NCI Office of Data Sharing (ODS) Data Jamboree (Abstract Submissions): Submission #26

serial: '26'
sid: '145168'
uuid: 8b1eb5cc-833f-4016-b6a6-2ed5b36957f4
uri: /nci/ods-data-jamboree/abstractsubmissions
created: '1750706700'
completed: '1750706737'
changed: '1750706737'
in_draft: '0'
current_page: ''
remote_addr: 10.208.28.103
uid: '0'
langcode: en
webform_id: nci_office_of_data_sharing_abstr
entity_type: node
entity_id: '2107'
locked: '0'
sticky: '0'
notes: ''
metatag: meta
data:
  category: 'Building specific disease cohorts, or visualization techniques'
  degree_s_: Ph.D
  email: mcieslik@med.umich.edu
  first_name: Marcin
  keywords_abstracts: 'MI-OncoSeq, immunotherapy, integrative genomics, precision oncology'
  last_name: Cieslik
  middle_initial: ''
  organization: 'University of Michigan'
  organization_address:
    address: ''
    address_2: ''
    city: 'Ann Arbor'
    country: ''
    postal_code: ''
    state_province: ''
  other_please_specify_: ''
  summary: "Childhood cancers are rare and genomically diverse, and a lack of centralized data has hindered research progress. This project will leverage the rich genomic and transcriptomic data from the Pediatric Michigan Oncology Sequencing (Peds-MI-OncoSeq) cohort, a key contribution to the NCI's Childhood Cancer Data Initiative (CCDI). We propose a dual-aim study to first, create a comprehensive multi-omic atlas to define the molecular architecture across a range of pediatric malignancies. Second, we will conduct a focused analysis on the neuroblastoma cohort to identify robust molecular biomarkers that predict patient response to anti-GD2 immunotherapy, a standard-of-care treatment. By integrating genomic and transcriptomic data, this work seeks to uncover fundamental cancer biology and produce clinically relevant predictors to improve therapeutic strategies for children with cancer."
  title: 'Assistant Professor'
  ttile: 'Integrative Genomic Analysis of Pediatric Cancer Data from Peds-MI-OncoSeq within the NCI Childhood Cancer Data Initiative (CCDI)'
  upload_abstract: '65603'