通过对转录组建模将癌症药物反应从细胞水平转化至患者水平的深度学习框架

Introduction: Measuring perturbations of the transcriptome upon drug treatment can be highly informative, yet perturbed transcriptome profiles are often unavailable at both the cell-level and the patient-level. A key challenge, therefore, is how to infer such unobserved drug responses. Because drug treatment perturbs not only individual gene expression levels but also the underlying gene-gene interactions, the major challenge is how to model perturbations of these gene-gene interactions at the gene-level in LINCS so that unobserved drug responses of cancer cells and patients can be inferred. Methods: To predict unobserved perturbation of transcriptome after drug treatment, we have developed a deep learning model that captures perturbations of gene-gene interactions in LINCS, Condition-Specific Gene-Gene Attention (CSG2A, Bioinformatics/ISMB 2024). CSG2A models dosage- and time-dependent transcriptome perturbation and serves as as perturbation-based pretrained model. As change in transcriptome can be also seen as transcriptomic cellular state change, we used an existing pre-trained rank-based model (Geneformer, Nature 2024). Given these models of transcriptomic changes, we model the cancer patient in two different approaches in terms of transcriptome profiles. In the first approach, a deep learning model, called THERAPI, embeds a tumor by combining arbitrary sets of cancer cells in GDSC, after which perturbations of gene-gene interactions are predicted with the perturbation-based and rank-based models. In the second approach, we proposed PREDIKTOR, which uses a network-based representation of the patient transcriptome with DysRegNet ( British Journal of Pharmacology 2024), and then predicts drug-induced perturbations of gene-gene interactions using the perturbation-based pre-trained model. Results: We developed two computational frameworks for predicting patient-level drug response from cell-line response, THERAPI and PREDIKTOR. Both THERAPI and PREDIKTOR outperformed existing deep learning models with significant margin up to 8.5% improvement in experiment with TCGA data and the I-SPY2 patient cohort. Conclusion: These two frameworks are being deployed on the Amazon cloud as “DrugVLAB TM response” so that any researchers around the world can utilize for drug response prediction. Our systems can evolve with upcoming deep learning models, propriety or public, for more accurate drug response prediction.