Sánchez-Corrales Yara E, Pohle Ruben V C, Castellano Sergi, Giustacchini Alice
Genetics and Genomic Medicine Department, Great Ormond Street Institute of Child Health, University College London, London, United Kingdom.
Molecular and Cellular Immunology Section, Great Ormond Street Institute of Child Health, University College London, London, United Kingdom.
Front Oncol. 2021 Apr 29;11:666829. doi: 10.3389/fonc.2021.666829. eCollection 2021.
Acute Myeloid Leukaemia (AML) is a phenotypically and genetically heterogenous blood cancer characterised by very poor prognosis, with disease relapse being the primary cause of treatment failure. AML heterogeneity arise from different genetic and non-genetic sources, including its proposed hierarchical structure, with leukemic stem cells (LSCs) and progenitors giving origin to a variety of more mature leukemic subsets. Recent advances in single-cell molecular and phenotypic profiling have highlighted the intra and inter-patient heterogeneous nature of AML, which has so far limited the success of cell-based immunotherapy approaches against single targets. Machine Learning (ML) can be uniquely used to find non-trivial patterns from high-dimensional datasets and identify rare sub-populations. Here we review some recent ML tools that applied to single-cell data could help disentangle cell heterogeneity in AML by identifying distinct core molecular signatures of leukemic cell subsets. We discuss the advantages and limitations of unsupervised and supervised ML approaches to cluster and classify cell populations in AML, for the identification of biomarkers and the design of personalised therapies.
急性髓系白血病（AML）是一种在表型和基因上具有异质性的血癌，其特征是预后极差，疾病复发是治疗失败的主要原因。AML的异质性源于不同的遗传和非遗传因素，包括其假定的层级结构，白血病干细胞（LSC）和祖细胞可产生多种更成熟的白血病亚群。单细胞分子和表型分析的最新进展凸显了AML患者内部和患者之间的异质性本质，这在目前限制了针对单一靶点的基于细胞的免疫治疗方法的成功。机器学习（ML）可独特地用于从高维数据集中找到非平凡模式并识别罕见亚群。在此，我们综述一些最近应用于单细胞数据的ML工具，这些工具可通过识别白血病细胞亚群不同的核心分子特征来帮助解开AML中的细胞异质性。我们讨论了无监督和有监督ML方法在AML中对细胞群体进行聚类和分类以识别生物标志物和设计个性化疗法的优势和局限性。
