bioRxiv · 10.64898/2025.12.08.693012
A transcription factor-responsive enhancer discovery platform for targeted immunotherapy
Abstract
Synthetic enhancers with high specificity are crucial for therapeutic gene control. However, experimental screens and machine learning-guided design typically require context-specific datasets, limiting generalizability. Because transcription factor (TF) activity reflects cellular state, TF-responsive enhancer libraries offer a universal starting point. Here, we developed TREND (transcription factor-responsive enhancer discovery), a massively parallel reporter assay of [~]2.7 million enhancer-barcode constructs representing 57,715 designs. TREND covers 1,068 motif-annotated proteins, including 729 confirmed TFs across 49 DNA-binding domain families. Applied to ovarian cancer, TREND identified enhancers that discriminate cancer from normal epithelial cells. These enhancers enabled protein-interaction-based AND-gate circuits with reduced OFF-state leakage and amplified ON-state output, driving tumor-restricted expression of combinatorial immune effectors and robust antitumor responses in murine ovarian cancer models. TREND also identified T-cell activation-responsive enhancers with greater inducibility and lower basal activity than conventional NFAT-motif-based elements. Together, TREND provides a generalizable framework for context-specific enhancer discovery and therapeutic gene regulation.
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Jia, Y., Chen, C.-Y., Zhu, B., Wu, Z., Wu, Y.-C., Wang, R., Salamah, A. I., Halder, S., Liu, Y.-N., Scimeca, L., Yin, H., Sabu, T., Antwi, E. B., Wang, Y., Wu, M.-R.. 2025-12-10. A transcription factor-responsive enhancer discovery platform for targeted immunotherapy. https://doi.org/10.64898/2025.12.08.693012
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