Benchmarking 13 tools for mutational signature attribution, including a new and improved algorithm

Abstract

Mutational signatures are characteristic patterns of mutations caused by endogenous mutational processes or by exogenous mutational exposures. There has been little benchmarking of approaches for determining which signatures are present in a sample and estimating the number of mutations due to each signature. This problem is referred to as "signature attribution". We show that there are often many combinations of signatures that can reconstruct the patterns of mutations in a sample reasonably well, even after encouraging sparse solutions. We benchmarked thirteen approaches to signature attribution, including a new approach called Presence Attribute Signature Activity (PASA), on large synthetic data sets (2,700 synthetic samples in total). These data sets recapitulated the single-base, insertion-deletion, and doublet-base mutational signature repertoires of 9 cancer types. For single-base substitution mutations, PASA and MuSiCal outperformed other approaches on all the cancer types combined. However, the ranking of approaches varied by cancer type. For doublet-base substitutions and small insertions and deletions, while PASA outperformed the other approaches in most of the nine cancer types, the ranking of approaches again varied by cancer type. We believe this variation reflects inherent difficulties in signature attribution. These difficulties stem from the fact that there are often many attributions that can reasonably explain the pattern of mutations in a sample and from the combinatorial search space due to the need to impose sparsity. Tables herein can provide guidance on the selection of mutational signature attribution approaches that are best suited to particular cancer types and study objectives.