ARC25 Agenda – Fri, Jan 31

3:15 to 4:15 pm and 4:30 to 5:30 pm, Park Suites (Seneca/Iroquois)

The Gabriella Miller Kids First Data Resource for pediatric cancer and structural birth defect research 

Session Presenter: Dr. David Higgins

VIRTUAL OPTION! Join us online via Zoom! (Meeting ID: 850 6198 3492, Passcode: 904514)

• • 3:15 to 4:15 pm: Overview of the Gabriella Miller Kids First Data Resource

  • 4:15 to 4:30 pm: Break

  • 4:30 to 5 pm: A Demonstration of the Gabriella Miller Kids First Data Resource

Nine-year-old brain tumor patient Gabriella Miller challenged members of Congress to “stop talking and start doing” when providing federal funding for research into cures for pediatric cancer and structural birth defects. Her advocacy resulted in the 2014 Gabriella Miller Kids First Research Act, allocating millions of dollars to the National Institutes of Health for pediatric research. In 2024, Congress passed the Gabriella Miller Kids First 2.0 Act, extending funding through 2028.

The Gabriella Miller Kids First Data Resource Center (Kids First DRC) has since honored her legacy by building a comprehensive data resource for genomic research into pediatric conditions. Genomic data from more than 26 000 participants spanning over 1900 conditions have been released for secondary research and analysis using the center’s web-based platforms.

This two-part workshop provides a thorough overview of the Gabriella Miller Kids First data resource and how it supports research into the genetic causes of pediatric cancer and structural birth defects. The first hour will cover an overview of the data resource, describing the different data types and platforms that are available for research use, how investigators have used the data in peer reviewed publications, and funding opportunities for projects related to Kids First data. The second hour will focus on a live demonstration of the Kids First DRC’s platforms, including the Kids First Portal for participant and variant discovery as well as CAVATICA for cloud-based bioinformatic analysis.