Hybrid Computational Modeling with Multi-Level Validation Identifies TK1-VIM as a Robust Therapeutic Pair in...

Area of Science:

• Computational biology
• Oncology
• Systems biology

Background:

• Triple-negative breast cancer (TNBC) presents a significant clinical challenge due to a lack of effective molecular targets and poor patient prognosis.
• Existing computational methods for target identification often yield targets with low druggability, high complexity, and insufficient validation.
• There is a critical need for robust and validated computational strategies to identify novel therapeutic targets in TNBC.

Purpose of the Study:

• To develop and validate a hybrid computational methodology for identifying druggable therapeutic target pairs in triple-negative breast cancer.
• To identify a robust, computationally validated therapeutic target pair for TNBC with a favorable therapeutic window.
• To provide a rigorous methodological framework for future computational drug target discovery.

Main Methods:

• A hybrid approach combining Boolean network modeling and semidefinite programming (SDP) was employed to analyze a TNBC cell line network.
• The identified therapeutic pair underwent multi-level validation, including Boolean simulations, bootstrap uncertainty quantification, and sensitivity analysis.
• Orthogonal validation was performed using AlphaGenome, a deep learning model, to assess target expression and potential transcriptomic perturbation in normal mammary tissues.

Main Results:

• The computational analysis identified TK1 and VIM as a robust therapeutic target pair for TNBC.
• Dual inhibition of TK1 and VIM demonstrated high efficacy, achieving 99.03% similarity to the apoptotic state with statistical superiority (p<0.001).
• The TK1-VIM pair exhibits full druggability, with available inhibitors and a favorable therapeutic window indicated by low transcriptomic perturbation in normal tissues.

Conclusions:

• The TK1-VIM pair represents a computationally robust and druggable therapeutic candidate strategy for triple-negative breast cancer.
• The developed hybrid methodology offers a rigorous benchmark for computational drug target identification, emphasizing robustness and validation.
• Further experimental validation is essential for the clinical translation of this promising therapeutic strategy.