A MicroRNA Signature Identifies Pancreatic Ductal Adenocarcinoma Patients at Risk for Lymph Node Metastases

Abstract

Background & aims: Pancreatic ductal adenocarcinomas (PDACs) frequently metastasize to the lymph nodes; strategies are needed to identify patients at highest risk for lymph node metastases. We performed genome-wide expression profile analyses of PDAC specimens, collected during surgery or endoscopic ultrasound-guided fine-need aspiration (EUS-FNA), to identify a microRNA (miRNA) signature associated with metastasis to lymph nodes.

Methods: For biomarker discovery, we analyzed miRNA expression profiles of primary pancreatic tumors from 3 public data sets (The Cancer Genome Atlas, GSE24279, and GSE32688). We then analyzed 157 PDAC specimens (83 from patients with lymph node metastases and 74 without) from Japan, collected from 2001 through 2017, for the training cohort and 107 PDAC specimens (63 from patients with lymph node metastases and 44 without) from a different medical center in Japan, from 2002 through 2016, for the validation cohort. We also analyzed samples collected by EUS-FNA before surgery from 47 patients (22 patients with lymph node metastases and 25 without; 17 for the training cohort and 30 from the validation cohort) and 62 specimens before any treatment from patients who received neoadjuvant chemotherapy (9 patients with lymph node metastasis and 53 without) for additional validation. Multivariate logistic regression analyses were used to evaluate the statistical differences in miRNA expression between patients with vs without metastases.

Results: We identified an miRNA expression pattern associated with diagnosis of PDAC metastasis to lymph nodes. Using logistic regression analysis, we optimized and trained a 6-miRNA risk prediction model for the training cohort; this model discriminated patients with vs without lymph node metastases with an area under the curve (AUC) of 0.84 (95% confidence interval [CI], 0.77-0.89). In the validation cohort, the model identified patients with vs without lymph node metastases with an AUC of 0.73 (95% CI, 0.64-0.81). In EUS-FNA biopsy samples, the model identified patients with vs without lymph node metastases with an AUC of 0.78 (95% CI, 0.63-0.89). The miRNA expression pattern was an independent predictor of PDAC metastasis to lymph nodes in the validation cohort (odds ratio, 17.05; 95% CI, 2.43-119.57) and in the EUS-FNA cohort (95% CI, 0.65-0.87).

Conclusions: Using data and tumor samples from 3 independent cohorts, we identified an miRNA signature that identifies patients at risk for PDAC metastasis to lymph nodes. The signature has similar levels of accuracy in the analysis of resected tumor specimens and EUS-FNA biopsy specimens. This model might be used to select treatment and management strategies for patients with PDAC.

Keywords: Cancer Progression; LNM; Prognosis; Prognostic Factor.

Figure 1.

Genome-wide discovery and validation of a novel miRNA signature to detect lymph node metastasis in PDAC patients. (A-C) The ROC curves demonstrate the diagnostic performance of the 7-miRNA signature for distinguish the patients with lymph node metastasis in (A) TCGA, (B) GSE24279 and (C) GSE32688 cohorts.

Figure 2.

Training and validation of a miRNA signature for identifying Lymph node metastasis in PDAC patients in two independent clinical cohorts. (A, B) A ROC curve of the 6-miRNA signature in the (A) training cohort (LNP = 83, LNN = 74, AUC = 0.84) and (B) validation cohort (LNP = 63, LNN = 44, AUC = 0.73). (C, D) Risk score distribution plot in (C) training cohort and (D) validation cohort. Modified risk score was obtained from subtracting individual risk score from Youden’s index value of risk model. (E, F) The new combination model, miRNA signature and CA19–9, outperformed the detection accuracy in both clinical cohorts (E: training cohort, F: validation cohort). LNN: lymph node metastasis negative, LNP: lymph node metastasis positive.

Figure 3.

Prognostic potential of the miRNA signature for PDAC patients in the clinical cohorts. (A, B) A comparison of (A) OS and (B) RFS between high and low-risk group estimated by 6-miRNA signature model in the training cohort. (C) Forest plot with hazard ratio of clinicopathological variables and signature risk score status in multivariate cox proportional analysis of OS in training cohort. (D, E) A comparison of (D) OS and (E) RFS between high and low-risk group estimated by 6-miRNA signature model in the training cohort. (F) Forest plot with hazard ratio of clinicopathological variables and signature risk score status in multivariate cox proportional analysis of OS in validation cohort.

Figure 4.

Higher-order validation of the miRNA signature in EUS-FNA biopsy specimens from PDAC patients. (A) A ROC curve of the 6-miRNA signature in EUS-FNA biopsy cohort (LNP = 22, LNN = 25, AUC = 0.78). (B) Risk score distribution plot in EUS-FNA biopsy cohort. (C) The ROC curves of each clinicopathological factors and the risk model constructed with 6-miRNA signature and CA19–9 (AUC = 0.81). (D) Forest plot with odds ratio of clinicopathological variables and signature risk score status in multivariate logistic regression analysis of LNM in additional validation cohort. LNN: lymph node metastasis negative, LNP: lymph node metastasis positive.

Figure 5.

Additional validation of performance of the miRNA signature for predicting residual nodal involvement following neoadjuvant therapy in EUS-FNA biopsy specimens. (A) A ROC curve of the 6-miRNA signature in additional validation cohort (pre-NAT EUS-FNA biopsies; ypN positive = 9, ypN negative = 53, AUC = 0.78). (B) Risk score distribution plots in an additional validation cohort. (C) The distribution of risk scores according to ypN status (P < 0.01, Mann Whitney test. (D) Forest plots with odds ratio for clinicopathological variables and risk scores in multivariate logistic regression analysis of ypN status in an additional validation cohort.