Back

Photoproximity labeling of c-Myc reveals SLK as a cancer specific co-regulator

Milione, R. R.; Tong, F.; Wolfe, K. L.; Linker, S. B.; Mu, X. J.; Oakley, J. V.; Nager, A. R.; Janiszewska, M.; Seath, C. P.

2025-10-04 cancer biology
10.1101/2025.10.02.680136 bioRxiv
Show abstract

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. 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 {micro}Map 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 Myc interactor, 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. Importantly, this stabilization is driven by a change in SLK splicing rather than expression at the protein or RNA levels. Furthermore, analysis of cancer patient data shows a strong correlation between this splice isoform and expression of c-Myc targets, suggesting this novel regulatory axis is operative across human cancer.

Published in Nature Chemical Biology (predicted rank #18) · training set

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.