Diagnosis of rare subtypes of acute myeloid leukaemia and related neoplasms

Comparison of efficacy and safety of different asparaginases in adult acute lymphoblastic leukemia based on nano-magnetic beads immunoassay

OBJECTIVE

To compare the efficacy and safety of different asparaginase formulations in the treatment of acute lymphoblastic leukemia (ALL) based on nano-magnetic bead immunoassay.

METHODS

Retrospective analysis of adult ALL patients' clinical data who admitted to The Affiliated Hospital of Changsha Health Vocational College from August 2020 to August 2023. Finally, 65 adult ALL patients were included in this study, including the polyethylene glycol conjugated asparaginase (PEG-ASNase) group (n = 32) and the L-asparaginase (L-ASNase) group (n = 33). Enzyme-linked immunosorbent assay (ELISA) based on magnetic nanoparticles was used to determine the activity of ASNase in both groups. The levels of asparagine or glutamine in two groups were detected by automatic biochemical analyzer during induction therapy, and the adverse events of the two groups were observed during the treatment.

RESULTS

PEG-ASNase demonstrated a slower decrease in enzyme activity, longer action duration, and higher safety profile compared to L-ASNase. PEG-ASNase group and L-ASNase group demonstrated a similar complete remission rate (71.88% vs. 60.61%). Event-free survival was higher in patients receiving PEG-ASNase than those receiving L-ASNase (42.4% and 18.7%). The observed adverse reactions included allergic reactions, pancreatic lesions, gastrointestinal reactions and liver function damage. The incidence of gastrointestinal reactions and liver function damage was higher in the L-ASNase group than that in PEG-ASNase group (45.45% and 33.33%).

CONCLUSION

This study provides valuable insights into the asparaginase treatments in clinical, highlighting the importance of PEG-ASNase for improving treatment protocols in adult ALL patients.

A Case of Acute Myeloid Leukemia Mimicking Blastic Plasmacytoid Dendritic Cell Neoplasm: Utility of the Proposed Upcoming WHO-5 Diagnostic Criteria

Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is an aggressive hematologic malignancy which is associated with a distinctive morphologic appearance. However, the morphology is not specific, and diagnostic characterization requires integration of immunophenotypic and genetic testing. We herein report a case of a 35-year-old female patient who presented with worsening cytopenia. A bone marrow aspirate identified medium-sized blastic cells with perinuclear microvacuoles ("pearl neckless"). Occasional blasts demonstrated a "hand mirror" appearance. Tandem flow cytometry showed an atypical population of dim CD45 events with expression of CD4, CD56, CD117, CD123, and monocytic markers such as CD64. Fluorescence in situ hybridization (FISH) showed evidence of a KMT2A rearrangement with an unknown partner on chromosome 19. Expression of MPO and muramidase was present. The final diagnosis was acute monocytic leukemia (AMoL). Due to the overlapping features of acute myeloid leukemia and BPDCN, the 5th Edition of the World Health Organization (WHO) Classification of Haematolymphoid Tumours provides new criteria for the diagnosis of BPDCN. Our case highlights the utility of these criteria.

Golgi Signaling Proteins GOLPH3, MYO18A, PITPNC1 and RAB1B: Implications in Prognosis and Survival Outcomes of AML Patients

INTRODUCTION

The role of different Golgi signaling proteins remains unexplored in the progression and spread of Acute myeloid leukemia (AML), whom all interact together in a way that facilitates proliferation and differentiation of myeloid lineage cells.Material & methods: This study comprised 70 newly diagnosed AML patients and 20 healthy controls to investigate the serum levels of signaling proteins; Golgi Phosphoprotein 3 (GOLPH3), Myosin 18A (MYO18A), Cytoplasmic Phosphatidylinositol Transfer Protein 1 (PITPNC1), and Ras-Associated Binding Protein 1B (RAB1B).

RESULTS

AML patients showed higher serum levels of GOLPH3, MYO18A, PITPNC1, and RAB1B when compared to control (p < 0.001). A significant negative correlation was found between the patients' overall survival and GOLPH3 (p = 0.001), MYO18A (p = 0.011), PITPNC1 (p = 0.001), and RAB1B (p = 0.042). Results were confirmed by Kaplen-Meier survival analysis showing lower survival estimates in patients with higher GOLPH3 (p = 0.014), MYO18A (p = 0.047), PITPNC1 (p = 0.008) and RAB1B (p = 0.033) serum levels.

DISCUSSION

Golgi apparatus acts as master brain in membrane trafficking and signaling events that affect cell polarity necessary for migration, division, or differentiation. This study aims to explore the association between signaling proteins and the diagnosis, prognosis, and survival of AML patients.

CONCLUSION

GOLPH3, MYO18A, PITPNC1, and RAB1B maybe promising diagnostic and prognostic biomarkers in AML patients.

Classification of acute myeloid leukemia M1 and M2 subtypes using machine learning

BACKGROUND

Classification of acute myeloid leukemia (AML) relies on manual analysis of bone marrow or peripheral blood smear images. We aimed to construct a machine learning model for automatic classification of AML-M1 and M2 subtypes in bone marrow smear images.

METHODS

Bone marrow smear images of AML patients were extracted from the Cancer Imaging Archive (TCIA) open database. Classification criteria of AML subtypes were based on the French-American-British (FAB) classification system. Random forest method and broad learning system (BLS) were used to develop the classification model. Morphological features, radiomics features, and clinical features were extracted. The performance of the classification model was evaluated by calculating accuracy, precision, recall, F1-score, and area under the curve (AUC). A total of 50 bone marrow smear images (AML-M1, 31 cases; AML-M2, 19 cases) with 500 slices were included in this study.

RESULTS

A total of 43 morphological features, 276 radiomics features, and 1 clinical feature were extracted. Finally, 9 variables including 2 morphological features, 6 radiomics features, and 1 clinical feature were selected into the classification model. The best classification performance was observed in the random forest model with 9 variables, with the average accuracy, AUC, F1-score, recall, and precision of the model being 0.998 ± 0.003, 0.998 ± 0.004, 0.998 ± 0.004, 0.996 ± 0.009, and 1 ± 0, respectively.

CONCLUSION

The random forest model performed well for the classification of AML-M1 and M2, which may provide a tool for clinicians to classify AML-M1 and M2.

The Plasmacytoid Dendritic Cell CD123+ Compartment in Acute Leukemia with or without RUNX1 Mutation: High Inter-Patient Variability Disclosed by Immunophenotypic Unsupervised Analysis and Clustering

Plasmacytoid dendritic cells (PDC) constitute a small subset of normal bone marrow (BM) cells but have also been shown to be present, sometimes in large numbers, in several hematological malignancies such as acute myeloid leukemia with RUNX1 mutation, chronic myelomonocytic leukemia or, obviously, blastic plasmacytoid dendritic cell neoplasms. These cells have been reported to display somewhat variable immunophenotypic features in different conditions. However, little is known of their plasticity within individual patients. Using an unsupervised clustering tool (FlowSOM) to re-visit flow cytometry results of seven previously analyzed cases of hematological malignancies (6 acute myeloid leukemia and one chronic myelomonocytic leukemia) with a PDC contingent, we report here on the unexpectedly high variability of PDC subsets. Although five of the studied patients harbored a RUNX1 mutation, no consistent feature of PDCs could be disclosed as associated with this variant. Moreover, the one normal single-node small subset of PDC detected in the merged file of six normal BM could be retrieved in the remission BM samples of three successfully treated patients. This study highlights the capacity of unsupervised flow cytometry analysis to delineate cell subsets not detectable with classical supervised tools.