Abstract Phosphorylation is a central regulator of protein function and oncogenic signaling, and advances in mass spectrometry now enable unbiased, proteome-wide identification of cancer-associated phosphosites. However, the functional relevance of most sites remains poorly understood and scattered across the literature.To address this gap, we developed PTMax, an AI-enabled resource that integrates comprehensive literature mining with systematic multi-omics data to advance functional interpretation of phosphorylation in cancer.To standardize phosphosite information reported across published studies, we enhanced our literature-mining pipeline to efficiently extract site-level evidence and associated functional information from full-text articles and pathway figures. Evidence aggregated from these sources was used to generate functional summaries, which were evaluated through both automated and manual quality assessments. PTMax additionally incorporates dozens of mass spectrometry-based phosphoproteomics datasets and multi-omics data from CPTAC cancer cohorts, including RNA, protein, phosphosite abundance, and phenotype associations. For each phosphosite, we computed two evidence scores that quantify literature-derived information richness and data-driven support, respectively. We also constructed signature sets that group phosphosites by cancer hallmarks, co-mentioned genes or diseases, and pathway figure associations, and generated a co-regulated phosphorylation network to facilitate pathway- and network-level interpretation.PTMax currently contains more than 40,000 literature-derived phosphosites extracted from over 500,000 sentences and 1,400 pathway figures, capturing ∼70% of low-throughput-validated and ∼80% of regulatory-annotated phosphosites in PhosphoSitePlus. Notably, over 30,000 sites lack prior regulatory evidence, underscoring the value of AI-driven literature mining. Integration with multi-omics resources adds ∼200,000 unique phosphosites, including 65,000 sites with quantitative associations and 26,000 linked to cancer phenotypes. The PTMax interface enables users to query individual genes or phosphosites and retrieve comprehensive, context-rich information together with user-friendly visualizations. In addition, pathway and network-based analysis modules help translate phosphosite lists into functional and signaling insights. In summary, PTMax unifies literature and figure mining with large-scale experimental datasets to deliver a comprehensive, multi-dimensional resource that advances the functional study of phosphorylation in cancer. Citation Format: Yanling Sun, Sara S. Savage, John M. Elizarraras, Eric Jaehnig, Bing Zhang, . PTMax: An AI-enabled platform integrating literature mining and multi-omics for functional interpretation of phosphorylation in cancer abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2703.
Sun et al. (Fri,) studied this question.