Zhou Yuru, Dai Quanhui, Xu Yanming, Wu Shuang, Cheng Minzhang, Zhao Bing
School of Basic Medical Sciences, Institute of Biomedical Innovation, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
Z Lab, bioGenous BIOTECH, Shanghai, China.
NPJ Precis Oncol. 2025 Aug 13;9(1):282. doi: 10.1038/s41698-025-01082-6.
A major challenge in effective cancer treatment is the variability of drug responses among patients. Patient-derived organoids greatly preserve the genetic and histological characteristics even the drug sensitivities of primary tumor tissues, therefore provide a compelling approach to predict clinical outcome. However, the individual organoid culture and following drug response test are time and cost-consuming, which hinders the potential clinical application. Here, we developed PharmaFormer, a clinical drug response prediction model based on custom Transformer architecture and transfer learning. PharmaFormer was initially pre-trained with the abundant gene expression and drug sensitivity data of 2D cell lines, and was then finalized through a model further fine-tuned with the limited organoid pharmacogenomic data accumulated at the present stage. Our results demonstrate that PharmaFormer, integrating both pan-cancer cell lines and organoids of a specific type of tumor, provides a dramatically improved accurate prediction of clinical drug response. This study highlights that advanced AI models combined with biomimetic organoid models will accelerate precision medicine and future drug development.
有效癌症治疗的一个主要挑战是患者之间药物反应的变异性。患者来源的类器官极大地保留了原发性肿瘤组织的遗传和组织学特征甚至药物敏感性，因此提供了一种预测临床结果的令人信服的方法。然而，单个类器官培养及后续药物反应测试既耗时又费钱，这阻碍了其潜在的临床应用。在此，我们开发了PharmaFormer，一种基于定制Transformer架构和迁移学习的临床药物反应预测模型。PharmaFormer最初使用二维细胞系丰富的基因表达和药物敏感性数据进行预训练，然后通过用现阶段积累的有限类器官药物基因组数据进一步微调模型来最终确定。我们的结果表明，整合泛癌细胞系和特定类型肿瘤类器官的PharmaFormer能显著提高临床药物反应的准确预测。这项研究强调，先进的人工智能模型与仿生类器官模型相结合将加速精准医学和未来药物开发。
