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PubMed2026

Serial CSF CA19-9 monitoring and CSF genomic profiling in ERBB2-mutant lung adenocarcinoma with leptomeningeal metastasis: a case report.

Leptomeningeal metastasis (LM) from ERBB2-mutant lung adenocarcinoma is difficult to treat and monitor because systemic disease and leptomeningeal disease may evolve discordantly. Evidence regarding cerebrospinal fluid (CSF) genomic profiling and serial CSF tumor marker monitoring in ERBB2-mutant non-small cell lung cancer with LM remains limited. We report a 48-year-old woman initially diagnosed with stage IB mucinous lung adenocarcinoma harboring an ERBB2 exon 20 p.G776delinsVC mutation. After surgery and adjuvant chemotherapy, she developed nodal recurrence and later presented with lower back pain and lower-limb numbness. LM was clinically diagnosed based on neurological symptoms, magnetic resonance imaging and CSF cytopathology. She received craniospinal irradiation, systemic therapy and subsequent intrathecal treatment. At month 33, CSF CA19-9 was markedly elevated despite no clear radiographic systemic progression. CSF next-generation sequencing(NGS) detected the same ERBB2 exon 20 p.G776delinsVC mutation as the primary lung tumor, with a higher variant allele fraction in CSF than in lung tissue.The patient subsequently received trastuzumab deruxtecan and sequential intrathecal therapy with pemetrexed, etoposide, cytarabine and pemetrexed rechallenge. Intrathecal treatment was adjusted according to serial CSF CA19-9 levels, neurological status, imaging findings, systemic disease activity and treatment-related toxicities. CSF CA19-9 was not used as a stand-alone criterion for progression or treatment modification.At the latest follow-up, she remained alive more than 36 months after the clinical diagnosis of LM. This case suggests that CSF NGS and serial CSF CA19-9 may provide further insights into disease activity within the integrated assessment of systemic and leptomeningeal disease. Their clinical applicability is still under investigation and necessitates future validation.

CA19-9↗

PubMed2026

Longitudinal analysis of circulating tumor DNA and CA19-9 dynamics in predicting disease relapse and monitoring treatment response in stage I-III pancreatic ductal adenocarcinoma: An interim analysis of a prospective observational study.

INTRODUCTION: Postoperative recurrence is the leading cause of mortality in resected pancreatic ductal adenocarcinoma (PDAC), yet reliable tools for early relapse detection and treatment response assessment remain lacking. METHODS: In a prospective cohort of 136 patients with resected stage I-III PDAC receiving adjuvant chemotherapy, we evaluated circulating tumor DNA (ctDNA) and CA19-9 as longitudinal biomarkers across multiple postoperative time windows. RESULTS: ctDNA consistently outperformed CA19-9 as an independent prognostic factor; ctDNA positivity at on-treatment and surveillance assessments achieved a positive predictive value of 91.7%, while persistent negativity identified the lowest-risk patients. Integrating CA19-9 with ctDNA resolved the ctDNA-alone gap in distinguishing treatment clearance from conversion, improving discrimination of responders from non-responders (HR 3.72; P = .008). A time-weighted dynamic ctDNA risk score (MinerVa-dynamic) further stratified ctDNA-negative patients into clinically distinct prognostic subgroups, achieving an area under the curve of 0.87 and 0.82 for one- and two-year disease-free survival prediction, respectively. CONCLUSIONS: These findings support a dual-biomarker longitudinal monitoring framework as a practical, individualized approach to postoperative surveillance and early therapeutic decision-making in PDAC.

CA19-9↗

PubMed2024

Development of a Fit-For-Purpose Multi-Marker Panel for Early Diagnosis of Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) suffers from a lack of an effective diagnostic method, which hampers improvement in patient survival. Carbohydrate antigen 19-9 (CA19-9) is the only FDA-approved blood biomarker for PDAC, yet its clinical utility is limited due to suboptimal performance. Liquid chromatography-mass spectrometry (LC-MS) has emerged as a burgeoning technology in clinical proteomics for the discovery, verification, and validation of novel biomarkers. A plethora of protein biomarker candidates for PDAC have been identified using LC-MS, yet few has successfully transitioned into clinical practice. This translational standstill is owed partly to insufficient considerations of practical needs and perspectives of clinical implementation during biomarker development pipelines, such as demonstrating the analytical robustness of proposed biomarkers which is critical for transitioning from research-grade to clinical-grade assays. Moreover, the throughput and cost-effectiveness of proposed assays ought to be considered concomitantly from the early phases of the biomarker pipelines for enhancing widespread adoption in clinical settings. Here, we developed a fit-for-purpose multi-marker panel for PDAC diagnosis by consolidating analytically robust biomarkers as well as employing a relatively simple LC-MS protocol. In the discovery phase, we comprehensively surveyed putative PDAC biomarkers from both in-house data and prior studies. In the verification phase, we developed a multiple-reaction monitoring (MRM)-MS-based proteomic assay using surrogate peptides that passed stringent analytical validation tests. We adopted a high-throughput protocol including a short gradient (<10 min) and simple sample preparation (no depletion or enrichment steps). Additionally, we developed our assay using serum samples, which are usually the preferred biospecimen in clinical settings. We developed predictive models based on our final panel of 12 protein biomarkers combined with CA19-9, which showed improved diagnostic performance compared to using CA19-9 alone in discriminating PDAC from non-PDAC controls including healthy individuals and patients with benign pancreatic diseases. A large-scale clinical validation is underway to demonstrate the clinical validity of our novel panel.

Humans↗

PubMed2026

Plasma Proteomic Profiling Identifies Candidate Biomarkers for Pancreatic Ductal Adenocarcinoma.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy that is often diagnosed after curative treatment is no longer feasible. Existing biomarkers, particularly CA19-9, have limited sensitivity and specificity. Plasma proteins that capture tumor-associated biological alterations may therefore provide useful signals for earlier detection. METHODS: Plasma samples from 99 patients with PDAC and 30 healthy controls were analyzed using data-independent acquisition (DIA) proteomics. Differentially expressed proteins were identified using predefined statistical thresholds and further examined by functional enrichment analysis. Selected candidate biomarkers were validated by ELISA in an independent subset. RESULTS: Among 565 quantified plasma proteins, 52 were differentially expressed between PDAC and controls. These proteins were enriched in extracellular processes, cholesterol metabolism, complement and coagulation cascades, and pancreatic secretion pathways. ELISA validation confirmed higher plasma levels of Cathepsin S, CTRB2, MARCO, PIGR, PRDX6, REG1A, Trypsin-2, and PEP-FAP in patients with PDAC compared with healthy controls. ROC analyses showed moderate-to-good discriminatory performance for several candidates, and the MARCO + PEP-FAP model improved classification compared with either marker alone. CONCLUSION: These findings reveal circulating proteins linked to key PDAC-related biological processes and identify eight candidates for further evaluation in multi-protein diagnostic panels. Larger validation studies incorporating clinically relevant disease control groups are warranted to determine their diagnostic specificity and clinical utility.

Humans↗

PubMed2026

Overcoming cancer resistance in pancreatic cancer: toward dynamic precision oncology.

Pancreatic ductal adenocarcinoma (PDAC) remains a highly lethal malignancy, largely because of its profound and evolving therapeutic resistance. Resistance is not determined by a single molecular alteration but arises from interconnected mechanisms, including intrinsic resistance, treatment-induced adaptive resistance, acquired resistance, genomic evolution, clonal selection, cancer stemness, phenotypic plasticity, metabolic adaptation, and tumor microenvironment-mediated effects. Emerging therapeutic approaches targeting KRAS/RAS signaling, stromal and immune components, metabolic dependencies, and DNA damage repair pathways offer opportunities to address these mechanisms, although durable efficacy remains limited by biological heterogeneity and adaptive responses. In this review, we examine therapeutic resistance as an evolutionary and multidimensional process and summarize emerging strategies for overcoming resistance. We further propose a Dynamic Precision Oncology (DPO) framework that extends conventional precision oncology beyond baseline molecular profiling by integrating longitudinal assessment of tumor genomics, circulating tumor DNA, CA19-9, imaging, radiomics, and clinical characteristics. This framework emphasizes iterative detection and characterization of emerging resistance, mechanism-informed treatment adaptation, and subsequent reassessment rather than automatic treatment modification based on a single biomarker. DPO may provide a conceptual framework for integrating evolving tumor biology into treatment decision-making, while prospective studies are needed to validate biomarkers, define actionable thresholds, and determine whether longitudinal resistance-guided strategies improve clinical outcomes in PDAC.

Humans↗

PubMed2026

Non-invasive strategy for gastric cancer detection: Integration of cell-free DNA fragmentomics and protein biomarkers.

Gastric cancer (GC) ranks as the fifth most common cancer worldwide, however, accurate and non-invasive diagnostic modalities for GC remain limited. Cell-free DNA (cfDNA) fragmentomics has emerged as a promising tool for cancer cell detection. Here we develop a gastric cancer detection model, named GaFraD model. The GaFraD model uses four cfDNA fragmentomics features, including fragment size ratio (FSR), copy number variation (CNV), 9-bp end motif (Motif), and fragment size at transcription start sites (TF). This model achieves an area under the receiver-operating characteristic curve (AUC) of 0.970 (95% CI: 0.944 - 0.990), a sensitivity of 95.0% and a specificity of 80.9%. By combining the GaFraD model and conventional protein biomarkers CA19-9 and PG-I/PG-II, the CONFIRM model was generated. The CONFIRM model attained an AUC of 0.986 (95% CI: 0.966 - 1.000), a sensitivity of 95.0% and a specificity of 95.6% in detecting GC. Moreover, the CONFIRM model achieved remarkable performance (AUC = 0.983, sensitivity 95.6%, specificity 94.2%) in distinguishing patients with early-stage GC from controls. Our work showed the high discriminatory power in distinguishing GC patients from controls, indicating the clinical potential of using cfDNA fragmentomics combined with protein biomarkers for non-invasive GC detection. The results of the study provide a new avenue for early, accurate, and non-invasive clinical diagnosis of GC.

Cell-free DNA↗

PubMed2025

KRAS Mutations in Duodenal Lavage Fluid After Secretin Stimulation for Detection of Pancreatic Cancer.

OBJECTIVE: Although pancreatic ductal adenocarcinoma (PDAC) is still a devastating disease, the survival rate for surgically removed PDACs has significantly improved in recent years. Early detection is essential in managing PDAC. BACKGROUND: The presence of KRAS mutations in PDAC leads to the initial genetic abnormality and offers a significant timeframe for identifying resectable PDACs. A minimally invasive and highly specific PDAC screening test is necessary to prevent the need for invasive follow-up tests. METHODS: Between July 2021 and March 2023, 169 cases were enrolled in 7 institutions. By administering secretin before esophagogastroduodenoscopy (EGD), the excretion of pancreatic juice into the papillary fluid can be stimulated, creating a resource for testing. Washing fluid was collected using a specialized catheter from control individuals (n=75) and patients with resectable PDAC (n=89) at the initial diagnosis. A highly sensitive technique was employed to study KRAS gene mutations. RESULTS: This study obtained an AUC of 0.934 (95% CI: 0.904, 0.964) when using KRAS mutations in duodenal lavage fluid to differentiate between patients with resectable PDAC and healthy controls. The estimated sensitivities were calculated with specificity set at 100%, resulting in a sensitivity of 83.1% (95% CI: 71.7%, 91.2%). The McNemer test showed a significantly higher sensitivity for KRAS mutations than serum CEA and CA19-9 ( P <0.0001). CONCLUSIONS: We created a method to identify resectable PDACs by analyzing KRAS mutation levels in duodenal fluid collected during EGD with secretin stimulation of pancreatic juice secretion.

Humans↗