Background/Objectives: The Janus kinase-signal transducer and activator of transcription (JAK-STAT) signaling pathway is a critical mediator of immune regulation, inflammation, and cancer progression. Although implicated in colorectal cancer (CRC) pathogenesis, its molecular heterogeneity and clinical significance remain insufficiently characterized—particularly within early-onset CRC (EOCRC) and across diverse treatment and demographic contexts. We present AI-HOPE-JAK-STAT, a novel conversational artificial intelligence platform built to enable the real-time, natural language-driven exploration of JAK/STAT pathway alterations in CRC. The platform integrates clinical, genomic, and treatment data to support dynamic, hypothesis-generating analyses for precision oncology.Methods: AI-HOPE-JAK-STAT combines large language models (LLMs), a natural language-to-code engine, and harmonized public CRC datasets from cBioPortal. Users define analytical queries in plain English, which are translated into executable code for cohort selection, survival analysis, odds ratio testing, and mutation profiling. To validate the platform, we replicated known associations involving JAK1, JAK3, and STAT3 mutations. Additional exploratory analyses examined age, treatment exposure, tumor stage, and anatomical site.Results: The platform recapitulated established trends, including improved survival among EOCRC patients with JAK/STAT pathway alterations. In FOLFOX-treated CRC cohorts, JAK/STAT-altered tumors were associated with significantly enhanced overall survival (p< 0.0001). Stratification by age revealed survival advantages in younger (age < 50) patients with JAK/STAT mutations (p= 0.0379). STAT5B mutations were enriched in colon adenocarcinoma and correlated with significantly more favorable trends (p= 0.0000). Conversely, JAK1 mutations in microsatellite-stable tumors did not affect survival, emphasizing the value of molecular context. Finally, JAK3-mutated tumors diagnosed at Stage I–III showed superior survival compared to Stage IV cases (p= 0.00001), reinforcing stage as a dominant clinical determinant.Conclusions: AI-HOPE-JAK-STAT establishes a new standard for pathway-level interrogation in CRC by empowering users to generate and test clinically meaningful hypotheses without coding expertise. This system enhances access to precision oncology analyses and supports the scalable, real-time discovery of survival trends, mutational associations, and treatment-response patterns across stratified patient cohorts.
背景/目的：Janus激酶-信号转导与转录激活因子（JAK-STAT）信号通路是免疫调节、炎症反应和癌症进展的关键调控因子。尽管该通路与结直肠癌（CRC）的发病机制相关，但其分子异质性及临床意义仍未得到充分阐明——尤其是在早发性结直肠癌（EOCRC）以及不同治疗和人口学背景下。我们开发了AI-HOPE-JAK-STAT，这是一个新型对话式人工智能平台，旨在实现对CRC中JAK/STAT通路改变的实时、自然语言驱动的探索。该平台整合了临床、基因组和治疗数据，以支持精准肿瘤学领域的动态、假设生成性分析。 方法：AI-HOPE-JAK-STAT结合了大型语言模型（LLMs）、自然语言到代码引擎以及来自cBioPortal的标准化公共CRC数据集。用户使用简单英语定义分析查询，这些查询被转换为可执行代码，用于队列选择、生存分析、比值比检验和突变谱分析。为验证该平台，我们复现了涉及JAK1、JAK3和STAT3突变的已知关联。额外的探索性分析考察了年龄、治疗暴露、肿瘤分期和解剖部位。 结果：该平台重现了已知趋势，包括携带JAK/STAT通路改变的EOCRC患者生存期改善。在接受FOLFOX治疗的CRC队列中，JAK/STAT改变的肿瘤与显著提高的总生存期相关（p < 0.0001）。按年龄分层显示，携带JAK/STAT突变的年轻（年龄 < 50岁）患者具有生存优势（p = 0.0379）。STAT5B突变在结肠腺癌中富集，并与显著更有利的趋势相关（p = 0.0000）。相反，微卫星稳定肿瘤中的JAK1突变不影响生存，这凸显了分子背景的重要性。最后，在I–III期诊断的JAK3突变肿瘤与IV期病例相比显示出更优的生存期（p = 0.00001），进一步证实了分期作为主导临床决定因素的作用。 结论：AI-HOPE-JAK-STAT通过使用户无需编码专业知识即可生成和检验具有临床意义的假设，为CRC通路水平研究设立了新标准。该系统提升了精准肿瘤学分析的可及性，并支持在分层患者队列中可扩展、实时地发现生存趋势、突变关联和治疗反应模式。
肿瘤(癌症)患者之家