High-order interaction modeling of tumor-microenvironment crosstalk for tumor growth

Area of Science:

• Computational biology
• Cancer research
• Systems biology

Background:

• Interactions between cancer cells and the tumor microenvironment (TME) influence tumor progression and drug resistance.
• The genomic mechanisms governing these TME interactions are not well understood, hindering the identification of therapeutic targets.
• Current understanding lacks a comprehensive view of the high-order genomic networks involved in tumor-TME crosstalk.

Purpose of the Study:

• To develop and apply a computational model integrating evolutionary game theory, ecosystem theory, and allometric scaling law.
• To chart the genomic atlas of high-order interaction networks between tumor cells, the TME, and tumor mass.
• To identify the causal influence of gene-induced tumor-TME crosstalk on tumor growth and discover actionable genetic targets.

Main Methods:

• Integration of evolutionary game theory and ecosystem theory.
• Application of allometric scaling law to model complex biological systems.
• Development of a computational model to analyze genomic data from tumor-TME interactions.
• Assessment of gene-induced crosstalk influencing tumor growth dynamics.

Main Results:

• The model successfully charts genomic interaction networks within the tumor microenvironment.
• Cooperation and competition between tumor cells and microenvironment components were shown to modulate tumor growth.
• Specific genes responsible for promoting or inhibiting tumor growth via TME crosstalk were identified.
• The findings highlight the potential for targeting these genes to alter tumor progression.

Conclusions:

• A novel computational framework enables precise inference of genomic underpinnings of tumor-TME interactions.
• The model provides a method to analyze any omics data to understand tumor progression.
• Identified genes serve as potential therapeutic targets for modulating tumor growth by altering tumor-TME crosstalk.
• This approach opens new avenues for precision oncology by dissecting complex cellular interactions.