This week in MathOnco 379

“This week in Mathematical Oncology” — August 13, 2026

> mathematical-oncology.org

From the editor:

In this issue, I’d like to draw your attention to the JAMA Oncology paper by Kit Gallagher, Sandy Anderson and others. They sought to answer the question “Can mathematical biomarkers derived from initial prostate-specific antigen dynamics predict patient-specific progression and survival under adaptive hormone therapy for prostate cancer?” The answer is a resounding yes, and the paper is a nice example of translational mathematical oncology.

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.

1. Decoding the cellular Allee effect through a stochastic modeling tool for assessing neighborhood and lineage impacts on cell growth
   Sebastian Student, Alicja Staśczak

2. Towards a clinically practical computational platform for systematically adapting radiation therapy for glioma patients
   Hugo Joseph Michel Miniere, David Hormuth, Ernesto Augusto Bueno da Fonseca Lima, ... Jodi Goldman, Caroline Chung, Thomas E Yankeelov

3. Mathematical Biomarkers of Adaptive Therapy Outcomes in Prostate Cancer
   Kit Gallagher, Maximilian A. Strobl, Robert A. Gatenby, Jingsong Zhang, Philip K. Maini, Alexander R. Anderson

4. Agent‐Based Simulations of Lung Tumor Evolution Suggest That Ongoing Cell Competition Drives Realistic Clonal Expansions
   Helena Coggan, James R. M. Black, Carlos Martínez‐Ruiz, Kristiana Grigoriadis, Jasmin Fisher, Nicholas McGranahan

5. Density dependence and evolvability limit adaptive therapy in non-small cell lung cancer mouse model
   Mariyah Pressley, Jessica J. Cunningham, Luiza Silva Simoes, Robert A. Gatenby, Joel S. Brown, Stanislav Avdieiev

6. Hierarchical mathematical modelling of patients with myeloproliferative neoplasms captures interferon-α treatment responses and allows for personalised and population predictions
   Tobias Idor Boklund, Gurvan Hermange, Jordan Snyder, ... Morten Andersen, Johnny T. Ottesen, Thomas Stiehl

7. Gene mutant dosage is associated with prognosis and metastatic tropism in 60,000 clinical cancer samples
   Nicola Calonaci, Eriseld Krasniqi, Daniel Colic, ... Biagio Ricciuti, Marcello Maugeri-Saccà, Giulio Caravagna

8. In silico clinical trials of BiTE expression by oncolytic viruses reveal the impact of patient heterogeneity on dosage protocol
   Adrianne L. Jenner, Robyn P. Araujo, Noa L. Levi, Guy Ungerechts, Christine E. Engeland, Johannes P.W. Heidbuechel

9. Assessing the Role of Model Complexity in Virtual Clinical Trial Outcomes
   Jana L. Gevertz, Joanna R. Wares

10. Inside the wavering mind of an NK cell: Mathematical Modeling of NK cell Activation 2256230
    Montana Ferita, Fred Adler

11. Mathematical modeling of T cell exhaustion in the tumor microenvironment.
    Adeniyi-Aogo TE, Talkington AM

12. Precision oncology paradigm: Integrating tumor-on-chip platforms, mathematical modeling, and AI for personalized cancer therapeutics
    Nafiseh Moghimi, Mohsen Rezaeian, Mohammad Kohandel

1. Identifying functional drivers of Hepatoblastoma outcomes via agent-based modeling and transcriptomics
   Ravoni A, Liu Y, Cairo S, Castiglione F, Nardini C

2. Preclinical Evaluation of Chemoradiation Resistance Using 18F-FDG PET/CT in Head and Neck Squamous Cell Carcinoma
   Casey C. Heirman, Ashlyn G. Rickard, Rico Castillo, ... Tammara Watts, Yvonne M. Mowery, Kyle J. Lafata

3. Where Physics Meets Privacy: Federated PINNs for Privacy-Preserving Brain Tumor Biomechanical Modeling
   Mahmuda Akter Sristy, Md Al-Mahfuz Chowdhury, Momota Ahsana Meem, Sajid Ahamed, Kazi Irfan Subhan

1. Getting over ANOVA: estimation graphics for multi-group comparisons
   Zinan Lu, Jonathan Anns, Yishan Mai, Rou Zhang, Kahseng Lian, Nicole MynYi Lee, Shan Hashir, Lucas Zhuoyu Wang, Yixuan Li, A. Rosa Castillo Gonzalez, Joses Ho, Hyungwon Choi, Sangyu Xu & Adam Claridge-Chang

2. AACR Data Science and Artificial Intelligence in Oncology Conference
   Christina Curtis, Stanford University
   Elana J. Fertig, University of Maryland School of Medicine
   Benjamin Haibe-Kains, UHN Princess Margaret Cancer Centre
   Dana Pe’er, Memorial Sloan Kettering Cancer Center
   Sohrab Shah, Memorial Sloan Kettering Cancer Center
   Yu Shyr, Vanderbilt University Medical Center

The newsletter now has a dedicated homepage where we post the cover artwork for each issue, curated by Maximilian Strobl, Veronika Hofmann, Yifan Chen, and Sarah Groves. We encourage submissions that coincide with the release of a recent paper from your group. This week’s artwork:

Based on the paper: Mathematical Biomarkers of Adaptive Therapy Outcomes in Prostate Cancer published in JAMA Oncology

Artist: Kit Gallagher, Maximilian Strobl, Sandy Anderson, Philip Maini

Caption: Adaptive Therapy has been developed as an alternative treatment scheduling paradigm to continuous therapy, applying breaks in treatment to resensitize the tumor to the applied therapeutic, and hence delay patient progression. Previous adaptive approaches have employed a ‘one size fits all’ approach to scheduling these breaks, applying the same algorithm to all patients despite their widely different tumor dynamics. This heterogeneity inspired the cover image, where each circular ‘flower’ motif depicts possible treatment responses for a single patient. The tumor response is wrapped into a circle, with the radius corresponding to the tumor size, and the shading corresponding to the drug treatment. Each flower represents three possible outcomes, with the outermost circle depicting the best outcome. These flowers decorate a spring-time tree, formed by considering the possible treatment decisions at each point in a patient’s history, where each branch represents a series of treatment periods or drug holidays, and forks denote a new treatment possibility. By navigating these decisions optimally, supported by predictive biomarkers, the patient’s time to progression is extended compared to conventional strategies.

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