Martí Duran-Ferrer

Splenic marginal zone lymphoma (SMZL) is a rare B-cell malignancy with notable genetic, epigenetic, and clinical heterogeneity. This study utilized coding and non-coding sequencing (n=74), including whole-genome sequencing (WGS) on 24 paired tumour-normal samples, targeted sequencing (n=55) and DNA methylation in 126 cases to characterize the disease. From WGS, we identified recurrent, predominantly clonal, coding mutations in KLF2 (50%), KMT2D (25%) and NOTCH2 (25%), alongside rare mutations in FLNC (8%), novel mutations in FAM135B (17%) and non-coding mutational hotspots in BCL6, PAX5 and BACH2, linked to aberrant somatic hypermutation. At least one non-coding hotspot was detected in 69% of cases. Copy-number aberrations (CNAs) were present in 73% of cases, including del(7q) (27%), gain(3q) (17%) and trisomy 12 (13%). DNA methylation profiling revealed two epigenetic subgroups: SMZL-HR (high-risk, n=67) and SMZL-LR (low-risk, n=59). SMZL-HR was associated with adverse features such as female sex, IGHV1-2*04 usage, KLF2 mutations, del(7q), shorter telomeres, and elevated epiCMIT scores. Transcriptomic analysis highlighted enhanced cell proliferation in SMZL-HR, with enrichment of E2F and G2M checkpoint pathways and epigenetic regulation via EZH2. SMZL-HR patients had significantly shorter time to first treatment (TTFT) (HR: 1.9, p=.003) and reduced overall survival (HR: 2.5, p=.039): 85% of SMZL-HR patients required treatment and showed a higher frequency of transformation (p=.007) and mortality (p<.001). Multivariate analysis confirmed SMZL-HR as an independent predictor of shorter TTFT (HR: 2.4, p=.001). These findings demonstrate the role of DNA methylation and molecular profiling in SMZL risk stratification.

Accurate pathological diagnosis of hematolymphoid neoplasms (HLN) is often challenging due to their complexity and heterogeneity. Genome-wide DNA methylation profiling has emerged as a valuable tool for tumor classification and diagnosis across malignancies, like central nervous system neoplasms. In this study, we explored the role of DNA methylation-based profiling in HLN. We generated the largest cross-platform HLN methylome cohort to date (1,156 samples) and identified 44 reproducible methylation classes (MCs) that aligned closely with WHO 5th edition/ICC entities, including subgroups with clinical and biological relevance. Copy number alterations were also inferred for all MCs. Additionally, a machine learning-based DNA methylation classifier was developed and validated on independent test set, demonstrating a modest 58% high-confidence score rate, nevertheless with a robust 97% concordance with the original diagnosis in these high-confidence score cases. Additionally, in discrepant high confidence score cases, while few, the methylation classifier demonstrated its potential utility as an adjunct where on additional review the diagnosis was revised in favor of the methylation prediction in the majority of cases (5/8 discrepant cases). Tumor purity was a significant contributor for a substantial proportion of low confidence score samples (scores below predetermined thresholds ,42%), affecting the classifier performance (X2 = 11.7, p<0.0008). Our findings suggest that specific hematolymphoid tumor types exhibit distinct methylation signatures that can be leveraged to accurately classify these tumors. As a pilot study, our results provide a foundation for the development of a comprehensive and clinically valuable methylation-based classifier for hematolymphoid tumors in the future.

The transcription factor lymphoid enhancer-binding factor 1 (LEF1) is aberrantly expressed across all subtypes and stages of chronic lymphocytic leukemia (CLL), yet the molecular mechanisms underlying its contribution to CLL pathogenesis remain poorly defined. Here, we conducted a comprehensive mechanistic dissection of LEF1 function in CLL using extensive functional analyses of patient-derived samples. We identified that, although LEF1 messenger RNA levels remain stable, patients with clinically aggressive disease show elevated LEF1 protein levels due to enhanced protein stability. LEF1 protein abundance is selectively modulated by lymph node–derived stimuli, including T-cell interactions and B-cell receptor signaling. Importantly, we uncovered a dual, context-dependent role for LEF1 that is determined by its protein levels. Low LEF1 protein, characteristic of indolent disease, supports B-cell activation, whereas increased protein abundance in aggressive disease promotes proliferation through the binding and induction of cell cycle and metabolic gene networks. We further showed that LEF1 exon 6 skipping is enriched in proliferative and aggressive CLL. Both in vitro and in vivo experiments revealed that LEF1-driven proliferation is mediated by these short, alternative spliced isoforms. Although all LEF1 isoforms bind to a core set of proliferation- and activation-related genes, they induce distinct transcriptional programs; full-length LEF1 promotes a quiescence gene signature and limits leukemic growth, whereas exon 6-skipping isoforms drive proliferation. Our findings establish LEF1 as an oncogenic transcription factor in CLL whose biological and clinical effects are modulated posttranscriptionally by both protein abundance and isoform composition.

Cancer development and response to treatment are evolutionary processes^1,2, but characterizing evolutionary dynamics at a clinically meaningful scale has remained challenging³. Here we develop a new methodology called EVOFLUx, based on natural DNA methylation barcodes fluctuating over time⁴, that quantitatively infers evolutionary dynamics using only a bulk tumour methylation profile as input. We apply EVOFLUx to 1,976 well-characterized lymphoid cancer samples spanning a broad spectrum of diseases and show that initial tumour growth rate, malignancy age and epimutation rates vary by orders of magnitude across disease types. We measure that subclonal selection occurs only infrequently within bulk samples and detect occasional examples of multiple independent primary tumours. Clinically, we observe faster initial tumour growth in more aggressive disease subtypes, and that evolutionary histories are strong independent prognostic factors in two series of chronic lymphocytic leukaemia. Using EVOFLUx for phylogenetic analyses of aggressive Richter-transformed chronic lymphocytic leukaemia samples detected that the seed of the transformed clone existed decades before presentation. Orthogonal verification of EVOFLUx inferences is provided using additional genetic data, including long-read nanopore sequencing, and clinical variables. Collectively, we show how widely available, low-cost bulk DNA methylation data precisely measure cancer evolutionary dynamics, and provides new insights into cancer biology and clinical behaviour.

Previous studies have reported that chronic lymphocytic leukemia (CLL) shows a de novo chromatin activation pattern compared with normal B cells. Here, we explored whether the level of chromatin activation is related to the clinical behavior of CLL. We identified that, in some regulatory regions, increased de novo chromatin activation is linked to clinical progression, whereas, in other regions, it is associated with an indolent course. We next developed 2 prognostic scores for progressive and indolent disease, respectively, calculated a single score representing the balance between them, and further generated surrogate scores based on gene and protein expression of the target genes. The balance score outperformed the clinical impact of the 2 individual scores, because it seemed to capture the prognostic information provided by each of them. Biologically, CLLs with higher balance score showed increased activation of tumor necrosis factor alpha (TNF-α)/NF-κB and mTOR signaling pathways. Regulatory programs related to progression were predominantly activated in the lymph node microenvironment, whereas those linked to indolent disease appeared to be microenvironment independent. Finally, we thoroughly validated the balance score as a powerful and independent quantitative prognostic factor for time to first treatment across independent CLL cohorts and data modalities, such as chromatin, transcriptome, or proteome data. Our findings support the concept that de novo acquisition of chromatin changes in CLL cells plays a dual biological role, and the balance between proprogression and proindolence is a strong independent determinant of CLL prognosis.

The recognition of B-cell prolymphocytic leukemia (B-PLL) as a separate entity is controversial based on the current classification systems. Here, we analyzed the DNA methylome of a cohort of 20 B-PLL cases diagnosed according to the guidelines of the International Consensus Classification/Fourth revised edition of the World Health Organization Classification, and compared them with chronic lymphocytic leukemia (CLL), mantle cell lymphoma (MCL), splenic marginal zone lymphoma (SMZL), and normal B-cell subpopulations. Unsupervised principal component analyses suggest that B-PLL is epigenetically distinct from CLL, MCL, and SMZL, which is further supported by robust differential methylation signatures in B-PLL. We also observe that B-PLL can be segregated into 2 epitypes with differential clinicobiological characteristics. B-PLL epitype 1 carries lower immunoglobulin heavy variable somatic hypermutation and a less profound germinal center–related DNA methylation imprint than epitype 2. Furthermore, epitype 1 is significantly enriched in mutations affecting MYC and SF3B1, and displays DNA hypomethylation and gene upregulation signatures enriched in MYC targets. Despite the low sample size, patients from epitype 1 have an inferior overall survival than those of epitype 2. This study provides relevant insights into the biology and differential diagnosis of B-PLL, and potentially identifies 2 subgroups with distinct biological and clinical features.

Cancer development, progression, and response to treatment are evolutionary processes, but characterising the evolutionary dynamics at sufficient scale to be clinically-meaningful has remained challenging. Here, we develop a new methodology called EVOFLUx, based upon natural DNA methylation barcodes fluctuating over time, that quantitatively infers evolutionary dynamics using only a bulk tumour methylation profile as input. We apply EVOFLUx to 1,976 well-characterised lymphoid cancer samples spanning a broad spectrum of diseases and show that tumour growth rates, malignancy age and epimutation rates vary by orders of magnitude across disease types. We measure that subclonal selection occurs only infrequently within bulk samples and detect occasional examples of multiple independent primary tumours. Clinically, we observe that tumour growth rates are higher in more aggressive disease subtypes, and in two series of chronic lymphocytic leukaemia patients, evolutionary histories are independent prognostic factors. Phylogenetic analyses of longitudinal CLL samples using EVOFLUx detect the seeds of future Richter transformation many decades prior to presentation. We provide orthogonal verification of EVOFLUx inferences using additional genetic and clinical data. Collectively, we show how widely- available, low-cost bulk DNA methylation data precisely measures cancer evolutionary dynamics, and provides new insights into cancer biology and clinical behaviour.

The value of genome-wide over targeted driver analyses for predicting clinical outcomes of cancer patients is debated. Here, we report the whole-genome sequencing of 485 chronic lymphocytic leukemia patients enrolled in clinical trials as part of the United Kingdom’s 100,000 Genomes Project. We identify an extended catalog of recurrent coding and noncoding genetic mutations that represents a source for future studies and provide the most complete high-resolution map of structural variants, copy number changes and global genome features including telomere length, mutational signatures and genomic complexity. We demonstrate the relationship of these features with clinical outcome and show that integration of 186 distinct recurrent genomic alterations defines five genomic subgroups that associate with response to therapy, refining conventional outcome prediction. While requiring independent validation, our findings highlight the potential of whole-genome sequencing to inform future risk stratification in chronic lymphocytic leukemia.

Richter transformation (RT) is a paradigmatic evolution of chronic lymphocytic leukemia (CLL) into a very aggressive large B cell lymphoma conferring a dismal prognosis. The mechanisms driving RT remain largely unknown. We characterized the whole genome, epigenome and transcriptome, combined with single-cell DNA/RNA-sequencing analyses and functional experiments, of 19 cases of CLL developing RT. Studying 54 longitudinal samples covering up to 19 years of disease course, we uncovered minute subclones carrying genomic, immunogenetic and transcriptomic features of RT cells already at CLL diagnosis, which were dormant for up to 19 years before transformation. We also identified new driver alterations, discovered a new mutational signature (SBS-RT), recognized an oxidative phosphorylation (OXPHOS)^high–B cell receptor (BCR)^low-signaling transcriptional axis in RT and showed that OXPHOS inhibition reduces the proliferation of RT cells. These findings demonstrate the early seeding of subclones driving advanced stages of cancer evolution and uncover potential therapeutic targets for RT.