Conversational Artificial Intelligence Agents-Enabled Dissection of RTK-RAS and MAPK Pathway Dependencies in Gemcitabine-Treated Pancreatic Ductal Adenocarcinoma (PDAC)

Abstract

Abstract Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy characterized by profound molecular heterogeneity and inconsistent responses to gemcitabine-based therapy. Although KRAS mutations are nearly ubiquitous, the broader RTK-RAS and MAPK signaling networks, and their association with therapeutic response, remain insufficiently characterized. We performed an integrative clinical-genomic study of 184 PDAC tumors, stratified by age at diagnosis and gemcitabine exposure, systematically evaluating somatic alterations within curated RTK–RAS/MAPK gene panels. Conversational artificial intelligence agents (AI-HOPE-RTK-RAS and AI-HOPE-MAPK) were deployed to dynamically construct cohorts and conduct pathway-level analyses, with results subsequently confirmed using conventional statistical approaches. Among late-onset PDAC cases, ERBB2 and RET mutations were significantly enriched in gemcitabine-treated tumors. In early-onset disease, CACNA2D family alterations were more common in untreated tumors, whereas FLNB and TP53 mutations were observed at higher frequencies in treated cases. Notably, late-onset patients who did not receive gemcitabine and lacked RTK-RAS or MAPK pathway alterations demonstrated significantly improved overall survival. These findings identify age- and treatment-specific signaling dependencies extending beyond canonical KRAS alterations and reinforce a precision oncology framework in PDAC. Conversational AI enabled rapid, multidimensional integration of clinical and genomic data, facilitating the identification of clinically meaningful pathway architectures.

Ähnliche Arbeiten