The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.
Nat Commun. 2021 May 11;12(1):2700. doi: 10.1038/s41467-021-22989-1.
Resistance to ionizing radiation, a first-line therapy for many cancers, is a major clinical challenge. Personalized prediction of tumor radiosensitivity is not currently implemented clinically due to insufficient accuracy of existing machine learning classifiers. Despite the acknowledged role of tumor metabolism in radiation response, metabolomics data is rarely collected in large multi-omics initiatives such as The Cancer Genome Atlas (TCGA) and consequently omitted from algorithm development. In this study, we circumvent the paucity of personalized metabolomics information by characterizing 915 TCGA patient tumors with genome-scale metabolic Flux Balance Analysis models generated from transcriptomic and genomic datasets. Metabolic biomarkers differentiating radiation-sensitive and -resistant tumors are predicted and experimentally validated, enabling integration of metabolic features with other multi-omics datasets into ensemble-based machine learning classifiers for radiation response. These multi-omics classifiers show improved classification accuracy, identify clinical patient subgroups, and demonstrate the utility of personalized blood-based metabolic biomarkers for radiation sensitivity. The integration of machine learning with genome-scale metabolic modeling represents a significant methodological advancement for identifying prognostic metabolite biomarkers and predicting radiosensitivity for individual patients.
抵抗电离辐射是许多癌症的一线治疗方法，但这也是一个主要的临床挑战。由于现有机器学习分类器的准确性不足，肿瘤放射敏感性的个体化预测目前尚未在临床上实施。尽管肿瘤代谢在辐射反应中起着公认的作用，但代谢组学数据在大型多组学倡议（如癌症基因组图谱 (TCGA)）中很少收集，因此也被排除在算法开发之外。在这项研究中，我们通过使用来自转录组和基因组数据集的基因组规模代谢通量平衡分析模型来描述 915 个 TCGA 患者肿瘤，从而避免了个性化代谢组学信息的缺乏。预测并实验验证了区分辐射敏感和辐射抵抗肿瘤的代谢生物标志物，从而将代谢特征与其他多组学数据集集成到基于集成的机器学习分类器中，以用于辐射反应。这些多组学分类器显示出更高的分类准确性，确定了临床患者亚组，并证明了基于个性化血液代谢生物标志物预测辐射敏感性的实用性。机器学习与基因组规模代谢建模的结合为识别预后代谢物生物标志物和预测个体患者的放射敏感性提供了重要的方法学进展。
