A transcription factor-responsive enhancer discovery platform for targeted immunotherapy

Synthetic enhancers with high specificity are crucial for therapeutic gene control. Although existing experimental screens and machine learning-based designs have advanced their discovery, these strategies remain context-restricted, typically requiring new cell-type- or state-specific libraries or datasets for each context, thus limiting their generalizability and scalability for broad biomedical applications. Because transcription factor (TF) activity naturally varies across cell types and cellular states, a high-coverage library of TF-responsive synthetic enhancers provides a universal starting point for identifying cell-type- or state-specific elements across diverse biological contexts. Harnessing this principle, we developed the TREND (transcription factor-responsive enhancer discovery) platform, a massively parallel reporter assay framework comprising 57,715 designs covering 1,068 TFs. To demonstrate its utility, we applied TREND to ovarian cancer and identified cancer-selective synthetic enhancers that discriminate cancer cells from normal epithelial cells. Building on these synthetic enhancers, we developed a protein-interaction-based AND-gate circuit that enables tighter control and enhanced transcriptional strength. In murine ovarian cancer models, these synthetic enhancers and AND-gate circuits achieved tumor-restricted activity and potent antitumor efficacy when driving combinatorial immune payloads. Extending TREND to immune cell engineering, we identified synthetic enhancers with stronger inducibility in the activated state and lower basal activity than conventional NFAT-motif-based enhancers in T cells. Together, these findings position TREND as a more generalizable framework for identifying synthetic enhancers with high contextual specificity, offering a versatile tool for precise gene regulation and context-aware therapeutic design.