Childhood Cancer Data Initiative Annual Symposium (Abstract Registration)

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# Created User IP address Abstract Title: Abstract: Abstract: Authors: Presenting Author: Institution: Email Address: Operations
65 Thu, 09/04/2025 - 16:55 chungs6 10.208.28.132 Genetic Data Modeling for the Childhood Cancer Clinical Data Commons (C3DC) 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.
  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
 Brian Furner University of Chicago bfurner@bsd.uchicago.edu
64 Tue, 09/02/2025 - 14:06 chungs6 10.208.24.230 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 at Children’s Hospital Los Angeles (CHLA) to the CCDI (dbGaP Study Accession: phs002518). We are now augmenting this dataset with new types of data, and to develop tools for integrating the diverse datasets in CCDI to improve diagnosis and treatment of all children with cancer. To date, almost 700 CNS and non-CNS solid tumors in our cohort have been identified, screened, and key whole slide images (WSI) selected. We developed a computational pipeline to access and organize metadata for each WSI, and to quantitatively assess tumor morphology of 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. Tools to leverage these multimodal data to improve diagnosis and characterization of the tumors is an active area of investigation. We have trained a CNS tumor classifier applicable with Methylseq samples. 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): PhD
    Organization: Department of Pathology and Laboratory Medicine, Children’s Hospital Los Angeles, Los Angeles, CA
  2. First Name: Jennifer
    Last Name: Cotter
    Degree(s): MD
    Organization: Department of Pathology and Laboratory Medicine, Children’s Hospital Los Angeles; Department of Pathology, Keck School of Medicine, University of Southern California; Los Angeles, CA
  3. First Name: David
    Middle Initial: N.
    Last Name: Buckley
    Degree(s): MS, PhD
    Organization: Department of Pathology and Laboratory Medicine, Children’s Hospital Los Angeles, Los Angeles, CA
  4. First Name: Shengmei
    Last Name: Zhou
    Degree(s): MD
    Organization: Department of Pathology and Laboratory Medicine, Children’s Hospital Los Angeles; Department of Pathology, Keck School of Medicine, University of Southern California; Los Angeles, CA
  5. First Name: William
    Last Name: Mango
    Organization: Department of Pathology and Laboratory Medicine, Children’s Hospital Los Angeles, Los Angeles, CA
  6. First Name: Bruce
    Last Name: Pawel
    Degree(s): MD
    Organization: Department of Pathology and Laboratory Medicine, Children’s Hospital Los Angeles; Department of Pathology, Keck School of Medicine, University of Southern California; Los Angeles, CA
  7. First Name: Fariba
    Last Name: Navid
    Organization: Cancer and Blood Disease Institute, Children’s Hospital Los Angeles; Department of Pediatrics, Keck School of Medicine, University of Southern California; Los Angeles, CA
  8. First Name: Jaclyn
    Last Name: Biegel
    Degree(s): PhD, FACMG
    Organization: Department of Pathology and Laboratory Medicine, Children’s Hospital Los Angeles; Department of Pathology, Keck School of Medicine, University of Southern California; Los Angeles, CA
  9. First Name: James
    Middle Initial: F.
    Last Name: Amatruda
    Degree(s): MD, PhD
    Organization: Cancer and Blood Disease Institute, Children’s Hospital Los Angeles; Departments of Pediatrics and Medicine, Keck School of Medicine, University of Southern California; Los Angeles, CA
 Alexander L Markowitz, PhD Department of Pathology and Laboratory Medicine, Children’s Hospital Los Angeles, Los Angeles, CA amarkowitz@chla.usc.edu
63 Tue, 09/02/2025 - 10:41 chungs6 10.208.28.30 Integrative analysis of the rare endocrine cancers using multi-omics data Endocrine cancers, including thyroid, adrenal, pituitary, and endocrine pancreatic cancers, are rare and understudied due to limited biospecimens and genomic data. We employed multi-omics approaches—RNA sequencing, whole-exome sequencing, and whole-genome sequencing—to elucidate molecular signatures and regulatory pathways in these cancers. We identified genes specific to various endocrine cancers, such as GNRHR, NPTX2, TAAR1, SLC6A5, and OPRM1. Co-expression network analysis revealed distinct neuronal pathways associated with cancer progression. Integrating protein-protein interaction and multi-omic aberrant gene interaction networks, we discovered pathways involving noradrenaline biosynthesis in pheochromocytomas, calcitonin regulation in medullary thyroid carcinoma, cholesterol biosynthesis in adrenocortical carcinoma, and the BRCA1-associated genome surveillance complex in papillary thyroid carcinoma. Our comprehensive multi-omic analysis offers novel insights into candidate therapeutic targets for these rare endocrine cancers.
  1. First Name: SV
    Last Name: Daulatabad
  2. First Name: A
    Last Name: Jain
  3. First Name: D
    Last Name: Nousome
  4. First Name: M
    Last Name: Tandon
  5. First Name: D
    Last Name: Varghese
  6. First Name: K
    Last Name: Pendo
  7. First Name: MFW
    Last Name: Malone
  8. First Name: J
    Last Name: del Rivero
  9. First Name: BC
    Last Name: Widemann
  10. First Name: KM
    Last Name: Reilly
 Vidhur Daulatabad National Institutes of Health/National Cancer Institute vidhur.daulatabad@nih.gov
62 Mon, 09/01/2025 - 22:56 Anonymous 10.208.24.230 Digital Early Diagosis for Pediatric Brain Tumors: Sedation-Minimizing, Data-Driven Pathway More than the tumor itself, the delayed diagnosis of pediatric brain tumor remains a global challenge. Through professional and public advice, the UK's HeadSmart awareness program could successfully reduce the median of total diagnostic interval (TDI) from 14.4 weeks in 2006 to 6.7 weeks in 2013. There are few low- and middle-income areas that still use similar structured techniques. But existing delays of low-grade tumors of 6-7 weeks highlights clinical and system-level issues. To improve the early-diagnosis route for pediatric brain tumors, we aim to design a system that combines age-stratified triage, sedation-minimizing imaging, mobile decision assistance, and real-world data collection. The system first defines red flag symptom threshold and imaging flow. This is further validated by the Parent Companion app for symptom tracking, and triage guidance. This significantly reduces TDI through better patient-system tracking, and promotes widespread adoption of digital tools to reduce manual burden of clinician’s automating tasks. The accuracy will be verified via vignette-based assessments and real-world discrepancy testing against standard head circumference measurements. This effort uses big data integration and digital health assessment to regulate and transform pediatric neuro-oncology diagnoses. This system aims to achieve data-driven early detection and scalable global childhood cancer therapy.
  1. First Name: Rishika
    Last Name: Sharma
    Degree(s): MSc Computer Science
    Organization: Open Health Systems Laboratory (OHSL)
  2. First Name: Dimple
    Last Name: Khona
    Degree(s): MD, MBA, FAAP
    Organization: Kaiser Permanente Orange County Anaheim Medical Center
 Rishika Sharma Open Health Systems Laboratory (OHSL) rishika.sharma@ohsl.us
61 Mon, 09/01/2025 - 21:52 Anonymous 10.208.24.230 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 some 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 to the late effects of radiation compared to adults. 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 discuss the status of ongoing clinical and radiotherapy data collection and describe the current 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
    Degree(s): MS
    Organization: Massachusetts General Hospital
  3. First Name: Benjamin
    Middle Initial: VM
    Last Name: Bajaj
    Degree(s): MPH
    Organization: Massachusetts General Hospital
  4. First Name: Jungwook
    Last Name: Shin
    Degree(s): PhD
    Organization: National Cancer Institute
  5. First Name: Matthew
    Middle Initial: M
    Last Name: Mille
    Degree(s): PhD
    Organization: National Cancer Institute
  6. First Name: Keith
    Middle Initial: T
    Last Name: Griffin
    Degree(s): PhD
    Organization: National Cancer Institute
  7. First Name: Sergio
    Last Name: Morato
    Degree(s): PhD
    Organization: National Cancer Institute
  8. First Name: Isabelle
    Last Name: van der Velpen
    Degree(s): MD, 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
    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
 Cari Kitahara National Cancer Institute kitaharac@mail.nih.gov
60 Mon, 09/01/2025 - 21:13 Anonymous 10.208.28.30 Generalizable Pediatric Sarcoma Histopathology Classification with Multi-Institutional Machine Learning Intro: Digitization of histopathology slides has allowed for the use of computational machine learning and artificial intelligence (AI)–based approaches to aid in diagnostics. These tools could be especially helpful for classifying pediatric sarcoma subtypes, which are rare and heterogeneous, and whose diagnoses often require costly genetic and molecular testing that may not be available to every patient. These machine learning models offer great promise but come with the caveat of being prone to overfitting to an individual institution’s microscope, scanner, and staining protocol, which can affect performance in real-world clinical settings where instruments and protocols differ across hospitals. Therefore, it can be difficult to make these models generalizable for use globally if they are not trained on a large and diverse dataset.

Methods: We have curated over 700 H&E images from four institutions spanning over 10 different sarcoma subtypes. We utilize an in-house, open-source pipeline for stain normalization, focus checking, and cropping to harmonize the images. AI models are then used to extract features from these images, which can be used for downstream SAMPLER-based machine learning.

Results: We achieve state-of-the-art results in classifying images as rhabdomyosarcoma vs non-rhabdomyosarcoma soft tissue sarcomas (AUC 0.969 ± 0.026), alveolar vs embryonal rhabdomyosarcoma (AUC 0.961 ± 0.021), and Ewing sarcoma (AUC 0.929). Importantly, our models generalize well when tested on data from previously unseen institutions, outperforming similar methods.

Conclusion: Our pipeline is well suited for additional collaboration and could be a tool to help bridge access to clinical resources globally.
  1. First Name: Adam
    Middle Initial: H.
    Last Name: Thiesen
    Organization: The Jackson Laboratory, UConn School of Medicine
  2. First Name: Sergii
    Last Name: Domanskyi
    Degree(s): Ph.D.
    Organization: The Jackson Laboratory
  3. First Name: Ali
    Last Name: Foroughi pour
    Degree(s): Ph.D.
    Organization: St. Jude Children's Research Hospital
  4. First Name: Jingyan
    Last Name: Zhang
    Organization: Johns Hopkins University
  5. First Name: Todd
    Middle Initial: B.
    Last Name: Sheridan
    Degree(s): M.D.
    Organization: Hartford Healthcare
  6. First Name: Steven
    Middle Initial: B.
    Last Name: Neuhauser
    Organization: The Jackson Laboratory
  7. First Name: Alyssa
    Middle Initial: E.
    Last Name: Stetson
    Degree(s): M.D. MPH
    Organization: Massachusetts General Hospital Department of Surgery
  8. First Name: Katelyn
    Last Name: Dannheim
    Degree(s): M.D.
    Organization: Massachusetts General Hospital Department of Pathology
  9. First Name: Danielle
    Middle Initial: B.
    Last Name: Cameron
    Degree(s): M.D. MPH
    Organization: Massachusetts General Hospital
  10. First Name: Shawn
    Last Name: Anh
    Degree(s): M.D. Ph.D.
    Organization: University of Pennsylvania Department of Surgery
  11. First Name: Hao
    Last Name: Wu
    Degree(s): M.D. Ph.D.
    Organization: Yale School of Medicine Department of Pathology
  12. First Name: Emily
    Middle Initial: R.
    Last Name: Christison-Lagay
    Degree(s): M.D.
    Organization: Yale School of Medicine Department of Surgery
  13. First Name: Carol
    Middle Initial: J.
    Last Name: Bult
    Degree(s): Ph.D.
    Organization: The Jackson Laboratory
  14. First Name: Jeffrey
    Middle Initial: H.
    Last Name: Chuang
    Degree(s): Ph.D.
    Organization: The Jackson Laboratory, UConn School of Medicine
  15. First Name: Jill
    Middle Initial: C.
    Last Name: Rubinstein
    Degree(s): M.D. Ph.D.
    Organization: The Jackson Laboratory, Hartford Healthcare, UConn School of Medicine
 Adam Thiesen The Jackson Laboratory adam.thiesen@jax.org
59 Mon, 09/01/2025 - 20:30 Anonymous 10.208.28.30 Pediatric Extension of OncoTree: An Open-Source, Cancer Classification System OncoTree is an open source, publicly accessible, community driven cancer classification system developed by Memorial Sloan Kettering Cancer Center and maintained by a cross-institutional committee of oncologists, pathologists, scientists, and engineers (OncoTree committee). Accessible through an intuitive web interface and API, OncoTree provides a structured hierarchical framework that standardizes cancer types, thereby supporting accurate diagnosis, therapeutic decision making, and data harmonization. Since its inception, OncoTree has grown to include over 900 tumor types, mapped to external terminologies such as NCI Thesaurus and UMLS, and is updated to align with standards like the World Health Organization (WHO) classification of tumors. All updates are publicly released with versioning, mapping tools, and detailed documentation to ensure transparency and reproducibility.

Initially focused on adult malignancies, OncoTree is now undergoing concentrated efforts to expand and refine its pediatric classification nodes. Pediatric cancers are biologically and clinically distinct from adult tumors, presenting unique challenges for classification. The pediatric extension of OncoTree aims to comprehensively represent childhood malignancies by incorporating WHO pediatric classifications, capturing rare and developmentally driven tumor entities, and restructuring ontology branches to better reflect pediatric tumor biology. This effort includes mapping diagnoses across ontology versions and external terminologies, identifying coverage gaps, and implementing systematic revisions under the governance of the OncoTree committee.

With clear documentation with release notes, source references, and support tools, this pediatric expansion will deliver a well-organized, transparent framework that improves the consistency of tumor classification, facilitates cross-study analyses, and strengthens the evidence base for clinical decision-making in childhood cancer care.
  1. First Name: Ritika
    Last Name: Kundra
    Degree(s): M.S.
    Organization: MSK
  2. First Name: Rima
    Last Name: AlHamad
    Degree(s): M.S.
    Organization: MSK
  3. First Name: Robert
    Last Name: Sheridan
    Degree(s): M.S.
    Organization: MSK
  4. First Name: Manda
    Last Name: Wilson
    Degree(s): M.S.
    Organization: MSK
  5. First Name: Avery
    Last Name: Wang
    Degree(s): M.S.
    Organization: MSK
  6. First Name: Benjamin
    Last Name: Preiser
    Degree(s): B.S.
    Organization: MSK
  7. First Name: Benjamin
    Last Name: Gross
    Degree(s): M.S.
    Organization: MSK
  8. First Name: Kelly
    Last Name: Cavender
    Degree(s): B.S.
    Organization: MSK
  9. First Name: Sarah
    Middle Initial: P
    Last Name: Suehnholz
    Degree(s): Ph.D.
    Organization: MSK
  10. First Name: Ramyasree
    Last Name: Madupuri
    Degree(s): M.S.
    Organization: MSK
  11. First Name: Baby
    Middle Initial: A
    Last Name: Satravada
    Degree(s): M.S.
    Organization: MSK
  12. First Name: Neerav
    Middle Initial: N
    Last Name: Shukla
    Degree(s): M.D.
    Organization: MSK
  13. First Name: Stephanie
    Last Name: Suser
    Degree(s): B.A.
    Organization: MSK
  14. First Name: Mark
    Last Name: Ewalt
    Degree(s): M.D.
    Organization: MSK
  15. First Name: Tejus
    Last Name: Bale
    Degree(s): M.D., Ph.D.
    Organization: MSK
  16. First Name: Maria
    Middle Initial: E
    Last Name: Arcila
    Degree(s): M.D.
    Organization: MSK
  17. First Name: Aijaz
    Last Name: Syed
    Degree(s): M.S.
    Organization: MSK
  18. First Name: Mihir
    Last Name: Kavatkar
    Degree(s): M.S.
    Organization: MSK
  19. First Name: Tali
    Last Name: Mazor
    Degree(s): Ph.D.
    Organization: DFCI
  20. First Name: Ethan
    Last Name: Cerami
    Degree(s): Ph.D.
    Organization: DFCI
  21. First Name: Justin
    Last Name: Jee
    Degree(s): M.D., Ph.D.
    Organization: MSK
  22. First Name: Christopher
    Last Name: Fong
    Degree(s): Ph.D.
    Organization: MSK
  23. First Name: Michele
    Last Name: Waters
    Degree(s): Ph.D.
    Organization: MSK
  24. First Name: Michael
    Middle Initial: F
    Last Name: Berger
    Degree(s): Ph.D.
    Organization: MSK
  25. First Name: Debyani
    Last Name: Chakravarty
    Degree(s): Ph.D.
    Organization: MSK
  26. First Name: Ingo
    Middle Initial: K
    Last Name: Mellinghoff
    Degree(s): M.D., F.A.C.P.
    Organization: MSK
  27. First Name: David
    Middle Initial: B
    Last Name: Solit
    Degree(s): M.D.
    Organization: MSK
  28. First Name: JianJiong
    Last Name: Gao
    Degree(s): Ph.D.
    Organization: MSK
  29. First Name: Nikolaus
    Last Name: Schultz
    Degree(s): Ph.D.
    Organization: MSK
 Ritika Kundra Memorial Sloan Kettering kundrar@mskcc.org
58 Mon, 09/01/2025 - 15:57 Anonymous 10.208.24.230 Next-generation cancer models for pediatric solid cancer. 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.
  1. First Name: Emon
    Last Name: Nasajpour
    Organization: Stanford University
  2. First Name: Dena
    Last Name: Panovska
    Organization: Stanford University
  3. First Name: Ruolun
    Last Name: Wei
    Organization: Stanford University
  4. First Name: Conrado
    Last Name: Soria
    Organization: Frederick National Laboratory for Cancer Research
  5. First Name: Eva
    Last Name: Tonsing-Carter
    Organization: National Cancer Institute
  6. First Name: Julyann
    Last Name: Perez-Mayoral
    Organization: National Cancer Institute
  7. First Name: Louis
    Last Name: Staudt
    Organization: National Cancer Institute
  8. First Name: Calvin
    Last Name: Kuo
    Organization: Stanford University
  9. First Name: Daniela
    Last Name: Gerhard
    Organization: National Cancer Institute
  10. First Name: Melissa
    Last Name: Porter
    Organization: National Cancer Institute
  11. First Name: Rachana
    Last Name: Agarwal
    Organization: Frederick National Laboratory for Cancer Research
  12. First Name: Claudia
    Middle Initial: K
    Last Name: Petritsch
    Organization: Stanford University
 Claudia K Petritsch Stanford University cpetri@stanford.edu
56 Mon, 09/01/2025 - 14:19 Anonymous 10.208.24.230 Transforming Childhood Cancer through Tumor Vessel-Targeting Therapy. Angiogenesis is a fundamental process driving tumor progression, metastasis, and therapeutic resistance in both adult and pediatric cancers. In childhood malignancies, dysregulated vascular growth not only supports tumor expansion but also contributes to the formation of an immunosuppressive tumor microenvironment. Targeting angiogenesis through inhibition of vascular endothelial growth factor (VEGF) signaling and vessel normalization has emerged as a promising therapeutic strategy. However, clinical translation in pediatric oncology has been limited by insufficient drug accumulation, systemic toxicities, and heterogeneous patient responses. Advances in nanotechnology now enable the design of precision nanoformulations that selectively modulate abnormal vasculature while minimizing adverse effects, thereby offering new opportunities for vessel-targeting therapies. Within the framework of the Childhood Cancer Data Initiative (CCDI), the integration of multi-omic datasets, imaging biomarkers, and therapeutic response profiles can provide critical insights into the molecular determinants of angiogenesis in childhood cancers. Leveraging these data-driven approaches has the potential to refine predictive biomarkers, guide patient stratification, and accelerate the development of effective vessel-targeted therapeutics tailored to pediatric oncology. Ultimately, the convergence of molecular biology, nanomedicine, and large-scale data sharing through CCDI represents a transformative pathway for advancing childhood cancer research and improving clinical outcomes.
Keywords:
Childhood cancer, angiogenesis, tumor vessel targeting, nanomedicine, CCDI, pediatric oncology.
  1. First Name: Priya Rithika
    Last Name: Vella
    Degree(s): BSc Hons Life Sciences (Biotechnology, Microbiology, Biochemistry)
    Organization: Malla reddy university
 Priya Rithika Vella Malla Reddy University. priyarithikavella@gmail.com
55 Sun, 08/31/2025 - 14:07 Anonymous 10.208.28.30 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, model, and pipeline, to streamline the use of CCDI clinicogenomics data of 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): M.S.
    Organization: Department of Biostatistics & Health Data Science, Indiana University Fairbanks School of Public Health
  2. First Name: Aishwarya
    Last Name: Budhkar
    Degree(s): M.S.
    Organization: Department of Computer Science, Indiana University Luddy School of Informatics, Computing, and Engineering
  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: Jing
    Last Name: Su
    Degree(s): Ph.D.
    Organization: Indiana University School of Medicine, IU Simon Comprehensive Cancer Center
  6. First Name: Kun
    Last Name: Huang
    Degree(s): Ph.D.
    Organization: Indiana University School of Medicine, IU Simon Comprehensive Cancer Center
 Zanyu Shi Indiana University Indianapolis zanyshi@iu.edu
54 Sun, 08/31/2025 - 03:56 Anonymous 10.208.24.230 GAIPO introduces explainable graph AI for clinicospatial data in precision oncology of pediatric cancer 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: Karen
    Middle Initial: E.
    Last Name: Pollok
    Degree(s): Ph.D.
    Organization: Indiana University School of Medicine, IU Simon Comprehensive Cancer Center
  4. First Name: Kun
    Last Name: Huang
    Degree(s): Ph.D.
    Organization: Indiana University School of Medicine, IU Simon Comprehensive Cancer Center
  5. 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
53 Fri, 08/29/2025 - 17:50 Anonymous 10.208.28.51 Neuroblastoma Patient-Derived Xenografts and Cell Lines from Postmortem Blood as Models to Understand and Reverse Therapy Resistance Patient-derived models of neuroblastoma are essential for defining resistance mechanisms and for preclinical studies seeking to reverse drug resistance. We have established and characterized patient derived xenografts (PDXs) and patient derived cell lines (PDCLs) from neuroblastoma patients with progressive disease from samples obtained post-mortem (PD-PM).Tumor and blood samples were cultured and/or subcutaneously xenografted into NOD SCIDγ mice. PDCLs and PDXs were validated by STR profiling and confirmed to be free of Epstein–Barr virus and mycoplasma. Whole exome sequencing identified mutations; telomere maintenance mechanisms were assessed using TERT qPCR, C-circle assay, and TERT break-apart FISH. Therapeutic responses were evaluated in subcutaneous xenografts. PD-PM specimens showed higher xenograft engraftment rates (68%; 21 of 31 specimens, 83% for PD-PM blood specimens) and higher PDCL take rates (54%, 25 of 46 specimens) compared to diagnosis (Dx, 17%) or progressive disease (PD, 11%, P<0.001) specimens. PD-PM PDXs exhibited higher mutation burdens than Dx PDXs (P=0.026), with 33% harboring canonical activating ALK mutations and another 33% having mutations in other RAS-MAPK signaling genes. Among 20 high TERT-expressing PD-PM PDXs, 13 had MYCN amplification; 4 MYCN non-amplified PDXs exhibited TERT rearrangements. One PD-PM PDX was alternative lengthening of telomeres positive. Relative to one Dx and one PD PDXs, duration of response to temozolomide + irinotecan was low in 3 PD-PM PDXs (P<0.001). Thus, PD-PM PDXs activate ALK or RAS-MAPK signaling, manifest high levels of therapy resistance, and provide models to study reversing drug resistance. These PDXs are freely available from the COG/ALSF Childhood Cancer Repository (https://cccells.org).
  1. First Name: Balakrishna
    Last Name: Koneru
    Degree(s): PhD
    Organization: Texas Tech University Health Sciences Center School of Medicine Lubbock, TX, USA
  2. First Name: Nighat
    Last Name: Noureen
    Degree(s): PhD
    Organization: Texas Tech University Health Sciences Center School of Medicine Lubbock, TX, USA
  3. First Name: Ashly
    Last Name: Hindle
    Degree(s): PhD
    Organization: Texas Tech University Health Sciences Center School of Medicine Lubbock, TX, USA
  4. First Name: Kristyn
    Last Name: McCoy
    Degree(s): MS
    Organization: Texas Tech University Health Sciences Center School of Medicine Lubbock, TX, USA
  5. First Name: Jonas
    Last Name: Nance
    Degree(s): BS
    Organization: Texas Tech University Health Sciences Center School of Medicine Lubbock, TX, USA
  6. First Name: Diana
    Last Name: Ixlamati-Nava
    Degree(s): BS
    Organization: Texas Tech University Health Sciences Center School of Medicine Lubbock, TX, USA
  7. First Name: Mohamad
    Last Name: Al-Rahwan
    Degree(s): MD
    Organization: Texas Tech University Health Sciences Center School of Medicine Lubbock, TX, USA
  8. First Name: Yael
    Middle Initial: P
    Last Name: Mosse
    Degree(s): MD
    Organization: Division of Oncology and Center for Childhood Cancer Research; Children’s Hospital of Philadelphia, Philadelphia, PA USA
  9. First Name: John
    Middle Initial: M
    Last Name: Maris
    Degree(s): MD
    Organization: Division of Oncology and Center for Childhood Cancer Research; Children’s Hospital of Philadelphia, Philadelphia, PA USA
  10. First Name: Shahab
    Last Name: Asgharzadeh
    Degree(s): MD
    Organization: Children's Hospital of Los Angeles, Los Angeles, CA, USA
  11. First Name: Araz
    Last Name: Marachelian
    Degree(s): MD
    Organization: Children's Hospital of Los Angeles, Los Angeles, CA, USA
  12. First Name: Meredith
    Middle Initial: S
    Last Name: Irwin
    Degree(s): MD
    Organization: Department of Pediatrics, Hospital for Sick Children and University of Toronto, Toronto, Canada
  13. First Name: Suzanne
    Last Name: Shusterman
    Degree(s): MD
    Organization: Dana-Farber/Harvard Cancer Center, Boston, MA, USA
  14. First Name: Stephen
    Middle Initial: S
    Last Name: Roberts
    Degree(s): MD PhD
    Organization: Oregon Health and Science University, Portland, OR USA
  15. First Name: Min
    Middle Initial: H
    Last Name: Kang
    Degree(s): PharmD
    Organization: Texas Tech University Health Sciences Center School of Medicine Lubbock, TX, USA
  16. First Name: C Patrick
    Last Name: Reynolds
    Degree(s): MD PhD
    Organization: Texas Tech University Health Sciences Center School of Medicine Lubbock, TX, USA
 C Patrick Reynolds Texas Tech University Health Sciences Center School of Medicine, Lubbock, TX, USA patrick.reynolds@ttuhsc.edu
52 Fri, 08/29/2025 - 17:37 Anonymous 10.208.28.51 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 560/85 neuroblastomas, 16/20 leukemias, 7/5 lymphomas, 20/3 Ewing sarcomas, 13/3 soft tissue sarcomas. 32/1 retinoblastomas, 1/2 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. Small samples more often are able to generate a PDCL and we have demonstrated comparability of low-passage hypoxia PDCLs to 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 58 PDCLs and 17 PDXs have been established from patients on the ANBL1531 phase III trial and PDCLs/PDXs from 30 patients on the ANBL2131 phase III study. The repository distributes PDCLs and PDXs to > 500 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: McCoy
    Degree(s): MS
    Organization: Texas Tech University Health Sciences Center School of Medicine Lubbock, TX
  3. First Name: Diana
    Last Name: Ixlamati-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): 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): PharmD
    Organization: Texas Tech University Health Sciences Center School of Medicine Lubbock, TX
 C Patrick Reynolds Texas Tech University Health Sciences Center School of Medicine Lubbock, TX patrick.reynolds@ttuhsc.edu
51 Fri, 08/29/2025 - 11:20 Anonymous 10.208.24.168 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 response was: NR (n=11), SD (n=2), PR (n=9), and CR (n=18) while nine 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 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: Kristyn
    Last Name: McCoy
    Degree(s): MS
    Organization: Texas Tech University Health Sciences Center
  4. First Name: Jonas
    Last Name: Nance
    Degree(s): BS
    Organization: Texas Tech University Health Sciences Center
  5. First Name: Diana
    Last Name: Ixlamati-Nava
    Degree(s): BS
    Organization: Texas Tech University Health Sciences Center
  6. First Name: Nighat
    Last Name: Noureen
    Degree(s): PhD
    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: Irwin
    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, 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
50 Fri, 08/29/2025 - 09:18 Anonymous 10.208.28.51 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 aims to collect treatment records from 17 hospitals, targeting 10,000 patients for each modality. 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. A total of 7,000 patients have been collected so far in an industry-standard medical image 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 built from patient 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): Ph.D
    Organization: National Institutes of Health/National Cancer Institute
  2. First Name: Matthew
    Middle Initial: M
    Last Name: Mille
    Degree(s): Ph.D
    Organization: National Institutes of Health/National Cancer Institute
  3. First Name: Caroline
    Last Name: Esposito
    Degree(s): BA
    Organization: National Institutes of Health/National Cancer Institute
  4. First Name: Todd
    Middle Initial: M
    Last Name: Gibson
    Degree(s): Ph.D
    Organization: National Institutes of Health/National Cancer Institute
  5. First Name: Keith
    Middle Initial: T
    Last Name: Griffin
    Degree(s): Ph.D
    Organization: National Institutes of Health/National Cancer Institute
  6. First Name: Jae Won
    Last Name: Jung
    Degree(s): Ph.D
    Organization: East Carolina University
  7. First Name: Choonik
    Last Name: Lee
    Degree(s): Ph.D
    Organization: University of Michigan
  8. First Name: Sergio
    Last Name: Morato
    Degree(s): Ph.D
    Organization: National Institutes of Health/National Cancer Institute
  9. First Name: Torunn
    Middle Initial: I
    Last Name: Yock
    Degree(s): M.D
    Organization: Massachusetts General Hospital and Harvard Medical School
  10. First Name: Amy
    Middle Initial: Berrington
    Last Name: de González
    Degree(s): D.Phill
    Organization: Institute for Cancer Research, UK
  11. First Name: Choonsik
    Last Name: Lee
    Degree(s): Ph.D
    Organization: National Institutes of Health/National Cancer Institute
  12. First Name: Cari
    Middle Initial: M
    Last Name: Kitahara
    Degree(s): Ph.D
    Organization: National Institutes of Health/National Cancer Institute
 Jungwook Shin on behalf of the Pediatric Proton/Photon Therapy Comparison cohort study consortium National Institutes of Health/National Cancer Institute jungwook.shin@nih.gov
49 Thu, 08/28/2025 - 17:00 Anonymous 10.208.28.90 AttentionAML: Accurate Molecular Categorization of Pediatric Acute Myeloid Leukemia by an Attention-Based Deep Learning Framework As an aggressive hematopoietic malignancy, acute myeloid leukemia (AML) is defined by aberrant clonal expansion of abnormal myeloid progenitor cells, typically leading to bone marrow failure and compromised hematopoiesis. Characterized by morphological, molecular, and genetic alterations, pediatric AML encompasses multiple distinct subtypes that would exhibit subtype-specific responses to treatment and prognosis, underscoring the critical need of accurately identifying AML subtypes for effective clinical management and tailored therapeutic approaches. Traditional wet lab approaches such as immunophenotyping, cytogenetic analysis, morphological analysis, or molecular profiling to identify AML subtypes are labor-intensive, costly, and time-consuming. To address these challenges, we propose AttentionAML, a novel attention-based deep learning framework for accurately categorizing AML subtypes based on transcriptomic data from the Childhood Cancer Data Initiative (CCDI) system. Benchmarking tests based on 1,661 AML patients suggested that AttentionAML outperformed state-of-the-art methods across all evaluated metrics (accuracy: 0.96, precision: 0.96, recall of 0.96, F1-score: 0.96, and Matthews correlation coefficient: 0.96). Furthermore, we also demonstrated the superiority of AttentionAML over conventional approaches in terms of AML patient clustering visualization and subtype-specific gene marker characterization. We believe AttentionAML will bring remarkable positive impacts on downstream AML risk stratification and personalized treatment design. To enhance its impact, a user-friendly Python package implementing AttentionAML is publicly available at our lab's Github repository at https://github.com/wan-mlab/AttentionAML.
  1. First Name: Lusheng
    Last Name: Li
    Organization: University of Nebraska Medical Center
  2. First Name: Joseph
    Middle Initial: D
    Last Name: Khoury
    Degree(s): M.D.
    Organization: University of Nebraska Medical Center
  3. First Name: Jieqiong
    Last Name: Wang
    Degree(s): Ph.D.
    Organization: University of Nebraska Medical Center
  4. First Name: Shibiao
    Last Name: Wan
    Degree(s): Ph.D.
    Organization: University of Nebraska Medical Center
 Shibiao Wan University of Nebraska Medical Center swan@unmc.edu
48 Thu, 08/28/2025 - 08:32 Anonymous 10.208.28.90 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
    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
47 Wed, 08/27/2025 - 23:25 Anonymous 10.208.28.150 Real-World Experience of Pediatric Precision Oncology Cohort At a Single Institution: An Interim Analysis Our primary aim is to study the real-world impact of integrative clinical sequencing (ICS) based pediatric precision oncology program on the outcomes of pediatric-AYA cancer patients at a single institution.
Methods: Pediatric-AYA oncology patients (age 0-25yr) were enrolled and sequenced using methodology described previously.1 Longitudinal clinical data extracted from EHR using NCI supported tool EMERSE were entered into a REDCap.
Results: Clinical characteristics are shown in Fig-1. Of 1000 patients enrolled, 925 (92.5%) were sequenced successfully, 235 (23.5%) underwent repeat ICS (Range 1-4). Interim analysis of the first 250 patients revealed 230 patients with full sequencing results. Of those, 133 (57.8%) patients had total 228 actionable alterations (AA) (mean1.71 AA /pt), of which 30 (13%) patients had germline AA. Of the total AA, 64 patients had 84 SNV (Single Nucleotide Variants), 33 patients had 38 actionable fusions, and 46 had 76 actionable CNA’s (Copy Number Alterations). Fifty-one (22.2%) patients received a total of 96 targeted therapies (TT) supported by variant level evidence (VLE)2, 3 (L1=39, L2=5, L3=4, L4=4, Other=44), while 16 (7.0%) patients received 26 TT without AA based on clinician preference. Eighty-two (35.6%) patients with AA did not receive any TT. Sixty-five (28.3%) patients underwent repeat ICS, among which 9.2% (6) had additional AA identified and 4.6% (3) received TT based on repeat ICS.
Conclusion: Interim analyses showed that 67 patients (29.1%) received TT with variable level of evidence. Outcomes of patients receiving TT including, EFS, OS and barrier in receiving TT despite having AA, are under further analyses.
  1. First Name: Lucas
    Last Name: Ebert
    Degree(s): B.S.
    Organization: Michigan Medicine
  2. First Name: Malay
    Last Name: Mody
    Degree(s): MPH
    Organization: Michigan Medicine
  3. First Name: Joshua
    Last Name: Goldman
    Degree(s): M.D.
    Organization: Michigan Medicine
  4. First Name: David
    Last Name: Hanauer
    Degree(s): M.D., M.S.
    Organization: Michigan Medicine
  5. First Name: Jamie
    Last Name: Estill
    Degree(s): M.S.
    Organization: Michigan Medicine
  6. First Name: Brian
    Last Name: Magnuson
    Degree(s): Ph.D.
    Organization: Michigan Medicine
  7. First Name: Carl
    Last Name: Koschmann
    Degree(s): M.D.
    Organization: Michigan Medicine
  8. First Name: Arul
    Last Name: Chinnaiyan
    Degree(s): M.D., Ph.D.
    Organization: Michigan Medicine
  9. First Name: Rajen
    Last Name: Mody
    Degree(s): M.D., M.S.
    Organization: Michigan Medicine
  10. First Name: Chandan
    Last Name: Kumar
    Degree(s): Ph.D.
    Organization: Michigan Medicine
 Lucas Ebert/ Malay Mody Michigan Medicine rmody@med.umich.edu
46 Tue, 08/26/2025 - 17:14 Anonymous 10.208.28.36 VCF Table Viewer: Flexible Visualization of CCDI VCF Files Variant calling pipelines produce variant caller format (VCF) files. VCF files have large amounts of information about called variants, especially if they are annotated by tools such as Variant Effect Predictor (VEP), but are difficult to read and interpret directly, particularly for non-computational biologists. Thus, there are many tools available to extract information from VCF files for visualization and analysis. We present here a new Shiny app, VCF Table Viewer, that extracts information from annotated VCF files and displays them in an interactive table. The table provides the ability to flexibly sort through a list of called variants while visualizing desired annotations, including color highlighting of various annotations. It also allows easy visualization of the bam file pileups in an embedded IGV tab for variants selected in the table, as well as plots of somatic allele frequencies from mutect2 calls over multiple samples when these are available. VCF Table Viewer provides a novel interface that facilitates the examination of variant calls by non-computational biologists. We provide examples from an ongoing clinical study of patients with familial platelet disorder. The code is freely available on Github and has been integrated into the Cancer Genomics Cloud with easy selection of VCF files from CCDI datasets for visualization.
  1. First Name: Michael
    Last Name: Sierk
    Degree(s): PhD
    Organization: National Cancer Institute
  2. First Name: Justin
    Last Name: Elfritz
    Degree(s): PhD
    Organization: IBM
  3. First Name: Kate
    Last Name: Isaac
    Degree(s): PhD
    Organization: Fred Hutch Cancer Center
  4. First Name: Thomas
    Last Name: O'Neal
    Degree(s): BA
    Organization: IBM
  5. First Name: Daoud
    Last Name: Meerzaman
    Degree(s): PhD
    Organization: National Cancer Institute
 Michael Sierk National Cancer Institute michael.sierk@nih.gov
45 Tue, 08/19/2025 - 12:15 Anonymous 10.208.28.152 Enrollment in Children’s Oncology Group’s Clinical Trials: Population-Based Linkage with the National Childhood Cancer Registry Background: Improvements in outcomes among children and adolescents diagnosed with cancer are attributable to many factors, including clinical trials such as those administered through the Children’s Oncology Group (COG), as well as population-based resources like the National Childhood Cancer Registry (NCCR). The objective of this study was to link COG trial data with the NCCR to evaluate overall enrollment patterns.

Methods: Data were received from the NCCR and COG, which were linked using an array of variables and then compared to evaluate enrollment patterns in COG studies from 2007-2018. Multivariable logistic regression was used to identify characteristics associated with not being enrolled in a COG study.

Results: Among 134,696 NCCR cancer patients, 51,062 matched with COG study enrollees. There were several differences in demographic and clinical characteristics between those enrolled and not enrolled in COG studies. Enrollment was higher among children aged 0-4 years compared to adolescents aged 15-19 years (53.7% vs 20.1%). Differences by race and ethnicity were also observed; for example, those who identified as non-Hispanic White were more likely to be enrolled than those who identified as non-Hispanic Asian/Pacific Islander (38.8% vs 32.9%). In a multivariable logistic regression model, several characteristics were significantly associated with not being enrolled in a COG study, including age at diagnosis, year of diagnosis, race and ethnicity, and cancer type.

Conclusion: Our results suggest that several groups are underrepresented in COG clinical trials. This information can help guide the prioritization of population groups for engagement in future studies.
  1. First Name: Philip
    Middle Initial: J
    Last Name: Lupo
    Degree(s): Ph.D.
    Organization: Emory University
  2. First Name: David
    Middle Initial: A
    Last Name: Siegel
    Degree(s): M.D., MPH
    Organization: Centers for Disease Control and Prevention
  3. First Name: Nicola
    Middle Initial: C
    Last Name: Schussler
    Degree(s): BS
    Organization: Information Management Services, Inc
  4. First Name: Todd
    Middle Initial: A
    Last Name: Alonzo
    Degree(s): Ph.D.
    Organization: University of Southern California
  5. First Name: Suzanne
    Last Name: Adams
    Degree(s): ODS-C, BS
    Organization: Information Management Services, Inc
  6. First Name: David
    Last Name: Angelaszek
    Degree(s): MS
    Organization: Information Management Services, Inc
  7. First Name: Shanthala
    Last Name: Basavappa
    Degree(s): MS
    Organization: Essex, an Emmes Company
  8. First Name: Tiffany
    Middle Initial: M
    Last Name: Chambers
    Degree(s): MPH
    Organization: Baylor College of Medicine
  9. First Name: Linda
    Last Name: Colye
    Degree(s): BS
    Organization: Information Management Services, Inc
  10. First Name: Eric
    Last Name: Durbin
    Degree(s): Dr.PH., MS
    Organization: University of Kentucky
  11. First Name: Johanna
    Middle Initial: L
    Last Name: Goderre
    Degree(s): MPH
    Organization: National Cancer Institute
  12. First Name: Tiffany
    Last Name: Hayes
    Degree(s): MPH
    Organization: Essex, an Emmes Company
  13. First Name: Will
    Last Name: Howe
    Degree(s): BS
    Organization: Information Management Services, Inc
  14. First Name: Elizabeth
    Last Name: Hsu
    Degree(s): Ph.D.
    Organization: National Cancer Institute
  15. First Name: Richard
    Last Name: Lee
    Degree(s): BS
    Organization: Information Management Services, Inc
  16. First Name: Denise
    Middle Initial: R
    Last Name: Lewis
    Degree(s): Ph.D., MPH
    Organization: National Cancer Institute
  17. First Name: Angela
    Middle Initial: B
    Last Name: Mariotto
    Degree(s): Ph.D.
    Organization: National Cancer Institute
  18. First Name: Brad
    Middle Initial: H
    Last Name: Pollock
    Degree(s): Ph.D., MPH
    Organization: University of California Davis
  19. First Name: Anca
    Last Name: Preda
    Degree(s): M.D., MPH
    Organization: National Cancer Institute
  20. First Name: Michael
    Middle Initial: E
    Last Name: Roth
    Degree(s): M.D.
    Organization: The University of Texas MD Anderson Cancer Center
  21. First Name: Jennifer
    Last Name: Stevens
    Degree(s): BS
    Organization: Information Management Services, Inc
  22. First Name: Tina
    Last Name: Terranova
    Degree(s): MSc
    Organization: ICF
  23. First Name: Sarah
    Middle Initial: L
    Last Name: Vargas
    Degree(s): Ph.D.
    Organization: Children's Oncology Group
  24. First Name: Douglas
    Middle Initial: S
    Last Name: Hawkins
    Degree(s): M.D.
    Organization: University of Washington
  25. First Name: Lynne
    Last Name: Penberthy
    Degree(s): M.D., MPH
    Organization: National Cancer Institute
 David Siegel Centers for Disease Control and Prevention irn3@cdc.gov