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Telomerase Dysfunction Strategy Overcomes Targeted Therapy Induced Drug Tolerant Persister in Pan-cancer via SUCLG2 downregulation

GSE308782 Homo sapiens (human) RNA-seq (gene expression) 36 samples Submitted 2026/05/21 Platform GPL29480
At a glance

RNA-seq (gene expression) · human · 36 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
36 samples: 12 conditions, mostly in duplicateGroups 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.
Summary (written by the submitters)
The evolution of cancer cells from a drug-tolerant persister (DTP) state into stably resistant clones is a primary barrier to the long-term efficacy of targeted therapies. Through longitudinal transcriptomic profiling across melanoma, non-small cell lung cancer (NSCLC), and colorectal cancer models, we uncover a conserved, biphasic regulation of telomerase as a fundamental driver of this process.
Read the full summary ↓
Combining targeted therapies with telomerase inhibition (genetic or pharmacological) in melanoma and NSCLC models suppressed DTP cell formation and resistance progression in vitro and in vivo. Mechanistic dissection via RNA-seq revealed SUCLG2 as a critical downstream effector of the telomerase inhibitor 6-thio-dG; SUCLG2 knockdown phenocopied combinatorial efficacy. Complementary ATAC-seq demonstrated 6-thio-dG–induced chromatin remodelling at the SUCLG2 locus, reducing accessibility to downregulate expression and thereby impeding DTP proliferation and resistance evolution. Collectively, we establish preemptive telomerase inhibition as a vulnerability-directed strategy to ablate adaptive resistance across malignancies.
This dataset
Open GSE308782 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 GSE308782_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 PRJNA1332717 and SRA study SRP625146. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
12 conditions, mostly in duplicate
HCC827 Day ×4 PC9 Day ×4 SK-MEL-28 Day ×4 SW480 Day ×4 A375 CTRL ×2 A375 THIO ×2 A375 TKI ×2 A375 COM ×2 +8 more

Supports a between-group comparison across 32 samples.

How we read this: 12 replicated groups read from 36 sample titles; they account for 32 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
Datasets on a similar topic

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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.