GRIP-Lung: Generative Model of Response to Drug-Induced Perturbation in Lung Cancer.

The prediction of drug response would significantly improve the treatment of lung cancer. Tumor heterogeneity and complex signal transduction pathways lead to varied treatment effects among patients, but traditional computational approaches struggle to model the nonlinear, high-dimensional relationship between genes and drug responses. In order to develop a Generative Adversarial Network (GAN)-based model that can predict drug-induced gene expression profiles from lung cancer cell lines, we developed GRIP-Lung (Generative Model of Response to Drug-Induced Perturbation in Lung Cancer). By making use of biologically informed embeddings of cell line identity as well as drug treatment conditions, this model is able to gain a fairly good understanding of cell types and their transcriptional perturbations induced by different drugs. The GRIP-Lung model displayed reasonably good prediction ability in terms of predictive accuracy and showed high concordance between the predicted and experimental expression profiles. We not only predicted transcriptional changes induced by drug therapy but also used single-sample Gene Set Enrichment Analysis (ssGSEA) to classify post-treatment response states based on characteristic molecular biomarkers, offering a means for selecting effective drugs to target specific heterogeneity within lung tumors. The proposed GRIP-Lung framework faithfully reproduces drug-induced transcriptional perturbations in lung cell line models. By integrating biologically informed embeddings and adversarial learning, the model advances drug response prediction. This makes it a flexible computational tool for drug repositioning.