Baxter Laboratory in Stem Cell Biology, Stanford University, Stanford, California, USA.
Department of Microbiology and Immunology, Stanford University, Stanford, California, USA.
Nat Med. 2018 May;24(4):474-483. doi: 10.1038/nm.4505. Epub 2018 Mar 5.
Insight into the cancer cell populations that are responsible for relapsed disease is needed to improve outcomes. Here we report a single-cell-based study of B cell precursor acute lymphoblastic leukemia at diagnosis that reveals hidden developmentally dependent cell signaling states that are uniquely associated with relapse. By using mass cytometry we simultaneously quantified 35 proteins involved in B cell development in 60 primary diagnostic samples. Each leukemia cell was then matched to its nearest healthy B cell population by a developmental classifier that operated at the single-cell level. Machine learning identified six features of expanded leukemic populations that were sufficient to predict patient relapse at diagnosis. These features implicated the pro-BII subpopulation of B cells with activated mTOR signaling, and the pre-BI subpopulation of B cells with activated and unresponsive pre-B cell receptor signaling, to be associated with relapse. This model, termed 'developmentally dependent predictor of relapse' (DDPR), significantly improves currently established risk stratification methods. DDPR features exist at diagnosis and persist at relapse. By leveraging a data-driven approach, we demonstrate the predictive value of single-cell 'omics' for patient stratification in a translational setting and provide a framework for its application to human cancer.
为了改善治疗效果，我们需要深入了解导致疾病复发的癌细胞群体。在这里，我们报告了一项基于单细胞的急性 B 淋巴细胞白血病诊断研究，该研究揭示了隐藏的、依赖发育的细胞信号状态，这些状态与复发具有独特的相关性。通过使用质谱流式细胞术，我们同时定量分析了 60 个原始诊断样本中涉及 B 细胞发育的 35 种蛋白质。然后，通过在单细胞水平上运行的发育分类器，将每个白血病细胞与其最近的健康 B 细胞群体相匹配。机器学习确定了扩展的白血病群体的六个特征，这些特征足以在诊断时预测患者的复发。这些特征表明，具有激活的 mTOR 信号的 pro-BII 亚群 B 细胞，以及具有激活和无反应的前 B 细胞受体信号的 pre-BI 亚群 B 细胞，与复发相关。该模型称为“依赖发育的复发预测因子”（DDPR），显著提高了目前确立的风险分层方法。DDPR 特征在诊断时存在，并在复发时持续存在。通过利用数据驱动的方法，我们在转化环境中证明了单细胞“组学”对患者分层的预测价值，并为其在人类癌症中的应用提供了框架。
