MOTIVATION: Cancers arise as the result of somatically acquired changes in the 
DNA of cancer cells. However, in addition to the mutations that confer a growth 
advantage, cancer genomes accumulate a large number of somatic mutations 
resulting from normal DNA damage and repair processes as well as carcinogenic 
exposures or cancer related aberrations of DNA maintenance machinery. These 
mutagenic processes often produce characteristic mutational patterns called 
mutational signatures. The decomposition of a cancer genome's mutation catalog 
into mutations consistent with such signatures can provide valuable information 
about cancer etiology. However, the results from different decomposition methods 
are not always consistent. Hence, one needs to be able to not only decompose a 
patient's mutational profile into signatures but also establish the accuracy of 
such decomposition.
RESULTS: We proposed two complementary ways of measuring confidence and 
stability of decomposition results and applied them to analyze mutational 
signatures in breast cancer genomes. We identified both very stable and highly 
unstable signatures, as well as signatures that previously have not been 
associated with breast cancer. We also provided additional support for the novel 
signatures. Our results emphasize the importance of assessing the confidence and 
stability of inferred signature contributions.
AVAILABILITY AND IMPLEMENTATION: All tools developed in this paper have been 
implemented in an R package, called SignatureEstimation, which is available from 
https://www.ncbi.nlm.nih.gov/CBBresearch/Przytycka/index.cgi\#signatureestimation.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics 
online.