Search Results (2,029)

Liquid biopsy is an emerging approach for the molecular characterization of hepatopancreatobiliary (HPB) malignancies through the analysis of circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), and extracellular vesicles (EVs). These biomarkers may complement conventional imaging and serum markers by providing additional information on tumor biology and disease evolution. This narrative review examines the available evidence concerning the potential applications of liquid biopsy in HPB surgical practice, with particular emphasis on preoperative prognostic assessment, postoperative molecular residual disease detection, recurrence monitoring, and treatment adaptation. Among the different circulating biomarkers, ctDNA currently has the most developed perioperative evidence base. Studies involving pancreatic ductal adenocarcinoma, hepatocellular carcinoma, and biliary tract cancers have demonstrated associations between ctDNA detection and unfavorable oncological outcomes, particularly postoperative recurrence. Nevertheless, the available evidence remains predominantly observational, with considerable heterogeneity in patient populations, sampling protocols, analytical platforms, and outcome definitions. Although the integration of circulating biomarkers with conventional anatomical criteria has generated interest in the concept of “biological resectability”, this framework remains hypothetical and has not been prospectively validated for surgical treatment selection. Similarly, ctDNA-guided modifications of neoadjuvant therapy, adjuvant treatment, or postoperative surveillance have not yet demonstrated clinical benefit in HPB malignancies. Liquid biopsy should therefore currently be regarded as a promising investigational prognostic tool rather than an established determinant of surgical or oncological management. Standardized methodologies and prospective interventional trials are required before its routine clinical implementation. Full article

Pancreatic ductal adenocarcinoma (PDAC) presents a stringent drug-delivery environment in which tumor accumulation does not necessarily translate into target-cell exposure or cargo activity. This critical narrative review develops a barrier-informed framework for evaluating nanoimmunotherapies across systemic exposure, tumor access, target-cell delivery, cargo release, target engagement, and pharmacological activity. The framework links vascular, stromal, immune, metabolic, and intracellular barriers to cargo–carrier selection, administration route, biological identity, pharmacokinetics, biodistribution, and intracellular trafficking. Platforms discussed include nanovaccines and nucleic-acid therapeutics, engineered extracellular vesicles, nanoengineered oncolytic-virus delivery, nanoparticle-assisted immune-cell programming, checkpoint modulation, and stromal or metabolic strategies relevant to immune-directed therapy. Functional-delivery evidence is integrated with critical quality attributes, mechanism-based potency, manufacturing comparability, biosafety, repeat-dose performance, and biomarker-guided clinical development. We propose a qualitative stage-gating framework based on concordant evidence of target-site exposure, target-cell activity, reproducible product quality, acceptable repeat-dose performance, and a testable biomarker strategy. This framework supports formulation selection, cross-platform comparison, and translational prioritization of PDAC nanoimmunotherapies. Full article

Pancreatic ductal adenocarcinoma (PDAC) is characterized by extensive neural remodeling and a profoundly immunosuppressive tumor microenvironment, both of which contribute to disease progression and therapeutic resistance. However, whether nociceptor-derived signals directly reprogram PDAC cells to promote immune evasion remains poorly understood. Here, we identify a previously unrecognized tumor-intrinsic neuroimmune mechanism driven by calcitonin gene-related peptide (CGRP) signaling through the receptor complex RAMP1/CALCRL on PDAC cells. CRISPR/Cas9-mediated deletion of Ramp1 or Calcrl did not alter PDAC cell proliferation in vitro but markedly suppressed tumor growth and prolonged survival in immunocompetent mice. These antitumor effects were lost in immunodeficient hosts, indicating that the pro-tumorigenic function of CGRP signaling depends on an intact host immune system. Consistently, tumor cell-specific loss of Ramp1 or Calcrl remodeled the tumor immune microenvironment, enhancing CD4⁺ and CD8⁺ T-cell effector activity. Mechanistically, CGRP stimulation promoted TGF-β2 expression and secretion by PDAC cells in a RAMP1-dependent manner. Tumor-derived TGF-β2 subsequently acted in a paracrine fashion to suppress CD8⁺ T-cell activation and cytotoxic function, whereas genetic disruption of RAMP1 or pharmacological inhibition of TGF-β signaling relieved this immunosuppression. Together, these findings define a CGRP–RAMP1/CALCRL–TGF-β2 signaling axis through which nociceptive input is converted into tumor-driven adaptive immune evasion in PDAC. This pathway reveals a previously underappreciated mechanism linking neural signaling to PDAC progression and identifies CGRP receptor signaling as a potential therapeutic vulnerability for restoring antitumor immunity and improving the responsiveness of PDAC to immunotherapy. Full article

Pancreatic ductal adenocarcinoma (PDAC) has a five-year relative survival of 8%. Feature selection optimizes accuracy, survival concordance, and network structure separately; whether uniting them matters has not been determined. The BIMS-GA optimizes the cross-validated AUC, Cox concordance, and STRING v11.0 protein–protein interaction coherence in one fitness function and was compared against fourteen alternative methods on GSE62452 (n = 130) and eight external evaluation sets. Using a nested protocol, the fifteen methods had pooled out-of-fold AUCs spanning 0.877–0.900; the BIMS-GA ranked thirteenth with 0.8855. No DeLong comparisons survived correction. Stability did not persist either: the ten-seed Jaccard index of 0.804 exceeded every comparator but fell to 0.521 in a second seed block. Network coherence separated them by two orders of magnitude, 0.480 against 0.005–0.192, in an ordering unbroken across twenty runs. Removing the network term raised the discovery AUC by 0.0117 and lowered external prognostic concordance by 0.0149 in all ten runs, acting as a regularizer. The twenty-gene panel comprises one laminin-332 and an integrin adhesion program with no isolated member, stratifying survival in four cohorts (hazard ratios of 2.92–4.13 fitted within each cohort; 1.02–2.17 transferred). Network coherence, not discrimination, is the axis that remained separate in every run examined, while short-run stability did not. Full article

Pancreatic ductal adenocarcinoma (PDAC) is rarely detected before the disease is advanced, and existing serum markers are insufficient for early detection. Its systemic immune alterations are incompletely characterized and rarely examined by sex. We measured 19 serum cytokines by bead-based multiplex assay in 22 patients with PDAC and 16 controls, comparing groups overall and separately in women and men, and examined co-regulation among them. In the full cohort, interleukin-7 (IL-7) was the only cytokine to discriminate patients from controls after correction for multiple testing. Adjustment for age strengthened this association, and IL-7 matched the discrimination of carbohydrate antigen 19-9 (CA19-9). After stratification by sex, IL-7 remained the only cytokine higher in women, though it did not survive correction within that subgroup, whereas in men, eight innate, T-helper 17 (Th17) and type I interferon cytokines (IL-1α, IL-1RA, IL-31, IFN-α, IL-17A, IL-17F, TNF-α, and TNF-β) separated patients from controls, although the male control group was small. The correlation network present in controls was largely absent in patients, which instead showed few associations centered on IL-2, IL-5 and IL-6. These findings identify IL-7 as a candidate discriminator of PDAC and show that the cytokines separating patients from controls differed by sex, warranting validation in a larger, sex-balanced cohort that also measures sex hormones. Full article

Class imbalance is common in biomedical machine learning, yet imbalance-correction strategies are typically evaluated using predictive performance rather than their effects on biological interpretation. We evaluated six resampling methods: Random Oversampling (ROS), Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic Sampling (ADASYN), Random Undersampling (RUS), ClusterCentroids, and AllKNN (All K-Nearest Neighbors), together with a no-resampling baseline, using DNA methylation data from The Cancer Genome Atlas Pancreatic Ductal Adenocarcinoma (PDAC) cohort. The analysis included 485,578 CpG markers of which 406,807 were retained after quality-control filtering. A continuous feature-ranking framework merging Random Forest importance and ANOVA F-statistics was applied across repeated runs. Biological relevance was evaluated through hypergeometric enrichment against a curated PDAC gene set and preranked Gene Set Enrichment Analysis (GSEA) using 309 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. All seven analysis strategies produced significant enrichment for literature-supported pancreatic cancer-associated genes, with hypergeometric p-values ranging from $3.34\times {10}^{-16}$ to $1.28\times {10}^{-7}$. SMOTE recovered the largest absolute number of PDAC-associated genes (141 of 13,827 selected genes), whereas RUS achieved the greatest fold enrichment (2.17-fold; 88 observed versus 40.61 expected). Notably, the no-resampling baseline remained highly competitive, recovering 112 PDAC-associated genes with 1.86-fold enrichment and a p-value of $5.27\times {10}^{-14}$, indicating that resampling was not necessary to recover biologically relevant features under the evaluated feature-ranking framework. Across methods, 169–234 of the 309 tested KEGG pathways were significant at false discovery rate $\le 0.05$. ClusterCentroids produced the largest number of significant pathways (234), followed by the no-resampling baseline (225) and RUS (223). Seven to nine predefined PDAC-focused pathways were recovered per method, with RUS and ClusterCentroids recovering all nine. Core pathways consistently identified across all methods included MAPK, PI3K–Akt, TGF-$\beta$, Notch, Hedgehog, focal adhesion, and cell-cycle signaling. These findings show that resampling strategies produce distinct trade-offs between biomarker coverage, biological specificity, and pathway breadth, supporting biologically informed evaluation rather than selection of a single universally optimal imbalance-correction method. Full article

Pancreatic ductal adenocarcinoma (PDAC) progression is driven by complex cellular interactions within the tumor microenvironment (TME), where endothelial cell (EC)-tumor-associated macrophage (TAM) crosstalk significantly influences angiogenesis and immune modulation, although the specific subsets and consequences remain unclear. Here, we integrated four single-cell RNA sequencing datasets (50 PDAC; 21 normal adjacent tissues) to profile EC and macrophage subpopulations, subsequently employing CellChat to decipher intercellular communication networks and identify altered ligand–receptor interactions in PDAC. Correlation and Kaplan–Meier survival analyses (TCGA-PAAD) assessed associations and prognostic value, while functional validation used EdU, CCK-8, and Transwell assays on PDAC cells. We identified a distinct endothelial subset (EC0) and macrophage subset (Mac2) with enhanced interaction signals in PDAC. This EC0–Mac2 crosstalk correlated with significant S100A6 upregulation, which was associated with poor patient prognosis. Exogenous S100A6 treatment markedly enhanced PDAC cell proliferation and migration in vitro. In conclusion, EC-Mac crosstalk may promote PDAC progression via S100A6, and this interaction pattern warrants further investigation as a candidate prognostic biomarker and potential target for disrupting stromal communication. Full article

Background/Objectives: Surveillance of intraductal papillary mucinous neoplasms (IPMNs) provides a clinically relevant window in which biological changes associated with pancreatic ductal adenocarcinoma (PDAC) development may potentially be investigated before advanced disease occurs. Nanoparticle-enabled protein corona (PC) fingerprinting has shown analytical potential in PDAC, but the extent to which different nanoplatforms recover distinct information across the biologically proximate IPMN-PDAC spectrum remains unclear. This exploratory study compared three nanoplatform configurations to identify a candidate workflow for subsequent validation. Methods: Plasma from 22 patients with de novo PDAC (n = 11), IPMN (n = 6), or IPMN-derived PDAC (n = 5) was incubated with graphene oxide (GO) nanosheets, gold nanoparticles (AuNPs), or cationic DOTAP-DOPE liposomes. Wash-resistant PCs were analyzed by SDS-PAGE, quantified across predefined molecular weight (MW) regions, and evaluated using exploratory linear discriminant analysis with repeated stratified cross-validation. Results: The three nanoplatforms generated non-equivalent electrophoretic PC architectures. In this small exploratory cohort, GO showed the broadest pattern of nominal clinical-group-associated variation in relative MW-region contributions and yielded internal cross-validation AUC estimates of 0.955 for IPMN versus de novo PDAC, 0.767 for IPMN versus IPMN-derived PDAC, and 0.927 for IPMN-derived versus de novo PDAC. Corresponding AUCs were 0.606–0.700 for AuNPs and 0.621–0.691 for liposomes. Conclusions: GO provided the broadest group-associated variation and numerically highest internal discrimination estimates, supporting its prioritization for independent validation and prospective longitudinal evaluation in IPMN surveillance. Full article

Background/Objectives: Pancreatic ductal adenocarcinoma (PDAC) has exceptionally high mortality, largely because of late diagnosis, while established blood biomarkers such as CA19-9 have limited sensitivity for early detection. We evaluated whether modelling serum proteomic measurements as supervised pairwise statistical graphs using synolitic graph neural networks (SGNNs) could provide a useful framework for pre-diagnostic PDAC classification. Methods: We analysed 217 pre-diagnostic serum samples from UKCTOCS, comprising 100 PDAC samples from 75 women and 117 control samples, profiled for 97 proteins. Samples were divided into a development set collected 0–1 year before diagnosis (n = 110) and a temporal holdout set collected 1–2 years before diagnosis (n = 107); 25 PDAC participants contributed samples to both windows. The final SGNN used fold-internal mutual-information selection of 30 proteins, pairwise RBF-SVM-derived edge information, minimum-connected sparsification, and a 10-member GATv2 ensemble per cross-validation fold. Conventional machine-learning comparators were tuned using development data only. Results: The SGNN achieved a mean 5-fold development ROC-AUC of 75.33 ± 10.43%, lower than the tuned Random Forest (81.67%); the other tuned comparators achieved 77.67% (XGBoost), 77.17% (logistic regression), and 73.17% (SVM). Averaging predictions across the five-fold-specific SGNN ensembles yielded a temporal-holdout ROC-AUC of 64.35% (95% CI 52.6–74.6%). In the participant-independent subset of 25 PDAC cases not represented in development and 57 controls, ROC-AUC was 66.04% (95% CI 52.56–78.88%). Conclusions: SGNNs provide a feasible graph-based representation of pre-diagnostic proteomic data but did not outperform optimised conventional machine-learning methods within development cross-validation. The temporal and participant-independent performance estimates warrant further investigation in larger fully independent cohorts. A graph-specific predictive advantage was not directly tested—no such advantage was demonstrated in this dataset—and the clinical utility has not been established. Full article

Background: This study aimed to define the clinical significance of pretreatment serum growth differentiation factor-15 (GDF-15), including its associations with clinical vulnerability, survival, and chemotherapy outcomes in advanced pancreatic ductal adenocarcinoma (PDAC). Methods: Pretreatment serum samples were prospectively collected from 80 patients with locally advanced or metastatic PDAC. GDF-15 was measured using an enzyme-linked immunosorbent assay and analyzed continuously and according to the cohort median. Associations with clinicopathological characteristics and overall survival (OS) were evaluated in the overall cohort. Treatment effectiveness, delivery, and safety were additionally assessed among 68 patients who received systemic chemotherapy. Results: The median GDF-15 concentration was 362.6 pg/mL. Higher GDF-15 was associated with poor performance status, involvement of multiple metastatic organ sites, an elevated neutrophil-to-lymphocyte ratio, and hypoalbuminemia. Eleven of the 12 patients unable to receive chemotherapy because of clinical deterioration had high GDF-15. The high GDF-15 group had shorter OS than the low GDF-15 group in the overall cohort (median, 6.1 vs. 13.9 months; hazard ratio [HR] = 1.96; 95% CI, 1.23–3.13; p = 0.002). The association between continuous GDF-15 and OS was attenuated and no longer statistically significant after multivariable adjustment (HR per doubling = 1.22; 95% CI, 0.97–1.52; p = 0.085). Among treated patients, progression-free survival and objective response did not differ significantly between groups, whereas high GDF-15 was associated with shorter OS (median, 8.7 vs. 14.2 months; HR = 1.72; 95% CI, 1.02–2.90; p = 0.025) and treatment duration (median, 3.2 vs. 6.3 months; p = 0.007). Relative dose intensity and subsequent treatment rates were similar. Febrile neutropenia occurred exclusively in the high GDF-15 group, which also accounted for four of five treatment-related deaths. Conclusions: Elevated pretreatment GDF-15 was associated with limited treatment fitness and inferior survival in patients with advanced PDAC, without clear evidence of intrinsic chemotherapy resistance. GDF-15 may represent a potential marker of systemic vulnerability in advanced PDAC, although its clinical utility requires external validation. Full article

Background: Artificial intelligence (AI) is now applied across the pancreatic ductal adenocarcinoma (PDAC) pathway, yet the literature has grown faster than the evidence needed to act on it. The question is not whether an algorithm can predict an endpoint, but whether it yields information reliable, generalizable, and actionable enough to change a clinically meaningful decision. Methods: In this narrative, in a non-systematic review of PDAC-specific literature, we grade applications on an author-defined evidence-readiness framework (development, internal, external, prospective validation, clinical impact, and implementation) and on four dimensions—validation, comparator, actionability, and impact. Findings: Contemporary meta-analyses show high pooled accuracy for AI-based early detection and for distinguishing PDAC from mass-forming pancreatitis (sensitivity ~0.88–0.92, specificity ~0.90–0.93), but with extreme heterogeneity, low radiomics quality, scarce external validation, and—for electronic health-record risk models—positive predictive values often below 1% at population prevalence. Computed tomography (CT)-based detection and risk enrichment are among the more mature applications identified, yet none has been shown to improve stage distribution, resection rate, or survival. Treatment prediction is held to a strict standard, distinguishing prognostic association from a validated treatment-by-biomarker interaction. Conclusions: The rate-limiting steps are no longer algorithmic but translational—external and prospective validation, calibration, appropriate comparators, and workflow integration. Full article

Background: Biliary tract cancers (BTCs) carry a poor prognosis driven by frequent diagnosis at advanced or metastatic stages. Inadequate tissue sampling impedes biomarker-directed selection of therapy that could potentially improve outcomes over limited chemotherapeutic options. Plasma cell-free DNA (cfDNA) yields limited sensitivity. However, given its direct contact with the tumor, bile holds promise as a locally enriched source of tumor-derived cfDNA for evaluation. Methods: Following the PRISMA-ScR guidelines, we searched PubMed/MEDLINE and EMBASE without date restrictions. Seventeen primary studies of bile cfDNA in cholangiocarcinoma (CCA), gallbladder carcinoma (GBC), pancreatic ductal adenocarcinoma (PDAC), and primary sclerosing cholangitis (PSC) were synthesized narratively. Results: Compared to plasma cfDNA, Bile cfDNA was isolated at higher concentrations and in longer fragments than plasma cfDNA; bile cfDNA demonstrated higher concordance with matched tumor tissue, capturing more actionable alterations. Pairing it with serum CA19-9 or newer bile-derived markers improved performance. Emerging data suggest a role in early detection of CCA during PSC surveillance. Conclusions: Bile cfDNA as a liquid biopsy medium offers analytic advantages over plasma for detecting and profiling biliary malignancy. Standardized collection, extraction, and reporting protocols and prospective multicenter validation are needed before routine use. Full article

Background/Objectives: Pancreatic ductal adenocarcinoma (PDAC) is a malignancy with poor prognosis, where SMAD4 protein loss is closely associated with increased tumor aggressiveness and metastatic potential. Current assessment of SMAD4 expression relies on IHC of tumor tissue obtained by biopsy or resection, in which limited sampling may not represent intratumoral heterogeneity. T1ρ-weighted imaging, an emerging functional MRI technique, shows promise in characterizing tumor microstructural properties. This study aimed to evaluate the value of T1ρ imaging combined with conventional MRI sequences and clinical parameters for non-invasive prediction of SMAD4 protein expression status in PDAC. Methods: This retrospective single-center study included 57 patients with histologically confirmed PDAC who underwent pretreatment MRI with T1ρ imaging between September 2024 and May 2025. Immunohistochemical analysis revealed SMAD4 intact in 28 tumors (49.1%) and SMAD4 loss in 29 tumors (50.9%). T1ρ values were measured and compared between SMAD4 intact and SMAD4 loss tumors. A multiparametric predictive model incorporating T1ρ values, lymph node metastasis status, and carbohydrate antigen 19-9 (CA19-9) levels was developed. Results: SMAD4 intact tumors demonstrated significantly higher T1ρ values compared to SMAD4 loss tumors (96.34 ± 14.12 vs. 71.93 ± 10.24 ms, p < 0.001). T1ρ achieved an AUC of 0.901 (95% CI: 0.817–0.986), with 82.14% sensitivity and 89.66% specificity, for predicting SMAD4 expression status. A multiparametric model combining T1ρ values, lymph node metastasis status, and CA19-9 levels yielded a numerically higher AUC of 0.932 (95% CI: 0.862–1.000), although the difference from T1ρ alone was not statistically significant (DeLong test, p > 0.05). Conclusions: T1ρ imaging showed potential for predicting SMAD4 status in PDAC. Full article

Background: Tertiary lymphoid structures (TLSs) may identify immunologically active pancreatic ductal adenocarcinoma (PDAC), but clinical definitions and spatial analyses have been inconsistent. Methods: We integrated a systematic review of 21 studies (22 reports) with patient-aware secondary analyses of seven public molecular datasets. Results: Pathology-defined TLS presence was associated with longer overall survival in five cohorts (1019 patients; random-effects hazard ratio 0.544, 95% confidence interval 0.398–0.743). In pathology-annotated spatial data, a mature-TLS program was enriched in TLS-positive regions at the patient level. B-cell and Tfh/CXCL13 programs showed reproducible positive associations with continuous TLS burden, whereas other program associations were less directionally consistent. An independent spatial cohort supported an rCAF-over-myCAF association with lymphoid probability. Chemokine-axis co-enrichment was observed but did not establish direct signaling. Conclusions: TLSs mark a favorable, spatially organized immune state in PDAC. The evidence supports biomarker and biological hypotheses while retaining boundaries on causality, treatment induction, and clinical prediction. Full article

Pancreatic diseases, particularly pancreatic ductal adenocarcinoma (PDAC), remain among the most challenging disorders to diagnose and treat owing to a series of interconnected biological barriers, including dense stromal desmoplasia, profound immunosuppression, hypoxia, therapeutic resistance, and the lack of effective early-detection strategies. Overcoming these barriers requires multifunctional platforms capable of integrating diagnosis, microenvironment regulation, and precision therapy within a single system. Metal–organic frameworks (MOFs), a class of highly tunable porous crystalline materials, have emerged as uniquely suited candidates for this purpose because of their programmable composition, structural versatility, catalytic activity, and capacity for multifunctional integration. Notably, MOFs have evolved from passive drug carriers into active regulators of disease-associated biological processes, enabling enhanced drug delivery, extracellular matrix remodeling, ferroptosis- and cuproptosis-based precision therapy, energy-triggered reactive oxygen species generation, immune microenvironment reprogramming, and imaging-guided theranostics. Beyond pancreatic cancer, emerging evidence further supports their potential applications in pancreatitis management and interventional therapeutic enhancement. In this review, we systematically summarize recent advances in the rational engineering of MOFs for pancreatic diseases, focusing on how material design strategies are leveraged to overcome key pathological barriers and improve therapeutic outcomes. We further discuss current challenges associated with biosafety, biodegradation, large-scale manufacturing, and clinical translation, while highlighting future opportunities for the development of next-generation MOF-based precision nanomedicines. By integrating advances across materials science, nanomedicine, and pancreatic disease biology, this review provides a comprehensive framework for the design and translational development of MOF-enabled diagnostic and therapeutic platforms. Full article