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Nat Med. 2018 May;24(4):474-483. doi: 10.1038/nm.4505. Epub 2018 Mar 5.

Single-cell developmental classification of B cell precursor acute lymphoblastic leukemia at diagnosis reveals predictors of relapse.

Author information

1
Baxter Laboratory in Stem Cell Biology, Stanford University, Stanford, California, USA.
2
Department of Microbiology and Immunology, Stanford University, Stanford, California, USA.
3
Department of Pathology, Stanford University, Stanford, California, USA.
4
PhD Program in Immunology, Stanford University, Stanford, California, USA.
5
Department of Pediatrics, Bass Center for Childhood Cancer, Stanford University, Stanford, California, USA.
6
M. Tettamanti Research Center, Pediatric Clinic University of Milano Bicocca, Monza, Italy.
7
Department of Obstetrics and Gynecology, Stanford University, Stanford, California, USA.
8
Department of Statistics, Stanford University, Stanford, California, USA.
9
Department of Health Research and Policy, Stanford University, Stanford, California, USA.

Abstract

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.

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