“This week in Mathematical Oncology” — Jan 22, 2026
> mathematical-oncology.org
From the editor:
In this issue, my group releases a preprint describing “75 years of mathematical oncology,” led by Franco Pradelli with help from our colleagues on the mathematical-oncology.org team.
The preprint includes an analysis of all 1,500 papers in this very newsletter, as a hand-curated database of the past 7 years of math oncology. We compare this to ~20k papers from the past seven decades on Scopus.
It’s great to see an idea from almost exactly 4 years ago come to fruition! Please let us know what you think, and whether you agree with our definition of mathematical oncology that attempts to summarize the field!
Bonus question: can you guess the earliest known example of mathematical oncology? A Markov chain model of disease status!
Enjoy,
Jeffrey West
jeffrey.west@moffitt.org
“Excluded phenotypes” restrict genetic paths toward adaptation in declining populations
James S. Andon, Charles D. Kocher, Ken A. Dill, Tina WangAdaptive therapy for non-small cell lung cancer via integrated Stackelberg game and deep reinforcement learning frameworks
Zhiqing Li, Yun Zhao, Zhiqiang Yu, Xuewen TanMathematical modeling of tumor-immune interactions in breast cancer must model tumor-immune interactions in breast cancer
Heiko EnderlingFirst Explore, Then Settle: A Theoretical Analysis of Evolvability as a Driver of Adaptation. Jiménez-Sánchez J, Ortega-Sabater C, Maini PK, Pérez-García VM, Lorenzi T.
75 Years of Mathematical Oncology
Franco Pradelli, Maximilian Strobl, Sadegh Marzban, François de Kermenguy, …, Sara Hamis, Dhananjay Bhaskar, Alexander R. A. Anderson, Jeffrey WestSelection for targeted therapy resistance leads to an indirect selection for higher phenotypic plasticity and enhanced evolvability to orthogonal stressors
Alicia Bjornberg, Aobuli Xieraili, Matthew Froid, Rowan Barker-Clarke, …, David Bassanta, Alexander RA Anderson, Virginia A Turati, Andriy MarusykSystematic Review of Evidence for the Cost of Therapeutic Resistance in Cancer. Kane B, Mestas L, Garza M, Soesilo T, Hufford M, Richker H, Maley C. A.
Machine Learning-Based Identification of Blood Biomarkers that Distinguish Precachectic and Cachectic Patients with Pancreatic Ductal Adenocarcinoma. Olumoyin KD, Park M, Davis EW, Permuth JB, Rejniak KA.
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: Mathematical Oncology: How Modeling Is Transforming Clinical Decision-Making published in Cancer Research
Artists: Kevin Scibilia, Kit Gallagher, Jill Gallaher, and Sandy Anderson
Caption: The widespread use of cytotoxic drugs has shaped the paradigm of uniformly administering a ‘maximum tolerated dose’ to patients; however, this approach fails to account for the dynamic and heterogeneous nature of challenging cancers. The cover image illustrates the translational capabilities of mathematical modeling to address these issues, transforming clinical patient information (depicted by the patient scan on the left) into digital avatars (shown by the blue wavy figure on the right) that enable personalized treatments (represented by the drug in the middle of the image). The underlying patient treatment and disease dynamics drive the digital representation, as indicated by the connecting waveforms between the two figures that emerge from the equations and mathematical analysis in the background.
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
Subscribers: 2-4-6-8 who do we appreciate