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Data-driven, harmonised classification system for myelodysplastic syndromes: a consensus paper from the International Consortium for Myelodysplastic Syndromes
The Lancet Haematology ( IF 15.4 ) Pub Date : 2024-10-09 , DOI: 10.1016/s2352-3026(24)00251-5 Prof Rami S Komrokji MBBS, Luca Lanino MD, Somedeb Ball MD, Jan P Bewersdorf MD, Monia Marchetti MD, Giulia Maggioni MD, Erica Travaglino BSc, Najla H Al Ali MSc, Prof Pierre Fenaux MD, Prof Uwe Platzbecker MD, Valeria Santini MD, Maria Diez-Campelo MD, Avani Singh MD, Akriti G Jain MD, Luis E Aguirre MD, Sarah M Tinsley-Vance PhD, Zaker I Schwabkey MD, Onyee Chan MD, Zhouer Xie MD, Andrew M Brunner MD, Andrew T Kuykendall MD, Prof John M Bennett MD, Rena Buckstein MD, Rafael Bejar MD, Prof Hetty E Carraway MD, Prof Amy E DeZern MD, Elizabeth A Griffiths MD, Prof Stephanie Halene MD, Prof Robert P Hasserjian MD, Jeffrey Lancet MD, Alan F List MD, Sanam Loghavi MD, Prof Olatoyosi Odenike MD, Eric Padron MD, Mrinal M Patnaik MBBS, Gail J Roboz MD, Maximilian Stahl MD, Prof Mikkael A Sekeres MD, David P Steensma MD, Prof Michael R Savona MD, Justin Taylor MD, Prof Mina L Xu MD, Kendra Sweet MD, David A Sallman MD, Prof Stephen D Nimer MD, Prof Christopher S Hourigan DM, Prof Andrew H Wei MBBS, Elisabetta Sauta PhD, Saverio D’Amico MSc, Gianluca Asti MSc, Prof Gastone Castellani PhD, Mattia Delleani MSc, Alessia Campagna MD, Uma M Borate MD, Prof Guillermo Sanz MD, Fabio Efficace PhD, Steven D Gore MD, Tae Kon Kim MD, Navel Daver MD, Prof Guillermo Garcia-Manero MD, Maria Rozman MD, Prof Alberto Orfao MD, Sa A Wang MD, Prof M Kathryn Foucar MD, Prof Ulrich Germing MD, Prof Torsten Haferlach MD, Phillip Scheinberg MD, Prof Yasushi Miyazaki MD, Marcelo Iastrebner MD, Austin Kulasekararaj MD, Prof Thomas Cluzeau MD, Shahram Kordasti MD, Prof Arjan A van de Loosdrecht MD, Prof Lionel Ades MD, Amer M Zeidan MD, Prof Matteo G Della Porta MD, International Consortium on Myelodysplastic Syndromes
The Lancet Haematology ( IF 15.4 ) Pub Date : 2024-10-09 , DOI: 10.1016/s2352-3026(24)00251-5 Prof Rami S Komrokji MBBS, Luca Lanino MD, Somedeb Ball MD, Jan P Bewersdorf MD, Monia Marchetti MD, Giulia Maggioni MD, Erica Travaglino BSc, Najla H Al Ali MSc, Prof Pierre Fenaux MD, Prof Uwe Platzbecker MD, Valeria Santini MD, Maria Diez-Campelo MD, Avani Singh MD, Akriti G Jain MD, Luis E Aguirre MD, Sarah M Tinsley-Vance PhD, Zaker I Schwabkey MD, Onyee Chan MD, Zhouer Xie MD, Andrew M Brunner MD, Andrew T Kuykendall MD, Prof John M Bennett MD, Rena Buckstein MD, Rafael Bejar MD, Prof Hetty E Carraway MD, Prof Amy E DeZern MD, Elizabeth A Griffiths MD, Prof Stephanie Halene MD, Prof Robert P Hasserjian MD, Jeffrey Lancet MD, Alan F List MD, Sanam Loghavi MD, Prof Olatoyosi Odenike MD, Eric Padron MD, Mrinal M Patnaik MBBS, Gail J Roboz MD, Maximilian Stahl MD, Prof Mikkael A Sekeres MD, David P Steensma MD, Prof Michael R Savona MD, Justin Taylor MD, Prof Mina L Xu MD, Kendra Sweet MD, David A Sallman MD, Prof Stephen D Nimer MD, Prof Christopher S Hourigan DM, Prof Andrew H Wei MBBS, Elisabetta Sauta PhD, Saverio D’Amico MSc, Gianluca Asti MSc, Prof Gastone Castellani PhD, Mattia Delleani MSc, Alessia Campagna MD, Uma M Borate MD, Prof Guillermo Sanz MD, Fabio Efficace PhD, Steven D Gore MD, Tae Kon Kim MD, Navel Daver MD, Prof Guillermo Garcia-Manero MD, Maria Rozman MD, Prof Alberto Orfao MD, Sa A Wang MD, Prof M Kathryn Foucar MD, Prof Ulrich Germing MD, Prof Torsten Haferlach MD, Phillip Scheinberg MD, Prof Yasushi Miyazaki MD, Marcelo Iastrebner MD, Austin Kulasekararaj MD, Prof Thomas Cluzeau MD, Shahram Kordasti MD, Prof Arjan A van de Loosdrecht MD, Prof Lionel Ades MD, Amer M Zeidan MD, Prof Matteo G Della Porta MD, International Consortium on 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.
中文翻译：
数据驱动的骨髓增生异常综合征统一分类系统：来自国际骨髓增生异常综合征联盟的共识论文
WHO 和 2022 年国际共识分类骨髓增生异常综合征的分类提高了诊断精度并完善了这些疾病的决策过程。然而，一些差异仍然存在，并可能导致它们在临床环境中的采用不一致。我们采用了一种数据驱动的方法来提供这两个分类系统之间的协调。我们研究了基因组特征的重要性及其对定义统一实体标签的聚类分配过程的影响。成立了一个由血液学家、血液病理学家和数据科学家组成的小组，他们是国际骨髓增生异常综合征联盟的成员，并采用了改进的 Delphi 共识过程来协调形态学定义的类别，但没有明显的基因组特征。该小组定期举行在线会议，并使用在线投票工具参与了两轮调查。我们确定了 9 个具有不同基因组特征的集群。层次结构重要性的簇以双等位基因 TP53 失活为特征。集群分配与爆炸计数无关。具有单等位基因 TP53 失活的个体被分配到其他集群。从层次结构上看，第二重要的组包括骨髓增生异常综合征伴 del（5q）。骨髓中分离的 del（5q） 和不到 5% 的原始细胞是最相关的标签定义特征。第三个最重要的集群包括 SF3B1 突变的骨髓增生异常综合征。不存在孤立的 del（5q）、del（7q）/-7、abn3q26.2、复杂核型、RUNX1 突变或双等位基因 TP53 是该类别协调标记的基础。 形态学定义的骨髓增生异常综合征实体显示出较大的基因组异质性，与多系发育不良、骨髓原始细胞、细胞减少或纤维化相比，单系发育不良无法有效捕获。我们研究了骨髓增生异常综合征（骨髓原始细胞超过 10%）与急性髓系白血病之间的生物学连续体，发现遗传特征仅部分重叠。调查后，原始细胞数低（即小于 5%）的骨髓增生异常综合征和原始细胞增加的骨髓增生异常综合征（即 5% 或更多）被认为是疾病实体。我们的数据驱动方法可以有效地协调当前骨髓增生异常综合征的分类，并为现实世界中的患者管理提供参考。
更新日期：2024-10-09
中文翻译：
数据驱动的骨髓增生异常综合征统一分类系统：来自国际骨髓增生异常综合征联盟的共识论文
WHO 和 2022 年国际共识分类骨髓增生异常综合征的分类提高了诊断精度并完善了这些疾病的决策过程。然而，一些差异仍然存在，并可能导致它们在临床环境中的采用不一致。我们采用了一种数据驱动的方法来提供这两个分类系统之间的协调。我们研究了基因组特征的重要性及其对定义统一实体标签的聚类分配过程的影响。成立了一个由血液学家、血液病理学家和数据科学家组成的小组，他们是国际骨髓增生异常综合征联盟的成员，并采用了改进的 Delphi 共识过程来协调形态学定义的类别，但没有明显的基因组特征。该小组定期举行在线会议，并使用在线投票工具参与了两轮调查。我们确定了 9 个具有不同基因组特征的集群。层次结构重要性的簇以双等位基因 TP53 失活为特征。集群分配与爆炸计数无关。具有单等位基因 TP53 失活的个体被分配到其他集群。从层次结构上看，第二重要的组包括骨髓增生异常综合征伴 del（5q）。骨髓中分离的 del（5q） 和不到 5% 的原始细胞是最相关的标签定义特征。第三个最重要的集群包括 SF3B1 突变的骨髓增生异常综合征。不存在孤立的 del（5q）、del（7q）/-7、abn3q26.2、复杂核型、RUNX1 突变或双等位基因 TP53 是该类别协调标记的基础。 形态学定义的骨髓增生异常综合征实体显示出较大的基因组异质性，与多系发育不良、骨髓原始细胞、细胞减少或纤维化相比，单系发育不良无法有效捕获。我们研究了骨髓增生异常综合征（骨髓原始细胞超过 10%）与急性髓系白血病之间的生物学连续体，发现遗传特征仅部分重叠。调查后，原始细胞数低（即小于 5%）的骨髓增生异常综合征和原始细胞增加的骨髓增生异常综合征（即 5% 或更多）被认为是疾病实体。我们的数据驱动方法可以有效地协调当前骨髓增生异常综合征的分类，并为现实世界中的患者管理提供参考。
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