用元学习预测肿瘤放疗敏感性，提升个性化治疗精度。
Exploring Strategies for Personalized Radiation Therapy: Part III Identifying genetic determinants for Radiation Response with Meta Learning
- 基于元学习框架，动态调整基因重要性以适应个体样本。
- 在腺癌和大细胞癌等高变异亚型中表现优异，准确率显著提升。
- 适合关注精准放疗与基因驱动机制的临床研究者使用。
癌症放疗反应受复杂且个体化的生物学因素影响，但现有治疗方案常采用统一剂量，未考虑肿瘤异质性。本研究提出一种元学习框架，仅需一次训练即可利用细胞系水平基因表达数据预测放射敏感性（以SF2衡量）。不同于依赖固定10基因签名的排名线性模型RSI，该模型通过微调使各基因重要性随样本变化，克服了静态模型假设基因贡献一致、忽略表达量及基因互作的局限。结果表明，该方法在未见样本上具有强泛化能力，在腺癌和大细胞癌等放射敏感性差异大的肿瘤亚型中表现突出。通过跨任务学习可迁移结构并保留样本特异性适应能力，实现对个体样本的快速适配，提升多种肿瘤亚型的预测准确性，并揭示基因影响的上下文依赖模式，有助于指导个性化放疗策略。
原文摘要 · Abstract (English)
Radiation response in cancer is shaped by complex, patient specific biology, yet current treatment strategies often rely on uniform dose prescriptions without accounting for tumor heterogeneity. In this study, we introduce a meta learning framework for one-shot prediction of radiosensitivity measured by SF2 using cell line level gene expression data. Unlike the widely used Radiosensitivity Index RSI a rank-based linear model trained on a fixed 10-gene signature, our proposed meta-learned model allows the importance of each gene to vary by sample through fine tuning. This flexibility addresses key limitations of static models like RSI, which assume uniform gene contributions across tumor types and discard expression magnitude and gene gene interactions. Our results show that meta learning offers robust generalization to unseen samples and performs well in tumor subgroups with high radiosensitivity variability, such as adenocarcinoma and large cell carcinoma. By learning transferable structure across tasks while preserving sample specific adaptability, our approach enables rapid adaptation to individual samples, improving predictive accuracy across diverse tumor subtypes while uncovering context dependent patterns of gene influence that may inform personalized therapy.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。