This week in MathOnco 323

Dosing strategies, heterogeneity, spatial data & modeling, jellyfish plots...

“This week in Mathematical Oncology” — Mar 6, 2025

> mathematical-oncology.org

From the editor:

Today’s issue focuses on topics important in math oncology like dosing strategies, heterogeneity, spatial data & modeling, and a neat new visualization technique called Jellyfish plots.

Thanks,

Jeffrey West
jeffrey.west@moffitt.org

1. Modeling critical dosing strategies for stromal-induced resistance to cancer therapy
   Anna K. Kraut, Colleen M. Garvey, Carly Strelez, Shannon M. Mumenthaler, Jasmine Foo

2. Numerical investigation of nanofluid flow over a bidirectional stretching surface in porous media
   Waseef Ullah, Zahid Ahmed, Khowllah Naeem

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

4. Model-Informed Selection of the Recommended Phase 2 Dosage for Anti-TIGIT Immunotherapy Leveraging co-Expressed PD-1 Inhibitor Target Engagement
   Irina Kareva, Ping Hu, Vadryn Pierre, Thomas Kitzing, Anja Victor, Emilia Richter, Wei Gao, Karthik Venkatakrishnan, Anup Zutshi

5. Jellyfish: integrative visualization of spatio-temporal tumor evolution and clonal dynamics

   Kari Lavikka, Altti Ilari Maarala, Jaana Oikkonen, Sampsa Hautaniemi

1. Validating the predictions of mathematical models describing tumor growth and treatment response
   Guillermo Lorenzo, David A. Hormuth II, Chengyue Wu, Graham Pash, Anirban Chaudhuri, Ernesto A. B. F. Lima, Lois C. Okereke, Reshmi Patel, Karen Willcox, Thomas E. Yankeelov

2. Optimizing the Efficacy of Vaccine-Induced Immunotherapy in Melanomas
   Ibrahim Chamseddine, Manoj Kambara, Priya Bhatt, Shari Pilon-Thomas, Katarzyna A. Rejniak

3. AI-driven 3D Spatial Transcriptomics
   Cristina Almagro-Pérez, Andrew H. Song, Luca Weishaupt, Ahrong Kim, …, Sizun Jiang, Ali Bashashati, Jonathan T.C. Liu, Faisal Mahmood

4. SIMPROV : Provenance capturing for simulation studies
   Andreas Ruscheinski, Anja Wolpers, Philipp Henning, Pia Wilsdorf, Adelinde M. Uhrmacher

5. Epithelial competition determines gene therapy potential to suppress Fanconi Anemia oral cancer risk
   Hunter Lee Colegrove, Raymond J. Monnat Jr., Alison F. Feder

1. Mechanistic learning in action at COMPO
   Sebastien Benzekry
   The Mathematical Oncology Blog

2. Tyler Cassidy

   Math Oncology Interviews by Thomas Hillen (YouTube)

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: Personalized predictions of Glioblastoma infiltration: Mathematical models, Physics-Informed Neural Networks and multimodal scans, published in Medical Image Analysis

Artist: Ray Zirui Zhang, John S. Lowengrub

Caption: •This artwork represents the integration of multimodal imaging, mathematical modeling, and physics-informed machine learning to create personalized digital twins for glioblastoma (GBM) infiltration predictions. On the left, a brain MRI showcases the tumor region, representing real-world medical imaging data. The central schematic illustrates a physics-informed neural network, a machine learning framework that embeds mathematical model to predict tumor growth and infiltration dynamics. The neural network integrates patient-specific data with reaction-diffusion equations, capturing both spatial and temporal dynamics of GBM growth. On the right, a 3D visualization combines predicted tumor regions (yellow and blue) and the brain’s anatomy (gray), highlighting the potential of digital twins for personalized predictions. This fusion of physics, machine learning, and multimodal data offers a novel framework for understanding GBM progression, advancing patient-specific treatment strategies. The image emphasizes the synergy between computational methods and clinical data, symbolizing a step toward precision oncology.

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