This week in MathOnco 173

Parasite-like interactions in cancer, Lynch syndrome, a Bayesian mechanistic/machine approach, Boolean models, and Allee effect

“This week in Mathematical Oncology” — Newsletter
> mathematical-oncology.org
Aug. 5, 2021

From the editor:
> jeffrey.west@moffitt.org

Hello!

So many cool articles this week. Parasite-like interactions in cancer, Lynch syndrome, a Bayesian mechanistic/machine approach, Boolean models, and Allee effect. Scroll down to see a few additions to the Jobs/Conferences/Special issues sections as well.

Enjoy,

- Jeffrey West

  1. Paracrine Behaviors Arbitrate Parasite-Like Interactions Between Tumor Subclones
    Robert J. Noble, Viola Walther, Christian Roumestand, Michael E. Hochberg, Urszula Hibner, Patrice Lassus

  2. A computational model for investigating the evolution of colonic crypts during Lynch syndrome carcinogenesis
    Saskia Haupt, Nils Gleim, Aysel Ahadova, Hendrik Bläker, Magnus von Knebel Doeberitz, Matthias Kloor, Vincent Heuveline

  3. The Drug Titration Paradox: Correlation of More Drug With Less Effect in Clinical Data
    Thomas W. Schnider, Charles F. Minto, Miodrag Filipovic

  4. HEDOS – a computational tool to assess radiation dose to circulating blood cells during external beam radiotherapy based on whole-body blood flow simulations
    Jungwook Shin, Shu Xing1, Lucas McCullum, Abdelkhalek Hammi, Jennifer Pursley, Camilo M Correa Alfonso, Julia Withrow, Sean Domal, Wesley E Bolch, Harald Paganetti, Clemens Grassberger

  5. Integrating single cell sequencing with a spatial quantitative systems pharmacology model spQSP for personalized prediction of triple-negative breast cancer immunotherapy response
    Shuming Zhang, Chang Gong, Alvaro Ruiz-Martinez, Hanwen Wang, Emily Davis-Marcisak, Atul Deshpande, Aleksander S. Popel, Elana J. Fertig

  6. Improving personalized tumor growth predictions using a Bayesian combination of mechanistic modeling and machine learning
    Pietro Mascheroni, Symeon Savvopoulos, Juan Carlos López Alfonso, Michael Meyer-Hermann, Haralampos Hatzikirou

  1. Patient-specific Boolean models of signaling networks guide personalized treatments
    Arnau Montagud, Jonas Béal, Luis Tobalina, Pauline Traynard, Vigneshwari Subramanian, Bence Szalai, Róbert Alföldi, László Puskás, Alfonso Valencia, Emmanuel Barillot, Julio Saez-Rodriguez, Laurence Calzone

  2. Travelling-wave analysis of a model of tumour invasion with degenerate, cross-dependent diffusion
    Chloé Colson, Faustino Sánchez-Garduño, Helen M. Byrne, Philip K. Maini, Tommaso Lorenzi

  3. A Novel Mathematical Model of Tumor Growth Kinetics with Allee Effect under Fuzzy Environment
    Rubeena Khaliq, Pervaiz Iqbal, Shahid Ahmad Bhat

  1. Coordination games in cancer
    The Mathematical Oncology Blog
    Péter Bayer: “Driver genes may help identify coordination games. In many cancers, a specific gene mutation is observed in almost all cells and is vital for the proliferation of cancer cells. Perhaps the best example is the oncogenic mutation of Epidermal Growth Factor in lung cancers (accounting for 15-20% of lung cancers). Unlike most lung cancer cohorts in which prolonged smoking and exposure to air pollutants are clear risk factors, EGFR-mut cancers tend to occur in younger patients who are non-smokers. For these patients, the EGFR-mutation occurs in essentially all the cancer cells of the primary and metastatic tumors. Furthermore, the mutational burden in EGFR-mut lung cancer is significantly smaller than EGFR WT lung cancers, indicating a molecularly more homogeneous intra-tumoral population. We propose that EGFR-mut versus EGFR WT lung cancers represent a coordination game.

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. This week’s submission is from Rob Noble:

Caption: Via experiments and mathematical modelling, we investigated a "winner" clone that inhibits a "loser" clone derived from the same tumour. In this image, the two curves depict the distinctive three-phase frequency dynamics of the winner (blue) and loser (red) in co-culture. In the first phase, the two clones barely interact, and the loser increases in frequency due to its higher intrinsic growth rate. In phase two, paracrine factors suppress the loser while boosting the winner's growth. Finally, both clones die off as toxins accumulate and resources are exhausted. Overall, the interactions are effectively equivalent to those of a parasite and its host, such that the loser both suffers from the winner's presence and enhances the winner’s fitness. The distinctive shape of the frequency curves is appropriately reminiscent of a parasitic tick, such as shown here overlaid on a microscope photo of the competing tumour cells. Read more here.

Created by: Rob Noble (@robjohnnoblerobjohnnoble.github.io)

Visit the mathematical oncology page to view jobs, meetings, and special issues. We will post new additions here, but the full list can found at mathematical-oncology.org.

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