#MathOnco Issue 109: go-or-grow, ctDNA, hematopoiesis, dNdS, storage effect, and more..
This week in
Math Oncology
Apr. 2, 2020 ~ Issue 109
From the editor
Hello everyone,
Today's issue of "This week in Mathematical Oncology" contains articles on go-or-grow, ctDNA, hematopoiesis, dNdS, and the storage effect. Scroll down for these and much more!
I also included an interesting book I recently started reading on the science of "why" - the math behind determining cause and effect. Pick up a copy, it's definitely relevant to math modeling!
Enjoy!
-Jeffrey West
#MathOnco Publications
Examining Go-or-Grow Using Fluorescent Cell-Cycle Indicators and Cell-Cycle-Inhibiting Drugs
Authors: Sean T. Vittadello, Scott W. McCue, Gency Gunasingh, Nikolas K.Haass, Matthew J. Simpson
The evolutionary dynamics and fitness landscape of clonal hematopoiesis
Authors: Caroline J. Watson, A. L. Papula, Gladys Y. P. Poon, Wing H. Wong, Andrew L. Young, Todd E. Druley, Daniel S. Fisher, Jamie R. Blundell
Mapping the breast cancer metastatic cascade onto ctDNA using genetic and epigenetic clonal tracking
Authors: George D. Cresswell, Daniel Nichol, Inmaculada Spiteri, Haider Tari, ..., Gaia Schiavon, Alan Ashworth, Peter Barry, Andrea Sottoriva
Image-based metric of invasiveness predicts response to adjuvant temozolomide for primary glioblastoma
Authors: Susan Christine Massey, Haylye White, Paula Whitmire, Tatum Doyle, ..., Jann N. Sarkaria, Leland S. Hu, Maciej M. Mrugala, Kristin R. Swanson
Measuring the distribution of fitness effects in somatic evolution by combining clonal dynamics with dN/dS ratios
Authors: Marc J Williams, Luis Zapata, Benjamin Werner, Chris P Barnes, Andrea Sottoriva, Trevor A Graham
#MathOnco Preprints
Circulating immune cell phenotype dynamics reflect the strength of tumor-immune cell interactions in patients during immunotherapy
Authors: Jason I Griffiths, Pierre Wallet, Lance T Pflieger, David Stenehjem, ..., Frederick R Adler, Jeffrey T Chang, Sunil Sharma, Andrea H Bild
What is the storage effect, why should it occur in cancers, and how can it inform cancer therapy?
Authors: Anna K. Miller, Joel S. Brown, David Basanta, Nancy Huntly
Utilizing Differential Evolution into optimizing targeted cancer treatments
Authors: Michail-Antisthenis Tsompanas, Larry Bull, Andrew Adamatzky, Igor Balaz
Practicing for the worst-case scenario of cancer biology
The Mathematical Oncology Blog
Noemi Andor: "This is the worst-case scenario of cancer biology – not that every tumor is unique, but that the most effective, long-term treatment for every tumor is unique too and that most tumors will adapt even to that “best treatment”, that in order to achieve a long-term response therapeutic actions must evolve as quickly as the tumor does. ... Let’s redefine what success means when it comes to working with cell lines, namely not to kill the entire cell line, but to kill a specific subpopulation within the cell line. Let cell lines be our training camps where we learn to control the evolution of a cancer population over time."
#MathOnco - Book of the month
The Book of Why:
The New Science of Cause and Effect
Judea Pearl: "Correlation is not causation. This mantra, chanted by scientists for more than a century, has led to a virtual prohibition on causal talk. Today, that taboo is dead. The causal revolution, instigated by Judea Pearl and his colleagues, has cut through a century of confusion and established causality--the study of cause and effect--on a firm scientific basis. His work explains how we can know easy things, like whether it was rain or a sprinkler that made a sidewalk wet; and how to answer hard questions, like whether a drug cured an illness. Pearl's work enables us to know not just whether one thing causes another: it lets us explore the world that is and the worlds that could have been."
Jobs
Computational Approaches to Breast Cancer Evolution - Postdoc (Marc Ryser)
Postdoctoral Fellow in Mathematical Oncology (Russell Rockne)
Pre-leukemic Dynamics – MSc or PhD Studentship (Morgan Craig)
Quantitative Systems Pharmacology (QSP) Modeler - Cell Therapy (Dean Bottino)
Math/statistical models of stem cell lineage dynamics and cancer genomics - Postdoc (Adam MacLean)
Postdoctoral Research Position in Computational Oncology (Tom Yankeelov)
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