Acute promyelocytic leukemia: Immunophenotype and differential diagnosis by flow cytometry

NET-related gene signature for predicting AML prognosis

Acute Myeloid Leukemia (AML) is a malignant blood cancer with a high mortality rate. Neutrophil extracellular traps (NETs) influence various tumor outcomes. However, NET-related genes (NRGs) in AML had not yet received much attention. This study focuses on the role of NRGs in AML and their interaction with the immunological microenvironment. The gene expression and clinical data of patients with AML were downloaded from the TCGA-LAML and GEO cohorts. We identified 148 NRGs through the published article. Univariate Cox regression was used to analyze the association of NRGs with overall survival (OS). The least absolute shrinkage and selection operator were utilized to assess the predictive efficacy of NRGs. Kaplan-Meier plots visualized survival estimates. ROC curves assessed the prognostic value of NRG-based features. A nomogram, integrating clinical information and prognostic scores of patients, was constructed using multivariate logistic regression and Cox proportional hazards regression models. Twenty-seven NRGs were found to significantly impact patient OS. Six NRGs-CFTR, ENO1, PARVB, DDIT4, MPO, LDLR-were notable for their strong predictive ability regarding patient survival. The ROC values for 1-, 3-, and 5-year survival rates were 0.794, 0.781, and 0.911, respectively. In the training set (TCGA-LAML), patients in the high NRG risk group showed a poorer prognosis (p < 0.001), which was validated in two external datasets (GSE71014 and GSE106291). The 6-NRG signature and corresponding nomograms exhibit superior predictive accuracy, offering insights for pre-immune response evaluation and guiding future immuno-oncology treatments and drug selection for AML patients.

MPO Expression of Background Neutrophils in MPO Negative Acute Promyelocytic Leukemia, An Easy Clue to Corroborate a Challenging Diagnosis: A Case Report and Review of Literature

Acute promyelocytic leukemia (APL) is characterized by the pathogenic driver fusion transcript PML-RARA resulting from the t(15;17) translocation. Early recognition of APL with prompt ATRA induction has a decisive impact on the early death rate. The preliminary diagnosis of APL relies heavily on cytomorphology and flow cytometry. In APL with variant morphology, such as the microgranular variant, immunophenotype, especially the bright MPO positivity is the basis of diagnosis. Till date, only five cases of APL with reduced/absent MPO have been described in literature. The identification of MPO deficiency based on genetic testing would involve at the least a MPO gene scanning with NGS, followed by microarray to identify somatic uniparental disomy in heterozygotes. This testing is not only redundant given the scant clinical implications of heterozygous MPO deficiency but also time consuming. An easy way to identify background MPO deficiency confounding the immunophenotype of a myeloid neoplasm is the MPO expression in background neutrophils gated on the initial flow cytometry. A dim MPO in the background neutrophils, in the morphological setting of APL, can identify underlying MPO deficiency, clarifying the immunophenotypic ambiguity and thus establishing an unequivocal diagnosis as seen in the current case.

Flow Cytometric Assessment of Myelodysplastic Syndromes/Neoplasms

Myelodysplastic syndromes/neoplasms (MDS) are a heterogeneous class of hematopoietic stem cell neoplasms characterized by ineffective hematopoiesis leading to peripheral cytopenias. This group of diseases is typically diagnosed using a combination of clinical, morphologic, and genetic criteria. Many studies have described the value of multiparametric flow cytometry (MFC) in the diagnosis, classification, and prognostication of MDS. This review summarizes the approach to MDS diagnosis and immunophenotypic characterization using MFC and describes the current state while highlighting future opportunities and potential pitfalls.

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.

Comprehensive analysis of clinical prognostic features and tumor microenvironment landscape of CD11b+CD64+ patients with acute myeloid leukemia

BACKGROUND

Immunophenotyping surface molecules detected in the clinic are mainly applied in diagnostic confirmation and subtyping. However, the immunomodulatory molecules CD11b and CD64, are highly associated with leukemogenesis. Hence, the prognostic value of them and their potential biological functions merit further investigation.

METHODS

Flow cytometry was operated to detect immunophenotypic molecules from AML bone marrow samples. Multivariate cox regression, Kaplan-Meier analyses, and nomogram were conducted to predict survival. Transcriptomic data, lymphocyte subsets, and immunohistochemical staining were incorporated to identify potential biological functions of prognostic immunophenotype in acute myeloid leukemia (AML).

RESULTS

We classified 315 newly diagnosed AML patients of our center based on the expression of CD11b and CD64. The CD11b+CD64+ populations were identified as independent risk factors for overall survival and event-free survival of AML, exhibiting specific clinicopathological features. The predictive models based on CD11b+CD64+ showed high classification performance. In addition, the CD11b+CD64+ subset, characterized by high inhibitory immune checkpoints, M2-macrophage infiltration, low anti-tumor effector cells infiltration, as well as abnormal somatic mutation landscape, presented a distinctive tumor microenvironmental landscape. The CD11b+CD64+ population showd a higher expression of BCL2, and the drug sensitivity indicated that they presented a lower half-maximal inhibitory concentration value for BCL2 inhibitor, and could benefit more from the above medicine.

CONCLUSIONS

This work might be of benefit to enhanced understanding of CD11b+CD64+ in the prognosis and leukemogenesis, and yielded novel biomarkers to guide immunotherapy and targeted therapy for AML.

Advances in Acute Myeloid Leukemia Classification, Prognostication and Monitoring by Flow Cytometry

Although final classification of acute myeloid leukemia (AML) integrates morphologic, cytogenetic, and molecular data, flow cytometry remains an essential component of modern AML diagnostics. Here, we review the current role of flow cytometry in the classification, prognostication, and monitoring of AML. We cover immunophenotypic features of key genetically defined AML subtypes and their effects on biological and clinical behaviors, review clinically tractable strategies to differentiate leukemias with ambiguous immunophenotypes more accurately and discuss key principles of standardization for measurable residual disease monitoring. These advances underscore flow cytometry's continued growth as a powerful diagnostic, management, and discovery tool.

Molecular Heterogeneity of Pediatric AML with Atypical Promyelocytes Accumulation in Children—A Single Center Experience

Acute promyelocytic leukemia (APL) pathogenesis is based on RARA gene translocations, which are of high importance in the diagnosis of and proper therapy selection for APL. However, in some cases acute myeloid leukemia (AML) demonstrates APL-like morphological features such as atypical promyelocytes accumulation. This type of AML is characterized by the involvement of other RAR family members or completely different genes. In the present study, we used conventional karyotyping, FISH and high-throughput sequencing in a group of 271 de novo AML with atypical promyelocytes accumulation. Of those, 255 cases were shown to carry a typical chromosomal translocation t(15;17)(q24;q21) with PML::RARA chimeric gene formation (94.1%). Other RARA-positive cases exhibited cryptic PML::RARA fusion without t(15;17)(q24;q21) (1.8%, n = 5) and variant t(5;17)(q35;q21) translocation with NPM1::RARA chimeric gene formation (1.5%, n = 4). However, 7 RARA-negative AMLs with atypical promyelocytes accumulation were also discovered. These cases exhibited TBL1XR1::RARB and KMT2A::SEPT6 fusions as well as mutations, e.g., NPM1 insertion and non-recurrent chromosomal aberrations. Our findings demonstrate the genetic diversity of AML with APL-like morphological features, which is of high importance for successful therapy implementation.