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  ClinOmicsTrailbc: a visual analytics tool for breast cancer treatment stratification

Schneider, L., Kehl, T., Thedinga, K., Grammes, N., Backes, C., Mohr, C., et al. (2019). ClinOmicsTrailbc: a visual analytics tool for breast cancer treatment stratification. Bioinformatics, 35(24), 5171-5181. doi:10.1093/bioinformatics/btz302.

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 Creators:
Schneider, L, Author
Kehl, T, Author
Thedinga, K, Author
Grammes, NL, Author
Backes, C, Author
Mohr, C, Author
Schubert, B, Author
Lenhof, K, Author
Gerstner, N, Author
Hartkopf, AD, Author
Wallwiener, M, Author
Kohlbacher, O1, Author           
Keller, A, Author
Meese, E, Author
Graf, N, Author
Lenhof, H-P, Author
Affiliations:
1Research Group Biomolecular Interactions, Max Planck Institute for Developmental Biology, Max Planck Society, ou_3380092              

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 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.

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 Dates: 2019-12
 Publication Status: Issued
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1093/bioinformatics/btz302
PMID: 31038669
 Degree: -

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Title: Bioinformatics
Source Genre: Journal
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Publ. Info: Oxford : Oxford University Press
Pages: - Volume / Issue: 35 (24) Sequence Number: - Start / End Page: 5171 - 5181 Identifier: ISSN: 1367-4803
CoNE: https://pure.mpg.de/cone/journals/resource/954926969991