A simple model-based approach to inferring and visualizing cancer mutation signatures
Yuichi Shiraishi, Georg Tremmel, Satoru Miyano, Matthew Stephens
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 features of each signature,
the improved robustness of signature inferences from small sample sizes,
and more detailed inference of signature characteristics such as strand
biases and sequence context effects at the base two positions 5' to the
mutated site.
The overall framework of our work is based on probabilistic models that
are closely
connected with ``mixed-membership models'' which are widely used in
population genetic admixture analysis, and in machine learning for
document clustering. We argue that recognizing these relationships
should help improve
understanding of mutation signature extraction problems,
and suggests ways to further improve the statistical methods.
Our methods are implemented in an R package
pmsignature (https://github.com/friend1ws/pmsignature)
and a web application available at
https://friend1ws.shinyapps.io/pmsignature_shiny/.
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