Closing the translational gap: integrating of high‐throughput data for 
personalized cancer treatments into clinical processes

Kohlbacher, O

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Closing the translational gap: integrating of high‐throughput data for personalized cancer treatments into clinical processes

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Kohlbacher, O
Department Protein Evolution, Max Planck Institute for Developmental Biology, Max Planck Society;

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https://www.sys-med.de/fileadmin/Meeting/2017/eMed_Abstract_book_2017_web.pdf
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Citation

Kohlbacher, O. (2017). Closing the translational gap: integrating of high‐throughput data for personalized cancer treatments into clinical processes. Talk presented at e:Med Meeting 2017 on Systems Medicine. Göttingen, Germany. 2017-11-21 - 2017-11-23.

Cite as: https://hdl.handle.net/21.11116/0000-000F-8C26-9

Abstract

While the analysis of high-throughput data (genomics, transcriptomics, proteomics, etc.) has by now become routine in biomedical research, the integration into routine healthcare is progressing at a slower pace. We will discuss some of the issues when translating research ideas into clinical practice. In two showcases centered around personalized cancer treatment we will then discuss how IT systems can enable the implementation of personalized treatments. The first showcase is the implementation of personalized cancer vaccination, where multi-omics data needs to be integrated to enable a patient-specific design of an optimal vaccine. The second project is the development of interactive molecular tumor board solutions (funded within the iD:Sem initiative of BMBF) that will hopefully enable more interactive and efficient discussions of personalized cancer treatment options in general.