This week in MathOnco 282

Spatial modeling, clinical trial design, oncolytic viruses, and stats

“This week in Mathematical Oncology” — January 25, 2023

> mathematical-oncology.org

From the editor:

To give you a tiny glimpse into the world of math onco, i encourage you to take a look at this tweet, where I show some statistics of this newsletter.

It’s interesting to note the hotspots of MathOnco folks both within America (California, Florida, New York, Mass, and Texas) and across the world (UK, India, Germany, France). It’s also interesting to note the holes, suggesting missed opportunities.

Enjoy,

Jeffrey West
jeffrey.west@moffitt.org

1. Exploring chronic and transient tumor hypoxia for predicting the efficacy of hypoxia-activated pro-drugs
   Shreya Mathur, Shannon Chen & Katarzyna A. Rejniak

2. Designing clinical trials for patients who are not average
   Thomas E. Yankeelov, David A. Hormuth II, Ernesto A.B.F. Lima, Guillermo Lorenzo, Chengyue Wu, Lois C. Okereke, Gaiane M. Rauch, Aradhana M. Venkatesan, Caroline Chung

3. Spatial modelling of the tumor microenvironment from multiplex immunofluorescence images: methods and applications
   Gayatri Kumar, Renganayaki Krishna Pandurengan, Edwin Roger Parra, Kasthuri Kannan, Cara Haymaker

4. Predicting the spatio-temporal response of recurrent glioblastoma treated with rhenium-186 labelled nanoliposomes
   Chase Christenson, Chengyue Wu, David A. Hormuth II, Shiliang Huang, Ande Bao, Andrew Brenner, Thomas E. Yankeelov

5. Evolutionary dynamics of glucose-deprived cancer cells: insights from experimentally informed mathematical modelling
   Luis Almeida, Jérôme Alexandre Denis, Nathalie Ferrand, Tommaso Lorenzi, Antonin Prunet, Michéle Sabbah and Chiara Villa

6. Global stability and parameter analysis reinforce therapeutic targets of PD-L1-PD-1 and MDSCs for glioblastoma
   Hannah G. Anderson, Gregory P. Takacs, Duane C. Harris, Yang Kuang, Jeffrey K. Harrison, and Tracy L. Stepien

1. Quantifying cell divisions along evolutionary lineages in cancer
   Kamila Naxerova, Martin Blohmer, David Cheek, Wei-Ting Hung, …, Sara Pai, Jochen Lennerz, Teh-Ying Chou, Matthias Kloor

2. Oscillations in a Spatial Oncolytic Virus Model
   Arwa Abdulla Baabdulla, Thomas Hillen

3. Mechanical constraints and cell cycle regulation in models of collective cell migration
   Carles Falcó, Daniel J. Cohen, José A. Carrillo, Ruth E. Baker

1. Learning from failed model predictions
   The Mathematical Oncology Blog
   Sara Hamis: “It’s 2024 and a new year to delve into mathematical oncology! This is a great time to pause and reflect on where the research field is heading. Undeniably, a central part of contemporary mathematical oncology entails fitting mathematical models to bio-medical data. The ongoing surge in bio-medical data is exciting and, as a modeller, there is nothing quite like the feeling of seeing your model beautifully predict unseen data. When this happens, you high-five your collaborators, have a good night’s sleep, and prepare to publish! But what happens when your model predictions do not match (all) unseen data? This blog post is about that.”

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: The bone ecosystem facilitates multiple myeloma relapse and the evolution of heterogeneous proteasome inhibitor resistant disease

Artist: Anna Miller, David Basanta, Maxi Strobl

Caption: In cancers, the complex ecosystem of different cell types and signaling molecules, known as the tumor microenvironment, plays a crucial role in shaping how cancer cells evolve and adapt. This process, called somatic evolution, leads to increased diversity (heterogeneity) within the tumor, which allows it to better cope with challenges like treatment and environmental pressures. The image you see here comes from a computer simulation of our agent-based model, which represents the bone ecosystem in multiple myeloma. Each pixel in the image represents either a cell or the concentration of a signaling molecule that cells use to communicate with each other. By studying how these different elements interact within the simulated ecosystem, we can gain valuable insights into how the real cancer ecosystem influences tumor evolution and treatment resistance.

Visit the mathematical oncology page to view jobs, meetings, and special issues. We will post new additions here, but the full list can found at mathematical-oncology.org.

1. Jobs

2. Conferences / Meetings

3. Special issues

---

Current subscriber count: >1.8k