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
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).
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
