Abstract C102: MCAT™: A 3D cancer functional genomics platform uncovers clinically predictive novel targets for Hepatocellular Carcinoma beyond traditional differentially expressed genes (DEG) and 2D cancer models

Abstract There remains a significant unmet need for novel therapeutic targets in cancer drug development. Traditional approaches—such as differentially expressed genes (DEG) analysis comparing tumor and normal tissues, or functional genomic screens using 2D cancer models (e.g., The Cancer Dependency Map (DepMap) from the Broad Institute)—have contributed to target discovery but carry notable limitations. DEG-based targets often lack causality, relying on correlative expression rather than functional impact, while 2D cancer models fail to recapitulate the tumor microenvironment and cellular architecture of patient tumors.To address these gaps, we introduce MCAT™, MEDiC’s Causal and Actionable Target, a next-generation 3D cancer spheroid-based functional genomics screening platform. Applied to hepatocellular carcinoma (HCC), MCAT™ identified a distinct set of novel functional targets (“MCAT 3D Hits”) that showed minimal overlap with targets derived from DEG-derived or DepMap 2D-derived screening.To assess clinical relevance of these target sets, we developed an AI prognostic model by using each target set (MCAT 3D, DEG, and DepMap 2D) as input features for patient survival prediction.MCAT 3D Hits consistently outperformed both DEG-derived and DepMap 2D-derived targets across multiple metrics, including survival classification accuracy (AUPRC: 0.931, ROC-AUC: 0.913, F1: 0.810). Especially, superior performance was also observed in the external dataset, demonstrating the model’s cross-dataset robustness. The results highlight both the clinical potential of these targets and the power of MCAT™ as a discovery platform.Beyond drug target discovery applications, MCAT-derived targets represent promising biomarkers for next-generation prognostic and predictive AI models in solid tumors. Together, these findings support the use of MCAT™ to uncover functionally relevant and clinically actionable targets that have been missed by traditional methods. Citation Format: Frances Kim, Jina Song, Maya Tureez, Un Jae Baek, Hojung Nam, Hong-Pyo Lee, Kyuho Han. MCAT™: A 3D cancer functional genomics platform uncovers clinically predictive novel targets for Hepatocellular Carcinoma beyond traditional differentially expressed genes (DEG) and 2D cancer models [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2025 Oct 22-26; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2025;24(10 Suppl):Abstract nr C102.