Characterisation of the genomic landscape in splenic marginal zone lymphoma

Abstract

Background: Somatic gene mutations can alter protein function, drive carcinogenesis and aid in the risk-adapted stratification of cancer patients. Splenic Marginal Zone Lymphoma (SMZL) is an indolent B-cell lymphoma comprising less than 2% of lymphoid neoplasms. Approximately 70% of patients develop a progressive disease requiring treatment whilst 30% of these will ultimately transform to a more aggressive lymphoma. There are currently no biomarkers recommended for establishing diagnosis, assessing prognosis, or determining the choice of therapy. This is in part due to superficial understanding of the molecular pathogenesis and heterogeneity of the disease.
Aims: The main aim of this study is to construct a detailed characterisation of the genetic landscape of SMZL through the identification of somatic variants in unmatched tumour samples in the largest SMZL cohort to date and explore their clinical significance by integrating relevant clinical data. In conjunction to the analysis of somatic variants, an important part of this project also centres around the bioinformatics processing and optimisation of pipelines to obtain the best sequencing results.
Methods: Tumour samples were sequenced using an amplicon-based approach consisting of 57 target genes. Paired end reads were aligned using BWA-mem to the hg38 reference genome and LocatIt was used to merge duplicate reads using unique molecular identifiers. Afterward, GATK’s haplotype caller was used for variant calling and Annovar software for annotation. Variants were filtered using an unsupervised machine learning algorithm and validated in-silico using a genome viewer. Subsequently, variants were filtered once more to reduce likely germline variants. Finally, additional clinical and genetic data was integrated with the curated variant list to correlate genomic results with clinical outcomes.
Results: In concordance with the literature NOTCH2 [13%], TP53 [12%] and KLF2 [12%] were found to be recurrently mutated among SMZL patients. As well as validating previous observations, key findings within this work included: 1) Genes KMT2D and CCND3 were found mutated in a much higher number of cases than was expected; 2) KLF2 and CCND3 harbour mutation hotspots which require functional validation but are predicted to affect protein function; 3) Evidence of somatic hypermutation (SHM) was found in the majority of cases, (only 8% showed no evidence of SHM); 4) Deletions of 7q were associated to IGHV1-2*04 usage, KLF2 and NOTCH2 mutations, short telomeres, and low levels of SHM; 5) Identification of two potential genomic subgroups, one group characterised by 7q deletions, KLF2 and NOTCH2 mutations and IGHV1-2*04 usage and a second group characterised by MYD88 mutations and mutated IGHV genes and; 6) Identification of telomere length and gains of 3q and 8q as new potential prognostic factors.
Conclusion: This project collects the largest cohort of SMZL cases assessed to date imparting clarity to the genetic landscape of this cancer. The data supports distinct sub-groups of SMZL driven by IGHV usage and consistent genomic lesions. Additional studies across multiple discovery and validation cohorts, as well as prospective clinical trials are required to validate results, particularly disease outcomes.

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Identifiers

ORCID for Sarah Ennis: orcid.org/0000-0003-2648-0869

ORCID for Jane Gibson: orcid.org/0000-0002-0973-8285

ORCID for Jon Strefford: orcid.org/0000-0002-0972-2881

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