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More than 25% of patients with AML carry no mutations in genes known to be associated with leukemia. Analyses of genomes, transcriptomes, and methylomes of AML samples implicate mutations in cytogenetically normal AML and provide insight into the relationships among causative genes. The molecular pathogenesis of acute myeloid leukemia (AML) has been studied with the use of cytogenetic analysis for more than three decades. Recurrent chromosomal structural variations are well established as diagnostic and prognostic markers, suggesting that acquired genetic abnormalities (i.e., somatic mutations) have an essential role in pathogenesis. 1 , 2 However, nearly 50% of AML samples have a normal karyotype, and many of these genomes lack structural abnormalities, even when assessed with high-density comparative genomic hybridization or single-nucleotide polymorphism (SNP) arrays 3 – 5 (see Glossary). Targeted sequencing has identified recurrent mutations in FLT3, NPM1, KIT, CEBPA, and TET2 . 6 – 8 Massively parallel . . .

t(8;21)(q22;q22) acute myelogenous leukemia (AML) is morphologically characterized by a continuum of heterogeneous leukemia cells from myeloblasts to differentiated myeloid elements. Thus, t(8;21) AML is an excellent model for studying heterogeneous cell populations and cellular evolution during disease progression. Using integrative analyses of immunophenotype, RNA-sequencing (RNA-seq), and single-cell RNA-sequencing (scRNA-seq), we identified three distinct intrapatient leukemic cell populations that were arrested at different stages of myeloid differentiation: CD34⁺CD117dim blasts, CD34⁺CD117bri blasts, and abnormal myeloid cells with partial maturation (AM). CD117 is also known as c-KIT protein. CD34⁺CD117dim cellswere blocked in the G0/G1 phase at disease onset, presentingwith the regular morphology of myeloblasts showing features of granulocyte-monocyte progenitors (GMP), and were drug-resistant to chemotherapy. Genes associated with cell migration and adhesion (LGALS1, EMP3, and ANXA2) were highly expressed in the CD34⁺CD117dim population. CD34⁺CD117bri blasts were blocked a bit later than the CD34⁺CD117dim population in the hematopoietic differentiation stage and displayed high proliferation ability. AM cells, which bear abnormal myelocyte morphology, especially over-expressed granule genes AZU1, ELANE, and PRTN3 and were sensitive to chemotherapy. scRNA-seq at different time points identified CD34⁺CD117dim blasts as an important leukemic cluster that expanded at postrelapse refractory stage after several cycles of chemotherapy. Patients with t(8;21) AML with a higher proportion of CD34⁺CD117dim cells had significantly worse clinical outcomes than those with a lower CD34⁺CD117dim proportion. Univariate and multivariate analyses identified CD34⁺CD117dim proportion as an independent factor for poor disease outcome. Our study provides evidence for the multidimensional heterogeneity of t(8;21)AML and may offer new tools for future disease stratification.

This investigation of 285 cases of acute myelogenous leukemia combined sophisticated analyses of gene-expression profiles with cytogenetic findings, mutational status, and morphologic characteristics to identify distinct groups of patients. These groupings were related to the outcome of treatment. Sophisticated analyses of gene-expression profiles with cytogenetic findings, mutational status, and morphologic characteristics. Acute myeloid leukemia (AML) is not a single disease but a group of neoplasms with diverse genetic abnormalities and variable responses to treatment. Cytogenetics and molecular analyses can be used to identify subgroups of AML with different prognoses. For instance, the translocations inv(16), t(8;21), and t(15;17) herald a favorable prognosis, whereas other cytogenetic aberrations indicate poor-risk leukemia. 1 – 5 Abnormalities involving 11q23, t(6;9), or 7(q) are defined as poor-risk markers by some groups 2 , 3 and as intermediate-risk markers by others. 3 – 5 These inconsistencies and the absence of cytogenetic abnormalities in a considerable proportion of patients argue for refinement of the classification . . .

This study demonstrates that the genes expressed by peripheral-blood monocytes of adults with acute myeloid leukemia provide prognostic information over and above that provided by established indicators such as cytogenetic status. The authors analyzed the gene-expression profiles of samples obtained from 116 patients, who were subsequently assigned to receive various intensive treatments. They identified good- and poor-outcome classes of gene expression that were associated with differences in overall survival — even when the analysis was restricted to specimens with a normal karyotype. Genes expressed by monocytes identified good- and poor-outcome classes even with a normal karyotype. Acute myeloid leukemia (AML) is the most common acute leukemia in adults. Chemotherapy induces a complete remission in 70 to 80 percent of younger patients (age, 16 to 60 years), but many of them have a relapse and die of their disease. Myeloablative conditioning followed by allogeneic stem-cell transplantation can prevent relapse, but this approach is associated with a high treatment-related mortality. 1 Therefore, accurate predictors of the clinical outcome are needed to determine appropriate treatment for individual patients. (See Glossary.) Currently used prognostic indicators include age, cytogenetic findings, the white-cell count, and the presence or absence of an antecedent hematologic . . .

Although cancer classification has improved over the past 30 years, there has been no general approach for identifying new cancer classes (class discovery) or for assigning tumors to known classes (class prediction). Here, a generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case. A class discovery procedure automatically discovered the distinction between acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) without previous knowledge of these classes. An automatically derived class predictor was able to determine the class of new leukemia cases. The results demonstrate the feasibility of cancer classification based solely on gene expression monitoring and suggest a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.

BCR-ABL -positive acute myeloid leukemia (AML) is a rare subtype of AML that is now included as a provisional entity in the 2016 revised WHO classification of myeloid malignancies. Since a clear distinction between de novo BCR-ABL + AML and chronic myeloid leukemia (CML) blast crisis is challenging in many cases, the existence of de novo BCR-ABL + AML has been a matter of debate for a long time. However, there is increasing evidence suggesting that BCR-ABL + AML is in fact a distinct subgroup of AML. In this study, we analyzed all published cases since 1975 as well as cases from our institution in order to present common clinical and molecular features of this rare disease. Our analysis shows that BCR-ABL predominantly occurs in AML-NOS, CBF leukemia, and AML with myelodysplasia-related changes. The most common BCR-ABL transcripts (p190 and p210) are nearly equally distributed. Based on the analysis of published data, we provide a clinical algorithm for the initial differential diagnosis of BCR-ABL + AML. The prognosis of BCR-ABL + AML seems to depend on the cytogenetic and/or molecular background rather than on BCR-ABL itself. A therapy with tyrosine kinase inhibitors (TKIs) such as imatinib, dasatinib, or nilotinib is reasonable, but—due to a lack of systematic clinical data—their use cannot be routinely recommended in first-line therapy. Beyond first-line treatment of AML, the use of TKI remains an individual decision, both in combination with intensive chemotherapy and/or as a bridge to allogeneic stem cell transplantation. In each single case, potential benefits have to be weighed against potential risks.

The incidence of acute myeloid leukemia (AML) is increasing annually, and timely diagnostic and treatments can substantially improve patient survival rates. AML typing traditionally relies on manual microscopy for classifying and counting myeloid cells, which is time-consuming, laborious, and subjective. Therefore, developing a reliable automated model for myeloid cell classification is imperative. This study evaluated the performance of five widely-used classification models on the largest publicly available bone marrow cell dataset (BM). However, the accuracy of the classification model is significantly affected by the imbalance in the distribution of bone marrow cell types. To address this issue, this study analyzed five different Loss functions and seven different attention mechanisms. When the classification models is chosen, Swin Transformer V2 was found to perform the best. However, the lightweight model RegNetX-3.2gf had significantly fewer parameters and a significantly faster inference speed than Swin Transformer V2, and its F1 Score was only 0.032 lower than that of Swin Transformer V2. Accordingly, RegNetX-3.2gf is strongly recommended for practical applications. During the evaluation of Loss function and attention mechanism, the Cost-Sensitive Loss Function (CS) and the channel attention mechanism Squeeze-and-Excitation Networks (SE) demonstrated superior performance. The optimal model (RegNetX-3.2gf + CS + SE) achieved an average precision of 68.183%, an average recall of 63.722%, and an average F1 Score of 65.155%. This model exhibited significantly improved performance compared to the original dataset results, achieving an enhancement of 17.183% in precision and 10.655% in the F1 Score. Finally, the class activation maps demonstrate that our model focused on the cells themselves, especially on the nucleus when making classifications. It proved that our model was reliable. This study provided an important reference for the study of bone marrow cell classification and a practical application of the model, promoting the development of the intelligent classification of AML.

Acute myeloid leukemia is a genetically and cellularly heterogeneous disease. We characterize 120 AMLs using genomic and transcriptomic analyses, including single-cell RNA sequencing. Our results reveal an extensive cellular heterogeneity that distorts the bulk transcriptomic profiles. Selective examination of the transcriptional signatures of >90,000 immature AML cells identifies four main clusters, thereby extending current genomic classification of AML. Notably, NPM1 -mutated AML can be stratified into two clinically relevant classes, with NPM1 class I associated with downregulation of MHC class II and excellent survival following hematopoietic stem cell transplantation. NPM1 class II is instead associated with resistance to allogeneic T cells in an ex vivo co-culture assay, and importantly, dismal survival following hematopoietic stem cell transplantation. These findings provide insights into the cellular state space of AML, define diagnostic entities, and highlight potential therapeutic intervention points. The clinical outcomes and treatment responses of acute myeloid leukemia (AML) patients are heterogeneous. Here, the authors use bulk and single-cell sequencing approaches and identify two transcriptomic subtypes within NPM1 -mutated AML with distinct immune evasion properties and responses to hematopoietic stem cell transplantation.

Acute undifferentiated leukemia is a rare type of acute leukemia that shows no evidence of differentiation along any lineage. Clinical, immunophenotypic and genetic data is limited and it is uncertain if acute undifferentiated leukemia is biologically distinct from acute myeloid leukemia with minimal differentiation, which also shows limited myeloid marker expression and has been reported to have a poor prognosis. We identified 92 cases initially diagnosed as acute undifferentiated leukemia or acute myeloid leukemia with minimal differentiation from pathology databases of nine academic institutions with available diagnostic flow cytometric data, cytogenetic findings, mutational and clinical data. Outcome analysis was performed using Kaplan Meier test for the 53 patients who received induction chemotherapy. Based on cytogenetic abnormalities ( N  = 30) or history of myelodysplastic syndrome ( N  = 2), 32 cases were re-classified as acute myeloid leukemia with myelodysplasia related changes. The remaining 24 acute undifferentiated leukemia patients presented with similar age, blood counts, bone marrow cellularity, and blast percentage as the remaining 30 acute myeloid leukemia with minimal differentiation patients. Compared to acute myeloid leukemia with minimal differentiation, acute undifferentiated leukemia cases were characterized by more frequent mutations in PHF6 (5/15 vs 0/19, p  = 0.016) and more frequent expression of TdT on blasts ( p  = 0.003) while acute myeloid leukemia with minimal differentiation cases had more frequent CD123 expression ( p  = 0.042). Outcome data showed no difference in overall survival, relapse free survival, or rates of complete remission between acute undifferentiated leukemia and acute myeloid leukemia with minimal differentiation groups ( p  > 0.05). Acute myeloid leukemia with myelodysplasia-related changes patients showed shorter survival when censoring for bone marrow transplant as compared to acute undifferentiated leukemia ( p  = 0.03) and acute myeloid leukemia with minimal differentiation ( p  = 0.002). In this largest series to date, the acute undifferentiated leukemia group shows distinct characteristics from acute myeloid leukemia with minimal differentiation, including more frequent PHF6 mutations and expression of TdT.

Acute myeloid leukemia is a category of diseases with a common aggressive clinical presentation but with a prognosis and management that is dependent upon the underlying genetic characteristics of the neoplasm. The purpose of this brief review is to update the practicing pathologist on the current standard of care in the genetic evaluation of acute myeloid leukemia and to highlight future directions in the classification, genetic assessment, and management of these devastating diseases.