Proton irradiation augments the reduction in tumor progression observed with advanced age

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

质子辐照因其离子束在放射治疗中的物理优势而被誉为提高肿瘤靶向性的重要手段。近期，我们实验室的研究成果表明，除了靶向优势外，质子辐照还能抑制血管生成和免疫相关因子，从而调节肿瘤进展。高能质子亦是构成宇航员所暴露的银河宇宙射线的主要成分。因此，对质子暴露的生物效应的深入理解将有助于提升癌症治疗的效果和对太空旅行中致癌风险的评估。此外，年龄在肿瘤发病率中扮演着重要角色，并在估计癌症风险时成为一项关键考量。本研究探讨了宿主年龄和质子暴露对肿瘤进展的影响。通过将小鼠Lewis肺癌细胞（LLC）注入年轻（68天）与老年（736天）小鼠体内，并观察是否伴随质子辐照，我们追踪了肿瘤滞后时间和生长动态。与年轻小鼠相比，老年小鼠的肿瘤进展得到了抑制。进一步的进展差异通过质子辐照（1GeV）得到调节，老年小鼠中抑制效果的提升尤为显著。通过全局转录组分析，确定TGFB1和TGFB2为关键因子，它们对观察到的肿瘤动力学做出了贡献。这些发现表明，老年宿主提供的全身肿瘤支持减少，而质子辐照可以进一步抑制这种支持。总体设计：为进行肿瘤组织的全基因组表达分析，使用了Illumina San Diego CA的Mouse WG-6 BeadArray芯片。利用Ambion Illumina TotalPrep Amplification Kit（Ambion Austin TX）对总RNA进行扩增，并从每个条件的重复生物学样本中进行标记。对于肿瘤重复样本，使用了来自青春期和老年小鼠的各30个肿瘤样本，共计60个肿瘤样本。所有重复样本均单独运行。对于每个年龄组，有10个肿瘤样本接受了质子辐照，而另外20个肿瘤样本来自未经辐照的小鼠（如上所述）。使用TRIzol（Invitrogen）提取和纯化总RNA，并使用Agilent Bioanalyzer进行定量。如果样本的28s/18s RIN比例大于2:1且RIN值大于7，则认为样本适合进行扩增和杂交。每个样本使用500ng的总RNA进行扩增，并将1.5ug产物加载到芯片上。杂交于55°C后，芯片被清洗，并使用Illumina iScan System进行扫描。使用GenomeStudio（Illumina）进行质量控制，对数据进行背景减除和秩不变归一化处理。数据被导入MultiExperiment Viewer MeV进行统计分析。通过MeV应用单因素方差分析（one-way ANOVA）和标准的Bonferroni校正（FDR <0.05）来确定具有统计学意义的基因，从而得到显著基因列表。平均基因表达信号低于10的基因被过滤掉，因为信号过弱。