Kooperativer Bibliotheksverbund

In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 33, No. 33 ( 2015-11-20), p. 3911-3920

Abstract: At the molecular level, myeloma is characterized by copy number abnormalities and recurrent translocations into the immunoglobulin heavy chain locus. Novel methods, such as massively parallel sequencing, have begun to describe the pattern of tumor-acquired mutations, but their clinical relevance has yet to be established. Methods We performed whole-exome sequencing for 463 patients who presented with myeloma and were enrolled onto the National Cancer Research Institute Myeloma XI trial, for whom complete molecular cytogenetic and clinical outcome data were available. Results We identified 15 significantly mutated genes: IRF4, KRAS, NRAS, MAX, HIST1H1E, RB1, EGR1, TP53, TRAF3, FAM46C, DIS3, BRAF, LTB, CYLD, and FGFR3. The mutational spectrum is dominated by mutations in the RAS (43%) and nuclear factor-κB (17%) pathways, but although they are prognostically neutral, they could be targeted therapeutically. Mutations in CCND1 and DNA repair pathway alterations (TP53, ATM, ATR, and ZNFHX4 mutations) are associated with a negative impact on survival. In contrast, those in IRF4 and EGR1 are associated with a favorable overall survival. We combined these novel mutation risk factors with the recurrent molecular adverse features and international staging system to generate an international staging system mutation score that can identify a high-risk population of patients who experience relapse and die prematurely. 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.

Type of Medium: Online Resource

ISSN: 0732-183X , 1527-7755

URL: Article

DOI: 10.1200/JCO.2014.59.1503

Language: English

Publisher: American Society of Clinical Oncology (ASCO)

Publication Date: 2015

detail.hit.zdb_id: 2005181-5

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: BMJ Open, BMJ, Vol. 12, No. 6 ( 2022-06), p. e056147-

Abstract: Multiple myeloma is a bone marrow cancer, which predominantly affects older people. The incidence is increasing in an ageing population. Over the last 10 years, patient outcomes have improved. However, this is less apparent in older, less fit patients, who are ineligible for stem cell transplant. Research is required in this patient group, taking into account frailty and aiming to improve: treatment tolerability, clinical outcomes and quality of life. Methods and analysis Frailty-adjusted therapy in Transplant Non-Eligible patients with newly diagnosed Multiple Myeloma is a national, phase III, multicentre, randomised controlled trial comparing standard (reactive) and frailty-adjusted (adaptive) induction therapy delivery with ixazomib, lenalidomide and dexamethasone (IRD), and to compare maintenance lenalidomide to lenalidomide+ixazomib, in patients with newly diagnosed multiple myeloma not suitable for stem cell transplant. Overall, 740 participants will be registered into the trial to allow 720 and 478 to be randomised at induction and maintenance, respectively. All participants will receive IRD induction with the dosing strategy randomised (1:1) at trial entry. Patients randomised to the standard, reactive arm will commence at the full dose followed by toxicity dependent reactive modifications. Patients randomised to the adaptive arm will commence at a dose level determined by their International Myeloma Working Group frailty score. Following 12 cycles of induction treatment, participants alive and progression free will undergo a second (double-blind) randomisation on a 1:1 basis to maintenance treatment with lenalidomide+placebo versus lenalidomide+ixazomib until disease progression or intolerance. Ethics and dissemination Ethical approval has been obtained from the North East—Tyne & Wear South Research Ethics Committee (19/NE/0125) and capacity and capability confirmed by local research and development departments for each participating centre prior to opening to recruitment. Participants are required to provide written informed consent prior to trial registration. Trial results will be disseminated by conference presentations and peer-reviewed publications. Trial registration number ISRCTN17973108 , NCT03720041 .

Type of Medium: Online Resource

ISSN: 2044-6055 , 2044-6055

URL: Article

DOI: 10.1136/bmjopen-2021-056147

Language: English

Publisher: BMJ

Publication Date: 2022

detail.hit.zdb_id: 2599832-8

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: Clinical Lymphoma Myeloma and Leukemia, Elsevier BV, Vol. 17, No. 1 ( 2017-02), p. e10-

Type of Medium: Online Resource

ISSN: 2152-2650

URL: Article

DOI: 10.1016/j.clml.2017.03.017

Language: English

Publisher: Elsevier BV

Publication Date: 2017

detail.hit.zdb_id: 2540998-0

detail.hit.zdb_id: 2193618-3

In: Blood, American Society of Hematology, Vol. 138, No. Supplement 1 ( 2021-11-05), p. 81-81

Abstract: Background Although the median age of patients with newly diagnosed multiple myeloma (MM) is 70-74 years, recruitment of frail older patients to clinical trials is poor. The International Myeloma Working Group (IMWG) frailty score predicts survival, adverse events and treatment tolerability using age, the Katz Activity of Daily Living, the Lawton Instrumental Activity of Daily Living, and the Charlson Comorbidity Index, rather than age alone. Despite IMWG score prognostic biomarker capability, to date no evidence exists of its predictive biomarker potential. The UK-MRA Myeloma Risk Profile (MRP) has also been shown in both clinical trial and real-world populations to be a prognostic biomarker in transplant ineligible patients but prospective comparisons of the two scores have not been previously conducted. Study Design/Methods The FiTNEss trial (Myeloma XIV, NCT03720041, Figure 1A) is a UK-MRA phase III, multi-centre, randomised controlled trial for newly diagnosed MM patients not suitable for stem cell transplant. The primary objectives are 1) to compare early treatment cessation ( & lt;60 days from randomisation) between patients randomised to standard (reactive) and frailty-adjusted (adaptive, based on IMWG score) induction therapy delivery with the triplet ixazomib, lenalidomide and dexamethasone (IRd) 2) to compare progression free survival for maintenance lenalidomide plus placebo (R) and lenalidomide plus ixazomib (IR). The FiTNEss trial is designed as an all-comers study with few exclusion criteria other than necessary for safety including some haematological and biochemical parameters, but there is no exclusion based on renal function. Patients with grade 2 or greater baseline peripheral neuropathy, current systemic infection or recent surgery or other cancer are excluded. Here we report the demographics for the first patients recruited, including IMWG frailty assessments and MRP to demonstrate the feasibility of recruiting frail patients to randomised phase III clinical trials. Results The FiTNEss trial opened on 04/08/2020 during the second wave of the COVID-19 pandemic in the UK. At the time of data cut off (14/07/2021) recruitment is active at 84 sites, with 180 patients randomised. Baseline characteristics for the randomised patients are shown in Figure 1B. The median age of patients is 77 years (range 64, 93) with 36.1% aged 76-80 and 26.1% over 80. In keeping with the older patient population 26.6% have an ECOG performance status of 2 or 3 and 31.7% ISS stage III. The IMWG frailty classification at baseline is FIT 43/180 (23.9%), UNFIT 53/180 (29.4%) and FRAIL 84/180 (46.7%). The effect of using age groups on the definition of patient frailty was explored. The IMWG frailty score defines all patients over 80 as FRAIL whilst an age of 76-80 contributes one point to the score. An analysis of patients' frailty was repeated with the contribution of age removed. For those aged over 80 years (n=47, 100% FRAIL) we found that 20 (42.6%) would have been re-classified as FIT and 18 (38.3%) as UNFIT, with only 9 (19.2%) retaining the FRAIL category. For those aged 76-80 (n=65, 53.8% UNFIT, 46.2% FRAIL) all 35 patients previously classified as UNFIT became FIT (53.8%) whilst 19 (29.2%) classed as FRAIL became UNFIT with 11 (16.9%) remaining FRAIL. The MRP classification, using age as a continuous variable, was Low-risk 45/180 (25.0%), Medium-risk 46/180 (25.6%), High-risk 75/180 (41.7%) and not available for 14/180 (7.8%) patients. Concordance between the IMWG frailty score and the MRP occurred in 48.9% of patients (88/180). 37.2% of FIT patients were classified as MRP Low-risk, 32.1% of UNFIT patients as MRP Medium-risk and 65.5% of FRAIL patients as MRP High-risk. Discussion The FiTNEss trial demonstrates the feasibility of recruiting older, less fit patients to clinical trials. Recruitment of patients classified as FRAIL was very high despite the COVID pandemic, likely due to the all-oral nature of the regimen under investigation enabling patients to avoid attendance at hospital day units for treatment and associated exposure risk. In the population recruited to date we found age to be a key contributor to the FRAIL category of the IMWG frailty score. Concordance between IMWG frailty score and MRP was highest in FRAIL/High-risk patients. The first interim analysis of the primary objectives is planned when 50% of required participants for R1 have reached 60 days post R1, which is anticipated in Q2 of 2022. Figure 1 Figure 1. Disclosures Cook: Amgen: Consultancy, Honoraria, Research Funding; BMS: Consultancy, Honoraria, Research Funding; Sanofi: Consultancy, Honoraria; Karyopharm: Consultancy, Honoraria; Roche: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Oncopeptides: Consultancy, Honoraria; Takeda: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding. Pawlyn: Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene / BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria. Royle: BMS: Research Funding; Merck Sharpe and Dohme: Research Funding; Amgen: Research Funding; Takeda: Research Funding. Coulson: BMS / Celgene: Honoraria; Merck Sharpe and Dohme: Research Funding; Amgen: Research Funding; Takeda: Research Funding. Jenner: BMS/Celgene: Consultancy, Honoraria, Speakers Bureau; Janssen: Consultancy, Honoraria, Speakers Bureau; Pfizer: Consultancy; Takeda: Consultancy. Kishore: Sanofi: Other: Attending fees; Celgene: Other: Attending fees; Takeda: Other: Attending fees; Jannsen: Other: Attending fees. Rabin: BMS / Celgene: Consultancy, Honoraria, Other: Travel support for meetings; Takeda: Consultancy, Honoraria, Other: Travel support for meetings; Janssen: Consultancy, Honoraria, Other: Travel support for meetings. Best: BMS/Celgene: Research Funding; Merck Sharpe and Dohme: Research Funding; Amgen: Research Funding; Takeda: Research Funding. Gillson: BMS / Celgene: Research Funding; Meck Sharpe and Dohme: Research Funding; Amgen: Research Funding; Takeda: Research Funding. Henderson: Takeda: Research Funding; Amgen: Research Funding; Merck Sharpe and Dohme: Research Funding; BMS / Celgene: Research Funding. Olivier: Merck Sharpe and Dohme: Research Funding; Takeda: Research Funding; Amgen: Research Funding; Celgene / BMS: Research Funding. Kaiser: AbbVie: Consultancy; GSK: Consultancy; Karyopharm: Consultancy, Research Funding; Pfizer: Consultancy; Amgen: Honoraria; Seattle Genetics: Consultancy; Takeda: Consultancy, Other: Educational support; Janssen: Consultancy, Other: Educational support, Research Funding; BMS/Celgene: Consultancy, Other: Travel support, Research Funding. Drayson: Abingdon Health: Current holder of individual stocks in a privately-held company. Jones: Janssen: Honoraria; BMS/Celgene: Other: Conference fees. Cairns: Merck Sharpe and Dohme: Research Funding; Amgen: Research Funding; Takeda: Research Funding; Celgene / BMS: Other: travel support, Research Funding. Jackson: celgene BMS: Consultancy, Honoraria, Research Funding, Speakers Bureau; amgen: Consultancy, Honoraria, Speakers Bureau; takeda: Consultancy, Honoraria, Research Funding, Speakers Bureau; GSK: Consultancy, Honoraria, Speakers Bureau; J and J: Consultancy, Honoraria, Speakers Bureau; oncopeptides: Consultancy; Sanofi: Honoraria, Speakers Bureau. OffLabel Disclosure: Frailty-score adapted dosing strategies

Type of Medium: Online Resource

ISSN: 0006-4971 , 1528-0020

URL: Article

DOI: 10.1182/blood-2021-146650

Language: English

Publisher: American Society of Hematology

Publication Date: 2021

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. 640-640

Abstract: Multiple myeloma (MM) is a disease characterized by the abnormal proliferation of plasma cells in the bone marrow. We and others have recently demonstrated the existence of different myeloma subclones phylogenetically related to the founding clone. This intra-clonal heterogeneity is the basis for disease progression, treatment resistance, and relapse. However, the clinical and biological relevance of the presence and diversity of different myeloma subclones has not been fully established. In this study, we used whole exome sequencing (WES) plus a pull down of the MYC, IGH, IGL and IGK loci as a tool to analyze the largest series of presenting cases of myeloma (463 patients) to date, which were entered into the Myeloma XI trial (NCT01554852). DNA from both tumor and peripheral blood samples were used in the exome capture protocol following the SureSelect Target Enrichment System for Illumina Paired-End Sequencing Library v1.5. Exome reads were used to call single nucleotide variants (SNVs), indels, translocations, and copy number aberrations. The proportion of tumor cells containing an SNV was inferred. The presence and proportion of subclones were defined in a subset of 437 patients using a genetic algorithm based-tool (GAUCHO), which also calculated different indices of clonal diversity: number of clones, mean pairwise genetic divergence, Shannon and Inverse Simpson diversity index and Berger-Parker dominance index. Based on these results, we aimed to determine the clinical implications of the number of mutations and the subclonal diversity of MM at presentation in progression free (PFS) and overall survival (OS). We found that MM patients with t(14;16) and t(14;20) had more exonic mutations (not including Ig variants) than the rest of samples (median 87 versus 43, p 〈 0.001). Additionally, we found that MM patients with an APOBEC signature or with mutations in ATM/ATR had significantly more mutations than patients without these genetic lesions with a median number of 137 mutations (range 20-569) and 84.5 (range 33-319) respectively (p 〈 0.001). Subsequently, we identified patients with high number of mutations ( 〉 59 mutations) that had a worse outcome in terms of OS (2-year OS rate of 71% (95% CI, 63-80%) vs. 82% (95% CI, 78-87%), p=0.02), but not progression free survival (median 22.5 (95% CI 18.7-30.2) vs. 27.5 (95% CI, 25.8-30.5) months, p=0.1) We reported recurrent mutated genes in myeloma with mutations being present at both clonal and subclonal levels (IRF4, RB1, DIS3, BRAF, KRAS, and NRAS), whereas other genes were mutated only at clonal (HIST1H1E, LTB, TP53 or EGR1), or subclonal levels (CYLD, TRAF3, MAX). These results give insights about the differences in mutation acquisition times and potential subclonal fitness. We inferred that the median number of clones present in this myeloma series was 5, and determined the prognostic value of the number and diversity of subclones in MM patients. The prognostic impact of having high number of clones was unclear as no significant differences were found. On the contrary, there was a significant difference in terms of outcome when calculating distinct measurements of subclonal diversity. Briefly, MM patients with high values of inverse Simpson diversity index had a significantly poorer PFS (median 13.2 (95% CI, 9.4-∞) vs. 26.9 months (95% CI, 24-30.2) months, p=0.02) and OS (66% (95% CI, 52-82%) vs. 81% (95% CI, 77-85%) alive at 2-years, p=0.01); and, alternatively, MM patients who did not have a dominant subclone accounting for 〉 25% of MM cells (low values of Berger-Parker Dominance index, n=56) had a significantly shorter PFS than those with a dominant clone accounting for more than 25% of cells with a median of 22 (95% CI, 12.3-26.3) vs. 27.5 months (95% CI, 23.9-30.9) respectively, p=0.02. Our results show that mutational load and subclonal diversity are poor prognostic factors in myeloma. Previous studies from massive-parallel sequencing and single cell analyses of myeloma plasma cells already revealed that myeloma had the features of an evolutionary ecosystem, where different tumour subclones coexist and have differential response to treatment. We have demonstrated in this study that measures of tumor diversity have important clinical consequences. To our knowledge, this is the first time that the use of clonal diversity indices as predictive biomarkers of progression is proposed in haematological malignancies, and more specifically, myeloma. Disclosures Walker: Onyx Pharmaceuticals: Consultancy, Honoraria.

Type of Medium: Online Resource

ISSN: 0006-4971 , 1528-0020

URL: Article

DOI: 10.1182/blood.V124.21.640.640

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. 126, No. 23 ( 2015-12-03), p. 2981-2981

Abstract: Introduction Identifying molecular high risk myeloma remains a diagnostic challenge. We previously reported co-segregation of 〉 1 adverse lesion [t(4;14), t(14;16), t(14;20), gain(1q), del(17p)] by iFISH to specifically characterise a group of high risk patients (Boyd et al., Leukemia 2012). However, implementation of this approach is difficult using FISH because of its technical limitations. We recently developed and validated a novel high-throughput all-molecular testing strategy against FISH (MyMaP- Myeloma MLPA and translocation PCR; Kaiser MF et al., Leukemia 2013; Boyle EM et al., Gen Chrom Canc 2015). Here, we molecularly characterised 1,036 patients from the NCRI Myeloma XI trial using MyMaP and validated the co-segregation approach. Materials, Methods and Patients Recurrent translocations and copy number changes were assayed for 1,036 patients enrolled in the NCRI Myeloma XI (NCT01554852) trial using CD138+ selected bone marrow myeloma cells taken at diagnosis. The trial included an intensive therapy arm for younger and fitter and a non-intense treatment arm for elderly and frail patients. Analysis was performed using MyMaP, which comprises TC-classification based multiplex qRT-PCR and multiplex ligation-dependent probe amplification (MLPA; MRC Holland). Median follow up for the analysis was 24 months. Results Adverse translocations [t(4;14), t(14;16), t(14;20)] were present in 18.2% of cases, del(17p) in 9.3%, gain(1q) in 34.5% and del(1p32) in 9.4% of cases. All adverse lesions were associated with significantly shorter PFS and OS by univariate analysis (P 〈 0.05 for all). Of the 1,036 analysed cases, 13.5% carried 〉 1 adverse lesion, 33.9% had one isolated adverse lesion and 52.6% had no adverse lesion. Presence of 〉 1, 1 or no adverse lesion was associated with a median PFS of 17.0, 23.9 and 30.6 months (P =3.0x10-9) and OS at 24 months of 67.9%, 75.0% and 86.0% (P =1.8x10-7), respectively. Del(1p) was associated with shorter PFS and OS for the intensive, but not for the non-intensive therapy arm and was independent of the co-segregation model by multivariate analysis regarding OS (P =0.006). We thus included del(1p) as an additional adverse lesion in the model for younger patients. The groups with 〉 1 (19.4% of cases), 1 (31.1%) and no adverse lesions (49.5%) were characterised by median PFS of 19.4, 29.4 and 39.1 months (P =1.2x10-10) and median 24-months survival of 73.8%, 86.4% and 91.5% (P =1.4x10-6), respectively. Hazard Ratio for 〉 1 adverse lesion was 3.0 (95% CI 2.1-4.1) for PFS and 3.8 (95% CI 2.2-6.5) for OS. By multivariate analysis, co-segregation of adverse lesions was independent of ISS for PFS/OS in the entire group of 1,036 cases and in the intensive treatment arm. We integrated adverse lesions and ISS into a combined model defining High Risk ( 〉 1 adv les + ISS 2 or 3; 1 adv les + ISS 3) and Low Risk (no adv les + ISS 1 or 2; 1 adv les + ISS 1) and the remainder as Intermediate Risk. The High Risk, Intermediate Risk and Low Risk groups of the total cohort included 11.2%, 41.2% and 41.6% of cases with median PFS of 15.8, 19.8 and 35.2 months (P 〈 2.2x10-16) and median OS at 24 months of 62.9%, 73.7%, and 90.7% (P =4.0x10-14), respectively. Integration of ISS into the model for younger patients resulted in highly specific identification of a High Risk group (15.6% of cases) with HR 3.8 (CI 2.6-5.4) for PFS and 6.2 (CI 3.3-11.6) for OS. Conclusions Co-segregation analysis of adverse genetic lesions is a specific molecular risk stratification tool which has now been validated in two large independent trials including a real-world population of all age groups (UK MRC Myeloma IX; NCRI Myeloma XI; total 1,905 patients). MyMaP is a validated all-molecular analysis approach that makes the otherwise technically challenging assessment of multiple genetic regions by FISH accessible using standard laboratory equipment without bioinformatics requirements. Disclosures Kaiser: BristolMyerSquibb: Consultancy; Chugai: Consultancy; Janssen: Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria. 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:Celgene: Consultancy, Research Funding, Speakers Bureau; BMS: Consultancy; Sanofi: Consultancy, Speakers Bureau; Amgen: Consultancy, Speakers Bureau; Takeda Oncology: Consultancy, Research Funding, Speakers Bureau; Janssen: Consultancy, Research Funding, Speakers Bureau. Gregory:Celgene: Honoraria; Janssen: Honoraria. Davies:Takeda-Milenium: Honoraria; Onyx-Amgen: Honoraria; Celgene: Honoraria; University of Arkansas for Medical Sciences: Employment. Jackson:Celgene: Honoraria; Takeda: Honoraria; Amgen: Honoraria. Morgan:Weisman Institute: Honoraria; Takeda-Millennium: Honoraria, Membership on an entity's Board of Directors or advisory committees; 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; 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.V126.23.2981.2981

Language: English

Publisher: American Society of Hematology

Publication Date: 2015

detail.hit.zdb_id: 1468538-3

detail.hit.zdb_id: 80069-7