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Computational methods for cancer driver discovery: A survey.

Vu Viet Hoang Pham¹, Lin Liu¹, Cameron Bracken^2,3

¹UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, AU.

|April 16, 2021

Summary

This summary is machine-generated.

This review categorizes computational tools for cancer driver discovery into single driver, driver module, and personalized methods. It evaluates their performance to guide researchers in selecting the best tools for identifying biologically significant cancer drivers.

Keywords:

cancer driver cancer driver discovery coding gene computational method microRNA

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Identifying cancer-driving genes is crucial for targeted cancer treatment.
Numerous computational tools exist for cancer driver discovery, but their diverse methodologies and data requirements complicate tool selection.
The field of cancer driver discovery is rapidly evolving, necessitating updated reviews.

Purpose of the Study:

To provide a comprehensive survey of computational methods for cancer driver discovery.
To categorize existing methods into single driver, driver module, and personalized approaches.
To evaluate and compare the performance of these tools for identifying biologically significant cancer drivers.

Main Methods:

Systematic review and categorization of computational cancer driver discovery tools.
Classification into three groups: single driver identification, driver module identification, and personalized cancer driver identification.
Performance evaluation and comparison of identified methods.

Main Results:

A categorized reference of computational methods for cancer driver discovery.
Comparative analysis of tool performance in identifying biologically relevant cancer drivers.
Demonstration of biological significance through Gene Ontology (GO) biological processes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment.
Identification of a cancer driver cohort that can stratify patient survival.

Conclusions:

This survey offers a valuable resource for researchers navigating the complex landscape of cancer driver discovery tools.
The performance evaluation provides insights into the capabilities of different methods for uncovering biologically significant drivers.
The identified cancer driver cohort highlights the potential for improved patient stratification and personalized treatment strategies.