The current tools available for stratifying radiation victims include a) measurement of lymphocyte depletion kinetics, b) measurement of time to onset of nausea and vomiting, and c) cytogenetics analysis. Unfortunately, lymphocyte kinetics require several (>3-7) daily complete blood counts to provide accurate prediction of dose received and decline can lag for 48 hours even in heavily exposed individuals. Nausea and vomiting are also non-specific signs of radiation exposure and many can be expected to have such symptoms simply due to psychogenic stress. Cytogenetics remains the clinical standard via measurement of the dicentric DNA breaks which can occur following exposure. However, cytogenetics analysis requires several days to complete and does not lend itself to high throughput screening of affected individuals.
In light of these limitations in biological dosimetry, new technologies can and should be applied to develop more rapid and predictive tools for caregivers to apply in the triage of radiation mass casualties. One advantageous approach would utilize high throughput gene expression analyses to identify patterns of molecular changes which occur following radiation exposure. Such an approach could lead to a validated panel of “radiation response” genes that have yet to be identified and could be translated into a rapidly applicable diagnostic screening test. Our group has utilized the application of genomic analyses to identify genes predictive of prognosis within several types of cancers as well as genes which predict patient response to chemotherapy. We believe that a similar strategy can be successfully applied to determine which genes will predict different levels of radiation exposure and possibly allow stratification of victims based upon their genomic profile.
The primary goal of this proposal is to take advantage of recent developments in the use of genome-scale measures of gene expression, together with advanced computational tools, to develop molecular signatures that reflect the exposure to radiation. More broadly, this project has the potential to lead to the identification of specific genes involved in radiation-induced cellular damage and, therefore, targets for radioprotective intervention.
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