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An ASCL1 C-Terminal Phospho-Degron Control ASCL1 Protein Stability in Small Cell Lung Cancer

GSE312626 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 9 samples Submitted 2026/07/31 Platform GPL34284
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 9 samples

What it is
ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism
Homo sapiens (human)
Topic
Small cell lung cancerThis series is part of our curated briefing on it.
Samples
9 samples: 2 × Flag ChIP-seq on CORL47 cells expressing DOX-ON ASCL1WT-Flag vs 2 × ASCL1 ChIP-seq on CORL47 cells expressing empty vector vs 2 × Flag ChIP-seq on CORL47 cells expressing DOX-ON ASCL1del217-236-FlagGroups 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)
ASCL1 is a lineage transcription factor dependency in small cell lung cancer (SCLC) and other high-grade neuroendocrine cancers, but remains undruggable. To uncover mechanisms that control ASCL1 abundance, we combined genome-wide CRISPR knockout and base-editor tiling screens with an endogenous ASCL1 reporter, quantitative proteomics, and structural modeling.
Read the full summary ↓
We identify HUWE1 as the dominant E3 ligase for ASCL1 in SCLC and define a conserved C-terminal phospho-degron centered on Ser207 and terminal Trp/Phe residues that is necessary for HUWE1 binding and ASCL1 protein degradation. Disrupting this degron prevents HUWE1 binding and hyperstabilizes ASCL1, locks it on chromatin, and paradoxically impairs SCLC proliferation. These findings reveal a more generalizable phospho-degron recognition module in HUWE1 as a targetable regulatory switch for ASCL1 protein degradation.
This dataset
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.

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 GSE312626_RAW.tar (it can run to tens of gigabytes).

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.

What the files usually look like: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1375549 and SRA study SRP651692. Searching any of these in the dataset finder brings you back here.

Study design
2 × Flag ChIP-seq on CORL47 cells expressing DOX-ON ASCL1WT-Flag vs 2 × ASCL1 ChIP-seq on CORL47 cells expressing empty vector vs 2 × Flag ChIP-seq on CORL47 cells expressing DOX-ON ASCL1del217-236-Flag

Supports a between-group comparison across 6 samples.

How we read this: 3 replicated groups read from 9 sample titles; they account for 6 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
More Small cell lung cancer datasets

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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.