PT  - JOURNAL ARTICLE
AU  - Helge Hass
AU  - Kristina Masson
AU  - Sibylle Wohlgemuth
AU  - Violette Paragas
AU  - John E Allen
AU  - Mark Sevecka
AU  - Emily Pace
AU  - Jens Timmer
AU  - Joerg Stelling
AU  - Gavin MacBeath
AU  - Birgit Schoeberl
AU  - Andreas Raue
TI  - Predicting ligand-dependent tumors from multi-dimensional signaling features
AID  - 10.1101/142901
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 142901
4099  - http://biorxiv.org/content/early/2017/05/27/142901.short
4100  - http://biorxiv.org/content/early/2017/05/27/142901.full
AB  - 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.