A patient-centric knowledge graph approach to prioritize mutants for selective anti-cancer targeting

Abstract

Personalized oncology has revolutionized cancer treatment by targeting specific genetic aberrations in tumors. However, the identification of suitable targets for anti-cancer therapies remains a challenge. In this study, we introduce a knowledge graph approach to prioritize cancer mutations with clinical, functional, and structural significance as potential therapeutic targets. Focusing on the human kinome, we integrate protein-protein interaction and patient-centric mutation networks to construct a comprehensive network enriched with transcriptomic, structural, and drug response data, together covering five layers of information. Moreover, we make the constructed knowledge graph publicly available, along with a plethora of scripts to facilitate further annotation and expansion of the network. Interactive visualization resources are also provided, ensuring accessibility for researchers regardless of computational expertise and enabling detailed analysis by cancer type and individual layers of information. This comprehensive resource has the potential to identify relevant mutations for targeted therapeutic interventions, thereby advancing personalized oncology and improving patient outcomes.