This week in MathOnco 248

Clinical translation of adaptive therapy, stackelberg evolutionary game theory, adaptive PARP inhibitor treatment, mutation fixation, somatic evolution, and more.

“This week in Mathematical Oncology” — Mar. 30, 2023

> mathematical-oncology.org

From the editor:

Today we feature articles on clinical translation of adaptive therapy, stackelberg evolutionary game theory, adaptive PARP inhibitor treatment, mutation fixation, somatic evolution, and more.

Enjoy,

Jeffrey West
jeffrey.west@moffitt.org

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“I have deeply regretted that I did not proceed far enough at least to understand something of the great leading principles of mathematics; for men thus endowed seem to have an extra sense."
- Darwin

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1. A survey of open questions in adaptive therapy: Bridging mathematics and clinical translation
   Jeffrey West, Fred Adler, Jill Gallaher, Maximilian Strobl, Renee Brady-Nicholls, Joel Brown, Mark Roberson-Tessi, Eunjung Kim, Robert Noble, Yannick Viossat, David Basanta, Alexander RA Anderson

2. Stackelberg evolutionary game theory: how to manage evolving systems
   Alexander Stein, Monica Salvioli, Hasti Garjani, Johan Dubbeldam, Yannick Viossat, Joel S. Brown and Kateřina Staňková

3. Neuroblastoma arises in early fetal development and its evolutionary duration predicts outcome
   Verena Körber, Sabine A. Stainczyk, Roma Kurilov, Kai-Oliver Henrich, Barbara Hero, Benedikt Brors, Frank Westermann & Thomas Höfer

4. Generalizable biomarker prediction from cancer pathology slides with self-supervised deep learning: A retrospective multi-centric study
   Jan Moritz Niehues, Philip Quirke, Nicholas P. West, Alexander Brobeil, Michael Hoffmeister, Jakob Nikolas Kather

1. Adaptive therapy for ovarian cancer: An integrated approach to PARP inhibitor scheduling
   Maximilian Strobl, Alexandra L. Martin, Jeffrey West, Jill Gallaher, Mark Robertson-Tessi, Robert Gatenby, Robert Wenham, Philip Maini, Mehdi Damaghi, Alexander Anderson

2. Mutant fixation in the presence of a natural enemy
   Dominik Wodarz, Natalia L. Komarova

3. Learning the functional landscape of microbial communities
   Abigail Skwara, Karna Gowda, Mahmoud Yousef, Juan Diaz-Colunga, Arjun S Raman, Alvaro Sanchez, Mikhail Tikhonov, Seppe Kuehn

4. Clonal differences underlie variable responses to sequential and prolonged treatment
   Dylan L Schaff, Aria J Fasse, Phoebe E White, Robert J Vander Velde, Sydney M Shaffer

5. Somatic Evolution of Cancer: A New Synthesis
   Ulfat Baing, Rohini Kharate, Milind Watve

6. Long-range interactions and disorder facilitate pattern formation in spatial complex systems
   Fabrizio Olmeda, Steffen Rulands

1. 12th Annual Southern California Systems Biology Conference

   #SoCalSysBio23: The conference will provide an opportunity for diverse research groups across Southern California institutions to showcase their current research activities in all areas of Systems Biology. The meeting format will consist of short faculty talks, with lightning talks and poster sessions for trainees. Come share your latest work, build community, and stimulate collaborations! Registration deadline: April 15 (click here to register).

2. Deconvolution of clinical variance in CAR-T pharmacology and response
   Daniel Kirouac: “Combining mathematical modelling, bioinformatics, and machine learning to understand the pharmacology of T cell therapeutics”

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: Metabolic activity grows in human cancers pushed by phenotypic variability in iScience.

Artist: Jesús J. Bosque (@JesusJBosque)

Caption: “PET scans provide information of paramount importance to the clinician, but are also a powerful tool to access tumour metabolism for the researcher. Using pre-treatment PET images from patients with cancer, we identified how the hotspot of metabolic activity (marked in red in the image) reflects underlying processes that drive the tumour to more aggressive states. To model it, we used a PDE model phenotypically structured on the proliferation, where cells can randomly change their proliferation rate upwards or downwards with equal probability. The model shows that this variability progressively shifts the proliferation profiles to higher values, what is depicted in the image by the curves showing the successive distributions. Interestingly, the model described very well the traits identified in the data, therefore, we discuss that random phenotypic changes suffice to fuel the observed increases in tumour metabolic activity.”

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