Purpose-In a large-scale radiological emergency, estimates of exposure doses and radiation injury would be required for individuals without physical dosimeters. Current methods are inadequate for the task, so we are developing gene expression profiles for radiation biodosimetry. This approach could provide both an estimate of physical radiation dose and an indication of the extent of individual injury or future risk.Methods and Materials-We used whole genome microarray expression profiling as a discovery platform to identify genes with the potential to predict radiation dose across an exposure range relevant for medical decision-making in a radiological emergency. Human peripheral blood from ten healthy donors was irradiated ex vivo, and global gene expression was measured six and 24 hours after exposure.Results-A 74-gene signature was identified that distinguishes between four radiation doses (0.5, 2, 5 and 8 Gy) and controls. Over a third of these genes are regulated by TP53. A Nearest Centroid classifier using these same 74 genes correctly predicted 98% of samples taken either six or 24 hours after treatment as unexposed, exposed to 0.5 Gy, to 2 Gy, or to 5 Gy and above. Expression patterns of five genes (CDKN1A, FDXR, SESN1, BBC3 and PHPT1) from this signature were also confirmed by real-time PCR.Conclusions-The ability of a single gene set to predict radiation dose throughout a window of time without need for individual pre-exposure controls represents an important advance in the development of gene expression for biodosimetry.

After a large-scale nuclear accident or an attack with an improvised nuclear device, rapid biodosimetry would be needed for triage. As a possible means to address this need, we previously defined a gene expression signature in human peripheral white blood cells irradiated ex vivo that predicts the level of radiation exposure with high accuracy. We now demonstrate this principle in vivo using blood from patients receiving total-body irradiation (TBI). Whole genome microarray analysis has identified genes responding significantly to in vivo radiation exposure in peripheral blood. A 3-nearest neighbor classifier built from the TBI patient data correctly predicted samples as exposed to 0, 1.25 or 3.75 Gy with 94% accuracy (P < 0.001) even when samples from healthy donor controls were included. The same samples were classified with 98% accuracy using a signature previously defined from ex vivo irradiation data. The samples could also be classified as exposed or not exposed with 100% accuracy. The demonstration that ex vivo irradiation is an appropriate model that can provide meaningful prediction of in vivo exposure levels, and that the signatures are robust across diverse disease states and independent sample sets, is an important advance in the application of gene expression for biodosimetry.

Purpose MicroRNAs (miRNAs), a class of noncoding small RNAs that regulate gene expression, are involved in numerous physiologic processes in normal and malignant cells. Our in vivo study measured miRNA and gene expression changes in human blood cells in response to ionizing radiation, to develop miRNA signatures that can be used as biomarkers for radiation exposure. Methods and Materials Blood from 8 radiotherapy patients in complete remission 1 or 2 was collected immediately before and 4 hours after total body irradiation with 1.25 Gy x-rays. Both miRNA and gene expression changes were measured by means of quantitative polymerase chain reaction and microarray hybridization, respectively. Hierarchic clustering, multidimensional scaling, class prediction, and gene ontology analysis were performed to investigate the potential of miRNAs to serve as radiation biomarkers and to elucidate their likely physiologic roles in the radiation response. Results The expression levels of 45 miRNAs were statistically significantly upregulated 4 hours after irradiation with 1.25 Gy x-rays, 27 of them in every patient. Nonirradiated and irradiated samples form separate clusters in hierarchic clustering and multidimensional scaling. Out of 223 differentially expressed genes, 37 were both down-regulated and predicted targets of the upregulated miRNAs. Paired and unpaired miRNA-based classifiers that we developed can predict the class membership of a sample with unknown irradiation status, with accuracies of 100% when all 45 upregulated miRNAs are included. Both miRNA control of and gene involvement in biologic processes such as hemopoiesis and the immune response are increased after irradiation, whereas metabolic processes are underrepresented among all differentially expressed genes and the genes controlled by miRNAs. Conclusions Exposure to ionizing radiation leads to the upregulation of the expression of a considerable proportion of the human miRNAome of peripheral blood cells. These miRNA expression signatures can be used as biomarkers of radiation exposure.

Cesium-137 is a radionuclide of concern in fallout from reactor accidents or nuclear detonations. When ingested or inhaled, it can expose the entire body for an extended period of time, potentially contributing to serious health consequences ranging from acute radiation syndrome to increased cancer risks. To identify changes in gene expression that may be informative for detecting such exposure, and to begin examining the molecular responses involved, we have profiled global gene expression in blood of male C57BL/6 mice injected with 137CsCl. We extracted RNA from the blood of control or 137CsCl-injected mice at 2, 3, 5, 20 or 30 days after exposure. Gene expression was measured using Agilent Whole Mouse Genome Microarrays, and the data was analyzed using BRB-ArrayTools. Between 466–6,213 genes were differentially expressed, depending on the time after 137Cs administration. At early times (2–3 days), the majority of responsive genes were expressed above control levels, while at later times (20–30 days) most responding genes were expressed below control levels. Numerous genes were overexpressed by day 2 or 3, and then underexpressed by day 20 or 30, including many Tp53-regulated genes. The same pattern was seen among significantly enriched gene ontology categories, including those related to nucleotide binding, protein localization and modification, actin and the cytoskeleton, and in the integrin signaling canonical pathway. We compared the expression of several genes three days after 137CsCl injection and three days after an acute external gamma-ray exposure, and found that the internal exposure appeared to produce a more sustained response. Many common radiation-responsive genes are altered by internally administered 137Cs, but the gene expression pattern resulting from continued irradiation at a decreasing dose rate is extremely complex, and appears to involve a late reversal of much of the initial response.

Purpose The issue of potential confounding factors is critical to the development of any approach to radiation biodosimetry, and has not been fully addressed for gene expression-based approaches. Materials and Methods As a step in this direction, we have investigated the effect of smoking on the global radiation gene expression response in ex vivo irradiated peripheral blood cells using microarray analysis. We also evaluated the ability of gene expression signatures to predict the radiation exposure level of ex-vivo-exposed samples from smokers and non-smokers of both genders. Results We identified 8 genes with a radiation response that was significantly affected by smoking status, and confirmed an effect of smoking on the radiation response of the four and a half LIM domains 2 (FHL2) gene using quantitative real-time polymerase chain reaction. The performance of our previously defined 74-gene signature in predicting the radiation dose to samples in this study was unaffected by differences in gender or smoking status, however, giving 98% correct prediction of dose category. This is the same accuracy as that found in the original study from which the signature was derived, using different donors. Conclusion The results support the development of peripheral blood gene expression as a viable strategy for radiation biodosimetry.