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The evolution and progression of multiple myeloma and its precursors over time is poorly understood. Here, we investigate the landscape and timing of mutational processes shaping multiple myeloma evolution in a large cohort of 89 whole genomes and 973 exomes. We identify eight processes, including a mutational signature caused by exposure to melphalan. Reconstructing the chronological activity of each mutational signature, we estimate that the initial transformation of a germinal center B-cell usually occurred during the first 2 nd -3 rd decades of life. We define four main patterns of activation-induced deaminase (AID) and apolipoprotein B mRNA editing catalytic polypeptide-like (APOBEC) mutagenesis over time, including a subset of patients with evidence of prolonged AID activity during the pre-malignant phase, indicating antigen-responsiveness and germinal center reentry. Our findings provide a framework to study the etiology of multiple myeloma and explore strategies for prevention and early detection. The initial mutational processes and how these lead to progression in multiple myeloma (MM) are unclear. Here, the authors identify mutational signatures that occur over time in a large cohort of MM patients and suggest features that may help in early diagnosis.

Cancers are caused by genomic alterations known as drivers. Hundreds of drivers in coding genes are known but, to date, only a handful of noncoding drivers have been discovered—despite intensive searching 1 , 2 . Attention has recently shifted to the role of altered RNA splicing in cancer; driver mutations that lead to transcriptome-wide aberrant splicing have been identified in multiple types of cancer, although these mutations have only been found in protein-coding splicing factors such as splicing factor 3b subunit 1 ( SF3B1 ) 3 – 6 . By contrast, cancer-related alterations in the noncoding component of the spliceosome—a series of small nuclear RNAs (snRNAs)—have barely been studied, owing to the combined challenges of characterizing noncoding cancer drivers and the repetitive nature of snRNA genes 1 , 7 , 8 . Here we report a highly recurrent A>C somatic mutation at the third base of U1 snRNA in several types of tumour. The primary function of U1 snRNA is to recognize the 5′ splice site via base-pairing. This mutation changes the preferential A–U base-pairing between U1 snRNA and the 5′ splice site to C–G base-pairing, and thus creates novel splice junctions and alters the splicing pattern of multiple genes—including known drivers of cancer. Clinically, the A>C mutation is associated with heavy alcohol use in patients with hepatocellular carcinoma, and with the aggressive subtype of chronic lymphocytic leukaemia with unmutated immunoglobulin heavy-chain variable regions. The mutation in U1 snRNA also independently confers an adverse prognosis to patients with chronic lymphocytic leukaemia. Our study demonstrates a noncoding driver in spliceosomal RNAs, reveals a mechanism of aberrant splicing in cancer and may represent a new target for treatment. Our findings also suggest that driver discovery should be extended to a wider range of genomic regions. A highly recurrent A>C somatic mutation in U1 small nuclear RNA, which alters the splicing pattern of genes that include known drivers of cancer, is identified in several types of tumour.

Analysis of mutational signatures is becoming routine in cancer genomics, with implications for pathogenesis, classification, prognosis, and even treatment decisions. However, the field lacks a consensus on analysis and result interpretation. Using whole-genome sequencing of multiple myeloma (MM), chronic lymphocytic leukemia (CLL) and acute myeloid leukemia, we compare the performance of public signature analysis tools. We describe caveats and pitfalls of de novo signature extraction and fitting approaches, reporting on common inaccuracies: erroneous signature assignment, identification of localized hyper-mutational processes, overcalling of signatures. We provide reproducible solutions to solve these issues and use orthogonal approaches to validate our results. We show how a comprehensive mutational signature analysis may provide relevant biological insights, reporting evidence of c-AID activity among unmutated CLL cases or the absence of BRCA1/BRCA2-mediated homologous recombination deficiency in a MM cohort. Finally, we propose a general analysis framework to ensure production of accurate and reproducible mutational signature data. Mutational signature analysis provides important information about the mutational processes underpinning different stages of tumorigenesis. Here, the authors compare publicly available signature extraction tools and suggest a framework for the generation of accurate and reproducible signature data.

Recent evidence suggests that the prognostic impact of gene mutations in patients with chronic lymphocytic leukemia (CLL) may differ depending on the immunoglobulin heavy variable (IGHV) gene somatic hypermutation (SHM) status. In this study, we assessed the impact of nine recurrently mutated genes (BIRC3, EGR2, MYD88, NFKBIE, NOTCH1, POT1, SF3B1, TP53, and XPO1) in pre-treatment samples from 4580 patients with CLL, using time-to-first-treatment (TTFT) as the primary end-point in relation to IGHV gene SHM status. Mutations were detected in 1588 (34.7%) patients at frequencies ranging from 2.3–9.8% with mutations in NOTCH1 being the most frequent. In both univariate and multivariate analyses, mutations in all genes except MYD88 were associated with a significantly shorter TTFT. In multivariate analysis of Binet stage A patients, performed separately for IGHV-mutated (M-CLL) and unmutated CLL (U-CLL), a different spectrum of gene alterations independently predicted short TTFT within the two subgroups. While SF3B1 and XPO1 mutations were independent prognostic variables in both U-CLL and M-CLL, TP53, BIRC3 and EGR2 aberrations were significant predictors only in U-CLL, and NOTCH1 and NFKBIE only in M-CLL. Our findings underscore the need for a compartmentalized approach to identify high-risk patients, particularly among M-CLL patients, with potential implications for stratified management.

Mantle cell lymphoma (MCL) is an aggressive tumor, but a subset of patients may follow an indolent clinical course. To understand the mechanisms underlying this biological heterogeneity, we performed whole-genome and/or whole-exome sequencing on 29 MCL cases and their respective matched normal DNA, as well as 6 MCL cell lines. Recurrently mutated genes were investigated by targeted sequencing in an independent cohort of 172 MCL patients. We identified 25 significantly mutated genes, including known drivers such as ataxia-telangectasia mutated (ATM), cyclin D1 (CCND1), and the tumor suppressor TP53 ; mutated genes encoding the anti-apoptotic protein BIRC3 and Toll-like receptor 2 (TLR2); and the chromatin modifiers WHSC1 , MLL2 , and MEF2B . We also found NOTCH2 mutations as an alternative phenomenon to NOTCH1 mutations in aggressive tumors with a dismal prognosis. Analysis of two simultaneous or subsequent MCL samples by whole-genome/whole-exome (n = 8) or targeted (n = 19) sequencing revealed subclonal heterogeneity at diagnosis in samples from different topographic sites and modulation of the initial mutational profile at the progression of the disease. Some mutations were predominantly clonal or subclonal, indicating an early or late event in tumor evolution, respectively. Our study identifies molecular mechanisms contributing to MCL pathogenesis and offers potential targets for therapeutic intervention.

Mantle cell lymphoma (MCL), a rare and aggressive B-cell non-Hodgkin lymphoma, mainly develops in the lymph node (LN) and creates a protective and immunosuppressive niche that facilitates tumor survival, proliferation and chemoresistance. To capture disease heterogeneity and tumor microenvironment (TME) cues, we have developed the first patient-derived MCL spheroids (MCL-PDLS) that recapitulate tumor oncogenic pathways and immune microenvironment in a multiplexed system that allows easy drug screening, including immunotherapies. MCL spheroids, integrated by tumor B cells, monocytes and autologous T-cells self-organize in disc-shaped structures, where B and T-cells maintain viability and proliferate, and monocytes differentiate into M2-like macrophages. RNA-seq analysis demonstrated that tumor cells recapitulate hallmarks of MCL-LN (proliferation, NF-kB and BCR), with T cells exhibiting an exhaustion profile (PD1, TIM-3 and TIGIT). MCL-PDLS reproduces in vivo responses to ibrutinib and demonstrates that combination of ibrutinib with nivolumab (anti-PD1) may be effective in ibrutinib-resistant cases by engaging an immune response with increased interferon gamma and granzyme B release. In conclusion, MCL-PDLS recapitulates specific MCL-LN features and in vivo responses to ibrutinib, representing a robust tool to study MCL interaction with the immune TME and to perform drug screening in a patient-derived system, advancing toward personalized therapeutic approaches.

Immunoglobulin (Ig) gene rearrangements and oncogenic translocations are routinely assessed during the characterization of B cell neoplasms and stratification of patients with distinct clinical and biological features, with the assessment done using Sanger sequencing, targeted next-generation sequencing, or fluorescence in situ hybridization (FISH). Currently, a complete Ig characterization cannot be extracted from whole-genome sequencing (WGS) data due to the inherent complexity of the Ig loci. Here, we introduce IgCaller, an algorithm designed to fully characterize Ig gene rearrangements and oncogenic translocations from short-read WGS data. Using a cohort of 404 patients comprising different subtypes of B cell neoplasms, we demonstrate that IgCaller identifies both heavy and light chain rearrangements to provide additional information on their functionality, somatic mutational status, class switch recombination, and oncogenic Ig translocations. Our data thus support IgCaller to be a reliable alternative to Sanger sequencing and FISH for studying the genetic properties of the Ig loci. Immunoglobulin (Ig) rearrangement and translocation information are usually obtained by targeted sequencing of the respective loci. Here, the authors present the IgCaller algorithm, which extracts Ig heavy and light chain genetic properties from short-read whole-genome sequencing results to provide a feasible alternative to direct sequencing.

RNA editing—primarily conversion of adenosine to inosine (A > I)—is a widespread posttranscriptional mechanism, mediated by Adenosine Deaminases acting on RNA (ADAR) enzymes to alter the RNA sequence of primary transcripts. Hence, in addition to somatic mutations and alternative RNA splicing, RNA editing can be a further source for recoding events. Although RNA editing has been detected in many solid cancers and normal tissue, RNA editing in chronic lymphocytic leukemia (CLL) has not been addressed so far. We determined global RNA editing and recurrent, recoding RNA editing events from matched RNA-sequencing and whole exome sequencing data in CLL samples from 45 untreated patients. RNA editing was verified in a validation cohort of 98 CLL patients and revealed substantially altered RNA editing profiles in CLL compared with normal B cells. We further found that RNA editing patterns were prognostically relevant. Finally, we showed that ADAR knockout decreased steady state viability of MEC1 cells and made them more susceptible to treatment with fludarabine and ibrutinib in vitro. We propose that RNA editing contributes to the pathophysiology of CLL and targeting the RNA editing machinery could be a future strategy to maximize treatment efficacy.

Cytidine to uridine (C-to-U) as well as adenosine to inosine (A-to-I) RNA editing denotes the posttranscriptional modification of RNA by specialized RNA deaminases. As RNA editing alters the sequence of the RNA, it can affect splicing, stability, miRNA binding and may also lead to recoding of the translated protein. Recently, we analysed recoding A-to-I RNA editing in chronic lymphocytic leukaemia (CLL) and could define prognostically relevant editing patterns. However, disease relevant C-to-U RNA editing in CLL remained unexplored. In this study, we examined C-to-U RNA editing in CLL and discovered a recoding RNA editing site within the MFN1 gene (hg38; chr3:179,375,230), which has recently been described as RNA editing site in brain samples. We found that MFN1 editing was not only present in CLL samples but also in naive B cell subsets, primarily occurred at unspliced RNA and correlated with intron retention. We further identified catalytically active ADARB1 as an essential regulator for MFN1 editing. Finally, MFN1 editing correlated with prolonged time to treatment and overall survival in CLL patients. Summarizing, we identified a novel ADARB1 function as C to U editing regulator, which regulates MFN1 splicing and MFN1 S329L recoding with pathogenic relevance in CLL.

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.Rustad et al. present a software package for the R statistical environment for the accurately quantify of somatic mutational signatures in hematological malignancies