This week in MathOnco 184

“This week in Mathematical Oncology” — Oct. 21, 2021
> mathematical-oncology.org
From the editor:

This week, we have manuscripts on topics like collateral sensitivity, optimal control, persistent homology, non-genetic inheritance, uncertainty quantification and more. If you missed it, check out the various resources (conferences, jobs, special issues) on the website.

Enjoy!

Jeffrey West
jeffrey.west@moffitt.org

  1. The role of memory in non-genetic inheritance and its impact on cancer treatment resistance
    Tyler Cassidy, Daniel Nichol, Mark Robertson-Tessi, Morgan Craig, Alexander R. A. Anderson

  2. Theoretical modeling of collaterally sensitive drug cycles: shaping heterogeneity to allow adaptive therapy
    Nara Yoon, Nikhil Krishnan, Jacob Scott

  3. Mathematical model of STAT signalling pathways in cancer development and optimal control approaches
    Jonggul Lee, Donggu Lee, Yangjin Kim

  4. Mathematical Modeling of Locoregional Recurrence Caused by Premalignant Lesions Formed Before Initial Treatment
    Mitsuaki Takaki, Hiroshi Haeno

  5. Persistent homology of tumor CT scans is associated with survival in lung cancer
    Eashwar Somasundaram, Adam Litzler, Raoul Wadhwa, Steph Owen, Jacob Scott

  6. Model comparison via simplicial complexes and persistent homology
    Sean T. Vittadello, Michael P. H. Stumpf

  7. Simulations reveal that different responses to cell crowding determine the expansion of p53 and Notch mutant clones in squamous epithelia
    Vasiliki Kostiou, Michael W. J. Hall, Philip H. Jones, Benjamin A. Hall

  1. Dynamics of fibril collagen remodeling by tumor cells using individual cell-based mathematical modeling
    Sharan Poonja, Mehdi Damaghi, Katarzyna A. Rejniak

  2. Uncertainty quantification and control of kinetic models for tumour growth under clinical uncertainties
    Andrea Medaglia, Giulia Colelli, Lisa Farina, Ana Bacila, Paola Bini, Enrico Marchioni, Silvia Figini, Anna Pichiecchio, Mattia Zanella

  1. Null-hacking, a lurking problem
    PsyArXiv Preprints
    John Protzko: “Pre-registration of analysis plans involves making data-analysis decisions before the data is run in order to prevent flexibly re-running it until a specific result appears (p-hacking). Just because a model and result is pre-registered, however, does not make it reflective of underlying reality. The complement to p-hacking, null-hacking, is the use of the same questionable research practices to re-analyze open data to return a null finding."

  2. Mistic, an open-source multiplexed image t-SNE
    The Mathematical Oncology Blog
    Sandhya Prabhakaran: “We were interested in understanding if and how treatment changed the tumor patterns across patients, and whether these patterns were predictive of patient response. As part of image pre-processing that involved cleaning and segmenting these images, we were also keen in arranging the images in a specific manner to visually bring out underlying patterns that would otherwise have been hard to detect. This was the motivation and need to develop Mistic, which is an open-source image t-SNE viewer for simultaneous viewing of multiple multiplexed 2D images using predefined coordinates (e.g. t-SNE or UMAP), randomly generated coordinates, or as vertical grids.

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.

Caption: The Galton board of phenotypic plasticity and resistance to therapy. An untreated tumour falls through a Galton board organized by treatment sensitivity, where the left most trajectories represent treatment resistance (purple cells) and the rightmost trajectories represent regrowth of a sensitive (orange cells) tumour. Transitions between drug sensitive and tolerant states is random and illustrated as a tumour bouncing in either direction-- towards drug resistance or sensitivity. Applying treatment closes the gates and biases the tumour’s trajectory towards resistance or regrowth. The tumour’s path is determined by both its current state; its history, as an explicit representation of stochastic phenotypic inheritance; and when treatment is applied, with the aim of selectively closing gates to direct the tumour towards extinction. In our recent work, we developed a simple mathematical model of this non-genetic phenotypic memory and fit the model to ex vivo NSCLC data. Using a combination of classical techniques from population dynamics, we derived simple thresholds that determine which gates to close to direct the tumours evolution away from resistance and towards extinction.

Created by: Tyler Cassidy (Twitter: @TCass20)

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