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Global Partnership, NJ-SHO, Germline Classification, Spatial Transcriptomics Tool

Published on June 21, 2024 in Cornerstone Blog · Last updated 1 month ago


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In the News


Children's Hospital of Philadelphia researchers are building breakthroughs in pediatric health with the development of new tools that streamline germline classification and identify patterns in large quantities of spatial transcriptomics data. Also featured in this week's news roundup is a data resource from NJ-SHO and a partnership between CHOP and the Department of Health – Abu Dhabi to advance research in pediatric oncology and gene therapy.

CHOP Partnership Inspires Global Innovation in Pediatric Health

Madeline Bell

Madeline Bell

CHOP and the Department of Health – Abu Dhabi (DoH) signed a memorandum of understanding to advance research in pediatric oncology and gene therapy. Madeline Bell, President and Chief Executive Officer of CHOP, met with Dr. Asma Ibrahim Al Mannaei, Executive Director of the Research and Innovation Center at DoH, to sign the memorandum June 9 and begin this new partnership.

"CHOP is dedicated to improving health outcomes for children around the world and to ensuring that all children have access to the care they need – including cell and gene therapies," Bell said in this Emirate's press release. "Our partnership with DoH will help us advance pediatric research and make breakthroughs that will benefit children in Abu Dhabi and across the globe."

With a special focus on reducing the global burden of childhood cancer, CHOP and DoH will work together to enhance research capabilities and training for healthcare professionals through the exchange of faculty, researchers, and students. This collaboration is designed to inspire knowledge sharing and learning opportunities with conferences, seminars, and workshops to advance pediatric care.

Researchers Develop New Tool for Better Classification of Inherited Disease-Causing Variants

Sharon J. Diskin
Sharon Diskin, PhD

A new tool — Automated Germline Variant Pathogenicity (AutoGVP) — developed in collaboration with researchers from CHOP, the Perelman School of Medicine at the University of Pennsylvania, and the National Cancer Institute (NCI) of the National Institutes of Health allows for streamlined germline variant classification that integrates with well-known large databases of sequencing data.

"Our goal was to create a publicly available tool that could evolve with [American College of Medical Genetics Association for Molecular Pathology (ACMG-AMP)] guidelines while still using many of the critical databases and approaches the research community has come to know," said senior study author Sharon J. Diskin, PhD, a member of the Center for Childhood Cancer Research and the Department of Biomedical and Health Informatics at CHOP, and associate professor of Pediatrics at Penn Medicine.

By automatically integrating ClinVar and modified InterVar variant annotations with the most current ACMG-AMP criteria, AutoGVP helps researchers keep pace with whole genome and exome sequencing research by determining if germline variants are likely responsible for a patient's disease.

"With AutoGVP, we can streamline variant classification and swiftly incorporate new information as more and more biobanks release large sequencing data," said first author Jung Kim, PhD, a staff scientist at the Division of Cancer Epidemiology and Genetics at the NCI. "Furthermore, AutoGVP reduces hands-on curating of variants and allows for reproducibility of the variant curation."

For example, the study authors are applying AutoGVP to genetic data from large neuroblastoma cohorts from Gabriella Miller Kids First (GMKF) as well as pediatric brain tumor patients from the Children's Brain Tumor Network (CBTN), a collaborative, multi-institutional research program dedicated to the study and treatment of childhood brain tumors. The Center for Data-Driven Discovery (D3B) at CHOP functions as the CBTN's Operations Center.

"Many samples we use for identifying pathogenic variants in pediatric brain tumors come from the centralized resource of the CBTN, and we would like to be able to share new findings with the CBTN sites in a more streamlined manner," said Jo Lynne Rokita, PhD, a supervisory bioinformatics scientist at the D3B and co-senior author of the study.

Learn more in this CHOP press release.

New Deep Learning Tool Helps Identify Patterns in Spatial Transcriptomic Data

Hakon Hakonarson Headshot
Hakon Hakonarson, MD, PhD

Researchers from the Center for Applied Genomics (CAG) have developed a new tool called spaVAE designed to identify patterns and relationships in large amounts of data collected from spatial transcriptomics technologies (SRTs) through deep learning. The findings from this study appear in Nature Methods.

"While the data being generated by SRTs is important, by itself it is difficult to understand the big picture of how this information can be translated in a way that helps drive discovery," said senior study author Hakon Hakonarson, MD, PhD, director of CAG. "This tool helps cluster the data to identify relationships between different data points and data types better than any other method currently available."

Although SRTs can profile tens of thousands of genes in different tissue locations, it can be challenging to identify which cells are expressing genes that may cause disease. spaVAE helps simplify this complex data by creating visual representations, combining data from different experiments, identifying differences in gene activity, and finding genes that vary across different spatial regions.

The tool also handles multiple types of data captured in the genome, proteome, transcriptome, metabolome, and epigenome. Researchers hope to apply this tool to other forms of spatial technology to further explore cell-to-cell communication.

Learn more in this CHOP press release.

NJ-SHO Provides Robust Resource in Population Health and Safety Data

Allison E. Curry, PhD, MPH
Allison Curry, PhD, MPH

In creating a robust health and safety dataset, CHOP is filling a gap in public health and injury prevention research. The NJ Safety and Health Outcomes (NJ-SHO) Center for Integrated Data is a data warehouse for researchers at CHOP and beyond who are interested in studying population health and safety. On June 5, Allison E. Curry, PhD, MPH, along with other members from the Center for Injury Research and Prevention (CIRP), presented NJ-SHO and all of its research capabilities at the Roberts Center.

The data warehouse is expansive and thorough, incorporating data from CHOP as well as New Jersey state resources. There are 18 years of complete data with over 125 million records on 24 million individuals, all anonymized and protected to prevent patient identification. With this data, CIRP has produced more than 50 research studies associated with injury prevention, collaborating with other centers around CHOP such as the Center for Autism Research (CAR) and the Center for Management of ADHD.

CIRP held this presentation to encourage all Centers at CHOP to utilize this data to help answer their own research questions. Any researcher who feels this kind of data could be useful for their study is encouraged to reach out to the members of CIRP to learn more about how they could make use of NJ-SHO.


Catch up on our headlines from our June 7 In The News:

  • Emily's Entourage Awards Inaugural Collaborative Research Grant to CHOP in Support of Cystic Fibrosis Treatments
  • John Maris, MD, Honored With Alex's Lemonade Stand 2024 Pitcher of Hope Award
  • American Association of Plastic Surgeons Recognizes Dr. Eric Chien-Wei Liao
  • 'The Pulse' Podcast Featuring Flaura Winston, MD, PhD, Now Available
  • Gregory Tasian, MD, MSCE, Presents Data Supporting Shock Wave Lithotripsy for Kidney Stones in Children
  • CHOP Researchers Aim to Improve Adolescent Health Discussions and Screening
  • CHOP Researchers Develop Screening Tool to Help Improve Access to Federal Nutrition Programs

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