Fundamentos en epigenética IDIPAPS / Universidad de Barcelona Iñaki Martín-Subero IDIPAPS / Universidad de Barcelona Barcelona, Spain Email: imartins@clinic.ub.es

El cuerpo humano: un solo genoma, muchos tipos de células diferentes

GENOMA = INFORMACIÓN ALMACENADA (3.000 millones nucleótidos) EPIGENOMA = INFORMACIÓN ORGANIZADA y EXPRESADA

Epigenomics: the science of gene regulation El estudio de los cambios en la estructura de la cromatina que regulan la expresión génica sin modificar la secuencia del ADN Aguilar & Craighead, Nature Nanotechnology 2013 DNA methylation in cancer Robertson, Nature Reviews Genetics 2005

Interpretation is dependent on experimental design & technology Laird, Nat Rev Genet 2010

Established knowledge is dependent on technology Laird, Nat Rev Genet 2010

Define the reference epigenomes of normal and neoplastic B cells WP4 - Epigenome of normal and neoplastic B- and T-cells REFERENCE EPIGENOMES (BS-Seq, ChIP-Seq, ATAC-Seq, RNA-Seq)

B-cell development and derived neoplasms Multiple myeloma (MM) (Queiros et al., Cancer cell, 2016) Mantle cell lymphoma (MCL) (Agirre et al., Genome Res 2015) (Kulis et al., Nat Genet 2015) (Caron et al., Cell Rep 2015) PC HSC CLP preB-I preB-II naiBC gcBC memBC Acute lymphoblastic leukemia (ALL) gc-derived B-cell lymphomas (FL, BL, DLBCL) Chronic lymphocytic leukemia (CLL) (Lee et al., NAR 2015) (Kulis et al., Nat Genet 2012) (Kretzmer et al., Nat Genet 2015)

Chronic Lymphocytic Leukemia (Spanish ICGC Consortium) Years 4 8 12 16 20 24 28 Months 100 200 300 400 50 Survival p = 0.001 IGHV mutated IGHV unmutated Most frequent leukemia in Western countries (5-7 cases /100,000 /year) Heterogeneous clinical evolution 2 major molecular subtypes depending on the IGHV mutational status

Throrough characterization of the mutational landscape of CLL (150 WG, 400 WE) Puente et al., Nature 2015

Identification of non-coding mutation hotspots ? Puente et al., Nature 2015

Identification of non-coding mutations in an enhancer region leading to PAX5 downregulation Puente et al., Nature 2015

Experimental design: cases, controls and techniques Whole genome bisulfite sequencing 2 CLLs 3 controls Approx. 50x coverage/sample 76.5% uniquely alignable reads >10x genomic sequencing 139 CLL cases 26 B cell controls 14 whole B cells 3 CD5+ naive B cells 3 naive B cells* 3 class-switched memory B cells* 3 non-class-switched memory B cells* * From single donors High density DNA methylation microarrays (450k Illumina) 139 CLLs 26 controls 487,577 genomic sites (all samples >95% pure) Gene expression profiling (U219 Affymetrix) Exome-Seq (n=88) SNP Arrays (n=139) Full clinical reports available 125 CLLs 20 controls >36,000 transcripts

DNA methylation and gene expression in CLLs with mutated and unmutated IGHV M-CLL Memory B cells Total PB B cells Naive B cells DNA methylation (Illumina 450k) U-CLL 194753 CpGs with SD>0.1 Gene expression (Affymetrix U219) 13098 tags with SD>0.5 Kulis et al., Nat Genet 2012

Clinical implications of DNA methylation patterns in CLL We carried out a consensus cluster analysis of CpGs differentially methylated in U-CLL vs M-CLL and related to normal B cells (1649 CpGs) DNA methylation IGHV somatic mutation 15% 0% 100% U-CLL M-CLL CD5+ B cells Naive B cells Memory B cells IgA/G+ Memory B cells IgM/D+ Naive B cell-like CLLs Memory B cell-like CLLs Enriched for U-CLLs Includes few M-CLLs Enriched for M-CLLs Includes few U-CLLs Consensus matrices Kulis et al., Nat Genet 2012

Clinical implications of DNA methylation patterns in CLL We carried out a consensus cluster analysis of CpGs differentially methylated in U-CLL vs M-CLL and related to normal B cells (1649 CpGs) U-CLL M-CLL CD5+ B cells Naive B cells Memory B cells IgA/G+ Memory B cells IgM/D+ Consensus matrices 15% IGHV somatic mutation 0% 100% DNA methylation 0% Naive B cell-like CLLs Memory B cell-like CLLs Intermediate CLLs Enriched for U-CLLs Includes few M-CLLs Enriched for M-CLLs Includes few U-CLLs Enriched for M-CLLs with low levels of IGHV mutation Includes few U-CLLs Kulis et al., Nat Genet 2012

Clinical features of the 3 CLL groups based on DNA methylation patters associated to normal B cells U-CLL M-CLL CD5+ B cells Naive B cells Memory B cells IgA/G+ Memory B cells IgM/D+ 15% IGHV somatic mutation Naive B cell-like CLLs Memory B cell-like CLLs Intermediate CLLs Years 0 5 10 15 20 25 Percentage of patients treated 1.0 0.8 0.6 0.4 0.2 0.0 MBC-like CLLs (n=52) Intermediate CLLs (n=19) NBC-like CLLs (n=43) P = 2 x 10-8 LDH high CD38 SF3B1 mutated NOTCH1 mutated Percentage of cases 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% ZAP70 11q deleted IGHV mutated MBC-like CLLs Intermediate CLLs NBC-like CLLs Kulis et al., Nat Genet 2012

Identification of epigenetic biomarkers for CLL Naive B cells n-CLL i-CLL m-CLL Memory B cells 1,649 CpGs DNA methylation 100% 0% Complexity reduction step Naive B cells n-CLL i-CLL m-CLL Memory B cells CpG 1 CpG 2 DNA methylation 100% 0% CpG 3 CpG 4 CpG 5 Queiros et al., Leukemia 2015

Study of the clinical impact of the three new groups of CLL a Initial series n-CLL b Initial series n-CLL i-CLL i-CLL 1 m-CLL 1 m-CLL n-CLL vs i-CLL - P=0.001 n-CLL vs i-CLL - P=0.003 0.8 i-CLL vs m-CLL - P=0.10 0.8 i-CLL vs m-CLL - P=0.102 Proportion of patients treated 0.6 Probability of survival 0.6 0. 4 0. 4 0.2 0.2 P=7.3x10-18 P=2.5x10-8 5 10 15 20 25 Years 5 10 15 20 25 Years c Validation series n-CLL d Validation series n-CLL i-CLL i-CLL 1 m-CLL 1 m-CLL n-CLL vs i-CLL - P=0.110 n-CLL vs i-CLL - P=0.280 0.8 i-CLL vs m-CLL - P=0.009 0.8 i-CLL vs m-CLL - P=0.03 Proportion of patients treated 0.6 0.6 Probability of survival 0. 4 0. 4 0.2 0.2 P=1.4x10-8 P=3.0x10-5 5 10 15 20 25 Years 5 10 15 20 25 Years Queiros et al., Leukemia 2015

Leukemic non-nodal MCL Mantle cell lymphoma (MCL) Aggressive B-cell non-Hodgkin lymphoma Characterized by the translocation t(11;14)(q13;q32) and over expression of cyclin D1 Clinical heterogeneity with 2 major groups: Conventional MCL Leukemic non-nodal MCL No or limited number of IGHV somatic mutations Genetic alterations Nodal disease Adverse clinical behavior Expression of SOX11 IGHV somatic mutations No or limited number of genetic alterations Non-nodal disease Indolent clinical behavior Lack of SOX11 expression

Biological and clinical differences in MCL subgroups   Cases with IGVH identity to germline >98% SOX11 positive cases Average number of copy number alterations per case Nodal presentation MCL Cluster 1 (n=62) 86% 93% 10 84% MCL Cluster 2 (n=20) 0% 5% 3,41 13% B-cell differentiation Conventional MCL PC 2 (7.77%) Leukemic non-nodal MCL PC1 (34.39%) Immature progenitors Naive B cells Germinal center B cells Memory B cells Plasmacells (Tonsil) Plasmacells (Bone marrow) SOX11-neg MCL; IGVH >98% germline SOX11-neg MCL; IGVH <98% germline SOX11-pos MCL; IGVH >98% germline SOX11-pos MCL; IGVH <98% germline Queiros, Beekman & Vilarrasa et al ., Cancer Cell 2016

DNA methylation heterogeneity and clinical behavior of MCL patients IGHV unmutated / SOX11-positive IGHV mutated / SOX11-negative 100,000 Hypomethylated CpG sites Hypermethylated CpG sites 40,000 C1 MCLs = GC-inexperienced MCLs (derived from less mature B cells) C2 MCLs = GC-experienced MCLs (more mature B cells) Lower number of DNA methylation changes 1 Higher number of DNA methylation changes Probability of survival p = 0.001 20 40 60 80 100 120 1 Lower number of DNA methylation changes Higher number of DNA methylation changes Probability of survival p = 1.36x10-5 25 50 75 100 125 Time (months)

DNA methylation levels Whole-genome bisulfite sequencing: differential DNA methylation between SOX11-positive and SOX11-negative MCLs Cluster 2 MCL (SOX11-) Cluster 1 MCL (SOX11+) The causes of SOX11 overexpression in Cluster 1 MCL are widely unknown Normal B cell differentiation 2 MCLs (SOX11+/SOX11-) DNA methylation levels

DMRs in a distant region point to potential SOX11 enhancer 50kb Demethylated in cluster 1 MCL case DMR cluster Demethylated in cluster 2 MCL case Enhancer region 3D interactions MCL cluster 1 case (SOX11+) 3D interactions No Enhancer No 3D interactions MCL cluster 2 case (SOX11-) No 3D interactions Queiros, Beekman & Vilarrasa et al ., Cancer Cell 2016

DMRs in a distant region point to potential SOX11 enhancer Queiros, Beekman & Vilarrasa et al ., Cancer Cell 2016

Resumen El epigenoma de las neoplasias hematológicas esta ampliamente alterado La asociación entre la metilación del ADN y la expresión génica es menos clara de lo que se acepta comúnmente. La mayoría de los cambios epigenéticos no se encuentran en regiones promotoras sino en regiones heterocromáticas y enhancers. La actividad reguladora de los enhancers en las neoplasias linfoides se modifica tanto por alteraciones geneticas como epigeneticas. Huellas epigenéticas asociadas a patrones de la diferenciación de los linfocitos B son últiles para clasificar las neoplasias B en entidades clinico-biológicas diferentes (p.ej. en la LLC) Una mejor comprensión de la desregulación génica en oncohematología require un analisis integrador de diversas marcas epigeneticas.

Acknowledgements