CD86 Is Associated with Immune Infiltration and Immunotherapy Signatures in AML and Promotes Its Progression

Abstract

Background: Cluster of differentiation 86 (CD86), also known as B7-2, is a molecule expressed on antigen-presenting cells that provides the costimulatory signals required for T cell activation and survival. CD86 binds to two ligands on the surface of T cells: the antigen CD28 and cytotoxic T lymphocyte-associated protein 4 (CTLA-4). By binding to CD28, CD86-together with CD80-promotes the participation of T cells in the antigen presentation process. However, the interrelationships among CD86, immunotherapy, and immune infiltration in acute myeloid leukemia (AML) are unclear.

Methods: The immunological effects of CD86 in various cancers (including on chemokines, immunostimulators, MHC, and receptors) were evaluated through a pan-cancer analysis using TCGA and GEO databases. The relationship between CD86 expression and mononucleotide variation, gene copy number variation, methylation, immune checkpoint blockers (ICBs), and T-cell inflammation score in AML was subsequently examined. ESTIMATE and limma packages were used to identify genes at the intersection of CD86 with StromalScore and ImmuneScore. Subsequently, GO/KEGG and PPI network analyses were performed. The immune risk score (IRS) model was constructed, and the validation set was used for verification. The predictive value was compared with the TIDE score.

Results: CD86 was overexpressed in many cancers, and its overexpression was associated with a poor prognosis. CD86 expression was positively correlated with the expression of CTLA4, PDCD1LG2, IDO1, HAVCR2, and other genes and negatively correlated with CD86 methylation. The expression of CD86 in AML cell lines was detected by QRT-PCR and Western blot, and the results showed that CD86 was overexpressed in AML cell lines. Immune infiltration assays showed that CD86 expression was positively correlated with CD8 T cell, Dendritic cell, macrophage, NK cell, and Th1_cell and also with immune examination site, immune regulation, immunotherapy response, and TIICs. ssGSEA showed that CD86 was enriched in immune-related pathways, and CD86 expression was correlated with mutations in the genes RB1, ERBB2, and FANCC, which are associated with responses to radiotherapy and chemotherapy. The IRS score performed better than the TIDE website score.

Conclusion: CD86 appears to participate in immune invasion in AML and is an important player in the tumor microenvironment in this malignancy. At the same time, the IRS score developed by us has a good effect and may provide some support for the diagnosis of AML. Thus, CD86 may serve as a potential target for AML immunotherapy.

Figure 1

CD86 was associated with immunoassay sites in pan-cancers. (a) Correlation between CD86 and immunomodulators (chemokines, receptors, MHC, and immunostimulators). (b–e) Correlation between CD86 and four immune checkpoints, PDCD1, CTLA4, CD274, and LAG3. The dots represent cancer types. The Y-axis represents the Pearson correlation, while the X-axis represents –log10P. (f) Correlation between diffuse carcinoma and 28 tumor-associated immune cells calculated with the ssGSEA algorithm. The color indicates visual cues the correlation coefficient (red is positive, blue is negative). The asterisks indicate a statistically significant P value calculated using Spearman correlation analysis. (^∗P < 0.05).

Figure 2

SNV, CNV, and methylation analysis of CD86 in AML. (a) KM curve with CD86 as cutoff in LAML; (b) mutation distribution of the top 10 genes with the highest mutation frequency in the median group of CD86 expression; (c) comparison of TMB distribution of CD86 expression median group; (d) CD86 gene expression difference among CD86 gene amplification groups; (e) correlation analysis between expression of CD86 gene and methylation. (f) The mRNA expression of CD86 in AML cell lines was detected by QRT-PCR. (g) Western blot was used to detect the expression of CD86 in AML cell lines.

Figure 3

CD86 was correlated with immunoassay sites. (a) Differences in expression of immunomodulators (chemokines, receptors, MHC, and immune stimulants) in LAML between the high and low CD86 groups; (b) differences in immune cell scores between high CD86 and low CD86 groups; (c) effector gene differences in immune cells associated with 5 TIICs (CD8 + T cells, NK cells, macrophages, Th1 cells, and dendritic cells) between the high and low CD86 groups; (d) correlation between CD86 and immune checkpoints. The colors and values represent Spearman correlation coefficients. (^∗P < 0.05; ^∗∗P < 0.01; ^∗∗∗P < 0.001; ^∗∗∗∗P < 0.0001; blank, P > 0.05).

Figure 4

CD86 predicts progression of immune checkpoint blockades (ICBs) in LAML. (a)–(b) Correlations between CD86 and the pan-cancer T-cell inflamed score and the individual genes included in the T-cell inflamed signature. The T-cell inflamed score is positively correlated with the clinical response to cancer immunotherapy; (c) correlations between CD86 and molecular subtypes using seven different algorithms and AML signatures; (d) mutational profiles of neoadjuvant chemotherapy-related genes in low- and high-CD86 groups. (e) correlations between CD86 and the enrichment scores of several therapeutic signatures such as targeted therapy and radiotherapy.

Figure 5

CD86, StromalScore, and ImmuneScore differential gene screening and PPI analysis. (a) Intersection of up-regulated genes in CD86, StromalScore, and ImmuneScore; (b) intersection of down-regulated genes in CD86, StromalScore, and ImmuneScore; (c)–(f) GO and KEGG functional enrichment analysis of differentially expressed genes in CD86, StromalScore, and ImmuneScore.

Figure 6

PPI model and KEGG/GO analysis. (a) PPI analysis diagram of module Mcode1; (b)–(e) GO and KEGG functional enrichment analysis of Mcode1 gene.

Figure 7

IRS construction and validation. (a) LASSO coefficient profiles of 40 prognostic RNAs in GEO training cohort. The coefficient profile plot was developed against the log (lambda) sequence; (b) the forest map shows the genetic multifactorial results of the final IRS model; (c) KM and ROC analysis of IRS model on TCGA training dataset; (d) KM and ROC analysis of IRS model on TCGA validation dataset; (e) KM and ROC analysis of IRS model on all TCGA data sets; (f) KM and ROC analysis of IRS model on all datasets of GSE10358; (g) KM and ROC analysis of the IRS model on the entire dataset of GSE37642.

Figure 8

Different risk IRS typing groups were associated with immunity. (a) Differential expression of high- and low-risk group and concentration of chemokine, immunostimulator, MHC, and receptor genes; (b) The CD86 expression level in IRS high- and low-risk group; (c) T-cell validation score of generalized carcinoma in IRS high- and low-risk group; (d) ssGSEA showed the correlation between IRS high- and low-risk group and 28 kinds of immune cells; (e) correlation between high- and low-risk groups and immunoassay sites.

Figure 9

Performance comparison of IRS and TIDE (a) IRS survival curve and ROC curve of dataset IMvigor210; (b) TIDE survival curve and ROC curve of dataset IMvigor210; (c) ROC curves of IRS and TIDE effect on immunotherapy in dataset IMvigor210; (d) IRS survival curve and ROC curve of dataset GSE91061; (e) TIDE survival curve and ROC curve of dataset GSE91061; (f) ROC curve of IRS and TIDE effect on immunotherapy in dataset GSE91061; (g) IRS survival curve and ROC curve of dataset GSE78220; (h) TIDE survival curve and ROC curve of dataset GSE78220; (i) ROC curves of IRS and TIDE effect on immunotherapy in dataset GSE78220; (j) IRS survival curve and ROC curve of dataset GSE135222; (k) TIDE survival curve and ROC curve of dataset GSE135222; (l) ROC curves of IRS and TIDE effects on immunotherapy in dataset GSE135222.