This week in MathOnco 174

Adaptive therapy predictions, somatic evolution, multi-drug synergy, spheroid structure, and modeling the mechanism

“This week in Mathematical Oncology” — Newsletter
August 12, 2021

From the editor:

As summer winds down, the pace of publication seems to ramp up. This week’s issue is filled with exciting topics: Adaptive therapy predictions, somatic evolution, multi-drug synergy, spheroid structure, and modeling the mechanism. Enjoy,

- Jeffrey West

  1. Predicting patient-specific response to adaptive therapy in metastatic castration-resistant prostate cancer using prostate-specific antigen dynamics
    Renee Brady-Nicholls, Jingsong Zhang, Tian Zhang, Andrew Z. Wang, Robert Butler, Robert A. Gatenby, Heiko Enderling

  2. Pan-cancer prediction of radiotherapy benefit using genomic-adjusted radiation dose (GARD): a cohort-based pooled analysis
    Jacob G Scott, Geoffrey Sedor, Patrick Ellsworth, Jessica A Scarborough, Kamran A Ahmed, Daniel E Oliver, Steven A Eschrich, Michael W Kattan, Javier F Torres-Roca

  3. The somatic molecular evolution of cancer: Mutation, selection, and epistasis
    Krishna Dasari, Jason A. Somarelli, Sudhir Kumar, Jeffrey P. Townsend

  4. A Spatial Quantitative Systems Pharmacology Platform spQSP-IO for Simulations of Tumor—Immune Interactions and Effects of Checkpoint Inhibitor Immunotherapy
    Chang Gong, Alvaro Ruiz-Martinez, Holly Kimko, Aleksander S. Popel

  5. PK/PD modeling analysis for dosing regimen selection of isatuximab as single agent and in combination therapy in patients with multiple myeloma
    Kimiko Koiwai, Raouf El-Cheikh, Hoai-Thu Thai, Claire Brillac, …, Marie-Laure Risse, Helgi van de Velde, Dorothée Semiond, Laurent Nguyen

  6. Evolutionary Dynamics of Treatment-Induced Resistance in Cancer Informs Understanding of Rapid Evolution in Natural Systems
    Mariyah Pressley, Monica Salvioli, David B. Lewis, Christina L. Richards, Joel S. Brown, Kateřina Staňková

  7. Collapse of Intra-Tumor Cooperation Induced by Engineered Defector Cells
    Marco Archetti

  8. MuSyC is a consensus framework that unifies multi-drug synergy metrics for combinatorial drug discovery
    David J. Wooten, Christian T. Meyer, Alexander L. R. Lubbock, Vito Quaranta, Carlos F. Lopez

  1. Quantitative analysis of tumour spheroid structure
    Alexander P Browning, Jesse A Sharp, Ryan J Murphy, Gency Gunasingh, Brodie Lawson, Kevin Burrage, Nikolas K Haass, Matthew J Simpson

  1. Do mechanisms matter?
    The Mathematical Oncology Blog
    Fred Adler: “Adaptive therapy depends on three key assumptions: resistance is costly, resistant cells can be suppressed by competition with sensitive cells, and therapy reduces the population of sensitive cells (although Viossat & Noble show that the first of these assumptions is not essential). However, what happens when these assumptions are tested against a wider set of ecological backgrounds? We expanded on the original work by Zhang et al […] to investigate four extensions of a basic competition model: 1) competition with healthy cells, 2) inclusion of resource dynamics, 3) an immune response, and 4) an Allee effect. We tested how a comprehensive set of intermittent and adaptive therapies balance two types of treatment failure (the time when resistant cells exceed some threshold and the time when the total cell population exceeds a different threshold) and two costs: average cell population and total treatment burden.

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Caption: We use clinical data of intermittent, adaptive prostate cancer hormone therapy to simulate tumor growth and treatment response dynamics. With that, we can predict possible patient-specific response trajectories to subsequent treatment cycles and recommend treatment adaptations when necessary. Click here to read more.

Created by: Heiko Enderling and Renee Brady-Nicholls.

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