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Patient-derived response estimates from zero-passage organoids of luminal breast cancer

GSE262110 Homo sapiens (human) RNA-seq (gene expression) 35 samples Submitted 2024/03/22 Platform GPL18573
At a glance

RNA-seq (gene expression) · human · 35 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
Breast cancerThis series is part of our curated briefing on it.
Samples
35 samples: 34 distinct titles, 1 of them repeatedGroups 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
Patient-derived response estimates from zero-passage organoids of luminal breast cancerBreast cancer research : BCR · 2024
Summary (written by the submitters)
Background: Primary luminal breast cancers lose hormone receptors rapidly in standard tissue culture. Breast cancer organoids are thought to retain tumor characteristics better, but long-term viability of luminal-subtype cases is a persistent challenge. Methods: We freshly isolated patient-derived cells from luminal tumor scrapes, miniaturized the organoid format into 5 µl replicates to increase throughput, and set an endpoint of 14 days to minimize drift.
Read the full summary ↓
Therapeutic hormone targeting was mimicked in these “zero–passage” organoids by withdrawing β-estradiol and adding tamoxifen. We tested sulforaphane as an electrophilic stress and commercial neutraceutical. Genetic perturbations were introduced by lentiviral transduction of two complementary sgRNAs and Cas9 stabilization for the first week of organoid culture. Transcriptional changes were measured by RT-qPCR or RNA sequencing; organoid phenotypes were quantified by serial brightfield imaging, digital image segmentation, and regression modeling of cellular doubling times. Results: We achieved >50% success in initiating luminal breast cancer organoids from tumor scrapes and maintaining them to the 14-day zero-passage endpoint. Few clinical parameters reliably impacted success. Abundance of ESR1 and PGR in zero-passage organoids consistently remained within the range of patient variability at the endpoint. However, responsiveness to hormone withdrawal and blockade was highly variable among luminal breast cancer cases. Combining sulforaphane with knockout of NQO1 (a phase II enzyme) also yielded a breadth of growth phenotypes, including growth inhibition with sulforaphane, growth promotion with NQO1 knockout, and growth antagonism when combined. Conclusions: Zero-passage organoids are a rapid and scalable way to interrogate properties of luminal breast cancer cells from patient-derived material.
Published in
Patient-derived response estimates from zero-passage organoids of luminal breast cancer
Przanowska RK, Labban N, Przanowski P et al. · Breast cancer research : BCR 2024 · PMID 39741344 · doi:10.1186/s13058-024-01931-5
This dataset
Open GSE262110 in finder → Find more human RNA-seq datasets → Breast 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 GSE262110_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 PRJNA1090405 and SRA study SRP496932. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39741344) ↗
Study design
34 distinct titles, 1 of them repeated
UVABCO78_TAM ×2

How we read this: Read from 35 sample titles: 34 distinct titles with little repetition. 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
More Breast cancer datasets

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