffusible ligands, facilitating fine-tuned biological control especially if combined with feedback mechanisms.

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