FLT3 mutation-related immune checkpoint molecule absent in melanoma 2 (AIM2) contributes to immune infiltration in pediatric and adult acute myeloid leukemia: evidence from bioinformatics analysis

Abstract

Background: The use of FMS-like tyrosine kinase 3 (FLT3) as a crucial target for kinase inhibitors is well established, but its association with immune infiltration remains unclear. This study aimed to explore the relationship between FLT3 mutations and immune checkpoint molecules (ICMs) in patients with acute myeloid leukemia (AML).

Methods: The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases were used to identify the ICMs associated with FLT3 mutations. A Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and gene set enrichment analysis (GSEA) were conducted to analyze the signaling pathways related to the ICMs. The single-sample GSEA (ssGSEA), Cibersort, and estimate algorithms were used to assess immune cell infiltration in AML.

Results: Absent in melanoma 2 (AIM2) exhibits elevated expression levels in AML patients harboring FLT3 mutation, contributing significantly to the progress of AML and establishing of an immunosuppressive microenvironment. AIM2 expression significantly correlated with sensitivity of clinically relevant drugs in ex vivo assays of AML. Additionally, AIM2 demonstrates substantial prognostic value and holds promise as a prospective immunotherapeutic target for AML. Our findings indicate a significant correlation between AIM2 and immune infiltration in AML cases, potentially affecting the presence of neutrophils, macrophages, effector memory T cells (Tem), and monocytes. Furthermore, AIM2 is closely linked to various signaling pathways, such as immune cytokine release, immune antigen presentation, and inflammasome signaling, which could play a role in immune cell enrichment in AML.

Conclusions: Our study identified AMI2 as an ICM linked to FLT3 mutations. AMI2 may be involved in the activation of suppressive immune cell populations, such as macrophages, neutrophils, and monocytes. AIM2 could serve as a promising immunotherapeutic target for combination therapy with FLT3 inhibitors in AML.

Keywords: Acute myeloid leukemia (AML); FMS-like tyrosine kinase 3 (FLT3); absent in melanoma 2 (AIM2); immune checkpoint; immune infiltration.

Figure 1

The relationship between FLT3 mutation and tumor immunity. FLT3 mutation-related AGs in AML were selected using data from TCGA database, including 135 FLT non-mutated and 58 FLT-mutated samples (A). Venn diagram representing the 11 ICMs related to FLT3 mutation in AML (B). Co-expression analysis of the relationship between 11 ICMs and FLT3 mutation (C), and the correlation among the 11 ICMs (D). Analysis of the biological functions of ICMs using the GO and KEGG databases (E). Heatmap showing six ICMs (AIM2, CCL1, CLU, NLRP2, TNFRSF18, and TNFRSF4) as key regulators in this biological process (F). FLT3, FMS-like tyrosine kinase 3; AG, activated gene; FC, fold change; ICM, immune checkpoint molecule; TPM, transcripts per million; BP, biological progress; MF, molecular function; KEGG, Kyoto Encyclopedia of Genes and Genomes; AML, acute myeloid leukemia; TCGA, The Cancer Genome Atlas; GO, Gene Ontology.

Figure 2

The correlation between six ICMs and the gene mutation in AML. The relationship between the expression of the six ICMs and the FLT3 mutation (A), IDH R132 mutation (B), IDH R140 mutation (C), RAS mutation (D), and NPM1 mutation (E) was analyzed using TCGA database. The expression of the six ICMs was also investigated in TCGA and GTEx databases (F). The GEO database (GSE2191) was used to detect AIM2 in pediatric AML and normal subjects (G). *, P<0.05; **, P<0.01; ***, P<0.001. FLT3, FMS-like tyrosine kinase 3; TPM, transcripts per million; IDH, isocitrate dehydrogenase; AML, acute myeloid leukemia; ICM, immune checkpoint molecule; TCGA, The Cancer Genome Atlas; GTEx, Genotype-Tissue Expression; GEO, Gene Expression Omnibus; AIM2, absent in melanoma 2.

Figure 3

The association between the six ICMs and the prognosis of AML patients. The correlation between the expression of six ICMs and the OS of AML patients was evaluated using TCGA database. HR, hazard ratio; CI, confidence interval; ICM, immune checkpoint molecule; AML, acute myeloid leukemia; OS, overall survival; TCGA, The Cancer Genome Atlas.

Figure 4

The association between AIM2 and immune infiltration. The ssGSEA (A), Cibersort (B), and estimate (C) algorithms were used to investigate the association between AIM2 and immune infiltration in AML patients. A subsequent analysis using the Cibersort algorithm revealed that the abundance of monocytes was higher in the AML patients with high AIM2 expression than those with low AIM2 expression (D). *, P<0.05; **, P<0.01; ***, P<0.001; ns, not significant. ssGSEA, single-sample gene set enrichment analysis; Tem, effector memory T cells; Th, T helper; iDC, immature dendritic cell; NK, natural killer; Tgd, gamma-delta T cells; DC, dendritic cell; TFH, T follicular helper; TReg, regulatory T cell; Tcm, central memory T cell; aDC, activated dendritic cell; pDC, plasmacytoid dendritic cell; AIM2, absent in melanoma 2; AML, acute myeloid leukemia.

Figure 5

Association between AIM2 expression and drug sensitivity in ex vivo assays in AML. Drug sensitivity according to AIM2 expression in ex vivo assays. Drugs with P<0.05, as determined by the Spearman correlation test, are indicated. AIM2, absent in melanoma 2; AML, acute myeloid leukemia.

Figure 6

AIM2 was found to be correlated with the biomarkers of neutrophils (A), macrophages (B), and monocytes (C) in AML. TPM, transcripts per million; AIM2, absent in melanoma 2; AML, acute myeloid leukemia.

Figure 7

Correlation between AIM2 and popular ICMs in AML. The relationship between AIM2 and 36 prevalent ICMs in AML is currently under investigation to validate the association between AIM2 and immune cell enrichment. AIM2, absent in melanoma 2; ICM, immune checkpoint molecule; AML, acute myeloid leukemia.

Figure 8

AIM2-related immune regulatory signaling pathway. To enhance understandings of the molecular signaling pathways associated with AIM2-mediated immune infiltration in AML, we first screened AIM2-related differentially expressed genes using the TCGA database, and then conducted a GSEA to investigate signal pathway enrichment. SARS-CoV-2, severe acute respiratory syndrome coronavirus 2; NF, nuclear factor; KEGG, Kyoto Encyclopedia of Genes and Genomes; BCR, B-cell receptor; IL, interleukin; NES, normalized effect size; PID, primary immunodeficiency; Th, T helper; MHC, major histocompatibility complex; AIM2, absent in melanoma 2; AML, acute myeloid leukemia; TCGA, The Cancer Genome Atlas; GSEA, gene set enrichment analysis.

Figure 9

AIM2-related canonical inflammasomes signaling pathway. A significant positive correlation was found between AIM2 and key inflammasome components, including CASP1, PYCARD, IL-18, IL-10, IL-1B, and NLRP3 (A). The association between key inflammasome components and FLT3 mutation status in AML (B). The expression of key inflammasome components was also investigated in TCGA and GTEx databases (C). *, P<0.05; **, P<0.01; ***, P<0.001. TPM, transcripts per million; AIM2, absent in melanoma 2; AML, acute myeloid leukemia; FLT3, FMS-like tyrosine kinase 3; TCGA, The Cancer Genome Atlas; GTEx, Genotype-Tissue Expression; CASP1, caspase 1; PYCARD, a N-terminal PYRIN-PAAD-DAPIN domain) and a C-terminal caspase-recruitment domain containing; IL-18, interleukin-18; IL-10, interleukin-10; IL-1B, interleukin-1β; NLRP3, NOD-, LRR-, and pyrin domain-containing protein 3.