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

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

Dataset DOI: [10.5061/dryad.pvmcvdnxc](10.5061/dryad.pvmcvdnxc)

## Description of the data and file structure

# DDPR

**Developmentally Dependent Predictor of Relapse**

This repository contains the original mass cytometry (.FCS) data generated for the *DDPR* project, as described in:

**Good et al.** *Single-Cell Developmental Classification of B-Cell Precursor Acute Lymphoblastic Leukemia at Diagnosis Reveals Predictors of Relapse*, *Nature Medicine*, 2018.

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## Overview

This dataset consists of single-cell mass cytometry (CyTOF) data from **60 pediatric B-cell precursor acute lymphoblastic leukemia (BCP-ALL) diagnostic samples** and **5 healthy adult bone marrow donors**. The study used a **35-marker antibody panel** to profile surface and intracellular protein expression, including basal and perturbed signaling states, at single-cell resoluti..., All human subjects data used in this study were obtained under protocols approved by the Institutional Review Boards at Stanford University and the University of Milan Bicocca. Written informed consent was obtained from the parents or legal guardians of all pediatric participants, including consent for future research use and public data sharing of de-identified samples.

The data submitted to Dryad have been fully de-identified in accordance with HIPAA and international data protection guidelines. Specifically, all direct personal identifiers (e.g., names, dates of birth, medical record numbers) were removed prior to analysis. Clinical metadata were limited to non-identifiable variables such as age at diagnosis, relapse status, and cytogenetic subtype. Time-to-event data were censored and/or interval-binned to prevent re-identification. No potentially identifying combinations of rare clinical features are present in the dataset. The mass cytometry data include only anonymized expressio...