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

7.项目摘要/摘要 胶质瘤是一种罕见的可治愈肿瘤，5年存活率低，仅为36%，远低于平均水平。 根据SEER和CBTRUS的数据，所有癌症的存活率为67.2%。恶性脑瘤会导致 被诊断为成年人的人的平均潜在寿命损失(YPLL)，超过了大多数 常见的癌症。与其他癌症一样，胶质瘤的存活率和YPLL没有改善，进展是 迫切需要。患者预后没有改善并不是因为缺乏新的发现，而是 由于在将这些知识转化为临床益处方面取得的成功有限。已经有了重要的发现 在过去的十年中，关于胶质瘤发生和生长的关键分子机制，已经 已被纳入世卫组织最新分类。IDH突变是脑胶质瘤发生和发展的首要事件 已经成为治疗胶质瘤的一种范式转变。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，并强制开发分子 用于高特异度和高灵敏度的胶质瘤成像。我们研究的长期目标是 可用于癌症患者的非侵入性分子成像方法的发展。IDH 突变在胶质瘤中很常见，并产生高水平的肿瘤代谢物2-羟基戊二酸(2HG)， 可作为诊断、预后、预测、手术和放射指导的生物标志物， 对化疗和靶向治疗的反应。此应用程序的目标是快速发展高 分辨率全脑定量2HG和代谢成像用于诊断、治疗指导和 突变型idh和野生型胶质瘤的监测。我们提案的中心假设是，下一步 第二代HG和代谢成像将使精确肿瘤学成为可能，并加快临床翻译 新的靶向治疗以改善突变型IDH和野生型胶质瘤患者的预后。三个具体目标 将为此执行：1)开发快速高分辨率全脑临床健壮的2HG和代谢 2)提高2HG和代谢显像的灵敏度、精密度、准确性和工作流程；3)临床应用 胶质瘤患者下一代2HG和代谢成像的翻译。有很强的理由支持 拟议的研究：1)没有针对IDH突变的替代体内成像方法，2)2HG 成像是完全非侵入性的，可以安全地重复，不需要任何辐射，可以快速提供结果 性价比高，3)提供对整个肿瘤和健康大脑的全面评估，而无需采样 活检的偏差，4)可以在手术前和不能手术的肿瘤中进行。方法是 创新是因为它采用了第一种可用的全脑2HG成像方法，这种方法将被加速 通过压缩感知，新颖的填隙硬件提高了数据质量，并在高吞吐量下进行了变换 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 为临床医生提供方便、准确的工具，对胶质瘤患者进行诊断、指导和监测。

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.