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Updated: May 5, 2026

Modeling Brain Metastasis Via Tail-Vein Injection of Inflammatory Breast Cancer Cells
Published on: February 4, 2021
Integrating structure to protein-protein interaction networks that drive metastasis to brain and lung in breast
H Billur Engin1, Emre Guney, Ozlem Keskin
1Center for Computational Biology and Bioinformatics and College of Engineering, Koc University, Istanbul, Turkey.
Abstract:
Blocking specific protein interactions can lead to human diseases. Accordingly, protein interactions and the structural knowledge on interacting surfaces of proteins (interfaces) have an important role in predicting the genotype-phenotype relationship. We have built the phenotype specific sub-networks of protein-protein interactions (PPIs) involving the relevant genes responsible for lung and brain metastasis from primary tumor in breast cancer. First, we selected the PPIs most relevant to metastasis causing genes (seed genes), by using the "guilt-by-association" principle. Then, we modeled structures of the interactions whose complex forms are not available in Protein Databank (PDB). Finally, we mapped mutations to interface structures (real and modeled), in order to spot the interactions that might be manipulated by these mutations. Functional analyses performed on these sub-networks revealed the potential relationship between immune system-infectious diseases and lung metastasis progression, but this connection was not observed significantly in the brain metastasis. Besides, structural analyses showed that some PPI interfaces in both metastasis sub-networks are originating from microbial proteins, which in turn were mostly related with cell adhesion. Cell adhesion is a key mechanism in metastasis, therefore these PPIs may be involved in similar molecular pathways that are shared by infectious disease and metastasis. Finally, by mapping the mutations and amino acid variations on the interface regions of the proteins in the metastasis sub-networks we found evidence for some mutations to be involved in the mechanisms differentiating the type of the metastasis.
Insights
Researchers mapped protein interactions in breast cancer metastasis, identifying potential links between immune responses and lung metastasis. Mutations may influence metastasis type, with microbial proteins involved in cell adhesion pathways for both metastasis and infectious diseases.
Area of Science:
- Genomics and Bioinformatics
- Structural Biology
- Cancer Research
Background:
- Protein-protein interactions (PPIs) are crucial in disease pathogenesis.
- Understanding genotype-phenotype relationships requires knowledge of protein interaction interfaces.
- Breast cancer metastasis involves complex genetic and molecular alterations.
Purpose of the Study:
- To construct phenotype-specific sub-networks of PPIs relevant to lung and brain metastasis in breast cancer.
- To model unknown protein complex structures and map mutations onto interaction interfaces.
- To investigate the functional and structural basis of metastasis and identify potential therapeutic targets.
Main Methods:
- Utilized the 'guilt-by-association' principle to identify metastasis-relevant PPIs.
- Modeled protein-protein interaction structures not present in the Protein Data Bank (PDB).
- Mapped mutations and amino acid variations onto protein interface structures.
Main Results:
- Functional analyses revealed a link between the immune system, infectious diseases, and lung metastasis.
- Structural analyses identified microbial protein-derived PPI interfaces associated with cell adhesion in both lung and brain metastasis sub-networks.
- Mutations were mapped to interface regions, providing evidence for their role in differentiating metastasis types.
Conclusions:
- PPI sub-networks offer insights into metastasis mechanisms, with distinct patterns for lung and brain metastasis.
- Shared molecular pathways involving microbial proteins and cell adhesion may link infectious diseases and metastasis.
- Identifying mutation-driven alterations in PPI interfaces could elucidate metastasis heterogeneity and inform targeted therapies.
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