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Related Experiment Video

Updated: Feb 22, 2026

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

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 Hass^1,2, Kristina Masson¹, Sibylle Wohlgemuth³

¹Merrimack Pharmaceuticals, Inc., Cambridge, MA 02139 USA.

NPJ Systems Biology and Applications

|September 26, 2017

Summary

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.

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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.