TY - JOUR T1 - Reference-free transcriptome exploration reveals novel RNAs for prostate cancer diagnosis JF - Life Science Alliance JO - Life Sci. Alliance DO - 10.26508/lsa.201900449 VL - 2 IS - 6 SP - e201900449 AU - Marina Pinskaya AU - Zohra Saci AU - Mélina Gallopin AU - Marc Gabriel AU - Ha TN Nguyen AU - Virginie Firlej AU - Marc Descrimes AU - Audrey Rapinat AU - David Gentien AU - Alexandre de la Taille AU - Arturo Londoño-Vallejo AU - Yves Allory AU - Daniel Gautheret AU - Antonin Morillon Y1 - 2019/12/01 UR - https://www.life-science-alliance.org/content/2/6/e201900449.abstract N2 - The use of RNA-sequencing technologies held a promise of improved diagnostic tools based on comprehensive transcript sets. However, mining human transcriptome data for disease biomarkers in clinical specimens are restricted by the limited power of conventional reference-based protocols relying on unique and annotated transcripts. Here, we implemented a blind reference-free computational protocol, DE-kupl, to infer yet unreferenced RNA variations from total stranded RNA-sequencing datasets of tissue origin. As a bench test, this protocol was powered for detection of RNA subsequences embedded into putative long noncoding (lnc)RNAs expressed in prostate cancer. Through filtering of 1,179 candidates, we defined 21 lncRNAs that were further validated by NanoString for robust tumor-specific expression in 144 tissue specimens. Predictive modeling yielded a restricted probe panel enabling more than 90% of true-positive detections of cancer in an independent The Cancer Genome Atlas cohort. Remarkably, this clinical signature made of only nine unannotated lncRNAs largely outperformed PCA3, the only used prostate cancer lncRNA biomarker, in detection of high-risk tumors. This modular workflow is highly sensitive and can be applied to any pathology or clinical application. ER -