This week in MathOnco 361
Where should you publish your math onco work?
“This week in Mathematical Oncology” — Feb 26, 2026
> mathematical-oncology.org
From the editor:
Welcome to another edition of the math oncology newsletter! In addition to the collection of relevant papers, we have also linked to a new Journal Finder tool. This tool summarizes the journals that publish math oncology papers — based on Franco Pradelli's recent bibliometrics preprint. Please let us know if you have any feedback!
Enjoy,
Jeffrey West
jeffrey.west@moffitt.org
TWiMO is brought to you by Maximilian Strobl, Sarah Groves, Veronika Hofmann, Yifan Chen, Franco Pradelli, and Sandy Anderson. Find out more about the team here.
Optimal control theory as a method for designing multidrug adaptive therapy regimens
Afton Widdershins, Elsa Hansen, Andrew Read & Raymond HohlDivergent understandings in comparative oncology
Zachary T. Compton, Amy M. Boddy, Lisa M. Abegglen, and Carlo C. MaleySciSciGPT: advancing human–AI collaboration in the science of science
Erzhuo Shao, Yifang Wang, Yifan Qian, Zhenyu Pan, Han Liu, Dashun WangSystems Biology of the Cancer Cell
Kevin A. Janes, Matthew J. LazzaraTumor mutational burden shapes success and resistance in cancer immunotherapy
Guim Aguadé-Gorgorió
In vivo lineage tracing across human tissues using methylation barcodes in the protocadherin gene cluster
Samuel F Hackett, Christopher T Boniface, Adriana V. A. Fonseca, …, Sadik Esener, Usha Menon, Jamie R Blundell
Where should you publish your Math Oncology?
mathematical-oncology.org/journals
This page presents an extended, interactive version of the scientific journal ranking we presented in 75 Years of Mathematical Oncology. We provide this ranking to support researchers in the field of mathematical oncology and beyond in exploring our findings independently and identifying journals of potential interest for their research. This ranking was derived from the analysis of our Scopus query-based dataset, which includes scientific publications matching a curated set of 1) oncology-related terms (e.g., Tumor, Cancer, Radiotherapy) and 2) mathematical-related terms (e.g., “Mathematical modeling”, “Agent-based Modeling”, etc.). Additional information is provided in our preprint’s Methods section. Data is through the end of 2024.Drugs Explained by Peter Bonate
The Mathematical Oncology Blog
Peter Bonate: “This book is a great resource for mathematical oncology researchers because it explains how drugs are actually developed and approved. You’ll gain practical insights into drug discovery, clinical trial design, and how drug development fits into a regulatory framework. The book connects the dots between scientific concepts and real-world applications, helping you understand how your quantitative models fit into the bigger picture of cancer drug development. It’s written in an accessible, mostly non-technical, engaging way that makes the complex drug development process easy to grasp, whether you’re focused on optimizing trials or predicting patient outcomes.”
The newsletter now has a dedicated homepage where we post the cover artwork for each issue, curated by Maximilian Strobl, Sarah Groves, and Veronika Hofmann. We encourage submissions that coincide with the release of a recent paper from your group. This week’s artwork:
Based on the paper: A multilevel formalism to model the hybrid E/M phenotypes in epithelial-mesenchymal plasticity published in the Biophysical Journal
Artist: Kishore Hari (@kishorehari139)
Caption: Hybrid epithelial-mesenchymal phenotypes have emerged as crucial components of cancer metastasis, as they enhance many pro-metastatic characteristics of cancer cells, including collective migration, stemness, and therapy resistance. A key characteristic of these hybrid phenotypes is the extensive heterogeneity in gene expression observed in cancer. The drivers, mechanisms, and implications of this heterogeneity for the stability of hybrid states remain highly debated. Using discrete models of gene regulatory networks, we show that stable hybrid states can be achieved through multiple stable expression levels across network genes, as opposed to the binary expression levels permitted in standard Boolean models. Multilevel models reveal that the network structure inherently supports stable hybrid states. While the relative abundance of hybrid phenotypes does not vary significantly in weakly stochastic environments expected in cell cultures or non-pathological conditions (blue ovals), stochastic environments expected in tumors and metastasis lead to a stable “hybrid-cloud” in the multilevel model, allowing cells to retain the pro-metastatic hybrid phenotype while dynamically shifting their expression patterns (red dots with black trajectories). Thus, we propose stable intermediate expression levels as a mechanism underlying the heterogeneous expression patterns observed in hybrid E/M phenotypes, providing cancer cells with a dynamic survival strategy through metastasis.
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
Approximate current subscriber count, N:
N(t) = 0.808t+80 (where t = days since Dec. 1st, 2017)