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Prediction of in vivo radiation dose status in radiotherapy patients using ex vivo and in vivo gene expression signatures.

ABSTRACT: 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.

SUBMITTER: Paul S

PROVIDER: S-EPMC3134561 | biostudies-literature | 2011 Mar

REPOSITORIES: biostudies-literature

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Prediction of in vivo radiation dose status in radiotherapy patients using ex vivo and in vivo gene expression signatures.

Paul Sunirmal S Barker Christopher A CA Turner Helen C HC McLane Amanda A Wolden Suzanne L SL Amundson Sally A SA

Radiation research 20110110 3

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 identifi ...[more]

PMID: 21388269

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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.

Dataset Information

Prediction of in vivo radiation dose status in radiotherapy patients using ex vivo and in vivo gene expression signatures.

Publications

Prediction of in vivo radiation dose status in radiotherapy patients using ex vivo and in vivo gene expression signatures.

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