Development of next generation 2HG and metabolic MR imaging for precision oncology of mutant IDH and wildtype glioma patients

7.项目总结/摘要 神经胶质瘤是一种罕见的可治愈的肿瘤，5年生存率为36%，远低于平均水平。 根据SEER和CBTRUS，所有癌症的存活率为67.2%。恶性脑肿瘤会导致 被诊断为成年人的个人平均潜在寿命损失（YPLL）为20年，超过大多数人 常见的癌症胶质瘤的生存率和YPLL没有像其他癌症那样得到改善， 迫切需要的。患者治疗效果没有改善并不是因为缺乏新的发现， 这是由于将这些知识转化为临床益处的成功有限。已经有了重要的发现 在过去的十年里，关于神经胶质瘤发生和生长的关键分子机制， 已纳入最新的WHO分类。IDH突变是胶质瘤发生的主要事件， 已经成为神经胶质瘤治疗的一个范式转变。神经肿瘤学专家（SNO，EANO）同意， 脑成像可以加速靶向治疗的临床试验， 高特异性和高灵敏度的胶质瘤成像。我们研究的长期目标是 开发可用于临床癌症患者的非侵入性分子成像方法。IDH 突变在神经胶质瘤中是常见的，并产生高水平的癌代谢物2-羟基戊二酸（2 HG）， 可以成像为诊断、预后、预测、指导手术和放射的生物标志物， 对化疗和靶向治疗的反应。该应用程序的目标是快速开发高 分辨率全脑定量2 HG和代谢成像，用于诊断、治疗指导和 监测突变IDH和野生型胶质瘤。我们建议的中心假设是， 第二代HG和代谢成像将实现精确的肿瘤学，并加速 新的靶向治疗，以改善突变IDH和野生型胶质瘤患者的结果。三个具体目标 将为此进行：1）开发快速高分辨率全脑临床稳健的2 HG和代谢 2）提高2 HG和代谢成像的灵敏度、精确度、准确度和工作流程，以及3）临床 下一代2 HG的翻译和神经胶质瘤患者的代谢成像。有很强的理由， 拟议的研究：1）没有特异性针对IDH突变的替代体内成像方法，2）2 HG 成像是完全非侵入性的，可以重复安全，没有任何辐射，可以快速提供结果， 成本有效，3）提供了对整个肿瘤和健康大脑的全面评估，而无需取样 活检的偏差，4）它可以在手术前和不能手术的肿瘤中进行。该方法是 创新，因为它采用了第一个可用的全脑2 HG成像方法，这将加快 通过压缩感知、新颖的shim硬件来提高数据质量，并在高吞吐量下进行变换 深度学习的自动化工具。拟议研究的贡献将是重大的，因为它将 为临床医生提供了一个用户友好和精确的工具，用于诊断，指导和监测胶质瘤患者。

7. Project Summary/Abstract Gliomas are rarely curable tumors with a low survival rate of 36% at five-years that is well below the average survival rate of 67.2% across all cancers, according to SEER and CBTRUS. Malignant brain tumors cause an average of 20 years of potential life lost (YPLL) for individuals diagnosed as adults, which exceeds most common cancers. Survival and YPLL have not improved for gliomas similarly to other cancers and progress is desperately needed. The lack of improvement in patient outcomes is not due to lack of new discoveries, but due to limited success in translating this knowledge into clinical benefit. Important discoveries have been made over the last decade regarding key molecular mechanisms involved in glioma initiation and growth, which have been incorporated in the latest WHO classification. IDH mutation is the primary event in glioma initiation and has become a paradigm shift in the treatment of glioma. Neuro-oncology experts (SNO, EANO) agree that brain imaging can accelerate clinical trials of targeted therapies and mandated the development of molecular imaging for highly specific and sensitive glioma imaging. The long-term goal of our research is the development of non-invasive molecular imaging methods that can be used clinically in cancer patients. IDH mutations are frequent in glioma and produce high levels of the oncometabolite 2-hydroxyglutarate (2HG) that can be imaged as a biomarker for diagnosis, prognosis, prediction, guidance of surgery and radiation, response to chemotherapy and targeted treatments. The objective of this application is to develop fast high resolution whole brain quantitative 2HG and metabolic imaging for diagnosis, treatment guidance and monitoring of mutant IDH and wildtype glioma. The central hypothesis of our proposal is that advancing next generation 2HG and metabolic imaging will enable precision oncology and accelerate clinical translation of novel targeted therapies to improve outcomes in mutant IDH and wildtype glioma patients. Three specific aims will be performed for this: 1) develop fast high-resolution whole-brain clinically robust 2HG and metabolic imaging, 2) improve sensitivity, precision, accuracy and workflow of 2HG and metabolic imaging, and 3) clinical translation of next generation 2HG and metabolic imaging in glioma patients. There are strong rationales for the proposed research: 1) there is no alternative in vivo imaging method specific for IDH mutations, 2) 2HG imaging is completely non-invasive, can be repeated safe without any radiation, can provide results fast and cost effective, 3) provides comprehensive evaluation of the entire tumor and healthy brain without the sampling bias of biopsies, 4) it can be performed pre-surgically and in tumors that cannot be operated. The approach is innovative because it employs the first available whole brain 2HG imaging method, which will be accelerated by compressed sensing, novel shim hardware to improve data quality, and transformed in a high throughput automated tool by deep learning. The contribution of the proposed research will be significant because it will provide clinicians with a user-friendly and precise tool for diagnostic, guiding and monitoring of glioma patients.