Discovering anticancer drug target combinations via network-informed signaling-based approach

Area of Science:

• Computational Biology
• Oncology
• Systems Biology

Background:

• Cancer treatment decisions are complex due to numerous drug combinations and the challenge of predicting patient response.
• Sequential single-drug treatments often lead to drug resistance, necessitating combination therapies.
• Current empirical drug combinations are limited by clinical experience and a lack of systematic target selection.

Purpose of the Study:

• To develop a novel strategy for selecting optimal drug target combinations that mimics natural cellular signaling.
• To identify key protein targets within cellular communication pathways to overcome drug resistance.
• To provide a systematic, network-informed approach for discovering effective anticancer drug combinations.

Main Methods:

• Utilized protein-protein interaction networks and shortest path analysis to map cellular communication pathways.
• Developed a strategy that identifies critical network nodes as potential combination drug targets.
• Mimicked cancer's resistance mechanisms by identifying parallel pathways that bypass drug-blocked routes.

Main Results:

• Identified key communication nodes as combination drug targets based on network topology.
• Validated the network-informed approach using clinical data from patient-derived breast and colorectal cancers.
• Demonstrated tumor reduction in breast cancer with Alpelisib + LJM716 and in colorectal cancer with Alpelisib + Cetuximab + Encorafenib.

Conclusions:

• The network-based strategy effectively discovers optimal protein co-target combinations to combat cancer drug resistance.
• The approach successfully identifies co-targets within alternative signaling pathways and their connecting nodes.
• This method offers a promising avenue for developing more effective and personalized combination cancer therapies.