Search Results (12)

Multispecific antibodies have redefined the immunotherapeutic landscape in hematologic malignancies. Bispecific antibodies (BsAbs), which redirect cytotoxic T cells toward malignant targets via dual antigen engagement, are now established components of treatment for diseases such as acute lymphoblastic leukemia (ALL), diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), and multiple myeloma (MM). Clinical trials of agents like blinatumomab, glofitamab, mosunetuzumab, and teclistamab have demonstrated deep and durable responses in heavily pretreated populations. Trispecific antibodies (TsAbs), although still investigational, represent the next generation of immune redirection therapies, incorporating additional tumor antigens or co-stimulatory domains (e.g., CD28, 4-1BB) to mitigate antigen escape and enhance T-cell persistence. This review provides a comprehensive evaluation of BsAbs and TsAbs across hematologic malignancies, detailing molecular designs, mechanisms of action, therapeutic indications, resistance pathways, and toxicity profiles including cytokine release syndrome (CRS), immune effector cell-associated neurotoxicity syndrome (ICANS), cytopenias, and infections. We further discuss strategies to mitigate adverse effects and resistance, such as antigen switching, checkpoint blockade combinations, CELMoDs, and construct optimization. Notably, emerging platforms such as tetrafunctional constructs, checkpoint-integrated multispecifics, and protease-cleavable masking designs are expanding the therapeutic index of these agents. Early clinical evidence also supports the feasibility of applying multispecific antibodies to solid tumors. Finally, we highlight the transformative role of artificial intelligence (AI) and machine learning (ML) in multispecific antibody development, including antigen discovery, biomarker-driven treatment selection, toxicity prediction, and therapeutic optimization. Together, BsAbs and TsAbs illustrate the convergence of molecular precision, clinical innovation, and AI-driven personalization, establishing a new paradigm for immune-based therapy across hematologic and potentially solid tumor malignancies. Full article

Kidney injury encompasses a broad spectrum of structural and functional abnormalities, directly associated with stem cell transplantation. Acute kidney injury and chronic kidney disease represent perilous complications of hematopoietic stem cell transplantation (HSCT), with an elevated risk of mortality and progression to end-stage renal disease. The early detection of these complications is, therefore, paramount, and research is increasingly focused on the identification of novel biomarkers of kidney damage. Recently, proenkephalin (PENK), a monomeric peptide that is freely filtered by the glomerulus and thus reflects glomerular filtration very well, has been shown to be an additional useful predictor of the occurrence of acute kidney injury and heart failure. Dickkopf-3 (DKK3) is a glycoprotein secreted by the renal tubular epithelium in response to stress and has been implicated in the development of interstitial fibrosis. It has therefore been evaluated primarily as a marker of fibrosis in chronic kidney disease (CKD), but may also help predict the development of acute kiney injury. Uromodulin is regarded as a renal marker. Previous studies have examined the potential of PENK, DKK-3 and uromodulin as a biomarker in individuals with preserved renal function. However, the urinary levels of PENK, DKK-3 and uromodulin in patients following HSCT have not yet been established. The objective of the present study was to assess urinary PENK, DKK-3, and uromodulin concentrations in patients who had been under ambulatory care of the Hematology, Transplantation and Internal Medicine Department for a minimum of three months following HSCT, and to investigate their correlations with kidney function, as reflected by serum creatinine and eGFR. The study population comprised 80 patients who had undergone allogeneic HSCT for various reasons, primarily hematological malignancies such as acute leukemias and lymphomas. In addition, 32 healthy volunteers were included in order to establish normal ranges for the biomarkers of interest. Urine concentrations of proenkephalin, DKK-3, and uromodulin were evaluated using a commercially available sandwich ELISA immunoassay. Demographic and clinical data were retrieved from the patients’ records. Statistical analyses were conducted using XLSLAT 2022 (Lumivero, Denver, CO, USA) and STATISTICAv13.0 (StatSoft, Tulsa, OH, USA). The results showed that PENK and DKK-3 levels were significantly higher in patients after HSCT compared to healthy volunteers. Furthermore, when patients were divided according to kidney function (below and over 60 mL/min/1.72 m²), it was found that the concentration of PENK and DKK-3 were significantly higher in 23 patients with CKD stage 3 relative to patients with eGFR over 60 mL min 1.72 m². In univariate correlations, PENK demonstrated an inverse relationship with eGFR (r: −0.21, p < 0.05), while DKK-3 exhibited no significant correlation with creatinine or eGFR.Patients following allogeneic HSCT, despite having normal or near-normal kidney function, exhibited evidence of kidney injury. However, further research is necessary to ascertain the clinical utility of the novel biomarker. Full article

Hematopoietic stem cell transplantation (HSCT) is a life-saving therapy for hematologic malignancies, such as leukemia and lymphoma and other severe conditions but is associated with significant risks, including graft versus host disease (GVHD), relapse, and treatment-related mortality. The increasing complexity of clinical, genomic, and biomarker data has spurred interest in machine learning (ML), which has emerged as a transformative tool to enhance decision-making and optimize outcomes in HSCT. This review examines the applications of ML in HSCT, focusing on donor selection, conditioning regimen, and prediction of post-transplant outcomes. Machine learning approaches, including decision trees, random forests, and neural networks, have demonstrated potential in improving donor compatibility algorithms, mortality and relapse prediction, and GVHD risk stratification. Integrating “omics” data with ML models has enabled the identification of novel biomarkers and the development of highly accurate predictive tools, supporting personalized treatment strategies. Despite promising advancements, challenges persist, including data standardization, algorithm interpretability, and ethical considerations regarding patient privacy. While ML holds promise for revolutionizing HSCT management, addressing these barriers through multicenter collaborations and regulatory frameworks remains essential for broader clinical adoption. In addition, the potential of ML can cope with some challenges such as data harmonization, patients’ data protection, and availability of adequate infrastructure. Future research should prioritize larger datasets, multimodal data integration, and robust validation methods to fully realize ML’s transformative potential in HSCT. Full article

Background: Hematologic malignancies are a group of heterogeneous neoplasms which originate from hematopoietic cells. The most common among them are leukemia, lymphoma, and multiple myeloma. Machine learning (ML) is a subfield of artificial intelligence that enables the analysis of large amounts of data, possibly finding hidden patterns. Methods: We performed a narrative review about recent applications of ML in the most common hematological malignancies. We focused on the most recent scientific literature about this topic. Results: ML tools have proved useful in the most common hematological malignancies, in particular to enhance diagnostic work-up and guide treatment. Conclusions: Although ML has multiple possible applications in this field, there are some issue that have to be fixed before they can be used in daily clinical practice. Full article

Background: The use of serum soluble interleukin 2 receptor (sIL-2R) for the diagnosis of febrile illnesses has not been examined. In this study, febrile patients were classified according to etiology and disease, and serum sIL-2R levels were evaluated. We determined whether serum sIL-2R is a useful marker for differentiating between malignant lymphoma (ML) and non-ML patients and between patients with ML and Kikuchi disease, which present similar clinical manifestations. Methods: This study was a cross-sectional study and included 344 patients with uncomplicated hemophagocytic syndrome, who had a fever of 38 °C or higher within 1 week of admission to our institution. Patient serum sIL-2R was measured, and the serum sIL-2R values are shown as median and IQR. Results: Serum sIL-2R increased above the upper reference limit in all disease groups with fever. The serum sIL-2R level in ML patients (n = 13) was 4760 (2120–6730) U/mL and significantly higher (p < 0.001) than the level of 998 (640–1625) U/mL in non-ML patients (n = 331). The serum sIL-2R level in ML patients (n = 13) was also significantly higher (p < 0.001) compared with that in patients with Kikuchi disease (n = 20; 705 (538–1091) U/mL). Conclusions: Serum sIL-2R tends to exceed the upper reference limit in patients with febrile illnesses. We conclude that the measurement of serum sIL-2R is useful for differentiating ML from non-ML and ML from Kikuchi disease. Full article

Hepatitis B virus (HBV) infection carries a risk of liver cancer and extrahepatic malignancy. However, the incidence trend and clinical course of malignant lymphoma (ML) in HBV patients are not well known. Data about ML newly diagnosed in chronic hepatitis B (CHB) patients from 2003 to 2016 were collected from National Health Insurance Service claims. A total of 13,942 CHB patients were newly diagnosed with ML from 2003 to 2016. The number of patients increased 3.8 times, from 442 in 2003 to 1711 in 2016. The 2-year survival rate of all patients was 76.8%, and the 5-year survival rate was 69.8%. The survival rate of patients taking antivirals due to high viral activity before their diagnosis with ML was significantly lower than that of patients with lower viral activity without antivirals (1 yr—77.3%, 3 yr—64.5%, and 5 yr—58.3% vs. 1 yr—84.0%, 3 yr—73.4%, and 5 yr—68.0%, respectively). The survival rate of patients with liver cirrhosis (LC) at baseline was significantly lower than that of those without LC. Cirrhotic patients taking antivirals before ML diagnosis had a worse prognosis than who did not. High viral activity in CHB patients with ML seems to be useful in predicting the prognosis for survival. Full article

Mantle cell lymphoma (MCL) is a rare lymphoid malignancy with a poor prognosis characterised by frequent relapse and short durations of treatment response. Most patients present with aggressive disease, but there exist indolent subtypes without the need for immediate intervention. The very heterogeneous behaviour of MCL is genetically characterised by the translocation t(11;14)(q13;q32), leading to Cyclin D1 overexpression with distinct clinical and biological characteristics and outcomes. There is still an unfulfilled need for precise MCL prognostication in real-time. Machine learning and deep learning neural networks are rapidly advancing technologies with promising results in numerous fields of application. This study develops and compares the performance of deep learning (DL) algorithms and radiomics-based machine learning (ML) models to predict MCL relapse on baseline CT scans. Five classification algorithms were used, including three deep learning models (3D SEResNet50, 3D DenseNet, and an optimised 3D CNN) and two machine learning models based on K-nearest Neighbor (KNN) and Random Forest (RF). The best performing method, our optimised 3D CNN, predicted MCL relapse with a 70% accuracy, better than the 3D SEResNet50 (62%) and the 3D DenseNet (59%). The second-best performing method was the KNN-based machine learning model (64%) after principal component analysis for improved accuracy. Our optimised CNN developed by ourselves correctly predicted MCL relapse in 70% of the patients on baseline CT imaging. Once prospectively tested in clinical trials with a larger sample size, our proposed 3D deep learning model could facilitate clinical management by precision imaging in MCL. Full article

It is desirable that noninvasive differential diagnosis takes place without lymph node biopsy for histiocytic necrotizing lymphadenitis (HNL) or malignant lymphoma (ML). In this study, we propose a novel scoring model for the differential diagnosis of these diseases using clinical information and clinical findings. We retrospectively analyzed the data from 15 HNL and 13 ML pediatric patients. First, a univariate analysis identified 14 clinical factors with significant differences. Second, a subsequent analysis using receiver operating characteristic (ROC) curve analysis identified three factors among them with area under the ROC curve values of >0.95: body temperature (°C), maximum lymph node size (cm), and serum β₂-microglobulin level (mg/L). Finally, the cut-off values of each of these three factors were determined and examined for the 28 cases. All 15 HNL cases were within 2–3 of the cut-off values among the three factors, no ML case was within two or more cut-off values. Thus, the diagnostic sensitivity and specificity of this novel scoring system were both 100%, indicating that clinical scoring with body temperature, maximum lymph node size, and β₂-microglobulin are useful for distinguishing between HNL and ML. Full article

The diagnosis of primary central nervous system (CNS) lymphoma, which is predominantly of the diffuse large B-cell lymphoma type (CNS DLBCL), is challenging. MicroRNAs (miRs) are gene expression-regulating non-coding RNAs that are potential biomarkers. We aimed to distinguish miR expression patterns differentiating CNS DLBCL and non-malignant CNS diseases with tumor presentation (n-ML). Next generation sequencing-based miR profiling of cerebrospinal fluids (CSFs) and brain tumors was performed. Sample source-specific (CSF vs. brain tumor) miR patterns were revealed. Even so, a set of 17 miRs differentiating CNS DLBCL from n-ML, no matter if assessed in CSF or in a tumor, was identified. Along with the results of pathway analyses, this suggests their pathogenic role in CNS DLBCL. A combination of just four of those miRs (miR-16-5p, miR-21-5p, miR-92a-3p, and miR-423-5p), assessed in CSFs, discriminated CNS DLBCL from n-ML samples with 100% specificity and 67.0% sensitivity. Analyses of paired CSF-tumor samples from patients with CNS DLBCL showed significantly lower CSF levels of miR-26a, and higher CSF levels of miR-15a-5p, miR-15b-5p, miR-19a-3p, miR-106b-3p, miR-221-3p, and miR-423-5p. Noteworthy, the same miRs belonged to the abovementioned set differentiating CNS DLBCL from non-malignant CNS diseases. Our results not only add to the basic knowledge, but also hold significant translational potential. Full article

Acrylamide, which is present in many daily foods, is a probable human carcinogen. In 2002, it was identified in several common foods. Subsequently, western epidemiologists began to explore the relationship between dietary acrylamide exposure and cancer risk; however, limited suggestive associations were found. This prospective study aimed to examine the association between dietary acrylamide intake and the risk of hematological malignancies, including malignant lymphoma (ML), multiple myeloma (MM), and leukemia. We enrolled 85,303 participants in the Japan Public Health Center-based Prospective study on diet and cancer as from 1995. A food frequency questionnaire that included data on acrylamide in all Japanese foods was used to assess dietary acrylamide intake. We applied multivariable adjusted Cox proportional hazards models to reckon hazard ratios (HRs) for acrylamide intake for both categorical variables (tertiles) and continuous variables. After 16.0 median years of follow-up, 326 confirmed cases of ML, 126 cases of MM, and 224 cases of leukemia were available for final multivariable-adjusted analysis. HRs were 0.87 (95% confidence interval [CI]: 0.64–1.18) for ML, 0.64 (95% CI: 0.38–1.05) for MM, and 1.01 (95% CI: 0.71–1.45) for leukemia. Our results implied that acrylamide may not be related to the risk of hematological malignancies. Full article

Primary central nervous system lymphoma (PCNSL) is a rare, highly aggressive, extranodal form of non-Hodgkin lymphoma, predominantly diagnosed as primary diffuse large B-cell lymphoma of the central nervous system (CNS DLBCL). Fast and precise diagnosis of PCNSL is critical yet challenging. microRNAs, important regulators in physiology and pathology are potential biomarkers. In 131 patients with CNS DLBCL and with non-malignant brain lesions (n-ML), miR-21, miR-19b and miR-92a, miR-155, miR-196b, miR-let-7b, miR-125b, and miR-9 were examined by RT-qPCR in brain biopsy samples (formalin-fixed paraffin-embedded tissues, FFPET; CNS DLBCL, n = 52; n-ML, n = 42) and cerebrospinal fluid samples (CSF; CNS DLBCL, n = 30; n-ML, n = 23) taken for routine diagnosis. FFPET samples were split into study and validation sets. Significantly higher CSF levels of miR-21, miR-19b, and miR-92a were identified in PCNSL but not in n-ML, and differentiated PCNSL from n-ML with 63.33% sensitivity and 80.77% specificity. In FFPETs, miR-155 and miR-196b were significantly overexpressed and miR-let-7b, miR-125b, and miR-9 were downregulated in PCNSL as compared to n-ML. Combined miR-155 and miR-let-7b expression levels in FFPETs discriminated PCNSL and n-ML with a 97% accuracy. In conclusion, tissue miR-155, miR-196b, miR-9, miR-125b, and miR-let-7b expression profiles differentiate PCNSL from n-ML. PCNSL CSFs and the relevant biopsy samples are characterized by specific, different microRNA profiles. A logistic regression model is proposed to discriminate between PCNSL and non-malignant brain lesions. None of the examined microRNAs influenced overall survival of PCNSL patients. Further ongoing developments involve next generation sequencing-based profiling of biopsy and CSF samples. Full article

Research on oncolytic viruses has mostly been directed towards the treatment of solid tumors, which has yielded limited information regarding their activity in hematological cancer. It has also been directed towards the treatment of humans, yet veterinary medicine may also benefit. Several strains of the Newcastle disease virus (NDV) have been used as oncolytics in vitro and in a number of in vivo experiments. We studied the cytolytic effect of NDV-MLS, a low virulence attenuated lentogenic strain, on a human large B-cell lymphoma cell line (SU-DHL-4), as well as on primary canine-derived B-cell lymphoma cells, and compared them to healthy peripheral blood mononuclear cells (PBMC) from both humans and dogs. NDV-MLS reduced cell survival in both human (42% ± 5%) and dog (34% ± 12%) lymphoma cells as compared to untreated controls. No significant effect on PBMC was seen. Cell death involved apoptosis as documented by flow-cytometry. NDV-MLS infections of malignant lymphoma tumors in vivo in dogs were confirmed by electron microscopy. Early (24 h) biodistribution of intravenous injection of 1 × 10¹² TCID₅₀ (tissue culture infective dose) in a dog with T-cell lymphoma showed viral localization only in the kidney, the salivary gland, the lung and the stomach by immunohistochemistry and/or endpoint PCR. We conclude that NDV-MLS may be a promising agent for the treatment of lymphomas. Future research is needed to elucidate the optimal therapeutic regimen and establish appropriate biosafety measures. Full article