#MathOnco Issue 60: measuring heterogeneity; epithelial-mesenchymal mechanism, macrophage-driven immunoediting; metastasis evol
This week in
Mathematical Oncology
April 4, 2019 ~ Issue 60
From the editor
Hello #MathOnco friends,
Today's issue contains a review on measuring genetic heterogeneity, as well as models on epithelial-mesenchymal mechanisms, macrophage-driven immunoediting, and evolution of metastases.
I'll also direct your attention to a blog post by Ryan O Schenck and I where we summarize and consolidate the exciting findings in math onco-related spatial models of heterogeneity in the past few months.
Please enjoy,
-Jeffrey West
#MathOnco Publications
Resolving genetic heterogeneity in cancer
Authors: Samra Turajlic, Andrea Sottoriva, Trevor Graham & Charles Swanton
Stochastic Evolution of Pancreatic Cancer Metastases During Logistic Clonal Expansion
Authors: Kimiyo N. Yamamoto, Lin L. Liu, Akira Nakamura, Hiroshi Haeno, Franziska Michor
Agent-based modeling of morphogenetic systems: Advantages and challenges
Authors: Chad M. Glen, Melissa L. Kemp , Eberhard O. Voit
#MathOnco Preprints
A Mechanism for Epithelial-Mesenchymal Heterogeneity in a Population of Cancer Cells
Authors: Shubham Tripathi, Herbert Levine, Mohit Kumar Jolly
Macrophage-mediated immunoediting drives ductal carcinoma evolution: Space is the game changer
Authors: Chandler Gatenbee, Jeffrey West, Mark Robertson-Tessi, Annie Baker, Nafia Guljar, Louise Jones, Trevor Graham, Alexander R.A. Anderson
Model-based tumor subclonal reconstruction
Authors: Giulio Caravagna, Timon Heide, Marc Williams, Luis Zapata, Daniel Nichol, ..., Chris P. Barnes, Guido Sanguinetti, Trevor A. Graham, Andrea Sottoriva
Is evolution predictable? Quantitative genetics under complex genotype-phenotype maps
Authors: Lisandro Milocco, Isaac Salazar-Ciudad
Can we afford to ignore the role of space in cancer and pre-cancerous tissue any longer?
Jeffrey West & Ryan O Schenck: "For those who are interested in agent-based models of cancer, this has been one of the most exciting months of mathematical oncology in recent memory. We might try to sum up what we have learned: the choice of model should be heavily influenced by the mode of evolution of the biological system of study. We also need unified metrics. The best metrics are ones that can easily compare model outputs directly to data. On the other hand, this where modeling can shine: showing that data can be recapitulated by several models or parameter combinations."
#MathOnco - Book of the month
The Oxford Handbook of Evolutionary Medicine
Martin Brune & Wulf Schiefenhovel: "Many adaptations to past ecologies have turned into risk factors for somatic disease and psychological disorder in our modern worlds (i.e. mismatch), among which epidemics of autoimmune diseases, cardiovascular diseases, diabetes and obesity, as well as several forms of cancer stand out. The Oxford Handbook of Evolutionary Medicine is a compilation of cutting edge insights into the evolutionary history of ourselves as a species, and how and why our evolved design may convey vulnerability to disease. "
Most clicked links of March
The Immune Checkpoint Kick Start: Optimization of Neoadjuvant Combination Therapy Using Game Theory
The impact of proliferation-migration tradeoffs on phenotypic evolution in cancer
PhysiCell: An open source physics-based cell simulator for 3-D multicellular systems
A numerical approach for a discrete Markov model for progressing drug resistance of cancer
Jobs
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