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: Not available
| 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:Effective clinical management of advanced epithelial ovarian cancer (EOC) remains a major challenge, with most patients relapsing despite standard therapies, emphasizing the need for individualized treatment strategies and predictive approaches. Here, functional ex vivo drug testing may provide a translational approach to match patients with effective, personalized therapies based on drug sensitivity. We present DRUGSENS, a scalable and clinically adaptable ex vivo drug response screening platform that enables quantitative assessment of single-cell, on-target effects in both cultured cell lines and patient-derived samples. DRUGSENS supports high-resolution analysis of cellular responses to individual therapeutic agents and clinically relevant drug combinations, with the potential to inform personalized treatment strategies across diverse tumor types and tissue contexts. This semi-automated ex vivo assay integrates immunofluorescence-based confocal imaging with computational analysis using QuPath, Fiji, and a developed DRUGSENS R package. We successfully generated individualized drug sensitivity profiles for 21 samples derived from 17 patients across both chemotherapy-naive and relapsed disease states, achieving a 100% success rate of culture efficiency and generating individualized drug profiles within a 10-day turnaround time. Notably, most resistant samples showed poor response to multiple agents, suggesting a multidrug-resistant phenotype. Ex vivo drug responses significantly correlated with clinical outcomes, including progression-free and overall survival. In conclusion, DRUGSENS platform enables rapid, clinically relevant functional profiling of therapeutic responses in EOC and represents a valuable tool to guide treatment recommendations and suggest drug prioritization for clinical application.
2026-07-22 | GSE308072 | 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:Ex vivo drug sensitivity testing predicts treatment outcomes in advanced ovarian cancer
| PRJNA1328690 | ENA
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