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In: Blood, American Society of Hematology, Vol. 126, No. 23 ( 2015-12-03), p. 189-189

Abstract: Background: Maximising response in myeloma (MM) patients with effective induction regimens prior to autologous stem cell transplant (ASCT) improves progression-free and overall survival. Triplet regimens combining an immunomodulatory agent (IMiD) and/or proteasome inhibitor (PI) are standard of care, however a more personalised approach is achieved by sequential triplet combinations based on an individual's response. Alternatively, quadruplet regimens may be more effective and new generation PIs such as carfilzomib, with less off-target activity, provide the opportunity to investigate this whilst minimising the risk of increased toxicity. The UK NCRI Myeloma XI trial is a large, phase III study aiming to answer these questions in transplant eligible (TE) patients comparing the quadruplet carfilzomib, cyclophosphamide, lenalidomide and dexamethasone to the sequential strategy of triplet IMiD combinations (with thalidomide or lenalidomide) followed by additional PI triplet therapy for those with a suboptimal response ( 〈 VGPR) prior to ASCT. Methods: In 2013, the TE pathway was amended to include KCRD: carfilzomib 36mg/m2 IV d1-2,8-9,15-16 (20mg/m2 #1d1-2), cyclophosphamide (cyclo) 500mg PO d1,8, lenalidomide (len) 25mg PO d1-21, dexamethasone (dex) 40mg PO d1-4,8-9,15-16). Patients are randomised to this up-front quadruplet or the sequential strategy of CRD: cyclo 500mg PO d1,8, len 25mg PO d1-21 PO daily, dex 40mg PO d1-4, 12-15 or CTD: cyclo 500mg PO d1,8,15 thalidomide 100-200mg PO daily, dex 40mg PO d1-4,12-15 given to max. response - patients with VGPR/CR proceed straight to ASCT, PR/MR are randomised to sequential CVD: cyclo 500mg d1,8,15, bortezomib 1.3mg/m2 IV/SC d1,4,8,11, dex 20mg PO d1,2,4,5,8,9,11,12 or nothing and SD/PD all receive sequential CVD. All treatments are given to max. response prior to ASCT, after which there is a maintenance randomisation. Patients: 1512 patients entered the TE pathway prior to amendment (756 CRD, 756 CTD). Of these, 201 patients with a suboptimal initial response went on to receive CVD, 142 following randomisation (initial response PR/MR) and 59 with NC/PD. 788 (of target n=1036) patients have been randomised post-amendment to date (394 KCRD, 197 CRD, 197 CTD). Results: TE patients receiving treatment prior to the amendment had response rates ≥VGPR: CRD 58% vs CTD 52%. For patients receiving the sequential triplet CVD due to a suboptimal response this was upgraded to ≥VGPR in 49% of those with initial MR/PR, 27% with NC/PD. This suggests the overall ≥VGPR rate to this treatment approach prior to ASCT would be approx. 75%. This now needs to be compared to the alternative approach of an upfront quadruplet. Comparing patients contemporaneously randomised to initial induction the patients receiving KCRD have completed a median 4 cycles (range 1-7), CRD 5 (range 1-10) and CTD 6 (range 1-9). Dose modifications have been required in 62% of patients receiving KCRD (56% to carfilzomib, 42% to lenalidomide) 44% CRD (40% to lenalidomide) and 65% CTD (59% to thalidomide). Data for study drug related toxicity in patients who have completed at least one cycle of initial induction are shown in table 1. Serious adverse events suspected to be due to trial medications have occurred in 37% on KCRD, 32% CRD and 35% CTD. Updated toxicity and preliminary response analysis on 23/09/15 will be presented at the meeting. This will include a response comparison at the end of initial induction regimen i.e. KCRD vs CRD vs CTD for an anticipated 700 contemporaneous patients who will have completed treatment. Updated response to the sequencing approach (with 250 patients having received sequential CVD) will also be presented and compared. Conclusions: In our study KCRD, an outpatient delivered 4-drug regimen combining second generation IMiD and PI drugs, is well-tolerated in TE NDMM patients, comparable to 3-drug regimens. Data will be presented at the meeting to compare the response rates achieved with the different regimens and treatment approaches. On behalf of the NCRI Haemato-oncology CSG Table 1. Comparative toxicities KCRD n=261 CRD n=143 CTD n=142 % (no. of patients) Peripheral neuropathy Sensory Gr II-IV 1.9 (5) 1.4 (2) 8.5 (12) Motor Gr II-IV 3.1 (8) 1 (1) 5.6 (8) VTE all grades 4.2 (11) 4.9 (7) 5.6 (8) Anaemia Gr III-IV 9.2 (24) 4.2 (6) 5.6 (8) Neutropenia Gr III-IV 14.9 (39) 16.1 (22) 13.3 (19) Thrombocytopenia Gr III-IV 8.4 (22) 1.4 (2) 1.4 (2) Infusion reaction Gr III-IV 0.4 (1) - - Disclosures Pawlyn: Celgene: Honoraria, Other: Travel support; The Institute of Cancer Research: Employment. Off Label Use: Carfilzomib as induction treatment for myeloma Lenalidomide and vorinostat as maintenance treatments for myeloma. Davies:University of Arkansas for Medical Sciences: Employment; Celgene: Honoraria; Onyx-Amgen: Honoraria; Takeda-Milenium: Honoraria. Jones:Celgene: Other: Travel support, Research Funding. Kaiser:Janssen: Honoraria; Chugai: Consultancy; Amgen: Consultancy, Honoraria; BristolMyerSquibb: Consultancy; Celgene: Consultancy, Honoraria, Research Funding. Jenner:Takeda: Honoraria; Amgen: Honoraria. Cook:Jazz Pharma: Consultancy, Honoraria, Speakers Bureau; Sanofi: Consultancy, Honoraria, Speakers Bureau; Takeda: Consultancy, Honoraria, Speakers Bureau; Amgen: Consultancy, Honoraria, Speakers Bureau; Chugai: Consultancy, Honoraria, Speakers Bureau; Janssen: Consultancy, Honoraria, Speakers Bureau; Bristol-Myers Squibb: Consultancy, Honoraria, Speakers Bureau; Celgene: Consultancy, Honoraria, Research Funding, Speakers Bureau. Russell:Therakos: Other: shares. Owen:Celgene: Honoraria, Research Funding; Janssen: Honoraria. Gregory:Janssen: Honoraria; Celgene: Honoraria. Jackson:Celgene: Honoraria; Amgen: Honoraria; Takeda: Honoraria. Morgan:Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda-Millennium: Honoraria, Membership on an entity's Board of Directors or advisory committees; CancerNet: Honoraria; Weisman Institute: Honoraria; MMRF: Honoraria; MMRF: Honoraria; University of Arkansas for Medical Sciences: Employment; Weisman Institute: Honoraria; CancerNet: Honoraria.

Type of Medium: Online Resource

ISSN: 0006-4971 , 1528-0020

URL: Article

DOI: 10.1182/blood.V126.23.189.189

Language: English

Publisher: American Society of Hematology

Publication Date: 2015

detail.hit.zdb_id: 1468538-3

detail.hit.zdb_id: 80069-7

In: Blood, American Society of Hematology, Vol. 126, No. 23 ( 2015-12-03), p. 371-371

Abstract: Introduction Multiple myeloma (MM) is characterised by the malignant expansion of clonal plasma cells in the bone marrow (BM). We and others have used massive parallel sequencing to describe the somatic aberrations acquired in different subclones in newly diagnosed MM (NDMM). These studies have showed that chemotherapy has an impact on intra-clonal heterogeneity, but more analyses are required in paired presentation/relapse samples and samples from multiple sites at the same and different time points. Materials and methods We have studied 49 paired presentation/relapse patients from a series of 463 NDMM patients entered into the Myeloma XI trial (NCT01554852). To understand the impact of spatial separation within the MM clone and the consideration that MM is a metastatic disease, we examined BM aspirates and compared them to targeted biopsies from extramedullary disease sites in 9 MM patients. These cases were 1 patient with samples bilaterally collected from the hip during the course of the disease, 4 MM cases with plasma cell leukemia (PCL), 3 MM cases with plasmacytomas, 1 MM patient with ascites, and 1 MM case with pleural effusion. DNA from both BM and peripheral blood samples were used for whole exome sequencing plus a pull down of the MYC, IGH, IGL and IGK loci following the SureSelect Target Enrichment System for Illumina Paired-End Sequencing Library v1.5. Exome reads were used to call single nucleotide variants, indels, translocations, and copy number aberrations. Mean sequencing depth was 59.3x. The proportion of mutant tumor cells carrying a mutation was inferred. The presence and proportion of subclones will be defined using bioinformatics tools. Results For the 463 NDMM samples, the following 15 significantly mutated genes are seen KRAS (n=103 mutations), NRAS (n=88), LTB (n=53), DIS3 (n=49), BRAF (n=37), EGR1 (n=22), FAM46C (n=20), IRF4 (n=19), TRAF3 (n=17), HIST1H1E (n=16), TP53 and FGFR3 (n=14), CYLD (n=13), MAX (n=12), and RB1 (n=5). These mutations were seen within all clonal cells and at subclonal levels, consistent with the mutations being acquired at different time points and being associated with different subclonal fitness. We show that NDMM have a mean number of exonic mutations of 61.1±13.0, in contrast to samples taken at the time of relapse, which show an average of 80.6±25.4, Figure 1A. We report diverse patterns of subclonal evolution: no change, subclonal tiding, and subclonal tiding with new subclones arising. We are currently examining samples taken during clinical remission to track subclones at the time of response. For patient with multiple samples taken at different timepoints, 77 mutations were shared across all samples but, of note, specific mutations were seen at the same timepoint in different sites (13/1662 R2R vs 13/1662 R2L), which illustrates the impact of sampling differences in reporting mutation calls and differential response to therapy, Figure 1B. This is also observed in a plasmacytoma case with both a BM aspirate sample containing 11 mutations (including NRAS c.183A 〉 T and BRAF c.1783T 〉 C), and a femur plasmacytoma with 18 mutations, of which only 2 are shared with the BM sample, Figure 3. One of these shared lesions is BRAF c.1783T 〉 C, the cancer clonal fraction of which increases ten-fold, suggesting that the sub-clone with this mutation disseminated from the BM and founded the plasmacytoma. Conclusion Our preliminary data demonstrate that MM subclones not only respond differently to clinical treatment, but also have different biological properties leading to cause extramedullary disease. To our knowledge, this is the first comprehensive genetic analysis of the spatio-temporal heterogeneity in myeloma and reveals genetic differences due to sampling bias. Figure 1. (A) Number of mutations in MM patients at clinical presentation and relapse. Each patient sample is represented by a dot. Lines and error bars correspond to the average and the standard error of the mean values, respectively. Difference was not statistically significant (p 〉 0.05, t-test). (B) MM patient analysed at presentation and following two relapses (top). The number of mutations increases through disease (bottom, left panel). Venn plot shows the number of shared and specific mutations for each time point (bottom, right panel). (C) Case with a MM sample (green) and a femur plasmacytoma (blue). Venn plot shows shared and specific mutations to the bone marrow or the plasmacytoma site. Figure 1. (A) Number of mutations in MM patients at clinical presentation and relapse. Each patient sample is represented by a dot. Lines and error bars correspond to the average and the standard error of the mean values, respectively. Difference was not statistically significant (p 〉 0.05, t-test). (B) MM patient analysed at presentation and following two relapses (top). The number of mutations increases through disease (bottom, left panel). Venn plot shows the number of shared and specific mutations for each time point (bottom, right panel). (C) Case with a MM sample (green) and a femur plasmacytoma (blue). Venn plot shows shared and specific mutations to the bone marrow or the plasmacytoma site. Disclosures Jones: Celgene: Other: Travel support, Research Funding. Peterson:University of Arkansas for Medical Sciences: Employment. Brioli:Celgene: Honoraria; Janssen: Honoraria. Pawlyn:Celgene: Honoraria, Other: Travel support; The Institute of Cancer Research: Employment. Gregory:Janssen: Honoraria; Celgene: Honoraria. Davies:Onyx-Amgen: Membership on an entity's Board of Directors or advisory committees; Array-Biopharma: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Takeda-Millennium: Membership on an entity's Board of Directors or advisory committees; University of Arkansas for Medical Sciences: Employment. Morgan:CancerNet: Honoraria; University of Arkansas for Medical Sciences: Employment; MMRF: Honoraria; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Weisman Institute: Honoraria; Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda-Millennium: Honoraria, Membership on an entity's Board of Directors or advisory committees.

Type of Medium: Online Resource

ISSN: 0006-4971 , 1528-0020

URL: Article

DOI: 10.1182/blood.V126.23.371.371

Language: English

Publisher: American Society of Hematology

Publication Date: 2015

detail.hit.zdb_id: 1468538-3

detail.hit.zdb_id: 80069-7

In: Blood, American Society of Hematology, Vol. 126, No. 23 ( 2015-12-03), p. 2983-2983

Abstract: Introduction Hyperdiploidy (HRD) comprises the largest pathogenetic subgroup of myeloma. However, its clinical and molecular characterisation is incomplete. Here, we investigate HRD using a novel high-throughput molecular analysis method (MyMaP - Myeloma MLPA and translocation PCR; Kaiser MF et al., Leukemia 2013; Boyle EM et al., Gen Chrom Canc 2015) in a large cohort of 1,036 patients from the UK NCRI Myeloma XI trial. Materials, Methods and Patients Copy number changes, including gain of chromosomes 5, 9 and 15, as well as translocation status were assayed for 1,036 patients enrolled in the UK NCRI Myeloma XI (NCT01554852) trial using CD138+ selected bone marrow myeloma cells taken at diagnosis. HRD was defined by triploidy of at least 2 of analysed chromosomes 5, 9 or 15. Analysis was performed on standard laboratory equipment with MyMaP, a combination of TC-classification based multiplex qRT-PCR and multiplex ligation-dependent probe amplification (MLPA; MRC Holland). The parallel assessment of multiple loci with copy number alteration (CNA) by MLPA allowed unbiased association studies using a Bayesian approach. Semi-quantitative gene expression data for CCND1 and CCND2 was generated as part of the multiplexed qRT-PCR analysis. Median follow up for the analysis was 24 months. Results Of the 1,036 analysed patients, 475 (46%) were HRD. Of these, 325 (68%) had gain(11q25), 141 (29.7%) gain(1q), 43 (9.1%) del(1p32) and 36 (7.5%) del(17p). Gain(11q25) was significantly associated with HRD (Bayes Factor BF01 〈 0.05) in the entire group of 1,036 cases and occurred in only 17% of non-HRD cases, but frequencies of the other copy number alterations (CNA) were similar to entire group. Although gain(1q) was negatively correlated with gain(11q25) within the HRD group (Corr-0.21, BF=0.0004), the two lesions co-occurred in 73 (15.4%) cases. Analysis of other CNA revealed that del(13q) was significantly less frequent (25%) in HRD cases than in non-HRD (56%) cases (BF 〈 0.0001). Interestingly, del(13q) within HRD was highly associated with gain(1q) (BF 〈 0.0001) and negatively correlated with gain(11q25) (BF 〈 0.0001). Thus, CNA status can help discriminate three distinct molecular subgroups of HRD: gain(11q25), gain(11q25)+gain(1q), gain(1q)[+/-del(13q)]. HRD cases were classified as D1, D2 or D1+D2 according to the TC classification based on qPCR CCND1 and CCND2 expression values and expression was correlated with copy number status. An association of the D1 subtype with gain(11q25) and of D2 with gain(1q) was confirmed. CCND1 expression was significantly (P 〈 0.001) higher in cases with gain(11q) [Mean Relative Quantitative (RQ) value 5,466] than in cases with gain(1q) [Mean RQ value 721] . In contrast, CCND2 expression values were significantly higher in cases with gain(1q) [Mean RQ 8,723] than in cases with gain(11q) [mean RQ 1,087] (P 〈 0.001). Co-occurrence of gain(11q) and gain(1q) was associated with intermediate values with CCND1 mean RQ 5,090 and CCND2 mean RQ 2,776, reminiscent of the D1+D2 subtype. HRD was associated with favourable outcome when compared to non-HRD cases with median PFS 28.8 vs. 21.7 months (P 〈 0.0001) and 24-months OS of 83% vs. 77% (median not reached), respectively. However, cases with t(11;14) had a median PFS of 27.0 months and 24-month OS of 80%, combarable to outcome of the HRD group. Within HRD cases, gain(1q) was associated with shorter PFS (P =0.02) and OS (P =0.009), associating the D2 group with inferior outcome. Presence of del(1p32) was associated with inferior PFS (P =0.01) and OS (P =0.0007) in the HRD subgroup and del(17p) was associated with inferior OS (P =0.04) with a trend for PFS. HRD cases with presence of any of the risk factors gain(1q), del(1p32) or del(17p) in comparison to those without had a median PFS of 25.1 vs 35.1 months (P =0.0001) and 24-month OS of 73.8% vs 89.0% (P 〈 0.0001). Conclusion We describe in a large trial cohort an association between gain(11q25) and the D1 hyperdiploid subtype as well as gain(1q) and the D2 subtype, a finding that has so far only been inferred by gene expression array data in the original TC classification. We also find an association with adverse outcome for the D2/gain(1q) subtype. Our findings demonstrate that the novel molecular approach MyMaP allows precise molecular sub-classification of HRD myeloma. Disclosures Kaiser: BristolMyerSquibb: Consultancy; Chugai: Consultancy; Janssen: Honoraria; Amgen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding. Pawlyn:Celgene: Honoraria, Other: Travel support; The Institute of Cancer Research: Employment. Jones:Celgene: Other: Travel support, Research Funding. Savola:MRC Holland: Employment. Owen:Celgene: Honoraria, Research Funding; Janssen: Honoraria. Cook:Takeda Oncology: Consultancy, Research Funding, Speakers Bureau; Amgen: Consultancy, Speakers Bureau; Sanofi: Consultancy, Speakers Bureau; BMS: Consultancy; Celgene: Consultancy, Research Funding, Speakers Bureau; Janssen: Consultancy, Research Funding, Speakers Bureau. Gregory:Janssen: Honoraria; Celgene: Honoraria. Davies:Onyx-Amgen: Honoraria; Celgene: Honoraria; University of Arkansas for Medical Sciences: Employment; Takeda-Milenium: Honoraria. Jackson:Amgen: Honoraria; Takeda: Honoraria; Celgene: Honoraria. Morgan:Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Weisman Institute: Honoraria; Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda-Millennium: Honoraria, Membership on an entity's Board of Directors or advisory committees; University of Arkansas for Medical Sciences: Employment; CancerNet: Honoraria; MMRF: Honoraria.

Type of Medium: Online Resource

ISSN: 0006-4971 , 1528-0020

URL: Article

DOI: 10.1182/blood.V126.23.2983.2983

Language: English

Publisher: American Society of Hematology

Publication Date: 2015

detail.hit.zdb_id: 1468538-3

detail.hit.zdb_id: 80069-7

In: Blood, American Society of Hematology, Vol. 132, No. Supplement 1 ( 2018-11-29), p. 2000-2000

Abstract: INTRODUCTION Features of high risk myeloma (MM) have been studied in detail but patients with longer term responses to first-line therapy are less well characterised. Identification of common features of this group may support optimised management. Here we analysed clinical and genetic characteristics of long-term responders of 4,249 trial patients from the UK MRC Myeloma IX (M-IX) and NCRI Myeloma XI (M-XI) trials. PATIENTS AND METHODS In M-IX patients were randomised between alkylating therapy (CVAD or MP) and thalidomide-based induction therapy (CTD). M-XI patients were randomised between thalidomide and lenalidomide based induction (CTD vs CRD) and a response-based bortezomib (CVD) intensification. Fitter patients received HD-Mel+ASCT consolidation. Patients were then randomised to thalidomide (M-IX) or lenalidomide (M-XI) maintenance or observation. Trials included symptomatic, newly diagnosed patients based on CRAB criteria. This analysis included 1,921 My-IX and 2,328 My-XI patients with median follow-up of 73 and 61 months (m), respectively. Genetic profiling was available for 1,866 patients. Patients with a long-term response post induction (PFS≥48m) were identified and their baseline characteristics, responses and treatment compared to those with PFS 〈 48m. OS difference was compared using the logrank test. Multivariate analysis was performed using logistic regression. RESULTS In M-IX, 283 (25.8%) of transplant-eligible (TE) patients had PFS ≥48m whereas 58 (7%) of transplant non-eligible (TNE) patients reached PFS≥48m. In M-XI 410 (34.2%) patients had PFS≥48m for TE and 116 (10.2%) for TNE. Extended progression free survival translated to overall survival (OS) benefit with a median post progression OS of 36.9m for PFS≥48m vs 16.7m for PFS 〈 48m (p 〈 0.0001) for M-IX. For M-XI, OS data had not reached maturity, however the probability of OS at 2 years post progression for those with PFS≥48m was 60% vs 36% for PFS 〈 48m. Clinical factors including ISS I (P 〈 0.0001) and lower performance status (WHO) (P 〈 0.0001) were positively associated with PFS≥48m. Relative risk by multivariate analyses appeared to be higher for these factors in TNE patients with odds ratio of 1.6 and 1.3 than in the TE group with odds 1.4 and 1.2 across M-IX and XI, respectively. The proportion of patients with a high risk lesion (Adverse translocation, Gain(1q) or Del(17p)) were lower in the PFS≥48m group than 〈 48m: 34.3% vs. 54.5% and 28.8% vs. 54% for TE and 10% vs. 51.2% and 35.4% vs 52.1% for TNE arms of M-IX and M-XI, respectively. 'Double hit' MM (≥2 high risk lesions) was rare with 5.8% of patients PFS≥48m compared to 16.6% of patients PFS 〈 48m across trials (P 〈 0.0001). Absence of gain(1q) was the only genetic factor retained within a multivariable analysis of baseline parameters associated with PFS≥48m in the TNE group, whereas for the TE group absence of all high risk lesions were associated with PFS≥48m (p 〈 0.0001). Hyperdiploidy was positively associated with PFS≥48m in the TE group (P=0.02) only by univariate analysis. The majority of patients with PFS ≥48m showed ≥VGPR after induction +/- consolidation: 211 (76.4%) and 340 (84%) of PFS ≥48m patients in the TE arms and 26 (49.1%) and 87 (76.3%) in the TNE arms of M-IX and M-XI, respectively. 86.7% of patients who achieved a ≥VGPR had a PFS ≥48m in the absence of high risk lesions compared to 72.8% with any high risk lesion present (P=0.004). Some patients with PFS≥48m had only reached PR after induction; 56 (20.3%) and 57 (14.1%) of PFS ≥48m patients in the TE arm and 15 (28.3%) and 24 (21.1%) in the TNE arms of M-IX and M-XI, respectively. Baseline factors that were associated with still being able to achieve PFS≥48m from induction after only achieving a PR included the lack of high risk genetic lesions (P 〈 0.0001) and low ISS (P=0.0002). In M-XI, the proportion of patients who only achieved a PR after induction and reached PFS≥48m was 10.6% for patients randomised to observation and 89.4% for patients with lenalidomide maintenance suggesting maintenance may be of particular benefit in this group. CONCLUSIONS Response assessment after induction+/-HD-Mel consolidation with baseline factors can define a patient group with superior outcomes in both TE and TNE patients and may influence future treatment strategies of MM patients undergoing first line therapy. Further analyses including modelling of predictors of response duration are ongoing and will be presented at the conference. Disclosures Shah: Celgene: Other: Travel, Accommodation expenses; Sanofi: Other: Travel and Accommodation expenses. Striha:Janssen: Research Funding; Abbvie: Research Funding; Celgene: Research Funding; MSD: Research Funding; Amgen: Research Funding. Hockaday:Celgene: Research Funding; Amgen: Research Funding; Abbvie: Research Funding; Janssen: Research Funding; MSD: Research Funding; Millenium: Research Funding. Pawlyn:Celgene Corporation: Consultancy, Honoraria, Other: Travel support; Amgen: Consultancy, Honoraria, Other: Travel Support; Janssen: Honoraria, Other: Travel support; Takeda Oncology: Consultancy, Other: Travel support. Jenner:Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novartis: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Drayson:Abingdon Health: Equity Ownership, Membership on an entity's Board of Directors or advisory committees. Owen:Celgene: Consultancy, Honoraria, Research Funding; Takeda: Honoraria, Other: Travel Support; Janssen: Consultancy, Other: Travel Support. Gregory:Celgene: Consultancy, Honoraria, Research Funding; Merck Sharp and Dohme: Research Funding; Janssen: Honoraria; Amgen: Research Funding. Morgan:Janssen: Research Funding; Takeda: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding. Davies:Janssen: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Takeda: Consultancy, Honoraria. Cook:Janssen: Consultancy, Honoraria, Research Funding, Speakers Bureau; Seattle Genetics: Honoraria; Glycomimetics: Consultancy, Honoraria; Takeda: Consultancy, Honoraria, Research Funding, Speakers Bureau; Amgen: Consultancy, Honoraria, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Research Funding, Speakers Bureau; Sanofi: Consultancy, Honoraria, Speakers Bureau; Bristol-Myers Squibb: Consultancy, Honoraria; Celgene Corporation: Consultancy, Honoraria, Research Funding, Speakers Bureau. Cairns:Celgene: Research Funding; Amgen: Research Funding; Merck Sharp and Dohme: Research Funding. Jackson:Roche: Consultancy, Honoraria, Speakers Bureau; Merck Sharp and Dohme: Consultancy, Honoraria, Speakers Bureau; Amgen: Consultancy, Honoraria, Speakers Bureau; Celgene: Consultancy, Honoraria, Other: Travel Support, Research Funding, Speakers Bureau; Takeda: Consultancy, Honoraria, Other: Travel Support, Research Funding, Speakers Bureau. Kaiser:Amgen: Consultancy, Honoraria; Takeda: Consultancy, Other: travel support; Janssen: Consultancy, Honoraria; Chugai: Consultancy; Bristol-Myers Squibb: Consultancy, Other: travel support; Celgene: Consultancy, Honoraria, Research Funding.

Type of Medium: Online Resource

ISSN: 0006-4971 , 1528-0020

URL: Article

DOI: 10.1182/blood-2018-99-110149

Language: English

Publisher: American Society of Hematology

Publication Date: 2018

detail.hit.zdb_id: 1468538-3

detail.hit.zdb_id: 80069-7

In: Blood, American Society of Hematology, Vol. 124, No. 21 ( 2014-12-06), p. 723-723

Abstract: Aberrant chromosomal translocations are seen in ~40% of presenting patients and predominantly involve the IGH locus at 14q32. The five main translocations involving the IGH locus are t(4;14), t(6;14), t(11;14), t(14;16) and t(14;20), which result in over-expression of MMSET/FGFR3, CCND3, CCND1, MAF and MAFB, respectively. In previous clinical trials we have shown that the t(4;14), t(14;16) and t(14;20) are associated with a poor prognosis. In initial sequencing studies of myeloma it has been noted that the spectrum of mutations fall into two groups, one of which is characterised by an APOBEC signature. This signature comprises of C 〉 T, C 〉 G and C 〉 A mutations in a TpC context and comprises only a subset of samples, with the rest having a rather generic mutation signature representing an intrinsic mutational process occurring as a result of the spontaneous deamination of methylated cytosine to thymine. Whole exome sequencing was performed on 463 presentation patients enrolled into the UK Myeloma XI trial. DNA was extracted from germline DNA and CD138+ plasma cells and whole exome sequencing was performed using SureSelect (Agilent). In addition to capturing the exome, extra baits were added covering the IGH, IGK, IGL and MYCloci in order to determine the breakpoints associated with translocations in these genes. Tumor and germline DNA were sequenced to a median of 60x and data processed to generate copy number, acquired variants and translocation breakpoints in the tumor. Progression-free and overall survival was measured from initial randomization and median follow up for this analysis was 25 months. These combined data allow us to examine the effect of translocations on the mutational spectra in myeloma and determine any associations with progression-free or overall survival. Translocations were detected in 232 (50.1%) patients of which 59 patients (12.7%) had a t(4;14), 86 patients (18.6%) a t(11;14), 17 patients (3.7%) a t(14;16), 5 patients (1%) a t(6;14) and 4 patients (0.9%) a t(14;20). MYC translocations were found in 85 patients (18.4%). Using the tiled regions we were able to detect a mutational signature, kataegis, where regional clustering of mutations can be indicative of somatic genomic rearrangements. We found the hallmarks of kataegis in 15 samples (3.2%), where there was enrichment for TpCpH mutations with an inter-mutational distance 〈 1 kb. Where we detected kataegis surrounding MYC there was also an inter-chromosomal translocation involving either IGK or IGL. Interestingly, the partner chromosomes also showed signs of kataegis e.g. in the t(2;8) kataegis was found at IGK and MYC and in the t(8;22) kataegis was found at MYC and IGL. APOBECs are thought to be involved in the generation of kataegis and as such this co-localisation is indicative of APOBEC involvement in the generation of MYCbreakpoints. We found mutation of translocation partner oncogenes, in particular CCND1 was mutated in 10 patients with the t(11;14). There was an association of mutated CCND1 with a poor prognosis when compared with non-mutated t(11;14) patients (OS median of 20.2 months vs. not reached, p=0.005). Mutations were also seen in FGFR3, MAF and MAFB but only in the samples with the respective translocations. The mutations are likely due to somatic hypermutation mediated by AID, an APOBEC family member. We found that t(14;16) and t(14;20) samples have a significantly higher number of mutations compare to the other translocation groups (p=1.65x10-5). These mutations were enriched for those with an APOBEC signature (T(C 〉 T)A, p=9.1x10-5; T(C 〉 T)T, p=0.0014; T(C 〉 G)A, p=0.001; T(C 〉 G)T, p=0.0064), indicating that the ‘maf’ translocation groups are characterized by APOBEC signature mutations, specifically APOBEC3B. When samples are assigned to either an APOBEC or non-APOBEC group the ‘maf’ translocations account for 66.6% of samples in the APOBEC group but only 1.3% of the non-APOBEC group. Here we show three different mutational signatures mediated by the APOBEC family: translocation partner mutation by AID, APOBEC signature mediated by APOBEC3B, and kataegis mediated by an unknown APOBEC family member. We also show for the first time a clinical impact of APOBEC mutations and their association with a poor prognosis. The poor prognosis of this mutational signature is inextricably linked to a high mutation load and the adverse t(14;16) and t(14;20) translocation subgroups. Disclosures Walker: Onyx Pharmaceuticals: Consultancy, Honoraria.

Type of Medium: Online Resource

ISSN: 0006-4971 , 1528-0020

URL: Article

DOI: 10.1182/blood.V124.21.723.723

Language: English

Publisher: American Society of Hematology

Publication Date: 2014

detail.hit.zdb_id: 1468538-3

detail.hit.zdb_id: 80069-7

In: Blood, American Society of Hematology, Vol. 124, No. 21 ( 2014-12-06), p. 2194-2194

Abstract: Dysregulation of the epigenome plays an important role in the pathogenesis of the plasma cell malignancy myeloma (MM). For example the H3K36 methyltransferase, MMSET, is overexpressed as a result of t(4;14) in 15% of patients and associated with a distinct DNA methylation pattern and shorter survival. Epigenetic modifiers may also be deregulated due to somatic mutations, seen in the histone demethylase, KDM6A/UTX (Van Haaften et al, Nat.Genet. 2009) and histone methyltransferase, MLL(Chapman et al, Nature 2011). We analysed the spectrum and clinical implications of epigenetic gene mutations in the largest series of newly diagnosed MM patients sequenced to date. Whole exome sequencing was performed on DNA extracted from tumour (CD138+) and peripheral blood samples from patients entering the NCRI Myeloma XI trial (n=463) using SureSelect (Agilent) with extra baits to cover IGH, IGK, IGL and MYC loci, median depth 60x. Data were processed to identify acquired variants, copy number, indels and translocation breakpoints and annotated for potentially deleterious mutations. Significantly mutated genes were detected using MutSigCV (v1.4) inputting all SNV and short indels (q-value threshold 0.1). Survival from initial randomization had median follow up of 25 months. The gene encoding the linker histone protein Histone 1.4, HIST1H1E was mutated in 2.8% of samples and one of the most significantly mutated genes in myeloma (p 〈 1x10-10, q 〈 1x10-10). Average cancer cell fraction in HIST1H1E mutated samples was close to 100%, suggesting these mutations may be an early event in MM pathogenesis. There were also recurrent mutations in genes encoding variants of the Histone 1 protein, HIST1H1B (0.22%), HIST1H1C (2.59%), HIST1H1D (0.65%); the percentage of patients with a mutation in any variant totals 6% (28/463). Mutations clustered in the globular domain and multiple sequence alignment revealed sites of recurrent mutation across variants. Along with the absence of mutations in the fifth common protein variant HIST1H1A,this suggests that these are not passenger mutations and may carry some significance to MM pathogenesis. Histone 1 mutations have been demonstrated to play an important role in other haematological malignancies but have not been previously characterized in MM. Potentially deleterious mutations in histone methyltransferase/demethylase enzymes were also seen in 24% of patients, though the percentage of patients with each gene mutated was low. The most frequently mutated gene family in the methyltransferases was MLL/2/3/4/5 (7% of patients). There were no mutations in EZH2, recurrently mutated in other B cell malignancies, and none of the MMSET activating mutations p.E1099K described in the MM1.S myeloma cell line were seen. The most frequently mutated demethylase gene was KDM3B in 1.5% of patients. KDM6A/UTX mutations occurred in 1.3% of patients and targeted analysis for deletion of whole exons increased the number of patients affected by a potentially inactivating lesion to 3%. Patients carrying a KDM6A mutation or deletion appear to have a shorter OS at current follow up than wild type (medians NR, log-rank p=0.0498, % alive at 2 years 51% CI 30-85 vs 80% CI 77-84). Data suggest that EZH2 inhibitors, currently in development for lymphoma, could be investigated for these patients as inhibiting the H3K27 methyltransferase may counteract the increased H3K27 methylation resulting from inactivation of the demethylase. DNA methylation modifiers were found to be mutated in 4% of patients. These include mutations previously reported in glioma (p.R132C in IDH1) and AML/MDS (p.R140W in IDH2 and p.C1378Y in TET2). Collectively, mutations in any DNA methylation modifier (TET1/2/3 n=9, IDH1/2 n=2 or DNMT1/3A/B n=6) are associated with a shorter OS (medians NR, p=0.045, % alive at 2 years 58% CI 39-88 vs 80% CI 76-84). Patients with these mutations might be amenable to demethylating agents such as azacytidine and newer agents such as IDH inhibitors currently in early stages of development. This is the first extensive analysis of the spectrum of mutations in epigenetic modifiers in a uniformly treated population in MM. An association with clinical outcome is suggested in our dataset but will need validation due to the low overall frequency of the mutations. This data further emphasises the importance of epigenetics in MM and provides new potential targets for personalised therapeutic strategies for patients. Disclosures Pawlyn: Celgene: Honoraria. Walker:Onyx Pharmaceuticals: Consultancy, Honoraria.

Type of Medium: Online Resource

ISSN: 0006-4971 , 1528-0020

URL: Article

DOI: 10.1182/blood.V124.21.2194.2194

Language: English

Publisher: American Society of Hematology

Publication Date: 2014

detail.hit.zdb_id: 1468538-3

detail.hit.zdb_id: 80069-7

In: Blood, American Society of Hematology, Vol. 128, No. 22 ( 2016-12-02), p. 804-804

Abstract: Introduction Epigenetic dysregulation is a hallmark of cancer and has significant impact on disease biology. The epigenetic structure of myeloma is heterogeneous and we previously demonstrated that gene specific DNA methylation changes are associated with outcome, using low-resolution arrays. We now performed a high-resolution genome wide DNA methylation analysis of a larger group of patients from a UK national phase III study to further define the role of epigenetic modifications in disease behaviour and outcome. Patients and Methods Highly purified ( 〉 95%) CD138+ myeloma bone marrow cells from 465 newly diagnosed patients enrolled in the UK NCRI Myeloma XI study were analysed. The extracted DNA was bisulfite-converted using the EZ DNA methylation kit (Zymo) and hybridized to Infinium HumanMethylation450 BeadChip arrays. Raw data was processed using the R Bioconductor package "minfi". SNP containing probes and probes on the sex chromosomes were removed. 464 samples and 441293 probes were retained following inspection of quality control metrics. Beta values were summarized across functional genomic units or differentially methylated regions (DMRs) that included: gene bodies, promoters, insulators, CpG-islands and enhancers. K-means was applied to each DMR to cluster patients into 2 groups (high or low methylation) per region. Filters were applied to define a clinically meaningful minimum group size and methylation differences between the groups. Overall survival (OS) and progression free survival (PFS) were assessed by a Cox proportional hazards regression model fitted to each DMR with a time-dependent covariate of the trial pathway. Pathway analyses were performed using GREAT (Stanford University) and GSEA (Broad Institute). Results We identified 589 differentially methylated regions that were significantly associated with PFS and OS when using a cut-off of P 〈 0.01 (log-rank). Of these, 114 DMRs were located within 10kb of a gene transcription start site (TSS). Among these, several genes implicated in myeloma disease biology, such as immune cell-cell interaction genes (e.g. CD226) or stemness-associated transcription factors (e.g. PAX4) were identified to be differentially methylated. Using pathway analysis on all 589 DMRs, Gene Ontology biologic groups were enriched for positive regulation of proliferation, cell migration and cytoskeleton organisation (FDR P 〈 0.05). This was further supported by enrichment of proliferative E2F1 transcription factor target structures (FDR P 〈 0.05). Matched gene expression profiles have been generated and integrated analyses correlating epigenetic with GEP and genetic risk data and individual gene level methylation-expression associations will be presented at the meeting. This data is also being integrated with drug resistance profiles from the Cancer Cell Line Encyclopedia (CCLE; Barretina, et al, 2016). Conclusion Epigenetic mechanisms play a significant role in influencing tumour cell behaviour. We have identified here differentially methylated regions that are significantly associated with patient outcome. Pathway analyses suggest an epigenetic regulation of biologic mechanisms involved in high risk disease, such as proliferation and migration. Integration of epigenetic data with matched gene expression profiles is currently ongoing to delineate independent epigenetic biomarkers associated with high risk disease behaviour. Disclosures Jones: Celgene: Honoraria, Research Funding. Pawlyn:Takeda Oncology: Consultancy; Celgene: Consultancy, Honoraria, Other: Travel Support. Jenner:Janssen: Consultancy, Honoraria, Other: Travel support, Research Funding; Novartis: Consultancy, Honoraria; Amgen: Consultancy, Honoraria, Other: Travel support; Takeda: Consultancy, Honoraria, Other: Travel support; Celgene: Consultancy, Honoraria, Research Funding. Cook:Amgen: Consultancy, Honoraria, Research Funding, Speakers Bureau; Glycomimetics: Consultancy, Honoraria; Takeda: Consultancy, Honoraria, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Research Funding, Speakers Bureau; Sanofi: Consultancy, Honoraria, Speakers Bureau; Bristol-Myers Squibb: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding, Speakers Bureau. Drayson:Abingdon Health: Equity Ownership, Membership on an entity's Board of Directors or advisory committees. Davies:Janssen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Takeda: Consultancy, Honoraria. Morgan:Univ of AR for Medical Sciences: Employment; Janssen: Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Bristol Meyers: Consultancy, Honoraria. Jackson:MSD: Consultancy, Honoraria, Speakers Bureau; Celgene: Consultancy, Honoraria, Other: Travel support, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Speakers Bureau; Amgen: Consultancy, Honoraria, Speakers Bureau; Roche: Consultancy, Honoraria, Speakers Bureau; Takeda: Consultancy, Honoraria, Other: Travel support, Research Funding, Speakers Bureau. Kaiser:Janssen: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Other: Travel Support; BMS: Consultancy, Other: Travel Support; Chugai: Consultancy.

Type of Medium: Online Resource

ISSN: 0006-4971 , 1528-0020

URL: Article

DOI: 10.1182/blood.V128.22.804.804

Language: English

Publisher: American Society of Hematology

Publication Date: 2016

detail.hit.zdb_id: 1468538-3

detail.hit.zdb_id: 80069-7

In: Blood, American Society of Hematology, Vol. 128, No. 22 ( 2016-12-02), p. 4407-4407

Abstract: Introduction A significant proportion of myeloma patients relapse early and show short survival with current therapies. Molecular diagnostic tools are needed to identify these high risk patients at diagnosis to stratify treatment and offer the prospect of improving outcomes. Two validated molecular approaches for risk prediction are widely used: 1) molecular genetic risk profiling [e.g. del(17p), t(4;14)] 2) gene expression (GEP) risk profiling, [e.g. EMC92 (Kuiper et al., Leukemia 2012)] . We profiled patients from a large multicentric UK National trial using both approaches for integrated risk stratification. Methods A representative group of 221 newly diagnosed, transplant eligible patients (median age 64 years) treated on the UK NCRI Myeloma XI trial were molecularly profiled. DNA and RNA were extracted from immunomagnetically CD138-sorted bone marrow plasma cells. Molecular genetic profiles, including t(4;14), t(14;16), Del(17p), Gain(1q) were generated using MLPA (MRC Holland) and a TC-classification based qRT-PCR assay (Boyle EM, et al., Gen Chrom Canc 2015, Kaiser MF, et al., Leukemia 2013). GEP risk status as per EMC92 was profiled on a diagnostic Affymetrix platform using the U133plus2.0-based, CE-marked MMprofiler (SkylineDx) which generates a standardised EMC92 risk score, called 'SKY92'. Progression-free (PFS) and overall survival (OS) were measured from initial randomization and median follow-up for the analysed group was 36 months. Statistical analyses were performed using R 3.3.0 and the 'survival' package. Results were confirmed in an independent dataset, MRC Myeloma IX, for which median follow-up was 82.7 months. Results Of the 221 analysed patients, 116 were found to carry an established genetic high risk lesion [t(4;14), t(14;16), del(17p) or gain(1q)]. We and others have recently demonstrated that adverse lesions have an additive effect and that co-occurrence of ≥2 high risk lesions is specifically associated with adverse outcome (Boyd KD et al, Leukemia 2011). 39/221 patients (17.6%) were identified as genetic high risk with ≥2 risk lesions (termed HR2). By GEP, 53/221 patients (24.0%) were identified as SKY92 high risk. Genetic and GEP high risk co-occurred in 22 patients (10.0%), 31 patients (14.0%) were high risk only by GEP and 17 patients (7.7%) by genetics only. SKY92 high risk status was associated with significantly shorter PFS (median 17.1 vs. 34.3 months; P 〈 0.0001; Hazard ratio [HR] 3.2 [95%CI: 2.2-4.7] ) and OS (median 36.0 vs. not reached; P 〈 0.0001; HR 3.9 [2.3-6.9]). Genetic risk by HR2 was similarly associated with adverse outcome: median PFS 17.0 vs. 33.6 months; P 〈 0.0001; HR 2.9 [1.9-4.4]), median OS 33.5 vs. not reached; P 〈 0.0001; HR 4.1 [2.3-7.2]). Importantly, by multivariate analysis GEP and genetic high risk status were independently associated with shorter PFS (P 〈 0.001) and OS (P 〈 0.005). We next investigated interactions between genetic and gene expression high risk status. Three groups were defined: 1) Patients with both SKY92 and genetic (HR2) high risk status (n=22), 2) either GEP or genetic high risk (n=48) or 3) absence of GEP or genetic (HR2) high risk status (n=151). Co-occurring GEP and genetic high risk status was associated with very short PFS (median 12.5 vs. 20.0 vs. 38.3 months; P 〈 0.0001) and OS (median 25.6 vs. 47.3 vs. not reached; P 〈 0.0001) [Figure]. When comparing this ultra-high risk group against the remainder of cases (n=199), their risk of progressing and dying early was significantly elevated (PFS HR 4.4 [2.5-6.7] ; OS HR 5.9 [3.1-11.0]). We confirmed this finding in 116 transplant-eligible patients from the MRC Myeloma IX trial. Patients carrying both EMC92 and genetic high risk status had a median PFS of 7.8 vs. 25.5 months and median OS of 9.5 vs. 62.1 months (both P 〈 0.0001). Moreover, all patients in this ultra-high risk group progressed within 24 months and died within 48 months. Conclusion We demonstrate, for the first time, that combined genetic and gene expression risk profiling identifies a group of patients with ultra-high risk disease behaviour with high fidelity, using molecular features of the disease. Our results indicate that GEP and genetic high risk profiling identify independently relevant, but inter-related features of high risk disease biology. Integrated genetic and gene expression risk profiling could serve as a valuable tool for risk stratified, innovative treatment approaches in myeloma. Figure Figure. Disclosures Jones: Celgene: Honoraria, Research Funding. Pawlyn:Takeda Oncology: Consultancy; Celgene: Consultancy, Honoraria, Other: Travel Support. Jenner:Amgen: Consultancy, Honoraria, Other: Travel support; Janssen: Consultancy, Honoraria, Other: Travel support, Research Funding; Novartis: Consultancy, Honoraria; Takeda: Consultancy, Honoraria, Other: Travel support; Celgene: Consultancy, Honoraria, Research Funding. Cook:Glycomimetics: Consultancy, Honoraria; Takeda: Consultancy, Honoraria, Research Funding, Speakers Bureau; Amgen: Consultancy, Honoraria, Research Funding, Speakers Bureau; Bristol-Myers Squibb: Consultancy, Honoraria; Sanofi: Consultancy, Honoraria, Speakers Bureau; Celgene: Consultancy, Honoraria, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Research Funding, Speakers Bureau. Drayson:Abingdon Health: Equity Ownership, Membership on an entity's Board of Directors or advisory committees. Davies:Takeda: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria. Morgan:Janssen: Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Univ of AR for Medical Sciences: Employment; Bristol Meyers: Consultancy, Honoraria; Takeda: Consultancy, Honoraria. Jackson:Celgene: Consultancy, Honoraria, Other: Travel support, Research Funding, Speakers Bureau; Takeda: Consultancy, Honoraria, Other: Travel support, Research Funding, Speakers Bureau; MSD: Consultancy, Honoraria, Speakers Bureau; Janssen: Consultancy, Honoraria, Speakers Bureau; Amgen: Consultancy, Honoraria, Speakers Bureau; Roche: Consultancy, Honoraria, Speakers Bureau. Kaiser:Takeda: Consultancy, Other: Travel Support; Amgen: Consultancy, Honoraria; BMS: Consultancy, Other: Travel Support; Janssen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Chugai: Consultancy.

Type of Medium: Online Resource

ISSN: 0006-4971 , 1528-0020

URL: Article

DOI: 10.1182/blood.V128.22.4407.4407

Language: English

Publisher: American Society of Hematology

Publication Date: 2016

detail.hit.zdb_id: 1468538-3

detail.hit.zdb_id: 80069-7

In: Blood, American Society of Hematology, Vol. 124, No. 21 ( 2014-12-06), p. 2189-2189

Abstract: Introduction Multiple myeloma is a clinically highly heterogeneous disease, which is reflected by both a complex genome and epigenome. Dynamic epigenetic changes are involved at several stages of myeloma biology, such as transformation and disease progression. Our previous genome wide epigenetic analyses identified prognostically relevant DNA hypermethylation at specific tumor suppressor genes (Kaiser MF et al., Blood 2013), indicating that specific epigenetic programming influences clinical behavior. This clinically relevant finding prompted further investigation of the epigenomic structure of myeloma and its interaction with genetic aberrations. Material and Methods Genome wide DNA methylation of CD138-purified myeloma cells from 464 patients enrolled in the NCRI Myeloma XI trial at presentation were analyzed using the high resolution 450k DNA methylation array platform (Illumina). In addition, 4 plasma cell leukemia (PCL) cases (two t(11;14) and two (4;14)) and 7 myeloma cell lines (HMCL) carrying different translocations were analysed. Analyses were performed in R Bioconductor packages after filtering and removal of low quality and non-uniquely mapping probes. Results Variation in genome wide DNA methylation was analyzed using unsupervised hierarchical clustering of the 10,000 most variable probes, which revealed epigenetically defined subgroups of disease. Presence of recurrent IGH translocations was strongly associated with specific epigenetic profiles. All 60 cases with t(4;14) clustered into two highly similar sub-clusters, confirming that overexpression of the H3K36 methyltransferase MMSET in t(4;14) has a defined and specific effect on the myeloma epigenome. Interestingly, HMCLs KMS-11 and LP-1, which carry t(4;14), MM1.S, a t(14;16) cell line with an E1099K MMSET activating mutation as well as two PCLs with t(4;14) all clustered in one sub-clade. The majority (59/85) of t(11;14) cases showed global DNA hypomethylation compared to t(4;14) cases and clustered in one subclade, indicating a epigenetic programming effect associated with CCND1, with a subgroup of t(11;14) cases showing a variable DNA methylation pattern. In addition to translocation-defined subgroups, a small cluster of samples with a distinct epigenetic profile was identified. In total 7 cases with a shared specific DNA methylation pattern (median inter-sample correlation 0.4) were identified. The group was characterized by DNA hypermethylation (4,341 hypermethylated regions vs. 750 hypomethylated regions) in comparison to all other cases. Intersection of regions hypermethylated in this subgroups with ENCODE datasets revealed mapping to poised enhancers and promoters in H1-hESC, indicating functionally relevant epigenetic changes. Gene set enrichment analysis (KEGG) demonstrated enrichment of developmental pathway genes, e.g. Hedgehog signaling (adj p=5x10exp-13), amongst others and all four HOX clusters were differentially methylated in this group. Of note, three of seven cases in this subgroup carried a t(11;14) and all t(11;14) or t(11;14)-like HMCLs clustered closely together with these patient cases, but not with the cluster carrying the majority of t(11;14) myeloma or t(11;14) PCLs. This potentially indicates that t(11;14) HMCL could be derived from a subgroup of patients with specific epigenetic characteristics. Conclusion Our results indicate that the recurrent IGH translocations are fundamentally involved in shaping the myeloma epigenome through either direct upregulation of epigenetic modifiers (e.g. MMSET) or through insufficiently understood mechanisms. However, developmental epigenetic processes seem to independently contribute to the complexity of the epigenome in some cases. This work provides important insights into the spectrum of epigenetic subgroups of myeloma and helps identify subgroups of disease that may benefit from specific epigenetic therapies currently being developed. Disclosures Walker: Onyx Pharmaceuticals: Consultancy, Honoraria.

Type of Medium: Online Resource

ISSN: 0006-4971 , 1528-0020

URL: Article

DOI: 10.1182/blood.V124.21.2189.2189

Language: English

Publisher: American Society of Hematology

Publication Date: 2014

detail.hit.zdb_id: 1468538-3

detail.hit.zdb_id: 80069-7

In: Blood, American Society of Hematology, Vol. 124, No. 21 ( 2014-12-06), p. 172-172

Abstract: Introduction Co-segregation of two or more adverse structural genetic aberrations in myeloma is associated with a particularly bad outcome and defines a molecular high risk subgroup of patients that is in urgent need of innovative treatment approaches (Boyd, Leukemia 2012). Interphase in situ fluorescence hybridization(iFISH) is the current clinical standard for detecting structural genetic aberrations in myeloma. However, iFISH is labor-intensive, slow and dependent on investigator expertise, which makes standardization difficult. There is an urgent need to develop a standardized and easily accessible all-molecular diagnostic test to enable the design of risk-stratified trials and, finally, risk-adapted precision medicine treatments for high risk patients. Material and Methods Bone marrow material from 1596 patients was received by a central laboratory for patients enrolled in the NCRI Myeloma XI trial (NCT01554852) at diagnosis from over 80 centers throughout the UK. Myeloma cells were purified to a purity of 〉 98% (median across samples) using an AutoMACS (Miltenyi Biotech) system and DNA and RNA were extracted using AllPrep columns (QIAGEN). Recurrent translocations were predicted by gene expression using a sensitive and specific TC-classification based multiplex qRT-PCR assay on a standard TaqMan (Life Technologies) real-time cycler (Kaiser et al., Leukemia 2013). Myeloma specific copy number alterations were assayed using the sensitive and specific multiplex ligation-dependent probe amplification assay (MLPA P425; MRC Holland; Alpar et al, Gen Chrom Cancer 2013) on a standard thermocycler and a standard ABI 3730 capillary electrophoresis Genetic Analyzer. Analysis of qRT-PCR and MLPA results was performed on a desktop computers using standard software without need for bioinformatics expertise or infrastructure. Results Translocation status was successfully analyzed for 1201 cases and copy number aberrations were successfully analyzed for 1232 cases. Matched translocation and copy number aberration data was available for 1044 cases. Genetic lesions associated with an adverse prognosis were detected with the following frequencies among the 1044 cases: t(4;14): 13%; t(14;16): 4%; t(14;20): 1%; del(1p32): 9%; gain(1q): 27%; amp(1q): 8%; del(17p): 9%. Non-high risk recurrent IGH translocations as well as copy number aberrations were assayed through both tests as well. Co-segregation analysis of all detected abnormalities using Fisher’s exact test, corrected for multiple testing, revealed co-occurrence more than expected by chance of the following lesions: t(4;14) and gain(1q): q=6.2x10-4; t(4;14) and amp(1q): q=2.1x10-7; del(1p32) and gain(1q): 1.1x10-3. Statistically significant co-occurrence was also observed for del(12p) and del(17p): q=2.1x10-5 as well as del(12p) and t(4;14): q=1.8x10-5. Survival data at the timepoint of analysis was available for 450 patients with a median follow-up of 25 months. Patients were classified as previously described (Boyd et al, Leukemia 2013) into molecular risk groups with standard risk defined by absence of adverse genetic lesions (n=224), intermediate risk with presence of one adverse genetic lesion (n=161) and high risk with presence of two adverse lesions (n=65). On Cox analysis, there was a significant difference in terms of PFS between these groups with a median PFS of 31.3 months (95% CI 28.5-35.2), 25.8 months (CI 22.1-27.6) and 16.2 months (CI 10.6-23.7) for groups with none, one, two or more genetic lesions, respectively. The 2-year OS was also significantly different between the groups with 84% (CI 79-89%) in standard risk, 78% (CI 71-85%) in intermediate risk and 65% (CI 53-78%) in high risk patients. Conclusion This all-molecular diagnostic approach for recurrent structural aberrations in myeloma offers a fast, robust and high throughput alternative to iFISH that can be run in any molecular diagnostic laboratory on standard equipment. The methods described here enable standardized and specific identification of a high risk subgroup of patients without the need for a bioinformatics infrastructure or expertise. The clinical applicability of this method makes it an ideal candidate method for prospective molecular risk-stratified clinical trials. Disclosures Walker: Onyx Pharmaceuticals: Consultancy, Honoraria. Savola:MRC-Holland: Employment.

Type of Medium: Online Resource

ISSN: 0006-4971 , 1528-0020

URL: Article

DOI: 10.1182/blood.V124.21.172.172

Language: English

Publisher: American Society of Hematology

Publication Date: 2014

detail.hit.zdb_id: 1468538-3

detail.hit.zdb_id: 80069-7