Computational Methods Summarizing Mutational Patterns in Cancer: Promise and Limitations for Clinical Applications

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Next-generation sequencing has expanded the catalog of cancer mutations.
• Computational approaches are essential for interpreting the complex cancer-genomic landscape.
• Numerous methods have been developed to extract meaningful insights from cancer mutation data.

Purpose of the Study:

• To review leading computational methods for deriving intricate mutational patterns with clinical relevance.
• To examine the utility and challenges of mutation signatures in clinical decision-making.
• To survey computational tools for identifying cancer-driver genes and predicting cancer phenotypes.

Main Methods:

• Review of mutation signature development and applications.
• Survey of computational tools for cancer-driver gene identification (single-driver, pathway, and network analyses).
• Examination of methods for inferring mutation combinations and integrating multi-omics data for prediction.

Main Results:

• Mutation signatures correlate environmental effects with cancer genomes and have potential clinical use cases.
• Computational tools can identify cancer-driver genes and predict tumor origin, treatment outcomes, prognosis, and cancer typing.
• Integration of multi-omics data improves cancer phenotype prediction.

Conclusions:

• Computational tools offer significant potential for cancer diagnosis and treatment, aiding in discovery and prediction.
• Limitations exist in the widespread clinical integration of these tools.
• Advances in machine learning offer solutions to overcome current challenges and enhance clinical utility.