Comparison of Novel Proteomic Expression Profiles for Radiation Exposure in Male and Female C57BL6 Mice.

There is a need for point-of-care diagnostics for future mass casualty events involving radiation exposure. The development of radiation exposure and dose prediction algorithms for biodosimetry is needed for screening of large populations during these scenarios, and exploration of the potential effects which sex, age, genetic heterogeneity, and physiological comorbidities may have on the utility of biodosimetry diagnostics is needed. In the current study, proteomic profiling was used to examine sex-specific differences in age-matched C57BL6 mice on the blood proteome after radiation exposure, and the usefulness of development and application of biodosimetry algorithms using both male and female samples. Male and female mice between 9-11 weeks of age received a dose of total-body irradiation (TBI) of either 2, 4 or 8 Gy and plasma was collected at days 1, 3 and 7 postirradiation. Plasma was then screened using the SomaScan v4.1 assay for ∼7,000 protein analytes. A subset panel of protein biomarkers demonstrated significant (FDR < 0.05 and |logFC| > 0.2) changes in expression after radiation exposure. All proteins were used for feature selection to build predictive models of radiation exposure using different sample and sex-specific cohorts. Both binary (prediction of any radiation exposure) and multidose (prediction of specific radiation dose) model series were developed using either female and male samples combined or only female or only male samples. The binary series (models 1, 2 and 3) and multidose series (models 4, 5 and 6) included female/male combined, female only and male only respectively. Detectable values were obtained for all ∼7,000 proteins included in the SomaScan assay for all samples. Each model algorithm built using a unique sample cohort was validated with a training set of samples and tested with a separate new sample series. Overall predictive accuracies in the binary model series was ∼100% at the model training level, and when tested with fresh samples, 97.9% for model 1 (female and male) and 100% for model 2 (female only) and model 3 (male only). When sex-specific models 2 and 3 were tested with the opposite sex, the overall predictive accuracy rate dropped to 62.5% for model 2 and remained 100% for model 3. The overall predictive accuracy rate in the multidose model series was 100% for all models at the model training level and, when tested with fresh samples, 83.3%, 75% and 83.3% for Multidose models 4-6, respectively. When sex-specific model 5 (female only) and model 6 (male only) were tested with the opposite sex, the overall predictive accuracy rate dropped to 52.1% and 68.8%, respectively. These models represent novel predictive panels of radiation-responsive proteomic biomarkers and illustrate the utility and necessity of considering sex-specific differences in development of radiation biodosimetry prediction algorithms. As sex-specific differences were observed in this study, and as use of point-of-care radiation diagnostics in future mass casualty settings will necessarily include persons of both sexes, consideration of sex-specific variation is essential to ensure these diagnostic tools have practical utility in the field.

The Future of Radioactive Medicine.

The discovery of X rays in the late 19th century heralded the beginning of a new age in medicine, and the advent of channeling the power of radiation to diagnose and treat human disease. Radiation has been leveraged in medicine in a multitude of ways and is a critical element of cancer care including screening, diagnosis, surveillance, and interventional treatments. Modern radiotherapy techniques include a multitude of methodologies utilizing both externally and internally delivered radiation from a variety of approaches. This review provides a comprehensive overview of contemporary radiotherapy methodologies, the field of radiopharmaceuticals and theranostics, effects of low dose radiation and highlights the phenomena of fear of exposure to radiation and its impact in modern medicine.

Prediction of Total-Body and Partial-Body Exposures to Radiation Using Plasma Proteomic Expression Profiles.

There is a need to identify new biomarkers of radiation exposure for not only systemic total-body irradiation (TBI) but also to characterize partial-body irradiation and organ specific radiation injury. In the current study, we sought to develop novel biodosimetry models of radiation exposure using TBI and organ specific partial-body irradiation to only the brain, lung or gut using a multivariate proteomics approach. Subset panels of significantly altered proteins were selected to build predictive models of radiation exposure in a variety of sample cohort configurations relevant to practical field application of biodosimetry diagnostics during future radiological or nuclear event scenarios. Female C57BL/6 mice, 8-15 weeks old, received a single total-body or partial-body dose of 2 or 8 Gy TBI or 2 or 8 Gy to only the lung or gut, or 2, 8 or 16 Gy to only the brain using a Pantak X-ray source. Plasma was collected by cardiac puncture at days 1, 3 and 7 postirradiation for total-body exposures and only the lung and brain exposures, and at days 3, 7 and 14 postirradiation for gut exposures. Plasma was then screened using the aptamer-based SOMAscan proteomic assay technology, for changes in expression of 1,310 protein analytes. A subset panel of protein biomarkers which demonstrated significant changes (P < 0.01) in expression after irradiation were used to build predictive models of radiation exposure using different sample cohorts. Model 1 compared controls vs. all pooled irradiated samples, which included TBI and all organ specific partial irradiation. Model 2 compared controls vs. TBI vs. partial irradiation (with all organ specific partial exposure pooled within the partial-irradiated group), and model 3 compared controls vs. each individual organ specific partial-body exposure separately (brain, gut and lung). Detectable values were obtained for all 1,310 proteins included in the SOMAscan assay for all samples. Each model algorithm built using a unique sample cohort was validated with a training set of samples and tested with a separate new sample series. Overall predictive accuracies of 89%, 78% and 55% resulted for models 1-3, respectively, representing novel predictive panels of radiation responsive proteomic biomarkers. Though relatively high overall predictive accuracies were achieved for models 1 and 2, all three models showed limited accuracy at differentiating between the controls and partial-irradiated body samples. In our study we were able to identify novel panels of radiation responsive proteins useful for predicting radiation exposure and to create predictive models of partial-body exposure including organ specific radiation exposures. This proof-of-concept study also illustrates the inherent physiological limitations of distinguishing between small-body exposures and the unirradiated using proteomic biomarkers of radiation exposure. As use of biodosimetry diagnostics in future mass casualty settings will be complicated by the heterogeneity of partial-body exposure received in the field, further work remains in adapting these diagnostic tools for practical use.

Non-patient related variables affecting levels of vascular endothelial growth factor in urine biospecimens.

Vascular endothelial growth factor (VEGF) is an angiogenic protein proposed to be an important biomarker for the prediction of tumour growth and disease progression. Recent studies suggest that VEGF measurements in biospecimens, including urine, may have predictive value across a range of cancers. However, the reproducibility and reliability of urinary VEGF measurements have not been determined. We collected urine samples from patients receiving radiation treatment for glioblastoma multiforme (GBM) and examined the effects of five variables on measured VEGF levels using an ELISA assay. To quantify the factors affecting the precision of the assay, two variables were examined: the variation between ELISA kits with different lot numbers and the variation between different technicians. Three variables were tested for their effects on measured VEGF concentration: the time the specimen spent at room temperature prior to assay, the addition of protease inhibitors prior to specimen storage and the alteration of urinary pH. This study found that VEGF levels were consistent across three different ELISA kit lot numbers. However, significant variation was observed between results obtained by different technicians. VEGF concentrations were dependent on time at room temperature before measurement, with higher values observed 3-7 hrs after removal from the freezer. No significant difference was observed in VEGF levels with the addition of protease inhibitors, and alteration of urinary pH did not significantly affect VEGF measurements. In conclusion, this determination of the conditions necessary to reliably measure urinary VEGF levels will be useful for future studies related to protein biomarkers and disease progression.