Combined microRNA and mRNA microfluidic TaqMan array cards for the diagnosis of malignancy of multiple types of pancreatico-biliary tumors in fine-needle aspiration material

Abstract

Pancreatic ductal adenocarcinoma (PDAC) continues to carry the lowest survival rates among all solid tumors. A marked resistance against available therapies, late clinical presentation and insufficient means for early diagnosis contribute to the dismal prognosis. Novel biomarkers are thus required to aid treatment decisions and improve patient outcomes.We describe here a multi-omics molecular platform that allows for the first time to simultaneously analyze miRNA and mRNA expression patterns from minimal amounts of biopsy material on a single microfluidic TaqMan Array card. Expression profiles were generated from 113 prospectively collected fine needle aspiration biopsies (FNAB) from patients undergoing surgery for suspect masses in the pancreas. Molecular classifiers were constructed using support vector machines, and rigorously evaluated for diagnostic performance using 10×10fold cross validation. The final combined miRNA/mRNA classifier demonstrated a sensitivity of 91.7%, a specificity of 94.5%, and an overall diagnostic accuracy of 93.0% for the differentiation between PDAC and benign pancreatic masses, clearly outperfoming miRNA-only classifiers. The classification algorithm also performed very well in the diagnosis of other types of solid tumors (acinar cell carcinomas, ampullary cancer and distal bile duct carcinomas), but was less suited for the diagnostic analysis of cystic lesions.We thus demonstrate that simultaneous analysis of miRNA and mRNA biomarkers from FNAB samples using multi-omics TaqMan Array cards is suitable to differentiate suspect solid pancreatic masses with high precision.

A perceptually optimised bivariate visualisation scheme for high-dimensional fold-change data

“…Heatmap-representations are a standard visualisation concept for a large variety of high dimensional molecular high-throughput profiles. Their correct interpretation can improve the analysis of mRNA or miRNA profiles as well as the analysis of methylation or mutation patterns (Gress et al 2017;Ogechukwu et al 2017;Taudien et al 2016;Gress et al 2011). However, heatmaps also provide an overwhelming amount of information.…”

Epigenomics of Pancreatic Cancer: A Critical Role for Epigenome-Wide Studies

“…Studies have started to identify cell subtypes based on epigenetic signatures in PDAC, and these subtypes have differential survival rates and responses to treatment (reviewed in Reference [93]). Through further research, common heterogeneity PDAC profiles and associated effective combination treatments can be developed to improve overall PDAC patient survival with an individualized approach [135,138,139].…”

Efficient cross-validation traversals in feature subset selection

“…Although feature profiles can, in general, be very high-dimensional ( n ≫ m ), the identification of informative feature subsets is also of interest for low-dimensional settings ( n → 0 ). For example, one might screen preselected panels of molecular profiles for feature subsets relevant to a specific type of disease [12][13][14][15][16] .…”