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Oquendo et al. J Transl Genet Genom 2021;5:89-111 https://dx.doi.org/10.20517/jtgg.2021.04 Page 105

knowledge in SMZL. The diagram outlines key areas for future research starting with unbiased whole genome sequencing of matched
germline-tumour samples across the disease history. Each time point will allow for the identification of key immunogenetic and
cytogenetic subgroups, as well as mutational signatures. Whole genome sequencing is essential for the identification of non-coding
mutations, and with large collaborative cohorts enabling the identification of SNPs associated with disease risk. This unbiased approach
will form the basis for more targeted studies, analysing additional contributory factors such as epigenome of SMZL and telomere
attrition mechanisms. The identification and analysis of suitable control MZ B-cells will be important work that will feed into wider
studies, particularly epigenetic studies and determining the cell of origin. Synthesis of new information will allow the identification of
biological sub-groups and direct downstream functional analyses. The ultimate goal is to translate newly acquired knowledge of the
underlying molecular mechanism of SMZL for direct patient benefit: to improve differential diagnosis and aid in the discovery of novel
therapeutic targets. SMZL: Splenic marginal zone lymphoma; MZ: marginal zone; CNAs: copy number alterations; WGS: whole genome
sequencing; CBL-MZ: clonal B cell lymphocytosis of marginal zone origin; IGHV: immunoglobulin heavy chain variable region; FACS:
fluorescence-activated cell sorting; TME: tumour microenvironment.

employ high-resolution DNA imaging technologies [136,137] .

Ongoing studies
Whilst it is clear that SMZL research remains in its infancy, a close and collaborative network of clinicians
and scientists are continuing to study the disease. Two ongoing projects are particularly exciting and
promise to significantly advance our biological understanding of the disease. Firstly, the IELSG46
(NCT02945319) is an observational study, led by Professor Davide Rossi, which aims to develop and
validate an accurate prognostication model using integrated molecular profiling of > 300 treatment naive
SMZL patients, where diagnosis is based on spleen histology and > 5 years of follow-up is available on all
patients. The initial plans were to perform targeted sequencing to detect somatic gene mutations and permit
IGHV analysis and FISH to define 7q deletion status, culminating with complex statistical methods to
develop hierarchical models to predict overall survival. However, the project has now developed to include
the genome wide analysis of mutations, CNAs and gene expression. This exciting multicentre study is
ongoing and has only been presented in abstract form [138,139] . In 2019, the consortium presented their early
work. Profiling a cohort of 382 SMZL patients, the authors define four molecular clusters (MC) with distinct
outcomes, employing machine learning approaches. MC1 and 2 were driven by KLF2 and NOTCH2
mutations, respectively, and enriched for IGHV1-2*04 and deletion of 7q. MC3 and 4 were defined by
KMT2D and TP53/ATM mutations, respectively. Clinical correlations demonstrated that MC1 and MC2
were those that exhibited inferior survival. The authors also reported a more restricted analysis of gene
expression data suggesting that patients can be clustered based on transcriptomic signatures. This study,
and a host of consequent manuscripts, will be truly important in defining the prognostic landscape of the
disease, and will be an important step towards identifying patients for precision medicine approaches.

Our group is also coordinating a large international study, distinct from IELSG46, focusing on detailed
DNA methylation profiling of SMZL cases and positioning these cases within a framework including a
spectrum of normal B-cell sub-sets and a myriad of other mature B-cell tumours. These data will then be
integrated with genome-wide mutational and CNA data, telomere dynamics, and for key biological
subgroups, the proliferative history based on methylation arrays, transcriptomic and chromatin accessibility
maps. The aims are to: (1) further characterize the epigenetic landscape of SMZL, and define new patient
subgroups based on state-of-the-art approaches; (2) define patient subgroups based on their relationship to
normal B-cell populations, providing insights into the potential cell-of-origin of key patient subgroups; (3)
compare SMZL to the DNA methylation patterns of other mature B-cell tumours, detailing approaches that
might aid in differential diagnosis; and (4) compare CBL-MZ to SMZL and aligned conditions, allowing
more accurate disease classification and improved prediction of those CBL-MZ cases destined to progress to
overt lymphoma. These are two examples of key data sets that will emerge in the literature in the coming
months and should advance our understanding of the molecular lesions that define the disease and provide
further support of their clinical utility.