PAN-CANCER IDENTIFICATION OF NON-CODING RNAS WITH BIOMARKER POTENTIAL IN PLASMA

Non-coding RNAs (ncRNAs) are associated with various hallmarks of cancer including proliferation, invasion, migration and angiogenesis. Compared to protein-coding genes, their expression is often highly cell-type specific, suggesting they may function as biomarkers for diagnosis and treatment response monitoring of cancer patients. To identify cancer-type-specific ncRNAs, we reprocessed small RNA and polyA+ RNA-sequencing data of The Cancer Genome Atlas (TCGA) and TARGET database, resulting in the annotation and quantification of miRNAs, isomiRs, sn(o)RNA fragments, tRNA fragments, mRNAs and lncRNAs in almost 12,000 patient samples covering 40 cancer types. Expression values were used to calculate a novel expression specificity score, resulting in more than 6000 genes with a cancer-type specific expression pattern. Whereas cancer-type-specific lncRNAs were identified in almost all cancer types, cancer-type-specific miRNAs were found in only a subset of cancer types. Of note, isomiRs showed a higher level of cancer-type specificity compared to canonical miRNAs. In order to evaluate the potential of these cancer-type-specific ncRNAs as non-invasive biomarkers, we collected a cohort of plasma samples from metastatic cancer patients representing 34 cancer types. For each patient, we prepared RNA from the total plasma and from extra-cellular vesicles. All samples were analysed using small RNA sequencing to quantify circulating miRNA expression levels. Quantification of lncRNAs in the plasma derived RNA is currently ongoing using a custom RNA-capture sequencing approach. Our preliminary findings indicate that a subset of cancer-type specific RNAs are detectable in plasma and may be used as biomarkers for diagnosis or disease monitoring.

Authors

Celine Everaert (1,2,3)
Kimberly Verniers (1,2,3)
Nurten Yigit (1,2,3)
Glenn Vergauwen (2,4)
An Hendrix (2,4)
Olivier Thas (2,5)
Jo Vandesompele (1,2,3)
Pieter Mestdagh (1,2,3)

Organisations

(1) Center for Medical Genetics, Ghent University, Ghent, Belgium
(2) Cancer Research Institute Ghent, Ghent University, Ghent, Belgium
(3) Bioinformatics Institute Ghent N2N, Ghent University, Ghent, Belgium
(4) Laboratory of Experimental Cancer Research, Ghent University, Ghent, Belgium
(5) Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium (6) National Institute for Applied Statistics Research Australia, University of Wollongong, Australia

Presenting author

Celine Everaert, PhD student, Center for Medical Genetics, Ghent University, Ghent, Belgium
Celine.Everaert@UGent.be
Contact us now

Flanders.bio Strategic Partners