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.

中文翻译：

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诊断时B细胞前体急性淋巴细胞白血病的单细胞发育分类揭示了复发的预测因子。

需要深入了解引起复发性疾病的癌细胞群，以改善预后。在这里，我们报告了诊断时B细胞前体急性淋巴细胞白血病的单细胞研究，该研究揭示了与复发唯一相关的隐藏的依赖于发育的细胞信号传导状态。通过使用流式细胞仪，我们同时定量了60个主要诊断样品中涉及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接近的健康B细胞群体进行匹配。机器学习确定了六种扩大的白血病人群的特征，这些特征足以预测诊断时的患者复发。这些特征与激活mTOR信号的B细胞前BII亚群有关，以及B细胞的pre-BI亚群具有激活的和无反应的pre-B细胞受体信号传导，与复发相关。该模型被称为“发展依赖的复发预测因子”（DDPR），显着改善了目前建立的风险分层方法。DDPR功能在诊断时就存在，并在复发时持续存在。通过利用数据驱动的方法，我们证明了单细胞“组学”在转化环境中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。DDPR功能在诊断时存在，并在复发时持续存在。通过利用数据驱动的方法，我们证明了单细胞“组学”在转化环境中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。DDPR功能在诊断时就存在，并在复发时持续存在。通过利用数据驱动的方法，我们证明了单细胞“组学”在转化环境中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。