This week in MathOnco 201

Spatial heterogeneity, autocrine signaling, microbial cancer diagnostics, coexistence theory, mutation rate evolution, and more...

“This week in Mathematical Oncology” — Mar. 17, 2022

> mathematical-oncology.org

From the editor:


It’s been great to experience a bit normalcy this week, attending the AACR special conference on cancer evolutionary dynamics in Tampa, FL. In case you missed it, check out the conference hashtag: #AACRevol22.

Otherwise, enjoy this week’s selection of publications!

Jeffrey West
jeffrey.west@moffitt.org

1. Population Dynamics of Epithelial-Mesenchymal Heterogeneity in Cancer Cells
   Paras Jain, Sugandha Bhatia, Erik W. Thompson, Mohit Kumar Jolly

2. Optimizing the future: how mathematical models inform treatment schedules for cancer
   Deepti Mathur, Ethan Barnett, Howard I. Scher, Joao B. Xavier

3. The impact of the spatial heterogeneity of resistant cells and fibroblasts on treatment response
   Masud M A, Jae-Young Kim, Cheol-Ho Pan, Eunjung Kim

4. Cancer's second genome: Microbial cancer diagnostics and redefining clonal evolution as a multispecies process
   Gregory D. Sepich-Poore, Caitlin Guccione, Lucie Laplane, Thomas Pradeu, Kit Curtius, Rob Knight

5. Autocrine signaling can explain the emergence of Allee effects in cancer cell populations
   Philip Gerlee, Philipp M. Altrock, Adam Malik, Cecilia Krona, Sven Nelander

6. Optimal strategy and benefit of pulsed therapy depend on tumor heterogeneity and aggressiveness at time of treatment initiation
   Deepti Mathur, Bradford P. Taylor, Walid K. Chatila, Howard I. Scher, Nikolaus Schultz, Pedram Razavi, Joao Xavier

7. Parameter estimation and sensitivity analysis for a model of tumor–immune interaction in the presence of immunotherapy and chemotherapy
   Hesham A. Elkaranshawy, Ahmed M. Makhlouf

8. Normalizing tumor microenvironment with nanomedicine and metronomic therapy to improve immunotherapy
   Fotios Mpekris, Chrysovalantis Voutouri, Myrofora Panagi, James W. Baish, Rakesh K. Jain, Triantafyllos Stylianopoulos,

1. Coexistence in diverse communities with higher-order interactions
   Theo Gibbs, Simon A. Levin, Jonathan M. Levine

2. Patient-specific forecasting of post-radiotherapy prostate-specific antigen kinetics enables early prediction of biochemical relapse
   Guillermo Lorenzo, Nadia di Muzio, Chiara Lucrezia Deantoni, Cesare Cozzarini, Andrei Fodor, Alberto Briganti, Francesco Montorsi, Victor M. Perez-Garcia, Hector Gomez, Alessandro Reali

3. Agent-based models help interpret patterns of clinical drug resistance by contextualizing competition between distinct drug failure modes
   Scott M Leighow, Benjamin Landry, Michael J. Lee, Shelly R. Peyton, Justin R. Pritchard

4. Mutation Rate Evolution Drives Immune Escape In Mismatch Repair-Deficient Cancer
   Hamzeh Kayhanian, Panagiotis Barmpoutis, Eszter Lakatos, William Cross, …, Nischalan Pillay, Manuel Rodriguez-Justo, Kai-Keen Shiu, Marnix Jansen

1. Bayes and Darwin: How replicator populations implement Bayesian computations

   BioEssays: Problems & Paradigms
   Dániel Czégel et. al.: “Bayesian learning theory and evolutionary theory both formalize adaptive competition dynamics in possibly high-dimensional, varying, and noisy environments. What do they have in common and how do they differ? We point out mathematical equivalences between their basic dynamical equations, generalizing the isomorphism between Bayesian update and replicator dynamics. We discuss how these mechanisms provide analogous answers to the challenge of adapting to stochastically changing environments at multiple timescales. A unified view of the theories of learning and evolution comes in sight.”

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.

Caption: The artwork represents our recent study on population dynamics of Epithelial-Mesenchymal heterogeneity in cancer cells. Our in-silico model considers the asymmetric distribution of the EMT-inducing transcription factor SNAIL during cell division to cause spontaneous phenotypic switching. Generating a single clone from a mesenchymal cell (top left bar-plot) leads to a heterogeneous (Epithelial, Hybrid, and Mesenchymal) population over one in-silico cell passage (top right bar-plot). Upon several such passages (bottom row), the cell population converges to an Epithelial dominated phenotypic distribution. Our results suggest asymmetric cell division as a mechanism that can explain some of the findings reported in PMC42-LA breast cancer cells (click here). 

Created by: Atchuta Srinivas Duddu & Paras Jain

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