KRAS Mutation Allele Frequency Dynamics in Plasma Extracellular Vesicles: Association with Survival in Localized Pancreatic Adenocarcinoma.

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) lacks consistent biomarkers to monitor treatment response and predict survival. Metabolically active extracellular vesicles (EVs) carrying tumor-specific KRAS mutations offer promise as disease-specific biomarkers.

PATIENTS AND

METHODS

Informed by genomic profiling of tumor tissue, plasma samples were prospectively collected from 44 patients, with confirmed KRAS-mutated PDAC, undergoing neoadjuvant therapy (NAT) followed by surgery between 2019 and 2021. Samples were obtained at diagnosis, post-NAT, and 1 month post surgery. EVs were isolated using lipid nanoprobe technology, and EV-associated KRAS mutations were detected using droplet digital polymerase chain reaction (ddPCR). Patients were grouped on the basis of temporal changes in EV-associated KRAS mutation allele frequency (MAF): no KRAS detected (ND), decreasing MAF (DD), and increasing MAF (ID).

RESULTS

Among 44 patients, 29 (65.9%) were ND, 8 (18.2%) DD, and 7 (15.9%) ID. Detectable EV-associated KRAS MAF was found in 21%, 30%, and 50% of patients with stages I, II, and III PDAC. No significant differences were noted in demographic or clinical variables (p > 0.05). The ND group had the longest restricted mean disease-free survival (rmDFS: 31.2 months), followed by DD (27.8 months) and ID (9.8 months; p = 0.010). Similarly, restricted mean overall survival (rmOS) was longest in the ND (40.3 months), followed by DD (35.7 months) and ID (17.7 months; p = 0.012). On multivariable analysis, increasing EV-KRAS MAF (ID group) independently predicted inferior rmDFS [hazard ratio (HR): 6.14; p = 0.001] and rmOS (HR: 6.95; p = 0.002).

CONCLUSIONS

Temporal increase of EV-KRAS MAF is a significant predictor of reduced DFS and OS in PDAC. Integrating EV-KRAS mutation allele frequency dynamics analysis with current biomarkers such as carbohydrate antigen 19-9 (CA19-9) could improve treatment monitoring and survival prognostication.