Genetic prognostication and treatment selection in Multiple Myeloma. Kihyun Kim Department of Medicine Sungkyunkwan University Samsung Medical Center

Transcription

1 Genetic prognostication and treatment selection in Multiple Myeloma Kihyun Kim Department of Medicine Sungkyunkwan University Samsung Medical Center

2 The International Congress of BMT 2018 COI disclosure Name of author : Kihyun Kim I have no personal or financial interests to declare. I have no financial support from an industry source at the current presentation.

3 Multiple Myeloma Kumar SK, et al. Leukemia

4 Clonal evolution & subclonal heterogeneity of myeloma Pawlyn et al. Nature reviews Cancer 2017, 17:534

5 high-risk cytogenetics in the era of novel agents Avet-Loiseau et al. Leukemia 2018, 32, 1267

6 Diagnostic techniques for cytogenetic abnormalities Conventional karyotyping Fluorescence in situ hybridization Purification of CD138-expressing plasma cells or dual staining for cytoplasmic immunoglobulin (Ig) and FISH Singe-nucleotide polymorphism based mapping arrays identify copy number variations (CNV). Translocations are not usually detected Array-based comparative genomic hybridization a tool for genome-wide classification of CNVs Gene expression profiling Next Generation Sequencing

7 Primary and secondary cytogenetic abnormalities in MM Kumar et al. Nat Rev Clin Oncol. 2018, 1759

8 Genetic lesions associated with high-risk multiple myeloma Class Lesion Genes affected Primary translocations Secondary translocations Copy number change Homozygous inactivation of TSGs Genetic changes associated with DNA repair deficiency Frequency in NDMM (approx. %) Change in Frequency at relapse Identifiable by ifish CNA GEP NGS t(4;14) MMSET 15 No change, t(14;16) MAF 3 clonal initiating Yes No Yes Yes t(14;20) MAFB 1.5 events MYC MYC 20 Increased Yes No No Yes JT1q BCL9, MCL1, IL6RA, CKS1B, ANP32E and others Isochromosome formation Many Unknown Unknown Hyperhaploidy Many Few Unknown Gain (1q) Del (1p) Genes located in the 1q transcriptional unit are: BCL9, MCL1, IL6RA, CKS1B, ANP32E and others Genes lost on 1p are: FAF1, CDKN2C, FAM46C, RPL5 and others Unknown Increased Yes No No Yes Gain: 30 Amp: 10 Increased 20 Increased Del (17p) TP53 and others 10 Increased RB1 2 TP53 4 Mutation +/ FAM46C 5 copy number change CYLD 3 Increased TRAF3 8 No Yes No Yes Yes Yes Yes Yes No Yes, with NGS No Yes with CNA Genome-wide LOH Many 5 Increased No No No Yes Pawlyn et al. Nature reviews Cancer 2017, 17:534

9 Common risk stratification approaches in MM Staging system Variables Stages International Staging System (ISS) Serum albumin and β 2 m levels I: serum albumin 3.5 g/dl and β 2 m <3.5 mg/dl II: neither stage I nor III III: β 2 m >5.5 mg/dl Revised International Staging Syst em (RISS) International Myeloma Working G roup (IMWG) risk staging msmart risk staging Gene-expression-based signatures Serum albumin, β 2 m, and LDH levels, and plasma cell FISH Serum albumin, β 2 m, and LDH levels, and plasma cell FISH Serum albumin, β 2 m and LDH levels, plasma cell FISH, and proliferation index UAMS Skyline 92 HOVON IFM I: ISS stage I, LDH normal, and standard-risk disease according to FISH II: neither stage I nor stage III III: ISS stage III plus abnormal LDH or high-risk disease according to FISH (del 17p and/or t(4;14) or t(14;16)) Low risk: ISS stage I or II, absence of t(4;14), del 17p13 and del 1q21, and <55 years of age Standard risk: all others High risk: ISS stage II or III and either t(4;14) or del 17p13 Standard risk: trisomies and/or t(11;14) Intermediate risk: t(4;14) or 1q amplification High risk: t(14;16), t(14;20), or del 17p Presence of alterations detected by each signature Kumar et al. Nat Rev Clin Oncol. 2018, 1759

10 Combined prognostic models of ISS and FISH IMWG MRC German Treatment Include both young patients (transplan t) and older patients (chemotherapy o nly) Young (intensive) and old patients (non-int ensive) with thalidomide-based combinatio n at induction and thalidomide maintenanc e on MRC IX trials Chemo-based induction followed b y HD Mel ASCT and maintenance Low-risk N Parameter ISS I/II with no adverse FISH ISS I/II with no adverse FISH or ISS I with 1 adverse FISH lesions ISS I with no adverse FISH % Patients 51% 38% 42% Int risk OS 76% at 4 years Median 67.8 months 72% at 5 years Parameter ISS III with no adverse FISH or ISS I an d t(4;14)/17p13 del ISS I with >1 adverse FISH lesions ISS II/III with 1 adverse FISH lesions or ISS III and n o adverse FISH ISS II/III with no adverse FISH or IS S I and t(4;14)/17p13 del % Patients 29% 48% 44% OS 45% at 4 years Median 41.3 months 62% at 5 years High-risk Parameter ISS II/III and t(4;14)/17p13 del ISS II/III with >1 adverse FISH lesions ISS II/III and t(4;14)/17p13 del % Patients 20% 14% 14% OS 33% at 4 years Median 19.4 months 41% at 5 years Chng et al. Leukemia, 2014, 269

11 Genetic markers t(4;14) and del17p, but not 1q gain or mutational data from TP53. The proportion of stage II is larger than stage I or III The high-risk group in the R-ISS classification comprised 10% and had a median PFS of 29 months and 5-year OS of 40%.

12 Genetic events that can be identified by FISH Primary genetic events Secondary genetic events IgH translocation Gene(s) Frequency (%) Deletion Gene(s) Frequency (%) t(4;14) FGFR3/MMSET 15 1p CDKN2C, FAF1, FAM46C t(6;14) CCND3 4 6q 33 t(11;14) CCND1 20 8p 25 t(14;16) MAF 4 13 RB1, DIS3 44 t(14;20) MAFB 1 11q BIRC2/BIRC3 7 Hyperdiploidy 30 14q TRAF q WWOX, CYLD 35 17p TP53 7 Gain Trisomies of chromosomes 3, 5, 7, 9, 11, 15, 19, 21 NA 50 1q CKS1B, ANP32E 40 Sonneveld et al. Blood. 2016;127(24):

13 Role of additional chromosomal changes in the prognostic value of t(4;14) and del(17p) in multiple myeloma: the IFM experience Hebraud et al. Blood. 2015;125(13):2095

14 Role of additional chromosomal changes in the prognostic value of t(4;14) and del(17p) in multiple myeloma: the IFM experience Hebraud et al. Blood. 2015;125(13):2095

15 Role of additional chromosomal changes in the prognostic value of t(4;14) and del(17p) in multiple myeloma: the IFM experience Hebraud et al. Blood. 2015;125(13):2095

16 multiple cytogenetic high-risk abnormalities Binder et al. Blood Cancer Journal (2017) 7, e600

17 Monosomy 13 vs. del 13q Binder et al. Blood Cancer Journal (2017) 7, e600

18 Coexistent hyperdiploidy does not abrogate poor prognosis in myeloma with adverse cytogenetics and may precede IGH translocations Blood. 2015;125(5):

19 Prediction of outcome in newly diagnosed myeloma Combined data of MRC IX (869) and NCRI XI (N=1036) Copy number abnormalidty & IgH translocation: MLPA (Multiplex ligation dependent probe amplification) and qrt-pcr a double-hit any two of the following: (1) t(4;14), t(14;16), t(14;20) (2) gain(1q) (3) del(17p) double-hit -ISS ultra high risk (ISS II or III and double-hit ; 12.0%) intermediate risk (ISS I and double-hit ; ISS II and 1 adverse lesion; ISS III and no or 1 adverse lesion; 44.1%) favourable risk groups (ISS I and no or 1 adverse lesion; ISS II and no adverse lesion; 43.9%). Shah et al. Leukemia (2018) 102

20 Prediction of outcome in newly diagnosed myeloma Shah et al. Leukemia (2018) 102

21

22 GEP-based prognostic signatures GEP signatures Methods Ref UAMS 70-gene Comparing the expression profiles of patients with top a nd bottom quartile of survival treatment on Total Therap y II Blood 2006; 109: IFM signature Centrosome index HZD cell death signature IL6-HMCL signature Model derived from iterative process of univariate cox an alysis, resampling analysis and then principle component analysis Based on expression of genes encoding components of t he centrosome Signature derived from genes homozygously deleted in myeloma as detected by array comparative genomic hybr idization 13 Genes signature build from genes accounted for heter ogeneity in human myeloma cells lines upon IL6 stimulati on J Clin Oncol 2008; 26: Blood 2008; 111: Clin Cancer Res 2010; 16: Haematologica 2011; 96: Proliferation index Based on proliferation genes Haematologica 2011; 96: EMC 92-gene signature Generated by supervised component analysis with simula ted annealing Leukemia 2012; 26: Chromosome instability genomic event count (CINGEC) signature Genes differentially expression between patients with the top quartile and bottom quartile of genomic complexity based on the number of acgh defined abnormalities incl uding both DNA gains and losses and breaks. PLoS One 2013; 8: e66361 Chng et al. Leukemia, 2014, 269

23 TC classification Bergsagel et al. Blood 2005, 296

24 UAMS classification Zhan et al. Blood. 2006, 2020

25 Clinical value of GEP-based molecular subtyping a data set of 1217 patients with multiple myeloma enrolled in Total Therapies TT2+: add thalidomide TT3a: bortezmib TT3b: lenalidomide maintenance Weinhold et al. Leukemia (2016) 30,

26 Clinical value of GEP-based molecular subtyping Weinhold et al. Leukemia (2016) 30,

27 Epigenetic inactivation of tumor suppressor genes Global methylation analysis MRC IX trial 195 genes with changes in methylation status the epigenetically regulated tumor modulating genes GPX3, RBP1, SPARC, and TGFBI. Kaiser et al. blood 2013,122(2):

28 203 Patients with MM Chapment et al. Nature, 2011, 470, Morgan et al. Nat Rev Cancer ;12(5):335 Lohr et al. Cancer Cell 2014, 25, 91

29 Common mutations in MM Gene Frequency (%) Function KRAS NRAS TP MAPK signalling pathway (cell survival and growth) MAPK signalling pathway (cell survival and growth) Tumour suppressor involved in response to DNA damage and apoptosis DIS3 11 Exosome endoribonuclease FAM46C ~11 Unclear BRAF 6 15 TRAF3 3 6 MAPK signalling pathway (cell survival and growth) NF-κB signalling pathway (cell survival and proliferation) ROBO1 2 5 Transmembrane receptor involved cell growth through crosstalk with MET signalling CYLD 2 3 NF-κB signalling pathway (cell survival and proliferation) EGR1 4 6 Transcription factor SP Antigen-response mechanisms in mature B cells FAT3 4 7 Cadherin superfamily member (cell adhesion) CCND1 3 Cell cycle progression

30 NGS 84 samples from 67 patients with MM exome sequencing, high-resolution copy-number arrays and cytogenetics Bolli et al Nat Commun. 2014;5:2997

31 Clonal selection and double-hit events involving tumor suppressor genes underlie relapse in myeloma longitudinal study of 33 patients entered into Total Therapy protocols gene expression profiling high-resolution copy number arrays whole-exome sequencing Weinhold et al. Blood. 2016;128(13):1735

32 clonal evolution in homogeneously treated patients targeted sequencing of bone marrow plasma cells in 43 multiple myeloma patients at diagnosis and at relapse from exactly the same intensive treatment. Corre et al. Leukemia 2018 e-pub online

33 Mutational Spectrum, Copy Number Changes, and Outcome of NDMM 463 NDMM, MRC Myeloma XI trial 15 significantly mutated genes IRF4, KRAS, NRAS, MAX, HIST1H1E, RB1, EGR1, TP53, TRAF3, FAM46C, DIS3, BRAF, LTB, CYLD, and FGFR3. Mutations in the RAS (43%) and nuclear factor-b (17%) pathways are prognostically neutral Mutations in CCND1 and DNA repair pathway alterations (TP53, ATM, ATR, and ZNFHX4 mutations) are associated with a negative impact on survival. IRF4 and EGR1 are associated with a favorable overall survival. Walker et al. J Clin Oncol 33:

34 Mutational Spectrum, Copy Number Changes, and Outcome of NDMM Group 1, ISS I and II with no copy number and structural abnormality [CNSA] or mutation; Group 2, ISS III with no CNSA or mutation or ISS I, II, and III with one CNSA or mutation Group 3, two CNSAs or mutations regardless of their ISS) Mutation:including TP53, ZFHX4, CCND1, ATM and ATR, MYC translocations, and amp(1q). Walker et al. J Clin Oncol 33:

35 Myeloma Genome Project Whole genome/exome data N=1273 the Myeloma XI trial the Dana-Faber Cancer Institute/Intergroupe Francophone du Myelome the Multiple Myeloma Research Foundation CoMMpass study 63 mutated driver genes Increased number of driver mutations is associated with poor outcome Walker et al. Blood. 2018;132(6):

36 Walker et al. Blood. 2018;132(6): Copy number (CN) clusters CN cluster % Hyperdiploid, gains of 1q, 6p, expression of CCND2 CN cluster % hyperdiploid with gain of 11q and expression of CCND1, mutation of FAM46C inversely associated with gain of 1q CN cluster 3 5.6% associated with t(14;16), deletions of 1p, 8p, 13q, 14q, and 16q, and gain of 1q. CN cluster 4 5.2% associated with t(4;14), del13q, and del14q CN cluster 5 6.7% associated with t(4;14), del4p, del13q, and del14q, mutations of NFKBIA, MAX, and TRAF3. CN cluster 6 5.9% associated with deletions of 8p, 14q, and 16q, gain of 1q, and mutation of CYLD. CN cluster 7 7.9% associated with t(4;14) and t(14;16), the APOBEC signature, deletions of 11q and 13q, gain of 1q, and mutation of DIS3, delfgfr3 CN cluster % associated with t(11;14) and mutations of CCND1 and PRKD2, but not with any deletions or gains. CN cluster 9 4.8% associated with t(11;14) and gain of 11q as well as mutation of BRAF

37 A high-risk, Double-Hit, group of newly diagnosed myeloma Myeloma genome project N=1273 Exclude aged >75 Walker et al. leukemia e-pub online

38 A high-risk, Double-Hit, group of newly diagnosed myeloma 6.1% of the population median PFS = 15.4 months, median OS = 20.7 months Walker et al. leukemia e-pub online

39 Targeted Sequencing 47 gene multiple myeloma mutation panel(m 3 P) Genomic profiles to guide precision therapy Small amounts of DNA Deep coverage results 39 the top mutated genes Resistance to IMiDs (CRBN, CUL4A, CUL4B, DDB1 and IRF4), proteasome inhibitors (PSMG2, PSMB5) and glucocorticoid therapies (NR3C1) 72 untreated high-risk (del17p) MM patients Mutations in 78% of the patients. TP53 mutation was increased (28%) no mutations in FAM46C. British Journal of Haematology, 2015, 168, 507

40 Targeted sequencing of refractory myeloma 50 multidrug refractory multiple myeloma 88%, 78%, and 68% were refractory to an IMiD, a PI, or both 47 gene multiple myeloma mutation panel(m 3 P) an increased prevalence of mutations in the Ras pathway genes KRAS,NRAS, and/or BRAF(72%), as well as TP53 (26%), CRBN(12%), and CRBN pathway genes (10%) Kortum et al. Blood. 2016;128:1226

41 Targeted Sequencing N=418 N. Bolli et al leukemia 2018 e-pub

42 Integrative network analysis The MMRF CoMMpass study is a longitudinal, prospective observational study MMNet an integrated network of MM incorporating RNA-seq, WES, WGS and clinical data from CoMMpass IA7 Laguna et al. Leukemia (2018) 120

43 Integrative network analysis Laguna et al. Leukemia (2018) 120

44 CoMMpass: MMNet-based patient clusters Cluster No. of Pts Ex. upregulated genes Ex. downregulated genes UAMS Transloc Somat Mut CNA Mut Burd CCND1 82 (18.2%) CCND1, DTX1, PAX5 CCND2, MEIS2, NEDD4, CDK6, PRAME CD-1, CD-2 t(11;14) MAF 30 (6.6%) NUAK1, CCND2, MAF-A, IL21R, MAF NCAM1, GLI3, BMP4, DKK1 MF t(14;16) t(14;20) SYNE2 1q amp, 13q del, 16q del UP CCND3 2 (0.45%) CCND3, PRICKLE4, USP49 t(6;14) DOWN MMSET 76 (16.8%) CCND3/ del FGFR3, CCND2, CDC42BPA, CLEC11A, NUAK1, WHSC1 MAGEC1, LAG3, PAX5, CCND1 MS t(4;14) DIS3, MAX, FGFR3 1q amp, 1p del, 12 del, 13q del, 14q del, 22q del 4 (8.8%) CCND3, MAP1A, USP49, PRICKLE4 SMOX, TPRG1, NFATC4 t(6;14) 6 del, 8 del, 14q del CK 38 (8.4%) XIRP1, CXCL8, IL1R2, SEMA7A, TREM1, TUBB3, IL6, DUSP8, DUSP4, BCL6 CCR2, NEK2, TOP2A, CD28, CDK1 PR (inv.) HY/NRAS 118 (26.2%) CCR5, PRAME, GLI3, LAG3, FRZB, MEIS2 CCND2, CDR1, CDKN1C, IGF1R HY NRAS Hyperdiploidy (3, 5, 7, 9, 11, 15, 19, 21 amp) IMM 42 (9.3%) S100A8, S100A12, S100A9, CXCR1, CCND2 SETP4, ABCG2, PALD1, SIX4 MYC 29 (6.4%) PKHD1, MAGEA3, MAGEA12, ASS1, MYC CD44, MAF, NUAK1 t(8;14) 15q amp CC 29 (6.4%) CCND2, MAGEA3, MAGEC1, MAGEA6, MEIS2 CCND1, SATB1, PMAIP1, KIF19 PR LB, CD-1 (inv.) DST 1p del, 4 del, 13q del, 14q del, 16 del DOWN Laguna et al. Leukemia (2018) 120 UP

45 Spatial genomic heterogeneity in multiple myeloma Rasche et al. Nat Commun. 2017;8(1):268

46 Spatial genomic heterogeneity in multiple myeloma Rasche et al. Nat Commun. 2017;8(1):268

47 Large focal lesions is a strong independent prognostic factor in multiple myeloma Rasche et al. Blood. 2018;132(1):59

48 Circulating tumour DNA sequence analysis as an alternative to MM BM aspirates a hybrid-capturebased Liquid Biopsy Sequencing (LB-Seq) method targeted deep sequencing of all protein coding exons of KRAS, NRAS, BRAF, EGFR and PIK3CA genes in 64 cfdna specimens from 53 myeloma patients 83 ng of cfdna or less (13 samples had ng of cfdna available), extracted from 3 to 13ml (median 8.5ml) of blood plasma, Kis et al. Nat Commun ;8:15086.

49 Cell-free DNA sequencing Guo et al leukemia e-pub

50 Whole-exome sequencing of cell-free DNA and circulating tumor cells in multiple myeloma ultra-low pass whole genome sequencing (ULP-WGS) Manier et al. NATURE COMMUNICATIONS 2018, 9:1691

51 Whole-exome sequencing of cell-free DNA and circulating tumor cells in multiple myeloma Manier et al. NATURE COMMUNICATIONS 2018, 9:1691

52 Genetic interrogation of circulating MM cells at single-cell resolution Lohr et al. Sci. Transl. Med. 8, 363ra147 (2016)

53 Linking transcriptional and genetic tumor heterogeneity through allele analysis of single-cell RNA-seq data Fan et al. Genome research :1217

54 CD38-APC Myeloma transcriptome analysis in variable disease states Tumor cell enrichment by anti- CD138 Ab CD138- PE comparison Bone marrow in stable disease>>bone marrow in aggressive disease>> Refractory extramedullary Unpublished data

55 Transcriptional programs controlling the myeloma progression Ubpublished data

56 Predicting treatment benefit in multiple myeloma through simulation of alternative treatment effects Ubels et al. NATURE COMMUNICATIONS DOI: /s

57 Conclusion With advances of techniques and knowledge of myeloma pathogenesis novel ways for genetic prognostication have been tried. CNVs and translocations have a stronger impact on prognosis than mutations, but extended genotyping shows novel prognostic categories. Methods for treatment selection especially for high risk myeloma patients are still unmet needs.