System level network data and models attack cancer drug resistance

Area of Science:

• Systems Biology
• Oncology
• Bioinformatics

Background:

• Drug resistance accounts for over 90% of cancer-related deaths, representing a critical challenge in oncology.
• Cancer drug resistance is understood as a system-level network phenomenon involving the entire cell.
• Previous research relied on small-scale interactomes, limiting comprehensive understanding.

Purpose of the Study:

• To leverage recent advances in proteome-wide interactome and signaling network data.
• To integrate drug-target interactions, resistance mutations, and multi-omics datasets.
• To pave the way for building proteome-wide drug resistance models.

Main Methods:

• Utilizing proteome-wide human interactome and signaling network data.
• Integrating drug-target interactions and drug resistance-inducing mutations.
• Incorporating cancer and drug resistance-related multi-omics datasets.

Main Results:

• Development of system-level signaling network models for therapy resistance.
• Capability to perform in silico clinical trials and drug combination screens.
• Established interoperability and reliability of drug resistance network data and models.

Conclusions:

• Recent data integration advances enable the construction of proteome-wide drug resistance models.
• These models are crucial for understanding and overcoming cancer drug resistance.
• The findings support directed drug development and personalized cancer therapies.