Acta Haematologica最新文献

Introduction: Acute myeloid leukemia (AML) is a heterogenous hematologic malignancy that maintains high relapse rates and poor survival despite ongoing treatment advances. There is critically unmet need for consistently providing long-term survival with minimal treatment toxicity for AML patients. Advances in artificial intelligence/machine learning (AI/ML) offer new approaches to addressing clinical challenges in AML.

Methods: In this systematic narrative review, 426 publications focusing on the intersection of AML and AI/ML between January 1, 2010, and July 30, 2024, are reviewed.

Results: The evolution of AI/ML tools over time is described from a clinically relevant perspective with a distinction between early epochs of AI/ML versus more contemporary algorithms, such as generative adversarial networks and transformer-based algorithms. This review highlights the utilization of contemporary AI/ML algorithms via addressing diagnostic challenges, molecular risk stratification problems, and clinical outcome prediction in the context of AML.

Conclusion: Overall, AI/ML represents a promising new frontier in approaching clinical problems in AML, though there are still opportunities for utilization, particularly in the setting of allogeneic stem cell transplantation.

Introduction: Many reports indicate that the occurrence of multiple myeloma (MM) is closely related to inflammation and immunity. Although the survival rates have been gradually improving in recent years, the cure rate is still not optimistic enough. Therefore, it is necessary to continue exploring the causes of MM.

Methods: This study utilizes Mendelian randomization (MR) analysis to establish the connection between inflammatory factors, immune cells, and the occurrence of MM.

Results: In MR studies, a significant correlation was observed between interleukin-1 receptor antagonist (IL-1Ra), tumor necrosis factor receptor 1 (TNFR1), memory B-cell percentage of B cells (memory B-cell %B cells), and immunoglobulin D-positive, CD24-negative percentage B cells (IgD+ CD24- %B cells) with the onset of MM. In particular, IgD+ CD24- %B cells showed a statistically significant inverse relationship with the development of MM (p < 0.05, OR <1), whereas IL-1Ra, TNFR1, and memory B-cell %B cells displayed a positive association with the onset of MM (p < 0.05, OR >1). These findings contribute valuable insights to the understanding of the pathogenesis of MM.

Conclusion: This study emphasizes the significant role of inflammatory factors and immune cells in multiple myeloma (MM) progression. IL-1Ra, TNFR1, and memory B-cell percentages are identified as risk factors, while IgD+ CD24- %B cells may protect against progression, suggesting new immunomodulatory treatment strategies. However, research on IgD+ CD24- %B cells and MM is limited, necessitating future studies to clarify their mechanisms and effects on the tumor microenvironment. There is also an urgent need for clinical trials to assess therapies targeting these cells, as well as long-term follow-ups to understand their dynamic changes in relation to disease progression. Further investigation using animal models is warranted to validate their functional role in MM development.

Introduction: Proton pump inhibitors (PPIs) are one of the most widely used drugs worldwide [Gut Liver. 2017;11(1):27-37]. The use of PPI has become a common practice and is overprescribed for all patients with cancer including patients with hematological malignancies. In the current study, we aimed to explore retrospectively the effect of PPI, on time to first treatment (TTFT) in a large cohort of patients with chronic lymphocytic leukemia (CLL) who were under watch-and-wait approach.

Methods: The cohort is based on anonymized data obtained from electronic medical records of Maccabi Healthcare Services (MHS) members, who is the second-largest healthcare organization in Israel, with 2.5 million insured patients, and received a diagnosis of CLL during this period.

Results: Our cohort included 3,474 patients with CLL who are treatment-naïve, and the median follow-up was 1,745 days (602-3,700). A total of 1,061 patients (30.5%) received a PPI agent, for a minimum of 3 months during the watch-and-wait period. The intake of PPI was found to be associated with a shorter TTFT: among PPI users, the 10-year treatment-free ratio is 79.2%, while among non-PPI users it is 90.6%.

Conclusion: Routine use of PPI in CLL patients may negatively impact their clinical course. Biology of this primary observation requires further investigation.

Introduction: Accurate prediction of survival in patients with acute myelogenous leukemia (AML) is challenging. Therefore, we developed a predictive survival model using endocrine-related gene expression to identify an endocrine signature for accurate stratification of AML prognosis.

Methods: RNA matrices and clinical data for AML were downloaded from a training dataset (Gene Expression Omnibus) and two validation datasets (the Cancer Genome Atlas and Therapeutically Applicable Research to Generate Effective Treatments).

Results: In relation to the survival outcome, a risk model was constructed by incorporating seven endocrine-related genes. The model exhibited favorable predictive efficacy in estimating 5-year survival rates, as demonstrated by both the training and validation cohorts. Multivariable analysis revealed that the endocrine signature demonstrated autonomous prognostic significance in the aforementioned cohorts. Prediction accuracy for 5-year overall survival increased using a nomogram combining endocrine risk score and classical prognostic factors compared with using classical prognostic factors alone. The model predictions were confirmed using AML cell lines.

Conclusion: The endocrine-related prognostic model established in this study improves AML survival prediction accuracy.

Introduction: Ruxolitinib is approved for treatment of myelofibrosis. We evaluated ruxolitinib in patients with anemia (hemoglobin <10 g/dL) or thrombocytopenia (platelet count ≤100 × 109/L) at diagnosis.

Methods: This was a retrospective, secondary analysis of a Cardinal Health Oncology Provider Extended Network medical chart review of adults with myelofibrosis diagnosed between 2012 and 2016 who received first-line ruxolitinib.

Results: 176 patients received first-line ruxolitinib and were included in this analysis. At diagnosis, 120 patients had hemoglobin concentrations <10 g/dL and 59 had a platelet count ≤100 × 109/L. Most patients (95%) with thrombocytopenia also had anemia. Median time of observation after diagnosis was 21.4 months. Among patients with anemia or thrombocytopenia, ruxolitinib dose at end of study was ≥10 mg twice daily (bid) in 88.3% and 83.1%, respectively. Ruxolitinib treatment was ongoing in 76.1% of patients overall and was rarely discontinued for anemia or thrombocytopenia (n = 2 total, 1.1%). Per the treating physician, 79.7% of patients had improved symptoms and 62.7% improved spleen size.

Conclusion: Most patients with myelofibrosis and anemia or thrombocytopenia at diagnosis tolerated and maintained a ruxolitinib dose ≥10 mg bid for nearly 2 years, resulting in clinical benefit. This real-world evidence supports observations from prospective clinical trials of ruxolitinib in myelofibrosis.

Background: Recent advancements in artificial intelligence (AI) hold significant promise for transforming hemophilia care. Summary: This review explores the impact of AI on critical aspects of hemophilia management, including bleeding risk prediction, biomarker identification, personalized treatment strategies, and patient education. Key Messages: We discuss the application of machine learning models in predicting bleeding risks among children with hemophilia engaging in physical activities, the use of AI in analyzing factor VIII protein structures to determine disease severity, and the development of AI-powered chatbots and digital platforms for patient education and self-management, particularly in resource-limited settings. Furthermore, we address the challenges inherent in implementing AI technologies in clinical practice, such as data privacy concerns, model interpretability, and the need for robust validation. By highlighting current advancements and future directions, we underscore the potential of AI to enhance personalized care and improve outcomes for individuals with hemophilia.

Introduction: Acute promyelocytic leukemia (APL) is genetically characterized by the fusion of promyelocytic leukemia (PML) gene with retinoic acid receptor alpha (RARα) resulting from a t(15;17)(q24;q21) chromosomal translocation. An infrequent but recurrent finding in APL is the formation of an isochromosome of the derivative chromosome 17; ider(17)(q10)t(15;17) or ider(17q). This rearrangement in APL results in an additional copy of the PML-RARα fusion gene as well as loss of 17p/TP53. Due to the infrequent occurrence of the ider(17q), the prognostic impact of this genetic finding is not well known. Case Presentation(s): Here, we describe the clinical characteristics and outcomes of our case series of 5 patients with ider(17q) APL treated at the University of Maryland and Johns Hopkins University.

Conclusion: In our series, patients with APL with ider(17q) did not have a worse prognosis.