SAFER: sub-hypergraph attention-based neural network for predicting effective responses to dose combinations

• Published in: BMC bioinformatics Vol. 25; no. 1; pp. 250 - 19
• Main Authors: Tang, Yi-Ching, Li, Rongbin, Tang, Jing, Zheng, W. Jim, Jiang, Xiaoqian
• Format: Journal Article
• Language: English
• Published: London BioMed Central 30.07.2024; BioMed Central Ltd; BMC
• Subjects: Algorithms; Antineoplastic Agents - pharmacology; Artificial intelligence; Bioinformatics; Biological effects; Biological models (mathematics); Biomedical and Life Sciences; Cancer; Cell culture; Cell Line, Tumor; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Context-aware models; Datasets; Dosage; Dose-response relationship (Biochemistry); Dose-Response Relationship, Drug; Dose–response drug combination data; Drug combination prediction; Drug dosages; Drug Synergism; Drug therapy, Combination; Fibrosis; Gene expression; Genomics; Graph attention mechanisms; Graph theory; Graphs; Humans; Hypergraph representation learning; Life Sciences; Lung cancer; Lung Neoplasms - drug therapy; Lung Neoplasms - metabolism; Microarrays; Neural networks; Neural Networks, Computer; Oncology; Pharmaceutical research; Protein interaction; Proteins; Response rates; Signal transduction; Signal Transduction - drug effects; Toxicity; Transcription factors; China; Graph attention mechanisms; Hypergraph representation learning; Dose–response drug combination data; Context-aware models; Drug combination prediction
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-024-05873-9

Background The potential benefits of drug combination synergy in cancer medicine are significant, yet the risks must be carefully managed due to the possibility of increased toxicity. Although artificial intelligence applications have demonstrated notable success in predicting drug combination synergy, several key challenges persist: (1) Existing models often predict average synergy values across a restricted range of testing dosages, neglecting crucial dose amounts and the mechanisms of action of the drugs involved. (2) Many graph-based models rely on static protein–protein interactions, failing to adapt to dynamic and higher-order relationships. These limitations constrain the applicability of current methods. Results We introduce SAFER, a Sub-hypergraph Attention-based graph model, addressing these issues by incorporating complex relationships among biological knowledge networks and considering dosing effects on subject-specific networks. SAFER outperformed previous models on the benchmark and the independent test set. The analysis of subgraph attention weight for the lung cancer cell line highlighted JAK-STAT signaling pathway, PRDM12, ZNF781, and CDC5L that have been implicated in lung fibrosis. Conclusions SAFER presents an interpretable framework designed to identify drug-responsive signals. Tailored for comprehending dose effects on subject-specific molecular contexts, our model uniquely captures dose-level drug combination responses. This capability unlocks previously inaccessible avenues of investigation compared to earlier models. Furthermore, the SAFER framework can be leveraged by future inquiries to investigate molecular networks that uniquely characterize individual patients and can be applied to prioritize personalized effective treatment based on safe dose combinations.