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Can a New Computational Method Help to Prioritize Noncoding Mutations that Disrupt Gene Expression in Pediatric Cancer?

Published on August 3, 2020 in Cornerstone Blog · Last updated 4 months 3 weeks ago


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It’s challenging for scientists to study noncoding regions because they can involve structural variants that are large, complex genomic alterations.

The findings:

A study team from Children’s Hospital of Philadelphia and the Perelman School of Medicine at the University of Pennsylvania developed a computational algorithm called PANGEA (predictive analysis of noncoding genomic enhancer/promoter alterations). This new tool helped them to identify and systematically prioritize 1,137 structural variants that may be causal noncoding mutations influencing the expression of more than 2,000 genes across five major types of pediatric cancers. Their analysis of B cell acute lymphoblastic leukemia (B-ALL), acute myeloid leukemia, neuroblastoma, Wilms tumor, and osteosarcoma also revealed that coding and noncoding mutations affect distinct sets of genes and pathways, with little overlap.

Why it matters:

The majority of the human genome consists of noncoding regions that act as a control switch to direct how genes are turned on and off. In cancer, which involves uncontrolled growth and division of cancer cells, noncoding mutations can play important roles as enhancers/promoters to oncogenes or from tumor suppressor genes. It’s challenging for scientists to study these noncoding regions because they can involve structural variants that are large, complex genomic alterations.

The new computational method PANGEA helps scientists to characterize a full spectrum of recurrent noncoding mutations. Researchers can use this approach to unveil their impact on gene expression and find novel cancer-related genes. By performing comparative analyses of both coding and noncoding mutations, researchers also can create integrated mutation and gene expression profiles that could define patient subtypes with different clinical outcomes.

Who conducted the study:

Kai Tan, PhD, professor of Pediatrics at CHOP and an investigator with the Department of Biomedical and Health Informatics, is senior author of the study. His colleagues included researchers from CHOP’s Division of Oncology and Center for Childhood Cancer Research, and the Perelman School of Medicine at the University of Pennsylvania. Grants from the National Institutes of Health, including the National Cancer Institute and the National Institute of General Medical Sciences, supported this work.

How they did it:

The study team used PANGEA to conduct an analysis of all types of noncoding mutations, including single nucleotide variants, small indels, copy number variations, and structural variants, in 501pediatric cancer patients. Their analysis of five pediatric cancer types identified 1,175 genes recurrently altered in their coding regions and 2,162 genes recurrently altered in their noncoding regions. In total, they identified disruption of enhancer function by recurrent noncoding mutations in 477 of 501 patients with pediatric cancer (95%). The researchers gave an example in the paper of how PANGEA worked to identify a noncoding mutation caused by structural variants and validated its role in overexpression of the gene CHD4 via enhancer hijacking in B-ALL.

Quick thoughts:

“Because 95 percent of the genome is noncoding, presumably there will be a lot of driver noncoding mutations,” Dr. Tan said. “If we can understand noncoding mutations better, we can stratify patient subgroups and identify new subgroups. Even better, if you combine coding mutations and noncoding mutations, maybe we can find good targets for future research and therapy to move into clinical practice.”

What’s next:

With this new computational tool, researchers can expand the types of pediatric cancer that they can study. As scientists begin to identify more noncoding mutations associated with these cancer types, it will facilitate future experimental work to characterize and validate predictions about their oncogenic roles.

Where the study was published:

The article appeared in Science Advances.

Where to learn more: 

Find out more about Dr. Tan’s Lab, read about this study in CHOP News, and get additional details about the PANGEA software package.