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Molecular and Structural Basis of Pan-Resistance to BTK Inhibitors and Degraders

GSE330399 Homo sapiens (human) RNA-seq (gene expression) 100 samples Submitted 2026/05/08 Platform GPL34281
At a glance

RNA-seq (gene expression) · human · 100 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
100 samples: 100 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)
Early-phase clinical trials of Bruton’s tyrosine kinase (BTK) degraders have demonstrated efficacy in patients with BTK inhibitor-resistant chronic lymphocytic leukemia (CLL). How clinical resistance to BTK degraders arises is unknown. Here we sequenced serial CLL samples from patients enrolled in the phase I trials of zelebrudomide and bexobrutideg and observed recurrent expansion of preexisting BTK A428D mutations at relapse.
Read the full summary ↓
Unlike previously studied BTK inhibitor resistance mutations, BTK A428D conferred pan-resistance to BTK inhibitors and degraders. In the absence of BTK-directed therapies, however, cells bearing BTK A428D exhibited a competitive disadvantage. A crystal structure of BTK A428D revealed that the mutant aspartate confers broad resistance to ATP-competitive small molecules by obstructing essential drug contacts with the kinase hinge. Combining BTK degraders with venetoclax mitigated expansion of BTK A428D. These results provide the basis for clinical resistance to BTK degraders and will inform therapy sequencing and the development of next-generation degrader therapies.
This dataset
Open GSE330399 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 GSE330399_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 PRJNA1463785 and SRA study SRP698726. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
100 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 100 sample titles: 100 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

+ 60 more — browse all 100 samples with per-sample file links →

More Chronic lymphocytic leukemia 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.