Dec 3, 2015

A Simple Model-Based Approach to Inferring and Visualizing Cancer Mutation Signatures

PLoS Genetics
Yuichi ShiraishiMatthew Stephens

Abstract

Recent advances in sequencing technologies have enabled the production of massive amounts of data on somatic mutations from cancer genomes. These data have led to the detection of characteristic patterns of somatic mutations or "mutation signatures" at an unprecedented resolution, with the potential for new insights into the causes and mechanisms of tumorigenesis. Here we present new methods for modelling, identifying and visualizing such mutation signatures. Our methods greatly simplify mutation signature models compared with existing approaches, reducing the number of parameters by orders of magnitude even while increasing the contextual factors (e.g. the number of flanking bases) that are accounted for. This improves both sensitivity and robustness of inferred signatures. We also provide a new intuitive way to visualize the signatures, analogous to the use of sequence logos to visualize transcription factor binding sites. We illustrate our new method on somatic mutation data from urothelial carcinoma of the upper urinary tract, and a larger dataset from 30 diverse cancer types. The results illustrate several important features of our methods, including the ability of our new visualization tool to clearly highlight the key fe...Continue Reading

  • References36
  • Citations21

References

  • References36
  • Citations21

Citations

Mentioned in this Paper

TP53 gene
Severe Acute Respiratory Syndrome
Size
Patterns
Tobacco
Lung
Genome
Malignant Neoplasm of Stomach
Somatic Mutation
Genetic Analysis

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