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.

项目摘要 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。在哈佛/加州大学旧金山分校罗宾，我们建议， 利用这些机会提高我们对辐射反应和抵抗力的认识。的 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，这两种肿瘤是最常见的。 神经外胚层起源的临床显著儿科肿瘤。我们假设肿瘤内 异质性驱动辐射反应和抗性，其机制可以通过以下方式确定： 在肿瘤形成之前、期间和之后， 放疗后分子数据科学和高级剂量学核心（数据科学核心）是一个 哈佛/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中心， 管理、分析和整合中心生成的所有分子数据库， 剂量学建模和预测计算建模平台。具体而言，核心小组将致力于： 检索，策划和管理与哈佛/UCSF罗宾中心相关的分子数据，并提供 为实现ROBIN目标有效提供所有数据访问所需的基础设施; 分析和整合项目1和2中生成的所有分子数据类型;（iii）生成高级 剂量学数据，包括放射性核素治疗剂量和肿瘤接受剂量的准确分布 细胞，这是研究项目1和2中提出的生物学假设所需的;（iv）进行 放射效应的机制建模和对放射疗法的抗性的演变，直接支持 项目1和2的目标，可以为数据收集提供信息，并解决罗宾中心的假设; 以及（v）与ROBIN网络和整个科学界分享发现和新方法。 为此，核心小组将与项目1和2以及其他核心小组密切协调其活动， 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。核心小组还将与交叉培训核心小组和 行政核心，以达到其他成员的罗宾网络和更大的科学界。的 核心将由两名研究人员，Michor博士和Seo博士指导，他们具有广泛和互补的知识。 我们拥有丰富的专业知识，并组建了一支经验丰富的调查人员团队，为核心的目标做出贡献。我们 预计数据科学核心的成果将被ROBIN的所有组成部分广泛利用 中心和更大的罗宾网络以及更广泛的科学界，并将显着 有助于理解数据科学和剂量学/癌症演变方面的一系列广泛的 癌症和治疗。