UC San Diego

Pancreatic cancer remains one of the most challenging malignancies to treat due to its late-stage diagnosis, aggressive progression, and high resistance to existing therapies. This review examines the latest advancements in early detection, and therapeutic strategies, with a focus on emerging biomarkers, tumor microenvironment (TME) modulation, and the integration of artificial intelligence (AI) in data analysis. We highlight promising biomarkers, including microRNAs (miRNAs) and circulating tumor DNA (ctDNA), that offer enhanced sensitivity and specificity for early-stage diagnosis when combined with multi-omics panels. A detailed analysis of the TME reveals how components such as cancer-associated fibroblasts (CAFs), immune cells, and the extracellular matrix (ECM) contribute to therapy resistance by creating immunosuppressive barriers. We also discuss therapeutic interventions that target these TME components, aiming to improve drug delivery and overcome immune evasion. Furthermore, AI-driven analyses are explored for their potential to interpret complex multi-omics data, enabling personalized treatment strategies and real-time monitoring of treatment response. We conclude by identifying key areas for future research, including the clinical validation of biomarkers, regulatory frameworks for AI applications, and equitable access to innovative therapies. This comprehensive approach underscores the need for integrated, personalized strategies to improve outcomes in pancreatic cancer.