This week in MathOnco 272

Aging/inflammation, oscillatory tumor growth patterns, stroma-mediated proliferation, and anti-PDL1 therapy.

“This week in Mathematical Oncology” — October 5, 2023

> mathematical-oncology.org

From the editor:

This week we have papers on aging/inflammation, oscillatory tumor growth patterns, stroma-mediated proliferation, and anti-PDL1 therapy.

Thanks,

Jeffrey West
jeffrey.west@moffitt.org

Is there something we missed?

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1. Aging, Inflammation, and Comorbidity in Cancers—A General In Silico Study Exemplified by Myeloproliferative Malignancies
   Johnny T. Ottesen, Morten Andersen

2. An image-based modeling framework for predicting spatiotemporal brain cancer biology within individual patients
   Kamila M. Bond, Lee Curtin, Sara Ranjbar, Ariana E. Afshari, Leland S. Hu, Joshua B. Rubin, Kristin R. Swanson

3. Mechanistic characterization of oscillatory patterns in unperturbed tumor growth dynamics: The interplay between cancer cells and components of tumor microenvironment
   Aymara Sancho-Araiz, Zinnia P. Parra-Guillen, Jean Bragard, Sergio Ardanza, Victor Mangas-Sanjuan, Iñaki F. Trocóniz

4. Cracking the pattern of tumor evolution based on single-cell copy number alterations Get access Arrow
   Ying Wang, Min Zhang, Jian Shi, Yue Zhu, Xin Wang, Shaojun Zhang, Fang Wang

5. Game-theoretical description of the go-or-grow dichotomy in tumor development for various settings and parameter constellations
   Shalu Dwivedi, Christina Glock, Sebastian Germerodt, Heiko Stark & Stefan Schuster

6. Stroma-mediated breast cancer cell proliferation indirectly drives chemoresistance by accelerating tumor recovery between chemotherapy cycles

   Daria Miroshnychenko, Tatiana Miti, Pragya Kumar, Anna Miller, Mark Laurie, Nathalia Giraldo, Marilyn M. Bui, Philipp M. Altrock, David Basanta, Andriy Marusyk

7. Robust positive control of tumour growth using angiogenic inhibition
   Mohamadreza Homayounzade, Maryam Homayounzadeh, Mohammad Hassan Khooban

1. Prediction of individual survival and trial outcome for anti-PDL1 treatment in non-small cell lung cancer using blood markers-based kinetics-machine learning
   Sébastien Benzekry, Mélanie Karlsen, Célestin Bigarré, Abdessamad El Kaoutari, Bruno Gomes, Martin Stern, Ales Neubert, Rene Bruno, François Mercier, Suresh Vatakuti, Peter Curle, Candice Jamois

1. Dr. Darryl Shibata: Investigating the Evolution of Cancer at Multiple Scales
   ”Darryl Shibata, M.D., is a CSBC investigator at the University of Southern California (USC) who uses evolutionary models to advance cancer systems biology. According to him, “the principles of evolution fit naturally into systems biology, since biology is basically built by evolution.””

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: A global sensitivity analysis of a mechanistic model of neoadjuvant chemotherapy for triple negative breast cancer constrained by in vitro and in vivo imaging data published in Engineering with Computers

Artist: G. Lorenzo (@guillelorenzogzi)

Caption: In our recent work, we investigate a new spatiotemporal biology-based model to forecast breast cancer growth and response to neoadjuvant therapy. This model accounts for tumor cell proliferation and invasion, mechanical constraints to tumor growth, as well as drug pharmacokinetics and pharmacodynamics, including drug-drug synergistic effects. We focus on two standard neoadjuvant chemotherapeutic regimens for locally advanced triple negative breast cancer: doxorubicin plus cyclophosphamide (DC), and paclitaxel plus carboplatin (PC). In this context, we constructed the parameter space for our model by combining patient-specific, MRI-informed values from our previous studies on breast cancer forecasting and in vitro measurements of drug pharmacodynamics and synergistic effects obtained via high-throughput, time-resolved, automated microscopy. The sensitivity analysis is run in two MRI-based scenarios corresponding to a well-perfused and a poorly perfused tumor. Out of the 15 parameters considered in this study, only 3 (DC) to 5 (PC) parameters, which represent drug-induced changes to tumor cell net proliferation, exhibit a relevant impact on model forecasts. Thus, these results dramatically limit the number of parameters that require in vivo MRI-constrained calibration, thereby facilitating the clinical application of our model.

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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• Junior Professor Chair Position: Computational modeling of omics data (Centre Inria d'Université Côte d'Azur, Marseille France)

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