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Cited by 35Sarcoma Diagnosis and TreatmentCancer Immunotherapy and BiomarkersSingle-cell and spatial transcriptomics

Immune classification of osteosarcoma

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Le, Trang, Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA MA 01003-9305, USA, Su, Sumeyye, Shahriyari, Leili·2021·Mathematical Biosciences and Engineering
DOI 10.3934/mbe.2021098PMC PMC7992873
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Abstract

Tumor immune microenvironment has been shown to be important in predicting the tumor progression and the outcome of treatments. This work aims to identify different immune patterns in osteosarcoma and their clinical characteristics. We use the latest and best performing deconvolution method, CIBERSORTx, to obtain the relative abundance of 22 immune cells. Then we cluster patients based on their estimated immune abundance and study the characteristics of these clusters, along with the relationship between immune infiltration and outcome of patients. We find that abundance of CD8 T cells, NK cells and M1 Macrophages have a positive association with prognosis, while abundance of γδ T cells, Mast cells, M0 Macrophages and Dendritic cells have a negative association with prognosis. Accordingly, the cluster with the lowest proportion of CD8 T cells, M1 Macrophages and highest proportion of M0 Macrophages has the worst outcome among clusters. By grouping patients with similar immune patterns, we are also able to suggest treatments that are specific to the tumor microenvironment.

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  • CD8+ T cells have a positive association with prognosis, whereas γδT cells are associated with poor prognosis.

    “A study claimed that CD8+ T cells have a positive association with prognosis, whereas γδT cells are associated with poor prognosis [58].”

    From Advances on immunotherapy for osteosarcoma (2024) · cites this paper as [58]

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Cited by

Sorted by citations

Authors
Le, T., Department of Mathematics and Statistics, U. O. M. A., Su, S., & Shahriyari, L.
Affiliations
University of Massachusetts Amherst (US)
Corresponding author
Shahriyari, Leili
ORCID
Venue
Mathematical Biosciences and Engineering
Publisher
American Institute of Mathematical Sciences (AIMS)
Published
2021
Volume
18
Issue
2
Pages
1879-1897
ISSN
  • 1547-1063 (ISSN-L)
  • 1551-0018
DOI
10.3934/mbe.2021098
PMC
PMC7992873
OpenAlex
W3131661228
Cited by
35
References
8
Citations by year
2026: 4 · 2025: 6 · 2024: 3 · 2023: 9 · 2022: 6 · 2021: 7
FWCI
4.06
Type
journal article
Language
en
Open access
Licence
cc-by-nc-sa
Locations
  • Mathematical Biosciences & Engineering (publishedVersion, cc-by-nc-sa)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion, cc-by)
  • Scholarworks (University of Massachusetts Amherst) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
National Cancer Institute (R21CA242933)
Topics
  • Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Sarcoma Diagnosis and Treatment
  • Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
Keywords
osteosarcoma, tumor immune microenvironment, immune cell deconvolution, CIBERSORTx, immune cell abundance, CD8+ T cells, M1 macrophages, M0 macrophages, γδ T cells, mast cells, dendritic cells, natural killer cells, patient clustering, prognostic factors, treatment response
MeSH terms
Bone Neoplasms, Humans, Macrophages, Osteosarcoma, CD8-Positive T-Lymphocytes, Tumor Microenvironment

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