Plasma Micro-RNA Alterations Appear Late in Pancreatic Cancer

Abstract

Objectives: The aim of this research was to study whether plasma microRNAs (miRNA) can be used for early detection of pancreatic cancer (PC) by analyzing prediagnostic plasma samples collected before a PC diagnosis.

Background: PC has a poor prognosis due to late presenting symptoms and early metastasis. Circulating miRNAs are altered in PC at diagnosis but have not been evaluated in a prediagnostic setting.

Methods: We first performed an initial screen using a panel of 372 miRNAs in a retrospective case-control cohort that included early-stage PC patients and healthy controls. Significantly altered miRNAs at diagnosis were then measured in an early detection case-control cohort wherein plasma samples in the cases are collected before a PC diagnosis. Carbohydrate antigen 19-9 (Ca 19-9) levels were measured in all samples for comparison.

Results: Our initial screen, including 23 stage I-II PC cases and 22 controls, revealed 15 candidate miRNAs that were differentially expressed in plasma samples at PC diagnosis. We combined all 15 miRNAs into a multivariate statistical model, which outperformed Ca 19-9 in receiver-operating characteristics analysis. However, none of the candidate miRNAs, individually or in combination, were significantly altered in prediagnostic plasma samples from 67 future PC patients compared with 132 matched controls. In comparison, Ca 19-9 levels were significantly higher in the cases at <5 years before diagnosis.

Conclusion: Plasma miRNAs are altered in PC patients at diagnosis, but the candidate miRNAs found in this study appear late in the course of the disease and cannot be used for early detection of the disease.

FIGURE 1

Source population and included cases in the 2 study cohorts. A, The screening cohort consisted of pancreatic cancer patients with TNM stage I-II disease and B, the prediagnostic cohort consisted of individuals who later developed pancreatic cancer. TNM indicates tumor-node-metastasis.

FIGURE 2

Differences between cases and controls over time. A to B shows the estimated (upper panels) and cross-validated (lower panels) multivariate statistical model (OPLA-DA) of the 15 candidate miRNAs. A, Prediagnostic samples at different time intervals before diagnosis and (B) at diagnosis. Each dot represents an individual. In a strong model, the cases would cluster around 1 and controls around 0. CV-ANOVA P values are derived from the cross validation. C to D shows boxplots of Ca 19–9 levels in (C) prediagnostic samples at different time intervals before diagnosis and (D) at diagnosis.

FIGURE 3

Receiver-operating characteristics (ROC) curves plotting the sensitivity and false-positive rate (1 – specificity) for the cross-validated multivariate model of 15 miRNAs and Ca 19–9 at (A) different time intervals in relation to diagnosis and (B) at diagnosis.