This week in MathOnco 263
Predictive models, ctDNA, subclonal interactions, adaptive therapy, resistance, and more...
“This week in Mathematical Oncology” — August 9, 2023
> mathematical-oncology.org
From the editor:
A few weeks off from the #MathOnco newsletter, we’re back today with articles on predictive models, ctDNA, subclonal interactions, adaptive therapy, resistance, and more...
Thanks,
Jeffrey West
jeffrey.west@moffitt.org
Predictive Model of Liver Toxicity to Aid the Personalized Selection of Proton Versus Photon Therapy in Hepatocellular Carcinoma
Ibrahim Chamseddine, Yejin Kim, Brian De, Harald Paganetti, Eugene J. Koay, Clemens GrassbergerCirculating tumor DNA to drive treatment in metastatic colorectal cancer
Giorgio Patelli, Gianluca Mauri, Federica Tosi, Alessio Amatu, …, Silvia Marsoni, Alberto Bardelli, Salvatore Siena, Andrea Sartore-BianchiComputational approaches to modelling and optimizing cancer treatment
Thomas O. McDonald, Yu-Chen Cheng, Christopher Graser, Phillip B. Nicol, Daniel Temko & Franziska MichorTumor Ecosystem: An Ecological View of Cancer Growth and Survival (Book)
Phei Er Saw, Erwei SongHaider Tari, Ketty Kessler, Nick Trahearn, Maria Vinci, Chris Jones, Andrea Sottoriva
The in silico lab: Improving academic code using lessons from biology
Jason Y. Cain, Jessica S. Yu, Neda BagheriComputational approaches to modelling and optimizing cancer treatment
Thomas O. McDonald, Yu-Chen Cheng, Christopher Graser, Phillip B. Nicol, Daniel Temko & Franziska MichorVirtual alignment of pathology image series for multi-gigapixel whole slide images
Chandler D. Gatenbee, Ann-Marie Baker, Sandhya Prabhakaran, Ottilie Swinyard, Robbert J. C. Slebos, Gunjan Mandal, Eoghan Mulholland, Noemi Andor, Andriy Marusyk, Simon Leedham, Jose R. Conejo-Garcia, Christine H. Chung, Mark Robertson-Tessi, Trevor A. Graham & Alexander R. A. Anderson
Adaptive therapy achieves long-term control of chemotherapy resistance in high grade ovarian cancer
Helen Hockings, Eszter Lakatos, Weini Huang, Maximilian Mossner, Mohammed Ateeb Khan, Stephen Metcalf, Francesco Nicolini, Kane Smith, Ann-Marie Baker, Trevor Graham, Michelle LockleyAddressing persistent challenges in digital image analysis of cancerous tissues
Sandhya Prabhakaran, Clarence Yapp, Gregory J. Baker, …, Jakob Troidl, Yubin Xie, Artem Sokolov, Darren R. TysonOn minimising tumoural growth under treatment resistance
Matthias M. Fischer, Nils BlüthgenEmergent digital bio-computation through spatial diffusion and engineered bacteria
Alex J.H. Fedorec, Neythen J. Treloar,Ke Yan Wen, …, Jack Rutter, Luca Rosa, Alexey Zaikin, Chris P. Barnes
The science of storytelling: the David Attenborough style of scientific presentation
William C. Ratcliff, Trends in Molecular MedicineNovel AI model predicts risk of hepatic toxicity after radiation therapy for HCC
”This paper describes a patient-specific prediction model for hepatic toxicity in which an ensemble convolutional neural network (CNNE) was trained on dose-volume histograms and clinical data from 117 patients with hepatocellular carcinoma. In an external validation cohort of 88 patients the CNNE consistently outperformed benchmark models, showing it can efficiently abstract dosimetric features from patients who receive either proton or photon therapy”
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: Hybrid cellular Potts and bead-spring modeling of cells in fibrous extracellular matrix, in Biophysical Journal
Artist: Erika Tsingos (@tisrenk), Roeland Merks (@roeland_merks), Leiden University (@LeidenScienceEN)
Caption: "Cells secrete a vast gamut of extracellular matrix proteins into the microenvironment. In our recent work we developed a hybrid cellular Potts model for cells interacting with extracellular matrix fibers modelled with molecular dynamics methods. In a typical simulation, a contracting cell pulled in fibers from all directions. Though the cell pulled isotropically, local heterogeneity in network structure resulted in anisotropic matrix displacement. To visualize this, we made all fibers distinct by giving them unique colors depending on their initial position. Then, we added a motion blur-like effect by darkening the colors for each subsequent timepoint, which we overlayed to create this artwork, which was also featured on the cover of Biophysical Journal. We will use this new model to investigate how the tug-of-war between cells in a fibrous matrix can steer pattern formation."
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