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Cited by 39Mathematical Biology Tumor GrowthImmune cells in cancerMathematical and Theoretical Epidemiology and Ecology Models

Data-Driven Mathematical Model of Osteosarcoma

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Le, Trang, Su, Sumeyye, Kirshtein, Arkadz, Shahriyari, Leili·2021·Cancers
DOI 10.3390/cancers13102367PMC PMC8156666
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Abstract

As the immune system has a significant role in tumor progression, in this paper, we develop a data-driven mathematical model to study the interactions between immune cells and the osteosarcoma microenvironment. Osteosarcoma tumors are divided into three clusters based on their relative abundance of immune cells as estimated from their gene expression profiles. We then analyze the tumor progression and effects of the immune system on cancer growth in each cluster. Cluster 3, which had approximately the same number of naive and M2 macrophages, had the slowest tumor growth, and cluster 2, with the highest population of naive macrophages, had the highest cancer population at the steady states. We also found that the fastest growth of cancer occurred when the anti-tumor immune cells and cytokines, including dendritic cells, helper T cells, cytotoxic cells, and IFN-γ, switched from increasing to decreasing, while the dynamics of regulatory T cells switched from decreasing to increasing. Importantly, the most impactful immune parameters on the number of cancer and total cells were the activation and decay rates of the macrophages and regulatory T cells for all clusters. This work presents the first osteosarcoma progression model, which can be later extended to investigate the effectiveness of various osteosarcoma treatments.

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  • As the tumor progresses, osteosarcoma cells evolve to generate variants that are resistant to the actions of DCs and phagocytes, resulting in diminished DC activation, ultimately allowing the tumor to evade immune surveillance.

    “However, as the tumor progresses, osteosarcoma cells evolve to generate variants that are resistant to the actions of DCs and phagocytes. This resistance results in diminished DC activation, ultimately allowing the tumor to evade the immune surveillance [60].”

    From Advances on immunotherapy for osteosarcoma (2024) · cites this paper as [60]

    Results §3.1 (page unavailable) says that, as tumors grow, some cancer cells become resistant to detection by naive dendritic cells; dendritic-cell numbers then decline, in what the authors call a likely immune-escape phase. It does not say the variants resist phagocytes or directly establish diminished dendritic-cell activation, and it presents immune escape as likely rather than certain. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Le, T., Su, S., Kirshtein, A., & Shahriyari, L.
Affiliations
  • University of Massachusetts Amherst (US)
  • Tufts University (US)
Corresponding author
Shahriyari, Leili
ORCID
Venue
Cancers
Publisher
MDPI AG
Published
2021-05-14
Volume
13
Issue
10
Pages
2367
ISSN
2072-6694 (ISSN-L)
DOI
10.3390/cancers13102367
PMC
PMC8156666
OpenAlex
W3161179735
Cited by
39
References
8
Citations by year
2026: 7 · 2025: 7 · 2024: 5 · 2023: 10 · 2022: 8 · 2021: 2
FWCI
2.51
Type
journal article
Language
en
Open access
Licence
Locations
  • Cancers (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • ScholarWorks@UMassAmherst (University of Massachusetts Amherst) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
National Institutes of Health (R21CA242933)
Topics
  • Physical Sciences › Mathematics › Modeling and Simulation › Mathematical Biology Tumor Growth
  • Life Sciences › Immunology and Microbiology › Immunology › Immune cells in cancer
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Mathematical and Theoretical Epidemiology and Ecology Models
Keywords
osteosarcoma, tumor microenvironment, immune cells, tumor progression, macrophages, regulatory T cells, dendritic cells, T helper cells, cytotoxic T lymphocytes, interferon-gamma, cytokines, gene expression profiles, tumor growth kinetics, tumor immunology

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