Evidence map›Paper›PMID 34068946›Full record

ArticleCancers2021

Data-Driven Mathematical Model of Osteosarcoma.

Trang Le, Sumeyye Su, Arkadz Kirshtein, Leili Shahriyari

Abstract read
In one paragraph

Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

28 citing papers in PubMed.

  1. Review
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  17. Review
  18. Strategies to Overcome Resistance to Immune-Based Therapies in Osteosarcoma.International journal of molecular sciences · 2023
    Review
  19. Article
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Trang LeDepartment of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003, USA.
Sumeyye SuDepartment of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003, USA.ORCID 0000-0001-5221-413X
Arkadz KirshteinDepartment of Mathematics, Tufts University, Medford, MA 02155, USA.ORCID 0000-0002-1205-3359
Leili ShahriyariDepartment of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003, USA.ORCID 0000-0001-6234-8449

Funding

Data-driven QSP software for personalized colon cancer treatmentR21CA242933 · NCI · UNIVERSITY OF MASSACHUSETTS AMHERST · PI PAL, SUVRA, ROY, SOUVIK · 2019 to 2020
$547k
NCI NIH HHS R21 CA242933NIH HHS R21CA242933
6 · The paper itself

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.

Indexed as

data-driven mathematical modelgene expression profilesimmune interactionsimmune variationsosteosarcomasensitivity analysistumor deconvolutiontumor microenvironment

Identifiers

PMID34068946
PMCPMC8156666

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.