Abstract 2496: GOALS: a novel approach for gene ontology analysis using layered shell to enhance functional insight mining in cancer genomics

Abstract Gene Ontologies (GOs) are standardized descriptions of gene functions in terms of biological processes, molecular functions, and cellular components, capturing their Parent-Child relationships in a structured framework and advancing cancer biological modeling to provide consistent and meaningful insights into functional genomics analysis. The conventional GO hierarchical structure is defined by human curation experts, with levels determined by the shortest path to the root term. However, grouping GOs poses challenges due to the uneven distribution of gene members within GO terms and inconsistencies in the level of detail across terms at the same GO level. In this work, we introduce Gene Ontology Analysis using Layered Shells (GOALS), a novel tool that discretizes GOAs into optimal layers. GOALS creates a scalable number of GO layers (typically within 10) while maintaining a balanced number of genes across GOs in each layer. Unlike existing tools, GOALS organizes GO terms using a bottom-up approach based on their co-membership network, discreting GOs to achieve an exponential fit with GO’s gene member size. Meanwhile, GOALS reveals clusters or supersets reflecting biological relevance by unsupervised clustering of GO’s latent projections. In the glioblastoma (GBM) and chronic myeloid leukemia (CML) case studies, GOALS identified distinct GO functional clusters with multi-GO layers to reveal multiple levels of detail—from specific to abstract contexts—to maximize signal discovery and uncover those signal’s association with drug resistance. More importantly, GOALS outperformed existing enrichment analysis tools by effectively classifying functional differences with varying treatment outcomes. GOALS offers a robust and innovative framework for exploring disordered GO clusters, mining GO activities, and analyzing potential GO-GO interplays. By addressing critical challenges in functional genomics, GOALS provides a powerful tool for advancing our understanding of cancer heterogeneity and uncovering actionable insights for therapeutic development. Citation Format: Zongliang Yue, Christopher D. Willey, Robert S. Welner, Rajesh Amin, Jake Y. Chen. GOALS: a novel approach for gene ontology analysis using layered shell to enhance functional insight mining in cancer genomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2496.