To the Editor: Acute myeloid leukemia (AML) is a heterogeneous disease characterized by proliferation of abnormal myeloblasts in the bone marrow. Traditional treatment, such as chemotherapy with or without hematopoietic stem cell transplantation (HSCT), has been used as the first-line therapy in AML. However, the clinical outcome of patients with AML varies greatly due to several factors, such as the tumor immune microenvironment (TME). Immune checkpoint inhibitors (ICIs) have recently gained popularity as therapeutic options for relapsed AML or measurable residual disease (MRD)-positive AML patients. Based on previous studies, immune dysregulation plays an important role in AML relapse, and immune-related genes may provide prognostic information for AML patients. Thus, exploring the immune-related genetic prognostic index (IRGPI) to predict prognosis and therefore provide personalized guidance for ICI therapy is urgently needed. In this study, we developed an immune-related genetic prognostic index for AML that was able to predict overall survival (OS) and ICI immunotherapy benefit. We focused on all immune-related genes in AML transcriptome data and screened immune-related genes associated with prognosis by weighted gene coexpression network analysis (WGCNA) to construct an immune-related gene prognostic index (IRGPI). We then characterized the immune and molecular features of the IRGPI, examined its prognostic ability for AML patients, and compared it with tumor immune dysfunction and exclusion (TIDE) as well as the tumor inflammation signature (TIS).[3,4] The results showed that the IRGPI is a promising prognostic biomarker for AML patients to predict the survival and benefits of ICI immunotherapy.
First, transcriptome and clinical information of the cohort The Cancer Genome Atlas (TCGA)-AML and all data for normal tissue samples from 336 whole blood samples in Genotype-Tissue Expression (GTEx) were retrieved via the UCSC Xena platform. RNA-seq data for 104 AML samples (GSE71014) and survival information were downloaded from the Gene Expression Omnibus (GEO). The clinical characteristics of 132 AML patients from TCGA-LAML used for survival analysis are listed in Supplementary Table 1, https://links.lww.com/CM9/B503. Lists of immune-related genes were downloaded from the ImmPort and InnateDB database.
A total of 14,672 differentially expressed genes were obtained in differential expression analysis. By intersecting these genes with the lists of immune-related genes obtained from ImmPort and InnateDB, 908 differentially expressed immune-related genes were identified, of which 189 genes were upregulated and 719 downregulated in tumor samples compared with normal samples. WGCNA was carried out on candidate genes to obtain immune-related hub genes (n = 908), revealing the top 117 immune-related hub genes with a threshold degree of >20. Expression of 24 of these immune-related hub genes correlated closely with AML patient OS, as determined by Kaplan–Meier (K–M) analysis (P <0.001).
To determine independent prognostic genes, multivariate Cox regression analysis for OS was performed among the 24 immune-related hub genes, and only seven genes (CLEC11A, IL1R2, IL1RL2, TRIM55, TREML2, CAMK2A, and BMP2) were found to significantly affect the OS of AML patients. Then, we constructed a prognostic index for all cancer samples calculated by the following formula: IRGPI = expression level of CLEC11A × (-0.59) + expression level of IL1R2 × 0.37 + expression level of IL1RL2 × 3.63 + expression level of TRIM55 × 4.98 + expression level of TREML2 × 1.25 + expression level of CAMK2A × 2.17 + expression level of BMP2 × 0.83. Taking the median IRGPI as the cut-off value, IRGPI-high patients had a lower OS than IRGPI-low patients (P <0.001) among AML samples from TCGA. Then, the role of the IRGPI was validated using the GSE71014 cohort. The patients in the IRGPI-low subgroup had a significantly better prognosis than those in the IRGPI-high subgroup (P = 0.044, data not shown), which was consistent with the result using the TCGA dataset and indicated the prognostic value of the IRGPI. Multivariate Cox regression analysis confirmed the IRGPI to be an independent prognostic factor after adjusting for other clinicopathologic factors (P <0.001).
Next, we explored the relationship between the IRGPI score and CD274 expression as well as CTLA4 and LAG3. The IRGPI score correlated significantly positively with CD274 expression (r = 0.42, P <0.001) and slightly with CTLA4 and LAG3 expression (r =0.3, P <0.001; r = 0.27, P <0.001) (data not shown).
To analyze the composition of immune cells in different IRGPI subgroups, we used the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm to evaluate differences in immune cells between the IRGPI-high and IRGPI-low groups. We found that monocytes and eosinophils were more abundant in the IRGPI-high subgroup but that resting mast cells were more abundant in the IRGPI-low subgroup. Further analysis showed that higher monocytes correlated with lower OS ability and that higher resting mast cells were associated with longer OS (data not shown).
We then used TIDE to evaluate the potential clinical efficacy of immunotherapy in the two IRGPI groups. Previous studies have shown that a higher TIDE prediction score is related to a higher potential for immune evasion, indicating that such patients are less likely to benefit from ICI therapy. Our results showed that the IRGPI-high group had a higher TIDE score than the IRGPI-low subgroup [Figure 1A], suggesting that IRGPI-low patients may benefit more from ICI therapy than IRGPI-high patients. Moreover, previous studies have shown that a higher TIDE prediction score is associated with a worse outcome. Therefore, IRGPI-low patients with a low TIDE score might have a better prognosis than IRGPI-high patients with a high TIDE score. In addition, the IRGPI-high group had a higher risk of T-cell exclusion and T-cell dysfunction than the IRGPI-low group [Figure 1B, C]. We also performed receiver operating characteristic (ROC) analysis of the IRGPI for OS at 1-year, 2-year, and 3-year follow-up, and Figure 1D shows a robust prediction ability at 3 years (AUC: 0.819). The area under the ROC curve for the IRGPI was higher than that for TIDE or TIS [Figure 1E]. Further analysis revealed a correlation between the IRGPI-high group and lower survival probability in the IMvigor210 cohort [Figure 1F], suggesting a strong prognosis ability compared with TIDE and TIS in AML and a possible indicator for ICI therapy benefit.
By using RNA-seq data of AML, we identified a prognostic biomarker, the IRGPI, for AML. The IRGPI proved to be a valid prognostic immune-related biomarker for AML, with better survival in IRGPI-low patients and worse survival in IRGPI-high patients in both TCGA and GEO cohorts.In our study, the composition of some immune cells differed between the two IRGPI subgroups: the IRGPI-high subgroup had higher levels of monocytes and neutrophils, whereas resting mast cells were significantly enriched in the IRGPI-low subgroup. It has been reported that monocytes can be driven by AML blasts to differentiate into the M2-like phenotype, which promotes a more immunosuppressive microenvironment and is averse to AML clearance. This is in accordance with the result that higher monocytes correlated with lower OS in our study. Therefore, the phenotype and functions of neutrophils in AML should be carefully evaluated in the future. Previous studies have proven that biomarkers, such as TIDE and TIS, can predict patient response to immunotherapy. TIDE can be used to identify two immune escape mechanisms that induce T-cell dysfunction in tumors with high CTL invasion and prevent T-cell invasion in tumors with low CTL levels. In addition, TIS provides both quantitative and qualitative information about the TME, and 18 genes that reflect an ongoing CD8 T-cell response have shown promising results in predicting response to anti-PD-1/PD-L1 agents. However, both TIDE and TIS focus on the immune function of T cells. Since the composition of immune cell subtypes and expression of immunosuppressive molecules were different between the two IRGPI subgroups, the IRGPI might reflect different immune benefits from ICR therapy identified with TIDE. Furthermore, both markers relate to patient response to immunotherapy rather than patient survival time. Encouragingly, the predictive value of the IRGPI in our study was comparable to that of TIDE and TIS, and the IRGPI might be a better predictor of OS at longer follow-up times. We further confirmed the prognostic value of the IRGPI in predicting the survival of patients who receive ICI therapy. Moreover, as the IRGPI is composed of only seven genes, it is easier to implement than TIDE and TIS.
In conclusion, the IRGPI is a promising immune-related prognostic biomarker that may help in distinguishing immune and molecular characteristics and predicting AML patient clinical outcomes. The IRGPI might serve as a prognostic indicator for response to ICI immunotherapy; however, our prognostic model was limited to data from public databases and lacked more prospective real-world data. Therefore, more studies are needed to verify our results. Second, as we only considered an index of immune-related genes, many prominent prognostic genes in AML might have been excluded. It is possible that the IRGPI can be applied jointly with other types of biomarkers to achieve higher prediction performance. Finally, it should be emphasized that the association between the risk score and immune activity has not yet been experimentally addressed.
This study was supported by grants from the National Natural Science Foundation of China (Nos. 81670166, 81870140, 82070184, and 81621001), Peking University People's Hospital Research and Development Funds (No. RDL2021-01), Beijing Life Oasis Public Service Center (No. CARTFR-01), and CSH Young Scholars and 3SBio Pharmaceutical joint research project (No. KYC2201001).
Conflicts of interest
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