Self-sufficient killing of malignant tumors by an engineered cancer-selective gene circuit.

Abstract

Gene therapies that selectively eliminate tumor cells within a heterogenous population remain an elusive goal in oncology, largely due to the difficulty to achieve cancer specificity by strict definition. Here, we engineer a therapeutic gene circuit capable of identifying cells that harbor oncogenic gain-of-function aberrations in E26 transformation-specific (ETS) transcription factors. Using a machine learning-guided random forest framework, we develop a cancer-selective promoter P_ETS∗ that only activates during ETS overexpression and/or gene fusion events, while remaining inactive under physiological RAF-MEK-ERK signaling or in rapidly proliferating healthy tissues. When delivered using adenoviral vectors, P_ETS∗ enables tumor-restricted viral replication in vivo as well as long-lasting tumor suppression and complete survival of treated mice. Specifically, intratracheal delivery of P_ETS∗-driven adenoviruses achieves sustained control of metastatic lung tumors for over 140 days. This work overcomes key barriers of synthetic biology and oncolytic virotherapy and could open up important avenues for future cancer treatment.