Journal or Book Title
MATHEMATICAL BIOSCIENCES AND ENGINEERING
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 gamma delta 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.
UMass Amherst Open Access Policy
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
National Cancer Institute of the National Institutes of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Cancer Institute (NCI) [R21CA242933]
Le, Trang; Su, Sumeyye; and Shahriyari, Leili, "Immune Classification of Osteosarcoma" (2021). MATHEMATICAL BIOSCIENCES AND ENGINEERING. 1323.