A comparison of WHO-5 and ICC classifications in a series of myeloid neoplasms, considerations for hematopathologists and molecular pathologists.

OBJECTIVES: The International Consensus Classification (ICC) and 5th Edition of the World Health Organization Classification (WHO-5) made substantive updates to the classification of myeloid neoplasms. This study compares the systems in a series of myeloid neoplasms with increased blasts, analyzing implications for diagnostic workflow and reporting. METHODS: Bone marrow biopsies categorized as myelodysplastic syndrome with excess blasts (MDS-EB) or acute myeloid leukemia (AML) by WHO-R4 were identified. Results of morphology review, karyotype, fluorescence in situ hybridization, and next-generation sequencing were compiled. Cases were retrospectively re-classified by WHO-5 and ICC. RESULTS: 46 cases were reviewed. 28 cases (61 %) had ≥20 % blasts, with the remaining cases having 5-19.5 % blasts. The most common differences in classification were 1) the designation of MDS versus MDS/AML (10/46, 22 %) for cases with 10-19 % blasts and 2) the ICC's designation of TP53 variants as a separate classifier for AML (8/46, 17 %). Bi-allelic/multi-hit TP53 alterations were identified in 15 cases (33 %). Variants of potential germline significance were identified in 29 (63 %) cases. CONCLUSIONS: While terminology differences between WHO-5 and ICC exist, both systems invoke similar opportunities for improved reporting: standardized classification of pathogenic variants (notably TP53), streamlined systems to evaluate for potential germline variants, and integrated reporting of morphologic and genetic data.

A practical approach on the classifications of myeloid neoplasms and acute leukemia: WHO and ICC.

In 2022, two new classifications of myeloid neoplasms and acute leukemias were published: the 5th edition WHO Classification (WHO-HAEM5) and the International Consensus Classification (ICC). As with prior classifications, the WHO-HAEM5 and ICC made updates to the prior classification (revised 4th edition WHO Classification, WHO-HAEM4R) based on a consensus of groups of experts, who examined new evidence. Both WHO-HAEM5 and ICC introduced several new disease entities that are based predominantly on genetic features, superseding prior morphologic definitions. While it is encouraging that two groups independently came to similar conclusions in updating the classification of myeloid neoplasms and acute leukemias, there are several divergences in how WHO-HAEM5 and ICC define specific entities as well as differences in nomenclature of certain diseases. In this review, we highlight the similarities and differences between the WHO-HAEM5 and ICC handling of myeloid neoplasms and acute leukemias and present a practical approach to diagnosing and classifying these diseases in this current era of two divergent classification guidelines.

NGS panel enhance precise diagnosis of myeloid neoplasms under WHO-HAEM5 and International Consensus Classification: An observational study.

This study aimed to assess hematological diseases next-generation sequencing (NGS) panel enhances the diagnosis and classification of myeloid neoplasms (MN) using the 5th edition of the WHO Classification of Hematolymphoid Tumors (WHO-HAEM5) and the International Consensus Classification (ICC) of Myeloid Tumors. A cohort of 112 patients diagnosed with MN according to the revised fourth edition of the WHO classification (WHO-HAEM4R) underwent testing with a 141-gene NGS panel for hematological diseases. Ancillary studies were also conducted, including bone marrow cytomorphology and routine cytogenetics. The cases were then reclassified according to WHO-HAEM5 and ICC to assess the practical impact of these 2 classifications. The mutation detection rates were 93% for acute myeloid leukemia (AML), 89% for myelodysplastic syndrome (MDS), 94% for myeloproliferative neoplasm (MPN), and 100% for myelodysplasia/myeloproliferative neoplasm (MDS/MPN) (WHO-HAEM4R). NGS provided subclassified information for 26 and 29 patients with WHO-HAEM5 and ICC, respectively. In MPN, NGS confirmed diagnoses in 16 cases by detecting JAK2, MPL, or CALR mutations, whereas 13 "triple-negative" MPN cases revealed at least 1 mutation. NGS panel testing for hematological diseases improves the diagnosis and classification of MN. When diagnosed with ICC, NGS produces more classification subtype information than WHO-HAEM5.

A practical algorithm for acute myeloid leukaemia diagnosis following the updated 2022 classifications.

Disease classification of complex and heterogenous diseases, such as acute myeloid leukaemia (AML), is continuously updated to define diagnoses, appropriate treatments, and assist research and education. Recent availability of molecular profiling techniques further benefits the classification of AML. The World Health Organization (WHO) classification of haematolymphoid tumours and the International Consensus Classification of myeloid neoplasms and acute leukaemia from 2022 are two updated versions of the WHO 2016 classification. As a consequence, the European LeukemiaNet 2022 recommendations on the diagnosis and management of AML in adults have been also updated. The current review provides a practical interpretation of these guidelines to facilitate the diagnosis of AML and discusses genetic testing, disease genetic heterogeneity, and FLT3 mutations. We propose a practical algorithm for the speedy diagnosis of AML. Future classifications may need to incorporate gene mutation combinations to enable personalised treatment regimens in the management of patients with AML.

Reporting bone marrow biopsies for myelodysplastic neoplasms and acute myeloid leukaemia incorporating WHO 5th edition and ICC 2022 classification systems: ALLG/RCPA joint committee consensus recommendations.

The classification of myeloid neoplasms continues to evolve along with advances in molecular diagnosis, risk stratification and treatment of disease. An approach for disease classification has been grounded in international consensus that has facilitated understanding, identification and management of molecularly heterogeneous entities, as well as enabled consistent patient stratification into clinical trials and clinical registries over time. The new World Health Organization (WHO) and International Consensus Classification (ICC) Clinical Advisory Committee releasing separate classification systems for myeloid neoplasms in 2022 precipitated some concern amongst haematopathology colleagues both locally and internationally. While both classifications emphasise molecular disease classification over the historical use of morphology, flow cytometry and cytogenetic based diagnostic methods, notable differences exist in how morphological, molecular and cytogenetic criteria are applied for defining myelodysplastic neoplasms (MDS) and acute myeloid leukaemias (AML). Here we review the conceptual advances, diagnostic nuances, and molecular platforms required for the diagnosis of MDS and AML using the new WHO and ICC 2022 classifications. We provide consensus recommendations for reporting bone marrow biopsies. Additionally, we address the logistical challenges encountered implementing these changes into routine laboratory practice in alignment with the National Pathology Accreditation Advisory Council reporting requirements for Australia and New Zealand.

AML leukocyte classification method for small samples based on ACGAN.

Leukemia is a class of hematologic malignancies, of which acute myeloid leukemia (AML) is the most common. Screening and diagnosis of AML are performed by microscopic examination or chemical testing of images of the patient's peripheral blood smear. In smear-microscopy, the ability to quickly identify, count, and differentiate different types of blood cells is critical for disease diagnosis. With the development of deep learning (DL), classification techniques based on neural networks have been applied to the recognition of blood cells. However, DL methods have high requirements for the number of valid datasets. This study aims to assess the applicability of the auxiliary classification generative adversarial network (ACGAN) in the classification task for small samples of white blood cells. The method is trained on the TCIA dataset, and the classification accuracy is compared with two classical classifiers and the current state-of-the-art methods. The results are evaluated using accuracy, precision, recall, and F1 score. The accuracy of the ACGAN on the validation set is 97.1 % and the precision, recall, and F1 scores on the validation set are 97.5 , 97.3, and 97.4 %, respectively. In addition, ACGAN received a higher score in comparison with other advanced methods, which can indicate that it is competitive in classification accuracy.

Statistical tests for intra-tumour clonal co-occurrence and exclusivity.

Tumour progression is an evolutionary process in which different clones evolve over time, leading to intra-tumour heterogeneity. Interactions between clones can affect tumour evolution and hence disease progression and treatment outcome. Intra-tumoural pairs of mutations that are overrepresented in a co-occurring or clonally exclusive fashion over a cohort of patient samples may be suggestive of a synergistic effect between the different clones carrying these mutations. We therefore developed a novel statistical testing framework, called GeneAccord, to identify such gene pairs that are altered in distinct subclones of the same tumour. We analysed our framework for calibration and power. By comparing its performance to baseline methods, we demonstrate that to control type I errors, it is essential to account for the evolutionary dependencies among clones. In applying GeneAccord to the single-cell sequencing of a cohort of 123 acute myeloid leukaemia patients, we find 1 clonally co-occurring and 8 clonally exclusive gene pairs. The clonally exclusive pairs mostly involve genes of the key signalling pathways.

CombiFlow: Flow cytometry-based identification and characterization of genetically and functionally distinct AML subclones.

Many cancers, including leukemias, are dynamic oligoclonal diseases. Tools to identify and prospectively isolate genetically distinct clones for functional studies are needed. We describe our CombiFlow protocol, which is a combinatorial flow cytometry-based approach to identify and isolate such distinct clones. CombiFlow enables the visualization of clonal evolution during disease progression and the identification of potential relapse-inducing cells at minimal residual disease (MRD) time points. The protocol can be adapted to various research questions and allows functional studies on live sorted cell populations. For complete details on the use and execution of this protocol, please refer to de Boer et al. (2018).

Advances in acute myeloid leukemia.

Acute myeloid leukemia (AML) is an uncommon but potentially catastrophic diagnosis with historically high mortality rates. The standard of care treatment remained unchanged for decades; however, recent discoveries of molecular drivers of leukemogenesis and disease progression have led to novel therapies for AML. Ongoing research and clinical trials are actively seeking to personalize therapy by identifying molecular targets, discovering patient specific and disease specific risk factors, and identifying effective combinations of modalities and drugs. This review focuses on important updates in diagnostic and disease classifications that reflect new understanding of the biology of AML, its mutational heterogeneity, some important genetic and environmental risk factors, and new treatment options including cytotoxic chemotherapy, novel targeted agents, and cellular therapies.

3+7 Combined Chemotherapy for Acute Myeloid Leukemia: Is It Time to Say Goodbye?

PURPOSE OF REVIEW: With the recent approval of multiple new drugs for the treatment of acute myeloid leukemia (AML), the relevance of conventional treatment approaches, such as daunorubicin and cytarabine ("3+7") induction chemotherapy, has been challenged. We review the AML risk stratification, the efficacy of the newly approved drugs, and the role of "3+7". RECENT FINDINGS: Treatment of AML is becoming more niched with specific subtypes more appropriately treated with gemtuzumab, midostaurin, and CPX-351. Although lower intensity therapies can yield high response rates, they are less efficient at preventing relapses. The only curative potential for poor-risk AML is still an allogeneic stem cell transplant. The number of AML subtypes where 3+7 alone is an appropriate therapeutic option is shrinking. However, it remains the backbone for combination therapy with newer agents in patients suitable for intensive chemotherapy.

DNA Methylation Signatures Predict Cytogenetic Subtype and Outcome in Pediatric Acute Myeloid Leukemia (AML).

Pediatric acute myeloid leukemia (AML) is a heterogeneous disease composed of clinically relevant subtypes defined by recurrent cytogenetic aberrations. The majority of the aberrations used in risk grouping for treatment decisions are extensively studied, but still a large proportion of pediatric AML patients remain cytogenetically undefined and would therefore benefit from additional molecular investigation. As aberrant epigenetic regulation has been widely observed during leukemogenesis, we hypothesized that DNA methylation signatures could be used to predict molecular subtypes and identify signatures with prognostic impact in AML. To study genome-wide DNA methylation, we analyzed 123 diagnostic and 19 relapse AML samples on Illumina 450k DNA methylation arrays. We designed and validated DNA methylation-based classifiers for AML cytogenetic subtype, resulting in an overall test accuracy of 91%. Furthermore, we identified methylation signatures associated with outcome in t(8;21)/RUNX1-RUNX1T1, normal karyotype, and MLL/KMT2A-rearranged subgroups (p < 0.01). Overall, these results further underscore the clinical value of DNA methylation analysis in AML.

Association Between Measurable Residual Disease in Patients With Intermediate-Risk Acute Myeloid Leukemia and First Remission, Treatment, and Outcomes.

Importance: Measurable residual disease (MRD) is widely used as a therapy-stratification factor for acute myeloid leukemia (AML), but the association of dynamic MRD with postremission treatment (PRT) in patients with intermediate-risk AML (IR-AML) has not been well investigated. Objective: To investigate PRT choices based on dynamic MRD in patients with IR-AML. Design, Setting, and Participants: This cohort study examined 549 younger patients with de novo IR-AML in the South China Hematology Alliance database during the period from January 1, 2012, to June 30, 2016, including 154 who received chemotherapy, 116 who received an autologous stem cell transplant (auto-SCT), and 279 who received an allogeneic SCT (allo-SCT). Subgroup analyses were performed according to dynamic MRD after the first, second, and third courses of chemotherapy. The end point of the last follow-up was August 31, 2020. Statistical analysis was performed from December 1, 2019, to September 30, 2020. Exposures: Receipt of chemotherapy, auto-SCT, or allo-SCT. Main Outcomes and Measures: The primary end points were 5-year cumulative incidence of relapse and leukemia-free survival. Results: Subgroup analyses were performed for 549 participants (314 male participants [57.2%]; median age, 37 years [range, 14-60 years]) according to the dynamics of MRD after 1, 2, or 3 courses of chemotherapy. Comparable cumulative incidences of relapse, leukemia-free survival, and overall survival were observed among participants who had no MRD after 1, 2, or 3 courses of chemotherapy. Participants who underwent chemotherapy and those who underwent auto-SCT had better graft-vs-host disease-free, relapse-free survival (GRFS) than those who underwent allo-SCT (chemotherapy: hazard ratio [HR], 0.35 [95% CI, 0.14-0.90]; P = .03; auto-SCT: HR, 0.07 [95% CI, 0.01-0.58]; P = .01). Among participants with MRD after 1 course of chemotherapy but no MRD after 2 or 3 courses, those who underwent auto-SCT and allo-SCT showed lower cumulative incidence of relapse (auto-SCT: HR, 0.25 [95% CI, 0.08-0.78]; P = .01; allo-SCT: HR, 0.08 [95% CI, 0.02-0.24]; P < .001), better leukemia-free survival (auto-SCT: HR, 0.26 [95% CI, 0.10-0.64]; P = .004; allo-SCT: HR, 0.21 [95% CI, 0.09-0.46]; P < .001), and overall survival (auto-SCT: HR, 0.22 [95% CI, 0.08-0.64]; P = .005; allo-SCT: HR, 0.25 [95% CI, 0.11-0.59]; P = .001) vs chemotherapy. In addition, auto-SCT showed better GRFS than allo-SCT (HR, 0.45 [95% CI, 0.21-0.98]; P = .04) in this group. Among participants with MRD after 1 or 2 courses of chemotherapy but no MRD after 3 courses, allo-SCT had superior cumulative incidence of relapse (HR, 0.10 [95% CI, 0.06-0.94]; P = .04) and leukemia-free survival (HR, 0.18 [95% CI, 0.05-0.68]; P = .01) compared with chemotherapy, but no advantageous cumulative incidence of relapse (HR, 0.15 [95% CI, 0.02-1.42]; P = .10) and leukemia-free survival (HR, 0.23 [95% CI, 0.05-1.08]; P = .06) compared with auto-SCT. Among participants with MRD after 3 courses of chemotherapy, allo-SCT had superior cumulative incidences of relapse, leukemia-free survival, and overall survival compared with chemotherapy (relapse: HR, 0.16 [95% CI, 0.08-0.33]; P < .001; leukemia-free survival: HR, 0.19 [95% CI, 0.10-0.35]; P < .001; overall survival: HR, 0.29 [95% CI, 0.15-0.55]; P < .001) and auto-SCT (relapse: HR, 0.25 [95% CI, 0.12-0.53]; P < .001; leukemia-free survival: HR, 0.35 [95% CI, 0.18-0.73]; P = .004; overall survival: HR, 0.54 [95% CI, 0.26-0.94]; P = .04). Among participants with recurrent MRD, allo-SCT was also associated with advantageous cumulative incidence of relapse, leukemia-free survival, and overall survival compared with chemotherapy (relapse: HR, 0.12 [95% CI, 0.04-0.33]; P < .001; leukemia-free survival: HR, 0.24 [95% CI, 0.10-0.56]; P = .001; overall survival: HR, 0.31 [95% CI, 0.13-0.75]; P = .01) and auto-SCT (relapse: HR, 0.28 [95% CI, 0.09-0.81]; P = .02; leukemia-free survival: HR, 0.30 [95% CI, 0.12-0.76]; P = .01; overall survival: HR, 0.26 [95% CI, 0.10-0.70]; P = .007). Conclusions and Relevance: This study suggests that clinical decisions based on dynamic MRD might be associated with improved therapy stratification and optimized PRT for patients with IR-AML. Prospective multicenter trials are needed to further validate these findings.

Machine learning integrates genomic signatures for subclassification beyond primary and secondary acute myeloid leukemia.

Although genomic alterations drive the pathogenesis of acute myeloid leukemia (AML), traditional classifications are largely based on morphology, and prototypic genetic founder lesions define only a small proportion of AML patients. The historical subdivision of primary/de novo AML and secondary AML has shown to variably correlate with genetic patterns. The combinatorial complexity and heterogeneity of AML genomic architecture may have thus far precluded genomic-based subclassification to identify distinct molecularly defined subtypes more reflective of shared pathogenesis. We integrated cytogenetic and gene sequencing data from a multicenter cohort of 6788 AML patients that were analyzed using standard and machine learning methods to generate a novel AML molecular subclassification with biologic correlates corresponding to underlying pathogenesis. Standard supervised analyses resulted in modest cross-validation accuracy when attempting to use molecular patterns to predict traditional pathomorphologic AML classifications. We performed unsupervised analysis by applying the Bayesian latent class method that identified 4 unique genomic clusters of distinct prognoses. Invariant genomic features driving each cluster were extracted and resulted in 97% cross-validation accuracy when used for genomic subclassification. Subclasses of AML defined by molecular signatures overlapped current pathomorphologic and clinically defined AML subtypes. We internally and externally validated our results and share an open-access molecular classification scheme for AML patients. Although the heterogeneity inherent in the genomic changes across nearly 7000 AML patients was too vast for traditional prediction methods, machine learning methods allowed for the definition of novel genomic AML subclasses, indicating that traditional pathomorphologic definitions may be less reflective of overlapping pathogenesis.

Isoform-specific and signaling-dependent propagation of acute myeloid leukemia by Wilms tumor 1.

Acute myeloid leukemia (AML) is caused by recurrent mutations in members of the gene regulatory and signaling machinery that control hematopoietic progenitor cell growth and differentiation. Here, we show that the transcription factor WT1 forms a major node in the rewired mutation-specific gene regulatory networks of multiple AML subtypes. WT1 is frequently either mutated or upregulated in AML, and its expression is predictive for relapse. The WT1 protein exists as multiple isoforms. For two main AML subtypes, we demonstrate that these isoforms exhibit differential patterns of binding and support contrasting biological activities, including enhanced proliferation. We also show that WT1 responds to oncogenic signaling and is part of a signaling-responsive transcription factor hub that controls AML growth. WT1 therefore plays a central and widespread role in AML biology.

Risk adapted therapeutic strategy in newly diagnosed acute myeloid leukemia: Refining the outcomes of ELN 2017 intermediate-risk patients.

INTRODUCTION: Despite advances in the treatment of acute myeloid leukemia (AML), cytotoxic chemotherapy remains the standard induction regimen. PATIENTS AND METHODS: In this single center retrospective study, we assessed outcomes of 99 consecutive adult AML patients treated with a risk-adapted strategy with a median follow-up of 35.5 months. RESULTS: We identified 24 (24 %), 55 (56 %) and 20 (20 %) patients classified as favorable-, intermediate-, and adverse- risk group respectively, according to the European LeukemiaNet (ELN) 2017 classification. Patients either received idarubicin and cytarabine induction chemotherapy with or without FLT3 inhibitors or hypomethylating agents based on age and comorbidity. The complete response (CR) rate was 76 % (82 % and 61 % in patients aged < 60 and ≥ 60, respectively). For the whole cohort, the 3-year overall survival (OS) was 53 %, being 62 % and 30 % in patients aged < 60 and ≥ 60, respectively. The 3-year leukemia-free survival (LFS) was 54 %, with 56 % and 45 % in patients aged < 60 and ≥ 60, respectively. The 3-year LFS were 58 %, 62 % and 25 % for patients within ELN favorable-, intermediate-, and adverse-risk groups respectively. Twenty-seven (36 %) out of 75 patients with intermediate- and adverse-risk disease underwent allogeneic hematopoietic cell transplantation (allo-HCT) in first CR with 92 % of them receiving post-transplant maintenance consisting of azacitidine in 19 (76 %) patients or sorafenib in 6 (24 %) patients. Of these patients younger than 60 years, the 3-year OS and LFS were 85 % and 69 %, respectively. CONCLUSION: These results indicate an improved OS for AML patients especially in intermediate-risk category who were treated with a total therapy consisting of induction chemotherapy followed by allo-HCT and post-transplant maintenance.

The transcriptome-wide landscape of molecular subtype-specific mRNA expression profiles in acute myeloid leukemia.

Molecular classification of acute myeloid leukemia (AML) aids prognostic stratification and clinical management. Our aim in this study is to identify transcriptome-wide mRNAs that are specific to each of the molecular subtypes of AML. We analyzed RNA-sequencing data of 955 AML samples from three cohorts, including the BeatAML project, the Cancer Genome Atlas, and a cohort of Swedish patients to provide a comprehensive transcriptome-wide view of subtype-specific mRNA expression. We identified 729 subtype-specific mRNAs, discovered in the BeatAML project and validated in the other two cohorts. Using unique proteomics data, we also validated the presence of subtype-specific mRNAs at the protein level, yielding a rich collection of potential protein-based biomarkers for the AML community. To enable the exploration of subtype-specific mRNA expression by the broader scientific community, we provide an interactive resource to the public.

Genomic characteristics and prognostic significance of co-mutated ASXL1/SRSF2 acute myeloid leukemia.

The ASXL1 and SRSF2 mutations in AML are frequently found in patients with preexisting myeloid malignancies and are individually associated with poor outcomes. In this multi-institutional retrospective analysis, we assessed the genetic features and clinical outcomes of 43 patients with ASXL1mut SRSF2mut AML and compared outcomes to patients with either ASXL1 (n = 57) or SRSF2 (n = 70) mutations. Twenty-six (60%) had secondary-AML (s-AML). Variant allele fractions suggested that SRSF2 mutations preceded ASXL1 mutational events. Median overall survival (OS) was 7.0 months (95% CI:3.8,15.3) and was significantly longer in patients with de novo vs s-AML (15.3 vs 6.4 months, respectively; P = .04 on adjusted analysis). Compared to ASXL1mut SRSF2wt and ASXL1wt SRSF2mut , co-mutated patients had a 1.4 and 1.6 times increase in the probability of death, respectively (P = .049), with a trend towards inferior OS (median OS = 7.0 vs 11.5 vs 10.9 months, respectively; P = .10). Multivariable analysis suggests this difference in OS is attributable to the high proportion of s-AML patients in the co-mutated cohort (60% vs 32% and 23%, respectively). Although this study is limited by the retrospective data collection and the relatively small sample size, these data suggest that ASXL1mut SRSF2mut AML is a distinct subgroup of AML frequently associated with s-AML and differs from ASXL1mut SRSF2wt /ASXL1wt SRSF2mut with respect to etiology and leukemogenesis.

Clinical features and outcomes of hypocellular acute myeloid leukemia in adults: A Korean AML registry data.

ABSTRACT: The hypocellular variant of acute myeloid leukemia (AML) is defined as bone marrow cellularity of <20% in a biopsy specimen at presentation. We performed a retrospective analysis of the clinical features and survival outcomes of hypocellular AML in a Korean population. We reviewed the medical records of all patients diagnosed with AML at nine hospitals participating in the Korean AML registry from 2006 to 2012. Overall survival (OS) and event-free survival (EFS) rates were calculated from the time of diagnosis until death or an event, respectively. In total, 2110 patients were enrolled and 102 (4.8%) were identified as having hypocellular AML. Patients with hypocellular AML were older than those with non-hypocellular AML (median age: 59 vs 49 years; P < .001) and presented with leukopenia more frequently (mean white blood cell count: 5810/µL vs 40549/µL; P < .001). There was no difference between patients with and without hypocellular AML in terms of the presence of antecedent hematologic disorders (5.9% vs 5.3%; P  = .809). FLT3-ITD and NPM1 mutations were less common in hypocellular than non-hypocellular AML (FLT3-ITD mutations: 1.2% vs 14.3%, P < .001; NPM1 mutations: 0% vs 9.5%, P = .019). No differences were seen between the hypocellular and non-hypocellular AML groups in the complete remission rate (53.9% vs 61.3%, P = .139) or early death rate (defined as any death before 8 weeks; 14.7% vs 13.0%, P = .629). The OS and EFS did not differ between the hypocellular and non-hypocellular AML groups (median OS: 16 vs 23 months, P = .169; median EFS: 6 vs 9 months, P = .215). Hypocellular AML is more frequently observed in older-aged patients and have fewer FLT3-ITD and NPM1 mutation, but the clinical outcomes of hypocellular AML do not differ from those of non-hypocellular AML.

How I diagnose and treat NPM1-mutated AML.

Mutations of the nucleophosmin (NPM1) gene, encoding for a nucleolar multifunctional protein, occur in approximately one-third of adult acute myeloid leukemia (AML). NPM1-mutated AML exhibits unique molecular, pathological, and clinical features, which led to its recognition as distinct entity in the 2017 World Health Organization (WHO) classification of myeloid neoplasms. Although WHO criteria for the diagnosis of NPM1-mutated AML are well established, its distinction from other AML entities may be difficult. Moreover, the percentage of blasts required to diagnose NPM1-mutated AML remains controversial. According to the European LeukemiaNet (ELN), determining the mutational status of NPM1 (together with FLT3) is mandatory for accurate relapse-risk assessment. NPM1 mutations are ideal targets for measurable residual disease (MRD) monitoring, since they are AML specific, frequent, very stable at relapse, and do not drive clonal hematopoiesis of undetermined significance. MRD monitoring by quantitative polymerase chain reaction of NPM1-mutant transcripts, possibly combined with ELN genetic-based risk stratification, can guide therapeutic decisions after remission. Furthermore, immunohistochemistry can be very useful in selected situations, such as diagnosis of NPM1-mutated myeloid sarcoma. Herein, we present 4 illustrative cases of NPM1-mutated AML that address important issues surrounding the biology, diagnosis, and therapy of this common form of leukemia.