RT Journal Article
SR Electronic
T1 Predicting ligand-dependent tumors from multi-dimensional signaling features
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 142901
DO 10.1101/142901
A1 Helge Hass
A1 Kristina Masson
A1 Sibylle Wohlgemuth
A1 Violette Paragas
A1 John E Allen
A1 Mark Sevecka
A1 Emily Pace
A1 Jens Timmer
A1 Joerg Stelling
A1 Gavin MacBeath
A1 Birgit Schoeberl
A1 Andreas Raue
YR 2017
UL http://biorxiv.org/content/early/2017/05/27/142901.abstract
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.