Abstract
BACKGROUND/OBJECTIVES: The incidence of early onset colorectal cancer (EOCRC) is increasing globally, particularly among underrepresented populations such as Hispanic/Latino individuals. TP53 is among the most frequently mutated pathways in CRC; however, its role in EOCRC, especially in relation to disparities and treatment outcomes, remains poorly defined. We developed AI-HOPE-TP53, a novel conversational AI agent, to enable a real-time, disparity-aware analysis of TP53 pathway alterations in EOCRC. METHODS: AI-HOPE-TP53 integrates a fine-tuned biomedical large language model (LLaMA 3) with harmonized datasets from cBioPortal (TCGA, MSK-IMPACT, AACR Project GENIE). Natural language queries are translated into workflows for mutation profiling, Kaplan-Meier survival analysis, and odds ratio estimation across clinical and demographic subgroups. RESULTS: The platform replicated known genotype-phenotype associations, including elevated TP53 mutation frequency in EOCRC and poorer prognosis in TP53-mutated tumors. Significant findings included a survival benefit for patients with early-onset TP53-mutant CRC treated with FOLFOX (p = 0.0149). Additional exploratory analyses showed a trend toward higher prevalence of TP53 pathway alterations in Hispanic/Latino EOCRC patients (OR = 2.13, p = 0.084) and identified sex-based disparities in treatment, with women being less likely than men to receive FOLFOX (OR = 0.845, p = 0.0138). CONCLUSIONS: AI-HOPE-TP53, developed in this study and made publicly available, is the first conversational AI platform tailored for pathway-specific and disparity-aware EOCRC research. By integrating clinical, genomic, and demographic data through natural language interaction, hypothesis generation and equity-focused analyses are enabled, with significant potential to advance precision oncology.