Integrating Single-Cell Foundation Models with Graph Neural Networks for Drug Response Prediction
Journal:
arXiv
Published Date:
Apr 19, 2025
Abstract
AI-driven drug response prediction holds great promise for advancing
personalized cancer treatment. However, the inherent heterogenity of cancer and
high cost of data generation make accurate prediction challenging. In this
study, we investigate whether incorporating the pretrained foundation model
scGPT can enhance the performance of existing drug response prediction
frameworks. Our approach builds on the DeepCDR framework, which encodes drug
representations from graph structures and cell representations from multi-omics
profiles. We adapt this framework by leveraging scGPT to generate enriched cell
representations using its pretrained knowledge to compensate for limited amount
of data. We evaluate our modified framework using IC$_{50}$ values on Pearson
correlation coefficient (PCC) and a leave-one-drug out validation strategy,
comparing it against the original DeepCDR framework and a prior
scFoundation-based approach. scGPT not only outperforms previous approaches but
also exhibits greater training stability, highlighting the value of leveraging
scGPT-derived knowledge in this domain.
