Figure 5.
Correlation analyses between immune patterns and immune checkpoint molecules (ICMs). (A) Heatmap representing the expression profiles of 69 immune checkpoint genes among the 3 novel immune clusters and 5 histological pituitary adenoma (PA) subtypes. (B) Regulatory networks of ICMs and tumor-infiltrating immune cells (TIICs). The yellow ovals represent the TIICs, and the blue triangles represent the ICMs. The green/red lines indicate negative/positive correlations between the ICMs and the TIICs. (C) Regression analyses between immune checkpoint receptor-ligand pairs. (D) Expression of immune checkpoint receptors and ligands among the 3 immune clusters. (E) The subclass mapping analysis demonstrated that immune cluster 1 was more sensitive to anti-CTLA4 therapy (Bonferroni-corrected P = 0.037) and that immune cluster 2 was more sensitive to anti–programmed cell death protein 1 (PD1) therapy (Bonferroni-corrected P = 0.009). In this figure, “R” is short for immunotherapy respondent.

Correlation analyses between immune patterns and immune checkpoint molecules (ICMs). (A) Heatmap representing the expression profiles of 69 immune checkpoint genes among the 3 novel immune clusters and 5 histological pituitary adenoma (PA) subtypes. (B) Regulatory networks of ICMs and tumor-infiltrating immune cells (TIICs). The yellow ovals represent the TIICs, and the blue triangles represent the ICMs. The green/red lines indicate negative/positive correlations between the ICMs and the TIICs. (C) Regression analyses between immune checkpoint receptor-ligand pairs. (D) Expression of immune checkpoint receptors and ligands among the 3 immune clusters. (E) The subclass mapping analysis demonstrated that immune cluster 1 was more sensitive to anti-CTLA4 therapy (Bonferroni-corrected P = 0.037) and that immune cluster 2 was more sensitive to anti–programmed cell death protein 1 (PD1) therapy (Bonferroni-corrected P = 0.009). In this figure, “R” is short for immunotherapy respondent.

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