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.
背景/目的：早发性结直肠癌（EOCRC）的发病率在全球范围内呈上升趋势，尤其在西班牙裔/拉丁裔等代表性不足的人群中更为明显。TP53是结直肠癌中最常发生突变的通路之一，但其在EOCRC中的作用，特别是在与健康差异和治疗结局的关系方面，仍不明确。为此，我们开发了AI-HOPE-TP53，一种新型对话式人工智能代理，旨在实现对EOCRC中TP53通路改变的实时、关注差异的分析。方法：AI-HOPE-TP53整合了一个经过微调的生物医学大语言模型（LLaMA 3）以及来自cBioPortal（TCGA、MSK-IMPACT、AACR Project GENIE）的标准化数据集。自然语言查询被转化为工作流程，用于跨临床和人口统计学亚组进行突变谱分析、Kaplan-Meier生存分析和比值比估计。结果：该平台复现了已知的基因型-表型关联，包括EOCRC中TP53突变频率升高以及TP53突变肿瘤预后较差。重要发现包括接受FOLFOX治疗的早发性TP53突变结直肠癌患者生存获益显著（p=0.0149）。其他探索性分析显示，西班牙裔/拉丁裔EOCRC患者中TP53通路改变的发生率有更高的趋势（OR=2.13，p=0.084），并识别出基于性别的治疗差异，女性患者接受FOLFOX治疗的可能性低于男性（OR=0.845，p=0.0138）。结论：本研究开发并公开提供的AI-HOPE-TP53，是首个专为通路特异性且关注健康差异的EOCRC研究量身定制的对话式人工智能平台。通过自然语言交互整合临床、基因组和人口统计学数据，该平台支持假设生成和聚焦公平性的分析，在推动精准肿瘤学发展方面具有巨大潜力。
肿瘤(癌症)患者之家