Acute myeloid leukemia (AML) is a biologically and genetically heterogeneous hematological malignance with an unsatisfactory risk stratification system. Recently, through the novel single-cell RNA sequencing technology, we revealed heterogeneous leukemia myeloblasts in RUNX1-RUNX1T1 AML. Thyrotropin-releasing hormone (TRH), as biomarkers of CD34⁺CD117^bri myeloblasts, were found to be prognostic in RUNX1-RUNX1T1 AML. However, the clinical and genetic features of TRH in AML patients are poorly understood. Here, with data from TCGA AML, TRH was found to be downregulated in patients older than 60 years old, with DNMT3A and NPM1 mutations, while overexpressed in patients with KIT mutations. This was further validated in three other cohorts of primary AML including Beat AML (n = 223), GSE6891 (n = 461), and GSE17855 (n = 237). Furthermore, we demonstrated that the expression of TRH in AML could be used to improve the ELN 2017 risk stratification system. In conclusion, our preliminary analysis revealed that TRH, a novel biomarker for AML patients, could be used to evaluate the survival of AML.

Background: Acute Myeloid Leukemia (AML) is a hematological cancer characterized by heterogeneous hematopoietic cells. Through the use of multidimensional sequencing technologies, we previously identified a distinct myeloblast population, CD34⁺CD117^dim, the proportion of which was strongly associated with the clinical outcome in t (8;21) AML. In this study, we explored the potential value of the CD34⁺CD117^dim population signature (117DPS) in AML stratification. Methods: Based on the CD34⁺CD117^dim gene signature, the least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to construct the 117DPS model using the gene expression data from Gene Expression Omnibus (GEO) database (GSE37642-GPL96 was used as training cohort; GSE37642-GPL570, GSE12417-GPL96, GSE12417-GPL570 and GSE106291 were used as validation cohorts). In addition, the RNA-seq data from The Cancer Genome Atlas (TCGA)-LAML and Beat AML projects of de-novo AML patients were also analyzed as validation cohorts. The differences of clinical features and tumor-infiltrating lymphocytes were further explored between the high-risk score group and low-risk score group. Results: The high-risk group of the 117DPS model exhibited worse overall survival than the low-risk group in both training and validation cohorts. Immune signaling pathways were significantly activated in the high-risk group. Patients with high-risk score had a distinct pattern of infiltrating immune cells, which were closely related to clinical outcome. Conclusion: The 117DPS model established in our study may serve as a potentially valuable tool for predicting clinical outcome of patients with AML.

Acute myeloid leukemia (AML) is a highly heterogeneous hematological malignancy that imposes great challenges in terms of drug resistance and relapse. Previous studies revealed heterogeneous leukemia cells and their relevant gene markers, such as CRIP1 as clinically prognostic in t (8;21) AML patients. However, the expression and role of CRIP1 in AML are poorly understood. We used the single-cell RNA sequencing and gene expression data from t (8;21) AML patients to analyze the immune and regulation networks of CRIP1. Two independent cohorts from GSE37642 and The Cancer Genome Atlas (TCGA) datasets were employed as validation cohorts. In addition, the methylation data from TCGA were used to analyze the methylation effect of the CRIP1 expression. Gene expression profile from t (8;21) AML patients showed that the CRIP1-high group exhibited an enrichment of immune-related pathways, including tumor necrosis factor (TNF)α signaling via nuclear factor kappa B (NFκB) pathways. Further studies using CIBERSORT showed that the CRIP1-high group had a significantly higher infiltration of exhausted CD8 T cells and activated mast cells. The CRIP1 expression was validated in the GSE37642-GPL96, GSE37642-GPL570, and TCGA datasets. In addition, with the methylation data, four CpG probes of CRIP1 (cg07065217, cg04411625, cg25682097, and 11763800) were identified as negatively associated with the CRIP1 gene expression in AML patients. Our data provide a comprehensive overview of the regulation of CRIP1 expression in AML patients. The evaluation of the TNFα-NFκB signaling pathway as well as the immune heterogeneity might provide new insights for exploring improvements in AML treatment.

Background: Comprehensive analysis of single-cell transcriptome and chromatin accessibility will contribute to interpret the heterogeneity of acute myeloid leukemia (AML). We hypothesize that integrating single-cell transcriptomic and chromatin accessibility landscapes underlying t(8;21) AML will provide valuable insights into its heterogeneous cellular properties and gene regulatory programs. Methods: Here, we conducted paired single-cell RNA-sequencing (scRNA-seq) and single-cell ATAC sequencing on bone marrow samples from newly diagnosed t(8;21) AML patients and healthy controls. Genetic signatures extracted from scRNA-seq were built and validated across three independent cohorts (German AMLCG1999, GSE106291 and TCGA LAML). Results: We identified TCF12, a core component of AML1-ETO-containing transcription factor complex (AETFC), as the most active transcription factor in blast cells, driving a universally repressed chromatin state. Furthermore, we delineated two functionally distinct T cell subsets, revealing that EOMES-mediated transcriptional regulation promotes the expansion of a cytotoxic T cell population (T cells_2; high GNLY, NKG7 and GZMB expression), with an increased clonality and a tendency for drug resistance. In addition, we discovered a novel leukemic CMP-like cluster characterized by high TPSAB1, HPGD and FCER1A expression. Leveraging machine learning-based integration of multi-omic profiles, we identified a robust 9-gene prognostic signature, which demonstrated significant predictive value for AML outcomes across three independent cohorts. Conclusions: This multi-omics study provides unprecedented insights into the transcriptional and epigenetic heterogeneity of t(8;21) AML, providing a potential actionable tool for clinical risk stratification.

Introduction: Immunotherapies targeting T cells in solid cancers are revolutionizing clinical treatment. Novel immunotherapies have had extremely limited benefit for acute myeloid leukemia (AML). Here, we characterized the immune microenvironment of t(8;21) AML patients to determine how immune cell infiltration status influenced prognosis.Methods: Through multi-omics studies of primary and longitudinal t(8;21) AML samples, we characterized the heterogeneous immune cell infiltration in the tumor microenvironment and their immune checkpoint gene expression. Further external cohorts were also included in this research.Results: CD8+ T cells were enriched and HAVCR2 and TIGIT were upregulated in the CD34⁺CD117^dim%-High group; these features are known to be associated with immune exhaustion. Data integration analysis of single-cell dynamics revealed that a subset of T cells (cluster_2) (highly expressing GZMB, NKG7, PRF1 and GNLY) evolved and expanded markedly in the drug-resistant stage after relapse. External cohort analysis confirmed that the cluster_2 T-cell signature could be utilized to stratify patients by overall survival outcome.Discussion: In conclusion, we discovered a distinct T-cell signature by scRNA-seq that was correlated with disease progression and drug resistance. Our research provides a novel system for classifying patients based on their immune microenvironment.

Acute myeloid leukemia (AML) with t(8;21)(q22;q22);(RUNX1::RUNX1T1) is highly heterogeneous and malignant. It has a relapse rate of nearly 40 %, resulting in clinical resistance or refractoriness to chemotherapy. Immune cells, particularly CD4(+) T and CD8(+) T lymphocytes, have been discovered to be dysfunctional in this condition, and functional recovery shows promising efficiency in preclinical trials. Here, with single-cell transcriptomic data from de novo AML patients with RUNX1::RUNX1T1 and at various stages following disease progression, we investigated the genes correlated with T-cell proliferation and activation. In leukemia cells, ADA, AHCY, GPN3 and LTBR were markedly highly expressed compared to those in T-cell at diagnosis, and they tended to increase with disease progression. Additionally, we discovered that AHCY was an effective biomarker to predict the overall survival as well as relapse-free survival of AML patients with RUNX1::RUNX1T1. The correlation of AHCY with infiltrated immune cells and immune checkpoints was also investigated. AML cohorts from two other independent studies, TCGA LAML (n = 145) and the GEO dataset (n = 104), also demonstrated an inferior outcome for AML patients with high AHCY expression. In conclusion, our research revealed that AHCY might function as a novel indicator to predict the prognosis and efficiency of T-cell proliferation and activation in AML patients with RUNX1::RUNX1T1.