This week in MathOnco 205
Precision medicine, resilience & aging, immunovirotherapies, experimental evolution, and more
“This week in Mathematical Oncology” — Apr. 14, 2022
From the editor:
Today’s edition contains publications on topics such as precision medicine, resilience & aging, 4D spheroids, immunovirotherapies, experimental evolution, and more.
PS. My apologies to those who received a duplicate version of the newsletter last week: apparently Substack had a glitch.
Improving immunovirotherapies: the intersection of mathematical modelling and experiments
Christine E. Engeland, Johannes P. W. Heidbuechel, Robyn P. Araujo, Adrianne L. Jenner
Optimal chemotherapy counteracts cancer adaptive resistance in a cell-based, spatially-extended, evolutionary model
Matteo Italia, Fabio Dercole, Roberto Lucchetti
Spatial mechanistic modeling for prediction of 3D multicellular spheroids behavior upon exposure to high intensity pulsed electric fields
Annabelle Collin, Hadrien Bruhier, Jelena Kolosnjaj, Muriel Golzio, Marie-Pierre Rols, Clair Poignard
Cancer Evolution in Precision Medicine Era
Dimitris Karagiannis, Theodoros Rampias
Resilience Integrates Concepts in Aging Research
Daniel Promislow, Rozalyn M. Anderson, Marten Scheffer, Bernard Crespi,…,
David S. Schneider, Sabrina L. Spencer, Dario Riccardo Valenzano, Michael E.
Tumour evolvability metrics predict recurrence beyond 10 years in locally-advanced prostate cancer
Andrea Sottoriva, Javier Fernandez-Mateos, George Cresswell, Nicholas Trahearn, …, Anna Wilkins, Michael Hubank, Rosalind Eeles, David Dearnaley
Experimental evolution in TP53 deficient gastric organoids recapitulates tumorigenesis
Kasper Karlsson, Moritz J. Przybilla, Hang Xu, Eran Kotler, …, Carlos J. Suarez, Christopher P. Barnes, Calvin J. Kuo, Christina Curtis
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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.
Caption: This artwork captures the notion of in silico clinical trials, which were recently discussed in our published work in the context of combined virotherapy and immunotherapy (viroimmunotherapy). In silico clinical trials, also known as virtual clinical trial, virtual twins, phase i trials, populations of models and random ODEs, are a technique used to create a population of in silico heterogeneous individuals based on real measurements of patient disease growth characteristics and treatment efficacy. By random sampling from distributions based on patient measurements, variations on the treatment protocol (e.g. days between consecutive treatment doses and multiplicity of doses) can be examined to predict population level responses as well as individual responses. From this, optimal “one-size-treats-all” or “personalised” treatments can be determined. This technique has been applied to viroimmunotherapy to reveal patterns in optimal dosage protocols based on tumour aggressivity, see our published work. Created with biorender.com.
Created by: Adrianne Jenner
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