Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance.
Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance.
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DOI：
10.1038/s41467-021-22989-1
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发表时间：
2021-05-11
影响因子：
16.6
通讯作者：
Kemp ML
中科院分区：
文献类型：
--
作者：
Lewis JE;Kemp ML
作为许多癌症的一线治疗手段，对电离辐射的耐药性是一项重大的临床挑战。由于现有机器学习分类器的准确性不足，目前尚未在临床上实现肿瘤放射敏感性的个性化预测。尽管肿瘤代谢在辐射反应中发挥着公认的作用，但代谢组学数据很少在大型多组学计划中收集，例如癌症基因组图谱（TCGA），因此在算法开发中被省略。在本研究中，我们利用转录组学和基因组数据集生成的基因组尺度代谢通量平衡分析模型，对915例TCGA患者肿瘤进行了表征，从而规避了个性化代谢组学信息的缺乏。预测和实验验证了区分辐射敏感和耐药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，用于辐射响应。这些多组学分类器显示了更高的分类准确性，确定了临床患者亚组，并证明了个性化血液代谢生物标志物对辐射敏感性的实用性。机器学习与基因组尺度代谢建模的整合代表了识别预后代谢物生物标志物和预测个体患者放射敏感性的重要方法学进步。肿瘤放射敏感性的个性化预测将促进癌症治疗的精准医学工作流程的发展。在这里，作者整合了机器学习和基因组尺度代谢建模方法来识别预测辐射反应的多组学生物标志物。
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. Personalized prediction of tumor radiosensitivity would facilitate development of precision medicine workflows for cancer treatment. Here, the authors integrate machine learning and genome-scale metabolic modeling approaches to identify multi-omics biomarkers predictive of radiation response.
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DOI：
10.1016/0360-3016(84)90027-0
发表时间：
1984-01-01
影响因子：
7
作者：
GRIFFIN, TW;PAJAK, TF;MARCIAL, VA
通讯作者：
MARCIAL, VA
影响因子：
64.8
作者：
通讯作者：
--
影响因子：
3.3
作者：
Deng, Qian;Huang, Chun-mei;Wang, Wei
通讯作者：
Wang, Wei
DOI：
10.1152/ajpendo.1996.271.3.e471
发表时间：
1996-09-01
影响因子：
5.1
作者：
Cameron, NE;Cotter, MA
通讯作者：
Cotter, MA
DOI：
10.1002/path.5028
发表时间：
2018-04
期刊：
The Journal of pathology
影响因子：
--
作者：
Cooper LA;Demicco EG;Saltz JH;Powell RT;Rao A;Lazar AJ
通讯作者：
Lazar AJ