Blood:机器学习揭示MDS基因型和形态学特征的相关性

2020-09-28 Q MedSci原创

骨髓增生异常综合征(MDS)是一组起源于造血干细胞的肿瘤性疾病,主要特征是骨髓造血功能异常、血细胞发育异常,表现为难治性的血细胞减少、造血功能衰竭,有向急性白血病转化的高风险,曾被称为白血病前期。

骨髓增生异常综合征(MDS)是一组起源于造血干细胞的肿瘤性疾病,主要特征是骨髓造血功能异常、血细胞发育异常,表现为难治性的血细胞减少、造血功能衰竭,有向急性白血病转化的高风险,曾被称为白血病前期。

一直以来,血细胞和骨髓细胞的形态学检查是诊断MDS的金标准,但该方法存在局限性,如病理评估的可靠性参差不齐,缺乏与遗传学数据的整合。体细胞事件影响形态特征,但形态和遗传变化的复杂性使明确其关联具有挑战性。

本研究使用一种用于识别形态学特征和基因组事件之间的共生模式的机器学习技术来分析MDS的新临床亚型。

本研究对1079名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。总共鉴定出1929个体细胞突变。

5种不同形态学特征人群的预后

定义了五种具有独特临床特征的不同形态学特征(Profile-1、Profile-2、Profile-3、Profile-4和Profile-5)。77%的高危型患者聚集在Profile-1中。所有的低危患者都聚集在剩下的4个类型中:Profile-2表现为全血细胞减少,Profile-3表现为单核细胞增多,Profile-4表现为巨核细胞增高,Profile-5表现为红系异常增生。这些特征也可以区分不同预后的患者。

低危MDS患者被分成8个与特定形态学特征相关的遗传特征(如Signature-A具有TET2突变,Signature-B同时具有TET2和SRSF2突变,Signature-G具有SF3B1突变)。

在独立队列中验证的基因数量

在一项独立队列的单独分析中,验证了6个形态学特征/遗传特征的关联性。

综上,该研究表明,形态学和基因型之间的非随机关系,甚至病因学关系,均可以被识别出来,以定义临床特征。这是第一次全面实施机器学习算法,以阐明MDS属性中遗传损伤、形态学和临床预后之间潜在的内在相互依赖关系。

原始出处:

Yasunobu Nagata, et al. Machine learning demonstrates that somatic mutations imprint invariant morphologic features in myelodysplastic syndromes. Blood. September 22, 2020.

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    2021-06-16 d830372
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    2020-09-30 cmuwisdom

    好文章

    0

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    2020-09-28 lovetcm

    #机器学习#用于#骨髓增生异常综合征#的形态学与基因型之间的关联分析,相对而言,会比经典的#主成分分析#更为有效,尤其是大样本时候。值得借鉴。

    0

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