A comprehensive comparison of tools for fitting mutational signatures

TL;DR

This study comprehensively evaluates twelve tools for fitting known mutational signatures to cancer mutation data, highlighting their performance differences and challenges in real-world applications.

Contribution

It provides the first extensive benchmark of mutational signature fitting tools using synthetic and real data, guiding tool selection and revealing limitations.

Findings

SigProfilerSingleSample performs best with fewer mutations.

SigProfilerAssignment/MuSiCal excel with larger mutation counts.

Constrained signature lists often lead to worse results.

Abstract

Mutational signatures connect characteristic mutational patterns in the genome with biological or chemical processes that take place in cancers. 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 twelve signature fitting tools on synthetic mutational catalogs with empirically-driven signature weights corresponding to eight cancer types. On average, SigProfilerSingleSample and SigProfilerAssignment/MuSiCal 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. Evaluation of real mutational catalogs suggests that the activity of signatures that are absent in the reference catalog poses considerable problems to all evaluated tools.