Targeted Therapy in Pediatric AML: An Evolving Landscape

AML/T cell interactomics uncover correlates of patient outcomes and the key role of ICAM1 in T cell killing of AML

T cells are important for the control of acute myeloid leukemia (AML), a common and often deadly malignancy. We observed that some AML patient samples are resistant to killing by human-engineered cytotoxic CD4+ T cells. Single-cell RNA-seq of primary AML samples and CD4+ T cells before and after their interaction uncovered transcriptional programs that correlate with AML sensitivity or resistance to CD4+ T cell killing. Resistance-associated AML programs were enriched in AML patients with poor survival, and killing-resistant AML cells did not engage T cells in vitro. Killing-sensitive AML potently activated T cells before being killed, and upregulated ICAM1, a key component of the immune synapse with T cells. Without ICAM1, killing-sensitive AML became resistant to killing by primary ex vivo-isolated CD8+ T cells in vitro, and engineered CD4+ T cells in vitro and in vivo. While AML heterogeneity implies that multiple factors may determine their sensitivity to T cell killing, these data show that ICAM1 acts as an immune trigger, allowing T cell killing, and could play a role in AML patient survival in vivo.

The role of flow cytometry in the classification of myeloid disorders

The World Health Organization classification (WHO-HAEM5) and the International Consensus Classification (ICC 2022) of myeloid neoplasms are based on the integration of clinical, morphologic, immunophenotypic, and genomic data. Flow cytometric immunophenotyping (FCIP) allows the identification, enumeration, and characterization of hematopoietic cells, and is therefore a powerful tool in the diagnosis, classification, and monitoring of hematological neoplasms. The vast majority of flow cytometry (FCM) studies in chronic myeloid neoplasms focus on its role in myelodysplastic neoplasms (MDS). FCM can also be helpful for the assessment of myeloproliferative neoplasms (MPN) and MDS/MPN, including the early detection of evolving myeloid or lymphoid blast crisis and the characterization of monocytic subsets. The classification of acute myeloid leukemia (AML) is primarily based on cytogenetic and molecular findings; however, FCIP is needed for subclassification of AML, not otherwise specified (NOS; ICC)/AML defined by differentiation (WHO-HAEM5). The main role of FCM in AML remains in making a rapid diagnosis and as a tool for measurable residual disease monitoring. Machine learning and artificial intelligence approaches can be used to analyze and classify FCM data. This article, based on an invited lecture at the 106th Annual Meeting of the German Society of Pathology in 2023, reviews the role of FCM in the classification of myeloid neoplasms, including recent publications on the application of artificial intelligence.

Identification and analysis of methylation signature genes and association with immune infiltration in pediatric acute myeloid leukemia

BACKGROUND

Acute myeloid leukemia (AML) is a common leukemia with low cure rate and poor prognosis among pediatric patients. The regulation of AML immune microenvironment and methylation remains to be explored. Pediatric and adult AML patients differ significantly in epigenetic factors, and the efficiency of treatment modalities varies between the two groups of patients.

METHODS

We collected mRNA, miRNA and DNA methylation data from pediatric AML patients across multiple databases. Differentially expression genes were identified, and a gene-miRNA regulatory network was constructed. Prognostic risk models were established by integrating LASSO and Cox regression, and a nomogram was generated. Based on this model, we investigated tumor-infiltrating immune cells and cell communication, analyzing the biological functions and pathways associated with prognostic factors. Furthermore, the relationships between all prognostic factors and gene modules were explored, and the impact of these factors on treatment modalities was determined.

RESULTS

We developed an efficient prognostic risk model and identified HOXA9, SORT1, SH3BP5, mir-224 and mir-335 as biomarkers. We validated these findings in an external dataset and observed a correlation between age and risk in pediatric patients. AML samples with lower risk scores have a better prognosis and higher expression of immune-upregulated biomarkers, and have lower immune scores. Furthermore, we detected discrepancies in immune cell infiltration and interactions between high- and low-risk group samples, which affected the efficacy of immunotherapy. We evaluated all prognostic factors and predicted the effect of immunotherapy and medicine.

CONCLUSION

This study comprehensively investigated the role of methylation signature genes in pediatric AML at the level of genomes and transcriptomes. The research aims to enhance the risk stratification, prognosis evaluation and assessment of treatment effectiveness of AML patients. This study also highlight the uniqueness of pediatric AML and foster the development of new immunotherapy and targeted therapy strategies.

AML/T cell interactomics uncover correlates of patient outcomes and the key role of ICAM1 in T cell killing of AML

T cells are important for the control of acute myeloid leukemia (AML), a common and often deadly malignancy. We observed that some AML patient samples are resistant to killing by human engineered cytotoxic CD4 + T cells. Single-cell RNA-seq of primary AML samples and CD4 + T cells before and after their interaction uncovered transcriptional programs that correlate with AML sensitivity or resistance to CD4 + T cell killing. Resistance-associated AML programs were enriched in AML patients with poor survival, and killing-resistant AML cells did not engage T cells in vitro . Killing-sensitive AML potently activated T cells before being killed, and upregulated ICAM1 , a key component of the immune synapse with T cells. Without ICAM1, killing-sensitive AML became resistant to killing to primary ex vivo -isolated CD8 + T cells in vitro , and engineered CD4 + T cells in vitro and in vivo . Thus, ICAM1 on AML acts as an immune trigger, allowing T cell killing, and could affect AML patient survival in vivo .

SIGNIFICANCE

AML is a common leukemia with sub-optimal outcomes. We show that AML transcriptional programs correlate with susceptibility to T cell killing. Killing resistance-associated AML programs are enriched in patients with poor survival. Killing-sensitive, but not resistant AML activate T cells and upregulate ICAM1 that binds to LFA-1 on T cells, allowing immune synapse formation which is critical for AML elimination.

GRAPHICAL ABSTRACT

Landscape of internal N7-methylguanosine of long non-coding RNA modifications in resistant acute myeloid leukemia

BACKGROUND

Growing evidence indicates that RNA methylation plays a fundamental role in epigenetic regulation, which is associated with the tumorigenesis and drug resistance. Among them, acute myeloid leukemia (AML), as the top acute leukemia for adults, is a deadly disease threatening human health. Although N7-methylguanosine (m7G) has been identified as an important regulatory modification, its distribution has still remained elusive.

METHODS

The present study aimed to explore the long non-coding RNA (lncRNA) functional profile of m7G in AML and drug-resistant AML cells. The transcriptome-wide m7G methylation of lncRNA was analyzed in AML and drug-resistant AML cells. RNA MeRIP-seq was performed to identify m7G peaks on lncRNA and differences in m7G distribution between AML and drug-resistant AML cells. The Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted to predict the possible roles and m7G-associated pathway.

RESULTS

Using m7G peak sequencing, it was found that a sequence motif was necessary for m7G methylation in drug-resistant AML lncRNA. Unsupervised hierarchical cluster analysis confirmed that lncRNA m7G methylation occurred more frequently in drug-resistant AML cells than in AML cells. RNA sequencing demonstrated that more genes were upregulated by methylation in drug-resistant AML cells, while methylation downregulated more genes in AML cells. The GO and KEGG pathway enrichment analyses revealed that genes having a significant correlation with m7G sites in lncRNA were involved in drug-resistant AML signaling pathways.

CONCLUSION

Significant differences in the levels and patterns of m7G methylation between drug-resistant AML cells and AML cells were revealed. Furthermore, the cellular functions potentially influenced by m7G in drug-resistant AML cells were predicted, providing evidence implicating m7G-mediated lncRNA epigenetic regulation in the progression of drug resistance in AML. These findings highlight the involvement of m7G in the development of drug resistance in AML.