Machine learning demonstrates that somatic mutations imprint invariant morphologic features in myelodysplastic syndromes
Machine learning demonstrates that somatic mutations imprint invariant morphologic features in myelodysplastic syndromes
复制标题
DOI：
10.1182/blood.2020005488
复制
发表时间：
2020-11-12
期刊：
影响因子：
20.3
通讯作者：
Maciejewski, Jaroslaw P.
中科院分区：
文献类型：
--
作者：
Nagata, Yasunobu;Zhao, Ran;Maciejewski, Jaroslaw P.
形态学解释是诊断骨髓增生异常综合征（MDS）的标准，但它有局限性，如病理评估的可靠性不一，缺乏与遗传数据的整合。体细胞事件形成形态特征，但形态和遗传变化的复杂性使得明确关联具有挑战性。本文使用机器学习技术来研究MDS的新型临床亚型，以确定形态特征和基因组事件之间的共同发生模式。我们对1079例MDS患者进行了测序，并分析了骨髓形态学变化和其他临床特征。共鉴定出1929个体细胞突变。定义了五种不同的形态学特征，具有独特的临床特征。77%的高风险患者聚集在剖面1。所有低风险（LR）患者都聚集在剩下的4种类型中：类型2的特征是全血细胞减少症，类型3是单核细胞增多症，类型4是巨核细胞升高，类型5是红细胞发育不良。这些特征也可以区分不同预后的患者。LR MDS患者被分为8个遗传特征（例如，特征A有TET2突变，特征B同时有TET2和SRSF2突变，特征G有SF3B1突变），表明与特定的形态学特征相关。在独立遗传特征独立分析独立队列的单独分析中证实了6种形态特征/遗传特征关联。我们的研究表明，形态学和基因型之间的非随机甚至病理关系可以确定临床特征。这是第一次全面实施机器学习算法来阐明MDS属性中遗传病变、形态和临床预后之间潜在的内在相互依赖性。
Morphologic interpretation is the standard in diagnosing myelodysplastic syndrome (MDS), but it has limitations, such as varying reliability in pathologic evaluation and lack of integration with genetic data. Somatic events shape morphologic features, but the complexity of morphologic and genetic changes makes clear associations challenging. This article interrogates novel clinical subtypes of MDS using a machine-learning technique devised to identify patterns of cooccurrence among morphologic features and genomic events. We sequenced 1079 MDS patients and analyzed bone marrow morphologic al-terations and other clinical features. A total of 1929 somatic mutations were identified. Five distinct morphologic profiles with unique clinical characteristics were defined. Seventyseven percent of higher-risk patients clustered in profile 1. All lower-risk (LR) patients clustered into the remaining 4 profiles: profile 2 was characterized by pancytopenia, profile 3 by monocytosis, profile 4 by elevated megakaryocytes, and profile 5 by erythroid dysplasia. These profiles could also separate patients with different prognoses. LR MDS patients were classified into 8 genetic signatures (eg, signature A had TET2 mutations, signature B had both TET2 and SRSF2 mutations, and signature G had SF3B1 mutations), demonstrating association with specific morphologic profiles. Six morphologic profiles/ genetic signature associations were confirmed in a separate analysis of an independent genetic signature separate analysis independent cohort. Our study demonstrates that nonrandom or even pathognomonic relationships between morphology and genotype to define clinical features can be identified. This is the first comprehensive implementation of machine learning algorithms to elucidate potential intrinsic interdependencies among genetic lesions, morphologies, and clinical prognostic in attributes of MDS.