An Integrative Drug-Induced Transcriptomic Analysis Identifies Novel MYC Antagonists and Potential Synergistic Drug Combinations.

Abstract

MYC is among the most frequently dysregulated oncogenes in human cancer, yet its direct targeting remains a significant challenge. Here, we present an in-silico integrative screening approach to identify compounds and combinations that can block MYC's oncogenic function by specifically disrupting its transcriptional regulatory function. Using a doxycycline (DOX)-inducible model, we established a MYC loss-of-function (LOF) gene signature that specifically captures the molecular consequences corresponding to the loss of MYC's ability in transcriptional regulation. By integrating large-scale post-perturbation transcriptomic profiling from the CMAP database, we screened over 8300 drug-induced profiles and identified 70 recurrent compounds that are predicted to antagonize MYC's transcriptional programs. To further enhance their therapeutic potential, we also developed an orthogonality analysis to pinpoint synergistic drug combinations that suppress MYC activity more effectively than single agents. Our scalable framework enables a rational and systematic identification of compounds with potential to antagonize MYC's oncogenic function by disrupting its transcriptional regulatory ability without necessarily decreasing its abundance. Our approach provides new insights on utilizing existing anticancer drugs to indirectly target MYC in MYC-driven cancer.