Molecular evaluation of gene mutation profiles and copy number variations in pediatric acute myeloid leukemia

Meena, Jagdish Prasad; Pathak, Nivedita; Gupta, Aditya; Bakhshi, Sameer; Gupta, Ritu; Makkar, Harshita; Seth, Rachna

doi:10.1016/j.leukres.2022.106954

Cited by 3 publications

(2 citation statements)

References 40 publications

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“…In the AML group, we observed notable preservation of correlations among markers predominantly expressed by lymphoid cells and disruption between myeloid markers. This suggests a potential compartmentalization within the leukemic process of AML, where the malignant blasts primarily affect the myeloid lineage while leaving the lymphoid lineage relatively intact (Jaddaoui et al, 2022; Khoury et al, 2022; Meena et al, 2022). The B-ALL group displayed a different pattern, with maintained correlations between T lymphocyte markers but disruptions observed for B lymphocyte and myeloid markers.…”

Section: Discussionmentioning

confidence: 99%

Immunophenotype signatures in acute leukemias unveiled by integrative systems immunology

Bahia,

Lima,

de Medeiros Oliveira

et al. 2024

Preprint

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Acute leukemias (ALs) are complex hematological disorders, and accurate diagnosis is crucial for guiding treatment decisions and predicting patient outcomes. While changes in cell marker levels are well documented, the impact of these changes on marker relationships through an integrative systems approach remains uncharacterized. To address this gap, we conducted a 12-year study investigating 41 markers, including ontogenic markers and those used to diagnose both common and rare leukemia types, using immunophenotyping flow cytometry (IFC) data from 1,069 leukocyte samples obtained from peripheral blood (PB) or bone marrow (BM) aspirates of patients with suspected ALs. Machine learning techniques, such as principal component analysis (PCA) and random forest (RF) classification, demonstrated the stratification power of the cellular markers. Hierarchical clustering analysis of leukocyte ontogenetic markers revealed disease-specific clusters, irrespective of sex or sample type (PB or BM). Additionally, we found that patients with acute myeloid leukemia (AML) showed mild disruption in cell marker correlations, whereas the most significant dysregulation was observed in patients with T-cell acute lymphoblastic leukemia (T-ALL). Importantly, we identified ontogenic correlation changes indicating clusters of immature versus mature leukocyte markers, as well as cell lineage-specific markers influencing cellular relationships. These findings underscore the value of integrating systems strategies into conventional IFC analyses to enhance synthetic diagnosis and deepen our understanding of ALs pathophysiology.

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Section: Discussionmentioning

confidence: 99%

Immunophenotype signatures in acute leukemias unveiled by integrative systems immunology

Bahia,

Lima,

de Medeiros Oliveira

et al. 2024

Preprint

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“…However, it was decreased compared to the proportion of AML, NOS (44.5%, 216/485). With the advances in gene detection technologies, patients are divided into more precise categories with clearer treatment strategies and prognoses ( 9 – 11 ). For instance, the mutations of ASXL1, BCOR, EZH2, SF3B1, SRSF2, STAG2, U2AF1 , and ZRSR2 genes are added as the defining cytogenetic abnormalities, and patients carrying one of which are considered a member of the AML-MR subgroup, while the subgroup defined by RUNX1 mutation is abolished.…”

Section: Discussionmentioning

confidence: 99%

The latest edition of WHO and ELN guidance and a new risk model for Chinese acute myeloid leukemia patients

Wang

Wei

et al. 2023

Front. Med.

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ObjectiveDiagnosis classification and risk stratification are crucial in the prognosis prediction and treatment selection of acute myeloid leukemia (AML). Here, we used a database of 536 AML patients to compare the 4th and 5th WHO classifications and the 2017 and 2022 versions of ELN guidance.MethodsAML patients were classified according to the 4th and 5th WHO classifications, as well as the 2017 and 2022 versions of the European LeukemiaNet (ELN) guidance. Kaplan–Meier curves with log-rank tests were used for survival analysis.ResultsThe biggest change was that 25 (5.2%), 8 (1.6%), and 1 (0.2%) patients in the AML, not otherwise specified (NOS) group according to the 4th WHO classification, were re-classified into the AML-MR (myelodysplasia-related), KMT2A rearrangement, and NUP98 rearrangement subgroups based on the 5th WHO classification. Referring to the ELN guidance, 16 patients in the favorable group, six patients in the adverse group, and 13 patients in the intermediate group based on the 2017 ELN guidance were re-classified to the intermediate and adverse groups based on the 2022 ELN guidance. Regrettably, the Kaplan–Meier curves showed that the survival of intermediate and adverse groups could not be distinguished well according to either the 2017 or 2022 ELN guidance. To this end, we constructed a risk model for Chinese AML patients, in which the clinical information (age and gender), gene mutations (NPM1, RUNX1, SH2B3, and TP53), and fusions (CBFB::MYH11 and RUNX1::RUNX1T1) were included, and our model could help divide the patients into favorable, intermediate, and adverse groups.ConclusionThese results affirmed the clinical value of both WHO and ELN, but a more suitable prognosis model should be established in Chinese cohorts, such as the models we proposed.

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Next-Generation Sequencing–Based Genomic Profiling of Children with Acute Myeloid Leukemia

Krizsán

Péterffy

Egyed

et al. 2023

The Journal of Molecular Diagnostics

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Molecular evaluation of gene mutation profiles and copy number variations in pediatric acute myeloid leukemia

Cited by 3 publications

References 40 publications

Immunophenotype signatures in acute leukemias unveiled by integrative systems immunology

Immunophenotype signatures in acute leukemias unveiled by integrative systems immunology

The latest edition of WHO and ELN guidance and a new risk model for Chinese acute myeloid leukemia patients

Next-Generation Sequencing–Based Genomic Profiling of Children with Acute Myeloid Leukemia

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