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In: Blood, American Society of Hematology, Vol. 125, No. 5 ( 2015-01-29), p. 831-840

Abstract: Coexistent hyperdiploidy or t(11;14) does not abrogate the poor prognosis associated with adverse cytogenetics in myeloma patients. Single-cell analysis reveals that hyperdiploidy may precede IGH translocation in the clonal history of a proportion of patients with both.

Type of Medium: Online Resource

ISSN: 0006-4971 , 1528-0020

URL: Article

DOI: 10.1182/blood-2014-07-584268

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. 121, No. 17 ( 2013-04-25), p. 3413-3419

Abstract: IGH translocations in myeloma can occur through at least 5 mechanisms. t(11;14) and t(14;20) DH-JH rearrangement-mediated translocations occur indicating these appear to occur in a pregerminal center cell.

Type of Medium: Online Resource

ISSN: 0006-4971 , 1528-0020

URL: Article

DOI: 10.1182/blood-2012-12-471888

Language: English

Publisher: American Society of Hematology

Publication Date: 2013

detail.hit.zdb_id: 1468538-3

detail.hit.zdb_id: 80069-7

In: Blood, American Society of Hematology, Vol. 123, No. 16 ( 2014-04-17), p. 2513-2517

Abstract: Inherited genetic variation increases risk to developing multiple myeloma through predisposition to MGUS. Loci identified that increase risk of developing MGUS include 2p23.3, 3p22.1, 3q26.2, 6p21.33, 7p15.3, 17p11.2, and 22q13.1.

Type of Medium: Online Resource

ISSN: 0006-4971 , 1528-0020

URL: Article

DOI: 10.1182/blood-2013-10-532283

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. 120, No. 21 ( 2012-11-16), p. 3490-3490

Abstract: Abstract 3490 IGH loci translocations in multiple myeloma are primary events in the aetiology of the disease. There are 5 main translocation partner chromosomes which result in the over-expression of key oncogenes. These translocations are t(4;14), t(6;14), t(11;14), t(14;16) and t(14;20) and result in the over-expression of MMSET and FGFR3, CCND3, CCND1, MAF and MAFB, respectively. The translocations have a major impact on response and survival with the t(4;14), t(14;16) and t(14;20) resulting in poor prognosis. It is therefore imperative that these chromosomal abnormalities be identified. Translocations have traditionally been identified by fluorescence in situ hybridisation (FISH). Using targeted capture techniques, similar to exome capture technology, followed by massively parallel sequencing it should be possible to identify the translocations and the specific breakpoints. We have developed a targeted capture using the SureSelect (Agilent) system by tiling RNA baits across the IGH locus. Baits covered the V, D and J segments as well as being tiled across the entire constant region, including the switch regions. DNA from samples (n=120) were assayed using 150 ng of DNA and a modified capture protocol. The translocation partner had previously been identified by FISH in 36 samples which comprised 11 t(4;14), 3 t(6;14), 11 t(11;14), 9 t(14;16), 2 t(14;20). The remaining 84 samples were assayed by RQ-PCR for over-expression of the partner oncogenes to determine the translocation. Several identified translocations were verified by PCR. In 90% of samples which had FISH performed the correct IGH translocation was detected using the capture technique. The number of paired reads detecting the translocation varied from 2 to 102. Breakpoints could be determined for all of these samples and were mapped for each translocation group. In the t(4;14) group the breakpoints were clustered around exons 1, 4 and 5, corresponding to the MB4-1, MB4-2 and MB4-3 IgH-MMSET hybrid transcripts. Of the 11 t(4;14) with FISH only 2 did not express FGFR3 and had deletion of der(14). In these samples the breakpoint was located between LETM1 and MMSET, confirming that loss of FGFR3 expression is due to deletion of der(14) and not due to the location of the breakpoint. The sample with the breakpoint furthest from MMSET was located 67 kbp upstream of the start of translation within LETM1, in a position similar to that found in the KMS-11 cell line. In the t(11;14) samples the breakpoints varied dramatically on chromosome 11 but were always centromeric to CCND1. Breakpoints varied from 1.1 kbp centromeric to the start of CCND1 transcription to 1.1 Mbp centromeric, within the PPP6R3 gene. However, most breakpoints (70%) were in the intergenic region between MYEOV and CCND1. The distance from the breakpoint to CCND1 did not inversely correlate with CCND1 expression, in fact the sample with the breakpoint furthest from CCND1, within PPP6R3, had the highest expression of CCND1 as determined by gene expression array. No samples had breakpoints within the mantle cell lymphoma major translocation cluster. However, 2 samples had their breakpoint within 100 bp of one another, indicating a possible common breakpoint. Of the t(6;14) samples 2 had breakpoints in the first intron of CCND3, upstream of the start of translation. The remaining sample had its breakpoint 550 kbp upstream of the transcription start site within UBR2. The t(14;16) samples all had their breakpoints within the last intron of WWOX, 0.48–1.03 Mbp centromeric of MAF and in the location of the common fragile site FRA16D. The breakpoints cluster into 2 groups on either side of the fragile site. The t(14;20) breakpoints were located in the 1.5 Mbp intergenic region centromeric of MAF. The breakpoint furthest from MAF was 1.2 Mbp centromeric of the gene. In conclusion, we have developed and validated a targeted capture and sequencing approach for identifying translocations into the IGH locus in myeloma. This approach is important because of its capacity for high throughput low cost testing strategies that can identify these important prognostic events making a myeloma specific diagnostic platform and personalised medicine a reality for patients with myeloma. Importantly sequence analysis of the peri-breakpoint regions gives insight into molecular mechanisms acting early in the process of myelomagenesis. Disclosures: No relevant conflicts of interest to declare.

Type of Medium: Online Resource

ISSN: 0006-4971 , 1528-0020

URL: Article

DOI: 10.1182/blood.V120.21.3490.3490

Language: English

Publisher: American Society of Hematology

Publication Date: 2012

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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

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

Abstract: 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.

Type of Medium: Online Resource

ISSN: 1078-0432 , 1557-3265

URL: Article

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

Language: English

Publisher: American Association for Cancer Research (AACR)

Publication Date: 2016

detail.hit.zdb_id: 1225457-5

detail.hit.zdb_id: 2036787-9

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

Type of Medium: Online Resource

ISSN: 0390-6078 , 1592-8721

URL: Article

DOI: 10.3324/haematol.2018.202200

Language: English

Publisher: Ferrata Storti Foundation (Haematologica)

Publication Date: 2019

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detail.hit.zdb_id: 2030158-3

detail.hit.zdb_id: 2805244-4

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

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

Type of Medium: Online Resource

ISSN: 2041-1723

URL: Article

DOI: 10.1038/ncomms7997

Language: English

Publisher: Springer Science and Business Media LLC

Publication Date: 2015

detail.hit.zdb_id: 2553671-0