Both anti-angiogenesis and gene therapy involve complex processes depending on non-point parameters belonging to a space of values. To successfully overcome the challenges involved in their therapeutic approaches, there is a need to analyze the sensitivity of these parameters. In this paper, a new mathematical model that combines immune system stimulations, inflammatory processes associated with tumor development, and gene therapy aimed at enhancing the efficacy of both treatments are explored. Using the global sensitivity methods of Sobol and Morris, the most important parameters are estimated. Estimation of the sensitivity variance revealed a strong interdependence between the parameters. Also, determinations of the conditions for effective therapy lead to a target of reducing the cancer cell numbers by at least $50\%$. This opened the way for delimiting the parameter spaces making it possible to reach the treatment target in addition to enhancing the estimation of the minimum time of remission. The combination of therapies and sensitivity analysis have demonstrated the robustness of therapy success.

抗血管生成和基因治疗都涉及复杂的过程，这取决于属于值空间的非点参数。为了成功克服其治疗方法所涉及的挑战，需要分析这些参数的敏感性。在本文中，探索了一种新的数学模型，该模型结合了免疫系统刺激、与肿瘤发展相关的炎症过程以及旨在提高两种治疗效果的基因治疗。使用 Sobol 和 Morris 的全局灵敏度方法，估计最重要的参数。敏感性方差的估计表明参数之间存在很强的相互依赖性。此外，确定有效治疗的条件导致将癌细胞数量减少至少一个目标。$50\%$. 这为界定参数空间开辟了道路，除了增强对最小缓解时间的估计之外，还可以达到治疗目标。治疗和敏感性分析的结合证明了治疗成功的稳健性。