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Mixed phenotype acute leukemia (MPAL) is a rare subtype of acute leukemia characterized by leukemic blasts presenting myeloid and lymphoid markers. Here we report data from integrated genomic analysis on 31 MPAL samples and compare molecular profiling with that from acute myeloid leukemia (AML), B cell acute lymphoblastic leukemia (B-ALL), and T cell acute lymphoblastic leukemia (T-ALL). Consistent with the mixed immunophenotype, both AML-type and ALL-type mutations are detected in MPAL. Myeloid-B and myeloid-T MPAL show distinct mutation and methylation signatures that are associated with differences in lineage-commitment gene expressions. Genome-wide methylation comparison among MPAL, AML, B-ALL, and T-ALL sub-classifies MPAL into AML-type and ALL-type MPAL, which is associated with better clinical response when lineage-matched therapy is given. These results elucidate the genetic and epigenetic heterogeneity of MPAL and its genetic distinction from AML, B-ALL, and T-ALL and further provide proof of concept for a molecularly guided precision therapy approach in MPAL. Mixed phenotype acute leukemia (MPAL) is a rare leukemia that presents both myeloid and lymphoid markers on blasts. Here the authors perform genomic analysis to show MPAL involves genetic and epigenetic heterogeneity and is genetically distinct from AML, B-ALL, and T-ALL.

Five-year survival has increased for many hematologic malignancies in the 21st century. However, whether this has translated into greater long-term survival is unknown. Here, we examine 10- and 20-year survival for patients with multiple myeloma (MM), acute lymphoblastic leukemia (ALL), acute myeloblastic leukemia (AML), chronic lymphoid leukemia (CLL), chronic myeloid leukemia (CML), non-Hodgkin lymphoma (NHL), and Hodgkin lymphoma (HL). Data were extracted from the Surveillance, Epidemiology, and End Results-9 database. Patients age 15+ with the above malignancies were included. The newly developed boomerang method was used to examine 10- and 20-year relative survival (RS) for patients in 2002–2006 and 2012–16. Ten and 20-year RS increased for each malignancy examined, with increases ranging from +4.4% units for 20-year RS for AML to +23.1% units for 10-year RS for CML. Ten year RS was >50% in 2012–16 for patients with CLL, CML, HL, NHL, and DLBCL, at 77.1%, 62.1%, 63.9%, 64.5%, and 63.0%, respectively. Survival dropped between 10 and 20 years after diagnosis for most malignancies. Long-term survival is increasing for common hematologic malignancies, but late mortality is an ongoing issue. Further study of long-term outcomes in curable malignancies to determine the reason for these later decreases in survival is indicated.

To report the findings of the 2019 Society for Hematopathology/European Association for Haematopathology Workshop within the categories of reactive eosinophilia, hypereosinophilic syndrome (HES), germline disorders with eosinophilia (GDE), and myeloid and lymphoid neoplasms associated with eosinophilia (excluding entities covered by other studies in this series). The workshop panel reviewed 109 cases, assigned consensus diagnosis, and created diagnosis-specific sessions. The most frequent diagnosis was reactive eosinophilia (35), followed by acute leukemia (24). Myeloproliferative neoplasms (MPNs) received 17 submissions, including chronic eosinophilic leukemia, not otherwise specified (CEL, NOS). Myelodysplastic syndrome (MDS), MDS/MPN, and therapy-related myeloid neoplasms received 11, while GDE and HES received 12 and 11 submissions, respectively. Hypereosinophilia and HES are defined by specific clinical and laboratory criteria. Eosinophilia is commonly reactive. An acute leukemic onset with eosinophilia may suggest core-binding factor acute myeloid leukemia, blast phase of chronic myeloid leukemia, BCR-ABL1-positive leukemia, or t(5;14) B-lymphoblastic leukemia. Eosinophilia is rare in MDS but common in MDS/MPN. CEL, NOS is a clinically aggressive MPN with eosinophilia as the dominant feature. Bone marrow morphology and cytogenetic and/or molecular clonality may distinguish CEL from HES. Molecular testing helps to better subclassify myeloid neoplasms with eosinophilia and to identify patients for targeted treatments.

PR1, an HLA-A2-restricted peptide derived from both proteinase 3 and neutrophil elastase, is recognized on myeloid leukemia cells by cytotoxic T lymphocytes (CTLs) that preferentially kill leukemia and contribute to cytogenetic remission. To evaluate safety, immunogenicity and clinical activity of PR1 vaccination, a phase I/II trial was conducted. Sixty-six HLA-A2+ patients with acute myeloid leukemia (AML: 42), chronic myeloid leukemia (CML: 13) or myelodysplastic syndrome (MDS: 11) received three to six PR1 peptide vaccinations, administered subcutaneously every 3 weeks at dose levels of 0.25, 0.5 or 1.0 mg. Patients were randomized to the three dose levels after establishing the safety of the highest dose level. Primary end points were safety and immune response, assessed by doubling of PR1/HLA-A2 tetramer-specific CTL, and the secondary end point was clinical response. Immune responses were noted in 35 of 66 (53%) patients. Of the 53 evaluable patients with active disease, 12 (24%) had objective clinical responses (complete: 8; partial: 1 and hematological improvement: 3). PR1-specific immune response was seen in 9 of 25 clinical responders versus 3 of 28 clinical non-responders ( P =0.03). In conclusion, PR1 peptide vaccine induces specific immunity that correlates with clinical responses, including molecular remission, in AML, CML and MDS patients.

Flow cytometry (FC) is critical for the diagnosis and monitoring of hematologic malignancies. Machine learning (ML) methods rapidly classify multidimensional data and should dramatically improve the efficiency of FC data analysis. We aimed to build a model to classify acute leukemias, including acute promyelocytic leukemia (APL), and distinguish them from nonneoplastic cytopenias. We also sought to illustrate a method to identify key FC parameters that contribute to the model's performance. Using data from 531 patients who underwent evaluation for cytopenias and/or acute leukemia, we developed an ML model to rapidly distinguish among APL, acute myeloid leukemia/not APL, acute lymphoblastic leukemia, and nonneoplastic cytopenias. Unsupervised learning using gaussian mixture model and Fisher kernel methods were applied to FC listmode data, followed by supervised support vector machine classification. High accuracy (ACC, 94.2%; area under the curve [AUC], 99.5%) was achieved based on the 37-parameter FC panel. Using only 3 parameters, however, yielded similar performance (ACC, 91.7%; AUC, 98.3%) and highlighted the significant contribution of light scatter properties. Our findings underscore the potential for ML to automatically identify and prioritize FC specimens that have critical results, including APL and other acute leukemias.

Background Leukemia is the most common subtype of cancer in Indian children and ranks among the top ten cancers in the adult population according to ICMR cancer registry 2022 (National Cancer Registry Programme, Cancer incidence and mortality in India 2022, 2022). Current leukemia diagnostics rely on sophisticated molecular tests that are not available at all healthcare centers, particularly in remote and tier-two cities. Access to specialized molecular diagnostics and prognostic tests is limited to fewer than 20 centers across India, serving a population of approximately 1.3 billion people (National Cancer Registry Programme, Cancer incidence and mortality in India 2022, 2022). This restricted accessibility results in delayed diagnosis, suboptimal risk stratification, increased economic burden, and higher mortality rates compared to high-income countries. Methods This is a multicenter, randomized, double-blind diagnostic interventional trial designed to validate a novel dried blood spot (DBS) based technology for leukemia transcript determination. The study will be conducted across five sites in India. Newly diagnosed acute leukemia patients ( n  = 300; 200 ALL and 100 AML) will be randomized into two arms: interventional arm (DBS-based testing) and comparator arm (conventional testing). The primary outcome is the sensitivity, specificity, and positive predictive value of the DBS-based method compared to conventional diagnostic methods. Secondary outcomes include overall survival, event-free survival, and relapse rates over one year of follow-up. Discussion This study addresses a critical gap in leukemia diagnostic accessibility in low- and middle-income countries. The novel DBS-based technology has the potential to withstand tropical temperature and humidity conditions for 24–48 h, making it suitable for transport from remote areas. Preliminary work has shown 100% specificity and 99% sensitivity compared to standard reference methods (PLoS One 13(1):e0191421, 2019). If validated, this technology could significantly improve leukemia outcomes through earlier diagnosis, better risk stratification, and reduced abandonment rates. Trial registration Clinical Trials Registry—India (CTRI): CTRI/2024/06/069601.

Chronic Eosinophilic Leukemia (CEL), a rare and intricate hematological disorder characterized by uncontrolled eosinophilic proliferation, presents clinical challenges owing to its infrequency. This study aimed to investigate epidemiology and develop a prognostic nomogram for CEL patients. Utilizing the Surveillance, Epidemiology and End Results database, CEL cases diagnosed between 2001 and 2020 were analyzed for incidence rates, clinical profiles, and survival outcomes. Patients were randomly divided into training and validation cohorts (7:3 ratio). LASSO regression analysis and Cox regression analysis were performed to screen the prognostic factors for overall survival. A nomogram was then constructed and validated to predict the 3- and 5-year overall survival probability of CEL patients by incorporating these factors. The incidence rate of CEL was very low, with an average of 0.033 per 100,000 person-years from 2001 to 2020. The incidence rate significantly increased with age and was higher in males than females. The mean age at diagnosis was 57 years. Prognostic analysis identified advanced age, specific marital statuses, and secondary CEL as independent and adverse predictors of overall survival. To facilitate personalized prognostication, a nomogram was developed incorporating these factors, demonstrating good calibration and discrimination. Risk stratification using the nomogram effectively differentiated patients into low- and high-risk groups. This study enhances our understanding of CEL, offering novel insights into its epidemiology, demographics, and prognostic determinants, while providing a possible prognostication tool for clinical use. However, further research is warranted to elucidate molecular mechanisms and optimize therapeutic strategies for CEL.

Leukemia is the most prevalent form of blood cancer, affecting individuals across all age groups. Early and accurate diagnosis is crucial for effective treatment and improved clinical outcomes. Peripheral blood smear analysis, a key non-invasive diagnostic tool, often suffers from subjective interpretation, inter-observer variability, and a lack of readily available expertise. Although deep learning approaches, particularly Convolutional Neural Networks (CNNs), have demonstrated exceptional performance in binary classification tasks, multiclass classification of leukemia subtypes remains challenging due to limited data availability and morphological similarities between subtypes. This study presents a novel hybrid methodology that combines pre-trained CNN architectures, including VGG16, InceptionV3, and ResNet50, with advanced classification models such as Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and the deep learning-based Multi-Layer Perceptron (MLP). The method leverages publicly available datasets, the Acute Lymphoblastic Leukemia Image Database (ALL-IDB) and the Munich AML Morphology Dataset, to classify healthy cells, lymphoblasts, and myeloblasts. Pre-trained CNNs are employed for feature extraction, while the classifiers refine the predictions for improved accuracy. The proposed approach demonstrated exceptional performance, with the InceptionV3 + SVM combination achieving the highest accuracy of 88%, followed closely by VGG16 + XGBoost at 87%. MLP-based models also achieved strong results, effectively capturing non-linear patterns in the data. In contrast, ResNet50 exhibited limitations, likely due to overfitting caused by the small dataset. The novelty of this work lies in the integration of pre-trained deep learning architectures with hybrid classification techniques, enabling robust multiclass classification in data-constrained scenarios. This innovative approach offers a scalable and precise diagnostic tool, improving the speed and reliability of leukemia subtype identification and providing significant potential to enhance clinical decision-making and patient care.

Abstract Background Advances in care have led to improvements in survival for adolescents and young adults (AYAs) diagnosed with cancer; however, the risk of early death remains high for certain cancers, particularly acute leukemias. Risk factors for early death in AYAs diagnosed with acute leukemia have not been well studied. Methods The Surveillance, Epidemiology, and End Results registry was used to assess risk of early death (within 2 months of diagnosis) in AYAs diagnosed with acute leukemia (n = 16 153). Early death proportion, by year, for AYAs diagnosed between 2006 and 2020 was described. Associations between incidence of early death and age at diagnosis, sex, race and ethnicity, socioeconomic status, rurality, acute leukemia type, and year of diagnosis were evaluated with logistic regression. Results Overall, 6.0% of AYAs experienced early death and there was a significant annual decrease in the odds of early death (odds ratio [OR] = 0.96, 95% confidence interval [CI] = 0.95 to 0.98, P < .0001) across the study period. Over the entire study period, AYAs diagnosed with acute promyelocytic leukemia (9.6%, 95% CI = 8.4 to 11.1) or other acute leukemias (13.3%, 95% CI = 10.5 to 16.7) had the highest proportion of early death and AYAs diagnosed with T lymphoblastic leukemia/lymphoma had the lowest (2.6%, 95% CI = 1.9 to 3.7). Older age at diagnosis, male sex, and Hispanic ethnicity were all associated with increased risk of early death. Conclusions A high proportion of AYAs with acute leukemia experience early death and risk varies by leukemia type and sociodemographic factors. A better understanding of the complex interplay between disease biology and sociodemographic factors is needed to guide risk prediction and prevention.