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The translational repair pathway is a frequent synthetic lethal target in cancer

GSE261804 Homo sapiens (human) RNA-seq (gene expression) 36 samples Submitted 2024/10/14 Platform GPL18573
At a glance

RNA-seq (gene expression) · human · 36 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
36 samples: 12 conditions, mostly in triplicateGroups 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
Synthetic lethality of mRNA quality control complexes in cancerNature · 2025
Summary (written by the submitters)
Synthetic lethality exploits the genetic vulnerabilities of cancer cells enabling, a targeted, precision approach to treat cancer. Synthetic lethal discovery approaches have led to clinical successes of PARP inhibitors and the progression of several next-generation therapeutic targets such as WRN, USP1, PKMYT1, POLQ, and PRMT5 into the clinic.
Read the full summary ↓
To date, however, most discovery efforts in the synthetic lethal space have focused on DNA repair. Here, we show, for the first time in humans, a frequent synthetic lethal interaction between two complexes of the translational repair pathway, PELO/HBS1L and the superkiller (SKI) complex. In distinct genetic contexts, either in 9p21.3 (FOCAD) deleted or MSI-H tumors, we found that phenotypically destabilized SKI complex leads to dependence on the PELO/HBS1L ribosomal rescue complex. We estimate that 20% of all tumors have a destabilized SKI complex. The concomitant loss of PELO in a SKI destabilized tumor drives an unfolded protein stress response, cell cycle arrest, and robust tumor growth inhibition. Furthermore, we demonstrate that the loss of HBS1L phenocopies PELO loss, presenting a second potential therapeutic target within the rescue complex. Our results indicate that PELO/HBS1L represent novel therapeutic targets whose dependence converges upon SKI complex destabilization, a common phenotypic biomarker in diverse genetic contexts and a significant patient population.
Published in
Synthetic lethality of mRNA quality control complexes in cancer
Prindle V, Richardson AE, Sher KR et al. · Nature 2025 · PMID 39910291 · doi:10.1038/s41586-024-08398-6
This dataset
Open GSE261804 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 GSE261804_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 PRJNA1089119 and SRA study SRP496072. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39910291) ↗
Study design
12 conditions, mostly in triplicate
MIAPACA-2, control 48, no dox, ×3 MIAPACA-2, control 48, dox, ×3 MIAPACA-2, PELO 48, no dox, ×3 MIAPACA-2, PELO 48, dox, ×3 MIAPACA-2, HBS1L 48, no dox, ×3 MIAPACA-2, HBS1L 48, dox, ×3 MIAPACA-2, control 96, no dox, ×3 MIAPACA-2, control 96, dox, ×3 +4 more

Supports a between-group comparison across 36 samples.

How we read this: 12 replicated groups read from 36 sample titles; they account for 36 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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