GEO series

B cell receptor-mediated amplification of the interferon response in CLL cells

GSE329311 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2026/05/15 Platform GPL24014

At a glance

RNA-seq (gene expression) · human · 12 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: Chronic lymphocytic leukemiaThis series is part of our curated briefing on it.
Samples: 12 samples: 12 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.

Summary (written by the submitters)

Abstract Chronic lymphocytic leukemia (CLL) comprises biologically distinct subtypes that differ in prognostic behavior and B cell receptor (BCR) signaling activity. We previously showed that unmutated CLL (U-CLL) cells exhibit enhanced type I interferon (IFN) responses, but the downstream effectors and signaling mechanisms remain incompletely defined.

Read the full summary ↓

In the present study, we investigated regulation of the interferon-stimulated gene lymphocyte activation gene 3 (LAG3) in primary CLL cells and its relationship to clinical subtype, MYC activity and BCR signaling. LAG3 expression was generally low at baseline but was robustly induced by IFN in a highly variable, patient-specific manner. Elevated IFN-induced LAG3 expression was associated with U-CLL, higher MYC activity and inferior overall survival, suggesting that it marks a more aggressive disease state. Bioinformatic analyses of primary CLL datasets and in vitro experiments indicated that high-LAG3 responses were linked to transcriptional signatures of active BCR signaling. IFN increased c-MYC and LAG3 expression in CLL cells and pharmacologic MYC inhibition reduced both responses. Mechanistically, IFN promoted association of ZAP70 with the IFNAR complex and inhibition of ZAP70 or multiple BCR-pathway components suppressed IFN-induced LAG3 expression. Moreover, BCR activation amplified IFN-driven LAG3 expression in normal B cells and in CLL cells, whereas BTK inhibition reduced LAG3 expression in patient samples in vivo. These findings support a model in which IFN signaling is rewired in aggressive CLL through cooperation with BCR-associated pathways to promote MYC-dependent LAG3 expression. This crosstalk may contribute to disease progression and helps explain a potential mechanism by which BCR-directed therapies attenuate pathogenic IFN responses in CLL.

This dataset

Open GSE329311 in finder → Find more human RNA-seq datasets → Chronic lymphocytic leukemia 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 GSE329311_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 PRJNA1458626 and SRA study SRP695543. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

12 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 12 sample titles: 12 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

GSM9701580 IonCode_0129_T01
GSM9701581 IonCode_0130_T02
GSM9701582 IonCode_0131_T03
GSM9701583 IonCode_0132_T04
GSM9701584 IonCode_0113_T05
GSM9701585 IonCode_0114_T06
GSM9701586 IonCode_0115_T07
GSM9701587 IonCode_0116_T08
GSM9701588 IonCode_0121_T09
GSM9701589 IonCode_0122_T10
GSM9701590 IonCode_0123_T11
GSM9701591 IonCode_0124_T12

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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.