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Long-term multi-modal recording reveals epigenetic adaptation routes in dormant breast cancer cells [scRNA-Seq]

GSE234181 Homo sapiens (human) Single-cell RNA-seq 184 samples Submitted 2024/04/16 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 184 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Homo sapiens (human)
Samples
184 samples: 44 conditions, mostly with about 4 replicates eachGroups 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
Long-term Multimodal Recording Reveals Epigenetic Adaptation Routes in Dormant Breast Cancer CellsCancer discovery · 2024
Summary (written by the submitters)
Patients diagnosed with hormone dependent breast cancer (HDBC) receive five or more years of adjuvant endocrine therapies (ETs). Adjuvant ETs delay relapse by targeting clinically undetectable micro-metastatic deposits, yet up to 50% of patients receiving ET relapse at a constant rate over the course of decades after surgery. The mechanisms driving these clinical dynamics are largely unknown but likely involve dormancy.
Read the full summary ↓
We developed two approaches to study the fate of dormant cells in long-term experiments. Firstly, we profiled samples from late relapse (10-35 yrs.) and patients treated with 16-44 months of neoadjuvant ETs until progression to dissect the contribution of genetic and transcriptional changes to tumour awakening. Next, we developed a first of its kind in vitro evolutionary study to systematically record adaptive strategies of individual lineages in unperturbed parallel experiments through several months. Collectively our data demonstrate that ETs induce a heritable cell state transition into dormancy via epigenetic reprogramming. Single lineages with divergent phenotypes awaken unpredictably via epigenetic erosion. Targeting the dormant epigenome shows promising activity against adapting cancer cells. Overall, this study uncovers the contribution of epigenetic adaptation to the evolution of resistance to ETs with profound implications for breast cancer.
Published in
Long-term Multimodal Recording Reveals Epigenetic Adaptation Routes in Dormant Breast Cancer Cells
Rosano D, Sofyali E, Dhiman H et al. · Cancer discovery 2024 · PMID 38527495 · doi:10.1158/2159-8290.CD-23-1161
This dataset
Open GSE234181 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 GSE234181_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA980249 and SRA study SRP441262. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38527495) ↗
Study design
44 conditions, mostly with about 4 replicates each
T47D_AI1m1_Cell.S ×8 T47D_AI2m1 _BC.S ×8 MCF7_T0_1 _Cell.S ×4 MCF7_T0_2 _Cell.S ×4 MCF7_T0_1_BC.S ×4 MCF7_T0_2 _BC.S ×4 MCF7_TAM1m1_Cell.S ×4 MCF7_TAM2m1_Cell.S ×4 +36 more

Supports a between-group comparison across 184 samples.

How we read this: 44 replicated groups read from 184 sample titles; they account for 184 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

+ 144 more — browse all 184 samples with per-sample file links →

Datasets on a similar topic

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