Virotherapy on the basis of oncolytic vaccinia virus (VACV) strains is a promising approach for cancer therapy. Recently, we showed that the oncolytic vaccinia virus GLV-1h68 has a therapeutic potential in treating human prostate and hepatocellular carcinomas in xenografted mice. In this study, we describe the use of dynamic boolean modeling for tumor growth prediction of vaccinia virus-injected human tumors. Antigen profiling data of vaccinia virus GLV-1h68-injected human xenografted mice were obtained, analyzed and used to calculate differences in the tumor growth signaling network by tumor type and gender. Our model combines networks for apoptosis, MAPK, p53, WNT, Hedgehog, the T-killer cell mediated cell death, Interferon and Interleukin signaling networks. The in silico findings conform very well with in vivo findings of tumor growth. Similar to a previously published analysis of vaccinia virus-injected canine tumors, we were able to confirm the suitability of our boolean modeling for prediction of human tumor growth after virus infection in the current study as well. In summary, these findings indicate that our boolean models could be a useful tool for testing of the efficacy of VACV-mediated cancer therapy already before its use in human patients.

基于溶瘤牛痘病毒（VACV）株的病毒疗法是一种有前途的癌症治疗方法。最近，我们显示溶瘤牛痘病毒GLV-1h68具有在异种移植小鼠中治疗人前列腺癌和肝细胞癌的治疗潜力。在这项研究中，我们描述了使用动态布尔模型对牛痘病毒注射的人肿瘤进行肿瘤生长预测。获得了注射牛痘病毒GLV-1h68的人类异种移植小鼠的抗原谱数据，并进行了分析，并根据肿瘤类型和性别来计算肿瘤生长信号网络的差异。我们的模型结合了凋亡，MAPK，p53，WNT，刺猬，T杀伤细胞介导的细胞死亡，干扰素和白介素信号网络的网络。将在硅片结果符合得很好，体内发现肿瘤生长。与先前发表的注射牛痘病毒的犬类肿瘤分析相似，在当前研究中，我们也能够确定布尔模型对于预测病毒感染后人类肿瘤生长的适用性。总而言之，这些发现表明，我们的布尔模型可能已经成为测试VACV介导的癌症疗法在人类患者中使用之前的有效性的有用工具。