  Mutational signatures connect characteristic mutational patterns in the
genome with biological processes that take place in the tumor tissues. Analysis
of mutational signatures can help elucidate tumor evolution, prognosis, and
therapeutic strategies. Although tools for extracting mutational signatures de
novo have been extensively benchmarked, a similar effort is lacking for tools
that fit known mutational signatures to a given catalog of mutations. We fill
this gap by comprehensively evaluating eleven signature fitting tools
(well-established as well as recent) on synthetic input data. To create
realistic input data, we use empirical signature weights in tumor tissue
samples from the COSMIC database. The study design allows us to assess the
effects of the number of mutations, type of cancer, and the catalog of
reference signatures on the results obtained with various fitting tools. We
find substantial performance differences between the evaluated tools. Averaged
over 120,000 simulated mutational catalogs corresponding to eight different
cancer types, SigProfilerSingleSample and SigProfilerAssignment perform best
for small and large numbers of mutations per sample, respectively. We further
show that ad hoc constraining the list of reference signatures is likely to
produce inferior results and that noisy estimates of signature weights in
samples with as few as 100 mutations can still be useful in downstream
analysis.
