Integrated multi-omic profiling reveals two biologically distinct subgroups of splenic marginal zone lymphoma with prognostic relevance
Notice bibliographique
Résumé
Abstract Splenic marginal zone lymphoma (SMZL) is a rare B-cell malignancy mainly affecting the spleen, bone marrow, and peripheral blood. Clinical outcomes are variable, with potential transformation into aggressive large B-cell tumors with poor survival rates. Despite advances in targeted therapies, specific biomarkers are urgently needed to guide treatment as incidence continues to rise. This study aims to extend our knowledge of SMZL biology by integrating genetic, phenotypic, transcriptomic, and epigenetic data to establish more precise molecular classifications aimed at guiding personalized treatments. We defined epigenetically distinct subtypes of SMZL using DNA methylation array data of 142 patients divided into a discovery (n=86, 60%) and a validation cohort (n=56, 40%). K-means clustering of the top 2000 most variable CpG sites consistently identified two similar clusters in both cohorts. Bootstrapped univariate survival analyses revealed significant differences in time to first treatment (TTFT) between these clusters in both the discovery (p=.007) and validation cohorts (p=.024). Subsequent clustering on the entire cohort allowed us to classify the subgroups as SMZL-HR (high risk, n=58, 41%) and SMZL-LR (low risk, n=84, 59%), reflecting the observed differences in TTFT. Twelve clinico-biological features were significantly enriched in SMZL-HR cases, including female sex (p<.001) , IGHV1-2*04 usage (p<.001), gene mutations (KLF2 (p<.001), KMT2D (p=.0015), TRAF3 (p<.001), NOTCH2 (p=.015), BCL10 (p=.007)) and chromosomal alterations (del(7q), gain(3q), gain(12q) (all p<0.01)). SMZL-HR patients also had higher rates of therapeutic intervention (p<0.001), disease transformation (p=0.01), and mortality (p<0.001) compared to SMZL-LR patients. Tumour mutational burden (TMB) (p<0.001) and the fraction of the SBS40 (p<0.001) mutational signatures were also increased in SMZL-HR compared to SMZL-LR. In contrast, SMZL-LR was associated with MYD88 mutations (p=.02), Trisomy 12 and 3 (p<.001 and p=.02). We found that the DNA methylation-based proliferative history score epiCMIT was significantly higher in SMZL-HR than SMZL-LR patients (p<0.001). TMB was positively correlated with epiCMIT (r=0.35, p<.001), reinforcing the link between extensive tumor proliferative histories and the acquisition of somatic mutations. Telomere length (TL) data (median 3.1, range: 2.38-7.57 kb) showed a significant negative correlation with epiCMIT (R=-0.3, p=.001). Transcriptomic comparisons of SMZL-HR and SMZL-LR revealed 399 differentially expressed genes (232 under-expressed, 167 overexpressed; FDR<.05, log fold change >1.5). Gene set enrichment analysis highlighted pathways linked to elevated cell division, specifically E2F targets (NES=1.98, p<.01) and the G2M checkpoint (NES=2.07, p<.01), and KAMMINGA_EZH2 targets (NES=1.91, p=.006). Taken together, these results suggest that SMZL-HR clones have a history of and higher potential for cellular division, potentiated by EZH2 targets associated with chromatin modification/stabilization, providing enhanced cellular resilience against replicative stress. Univariate Cox regression analysis tested the impact 60 clinico-biological features on TTFT and overall survival (OS). SMZL-HR status (HR: 2.0, p=.0013) and epiCMIT >median (HR: 1.67, p=.014) were significantly linked to shorter TTFT (5 vs. 16 months). Additionally, 46% of patients were classified into a poor-risk “NNK-like” group and 20% into the “High-M” group, as defined by Bonfiglio and Arribas, respectively. Both groups were associated with shorter TTFT (HR: 1.57 and 1.9, p=0.002 and 0.008, respectively). Significant predictors of shorter OS included the SMZL-HR epitype (HR: 2.5, p=.025). SMZL-HR patients had significantly shorter TTFT regardless of their Bonfiglio/Arribas classification. Multivariate Cox analysis (including 130 patients with 91 events) with 4 covariates (SMZL-HR, epiCMIT, NNK-like, High-M), revealed that SMZL-HR was the only independent variable in the final model (HR: 2.63, p=.001). Overall, this study presents a comprehensive framework that integrates (epi)genomic data with survival analysis, identifying two distinct disease entities, each with a discrete biological and clinical landscapes. This enhanced understanding supports the potential for improved personalized treatment strategies as well as better prognostic assessment for patients with SMZL.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».