Complex immune microenvironment of chordoma: a road map for future treatment

Abstract

BackgroundChordoma, a rare bone tumor, presents limited treatment options and patients typically exhibit poor survival outcomes. While immunotherapy has shown promising results in treating various tumors, research on the immune microenvironment of chordomas is still in its early stages. Therefore, understanding how the immune microenvironment of chordomas influences the outcomes of immunotherapy is crucial.MethodsWe employed single-cell RNA sequencing (scRNA-seq), bulk RNA-seq, CellChat, gene set variation analysis, as well as calculation of immune features to further dissect the complex immune microenvironment of chordoma.ResultsPrevious research by van Oostet alargued that compared with other sarcomas, chordomas typically exhibit an immunologically “hot” microenvironment, a conclusion with which we concur based on their research findings. Additionally, the authors suggest that T cell-mediated immunotherapy is feasible for the majority of chordomas. However, we are inclined to categorize them as an immune-excluded phenotype according to the latest classification methods, rather than persisting with the concepts of “cold” and “hot”. Unlike them, we explored immune infiltration scores (IS), T lymphocyte scoring (TLS), and human leucocyte antigen class I (HLA-I) using Bulk RNA-seq data from 126 chordoma patients and found that higher IS, TLS, and higher HLA-I expression were associated with poorer patient prognosis. Additionally, CellChat analysis of scRNA-seq results from six chordoma patients revealed no direct interaction between T cells and tumor cells.ConclusionsThese findings suggested that the efficacy of T cell-based immunotherapy may be limited or even ineffective for patients with chordoma.

The tumor-stroma ratio in giant cell tumor of bone: associations with the immune microenvironment and responsiveness to denosumab treatment

“…X-Tile software was used to determine cutoff values for continuous variables in survival analysis, where overall survival (OS) was the outcome parameter [ 24 ]. Patients were classified into two subgroups based on the cutoff point (≤ cutoff or > cutoff) and adjusted accordingly [ 25 ]. Univariate analysis was conducted using the Kaplan–Meier method to analyze the differences in survival between groups.…”