Developing mathematical model driven optimized recurrent glioblastoma therapies

开发数学模型驱动优化的复发性胶质母细胞瘤疗法

• 批准号: 10288768
• 负责人: Heiko Enderling
• 金额: $ 23.1万
• 依托单位: H. LEE MOFFITT CANCER CTR & RES INST
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10288768
• 关键词: Adult; Antigens; Attenuated; Biological Response Modifier Therapy; Biology; Brain; Cancer Center; Cell Death; Cells; Characteristics; Classification; Clinical; Clinical Research; Clinical Trials; Complex; Computational algorithm; Consult; Data; Diffuse; Discipline; Evolution; Face; Funding; Genetic Programming; Glioblastoma; Glioma; Goals; Holidays; Immune response; Immunotherapeutic agent; Immunotherapy; Individual; Interdisciplinary Study; Learning; Liquid substance; Malignant Neoplasms; Mathematics; Measurement; Medical; Methods; Modeling; Nature; Neuraxis; Neuroglia; Nivolumab; Oncology; Outcome; Patient-Focused Outcomes; Patients; Performance; Population; Prediction of Response to Therapy; Primary Brain Neoplasms; Procedures; Prognosis; Protocols documentation; Radiation; Radiation Dose Unit; Radiation Oncology; Radiation therapy; Reaction Time; Recording of previous events; Recovery; Recurrence; Residual state; Resistance; Resistance development; Risk; Sample Size; Sampling; Schedule; Science; Sensitivity and Specificity; Survival Rate; T-Lymphocyte; Testing; Time; Training; Translating; Treatment Protocols; Trust; Tumor Volume; Tumor-Derived; Validation; aggressive therapy; base; bevacizumab; cancer cell; chemotherapy; clinical practice; clinically significant; cohort; contrast enhanced; cost; demographics; design; effective therapy; exhaustion; heuristics; immunogenic; improved; improved outcome; individual patient; innovation; ipilimumab; mathematical algorithm; mathematical model; neoplastic cell; neuro-oncology; novel; outcome prediction; particle; patient response; pembrolizumab; preclinical study; predictive modeling; prevent; prospective; response; statistics; tool; treatment response; treatment strategy; tumor

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.

摘要 高级别胶质瘤，包括GBM，是成人中最常见的原发性脑肿瘤。GBM治疗是 复发性高级别胶质瘤（rHGG）仍然是致命的，尽管进行了积极的治疗。的一部分 治疗神经胶质瘤的挑战是其在天然免疫抑制中枢神经系统中的定位。 大分割立体定向放射治疗（HFSRT）结合免疫治疗已显示出良好的前景 在rHGG的临床前和临床研究中的抗肿瘤活性。辐射诱发免疫原性癌症 细胞死亡并促进肿瘤衍生抗原向抗肿瘤T细胞的呈递， 与免疫疗法协同作用以增强针对肿瘤细胞的免疫应答。治疗反应 取决于多种因素，包括患者、肿瘤和治疗参数。如何更好地 联合收割机与化疗或免疫治疗的结合仍然是未知的。Current protocols of 将放射与不同的疗法相结合，而不考虑进化动力学， 患者的肿瘤产生耐药性并最终进展。我们假设进化的原理- 必须探索指导性疗法，以积极主动地抵制耐药性的发展。数学 建模可以提供必要的工具来破译rHGG过程中复杂的进化动力学 疗法经过训练和测试的数学和计算算法可以模拟各种治疗 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 建模以从过去的临床研究中学习，以设计rHGG的前瞻性临床试验。使用数学 和计算算法来彻底探索不同的治疗方案， 临床可测试的协议，并最终改善rHGG的结果。这种跨学科的团队科学 方法结合了我们在神经肿瘤学和放射肿瘤学与数学肿瘤学的专业知识， 统计莫菲特癌症中心拥有跨传统部门的跨学科研究的丰富文化 障碍，正如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，我们建立在强大的初步数据， 我们的专业知识和探索进化的原则指导治疗的第一次在rHGG。