Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.] · 2025
Prediction model for mutation detection via circulating tumor DNA analysis in pancreatic cancer.
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Suzuki Rei, Asama Hiroyuki, Shimizu Hiroshi, Sato Kentaro, Osawa Kento, Ohira Rei, Kudo Keisuke, Sugimoto Mitsuru, Ohira Hiromasa
Аннотация
BACKGROUND: Comprehensive genomic profiling (CGP) is essential for precision medicine in pancreatic cancer (PC) patients. In unresectable cases, obtaining sufficient tissue is challenging, making plasma-based liquid CGP a common choice. The successful detection of KRAS mutations, which are present in more than 90 % of pancreatic cancers, indicates that liquid CGP has successfully detected circulating tumor DNA. This study aimed to develop a prediction model for detecting KRAS mutations in liquid CGP among patients with PC using the Japanese National Database.
METHODS: We evaluated the detection of potentially targetable gene mutations, including BRAF V600E, BRCA1/2, and PALB2. Multivariate logistic regression analysis was performed to identify independent predictors of KRAS mutation, and a scoring system was developed.
RESULTS: Targetable gene mutations were significantly more frequent in PC patients with KRAS mutations than in those without (4.8 % vs. 0.5 %, P < 0.001). A predictive model was created using four clinical variables (metastasis to the liver, bone, or peritoneum and treatment response before blood collection) in the training cohort (n = 1574). The percentage of patients with KRAS mutations was predicted to be 29.6 % at low probabilities (-1 to 3 points) and 84.4 % at high probabilities (9 or more points). In the validation cohort (n = 1052), the model showed moderately good discrimination, with a diagnostic accuracy of 0.70. We developed a web-based application for this predictive model (https://pdac-KRAS-detection.shinyapps.io/KRAS-prediction-tool/).
CONCLUSIONS: We developed and validated a predictive model for KRAS mutations. This model may be a useful clinical decision-making tool for liquid CGP in patients with PC.
METHODS: We evaluated the detection of potentially targetable gene mutations, including BRAF V600E, BRCA1/2, and PALB2. Multivariate logistic regression analysis was performed to identify independent predictors of KRAS mutation, and a scoring system was developed.
RESULTS: Targetable gene mutations were significantly more frequent in PC patients with KRAS mutations than in those without (4.8 % vs. 0.5 %, P < 0.001). A predictive model was created using four clinical variables (metastasis to the liver, bone, or peritoneum and treatment response before blood collection) in the training cohort (n = 1574). The percentage of patients with KRAS mutations was predicted to be 29.6 % at low probabilities (-1 to 3 points) and 84.4 % at high probabilities (9 or more points). In the validation cohort (n = 1052), the model showed moderately good discrimination, with a diagnostic accuracy of 0.70. We developed a web-based application for this predictive model (https://pdac-KRAS-detection.shinyapps.io/KRAS-prediction-tool/).
CONCLUSIONS: We developed and validated a predictive model for KRAS mutations. This model may be a useful clinical decision-making tool for liquid CGP in patients with PC.