arXiv cs.AI· Y. Zhu, K. Marlor, V. Lavelle, T. S. Brettin, M. Sacco, C. Siniscalchi, H. W. Raeder, M. R. Weil, R. L. Stevens·· 1 天前AI 评分26
用于评估和训练 AI 药物反应预测模型的 PDO 药筛数据
PDO Drug Screening Data for Evaluating and Training AI Drug Response Prediction Models
AI 导读
研究团队利用患者来源类器官（PDO）开展四项药筛研究，生成多组学图谱、药物特征和治疗反应数据，用于训练与评估抗癌药物反应预测（DRP）模型。他们按仅用细胞系数据、细胞系与 PDO 数据联合训练、细胞系预训练后 PDO 微调三种场景重训多个公开 DRP 模型，并在 PDO 上比较预测性能，同时评估不同实验室与检测技术产生的药筛活性数据一致性。
正文 · 原文
Abstract:Anti-cancer drug response prediction (DRP) is critically important for precision oncology and therapeutic development. Currently, most DRP models are developed using drug screening data from immortalized cancer cell lines, which can differ substantially from patient tumors at the genomic and molecular levels, even though patient tumor response is the ultimate prediction target. To help bridge this gap and bring DRP models closer to their intended clinical application, we conducted four drug screening studies using patient-derived organoids (PDOs), which more faithfully recapitulate patient tumors than immortalized cell lines. These studies generated data for improving and evaluating DRP models, including multi-omics profiles of PDO lines, drug feature data, and treatment response measurements. Using these data, we retrained and evaluated multiple publicly available DRP models under three model-building scenarios: training models using cell line data only, training models using combined cell line and PDO data, and pretraining models on cell line data followed by fine-tuning on PDO data. We then evaluated and compared model prediction performance on PDOs across these analysis scenarios. In addition, we also assessed the consistency of drug screening viability data generated by different laboratories using different assay technologies and/or experimental protocols.
| Subjects: | Quantitative Methods (q-bio.QM); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.15769 [q-bio.QM] |
| (or arXiv:2611.15769v1 [q-bio.QM] for this version) | |
| https://doi.org/10.48550/arXiv.2611.15769 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yitan Zhu [view email]
[v1]
Wed, 11 Nov 2026 17:49:41 UTC (2,146 KB)
来源：arXiv cs.AI · arxiv.org