用scGPT增强细胞表征，提升抗癌药反应预测准确率
Integrating Single-Cell Foundation Models with Graph Neural Networks for Drug Response Prediction
- 用scGPT预训练模型生成更丰富的细胞特征
- 在PCC指标上优于原有DeepCDR和scFoundation方法
- 特别适合数据少的癌症类型药物响应研究
基于AI的药物反应预测有望推动个性化癌症治疗。然而，癌症内在异质性及数据生成成本高，使得精准预测困难。本研究探讨将预训练的scGPT基础模型融入现有药物反应预测框架是否有效。方法基于DeepCDR框架，该框架通过图结构编码药物，通过多组学数据编码细胞。我们改进该框架，利用scGPT生成更具信息量的细胞表征，以弥补数据不足。在Pearson相关系数（PCC）与留一药验证策略下评估，使用IC₅₀值进行对比，结果表明，scGPT不仅性能超越原有DeepCDR和scFoundation方法，且训练更稳定，证明了利用scGPT知识在该领域的重要价值。
原文摘要 · Abstract (English)
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.
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