Heterogeneous Driving Effects Guide Personalized Tumor Treatments Targeting N6-Methyladenosine

Abstract Alterations to N6-methyladenosine (m6A) modifications can promote malignant progression by modulating gene expression through regulation of transcript metabolism. Quantifying the causal impact of m6A dysregulation at the population level could help guide personalized therapeutic interventions. In this study, we developed a causal framework that enables precise estimation of the driving effects (DE) of m6A dysregulation on tumor survival, establishing DE-based enhanced prioritization rules for anti-m6A therapies. The global average DE of m6A dysregulation across 9,647 tumors resulted in a decrease in overall survival of 180.1 days, considering a 5-year survival endpoint, suggesting potential overall benefits of treatment. Profiling of tumors most susceptible to m6A dysregulation and application of a modifier-mining tool revealed an effect modification in the PI3K/AKT/mTOR pathway. Specifically, the benefits of m6A-targeted treatment varied depending on baseline mTOR levels, which were validated in vitro and in vivo. Overall, focusing on m6A, this study established a paradigm leveraging DE over conventional risk to optimize personalized therapy. Significance: A causal inference framework based on m6A modifications that quantifies driving effects and characterizes high-benefit tumor profiles offers an improved approach for guiding personalized tumor treatment decisions. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI .