Developing mathematical model driven optimized recurrent glioblastoma therapies

摘要 包括基底膜在内的高级别胶质瘤是成人最常见的原发脑瘤。GBM治疗是 不能治愈，尽管积极治疗，复发的高级别胶质瘤(RHGG)仍然是致命的。部分内容 治疗胶质瘤的挑战是它在天然免疫抑制的中枢神经系统中的定位。 低分割立体定向放射治疗(HFSRT)结合免疫治疗显示出良好的前景 重组人粒细胞集落刺激因子临床前和临床研究中的抗肿瘤活性。辐射会导致免疫原性癌症 细胞死亡，促进肿瘤来源的抗原呈递给抗肿瘤T细胞，并作用于 与免疫疗法协同作用，增强对肿瘤细胞的免疫反应。治疗反应 取决于多种因素，包括患者、肿瘤和治疗参数。因此，如何最好地 将放射治疗与化疗或免疫疗法结合起来仍不清楚。当前的协议 在没有考虑进化动力学的情况下，将放射与不同的治疗方法相结合，并且每一种 患者的肿瘤会产生抵抗力，并最终发展。我们假设进化论原理- 必须探索有指导意义的治疗方法，以积极对抗耐药性的发展。数学 建模可能为破译rHGG过程中复杂的进化动力学提供必要的工具 心理治疗。经过训练和测试的数学和计算算法可以模拟各种治疗方法 所有可能的组合中的协议。我们的创新方法和目标是将数学 建模以学习过去的临床研究，以设计一项针对重组人粒细胞集落刺激因子的前瞻性临床试验。使用数学 而穷尽探索不同治疗方案的计算算法是改进的关键， 临床可测试的方案，并最终改善了rHGG结果。这个跨学科的科学团队 方法将我们在神经肿瘤学和放射肿瘤学方面的专业知识与数学肿瘤学和 统计数据。莫菲特癌症中心拥有丰富的跨传统科室的跨学科研究文化 障碍，从将数学和计算概念转化为 实验生物学以及临床试验和实践。在这里，我们建立在稳健的初步数据之上，以利用 我们的专业知识和探索进化原则指导的疗法首次在rHGG。

Abstract High-grade gliomas, including GBM, are the most common primary brain tumors in adults. GBM treatment is not curative, and recurrent high-grade glioma (rHGG) remains fatal, despite aggressive therapy. Part of the challenge in treating glioma is its localization within the naturally immunosuppressive central nervous system. Hypofractionated stereotactic radiotherapy (HFSRT) combined with immunotherapy has shown promising antitumor activity in both preclinical and clinical studies in rHGG. Radiation induces an immunogenic cancer cell death and promotes the presentation of tumor-derived antigens to antitumor T cells, and acts synergistically with immunotherapy to enhance the immune response against tumor cells. Treatment response depends on a myriad of factors, including patient, tumor, and treatment parameters. Thus, how to best combine radiation with chemotherapy or immunotherapeutics remains unknown. Current protocols of combining radiation with different therapies are applied without considering evolutionary dynamics, and every patient's tumor develops resistance and eventually progresses. We hypothesize that evolutionary principle- guided therapies must be explored to pro-actively counteract the development of resistance. Mathematical modeling may provide the necessary tools to decipher the complex evolutionary dynamics during rHGG therapy. Trained and tested mathematical and computational algorithms can simulate a variety of treatment protocols in all possible combinations. Our innovative approach and goals are to integrate mathematical modeling to learn from past clinical studies to design a prospective clinical trial in rHGG. Using mathematical and computational algorithms to exhaustively explore different treatment protocols holds the key to improved, clinically-testable protocols, and ultimately improved rHGG outcomes. This interdisciplinary team science approach combines our expertise in neuro-oncology and radiation oncology with mathematical oncology and statistics. Moffitt Cancer Center has a rich culture of interdisciplinary research across conventional department barriers, as evidenced by a strong history of translating mathematical and computational concepts into experimental biology as well as clinical trial and practice. Here we build on robust preliminary data to harness our expertise and explore evolutionary principles-guided therapies for the first time in rHGG.