The Genomic Revolution in Diagnosing Infant Brain Tumors

Our team recently published an important study in Neuro-Oncology that sheds new light on high-grade gliomas occurring in young children. Using integrated genomic analysis, we found these tumors are heterogeneous, with specific molecular features predicting divergent outcomes.

This study demonstrates the importance of an integrated diagnostic approach, combining histological and molecular techniques, to accurately classify pediatric brain tumors.

What is Integrated Diagnosis?

Integrated diagnosis refers to incorporating multiple layers of data - histological, molecular, genomic, and clinical - to arrive at a comprehensive tumor classification and diagnosis.

This integrated approach can improve IHG management in several key ways:

1. Accurate diagnosis - Integrating histology, DNA methylation profiles, sequencing, and clinical data allows accurate separation of IHG from other infant brain tumors like low-grade gliomas or pediatric-type high-grade gliomas. This enables prognostication and treatment planning specific to IHG.

2. Risk stratification - Molecular findings can identify subgroups within IHG with distinct outcomes. For example, MET or ALK fusions may confer better outcomes compared to wild-type IHG. This allows risk-adapted treatment strategies.

3. Targeted therapies - The common gene fusions in IHG present druggable targets. Knowing the specific alteration allows selecting matched targeted agents like ALK or MET inhibitors as potential alternatives to traditional chemotherapy.

4. Clinical trial design - Better classification of IHG enables improved clinical trial design and cohort selection to evaluate novel upfront or salvage therapies for this specific disease entity.?

5. Reduced toxicity - As survival is excellent for IHG, treatment de-escalation to reduce surgical morbidity and long-term effects of chemotherapy can be considered for lower-risk subgroups while maintaining survival rates.

How did Genomics/Bioinformatics contribute to this analysis?

This paper on infant high-grade gliomas provides an excellent example of how bioinformatics can translate into clinical impact:

• Bioinformatic analysis of DNA methylation profiles and next-generation sequencing data allowed molecular classification of these tumors into biological subtypes like IHG and HGG.
• This molecular stratification revealed that histologic diagnoses alone were insufficient - tumors that appeared "high-grade" could actually have excellent outcomes (IHG).
• Integrating histology with molecular class led to a revised diagnosis, changing clinical management. IHGs were spared aggressive therapy reserved for pediatric HGG.
• Bioinformatic discovery of recurrent ALK, NTRK, and MET fusions in IHG present druggable targets for novel therapies like TRK inhibitors. This enables a precision medicine approach.
• Molecular subgroups within IHG could be identified that may benefit from treatment escalation or de-escalation to optimize survival versus toxicity.
• Distinct imaging characteristics were found linked to the molecular subclasses, allowing non-invasive prediction of diagnosis.
• A public data portal was generated to disseminate the multi-dimensional data to the scientific community.

In summary, this study exemplifies how bioinformatic analysis of genomics data can redefine disease entities, improve diagnosis/prognosis, identify therapeutic targets, enable precision medicine approaches, and generate publicly available databases for further research. This showcases the immense potential to translate computational biology findings into real-world impact for patients.

How can you query and access the data?

We have created an interactive data portal that allows exploration of the clinical, molecular, and imaging data. Users can filter, visualize, and download data from this portal. This can be accessed at the following URL:

Some key features of the portal:

• Allows exploration of multi-dimensional datasets including clinical, molecular, and imaging data
• Developed using ProteinPaint, a visualization framework created at St. Jude Children's Research Hospital
• Users can filter cases based on clinical features, molecular characteristics, or integrated diagnosis
• Oncoplots visualize somatic mutations, CNVs, and fusions for each case
• Survival analysis can be performed for user-defined subsets of cases
• Methylation data are shown in an interactive scatterplot vs reference brain tumor types
• Links are provided to raw data files in public repositories
• Data can be downloaded in various formats (pdf, png, json)
• An easy-to-use interface enables non-computational researchers to query complex genomic data
• A customizable platform facilitates data sharing and dissemination to the scientific community
• Accelerates follow-up research by avoiding lengthy data access procedures

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