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.

项目总结