This week in MathOnco 332

Plasticity, adaptive therapy, drug tolerance, and more...

“This week in Mathematical Oncology” — May 22, 2025

> mathematical-oncology.org

From the editor:

Our team will be taking a regularly scheduled break from publishing the newsletter during the month of June. In the meantime, these next few issues might be a little fuller than normal to make up ground!

Enjoy,

Jeffrey West
jeffrey.west@moffitt.org

1. Eco-evolutionary Guided Pathomic Analysis Detects Biomarkers to Predict Ductal Carcinoma In Situ Upstaging
   Yujie Xiao, Manal Elmasry, Ji Dong K. Bai, ; Andrew Chen, …, Joseph O. Johnson, Prateek Prasanna, Chao Chen, Mehdi Damaghi

2. Adaptive Therapy Exploits Fitness Deficits in Chemotherapy-Resistant Ovarian Cancer to Achieve Long-Term Tumor Control
   Helen Hockings, Eszter Lakatos, Weini Huang, Maximilian Mossner, …, Jacqueline McDermott, Kane Smith, Ann-Marie Baker, Trevor A. Graham, Michelle Lockley

3. A model of intra-tumor and inter-patient heterogeneity explains clinical trials of curative combination therapy for lymphoma.
   Pomeroy AE, Palmer AC

4. Understanding therapeutic tolerance through a mathematical model of drug-induced resistance.
   Gevertz JL, Greene JM, Prosperi S, Comandante-Lou N, Sontag ED

5. Mathematical modeling unveils the timeline of CAR-T cell therapy and macrophage-mediated cytokine release syndrome.
   Santurio DS, Barros LRC, Glauche I, Fassoni AC.

6. Mathematical modeling in radiotherapy for cancer: a comprehensive narrative review.
   Zheng D, Preuss K, Milano MT, He X, Gou L, Shi Y, Marples B, Wan R, Yu H, Du H, Zhang C.

7. MRI-based digital twins to improve treatment response of breast cancer by optimizing neoadjuvant chemotherapy regimens.
   Wu C, Lima EABF, Stowers CE, Xu Z, Yam C, Son JB, Ma J, Rauch GM, Yankeelov TE.

8. Mathematical modeling unveils the timeline of CAR-T cell therapy and macrophage-mediated cytokine release syndrome.
   Santurio DS, Barros LRC, Glauche I, Fassoni AC.

9. A Machine Learning-Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia
   Yingjia Chen, Liye He, Aleksandr Ianevski, Kristen Nader, …, Krister Wennerberg, Caroline A. Heckman, Anil K. Giri, Tero Aittokallio

1. Impacts of competition and phenotypic plasticity on the viability of adaptive therapy.
   Vibishan B, Jain P, Sharma V, Hari K, Kadelka C, George JT, Jolly MK.

2. PSA/testosterone ratio as a potential biomarker to identify early progressors of adaptive therapy in metastatic castration sensitive prostate cancer
   Jill A. Gallaher, Robert A. Gatenby, Joel S. Brown, Alexander R. A. Anderson, Jingsong Zhang

3. Predictive Digital Twins with Quantified Uncertainty for Patient-Specific Decision Making in Oncology
   Graham Pash, Umberto Villa, David A. Hormuth II, Thomas E. Yankeelov, Karen Willcox

4. Possible mechanisms underlying time perception: decoupling internal and external time
   Irina Kareva, Georgy Karev

1. Math Oncology Interviews by Thomas Hillen (YouTube)

2. INSITE: In Silico Trials using Simulation and Artificial Intelligence for Therapy Evaluation

   The Mathematical Oncology Blog
   Rebecca Bekker, Renee Brady-Nicholls, Lisette de Pillis, Jana Gevertz, Harsh Jain: “Clinical trials evaluate the effects of a planned intervention on health-related outcomes. Unfortunately, they are time-consuming, expensive, and often end in failure. However, these failures do not necessarily mean that the therapeutic intervention is ineffective. Rather, it may be due to the selection of a non-optimal treatment schedule or less than ideal patient selection. In silico (or virtual) clinical trials are powerful tools which can address these issues. Specifically, they can be used to: 1. stratify patients according to response (thereby identifying the ideal patient subgroup for a specific therapeutic intervention); 2. investigate and identify the optimal schedule for individual patients, or entire subgroups; and 3. identify beneficial combination therapies.”

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: Modeling the dynamics of EMT reveals genes associated with pan-cancer intermediate states and plasticity in npj systems biologys and applications

Artist: MeiLu McDermott (Maclean lab)

Caption: The epithelial-to-mesenchymal transition (EMT), a critical step in cancer metastasis, is not a simple flip of a biological switch. Instead, cells pass through stable intermediate states that blend epithelial and mesenchymal features. We developed a mathematical model describing cell population dynamics and transition rates between EMT cell fates, and fit it directly to scRNAseq data. By modeling how cells move between states, we uncover shared intermediate state genes and provide a quantitative view of how EMT unfolds through epithelial, intermediate, and mesenchymal states.

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.

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