Abstract

Introduction: In recent years, strategies have been developed to identify specific mutation patterns within next-generation sequencing data. Distinct mutational patterns can be linked to underlying mutagenic processes in human cancer. One approach analyzes single base substitutions in the context of their neighboring bases as trinucleotides. The relative prevalence of all possible 96 altered trinucleotides defines distinctive mutational signatures. The activity of activation-induced cytidine deaminase (AID) initiates a specific mutational process in B cells. AID induces deamination of deoxycytidine into deoxyuridine. Subsequent mechanisms to repair the resulting mismatch lead to different genomic alterations that can be assigned to three mutational signatures: a canonical signature characterized by C>T/G transitions at WRCY motifs, a non-canonical signature defined by A>C transversions at WAN motifs, and a third AID signature characterized by C>T transitions at RCG motifs with preference for methylated CpG (W: A or T; R: purine; Y: pyrimidine, N: any nucleotide). The latter signature has specifically been designated as AID-mediated CpG-methylation-dependent mutagenesis. AID activity has been linked to the pathogenesis of several B-cell lymphomas, including follicular lymphoma (FL), chronic lymphocytic leukemia (CLL), and mantle cell lymphoma (MCL). Therefore, we searched for the contribution of different AID signatures in these B-cell malignancies.

Methods: We analyzed the mutational landscape in whole exome (WES) and whole genome (WGS) sequencing data from 41 FL, 30 CLL, 2 MBL, and 43 MCL cases. Somatic variants were called by comparison of tumor and germline DNA with an in-house developed pipeline. Mutational signatures were defined according to the 96-base substitution model (Alexandrov et al. 2013) by an unsupervised machine learning with implementation of the SomaticSignatures R package (Gehring et al. 2015). In addition, MutationalPattern R package (Blokzijl et al. 2018) was executed for comparison to mutational signatures defined in COSMIC.

Results: In unsupervised analyses of FL, CLL/MBL, and MCL cases, 77% of the mutation spectrum variance was attributable to four signatures (S1-4). In FL, the mutational landscape was dominated by S4 characterized by mutations in both canonical and non-canonical AID motifs (40%, 95% CI: 35-76%). The second most frequent signature (S2; 27%, 21-49%) was characterized by C>A transitions in the context of the non-canonical AID and the CpG hotspot motifs (RCG). The mutational landscape of CLL and MBL was strongly dominated by signature S3 (50%, 45-95%). S3 contains mutations in RCG motifs as well as mutations in non-canonical AID motifs (NTW), but with a lower contribution that in S4. In contrast, the mutational landscape of MCL was dominated by S1 (31%, 24-55%) characterized by C>T transitions in the RCG motif in addition to a striking prevalence of the TCT>TTT transition that is known to be associated with the activity of APOBEC enzymes. In comparison to the mutational signatures in COSMIC, the lymphomas analyzed here carry a strong similarity to the COSMIC signatures 1, 5, and 25. These signatures are observed across a wide spectrum of cancer types and are either of unknown etiology (S5 and S25) or associated with age (S1).

Conclusions: The most common point mutations in CLL/MBL and FL are C>T transitions and indicate a strong influence of AID on their mutational landscape. In the indolent B-cell malignancies, all three known AID-related signatures, i.e. canonical, non-canonical, and CpG-methylation-dependent can be found. In contrast, the genomic landscape of MCL is dominated by variants in CpG-methylation-dependent mutagenesis sites and by an APOBEC-related motif. In addition to AID-related signatures, we also found consensus signatures described in COSMIC such as the age-related spontaneous deamination signature 1. Our work independently confirms the role of AID in B-cell lymphoma pathogenesis but points to disease-specific mechanisms that modulate AID in the respective lymphoma cell of origin. In addition, our data suggest that distinctive repair mechanisms operate in different entities.

Disclosures

No relevant conflicts of interest to declare.

Author notes

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Asterisk with author names denotes non-ASH members.