Shared Resource Core 1: Molecular Data Science and Advanced Dosimetry

共享资源核心 1：分子数据科学和高级剂量测定

• 批准号: 10712295
• 负责人: Franziska Michor
• 金额: $ 25.36万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-19 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10712295
• 关键词: ATAC-seq; Address; Age; Anatomy; Artificial Intelligence; Biological; Biological Assay; Blood flow; Cancer Biology; Cells; Clinical; Clonal Evolution; Collaborations; Communities; Complex; Computer Models; Computing Methodologies; DNA Repair; Data; Data Collection; Data Science; Data Science Core; Data Security; Data Set; Data Storage and Retrieval; Databases; Dose; Ensure; Epigenetic Process; Evolution; Face; Funding; Genomics; Goals; Grant; Image; Immunofluorescence Immunologic; Infrastructure; Link; Malignant Childhood Neoplasm; Malignant Neoplasms; Methodology; Methods; Modeling; Molecular; Natural Language Processing; Neuroblastoma; Neuroepithelial, Perineurial, and Schwann Cell Neoplasm; Outcome; Patients; Pediatric Neoplasm; Peripheral Blood Involvement; Population; Publishing; Radiation Oncology; Radiation therapy; Radionuclide therapy; Research Personnel; Resistance; Resource Sharing; Resources; Specimen; Structure; System; The Cancer Genome Atlas; Training; Tumor Tissue; Weight; Work; access restrictions; analysis pipeline; cancer genomics; clinically significant; data exchange; data hub; data integrity; data management; data repository; design; diffuse midline glioma; dosimetry; exome; experience; genomic data; improved; individual patient; interest; member; metaiodobenzylguanidine; miRNA expression profiling; neoplastic cell; novel; profiles in patients; radiation effect; radiation resistance; radiation response; radiomics; repository; single-cell RNA sequencing; structured data; transcriptome sequencing; transcriptomics; treatment planning; tumor; tumor heterogeneity; tumor microenvironment; whole genome

PROJECT SUMMARY The convergence of radiation oncology, cancer genomics, data science, computational modeling, and artificial intelligence presents spectacular opportunities and challenges. In the Harvard/UCSF ROBIN, we propose to capitalize on these opportunities to improve our understanding of radiation response and resistance. The clinical vehicles for our ROBIN Center are diffuse midline gliomas (DMGs) and neuroblastoma, the two most clinically significant pediatric tumors of neural ectodermal origin. We hypothesize that intratumoral heterogeneity drives radiation response and resistance, and that the mechanisms thereof can be identified by deciphering tumor cell-intrinsic and -extrinsic components of the tumor microenvironment before, during, and after radiotherapy. The Molecular Data Science and Advanced Dosimetry Core (Data Science Core) is an essential component of the Harvard/UCSF ROBIN Center. It will serve as the hub for data storage and management, analysis, and integration of all molecular datatypes generated by the Center, advanced dosimetry modeling, and predictive computational modeling platforms. Specifically, the Core will work to (i) retrieve, curate, and manage molecular data related to the Harvard/UCSF ROBIN Center and provide the infrastructure necessary to efficiently provide access to all data required for achieving the ROBIN goals; (ii) analyze and integrate all molecular data types generated in Projects 1 and 2; (iii) generate advanced dosimetry data, including accurate distributions of dose for radionuclide therapy and dose received by tumor cells, that are required to investigate the biological hypotheses proposed in Projects 1 and 2; (iv) conduct mechanistic modeling of radiation effects and evolution of resistance to radiotherapy that directly supports the aims of Projects 1 and 2 and can inform data collection and address the hypotheses of the ROBIN Center; and (v) share findings and novel methods with the ROBIN Network and the scientific community at large. Toward these ends, the Core will coordinate its activities closely with Projects 1 and 2 and the other cores, in particular with the AI Core, focusing on imaging, radiomics, and natural language processing, and the Molecular Characterization Trial. The Core will also work closely with the Cross-Training Core and the Administrative Core to reach other members of the ROBIN Network and the larger scientific community. The Core will be directed by two investigators, Drs. Michor and Seo, who have expansive and complementary expertise and have assembled a team of experienced investigators to contribute to the goals of the Core. We anticipate that the results of the Data Science Core will be broadly utilized by all components of the ROBIN Center and the larger ROBIN Network as well as the broader scientific community, and will significantly contribute to understanding data science and dosimetry/cancer evolution aspects across a broad suite of cancers and treatments.

项目概要 放射肿瘤学、癌症基因组学、数据科学、计算建模和人工智能的融合 智能带来了巨大的机遇和挑战。在Harvard/UCSF ROBIN中，我们建议 利用这些机会来提高我们对辐射响应和抵抗力的理解。这 我们 ROBIN 中心的临床工具是弥漫性中线胶质瘤 (DMG) 和神经母细胞瘤，这是两种最常见的肿瘤 具有临床意义的神经外胚层起源的小儿肿瘤。我们假设瘤内 异质性驱动辐射响应和抵抗，其机制可以通过以下方式确定： 在之前、期间和期间破译肿瘤微环境的肿瘤细胞内在和外在成分 放射治疗后。分子数据科学和高级剂量测定核心（数据科学核心）是 哈佛大学/加州大学旧金山分校罗宾中心的重要组成部分。它将作为数据存储的枢纽 管理、分析和集成中心生成的所有分子数据类型，高级 剂量测定建模和预测计算建模平台。具体来说，核心将致力于 (i) 检索、整理和管理与哈佛/加州大学旧金山分校罗宾中心相关的分子数据，并提供 有效提供对实现 ROBIN 目标所需的所有数据的访问所需的基础设施； (二) 分析和整合项目 1 和 2 中生成的所有分子数据类型； (iii) 生成先进的 剂量测定数据，包括放射性核素治疗剂量和肿瘤接受剂量的准确分布 研究项目 1 和 2 中提出的生物学假设所需的细胞； (四) 行为 辐射效应和放射治疗耐药性演变的机械模型，直接支持 项目 1 和 2 的目标，可以为数据收集提供信息并解决 ROBIN 中心的假设； (v) 与 ROBIN 网络和整个科学界分享研究结果和新颖方法。 为了实现这些目标，核心将与项目 1 和 2 以及其他核心密切协调其活动， 特别是人工智能核心，专注于成像、放射组学和自然语言处理，以及 分子表征试验。该核心还将与交叉培训核心和 行政核心，以覆盖 ROBIN 网络的其他成员和更大的科学界。这 核心将由两名研究人员指导，博士。 Michor 和 Seo 具有广阔的互补性 专业知识，并组建了一支经验丰富的研究人员团队，为核心的目标做出贡献。我们 预计数据科学核心的结果将被 ROBIN 的所有组成部分广泛利用 中心和更大的 ROBIN 网络以及更广泛的科学界，并将显着 有助于理解广泛的数据科学和剂量测定/癌症进化方面 癌症和治疗。