Radiation Oncology Branch, National Cancer Institute, Bethesda, Maryland.
Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics & Information Technology, National Cancer Institute, National Institute of Health, Rockville, Maryland.
Radiat Res. 2024 Oct 1;202(4):697-705. doi: 10.1667/RADE-24-00092.1.
In future mass casualty medical management scenarios involving radiation injury, medical diagnostics to both identify those who have been exposed and the level of exposure will be needed. As almost all exposures in the field are heterogeneous, determination of degree of exposure and which vital organs have been exposed will be essential for effective medical management. In the current study we sought to characterize novel proteomic biomarkers of radiation exposure and develop exposure and dose prediction algorithms for a variety of exposure paradigms to include uniform total-body exposures, and organ-specific partial-body exposures to only the brain, only the gut and only the lung. C57BL6 female mice received a single total-body irradiation (TBI) of 2, 4 or 8 Gy, 2 and 8 Gy for lung or gut exposures, and 2, 8 or 16 Gy for exposure to only the brain. Plasma was then screened using the SomaScan v4.1 assay for ∼7,000 protein analytes. A subset panel of protein biomarkers demonstrating significant (FDR<0.05 and |logFC|>0.2) changes in expression after radiation exposure was characterized. All proteins were used for feature selection to build 7 different predictive models of radiation exposure using different sample cohort combinations. These models were structured according to practical field considerations to differentiate level of exposure, in addition to identification of organ-specific exposures. Each model algorithm built using a unique sample cohort was validated with a training set of samples and tested with a separate new sample series. The overall predictive accuracy for all models was 100% at the model training level. When tested with reserved samples Model 1 which compared an "exposure" group inclusive of all TBI and organ-specific partial-body exposures in the study vs. control, and Model 2 which differentiated between control, TBI and partials (all organ-specific partial-body exposures) the resulting prediction accuracy was 92.3% and 95.4%, respectively. For identification of organ-specific exposures vs. control, Model 3 (only brain), Model 4 (only gut) and Model 5 (only lung) were developed with predictive accuracies of 78.3%, 88.9% and 94.4%, respectively. Finally, for Models 6 and 7, which differentiated between TBI and separate organ-specific partial-body cohorts, the testing predictive accuracy was 83.1% and 92.3%, respectively. These models represent novel predictive panels of radiation responsive proteomic biomarkers and illustrate the feasibility of development of biodosimetry algorithms with utility for simultaneous classification of total-body, partial-body and organ-specific radiation exposures.
在未来涉及放射性损伤的大规模人员伤亡医疗管理场景中，将需要医疗诊断来识别暴露人员和暴露水平。由于野外几乎所有的暴露都是不均匀的，因此确定暴露程度以及哪些重要器官已经暴露对于有效的医疗管理至关重要。在本研究中，我们试图描述放射性暴露的新型蛋白质组学生物标志物，并为各种暴露模式开发暴露和剂量预测算法，包括全身均匀照射和仅脑、仅肠道和仅肺部的部分身体照射。C57BL6 雌性小鼠接受单次全身照射（TBI）2、4 或 8Gy、2 和 8Gy 肺或肠道暴露，以及 2、8 或 16Gy 仅脑暴露。然后使用 SomaScan v4.1 检测试剂盒筛选血浆中约 7000 种蛋白质分析物。选择一组具有显著变化（FDR<0.05 和 |logFC|>0.2）的蛋白质生物标志物进行特征描述。所有蛋白质都用于特征选择，以使用不同的样本队列组合构建 7 种不同的辐射暴露预测模型。这些模型根据实际现场考虑因素构建，以区分暴露水平，以及识别特定器官的暴露。使用独特的样本队列构建的每个模型算法都使用训练样本进行验证，并使用单独的新样本系列进行测试。所有模型在模型训练水平的总体预测准确性均为 100%。当使用保留样本测试时，模型 1 将研究中的所有 TBI 和特定器官部分身体暴露的“暴露”组与对照组进行比较，模型 2 区分对照组、TBI 和部分（所有特定器官部分身体暴露），预测准确率分别为 92.3%和 95.4%。对于特定器官暴露与对照组的识别，模型 3（仅脑）、模型 4（仅肠道）和模型 5（仅肺）的预测准确率分别为 78.3%、88.9%和 94.4%。最后，对于模型 6 和 7，它们区分 TBI 和单独的特定器官部分身体队列，测试预测准确率分别为 83.1%和 92.3%。这些模型代表了放射性反应蛋白质组生物标志物的新型预测面板，并说明了开发具有同时分类全身、部分身体和特定器官放射性暴露的实用生物剂量测定算法的可行性。
