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Genetic charcterization of myeloproliferative neoplasms (MPN)

GSE277354 Homo sapiens (human) RNA-seq (gene expression) 402 samples Submitted 2024/09/20 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 402 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
Myeloproliferative neoplasmsThis series is part of our curated briefing on it.
Samples
402 samples: 402 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 myeloproliferative neoplasms based on genetics only and prognostication of transformation to blast phaseLeukemia · 2024
Summary (written by the submitters)
Myeloproliferative neoplasms (MPN) are a heterogeneous group of clonal disorders characterized by aberrant hematopoietic proliferation and an intrinsic risk of progression to blast phase. The WHO classification 2022 identifies chronic myeloid leukemia and the BCR::ABL1 negative MPNs polycythemia vera, primary myelofibrosis and essential thrombocythemia as individual entities.
Read the full summary ↓
However, overlaps, borderline findings or transitions between MPN subtypes occur and incomplete clinical data often complicates diagnosis. By conducting a thorough genetic analysis, we've developed a model that relies on 12 genetic markers to accurately stratify MPN patients. The model can be simplified into a decision tree for routine use. Comparing samples at chronic and blast phase revealed, that one third of patients lost their MPN driver-gene mutation, while mutations in splicing and chromatin modifying genes were stable, indicating a shared founder clone of chronic and blast phase with different driver mutations and therefore different progressing capacities. This was further supported by gain of typical de novo AML gene mutations, accompanied by gain of complex karyotypes and RAS pathway gene mutations. Our data suggest to perform a broader genetic screening at diagnosis and also at clinical progression, as driver mutations may change and the MPN driver mutations present at diagnosis may disappear.
Published in
Characterization of myeloproliferative neoplasms based on genetics only and prognostication of transformation to blast phase
Walter W, Nadarajah N, Hutter S et al. · Leukemia 2024 · PMID 39341969 · doi:10.1038/s41375-024-02425-1
This dataset
Open GSE277354 in finder → Find more human RNA-seq datasets → Myeloproliferative neoplasms 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 GSE277354_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 PRJNA1162027 and SRA study SRP533132. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39341969) ↗
Study design
402 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 the first 400 of 402 sample titles: 400 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

+ 362 more — browse all 402 samples with per-sample file links →

More Myeloproliferative neoplasms datasets

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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.