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.2025.08.002
Himachandana Atluri, Alok Swaroop, Lucy A. Godley
Germline predisposition syndromes to myeloid malignancies have been recognized increasingly over the last decade. Although many of these genetic syndromes present early in life, the age at which a hematopoietic malignancy develops can vary widely depending on the specific gene involved and its role in hematopoiesis. Herein, we aim to review age-related penetrance and phenotype of key germline predisposition syndromes including: SAMD9/9L, GATA2, inherited bone marrow failure syndromes, RUNX1, CEBPA, TP53, and DDX41. We describe optimal diagnostic strategies for these patients, and explain how recognition of germline predisposition allows for the development of optimal treatment plan for the affected individual and counseling of at-risk family members.
{"title":"Germline predispositions to myeloid malignancies: Across the lifespan","authors":"Himachandana Atluri, Alok Swaroop, Lucy A. Godley","doi":"10.1053/j.seminhematol.2025.08.002","DOIUrl":"10.1053/j.seminhematol.2025.08.002","url":null,"abstract":"<div><div>Germline predisposition syndromes to myeloid malignancies have been recognized increasingly over the last decade. Although many of these genetic syndromes present early in life, the age at which a hematopoietic malignancy develops can vary widely depending on the specific gene involved and its role in hematopoiesis. Herein, we aim to review age-related penetrance and phenotype of key germline predisposition syndromes including: <em>SAMD9/9L, GATA2,</em> inherited bone marrow failure syndromes, <em>RUNX1, CEBPA, TP53,</em> and <em>DDX41.</em> We describe optimal diagnostic strategies for these patients, and explain how recognition of germline predisposition allows for the development of optimal treatment plan for the affected individual and counseling of at-risk family members.</div></div>","PeriodicalId":21684,"journal":{"name":"Seminars in hematology","volume":"62 3","pages":"Pages 131-140"},"PeriodicalIF":4.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145228619","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}
Pub Date : 2025-06-01DOI: 10.1053/j.seminhematol.2025.07.001
Frank Y. Huang , Andreas Trumpp , Patrick Stelmach
Acute myeloid leukemia (AML) is an aggressive blood cancer in which disease initiation and relapse are driven by leukemic cells with stem-like properties, known as leukemic stem cells (LSCs). The LSC compartment is highly heterogenous and this contributes to differences in therapy response. This heterogeneity is determined by genetic and nongenetic factors including somatic mutations, the cell of origin, transcriptional and epigenetic states as well as phenotypic plasticity. While this complicates the identification and eradication of LSCs, it also presents an opportunity to tailor therapeutic strategies to the phenotypic and functional states of LSCs present in a patient, exploiting their specific vulnerabilities. The emergence of single-cell multiomics technologies has transformed our ability to dissect cellular heterogeneity in AML, enabling simultaneous interrogation of genomic, transcriptomic, epigenomic and proteomic layers and providing high-resolution molecular snapshots of individual cells. In this review, we discuss causes and consequences of LSC heterogeneity, highlight advances in single-cell multiomics technologies to resolve it and outline how they can address shortcomings in our understanding of LSC heterogeneity and plasticity to revolutionize diagnostics and disease monitoring of AML.
{"title":"Resolving leukemic stem cell heterogeneity and plasticity with single-cell multiomics","authors":"Frank Y. Huang , Andreas Trumpp , Patrick Stelmach","doi":"10.1053/j.seminhematol.2025.07.001","DOIUrl":"10.1053/j.seminhematol.2025.07.001","url":null,"abstract":"<div><div>Acute myeloid leukemia (AML) is an aggressive blood cancer in which disease initiation and relapse are driven by leukemic cells with stem-like properties, known as leukemic stem cells (LSCs). The LSC compartment is highly heterogenous and this contributes to differences in therapy response. This heterogeneity is determined by genetic and nongenetic factors including somatic mutations, the cell of origin, transcriptional and epigenetic states as well as phenotypic plasticity. While this complicates the identification and eradication of LSCs, it also presents an opportunity to tailor therapeutic strategies to the phenotypic and functional states of LSCs present in a patient, exploiting their specific vulnerabilities. The emergence of single-cell multiomics technologies has transformed our ability to dissect cellular heterogeneity in AML, enabling simultaneous interrogation of genomic, transcriptomic, epigenomic and proteomic layers and providing high-resolution molecular snapshots of individual cells. In this review, we discuss causes and consequences of LSC heterogeneity, highlight advances in single-cell multiomics technologies to resolve it and outline how they can address shortcomings in our understanding of LSC heterogeneity and plasticity to revolutionize diagnostics and disease monitoring of AML.</div></div>","PeriodicalId":21684,"journal":{"name":"Seminars in hematology","volume":"62 3","pages":"Pages 218-225"},"PeriodicalIF":4.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145006541","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.2025.06.001
Nicolas Duployez , Claude Preudhomme
Over the past decades, the progressive identification of chromosomal abnormalities and gene mutations has transformed acute myeloid leukemia (AML) from a morphologically defined disease into a genetically stratified malignancy. The coexistence and competition of multiple mutations within leukemic clones underscore the complexity of AML and the need for therapeutic strategies that address clonal interference and mutational synergy. Molecular profiling now offers a more accurate definition of AML ontogeny, surpassing clinical history and revealing biologically and prognostically distinct subtypes. At the same time, new classifications focusing on genetic characteristics have enabled a more coherent and clinically meaningful categorization of the disease. These advances have contributed directly to risk stratification and treatment selection, and thus to more appropriate management.
{"title":"The genomic landscape of acute myeloid leukemia: Redefining classifications, ontogeny, and therapeutic strategies","authors":"Nicolas Duployez , Claude Preudhomme","doi":"10.1053/j.seminhematol.2025.06.001","DOIUrl":"10.1053/j.seminhematol.2025.06.001","url":null,"abstract":"<div><div>Over the past decades, the progressive identification of chromosomal abnormalities<span><span> and gene mutations has transformed </span>acute myeloid leukemia<span><span><span> (AML) from a morphologically defined disease into a genetically stratified malignancy. The coexistence and competition of multiple mutations within leukemic clones underscore the complexity of AML and the need for therapeutic strategies that address clonal interference and mutational synergy. </span>Molecular profiling<span> now offers a more accurate definition of AML ontogeny, surpassing clinical history and revealing biologically and prognostically distinct subtypes. At the same time, new classifications focusing on genetic characteristics have enabled a more coherent and clinically meaningful categorization of the disease. These advances have contributed directly to </span></span>risk stratification and treatment selection, and thus to more appropriate management.</span></span></div></div>","PeriodicalId":21684,"journal":{"name":"Seminars in hematology","volume":"62 3","pages":"Pages 141-154"},"PeriodicalIF":4.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144592096","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.2025.08.003
Marion Subklewe , Sergio Rutella , Antonio Curti
Immunotherapy has dramatically improved outcomes in lymphoid malignancies. In B cell cancers, CD19-directed CAR T cells and T-cell engagers have produced high remission rates and durable responses, now forming the cornerstone of treatment in many relapsed or refractory settings. In contrast, acute myeloid leukemia (AML) has not experienced a comparable breakthrough. To date, only antibody-drug conjugates have reached regulatory approval, with gemtuzumab ozogamicin approved in combination with intensive induction and consolidation therapy for newly diagnosed CD33-positive AML. This divergence is rooted in the biological and immunologic complexity of AML. Unlike B-cell malignancies with lineage-restricted surface markers such as CD19, AML lacks leukemia-specific antigens. Most targets are shared with normal hematopoietic progenitors, leading to on-target/off-leukemia toxicity. Moreover, AML exerts local and systemic immunosuppression through both tumor-intrinsic and microenvironmental mechanisms, limiting T-cell persistence and function. This review will introduce the current immunotherapy platforms under investigation in AML, starting with antibody-based approaches, followed by T-cell redirecting therapies, and culminating in an overview of immune resistance, the bone marrow microenvironment, and strategies toward personalized combinatorial immunotherapy. By synthesizing recent clinical data and mechanistic insights, including those from early CAR and T-cell engager trials, we aim to provide a translational framework for how immunotherapy might still reshape AML care—through integration of immune contexture of the bone marrow environment aiming for rational combinatorial approaches.
{"title":"The immunotherapy landscape in AML: Defining knowledge gaps toward rational combinatorial strategies","authors":"Marion Subklewe , Sergio Rutella , Antonio Curti","doi":"10.1053/j.seminhematol.2025.08.003","DOIUrl":"10.1053/j.seminhematol.2025.08.003","url":null,"abstract":"<div><div>Immunotherapy has dramatically improved outcomes in lymphoid malignancies. In B cell cancers, CD19-directed CAR T cells and T-cell engagers have produced high remission rates and durable responses, now forming the cornerstone of treatment in many relapsed or refractory settings. In contrast, acute myeloid leukemia (AML) has not experienced a comparable breakthrough. To date, only antibody-drug conjugates have reached regulatory approval, with gemtuzumab ozogamicin approved in combination with intensive induction and consolidation therapy for newly diagnosed CD33-positive AML. This divergence is rooted in the biological and immunologic complexity of AML. Unlike B-cell malignancies with lineage-restricted surface markers such as CD19, AML lacks leukemia-specific antigens. Most targets are shared with normal hematopoietic progenitors, leading to on-target/off-leukemia toxicity. Moreover, AML exerts local and systemic immunosuppression through both tumor-intrinsic and microenvironmental mechanisms, limiting T-cell persistence and function. This review will introduce the current immunotherapy platforms under investigation in AML, starting with antibody-based approaches, followed by T-cell redirecting therapies, and culminating in an overview of immune resistance, the bone marrow microenvironment, and strategies toward personalized combinatorial immunotherapy. By synthesizing recent clinical data and mechanistic insights, including those from early CAR and T-cell engager trials, we aim to provide a translational framework for how immunotherapy might still reshape AML care—through integration of immune contexture of the bone marrow environment aiming for rational combinatorial approaches.</div></div>","PeriodicalId":21684,"journal":{"name":"Seminars in hematology","volume":"62 3","pages":"Pages 209-217"},"PeriodicalIF":4.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145228612","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}
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