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Updated: May 6, 2026
Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres
Published on: July 22, 2020
Transcriptome-wide gene expression outlier analysis pinpoints therapeutic vulnerabilities in colorectal cancer
Elisa Mariella1,2, Gaia Grasso1,2, Martina Miotto1,2
1Department of Oncology, Molecular Biotechnology Center, University of Torino, Italy.
Abstract:
Multiple strategies are continuously being explored to expand the drug target repertoire in solid tumors. We devised a novel computational workflow for transcriptome-wide gene expression outlier analysis that allows the systematic identification of both overexpression and underexpression events in cancer cells. Here, it was applied to expression values obtained through RNA sequencing in 226 colorectal cancer (CRC) cell lines that were also characterized by whole-exome sequencing and microarray-based DNA methylation profiling. We found cell models displaying an abnormally high or low expression level for 3533 and 965 genes, respectively. Gene expression abnormalities that have been previously associated with clinically relevant features of CRC cell lines were confirmed. Moreover, by integrating multi-omics data, we identified both genetic and epigenetic alternations underlying outlier expression values. Importantly, our atlas of CRC gene expression outliers can guide the discovery of novel drug targets and biomarkers. As a proof of concept, we found that CRC cell lines lacking expression of the MTAP gene are sensitive to treatment with a PRMT5-MTA inhibitor (MRTX1719). Finally, other tumor types may also benefit from this approach.
Insights
Researchers developed a new computational method to find gene expression outliers in colorectal cancer (CRC) cells. This approach identified thousands of abnormal gene expression events, revealing potential new drug targets and biomarkers for cancer therapy.
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.
