This week in MathOnco 304

Drug antagonism, Identifiability, plasticity, virtual trials, and more...

“This week in Mathematical Oncology” — Aug 22, 2024

> mathematical-oncology.org

From the editor:

Today’s issue contains topics like drug antagonism, Identifiability, plasticity, virtual trials, and more.

Also be sure to check out the new blog post & corresponding cover artwork from Simon Syga!

Enjoy,

Jeffrey West
jeffrey.west@moffitt.org

1. Modeling tumors as complex ecosystems
   Guim Aguadé-Gorgorió, Alexander R.A. Anderson, Ricard Solé

2. Evolution of phenotypic plasticity leads to tumor heterogeneity with implications for therapy
   Simon Syga ,Harish P. Jain,Marcus Krellner,Haralampos Hatzikirou,Andreas Deutsch

3. Assessing the Role of Patient Generation Techniques in Virtual Clinical Trial Outcomes
   Jana L. Gevertz, Joanna R. Wares

4. Ultrasensitive Response Explains the Benefit of Combination Chemotherapy Despite Drug Antagonism
   Sarah C. Patterson, Amy E. Pomeroy, Adam C. Palmer

5. How We Treat Metastatic Castration-Sensitive Prostate Cancer
   Filip Ionescu, Jingsong Zhang

1. Growth rate-driven modelling reveals how phenotypic adaptation drives drug resistance in BRAFV600E-mutant melanoma
   Sara Hamis, Alexander P Browning, Adrianne L Jenner, Chiara Villa, Philip Maini, Tyler Cassidy

2. Identifiability of heterogeneous phenotype adaptation from low-cell-count experiments and a stochastic model
   Alexander P Browning, Rebecca M Crossley, Chiara Villa, Philip K Maini, Adrianne L Jenner, Tyler Cassidy, and Sara Hamis

3. SpatialCTD: A Large-Scale Tumor Microenvironment Spatial Transcriptomic Dataset to Evaluate Cell Type Deconvolution for Immuno-Oncology
   Jiayuan Ding, Lingxiao Li, Qiaolin Lu, Julian Venegas, …, Lulu Shang, Patrick Danaher, Yuying Xie, Jiliang Tang

4. Virtual patient analysis identifies strategies to improve the performance of predictive biomarkers for PD-1 blockade
   Theinmozhi Arulraj, Hanwen Wang, Atul Deshpande, Ravi Varadhan, …, Elizabeth M. Jaffee, Elana J. Fertig, Cesar A. Santa-Maria, Aleksander S. Popel

1. Evolution of Phenotypic Plasticity Leads to Tumor Heterogeneity with Implications for Therapy
   The Mathematical Oncology Blog
   Simon Syga: “Tumors can be viewed as ecosystems consisting of cancer cells that differ in genotype and phenotypic traits such as metabolism, motility, proliferation, and treatment resistance. These heterogeneous populations of cancer cells interact with one another as well as with their surrounding microenvironment, which includes stromal cells, immune cells, extracellular matrix components, and signaling molecules. As a consequence, no tumor is alike, even for patients with the same diagnosis!”

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: Evolution of phenotypic plasticity leads to tumor heterogeneity with implications for therapy published in PLOS Computational Biology.

Artist: Simon Syga (@sisyga91)

Caption: Tumor heterogeneity is a major obstacle to effective cancer treatment. Multiple factors, including irreversible mutations and reversible phenotypic changes, cause it. But how can we model the interplay between both processes and what's their effect on cancer treatment? Our new study sheds light on this interplay, focusing on the phenotypic switch between migration and proliferation, essential in glioblastoma, the most deadly brain tumor. We study this hypothesis using a novel, spatially explicit model that tracks individual cells’ phenotypic and genetic states. When a new cancer cell is born, a mutation can change its genotype and, thereby, its regulation of the phenotypic switch. We observe that cells at the tumor edge evolve to favor migration over proliferation and vice versa in the tumor bulk. The evolutionary process results in a heterogeneous tumor with a dense tumor core of fast-growing cells (teal) and a diffuse rim of invasive cells (brown). Notably, different genetic configurations, i.e., different regulations of the phenotypic switch, can result in this pattern of phenotypic heterogeneity. We investigate implications for cancer treatment and discover that phenotypic, rather than genetic, heterogeneity predicts tumor recurrence after therapy. This offers new insights into the significant variability in glioblastoma recurrence times post-treatment.

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