← BioTransfer GEO Dataset Finder
GEO series

Gene expression profiling reveals two overarching types of anaplastic large cell lymphoma with distinct targetable biology: an LLMPP study

GSE280480 Homo sapiens (human) RNA-seq (gene expression) 393 samples Submitted 2026/01/09 Platform GPL20301
At a glance

RNA-seq (gene expression) · human · 393 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
393 samples: 393 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
Gene expression profiling reveals 2 overarching types of ALCL with distinct targetable biology: an LLMPP studyBlood · 2026
Summary (written by the submitters)
Anaplastic large cell lymphomas (ALCLs) are CD30+ T-cell lymphomas that share pathologic features but differ in presentation, outcome, and genetics. Current classification incorporates clinical presentation and ALK status, but inadequately addresses molecular heterogeneity and therapeutic vulnerabilities. We studied 689 ALCLs in the Lymphoma/Leukemia Molecular Profiling Project and performed expert consensus review, genetic subtyping (ALK, DUSP22, TP63, and triple-negative), and immunohistochemistry for phospho-STAT3Tyr705.
Read the full summary ↓
RNAseq with unsupervised gene expression profiling (n=393) identified 2 main molecular types of ALCL that could be predicted with 91% accuracy based on the presence (Type I) or absence (Type II) of phospho-STAT3Y705 expression (P<0.0001). Type I ALCLs included ALK+ ALCL and a distinct subset of triple-negative ALCLs (TN-I); Type II ALCLs included tumors with DUSP22 and/or TP63 rearrangements and the remaining triple-negative ALCLs (TN-II). Type I ALCLs were enriched for JAK-STAT3 (FDR<0.0001), whereas Type II ALCLs were enriched for non-tyrosine kinase pathways, particularly epigenetic regulators (FDR=0.003). EZH2 was the top gene (FDR=1.3×10-97) and EZH2 and H3K27me3 were overexpressed by immunohistochemistry (P<0.0001). Prognosis in systemic ALCL (N=257) was favorable for DUSP22-rearranged ALCL (5-year OS, 95%) and ALK+ ALCL (88%), intermediate for triple-negative ALCL (TN-I, 52% and TN-II, 37%), and poor for TP63-rearranged ALCL (0%; P<0.0001). We introduce an integrated molecular classification that preserves currently diagnosed ALCL entities but identifies 4 molecularly distinct ALK− ALCL subtypes (DUSP22-rearranged, TP63-rearranged, TN-I, and TN-II). This classification can be easily implemented on paraffin tissue in routine practice or clinical trials and stratifies ALCL into diagnostically, prognostically, biologically, and potentially therapeutically relevant subtypes.
Published in
Gene expression profiling reveals 2 overarching types of ALCL with distinct targetable biology: an LLMPP study
Feldman AL, Dasari S, Rimsza LM et al. · Blood 2026 · PMID 41329859 · doi:10.1182/blood.2024027700
This dataset
Open GSE280480 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 GSE280480_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 PRJNA1178681. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41329859) ↗
Study design
393 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 393 sample titles: 393 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

+ 353 more — browse all 393 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.