Salivary transcriptomic biomarkers for detection of resectable pancreatic cancer

Abstract

Background & aims: Lack of detection technology for early pancreatic cancer invariably leads to a typical clinical presentation of incurable disease at initial diagnosis. New strategies and biomarkers for early detection are sorely needed. In this study, we have conducted a prospective sample collection and retrospective blinded validation to evaluate the performance and translational utilities of salivary transcriptomic biomarkers for the noninvasive detection of resectable pancreatic cancer.

Methods: The Affymetrix HG U133 Plus 2.0 Array (Affymetrix, Santa Clara, CA) was used to profile transcriptomes and discover altered gene expression in saliva supernatant. Biomarkers discovered from the microarray study were subjected to clinical validation using an independent sample set of 30 pancreatic cancer patients, 30 chronic pancreatitis patients, and 30 healthy controls.

Results: Twelve messenger RNA biomarkers were discovered and validated. The logistic regression model with the combination of 4 messenger RNA biomarkers (KRAS, MBD3L2, ACRV1, and DPM1) could differentiate pancreatic cancer patients from noncancer subjects (chronic pancreatitis and healthy control), yielding a receiver operating characteristic plot, area under the curve value of 0.971 with 90.0% sensitivity and 95.0% specificity.

Conclusions: The salivary biomarkers possess discriminatory power for the detection of resectable pancreatic cancer, with high specificity and sensitivity. This report provides the proof of concept of salivary biomarkers for the noninvasive detection of a systemic cancer and paves the way for prediction model validation study followed by pivotal clinical validation.

Figure 1

Schematic of the Strategy Used for the Discovery and Validation of Salivary Biomarkers. PC: pancreatic cancer; H: healthy control; CP: chronic pancreatitis.

Figure 2

Heatmap of 35 qPCR verified genes (23 up-regulated and 12 down-regulated) based on the microarray data. Hierarchical clustering and gene function enrichment was performed using Euclidean distance metric and Centroid linkage method (unsupervised clustering). Pancreatic cancer patients (n=12) and healthy controls (n=12) could be classified into distinct groups, indicating the discriminatory power of salivary mRNA biomarkers. The GEO database access number of all microarray experiments is GSE14245.

Figure 3

ROC curve and Interactive dot diagram for the logistic regression model. (A) The logistic regression model using four biomarkers (KRAS, MBD3L2, ACRV1 and DPM1) yielded an AUC value of 0.971 (cutoff 0.433). (B) Interactive dot diagram was based on the qPCR data of the non-cancer group (n = 60) and cancer group (n = 30).