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

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A systematic view of computational methods for identifying driver genes based on somatic mutation data.

Yingxin Kan¹, Limin Jiang^1,2, Jijun Tang^2,3

¹School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China.

Briefings in Functional Genomics

|July 27, 2021

Summary

Identifying cancer driver genes is crucial for diagnosis and treatment. This study reviews computational methods using somatic mutation data, offering a systematic approach for future research.

Keywords:

cancer driver genes clustering computational tools driver mutations frequency functional impact

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Driver gene mutations are critical in cancer development and biomedical research.
Identifying these genes is essential for accurate cancer diagnosis and treatment strategies.
Computational methods offer greater efficiency than traditional experiments for analyzing large mutation datasets.

Purpose of the Study:

To systematically review and categorize eight common computational algorithms for identifying cancer driver genes using only somatic mutation data.
To establish a general process for nominating candidate cancer driver genes.
To evaluate the performance of representative methods across multiple cancer types.

Main Methods:

Grouping computational methods based on the mutation features they utilize.
Developing a standardized process for nominating candidate driver genes.
Evaluating three representative algorithms on 10 cancer types from The Cancer Genome Atlas Program and five projects from the International Cancer Genome Consortium.
Comparing method results using various parameters and assessing them via CGC, OG/TSG, Q-value, and QQ plots.

Main Results:

Categorization of eight computational algorithms based on mutation feature usage.
A defined process for nominating candidate cancer driver genes.
Comparative evaluation of three methods across diverse cancer datasets.
Performance assessment using multiple metrics including CGC, OG/TSG, Q-value, and QQ plots.

Conclusions:

Somatic mutation data analysis provides an efficient approach to identifying cancer driver genes.
The presented systematic view of mutation features and evaluation methods lays the groundwork for integrating diverse data types.
This work facilitates the development of more robust driver gene identification strategies for precision oncology.