Biomarker integration for improved biodosimetry of mixed neutron + photon exposures

Abstract

There is a persistent risk of a large-scale malicious or accidental exposure to ionizing radiation that may affect a large number of people. Exposure will consist of both a photon and neutron component, which will vary in magnitude between individuals and is likely to have profound impacts on radiation-induced diseases. To mitigate these potential disasters, there exists a need for novel biodosimetry approaches that can estimate the radiation dose absorbed by each person based on biofluid samples, and predict delayed effects. Integration of several radiation-responsive biomarker types (transcripts, metabolites, blood cell counts) by machine learning (ML) can improve biodosimetry. Here we integrated data from mice exposed to various neutron + photon mixtures, total 3 Gy dose, using multiple ML algorithms to select the strongest biomarker combinations and reconstruct radiation exposure magnitude and composition. We obtained promising results, such as receiver operating characteristic curve area of 0.904 (95% CI: 0.821, 0.969) for classifying samples exposed to ≥ 10% neutrons vs. < 10% neutrons, and R2 of 0.964 for reconstructing photon-equivalent dose (weighted by neutron relative biological effectiveness) for neutron + photon mixtures. These findings demonstrate the potential of combining various -omic biomarkers for novel biodosimetry.

Validation of protein biomarker panel in peripheral blood leukocytes of juvenile and adult C57BL/6 mice up to seven days post-exposure for machine learning-based radiation biodosimetry

“…This study examines the ln-transformed expression of three individual IC protein biomarkers (ACTN1, DDB2, FDXR) in three cell populations (all leukocytes, B-cells, T-cells), as well as ln-transformed percentages of B-cells and T-cells, at 5 doses (0, 1, 2, 3, 4 Gy) on 3 days post-exposure (1,4,7) in adult and juvenile cohorts comprised of males and females. Each of these variables in this expansive study serves as a potential predictor for radiation dose classi cation (Exposure Index) or quantitative reconstruction (Dose), as detailed fully in the Materials and Methods.…”

“…Biomarkers which measure radiation-induced biological effects in individuals can serve as useful diagnostic tools for biodosimetry and guiding patient-speci c medical treatment decisions [1][2][3][4] . The combination of biomarkers in an integrated panel can be especially useful for the development of a bioassay capable of detecting radiation exposure across a range of conditions (such as dose ranges, post-exposure time points, and demographics) with improved accuracy [5][6][7][8][9] , as compared to the performance of individual biomarkers alone. The practical time considerations for various stages of the emergency response, including deployment of emergency response teams ("boots on the ground"), organization of potentially exposed individuals for the collection of samples, and assay time-to-result, underscore the critical need for a same-day result, high-throughput bioassay that detects radiation exposure in the general population up to a week later.…”

“…Stacking is a powerful ML technique because the use of various models can complement each other on di cult to predict samples, and the ensemble of models can often outperform any individual model [23][24][25][26] . To our knowledge, our group is the rst to implement the stacking methodology to radiation biodosimetry 7 .…”

Impact of Partial Body Shielding from Very High Dose Rates on Untargeted Metabolomics in Biodosimetry

“…This study is an expansion of our ongoing investigations into the efficacy of biofluid metabolomic signatures across realistic exposure scenarios. As previous studies have explored the impact of neutron exposures − and dose rate (e.g., refs − and see ref for a compiled list), here we compared metabolite levels in urine and serum from male and female C57BL/6 mice following a sham irradiation, TBI of either 4 or 8 Gy, and an upper body irradiation (UBI) and lower body irradiation (LBI) with 8 Gy using a VHDR. We predicted, as we have seen in previous studies, that although certain responses to radiation injury will be specific to factors such as sex or exposure type, there will be metabolites that are universally changed irrespective of these factors.…”