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Inhibition of BIRC5 and MCL-1 as a potential treatment strategy to overcome drug resistance in Mantle Cell Lymphoma

GSE312789 Homo sapiens (human) RNA-seq (gene expression) 56 samples Submitted 2026/03/18 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 56 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
56 samples
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
Inhibition of BIRC5 and MCL-1 as a potential treatment strategy to overcome drug resistance in Mantle Cell LymphomaBlood cancer journal · 2026
Summary (written by the submitters)
Mantle cell lymphoma (MCL) is a difficult-to-cure, highly heterogeneous, and aggressive form of non-Hodgkin lymphoma with a high recurrence rate and poor long-term prognosis. Using a novel optimization-regularization-based computational prediction algorithm called "secDrug," we have identified therapeutic targets for treating resistant cancers.
Read the full summary ↓
When applied to B-cell malignancies, the secDrug algorithm predicted the pro-survival proteins BIRC5 and MCL1 as top targets. Here, using a panel of MCL cell lines representing PI/BTKi sensitive, innate resistance, and clonally derived acquired resistance, we demonstrated that small molecule-based inhibition of BIRC5 and MCL1 is effective in killing MCL cells as single agents and in combination with Bortezomib or Ibrutinib. Further, using RNAseq (transcriptomics) and funcional genomics, we identified genes and molecular networks associated with their mechanism of action and synergy. Our results underlined the clinical potential of these candidates in curbing MCL progression and drug resistance.
Published in
Inhibition of BIRC5 and MCL-1 as a potential treatment strategy to overcome drug resistance in Mantle Cell Lymphoma
Pfitzer J, Chakravarti S, Mazumder S et al. · Blood cancer journal 2026 · PMID 41813647 · doi:10.1038/s41408-026-01467-1
This dataset
Open GSE312789 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 GSE312789_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 PRJNA1376135 and SRA study SRP651887. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41813647) ↗
Study design
A cohort of 56 individual samples

The grouping that defines any comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Sample titles run AKM1 … AKM56 and carry no group information.

Samples in this study

+ 16 more — browse all 56 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.