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慢性粒单核细胞白血病的预后模型

Models of Prognostication in Chronic Myelomonocytic Leukemia.

作者信息

Onida Francesco

机构信息

University of Milan - Dept. of Oncology and Hemato-Oncology Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico - Hematology/BMT Unit, Via Francesco Sforza no.35, 20122, Milan, Italy.

出版信息

Curr Hematol Malig Rep. 2017 Dec;12(6):513-521. doi: 10.1007/s11899-017-0416-8.

Abstract

PURPOSE OF REVIEW

Within the group of the myelodysplastic/myeloproliferative overlap neoplasms of the adult age, chronic myelomonocytic leukemia (CMML) is characterized by an extremely variable clinical course. This review aims to cover over the years main advancements in the identification of CMML clinical and biological features associated to survival outcomes and the consequent development of prognostic tools for individual patient treatment decision making.

RECENT FINDINGS

According to the last WHO classification of myeloid neoplasms, three subgroups of patients may be recognized on the base of percentage of blasts in marrow and in peripheral blood (CMML-0, CMML-1, and CMML-2), with corresponding decreasing life-expectations. Nonetheless, in each of the subgroups, prominent disparateness of biological characteristics associates to a large heterogeneity of clinical presentation, with very different prognostic implications. Recent findings indicate that the integration of clinical and molecular data appears to provide the most helpful prognostic information. Together with an orderly enlightenment for the increasing conception of specific disease characteristics, this review offers a comprehensive and thoughtful summary of the prognostic models developed over the last three decades for CMML.

摘要

综述目的

在成人骨髓增生异常/骨髓增殖性重叠肿瘤组中,慢性粒单核细胞白血病(CMML)的临床病程极具变异性。本综述旨在涵盖多年来在识别与生存结果相关的CMML临床和生物学特征方面的主要进展,以及由此产生的用于个体患者治疗决策的预后工具的发展。

最新发现

根据世界卫生组织(WHO)最新的髓系肿瘤分类,可根据骨髓和外周血中原始细胞的百分比识别出三组患者(CMML-0、CMML-1和CMML-2),其预期寿命相应降低。尽管如此,在每个亚组中,生物学特征的显著差异与临床表现的高度异质性相关,具有非常不同的预后意义。最近的研究结果表明,临床和分子数据的整合似乎能提供最有用的预后信息。除了对特定疾病特征的认识不断增加进行有序的启发外,本综述还对过去三十年中为CMML开发的预后模型进行了全面而深入的总结。

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