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

基本信息

批准号：
10437915
负责人：
Heiko Enderling
金额：
$ 18.86万
依托单位：
H. LEE MOFFITT CANCER CTR & RES INST
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-01 至 2024-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10437915
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

项目摘要

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.

抽象的包括GBM在内的高级神经瘤是成年人中最常见的原发性脑肿瘤。 GBM治疗是尽管侵略性治疗，但没有治愈性，并且复发性高度神经胶质瘤（RHGG）仍然致命。一部分治疗神经胶质瘤的挑战是其在自然免疫抑制中枢神经系统中的定位。与免疫疗法相结合的降压立体定位放射疗法（HFSRT）已显示出有希望的 RHGG临床前和临床研究中的抗肿瘤活性。辐射诱导免疫原性癌症细胞死亡并促进肿瘤衍生的抗原向抗肿瘤T细胞的表现，并作用通过免疫疗法协同作用，增强对肿瘤细胞的免疫反应。治疗反应取决于无数因素，包括患者，肿瘤和治疗参数。因此，如何最好辐射与化学疗法或免疫治疗剂结合在一起仍然未知。当前的协议在不考虑进化动力的情况下，将辐射与不同的疗法相结合，并且每个疗法患者的肿瘤会发展出抵抗力并最终进展。我们假设这一进化原理 - 必须探索指导疗法以积极地抵消抗药性的发展。数学建模可以提供必要的工具来破译RHGG期间复杂的进化动力学治疗。经过训练和测试的数学和计算算法可以模拟各种处理所有可能组合的协议。我们的创新方法和目标是整合数学建模以从过去的临床研究中学习，以设计RHGG中的前瞻性临床试验。使用数学详尽探索不同治疗方案的计算算法是改进的关键，临床检验的方案，并最终改善了RHGG结果。这个跨学科的团队科学方法结合了我们在神经肿瘤学和辐射肿瘤学方面的专业知识与数学肿瘤学和统计数据。莫菲特癌症中心在传统部门拥有丰富的跨学科研究文化障碍，可以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，我们以强大的初步数据为基础我们的专业知识和探索进化原理引导的疗法首次在RHGG中。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Author Correction: Intermittent radiotherapy as alternative treatment for recurrent high grade glioma: a modeling study based on longitudinal tumor measurements.

DOI：
10.1038/s41598-022-08620-3
发表时间：
2022-03-15
期刊：
Scientific reports
影响因子：
4.6
作者：
Brüningk SC;Peacock J;Whelan CJ;Brady-Nicholls R;Yu HM;Sahebjam S;Enderling H
通讯作者：
Enderling H

Rethinking the immunotherapy numbers game.