A Tumor-Agnostic, Topology-Informed Scoring Framework for Drug Repurposing: Application to CDK4/6 Inhibitor...

Area of Science:

• Oncology
• Bioinformatics
• Genomics

Background:

• Therapeutic resistance to CDK4/6 inhibitors (CDK4/6i) is a major challenge in HR+ breast cancer.
• Existing network-based methods for identifying salvage therapies lack a unified framework for translating network topology into actionable drug targets.
• There is a need for robust computational tools to predict effective drug repurposing strategies against therapy resistance.

Purpose of the Study:

• To develop and validate a novel computational framework, the Topology-Integrated Hubness Score (TIHS), for identifying therapeutic strategies against CDK4/6 inhibitor resistance.
• To prioritize drug repurposing candidates by integrating network topology with drug-target affinity.
• To experimentally validate the top-ranked candidate for overcoming resistance in HR+ breast cancer.

Main Methods:

• Developed the Topology-Integrated Hubness Score (TIHS) by integrating five orthogonal network metrics.
• Validated TIHS using cross-cohort statistical benchmarking and RNA-sequencing data from a palbociclib-resistant MCF7 model.
• Applied TIHS to prioritize drug candidates by overlaying network hubness with drug-target affinity profiles.

Main Results:

• TIHS demonstrated high cross-dataset stability (cosine similarity ≥ 0.98) and outperformed single-metric approaches in predicting drug sensitivity.
• Sorafenib was identified as a top-ranked candidate for reversing CDK4/6 inhibitor resistance.
• Experimental validation confirmed sorafenib resensitized resistant cells and molecular dynamics simulations supported binding to the hub gene FGFR3, which was mechanistically required for resensitization.

Conclusions:

• TIHS provides a mechanism-agnostic framework to bridge resistance-state transcriptomes and clinical decision-making.
• Targeting topology-defined hubs is a viable strategy for overcoming therapeutic resistance in cancer.
• This study validates TIHS as a powerful tool for identifying novel therapeutic strategies through computational prioritization and experimental verification.