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Biomarkers to Predict Lethal Radiation Injury to the Rat Lung.

ABSTRACT: Currently, there are no biomarkers to predict lethal lung injury by radiation. Since it is not ethical to irradiate humans, animal models must be used to identify biomarkers. Injury to the female WAG/RijCmcr rat has been well-characterized after exposure to eight doses of whole thorax irradiation: 0-, 5-, 10-, 11-, 12-, 13-, 14- and 15-Gy. End points such as SPECT imaging of the lung using molecular probes, measurement of circulating blood cells and specific miRNA have been shown to change after radiation. Our goal was to use these changes to predict lethal lung injury in the rat model, 2 weeks post-irradiation, before any symptoms manifest and after which a countermeasure can be given to enhance survival. SPECT imaging with ^99mTc-MAA identified a decrease in perfusion in the lung after irradiation. A decrease in circulating white blood cells and an increase in five specific miRNAs in whole blood were also tested. Univariate analyses were then conducted on the combined dataset. The results indicated that a combination of percent change in lymphocytes and monocytes, as well as pulmonary perfusion volume could predict survival from radiation to the lungs with 88.5% accuracy (95% confidence intervals of 77.8, 95.3) with a p-value of < 0.0001 versus no information rate. This study is one of the first to report a set of minimally invasive endpoints to predict lethal radiation injury in female rats. Lung-specific injury can be visualized by ^99mTc-MAA as early as 2 weeks after radiation.

SUBMITTER: Medhora M

PROVIDER: S-EPMC10053311 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Biomarkers to Predict Lethal Radiation Injury to the Rat Lung.

Medhora Meetha M Gao Feng F Gasperetti Tracy T Narayanan Jayashree J Himburg Heather H Jacobs Elizabeth R ER Clough Anne V AV Fish Brian L BL Szabo Aniko A

International journal of molecular sciences 20230315 6

Currently, there are no biomarkers to predict lethal lung injury by radiation. Since it is not ethical to irradiate humans, animal models must be used to identify biomarkers. Injury to the female WAG/RijCmcr rat has been well-characterized after exposure to eight doses of whole thorax irradiation: 0-, 5-, 10-, 11-, 12-, 13-, 14- and 15-Gy. End points such as SPECT imaging of the lung using molecular probes, measurement of circulating blood cells and specific miRNA have been shown to change after ...[more]

PMID: 36982722

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Project description:PurposeExposure to radiation provokes cellular responses, which are likely regulated by gene expression networks. MicroRNAs are small non-coding RNAs, which regulate gene expression by promoting mRNA degradation or inhibiting protein translation. The expression patterns of both mRNA and miRNA during the radiation-induced lung injury (RILI) remain less characterized and the role of miRNAs in the regulation of this process has not been studied. The present study sought to evaluate miRNA and mRNA expression profiles in the rat lung after irradiation.Methods and materialsMale Wistar rats were subjected to single dose irradiation with 20 Gy using 6 MV x-rays to the right lung. (A dose rate of 5 Gy/min was applied). Rats were sacrificed at 3, 12 and 26 weeks after irradiation, and morphological changes in the lung were examined by haematoxylin and eosin. The miRNA and mRNA expression profiles were evaluated by microarrays and followed by quantitative RT-PCR analysis.ResultsA cDNA microarray analysis found 2183 transcripts being up-regulated and 2917 transcripts down-regulated (P???0.05, ?2.0 fold change) in the lung tissues after irradiation. Likewise, a miRNAs microarray analysis indicated 15 miRNA species being up-regulated and 8 down-regulated (P???0.05). Subsequent bioinformatics analyses of the differentially expressed mRNA and miRNAs revealed that alterations in mRNA expression following irradiation were negatively correlated with miRNAs expression.ConclusionsOur results provide evidence indicating that irradiation induces alterations of mRNA and miRNA expression in rat lung and that there is a negative correlation of mRNA and miRNA expression levels after irradiation. These findings significantly advance our understanding of the regulatory mechanisms underlying the pathophysiology of radiation-induced lung injury. In summary, RILI does not develop gradually in a linear process. In fact, different cell types interact via cytokines in a very complex network. Furthermore, this study suggests that microRNAs may serve an important role in the pathogenesis of RILI and that understanding their role in RILI may have a significant effect on patient management and diagnosis in the future.

Project description:BackgroundRadiation-induced lung fibrosis (RILF) is an important late toxicity in patients with non-small-cell lung cancer (NSCLC) after radiotherapy (RT). Clinically significant RILF can impact quality of life and/or cause non-cancer related death. This study aimed to determine whether pre-treatment plasma cytokine levels have a significant effect on the risk of RILF and investigate the abilities of machine learning algorithms for risk prediction.MethodsThis is a secondary analysis of prospective studies from two academic cancer centers. The primary endpoint was grade≥2 (RILF2), classified according to a system consistent with the consensus recommendation of an expert panel of the AAPM task for normal tissue toxicity. Eligible patients must have at least 6 months' follow-up after radiotherapy commencement. Baseline levels of 30 cytokines, dosimetric, and clinical characteristics were analyzed. Support vector machine (SVM) algorithm was applied for model development. Data from one center was used for model training and development; and data of another center was applied as an independent external validation.ResultsThere were 57 and 37 eligible patients in training and validation datasets, with 14 and 16.2% RILF2, respectively. Of the 30 plasma cytokines evaluated, SVM identified baseline circulating CCL4 as the most significant cytokine associated with RILF2 risk in both datasets (P = 0.003 and 0.07, for training and test sets, respectively). An SVM classifier predictive of RILF2 was generated in Cohort 1 with CCL4, mean lung dose (MLD) and chemotherapy as key model features. This classifier was validated in Cohort 2 with accuracy of 0.757 and area under the curve (AUC) of 0.855.ConclusionsUsing machine learning, this study constructed and validated a weighted-SVM classifier incorporating circulating CCL4 levels with significant dosimetric and clinical parameters which predicts RILF2 risk with a reasonable accuracy. Further study with larger sample size is needed to validate the role of CCL4, and this SVM classifier in RILF2.

Dataset Information

Biomarkers to Predict Lethal Radiation Injury to the Rat Lung.

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Biomarkers to Predict Lethal Radiation Injury to the Rat Lung.

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