ArticleNPJ systems biology and applications2017
Predicting ligand-dependent tumors from multi-dimensional signaling features.
Article in NPJ systems biology and applications, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Smart nanoparticle delivery systems for curcumin: a targeted strategy to enhance anticancer efficacy and bioavailability.Journal of materials science. Materials in medicine · 2026Review
- Mechanistic modeling of cell viability assays with in silico lineage tracing.PLoS computational biology · 2025Article
- Long-Term Single-Molecule Tracking in Living Cells using Weak-Affinity Protein Labeling.Angewandte Chemie (International ed. in English) · 2025Article
- Efficient parameter estimation for ODE models of cellular processes using semi-quantitative data.Bioinformatics (Oxford, England) · 2024Article
- DynProfiler: a Python package for comprehensive analysis and interpretation of signaling dynamics leveraged by deep learning techniques.Bioinformatics advances · 2024Article
- Posterior marginalization accelerates Bayesian inference for dynamical models of biological processes.iScience · 2023Article
- Uncovering specific mechanisms across cell types in dynamical models.PLoS computational biology · 2023Article
- Biased activation of the receptor tyrosine kinase HER2.Cellular and molecular life sciences : CMLS · 2023Article
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- A text-based computational framework for patient -specific modeling for classification of cancers.iScience · 2022Article
- Mini-batch optimization enables training of ODE models on large-scale datasets.Nature communications · 2022Article
- Functional signaling test identifies HER2 negative breast cancer patients who may benefit from c-Met and pan-HER combination therapy.Cell communication and signaling : CCS · 2022Article
- MECHANISTIC AND DATA-DRIVEN MODELS OF CELL SIGNALING: TOOLS FOR FUNDAMENTAL DISCOVERY AND RATIONAL DESIGN OF THERAPY.Current opinion in systems biology · 2021Article
- Article
- Single-Molecule Super-Resolution Microscopy Reveals Heteromeric Complexes of MET and EGFR upon Ligand Activation.International journal of molecular sciences · 2020Article
- Model-based analysis of response and resistance factors of cetuximab treatment in gastric cancer cell lines.PLoS computational biology · 2020Article
- Efficient parameterization of large-scale dynamic models based on relative measurements.Bioinformatics (Oxford, England) · 2020Article
- Learning stable and predictive structures in kinetic systems.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- Extensions of ℓBMC bioinformatics · 2019Article
- Community-driven roadmap for integrated disease maps.Briefings in bioinformatics · 2019Review
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.