This week in MathOnco 169
This week in MathOnco 169
Data vis for R, metabolic fitness landscapes, 3D cell motility, optimal dosing, amplifiers of natural selection, and more...
“This week in Mathematical Oncology” — Newsletter
> mathematical-oncology.org
July 8, 2021
From the editor:
> jeffrey.west@moffitt.org
Today’s issue contains exciting papers on metabolic fitness landscapes, optimal dosing, 3D cell motility, amplifiers of natural selection, data vis for R, and more. Enjoy!
- Jeffrey West
Metabolic fitness landscapes predict the evolution of antibiotic resistance
Fernanda Pinheiro, Omar Warsi, Dan I. Andersson, Michael LässigIdentification of optimal dosing schedules of dacomitinib and osimertinib for a phase I/II trial in advanced EGFR-mutant non-small cell lung cancer
Kamrine E. Poels, Adam J. Schoenfeld, Alex Makhnin, Yosef Tobi, …, Aaron Hata, Scott L. Weinrich, Helena A. Yu, Franziska MichorSpace-velocity thermostatted kinetic theory model of tumor growth
Léon Masurel, Carlo Bianca, Annie LemarchandFast and strong amplifiers of natural selection
Josef Tkadlec, Andreas Pavlogiannis, Krishnendu Chatterjee, Martin A. NowakA mathematical framework for modelling 3D cell motility: applications to glioblastoma cell migration
M Scott, K Żychaluk, R N BearonA Mechanistic DNA Repair and Survival Model (Medras): Applications to Intrinsic Radiosensitivity, Relative Biological Effectiveness and Dose-Rate
Stephen Joseph McMahon, Kevin M. Prise
Quantifying T- and B-cell immune receptor distribution diversity to uncover their clinical relevance in clear cell renal cell carcinoma
Meghan C. Ferrall-Fairbanks, Nicholas Chakiryan, Boris I. Chobrutskiy, Youngchul Kim, …, Esther N. Katende, George Blanck, Brandon J. Manley, Philipp M. Altrock
Data visualisation using R, for researchers who don’t use R
Emily Nordmann, Phil McAleer, Wilhelmiina Toivo, Helena Paterson, Lisa DeBruine
"In this tutorial, we aim to provide a practical introduction to data visualisation using R, specifically aimed at researchers who have little to no prior experience of using R. First we detail the rationale for using R for data visualisation and introduce the “grammar of graphics” that underlies data visualisation using the ggplot package. The tutorial then walks the reader through how to replicate plots that are commonly available in point-and-click software such as histograms and boxplots, as well as showing how the code for these “basic” plots can be easily extended to less commonly available options such as violin-boxplots.”
The newsletter now has a dedicated homepage (thisweekmathonco.substack.com), which allows us to post cover artwork for each issue. We encourage submissions that coincide with the release of a recent paper from your group. Today’s cover art is by Maximilian Strobl (Moffitt Cancer Center):
Caption: To mix things, I thought I would tell you a little about myself today. I’m very interested in adaptive cancer therapy – the idea to leverage competition between drug-sensitive and resistant cells through strategic treatment adaptation. The attached simulations show a simple agent-based model of adaptive therapy (written in HAL) under increasing rates of cell turnover (left to right) and resistance costs (top to bottom). Importantly, you can see that the time for which the resistant colonies (pink) can be contained by sensitive cells (green) is modulated by a combination of both cost and turnover. As such, our research suggests that the rate of cell turnover in a tumor may be an important factor to consider when deciding whether and how to apply adaptive therapy. To read more about this work, see our paper here and our recent pre-print on the biorxiv.
Created by Maximilian Strobl (Newsletter Cover Editor)
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