Figure 5. Peripheral blood profiles of irradiation and LPS- treatment are highly specific. (A) Heat maps representing unique metagene profiles are shown which were utilized to distinguish 3 different levels of irradiation (left) and to distinguish LPS-treatment (right) in C57Bl6 mice. (B) The graph at left represents the predictive capabilities of the PB irradiation signatures in the female C57Bl6 mice in predicting dosage profiles at 50 cGy (black), 200 cGy (green), and 1000 cGy (red); the middle graph represents the predictive capabilities of the irradiation signatures when validated against the LPS-treated samples (squares); at right, the LPS signature was validated against the C57Bl6 irradiated mice and the predicted probabilities for 50 cGy (black), 200 cGy (green), and 1000 cGy (red) are shown. doi:10.1371/journal.pone.0001912.g005 accuracy. No overlap was observed between the genes which comprised the PB signature of LPS-sepsis and the PB signatures of radiation exposure in C57Bl6 mice (Table S5). PB signatures of radiation and chemotherapy are specific in humans In order to extend the analysis of PB signature specificity to humans, we collected PB from a population of healthy individuals (n = 18), patients who had undergone total body irradiation as conditioning prior to hematopoietic stem cell transplantation (n = 47) and patients who had undergone alkylator-based chemo- therapy conditioning alone (n = 41). RNA of sufficient quality was available from 18 healthy donor samples, 36 pre-irradiated patients, 34 post-irradiated patients, 36 pre-chemotherapy treat- ment patients and 32 post-chemotherapy patients (Table S6). A supervised binary regression analysis identified a metagene profile of 25 genes that distinguished the healthy individuals and the non- irradiated patients from the irradiated patients (Figure 6A). A leave-one-out cross validation analysis demonstrated that this PB predictor of human radiation response was 100% accurate in predicting the healthy individuals and the non-irradiated patients and 91% accurate at predicting the irradiated patients (Figure 6A). In order to test the specificity of this PB signature of human radiation response, we next tested its accuracy in predicting the status of patients who had undergone chemotherapy treatment alone. This signature correctly predicted 89% of the non- irradiated, pre-chemotherapy patients as non-irradiated and 75% of the chemotherapy-treated patients as non-irradiated (Figure 6A). Interestingly, 2 of the post-chemotherapy patients had a prior history of total lymphoid irradiation and both of these were mispredicted as ‘‘irradiated’’, suggesting perhaps that a durable molecular response to radiation was evident in these patients. Considering the entire population, the overall accuracy of Radiation Signatures the PB predictor of radiation was 90%. Within the chemotherapy- treated patients, a PB signature could be identified that appeared to distinguish untreated patients from chemotherapy-treated patients (Figure 6B). A leave-one-out cross-validation analysis demonstrated that this PB signature of chemotherapy treatment was 81% accurate at distinguishing the untreated patients and 78% accurate at predicting the chemotherapy-treated patients (Figure 6B). Further- more, the chemotherapy metagene profile demonstrated 100% accuracy in predicting the status of healthy individuals, 92% accuracy in predicting the non-irradiated patients, and 62% accuracy in predicting the PB samples from irradiated patients as not having received chemotherapy (Figure 6B). The overall accuracy of the PB predictor of chemotherapy-treatment was 81%. Interestingly, no overlapping genes were identified between the PB signature of radiation and the PB signature of chemotherapy treatment (Tables S7 and S8). It is also worth noting that all 12 of the post-irradiation patients whose status was mispredicted by the PB chemotherapy signature had received prior chemotherapy in the treatment of their underlying disease. Discussion Numerous studies now highlight the power of gene expression profiling to characterize the biological phenotype of complex diseases. We and others have shown the potential clinical utility of gene expression profiles in cancer research, in which the identifica- tion of patterns of gene expression within tumors has led to the characterization of tumor subtypes, prognostic categories and prediction of therapeutic response [33–37]. Beyond analysis of tumor tissues, it has also been suggested that gene expression profiling of the peripheral blood can provide indication of infections, cancer, heart disease, allograft rejection, environmental exposures and as a means of biological threat detection [3–11,38,39]. While the concept of PB cells as sentinels of disease is not new, it remains unclear whether PB gene expression profiles that have been associated with various conditions are specific for those diseases or rather reflect a common molecular response to a variety of genotoxic stresses. Given the dynamic nature of the cellular composition of PB blood [12] and the complexity of cellular responses over time [12], the durability of PB signatures over time is also uncertain and could affect the diagnostic utility of this approach for public health screening. We sought to address the capacity for PB gene expression profiles to distinguish an environmental exposure, in this case ionizing radiation, versus other medical conditions and to examine the impact of time, gender and genotype on the accuracy of these profiles. We found that PB gene expression signatures can be identified which accurately predict irradiated from non-irradiated mice and distinguish different levels of radiation exposure, all within a heterogeneous population with respect to gender, genotype and time from exposure. These results suggest the potential for PB gene expression profiling to be applied successfully in the screening for an environmental exposure. Previous studies have indicated that inter-individual variation in gene expression occurs within healthy individuals [12] and may therefore limit the accuracy of PB gene expression profiling to detect diseases or exposures. Our results demonstrate that the environmental exposure tested here, ionizing radiation, induced a pronounced and characteristic alteration in PB gene expression such that a PB expression profile was highly predictive of radiation status in a population with variable gender, genotype and time of analysis. From a practical standpoint, these data suggest the potential utility of this approach for biodosimetric screening of a heterogeneous human population in the event of a purposeful or accidental radiological or nuclear event [15–17]. PLoS ONE | www.plosone.org 6 April 2008 | Volume 3 | Issue 4 | e1912