Childhood Cancer Data Initiative Annual Symposium (Abstract Registration)

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# Created User IP address Abstract Title: Abstract: Authors: Presenting Author: Institution: Email Address: Operations
87 Fri, 09/04/2026 - 16:53 Anonymous 10.208.24.244 Neuroblastoma Patient-Derived Xenografts and Cell Lines from Postmortem Blood as Models to Understand and Reverse Therapy Resistance Background: Patient-derived models of neuroblastoma that recapitulate therapy resistance in patients are essential for defining resistance mechanisms. We established and characterized patient derived xenografts (PDXs) and patient derived cell lines (PDCLs) from neuroblastoma patients with progressive disease established post-mortem (PD-PM).

Methods: Tumor and blood samples were cultured and/or xenografted in NOD SCIDγ mice. PDCLs and PDXs were validated by STR profiling. Mutations were identified by whole-exome sequencing and telomere maintenance mechanisms by TERT qPCR, C-circle assay, and TERT break-apart FISH. Therapeutic responses were evaluated in subcutaneous xenografts.

Results: PD-PM specimens showed higher engraftment rates as xenografts (68%; 21/31 specimens, 83% for PD-PM blood specimens) and had higher take rates as PDCLs (54%, 25/46 specimens) compared to diagnosis (Dx, 17%) or progressive disease (PD, 11%, P<0.001) specimens. PD-PM PDXs had higher mutation burdens than Dx PDXs (P=0.026); 33% harbored activating ALK mutations and 33% mutations in other RAS-MAPK genes. Of 20 high TERT-expressing PD-PM PDXs, 13 had MYCN amplification and 4 MYCN-non-amplified PDXs had TERT rearrangements. One PD-PM PDX was ALT-positive. Temozolomide + irinotecan responses were shorter in 3 PD-PM than Dx and PD PDXs (P<0.001). In a PD-PM PDX, O6-BG reversed temozolomide + irinotecan resistance (P=0.002), while nanoliposomal irinotecan extended event-free survival compared to irinotecan (P=0.003).

Conclusions: PD-PM PDXs activate ALK or RAS-MAPK signaling, manifest high levels of chemoresistance, and provide models to study reversing neuroblastoma drug resistance. The Children’s Oncology Group (COG) panel of PDXs is freely available from the Alex's Lemonade Stand Foundation (ALSF)/COG Childhood Cancer Repository (https://cccells.org).
  1. First Name: Balakrishna
    Last Name: Koneru
    Degree(s): Ph.D
    Organization: Pediatric Cancer Research Center, School of Medicine, Texas Tech University Health Sciences Center, Lubbock, TX, USA
  2. First Name: Nighat
    Last Name: Noureen
    Degree(s): Ph.D
    Organization: Pediatric Cancer Research Center, School of Medicine, Texas Tech University Health Sciences Center, Lubbock, TX, USA
  3. First Name: Ashly
    Last Name: Hindle
    Degree(s): Ph.D
    Organization: Pediatric Cancer Research Center, School of Medicine, Texas Tech University Health Sciences Center, Lubbock, TX, USA
  4. First Name: Kristyn
    Last Name: Mccoy
    Degree(s): MS
    Organization: Pediatric Cancer Research Center, School of Medicine, Texas Tech University Health Sciences Center, Lubbock, TX, USA
  5. First Name: Jonas
    Last Name: Nance
    Degree(s): BS
    Organization: Pediatric Cancer Research Center, School of Medicine, Texas Tech University Health Sciences Center, Lubbock, TX, USA
  6. First Name: Diana
    Last Name: Ixtlamati-Nava
    Degree(s): BS
    Organization: Pediatric Cancer Research Center, School of Medicine, Texas Tech University Health Sciences Center, Lubbock, TX, USA
  7. First Name: Charles
    Last Name: Zhu
    Degree(s): BS
    Organization: Pediatric Cancer Research Center, School of Medicine, Texas Tech University Health Sciences Center, Lubbock, TX, USA
  8. First Name: Tenley
    Last Name: Lehman
    Degree(s): BS
    Organization: Pediatric Cancer Research Center, School of Medicine, Texas Tech University Health Sciences Center, Lubbock, TX, USA
  9. First Name: Mohamad
    Middle Initial: Al
    Last Name: Rahawan
    Degree(s): M.D.
    Organization: Department of Pediatrics, School of Medicine, Texas Tech University Health Sciences Center, Lubbock, TX, USA
  10. First Name: Yael
    Middle Initial: P
    Last Name: Mosse
    Degree(s): M.D.
    Organization: Division of Oncology and Center for Childhood Cancer Research, Children’s Hospital of Philadelphia, Philadelphia, PA USA; Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA USA
  11. First Name: John
    Middle Initial: M
    Last Name: Maris
    Degree(s): M.D.
    Organization: Division of Oncology and Center for Childhood Cancer Research, Children’s Hospital of Philadelphia, Philadelphia, PA USA; Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA USA
  12. First Name: Shahab
    Last Name: Asgharzadeh
    Degree(s): M.D.
    Organization: Children's Hospital of Los Angeles, Los Angeles, CA, USA
  13. First Name: Araz
    Last Name: Marachelian
    Degree(s): M.D.
    Organization: Children's Hospital of Los Angeles, Los Angeles, CA, USA
  14. First Name: Meredith
    Middle Initial: S
    Last Name: Irwin
    Degree(s): M.D.
    Organization: Department of Pediatrics, Hospital for Sick Children and University of Toronto, Toronto, Canada
  15. First Name: Suzanne
    Last Name: Shusterman
    Degree(s): M.D.
    Organization: Dana-Farber/Harvard Cancer Center, Boston, MA, USA
  16. First Name: Stephen
    Middle Initial: S
    Last Name: Roberts
    Degree(s): M.D.
    Organization: Oregon Health and Science University, Portland, OR USA
  17. First Name: Min
    Middle Initial: H
    Last Name: Kang
    Degree(s): PharmD
    Organization: Pediatric Cancer Research Center, School of Medicine, Texas Tech University Health Sciences Center, Lubbock, TX, USA
  18. First Name: Patrick
    Last Name: Reynolds
    Degree(s): M.D. Ph.D
    Organization: Pediatric Cancer Research Center, School of Medicine, Texas Tech University Health Sciences Center, Lubbock, TX, USA
 BALAKRISHNA KONERU Texas Tech University Health Sciences Center balakrishna.koneru@ttuhsc.edu
85 Fri, 09/04/2026 - 16:31 Anonymous 10.208.28.41 The Children’s Oncology Group and Alex’s Lemonade Stand Foundation Childhood Cancer Repository The Children’s Oncology Group (COG) childhood cancer repository (www.CCcells.org), supported by ALSF, establishes, validates, and banks patient-derived cell lines (PDCLs) and patient-derived xenografts (PDXs) from childhood cancers. Viable tumor, blood, or bone marrow samples obtained under informed consent via COG protocols are sent to a centralized resource lab to establish PDCLs and PDXs. PDCLs/PDXs are validated by short tandem repeat assay to patient material, verified free of human and mouse pathogens, tumor type validated by biomarkers, and telomere maintenance mechanism assessed. The repository has available PDCLs/PDXs from 578/87 neuroblastomas, 16/20 leukemias, 7/5 lymphomas, 20/3 Ewing sarcomas, 13/3 soft tissue sarcomas. 32/1 retinoblastomas, 1/ 5 osteosarcomas, 2/5 Wilm’s tumors, and 11 PDCLs from brain tumors. Hypoxic culture conditions are used to establish PDCLs, enhancing success rates and minimizing selection against cells by hyperoxia. Cultures in hypoxia are more often able to generate a PDCL and low-passage PDCLs in hypoxia generate xenografts comparable in RNA expression and drug response profiles to PDXs. Neuroblastoma PDCLs and PDXs are being established from patients enrolled on COG phase III studies at diagnosis and from disease persisting or progressing during and after therapy to enable future studies comparing genomics and drug response of PDCLs and PDXs to patient data. To date 56 PDCLs and 19 PDXs have been established from patients on the ANBL1531 phase III trial and 41 PDCLs and 4 PDXs from 41 patients on the ANBL2131 phase III study. The repository distributes PDCLs and PDXs to > 700 laboratories in 30 countries.
  1. First Name: C Patrick
    Last Name: Reynolds
    Degree(s): MD PhD
    Organization: Texas Tech University Health Sciences Center School of Medicine Lubbock, TX
  2. First Name: Kristyn
    Last Name: MCoy
    Degree(s): BS, MS
    Organization: Texas Tech University Health Sciences Center School of Medicine Lubbock, TX
  3. First Name: Diana
    Last Name: Ixtlamati-Nava
    Degree(s): BS
    Organization: Texas Tech University Health Sciences Center School of Medicine Lubbock, TX
  4. First Name: Jonas
    Last Name: Nance
    Degree(s): BS
    Organization: Texas Tech University Health Sciences Center School of Medicine Lubbock, TX
  5. First Name: Balakrishna
    Last Name: Koneru
    Degree(s): BS, MS, PhD
    Organization: Texas Tech University Health Sciences Center School of Medicine Lubbock, TX
  6. First Name: Min
    Middle Initial: H.
    Last Name: Kang
    Degree(s): BS, PharmD
    Organization: Texas Tech University Health Sciences Center School of Medicine Lubbock, TX
 Min H. Kang, PharmD Texas Tech University Health Sciences Center School of Medicine Lubbock, TX Min.Kang@ttuhsc.edu
84 Fri, 09/04/2026 - 15:54 Anonymous 10.208.24.244 Nanopore Sequencing for Pediatric CNS Tumor Classification Central nervous system (CNS) tumors are the most common solid tumors diagnosed in children, making up about 17% of diagnosed childhood cancer cases. Most pediatric CNS tumor cases require a combination of imaging, histopathological, and molecular diagnoses to determine the tumor (sub)type. My project utilizes an emerging molecular diagnostic technology -- nanopore sequencing -- to quickly and accurately generate high-depth whole genome DNA methylation profiles for a diverse cohort of 150 pediatric CNS tumors, and I am currently using this data to train and validate a pediatric-specific deep learning classifier. As part of a clinical research collaboration, I tested the previously-developed machine learning classifier Sturgeon on high-depth nanopore DNA methylation data for 7 matched pairs of standard patient tissue and surgical tumor aspirate (n=14). For each sample, I demonstrated between 7-34x genomic coverage, classified the sample’s tumor (sub)type, and used in-house software to validate key subtype-defining features and identify tumor-specific copy number variation(s). Through this initial analysis, I was able to identify key shortcomings in existing classifier(s), including inappropriate labeling for pediatric samples, insufficient adjustment for low tumor purity samples, and confident misclassification of rare/unique tumors. After making adjustments to existing preprocessing methods, all 14 samples were then classified correctly with high confidence. Matched samples also showed strong pairwise similarities, reflective of interchangeable use of the materials. Together, I used nanopore DNA methylation profiling to demonstrate fast and accurate classification of CNS tumor aspirate and to highlight areas of improvement for the development of a pediatric-specific CNS tumor classifier.
  1. First Name: Allison
    Middle Initial: A
    Last Name: Murray
    Organization: University of North Carolina Chapel Hill
  2. First Name: Breanna
    Middle Initial: E
    Last Name: Mann
    Degree(s): Ph.D.
    Organization: University of North Carolina Chapel Hill
  3. First Name: Andrew
    Middle Initial: B
    Last Name: Satterlee
    Degree(s): Ph.D.
    Organization: University of North Carolina Chapel Hill
  4. First Name: David
    Middle Initial: E
    Last Name: Kram
    Degree(s): M.D., MCR
    Organization: University of North Carolina Chapel Hill
  5. First Name: Jeremy
    Middle Initial: R
    Last Name: Wang
    Degree(s): Ph.D.
    Organization: University of North Carolina Chapel Hill
 Allison A Murray University of North Carolina Chapel Hill amurray1@unc.edu
86 Fri, 09/04/2026 - 15:45 Anonymous 10.208.24.244 The Integrated Oncogenomics Portal (IOCP): A Cloud-Native Pipeline and Database for Processing and Sharing CCDI & other Childhood Cancer Genomics Data NCI’s Childhood Cancer Data Initiative(CCDI) is assembling high-value pediatric cancer datasets, creating a critical need for scalable, reproducible infrastructure to transform them into harmonized, interpretable, and reusable results. The Integrated Oncogenomics Portal(IOCP) addresses this need through a cloud-native analysis pipeline and data portal that identify clinically and biologically relevant genomic alterations and enable interactive exploration at the patient, cohort, and cancer-type levels.

The first component is a nextflow pipeline, with parallel hg19/hg38 builds, covering RNA and DNA analysis: somatic-germline variant calling, copy-number, fusions, TMB, MSI, mutational signatures, HLA/neoantigen prediction. It is version-controlled and portable across cloud platforms; genomic datasets can be read directly from their source location without the need to transfer. We are currently processing the PediatricMATCH cohort(phs002883), followed by BeatAML(phs002599), MCI(phs002790), and Oncokids(phs002518).

The second component disseminates results through cloud oncogenomics database enabling case and cohort-level exploration, interactive-IGV, survival-analyses, and AVIA annotation integrating genomic, population, functional, cancer-specific, and clinical evidence for variant interpretation and prioritization. Pipeline results are also published to CCDI cBioPortal Cancer Data Explore for standardized OncoPrint visualization and cross-cohort queries. By integrating CCDI cohorts with published pediatric cancer genomic datasets, IOCP combines analytical depth with broad interoperability.

IOCP provides a reproducible framework for advancing CCDI’s goal of interoperable pediatric cancer research through harmonized and discoverable genomic results. Beyond CCDI cohorts, the database integrates published pediatric cancer genomics data and offers an LLM-enabled conversational interface letting users query in natural language and receive responses grounded in IOCP data.
  1. First Name: Vineela
    Last Name: Gangalapudi
    Degree(s): Masters
    Organization: ABCS, FNLCR, NCI Genetics Branch
  2. First Name: Hsien Chao
    Last Name: Chou
    Degree(s): Ph.D.
    Organization: NCI Genetics Branch
  3. First Name: Xinyu
    Last Name: Wen
    Degree(s): Masters
    Organization: ABCS, FNLCR, NCI Genetics Branch
  4. First Name: Jun
    Last Name: Wei
    Degree(s): Ph.D.
    Organization: NCI Genetics Branch
  5. First Name: Javed
    Last Name: Khan
    Degree(s): M.D.
    Organization: NCI Genetics Branch
  6. First Name: Srujan
    Last Name: Boppana
    Degree(s): Bachelor of Engineering
    Organization: NCI CBIIT
  7. First Name: Lauren
    Last Name: Funston
    Degree(s): Bachelors in Computer Science
    Organization: NCI CBIIT
  8. First Name: Subhashini
    Last Name: Jagu
    Degree(s): Ph.D.
    Organization: Office of Data Sharing, NCI
  9. First Name: Simone
    Last Name: Kats
    Degree(s): Masters
    Organization: Office of Data Sharing, NCI
  10. First Name: Bahar
    Last Name: Sayoldin
    Degree(s): Masters
    Organization: FNLCR, Office of Data Sharing, NCI
  11. First Name: Catherine
    Last Name: Bullen
    Degree(s): Ph.D.
    Organization: FNLCR, Office of Data Sharing, NCI
 Vineela Gangalapudi ABCS, FNLCR, NCI Genetics Branch vineela.gangalapudi@nih.gov
83 Thu, 09/03/2026 - 19:43 Anonymous 10.208.24.244 Single-Cell Multiome Sequencing Reveals Distinct Transcriptional Programs Associated with ecDNA Amplifications in Medulloblastoma Extrachromosomal DNA (ecDNA) is a key driver of intratumoral heterogeneity and has been linked to therapeutic resistance and poor patient outcomes. However, technical limitations in identifying, isolating, and analyzing ecDNA at the single-cell level have hindered our understanding of its role in tumor development and treatment response. We have previously developed a single-cell multiome (RNA + ATAC) sequencing approach that enables the simultaneous analyses of ecDNA and its gene expression profiles. Initial application of this method in a medulloblastoma tumor of the sonic hedgehog subgroup revealed distinct transcriptional signatures in ecDNA-positive compared to ecDNA-negative and non-malignant cells. Building on these findings, we performed single-cell multiome sequencing on an additional cohort of medulloblastoma specimens harboring ecDNA amplifications, obtained from Rady Children's Hospital and the Children's Brain Tumor Network (CBTN). Initial analyses revealed distinct functional characteristics between ecDNA-positive and ecDNA-negative tumor cells. For example, ecDNA-positive tumor cells show up-regulation of DNA replication, recombination, and damage repair pathways, while ecDNA-negative cells demonstrated increased expression of synaptic activity, cell signaling, and morphogenesis pathways. Our ongoing analysis focuses on further defining the transcriptional programs that distinguish ecDNA-positive from ecDNA-negative tumor cells, ultimately informing future functional genetic and pharmacological studies targeting transcriptional dependencies in ecDNA-driven medulloblastoma tumors.
  1. First Name: Hui
    Last Name: Hui
    Degree(s): Master
    Organization: University of California San Diego
  2. First Name: Jessica
    Last Name: Wang
    Degree(s): Bachelor
    Organization: Sanford Burnham Prebys Medical Discovery Institute
  3. First Name: Rishaan
    Last Name: Kenkre
    Degree(s): Bachelor
    Organization: Sanford Burnham Prebys Medical Discovery Institute
  4. First Name: Yan Yuen
    Last Name: Lo
    Degree(s): Bachelor
    Organization: Sanford Burnham Prebys Medical Discovery Institute
  5. First Name: Jon
    Last Name: Larson
    Degree(s): PhD
    Organization: Sanford Burnham Prebys Medical Discovery Institute
  6. First Name: Sunita
    Last Name: Sridhar
    Degree(s): MD
    Organization: Rady Children’s Hospital
  7. First Name: Owen
    Last Name: Chapman
    Degree(s): PhD
    Organization: Nagoya City University Graduate School of Medicine
  8. First Name: Lukas
    Last Name: Chavez
    Degree(s): PhD
    Organization: Sanford Burnham Prebys Medical Discovery Institute
 Hui Hui Sanford Burnham Prebys Medical Discovery Institute ahui@sbpdiscovery.org
82 Thu, 09/03/2026 - 13:15 Anonymous 10.208.24.244 Real-World Molecularly Targeted Treatment Registry (MaTTeR): a Pilot Study to Enrich CCDI Data Utilizing Directed EMR Extraction The incorporation of genomic profiling into the care of pediatric, adolescent and young adult (AYA) cancer patients has resulted in identification of targetable alterations and use of molecularly targeted therapy (MTT), necessitating collection and reporting of outcomes of real-world use of MTT. Using genomic data contributed to the CCDI and our institutional cohorts, we identified a cohort of patients who received MTT outside of clinical trials. We included patients with cancer seen at the Dana-Farber/Boston Children’s, who enrolled in a clinical sequencing/banking study and had next-generation sequencing (NGS) performed (OncoPanel, Rapid Heme and Fusion Panels). Sequencing results were used to identify patients with potentially targetable alterations in a list of 66 genes. Patients who received MTT were identified, and data including dosing, toxicity and response were extracted from the medical record. Between 2013 and 2024, 2163 patients had tumor profiling via targeted NGS. 35% (762/2163) had an actionable alteration detected for which MTT could have been administered. 112 patients received at least one regimen that included an MTT (70 patients with brain tumors, 28 with solid tumors, 14 with hematologic malignancies). Alterations in BRAF, NF1, ALK, FLT3, and PIK3CA led to the most MTT use. Of the 112 patients who received MTT, 81% (91/112) received at least one targeted therapy regimen outside of a clinical trial. This project will create a registry of MTT use within the CCDI that can be expanded to include additional patients and be a resource for MTT use for the pediatric and AYA cancer community.
  1. First Name: Suzanne
    Middle Initial: J.
    Last Name: Forrest
    Degree(s): M.D.
    Organization: Dana-Farber/Boston Children's Cancer and Blood Disorders Center
  2. First Name: Evelina
    Last Name: Ceca
    Organization: Dana-Farber/Boston Children's Cancer and Blood Disorders Center
  3. First Name: Kee Kiat
    Last Name: Yeo
    Degree(s): MD
    Organization: Dana-Farber/Boston Children's Cancer and Blood Disorders Center
  4. First Name: Maeve
    Last Name: Smart
    Organization: Dana-Farber/Boston Children's Cancer and Blood Disorders Center
  5. First Name: Olivia
    Last Name: Puopolo
    Organization: Dana-Farber/Boston Children's Cancer and Blood Disorders Center
  6. First Name: Elizabeth
    Middle Initial: F.
    Last Name: Cohen-Sacks
    Organization: Dana-Farber Cancer Institute
  7. First Name: Sabrina
    Last Name: Testa
    Organization: Dana-Farber/Boston Children's Cancer and Blood Disorders Center
  8. First Name: Ledia
    Middle Initial: A.
    Organization: Dana-Farber Cancer Institute
  9. First Name: Katherine
    Middle Initial: A.
    Last Name: Janeway
    Organization: Dana-Farber/Boston Children's Cancer and Blood Disorders Center
  10. First Name: Yana
    Last Name: Pikman
    Organization: Dana-Farber/Boston Children's Cancer and Blood Disorders Center
 Katherine A. Janeway Dana-Farber/Boston Children's Cancer and Blood Disorder Center Suzanne_forrest@dfci.harvard.edu
81 Thu, 09/03/2026 - 11:42 Anonymous 10.208.28.41 AI-driven multimodal analysis integrating WSI, Methyl-Seq, and OncoKids Cancer Panel to improve diagnostic precision of pediatric tumors. Previously, we added clinical and genomic data from more than 1,000 pediatric cancer patients from our racially and ethnically diverse patient population in Southern California to the CCDI (dbGaP: phs002518). The goal of our current P30 Supplement Project is to augment the CHLA dataset with new types of data from our unique patient population, and to develop tools for integrating the diverse datasets in CCDI to improve diagnosis and treatment of all children with cancer. To date, we have identified, screened, and selected the key whole slide images (WSI) from almost 700 CNS and non-CNS solid tumors in our cohort. We have developed a computational pipeline to access and organize metadata for each WSI. Additionally, our pipeline includes an analytical workflow to quantitatively assess WSI slides. In parallel, we have also generated whole-genome enzymatic methyl-seq data from 170 CNS tumors and developed a methyl-seq bioinformatics pipeline with a CNS tumor classifier compatible with methyl-seq data. We are actively developing tools to leverage these multimodal data for improved diagnosis and characterization of the tumors. Our pipeline is fully automated from raw bioinformatics processing to a classification report detailing predicted classification, classification score, UMAP clustering analysis, copy number profiling, and quality control metrics. Additionally, we have developed AI/ML tools to converge results from WSI analysis, methylation values, and OncoKids Cancer Panel results to enhance diagnostic decision-making. Our long-term goals are to incorporate additional data formats into our suite of multimodal analytic tools and to make these tools available to the CCDI community.
  1. First Name: Alexander
    Middle Initial: L.
    Last Name: Markowitz
    Degree(s): Ph.D.
    Organization: Children's Hospital Los Angeles, Keck School of Medicine, University of Southern California
  2. First Name: Jennifer
    Middle Initial: A.
    Last Name: Cotter
    Degree(s): M.D.
    Organization: Children's Hospital Los Angeles, Keck School of Medicine, University of Southern California
  3. First Name: David
    Middle Initial: N.
    Last Name: Buckley
    Degree(s): Ph.D.
    Organization: Children's Hospital Los Angeles
  4. First Name: Shengmei
    Last Name: Zhou
    Degree(s): M.D.
    Organization: Children's Hospital Los Angeles, Keck School of Medicine, University of Southern California
  5. First Name: William
    Last Name: Mango
    Organization: Children's Hospital Los Angeles
  6. First Name: Bruce
    Last Name: Pawel
    Degree(s): M.D.
    Organization: Children's Hospital Los Angeles, Keck School of Medicine, University of Southern California
  7. First Name: Fariba
    Last Name: Navid
    Degree(s): M.D.
    Organization: Children's Hospital Los Angeles, Keck School of Medicine, University of Southern California
  8. First Name: Jaclyn
    Last Name: Biegel
    Degree(s): Ph.D., FACMG
    Organization: Children's Hospital Los Angeles, Keck School of Medicine, University of Southern California
  9. First Name: James
    Middle Initial: F.
    Last Name: Amatruda
    Degree(s): M.D.,Ph.D.
    Organization: Children's Hospital Los Angeles, Keck School of Medicine, University of Southern California
 Alexander L. Markowitz Children's Hospital Los Angeles, Keck School of Medicine, University of Southern California amarkowitz@chla.usc.edu
80 Wed, 09/02/2026 - 12:08 Anonymous 10.208.28.41 Progress-to-date toward building a multicenter study comparing second cancer risks after radiotherapy: the Pediatric Proton and Photon Therapy Comparison Cohort Proton radiotherapy has emerged as the preferred radiotherapy modality for many cancers, especially in children. Proton radiotherapy is expected to reduce the risk of subsequent cancer and other adverse long-term health effects compared to photon radiotherapy because the physical properties of protons allow for lower radiation exposure to surrounding normal tissues. However, the magnitude of the purported reduction in risk remains uncertain, and no randomized clinical trials have compared the two radiotherapy types in children, who are more susceptible than adults to the late effects of radiation. Observational studies, while generally reassuring, have had important methodological limitations. CCDI funding enabled establishment of the NCI Pediatric Proton and Photon Therapy Comparison Cohort, a large-scale multicenter study with the primary aim to compare the risk of subsequent cancers in pediatric cancer patients treated with proton versus photon radiotherapy. Patient and treatment data, including electronic radiotherapy records, are being collected for eligible patients treated 2006-2025 at 17 participating centers. Long-term follow-up for incident second cancers will be conducted via linkage with state cancer registries. State-of-the-art radiation dose reconstruction methods developed for application in this cohort will allow for assessment of radiation dose-response and dose volume effects. Our poster will update the status of ongoing clinical and radiotherapy data collection and will describe the currently available study population by demographic and treatment factors. Research from this cohort is expected to inform clinical practice for pediatric cancer patients by providing the first large-scale systematic comparison of subsequent cancer risk after proton compared to photon therapy.
  1. First Name: Todd
    Middle Initial: M
    Last Name: Gibson
    Degree(s): PhD
    Organization: National Cancer Institute
  2. First Name: Sara
    Middle Initial: L
    Last Name: Dennehy
    Organization: Massachusetts General Hospital
  3. First Name: Benjamin
    Middle Initial: VM
    Last Name: Bajaj
    Organization: Massachusetts General Hospital
  4. First Name: Isabelle
    Last Name: van der Velpen
    Degree(s): MD, PhD
    Organization: National Cancer Institute
  5. First Name: Jungwook
    Last Name: Shin
    Degree(s): PhD
    Organization: National Cancer Institute
  6. First Name: Matthew
    Middle Initial: M
    Last Name: Mille
    Degree(s): PhD
    Organization: National Cancer Institute
  7. First Name: Keith
    Middle Initial: T
    Last Name: Griffin
    Degree(s): PhD
    Organization: National Cancer Institute
  8. First Name: Sergio
    Last Name: Morato
    Degree(s): PhD
    Organization: National Cancer Institute
  9. First Name: Caroline
    Last Name: Esposito
    Degree(s): BA
    Organization: National Cancer Institute
  10. First Name: Torunn
    Middle Initial: I
    Last Name: Yock
    Degree(s): MD
    Organization: Massachusetts General Hospital
  11. First Name: Amy
    Last Name: Berrington de Gonzalez
    Degree(s): DPhil
    Organization: Institute for Cancer Research
  12. First Name: Choonsik
    Last Name: Lee
    Degree(s): PhD
    Organization: National Cancer Institute
  13. First Name: Cari
    Middle Initial: M
    Last Name: Kitahara
    Degree(s): PhD
    Organization: National Cancer Institute
 Todd M Gibson National Cancer Institute todd.gibson@nih.gov
79 Wed, 09/02/2026 - 04:13 Anonymous 10.208.28.41 DATA-DRIVEN CHARACTERIZATION OF ANGIOGENESIS AND TREATMENT RESISTANCE IN PEDIATRIC HIGH-GRADE GLIOMA Pediatric high-grade gliomas (pHGG) are aggressive and molecularly diverse brain tumors with limited treatment options and poor outcomes. Their diffuse growth and marked molecular heterogeneity contribute to frequent treatment failure. In addition to tumor-intrinsic factors, the tumor microenvironment and its interaction with the vasculature are increasingly recognized as important contributors to disease progression. Angiogenesis, vascular remodeling, hypoxia, and tumor–vascular interactions may influence tumor growth and treatment response, but their molecular links to treatment resistance in pHGG. The CCDI provides an opportunity to investigate these relationships using integrated pediatric molecular and clinical datasets.
To identify angiogenesis-associated molecular signatures linked to aggressive tumor biology and treatment resistance in pHGG and to prioritize biologically relevant and potentially actionable vascular targets.
Publicly available CCDI molecular and clinical datasets will be analyzed using an integrated computational framework. Transcriptomic data will undergo standardized preprocessing and differential-expression analysis, followed by functional and pathway enrichment focused on angiogenesis, vascular remodeling, hypoxia, tumor–vascular interactions, and treatment resistance. Protein–protein interaction and network analyses will be used to identify functionally connected genes and potential regulatory hubs. Where clinical and treatment-response information is available, prioritized candidates will be assessed for associations with disease characteristics and clinical outcomes. Candidate selection will integrate differential expression, pathway involvement,clinical relevance, biological plausibility, and therapeutic tractability. The study is expected to identify angiogenesis-related signatures and vascular regulatory nodes associated with aggressive and treatment-resistant pHGG. By examining vascular biology within the broader tumor molecular landscape, this approach may generate clinically relevant and testable hypotheses for future functional validation.
  1. First Name: Dr. Ravi
    Last Name: Teja Banda
    Degree(s): MBBS, MD, DM
    Organization: Continental Hospital
  2. First Name: Priya Rithika
    Last Name: Vella
    Degree(s): BSc Hons Life Science
    Organization: Continental Hospital
Priya Rithika Vella Continental Hospital Priyarithikavella@gmail.com
78 Fri, 08/28/2026 - 08:53 Anonymous 10.208.28.32 Increasing the National Childhood Cancer Registry (NCCR) user base through the development and deployment of educational materials As one of the tools in the Childhood Cancer Data Initiative’s (CCDI) data ecosystem, the National Childhood Cancer Registry (NCCR), provides comprehensive, high-value data resources critical for advancing childhood cancer research. NCCR is undertaking a strategic initiative to broaden its user base. We are developing and piloting a suite of educational materials designed to increase effective engagement with NCCR data products. Initial consultation with graduate-level educators revealed strong interest in accessible childhood cancer data resources for use in workshops, graduate courses, and clinician-researcher training. In response, we convened an interdisciplinary NCCR Educational Materials Working Group (EDWG) comprised of experienced instructors from leading institutions, NCCR subject matter experts, and NCI project managers. The EDWG is creating a modular instructional unit featuring customizable session formats, student handouts, grading rubrics, data access instructions, compliance tools, case studies, exams, and introductory video content.

These materials will be piloted in academic courses and professional workshops. Feedback from students will be collected by professors, and professors will guide refinement of materials before broader dissemination to schools of public health, medicine, epidemiology, and health services research, as well as cancer centers and relevant professional organizations. This effort may be a model for other efforts to increase the user base of CCDI tools and platforms. By expanding access to high-quality training resources, this initiative aims to strengthen data literacy, foster greater engagement with NCCR, and support research capacity-building across the childhood cancer research community.
  1. First Name: Tracy
    Middle Initial: E.
    Last Name: Thompson
    Degree(s): MPH
    Organization: DCCPS, NCI
  2. First Name: Johanna
    Last Name: Goderre
    Degree(s): MPH
    Organization: DCCPS, NCI
 Johanna Goderre NCI johanna.goderrejones@nih.gov
77 Thu, 08/27/2026 - 14:20 Anonymous 10.208.24.244 Radiation dosimetry for the first large-scale systemic comparison of the risk of second cancers in children treated with proton versus photon therapy The Pediatric Proton/Photon Therapy Comparison (PPTC) cohort study is the first large-scale study comparing the risk of second cancers in children treated with proton versus photon radiotherapy. The study has collected treatment records for more than 10,000 pediatric cancer patients from 17 hospitals. Because the cohort data are both large and distributed across multiple databases, we have deployed a cloud-based system to streamline data collection, monitoring, validation, and transfer to a high-performance computing (HPC) cluster. Data for ~7,500 patients have been collected to date in an industry-standard medical imaging format (DICOM), including radiation field parameters, planning computed tomography (CT) images with clinician-delineated anatomical structures, and dose distributions from the treatment planning system (TPS). Another critical component is the development of scalable methods for estimating individualized, organ-level radiation dose for epidemiological dose-response analyses. Planning CT scans typically cover only the treatment region, often omitting organs of interest for late effects research. To address this, we developed a method to extend partial-body CT images using a library of surrogate anatomies. In addition, due to variability and inconsistency in organ delineation and naming, we utilize a deep learning–based automatic segmentation tool to standardize organ delineation. Finally, we address limitations of TPS dose estimates by performing advanced Monte Carlo radiation transport simulations of both modalities. These simulations integrate patient data and detailed physics modeling and are efficiently executed on the NIH HPC cluster. This poster presents our efforts to implement a state-of-the-art dosimetry platform—from data collection to individualized dose calculations.
  1. First Name: Jungwook
    Last Name: Shin
    Degree(s): PhD
    Organization: Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD
  2. First Name: Matthew
    Middle Initial: M
    Last Name: Mille
    Degree(s): PhD
    Organization: Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD
  3. First Name: Caroline
    Last Name: Esposito
    Degree(s): BA
    Organization: Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD
  4. First Name: Todd
    Middle Initial: M
    Last Name: Gibson
    Degree(s): PhD
    Organization: Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD
  5. First Name: Keith
    Middle Initial: T
    Last Name: Griffin
    Degree(s): PhD
    Organization: Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD
  6. First Name: Jae Won
    Last Name: Jung
    Degree(s): PhD
    Organization: Department of Radiation Oncology, East Carolina University, Greenville, NC
  7. First Name: Choonik
    Last Name: Lee
    Degree(s): PhD
    Organization: Department of Radiation Oncology, University of Michigan, Ann Arbor, MI
  8. First Name: Sergio
    Last Name: Morató
    Degree(s): PhD
    Organization: Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD
  9. First Name: Torunn
    Middle Initial: I
    Last Name: Yock
    Degree(s): MD
    Organization: Massachusetts General Hospital and Harvard Medical School, Boston, MA
  10. First Name: Amy
    Last Name: Berrington de González
    Degree(s): DPhil
    Organization: Division of Genetics and Epidemiology, Institute for Cancer Research, London, UK
  11. First Name: Choonsik
    Last Name: Lee
    Degree(s): PhD
    Organization: Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD
  12. First Name: Cari
    Middle Initial: M
    Last Name: Kitahara
    Degree(s): PhD
    Organization: Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD
 Matthew M. Mille Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD matthew.mille@nih.gov
76 Wed, 08/26/2026 - 15:12 Anonymous 10.208.24.244 Characterizing the Children’s Oncology Group panel of neuroblastoma patient-derived xenografts for response to induction and salvage chemotherapy Tumor biopsies are often not obtained at the time of progressive disease (PD) in neuroblastoma (NB). Patient-derived xenografts (PDXs) established from bone marrow or blood of NB patients enable studies to define molecular mechanisms and approaches to overcome therapy resistance. Tumor, marrow, or peripheral blood samples from 40 high-risk patients, 19 at diagnosis (Dx) and 21 at PD (12 of 21 at post-mortem), were received via Children’s Oncology Group (COG) protocol ANBL00B1 and established as PDXs. PDXs were classified by the response to chemotherapy (Cyclo/Topo, cyclophosphamide + topotecan, 3 x 21-day cycles, or TMZ/IRI, relapse chemo, temozolomide + irinotecan, 2 x 21-day cycles) as non-responders (NR), stable disease (SD), partial responders (PR), and complete responders (CR). Engraftment rates were 17% for Dx and 24% for PD samples. Based on an algorithm we developed for PDX response evaluation, Cyclo/Topo responses were NR (n=11), SD (n=2), PR (n=9), and CR (n=18), and 9 of 11 showed CR to TMZ/IRN. Nine of 13 (69%) of the NR+SD PDXs were established from post-mortem PD (PD-PM) samples, while 12 of 18 (67%) of the CR group were from Dx samples. The response of PDXs to Cyclo/Topo was greater for pretherapy (Dx) PDXs than for PD-PM or PD PDXs; six of the 11 PD models showed significantly better EFS for TMZ/IRN relative to Cyclo/Topo (P<0.05, n=6 PDXs). This panel of 40 NB PDXs, characterized for their response to chemotherapy, will enable studies to define the molecular mechanisms of NB therapy resistance and is available at www.CCcells.org.
  1. First Name: Min
    Middle Initial: H
    Last Name: Kang
    Degree(s): PharmD
    Organization: Texas Tech University Health Sciences Center
  2. First Name: In-Hyoung
    Last Name: Yang
    Degree(s): PhD
    Organization: Texas Tech University Health Sciences Center
  3. First Name: Nighat
    Last Name: Noureen
    Degree(s): PhD
    Organization: Texas Tech University Health Sciences Center
  4. First Name: Kristyn
    Last Name: McCoy
    Degree(s): MS
    Organization: Texas Tech University Health Sciences Center
  5. First Name: Jonas
    Last Name: Nance
    Degree(s): BS
    Organization: Texas Tech University Health Sciences Center
  6. First Name: Diana
    Last Name: Ixlamati-Nava
    Degree(s): BS
    Organization: Texas Tech University Health Sciences Center
  7. First Name: Ashly
    Last Name: Hindle
    Degree(s): PhD
    Organization: Texas Tech University Health Sciences Center
  8. First Name: Meredith
    Middle Initial: S
    Last Name: Erwin
    Degree(s): MD
    Organization: Hospital for Sick Children, University of Toronto
  9. First Name: Michael
    Middle Initial: D
    Last Name: Hogarty
    Degree(s): MD
    Organization: Children's Hospital of Philadelphia and University of Pennsylvania
  10. First Name: Charles
    Middle Initial: P
    Last Name: Reynolds
    Degree(s): MD, PhD
    Organization: Texas Tech University Health Sciences Center
 Min H. Kang Texas Tech University Health Sciences Center min.kang@ttuhsc.edu
75 Wed, 08/26/2026 - 12:30 Anonymous 10.208.24.244 Leveraging the ExtractEHR+ Toolkit to Enhance Medication Data in the Children’s Brain Tumor Network and Childhood Cancer Data Initiative Background:
The National Cancer Institute (NCI) Childhood Cancer Data Initiative (CCDI) has enabled the distribution of previously unshared data. A critical component to maximize the utility of these data is to have longitudinal clinical data, such as treatment data. Manual abstraction has typically been used to capture treatment data, but this process is time consuming and subject to human error. This study aimed to use automated extraction and processing of electronic health record (EHR) data using the ExtractEHR+ Toolkit to describe chemotherapy exposures for children enrolled in the Children’s Brain Tumor Network (CBTN) who have contributed data to the CCDI.

Methods:
Patients enrolled in CBTN at two hospitals were included. ExtractEHR extracted all medication orders and administrations, including outpatient prescriptions, heights, and weights. Medication order and administration data were merged and MedCleanEHR centrally cleaned these data to identify unique chemotherapy exposures and dose amounts. Height and weight data were used to calculate body surface area and merged with medication data. For prescriptions where a discrete dose field was not included, regular expressions were used to identify doses in free text fields. MedCleanEHR calculated cumulative chemotherapy doses each patient received.

Results:
The cohort included 1628 patients. ExtractEHR successfully pulled 3,849,099 medication administrations and 839,736 medication orders. Once cleaned, there were 98,877 unique chemotherapy administrations and 8,781 chemotherapy prescriptions.

Conclusions:
The ExtractEHR+ Toolkit can efficiently ascertains chemotherapy exposures and dosing for patients in the CBTN cohort. These data will be transferred to the CCDI to enhance clinical data in the CCDI ecosystem.
  1. First Name: Tamara
    Middle Initial: P
    Last Name: Miller
    Degree(s): MD, MSCE
    Organization: Emory University/Children's Healthcare of Atlanta
  2. First Name: Gregory
    Last Name: Joseph
    Degree(s): MS
    Organization: Emory University
  3. First Name: Edward
    Last Name: Krause
    Degree(s): MS
    Organization: Children's Hospital of Philadelphia
  4. First Name: Yun Gun
    Last Name: Jo
    Organization: Children's Hospital of Philadelphia
  5. First Name: Judy
    Last Name: Lee
    Organization: Children's Healthcare of Atlanta
  6. First Name: Hannah
    Last Name: Calkins
    Degree(s): MSIS
    Organization: Children's Hospital of Philadelphia
  7. First Name: Adam
    Middle Initial: C
    Last Name: Resnick
    Degree(s): PhD
    Organization: Children's Hospital of Philadelphia
  8. First Name: Richard
    Last Name: Aplenc
    Degree(s): MD, PhD
    Organization: Children's Hospital of Philadelphia
  9. First Name: Allison
    Middle Initial: P
    Last Name: Heath
    Degree(s): PhD
    Organization: Children's Hospital of Philadelphia
 Tamara P. Miller, MD, MSCE Emory University/Children's Healthcare of Atlanta tamara.miller@emory.edu
74 Wed, 08/26/2026 - 00:12 Anonymous 10.208.28.32 Graph Artificial Intelligence for Pediatric Oncology (GAIPO): a graph AI platform for precision oncology using pediatric clinical and omics data Advances in artificial intelligence (AI) are shifting the paradigm in precision medicine for pediatric cancer, including biomarker identification, drug discovery, and survival analysis. The Childhood Cancer Data Initiative (CCDI) ecosystem provides essential clinicogenomic data for deep learning in pediatric cancer research. To enable the efficient use of the CCDI resource for AI model training and implementation, we developed a generic graph AI platform, Graph Artificial Intelligence for Pediatric Oncology (GAIPO), as well as the standards of data, models, and pipelines, to streamline the use of CCDI clinicogenomics data across various data modalities from bulk and single-cell omics data to clinical information in AI development. GAIPO provides a comprehensive workflow: (1) Data fetching through CCDI Data Federation Resource API and cBioPortal API for clinical metadata, multi-omics, and spatial transcriptomics data; (2) Data modeling through mapping and harmonizing the fetched clinical and genomics data according to the CCDI data model; (3) Graph construction functionalities through GraphML from NetworkX; (4) Graph AI implementation of existing models; and (5) Post-analysis, such as vital feature selection and survival analysis. We demonstrate the capabilities of our GAIPO in treating two pediatric cancers, glioma and Wilms tumor, with potential applicability to other pediatric cancers. We apply graph AI models, MOGONET and PCGS, to integrate multi-omics data using explainable graph convolutional networks, allowing patient classification and biomarker identification. Notably, the new PCGS model identifies background-specific key features for biomarker discovery, risk group identification, and survival analysis in glioma and Wilms tumor, with potential applicability to other pediatric cancers.
  1. First Name: Zanyu
    Last Name: Shi
    Degree(s): Ph.D.
    Organization: Indiana University Indianapolis
  2. First Name: Aishwarya
    Last Name: Budhkar
    Degree(s): M.S.
    Organization: Indiana University Bloomington
  3. First Name: Waqas
    Last Name: Amin
    Degree(s): Ph.D.
    Organization: Indiana University School of Medicine, IU Simon Comprehensive Cancer Center
  4. First Name: Karen
    Middle Initial: E.
    Last Name: Pollok
    Degree(s): Ph.D.
    Organization: Indiana University School of Medicine, IU Simon Comprehensive Cancer Center
  5. First Name: Yongqun
    Last Name: He
    Degree(s): Ph.D.
    Organization: University of Michigan Medical School
  6. First Name: Kun
    Last Name: Huang
    Degree(s): Ph.D.
    Organization: Indiana University School of Medicine, IU Simon Comprehensive Cancer Center
  7. First Name: Jing
    Last Name: Su
    Degree(s): Ph.D.
    Organization: Indiana University School of Medicine, IU Simon Comprehensive Cancer Center
 Jing Su Indiana University School of Medicine, IU Simon Comprehensive Cancer Center su1@iu.edu
73 Tue, 08/25/2026 - 13:02 Anonymous 10.208.24.128 Machine-Readable Metadata and AI-Assisted Discovery for the National Childhood Cancer Registry Data Platform The NCCR Data Platform provides linked, de-identified cancer data for patients ages 0-39 across 9 datasources including tumor registry (1.4M patients, 21 states), pharmacy claims (12.6M records), medical claims (116M records), clinical trials, radiation oncology, and socioeconomic measures. Researchers face barriers discovering available data before investing in IRB-approved access requests.

We developed a machine-readable metadata layer using semantic web standards, consisting of: dataset catalog records conforming to NLM's DATMM 6.0.0 with 9 standalone Dataset entries; a lightweight OWL ontology defining classes for variables, value sets, cohort filters, and portable cohort definitions; and an instance data layer containing 42,067 RDF triples representing 533 variables, 3,715 coded values with observed frequencies, and 51 cohort filter definitions linked to 14 biomedical vocabularies. We additionally developed a CLI tool for metadata-driven cohort discovery and a portable cohort definition format. All artifacts are open source.

The metadata enables data discovery without patient-level access. Researchers can query which variables exist, what values are permissible, and how many records each value contains. The cohort definition format supports reproducible, shareable patient selection criteria. Critically, the structured metadata enables AI-assisted discovery: when provided to a large language model, it allows grounded answers about data availability — including record counts, filter criteria, and cross-datasource relationships — without hallucination.

Publishing NCCR metadata as linked data addresses FAIR compliance, NIH CADR requirements, researcher education, and AI-powered discovery simultaneously. The approach generalizes to other Controlled Access Data Repositories and represents a novel pathway for making complex research platforms discoverable to a broader community.
  1. First Name: Radu
    Last Name: Robotin
    Degree(s): PhD
    Organization: NCI/DCCPS/SRP
  2. First Name: Johanna
    Last Name: Goderre Jones
 Radu Robotin NCI radu.robotin@nih.gov
72 Tue, 08/25/2026 - 09:28 Anonymous 10.208.24.128 GAIPO enables spatial omics on Childhood Cancer Data Initiative ecosystem for precision pediatric oncology The Childhood Cancer Data Initiative (CCDI) strives to accelerate pediatric cancer research by uniting clinical records with emerging spatial omics data but lacks a unified framework for joint analysis. Although CCDI and public repositories provide rich patient records and high-resolution spatial profiles, no unified data standards and framework exist to extract, harmonize, and process these multimodal datasets for interpretable discovery.

We developed the GAIPO (Graph Artificial Intelligence for Pediatric Oncology) clinicospatial data standard and platform, enabling graph artificial intelligence (AI) on the CCDI ecosystem. The GAIPO clinicospatial standard unifies spatial assay formats, spatial resolutions, and registration of spatial data across modalities. To promote interoperability and reproducibility, we define a spatial data model that encompasses data standards, formats, and interfaces to integrate imaging and omics data through spatial graph representation, thereby enabling the implementation of explainable graph AI models. The GAIPO platform provides: flexible interfaces to CCDI APIs and public portals for retrieval of clinical metadata, spatial omics data, and associated histopathology images; a preprocessing pipeline for omics and imaging data; a graph AI module that integrates multiple modalities for downstream analysis; and an interpretation module to identify important features and cells and reveal critical enrichment pathways and cell-cell interactions. We demonstrated GAIPO’s functionalities by using xSiGra as the explainable graph AI model, graph Grad-CAM as the interpretability module, ScPCA as the CCDI-participating resource, and Wilms tumor as the pediatric cancer.

The GAIPO standards and platform facilitate graph AI on clinicospatial data in precision oncology for pediatric cancers.
  1. First Name: Aishwarya
    Last Name: Budhkar
    Degree(s): M.S.
    Organization: Indiana University Bloomington
  2. First Name: Zanyu
    Last Name: Shi
    Degree(s): M.S.
    Organization: Indiana University Indianapolis
  3. First Name: Waqas
    Last Name: Amin
    Degree(s): M.D.
    Organization: Indiana University School of Medicine
  4. First Name: Karen
    Middle Initial: E.
    Last Name: Pollok
    Degree(s): Ph.D.
    Organization: Indiana University School of Medicine, IU Simon Comprehensive Cancer Center
  5. First Name: Xuhong
    Last Name: Zhang
    Degree(s): Ph.D.
    Organization: Indiana University Bloomington
  6. First Name: Kun
    Last Name: Huang
    Degree(s): Ph.D.
    Organization: Indiana University School of Medicine, IU Simon Comprehensive Cancer Center
  7. First Name: Jing
    Last Name: Su
    Degree(s): Ph.D.
    Organization: Indiana University School of Medicine, IU Simon Comprehensive Cancer Center
 Aishwarya Budhkar Indiana University Bloomington abudhkar@iu.edu
71 Tue, 08/25/2026 - 00:42 Anonymous 10.208.28.32 Next-generation models to advance pediatric solid cancer treatments. 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.
  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
Claudia Katharina Petritsch Stanford University cpetri@stanford.edu
70 Wed, 08/19/2026 - 18:04 Anonymous 10.208.24.128 Pediatric Cancer Registries and Data Systems in Sub-Saharan Africa: A Scoping Review of Coverage, Interoperability, and Data-Sharing Readiness. Background
Pediatric cancer registries are essential for surveillance, outcomes research, health-system planning, and participation in collaborative data ecosystems.However, readiness of existing systems in sub-Saharan Africa (SSA) for sustainable cross-institutional data sharing remains unclear.

Aim

To map pediatric cancer registries and data systems in SSA and assess their coverage,data elements and standardization,interoperability,governance,and readiness for sustainable cross-institutional data sharing.

Methods
Following PRISMA-ScR principles, bibliographic and supplementary sources were systematically searched through August 2026 for primary studies describing patient-level pediatric cancer registries, databases, or registry-development initiatives in SSA. Eighteen eligible publications were charted across predefined domains: coverage/case ascertainment, data elements/standardization, interoperability, governance/data access, institutional capacity, and sustainability.Interoperability was assessed across semantic, technical, organizational, and patient-linkage dimensions.

Results

Population- and hospital-based registries, multicountry networks, and prospective digital databases were identified. ICCC-3, ICD-O-3, and Toronto staging demonstrated semantic standardization, including multicountry implementation (Liu et al., 2023; Mallon et al., 2023). Selected systems supported multicentre data aggregation, staging, longitudinal follow-up, survival, and research-ready clinical and socioeconomic data (Mallon et al., 2022; Parkin et al., 2021; Davidson et al., 2022). However, population coverage was uneven, while patient-level linkage and technical interoperability across clinical, pathology, registry, and national information systems were limited or insufficiently reported. Governance, workforce capacity, and sustainable financing were heterogeneous.

Conclusion

SSA has important foundations for a shareable pediatric cancer data ecosystem, but readiness remains uneven. Priorities include common data elements, patient-level linkage, technical interoperability, longitudinal outcome capture, and governed cross-institutional sharing to strengthen the contribution of SSA data to collaborative ecosystems such as CCDI.
  1. First Name: Korede
    Middle Initial: T
    Last Name: Akindele
    Degree(s): MSc
    Organization: The Dorcas Cancer Foundation
  2. First Name: Adedayo
    Last Name: Joseph
    Organization: Medserve Luth Cancer Center
  3. First Name: Fagbemide
    Last Name: Oluwafunmilayo
    Organization: Lagos University Teaching Hospital
  4. First Name: Atolagbe
    Last Name: Oluwakemi
    Organization: The Dorcas Cancer Foundation
  5. First Name: Adeseye
    Last Name: Akinsete
    Organization: Nigeria Society of Pediatric Oncology
 Korede Akindele The Dorcas Cancer Foundation araireakindele@gmail.com
69 Wed, 08/19/2026 - 13:40 Anonymous 10.208.28.32 Expanding In Vivo Genotyping Across the Clinical Spectrum of Retinoblastoma: Aqueous Humor Liquid Biopsy Enables Comprehensive Biallelic RB1 Characterization Without Tumor Tissue We previously established aqueous humor (AH) as a tumor-proximal liquid biopsy provides access to tumor-derived molecular information in retinoblastoma, a cancer in which diagnostic tumor biopsy is contraindicated. The critical next question was whether this signal could reconstruct the genotype of the living tumor—and whether molecular detectability persists when tumors are small and eye-sparing.

We analyzed diagnostic AH from 65 eyes of 53 patients using LBSeq4Kids, a clinically validated assay optimized for AH specimens. RB1 sequence variants, deletions, promoter alterations, and loss of heterozygosity were assessed, with germline RB1 status determined by blood testing when available. At least one RB1 alteration was identified in 57 of 65 eyes (87.7%), while complete biallelic molecular characterization was achieved in 53 eyes (81.5%). Importantly, molecular detectability was preserved across disease stage: complete characterization was 81.8% in B/C eyes and 81.1% in D/E eyes. At the individual alteration level, B/C eyes achieved 90.0% detection, comparable to published tumor-tissue cohorts (83.1–94.8%). AH captured the clinically relevant spectrum of RB1 alterations, including sequence variants, promoter alterations, deletions, and loss of heterozygosity.

These findings advance AH liquid biopsy from detecting tumor-derived molecular signal to reconstructing the genotype of the living tumor. The preserved detectability in small, eye-sparing tumors indicates that AH can provide molecular access even when tumor tissue is unavailable. Because B–E disease represents the vast majority of eyes in large international clinical cohorts, this approach has the potential to expand molecular genotyping beyond tissue-accessible patients and enable multicenter studies linking RB1 genotype with clinical phenotype.
  1. First Name: Liya
    Last Name: Xu
    Degree(s): PhD
    Organization: Children's Hospital of Los Angeles
  2. First Name: Scarlet
    Last Name: Affa
    Degree(s): BS
    Organization: Children's Hospital of Los Angeles
  3. First Name: Nerea
    Last Name: Goni
    Degree(s): Master
    Organization: University of Southern California
  4. First Name: Laura
    Last Name: Kagami
    Degree(s): MD
    Organization: Children's Hospital of Los Angeles
  5. First Name: Jaclyn
    Last Name: Biegel
    Degree(s): PhD
    Organization: Children's Hospital of Los Angeles
  6. First Name: Jesse
    Last Name: Berry
    Degree(s): MD
    Organization: Children's Hospital of Los Angeles
 Liya Xu Children's Hospital Los Angeles liyaxu@usc.edu
68 Tue, 08/18/2026 - 20:01 Anonymous 10.208.28.32 Hybrid Semantic–Lexical Retrieval for Source-to-CDE Mapping in the NCI caDSR Accurate mapping of source data elements to standardized common data elements (CDEs) is essential for biomedical data integration, but short, abbreviated, and heterogeneous source descriptions make both lexical and semantic matching unreliable. We constructed a benchmark of approximately 69,000 expert-linked source-to-CDE mappings from the National Cancer Institute’s caDSR and used it to evaluate bi-encoders, text representations, and two-stage fine-tuning. We then developed a hybrid system that combines semantic retrieval, CDE Match-inspired keyword retrieval, cross-encoder scoring, and supervised reranking. Across an internal test set and five distribution-shifted holdouts, the system achieved Recall@5 of 0.971 internally and 0.802–0.972 externally. It achieved the highest Recall@5 among evaluated methods on five of six datasets; on GDC, performance was near ceiling at 70/72, within one query of the official NCI CDE Match service and its Python approximation at 71/72.
  1. First Name: James
    Middle Initial: H
    Last Name: Tanis
    Degree(s): Ph.D.
    Organization: NCI
  2. First Name: Denise
    Middle Initial: B
    Last Name: Warzel
    Degree(s): M.Sc.
    Organization: NCI
  3. First Name: Rakesh
    Last Name: Khanna
    Degree(s): B.S.
    Organization: NCI
  4. First Name: Qingrong
    Last Name: Chen
    Degree(s): Ph.D.
    Organization: NCI
  5. First Name: Chunhua
    Last Name: Yan
    Degree(s): Ph.D.
    Organization: NCI
  6. First Name: Ravi
    Last Name: Valleleth
    Degree(s): B.S.
    Organization: NCI
  7. First Name: Matthew
    Middle Initial: T
    Last Name: Nash
    Degree(s): B.S.
    Organization: NCI
  8. First Name: Deepali
    Last Name: Manathara
    Degree(s): B.S.
    Organization: NCI
  9. First Name: Bradley
    Middle Initial: B
    Last Name: Biggers
    Degree(s): B.S.
    Organization: NCI
  10. First Name: Joyce
    Middle Initial: B
    Last Name: Soares
    Degree(s): M.Sc.
    Organization: NCI
  11. First Name: Daoud
    Last Name: Meerzaman
    Degree(s): Ph.D.
    Organization: NCI
 James H Tanis NCI james.tanis@nih.gov