Childhood Cancer Data Initiative Annual Symposium (Abstract Registration): Submission #65

Submission information
Submission Number: 65
Submission ID: 150963
Submission UUID: 7789d6fc-1299-408a-8bdc-d4de3ce112ba

Created: Thu, 09/04/2025 - 16:55
Completed: Thu, 09/04/2025 - 17:00
Changed: Thu, 09/04/2025 - 17:00

Remote IP address: 10.208.28.132
Submitted by: chungs6
Language: English

Is draft: No
Abstract Title: Genetic Data Modeling for the Childhood Cancer Clinical Data Commons (C3DC)
Abstract: The Childhood Cancer Clinical Data Commons (C3DC) is an important addition to the CCDI Data Ecosystem. With its 6th release in June 2025, its available data currently includes 18,594 participants from the Molecular Characterization Initiative (MCI) and the TARGET Initiative. The scope of the data has initially been limited to disease site and diagnosis, treatment types and agents, treatment response, and survival status. However, recent modeling has been undertaken in order to augment this data with genetic findings. The C3DC data model is adapted from the Data for the Common Good (D4CG) Pediatric Cancer Data Commons (PCDC) data model, which was built through iterative consensus by dozens of international oncology subject matter experts. The source format of clinical genomic data varies greatly depending on the specific test/panel and the proprietary structure/format of the test reports that each laboratory uses. This model is able to accommodate genetic findings at three general levels of granularity. The least granular is simply a test name and an unstructured text blob of results. The middle level (which we have seen to be the most common) also includes a test name and unstructured results, but adds additional fields for the standardized representation of the specific alterations in either ISCN (chromosomal) or HGVS (genic) nomenclatures. The most granular is to represent the elements of the unstructured text blob as discrete fields in addition to the ISCN or HGVS strings. These fields cover a wide breadth of information, from copy number status to allele frequency.
Abstract:
Authors:
  1. First Name: Michael
    Last Name: Watkins
    Degree(s): PhD
    Organization: University of Chicago
  2. First Name: Brian
    Last Name: Furner
    Organization: University of Chicago
  3. First Name: Samuel
    Last Name: Volchenboum
    Degree(s): MD, PhD
    Organization: University of Chicago
  4. First Name: Patrick
    Last Name: Dunn
    Degree(s): PhD
    Organization: Frederick National Laboratory for Cancer Research
  5. First Name: Sean
    Last Name: Burke
    Degree(s): PhD
    Organization: Frederick National Laboratory for Cancer Research
  6. First Name: Maureen
    Last Name: Ryan
    Organization: National Cancer Institute
  7. First Name: Janice
    Last Name: Knable
    Organization: National Cancer Institute
  8. First Name: Austin
    Last Name: Fitts
    Degree(s): PharmD
    Organization: National Cancer Institute
  9. First Name: Subhashini
    Last Name: Jagu
    Degree(s): PhD
    Organization: National Cancer Institute
Presenting Author: Brian Furner
Institution: University of Chicago
Email Address: bfurner@bsd.uchicago.edu