Patients’ characteristics
The authors prospectively evaluated 157 patients included in this study, 5 patients withdraw, 3 patients lost in follow up. So the authors analyzed 149 patients, including 105 pancreatic canc patients [age 58.38 ± 11.01 years; male 69/105 (65.71%), 14 cases had TNM staging I-II cancers and 91 cases staging III-IV cancers], and 44 cases with non-malignant pancreatic masses [age 52.66 ± 13.81 years; male 36/44 (81.82%)] (Table 1). These 105 pancreatic cancer cases consisted of 102 pancreatic ductal adenocarcinoma and 3 acinar cell carcinoma. These non-malignant 44 cases consisted of 26 autoimmune pancreatitis (AIP), 10 chronic pancreatitis and 3 pancreatic tuberculosis. The final diagnoses were based on evaluating surgical pathology (n = 29), and clinical courses after 2 years’ follow-up (n = 105). One case with KRAS G12D mutation of chronic pancreatitis was found to be malignant during follow-up.
Diagnostic value of KRAS gene mutations with EUS-FNA specimens in pancreatic adenocarcinoma
Next, authors analyzed the KRAS gene mutations in codons 12 in EUS-FNA samples from primary pancreatic adenocarcinoma (n = 105, Fig. 2, a representative patient) by using digital droplet polymerase chain reaction (ddPCR, Figure.3A-D). The sensitivity, specificity, PPV, NPV, and accuracy of the EUS-FNA alone were, 71.4%, 86.4%, 92.6%, 55.9% and 75.8%, respectively, whereas these values of the EUS-FNA with KRAS mutation analysis were 91.6%, 80.9%, 92.5%, 79.1% and 88.6%, respectively. The sensitivity and accuracy of EUS-FNA diagnose was increased from 71.4–91.6% (P < 0.001) and 75.8–88.6% (P < 0.001), respectively, when KRAS mutation ddPCR analysis was added to standard EUS-FNA assessment. Our study demonstrated that KRAS mutation analysis significantly improves the sensitivity and accuracy of EUS-FNA in pancreatic adenocarcinoma. Detecting KRAS gene mutations improved the diagnose of pancreatic adenocarcinoma with EUS-FNA.
Diagnostic value of KRAS gene mutation in ctDNA and serum CA19-9
Aiming for a non-invasive method for detecting KRAS mutations, the authors evaluated KRAS mutation analysis of ctDNA in all matched plasma samples. The concordance of results obtained from EUS-FNA samples and plasma samples was evaluated. As shown in Table 2, the sensitivity and accuracy of KRAS mutations in EUS-FNA samples were 91.6% and 88.6%, while the respective values of KRAS mutation in ctDNA were 32.8% and 42.7%. In the pancreatic adenocarcinoma group, KRAS gene mutations were found in 88 (83.8%) of 105 cases in primary cancer, while only 37 (35.3%) cases of KRAS mutation were found in ctDNA (p < 0.001, χ2 test, Table S1). The accuracy of non-invasive ctDNA KRAS in detecting pancreatic adenocarcinoma was not as high as EUS-FNS KRAS mutation analysis (P < 0.0001, Table 2 and Table S1). But the sensitivity and accuracy of combined KRAS mutations in plasma ctDNA and CA19-9 were 78.9% and 76.2%, respectively. These results indicated that detecting of circulating biomarkers combination (ctDNA and CA19-9) complements the use of other diagnostic techniques in the diagnosis of pancreatic cancer.
Prognostic value of KRAS gene mutation in pancreatic adenocarcinoma
The authors analyzed the effect of G12D, G12V, and G12R mutations in the following Kaplan-Meier study. The median survival time (MST) was significantly shorter in patients with G12D mutations (180 days) compared with patients with other mutations (240 days) in their EUS-FNA tissue samples and ctDNA sample (long-rank test, P = 0.001 and P = 0.0008, respectively) (Figures. 4A and 4B). In contrast, the MST was not found to be significantly different between the patients with wild-type KRAS (240 days) and those with KRAS mutations (210 days) in their EUS-FNA tissue samples and ctDNA sample (long-rank test, P = 0.7088 and P = 0.3076, respectively) (Figures. 4C and 4D).
Furthermore, Univariate analysis demonstrated that both G12D KRAS mutation in EUS-FNA tissue samples (HR, 1.94; 95%CI, 1.12–3.36, P < 0.0001) and ctDNA (HR, 1.579; 95%CI, 1.383–3.520, P < 0.0005) were significant factors for poor survival. Multivariate analysis demonstrated that G12D mutation in both EUS-FNA tissue samples (HR, 1.495, 95% CI, 1.325–1.753, P = 0.0010) and ctDNA (HR, 1.417, 95% CI, 1.199–2.870, P = 0.0199) were independently associated with poor overall survival (Table 3). The following factors were analyzed as possible risk factors for survival: age, sex, TNM stage, location of mass, tumor size, CEA, CA19-9, and KRAS mutations (G12D, G12V, and G12R). In univariate analysis, both baseline CA19-9 and ctDNA KRAS were associated with overall survival (OS), which was expected given the known positive correlation between the two variables.