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Identification of Single Nucleotide Non-coding Driver Mutations in Cancer
Kok A Gan1, Sebastian Carrasco Pro1, Jared A Sewell1
1Department of Biology, Boston University, Boston, MA, United States.
Abstract:
Recent whole-genome sequencing studies have identified millions of somatic variants present in tumor samples. Most of these variants reside in non-coding regions of the genome potentially affecting transcriptional and post-transcriptional gene regulation. Although a few hallmark examples of driver mutations in non-coding regions have been reported, the functional role of the vast majority of somatic non-coding variants remains to be determined. This is because the few driver variants in each sample must be distinguished from the thousands of passenger variants and because the logic of regulatory element function has not yet been fully elucidated. Thus, variants prioritized based on mutational burden and location within regulatory elements need to be validated experimentally. This is generally achieved by combining assays that measure physical binding, such as chromatin immunoprecipitation, with those that determine regulatory activity, such as luciferase reporter assays. Here, we present an overview of in silico approaches used to prioritize somatic non-coding variants and the experimental methods used for functional validation and characterization.
Insights
Millions of somatic variants in tumors are in non-coding DNA, potentially altering gene regulation. This study reviews computational and experimental methods to identify and validate functional non-coding variants in cancer.
Area of Science:
- Genomics
- Cancer Biology
- Molecular Biology
Background:
- Whole-genome sequencing reveals millions of somatic variants in tumors, predominantly in non-coding regions.
- These non-coding variants may influence gene regulation, but their functional roles are largely unknown.
- Distinguishing driver from passenger variants and understanding regulatory element function are key challenges.
Purpose of the Study:
- To provide an overview of computational approaches for prioritizing somatic non-coding variants.
- To present experimental methods for the functional validation and characterization of these variants.
Main Methods:
- In silico prioritization strategies based on mutational burden and location in regulatory elements.
- Experimental validation combining physical binding assays (e.g., ChIP) and regulatory activity assays (e.g., luciferase reporter assays).
Main Results:
- Discusses the integration of computational predictions with experimental validation for non-coding variants.
- Highlights the importance of a multi-modal approach to assess variant function.
Conclusions:
- Systematic characterization of somatic non-coding variants is crucial for understanding cancer driver mutations.
- Combining in silico prioritization with robust experimental validation is essential for functional genomics research in cancer.
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