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Incorporation of genomic determinants improves diagnostic accuracy of oligomonocytic chronic myelomonocytic leukemia

GSE314218 Homo sapiens (human) RNA-seq (gene expression) 47 samples Submitted 2025/12/31 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 47 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)
Topic
Myelodysplastic syndromesThis series is part of our curated briefing on it.
Samples
47 samples: 47 × Untreated CD34+ bone marrow sampleGroups 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
Incorporation of Genomic Determinants Improves Diagnostic Accuracy of Oligomonocytic Chronic Myelomonocytic LeukemiaBlood cancer discovery · 2026
Summary (written by the submitters)
Recent updates to the monocyte count thresholds for diagnosing chronic myelomonocytic leukemia (CMML) have been implemented to recognize oligomonocytic CMML (OM-CMML) as an early stage within the CMML spectrum. However, the clinical validity of these changes remains uncertain without considering biological factors. In this study, we compared a large cohort of patients (N=911) with OM-CMML (n=249), CMML (n=359), and myelodysplastic syndromes (n=303) using unsupervised clustering to assess the role of genomic determinants in improving CMML diagnostic accuracy.
Read the full summary ↓
Our findings show that CMML molecular signatures, characterized by biallelic TET2 mutations or SRSF2-TET2 co-mutations, are associated with a distinct transcriptome, monocytic bias, classical monocytosis, and progression to overt CMML in OM-CMML cases. By developing a weighted and reproducible model, we demonstrate that genomic features, combined with bone marrow monocyte frequencies, can reliably predict CMML progression in OM-CMML. These results support the integration of genomic determinants into the CMML diagnostic framework to enhance diagnostic accuracy and suggest that incorporation of our proposed clinic-ready diagnostic workflow into clinical practice might reliably identify early stage CMML with oligomonocytic features.
Published in
Incorporation of Genomic Determinants Improves Diagnostic Accuracy of Oligomonocytic Chronic Myelomonocytic Leukemia
Montalban-Bravo G, Wu C, Rodriguez-Sevilla JJ et al. · Blood cancer discovery 2026 · PMID 41587375 · doi:10.1158/2643-3230.BCD-25-0264
This dataset
Open GSE314218 in finder → Find more human RNA-seq datasets → Myelodysplastic syndromes 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 GSE314218_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 PRJNA1390278 and SRA study SRP656175. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41587375) ↗
Study design
47 × Untreated CD34+ bone marrow sample

Supports a between-group comparison across 47 samples.

How we read this: 1 replicated groups read from 47 sample titles; they account for 47 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

+ 7 more — browse all 47 samples with per-sample file links →

More Myelodysplastic syndromes datasets

Search all human RNA-seq datasets in GEO →

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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.