Mmsig: a fitting approach to accurately identify somatic mutational signatures in hematological malignancies

Even H Rustad,Elias Campo,Nicos Angelopoulos,Bachisio Ziccheddu,Ferran Nadeu,Francesco Maura,Niccolò Bolli,Xose S Puente,Ola Landgren

doi:10.1038/s42003-021-01938-0

Abstract

Mutational signatures have emerged as powerful biomarkers in cancer patients, with prognostic and therapeutic implications. Wider clinical utility requires access to reproducible algorithms, which allow characterization of mutational signatures in a given tumor sample. Here, we show how mutational signature fitting can be applied to hematological cancer genomes to identify biologically and clinically important mutational processes, highlighting the importance of careful interpretation in light of biological knowledge. Our newly released R package mmsig comes with a dynamic error-suppression procedure that improves specificity in important clinical and biological settings. In particular, mmsig allows accurate detection of mutational signatures with low abundance, such as those introduced by APOBEC cytidine deaminases. This is particularly important in the most recent mutational signature reference (COSMIC v3.1) where each signature is more clearly defined. Our mutational signature fitting algorithm mmsig is a robust tool that can be implemented immediately in the clinic.

Highlights

We show how mutational signature fitting can be used to identify clinically relevant mutational processes in hematological malignancies
Recent whole-genome sequencing (WGS) studies have revealed more than 40 signatures of single base substitutions (SBS), representing processes active in all tissues, as well as celltype-specific intrinsic processes (e.g., APOBEC-family cytidine deaminases, such as activation-induced cytidine deaminase, AID), processes related to exogenous agents, and deficiencies of specific DNA repair mechanisms[1,2,3,7,8,9,10,11,12]
We previously focused on the challenges and pitfalls of the first two steps, which may result in falsely identifying signatures that are not active in a given disease, such as HRD-SBS3 in multiple myeloma (MM)[10,14]

Summary

Introduction

We show how mutational signature fitting can be used to identify clinically relevant mutational processes in hematological malignancies. Using the latest COSMIC reference (i.e., COSMIC v3.1; https:// cancer.sanger.ac.uk/cosmic/signatures/SBS/index.tt) and applying three different fitting algorithms (deconstructSigs, mutationalPatterns, and mmsig) to each of the 82 MM samples without the use of error-correction showed similar results (Fig. 2A). As expected, updating to a reference set where each mutational signature is more clearly defined resulted in a clear overall increase in reconstruction accuracy for all three fitting algorithms (Fig. 3D).

Results

Conclusion