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Childhood Cancer Data Initiative–Funded Projects

CCDI awarded administrative supplements to NCI-Designated Cancer Centers to investigate how data within the CCDI Data Ecosystem can be used to drive innovative discoveries and foster collaborative research in childhood cancer. Research conducted using this funding could help identify critical scientific questions and determine the analytical tools that need to be developed.

The projects are summarized and listed below in alphabetical order by institution.

Project Name and Institution

Project Team Summary

Creating the Childhood Cancer Isoform Atlas: Informatics Tools and Multi-Omics Insights for Immunotherapy Targets

Abramson Cancer Center, University of Pennsylvania

Yi Xing, Ph.D.

Richard Aplenc, M.D., Ph.D.

Alternative splicing is a cellular process that allows a gene to code for many different proteins, but this process is often disrupted in cancer. A multidisciplinary team at the University of Pennsylvania proposes to create informatics tools that enable the use of CCDI data to map the various proteins, or isoforms, that result from alternative splicing. This project aims to: 1) map alternative isoforms into a new resource called the Childhood Cancer Isoform Atlas, 2) identify isoforms that could be immunotherapy targets, and 3) integrate and visualize data on isoforms and targets in childhood cancers. All software developed will be open source and accessible to the research community, facilitating the discovery of immunotherapy targets for hard-to-treat childhood cancers.

Real-World Molecularly Targeted Treatment Registry (MaTTeR): A Pilot Study to Enrich CCDI Data Utilizing Directed Electronic Medical Record Extraction

Boston Children’s Hospital and Dana-Farber Cancer Institute

Yana Pikman, M.D.

Suzanne Forrest, M.D.

Kee Yeo, M.D.

Katherine Janeway, M.D.

Doctors are increasingly using therapies that target specific gene changes in cancer cells, known as molecularly targeted therapies (MTT). Collecting and sharing data on how well these therapies work (their efficacy and toxicity), as well as on specific doses and drug combinations related to MTTs, is critical, especially when they are given outside of clinical trials. This project aims to implement an “Electronic Medical Record Search Engine” to identify patients who received MTTs outside of clinical trials. Investigators will then create a Real-World Molecularly Targeted Treatment Registry (MaTTeR) within the CCDI framework, using genomic data from the Dana-Farber Cancer Institute and the CCDI Data Ecosystem. They also propose to launch a data visualization platform in the ecosystem that doctors and researchers can use to explore and apply MaTTeR in their clinical care and research projects.

Enhancing Precision of Pediatric Cancer Molecular Targets by Aggregating CCDI Genomic Data to Pediatric Cancer Knowledgebase

Comprehensive Cancer Center, St. Jude Children’s Research Hospital

Jinghui Zhang, Ph.D.

Xiaotu Ma, Ph.D.

Clay Mcleod, M.S.

Michael Rusch, B.A.

In recent years, doctors and researchers have learned a lot about how genetic changes drive childhood and young adult cancers. Pediatric Cancer Knowledgebase version 2 provides dynamic visualizations of genetic changes in 300 molecular subtypes of childhood cancer. This project’s goal is to enhance the FDA’s Relevant Molecular Target List, characterized in CCDI’s Molecular Targets Platform, to support cancer care. This involves developing an application programming interface for summarizing statistics and patterns related to genetic changes in childhood cancers and for integrating CCDI data sets.

Machine Learning Framework for Accurate Childhood Acute Myeloid Leukemia Subtype Identification

Fred & Pamela Buffett Cancer Center, University of Nebraska

Shibiao Wan, Ph.D.

Joseph Khoury, M.D.

Jieqiong Wang, Ph.D.

Acute myeloid leukemia (AML) in children has many different subtypes, each characterized by different genetic alterations. This project aims to improve the identification of subtypes by integrating multi-omics data, including genomics, transcriptomics, and epigenetics. The team proposes to develop a machine learning framework that would refine risk stratification, diagnosis, and treatment selection for children with AML. This approach also holds promise for identifying subtypes of other childhood and young adult cancers, including ultra-rare tumors.

Unlocking the Potential of Extrachromosomal Circular DNA (eccDNA) as Prognostic Markers in Childhood and AYA Cancers

Sanford Burnham Prebys Medical Discovery Institute

Lukas Chavez, Ph.D.

Yuk-Lap (Kevin) Yip, Ph.D.

Extrachromosomal circular DNA (eccDNA) is a type of DNA that plays a role in the amplification of oncogenes in cancer. This project aims to increase understanding of how eccDNA affects the development, spread, and prognosis of childhood, adolescent, and young adult cancers. Investigators will use a computational pipeline to identify eccDNAs from whole-genome sequencing data from more than 3,500 tumor samples. These findings will be made available to the public through the CCDI Data Ecosystem.

Automated Classification of Pediatric Soft Tissue Sarcoma from Histopathology Images

The Jackson Laboratory

Jill Rubinstein, M.D., Ph.D.

Jeffrey Chuang, Ph.D.

Carol Bult, Ph.D.

Soft tissue sarcomas, while rare in children and young adults, have a range of subtypes with varying prognoses and clinical characteristics. The Jackson Laboratory investigators have expertise in gathering, integrating, and analyzing data from diverse sources and in computational oncology—using computers to model tumor characteristics, responses to therapies, and more. The aim of this project is to expand the collection of digitized whole-slide images of pediatric soft tissue sarcomas and to use computational techniques to classify and diagnose soft tissue sarcomas more accurately.

Enhancing Pediatric Cancer Research with AI-Driven Diagnostics

USC Norris Comprehensive Cancer Center, University of Southern California and Children’s Hospital Los Angeles

James Amatruda, M.D.

Jaclyn Biegel, Ph.D.

Xiaowu Gai, Ph.D.

Bruce Pawel, M.D.

Jennifer Cotter, M.D.

Mikako Warren, M.D.

Fariba Navid, M.D.

The USC Norris Comprehensive Cancer Center (NCCC), in collaboration with Children’s Hospital Los Angeles (CHLA), proposes to develop an online diagnostic resource powered by augmented artificial intelligence (AI). This AI will be used to create an AI-powered classifier that can sort vast amounts of imaging and molecular data to help determine a specific diagnosis for central nervous system (CNS) tumors, sarcomas, and ultimately all childhood and young adult cancers. The classifier is called “Multi-Modal AI-Based Diagnosis for Pediatric Oncology.” Additionally, NCCC and CHLA will collect whole-slide images from 599 solid tumors and whole-genome methylome data from 200 CNS tumors. These data will be added to the existing “OncoKids - NGS Panel for Pediatric Malignancies” data set within the CCDI Data Ecosystem.

Leveraging ExtractEHR and FHIR Framework for Enhancing Clinical Data Integration

Winship Cancer Institute, Emory University, and Children’s Hospital of Philadelphia

Tamara Miller, M.D.

Allison Heath, Ph.D.

Richard Aplenc, M.D., Ph.D.

ExtractEHR retrieves childhood cancer data such as hospital encounters, laboratory test results, medications, outcomes, pathology reports, etc., from electronic health records (EHR). It then transforms these data into a format that is readable and understandable for health care professionals. Fast Healthcare Interoperability Resources (FHIR) establish a framework for standardizing EHR data, making it easier and faster to exchange and share. The goal of this project is to use ExtractEHR and FHIR to match patients’ treatment and outcomes data with their molecular data in the CCDI Data Ecosystem. The team proposes to extract clinical data from two large children’s hospitals, Children’s Healthcare of Atlanta and the Children’s Hospital of Philadelphia, and incorporate them into the CCDI Data Ecosystem.
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