Identification of Leukemia-Associated Immunophenotypes by Databaseguided Flow Cytometry Provides a Highly Sensitive and Reproducible Strategy for the Study of Measurable Residual Disease in Acute Myeloblastic Leukemia

Acute Myeloid Leukemia: Diagnosis and Evaluation by Flow Cytometry

With recent technological advances and significant progress in understanding the pathogenesis of acute myeloid leukemia (AML), the updated fifth edition WHO Classification (WHO-HAEM5) and the newly introduced International Consensus Classification (ICC), as well as the European LeukemiaNet (ELN) recommendations in 2022, require the integration of immunophenotypic, cytogenetic, and molecular data, alongside clinical and morphologic findings, for accurate diagnosis, prognostication, and guiding therapeutic strategies in AML. Flow cytometry offers rapid and sensitive immunophenotyping through a multiparametric approach and is a pivotal laboratory tool for the classification of AML, identification of therapeutic targets, and monitoring of measurable residual disease (MRD) post therapy. The association of immunophenotypic features and recurrent genetic abnormalities has been recognized and applied in informing further diagnostic evaluation and immediate therapeutic decision-making. Recently, the evolving role of machine learning models in assisting flow cytometric data analysis for the automated diagnosis and prediction of underlying genetic alterations has been illustrated.

Exploring the interplay between microRNA expression and DNA mutation analysis in AML patients

MicroRNAs (miRNAs) are key regulators in Acute Myeloid Leukemia AML, affecting gene expression, including that of CD markers and impacting mutations within leukemic cells. Mutations in AML can alter miRNA profiles, which can affect the expression of CD markers and contribute to disease progression by influencing cellular processes such as differentiation, proliferation, and apoptosis. Here, we examined the interplay of cell surface protein expression (CD markers), DNA mutations, and microRNA expression in AML patients. We included 32 recently diagnosed AML patients, and CD marker expression was evaluated using flow cytometry and molecular techniques. This study aims to delve into this relationship within the context of AML, elucidating its potential implications for diagnosis, prognosis, and therapeutic interventions. Mutations were scrutinized in six patients using Whole-Exome Sequencing (WES), while quantitative PCR (qPCR) was employed to investigate the expression levels of nine microRNAs. Subsequently, a comprehensive interaction network was constructed using Cytoscape software, focusing on genes with significant mutations and their corresponding microRNAs. Cell surface protein expression analysis revealed upregulation of CD45, CD99, CD34, HLA-DR, CD38, CD13, CD33, MPO, CD15 and CD117 in AML patients. The molecular analysis results unveiled mutations in specific genes (FLT3, KIT, PTPN11, BCR, DNMT3A, and NRAS) targeted by nine microRNAs. Notably, eight microRNAs exhibited heightened expression levels. Network analysis highlighted interactions between the PTPN11 gene and six scrutinized microRNAs. Understanding the regulatory dynamics between gene mutations and microRNAs in AML patients is pivotal for unraveling the disease's molecular mechanisms and identifying potential therapeutic targets. Further exploration into the functional roles of microRNAs in gene regulation and AML pathogenesis is warranted to validate their potential as therapeutic targets, diagnostic markers, and advanced treatment strategies.

Immunophenotypic clustering in paediatric acute myeloid leukaemia

Acute myeloid leukaemia (AML) is a highly heterogeneous disease, exhibiting diverse subtypes according to the characteristics of tumour cells. The immunophenotype is one of the aspects acquired routinely through flow cytometry in the diagnosis of AML. Here, we characterized the antigen expression in paediatric AML cases across both morphological and molecular genetic subgroups. We discovered a subgroup of patients with unfavourable prognosis that can be immunologically characterized, irrespective of morphological FAB results or genetic aberrations. Cox regression analysis unveiled key antigens influencing the prognosis of AML patients. In terms of underlying genotypes, we observed that the antigenic profiles and outcomes of one specific group, primarily composed of CBFA2T3::GLIS2 and FUS::ERG, were analogous to the reported RAM phenotype. Overall, our data highlight the significance of immunophenotype to tailor treatment for paediatric 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.

The shape of cancer relapse: Topological data analysis predicts recurrence in paediatric acute lymphoblastic leukaemia

Although children and adolescents with acute lymphoblastic leukaemia (ALL) have high survival rates, approximately 15-20% of patients relapse. Risk of relapse is routinely estimated at diagnosis by biological factors, including flow cytometry data. This high-dimensional data is typically manually assessed by projecting it onto a subset of biomarkers. Cell density and "empty spaces" in 2D projections of the data, i.e. regions devoid of cells, are then used for qualitative assessment. Here, we use topological data analysis (TDA), which quantifies shapes, including empty spaces, in data, to analyse pre-treatment ALL datasets with known patient outcomes. We combine these fully unsupervised analyses with Machine Learning (ML) to identify significant shape characteristics and demonstrate that they accurately predict risk of relapse, particularly for patients previously classified as 'low risk'. We independently confirm the predictive power of CD10, CD20, CD38, and CD45 as biomarkers for ALL diagnosis. Based on our analyses, we propose three increasingly detailed prognostic pipelines for analysing flow cytometry data from ALL patients depending on technical and technological availability: 1. Visual inspection of specific biological features in biparametric projections of the data; 2. Computation of quantitative topological descriptors of such projections; 3. A combined analysis, using TDA and ML, in the four-parameter space defined by CD10, CD20, CD38 and CD45. Our analyses readily extend to other haematological malignancies.