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.

Artificial intelligence (AI) is transforming gastrointestinal (GI) endoscopy into a more standardized, data-driven, and workflow-integrated field. Advances in computer-assisted detection (CADe), diagnosis (CADx), quality assessment (CAQ), natural language processing (NLP), and multimodal deep learning have expanded AI applications across colonoscopy, upper endoscopy, endoscopic ultrasound (EUS), ERCP, cholangioscopy, and capsule endoscopy. These systems have demonstrated improvements in lesion detection, procedural quality assessment, workflow efficiency, and diagnostic support. However, current evidence remains largely focused on surrogate outcomes rather than patient-centered clinical benefits, while challenges related to generalizability, explainability, regulatory oversight, automation bias, and workflow integration continue to limit widespread adoption. Future progress will depend on prospective real-world validation, diverse datasets, explainable AI frameworks, and careful integration of human–AI interaction into clinical practice. Overall, AI is evolving from a supportive adjunct into an increasingly integrated component of gastrointestinal endoscopy with the potential to improve procedural quality, diagnostic consistency, and clinical efficiency.

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.

Most genetic variants associated with complex traits are hypothesized to regulate gene expression. To understand the genetics underlying gene expression variability, we characterized 14,324 RNA-sequencing samples from the Trans-Omics for Precision Medicine program and performed expression and splicing quantitative trait locus (e/sQTL) analyses in six tissues and cell types, including whole blood ( n = 6454) and lung ( n = 1291). We detected tens of thousands of secondary cis-e/sQTLs, showing that secondary cis-e/sQTL discovery remains unsaturated. We fine-mapped UK Biobank–derived genome-wide association study (GWAS) signals from 164 traits and identified e/sQTL colocalizations for 10,611 GWAS signals, including 7096 that colocalize with secondary e/sQTLs. Our results suggest that even larger e/sQTL analyses will uncover additional secondary e/sQTLs, further benefiting GWAS interpretation.

Abstract Frontotemporal dementia (FTD) is characterized by selective neuronal vulnerability, yet the features that predispose specific neuron types to degeneration remain unclear. We performed single-nucleus RNA sequencing of frontoinsular cortex, a region affected early in behavioral variant FTD, across individuals with C9orf72 -associated and sporadic FTD-MND spectrum disease. By enriching for large projection neurons, we resolved molecular subtypes of layer 5 extratelencephalic neurons, including von Economo neurons, and identified selective depletion of specific layer 2/3 and layer 5 neuron subtypes, convergent across genotypes. Despite selective neuronal loss, disease-associated transcriptional changes were convergent across excitatory neuron populations, suggesting that they reflect upstream pathophysiology or shared responses to local neurodegeneration. By relating neighborhood-level depletion in disease to gene expression in controls, we found that baseline cellular respiration and ATP synthesis predict neuronal vulnerability in disease. These findings define molecular correlates of selective neuronal vulnerability in FTD and provide a framework linking cell type and state to neurodegeneration.

Importance High-quality evidence guiding opioid prescribing decisions for acute pain across common diagnoses is lacking. Objective To describe pain trajectories and patterns of opioid and nonopioid treatment use among individuals who were offered opioids for the treatment of acute pain. Design, Setting, and Participants This was a prospective cohort study of patients recruited from 5 US health systems between September 2020 and March 2023 in emergency departments (EDs), primary care clinics, dental practices, or after cesarean delivery or knee replacement. Eligible patients were opioid-naive adults aged 18 years or older at all study sites, as well as adolescents aged 15 to17 years undergoing impacted molar extraction at 1 site, who were offered an opioid prescription for acute pain. Analysis was conducted April 2023 through February 2026. Exposure Offer of a prescription for an opioid analgesic. Main Outcomes and Measures Time to pain resolution (3 consecutive reports of no pain), patterns of opioid and nonopioid treatment use, and opioid-related adverse effects, ascertained from digital questionnaires. Results Among 1708 enrolled patients (median age, 38 years [IQR, 28-52 years]; 615 [36.0%] reporting race or ethnicity underrepresented in studies of acute pain management) followed up for 180 days, 915 (53.6%) were recruited in EDs, 307 (18.0%) in primary or urgent care, 263 (15.4%) in dental settings, and 223 (13.1%) in inpatient settings. Pain sources included dental (302 patients [17.7%]), trauma or injury (302 [17.7%]), obstetric (176 [10.3%]), musculoskeletal (131 [7.7%]), and low back (100 [5.9%]). Among 1502 patients reporting pain level at least once, median time to pain resolution irrespective of the treatment approach was 20 days (IQR, 8-88 days), with longer durations for surgical pain (74 days [IQR, 30 days to not reached]) and low back pain (69 days [IQR, 18 days to not reached]). The median time to opioid discontinuation among 1189 patients (69.6%) who reported any opioid use was 7 days (IQR, 2-31 days); an estimated 10.0% (95% CI, 7.7%-12.7%) of patients used opioids for at least 90 days, with higher rates in people reporting frequent pain before enrollment. Of 1482 patients (86.8%) completing at least 1 survey during the first 2 weeks of follow-up, 1153 (77.8%) reported using any opioids and 1287 (86.8%) reported using acetaminophen or ibuprofen. Among 619 (52.1%) patients with any opioid use who reported the dose of opioids taken in the first 15 days, daily doses were low (median, 10 [IQR, 5-15] morphine milligram equivalents). Most respondents reported leftover opioids (657 of 982 responding [66.9%]). Conclusions and Relevance In this cohort study of opioid-naive patients with acute pain, opioid use was generally low dose and of short duration, although some patients reported prolonged opioid use; most reported achieving pain resolution within 3 weeks, with longer times for surgical and low back pain. The findings suggest current guidelines for multimodal treatment and for short-duration opioid prescriptions if needed will serve many but not all patients, and treatment should be tailored to address individual patients’ needs.

Autonomous robotic-assisted surgery (RAS) has emerged as a promising objective in biomedical technology, further enhanced by miniaturization toward microrobotic-assisted surgery (μ-RAS). This reduction in scale promises minimally invasive, partially or fully automated surgical procedures, with the potential to reduce patient recovery times, lower medical costs, and enable previously unavailable procedural options. This perspective highlights the specific advances in RAS that potentially map to the microscale (μ-RAS), organized across five surgical domains: endovascular, endoluminal, laparoscopic, ophthalmic, and orthopedic. We examine both clinical demands and technological advances in surgical robotics and identify the key innovations required for progress across these surgical fields. Our contribution is distinct in combining the perspectives of both surgical experts and bioengineering innovators, outlining a roadmap for the advancement and eventual integration of autonomous RAS and μ-RAS into mainstream surgical practice.