Prediction System for KRAS Mutation Detection in Circulating Tumor DNA in Unresectable Pancreatic Cancer

Background/Aim: Unresectable pancreatic cancer (PC) is an aggressive malignancy with a poor prognosis and limited treatment options. Advances in molecular profiling and liquid biopsy, specifically the detection of circulating tumor DNA (ctDNA), offer new avenues for personalized therapy. KRAS mutations are present in approximately 63-70% of PC patients in circulating cell free DNA in the blood of approximately 63-70% of patients with PC; however, their detection via liquid biopsy can be influenced by disease stage, metastasis site, and ctDNA concentration. The aim of this retrospective study was to develop a prediction model for KRAS mutation detection in unresectable patients with PC using clinical variables.

Patients and Methods: We retrospectively analyzed 32 patients who underwent ctDNA testing from 2019 to 2024, utilizing either FoundationOne^® Liquid CDx or Guardant360^® CDx panels. Multivariate analysis of the clinical factors was performed via logistic regression with stepwise selection to elucidate independent predictors of KRAS mutation detection. A nomogram was developed based on the independent predictors of successful KRAS detection.

Results: Multivariate analysis revealed that liver metastasis, multiple metastatic sites, and disease progression were significant predictors of successful KRAS mutation detection. A nomogram and the receiver operating characteristic curve demonstrated high predictive accuracy, with a sensitivity of 70%, specificity of 90.9%, and area under curve of 0.83.

Conclusion: Our prediction system effectively stratified patients by the likelihood of KRAS mutation detection, offering a practical tool for selecting candidates for liquid biopsy. These findings underscore the importance of personalized approaches in unresectable PC management and suggest that patients without these key clinical factors may not benefit from ctDNA testing. Future studies should validate this model in larger cohorts.