Citations

Abstract Background: Pancreatic ductal adenocarcinoma (PDAC) is the most lethal common cancer (5-year survival ∼13%), emerging through years-to-decades progression involving chronic fibro-inflammatory injury and precursor transformation processes including ADM and PanIN. KRAS-driven acinar-to-ductal metaplasia (ADM) is among the earliest experimentally tractable and potentially interceptable events; KRAS (mutated in >90% of PDAC) has remained difficult to therapeutically target, particularly for prevention; emerging KRAS inhibitors are developed for treatment of established malignancy rather than early interception. We aimed to prioritize KRAS-axis interception candidates via AI-guided network analysis of TCGA-PAAD restricted to a curated KRAS-driven ADM/PanIN-initiation mechanism module. Methods: The 38-gene module (n=183 TCGA-PAAD samples; 37 genes available, PRSS2 absent) spans KRAS-MAPK, RTKs, TGF-β/SMAD, ECM, inflammation, transcription, tumor suppressors, and ductal/acinar genes from canonical pancreatic-cancer-progression biology, linking established-tumor transcriptomes to early-interception biology. A stability-selected Spearman-correlation network served as the network backbone (PCMCI+/NOTEARS extensions ongoing), paired with patient-level bootstrap filtering (B=200 resamples; ≥70% retention). A composite Interception Score I(v) = α·CB + β·Σ|w| + γ·U + δ·R integrating betweenness centrality, summed edge-weight influence, druggability (literature-curated drug-target prior), and bootstrap robustness ranked candidate interception nodes (unoptimized weights; tuning ongoing). Results: Spearman correlation yielded a 37-node, 248-edge network baseline; bootstrap resampling (B=200) evaluated 856 distinct candidate edges, of which 194 (22.7%) achieved ≥70% retention, defining the stable subgraph used for scoring. The Interception Score placed 7 KRAS-axis genes among the top-10 candidates: ERBB2 (0.953), KRAS (0.743), ERBB3 (0.742), MAPK1 (0.708), EGFR (0.675), MAP2K2 (0.605), and BRAF (0.589). Inflammation (IL6, 0.755), TGF-β/SMAD2 (0.726), and ECM/MMP2 (0.638) also ranked highly, recapitulating PDAC initiation mechanisms (stromal-epithelial crosstalk; inflammatory-fibrotic priming). ERBB2 (HER2), a clinically druggable RTK with approved inhibitors in other malignancies, emerged as the top-ranked candidate, supporting evaluation of HER2-axis signaling for interception in molecularly selected PDAC subsets. These findings align with canonical PDAC biology, supporting biological plausibility prior to formal weight optimization and external validation. Conclusions: AI-guided network analysis of TCGA-PAAD prioritizes biologically coherent KRAS-axis and stromal-inflammatory candidates with clinically available HER2-, EGFR-, MEK-, BRAF-, and KRAS-pathway inhibitors, supporting hypothesis generation for future pancreatic cancer interception studies. Stage-resolved analysis, causal-inference extensions, and sex-/age-stratified validation are ongoing. Citation Format: Jianfu Li, Yingyun Yang, Michael Wallace, Cui Tao, Yan Bi. AI-Driven Network-Based Discovery on TCGA-PAAD Identifies KRAS-Axis Drug-Repurposing Targets for Pancreatic Cancer Interception [abstract]. In: Proceedings of AACR Drug Discovery and Development (AACR D3) Conference; 2026 Jul 21-24; Boston, MA. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(14_Suppl):Abstract nr A029.

Abstract Background: Early-onset pancreatic cancer (EOPC; age <50 y at diagnosis) is rare (≈1.3/100,000), biologically distinct, and germline-enriched (BRCA1/2, PALB2, ATM, CDKN2A, Lynch). Despite NCCN and CAPS Consortium recognition, its pre-diagnostic electronic health record (EHR) trajectory is uncharacterized. We sought reproducible signatures to guide subtype-aware surveillance for this under-screened young-adult population, where conventional guidelines do not apply. Methods: We assembled an EOPC cohort from Mayo Clinic (3 sites, 2001–2023; n=169 EOPC, n=3,162 older-onset PDAC), identified by ICD-10 C25.0–C25.3, C25.7, C25.9 or ICD-9 157.x with age <50 at first diagnosis. For each patient, 3-year pre-diagnostic trajectories of 10 routine laboratory analytes (bilirubin, CA 19-9, CEA, lipase, amylase, glucose, HbA1c, ALT, AST, alkaline phosphatase) were summarized over 30/90/180/365-day windows (mean, slope, delta; 1,706 features total; 131 retained at ≥10% non-zero). Features were winsorized (1–99 pct), standardized, and projected onto 15 PCA components capturing 81.7% variance. K-means clustering (k=2–6, min cluster fraction ≥10%) was assessed by silhouette and 100-resample bootstrap Adjusted Rand Index (ARI). Cluster signatures were compared against the older-onset PDAC cohort projected into the same component space. Results: Two reproducible pre-diagnostic clusters emerged (k=2; silhouette 0.469; bootstrap ARI 0.440). Cluster A: "occult prodrome" (87.0%; n=147; mean age 44.9 y) showed no clear signal across the 10 monitored analytes, with all analyte z-scores near zero through the 3-year window. Cluster B: "metabolic-biliary active" (13.0%; n=22; mean age 44.4 y) was characterized by elevated mean HbA1c (z=+1.08), rising bilirubin slope (z=+0.54), and rising ALT/alkaline phosphatase slopes (z=+0.51 each), consistent with combined diabetic-onset and biliary-obstructive prodrome. The two-cluster structure reproduced in older-onset Mayo PDAC at 87.3% / 12.7%, nearly identical to the EOPC distribution, indicating a fundamental pre-diagnostic dichotomy of pancreatic cancer rather than an age-specific phenomenon. The dominant occult phenotype across age strata indicates that lab-trajectory–anchored surveillance can only address the active minority and motivates complementary genetic-, demographic-, or imaging-anchored pathways for the occult majority. Conclusions: EOPC pre-diagnostic trajectories partition into a dominant occult prodrome (∼87%) without detectable signal in the 10 routine labs evaluated and a minority metabolic-biliary active prodrome (∼13%) with combined diabetic and obstructive features. The high occult prevalence motivates germline-guided active surveillance for genetically at-risk young adults rather than symptom-driven workup, anchoring next-generation surveillance trial designs in rare and hereditary pancreatic malignancies. External validation in independent institutional cohorts (Mayo Clinic Platform / MERCI, or comparable EHR networks) is the planned next step. Citation Format: Jianfu Li Li, Yingyun Yang, Wallace MIchael, Yan Bi. Pre-diagnostic clinical-trajectory signatures of early-onset pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Breaking Barriers in the Fight against Rare Cancers; 2026 Jul 18-20; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2026;86(14_Suppl):Abstract nr A046.

Abstract Alzheimer’s Disease (AD) 1 is a progressive neurodegenerative disorder with limited therapeutic options, driving interest in drug repurposing to accelerate treatment discovery. Drug repurposing has emerged as a promising strategy to accelerate therapeutic discovery by repositioning existing drugs for new clinical indications. Recent computational repurposing approaches, including knowledge graph reasoning, transcriptomic signature analysis, and integrative literature mining, have demonstrated strong predictive capabilities 2 . However, these methods often yield divergent drug rankings, which makes it difficult to decide which candidates to advance for experimental follow-up and results in substantial gaps between computational predictions and feasible in vivo validation 2 .To bridge this computational-experimental gap, we proposed an advanced prioritization framework leveraging large language models (LLMs). Our method systematically evaluated three state-of-the-art (SOTA) and representative computational methods (TxGNN 3 , Composition-based Graph Convolutional Network (CompGCN) 4 , and a regularized logistic regression (RLR) 5 , to analyze both their predictive performance and pharmaceutical class distributions. By integrating the strengths and divergences of these models, we generated a unified, streamlined list of 90 candidate drugs for further prioritization. We then utilized an LLM-based agent to perform evidence synthesis from biomedical literature abstracts for each candidate. This process mimics expert manual curation but significantly reduces human effort and time by efficiently distilling vast textual data into actionable insights. Applying consistent and transparent selection criteria, we obtained a refined and prioritized list of drug candidates suitable for subsequent in vivo experimental validation. The robustness and clinical relevance of our framework were validated using real-world data from Alzheimer’s patient cohorts, clinical trial registries, and expert pharmacological reviews. This comprehensive validation confirmed that our LLM-driven approach enhances efficiency, consistency, scalability, and generalizability. By integrating computational predictions with scalable evidence synthesis and multifaceted validation, our framework facilitated rapid and informed prioritization of repurposed drugs. Our framework can potentially accelerate the translational pathway toward viable AD therapeutics. Moreover, the versatility of our framework can also be applied to drug repurposing efforts for other diseases beyond AD.

Abstract Background Acupuncture, a key modality in traditional Chinese medicine, is gaining global recognition as a complementary therapy and a subject of increasing scientific interest. However, fragmented and unstructured acupuncture knowledge spread across diverse sources poses challenges for semantic retrieval, reasoning, and in-depth analysis. To address this gap, we developed AcuKG, a comprehensive knowledge graph that systematically organizes acupuncture-related knowledge to support sharing, discovery, and artificial intelligence–driven innovation in the field. Methods AcuKG integrates data from multiple sources, including online resources, guidelines, PubMed literature, ClinicalTrials.gov, and multiple ontologies (SNOMED CT, UBERON, and MeSH). We employed entity recognition, relation extraction, and ontology mapping to establish AcuKG, with human-in-the-loop to ensure data quality. Two cases evaluated AcuKG’s usability: (1) how AcuKG advances acupuncture research for obesity and (2) how AcuKG enhances large language model (LLM) application on acupuncture question-answering. Results AcuKG comprises 1839 entities and 11 527 relations, mapped to 1836 standard concepts in 3 ontologies. Two use cases demonstrated AcuKG’s effectiveness and potential in advancing acupuncture research and supporting LLM applications. In the obesity use case, AcuKG identified highly relevant acupoints (eg, ST25, ST36) and uncovered novel research insights based on evidence from clinical trials and literature. When applied to LLMs in answering acupuncture-related questions, integrating AcuKG with GPT-4o and LLaMA 3 significantly improved accuracy (GPT-4o: 46% → 54%, P = .03; LLaMA 3: 17% → 28%, P = .01). Conclusion AcuKG is an open dataset that provides a structured and computational framework for acupuncture applications, bridging traditional practices with acupuncture research and cutting-edge LLM technologies.

Objective: Our project aims to use Artificial Intelligent (AI)-based methods to address overlooked issues in adolescent nutrition/diet and microbiome, and to identify biomarkers and risk factors that affect long-term health outcomes. Hypothesis: Many diseases, including diabetes, dementia, and Alzheimer’s disease, are linked to microbiome dysbiosis and diet-related risk factors, which can develop during adolescence and influence health outcomes throughout adulthood. Methods and Data: We conducted a systematic literature review on observational, clinical, and laboratory data to examine the relationships among diet quality, nutrition component, microbiome profiles, and energy expenditure, and their effects on behavior and health outcomes/diseases across adolescence, adulthood, and elderly populations (65+ yrs). We designed an analysis workflow to systematically extract and categorize data. Using AI-based methods we constructed knowledge models to uncover the relationships among key concepts on nutrition, diet, microbiome, genetic factors, age, sex, socioeconomic factors, and disease outcomes. Using the citations from PubMed literature, the Dietary Guidelines for Americans, and recommendations from authoritative health organizations, we developed a novel ChatGPT-based chatbot platform, Teen Fuel Up, designed to provide teens with real-time assistance to published knowledge about diet and nutrition. Conclusions: AI-based methods are highly effective in knowledge discovery and health promotion, identifying connections between adolescent nutrition, the microbiome, and chronic diseases later in life, as well as their differences using sex as a biological variable to increase scientific understanding of conditions that are specific to women. We will continue developing the ChatGPT-based chatbot to provide real-time cited knowledge that adapts to the evolving dietary needs of individuals. National Institute of Allergy And Infectious Diseases of the National Institutes of Health, grant number R01AI130460 and U24AI171008, and CPRIT award RP220244. This abstract was presented at the American Physiology Summit 2025 and is only available in HTML format. There is no downloadable file or PDF version. The Physiology editorial board was not involved in the peer review process.

Abstract Objective The rapid evolution of the COVID-19 virus has led to the development of different vaccine shots, each designed to combat specific variants and enhance overall efficacy. While vaccines have been crucial in controlling the spread of the virus, they can also cause adverse events (AEs). Understanding these relationships is vital for vaccine safety monitoring and surveillance. Methods In our study, we collected data from the Vaccine Adverse Event Reporting System (VAERS) and social media platforms (Twitter and Reddit) to extract relationships between COVID-19 vaccine shots and adverse events. The dataset comprised 771 relation pairs, enabling a comprehensive analysis of adverse event patterns. We employed state-of-the-art GPT models, including GPT-3.5 and GPT-4, alongside traditional models such as Recurrent Neural Networks (RNNs) and BioBERT, to extract these relationships. Additionally, we used two sets of post-processing rules to further refine the extracted relations. Evaluation metrics including precision, recall, and F1-score were used to assess the performance of our models in extracting these relationships accurately. Results The most commonly reported AEs following the primary series of COVID-19 vaccines include arm soreness, fatigue, and headache, while the spectrum of AEs following boosters is more diverse. In relation extraction, fine-tuned GPT-3.5 with Sentence-based Relation Identification achieved the highest precision of 0.94 and a perfect recall of 1, resulting in an impressive F1 score of 0.97. Conclusion This study advances biomedical informatics by showing how large language models and deep learning models can extract relationships between vaccine shots and adverse events from VAERS and social media. These findings improve vaccine safety monitoring and clinical practice by enhancing our understanding of post-vaccination symptoms. The study sets a precedent for future research in natural language processing and biomedical informatics, with potential applications in pharmacovigilance and clinical decision-making.