Mathematical Oncology: Using Mathematics to Enable Cancer Discoveries

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".

Deadline for manuscript submissions: 15 June 2025 | Viewed by 1755

Special Issue Editors


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Guest Editor
City of Hope National Med Center, Duarte, CA, USA
Interests: mathematical oncology; personalized medicine; mathematical modeling; state transition; model discovery; radiation therapy; targeted radionuclides; glioblastoma; acute myeloid leukemia
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Physics and Astronomy, College of Charleston, Charleston, SC, USA
Interests: mathematical oncology; vascular networks; metabolic scaling; image analysis; model discovery

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Guest Editor
Integrated Mathematical Oncology at the Moffitt Cancer Center, Tampa Bay, FL, USA
Interests: mathematical oncology; predictive modeling; patient-specific, personalized, dynamic modeling; computational biology
Precision Neurotherapeutics Innovation Program at Mayo Clinic, Phoenix, AZ, USA
Interests: mathematical oncology; mathematical neuro-oncology; prognostic biomarkers; glioblastoma; subpopulation dynamics

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Guest Editor
Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
Interests: mathematical and computational oncology; clinical cancer imaging; medical image analysis; patient-specific modeling; breast cancer; glioblastoma
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Mathematical modeling is proving to be an invaluable component for the future of oncology. Applications have spanned finding analytic or numerical analysis of first-principles models to agent-based modeling of complex dynamics. These methods are providing essential insights into the underlying cancer biology and identify unique opportunities to cancer prognosis, prediction, treatment planning, and intervention. Moreover, improvements in imaging systems and experimental techniques are providing unparalleled data with which to test, validate, and enhance proposed models. Given the rapid pace of growth seen in this field, many of these advances are being pioneered by early career researchers who are in the process of translating rigorous training into independent investigation.

In this Special Issue, we seek to highlight contributions to mathematical oncology led by early career researchers.  We invite submissions that present contributions to mathematical oncology, with a focus on original research that merges modeling with data.

Dr. Russell Rockne
Dr. Alexander Brummer
Dr. Renee Brady-Nicholls
Dr. Lee Curtin
Dr. Chengyue Wu
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • mathematical oncology
  • computational biology
  • mathematical modeling
  • computational oncology
  • data-driven modeling
  • physical oncology

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Published Papers (1 paper)

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Research

21 pages, 3545 KiB  
Article
Agent-Based Modeling of Virtual Tumors Reveals the Critical Influence of Microenvironmental Complexity on Immunotherapy Efficacy
by Yixuan Wang, Daniel R. Bergman, Erica Trujillo, Anthony A. Fernald, Lie Li, Alexander T. Pearson, Randy F. Sweis and Trachette L. Jackson
Cancers 2024, 16(17), 2942; https://doi.org/10.3390/cancers16172942 - 23 Aug 2024
Viewed by 642
Abstract
Since the introduction of the first immune checkpoint inhibitor (ICI), immunotherapy has changed the landscape of molecular therapeutics for cancers. However, ICIs do not work equally well on all cancers and for all patients. There has been a growing interest in using mathematical [...] Read more.
Since the introduction of the first immune checkpoint inhibitor (ICI), immunotherapy has changed the landscape of molecular therapeutics for cancers. However, ICIs do not work equally well on all cancers and for all patients. There has been a growing interest in using mathematical and computational models to optimize clinical responses. Ordinary differential equations (ODEs) have been widely used for mechanistic modeling in immuno-oncology and immunotherapy. They allow rapid simulations of temporal changes in the cellular and molecular populations involved. Nonetheless, ODEs cannot describe the spatial structure in the tumor microenvironment or quantify the influence of spatially-dependent characteristics of tumor-immune dynamics. For these reasons, agent-based models (ABMs) have gained popularity because they can model more detailed phenotypic and spatial heterogeneity that better reflect the complexity seen in vivo. In the context of anti-PD-1 ICIs, we compare treatment outcomes simulated from an ODE model and an ABM to show the importance of including spatial components in computational models of cancer immunotherapy. We consider tumor cells of high and low antigenicity and two distinct cytotoxic T lymphocyte (CTL) killing mechanisms. The preferred mechanism differs based on the antigenicity of tumor cells. Our ABM reveals varied phenotypic shifts within the tumor and spatial organization of tumor and CTLs despite similarities in key immune parameters, initial simulation conditions, and early temporal trajectories of the cell populations. Full article
(This article belongs to the Special Issue Mathematical Oncology: Using Mathematics to Enable Cancer Discoveries)
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