Abstract Predicting tumor sensitivity to therapeutic agents is a central problem in precision oncology, yet developing models that can generalize to new, un-screened cancer types remains a significant challenge. Current precision oncology approaches benefit only a small fraction of cancer patients, partly due to the difficulty of computationally modeling the complex relationships among tumors, somatic mutations, and drug-gene pathways. To address this gap, we present PRELUDE, a heterogeneous graph neural network (GNN) framework designed to leverage these biological relationships to identify cancer cell-specific drug vulnerabilities. Our approach begins with the careful curation of a knowledge graph composed of: (1) drug-cell interactions from large-scale screening panels, (2) drug-gene relationships from curated inhibitory target databases, (3) cell-gene links derived from somatic loss-of-function mutation data, and (4) a comprehensive gene-gene interaction network We show that PRELUDE outperforms existing precision oncology baselines. Our curriculum learning approach forces the model to learn generalizable, biology-driven patterns, demonstrated by its ability to accurately predict responses for cell lines completely removed from the training graph, mimicking the challenge of predicting responses for new patients. Furthermore, our approach is interpretable, identifying effective drug target genes that interact with mutated genes in cancer cells. These findings highlight the potential of graph-based methods to enhance predictive modeling in precision oncology and support their broader adoption in data-driven cancer research. Citation Format: Luis E. Tafoya, Mikaela Dicome, Yue Hu, Macaulay Oladimeji, David Arredondo, Yanfu Zhang, Kushal Virupakshappa, Avinash Sahu. PRELUDE: A graph neural network for drug response prediction abstract. In: Proceedings of the AACR Special Conference in Cancer Research: Cancer Evolution: The Dynamics of Progression and Persistence; 2025 Dec 4-6; Albuquerque, NM. Philadelphia (PA): AACR; Cancer Res 2025;85 (23Suppl): Abstract nr A033.
David Arredondo (Thu,) studied this question.