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Photoproximity labeling of c-Myc reveals SLK as a cancer-specific co-regulator [ChIP-Seq]

GSE330345 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 20 samples Submitted 2026/05/17 Platform GPL18573
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 20 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)
Samples
20 samples: 6 conditions, mostly in triplicateGroups 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)
Transcription factors (TFs) have long been aspirational therapeutic targets for the treatment of diseases, as their dysregulation is a common mechanism for altered cell states. Despite this, many TFs implicated in disease have disordered structures and lack canonical binding pockets, rendering them non-trivial targets for small molecule-based therapies.
Read the full summary ↓
Directly inhibiting TF function has proven difficult, but indirect inhibition by targeting the effector molecules that modulate TF function is a promising, yet underexplored, alternative approach. Here we report a strategy for capturing cancer-specific protein-protein interactions using context-dependent µMap photoproximity labeling. Using an intein-based method for catalyst conjugation in biochemically intact nuclei, we demonstrate that we can capture unique protein interactomes of c-Myc in healthy and cancerous prostate cell lines, and that these unique interactors can be mined to identify druggable vulnerabilities. We find that a cancer-specific c-Myc interactor, STE20 like kinase (SLK), selectively promotes c-Myc stabilization at the protein level, drives epithelial morphology, and is essential for tumorigenesis, validating it as a viable therapeutic target. Mechanistically, this stabilization is driven by SLK-mediated phosphorylation of c-Myc at serine 329, which antagonizes GSK3β-dependent phosphorylation of the c-Myc phosphodegron and effectively increases the stability of c-Myc. This cancer-selective interaction is enabled by a change in SLK splicing that promotes nuclear localization of the long isoform, rather than changes at the protein or total RNA level. Furthermore, analysis of cancer patient data shows a strong correlation between the SLK long splice isoform and expression of c-Myc targets across multiple tumor types. Importantly, the SLK-c-Myc interaction is validated in cancer cell lines from diverse tissues, suggesting this novel regulatory axis is broadly operative across human cancer.
This dataset
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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 GSE330345_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 peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1463689 and SRA study SRP698648. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
6 conditions, mostly in triplicate
PC-3 WT, c-Myc ChIP, ×3 PC-3 WT, RNA Pol II ChIP, ×3 PC-3 WT, IgG Control, ×3 PC-3 PSD10 (SLK-KO), c-Myc ChIP, ×3 PC-3 PSD10 (SLK-KO), RNA Pol II ChIP, ×3 PC-3 PSD10 (SLK-KO), IgG Control, ×3 PC-3 WT, Input Control, ×1 PC-3 PSD10 (SLK-KO), Input Control, ×1

Supports a between-group comparison across 18 samples.

How we read this: 6 replicated groups read from 20 sample titles; they account for 18 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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