This week in MathOnco 314

Hypoxia, immune checkpoint, combination therapy, QSP, drug resistance, and more...

“This week in Mathematical Oncology” — Nov 21, 2024

> mathematical-oncology.org

From the editor:

Today’s issue contains articles on topics like hypoxia, immune checkpoint, combination therapy, QSP, drug resistance, and more. Also, the Math Onco interview series by Thomas HIllen continues with Doron Levy, below!

Thanks,

Jeffrey West
jeffrey.west@moffitt.org

1. Characterising Cancer Cell Responses to Cyclic Hypoxia Using Mathematical Modelling
   Giulia Celora, Ruby Nixson, Joe Pitt-Francis, Philip Maini, Helen Byrne

2. AMBER: A Modular Model for Tumor Growth, Vasculature and Radiation Response
   Louis V. Kunz, Jesús J. Bosque, Mohammad Nikmaneshi, Ibrahim Chamseddine, Lance L. Munn, Jan Schuemann, Harald Paganetti & Alejandro Bertolet

3. Optimized patient-specific immune checkpoint inhibitor therapies for cancer treatment based on tumor immune microenvironment modeling
   Yao Yao, Youhua Frank Chen, Qingpeng Zhang

4. PULSAR Effect: Revealing potential synergies in combined radiation therapy and immunotherapy via differential equations
   Samiha Rouf, Casey Moore, Debabrata Saha, Dan Nguyen, MaryLena Bleile, Robert Timmerman, Hao Peng, Steve Jiang

5. Accumulation of Oncogenic Mutations During Progression from Healthy Tissue to Cancer

   Ruibo Zhang, Ivana Bozic

6. Chaotic dynamics and optimal therapeutic strategies for Caputo fractional tumor immune model in combination therapy
   Jia Li, Xuewen Tan, Wanqin Wu, Xinzhi Liu

7. Combining Biology-based and MRI Data-driven Modeling to Predict Response to Neoadjuvant Chemotherapy in Patients with Triple-Negative Breast Cancer
   Casey E. Stowers, Chengyue Wu, Zhan Xu, Sidharth Kumar, Clinton Yam, Jong Bum Son, Jingfei Ma, Jonathan I. Tamir, Gaiane M. Rauch, Thomas E. Yankeelov

8. A New Perspective on Tumor Progression: Evolution via Selection for

   FunctionFunction
   Frédéric Thomas, James Degregori, Andriy Marusyk, Antoine M Dujon, Beata Ujvari, Jean-Pascal Capp, Robert Gatenby, Aurora M Nedelcu

1. Predicting survival in prospective clinical trials using weakly-supervised QSP
   Vincent Lemaire, Matthew West, Kenta Yoshida, Jiajie Yu

2. A hybrid computational model of cancer spheroid growth with ribose-induced collagen stiffening
   Margherita Botticelli, John Metzcar, Thomas Phillips, Susan Cox, Pradeep Keshavanarayana, Fabian Spill

3. Personalized cancer treatment strategies incorporating irreversible and reversible drug resistance mechanisms

   Wei He, Matthew D. McCoy, Rebecca B. Riggins, Robert A. Beckman, Chen-Hsiang Yeang

4. Predicting treatment-free remission outcomes in Chronic Myeloid Leukemia patients using an integrated model of tumor-immune dynamics
   Artur C. Fassoni, Agnes Yong, Richard E. Clark, Ingo Roeder, Ingmar Glauche

1. Doron Levy

   Math Oncology Interviews by Thomas Hillen (YouTube)

The newsletter now has a dedicated homepage where we post the cover artwork for each issue. We encourage submissions that coincide with the release of a recent paper from your group. This week’s artwork:

Based on the paper: Dynamics and bifurcations in a model of chronic myeloid leukemia with optimal immune response windows in Journal of Mathematical Biology

Artist: Artur C. Fassoni

Caption: The advent of targeted therapy with Tyrosine Kinase Inhibitors (TKIs) has changed the paradigm for Chronic Myeloid Leukemia, with most patients achieving remission after a few years of treatment. Currently, the goal of clinical research is to predict which patients can safely discontinue TKI without relapse, relying on the immune system to control the residual leukemic cells that remain after discontinuation. Mathematical models are useful tools to characterize the dynamics of tumor-immune interactions in CML and to describe the post-treatment states in terms of attractors. In a recent model, we showed that three stable states can coexist, meaning that tumor elimination, control, or relapse are possible outcomes depending on the patient's immune system and treatment schedule. This figure shows an artistic representation for the complex, infinite-dimensional space corresponding to the "real model" (reality) with trajectories describing different outcomes: red for relapse, yellow for tumor control, and green for tumor elimination. Figure created with Shakker.

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