Proton irradiation is touted for its improved tumor targeting due to the physical advantages of ion beams for radiotherapy. Recent studies from our laboratory have shown that in addition to targeting advantages proton irradiation can inhibit angiogenic and immune factors and thereby modulate tumor progression. High-energy protons also constitute a principal component of the galactic cosmic rays to which astronauts are exposed. Increased understanding of the biological effects of proton exposure would thus contribute to both improved cancer therapy and carcinogenesis risk assessment for space travel. In addition age plays a major role in tumor incidence and is a critical consideration for estimating cancer risk. We investigated the effects of host age and proton exposure on tumor progression. Tumor lag time and growth dynamics were tracked following injection of murine Lewis lung carcinoma (LLC) cells into young (68 day) versus old (736 day) mice with or without coincident irradiation. Tumor progression was suppressed in old compared to young mice. Differences in progression were further modulated by proton irradiation (1GeV) with increased inhibition evident in old mice. Through global transcriptome analysis TGFB1 and TGFB2 were determined to be key players that contributed to the tumor dynamics observed. These findings point to older hosts providing decreased systemic tumor support which can be further inhibited by proton irradiation. Overall design: For genome-wide expression profiling of tumor tissue Mouse WG-6 BeadArray chips (Illumina San Diego CA) were used. Total RNA was amplified with the Ambion Illumina TotalPrep Amplification Kit (Ambion Austin TX) and labeled from all replicate biological samples for each condition. For tumor replicates thirty tumor samples from adolescent and thirty tumor samples from old mice for a total of 60 tumor samples were used. All replicate samples were run individually. For each age group ten tumor samples had received proton irradiation while twenty tumor samples were from unirradiated mice (as described above). Total RNA was isolated and purified using TRIzol (Invitrogen) and quantified using an Agilent Bioanalyzer. Samples were deemed suitable for amplification and hybridization if they had 28s/18s = 2:1 RIN >7. Total RNA of 500ng per sample was amplified using AmbionTotalPrep and 1.5ug of the product was loaded onto the chips. Following hybridization at 55C the chips were washed and then scanned using the Illumina iScan System. The data was checked with GenomeStudio (Illumina) for quality control. In GenomeStudio data was background subtracted and rank invariant normalization was applied. Data was imported into MultiExperiment Viewer MeV for statistical analysis. The statistically significant genes were determined using MeV by applying a one-way ANOVA analysis with standard Bonferroni correction with a FDR <0.05 that resulted in a list of significant genes. Average gene expression signals <10 were filtered out due to signal being

To further development of our gene expression approach to biodosimetry we have employed whole genome microarray expression profiling as a discovery platform to identify genes with the potential to distinguish radiation dose across an exposure range relevant for medical decision-making in a radiological emergency. Human peripheral blood from healthy donors was irradiated ex vivo and a 74-gene consensus signature was identified that distinguished between four radiation doses (0.5 2 5 and 8 Gy) and control samples. The same set of genes separated samples by exposure level at both six and 24 hours after treatment with overlap evident only at the highest two doses (5 and 8 Gy). Expression of five genes (CDKN1A FDXR SESN1 BBC3 and PHPT1) from this signature was quantified in the same RNA samples by real-time PCR confirming low variability between donors as well as the predicted radiation response pattern. Experiment Overall Design: Radiation induced gene expression in human blood was measured at 6 and 24 hours after exposure to doses of 0 0.5 2 5 and 8 Gy g-rays. Five independent experiments were performed at each time (6 or 24 hours) using different donors for each experiment

To further development of our gene expression approach to biodosimetry we have employed whole genome microarray expression profiling as a discovery platform to identify genes with the potential to distinguish radiation dose across an exposure range relevant for medical decision-making in a radiological emergency. Human peripheral blood from healthy donors was irradiated ex vivo and a 74-gene consensus signature was identified that distinguished between four radiation doses (0.5 2 5 and 8 Gy) and control samples. The same set of genes separated samples by exposure level at both six and 24 hours after treatment with overlap evident only at the highest two doses (5 and 8 Gy). Expression of five genes (CDKN1A FDXR SESN1 BBC3 and PHPT1) from this signature was quantified in the same RNA samples by real-time PCR confirming low variability between donors as well as the predicted radiation response pattern. Experiment Overall Design: Radiation induced gene expression in human blood was measured at 6 and 24 hours after exposure to doses of 0 0.5 2 5 and 8 Gy g-rays. Five independent experiments were performed at each time (6 or 24 hours) using different donors for each experiment

Proton irradiation is touted for its improved tumor targeting due to the physical advantages of ion beams for radiotherapy. Recent studies from our laboratory have shown that in addition to targeting advantages proton irradiation can inhibit angiogenic and immune factors and thereby modulate tumor progression. High-energy protons also constitute a principal component of the galactic cosmic rays to which astronauts are exposed. Increased understanding of the biological effects of proton exposure would thus contribute to both improved cancer therapy and carcinogenesis risk assessment for space travel. In addition age plays a major role in tumor incidence and is a critical consideration for estimating cancer risk. We investigated the effects of host age and proton exposure on tumor progression. Tumor lag time and growth dynamics were tracked following injection of murine Lewis lung carcinoma (LLC) cells into young (68 day) versus old (736 day) mice with or without coincident irradiation. Tumor progression was suppressed in old compared to young mice. Differences in progression were further modulated by proton irradiation (1GeV) with increased inhibition evident in old mice. Through global transcriptome analysis TGFB1 and TGFB2 were determined to be key players that contributed to the tumor dynamics observed. These findings point to older hosts providing decreased systemic tumor support which can be further inhibited by proton irradiation. Overall design: For genome-wide expression profiling of tumor tissue Mouse WG-6 BeadArray chips (Illumina San Diego CA) were used. Total RNA was amplified with the Ambion Illumina TotalPrep Amplification Kit (Ambion Austin TX) and labeled from all replicate biological samples for each condition. For tumor replicates thirty tumor samples from adolescent and thirty tumor samples from old mice for a total of 60 tumor samples were used. All replicate samples were run individually. For each age group ten tumor samples had received proton irradiation while twenty tumor samples were from unirradiated mice (as described above). Total RNA was isolated and purified using TRIzol (Invitrogen) and quantified using an Agilent Bioanalyzer. Samples were deemed suitable for amplification and hybridization if they had 28s/18s = 2:1 RIN >7. Total RNA of 500ng per sample was amplified using AmbionTotalPrep and 1.5ug of the product was loaded onto the chips. Following hybridization at 55C the chips were washed and then scanned using the Illumina iScan System. The data was checked with GenomeStudio (Illumina) for quality control. In GenomeStudio data was background subtracted and rank invariant normalization was applied. Data was imported into MultiExperiment Viewer MeV for statistical analysis. The statistically significant genes were determined using MeV by applying a one-way ANOVA analysis with standard Bonferroni correction with a FDR <0.05 that resulted in a list of significant genes. Average gene expression signals <10 were filtered out due to signal being