Childhood Cancer Data Initiative Annual Symposium (Abstract Registration): Submission #58

serial: '58'
sid: '150485'
uuid: 23993711-100d-4651-8a73-25592ea841e0
uri: /nci/ccdisymposium/abstract
created: '1756756631'
completed: '1756756928'
changed: '1756756928'
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langcode: en
webform_id: ccdi_symposium_abstract
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entity_id: '2139'
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data:
  authors_:
    - add_author_degree: ''
      add_author_first_name: Emon
      add_author_last_name: Nasajpour
      add_author_middle: ''
      add_author_organization: 'Stanford University'
    - add_author_degree: ''
      add_author_first_name: Dena
      add_author_last_name: Panovska
      add_author_middle: ''
      add_author_organization: 'Stanford University'
    - add_author_degree: ''
      add_author_first_name: Ruolun
      add_author_last_name: Wei
      add_author_middle: ''
      add_author_organization: 'Stanford University'
    - add_author_degree: ''
      add_author_first_name: 'Conrado '
      add_author_last_name: Soria
      add_author_middle: ''
      add_author_organization: 'Frederick National Laboratory for Cancer Research'
    - add_author_degree: ''
      add_author_first_name: Eva
      add_author_last_name: Tonsing-Carter
      add_author_middle: ''
      add_author_organization: 'National Cancer Institute'
    - add_author_degree: ''
      add_author_first_name: Julyann
      add_author_last_name: Perez-Mayoral
      add_author_middle: ''
      add_author_organization: 'National Cancer Institute'
    - add_author_degree: ''
      add_author_first_name: Louis
      add_author_last_name: Staudt
      add_author_middle: ''
      add_author_organization: 'National Cancer Institute'
    - add_author_degree: ''
      add_author_first_name: 'Calvin '
      add_author_last_name: Kuo
      add_author_middle: ''
      add_author_organization: 'Stanford University'
    - add_author_degree: ''
      add_author_first_name: Daniela
      add_author_last_name: Gerhard
      add_author_middle: ''
      add_author_organization: 'National Cancer Institute'
    - add_author_degree: ''
      add_author_first_name: Melissa
      add_author_last_name: Porter
      add_author_middle: ''
      add_author_organization: 'National Cancer Institute'
    - add_author_degree: ''
      add_author_first_name: Rachana
      add_author_last_name: Agarwal
      add_author_middle: ''
      add_author_organization: 'Frederick National Laboratory for Cancer Research'
    - add_author_degree: ''
      add_author_first_name: Claudia
      add_author_last_name: Petritsch
      add_author_middle: K
      add_author_organization: 'Stanford University'
  abstract: |+
    The Human Cancer Models Initiative (HCMI) is a global initiative founded by the National Cancer Institute (NCI), Cancer Research UK, Wellcome Sanger Institute, and the foundation Hubrecht Organoid Technology. The mission is to generate patient-derived Next-generation Cancer Models (NGCMs) from diverse tumor types, including rare adult and pediatric cancers, as a community resource. Unlike traditional cancer models, NGCMs are cultured under optimized conditions that better preserve the characteristics of the parental tumors. This preservation is validated through molecular and phenotypic analyses of tumor tissue and models, which are shared with the community alongside standard operating procedures (SOPs), informed consent templates, and clinical data case report forms (CRFs).
    The Stanford University Cancer Model Development Center (referred to as STAN CMDCs) is one of several CMDCs that ensure the integrity and quality of this initiative. STAN CMDC is dedicated to pediatric solid tumors, emphasizing central nervous system (CNS) tumors, the leading cause of cancer-related death in children. We developed a standardized bioprocessing pipeline which yielded a functional tumor bank, achieving a 60-70% success rate in establishing NGCMs for long-term passaging. We have successfully generated and submitted 85 NGCMs along with case-associated clinical and biospecimen data, as well as internal QC data validating the derived cancer models. 
    These models partially capture the heterogeneity of pediatric CNS tumors, neuroblastoma, hepatoblastoma, Wilms tumor, and brain metastases from neuroblastoma and rare sarcoma-related cancers. Longitudinal biobanking has identified and characterized novel onco-fusion proteins, rare tumor entities, and recurrences, thereby enhancing therapeutic efficacy and promoting personalized treatment strategies.

  abstract_title_: 'Next-generation cancer models for pediatric solid cancer. '
  email_address_: cpetri@stanford.edu
  institution_: 'Stanford University'
  presenting_author_: 'Claudia K Petritsch'