Matteo G Della Porta, Jan Philipp Bewersdorf, Yu-Hung Wang, Robert P Hasserjian
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引用次数: 0
摘要
骨髓增生异常综合征/肿瘤(MDS)和急性髓性白血病(AML)是肿瘤性造血细胞增生,根据形态学、临床和遗传学特征进行诊断和分类。具体来说,骨髓细胞在血液和骨髓中的比例是历来将 MDS 与 AML 区分开来的一个关键特征,它与其他几个形态学参数一起,定义了 MDS 中不同的疾病实体。MDS 和 AML 都有反复出现的遗传异常,这对它们的定义和亚分类产生了越来越大的影响。例如,在 2022 年,基于 SF3B1 突变或 TP53 双拷贝异常的存在,两个新的 MDS 实体被确认。与形态学分析相比,基因组信息更加客观,可重复性更高,因为形态学分析受观察者间差异和任意数字截断的影响。然而,由于其复杂性,在骨髓肿瘤分类中将基因组数据与传统的形态学特征相结合已被证明具有挑战性;基因表达和甲基化分析也能提供有关疾病发病机制的信息,从而增加了复杂性。新的机器学习技术有可能有效整合多种诊断模式,并改进历史分类系统。展望未来,将机器学习和先进的统计方法应用于大型患者队列,可以通过推进无偏见的、稳健的、以前未被发现的疾病亚组来完善未来的分类。未来的分类可能会纳入这些更新的技术和更高层次的分析,强调基因组疾病实体而非传统形态学定义的实体,从而促进更准确的诊断和患者风险分层。
Future directions in myelodysplastic syndromes/neoplasms and acute myeloid leukaemia classification: from blast counts to biology.
Myelodysplastic syndromes/neoplasms (MDS) and acute myeloid leukaemia (AML) are neoplastic haematopoietic cell proliferations that are diagnosed and classified based on a combination of morphological, clinical and genetic features. Specifically, the percentage of myeloblasts in the blood and bone marrow is a key feature that has historically separated MDS from AML and, together with several other morphological parameters, defines distinct disease entities within MDS. Both MDS and AML have recurrent genetic abnormalities that are increasingly influencing their definitions and subclassification. For example, in 2022, two new MDS entities were recognised based on the presence of SF3B1 mutation or bi-allelic TP53 abnormalities. Genomic information is more objective and reproducible than morphological analyses, which are subject to interobserver variability and arbitrary numeric cut-offs. Nevertheless, the integration of genomic data with traditional morphological features in myeloid neoplasm classification has proved challenging by virtue of its sheer complexity; gene expression and methylation profiling also can provide information regarding disease pathogenesis, adding to the complexity. New machine-learning technologies have the potential to effectively integrate multiple diagnostic modalities and improve on historical classification systems. Going forward, the application of machine learning and advanced statistical methods to large patient cohorts can refine future classifications by advancing unbiased and robust previously unrecognised disease subgroups. Future classifications will probably incorporate these newer technologies and higher-level analyses that emphasise genomic disease entities over traditional morphologically defined entities, thus promoting more accurate diagnosis and patient risk stratification.
期刊介绍:
Histopathology is an international journal intended to be of practical value to surgical and diagnostic histopathologists, and to investigators of human disease who employ histopathological methods. Our primary purpose is to publish advances in pathology, in particular those applicable to clinical practice and contributing to the better understanding of human disease.