Customized workflow development and data integration concepts in Systems Medicine Markus Wolfien markus.wolfien@uni-rostock.de Gustav Steinhoff gustav.steinhoff@med.uni-rostock.de Olaf Wolkenhauer olaf.wolkenhauer@uni-rostock.de www.sbi.uni-rostock.de References [1] Lott SC, Wolfien M, Riege K, Bagnacani A, Wolkenhauer O, Hoffmann S, Hess WR. Customized workflow development and data modularization concepts for RNA-Sequencing and metatranscriptome experiments. Journal of Biotechnology. 2017 doi.org/10.1016/j.jbiotec.2017.06.1203 [2] Wolfien M, Rimmbach R, Schmitz U, Jung JJ, Krebs S, Steinhoff G, David R, Wolkenhauer O. TRAPLINE: A standardized and automated pipeline for RNA sequencing data analysis, evaluation and annotation. BMC Bioinformatics. 2016. doi: 10.1186/s12859-015-0873-9. [3] Steinhoff G, Nesteruk J, Wolfien M, Kundt G, The PERFECT Trial Investigators. Cardiac Function Improvement and Bone Marrow Response Outcome Analysis of the Randomized Perfect Phase III Clinical Trial of Intramyocardial CD133 + Application After Myocardial Infarction. EBioMedicine. 2017. doi.org/10.1016/j.ebiom.2017.07.022 Workflows: Symbiosis of research and tool integration for clinical application What is TRAPLINE? bit.ly/TRAPLINE_RNAseq 0 Integrating data using a Systems Medicine approach Systems Biology Data integration (DisGeNet, miRCancer, TriplexRNA, …) ATCATG101000111 TAGTG101010101111 TTCATAA00011011 AAGCA1010011 Statistics Evaluation of omics data (RPKM, FDR, Bayes, …) Bioinformatics Automated and scalable data processing, e.g. TRAPLINE [2] Med. Informatics Data management Clinic Improved diagnosis, prognosis and therapy Unexplained symptoms New insights mRNA1 miRNA1 mRNA2 miRNA2 mRNA3 Up regulated Down regulated Disease associated GO term 101000111ATCATG 101010101111TTCATAA 00011011AAGCA 1010011TAGTG 0 -10 10 log2(fold change) significant yes no 0 1 2 3 4 -log10(p value) Disease Biomarkers miRNA 2 mRNA 1 miRNA 1 mRNA 2 mRNA 3 Machine learning Data classification and prediction Application of the integrative workflow: The Phase III clinical trial PERFECT Workflow management frameworks and cloud computing services are bridging the gap between tool developers and end users, aiming towards an easy applicable and up-scalable computational data analysis. This in turn allows for an improved data reproducibility, process documentation, and monitoring of submitted jobs. Finally, workflows facilitate the use of state-of-the-art computational tools which would be hard to access for non-experts without graphical user interface frameworks. However, the use of workflows could be even more simplified for experimental and clinical researchers by strengthening the specific focus on the addressed research hypothesis and lessening the effort for the selection of the most appropriate tool [1]. Regenerative therapies using stem cells for the repair of heart tissue have been at the forefront of preclinical and clinical development during the past 16 years. To build upon this progress, the Phase III clinical trial PERFECT was designed to assess clinical safety and efficacy of intramyocardial CD133+ bone marrow stem cell treatment combined with coronary artery bypass graft (CABG) for induction of cardiac repair. The primary endpoint was delta left ventricular ejection fraction (∆LVEF) at 180 days compared to baseline measured with magnet resonance imaging (MRI). Responders (R) classified by ∆LVEF ≥5% after 180 d were 60% of the patients (35/58) in both treatment groups (+17.1% in R vs. non-responders (NR). The PERFECT trial shows that cardiac tissue repair and restitution of left ventricular function can be successfully installed in ischemic heart disease by CABG surgery associated with presence of enhanced circulating CD133+ and CD34+ EPC level. Using this new computer-aided diagnostic technology, responsive patient characteristics can be accurately identified prior to treatment with bypass surgery and stem cells [3]. Outcome results of the PERFECT trial.