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NPJ Syst Biol Appl. 2017 Sep 20;3:27. doi: 10.1038/s41540-017-0030-3. eCollection 2017.

Predicting ligand-dependent tumors from multi-dimensional signaling features.

Author information

1
Merrimack Pharmaceuticals, Inc., Cambridge, MA 02139 USA.
2
Institute of Physics, University of Freiburg, Freiburg, Germany.
3
Department of Biosystems Science and Engineering and SIB Swiss Institute of Bioinformatics, ETH Zuerich, Zuerich, Switzerland.
4
Celgene, San Francisco, CA 94158 USA.
5
BIOSS Centre for Biological Signalling Studies, University of Freiburg, Freiburg im Breisgau, Germany.

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.

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