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Characterizing the Impact of MLL Fusion Variants and Fusion Partners on Leukemia Plasticity Using a Human CRISPR-Engineered MLL-Rearranged Leukemia Model

GSE311674 Homo sapiens (human) RNA-seq (gene expression) 13 samples Submitted 2026/03/01 Platform GPL18573
At a glance

RNA-seq (gene expression) · human · 13 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
13 samples: 13 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
Characterizing the impact of MLL fusion variants and fusion partners on leukemia plasticity using a human CRISPR-engineered MLL-rearranged leukemia modelNeoplasia (New York, N.Y.) · 2026
Summary (written by the submitters)
Acute leukemias involving MLL (KMT2A) rearrangements are aggressive hematologic malignancies associated with poor prognosis. MLL fusions typically involve MLL exons 8-14, with AF4 (AFF1) and AF9 (MLLT3) being frequent partners. To investigate how fusion partners and breakpoint locations influence the disease, we developed a CRISPR/Cas9 model by introducing MLL-AF4 or MLL-AF9 fusions with MLL breakpoints in intron 9 or 11 into cord blood CD34+ cells.
Read the full summary ↓
The MLL-rearranged cells showed increased proliferation and stemness, as well as an altered immunophenotype involving the upregulation of AML markers. Transcriptomic profiling revealed breakpoint- and partner-specific gene expression patterns that influence the characteristics of the disease. MLL(intron 9)-AF9 cells engrafted robustly in NSG mice and showed high lineage plasticity, switching from a myeloid to a B-lymphoid identity in vivo. Our model enables mechanistic studies across MLL fusion variants and may guide the development of targeted therapies for MLL-rearranged leukemias.
Published in
Characterizing the impact of MLL fusion variants and fusion partners on leukemia plasticity using a human CRISPR-engineered MLL-rearranged leukemia model
Radszuweit P, Fitzel R, Bruestl S et al. · Neoplasia (New York, N.Y.) 2026 · PMID 42001795 · doi:10.1016/j.neo.2026.101308
This dataset
Open GSE311674 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 GSE311674_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 PRJNA1370112 and SRA study SRP649291. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42001795) ↗
Study design
13 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 13 sample titles: 13 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
Datasets on a similar topic

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