This week in MathOnco 160

Imaging personalization, neutral evo., microenvironmental fluctuations, comp. bio field guide, and more

“This week in Mathematical Oncology” — Newsletter
Apr. 29, 2021
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jeffrey.west@moffitt.org
From the editor:

Summary of contents:

  1. Image-based personalization of computational models for predicting response of high-grade glioma to chemoradiation
    David A. Hormuth II, Karine A. Al Feghali, Andrew M. Elliott, Thomas E. Yankeelov, Caroline Chung

  2. Signatures of neutral evolution in exponentially growing tumors: A theoretical perspective
    Hwai-Ray Tung, Rick Durrett

  3. A Voxel Model to Decipher the Role of Molecular Communication in the Growth of Glioblastoma Multiforme
    Hamdan Awan, Sasitharan Balasubramaniam, Andreani Odysseos

  4. Towards decoding the coupled decision-making of metabolism and epithelial-to-mesenchymal transition in cancer
    Dongya Jia, Jun Hyoung Park, Harsimran Kaur, Kwang Hwa Jung, …, Mohit Kumar Jolly, Benny Abraham Kaipparettu, José N. Onuchic, Herbert Levine

  5. Molecular Heterogeneity and Evolution in Breast Cancer
    Jennifer L. Caswell-Jin, Carina Lorenz, Christina Curtis

  6. The impact of random microenvironmental fluctuations on tumor control probability
    Farinaz Forouzannia, Vahid Shahrezaei, Mohammad Kohandel

  1. A field guide to cultivating computational biology
    Anne Carpenter, Casey Greene, Piero Carnici, Benilton Carvalho, Michiel de Hoon, Stacey Finley, Kim-Anh Le Cao, Jerry Lee, Luigi Marchionni, Suzanne Sindi, Fabian Theis, Gregory Way, Jean Yang, Elana Fertig

  2. HOXA9 acts as a regulatory switch in acute myeloid leukaemia and myeloproliferative neoplasms
    Laure Talarmain, Matthew A. Clarke, David Shorthouse, Jasmin Fisher, Benjamin A Hall

1. Forcing cells to change lineages by cell-cell communication
The Mathematical Oncology Blog

Adam MacLean: “Megan Franke, a PhD student in our lab, came to me with an idea: a new modeling framework to combine gene regulatory networks with cell-cell communication at the single-cell level. This addressed what we saw as a rather urgent unmet need: models of gene regulatory dynamics generally dismiss any dynamic signaling processes originating outside the cell. Yet, we know: all cells exist in a noisy and tumultuous extracellular signaling milieu.”

The newsletter now has a dedicated homepage (thisweekmathonco.substack.com), which allows us to post cover artwork for each issue. Go explore the previous artwork here, and if you’d like to submit a cover for consideration please reply to this email. We encourage submissions that coincide with the release of a recent paper from your group. Today’s submission is below:

by David A. Hormuth, II – Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin

In our recent efforts to translate our pre-clinical models of glioma growth and response to the clinical setting we employed multiparametric MRI collected before and after radiation therapy to initialize and parameterize our model of tumor growth and response. Anatomical imaging was used to estimate the tumor burden, while diffusion weighted imaging was applied to estimate cell density. We then applied our modeling framework to forecast response to chemoradiation for each patient and overlaid the predicted distribution of tumor cells on anatomical images. This effort was a collaboration between the Oden Institute (Hormuth and Yankeelov) and the M.D. Anderson Cancer Center (Al Feghali, Elliott, and Chung). See the full publication in Sci Reports here.
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