Biochim Biophys Acta Rev Cancer. 2025 Oct 23:189484. doi: 10.1016/j.bbcan.2025.189484. Online ahead of print.

ABSTRACT

The five-year survival rate for Pancreatic Ductal Adenocarcinoma (PDAC) remains below 10 %, primarily due to the limited efficacy of conventional chemotherapy and immune checkpoint inhibitors against its triple-immune-sequestered, low-TMB tumor microenvironment(TME). This situation has been furter exacerbated by the stagnation of traditional vaccine development, driven by inefficient antigen screening and high tumor heterogeneity. Artificial intelligence (AI) exhibits remarkable advantages in the design of pancreatic ductal adenocarcinoma (PDAC) vaccines. It can integrate multi - omics data to efficiently unearth cryptic neoantigens from low - tumor mutation burden (TMB) samples, significantly enhancing the screening efficiency. Through dynamic modeling, AI can rationally plan the timing of combined vaccine therapies, effectively reducing the degree of T - cell exhaustion. By leveraging the digital twin model, AI can remarkably improve the matching accuracy between antigens and human leukocyte antigen (HLA). Additionally, it can construct a monitoring system to provide early warnings of antigen loss risks, thus gaining adjustment time for clinical treatments.This review aims to accomplish three primary objectives: demonstrate AI's potential in breaking the therapeutic impasse to overcome manufacturing-related treatment delays for 25-30 % of patients; further delineates the logical progression of AI from concept to clinical application; thereby provides a translational framework to bridge the gap between research and patient benefit.

PMID:41138796 | DOI:10.1016/j.bbcan.2025.189484

bioRxiv [Preprint]. 2025 May 5:2025.05.01.651678. doi: 10.1101/2025.05.01.651678.

ABSTRACT

Pancreatic cancer remains as one of the most challenging malignancies to diagnose and treat due to the late development of symptoms and limited early diagnostic options. Intraductal papillary mucinous neoplasms (IPMNs) are non-invasive precursors to invasive pancreatic ductal adenocarcinoma (PDAC)and an understanding of the changes in patterns of protein expression that accompany the progression from normal ductal (ND) cell, to IPMN to PDAC may provide avenues for improved earlier detection. In this study, we present an optimized spatial tissue proteomics workflow, termed SP-Max (Spatial Proteomics Optimized for Maximum Sensitivity and Reproducibility in Minimal Sample), designed to maximize protein recovery and quantification from limited laser micro dissected (LMD) samples. Our workflow enabled the identification of more than 6,000 proteins and the quantification of over 5,200 protein groups from FFPE tissue contours of pancreatic tissues. Comparative analyses across ND, IPMN, and PDAC revealed critical molecular differences in protein pathways and potential markers of progression. SP-Max provides a systematic, reproducible approach that significantly enhances our ability to study precancerous lesions and cancer progression in pancreatic tissues at unprecedented resolution.

PMID:40654937 | PMC:PMC12247709 | DOI:10.1101/2025.05.01.651678

Support Care Cancer. 2025 Jun 19;33(7):596. doi: 10.1007/s00520-025-09670-9.

ABSTRACT

PURPOSE: Anorexia is a frequent and serious symptom in patients with lung cancer, often leading to malnutrition and cachexia, and negatively affecting quality of life and survival. This scoping review systematically synthesizes current evidence on biomarkers associated with cancer-related anorexia (CRA) in lung cancer, aiming to clarify biological mechanisms and inform targeted interventions.

METHODS: We performed a comprehensive literature search of studies evaluating the associations between CRA and various biomarkers in patients with lung cancer. Data were extracted and analyzed for pathway, genomic, transcriptomic, epigenetic, proteomic, metabolic, and composite biomarkers.

RESULTS: A total of 33 studies were included, identifying more than 100 biomarkers closely associated with CRA in lung cancer. These include inflammatory cytokines, energy metabolism markers, epigenetic and transcriptomic alterations, and disruptions in multiple cellular signaling pathways. Our analysis demonstrates that CRA is not the result of a single factor but reflects widespread dysregulation across metabolic, immune, and signaling networks. Some studies suggest that nutritional and anti-inflammatory interventions, such as n-3 fatty acid and antioxidant supplementation, can modulate biomarker profiles and potentially improve clinical outcomes.

CONCLUSION: CRA in lung cancer is a multifactorial syndrome involving complex interactions among inflammatory, metabolic, and signaling pathways. Multi-omics biomarker integration holds promise for early detection and individualized treatment, but larger, multi-center studies are needed to confirm clinical utility and optimize management strategies. Precision interventions based on biomarker profiles should be further explored in future research and practice.

PMID:40536584 | DOI:10.1007/s00520-025-09670-9