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Updated: Jul 13, 2026
09:49
Oncogenic Gene Fusion Detection Using Anchored Multiplex Polymerase Chain Reaction Followed by Next Generation Sequencing
Published on: July 5, 2019
Co-occurrence analysis of insertional mutagenesis data reveals cooperating oncogenes
Jeroen de Ridder1, Jaap Kool, Anthony Uren
1Information and Communication Theory Group, Faculty of EEMCS, Delft University of Technology, Delft, The Netherlands.
Bioinformatics (Oxford, England)
|July 25, 2007
Summary
This study introduces a new computational method to detect cooperating mutations in cancer gene data. It identified 86 significant co-mutations, revealing how oncogenes cooperate in tumor development.
Area of Science:
- Genomics
- Cancer Biology
- Computational Biology
Background:
- Cancer arises from accumulated mutations disrupting cellular pathways.
- Insertional mutagenesis screens in mice identify cancer gene locations.
- Multiple mutations within a tumor suggest gene cooperation in cancer development.
Purpose of the Study:
- To develop a computational method for detecting statistically significant co-mutations in insertional mutagenesis data.
- To identify cooperating oncogenes and their pathways involved in tumor formation.
Main Methods:
- Developed a two-dimensional Gaussian Kernel Convolution (2DGKC) method.
- Defined Common Co-occurrence of Insertions (CCI) to identify statistically significant co-mutations across multiple screens.
- Analyzed insertion data across multiple scales and visualized results in a scale space.
Main Results:
- Discovered 86 statistically significant co-mutations using the 2DGKC method.
- Identified cooperating oncogenes crucial for tumor development.
- Combined co-occurrence data with gene family information to reveal significant oncogene family cooperations, such as Myc with the Pim family.
Conclusions:
- The 2DGKC method effectively identifies cooperating mutations in cancer gene databases.
- The study reveals novel insights into oncogene cooperation and its role in tumorigenesis.
- Findings provide a foundation for understanding complex genetic interactions in cancer.
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