Predictive Biomarkers for Early Diagnosis of Pancreatic Ductal Adenocarcinoma (PDAC)

Overview

Pancreatic ductal adenocarcinoma (PDAC) is one of the cancers with the poorest prognosis and is often diagnosed at an advanced stage, resulting in very low 5-year survival rates. Many PDACs arise from non-invasive precursor lesions that develop over years, including pancreatic intraepithelial neoplasia (PanIN) and intraductal papillary mucinous neoplasms (IPMN). Only a minority of pancreatic cystic lesions progress to invasive carcinoma, and current clinical and radiologic criteria have limited accuracy in predicting which patients are at high risk. This observational translational study will enroll adult patients undergoing pancreatic surgical resection for suspected pancreatic neoplasm (IPMN, PanIN, or PDAC) at IRCCS "Saverio de Bellis". Residual tumor tissue not required for diagnostic purposes, and when available adjacent non-neoplastic tissue, will be collected, coded, and pseudonymized for molecular analyses. Tumor cells will be used to generate three-dimensional cultures (tumorspheres and organoids) and will undergo genomic characterization by next-generation sequencing, RNA-sequencing-based transcriptomic profiling, protein expression analyses, advanced imaging, and in-vitro drug response assays. The main goal is to identify and validate molecular biomarkers predictive of progression to PDAC, improve risk stratification of patients with pancreatic precursor lesions, and support the development of innovative precision medicine strategies.

Pancreatic ductal adenocarcinoma (PDAC) is currently one of the leading causes of cancer-related mortality worldwide and is characterized by late diagnosis, high biological aggressiveness, and marked resistance to systemic therapies. Epidemiologic projections indicate that, by 2030, PDAC will become the second most common cause of cancer death in Western countries, highlighting the need for improved early-diagnosis strategies and therapeutic interventions. Most PDACs arise from non-invasive precursor lesions through a multistep neoplastic transformation process. Key precursor entities include pancreatic intraepithelial neoplasia (PanIN) and mucinous cystic lesions, particularly intraductal papillary mucinous neoplasms (IPMN), which are clinically detectable by imaging, biologically heterogeneous, and associated with variable malignant potential. The widespread use of high-resolution imaging has increased incidental detection of pancreatic cysts, but only a subset of these lesions will progress to invasive carcinoma, making clinical management challenging. In this context, the identification of reliable molecular biomarkers capable of predicting progression to invasive carcinoma is a clinical and scientific priority. This single-center, prospective observational translational study will include three arms: patients with IPMN, patients with PanIN, and patients with PDAC confirmed by histopathologic examination after pancreatic resection. Residual tissue not needed for diagnostic histopathology will be processed following standardized procedures to ensure high-quality biospecimens and full traceability. Fresh samples will be used for isolation of tumor stem-like cells and establishment of three-dimensional cultures (tumorspheres and organoids); additional material will be fixed in formalin and embedded in paraffin (FFPE) for histopathologic and immunohistochemical analyses. Molecular analyses will comprise next-generation sequencing (NGS) to identify somatic mutations, genomic instability, tumor mutational burden (TMB), and alterations in key oncogenic genes and pathways; RNA-sequencing (RNA-seq) to characterize transcriptional programs associated with neoplastic transformation; protein expression studies by immunofluorescence, immunohistochemistry, and Western blot focusing on proliferation, apoptosis, and tumor stemness markers; advanced cellular imaging to study proliferation, survival, invasiveness, and metabolic adaptation under stress; and in-vitro pharmacologic assays to assess sensitivity of patient-derived models to inhibitors targeting the identified biomarkers. Statistical analyses will include Monte Carlo-based sample-size estimation, group comparisons using parametric and non-parametric tests, logistic regression models, ROC curve analyses, and high-dimensional bioinformatic pipelines for differential expression, clustering, and multivariate pattern recognition. The ultimate aim is to define and validate molecular signatures predictive of progression toward PDAC, correlate them with clinical and pathological features, and generate organoid models for preclinical studies in precision oncology.