DOI: 10.5061/dryad.pvmcvdnxc
title: Single-cell developmental classification of B cell precursor acute lymphoblastic leukemia at diagnosis reveals predictors of relapse

description: 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.
description: # 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. --- ## 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 resolution.
 Each leukemia sample has been: * **Bead-normalized** (Finck *et al.*,
 Cytometry A, 2013) * **Debarcoded** using palladium-based cell barcoding
 (Zunder *et al.*, Nat Protoc, 2015) * **Gated on lineage-negative (Lin⁻)
 B-lineage cells**, excluding T cells, myeloid cells, and other lineages
 (see Supplementary Fig. 1a of the paper for gating strategy) --- ##
 Contents * **Bead-normalized, debarcoded FCS files** for all 60 patients
 and 5 healthy controls (in a single zip file) * Samples stained with a
 **panel of 35 antibodies**, including markers of B-cell development and
 intracellular signaling (see Supplementary Table 2) * Samples treated with
 **various ex vivo perturbations**, including: * Pervanadate * B-cell
 receptor (BCR) crosslinking * IL-7 * Thymic stromal lymphopoietin (TSLP) *
 BEZ235 (PI3K/mTOR inhibitor) * Dasatinib (ABL/SRC kinase inhibitor) *
 Tofacitinib (JAK inhibitor)\ (see Supplementary Table 3 for concentrations
 and timepoints) --- ## Clinical Metadata Patient metadata are provided in
 **Supplementary Table 1** of Good and Sarno et al., including: *
 Diagnostic cytogenetics * Age and WBC count at diagnosis * Minimal
 residual disease (MRD) risk * Final risk group (per AIEOP-BFM 2000
 protocol) * Relapse status and time-to-relapse or last follow-up Median
 follow-up time: 5.5 years\ Relapse occurred in 17 of 54 patients with ≥3
 years of follow-up. --- ## Citation If using this dataset, consider citing
 the original paper: **Good Z, Sarno J, Jager A, et al.** *Single-Cell
 Developmental Classification of B-Cell Precursor Acute Lymphoblastic
 Leukemia at Diagnosis Reveals Predictors of Relapse*. *Nat Med*.
 2018;24(4):474–483. doi:10.1038/nm.4505 ### Files and variables #### File:
 ddpr_data.zip **Description:**  ## Files and Variables This zip file
 contains **mass cytometry (.fcs) files** from a study of **B-cell
 precursor acute lymphoblastic leukemia (BCP-ALL)** and healthy bone marrow
 controls. These files are bead-normalized, debarcoded, and gated on
 **lineage-negative (Lin⁻) B-lineage cells**. Each file corresponds to a
 specific sample (healthy donor or patient) and experimental condition. All
 data files are in `.fcs` (Flow Cytometry Standard) format and are
 organized by: * **Donor/sample ID** (e.g., `UPN1`, `UPN35`, `Healthy3`) *
 **Stimulation or perturbation condition** (e.g., `Basal`, `IL-7`,
 `Pervanadate`, `BCR-Crosslink`, `Dasatinib`, `BEZ-235`, `Tofacitinib`,
 `TSLP`) ### 🗃️ File Naming Convention `[SampleID]_[Condition].fcs` *
 `SampleID`: UPN codes (e.g., `UPN4`, `UPN35-Relapse`) or `Healthy[1–5]` *
 `Condition`: The stimulation or treatment applied: * `Basal`: No
 stimulation * `BCR-Crosslink`: B-cell receptor engagement * `IL-7`,
 `TSLP`: Cytokine stimulation * `Pervanadate`: Broad phosphatase inhibition
 * `BEZ-235`: PI3K/mTOR inhibition * `Dasatinib`: ABL/Src family kinase
 inhibition * `Tofacitinib`: JAK inhibition For some samples, especially
 healthy donors, files are split into parts (e.g.,
 `Healthy3_Basal_Part1.fcs`), which can be merged during downstream
 analysis. ### .FCS Files * **525 `.fcs` files** * 60+ unique UPN patient
 samples * 5 healthy donor samples * Multiple perturbation conditions per
 sample ### 📈 Variables (Channels) Each `.fcs` file contains **single-cell
 expression measurements** for the following: #### 🔬 Cell Identification
 and Quality Control * `191Ir`, `193Ir`: DNA intercalators (nuclear
 content) * `195Pt`: Cisplatin (viability stain) * `Event_length`: Pulse
 width for singlet discrimination #### 🔄 Lineage Gating Markers * `CD3e`,
 `CD16`, `CD33`, `CD61`, `CD235a`, `CD38High`: Excluded during
 lineage-negative gating #### 📊 Phenotypic and Developmental Markers
 (subset) * `CD10`, `CD19`, `CD20`, `CD22`, `CD24`, `CD34`, `CD38`, `CD43`,
 `CD45`, `CD58`, `CD127` * `HLA-DR`, `TdT`, `PAX5`, `IKAROS`, `RAG1`,
 `Tslpr`, `CD79b`, `CD179a`, `CD179b` * `IgHi`, `IgHs`, `Ki-67` #### ⚡
 Signaling Markers (phospho-proteins) * `pS6`, `p4EBP1`, `pSTAT5`, `pSYK`,
 `pCREB`, `pPLCγ2`, `pAKT`, `pERK1/2`, `pIKAROS` --- ### 🧪 Units of
 Measurement * **Protein expression**: signal intensity (mass cytometry ion
 counts) * **Each row** in an `.fcs` file = one single cell * **Each
 column** = measured parameter (as described above) --- ### ⚠️ Missing
 Values * `.fcs` files do not use explicit NA markers * Undetected or
 low-signal proteins will have low numeric values (typically near 0) * No
 rows are removed; all single-cell events after gating are preserved ---
 ### 🔗 Metadata Reference Sample-level metadata (age, diagnosis, relapse
 status, risk stratification, cytogenetics) are described in: **Good Z, et
 al.** *Single-Cell Developmental Classification of B-Cell Precursor Acute
 Lymphoblastic Leukemia at Diagnosis Reveals Predictors of Relapse*,
 *Nature Medicine*, 2018.
 [https://doi.org/10.1038/nm.4505](https://doi.org/10.1038/nm.4505) For
 antibody panel details and perturbation conditions, refer to Supplementary
 Tables 2 and 3 of the above publication. ### Complete File List:
 Healthy1_BCR-Crosslink.fcs UPN29_IL-7.fcs Healthy1_Basal.fcs
 UPN29_Pervanadate.fcs Healthy1_IL-7.fcs UPN29_TSLP.fcs
 Healthy1_Pervanadate.fcs UPN29_Tofacitinib.fcs Healthy1_TSLP.fcs
 UPN2_BCR-Crosslink.fcs Healthy2_BCR-Crosslink_Part1.fcs UPN2_BEZ-235.fcs
 Healthy2_BCR-Crosslink_Part2.fcs UPN2_Basal.fcs
 Healthy2_BCR-Crosslink_Part3.fcs UPN2_Dasatinib.fcs
 Healthy2_BCR-Crosslink_Part4.fcs UPN2_IL-7.fcs Healthy2_Basal_Part1.fcs
 UPN2_Pervanadate.fcs Healthy2_Basal_Part2.fcs UPN2_TSLP.fcs
 Healthy2_Basal_Part3.fcs UPN2_Tofacitinib.fcs Healthy2_Basal_Part4.fcs
 UPN30_BCR-Crosslink.fcs Healthy2_IL-7_Part1.fcs UPN30_BEZ-235.fcs
 Healthy2_IL-7_Part2.fcs UPN30_Basal.fcs Healthy2_IL-7_Part3.fcs
 UPN30_Dasatinib.fcs Healthy2_IL-7_Part4.fcs UPN30_IL-7.fcs
 Healthy2_Pervanadate.fcs UPN30_Pervanadate.fcs Healthy2_TSLP_Part1.fcs
 UPN30_TSLP.fcs Healthy2_TSLP_Part2.fcs UPN30_Tofacitinib.fcs
 Healthy2_TSLP_Part3.fcs UPN31_BCR-Crosslink.fcs Healthy2_TSLP_Part4.fcs
 UPN31_BEZ-235.fcs Healthy3_BCR-Crosslink_Part1.fcs UPN31_Basal.fcs
 Healthy3_BCR-Crosslink_Part2.fcs UPN31_Dasatinib.fcs
 Healthy3_BCR-Crosslink_Part3.fcs UPN31_IL-7.fcs
 Healthy3_BCR-Crosslink_Part4.fcs UPN31_Pervanadate.fcs
 Healthy3_BCR-Crosslink_Part5.fcs UPN31_TSLP.fcs
 Healthy3_BCR-Crosslink_Part6.fcs UPN31_Tofacitinib.fcs
 Healthy3_BCR-Crosslink_Part7.fcs UPN35-Relapse_BCR-Crosslink.fcs
 Healthy3_BCR-Crosslink_Part8.fcs UPN35-Relapse_BEZ-235.fcs
 Healthy3_BCR-Crosslink_Part9.fcs UPN35-Relapse_Basal.fcs
 Healthy3_BEZ-235_Part1.fcs UPN35-Relapse_Dasatinib.fcs
 Healthy3_BEZ-235_Part2.fcs UPN35_BCR-Crosslink.fcs
 Healthy3_BEZ-235_Part3.fcs UPN35_BEZ-235.fcs Healthy3_Basal_Part1.fcs
 UPN35_Basal.fcs Healthy3_Basal_Part10.fcs UPN35_Dasatinib.fcs
 Healthy3_Basal_Part2.fcs UPN3_BCR-Crosslink.fcs Healthy3_Basal_Part3.fcs
 UPN3_BEZ-235.fcs Healthy3_Basal_Part4.fcs UPN3_Basal.fcs
 Healthy3_Basal_Part5.fcs UPN3_Dasatinib.fcs Healthy3_Basal_Part6.fcs
 UPN3_IL-7.fcs Healthy3_Basal_Part7.fcs UPN3_Pervanadate.fcs
 Healthy3_Basal_Part8.fcs UPN3_TSLP.fcs Healthy3_Basal_Part9.fcs
 UPN3_Tofacitinib.fcs Healthy3_Dasatinib_Part1.fcs
 UPN45-Relapse_BCR-Crosslink.fcs Healthy3_Dasatinib_Part2.fcs
 UPN45-Relapse_BEZ-235.fcs Healthy3_IL-7_Part1.fcs UPN45-Relapse_Basal.fcs
 Healthy3_IL-7_Part2.fcs UPN45-Relapse_Dasatinib.fcs
 Healthy3_IL-7_Part3.fcs UPN45_BCR-Crosslink.fcs Healthy3_IL-7_Part4.fcs
 UPN45_BEZ-235.fcs Healthy3_IL-7_Part5.fcs UPN45_Basal.fcs
 Healthy3_IL-7_Part6.fcs UPN45_Dasatinib.fcs Healthy3_IL-7_Part7.fcs
 UPN47_BCR-Crosslink.fcs Healthy3_IL-7_Part8.fcs UPN47_BEZ-235.fcs
 Healthy3_IL-7_Part9.fcs UPN47_Basal.fcs Healthy3_Pervanadate_Part1.fcs
 UPN47_Dasatinib.fcs Healthy3_Pervanadate_Part2.fcs UPN47_IL-7.fcs
 Healthy3_Pervanadate_Part3.fcs UPN47_Pervanadate.fcs
 Healthy3_TSLP_Part1.fcs UPN47_TSLP.fcs Healthy3_TSLP_Part2.fcs
 UPN47_Tofacitinib.fcs Healthy3_TSLP_Part3.fcs UPN48_BCR-Crosslink.fcs
 Healthy3_TSLP_Part4.fcs UPN48_BEZ-235.fcs Healthy3_TSLP_Part5.fcs
 UPN48_Basal.fcs Healthy3_TSLP_Part6.fcs UPN48_Dasatinib.fcs
 Healthy3_TSLP_Part7.fcs UPN48_IL-7.fcs Healthy3_TSLP_Part8.fcs
 UPN48_Pervanadate.fcs Healthy3_TSLP_Part9.fcs UPN48_TSLP.fcs
 Healthy3_Tofacitinib_Part1.fcs UPN48_Tofacitinib.fcs
 Healthy3_Tofacitinib_Part2.fcs UPN49_BCR-Crosslink.fcs
 Healthy3_Tofacitinib_Part3.fcs UPN49_BEZ-235.fcs
 Healthy3_Tofacitinib_Part4.fcs UPN49_Basal.fcs
 Healthy4_BCR-Crosslink_Part1.fcs UPN49_Dasatinib.fcs
 Healthy4_BCR-Crosslink_Part2.fcs UPN49_IL-7.fcs
 Healthy4_BCR-Crosslink_Part3.fcs UPN49_Pervanadate.fcs
 Healthy4_BCR-Crosslink_Part4.fcs UPN49_TSLP.fcs
 Healthy4_BCR-Crosslink_Part5.fcs UPN49_Tofacitinib.fcs
 Healthy4_BCR-Crosslink_Part6.fcs UPN4_BCR-Crosslink.fcs
 Healthy4_Basal_Part1.fcs UPN4_BEZ-235.fcs Healthy4_Basal_Part2.fcs
 UPN4_Basal.fcs Healthy4_Basal_Part3.fcs UPN4_Dasatinib.fcs
 Healthy4_Basal_Part4.fcs UPN4_IL-7.fcs Healthy4_Basal_Part5.fcs
 UPN4_Pervanadate.fcs Healthy4_Basal_Part6.fcs UPN4_TSLP.fcs
 Healthy4_IL-7_Part1.fcs UPN4_Tofacitinib.fcs Healthy4_IL-7_Part2.fcs
 UPN50_BCR-Crosslink.fcs Healthy4_IL-7_Part3.fcs UPN50_BEZ-235.fcs
 Healthy4_IL-7_Part4.fcs UPN50_Basal.fcs Healthy4_IL-7_Part5.fcs
 UPN50_Dasatinib.fcs Healthy4_IL-7_Part6.fcs UPN50_IL-7.fcs
 Healthy4_Pervanadate.fcs UPN50_Pervanadate.fcs Healthy4_TSLP_Part1.fcs
 UPN50_TSLP.fcs Healthy4_TSLP_Part2.fcs UPN50_Tofacitinib.fcs
 Healthy5_Basal_Part1.fcs UPN51_BCR-Crosslink.fcs Healthy5_Basal_Part2.fcs
 UPN51_BEZ-235.fcs Healthy5_Basal_Part3.fcs UPN51_Basal.fcs
 UPN1-Relapse_BCR-Crosslink.fcs UPN51_Dasatinib.fcs
 UPN1-Relapse_BEZ-235.fcs UPN51_IL-7.fcs UPN1-Relapse_Basal.fcs
 UPN51_Pervanadate.fcs UPN1-Relapse_Dasatinib.fcs UPN51_TSLP.fcs
 UPN10-Relapse_BCR-Crosslink.fcs UPN51_Tofacitinib.fcs
 UPN10-Relapse_BEZ-235.fcs UPN52_BCR-Crosslink.fcs UPN10-Relapse_Basal.fcs
 UPN52_BEZ-235.fcs UPN10-Relapse_Dasatinib.fcs UPN52_Basal.fcs
 UPN10_BCR-Crosslink.fcs UPN52_Dasatinib.fcs UPN10_BEZ-235.fcs
 UPN52_IL-7.fcs UPN10_Basal.fcs UPN52_Pervanadate.fcs UPN10_Dasatinib.fcs
 UPN52_TSLP.fcs UPN11_BCR-Crosslink.fcs UPN52_Tofacitinib.fcs
 UPN11_BEZ-235.fcs UPN53_BCR-Crosslink.fcs UPN11_Basal.fcs
 UPN53_BEZ-235.fcs UPN11_Dasatinib.fcs UPN53_Basal.fcs UPN11_IL-7.fcs
 UPN53_Dasatinib.fcs UPN11_Pervanadate.fcs UPN53_IL-7.fcs UPN11_TSLP.fcs
 UPN53_Pervanadate.fcs UPN11_Tofacitinib.fcs UPN53_TSLP.fcs
 UPN12_BCR-Crosslink.fcs UPN53_Tofacitinib.fcs UPN12_BEZ-235.fcs
 UPN54_BCR-Crosslink.fcs UPN12_Basal.fcs UPN54_BEZ-235.fcs
 UPN12_Dasatinib.fcs UPN54_Basal.fcs UPN12_IL-7.fcs UPN54_Dasatinib.fcs
 UPN12_Pervanadate.fcs UPN54_IL-7.fcs UPN12_TSLP.fcs UPN54_Pervanadate.fcs
 UPN12_Tofacitinib.fcs UPN54_TSLP.fcs UPN13_BCR-Crosslink.fcs
 UPN54_Tofacitinib.fcs UPN13_BEZ-235.fcs UPN55_BCR-Crosslink.fcs
 UPN13_Basal.fcs UPN55_BEZ-235.fcs UPN13_Dasatinib.fcs UPN55_Basal.fcs
 UPN13_IL-7.fcs UPN55_Dasatinib.fcs UPN13_Pervanadate.fcs UPN55_IL-7.fcs
 UPN13_TSLP.fcs UPN55_Pervanadate.fcs UPN13_Tofacitinib.fcs UPN55_TSLP.fcs
 UPN14_BCR-Crosslink.fcs UPN55_Tofacitinib.fcs UPN14_BEZ-235.fcs
 UPN56_BCR-Crosslink.fcs UPN14_Basal.fcs UPN56_BEZ-235.fcs
 UPN14_Dasatinib.fcs UPN56_Basal.fcs UPN14_IL-7.fcs UPN56_Dasatinib.fcs
 UPN14_Pervanadate.fcs UPN56_IL-7.fcs UPN14_TSLP.fcs UPN56_Pervanadate.fcs
 UPN14_Tofacitinib.fcs UPN56_TSLP.fcs UPN15_BCR-Crosslink.fcs
 UPN56_Tofacitinib.fcs UPN15_BEZ-235.fcs UPN57_BCR-Crosslink.fcs
 UPN15_Basal.fcs UPN57_BEZ-235.fcs UPN15_Dasatinib.fcs UPN57_Basal.fcs
 UPN15_IL-7.fcs UPN57_Dasatinib.fcs UPN15_Pervanadate.fcs UPN57_IL-7.fcs
 UPN15_TSLP.fcs UPN57_Pervanadate.fcs UPN15_Tofacitinib.fcs UPN57_TSLP.fcs
 UPN16_BCR-Crosslink.fcs UPN57_Tofacitinib.fcs UPN16_BEZ-235.fcs
 UPN58_BCR-Crosslink.fcs UPN16_Basal.fcs UPN58_BEZ-235.fcs
 UPN16_Dasatinib.fcs UPN58_Basal.fcs UPN16_IL-7.fcs UPN58_Dasatinib.fcs
 UPN16_Pervanadate.fcs UPN58_IL-7.fcs UPN16_TSLP.fcs UPN58_Pervanadate.fcs
 UPN16_Tofacitinib.fcs UPN58_TSLP.fcs UPN17_BCR-Crosslink.fcs
 UPN58_Tofacitinib.fcs UPN17_BEZ-235.fcs UPN5_BCR-Crosslink.fcs
 UPN17_Basal.fcs UPN5_BEZ-235.fcs UPN17_Dasatinib.fcs UPN5_Basal.fcs
 UPN17_IL-7.fcs UPN5_Dasatinib.fcs UPN17_Pervanadate.fcs UPN5_IL-7.fcs
 UPN17_TSLP.fcs UPN5_Pervanadate.fcs UPN17_Tofacitinib.fcs UPN5_TSLP.fcs
 UPN18_BCR-Crosslink.fcs UPN5_Tofacitinib.fcs UPN18_BEZ-235.fcs
 UPN60-Blood_Basal.fcs UPN18_Basal.fcs UPN60_Basal.fcs UPN18_Dasatinib.fcs
 UPN61-Blood_Basal.fcs UPN18_IL-7.fcs UPN62-Blood_Basal.fcs
 UPN18_Pervanadate.fcs UPN62_Basal.fcs UPN18_TSLP.fcs UPN63-Blood_Basal.fcs
 UPN18_Tofacitinib.fcs UPN63_Basal.fcs UPN19_BCR-Crosslink.fcs
 UPN64-Blood_Basal.fcs UPN19_BEZ-235.fcs UPN65-Blood_Basal.fcs
 UPN19_Basal.fcs UPN67_BCR-Crosslink.fcs UPN19_Dasatinib.fcs
 UPN67_BEZ-235.fcs UPN19_IL-7.fcs UPN67_Basal.fcs UPN19_Pervanadate.fcs
 UPN67_Dasatinib.fcs UPN19_TSLP.fcs UPN67_IL-7.fcs UPN19_Tofacitinib.fcs
 UPN67_Pervanadate.fcs UPN1_BCR-Crosslink.fcs UPN67_TSLP.fcs
 UPN1_BEZ-235.fcs UPN67_Tofacitinib.fcs UPN1_Basal.fcs
 UPN68_BCR-Crosslink.fcs UPN1_Dasatinib.fcs UPN68_BEZ-235.fcs UPN1_IL-7.fcs
 UPN68_Basal.fcs UPN1_Pervanadate.fcs UPN68_Dasatinib.fcs UPN1_TSLP.fcs
 UPN68_IL-7.fcs UPN1_Tofacitinib.fcs UPN68_Pervanadate.fcs
 UPN20_BCR-Crosslink.fcs UPN68_TSLP.fcs UPN20_BEZ-235.fcs
 UPN68_Tofacitinib.fcs UPN20_Basal.fcs UPN69_BCR-Crosslink.fcs
 UPN20_Dasatinib.fcs UPN69_BEZ-235.fcs UPN20_IL-7.fcs UPN69_Basal.fcs
 UPN20_Pervanadate.fcs UPN69_Dasatinib.fcs UPN20_TSLP.fcs UPN69_IL-7.fcs
 UPN20_Tofacitinib.fcs UPN69_Pervanadate.fcs UPN21_BCR-Crosslink.fcs
 UPN69_TSLP.fcs UPN21_BEZ-235.fcs UPN69_Tofacitinib.fcs UPN21_Basal.fcs
 UPN6_BCR-Crosslink.fcs UPN21_Dasatinib.fcs UPN6_BEZ-235.fcs UPN21_IL-7.fcs
 UPN6_Basal.fcs UPN21_Pervanadate.fcs UPN6_Dasatinib.fcs UPN21_TSLP.fcs
 UPN6_IL-7.fcs UPN21_Tofacitinib.fcs UPN6_Pervanadate.fcs
 UPN22-Relapse_BCR-Crosslink.fcs UPN6_TSLP.fcs UPN22-Relapse_BEZ-235.fcs
 UPN6_Tofacitinib.fcs UPN22-Relapse_Basal.fcs UPN7_BCR-Crosslink.fcs
 UPN22-Relapse_Dasatinib.fcs UPN7_BEZ-235.fcs UPN22_BCR-Crosslink.fcs
 UPN7_Basal.fcs UPN22_BEZ-235.fcs UPN7_Dasatinib.fcs UPN22_Basal.fcs
 UPN7_IL-7.fcs UPN22_Dasatinib.fcs UPN7_Pervanadate.fcs
 UPN23_BCR-Crosslink.fcs UPN7_TSLP.fcs UPN23_BEZ-235.fcs
 UPN7_Tofacitinib.fcs UPN23_Basal.fcs UPN8_BCR-Crosslink.fcs
 UPN23_Dasatinib.fcs UPN8_BEZ-235.fcs UPN23_IL-7.fcs UPN8_Basal.fcs
 UPN23_Pervanadate.fcs UPN8_Dasatinib.fcs UPN23_TSLP.fcs UPN8_IL-7.fcs
 UPN23_Tofacitinib.fcs UPN8_Pervanadate.fcs UPN24_BCR-Crosslink.fcs
 UPN8_TSLP.fcs UPN24_BEZ-235.fcs UPN8_Tofacitinib.fcs UPN24_Basal.fcs
 UPN90-Relapse_Basal.fcs UPN24_Dasatinib.fcs UPN90-Relapse_Dasatinib.fcs
 UPN24_IL-7.fcs UPN90-Relapse_IL-7.fcs UPN24_Pervanadate.fcs
 UPN90_Basal.fcs UPN24_TSLP.fcs UPN90_Dasatinib.fcs UPN24_Tofacitinib.fcs
 UPN90_IL-7.fcs UPN25_BCR-Crosslink.fcs UPN91_Dasatinib.fcs
 UPN25_BEZ-235.fcs UPN91_IL-7.fcs UPN25_Basal.fcs UPN91_basal.fcs
 UPN25_Dasatinib.fcs UPN92_Basal.fcs UPN25_IL-7.fcs UPN92_Dasatinib.fcs
 UPN25_Pervanadate.fcs UPN92_IL-7.fcs UPN25_TSLP.fcs UPN93_Basal.fcs
 UPN25_Tofacitinib.fcs UPN93_Dasatinib.fcs UPN26_BCR-Crosslink.fcs
 UPN93_IL-7.fcs UPN26_BEZ-235.fcs UPN94_Basal.fcs UPN26_Basal.fcs
 UPN94_Dasatinib.fcs UPN26_Dasatinib.fcs UPN94_IL-7.fcs UPN26_IL-7.fcs
 UPN95-Relapse_Basal.fcs UPN26_Pervanadate.fcs UPN95-Relapse_Dasatinib.fcs
 UPN26_TSLP.fcs UPN95-Relapse_IL-7.fcs UPN26_Tofacitinib.fcs
 UPN95_Basal.fcs UPN27_BCR-Crosslink.fcs UPN95_Dasatinib.fcs
 UPN27_BEZ-235.fcs UPN95_IL-7.fcs UPN27_Basal.fcs UPN96_Basal.fcs
 UPN27_Dasatinib.fcs UPN96_Dasatinib.fcs UPN27_IL-7.fcs UPN96_IL-7.fcs
 UPN27_Pervanadate.fcs UPN97_Basal.fcs UPN27_TSLP.fcs UPN97_Dasatinib.fcs
 UPN27_Tofacitinib.fcs UPN97_IL-7.fcs UPN28_BCR-Crosslink.fcs
 UPN98_Basal.fcs UPN28_BEZ-235.fcs UPN98_Dasatinib.fcs UPN28_Basal.fcs
 UPN98_IL-7.fcs UPN28_Dasatinib.fcs UPN9_BCR-Crosslink.fcs UPN28_IL-7.fcs
 UPN9_BEZ-235.fcs UPN28_Pervanadate.fcs UPN9_Basal.fcs UPN28_TSLP.fcs
 UPN9_Dasatinib.fcs UPN28_Tofacitinib.fcs UPN9_IL-7.fcs
 UPN29_BCR-Crosslink.fcs UPN9_Pervanadate.fcs UPN29_BEZ-235.fcs
 UPN9_TSLP.fcs UPN29_Basal.fcs UPN9_Tofacitinib.fcs UPN29_Dasatinib.fcs ##
 Code/software ## Software and Workflow The data files are in Flow
 Cytometry Standard (.fcs) format and can be analyzed using free,
 open-source software. Due to the complexity of high-dimensional cytometry
 data, we recommend using code-based analysis workflows under the
 supervision of a trained biostatistician to explore these data. ### R We
 recommend using the following R packages: * `flowCore`: for reading and
 preprocessing `.fcs` files * `tidyflowcore`: for tidyverse-style workflows
 with flow cytometry data * `tidytof`: for downstream analysis, including
 clustering and visualization ### Python For Python users, we recommend: *
 `pytometry`: for reading, transforming, and analyzing `.fcs` files using
 the Scanpy ecosystem These packages allow users to load, subset, and
 analyze single-cell protein expression data from mass cytometry
 experiments. No analysis scripts are included in this dataset submission.
 ## Access information Other publicly accessible locations of the data: *
 [https://github.com/kara-davis-lab/DDPR](https://github.com/kara-davis-lab/DDPR) Data was derived from the following sources: * [https://github.com/kara-davis-lab/DDPR](https://github.com/kara-davis-lab/DDPR) ## Human subjects data 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 expression values without any patient-identifying information.