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ClinOmicsTrailbc: a visual analytics tool for breast cancer treatment stratification

Abstract: 
Motivation Breast cancer is the second leading cause of cancer death among women. Tumors, even of the same histopathological subtype, exhibit a high genotypic diversity that impedes therapy stratification and that hence must be accounted for in the treatment decision-making process. Results Here, we present ClinOmicsTrailbc, a comprehensive visual analytics tool for breast cancer decision support that provides a holistic assessment of standard-of-care targeted drugs, candidates for drug repositioning, and immunotherapeutic approaches. To this end, our tool analyzes and visualizes clinical markers and (epi-)genomics and transcriptomics datasets to identify and evaluate the tumor’s main driver mutations, the tumor mutational burden, activity patterns of core cancer-relevant pathways, drug-specific biomarkers, the status of molecular drug targets, and pharmacogenomic influences. In order to demonstrate ClinOmicsTrailbc’s rich functionality, we present three case studies highlighting various ways in which ClinOmicsTrailbc can support breast cancer precision medicine. ClinOmicsTrailbc is a powerful integrated visual analytics tool for breast cancer research in general and for therapy stratification in particular, assisting oncologists to find the best possible treatment options for their breast cancer patients based on actionable, evidence-based results. Availability ClinOmicsTrailbc can be freely accessed at https://clinomicstrail.bioinf.uni-sb.de. Supplementary information Supplementary data are available at Bioinformatics online.
Journal: 
Bioinformatics
Publication Date: 
30 Apr 2019
Citation: 
[SKT+19] Schneider, L., Kehl, T., Thedinga, K., Grammes, N. L., Backes, C., Mohr, C., Schubert, B., Lenhof, K., Gerstner, N., Hartkopf. A. D., Wallwiener, M., Kohlbacher, O., Keller, A., Meese, E., Graf, N., Lenhof, H.-P. ClinOmicsTrail bc: a visual analytics tool for breast cancer treatment stratification, Bioinformatics, btz302, 2019. DOI: 10.1093/bioinformatics/btz302.