Pub Date : 2025-11-20DOI: 10.1053/j.seminhematol.2025.11.003
Jun Ho Yi, Dok Hyun Yoon, Kihyun Kim
Significant advances have been made in the treatment of multiple myeloma, with B-cell maturation antigen (BCMA)-targeting immunotherapies at the forefront of these developments. Three treatment modalities-including antibody-drug conjugates, bispecific antibodies, and chimeric antigen receptor-T cell therapies-have demonstrated durable responses, each associated with class-specific unique adverse events. Moreover, their efficacy and safety in later stages of multiple myeloma also provide the rationale for their use in earlier lines of therapy. In this review, we briefly outline the background biology of BCMA and discuss the mechanisms of action and structural features of these 3 agents, together with the most recent clinical data from early-phase trials. We also describe class-specific adverse events and mechanisms of resistance, as well as strategies to overcome them, thereby offering a framework to catch up with future developments in BCMA-targeted immunotherapy for multiple myeloma.
{"title":"Targeting B-cell maturation antigen in relapsed or refractory multiple myeloma: On the verge of its prime time.","authors":"Jun Ho Yi, Dok Hyun Yoon, Kihyun Kim","doi":"10.1053/j.seminhematol.2025.11.003","DOIUrl":"https://doi.org/10.1053/j.seminhematol.2025.11.003","url":null,"abstract":"<p><p>Significant advances have been made in the treatment of multiple myeloma, with B-cell maturation antigen (BCMA)-targeting immunotherapies at the forefront of these developments. Three treatment modalities-including antibody-drug conjugates, bispecific antibodies, and chimeric antigen receptor-T cell therapies-have demonstrated durable responses, each associated with class-specific unique adverse events. Moreover, their efficacy and safety in later stages of multiple myeloma also provide the rationale for their use in earlier lines of therapy. In this review, we briefly outline the background biology of BCMA and discuss the mechanisms of action and structural features of these 3 agents, together with the most recent clinical data from early-phase trials. We also describe class-specific adverse events and mechanisms of resistance, as well as strategies to overcome them, thereby offering a framework to catch up with future developments in BCMA-targeted immunotherapy for multiple myeloma.</p>","PeriodicalId":21684,"journal":{"name":"Seminars in hematology","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145757647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-20DOI: 10.1053/j.seminhematol.2025.11.002
Kyung-Nam Koh, Dae Hee Kim, Hyori Kim
The success of bispecific T cell engagers (BiTEs) in hematological malignancies has catalyzed the development of trispecific antibodies that simultaneously target 3 molecular entities. These next-generation immunotherapeutics address the key limitations of bispecific constructs including antigen escape, limited T cell activation, and on-target off-tumor toxicity. Trispecific constructs employ 2 primary strategies: dual tumor antigen targeting combined with CD3 engagement to prevent antigen escape, and integration of co-stimulatory signals (CD28, 4-1BB) to enhance T cell function. Early clinical data demonstrated promising efficacy signals, particularly in multiple myeloma where BCMA×CD38×CD3 constructs achieved 90% overall response rates in early-phase trials. Safety profiles mirror bispecific antibodies with cytokine release syndrome and neurotoxicity as primary concerns. Trispecific T cell engagers represent a significant advancement in precision immunotherapy for hematological malignancies. Although early clinical results are encouraging, challenges remain in optimal target selection, manufacturing complexity, and resistance mechanisms. Ongoing clinical trials will define their role in the evolving treatment landscape.
{"title":"Trispecific T cell engagers in hematological malignancies: Advancing beyond bispecific antibodies.","authors":"Kyung-Nam Koh, Dae Hee Kim, Hyori Kim","doi":"10.1053/j.seminhematol.2025.11.002","DOIUrl":"https://doi.org/10.1053/j.seminhematol.2025.11.002","url":null,"abstract":"<p><p>The success of bispecific T cell engagers (BiTEs) in hematological malignancies has catalyzed the development of trispecific antibodies that simultaneously target 3 molecular entities. These next-generation immunotherapeutics address the key limitations of bispecific constructs including antigen escape, limited T cell activation, and on-target off-tumor toxicity. Trispecific constructs employ 2 primary strategies: dual tumor antigen targeting combined with CD3 engagement to prevent antigen escape, and integration of co-stimulatory signals (CD28, 4-1BB) to enhance T cell function. Early clinical data demonstrated promising efficacy signals, particularly in multiple myeloma where BCMA×CD38×CD3 constructs achieved 90% overall response rates in early-phase trials. Safety profiles mirror bispecific antibodies with cytokine release syndrome and neurotoxicity as primary concerns. Trispecific T cell engagers represent a significant advancement in precision immunotherapy for hematological malignancies. Although early clinical results are encouraging, challenges remain in optimal target selection, manufacturing complexity, and resistance mechanisms. Ongoing clinical trials will define their role in the evolving treatment landscape.</p>","PeriodicalId":21684,"journal":{"name":"Seminars in hematology","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145782552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-10DOI: 10.1053/j.seminhematol.2025.11.001
Mooyoung Jung, Jeong Yeon Hwang, Hyunbo Shim
Emerging antibody-based therapeutic modalities such as CAR-Ts and bispecific antibodies have proven highly efficacious in treating diseases, including hematological malignancies. However, the complex molecular architectures of these novel agents present significant challenges in their design and production, for which binding moieties with small size and favorable physicochemical properties may offer a promising solution. Single domain antibodies (sdAbs), typically derived from the heavy chain antibodies of camelids and cartilaginous fishes but increasingly from synthetic and other sources as well, are small (12-15 kDa), well expressed, and exhibit favorable physicochemical properties, making them ideal targeting domains for these new modalities. In this article, we review the origins and characteristics of sdAbs, along with recent studies on CAR-T cell therapies and bispecific antibodies for hematological malignancies that incorporate sdAbs into their constructs, with emphasis on their structures, binding properties, and therapeutic efficacies. Together, these developments underscore the promise of sdAb-based CAR-Ts and bispecific antibodies as next-generation therapeutics, with the potential to expand treatment options and improve outcomes in hematological malignancies and beyond.
{"title":"Harnessing single-domain antibodies for CAR-T and bispecific antibody development.","authors":"Mooyoung Jung, Jeong Yeon Hwang, Hyunbo Shim","doi":"10.1053/j.seminhematol.2025.11.001","DOIUrl":"https://doi.org/10.1053/j.seminhematol.2025.11.001","url":null,"abstract":"<p><p>Emerging antibody-based therapeutic modalities such as CAR-Ts and bispecific antibodies have proven highly efficacious in treating diseases, including hematological malignancies. However, the complex molecular architectures of these novel agents present significant challenges in their design and production, for which binding moieties with small size and favorable physicochemical properties may offer a promising solution. Single domain antibodies (sdAbs), typically derived from the heavy chain antibodies of camelids and cartilaginous fishes but increasingly from synthetic and other sources as well, are small (12-15 kDa), well expressed, and exhibit favorable physicochemical properties, making them ideal targeting domains for these new modalities. In this article, we review the origins and characteristics of sdAbs, along with recent studies on CAR-T cell therapies and bispecific antibodies for hematological malignancies that incorporate sdAbs into their constructs, with emphasis on their structures, binding properties, and therapeutic efficacies. Together, these developments underscore the promise of sdAb-based CAR-Ts and bispecific antibodies as next-generation therapeutics, with the potential to expand treatment options and improve outcomes in hematological malignancies and beyond.</p>","PeriodicalId":21684,"journal":{"name":"Seminars in hematology","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145678778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-29DOI: 10.1053/j.seminhematol.2025.08.004
Hyeonmin Lee, Yonghee Lee, Junho Chung
Bispecific T cell engagers (bispecific TCEs) are engineered antibodies that redirect T cells to mediate tumor cell killing by simultaneously binding to CD3 on T cells and tumor-associated antigens. As of July 2025, ten bispecific TCEs are clinically available. The CD3-binding antibodies in these bispecific TCEs can be classified into 6 groups based on the amino acid sequence similarity across their 6 complementarity-determining regions (CDRs). Specifically, antibodies were assigned to the same family if their six CDRs-HCDR1-3 and LCDR1-3-exhibited ≥80% pairwise sequence identity upon multiple sequence alignment. Family 1, derived from OKT3-a mouse hybridoma generated by immunizing BALB/c mice with human T cells-includes only blinatumomab; Family 2, derived from SP34-a rhesus monkey (Macaca mulatta) derived hybridoma specific for human T cells-comprises 5 antibodies; and Family 6, derived from UCHT1-a mouse hybridoma generated by immunizing mice with human T cells-contains only tebentafusp. The origin of the remaining 3 antibodies has not been disclosed and they possess unique CD3-binding sequences. We classified them into their own distinct families (Families 3, 4, and 5). Interestingly, mosunetuzumab (Family 4) showed remarkably lower incidence of adverse events such as cytokine release syndrome (CRS), immune effector cell-associated neurotoxicity syndrome (ICANS), and infection compared to other bispecific TCEs even though its affinity for CD3ε was not significantly different. The epitopes of 4 antibodies in Family 2, teclistamab, talquetamab, glofitamab, and tarlatamab were previously defined to be located at the N-terminal region of CD3ε via hydrogen-deuterium exchange mass spectrometry (HDX-MS) analysis. In our in silico epitope prediction analysis, the N-terminal region was included in the epitope region of all bispecific TCEs regardless of their family. Blinatumomab (Family 1) and tebentafusp (Family 6) did not bind to the CD3ε homolog of the cynomolgus monkey, whereas the other 8 bispecific TCEs did. This lack of cross-reactivity poses clear disadvantages in their preclinical development, particularly for toxicity and safety evaluation in nonhuman primate models.
{"title":"Characterization of anti-CD3 antibodies in clinically available bispecific T cell engagers.","authors":"Hyeonmin Lee, Yonghee Lee, Junho Chung","doi":"10.1053/j.seminhematol.2025.08.004","DOIUrl":"https://doi.org/10.1053/j.seminhematol.2025.08.004","url":null,"abstract":"<p><p>Bispecific T cell engagers (bispecific TCEs) are engineered antibodies that redirect T cells to mediate tumor cell killing by simultaneously binding to CD3 on T cells and tumor-associated antigens. As of July 2025, ten bispecific TCEs are clinically available. The CD3-binding antibodies in these bispecific TCEs can be classified into 6 groups based on the amino acid sequence similarity across their 6 complementarity-determining regions (CDRs). Specifically, antibodies were assigned to the same family if their six CDRs-HCDR1-3 and LCDR1-3-exhibited ≥80% pairwise sequence identity upon multiple sequence alignment. Family 1, derived from OKT3-a mouse hybridoma generated by immunizing BALB/c mice with human T cells-includes only blinatumomab; Family 2, derived from SP34-a rhesus monkey (Macaca mulatta) derived hybridoma specific for human T cells-comprises 5 antibodies; and Family 6, derived from UCHT1-a mouse hybridoma generated by immunizing mice with human T cells-contains only tebentafusp. The origin of the remaining 3 antibodies has not been disclosed and they possess unique CD3-binding sequences. We classified them into their own distinct families (Families 3, 4, and 5). Interestingly, mosunetuzumab (Family 4) showed remarkably lower incidence of adverse events such as cytokine release syndrome (CRS), immune effector cell-associated neurotoxicity syndrome (ICANS), and infection compared to other bispecific TCEs even though its affinity for CD3ε was not significantly different. The epitopes of 4 antibodies in Family 2, teclistamab, talquetamab, glofitamab, and tarlatamab were previously defined to be located at the N-terminal region of CD3ε via hydrogen-deuterium exchange mass spectrometry (HDX-MS) analysis. In our in silico epitope prediction analysis, the N-terminal region was included in the epitope region of all bispecific TCEs regardless of their family. Blinatumomab (Family 1) and tebentafusp (Family 6) did not bind to the CD3ε homolog of the cynomolgus monkey, whereas the other 8 bispecific TCEs did. This lack of cross-reactivity poses clear disadvantages in their preclinical development, particularly for toxicity and safety evaluation in nonhuman primate models.</p>","PeriodicalId":21684,"journal":{"name":"Seminars in hematology","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145131958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01DOI: 10.1053/j.seminhematol.2025.06.004
Kasper J. Croese , Jacqueline Cloos , Jesse M. Tettero
The detection of measurable residual disease (MRD) in acute myeloid leukaemia (AML) has emerged as one of the strongest prognostic indications of adverse outcomes across different treatment settings and disease stages, independent of baseline genetic risk classification. Multiple techniques for MRD-assessment have been developed and clinically validated, including multiparameter flow cytometry (MFC) and molecular assays such as quantitative PCR (qPCR) and next-generation sequencing (NGS). These approaches have been incorporated into routine clinical practice to evaluate treatment efficacy and refine disease risk stratification. Beyond the prognostic significance, MRD monitoring offers a powerful tool for monitoring subclinical disease, enabling early relapse detection and influencing therapeutic decisions, including consolidation strategies, transplant conditioning, and pre-emptive interventions. In non-intensive treatment settings, MRD may help tailor treatment duration and identify patients eligible for therapy cessation. As the therapeutic landscape of AML continues to evolve with novel agents and strategies, the role and clinical applications of MRD are becoming increasingly relevant. This review summarizes current MRD assessment techniques, optimal measurement timepoints, and clinical applications across different therapeutic settings. We also highlight ongoing innovations and future directions that aim to fully integrate MRD into precision management of patients with AML.
{"title":"Measurable residual disease monitoring in acute myeloid leukaemia: Techniques, timing and therapeutic implications","authors":"Kasper J. Croese , Jacqueline Cloos , Jesse M. Tettero","doi":"10.1053/j.seminhematol.2025.06.004","DOIUrl":"10.1053/j.seminhematol.2025.06.004","url":null,"abstract":"<div><div>The detection of measurable residual disease (MRD) in acute myeloid leukaemia (AML) has emerged as one of the strongest prognostic indications of adverse outcomes across different treatment settings and disease stages, independent of baseline genetic risk classification. Multiple techniques for MRD-assessment have been developed and clinically validated, including multiparameter flow cytometry (MFC) and molecular assays such as quantitative PCR (qPCR) and next-generation sequencing (NGS). These approaches have been incorporated into routine clinical practice to evaluate treatment efficacy and refine disease risk stratification. Beyond the prognostic significance, MRD monitoring offers a powerful tool for monitoring subclinical disease, enabling early relapse detection and influencing therapeutic decisions, including consolidation strategies, transplant conditioning, and pre-emptive interventions. In non-intensive treatment settings, MRD may help tailor treatment duration and identify patients eligible for therapy cessation. As the therapeutic landscape of AML continues to evolve with novel agents and strategies, the role and clinical applications of MRD are becoming increasingly relevant. This review summarizes current MRD assessment techniques, optimal measurement timepoints, and clinical applications across different therapeutic settings. We also highlight ongoing innovations and future directions that aim to fully integrate MRD into precision management of patients with AML.</div></div>","PeriodicalId":21684,"journal":{"name":"Seminars in hematology","volume":"62 3","pages":"Pages 167-176"},"PeriodicalIF":4.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144567793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01DOI: 10.1053/S0037-1963(25)00039-3
{"title":"outside front cover, PMS 8883 metallic AND 4/C","authors":"","doi":"10.1053/S0037-1963(25)00039-3","DOIUrl":"10.1053/S0037-1963(25)00039-3","url":null,"abstract":"","PeriodicalId":21684,"journal":{"name":"Seminars in hematology","volume":"62 3","pages":"Page CO1"},"PeriodicalIF":4.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145493194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01DOI: 10.1053/j.seminhematol.2025.06.002
Torsten Haferlach , Jan-Niklas Eckardt , Wencke Walter , Sven Maschek , Jakob Nikolas Kather , Christian Pohlkamp , Jan Moritz Middeke
The landscape of acute myeloid leukemia (AML) diagnostics is undergoing a pivotal shift towards a transformative era, driven by the integration of artificial intelligence (AI). This review delves into the pivotal role of AI in reshaping AML diagnostics in the 21st century, highlighting advancements, challenges, and future prospects. AML, marked by the immediate need for accurate diagnosis and treatment, requires precise analysis against the complexity of various diagnostic methods such as cytomorphology, immunophenotyping, cytogenetics, and molecular testing. The introduction of AI in this field promises to address the critical need for rapid and standardized diagnostics, thereby enhancing patient care. AI technologies, including deep learning (DL) and machine learning (ML), are revolutionizing the interpretation of complex diagnostic data. With the use of AI-based models such as deep learning (DL) classifiers or automated karyotyping, promising tools do already exist. When it comes to reporting and reasoning, large language models (LLM) show their potential in efficient data processing and better clinical decision-making. This includes the use of large language models (LLMs) for generating comprehensive diagnostic reports that integrate multi-layered diagnostic information. However, there is a critical need for transparency and interpretability in AI-driven diagnostics. Explainable AI (XAI) models address this need building trust among clinicians and patients. Moreover, this review addresses the growing field of synthetic data that are becoming increasingly accessible due to advances in AI and computational technology. While synthetic data present a promising avenue for augmenting clinical research and potentially optimizing clinical trials in fields such as AML, their application requires careful ethical, regulatory, and methodological considerations. There are several limitations and challenges to consider regarding not only synthetic data but also AI models in general. This includes regulatory hurdles due to the dynamic nature of AI, as well as data privacy concerns and interoperability between different systems. In conclusion, AI has the potential to completely change how we diagnose and treat AML by offering faster, more accurate, and more comprehensive diagnostic insights. This potential is especially crucial for preserving knowledge in times of shortages of human experts. However, realizing this potential will require overcoming significant challenges and fostering collaboration between technologists and clinicians. As we move forward, the synergy between AI and human expertise will undoubtedly redefine the landscape of AML diagnostics, leading in a new era of precision medicine in hematology.
{"title":"AML diagnostics in the 21st century: Use of AI","authors":"Torsten Haferlach , Jan-Niklas Eckardt , Wencke Walter , Sven Maschek , Jakob Nikolas Kather , Christian Pohlkamp , Jan Moritz Middeke","doi":"10.1053/j.seminhematol.2025.06.002","DOIUrl":"10.1053/j.seminhematol.2025.06.002","url":null,"abstract":"<div><div>The landscape of acute myeloid leukemia (AML) diagnostics is undergoing a pivotal shift towards a transformative era, driven by the integration of artificial intelligence (AI). This review delves into the pivotal role of AI in reshaping AML diagnostics in the 21st century, highlighting advancements, challenges, and future prospects. AML, marked by the immediate need for accurate diagnosis and treatment, requires precise analysis against the complexity of various diagnostic methods such as cytomorphology, immunophenotyping, cytogenetics, and molecular testing. The introduction of AI in this field promises to address the critical need for rapid and standardized diagnostics, thereby enhancing patient care. AI technologies, including deep learning (DL) and machine learning (ML), are revolutionizing the interpretation of complex diagnostic data. With the use of AI-based models such as deep learning (DL) classifiers or automated karyotyping, promising tools do already exist. When it comes to reporting and reasoning, large language models (LLM) show their potential in efficient data processing and better clinical decision-making. This includes the use of large language models (LLMs) for generating comprehensive diagnostic reports that integrate multi-layered diagnostic information. However, there is a critical need for transparency and interpretability in AI-driven diagnostics. Explainable AI (XAI) models address this need building trust among clinicians and patients. Moreover, this review addresses the growing field of synthetic data that are becoming increasingly accessible due to advances in AI and computational technology. While synthetic data present a promising avenue for augmenting clinical research and potentially optimizing clinical trials in fields such as AML, their application requires careful ethical, regulatory, and methodological considerations. There are several limitations and challenges to consider regarding not only synthetic data but also AI models in general. This includes regulatory hurdles due to the dynamic nature of AI, as well as data privacy concerns and interoperability between different systems. In conclusion, AI has the potential to completely change how we diagnose and treat AML by offering faster, more accurate, and more comprehensive diagnostic insights. This potential is especially crucial for preserving knowledge in times of shortages of human experts. However, realizing this potential will require overcoming significant challenges and fostering collaboration between technologists and clinicians. As we move forward, the synergy between AI and human expertise will undoubtedly redefine the landscape of AML diagnostics, leading in a new era of precision medicine in hematology.</div></div>","PeriodicalId":21684,"journal":{"name":"Seminars in hematology","volume":"62 3","pages":"Pages 226-234"},"PeriodicalIF":4.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144567792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01DOI: 10.1053/j.seminhematol.2024.11.002
Valeria Visconte , Jaroslaw P. Maciejewski , Luca Guarnera
The introduction of artificial intelligence (AI), and in particular machine learning (ML), has revolutionized biomedical research at the clinical level, a trend that also includes hematologic malignancies and myeloid neoplasia (MN). ML encompasses a wide range of applications such as enhanced diagnostics, outcome predictions, decision trees and clustering. Despite several reports in recent years and the achievement of promising results, none of the ML-based pipelines have been directly translated into clinical practice. ML offers the potential to help refine risk stratification and increase accuracy to correctly predict clinical outcomes and disease classification. One of the complications in the clinical utilization of ML is that a large percentage of hematologists have limited familiarity with these tools which can cause skepticism. Concerns have also been raised by patients that are worried about privacy issues, reliability of the outcomes, and loss of human interaction. In this review, we aim to pinpoint the main mechanisms and applications of ML, as well as application in MN and Myelodysplastic Syndrome, highlighting strengths and limitations, and addressing the potential promise in clinical implementation of ML-pipelines.
{"title":"The potential promise of machine learning in myelodysplastic syndrome","authors":"Valeria Visconte , Jaroslaw P. Maciejewski , Luca Guarnera","doi":"10.1053/j.seminhematol.2024.11.002","DOIUrl":"10.1053/j.seminhematol.2024.11.002","url":null,"abstract":"<div><div><span>The introduction of artificial intelligence (AI), and in particular machine learning (ML), has revolutionized biomedical research at the clinical level, a trend that also includes </span>hematologic malignancies<span><span><span> and myeloid neoplasia (MN). ML encompasses a wide range of applications such as enhanced diagnostics, outcome predictions, decision trees and clustering. Despite several reports in recent years and the achievement of promising results, none of the ML-based pipelines have been directly translated into clinical practice. ML offers the potential to help refine </span>risk stratification<span> and increase accuracy to correctly predict clinical outcomes and disease classification. One of the complications in the clinical utilization of ML is that a large percentage of hematologists have limited familiarity with these tools which can cause skepticism. Concerns have also been raised by patients that are worried about privacy issues, reliability of the outcomes, and loss of human interaction. In this review, we aim to pinpoint the main mechanisms and applications of ML, as well as application in MN and </span></span>Myelodysplastic Syndrome, highlighting strengths and limitations, and addressing the potential promise in clinical implementation of ML-pipelines.</span></div></div>","PeriodicalId":21684,"journal":{"name":"Seminars in hematology","volume":"62 3","pages":"Pages 235-242"},"PeriodicalIF":4.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142771772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Therapy-related acute myeloid leukemia (tAML) and AML arising from previous hematologic disorders (secondary AML, sAML) share similar biological features, including karyotype abnormalities and gene specific mutations, patient-related risk factors. Older age and lower performance status also contribute to dimal prognosis, and dismal prognosis, both in terms of response rate and overall survival. However, these 2 entities significantly differ in leukemogenic trajectories. In this line, recent advances allowed for a better understanding of differential clonal progression processes in the broad landscape of sAMLs. Thus, in this manuscript, we reviewed clinical and biological characteristics of tAML and sAML, highlighting commonalities and divergent features and discussed classification aspects. We also gathered the newest evidence of leukemogenic trajectories leading from bone marrow failure syndromes, myelodysplastic syndromes (MDS), myeloproliferative neoplasms (MPN) and MDS/MPN overlap syndromes to sAML, as well as leukemias arising from donors’ cells in the setting of allogenic transplantation. Furthermore, we reviewed germline and acquired predisposition to leukemias and discussed the therapeutic landscape and future directions.
{"title":"Secondary and therapy-related acute myeloid leukemias: Overlapping features, distinct trajectories","authors":"Luca Guarnera MD , Emiliano Fabiani PhD , Giorgia Silvestrini PhD , Enrico Attardi PhD , Maria Teresa Voso MD","doi":"10.1053/j.seminhematol.2025.06.005","DOIUrl":"10.1053/j.seminhematol.2025.06.005","url":null,"abstract":"<div><div>Therapy-related acute myeloid leukemia (tAML) and AML arising from previous hematologic disorders (secondary AML, sAML) share similar biological features, including karyotype abnormalities and gene specific mutations, patient-related risk factors. Older age and lower performance status also contribute to dimal prognosis, and dismal prognosis, both in terms of response rate and overall survival. However, these 2 entities significantly differ in leukemogenic trajectories. In this line, recent advances allowed for a better understanding of differential clonal progression processes in the broad landscape of sAMLs. Thus, in this manuscript, we reviewed clinical and biological characteristics of tAML and sAML, highlighting commonalities and divergent features and discussed classification aspects. We also gathered the newest evidence of leukemogenic trajectories leading from bone marrow failure syndromes, myelodysplastic syndromes (MDS), myeloproliferative neoplasms (MPN) and MDS/MPN overlap syndromes to sAML, as well as leukemias arising from donors’ cells in the setting of allogenic transplantation. Furthermore, we reviewed germline and acquired predisposition to leukemias and discussed the therapeutic landscape and future directions.</div></div>","PeriodicalId":21684,"journal":{"name":"Seminars in hematology","volume":"62 3","pages":"Pages 155-166"},"PeriodicalIF":4.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144718388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01DOI: 10.1053/j.seminhematol.2025.10.001
Frederik Damm , Lars Bullinger
{"title":"Updates on current and future research in acute myeloid leukemia","authors":"Frederik Damm , Lars Bullinger","doi":"10.1053/j.seminhematol.2025.10.001","DOIUrl":"10.1053/j.seminhematol.2025.10.001","url":null,"abstract":"","PeriodicalId":21684,"journal":{"name":"Seminars in hematology","volume":"62 3","pages":"Pages 129-130"},"PeriodicalIF":4.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145493195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}