Developing human radiation biodosimetry models: Testing cross-species conversion approaches using an ex vivo model system
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ABSTRACT:
OTHER RELATED OMICS DATASETS IN: GSE102971
PROVIDER: PRJNA399654 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:In the event of a large-scale radiation exposure incident, accurate and quick assessment of radiation dose would be critical for triage and medical treatment of large numbers of potentially exposed individuals. Current methods of biodosimetry, such as the dicentric chromosome assay, are time consuming and require sophisticated equipment and highly trained personnel. Therefore, scalable biodosimetry approaches, including gene expression profiles in peripheral blood cells, are being investigated. Due to limited availability of appropriate human samples, however, biodosimetry development has relied heavily on mouse models, which are not directly applicable to human response. Therefore, to explore the feasibility of using non-human primate models to build and test a biodosimetry algorithm for use in humans, we ex vivo-irradiated peripheral blood samples from both humans and rhesus macaques to 0, 2, 5, 6, and 7 Gy, and compared the gene expression profiles 24 hours later using Agilent human microarrays. Among the dose-responsive genes in human and using non-human primate, 52 genes showed highly correlated expression patterns between the species, and were enriched in p53/DNA damage response, apoptosis, and cell cycle-related genes. When these interspecies-correlated genes were used to build biodosimetry models with using non-human primate data, the mean prediction accuracy on non-human primate samples was about 90% within 1 Gy of delivered dose in leave-one-out cross-validation. However, tests on human samples suggested that human gene expression values may need to be adjusted prior to application of the non-human primate model. A ‘multi-gene’ approach utilizing all gene values for cross-species conversion and applying the converted values on the non-human primate biodosimetry models, gave a leave-one-out cross-validation prediction accuracy for human samples highly comparable (up to 94%) to that for non-human primate. Overall, this study demonstrates that a robust NHP biodosimetry model can be built using interspecies-correlated genes, and that, by using multiple regression-based cross-species conversion of expression values, absorbed dose in human samples can be accurately predicted by the non-human primate model.
2017-08-24 | GSE102971 | GEO
Project description:In the event of a large-scale radiation exposure, accurate and quick assessment of radiation dose received would be critical for triage and medical treatment of large numbers of potentially exposed individuals. Current methods of biodosimetry, such as the dicentric chromosome assay, are time consuming and require sophisticated equipment and highly trained personnel. Therefore, scalable biodosimetry approaches, including gene expression profiles in peripheral blood cells, are being investigated. Due to the limited availability of appropriate human samples, biodosimetry development has relied heavily on mouse models, which are not directly applicable to human response. Therefore, to explore the feasibility of using non-human primate (NHP) models to build and test a biodosimetry algorithm for use in humans, we irradiated ex vivo peripheral blood samples from both humans and rhesus macaques with doses of 0, 2, 5, 6 and 7 Gy, and compared the gene expression profiles 24 h later using Agilent human microarrays. Among the dose-responsive genes in human and using non-human primate, 52 genes showed highly correlated expression patterns between the species, and were enriched in p53/DNA damage response, apoptosis and cell cycle-related genes. When these interspecies-correlated genes were used to build biodosimetry models with using NHP data, the mean prediction accuracy on non-human primate samples was about 90% within 1 Gy of delivered dose in leave-one-out cross-validation. However, tests on human samples suggested that human gene expression values may need to be adjusted prior to application of the NHP model. A "multi-gene" approach utilizing all gene values for cross-species conversion and applying the converted values on the NHP biodosimetry models, gave a leave-one-out cross-validation prediction accuracy for human samples highly comparable (up to 94%) to that for non-human primates. Overall, this study demonstrates that a robust NHP biodosimetry model can be built using interspecies-correlated genes, and that, by using multiple regression-based cross-species conversion of expression values, absorbed dose in human samples can be accurately predicted by the NHP model.
| S-EPMC5996993 | biostudies-literature
Project description:Understanding the possible impact of potential confounding factors is necessary for any approach to radiation biodosimetry. Potential confounding factors have not been fully addressed for gene expression-based biodosimetry approaches, such as we are developing. To begin addressing this need, we have used an ex vivo irradiated peripheral blood cell model to investigate the potential effect of smoking on the global radiation gene expression response, and looked for genes that respond to radiation differently in smokers and non-smokers, and also in males and females. The results indicate that only a small number of genes may be significantly confounded by either factor, supporting the idea of developing peripheral blood gene expression strategies for radiation biodosimetry.
2011-03-16 | GSE23515 | GEO
Project description:Understanding the possible impact of potential confounding factors is necessary for any approach to radiation biodosimetry. Potential confounding factors have not been fully addressed for gene expression-based biodosimetry approaches, such as we are developing. To begin addressing this need, we have used an ex vivo irradiated peripheral blood cell model to investigate the potential effect of smoking on the global radiation gene expression response, and looked for genes that respond to radiation differently in smokers and non-smokers, and also in males and females. The results indicate that only a small number of genes may be significantly confounded by either factor, supporting the idea of developing peripheral blood gene expression strategies for radiation biodosimetry. Blood from each of 24 different donors was exposed to four doses of ionizing radiation (0, 0.1, 0.5, or 2 Gy) and analyzed using single-color microarray hybridization. The donors represented equal numbers of male and female smokers (1 or more packs a day) and non-smokers. There are 95 data sets in the study as the sample from one of the female smokers exposed to 2 Gy was lost.
2011-03-16 | E-GEOD-23515 | biostudies-arrayexpress
Project description:After defining a gene expression signature that predicted radiation exposure dose with high accuracy in human peripheral white blood cells irradiated ex vivo, we now demonstrate the predictive power of gene expression signatures in blood from patients undergoing total body irradiation. Using whole genome microarray analysis, we have identified genes that respond to radiation exposure in cancer patients in vivo. A 3-nearest neighbor classifier built from these genes correctly predicted samples as exposed to 0, 1.25 or 3.75 Gy with 94% accuracy 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 using multiple methods. The demonstration that ex vivo irradiation is an appropriate model that can provide meaningful prediction of in vivo exposure, and that the signatures are robust across diverse disease states, is an important advance in the application of gene expression for biodosimetry. Translation of these signatures to a fully automated “lab-on-a-chip” device will enable high-throughput screening for large-scale radiological emergencies, as well as making such tests practical for clinical uses. Radiation induced gene expression was measured in vivo in TBI patients at 4 hours after 1.25Gy exposure or at 24 hours after 3.75Gy exposure with three 1.25Gy split doses (approximately 4 hours apart). A total of 18 TBI patients, diagnosed with a variety of cancers were used in this study. Blood from 14 healthy control individuals was also used for comparison.
2011-01-31 | E-GEOD-20162 | biostudies-arrayexpress
Project description:After defining a gene expression signature that predicted radiation exposure dose with high accuracy in human peripheral white blood cells irradiated ex vivo, we now demonstrate the predictive power of gene expression signatures in blood from patients undergoing total body irradiation. Using whole genome microarray analysis, we have identified genes that respond to radiation exposure in cancer patients in vivo. A 3-nearest neighbor classifier built from these genes correctly predicted samples as exposed to 0, 1.25 or 3.75 Gy with 94% accuracy 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 using multiple methods. The demonstration that ex vivo irradiation is an appropriate model that can provide meaningful prediction of in vivo exposure, and that the signatures are robust across diverse disease states, is an important advance in the application of gene expression for biodosimetry. Translation of these signatures to a fully automated “lab-on-a-chip” device will enable high-throughput screening for large-scale radiological emergencies, as well as making such tests practical for clinical uses.
2011-01-31 | GSE20162 | GEO
Project description:Ex Vivo Lung Perfusion as a Human Platform for Preclinical Small Molecule Testing
| PRJNA486099 | ENA
Project description:To further development of our gene expression approach to biodosimetry, we have employed whole genome microarray expression profiling as a discovery platform to identify genes with the potential to distinguish radiation dose across an exposure range relevant for medical decision-making in a radiological emergency. Human peripheral blood from healthy donors was irradiated ex vivo, and a 74-gene consensus signature was identified that distinguished between four radiation doses (0.5, 2, 5 and 8 Gy) and control samples. The same set of genes separated samples by exposure level at both six and 24 hours after treatment, with overlap evident only at the highest two doses (5 and 8 Gy). Expression of five genes (CDKN1A, FDXR, SESN1, BBC3 and PHPT1) from this signature was quantified in the same RNA samples by real-time PCR, confirming low variability between donors as well as the predicted radiation response pattern. Keywords: Dose Response, stress response, radiation response, biodosimetry
2008-07-01 | GSE8917 | GEO