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Spontaneous and Induced Mutations

Spontaneous and Induced Mutations

Spontaneous mutations arise infrequently during DNA replication due to errors in the process. A key factor behind these errors is tautomeric shifts in nitrogenous bases, where bases transition from keto to enol forms or amino to imino forms. This shift can alter base-pairing rules, leading to mutations. Additionally, reactive oxygen species (ROS) arising from aerobic metabolism can damage DNA, resulting in depurination (loss of a purine base) or depyrimidination (loss of a pyrimidine base).

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Related Experiment Video

Updated: Apr 20, 2026

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

Published on: July 22, 2020

HotSpotter: efficient visualization of driver mutations.

Jason Roszik, Scott E Woodman¹

¹Department of Melanoma Medical Oncology, The University of Texas MD Anderson Cancer Center, 7455 Fannin St, Houston, TX 77054, USA. swoodman@mdanderson.org.

|December 2, 2014

Summary

This summary is machine-generated.

Identifying cancer driver mutations is crucial. This study introduces HotSpotter, a tool to visualize mutation data, revealing less frequent or clustered mutations that indicate potential drivers, as demonstrated in endometrial cancer.

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Area of Science:

Oncology
Genetics
Bioinformatics

Background:

Driver mutations are key to cancer evolution and are typically identified by their frequency within a gene.
However, driver mutations can be infrequent at specific sites but clustered within a gene region, or have low frequency across different cancer types.
These less frequent or regionally clustered mutations may still confer a selective growth advantage.

Purpose of the Study:

To present a novel method for visualizing cancer mutation datasets.
To enable the identification of potential gene mutation 'hotspot' sites and regions.
To detect driver mutations that might be overlooked by conventional frequency-based analyses.

Main Methods:

Development of a rapid and user-friendly visualization tool named HotSpotter.
Overlaying gene mutation locations and frequencies onto a comprehensive cancer mutation reference set.
Utilizing the tool to analyze mutation data for potential driver identification.

Main Results:

The HotSpotter tool facilitates easy visualization and identification of mutation hotspots.
Application of the tool identified potentially functionally relevant hotspot regions in the NFE2L2 gene in endometrial cancer.
These NFE2L2 hotspots were not apparent through other standard analytical methods.

Conclusions:

HotSpotter is an efficient tool for visualizing gene mutation data, including location and frequency.
It aids in identifying potential driver mutations, particularly those with low overall frequency or localized in hotspot regions.
The tool enhances the discovery of functionally significant mutations across various cancer types.