System-based approaches as prognostic tools for glioblastoma

  • Background: The evasion of apoptosis is a hallmark of cancer. Understanding this process holistically and overcoming apoptosis resistance is a goal of many research teams in order to develop better treatment options for cancer patients. Efforts are also ongoing to personalize the treatment of patients. Strategies to confirm the therapeutic efficacy of current treatments or indeed to identify potential novel additional options would be extremely beneficial to both clinicians and patients. In the past few years, system medicine approaches have been developed that model the biochemical pathways of apoptosis. These systems tools incorporate and analyse the complex biological networks involved. For their successful integration into clinical practice, it is mandatory to integrate systems approaches with routine clinical and histopathological practice to deliver personalized care for patients. Results: We review here the development of system medicine approaches that model apoptosis for the treatment of cancer with a specific emphasis on the aggressive brain cancer, glioblastoma. Conclusions: We discuss the current understanding in the field and present new approaches that highlight the potential of system medicine approaches to influence how glioblastoma is diagnosed and treated in the future.

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Author:Manuela Salvucci, Zaitun Zakaria, Steven Carberry, Amanda Tivnan, Volker SeifertORCiD, Donat KögelORCiD, Brona M. Murphy, Jochen H. M. PrehnORCiD
URN:urn:nbn:de:hebis:30:3-533265
DOI:https://doi.org/10.1186/s12885-019-6280-2
ISSN:1471-2407
Pubmed Id:https://pubmed.ncbi.nlm.nih.gov/31718568
Parent Title (English):BMC cancer
Publisher:BioMed Central ; Springer
Place of publication:London ; Berlin ; Heidelberg
Document Type:Article
Language:English
Year of Completion:2019
Date of first Publication:2019/11/12
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2020/03/30
Tag:Apoptosis; Computational model; Glioblastoma; Molecular signatures; Network model; Numerical simulation; Precision oncology; Prognostic biomarker; Systems biology; Systems medicine
Volume:19
Issue:1, Art. 1092
Page Number:17
First Page:1
Last Page:17
Note:
Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
HeBIS-PPN:463771265
Institutes:Medizin / Medizin
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Sammlungen:Universitätspublikationen
Licence (German):License LogoCreative Commons - Namensnennung 4.0