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 辐射肿瘤学，癌症基因组学，数据科学，计算建模和人工的收敛性 情报带来了壮观的机遇和挑战。在哈佛/UCSF罗宾中，我们建议 利用这些机会，以提高我们对辐射反应和抵抗的理解。这 我们的罗宾中心的临床车辆是弥漫性中线神经胶质瘤（DMG）和神经母细胞瘤，这是两个最大的 神经外皮起源的临床上显着的小儿肿瘤。我们假设肿瘤内 异质性驱动辐射反应和抗性，并且可以通过 肿瘤细胞中的解密肿瘤细胞内部和 - 超级环境的 - 之前，期间和 放疗后。分子数据科学和高级剂量核心（数据科学核心）是 哈佛/UCSF罗宾中心的重要组成部分。它将用作数据存储的枢纽， 中心生成的所有分子数据类型的管理，分析和集成，高级 剂量模型和预测计算建模平台。具体而言，核心将对（i）作用 检索，策划和管理与哈佛/UCSF罗宾中心有关的分子数据，并提供 有效地提供实现罗宾目标所需的所有数据所需的基础架构； （ii） 分析和整合项目1和2中产生的所有分子数据类型； （iii）生成高级 剂量测定数据，包括放射性核素治疗的剂量的准确分布和肿瘤接受的剂量 细胞，需要研究项目1和2中提出的生物学假设所需的细胞； （iv）行为 辐射效应的机械模型和对放射疗法的抵抗力的演变直接支持 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； （v）与罗宾网络和整个科学界分享发现和新方法。 在这些目的方面，核心将与项目1和2和其他核心紧密协调其活动 特别是AI核心，专注于成像，放射线学和自然语言处理，以及 分子表征试验。核心还将与交叉训练核心紧密合作 行政核心，以吸引罗宾网络的其他成员和更大的科学界。这 核心将由两名调查员Drs指导。 Michor和Seo，他们有广泛和互补的 专业知识，并组建了一支经验丰富的调查员团队，以促进核心的目标。我们 预计数据科学核心的结果将被罗宾的所有组成部分广泛使用 中心和较大的罗宾网络以及更广泛的科学界，将大大显着 有助于了解数据科学和剂量法/癌症演化方面 癌症和治疗。