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Updated: Feb 22, 2026
A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds
Published on: April 6, 2016
Predicting ligand-dependent tumors from multi-dimensional signaling features
Helge Hass1,2, Kristina Masson1, Sibylle Wohlgemuth3
1Merrimack Pharmaceuticals, Inc., Cambridge, MA 02139 USA.
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.
Insights
This study introduces ligand addiction as a driver of tumor growth, developing a computational model to predict ligand-dependent proliferation. The findings suggest a link between high growth factor expression and tumor growth, offering new therapeutic avenues.
Area of Science:
- Oncology
- Computational Biology
- Molecular Biology
Background:
- Targeted therapies benefit a small subset of solid tumors driven by single oncogenes.
- High tumor ligand levels correlate with poor patient survival, yet unselected therapies show limited efficacy.
- Ligand addiction is proposed as a novel driver of tumor growth.
Purpose of the Study:
- To explore ligand addiction as a driver of tumor growth.
- To develop a predictive model for ligand-dependent cancer cell proliferation.
- To investigate the relationship between growth factors and tumor growth in vivo.
Main Methods:
- Utilized Bagged Decision Trees (BDT) on high-dimensional signaling features from a computational model.
- Developed a mechanistic, multi-pathway model incorporating receptor heterodimerization.
- Trained the model on seven cancer cell lines and validated its predictive capacity on two independent cell lines by adjusting receptor expression.
Main Results:
- Successfully predicted ligand-dependent proliferation across 58 cell lines using the BDT approach.
- The computational model accurately predicted signaling in independent cell lines by modifying receptor expression levels.
- Predicted tumor growth response in patient samples correlated with high growth factor expression in the tumor microenvironment.
Conclusions:
- Ligand addiction represents a potential driver of tumor growth.
- The developed computational model offers a method to predict ligand-dependent proliferation.
- Co-evolution of growth factors and tumor microenvironment is suggested, impacting in vivo tumor growth.
