Predicting ligand-dependent tumors from multi-dimensional signaling features
Abstract: Targeted therapies have shown significant patient benefit in about 5–10% of solid tumors that are addicted to a single oncogene. Here, we explore the idea of ligand addiction as a driver of tumor growth. High ligand levels in tumors have been shown to be associated with impaired patient survival, but targeted therapies have not yet shown great benefit in unselected patient populations. Using an approach of applying Bagged Decision Trees (BDT) to high-dimensional signaling features derived from a computational model, we can predict ligand dependent proliferation across a set of 58 cell lines. This mechanistic, multi-pathway model that features receptor heterodimerization, was trained on seven cancer cell lines and can predict signaling across two independent cell lines by adjusting only the receptor expression levels for each cell line. Interestingly, for patient samples the predicted tumor growth response correlates with high growth factor expression in the tumor microenvironment, which argues for a co-evolution of both factors in vivo
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Anmerkungen
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npj Systems biology and applications. - 3 (2017) , 27, ISSN: 2056-7189
- Ereignis
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Veröffentlichung
- (wo)
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Freiburg
- (wer)
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Universität
- (wann)
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2019
- Urheber
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Haß, Helge
Masson, Kristina
Wohlgemuth, Sibylle
Paragas, Violette
Allen, John E.
Sevecka, Mark
Pace, Emily
Timmer, Jens
Stelling, Jörg
MacBeath, Gavin
Schoeberl, Birgit
Raue, Andreas
- Beteiligte Personen und Organisationen
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Data Analysis and Modeling of Dynamic Processes in the Life Science
- DOI
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10.1038/s41540-017-0030-3
- URN
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urn:nbn:de:bsz:25-freidok-1402783
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
- 15.08.2025, 07:20 MESZ
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Beteiligte
- Haß, Helge
- Masson, Kristina
- Wohlgemuth, Sibylle
- Paragas, Violette
- Allen, John E.
- Sevecka, Mark
- Pace, Emily
- Timmer, Jens
- Stelling, Jörg
- MacBeath, Gavin
- Schoeberl, Birgit
- Raue, Andreas
- Data Analysis and Modeling of Dynamic Processes in the Life Science
- Universität
Entstanden
- 2019
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