Anticancer Target Combinations: Network-Informed Signaling-Based Approach to Discovery.

Area of Science:

• Oncology
• Computational Biology
• Pharmacology

Background:

• Anticancer drug discovery faces challenges with sequential single therapies due to resistance.
• Combinatorial drug strategies are vast and require precise selection of co-targets.
• Current combination selection relies heavily on empirical observations and clinical practice.

Purpose of the Study:

• To develop and validate a signaling pathway-based method for discovering optimal protein co-targets for cancer drug combinations.
• To address and temper drug resistance by identifying strategies that block parallel or complementary cancer signaling pathways.
• To test the pipeline on patient-derived data for breast and colorectal cancers.

Main Methods:

• A network-informed, signaling-based pipeline was developed to identify co-targets.
• The pipeline utilizes network concepts, metrics, and tissue-specific co-existing mutations.
• Applied to patient-derived ESR1|PIK3CA and BRAF|PIK3CA subnetworks in breast and colorectal cancers.

Main Results:

• For breast cancer, suggested co-targeting of the ESR1|PIK3CA subnetwork with an alpelisib-LJM716 combination.
• For colorectal cancer, suggested co-targeting of the BRAF|PIK3CA subnetwork with alpelisib, cetuximab, and encorafenib.
• Results were validated using patient-based xenografts.

Conclusions:

• The developed pipeline offers a promising, signaling-based approach for selecting effective drug combinations.
• This method aids in overcoming cancer drug resistance by targeting complementary signaling pathways.
• The findings provide a foundation for more rational and effective combinatorial cancer therapies.