探索个性化放射治疗策略：第三部分 利用元学习识别放射反应的遗传决定因素
Exploring Strategies for Personalized Radiation Therapy: Part III Identifying genetic determinants for Radiation Response with Meta Learning
AI总结：
本研究提出一种元学习框架，利用细胞系基因表达数据单样本预测以SF2衡量的放射敏感性，克服了RSI等静态模型的局限，实现了对个体样本的快速适应，提高了跨肿瘤亚型的预测准确性并揭示了指导个性化治疗的基因影响模式。
摘要：
癌症的放射反应受复杂的患者特异性生物学影响，然而当前的治疗策略通常依赖统一的剂量处方，未考虑肿瘤异质性。在本研究中，我们引入了一种元学习框架，利用细胞系级别的基因表达数据，对以 SF2 衡量的放射敏感性进行单样本预测。与广泛使用的 Radiosensitivity Index RSI（一种基于固定 10 基因特征训练的秩线性模型）不同，我们提出的元学习模型允许通过微调使每个基因的重要性随样本而变化。这种灵活性解决了 RSI 等静态模型的关键局限，这些模型假设不同肿瘤类型中基因贡献一致，并忽略了表达幅度和基因-基因相互作用。结果表明，元学习对未见样本具有稳健的泛化能力，并在放射敏感性变异性高的肿瘤亚群（如腺癌和大细胞癌）中表现良好。通过学习跨任务的可迁移结构并保持样本特异性适应能力，我们的方法实现了对个体样本的快速适应，提高了跨不同肿瘤亚型的预测准确性，同时揭示了可能指导个性化治疗的基因影响的上下文依赖模式。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
