Radiation Oncology at the Interface of Pediatric Cancer Biology and Data Science

项目摘要 来自Dana-Farber/哈佛癌症中心（DF/HCC）的基础科学家和临床研究人员与 他们在加州大学弗朗西斯科分校（UCSF）的海伦迪勒家庭综合大学的同行 癌症中心提出一个哈佛/加州大学旧金山分校罗宾中心。我们的目标是提高效力， 选择两种关键的放射方式-外部束放射和放射性药物。很有特色 我们ROBIN中心的一个特点是选择了两种临床上具有侵袭性的儿科肿瘤--弥漫性中线胶质瘤 (DMG)和高风险神经母细胞瘤（NBL）-作为研究计划的科学工具。基因驱动因素 这两种儿科癌症也在更常见的成人实体瘤中发现。此外，低突变 这些儿科癌症的负担为“低N/高含量”分子提供了干净的遗传背景。 ROBIN RFA规定的表征试验（MCT）。DMG和NBL中的恶性细胞是 在发育阶段上是异质的。我们研究计划的中心假设是， 发育异质性在辐射反应机制水平上得到回应， 阻力我们的研究计划包括两个研究项目，用于测试这一假设的预测。 项目1利用了接受放疗的儿童的放疗前后肿瘤组织的配对样本 前瞻性地用统一的放射治疗方案来测试辐射选择放射抗性的预测， 肿瘤内变异。项目2验证131 I-MIBG治疗侵袭性神经母细胞瘤的预测 选择对治疗反应不利的肿瘤内亚型。我们的ROBIN MCT提供 完成这两个项目的目标和具体目标所需的资源。多模态分析和 项目和MCT数据的数学建模将在临床人工 智能和成像核心以及分子数据科学和高级剂量学核心。十字架- 培训核心和行政核心将支持科学活动，并作为中心的 “外向”面对更广泛的科学界和公众。 该中心将作为国家癌症研究所校外支持使命的“增值”组成部分， 利用（i）DF/HCC和UCSF的P30核心设施，（ii）U 54资助的DFCI物理科学肿瘤学 中心，以及（iii）NCI的实验治疗药物临床试验网络和国家临床试验 网络.相反，授予哈佛/加州大学旧金山分校中心的NCI罗宾奖将为该领域带来“增值 通过催化放射生物学、人工智能和 数据科学-在波士顿和旧金山弗朗西斯科建立良好的学科，但从未被应用 与抗辐射性的问题相协调。中心共同领导，教授达芙妮哈斯-科根医学博士。和 Franziska Michor博士在多研究者、多机构的管理方面有丰富的经验 研究计划以及放射肿瘤学和高级数据科学的互补技能。

Project Summary Basic scientists and clinical investigators from Dana-Farber/Harvard Cancer Center (DF/HCC) join forces with their counterparts at University of California, San Francisco’s (UCSF) Helen Diller Family Comprehensive Cancer Center to propose a Harvard/UCSF ROBIN Center. Our Objective is to improve potency and selectivity of two key radiation modalities--external beam radiation and radiopharmaceuticals. A distinctive feature of our ROBIN Center is its selection of two clinically aggressive pediatric tumors--diffuse midline glioma (DMG) and high-risk neuroblastoma (NBL) – as scientific vehicles for the study plan. The genetic drivers of these two pediatric cancers are found also in more common adult solid tumors. Moreover, the low mutational burden of these pediatric cancers provides a clean genetic background for the “low N/high content” Molecular Characterization Trials (MCTs) specified by the ROBIN RFA. The malignant cells within DMG and NBL are heterogenous with respect to developmental stage. The central hypothesis of our study plan is that this developmental heterogeneity is echoed at the level of radiation-response mechanisms and enables radiation resistance. Our study plan features two research projects configured to test predictions of this hypothesis. Project 1 draws upon paired samples of pre-and post-radiotherapy tumor tissue from children treated prospectively with a uniform radiotherapy regimen to test the prediction that radiation selects for radioresistant intra-tumoral variants. Project 2 tests the prediction that 131I-MIBG therapy of aggressive neuroblastoma selects for intra-tumoral subtypes with unfavorable responses to treatment. Our ROBIN MCT provides resources needed to complete the objectives and specific aims of the two Projects. Multimodal analysis and mathematical modeling of data from the Projects and the MCT will be performed in a Clinical Artificial Intelligence and Imaging Core and a Molecular Data Science and Advanced Dosimetry Core. A Cross- Training Core and an Administrative Core will support scientific activities and serve also as the Center’s “outward face” to the broader scientific community and lay public. The Center will serve as a “value-added” component of the NCI’s extramural support mission by leveraging (i) P30 core facilities of DF/HCC and UCSF, (ii) a U54-funded DFCI Physical Sciences Oncology Center, and (iii) the NCI's Experimental Therapeutics Clinical Trials Network and National Clinical Trials Networks. Conversely, an NCI ROBIN award to the Harvard/UCSF Center would be “value-added” to the field of radiation oncology by catalyzing productive interactions between radiation biology, artificial intelligence, and data science – disciplines that are well-established in Boston and San Francisco but have never been applied coordinately to the problem of radioresistance. Center co-leads, Professors Daphne Haas-Kogan M.D. and Franziska Michor, Ph.D. have much experience in management of multi-investigator, multi-institutional research programs together with complementary skillsets in radiation oncology and advanced data science.