Abstract LB112: Knowledge graph AI-based prioritization of drug target candidates across 503 cancers
Authors: Bob Zimmermann, Radosław Bielecki, Roy Ronen, Janusz Dutkowski
Journal: Cancer Research (2025)

Abstract Background: Drug target discovery is a vital step in drug development. Novel computational methods taking advantage of multimodal data sets and diverse algorithmic approaches are beginning to provide key insights to inform this process. Comprehensive biomedical knowledge graphs provide a particularly promising paradigm for target discovery and prioritization, being able to encode multi-modal relational evidence without the need for feature engineering or selection. Within this context, target prediction can be formulated as a link prediction problem using knowledge graph embeddings (KGEs), which learn vector representations of graph entities and provide a flexible framework to simultaneously prioritize drug targets and interpret them within the broader context of their underlying biology. Approach: We developed a comprehensive data-driven biomedical knowledge graph spanning over 4 billion relations across over 1 million entities including genes, diseases, drugs, pathways, tissues, cell types and other biomedical entities. Using a subset of this knowledge graph derived directly from molecular networks as well as analyses of large-scale cancer genomics data (including gene and protein expression, CRISPR sensitivity, somatic mutations and hypermethylation), we trained, evaluated and optimized 17 state-of-the-art KGE models, further incorporating a specialized negative sampling strategy and custom stopping criteria. The KGE models were used to predict disease-target associations as well as identify target classes along with their inter- and intra-class interaction profile signatures. Results: Cancer target predictions were evaluated using a benchmark set of known drug targets. The best KGE methods sharing molecular and drug target information across cancer types achieved an average AUPRC of 0.794 and significantly outperformed standard machine learning approaches trained on each cancer separately. KGE gene embeddings formed distinct clusters aligned with target-disease associations and gene-gene interaction profiles. Analysis of these clusters revealed sets of cancer drug targets enriched in distinct biological functions, including immune checkpoints, GPCRs, cell cycle regulators, and ribosomally-associated genes. Many of the predicted new targets were embedded close to genes with established target roles - providing alternative target candidates which share common molecular pathways and/or biological mechanisms with already established targets. Notably, we also identified gene clusters with few or no known targets whose cancer relationships were nevertheless strongly supported by the underlying data, highlighting the potential of our approach to discover target candidates within new functional classes. Citation Format: Bob Zimmermann, Radosław Bielecki, Roy Ronen, Janusz Dutkowski. Knowledge graph AI-based prioritization of drug target candidates across 503 cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_2):Abstract nr LB112.