Toblerone: detecting exon deletion events in cancer using RNA-seq

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Cancer arises from genomic mutations, including oncogene activation and tumor suppressor gene repression.
• IKZF1 gene deletions in acute lymphoblastic leukemia (ALL) correlate with poor patient prognosis and increased relapse rates.
• Accurate detection of IKZF1 deletions is crucial for informing ALL treatment strategies.

Purpose of the Study:

• To develop and validate a method for detecting exon deletions in genes using RNA-sequencing (RNA-seq) data.
• To apply this method to identify IKZF1 deletions in pediatric B-ALL samples.
• To create a user-friendly application for non-bioinformaticians to analyze RNA-seq data for IKZF1 deletions.

Main Methods:

• Developed a bioinformatics pipeline utilizing a custom transcriptome reference with known exon deletions.
• Employed a pseudoalignment algorithm to map RNA-seq reads and identify those supporting deletions.
• Integrated gene expression analysis and cohort-wide comparisons to validate deletion evidence.

Main Results:

• Successfully applied the Toblerone algorithm to a cohort of 99 pediatric B-ALL samples.
• Identified and validated IKZF1 deletions within the studied cohort.
• Developed a graphical desktop application for accessible deletion analysis.

Conclusions:

• The Toblerone method provides an effective means for detecting gene exon deletions from RNA-seq data.
• This approach, particularly for IKZF1 deletions in ALL, can aid in prognostic assessment and treatment decisions.
• The accompanying graphical application democratizes the analysis of RNA-seq data for clinical applications.