Transcriptome-wide gene expression outlier analysis pinpoints therapeutic vulnerabilities in colorectal cancer.

Area of Science:

• Computational biology
• Genomics
• Cancer research

Background:

• Expanding the drug target repertoire in solid tumors is crucial.
• Systematic identification of gene expression abnormalities is needed for cancer research.

Purpose of the Study:

• To develop and apply a novel computational workflow for transcriptome-wide gene expression outlier analysis in colorectal cancer (CRC).
• To identify overexpression and underexpression events in CRC cell lines.
• To integrate multi-omics data to uncover genetic and epigenetic drivers of outlier expression.

Main Methods:

• Developed a computational workflow for transcriptome-wide gene expression outlier analysis.
• Applied the workflow to RNA sequencing data from 226 CRC cell lines.
• Integrated whole-exome sequencing and DNA methylation profiling data.
• Validated previously known gene expression abnormalities associated with CRC features.

Main Results:

• Identified 3533 genes with abnormally high expression and 965 genes with abnormally low expression in CRC cell lines.
• Confirmed known gene expression abnormalities linked to clinically relevant CRC features.
• Discovered genetic and epigenetic alterations underlying outlier gene expression.
• Demonstrated MTAP gene absence in CRC cell lines confers sensitivity to a PRMT5-MTA inhibitor (MRTX1719).

Conclusions:

• The developed atlas of CRC gene expression outliers serves as a valuable resource for discovering novel drug targets and biomarkers.
• The computational approach is applicable to other tumor types.
• This study highlights a specific therapeutic vulnerability in MTAP-deficient CRC.