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Cancer Susceptibility to Stapled Oncolytic Peptides is Dictated by Membrane Cholesterol and Inflammatory Signaling

GSE315958 Homo sapiens (human) RNA-seq (gene expression) 21 samples Submitted 2026/02/25 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 21 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
21 samples: 7 conditions, mostly in duplicateGroups 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)
Relapsed and refractory cancers effectively overcome diverse modalities of cancer treatment, whose principal targets are nucleic acids and proteins. The plasma membranes of cancer cells represent an alternative and underutilized target, with the potential for membrane lysis to induce a rapid, pro-inflammatory cell death that circumvents the challenges of intratumor heterogeneity and immune evasion.
Read the full summary ↓
Here, we repurposed StAMP51.2, a stapled Magainin II peptide previously optimized for selective membrane lysis of gram-negative bacteria, to target cancer cell membranes. Using PRISM, a high throughput cancer cytotoxicity screen, we identified cancer cells most vulnerable to StAMP51.2 and biomarkers of susceptibility, specifically reduced cholesteryl esters and elevated triacylglycerols. We validated this signature in a pair of sensitive (OCI-AML3) and resistant (K562) leukemia cell lines, correlating their differential responses to distinct lipidomic profiles. Susceptibility of OCI-AML3 cells in culture extended to the in vivo context, where StAMP51.2 suppressed leukemic growth in orthotopic and intraperitoneal models. To further characterize the mechanism of action, we undertook the challenge of generating StAMP51.2-resistant OCI-AML3 cells, which required four months of low-level exposure. Strikingly, drug-resistant OCI-AML3 cells recapitulated the lipidomic phenotype of naturally-resistant K562 cells. Transcriptomic analyses further revealed that lipid reprogramming was accompanied by pervasive downregulation of inflammatory signaling. Thus, in advancing StAMP51.2 as an oncolytic prototype, we uncovered an immune regulatory axis that links membrane integrity to inflammatory signaling.
This dataset
Open GSE315958 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 GSE315958_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 PRJNA1400017 and SRA study SRP660702. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
7 conditions, mostly in duplicate
K562 Parental Sample ×3 OCI-AML3 Parental Sample ×3 OCI-AML3 Vehcile Sample 2 Sequencing ×2 OCI-AML3 Vehicle Sample 3 Sequencing ×2 OCI-AML3 StAMP Resistant BioRep1 Sample 1 Seq… ×2 OCI-AML3 StAMP Resistant BioRep1 Sample 2 Seq… ×2 OCI-AML3 StAMP Resistant BioRep1 Sample 3 Seq… ×2 OCI-AML3 Vehcile Sample 1 Sequencing ×1 +4 more

Supports a between-group comparison across 16 samples.

How we read this: 7 replicated groups read from 21 sample titles; they account for 16 of them. 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
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