[Expectations and challenges for clinical application of cancer genome panels in acute myeloid leukemia].

The blood cancer field has played a pioneering role in advancing precision medicine, with milestones such as development of ABL1 inhibitors for chronic myeloid leukemia. The significance of gene mutation information in AML treatment has increased, evident in classifications and guidelines from organizations such as WHO and ELN. This article examines the anticipated roles of cancer genome panels (CGPs) in AML treatment from three perspectives: diagnosis, risk stratification, and treatment selection. Use of CGPs enables more accurate diagnosis and risk stratification. In treatment selection, CGPs not only complements but also substitutes existing companion diagnostics, and is expected to be a crucial information source for future drug adoption and investigation of tumor-agnostic therapies. However, various challenges remain to be addressed, including the purpose and timing of CGPs, the time required for the tests, and how to utilize expert panels.

Detection of novel PPP1R1B::STARD3 fusion transcript in acute myeloid leukemia: a case report.

BACKGROUND: Acute myeloid leukemia (AML) is the second most common type of leukemia in children. Although prognostic and diagnostic tests of AML patients have improved, there is still a great demand for new reliable clinical biomarkers for AML. Read-through fusion transcripts (RTFTs) are complex transcripts of adjacent genes whose molecular mechanisms are poorly understood. This is the first report of the presence of the PPP1R1B::STARD3 fusion transcript in an AML patient. Here, we investigated the presence of PPP1R1B::STARD3 RTFT in a case of AML using paired-end RNA sequencing (RNA-seq). CASE PRESENTATION: A Persian 12-year-old male was admitted to Dr. Sheikh Hospital of Mashhad, Iran, in September 2019 with the following symptoms, including fever, convulsions, hemorrhage, and bone pain. The patient was diagnosed with AML (non-M3-FAB subtype) based on cell morphologies and immunophenotypical features. Chromosomal analysis using the G-banding technique revealed t (9;22) (q34;q13). CONCLUSIONS: Single-cell RNA sequencing (scRNA-seq) analysis suggested that the PPP1R1B promoter may be responsible for the PPP1R1B::STARD3 expression. Alterations in the level of lipid metabolites implicate cancer development, and this fusion can play a crucial role in the cholesterol movement in cancer cells. PPP1R1B::STARD3 may be considered a candidate for targeted therapies of the cholesterol metabolic and the PI3K/AKT signaling pathways involved in cancer development and progression.

Acute Myeloid Leukemia with Myelodysplasia - Related Changes after Isolated Myeloid Sarcoma.

BACKGROUND: As a tumor mass, a myeloid sarcoma consists of myeloid blasts and presents at an anatomical site other than the bone marrow. In about one quarter of cases, myeloid sarcoma happens without an underlying acute myeloid leukemia or other myeloid neoplasm, and it may precede or coincide with AML or form acute blastic transformation of MDSs, MPNs, or MDS/MPNs. METHODS: Herein, we described a rare case of acute myeloid leukemia with myelodysplasia-related changes (AML-MRC), with WT1 mutation and high expression of TP53 after isolated myeloid sarcoma of lymph nodes showing a higher proportion of blasts, dysplasia of both megakaryocytes and granulocytes. CONCLUSIONS: The case highlights the importance of a bone marrow examination, including morphology, immunophenotyping, cytogenetic, and molecular examination in all cases to exclude the possibility of myeloid sarcoma, especially the morphological feature of bone marrow dysplasia in the early stage before AML.

Lysosome-related genes predict acute myeloid leukemia prognosis and response to immunotherapy.

Background: Acute myeloid leukemia (AML) is a highly aggressive and pathogenic hematologic malignancy with consistently high mortality. Lysosomes are organelles involved in cell growth and metabolism that fuse to form specialized Auer rods in AML, and their role in AML has not been elucidated. This study aimed to identify AML subtypes centered on lysosome-related genes and to construct a prognostic model to guide individualized treatment of AML. Methods: Gene expression data and clinical data from AML patients were downloaded from two high-throughput sequencing platforms. The 191 lysosomal signature genes were obtained from the database MsigDB. Lysosomal clusters were identified by unsupervised consensus clustering. The differences in molecular expression, biological processes, and the immune microenvironment among lysosomal clusters were subsequently analyzed. Based on the molecular expression differences between lysosomal clusters, lysosomal-related genes affecting AML prognosis were screened by univariate cox regression and multivariate cox regression analyses. Algorithms for LASSO regression analyses were employed to construct prognostic models. The risk factor distribution, KM survival curve, was applied to evaluate the survival distribution of the model. Time-dependent ROC curves, nomograms and calibration curves were used to evaluate the predictive performance of the prognostic models. TIDE scores and drug sensitivity analyses were used to explore the implication of the model for AML treatment. Results: Our study identified two lysosomal clusters, cluster1 has longer survival time and stronger immune infiltration compared to cluster2. The differences in biological processes between the two lysosomal clusters are mainly manifested in the lysosomes, vesicles, immune cell function, and apoptosis. The prognostic model consisting of six prognosis-related genes was constructed. The prognostic model showed good predictive performance in all three data sets. Patients in the low-risk group survived significantly longer than those in the high-risk group and had higher immune infiltration and stronger response to immunotherapy. Patients in the high-risk group showed greater sensitivity to cytarabine, imatinib, and bortezomib, but lower sensitivity to ATRA compared to low -risk patients. Conclusion: Our prognostic model based on lysosome-related genes can effectively predict the prognosis of AML patients and provide reference evidence for individualized immunotherapy and pharmacological chemotherapy for AML.

White blood cell count nadir to zero following intensive chemotherapy as a predictive factor for patients with acute myeloid leukemia.

Predictors for response to intensive therapy in AML have focused on baseline factors: percent leukemic blasts in marrow, cytogenetic/molecular genetic abnormalities, and presence of secondary AML. Non-baseline dynamic factors, occurring after induction but before response, may be useful for decisions related to salvage chemotherapy. We hypothesized white blood cell (WBC) count nadir after induction may be a real time indicator of treatment efficacy. We also examined whether time to stem cell transplant (SCT) or baseline molecular genetic abnormalities are associated with a low nadir. Data showed WBC nadir = 0 was a negative predictor for response to intensive induction and was correlated with reduced overall survival and progression free survival. Patients with WBC nadir = 0 did not have a significantly longer time to SCT, and none of the mutations increased the likelihood of reaching WBC nadir = 0. WBC nadir may be a useful real-time monitor in AML patients receiving intensive induction chemotherapy.

Frequency and Aberrant CD Marker Profile of Acute Leukaemias.

OBJECTIVE: To determine the frequency of different types of acute leukaemia and their subtypes along with associated aberrant CD markers. STUDY DESIGN: Descriptive study. Place and Duration of the Study: Department of Immunology Armed Forces Institute of Pathology, National University of Medical Sciences, Rawalpindi, Pakistan, from November 2021 to October 2023. METHODOLOGY: All samples received for flow cytometric immunophenotyping with suspicion of acute leukaemia were included in the study. Cells were stained with fluorochrome labelled monoclonal antibodies against lineage-specific cluster of differentiation (CD) markers through a lyse-wash procedure. Acquisition and analysis were done using multi-parameter BD FACS Canto II Flow cytometer and BD FACS Diva software, respectively. Data were entered and analysed using SPSS v 23.0. RESULTS: Over a period of 2 years, a total of 1,115 suspected patients were tested for acute leukaemia. Among them, 728 (65.3%) were males and 387 (34.7%) were females, with mean age 28 ± 21 years, ranging from 1 week to 87 years. Among a total of 875/1115 (78.5%) diagnosed cases of acute leukaemia, AML was the most common leukaemia present in 408/875 (46.6%) patients followed by B-ALL and T-ALL in 384/875 (43.8%) and 70/87 (8%) patients, respectively (p = 0.5712). Aberrant CD markers were detected in 109/875 (12.5%) leukaemias (p = 0.0628). The most common aberrant CD markers in B-ALL were CD13 and CD33 present in 30/384 (7.8%) cases separately. Among AML and T-ALL most common aberrant CD markers were CD7 and CD33 present in 25/408 (6.13%) and 7/70 (10%) cases, respectively. CONCLUSION: Special consideration should be given to the presence of aberrant CD markers when assigning lineages to acute leukaemias. They may be important diagnostic, prognostic, and management tools for institution of immunotherapy. KEY WORDS: Aberrant CD markers, Acute leukaemia, CD Markers, Flow cytometry, Immunophenotyping.

Higher TIGIT+ γδ TCM cells may predict poor prognosis in younger adult patients with non-acute promyelocytic AML.

Introduction: γδ T cells recognize and exert cytotoxicity against tumor cells. They are also considered potential immune cells for immunotherapy. Our previous study revealed that the altered expression of immune checkpoint T-cell immunoreceptor with immunoglobulin and ITIM domain (TIGIT) on γδ T cells may result in immunosuppression and is possibly associated with a poor overall survival in acute myeloid leukemia (AML). However, whether γδ T-cell memory subsets are predominantly involved and whether they have a relationship with clinical outcomes in patients with AML under the age of 65 remain unclear. Methods: In this study, we developed a multicolor flow cytometry-based assay to monitor the frequency and distribution of γδ T-cell subsets, including central memory γδ T cells (TCM γδ), effector memory γδ T cells (TEM γδ), and TEM expressing CD45RA (TEMRA γδ), in peripheral blood from 30 young (≤65 years old) patients with newly diagnosed non-acute promyelocytic leukemia (also known as M3) AML (AMLy-DN), 14 young patients with AML in complete remission (AMLy-CR), and 30 healthy individuals (HIs). Results: Compared with HIs, patients with AMLy-DN exhibited a significantly higher differentiation of γδ T cells, which was characterized by decreased TCM γδ cells and increased TEMRA γδ cells. A generally higher TIGIT expression was observed in γδ T cells and relative subsets in patients with AMLy-DN, which was partially recovered in patients with AMLy-CR. Furthermore, 17 paired bone marrow from patients with AMLy-DN contained higher percentages of γδ and TIGIT+ γδ T cells and a lower percentage of TCM γδ T cells. Multivariate logistic regression analyses revealed the association of high percentage of TIGIT+ TCM γδ T cells with an increased risk of poor induction chemotherapy response. Conclusions: In this study, we investigated the distribution of γδ T cells and their memory subsets in patients with non-M3 AML and suggested TIGIT+ TCM γδ T cells as potential predictive markers of induction chemotherapy response.

A Case of Chronic Eosinophilic Leukemia with CSF3R-Mutant Clone and Transformed to Secondary Acute Myeloid Leukemia.

BACKGROUND: Chronic eosinophilic leukemia (CEL) is a rare invasive disease characterized by non-specific cytogenetic abnormalities or elevated mother cells, poor prognosis, and a high risk of conversion to acute leukemia. METHODS: We described the data of a patient with CEL-NOS. RESULTS: This case is a CEL-NOS with four mutations in CSF3R-T618I, DNMT3A Q816, ASXL1, and IDH2. CONCLUSIONS: The patient rapidly evolves into secondary acute myeloid leukemia (AML).

Severe Infection Mimicking AML due to Bone Marrow Suppression: a Diagnostic Challenge.

BACKGROUND: Infection may lead to agranulocytosis due to bone marrow suppression. However, a rare case with infection presented with morphological features of acute myeloid leukemia (AML). METHODS: We report a case of extreme agranulocytosis due to severe infection mimicking acute myeloid leukemia. The case was definitively diagnosed by subsequent morphology, flow cytometry, and bone marrow biopsy, and subsequent successful anti-infective treatment confirmed the diagnosis. CONCLUSIONS: To date, no case of a patient diagnosed with severe infection mimicking AML has been reported. The case emphasizes the importance of an integrated diagnostic work-up, especially careful clinical observation and differential diagnosis.

Comprehensive Molecular Profiling of NPM1-Mutated Acute Myeloid Leukemia Using RNAseq Approach.

Acute myeloid leukemia (AML) is a complex hematologic malignancy with high morbidity and mortality. Nucleophosmin 1 (NPM1) mutations occur in approximately 30% of AML cases, and NPM1-mutated AML is classified as a distinct entity. NPM1-mutated AML patients without additional genetic abnormalities have a favorable prognosis. Despite this, 30-50% of them experience relapse. This study aimed to investigate the potential of total RNAseq in improving the characterization of NPM1-mutated AML patients. We explored genetic variations independently of myeloid stratification, revealing a complex molecular scenario. We showed that total RNAseq enables the uncovering of different genetic alterations and clonal subtypes, allowing for a comprehensive evaluation of the real expression of exome transcripts in leukemic clones and the identification of aberrant fusion transcripts. This characterization may enhance understanding and guide improved treatment strategies for NPM1mut AML patients, contributing to better outcomes. Our findings underscore the complexity of NPM1-mutated AML, supporting the incorporation of advanced technologies for precise risk stratification and personalized therapeutic strategies. The study provides a foundation for future investigations into the clinical implications of identified genetic variations and highlights the importance of evolving diagnostic approaches in leukemia management.

Deep learning assists in acute leukemia detection and cell classification via flow cytometry using the acute leukemia orientation tube.

This study aimed to evaluate the sensitivity of AI in screening acute leukemia and its capability to classify either physiological or pathological cells. Utilizing an acute leukemia orientation tube (ALOT), one of the protocols of Euroflow, flow cytometry efficiently identifies various forms of acute leukemia. However, the analysis of flow cytometry can be time-consuming work. This retrospective study included 241 patients who underwent flow cytometry examination using ALOT between 2017 and 2022. The collected flow cytometry data were used to train an artificial intelligence using deep learning. The trained AI demonstrated a 94.6% sensitivity in detecting acute myeloid leukemia (AML) patients and a 98.2% sensitivity for B-lymphoblastic leukemia (B-ALL) patients. The sensitivities of physiological cells were at least 80%, with variable performance for pathological cells. In conclusion, the AI, trained with ResNet-50 and EverFlow, shows promising results in identifying patients with AML and B-ALL, as well as classifying physiological cells.

NET-related gene signature for predicting AML prognosis.

Acute Myeloid Leukemia (AML) is a malignant blood cancer with a high mortality rate. Neutrophil extracellular traps (NETs) influence various tumor outcomes. However, NET-related genes (NRGs) in AML had not yet received much attention. This study focuses on the role of NRGs in AML and their interaction with the immunological microenvironment. The gene expression and clinical data of patients with AML were downloaded from the TCGA-LAML and GEO cohorts. We identified 148 NRGs through the published article. Univariate Cox regression was used to analyze the association of NRGs with overall survival (OS). The least absolute shrinkage and selection operator were utilized to assess the predictive efficacy of NRGs. Kaplan-Meier plots visualized survival estimates. ROC curves assessed the prognostic value of NRG-based features. A nomogram, integrating clinical information and prognostic scores of patients, was constructed using multivariate logistic regression and Cox proportional hazards regression models. Twenty-seven NRGs were found to significantly impact patient OS. Six NRGs-CFTR, ENO1, PARVB, DDIT4, MPO, LDLR-were notable for their strong predictive ability regarding patient survival. The ROC values for 1-, 3-, and 5-year survival rates were 0.794, 0.781, and 0.911, respectively. In the training set (TCGA-LAML), patients in the high NRG risk group showed a poorer prognosis (p < 0.001), which was validated in two external datasets (GSE71014 and GSE106291). The 6-NRG signature and corresponding nomograms exhibit superior predictive accuracy, offering insights for pre-immune response evaluation and guiding future immuno-oncology treatments and drug selection for AML patients.

[Research Progress on the Prognostic Evaluation of MRD in Acute Myeloid Leukemia --Review].

Acute myeloid leukemia (AML) is a highly heterogeneous group of malignant tumors in the blood system. Although many AML patients have achieved survive for a long time through chemotherapy and targeted therapy combined with/without HSCT, but some of them still be difficult to achieve remission or early relapse after remission. Therefore, refining risk stratification and achieving individualized treatment based on prognostic indicators is of great significance. As the research on prognostic indicators of AML deepens increasingly, the prognostic stratification has been continuously improved, from the MICM typing index to the comprehensive evaluation of biological disease characteristics such as MRD. This article reviews the development of prognostic indicators for AML and the research progress of MRD on AML prognosis evaluation to better identify patients with different risks and formulate and implement accurate diagnosis and treatment programs.

High throughput screening aids clinical decision-making in refractory acute myeloid leukaemia.

BACKGROUND: Despite advances in therapeutics for adverse-risk acute myeloid leukaemia (AML), overall survival remains poor, especially in refractory disease. Comprehensive tumour profiling and pre-clinical drug testing can identify effective personalised therapies. CASE: We describe a case of ETV6-MECOM fusion-positive refractory AML, where molecular analysis and in vitro high throughput drug screening identified a tolerable, novel targeted therapy and provided rationale for avoiding what could have been a toxic treatment regimen. Ruxolitinib combined with hydroxyurea led to disease control and enhanced quality-of-life in a patient unsuitable for intensified chemotherapy or allogeneic stem cell transplantation. CONCLUSION: This case report demonstrates the feasibility and role of combination pre-clinical high throughput screening to aid decision making in high-risk leukaemia. It also demonstrates the role a JAK1/2 inhibitor can have in the palliative setting in select patients with AML.

Limited efficacy of 3 + 7 plus gemtuzumab ozogamycin in newly diagnosed fit intermediate genetic risk acute myeloid leukemia patients.

BACKGROUND: Gemtuzumab-ozogamycin (GO) is approved in combination with high-dose chemotherapy for treatment-naïve low- and intermediate-risk acute myeloid leukemia (AML). AIMS: In this retrospective real-life multicenter study, we reported efficacy and safety of GO plus high-dose chemotherapy in newly diagnosed AML patients. METHODS AND RESULTS: A total of 31 fit low- and intermediate-risk AML patients treated with GO-based regimens were retrospectively included in this real-life multicenter study, and results were compared with a control cohort treated with 3 + 7 alone. Complete remission (CR) rate after induction was 77%, and most responders (45%) underwent two GO-based consolidation, and minimal residual disease (MRD) negativity was observed in 17 cases (55%) after the end of consolidation. Low genetic risk was associated with increased CR rate compared with intermediate-risk AML (88% vs. 33%; p < .001), as well as prolonged overall survival (OS; hazard ratio, 0.16; 95% confidential interval, 0.02-0.89; p < .001). GO addition resulted in a survival benefit for low-risk AML (median OS not reached vs. 25 months; p = .19) while not for intermediate-risk subjects (10 vs. 13 months; p = .92), compared with the control group. Moreover, GO-treated patients experienced fever of unknown origin or sepsis in 42% or 36% of cases, respectively, with one death during induction due to septic shock, with similar rates compared with the control group (p = .3480 and p = .5297, respectively). No cases of veno-occlusive disease after allogeneic transplantation were observed. CONCLUSIONS: Our real-life multicenter study confirmed GO-based treatment efficacy with high MRD negativity rates in fit newly diagnosed AML patients, especially in those with low genetic risk and core binding factor, while limited benefits were observed in intermediate-risk AML. However, further validation on larger prospective cohorts is required.

Evaluation of a machine-learning model based on laboratory parameters for the prediction of acute leukaemia subtypes: a multicentre model development and validation study in France.

BACKGROUND: Acute leukaemias are life-threatening haematological cancers characterised by the infiltration of transformed immature haematopoietic cells in the blood and bone marrow. Prompt and accurate diagnosis of the three main acute leukaemia subtypes (ie acute lymphocytic leukaemia [ALL], acute myeloid leukaemia [AML], and acute promyelocytic leukaemia [APL]) is of utmost importance to guide initial treatment and prevent early mortality but requires cytological expertise that is not always available. We aimed to benchmark different machine-learning strategies using a custom variable selection algorithm to propose an extreme gradient boosting model to predict leukaemia subtypes on the basis of routine laboratory parameters. METHODS: This multicentre model development and validation study was conducted with data from six independent French university hospital databases. Patients aged 18 years or older diagnosed with AML, APL, or ALL in any one of these six hospital databases between March 1, 2012, and Dec 31, 2021, were recruited. 22 routine parameters were collected at the time of initial disease evaluation; variables with more than 25% of missing values in two datasets were not used for model training, leading to the final inclusion of 19 parameters. The performances of the final model were evaluated on internal testing and external validation sets with area under the receiver operating characteristic curves (AUCs), and clinically relevant cutoffs were chosen to guide clinical decision making. The final tool, Artificial Intelligence Prediction of Acute Leukemia (AI-PAL), was developed from this model. FINDINGS: 1410 patients diagnosed with AML, APL, or ALL were included. Data quality control showed few missing values for each cohort, with the exception of uric acid and lactate dehydrogenase for the cohort from Hôpital Cochin. 679 patients from Hôpital Lyon Sud and Centre Hospitalier Universitaire de Clermont-Ferrand were split into the training (n=477) and internal testing (n=202) sets. 731 patients from the four other cohorts were used for external validation. Overall AUCs across all validation cohorts were 0·97 (95% CI 0·95-0·99) for APL, 0·90 (0·83-0·97) for ALL, and 0·89 (0·82-0·95) for AML. Cutoffs were then established on the overall cohort of 1410 patients to guide clinical decisions. Confident cutoffs showed two (0·14%) wrong predictions for ALL, four (0·28%) wrong predictions for APL, and three (0·21%) wrong predictions for AML. Use of the overall cutoff greatly reduced the number of missing predictions; diagnosis was proposed for 1375 (97·5%) of 1410 patients for each category, with only a slight increase in wrong predictions. The final model evaluation across both the internal testing and external validation sets showed accuracy of 99·5% for ALL diagnosis, 98·8% for AML diagnosis, and 99·7% for APL diagnosis in the confident model and accuracy of 87·9% for ALL diagnosis, 86·3% for AML diagnosis, and 96·1% for APL diagnosis in the overall model. INTERPRETATION: AI-PAL allowed for accurate diagnosis of the three main acute leukaemia subtypes. Based on ten simple laboratory parameters, its broad availability could help guide initial therapies in a context where cytological expertise is lacking, such as in low-income countries. FUNDING: None.

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.

Rapid progression from MDS to AML with gastric submucosal tumour as an extramedullary infiltration.

We present a rare case of myeloid sarcoma in the stomach of an elderly woman initially diagnosed with anaemia. Myeloid sarcoma, an unusual extramedullary manifestation of acute myeloid leukaemia (AML), primarily affects lymph nodes, bones, spine and skin, with gastrointestinal involvement being infrequent. Despite normal results from the initial endoscopy, a follow-up examination after 4 months revealed multiple submucosal gastric tumours. These developments coincided with worsening of anaemia and an increase in peripheral myeloblasts. Pathological evaluation and immunohistochemical staining confirmed gastric extramedullary infiltration associated with AML. This case highlights the importance of comprehensive diagnostic processes when suspecting leukaemic transformations, especially in myelodysplastic syndrome (MDS). Due to financial constraints, additional critical studies such as cytogenetics and next-generation sequencing were not performed. Nonetheless, this rare case demonstrates the visual observation of rapid progression from MDS to AML and concurrent early myeloid sarcoma development in an elderly patient.