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745 A Comprehensive DNA Methylome Analysis of Stereotyped and Non-Stereotyped CLL Reveals an Epigenetic Signature with Strong Clinical Impact Encompassing IGHV Status, Stereotypes and IGLV3-21R110

Program: Oral and Poster Abstracts
Type: Oral
Session: 641. Chronic Lymphocytic Leukemias: Basic and Translational: Novel Therapies and Biomarkers
Hematology Disease Topics & Pathways:
Research, Fundamental Science, Lymphoid Leukemias, adult, Translational Research, CLL, elderly, bioinformatics, Diseases, Lymphoid Malignancies, computational biology, young adult , Technology and Procedures, profiling, Study Population, Human, omics technologies
Monday, December 12, 2022: 10:30 AM

Martí Duran-Ferrer, PhD1,2*, Larry Mansouri3*, Ferran Nadeu, PhD4,5*, Guillem Clot, PhD1,4*, Sujata Bhoi6*, Lesley Ann Sutton7*, Panagiotis Baliakas, MD8*, Sara Ek, PhD, Professor9*, Venera Kuci Emruli10*, Karla Plevova, PhD11*, Zadie Davis12*, Hanna Goransson-Kultima13*, Anders Isaksson14*, Karin E. Smedby, MD, PhD15*, Gianluca Gaidano, MD, PhD16, Anton W. Langerak, PhD17*, Frederic Davi, MD, PhD18*, Davide Rossi, MD, PhD19,20, David Oscier12, Sarka Pospisilova, Prof PhD21*, Maria Karypidou22*, Andreas Agathangelidis, PhD23*, Wolfgang Huber24*, Junyan Lu25*, Thorsten Zenz26, Julio Delgado, MD27, Armando Lopez-Guillermo, MD1,27,28, Paolo Ghia, MD, PhD29, Elías Campo, PhD MD4,30,31,32, Kostas Stamatopoulos, MD, PhD33*, Richard Rosenquist, MD34 and José I. Martín-Subero, PhD1,4,31,35*

1Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
2Centro de Investigación Biomédica en Red de Cáncer, Universitat de Barcelona, Barcelona, Spain
3Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm,, Stockholm, Sweden
4Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain
5Molecular Pathology of Lymphoid Neoplasms Group, IDIBAPS, Barcelona, Barcelona, Spain
6Department of Immunology, Genetics and Pathology,, Uppsala University, Uppsala, Sweden
7Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden, Stockholm, Sweden
8Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Uppsala, Sweden
9Department of Immunotechnology, Lund University, Lund, Sweden
10Lund University, Lund, Sweden
11Central European Institute of Technology, Masaryk University, Brno, Czech Republic
12Royal Bournemouth Hospital, Bournemouth, United Kingdom
13Uppsala University, Uppsala, Sweden
14Uppsala University, Uppsala, SWE
15Clinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
16Division of Hematology, Department of Translational Medicine, Università del Piemonte Orientale, Novara, Italy
17Department of Immunology, Laboratory Medical Immunology, Erasmus MC, Rotterdam, Netherlands
18Department of Biological Haematology, Sorbonne Université, INSERM, Centre de recherche des Cordeliers, AP-HP, Pitié-Salpêtrière Hospital, Paris, FRA
19Division of Hematology,, Oncology Institute of Southern Switzerland, Bellinzona, Switzerland
20Institute of Oncology Research, Bellinzona, Switzerland
21Department of Internal Medicine - Hematology and Oncology / Center of Molecular Biology and Gene Therapy, University Hospital Brno and Faculty of Medicine, Brno, Czech Republic
22Centre For Research and Technology Hellas, Thessaloniki, GRC
23Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece
24EMBL, Heidelberg, DEU
25European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
26Department of Medical Oncology and Hematology, University Hospital Zurich, Zurich, BW, Switzerland
27Servicio de Hematología, Hospital Clínic, IDIBAPS, Barcelona, Spain
28Hematopathology Unit, Hospital Clínic, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), CIBERONC, Universitat de Barcelona, Barcelona, Spain
29L'Università Vita-Salute San Raffaele Milano and IRCCS Istituto Scientifico San Raffaele, Milano, Italy
30Hematopathology Section, Pathology Department, Hospital Clinic, Barcelona, Spain
31Departament de Fonaments Clínics, Facultat de Medicina, Universitat de Barcelona, Barcelona, Spain
32IDIBAPS, Barcelona, Spain
33Institute of Applied Biosciences, Centre For Research and Technology Hellas, Thessaloniki, Greece
34Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
35Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain

Although up to 41% of chronic lymphocytic leukemia (CLL) patients belong to subsets with stereotyped or quasi-identical B cell receptors, a thorough epigenomic characterization of CLL stereotypy has been limited by the low frequency (<2.5%) of each individual subset. To address this challenge, we assembled a series of 995 cases profiled by 450k and EPIC DNA methylation arrays. The dataset was enriched with the 8 most frequent subsets (#1-8, n=180) and other less common and satellite stereotypes (n=18). The remaining cases were not recognized as any known subset (n=451) or were not classified (n=346), and were used for control and validation purposes.

Unsupervised analyses showed subset and non-subset cases overlapping, suggesting that stereotypy per se is not a major source of DNA methylation variability. Instead, and reinforcing previous reports, we found that the major principal components were related to the 3 cell-of-origin groups or epitypes (naïve (n)-CLL, intermediate (i)-CLL and memory (m)-CLL), and to the CLL proliferative history measured by the epiCMIT mitotic clock. Nonetheless, focused analyses of each epitype revealed specific clustering and differential patterns for all subsets, with subsets #2, #5, #8 demonstrating the most prominent DNA methylation signatures.

Focusing on #2, the most frequent subset almost exclusively composed of i-CLL, we found mainly a loss of DNA methylation compared to non-subset i-CLL cases (217 differentially methylated CpGs). Part of this methylation loss was also observed in post-germinal center B cells and mainly reflected the higher epiCMIT of subset #2 (P=0.002). The majority of the remaining CpGs were de novo hypomethylated in subset #2 compared to non-subset and normal B cell samples, and targeted regulatory elements, which may represent a hypomethylation linked to subset #2-specific regulatory programs. Noticeably, a group of non-subset cases displayed molecular features similar to subset #2, including the hypomethylation signature, epiCMIT levels, IGHV gene mutational status, and enrichment for the IGLV3-21R110 mutation. As all but one subset #2 patient carried the IGLV3-21R110, we then grouped patients having either of the two features and found a ~50% increase of differentially methylated CpGs (n=315), strongly supporting that the driver of the differential methylation patterns in subset #2 was indeed the presence of the IGLV3-21R110.

A subsequent comparison of the identified signature with n-CLLs and m-CLLs showed that i-CLLs with IGLV3-21R110 shared methylation patterns with n-CLLs, whereas i-CLLs without such mutation resembled m-CLLs. In fact, a consensus clustering approach of the 315 CpGs with 631 cases profiled with the 450k array resulted in 2 robust clusters, which could also be identified with high accuracy with few CpGs (~98% cross-validated accuracy, ~5 CpGs). The first cluster was composed globally, but not entirely, by unmutated IGHV (U-CLL), n-CLL, subsets #1-3 and #5-8, and cases with the IGLV3-21R110 from the mutated IGHV (M-CLL), m-CLL and i-CLL subgroups (n=313), while the second cluster encompassed M-CLL, m-CLL and i-CLL lacking the IGLV3-21R110 and subset #4 (n=318). As expected, these two clusters showed a dramatically different clinical outcome, with a difference in median time to first treatment from diagnosis of ~25 years (P=3x10-77). Remarkably, this high prognostic power was also observed for overall survival, both from diagnosis and from date of sampling, and was independent from IGHV mutational status, subsets #1-8, IGLV3-21R110, epitypes, and epiCMIT, as indicated by multivariate Cox models. In fact, few U-CLL cases classified as cluster 2 showed the same favorable outcome than M-CLL. Conversely, M-CLL, m-CLL and i-CLL cases classified as cluster 1 displayed the same adverse outcome similar to U-CLL or n-CLL, even those few cases without the IGLV3-21R110. Finally, the molecular (n=364) and clinical (n=257) features of these 2 groups were collectively validated through 3 independent cohorts containing both 450k and EPIC data.

Altogether, this study reveals novel insights into the epigenome of CLL stereotypy and into the yet poorly characterized i-CLL epitype. Our analyses unveil an epigenetic signature that dichotomizes CLLs into two clusters with markedly different clinical outcome, which mainly, but not exclusively, encompass IGHV mutational status, epitypes, stereotypes and IGLV3-21R110.

Disclosures: Smedby: Janseen Cilag: Research Funding. Gaidano: Beigene: Membership on an entity's Board of Directors or advisory committees; Incyte: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees; Astra-Zeneca: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Abbvie: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Langerak: Genentech: Research Funding; Gilead: Speakers Bureau; Janssen: Speakers Bureau; Roche: Research Funding. Rossi: AstraZeneca: Consultancy, Honoraria, Other: Travel Support, Research Funding; Janssen: Consultancy, Honoraria, Other: Travel Support, Research Funding; AbbVie: Consultancy, Honoraria, Other: Travel Support, Research Funding; Gilead: Other: honoraria, advisory board fees , Research Funding; MSD: Other: advisory board fees ; BMS: Consultancy, Honoraria, Other: Travel Support; BeiGene: Consultancy, Honoraria, Other: Travel Support, Research Funding. Zenz: Incyte: Consultancy, Honoraria; Janpix: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria; Gilead: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Roche: Consultancy, Honoraria. Lopez-Guillermo: Abbvie: Membership on an entity's Board of Directors or advisory committees; Celgene, Gilead Sciences, Roche, Incyte: Membership on an entity's Board of Directors or advisory committees; Roche: Research Funding. Ghia: AstraZeneca: Consultancy, Honoraria, Research Funding; BMS: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria, Research Funding; BeiGene: Consultancy, Honoraria; Lilly/Loxo: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Research Funding; MSD: Consultancy, Honoraria; Roche: Consultancy, Honoraria. Stamatopoulos: AbbVie: Research Funding. Rosenquist: AbbVie: Honoraria; Astrazeneca: Honoraria; Janssen: Honoraria; Illumina: Honoraria; Roche: Honoraria.

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