This week in MathOnco 333

Final issue before the break!

“This week in Mathematical Oncology” — May 29, 2025

> mathematical-oncology.org

From the editor:

As I mentioned last week, our team is taking the month of June as a short break from the newsletter. In the meantime, please feel free to submit your papers by replying to this email, and we’ll be back in July.

Have a great summer!

Enjoy,

Jeffrey West
jeffrey.west@moffitt.org

1. Bringing evolutionary cancer therapy to the clinic: a systems approach
   Arina Soboleva, Irene Grossmann, Anne-Marie C. Dingemans, Jafar Rezaei & Kateřina Staňková

2. Evolutionary and immune microenvironment dynamics during neoadjuvant treatment of esophageal adenocarcinoma.
   Barroux M, Househam J, Lakatos E, Ronel T, …, Bengsch B, Schmid RM, Siveke JT, Quante M, Graham TA.

3. Optimal adaptive therapeutic schedules for metastatic castrate-resistant prostate cancer based on bilevel optimization problem.
   Wang D, Lei J.

4. Phenotype structuring in collective cell migration: a tutorial of mathematical models and methods
   Tommaso Lorenzi, Kevin J. Painter & Chiara Villa

5. Cell populations simulated in silico within SimulCell accurately reproduce the behaviour of experimental cell cultures
   Elvira Toscano, Elena Cimmino, Angelo Boccia, Leandra Sepe & Giovanni Paolella

6. Parallel and convergent dynamics in the evolution of primary breast and lung adenocarcinomas
   Robert A. Gatenby, Jamie K. Teer, Kenneth Y. Tsai, Joel S. Brown

7. Estimating treatment sensitivity in synthetic and in vitro tumors using a random differential equation model
   Natalie Meacham & Erica M. Rutter

1. 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

2. The adaptive state determines the impact of mutations on evolving populations
   Malgorzata Tyczynska Weh, Pragya Kumar, Viktoriya Marusyk, Andriy Marusyk, David Basanta

3. CLONEID: A Framework for Monitoring and Steering Subclonal Dynamics
   Thomas Veith, Richard Beck, Vural Tagal, Tao Li, , …, Steven Eschrich, Janine Lupo, Hanlee P. Ji, Aaron Diaz, Noemi Andor

4. Fluctuating growth rates link turnover and unevenness in species-rich communities
   Emil Mallmin, Arne Traulsen, Silvia De Monte

5. Overcoming treatment resistance mediated by the bone marrow vascular niche in acute myeloid leukemia
   Matthew Froid, Sergio Branciamore, Ziang Chen, David Frankhouser, …, Bin Zhang, Guido Marcucci, Russell Rockne, David Basanta

1. Marcello Delitala

   Math Oncology Interviews by Thomas Hillen (YouTube)

1. Biophysically-Constrained GBM Predictions via Deep-Learning Integrating Structural SINDY with Mechanistic Filters Workshop Report

   The Mathematical Oncology Blog

   Divyanshu Tak, Guillermo Lorenzo, Jana Lipkova, Morten Andersen, Matthew Faria, Russell Rockne, Sarah Brüningk, Philipp Altrock: “Glioblastoma multiforme (GBM) remains one of the most aggressive and challenging malignancies of the central nervous system, with a devastating prognosis despite advances in treatment protocols. The median overall survival for GBM patients is under 15 months following initial diagnosis, primarily due to tumor recurrence (Kwak et al., 2024; Stupp et al., 2005; Molinaro et al., 2020). Standard treatment approaches often fail to address the (radiologically invisible) infiltration of malignant cells into surrounding tissue (Akbari et al., 2016; Chang et al., 2007). Despite years of research, including from members in our team, the inherent variability exhibited by GBM means that no single imaging metric has proven adequate to accurately map tumor infiltration. We challenged ourselves to address this critical gap by integrating dynamic and structural brain imaging through a new concept in mechanistic learning that we refer to as mechanistic filters.”

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 preprint: PSA/testosterone ratio as a potential biomarker to identify early progressors of adaptive therapy in metastatic castration sensitive prostate cancer

Artist: Jill Gallaher and Sandy Anderson

Caption: Our new pre-print updates on an ongoing adaptive therapy trial for men with castrate-sensitive prostate cancer. Median time to progression has not yet been reached, but 6 of 16 patients have progressed. We focus on identifying biomarkers that may predict these early progressors after just one on-off cycle of treatment. We identify significant features in the dynamics of testosterone and prostate-specific antigen (PSA). A higher average PSA-to-testosterone ratio, which crucially couples the tumor growth to its resource, is identified as a potential biomarker for early progression. Further, fitting a mechanistic mathematical model to the first cycle suggests progressors have a more pre-existing resistance. We illustrate the actual patient data from the first cycle graphically, with time normalized by the cycle duration. On the left, the PSA dynamics are in the numerator, and testosterone dynamics are in the denominator. Their ratio is on the right. The earliest progressors are colored red and thicker and the later progressors are yellow and thinner, which transitions to the gray, dotted thin-lined nonprogressors.

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

• Postdoc Mathematical Modeling of Bacterial Growth at Roskilde University Denmark

2. Conferences / Meetings

3. Special issues

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