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Updated: Sep 28, 2025

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres
Published on: July 22, 2020
SWAN pathway-network identification of common aneuploidy-based oncogenic drivers.
Robert R Bowers1, Christian M Jones1, Edwin A Paz2
1Department of Biochemistry and Molecular Biology, Medical University of South Carolina, Charleston, SC, USA.
Shifted Weighted Annotation Network (SWAN) analysis identifies cancer drivers by assessing cumulative monoallelic gene changes. This approach prioritizes oncogenes and tumor suppressors, revealing new therapeutic targets in solid tumors.
Area of Science:
- Genomics
- Cancer Biology
- Bioinformatics
Background:
- Monoallelic genetic differences, like those in siblings, are fundamental to evolution.
- Cancerous solid tumors display aneuploidy, characterized by numerous monoallelic gene-level copy-number alterations (CNAs).
- Tumor aneuploidy presents a high-noise environment, complicating the identification of driver mutations.
Purpose of the Study:
- To develop a method for analyzing the biological impact of cumulative monoallelic genetic changes in cancer.
- To identify cancer drivers, including tumor suppressors and oncogenes, within the complex landscape of CNAs.
- To uncover novel druggable vulnerabilities in solid tumors.
Main Methods:
- Development of Shifted Weighted Annotation Network (SWAN) analysis.
- Integrated pathway-network analysis of CNAs, RNA expression, and mutations.
- Deployment via a user-friendly web platform for prioritizing genetic alterations.
Main Results:
- SWAN effectively assesses biology affected by cumulative monoallelic changes.
- The analysis successfully prioritizes known and novel tumor suppressors and oncogenes.
- Commonly suppressed pathways include protein homeostasis, phospholipid dephosphorylation, and ion transport.
- An atlas of CNA-altered pathways across cancer types has been generated.
Conclusions:
- SWAN analysis provides a robust method for navigating complex cancer genetics.
- Identified CNA network shifts highlight promising new therapeutic targets for solid tumors.
- The findings offer a new perspective on exploiting genetic vulnerabilities in cancer treatment.
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