Proliferative History Is a Novel Driver of Clinical Outcome in Splenic Marginal Zone Lymphoma
Parker, H.; Mirandari, A.; Jaramillo Oquendo, C.; Duran-Ferrer, M.; Stevens, B.; Buermann, L.; Amarasinghe, H. E.; Thomas, J.; Kadalayil, L.; Carr, L.; Syeda, S.; Sakthipakan, M.; Parry, M.; Davis, Z.; McIver-Brown, N.; Xochelli, A.; Ennis, S.; Scarfo, L.; Ghia, P.; Kalpadakis, C.; Pangalis, G.; Rossi, D.; Wagner, S.; Ahearne, M.; Seifert, M.; Plass, C.; Weichenhan, D.; Kimby, E.; Sutton, L.-A.; Rosenquist, R.; Forconi, F.; Stamatopoulos, K.; Salido, M.; Ferrer, A.; Thieblemont, C.; Ljungstrom, V.; Amini, R.-M.; Oscier, D.; Walewska, R.; Rose-Zerilli, M. J.; Gibson, J.; Martin-Subero, J. I.; O
Show abstract
The epiCMIT (epigenetically-determined Cumulative MIToses) mitotic clock traces B-cell mitotic history via DNA methylation changes in heterochromatin and H3K27me3-containing chromatin. While high scores correlated with poor outcomes in CLL and MCL, its prognostic significance in SMZL remains unknown. Derived from 142 SMZL cases using DNA methylation microarrays, epiCMIT values were correlated with genomic, transcriptomic, and clinical data. EpiCMIT as a continuous variable was significantly higher in females (p=0.02), patients with IGHV1-2*04 allele usage (p<0001), intermediate IGHV somatic hypermutation load (97-99.9% identity, p=0.04), elevated mutational burden (25 vs. 17 mut/Mb, p=0.001), driver gene mutations [KLF2 (p<0.001), NOTCH2 (p<0.01), TP53 (p=0.01), KMT2D (p<0.001)], and del(7q) (p=0.01). Negative correlation between epiCMIT and telomere length (r=-0.29 p<0.001) supported the association between cumulated proliferation and telomere attrition. While univariate analysis highlighted epiCMIT as robust predictor of shorter treatment-free survival (TFS), multivariate analysis confirmed epiCMIT as an independent marker for shorter TFS. In summary, our matched multi-omic datasets facilitate the clinico-biological characterization of SMZL and introduces epiCMIT as a strong prognostic marker, identifying high-risk patients and predicting reduced treatment-free survival, hence providing a new tool for risk-adapted patient management.
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