Acute myeloid leukemia (AML) is still incurable due to its heterogeneity and complexity of tumor microenvironment. It is imperative therefore to understand the molecular pathogenesis of AML and identify leukemia-associated biomarkers to formulate effective treatment strategies. Here, we systematically analyzed the clinical characters and natural killer (NK) cells portion in seventy newly-diagnosis (ND) AML patients. We found that the proportion of NK cells in the bone marrow of ND-AML patients could predict the prognosis of patients by analyzing the types and expression abundance of NK related ligands in tumor cells. Furthermore, MCL1 inhibitor but not BCL2 inhibitor combined with NK cell-based immunotherapy could effectively improve the therapeutic efficiency via inhibiting proliferation and inducing apoptosis of AML primary cells as well as cell lines in vitro. There results provide valuable insights that could help for exploring new therapeutic strategies for leukemia treatment.

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.

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.

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: Extranodal natural killer (NK)/T-cell lymphoma (ENKTL) is a rare aggressive type of non-Hodgkin's lymphoma associated with Epstein-Barr virus infect