Kooperativer Bibliotheksverbund

In: Clinical Cancer Research, American Association for Cancer Research (AACR), Vol. 22, No. 23 ( 2016-12-01), p. 5783-5794

Kurzfassung: Purpose: Epigenetic dysregulation is known to be an important contributor to myeloma pathogenesis but, unlike other B-cell malignancies, the full spectrum of somatic mutations in epigenetic modifiers has not been reported previously. We sought to address this using the results from whole-exome sequencing in the context of a large prospective clinical trial of newly diagnosed patients and targeted sequencing in a cohort of previously treated patients for comparison. Experimental Design: Whole-exome sequencing analysis of 463 presenting myeloma cases entered in the UK NCRI Myeloma XI study and targeted sequencing analysis of 156 previously treated cases from the University of Arkansas for Medical Sciences (Little Rock, AR). We correlated the presence of mutations with clinical outcome from diagnosis and compared the mutations found at diagnosis with later stages of disease. Results: In diagnostic myeloma patient samples, we identify significant mutations in genes encoding the histone 1 linker protein, previously identified in other B-cell malignancies. Our data suggest an adverse prognostic impact from the presence of lesions in genes encoding DNA methylation modifiers and the histone demethylase KDM6A/UTX. The frequency of mutations in epigenetic modifiers appears to increase following treatment most notably in genes encoding histone methyltransferases and DNA methylation modifiers. Conclusions: Numerous mutations identified raise the possibility of targeted treatment strategies for patients either at diagnosis or relapse supporting the use of sequencing-based diagnostics in myeloma to help guide therapy as more epigenetic targeted agents become available. Clin Cancer Res; 22(23); 5783–94. ©2016 AACR.

Materialart: Online-Ressource

ISSN: 1078-0432 , 1557-3265

URL: Article

DOI: 10.1158/1078-0432.CCR-15-1790

Sprache: Englisch

Verlag: American Association for Cancer Research (AACR)

Publikationsdatum: 2016

ZDB Id: 1225457-5

ZDB Id: 2036787-9

In: Blood, American Society of Hematology, Vol. 134, No. Supplement_1 ( 2019-11-13), p. 3153-3153

Kurzfassung: Background Transplant non-eligible (TNE) myeloma patients are a very heterogeneous group that is not well-defined on the basis of age alone, but rather by the interplay of age, physical function, cognitive function and comorbidity better defined as 'frailty'. The International Myeloma Working Group (IMWG) has published a scoring system for myeloma patient frailty that predicts survival, adverse events and treatment tolerability using age, the Katz Activity of Daily Living (ADL), the Lawton Instrumental Activity of Daily Living (IADL), and the Charlson Comorbidity Index (CCI). It has been proposed to be useful in determining the feasibility of treatment regimens and appropriate dose reductions but has not been validated prospectively. We hypothesize that by defining subgroups of patients based on the IMWG frailty score, and guiding up-front dose adjustments we can personalize therapy to improve treatment tolerability and therefore short-term outcomes, along with quality of life. In addition we plan to compare the use of single agent immunomodulatory (IMiD) based maintenance therapy with an IMiD and proteasome inhibitor maintenance doublet to try and improve long-term outcomes for patients. Study Design and Methods Myeloma XIV (NCT03720041) is a phase III, multi-center, randomized controlled trial to compare standard (reactive) and frailty-adjusted (adaptive) induction therapy delivery with the novel triplet ixazomib, lenalidomide and dexamethasone (IRd), and to compare maintenance lenalidomide (R) to lenalidomide plus ixazomib (IR) in patients with newly diagnosed multiple myeloma not suitable for a stem cell transplant. The trial outline is shown in Figure 1. All participants receive induction treatment with ixazomib, lenalidomide and dexamethasone and are randomized (R1) on a 1:1 basis at trial entry to the use of frailty score-adjusted up-front dose reductions vs. standard up-front dosing followed by toxicity dependent reactive dose modifications during therapy. Following 12 cycles of induction treatment participants alive and progression-free undergo a second randomization (R2) on a 1:1 basis to maintenance treatment with lenalidomide plus placebo versus lenalidomide plus ixazomib. Participants and their treating physicians are blinded to maintenance allocation. The primary objectives of the study are to determine: Early treatment cessation (within 60 days of randomization) for standard versus frailty-adjusted up-front dosingProgression-free survival (PFS, from maintenance randomization) for lenalidomide + placebo (R) versus lenalidomide + ixazomib (IR) The secondary objectives of the study include determining: progression-free survival (PFS) for standard versus frailty-adjusted up-front dosing reductions, overall survival (OS), overall response rate (ORR), treatment compliance and total amount of therapy delivered, toxicity & safety including the incidence of Second Primary Malignancies (SPMs), Quality of Life (QoL), cost-effectiveness of standard versus frailty-adjusted up-front dosing of IRd and cost-effectiveness of IR versus R. Exploratory analyses include the association of molecular subgroups with response, PFS and OS. Seven hundred and forty participants will be enrolled into the trial at R1 to give 80% power to demonstrate a difference in early cessation and ensure that at least 478 participants remain and are randomized at R2 (based on attrition rates in our previous study Myeloma XI). At R2 478 patients will give us 80% power to detect an eight month difference in PFS between R and IR. Disclosures Cairns: Celgene, Amgen, Merck, Takeda: Other: Research Funding to Institution. Pawlyn:Amgen, Janssen, Celgene, Takeda: Other: Travel expenses; Amgen, Celgene, Takeda: Consultancy; Amgen, Celgene, Janssen, Oncopeptides: Honoraria. Royle:Celgene, Amgen, Merck, Takeda: Other: Research Funding to Institution. Best:Celgene, Amgen, Merck, Takeda: Other: Research Funding to Institution. Bowcock:Takeda: Honoraria, Research Funding. Boyd:Takeda: Consultancy, Honoraria; Novartis: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria. Drayson:Abingdon Health: Consultancy, Equity Ownership. Henderson:Celgene, Amgen, Merck, Takeda: Other: Research Funding to Institution. Jenner:Abbvie, Amgen, Celgene, Novartis, Janssen, Sanofi Genzyme, Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Jones:Celgene: Honoraria, Research Funding. Kaiser:Takeda, Janssen, Celgene, Amgen: Honoraria, Other: Travel Expenses; Celgene, Janssen: Research Funding; Abbvie, Celgene, Takeda, Janssen, Amgen, Abbvie, Karyopharm: Consultancy. Kishore:Celgene, Takeda, Janssen: Honoraria, Speakers Bureau; Celgene, Jazz, Takeda: Other: Travel expenses. Mottram:Celgene, Amgen, Merck, Takeda: Other: Research Funding to Institution. Owen:Janssen: Other: Travel expenses; Celgene, Janssen: Consultancy; Celgene, Janssen: Honoraria; Celgene: Research Funding. Jackson:Celgene, Amgen, Roche, Janssen, Sanofi: Honoraria. Cook:Celgene, Janssen-Cilag, Takeda: Honoraria, Research Funding; Amgen, Bristol-Myers Squib, GlycoMimetics, Seattle Genetics, Sanofi: Honoraria; Janssen, Takeda, Sanofi, Karyopharm, Celgene: Consultancy, Honoraria, Speakers Bureau. OffLabel Disclosure: Frailty adjusted dosing. Ixazomib and lenalidomide combination as maintenance.

Materialart: Online-Ressource

ISSN: 0006-4971 , 1528-0020

URL: Article

DOI: 10.1182/blood-2019-126207

Sprache: Englisch

Verlag: American Society of Hematology

Publikationsdatum: 2019

ZDB Id: 1468538-3

ZDB Id: 80069-7

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

Kurzfassung: 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.

Materialart: Online-Ressource

ISSN: 0006-4971 , 1528-0020

URL: Article

DOI: 10.1182/blood.V124.21.723.723

Sprache: Englisch

Verlag: American Society of Hematology

Publikationsdatum: 2014

ZDB Id: 1468538-3

ZDB Id: 80069-7

In: Haematologica, Ferrata Storti Foundation (Haematologica), Vol. 104, No. 7 ( 2019-07), p. 1440-1450

Materialart: Online-Ressource

ISSN: 0390-6078 , 1592-8721

URL: Article

DOI: 10.3324/haematol.2018.202200

Sprache: Englisch

Verlag: Ferrata Storti Foundation (Haematologica)

Publikationsdatum: 2019

ZDB Id: 2186022-1

ZDB Id: 2030158-3

ZDB Id: 2805244-4

In: Blood, American Society of Hematology, Vol. 124, No. 21 ( 2014-12-06), p. 637-637

Kurzfassung: Background: The main genetic features of myeloma identified so far have been the presence of balanced translocations at the immunoglobulin heavy chain (IGH) region and copy number abnormalities. Novel methodologies such as massively parallel sequencing have begun to describe the pattern of tumour acquired mutations detected at presentation but their biological and clinical relevance has not yet been fully established. Methods: Whole exome sequencing was performed on 463 presentation patients enrolled into the large UK, phase III, open label, 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 MYC loci in order to determine the breakpoints associated with translocations in these genes. Tumour and germline DNA were sequenced to a median of 60x and data processed to generate copy number, acquired variants and translocation breakpoints in the tumour. 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. Results: We identified 15 significantly mutated genes comprising IRF4, KRAS, NRAS, MAX, HIST1H1E, RB1, EGR1, TP53, TRAF3, FAM46C, DIS3, BRAF, LTB, CYLD and FGFR3. By analysing the correlation between mutations and cytogenetic events using a probabilistic approach, we describe the co-segregation of t(11;14) with CCND1 mutations (Corr 0.28,BF=1.5x106 (Bayes Factor)) and t(4;14) with FGFR3 (Corr=0.40, BF=1.12x1014) and PRKD2 mutations (Corr=0.23, BF=3507). The mutational spectrum is dominated by mutations in the RAS (43%) and NF-κB (17%) pathway, however they are prognostically neutral. We describe for the first time in myeloma mutations in genes such as CCND1 and DNA repair pathway alterations (TP53, ATM, ATR and ZFHX4 mutations) that are associated with a negative impact on survival in contrast to those in IRF4 and EGR1 that are associated with a favourable overall-survival. By combining these novel risk factors with the previously described adverse cytogenetic features and ISS we were able to demonstrate in a multivariate analysis the independent prognostic relevance of copy number and structural abnormalities (CNSA) such as del(17p), t(4;14), amp(1q), hyperdiploidy and MYC translocations and mutations in genes such as ATM/ATR, ZFHX4, TP53 and CCND1. We demonstrate that the more adverse features a patient had the worse his outcome was for both PFS (one lesion: HR=1.6, p=0.0012, 2 lesions HR=3.3, p 〈 0.001, 3 lesions HR=15.2, p 〈 0.001) and for OS (one lesion: HR=2.01, p=0.0032, 2 lesions HR=4.79, p 〈 0.001, 3 lesions HR=9.62, p 〈 0.001). When combined with ISS, we identified 3 prognostic groups (Group 1: ISS I/II with no CNSA or mutation, Group 2: ISS III with no CNSA or mutation or ISS I/II/III with one CNSA or mutation, Group 3: Two CNSA or mutation regardless of their ISS) thus identifying three distinct prognostic groups with a high risk population representing 13% of patients that both relapsed [median PFS 10.6 months (95% CI 8.7-17.9) versus 27.7 months (95% CI 25.99-31.1), p 〈 0.001] and died prematurely [median overall survival 23.2 months (95% CI 18.2-35.3.) versus not reached, p 〈 0.001] regardless of their ISS score. Finally, we have also identified a list of potentially actionable mutations for which targeted therapy already exists opening the way into personalized medicine in myeloma. Conclusion: We have refined our understanding of genetic events in myeloma and identified clinically relevant mutations that may be used to better stratify patients at presentation. Identifying high risk populations or patients that may benefit from targeted therapy may open the way into personalized medicine for myeloma. Disclosures Walker: Onyx Pharmaceuticals: Consultancy, Honoraria.

Materialart: Online-Ressource

ISSN: 0006-4971 , 1528-0020

URL: Article

DOI: 10.1182/blood.V124.21.637.637

Sprache: Englisch

Verlag: American Society of Hematology

Publikationsdatum: 2014

ZDB Id: 1468538-3

ZDB Id: 80069-7

In: Blood, American Society of Hematology, Vol. 130, No. 14 ( 2017-10-05), p. 1639-1643

Kurzfassung: A significant proportion of MM is dominated by neutral evolutionary dynamics. Neutral MM tumors are characterized by shorter survival, consistent with reduced sensitivity to drugs targeting the MM microenvironment.

Materialart: Online-Ressource

ISSN: 0006-4971 , 1528-0020

URL: Article

DOI: 10.1182/blood-2016-11-750612

Sprache: Englisch

Verlag: American Society of Hematology

Publikationsdatum: 2017

ZDB Id: 1468538-3

ZDB Id: 80069-7

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

Kurzfassung: 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.

Materialart: Online-Ressource

ISSN: 0006-4971 , 1528-0020

URL: Article

DOI: 10.1182/blood.V124.21.2194.2194

Sprache: Englisch

Verlag: American Society of Hematology

Publikationsdatum: 2014

ZDB Id: 1468538-3

ZDB Id: 80069-7

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

Kurzfassung: 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.

Materialart: Online-Ressource

ISSN: 0006-4971 , 1528-0020

URL: Article

DOI: 10.1182/blood.V128.22.804.804

Sprache: Englisch

Verlag: American Society of Hematology

Publikationsdatum: 2016

ZDB Id: 1468538-3

ZDB Id: 80069-7

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

Kurzfassung: 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.

Materialart: Online-Ressource

ISSN: 0006-4971 , 1528-0020

URL: Article

DOI: 10.1182/blood.V128.22.4407.4407

Sprache: Englisch

Verlag: American Society of Hematology

Publikationsdatum: 2016

ZDB Id: 1468538-3

ZDB Id: 80069-7

In: Nature Communications, Springer Science and Business Media LLC, Vol. 6, No. 1 ( 2015-04-23)

Materialart: Online-Ressource

ISSN: 2041-1723

URL: Article

DOI: 10.1038/ncomms7997

Sprache: Englisch

Verlag: Springer Science and Business Media LLC

Publikationsdatum: 2015

ZDB Id: 2553671-0