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Genomics of Multiple Myeloma Gareth J Morgan Director Nyulangone Myeloma Research Program New York City Talk Outline

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Genomics of Multiple Myeloma Gareth J Morgan Director Nyulangone Myeloma Research Program New York City Talk Outline Genomics of multiple myeloma Gareth J Morgan Director NYULangone Myeloma Research Program New York City Talk outline 1. Introduction 2. Aetiologic events 3. The Myeloma Genome project 4. Epigenetic events 5. Risk stratification 6. Impact on the microenvironment 7. Structural events and identifying new drivers One size fits all We know its illogical – “MULTIPLE” myelomas Natural history of Multiple Myeloma. Myeloma remains an incurable disease for most patients Durie BGM, ed. Multiple Myeloma Concise Review 2007. MGUS, monoclonal gammopathy of unknown significance; M International Myeloma Foundation. American Cancer Society. protein, myeloma protein. Cancer Facts & Figures, 2008. Personalize therapeutic decisions. Younger, fit •Achieving longest possible remission/sustained disease control while preserving QoL? Elderly, fit •Achieving and maintaining responses while preserving QoL? Frail/comorbidities •Tolerability while preserving QoL? Very frail • Palliative care while preserving QoL? Personalized medicine Diagnostic test Multistep Progression System Initiation Progression Germinal centre Bone marrow Peripheral blood Post-GC Smouldering Plasma Cell MGUS Myeloma B cell Myeloma Leukemia inherited variants PRIMARY GENETIC EVENTS IGH TRANSLOCATIONS HYPERDIPLOIDY SECONDARY GENETIC EVENTS COPY NUMBER ABNORMALITIES DNA HYPOMETHYLATION ACQUIRED MUTATIONS 2. Aetiologic Copy Number Abnormalities and Structural Variants 2. Aetiologic Events in Multiple Myeloma Primary genetic events 11q13 (Cyclin D1) (initiation events) 15% IGH translocations (50%) • t(4;14) FGFR3/MMSET (15%) 6p21 (Cyclin D3) • t(6;14) CCND3 (4%) 3% • t(11;14) CCND1 (20%) • t(14;16) MAF (4%) • t(14;20) MAFB 4p16 (MMSET) None 15% Hyperdiploidy (50%) 60% •Trisomies of chromosomes 3, 5, 7, 9, 11, 15, 19, 21 16q23 (c-MAF) 5% 20q12 (MAFB) 2% Morgan GJ, Walker BA and Davies FE. Nature Reviews Cancer. In 2012 Translocation Cyclin-D classification “A unifying aetiologic classification of MM is based on the presence of chromosomal translocations and the deregulation of a D group cyclin either directly by a translocation or by an unknown mechanism.” MM is comprised of at least six molecular categories. Stein C et al Oncotarget 2017. G1/S checkpoint 11 Pawlyn C and Morgan GJ. Nat Rev Cancer. 2017;17(9):543-556 3. Myeloma Genome Project Today ISS DNA Group 1 • ~600 ex. panel Molecular Stratification profiling of patients • ~1700 WES Group 2 MGP • ~1000 WGS Clinical RNA Group 3 • ~1100 RNAseq Group 4 Molecular Risk Stratification N = 1273 exomes Walker et al. Leukemia 2018 Bi-allelic Inactivation of Tumour Suppressor Genes Multiple Myeloma NFkB G1S P53 Pan-cancer Homozygous Deletions Multiple Myeloma Driver Genes (n=63) Walker et al. Blood 2018 Mutation Frequencies of 63 Driver Genes RAS Walker et al. Blood 2018 Mutation Frequencies of 63 Driver Genes VEMURAFINIB DEBRAFINIB RAS Cancer Clonal Fraction of Driver Genes Walker et al. Blood 2018 Myeloma as an Evolutionary Ecosystem Selective pressures Ecosystem 1 Ecosystem 2 Ecosystem 3 EMD Diffuse Single founder Ecosystem tumour 4 initiating cell Focal MGUS MM PCL 21 Mutation Targeted Therapy; Implications of Sub-clonal Heterogeneity Venetoclax t(11;14) Oncogene Dependancies • Distribution of mutations and copy number variables s not random providing evidence for distinct subgroups of disease. • The aetiologic events provide a distinct genetic background on which collaborating variants are super imposed. Clustering by Copy Number Abnormality; Nine Copy Number Sub-Groups 4. Epigenetic Changes; t(4;14) FGFr3 Enhancer Hybrid gene Ig NSD2 Immunoglobulin gene locus NSD2 is H3K36me3 Wipes Out Repressive H3K27me3 Peaks to Activate Genes - NSD2 + NSD2 Balance of H3K27me/Ac regulates key processes The complex is frequently targeted by mutation in MM H3K27me3 ASC-2 MLL2/3 JARID2 PTIP ASXL1 RbAp p300 UTX 46/47 EED SUZ12 Me EZH2 Me Ac ON OFF Modified from Ezponda & Licht, Clin Cancer Res 2014 Epigenetic Driver Mutations Gene Cytoband Full name Function ARID1A 1p36.11 AT-rich Interaction Domain 1A SWI/SNF 31/1273 (2.44%) 1p36.11 TSG score ARID2 12p12 AT-rich Interaction Domain 2 PBAF complex 17/1273 (1.34%) 12q12 TSG score CREBBP 16p13.3 CREB Binding Protein BRD and HAT 30/1273 (2.36%) 16p13.3 TSG score DNMT3A 2p23.3 DNA Methyltransferase a DNA methylation 22/1273 (1.73%) 2p23.3 TSG score EP300 22q13.2 E1A Binding Protein P300 HAT 25/1273 (1.96%) 22q13.2 TSG score HIST1H1E 6p22.2 Histone Cluster 1 H1 Family Member Epigenetic 47/1273 (3.69%) 6p22.2 TSG score E IDH1 2q34 Isocitrate Dehydrogenase DNA methylation 7/1273 (0.55%) 2q34 ONC score IDH2 15q26.1 Isocitrate Dehydrogenase DNA methylation 4/1273 (0.31%) 15q26.1 ONC score KDM5C (Jarid1c) Xp11.22 Lysine Demethylase 5C H3K4 21/1273 (1.65%) Xp11.2 TSG score demethylase 2 KDM6A (UTX) Xp11.3 Lysine Demethylase 6A H3K27 19/1273 (1.49%) Xp11.3 TSG score demethylase KMT2B (MLL2) 19q13.12 Lysine Methyltransferase 2B H3K4 methylation 28/1273 (2.2%) 19q13.1 TSG score 2 KMT2C (MLL3) 7q36.1 Lysine Methyltransferase 2C H3K4 methylation 34/1273 (2.67%) 7q36.1 TSG score NCOR1 17p12 Nuclear Receptor Corepressor 1 gene repression 17/1273 (1.34%) 17p12 TSG score SETD2 3p21.31 SET Domain Containing 2 H3K36 me3 24/1273 (1.89%) 3p21.31 TSG score TET2 4q24 Hydroxylation 5MeC DNA methylation 24/1273 (1.89%) 4q24 TSG score Myeloma genome project Walker et al. Blood 2018 5. Risk Stratification Interphase FISH – Adverse – t(4;14), t(14;16), del17p. – Standard – hyperdiploidy, t(11;14). – Ultra high risk – adverse translocation plus del17p Revised International Staging System R-ISS Stg Factor % of pts 5 yr PFS 5 yr OS Absence of adverse factors (no high LDH, ISS 2 or 3, t(4;14) I 28% 55% 82% t(14;16) or del(17p)) II Not R-ISS I or III 62% 36% 62% III ISS 3 and either high-risk CA by iFISH or high LDH 10% 24% 40% Non–transplantation- Transplantation- Immunomodulatory-based Proteasome inhibitor- based based regimens based regimens regimens regimens 1. Palumbo A, et al. J Clin Oncol 2015;33:2863-2869. Double Hit MM a new disease segment. “Double hit” MM a. Biallelic P53 b. ISS III amp CKS1B c. 6% NDMM Comparison of Double Hit MM to IMWG Risk Groups Integrating mutation and expression data 12 Stable Patient Clusters in NDMM Cluster Sizes iCC-1 49 patients 10% iCC-2 25 patients 5% iCC-3 60 patients 12% are iCC-4 42 patients 8% iCC-5 59 patients 11% ) samples iCC-6 39 patients 8% Matrix two iCC-7 38 patients 7% together iCC-8 59 patients 11% where iCC-9 59 patients 11% grouped Consensus iCC-10 28 patients 5% iterations iCC-11 25 patients 5% (% iCC-12 31 patients 6% • Multi-dimensional clustering analysis identified 12 stable patient segments (all ≥5%) with distinct outcomes • This is the first large-scale analysis of ndMM to perform unsupervised biomarker analysis to stratify pts based on SV, CNA, GEP, and SNV differences 34 Molecular Features of Cluster 8 • 24/59 (41%) were ISS3 • 19/59 (32%) were ≥ 65 yrs Copy Cytogenetics Number Del1p13/22-21 (1e-3) Amp 1q21-25 (1e-2) t(4;14) (1e-3) Del13q12-34 (1e-5/3) Del14q12-32 (1e-7) t(14;16) (1e-4) Del16q12-24 (1e-4) GE GE profile driven by significant low- expression of C8:NS_MAX,NS_TRAF2,CDKN2C_Loss,RPL5_ transcripts and Loss,FAM46C_Loss,FGFR3_Loss,TRAF2_Loss, up-regulation of BR cell-cycle CA2_Loss,RB1_Loss,DIS3_Loss,ABCD4_Loss, pathways TRAF3_Loss,CKS1B_Amp,t_14_16,t_4_14,m utations_ 36 per_mb,APOBEC,ISS_S3" Ortiz et al, Manuscript in preparation.. Poster presented at EHA 2018. Identifying Molecular Drivers of High-Risk Genetic Biological Proteomic Computational • CRISPR screens • Proliferation • Interaction mapping • Validation • shRNA • Cell cycle • Immunoprecipitation • Master regulators knockdowns • Apoptosis • Mass Spectrometry • Bionetworks • Targeted gene • 3D & Co-culture • ChIP-Seq manipulations assays • Chemical inhibition • Immune assays • Pathways, genes and functional networks • Biomarker • Preclinical / ex-vivo models for testing function 6. Impact on the Microenvironment HR PCL Normal cell MGUS SMM MM EMD MYC Recurrent myeloma mutations translocations Recurrent copy number changes P53 inactivation Amp1q Whole arm translocations RB1 inactivation CDKN2C inactivation Increase in tumour promoting cells High-risk states T-regs HR PCL pDCs MDSCs Normal cell MGUS SMM MM EMD MYC translocations P53 inactivation Amp1q Whole arm translocations RB1 inactivation CDKN2C Decrease in tumour suppressing cells High-risk states DCs HR PCL NK Cytotoxic T cells cells Norm al B cells cell MGUS SMM MM Th cells EMD MYC translocations P53 inactivation Amp1q Whole arm translocations RB1 inactivation CDKN2C inactivation Do the tumour cells contain information which is able to disorganize the microenvironment? De-convoluting the immune micro-environment. The Rosetta project. Sub-groups identified with distinct features independent of the major molecular subgroups of disease. Cluster 5. Cluster 5. High-risk microenvironment T cells NK cells Macrophage Mast cells Eosinophils M2 M1 Plasma cell features associated with high-risk microenvironment T cells NK cells Macrophage Mast cells Eosinophils M2 M1 VCAM Interferon response Proteoglycan 2 genes IFIT1/3, ISG15, SMAD, RASSF6, IFI6, IFI44L GBP1, MITD1, Cancer testis antigen MAD2L1, NDC80 APOBEC3B FAM133A Hepatocyte growth factor Plasma cell Extracellular matrix Stromal cell 7. Identifying New Drivers A Significant Percentage of Patients (15%) Have No Detectable Driver Walker et al. Blood 2018 We are missing drivers either because they occur by a mechanism its difficult to find or they occur in a part of the genome we haven’t examined. The Non-coding Regions of the Multiple Myeloma Genome 10x Chromium WGS Enriched for high risk molecular features SVs SNVs Copy Number N = 95 whole genome sequencing Structural Variants and Gene Deregulation – Amplification – Fusion genes – Gene overexpression by superenhancers – Gene knockout 1q amp and JT1q12 • 1q+ is present in 40% presenting MM and is a poor prognostic feature. • Frequently amplified by duplication or breakage fusion breakage cycles.
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