AI-Driven Combination Therapy for Counteracting Dysregulated Genes in Lung Adenocarcinoma: Contribution-Aware...

Background/Objectives: Lung adenocarcinoma (LUAD) is molecularly heterogeneous and often requires rational drug combinations rather than single-agent therapy. Many computational repurposing methods use global signature matching or network scores, but they often treat dysregulated genes equally and optimize a single scalar objective. This study aimed to develop a contribution-aware computational framework for prioritizing repurposed multi-drug combinations that counteract LUAD driver modules; Methods: Ten LUAD driver scenarios were curated from the LUAD and non-small cell lung cancer literature and encoded as gene-level counteraction vectors across 44 unique genes. Direction-aware drug-gene interactions from the Comparative Toxicogenomics Database were processed into a weighted contribution matrix. A genetic algorithm was then used to search for small combinations of up to six drugs. The fitness function combined mean absolute error with terms for waste, mismatch, entropy, coverage, combination size, and optional cost. Orthogonal computational support was assessed using CLUE/Connectivity Map transcriptomic reversal analysis; Results: After filtering and optimization, 42 drugs and chemicals remained as candidate components across the scenarios. Increasing the combination size from one to three drugs usually reduced the mean absolute error, whereas larger combinations provided more limited gains. Compared with an MAE-only baseline, the full contribution-aware objective improved or preserved MAE in 54 of 60 scenario-drug-count comparisons. Drug and gene clustering identified interchangeable candidate groups and shared mechanisms across LUAD scenarios. CLUE-based analysis provided strong or moderate transcriptomic reversal support for several prioritized compounds; Conclusions: The proposed framework provides a transparent, scenario-based method for prioritizing repurposed drug combinations in LUAD. The results are computational and hypothesis-generating. They should guide future experimental testing, not clinical treatment decisions.