GEO series

Genomic heterogeneity drives mechanical adaptation in human tumor cells [RRBS]

GSE255831 Homo sapiens (human) DNA methylation 16 samples Submitted 2026/02/18 Platform GPL24676

At a glance

DNA methylation · human · 16 samples

What it is: DNA methylationChemical marks on DNA that help switch genes on or off.
Organism: Homo sapiens (human)
Samples: 16 samples: 16 samples, each with a distinct title — no replicated groupsGroups 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: Multi-omics investigation reveals molecular determinants of cancer cell evolution on soft extracellular matrixbioRxiv : the preprint server for biology · 2025

Summary (written by the submitters)

The progression of many solid tumors is accompanied by temporal and spatial changes in the stiffness of the extracellular matrix (ECM). Cancer cells adapt to soft and stiff ECM through mechanisms that are not fully understood. In particular, it is well known that there is significant genetic heterogeneity from cell to cell in tumors, but how ECM stiffness as a parameter might interact with that genetic variation is not known.

Read the full summary ↓

Here, we used the method of experimental evolution to study response of genetically variable and clonal tumor cell populations to ECM stiffness. Cell fitness increased on soft ECM over a period of several weeks in genetically variable but not clonal populations. DNA barcode-enabled clonal tracking revealed that sustained culture on soft ECM selects for a few genetic variants. These data provide the first evidence that ECM stiffness exerts natural selection on genetically variable tumor populations. Genome-wide analysis including RNA-seq, ATAC-seq and DNA methylation profiling reveal substantial differences in gene expression between selected populations and ancestral cells which are partially explained by epigenetic modifications. Soft-selected cells are highly migratory with enriched oncogenic signatures and exhibit highly unusual behaviors like spreading and traction force generation on ECMs as soft as 1 kPa. Cell spreading is the directly selected trait but not levels of integrins or adhesion proteins like talin. Overall, these data show that ECM stiffness in solid tumors may drive malignant behaviors through evolution by natural selection.

Published in

Multi-omics investigation reveals molecular determinants of cancer cell evolution on soft extracellular matrix

Sahoo S, Khanna S, Wang TC et al. · bioRxiv : the preprint server for biology 2025 · PMID 41446202 · doi:10.64898/2025.12.17.694784

This dataset

Open GSE255831 in finder → Find more human methylation 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 GSE255831_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: Look for beta-value tables or raw IDAT files among the supplementary files.

Also filed as BioProject PRJNA1076642 and SRA study SRP489794. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41446202) ↗

Study design

16 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 16 sample titles: 16 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

GSM8081378 MDA-MB-231-ancestral-1kPa-R1 (RRBS)
GSM8081379 MDA-MB-231-ancestral-1kPa-R2 (RRBS)
GSM8081380 MDA-MB-231-ancestral-1kPa-R3 (RRBS)
GSM8081381 MDA-MB-231-ancestral-1kPa-R4 (RRBS)
GSM8081382 MDA-MB-231-ancestral-308Kpa-R1 (RRBS)
GSM8081383 MDA-MB-231-ancestral-308Kpa-R2 (RRBS)
GSM8081384 MDA-MB-231-ancestral-308Kpa-R3 (RRBS)
GSM8081385 MDA-MB-231-ancestral-308Kpa-R4 (RRBS)
GSM8081386 MDA-MB-231-adapted-1kPa-R1 (RRBS)
GSM8081387 MDA-MB-231-adapted-1kPa-R2 (RRBS)
GSM8081388 MDA-MB-231-adapted-1kPa-R3 (RRBS)
GSM8081389 MDA-MB-231-adapted-1kPa-R4 (RRBS)
GSM8081390 MDA-MB-231-adapted-308Kpa-R1 (RRBS)
GSM8081391 MDA-MB-231-adapted-308Kpa-R2 (RRBS)
GSM8081392 MDA-MB-231-adapted-308Kpa-R3 (RRBS)
GSM8081393 MDA-MB-231-adapted-308Kpa-R4 (RRBS)

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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.