This week in MathOnco 239

Markov models, cell tracking, tumor-immune dynamics, and evolutionary rescue

“This week in Mathematical Oncology” — Jan. 19, 2023

> mathematical-oncology.org

From the editor:

Today we feature articles with Markov models, cell tracking, tumor-immune dynamics, and evolutionary rescue. That last topic has a corresponding blog post and feature cover image, too.

Also — be sure not to miss David Basanta’s year in review post from 2022!

Thanks,

Jeffrey West
jeffrey.west@moffitt.org

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“The future is already here — it’s just not evenly distributed.”
— W. Gibson

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1. A Markovian decision model of adaptive cancer treatment and quality of life
   Péter Bayer, Joel S. Brown, Johan Dubbeldam, Mark Broom

2. Evolution tumour models paving the way for understanding oral carcinogenesis
   Roberta Rayra Martins-Chaves, Rubens Signoretti Oliveira Silva, Thaís dos Santos Fontes Pereira, Felipe Paiva Fonseca, Ricardo Santiago Gomez

3. An introduction to causal inference for pharmacometricians
   James A. Rogers, Hugo Maas, Alejandro Pérez Pitarch

4. Quantifying the Morphology and Mechanisms of Cancer Progression in 3D in-vitro environments: Integrating Experiments and Multiscale Models
   Nikolaos M. Dimitriou, Salvador Flores-Torres, Joseph Matthew Kinsella, Georgios D. Mitsis

5. A Systems Biology Approach for Addressing Cisplatin Resistance in Non-Small Cell Lung Cancer
   Sravani Ramisetty, Prakash Kulkarni, Supriyo Bhattacharya, Arin Nam, …, Swapnil Rajurkar, Erminia Massarelli, Ravi Salgia, Atish Mohanty

1. Fast and Accurate Cell Tracking: a real-time cell segmentation and tracking algorithm to instantly export quantifiable cellular characteristics from large scale image data
   Ting-Chun Chou, Li You, Cecile Beerens, Kate J. Feller, Miao-Ping Chien

2. Dynamical modelling of proliferative-invasive plasticity and IFNγ signaling in melanoma reveals mechanisms of PD-L1 expression heterogeneity
   Seemadri Subhadarshini, Sarthak Sahoo, Jason A. Somarelli, Mohit Kumar Jolly

3. A phenotype-structured model for the tumour-immune response
   Camille Pouchol, Jean Clairambault, Zineb Kaid

4. Modelling drug responses and evolutionary dynamics using triple negative breast cancer patient-derived xenografts
   Abigail Shea, Yaniv Eyal-Lubling, Daniel Guerrero-Romero, Raquel Manzano Garcia, …, Carlos Caldas, Jean Abraham, Oscar M Rueda, Alejandra Bruna

5. Reinforcement Learning informs optimal treatment strategies to limit antibiotic resistance
   Davis T. Weaver, Jeff Maltas, Jacob G. Scott

1. 2022 Recap: A year in review
   The Mathematical Oncology Blog
   David Basanta: “This is the 4th year I get to write this end-of-the-year post for the MathOnco blog, and this year, I would like to talk about something other than the posts we got from you, which obviously were excellent! For one thing, we have a new member in the blog team: Alexander Zeilmann, from Heidelberg University. While, as you know, most of the work required to maintain this blog is done by this community writing those lovely, insightful and thought-provoking posts, there is still the job of (sometimes) soliciting them, lightly editing them, uploading them to the site and using social media to advertise them. Alex, Jeff and I have been doing that this year and we are very happy having Alex in the team!”

2. Evolutionary rescue dynamics of resistant mutants
   The Mathematical Oncology Blog
   Serhii Aif: “The fitness cost of resistance has been one of the key assumptions in adaptive therapy. Its utilization aims to slow the development of resistance and increase the effectiveness of treatment. One common approach is to leverage the competition for space and nutrients between sensitive and resistant cells to maintain a tolerable tumor size and reduce the probability of progression. Understanding the cost of resistance and how space might change the dynamics of its evolution has implications on improving the effectiveness of treatment. […] In this paper, we investigate whether the probability of such an evolutionary rescue can be increased in dense populations by the aforementioned fitness screening as a result of collective cell dynamics.”

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: “Evolutionary rescue of resistant mutants is governed by a balance between radial expansion and selection in compact populations” in Nature Communications

Artists: Serhii Aif (@sergiyayf) and Jona Kayser (@JonaKayser)

Caption: "In our recent paper, we study how resistant mutants carrying a fitness deficit can be rescued from purifying selection by continued evolution. The image shows a colony comprised of genetically tailored yeast cells allowing us to track the evolutionary trajectory of fluorescently labeled resistant mutants (red) with high spatial and temporal resolution. The opposing forces of radial population expansion and natural selection result in the formation of narrow but stable red streaks, formed by slower-growing resistant cells. This inflation-selection balance, a phenomenon first described in our work, inherently promotes the evolutionary rescue of resistant mutants by subsequent compensatory mutations (blue). As a result, the risk of therapy failure - made visible by the presence of resurgent growth domes at the periphery of the depicted colony - is dramatically increased."

Visit the mathematical oncology page to view jobs, meetings, and special issues. We will post new additions here, but the full list can found at mathematical-oncology.org.

1. Jobs

• NEW: Postdoctoral Research Associate in the Spatial Analysis and Multiscale Modelling of Solid Tumours and Their Response to Treatment (3 years) (Helen Byrne, University of Oxford) – 31st January, 2023.

2. Conferences / Meetings

3. Special issues

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