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Dominant-negative TP53 mutations potentiated by the HSF1-regulated proteostasis network [2]

GSE304317 Homo sapiens (human) RNA-seq (gene expression) 24 samples Submitted 2025/12/30 Platform GPL34295
At a glance

RNA-seq (gene expression) · human · 24 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
24 samples: 6 × A549 parental DMSO vs 6 × A549 parental DOX vs 6 × A549 cHSF1 DOX vs 6 × A549 dncHSF1 DOXGroups 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
Dominant-negative TP53 mutations potentiated by the HSF1-regulated proteostasis networkMolecular cell · 2026
Summary (written by the submitters)
Protein mutational landscapes are sculpted by the impacts of the resulting amino acid substitutions on the protein’s stability and folding or aggregation kinetics. These properties can, in turn, be modulated by the composition and activities of the cellular proteostasis network. Heat shock factor 1 (HSF1) is the master regulator of the cytosolic and nuclear proteostasis networks, dynamically tuning the expression of cytosolic and nuclear chaperones and quality control factors to meet demand.
Read the full summary ↓
Chronic increases in HSF1 levels and activity are prominent hallmarks of cancer cells. One plausible explanation for this observation is that the consequent upregulation of proteostasis factors could biophysically facilitate the acquisition of oncogenic mutations. Here, we experimentally evaluate the impacts of chronic HSF1 activation on the mutational landscape accessible to the quintessential oncoprotein p53. Specifically, we apply quantitative deep mutational scanning of p53 to assess how HSF1 activation shapes the mutational pathways by which p53 can escape cytotoxic pressure conferred by the small molecule nutlin-3, which is a potent antagonist of the p53 negative regulator MDM2. We find that HSF1 activation broadly increases the fitness of dominant-negative substitutions within p53. This effect of HSF1 activation was particularly notable for non-conservative, biophysically unfavorable amino acid substitutions within buried regions of the p53 DNA-binding domain. These results indicate that chronic HSF1 activation shapes the oncogenic mutational landscape, preferentially supporting the acquisition of cancer-associated substitutions that are biophysically destabilizing. Along with providing the first experimental and quantitative insights into how HSF1 influences oncoprotein mutational spectra, these findings also implicate HSF1 inhibition as a strategy to alter the accessible mutational landscape of p53.
Published in
Dominant-negative TP53 mutations potentiated by the HSF1-regulated proteostasis network
Halim S, Sebastian RM, Liivak KE et al. · Molecular cell 2026 · PMID 41539306 · doi:10.1016/j.molcel.2025.12.013
This dataset
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.

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 GSE304317_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1300371 and SRA study SRP605821. Searching any of these in the dataset finder brings you back here.

Study design
6 × A549 parental DMSO vs 6 × A549 parental DOX vs 6 × A549 cHSF1 DOX vs 6 × A549 dncHSF1 DOX

Supports a between-group comparison across 24 samples.

How we read this: 4 replicated groups read from 24 sample titles; they account for 24 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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