[3H5-OS-10c] OS-10

[3H5-OS-10c-04] Drug Response Prediction via Transfer Learning on Graph Neural Networks with Knockdown Cell Line Gene Regulatory Networks

〇Sho Oshima¹, Yuji Okamoto¹, Taisei Tosaki^1,2, Yasushi Okuno^1,2 (1. Kyoto university, 2. Riken Center for Computational Science)

Keywords:Graph Neural Network, Gene Regulatory Network, Transfer Learning, Survival Time Analysis, Cancer Precision Medicine

Comprehensive identification of target genes and accurate prediction of their therapeutic effects are of paramount importance in personalized cancer medicine. However, conventional approaches using animal models or cell lines are costly, time-consuming, and limited in their ability to achieve thorough analyses.
Here we show that a new Graph Neural Network (GNN)-based learning scheme, which aligns experimental data from cancer cell lines with Virtual Knockdown manipulations in cancer patients, enables accurate prognosis prediction after gene suppression through transfer learning. Focusing on the HER2-positive subtype of breast cancer, we conducted a Virtual Knockdown of ERBB2 (Her2). Our findings not only confirmed improvements in survival rates but also suggested better hazard ratios compared to clinical data, demonstrating the biological consistency and utility of our approach for treatment efficacy prediction.
This research is expected to serve as a valuable foundation for enhancing the reliability of molecular-targeted drug design, discovering new therapeutic targets, and advancing clinical applications. Moving forward, the application of this method to a broader range of cancer types and further integration with clinical data will enable the development of more versatile and precise models for predicting treatment outcomes.