Childhood Cancer Data Initiative Annual Symposium (Abstract Registration): Submission #71

Submission information
Submission Number: 71
Submission ID: 191908
Submission UUID: 31dd971c-1433-4f0e-b25d-739c4cd26b94

Created: Tue, 08/25/2026 - 00:42
Completed: Tue, 08/25/2026 - 00:55
Changed: Tue, 08/25/2026 - 00:55

Remote IP address: 10.208.28.32
Submitted by: Anonymous
Language: English

Is draft: No
Abstract Title: Next-generation models to advance pediatric solid cancer treatments.
Abstract: The high attrition rate in oncology is attributed to an over-reliance on 2D cell cultures and animal models, which often fail to accurately replicate patient tumor biology. The adoption of advanced, clinically relevant preclinical models is essential for better identifying and prioritizing agents with a higher likelihood of success in clinical trials. The Human Cancer Models Initiative (HCMI) is a global initiative founded by the National Cancer Institute (NCI). The mission is to generate patient-derived next-generation cancer models from diverse tumor types as a community resource. Unlike traditional cancer models, the new models are cultured under optimized, predominantly 3D conditions that better preserve the characteristics of the parental tumors than historical culture conditions. This preservation is validated through phenotypic and molecular analyses of tumor tissue and models, which are shared alongside associated clinical and molecular data. To contribute towards the goal of the HCMI the Stanford CDMC is dedicated to models of pediatric solid tumors, emphasizing central nervous system (CNS) tumors, the leading cause of cancer-related death in children. We have generated <85 pediatric cancer models along with case-associated clinical and biospecimen data, as well as internal QC data validating the derived cancer models, for further characterization and distribution via the HCMI pipeline. Our next-generation cancer 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, and therapeutic vulnerabilities through multi-omics.
Authors:
  1. First Name: Emon
    Last Name: Nasajpour
    Degree(s): BSc
    Organization: Stanford University
  2. First Name: Ruolun
    Last Name: Wei
    Degree(s): MD, PhD
    Organization: Stanford University
  3. First Name: Conrado
    Last Name: Soria
    Organization: Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc.
  4. First Name: Calvin
    Last Name: Kuo
    Degree(s): MD, PhD
    Organization: Stanford University
  5. First Name: Rachana
    Last Name: Agarwal
    Degree(s): PhD
    Organization: Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc.
  6. First Name: Claudia
    Middle Initial: Katharina
    Last Name: Petritsch
    Degree(s): PhD
    Organization: Stanford University
Presenting Author: Claudia Katharina Petritsch
Institution: Stanford University
Email Address: cpetri@stanford.edu