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

Keyes, Timothy1; Good, Zinaida1; Sarno, Jolanda1; Jager, Astraea1; Davis, Kara L1

Research facility: Stanford University School of Medicine
Published Jul 11, 2025 on Dryad. https://doi.org/10.5061/dryad.pvmcvdnxc

Data files

Jul 11, 2025 version files 5.86 GB

Abstract

This dataset supports the publication "Single-Cell Developmental Classification of B-Cell Precursor Acute Lymphoblastic Leukemia at Diagnosis Reveals Predictors of Relapse" (Nature Medicine, 2018). It contains single-cell mass cytometry measurements from 60 pediatric patients diagnosed with B-cell precursor acute lymphoblastic leukemia (BCP-ALL), along with data from 5 healthy bone marrow donors. Using a panel of 35 surface and intracellular proteins, each leukemia cell was classified into its closest developmental B-cell stage using a supervised single-cell developmental classifier. Features derived from these classified cell populations—including protein expression levels and signaling responses under basal and perturbed conditions—were used to construct a machine learning model (Developmentally Dependent Predictor of Relapse, or DDPR) that stratifies patients at diagnosis based on future risk of relapse. This dataset includes raw, bead-normalized ion count data. It enables exploration of developmental heterogeneity in BCP-ALL and provides a resource for studying relapse-associated signaling states in leukemic populations.