A Translatable Predictor of Human Radiation Exposure

Abstract

Terrorism using radiological dirty bombs or improvised nuclear devices is recognized as a major threat to both public health and national security. In the event of a radiological or nuclear disaster, rapid and accurate biodosimetry of thousands of potentially affected individuals will be essential for effective medical management to occur. Currently, health care providers lack an accurate, high-throughput biodosimetric assay which is suitable for the triage of large numbers of radiation injury victims. Here, we describe the development of a biodosimetric assay based on the analysis of irradiated mice, ex vivo-irradiated human peripheral blood (PB) and humans treated with total body irradiation (TBI). Interestingly, a gene expression profile developed via analysis of murine PB radiation response alone was inaccurate in predicting human radiation injury. In contrast, generation of a gene expression profile which incorporated data from ex vivo irradiated human PB and human TBI patients yielded an 18-gene radiation classifier which was highly accurate at predicting human radiation status and discriminating medically relevant radiation dose levels in human samples. Although the patient population was relatively small, the accuracy of this classifier in discriminating radiation dose levels in human TBI patients was not substantially confounded by gender, diagnosis or prior exposure to chemotherapy. We have further incorporated genes from this human radiation signature into a rapid and high-throughput chemical ligation-dependent probe amplification assay (CLPA) which was able to discriminate radiation dose levels in a pilot study of ex vivo irradiated human blood and samples from human TBI patients. Our results illustrate the potential for translation of a human genetic signature for the diagnosis of human radiation exposure and suggest the basis for further testing of CLPA as a candidate biodosimetric assay.

Predicting ionizing radiation exposure using biochemically-inspired genomic machine learning

“…In fact, Lucas et al . have shown that the murine signatures they developed are not translatable to human samples 42 . Furthermore, only two genes, including BAX , are shared by the human and murine signatures derived by Dressman et al .…”

“…Furthermore, only two genes, including BAX , are shared by the human and murine signatures derived by Dressman et al . 42 .…”

Radiation Dose-Rate Effects on Gene Expression in a Mouse Biodosimetry Model

“…We also evaluated the biological processes implicated by gene ontology analysis using DAVID. Although several groups are working towards the development of gene expression as an approach for radiation biodosimetry (6–9, 33, 34), such studies, as well as the development of other radiation biodosimetry methods, have focused on the classification of acute exposures. Among studies using C57BL/6 mice for acute biodosimetry development, Filiano et al .…”

Comparable dose estimates of blinded whole blood samples are obtained independently of culture conditions and analytical approaches. Second RENEB gene expression study

“…Dose assessment in irradiated blood samples can be obtained by either using dose predictive gene or exon signatures (Paul & Amundson 2008;Kabacik et al 2011;Boldt et al 2012;Knops et al 2012;Lucas et al 2014;Macaeva et al 2016) or by monitoring the modification of transcription of single radiation-responsive genes like ferredoxin reductase (FDXR) (Manning et al 2013;Abend et al 2016).…”