GEO series

Mutant p53 negatively regulates ER⍺ activity to drive resistance to endocrine therapy

GSE292625 Homo sapiens (human) RNA-seq (gene expression) 18 samples Submitted 2026/01/28 Platform GPL30173

At a glance

RNA-seq (gene expression) · human · 18 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: Ovarian cancerThis series is part of our curated briefing on it.
Samples: 18 samples: 6 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: Mutant p53 binds and controls estrogen receptor activity to drive endocrine resistance in ovarian cancerGenes & development · 2026

Summary (written by the submitters)

High-grade serous ovarian cancer (HGSOC) is a highly lethal gynecologic malignancy in women. Women diagnosed with HGSOC initially respond to chemotherapy but there is a greater than 80% rate of relapse. There is thus a significant unmet need for new therapeutic targets for HGSOC. Estrogen receptor alpha (ER⍺) is a particularly attractive candidate, as 70-80% of HGSOC tumors stain positively for ER⍺ and there are approved inhibitors that show limited toxicity.

Read the full summary ↓

Unlike the case for breast cancer however, endocrine therapy for HGSOC has not shown consistently promising results. In this work we show that mutant forms of p53, which occur in 70% of HGSOC, inhibits ER⍺ function and confer resistance to fulvestrant and elacestrant. Mechanistically we show that mutant p53 only inhibits one arm of the ER⍺ pathway: the transactivation of jointly regulated ER⍺-SP1 target genes, like DEPTOR, which encodes an mTOR inhibitor. We show that silencing mutant p53 restores the ability of ER⍺ to transactivate ER⍺-SP1 target genes, and renders HGSOC sensitive to endocrine therapy. This work resolves part of the underlying reason for endocrine resistance of HGSOC, and reveals one mechanism to reverse such: refold or ablate mutant p53.

Published in

Mutant p53 binds and controls estrogen receptor activity to drive endocrine resistance in ovarian cancer

Shao C, Indeglia A, Foster M et al. · Genes & development 2026 · PMID 41193244 · doi:10.1101/gad.352953.125

This dataset

Open GSE292625 in finder → Find more human RNA-seq datasets → Ovarian cancer 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 GSE292625_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 PRJNA1240323 and SRA study SRP572261. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41193244) ↗

Study design

6 conditions, mostly in triplicate

PEO1, sip53, E2, 0h, ×3 PEO1, sip53, E2, 2h, ×3 PEO1, sip53, E2, 8h, ×3 PEO1, sipNC, E2, 0h, ×3 PEO1, sipNC, E2, 2h, ×3 PEO1, sipNC, E2, 8h, ×3

Supports a between-group comparison across 18 samples.

How we read this: 6 replicated groups read from 18 sample titles; they account for 18 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

GSM8862509 PEO1, sip53, E2, 0h, 1 PEO1, sip53, E2, 0h,
GSM8862510 PEO1, sip53, E2, 0h, 2 PEO1, sip53, E2, 0h,
GSM8862511 PEO1, sip53, E2, 0h, 3 PEO1, sip53, E2, 0h,
GSM8862512 PEO1, sip53, E2, 2h, 1 PEO1, sip53, E2, 2h,
GSM8862513 PEO1, sip53, E2, 2h, 2 PEO1, sip53, E2, 2h,
GSM8862514 PEO1, sip53, E2, 2h, 3 PEO1, sip53, E2, 2h,
GSM8862515 PEO1, sip53, E2, 8h, 1 PEO1, sip53, E2, 8h,
GSM8862516 PEO1, sip53, E2, 8h, 2 PEO1, sip53, E2, 8h,
GSM8862517 PEO1, sip53, E2, 8h, 3 PEO1, sip53, E2, 8h,
GSM8862518 PEO1, sipNC, E2, 0h, 1 PEO1, sipNC, E2, 0h,
GSM8862519 PEO1, sipNC, E2, 0h, 2 PEO1, sipNC, E2, 0h,
GSM8862520 PEO1, sipNC, E2, 0h, 3 PEO1, sipNC, E2, 0h,
GSM8862521 PEO1, sipNC, E2, 2h, 1 PEO1, sipNC, E2, 2h,
GSM8862522 PEO1, sipNC, E2, 2h, 2 PEO1, sipNC, E2, 2h,
GSM8862523 PEO1, sipNC, E2, 2h, 3 PEO1, sipNC, E2, 2h,
GSM8862524 PEO1, sipNC, E2, 8h, 1 PEO1, sipNC, E2, 8h,
GSM8862525 PEO1, sipNC, E2, 8h, 2 PEO1, sipNC, E2, 8h,
GSM8862526 PEO1, sipNC, E2, 8h, 3 PEO1, sipNC, E2, 8h,

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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.