Back

MYC-MIZ1 Complexes at Enhancers Tune Neuroendocrine Identity of Small Cell Lung Cancer

Froehlich, L. M.; Tumbrink, H. L.; Adhikari, B.; Rempe, M.; Ostendorp, J.; Zickler, P.; Hoehne-Wiechmann, M.; Heimsoeth, A.; Tang, Y.; Lennartz, S.; Schwaebe, A.; Werr, L.; Fischer, M.; Quaas, A.; Gruell, H.; Garbert, K.; Morgenthaler, D.; Touet, M.; Hildebrand, J. A.; Weigert, O.; Beleggia, F.; Papadopoulos, D.; Wolf, E.; Braegelmann, J.; Frede, J.; Haensel-Hertsch, R.; Sos, M. L.

2026-08-20 cancer biology
10.64898/2026.08.19.745709 bioRxiv
Show abstract

MYC family members have been extensively studied as undruggable transcription factors regulating oncogenic signaling in highly aggressive tumors such as small cell lung cancer (SCLC), via promoter binding. Here, leveraging the previously described Myc-driven SCLC mouse model (RPM), we generated RPM-Miz1{Delta}POZ (RPMM) mice to uncover a Myc-dependent regulation of neuroendocrine (NE) differentiation, via enhancers. Our functional and genomic analyses reveal that Miz1 facilitates Myc binding to low-affinity E-boxes at distal chromosomal regions, thereby enabling Myc occupancy at sites with otherwise limited intrinsic affinity. We further show that SCLC patients and cellular models share an enrichment of low-affinity E-Box Myc binding motifs at enhancer regions that loop to genes of classic neuroendocrine differentiation. Integrated epigenetic and genomic analyses with AI-modeling implicate Myc/Miz1 binding at enhancers as the determinant for the expression of bona-fide neuroendocrine genes. In RPMM tumors, the suppression of neuroendocrine identity is paralleled by a redistribution of Myc protein towards promoter-proximal regions, hyper-activation of Myc transcriptional programs, apoptotic priming and enhanced sensitivity to etoposide. Together, these findings uncover Miz1/Myc-engaged enhancers as a central hub for neuroendocrine lineage programs and provide a mechanistic basis for a targeted inhibition of Miz1 to boost chemosensitivity in SCLC.

Matching journals

The top 5 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.