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Predicting ligand-dependent tumors from multi-dimensional signaling features

Helge Hass, Kristina Masson, Sibylle Wohlgemuth, Violette Paragas, John E Allen, Mark Sevecka, Emily Pace, Jens Timmer, Joerg Stelling, Gavin MacBeath, Birgit Schoeberl, Andreas Raue
doi: https://doi.org/10.1101/142901
Helge Hass
1Merrimack Pharmaceuticals, Inc., Cambridge, MA 02139, USA
2Institute of Physics, University of Freiburg, Freiburg, Germany
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Kristina Masson
1Merrimack Pharmaceuticals, Inc., Cambridge, MA 02139, USA
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Sibylle Wohlgemuth
3Department of Biosystems Science and Engineering and SIB Swiss Institute of Bioinformatics, ETH Zuerich, Zuerich, Switzerland
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Violette Paragas
1Merrimack Pharmaceuticals, Inc., Cambridge, MA 02139, USA
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John E Allen
1Merrimack Pharmaceuticals, Inc., Cambridge, MA 02139, USA
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Mark Sevecka
1Merrimack Pharmaceuticals, Inc., Cambridge, MA 02139, USA
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Emily Pace
4Celgene, San Francisco, CA 94158, USA
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Jens Timmer
2Institute of Physics, University of Freiburg, Freiburg, Germany
5BIOSS Centre for Biological Signalling Studies, University of Freiburg, Germany
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Joerg Stelling
3Department of Biosystems Science and Engineering and SIB Swiss Institute of Bioinformatics, ETH Zuerich, Zuerich, Switzerland
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Gavin MacBeath
1Merrimack Pharmaceuticals, Inc., Cambridge, MA 02139, USA
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Birgit Schoeberl
1Merrimack Pharmaceuticals, Inc., Cambridge, MA 02139, USA
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Andreas Raue
1Merrimack Pharmaceuticals, Inc., Cambridge, MA 02139, USA
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  • For correspondence: araue@merrimack.com
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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 a novel 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.

Summary Prediction of ligand-induced growth of cancer cell lines, which correlates with ligand-blocking antibody efficacy, could be significantly improved by learning from features of a mechanistic signaling model, and was applied to reveal a correlation between growth factor expression and predicted response in patient samples.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted May 27, 2017.
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Predicting ligand-dependent tumors from multi-dimensional signaling features
Helge Hass, Kristina Masson, Sibylle Wohlgemuth, Violette Paragas, John E Allen, Mark Sevecka, Emily Pace, Jens Timmer, Joerg Stelling, Gavin MacBeath, Birgit Schoeberl, Andreas Raue
bioRxiv 142901; doi: https://doi.org/10.1101/142901
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Predicting ligand-dependent tumors from multi-dimensional signaling features
Helge Hass, Kristina Masson, Sibylle Wohlgemuth, Violette Paragas, John E Allen, Mark Sevecka, Emily Pace, Jens Timmer, Joerg Stelling, Gavin MacBeath, Birgit Schoeberl, Andreas Raue
bioRxiv 142901; doi: https://doi.org/10.1101/142901

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