GEO series

Molecular Characterization of Chemoresistant Cell Populations Improves Risk Stratification and Therapy Response Prediction in Pediatric AML [pAML Patients scRNA-seq]

GSE270894 Homo sapiens (human) Single-cell RNA-seq 20 samples Submitted 2025/11/26 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 20 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Homo sapiens (human)
Topic: Acute myeloid leukemiaThis series is part of our curated briefing on it.
Samples: 20 samples: 20 samples, each with a distinct title — no replicated groupsGroups 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: Characterization of Chemoresistant Cell Populations Improves Risk Stratification and Therapy Prediction in Pediatric AMLbioRxiv : the preprint server for biology · 2025

Summary (written by the submitters)

Most pediatric acute myeloid leukemia (pAML) patients achieve complete remission after chemotherapy. However, relapse remains common, and nearly 40% ultimately die of the disease. Prognosis is guided by cytogenetic biomarkers and measurable residual disease (MRD) after one chemotherapy cycle, at which point the highest-risk patients are referred to targeted experimental therapies and stem cell transplantation (SCT).

Read the full summary ↓

Because these therapies are most effective before relapse, accurate early risk prediction is essential for improving outcomes. To address help this need, we analyzed paired diagnosis–relapse samples from 33 pAML patients at single-cell resolution and identified chemoresistant pAML populations whose abundance at diagnosis significantly improved risk prediction. Incorporation of these populations into a risk model uncovered a previously unrecognized subgroup—representing 20% of pAML cases—with event-free survival (EFS) below 40%, accounting for half of the deaths among patients that were not prescribed SCT after a cycle of chemotherapy. Furthermore, molecular characterization of these chemoresistant populations revealed therapeutic targets and targeted interventions relevant to most high-risk patients, paving the way for more effective treatments for high risk pAML patients.

Published in

Characterization of Chemoresistant Cell Populations Improves Risk Stratification and Therapy Prediction in Pediatric AML

NajafPanah MJ, Stevens AM, Krueger MJ et al. · bioRxiv : the preprint server for biology 2025 · PMID 41256415 · doi:10.1101/2025.09.25.678688

This dataset

Open GSE270894 in finder → Find more human RNA-seq datasets → Acute myeloid leukemia landscape →

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 GSE270894_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 PRJNA1128710 and SRA study SRP516312. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41256415) ↗

Study design

20 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 20 sample titles: 20 distinct titles with little repetition. 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

GSM8354306 TCH1-TCH3, D and R (SC)
GSM8354307 TCH4-TCH6, D and R (SC)
GSM8354308 PAPZCL_D (SC)
GSM8354309 PAPZCL_R (SC)
GSM8354310 PARBIU_D (SC)
GSM8354311 PARBIU_R (SC)
GSM8354312 PAUMTZ_D (SC)
GSM8354313 PAUMTZ_R (SC)
GSM8354314 PAUNSV_D (SC)
GSM8354315 PAUNSV_R (SC)
GSM8354316 PAUVAT_D (SC)
GSM8354317 PAUVAT_R (SC)
GSM8354318 PAVBFN_D (SC)
GSM8354319 PAVBFN_R (SC)
GSM8354320 PAVTLN_D (SC)
GSM8354321 PAVTLN_R (SC)
GSM8354322 PAWNPU_R (SC)
GSM8354323 TCHnorm1.n.S (SC)
GSM8354324 TCHnorm2.n.S (SC)
GSM8354325 TCHnorm2.n.U (SC)

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