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An integrative multiparametric approach stratifies distinct phenotypes of blast phase chronic myelomonocytic leukemia with differential maturation status and drug sensitivity profiles

GSE251806 Homo sapiens (human) RNA-seq (gene expression) 49 samples Submitted 2024/12/20 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 49 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
49 samples: 49 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
An integrative multiparametric approach stratifies putative distinct phenotypes of blast phase chronic myelomonocytic leukemiaCell reports. Medicine · 2025
Summary (written by the submitters)
Approximately 30% of chronic myelomonocytic leukemia patients undergo transformation to a chemo-refractory blastic phase (BP-CMML). Seeking novel therapeutic approaches we profiled blast transcriptomes from 42 BP-CMMLs, observing extensive transcriptional heterogeneity and poor alignment to current AML classifications. BP-CMMLs displayed distinctive transcriptomic profiles, including enrichment for quiescence and variability in drug response signatures.
Read the full summary ↓
Integrating clinical, immunophenotype and transcriptome parameters, RandomForest unsupervised clustering distinguished immature and mature subtypes characterized by differential expression of transcriptional modules, oncogenes, apoptotic regulators and patterns of surface marker expression. Subtypes differed in predicted response to AML drugs, validated ex vivo in primary samples. Iteratively refined stratification resolved a classification structure comprising five subtypes along a maturation spectrum, predictive of response to novel agents including consistent patterns for RTK, CDK, MTOR and MAPK inhibitors. Finally, we generated a clinically-applicable decision tree to stratify BP-CMML with high specificity and sensitivity, and potentially guide personalized drug selection for improved outcomes.
Published in
An integrative multiparametric approach stratifies putative distinct phenotypes of blast phase chronic myelomonocytic leukemia
Gurashi K, Wang YH, Amaral FMR et al. · Cell reports. Medicine 2025 · PMID 39892394 · doi:10.1016/j.xcrm.2025.101933
This dataset
Open GSE251806 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 GSE251806_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1055478 and SRA study SRP479575. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39892394) ↗
Study design
49 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 49 sample titles: 49 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

+ 9 more — browse all 49 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

Share this dataset

Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.