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Targeting signaling rewiring and resistant subpopulations in Philadelphia Chromosome-like Acute Lymphoblastic Leukemia [Bulk RNA-seq]

GSE263709 Homo sapiens (human) Single-cell RNA-seq 39 samples Submitted 2025/01/09 Platform GPL20301
At a glance

Single-cell RNA-seq · human · 39 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Homo sapiens (human)
Samples
39 samples: 14 conditions, mostly in duplicateGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper
Targeting senescent stemlike subpopulations in Philadelphia chromosome-like acute lymphoblastic leukemiaBlood · 2025
Summary (written by the submitters)
Philadelphia chromosome-like acute lymphoblastic leukemia (Ph-like ALL) is a common high-risk leukemia subtype defined by a kinase-activated gene expression pattern and chemoresistance, with low overall survival rates in both children and adults. Clinical responses of patients with Ph-like ALL to tyrosine kinase inhibitor (TKI)-based therapies are incomplete, suggesting partial oncogene addiction and highlighting the need to elucidate alternative biologic dependencies to cure patients.
Read the full summary ↓
Herein, we report integrated bulk and single-cell multiomics studies of Ph-like ALL cells treated in vitro and in vivo with targeted agents to comprehensively define signaling adaptations and resistant subpopulations. Characterizing residual/resistant human ALL cells harvested from murine spleens revealed transcriptional regulatory network changes and kinase signaling adaptations. We identified key transcription factors including c-MYC and AP1 that mediate transcriptional rewiring associated with therapeutic escape, and demonstrate a specific MYC dependency in Ph-like ALL cells. We found a sub-population of leukemia blasts that have high senescence-associated stem cell-like features that is effectively eradicated only with combined STAT and BCL-2 inhibition. This is clinically significant since both conventional chemotherapy and ruxolitinib are ineffective against senescent stem-like blasts, which may mediate therapeutic tolerance and repopulate relapse. Our findings demonstrate that systems level discovery of key oncogenic dependencies and escape pathways can be exploited for co-targeting or sequential treatment to optimally eradicate blasts and cure this high-risk leukemia subtype.
Published in
Targeting senescent stemlike subpopulations in Philadelphia chromosome-like acute lymphoblastic leukemia
Ding YY, Sussman JH, Madden K et al. · Blood 2025 · PMID 39774844 · doi:10.1182/blood.2024026482
This dataset
Open GSE263709 in finder → Find more human RNA-seq datasets →
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE263709_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1098764 and SRA study SRP500834. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39774844) ↗
Study design
14 conditions, mostly in duplicate
ALL4364-Rep1-D ×4 ALL4364-Rep2-D ×4 ALL2128-D0 ×2 ALL2128-D28 ×2 ALL2128-D56 ×2 ALL2128-D7 ×2 ALL4988-D3-Control ×2 ALL4988-D3 ×2 +13 more

Supports a between-group comparison across 32 samples.

How we read this: 14 replicated groups read from 39 sample titles; they account for 32 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study
Datasets on a similar topic

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