Rami S Komrokji, Luca Lanino, Somedeb Ball, Jan P Bewersdorf, Monia Marchetti, Giulia Maggioni, Erica Travaglino, Najla H Al Ali, Pierre Fenaux, Uwe Platzbecker, Valeria Santini, Maria Diez-Campelo, Avani Singh, Akriti G Jain, Luis E Aguirre, Sarah M Tinsley-Vance, Zaker I Schwabkey, Onyee Chan, Zhouer Xie, Andrew M Brunner, Andrew T Kuykendall, John M Bennett, Rena Buckstein, Rafael Bejar, Hetty E Carraway, Amy E DeZern, Elizabeth A Griffiths, Stephanie Halene, Robert P Hasserjian, Jeffrey Lancet, Alan F List, Sanam Loghavi, Olatoyosi Odenike, Eric Padron, Mrinal M Patnaik, Gail J Roboz, Maximilian Stahl, Mikkael A Sekeres, David P Steensma, Michael R Savona, Justin Taylor, Mina L Xu, Kendra Sweet, David A Sallman, Stephen D Nimer, Christopher S Hourigan, Andrew H Wei, Elisabetta Sauta, Saverio D'Amico, Gianluca Asti, Gastone Castellani, Mattia Delleani, Alessia Campagna, Uma M Borate, Guillermo Sanz, Fabio Efficace, Steven D Gore, Tae Kon Kim, Navel Daver, Guillermo Garcia-Manero, Maria Rozman, Alberto Orfao, Sa A Wang, M Kathryn Foucar, Ulrich Germing, Torsten Haferlach, Phillip Scheinberg, Yasushi Miyazaki, Marcelo Iastrebner, Austin Kulasekararaj, Thomas Cluzeau, Shahram Kordasti, Arjan A van de Loosdrecht, Lionel Ades, Amer M Zeidan, Matteo G Della Porta
{"title":"数据驱动的骨髓增生异常综合征统一分类系统:骨髓增生异常综合征国际联盟共识文件。","authors":"Rami S Komrokji, Luca Lanino, Somedeb Ball, Jan P Bewersdorf, Monia Marchetti, Giulia Maggioni, Erica Travaglino, Najla H Al Ali, Pierre Fenaux, Uwe Platzbecker, Valeria Santini, Maria Diez-Campelo, Avani Singh, Akriti G Jain, Luis E Aguirre, Sarah M Tinsley-Vance, Zaker I Schwabkey, Onyee Chan, Zhouer Xie, Andrew M Brunner, Andrew T Kuykendall, John M Bennett, Rena Buckstein, Rafael Bejar, Hetty E Carraway, Amy E DeZern, Elizabeth A Griffiths, Stephanie Halene, Robert P Hasserjian, Jeffrey Lancet, Alan F List, Sanam Loghavi, Olatoyosi Odenike, Eric Padron, Mrinal M Patnaik, Gail J Roboz, Maximilian Stahl, Mikkael A Sekeres, David P Steensma, Michael R Savona, Justin Taylor, Mina L Xu, Kendra Sweet, David A Sallman, Stephen D Nimer, Christopher S Hourigan, Andrew H Wei, Elisabetta Sauta, Saverio D'Amico, Gianluca Asti, Gastone Castellani, Mattia Delleani, Alessia Campagna, Uma M Borate, Guillermo Sanz, Fabio Efficace, Steven D Gore, Tae Kon Kim, Navel Daver, Guillermo Garcia-Manero, Maria Rozman, Alberto Orfao, Sa A Wang, M Kathryn Foucar, Ulrich Germing, Torsten Haferlach, Phillip Scheinberg, Yasushi Miyazaki, Marcelo Iastrebner, Austin Kulasekararaj, Thomas Cluzeau, Shahram Kordasti, Arjan A van de Loosdrecht, Lionel Ades, Amer M Zeidan, Matteo G Della Porta","doi":"10.1016/S2352-3026(24)00251-5","DOIUrl":null,"url":null,"abstract":"<p><p>The WHO and International Consensus Classification 2022 classifications of myelodysplastic syndromes enhance diagnostic precision and refine decision-making processes in these diseases. However, some discrepancies still exist and potentially cause inconsistency in their adoption in a clinical setting. We adopted a data-driven approach to provide a harmonisation between these two classification systems. We investigated the importance of genomic features and their effect on the cluster assignment process to define harmonised entity labels. A panel of expert haematologists, haematopathologists, and data scientists who are members of the International Consortium for Myelodysplastic Syndromes was formed and a modified Delphi consensus process was adopted to harmonise morphologically defined categories without a distinct genomic profile. The panel held regular online meetings and participated in a two-round survey using an online voting tool. We identified nine clusters with distinct genomic features. The cluster of highest hierarchical importance was characterised by biallelic TP53 inactivation. Cluster assignment was irrespective of blast count. Individuals with monoallelic TP53 inactivation were assigned to other clusters. Hierarchically, the second most important group included myelodysplastic syndromes with del(5q). Isolated del(5q) and less than 5% of blast cells in the bone marrow were the most relevant label-defining features. The third most important cluster included myelodysplastic syndromes with mutated SF3B1. The absence of isolated del(5q), del(7q)/-7, abn3q26.2, complex karyotype, RUNX1 mutations, or biallelic TP53 were the basis for a harmonised label of this category. Morphologically defined myelodysplastic syndrome entities showed large genomic heterogeneity that was not efficiently captured by single-lineage versus multilineage dysplasia, marrow blasts, hypocellularity, or fibrosis. We investigated the biological continuum between myelodysplastic syndromes with more than 10% bone marrow blasts and acute myeloid leukaemia, and found only a partial overlap in genetic features. After the survey, myelodysplastic syndromes with low blasts (ie, less than 5%) and myelodysplastic syndromes with increased blasts (ie, 5% or more) were recognised as disease entities. Our data-driven approach can efficiently harmonise current classifications of myelodysplastic syndromes and provide a reference for patient management in a real-world setting.</p>","PeriodicalId":48726,"journal":{"name":"Lancet Haematology","volume":" ","pages":"e862-e872"},"PeriodicalIF":15.4000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Data-driven, harmonised classification system for myelodysplastic syndromes: a consensus paper from the International Consortium for Myelodysplastic Syndromes.\",\"authors\":\"Rami S Komrokji, Luca Lanino, Somedeb Ball, Jan P Bewersdorf, Monia Marchetti, Giulia Maggioni, Erica Travaglino, Najla H Al Ali, Pierre Fenaux, Uwe Platzbecker, Valeria Santini, Maria Diez-Campelo, Avani Singh, Akriti G Jain, Luis E Aguirre, Sarah M Tinsley-Vance, Zaker I Schwabkey, Onyee Chan, Zhouer Xie, Andrew M Brunner, Andrew T Kuykendall, John M Bennett, Rena Buckstein, Rafael Bejar, Hetty E Carraway, Amy E DeZern, Elizabeth A Griffiths, Stephanie Halene, Robert P Hasserjian, Jeffrey Lancet, Alan F List, Sanam Loghavi, Olatoyosi Odenike, Eric Padron, Mrinal M Patnaik, Gail J Roboz, Maximilian Stahl, Mikkael A Sekeres, David P Steensma, Michael R Savona, Justin Taylor, Mina L Xu, Kendra Sweet, David A Sallman, Stephen D Nimer, Christopher S Hourigan, Andrew H Wei, Elisabetta Sauta, Saverio D'Amico, Gianluca Asti, Gastone Castellani, Mattia Delleani, Alessia Campagna, Uma M Borate, Guillermo Sanz, Fabio Efficace, Steven D Gore, Tae Kon Kim, Navel Daver, Guillermo Garcia-Manero, Maria Rozman, Alberto Orfao, Sa A Wang, M Kathryn Foucar, Ulrich Germing, Torsten Haferlach, Phillip Scheinberg, Yasushi Miyazaki, Marcelo Iastrebner, Austin Kulasekararaj, Thomas Cluzeau, Shahram Kordasti, Arjan A van de Loosdrecht, Lionel Ades, Amer M Zeidan, Matteo G Della Porta\",\"doi\":\"10.1016/S2352-3026(24)00251-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The WHO and International Consensus Classification 2022 classifications of myelodysplastic syndromes enhance diagnostic precision and refine decision-making processes in these diseases. 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Individuals with monoallelic TP53 inactivation were assigned to other clusters. Hierarchically, the second most important group included myelodysplastic syndromes with del(5q). Isolated del(5q) and less than 5% of blast cells in the bone marrow were the most relevant label-defining features. The third most important cluster included myelodysplastic syndromes with mutated SF3B1. The absence of isolated del(5q), del(7q)/-7, abn3q26.2, complex karyotype, RUNX1 mutations, or biallelic TP53 were the basis for a harmonised label of this category. Morphologically defined myelodysplastic syndrome entities showed large genomic heterogeneity that was not efficiently captured by single-lineage versus multilineage dysplasia, marrow blasts, hypocellularity, or fibrosis. We investigated the biological continuum between myelodysplastic syndromes with more than 10% bone marrow blasts and acute myeloid leukaemia, and found only a partial overlap in genetic features. After the survey, myelodysplastic syndromes with low blasts (ie, less than 5%) and myelodysplastic syndromes with increased blasts (ie, 5% or more) were recognised as disease entities. 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Data-driven, harmonised classification system for myelodysplastic syndromes: a consensus paper from the International Consortium for Myelodysplastic Syndromes.
The WHO and International Consensus Classification 2022 classifications of myelodysplastic syndromes enhance diagnostic precision and refine decision-making processes in these diseases. However, some discrepancies still exist and potentially cause inconsistency in their adoption in a clinical setting. We adopted a data-driven approach to provide a harmonisation between these two classification systems. We investigated the importance of genomic features and their effect on the cluster assignment process to define harmonised entity labels. A panel of expert haematologists, haematopathologists, and data scientists who are members of the International Consortium for Myelodysplastic Syndromes was formed and a modified Delphi consensus process was adopted to harmonise morphologically defined categories without a distinct genomic profile. The panel held regular online meetings and participated in a two-round survey using an online voting tool. We identified nine clusters with distinct genomic features. The cluster of highest hierarchical importance was characterised by biallelic TP53 inactivation. Cluster assignment was irrespective of blast count. Individuals with monoallelic TP53 inactivation were assigned to other clusters. Hierarchically, the second most important group included myelodysplastic syndromes with del(5q). Isolated del(5q) and less than 5% of blast cells in the bone marrow were the most relevant label-defining features. The third most important cluster included myelodysplastic syndromes with mutated SF3B1. The absence of isolated del(5q), del(7q)/-7, abn3q26.2, complex karyotype, RUNX1 mutations, or biallelic TP53 were the basis for a harmonised label of this category. Morphologically defined myelodysplastic syndrome entities showed large genomic heterogeneity that was not efficiently captured by single-lineage versus multilineage dysplasia, marrow blasts, hypocellularity, or fibrosis. We investigated the biological continuum between myelodysplastic syndromes with more than 10% bone marrow blasts and acute myeloid leukaemia, and found only a partial overlap in genetic features. After the survey, myelodysplastic syndromes with low blasts (ie, less than 5%) and myelodysplastic syndromes with increased blasts (ie, 5% or more) were recognised as disease entities. Our data-driven approach can efficiently harmonise current classifications of myelodysplastic syndromes and provide a reference for patient management in a real-world setting.
期刊介绍:
Launched in autumn 2014, The Lancet Haematology is part of the Lancet specialty journals, exclusively available online. This monthly journal is committed to publishing original research that not only sheds light on haematological clinical practice but also advocates for change within the field. Aligned with the Lancet journals' tradition of high-impact research, The Lancet Haematology aspires to achieve a similar standing and reputation within its discipline. It upholds the rigorous reporting standards characteristic of all Lancet titles, ensuring a consistent commitment to quality in its contributions to the field of haematology.