Network-based elucidation of colon cancer drug resistance mechanisms by phosphoproteomic time-series analysis

Area of Science:

• Molecular Biology
• Bioinformatics
• Cancer Research

Background:

• Aberrant signaling pathways are key drivers of cancer development and progression.
• Targeted therapies face challenges due to adaptive resistance mechanisms involving signaling network rewiring.
• Existing methods struggle to fully capture dynamic cellular responses to drug perturbations.

Purpose of the Study:

• To introduce Virtual Enrichment-based Signaling Protein-activity Analysis (VESPA), a novel algorithm for elucidating cell response and adaptation mechanisms to drug treatments.
• To analyze phosphoproteomic time-series data from colorectal cancer cells treated with inhibitors.
• To identify kinase and phosphatase activity and understand signaling network rewiring in response to targeted therapies.

Main Methods:

• Development and application of the VESPA algorithm, which infers enzyme activity from substrate phosphorylation states.
• Analysis of 7-point phosphoproteomic time series data from colorectal cancer cells.
• Integration of tumor-specific enzyme/substrate interactions to account for signal crosstalk and sparse data.
• Experimental validation using CRISPR knock-out assays.

Main Results:

• VESPA accurately infers kinase and phosphatase activity, revealing time-dependent signaling pathway responses to drug perturbations.
• The algorithm successfully elucidates adaptive resistance mechanisms and network rewiring in cancer cells.
• Experimental validation confirmed the predicted adaptive responses and rewiring events.

Conclusions:

• VESPA provides a powerful tool for dissecting complex cell signaling dynamics and adaptive mechanisms in response to drug treatment.
• The findings highlight the importance of understanding network rewiring for overcoming therapeutic resistance in cancer.
• The algorithm shows broad applicability for studying cellular adaptation in cancer and other diseases.