Pancreatic cancer immunotherapy biomarkers: from traditional markers to multimodal integration and dynamic monitoring

Pancreatic ductal adenocarcinoma (PDAC) remains an intractable cancer marked by delayed diagnosis, rapid progression, and significant resistance to current treatments. Conventional biomarkers, such as CA19-9, have insufficient sensitivity and specificity. Meanwhile, the practical use of newer markers such as the tumor mutational burden and microsatellite instability is limited by the absence of standardized testing protocols and definitive threshold values. Circulating tumor DNA and exosomal miRNA hold promise for continuously tracking tumor dynamics and effectiveness of immunotherapy, but additional validation is necessary before their routine clinical application. Recent advancements in multiomics, nanotechnology, and artificial intelligence have opened new possibilities for more accurate and comprehensive biomarkers. For instance, Shah et al. developed shortwave-infrared-emitting nanoprobes to specifically target CD8 + cytotoxic T cells, permitting high-sensitivity in vivo imaging in breast cancer models. Batool et al. utilized nanoplasmonic sensors to detect changes in serum programmed death-ligand 1 and cytokine levels within 1–2 weeks post-treatment, achieving picomolar sensitivity. Chang et al. combined fluorescence and photoacoustic imaging in the NanoTrackThera platform, facilitating the real-time monitoring of immunotherapy efficacy. This review highlights the evolution of PDAC biomarkers from traditional markers to multimodal integration and dynamic monitoring. The limitations of current markers and potential of emerging technologies, including metabolic reprogramming markers, epigenetic regulators, and AI-driven predictive models, are discussed. Future directions include multicenter prospective trials to validate multimodal models, standardize detection methods, and increase interdisciplinary collaboration. By integrating genomic, epigenetic, metabolic, and microbiome data, these models can better capture the complexity of PDAC, thereby improving patient outcomes through precision immunotherapy.