This week in MathOnco 298

Cell-state transitions, adaptive therapy, spatio-temporal dynamics, and more...

“This week in Mathematical Oncology” — July 4, 2024

> mathematical-oncology.org

From the editor:

We’re back! Much has happened during the interim — we did a giveaway to mark 2,000 subs, and we recruited a few more fantastic volunteers to our team (we’ll do a proper introduction soon).

I’m sad to be missing those of you at SMB2024, but from afar it looks like a fun conference with a lot of great MathOnco.

Thanks,

Jeffrey West
jeffrey.west@moffitt.org

1. LinG3D: visualizing the spatio-temporal dynamics of clonal evolution
   Anjun Hu, Awino Maureiq E. Ojwang’, Kayode D. Olumoyin & Katarzyna A. Rejniak

2. Ductal carcinoma in situ develops within clonal fields of mutant cells in morphologically normal ducts
   Stefan J Hutten, Hendrik A Messal, Esther H Lips, Michael Sheinman, …, Jacco van Rheenen, Jelle Wesseling, Colinda LGJ Scheele

3. Recent advances in immunotherapy in cancer treatment
   Ayyub A. Patel

4. Deciphering oxygen distribution and hypoxia profiles in the tumor microenvironment: a data-driven mechanistic modeling approach
   Pawan Kumar, Matthieu Lacroix, Pierrick Dupré, Janan Arslan, Lise Fenou, Beatrice Orsetti, Laurent Le Cam, Daniel Racoceanu and Ovidiu Radulescu

5. Dynamic analysis of a drug resistance evolution model with nonlinear immune response
   Tengfei Wang, Xiufen Zou

6. Adaptive Cancer Therapy in the Age of Generative Artificial Intelligence
   Youcef Derbal

7. Cell-state transitions and density-dependent interactions together explain the dynamics of spontaneous epithelial-mesenchymal heterogeneity
   Paras Jain, Ramanarayanan Kizhuttil, Madhav B. Nair, Sugandha Bhatia, Erik W. Thompson, Jason T. George, Mohit Kumar Jolly

1. Mutant scaling laws reveal that accelerated mutant evolution via gene amplifications requires spatially structured population growth
   Natalia L. Komarova, Justin Pritchard, Dominik Wodarz

2. Characterising cancer cell responses to cyclic hypoxia using mathematical modelling
   Giulia Celora, Ruby Nixson, Joe Pitt-Francis, Philip Maini, Helen Byrne

3. Toward Artificial Open-Ended Evolution within Lenia using Quality-Diversity
   Maxence Faldor, Antoine Cully

1. Exploiting temporal aspects of cancer immunotherapy
   Rachael M. Zemek, Valsamo Anagnostou, Inês Pires da Silva, Georgina V. Long & Willem Joost Lesterhuis

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: Mathematical Model-Driven Deep Learning Enables Personalized Adaptive Therapy published in Cancer Research

Artist: Kit Gallagher (@SciKit_G)

Caption: Adaptive Therapy has been developed as an alternative treatment scheduling paradigm, aiming to control rather than try to cure late-stage cancers by including breaks in treatment to resensitise the tumour to the applied therapeutic. 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 tumour dynamics. By integrating mathematical modelling and machine learning into the decision-making process, we can tailor these schedules to individual patients, as they follow a unique trajectory under treatment. Shown are three such response trajectories, spiralling out from a common centre as the patients' response to treatment diverges over time, and the scheduling adapts to this. Each trajectory alternates between crosses that represent the patient's clinical data for a single treatment cycle, from which we fit a mathematical model (given by the solid line), and use this model to drive a machine learning framework (1s and 0s) which arrives at treatment recommendation for the next cycle, before we repeat this process.

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