In silico signaling modeling to understand cancer pathways and treatment responses

Kunz M, Jeromin J, Fuchs M, Christoph J, Veronesi G, Flentje M, Nietzer SL, Dandekar G, Dandekar T (2019)

Publication Type: Journal article

Publication year: 2019

Journal

Briefings in Bioinformatics Oxford University Press

DOI: 10.1093/bib/bbz033

Abstract

Precision medicine has changed thinking in cancer therapy, highlighting a better understanding of the individual clinical interventions. But what role do the drivers and pathways identified from pan-cancer genome analysis play in the tumor? In this letter, we will highlight the importance of in silico modeling in precision medicine. In the current era of big data, tumor engines and pathways derived from pan-cancer analysis should be integrated into in silico models to understand the mutational tumor status and individual molecular pathway mechanism at a deeper level. This allows to pre-evaluate the potential therapy response and develop optimal patient-tailored treatment strategies which pave the way to support precision medicine in the clinic of the future.

Authors with CRIS profile

Meik Kunz Lehrstuhl für Medizinische Informatik Jan Christoph Lehrstuhl für Medizinische Informatik

Involved external institutions

Julius-Maximilians-Universität Würzburg DE Germany (DE) Humanitas Research Hospital / IRCCS Istituto Clinico Humanitas IT Italy (IT) Universitätsklinikum Würzburg DE Germany (DE)

How to cite

APA:

Kunz, M., Jeromin, J., Fuchs, M., Christoph, J., Veronesi, G., Flentje, M.,... Dandekar, T. (2019). In silico signaling modeling to understand cancer pathways and treatment responses. Briefings in Bioinformatics. https://doi.org/10.1093/bib/bbz033

MLA:

Kunz, Meik, et al. "In silico signaling modeling to understand cancer pathways and treatment responses." Briefings in Bioinformatics (2019).

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