Exploration of Coding and Non-coding Variants in Cancer Using GenomePaint

Area of Science:

• Genomics
• Cancer Biology
• Bioinformatics

Background:

• Cancer research generates vast amounts of multi-omics data (whole-genome, transcriptome, epigenomics).
• Integrating and visualizing these diverse datasets is crucial for understanding cancer mechanisms.
• Existing tools may not fully capture the interplay between genetic variations, gene expression, and clinical outcomes.

Purpose of the Study:

• To introduce GenomePaint, an interactive platform for visualizing and analyzing multi-omics cancer data.
• To demonstrate GenomePaint's capability in exploring the relationships between DNA variations, RNA expression, and patient outcomes.
• To showcase how GenomePaint facilitates the discovery of novel biological insights in cancer.

Main Methods:

• Development of an interactive visualization platform (GenomePaint) integrating whole-genome, whole-exome, transcriptome, and epigenomic data.
• Incorporation of 3D genome data to assess the functional impact of regulatory non-coding variants.
• Correlation analysis of mutation and expression patterns with patient outcomes.
• Support for custom data uploads for flexible analysis.

Main Results:

• GenomePaint effectively visualizes the inter-relatedness of DNA variations and RNA expression across tumor samples.
• The platform enabled the identification of aberrant splicing in CREBBP and cis activation of MYC in acute lymphoblastic leukemia.
• Analysis of adult glioblastomas revealed inter- and intra-tumor heterogeneity at the EGFR locus.

Conclusions:

• GenomePaint provides a powerful tool for deep multi-omics exploration of individual cancer genomes.
• The platform facilitates the discovery of critical biological insights, such as gene dysregulation and tumor heterogeneity.
• These findings underscore the potential of GenomePaint for guiding further experimental validation in cancer research.