Faculty Bibliography

Analysis of the genetic basis for multiple myeloma (MM) has informed many of our current concepts of the biology that underlies disease initiation and progression. Studying these events in further detail is predicted to deliver important insights into its pathogenesis, prognosis and treatment. Information from whole genome sequencing of structural variation is revealing the role of these events as drivers of MM. In particular, we discuss how the insights we have gained from studying chromothripsis suggest that it can be used to provide information on disease initiation and that, as a consequence, it can be used for the clinical classification of myeloma precursor diseases allowing for early intervention and prognostic determination. For newly diagnosed MM, the integration of information on the presence of chromothripsis has the potential to significantly enhance current risk prediction strategies and to better characterize patients with high-risk disease biology. In this article we summarize the genetic basis for MM and the role played by chromothripsis as a critical pathogenic factor active at early disease phases.

The treatment landscape for multiple myeloma (MM) has dramatically changed over the last three decades, moving from no US Food and Drug Administration approvals and two active drug classes to over 19 drug approvals and at least eight different active classes. The advances seen in MM therapy have relied on both a structured approach to obtaining new labels and cautious off-label drug use. Although there are country and regional differences in drug approval processes, many of the basic principles behind off-label drug use in MM can be summarized into four main categories: 1) use of a therapy prior to the current approval regulations; 2) widespread use of a therapy following the release of promising clinical trial results but prior to drug approval; 3) use of a cheap therapy supported by clinical safety and efficacy data but without commercial backing; and 4) niche therapies for small well-defined patient populations where large clinical trials with sufficient power may be difficult to perform. This review takes a historical approach to discuss how off-label drug use has helped to shape the current treatment approach for MM.

Introduction: A number of cytogenetic abnormalities (CAs) are associated with poorer prognosis in MM, including del(17p), t(4;14), t(14;16), and amp1q21. There is a general consensus that treatment with proteasome inhibitors (PIs) benefits pts carrying these CAs (Sonneveld Blood 2016). This meta-analysis of four phase 3 studies assesses PFS benefit in pts receiving the oral PI ixa vs pbo regarding the specific adverse CAs.
Method(s): Pts in TOURMALINE-MM1 (N=722; relapsed/refractory MM; Moreau N Engl J Med 2016) and MM2 (N=705; newly diagnosed MM; Facon Blood 2021) received ixa plus lenalidomide-dexamethasone (Rd) vs pbo-Rd (1:1). Pts in TOURMALINE-MM3 (N=656; Dimopoulos Lancet 2019) and TOURMALINE-MM4 (N=706; Dimopoulos J Clin Oncol 2020) received ixa vs pbo (3:2) as maintenance following autologous stem cell transplant or as post-induction maintenance in transplant-ineligible pts, respectively. In TOURMALINE-MM1/MM2, CAs were centrally assessed on CD138 positive sorted cells from bone marrow samples collected at study entry using fluorescence in situ hybridization (FISH). Cutoff values for defining the presence of del(17p), t(4;14), and t(14;16) were 5%, 3%, and 3% positive cells, respectively, based on the false-positive rates (technical cutoffs) of the FISH probes used, and cutoff values of 3% (MM1) and 20% (MM2) were used for amp1q21. In TOURMALINE-MM3/MM4, cytogenetic assessment was performed locally using FISH or conventional karyotyping with locally defined thresholds for positivity.
Result(s): 270/1227 (22%) vs 227/1019 (22%) evaluable pts receiving ixa-based vs pbo-based therapy had high-risk CAs [del(17p), t(4;14), t(14;16)]: 75 vs 62 in MM1, 60 vs 63 in MM2, 61 vs 54 in MM3, and 74 vs 48 in MM4. 957/1227 (78%) vs 792/1019 (78%) had complementary standard-risk CAs: 200 vs 216 in MM1, 231 vs 234 in MM2, 252 vs 152 in MM3, and 275 vs 190 in MM4. 555/1142 (49%) vs 479/955 (50%) evaluable pts receiving ixa-based vs pbo-based therapy had expanded high-risk CAs (high-risk CAs +/- amp1q21): 155 vs 154 in MM1, 134 vs 146 in MM2, 116 vs 88 in MM3, and 150 vs 91 in MM4. 587/1142 (51%) vs 476/955 (50%) had complementary standard-risk CAs: 122 vs 126 in MM1, 164 vs 153 in MM2, 154 vs 89 in MM3, and 148 vs 108 in MM4. After a median follow-up in the pooled analysis of 25.6 months (mos; 12.7, 54.6, 29.7, and 21.3 mos in MM1, MM2, MM3, and MM4, respectively), the hazard ratio (HR) for PFS with ixa-based vs pbo-based therapy in pts with high-risk CAs was 0.74 (95% confidence interval [CI] 0.59-0.93; median 17.8 vs 13.2 mos) and 0.70, (95% CI 0.62-0.80; median 26.3 vs 17.6 mos) in pts with standard-risk CAs. In the subgroup analyses of expanded high-risk CAs, the HR for PFS with ixa-based vs pbo-based therapy in pts in the expanded high-risk group was 0.75 (95% CI 0.64-0.87; median 18.1 vs 14.1 mos; Figure 1) and 0.71 (95% CI 0.59-0.85; median 36.1 vs 21.4 mos) in the complementary standard-risk group. Analyses of PFS according to the presence of individual CAs (Figure 2) indicated differing magnitudes of PFS benefit. Notably, in pts with t(4;14) (n=124 vs n=102), the HR for PFS with ixa-based vs pbo-based therapy was 0.68 (95% CI 0.48-0.96; median 22.4 vs 13.2 mos), while for pts with amp1q21 (n=380 vs n=312), the HR was 0.77 (95% CI 0.63-0.93; median 18.8 vs 14.5 mos) and for pts with del(17p) (n=141 vs n=107) the HR was 0.80 (95% CI 0.59-1.09; median 15.7 vs 13.2 mos).
Conclusion(s): This pooled analysis demonstrated a PFS benefit with ixa-based therapy vs pbo-based therapy regardless of the presence of specific adverse CAs, with a similar magnitude of benefit in pts with (expanded) high-risk CAs and the respective complementary standard-risk groups. However, due to the differences in study eligibility criteria and pt populations, ixa combined with Rd or as single-agent maintenance therapy may not abrogate the negative impact of high-risk CAs. Analyses of PFS in subgroups with specific CAs indicated that the greatest magnitudes of benefit (lowest HRs) with ixa-based vs pbo-based therapy were in pts with t(4;14) (HR 0.68) and pts with amp1q21 (HR 0.77), suggesting that the improved outcome with ixa-based vs pbo-based therapy in the expanded high-risk subgroup was primarily driven by PFS differences in pts with these more common CAs. Further study is needed, and additional sensitivity analyses will be presented in subsequent publications. [Formula presented] Disclosures: Chng: BMS/Celgene: Honoraria, Research Funding; Amgen: Honoraria; Takeda: Honoraria; Abbvie: Honoraria; Sanofi: Honoraria; Pfizer: Honoraria; Johnson and Johnson: Honoraria, Research Funding. Lonial: BMS/Celgene: Consultancy, Honoraria, Research Funding; AMGEN: Consultancy, Honoraria; GlaxoSmithKline: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Research Funding; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees; Merck: Honoraria. Morgan: Takeda: Honoraria. Iida: Amgen: Research Funding; Daiichi Sankyo: Research Funding; Glaxo SmithKlein: Research Funding; Ono: Honoraria, Research Funding; Takeda: Honoraria, Research Funding; Sanofi: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Chugai: Research Funding; Abbvie: Research Funding; Janssen: Honoraria, Research Funding; Bristol-Myers Squibb: Research Funding. Moreau: Sanofi: Honoraria; Celgene BMS: Honoraria; Abbvie: Honoraria; Amgen: Honoraria; Janssen: Honoraria; Oncopeptides: Honoraria. Kumar: Bluebird Bio: Consultancy; Carsgen: Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Antengene: Consultancy, Honoraria; Novartis: Research Funding; Oncopeptides: Consultancy; Tenebio: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche-Genentech: Consultancy, Research Funding; Beigene: Consultancy; Merck: Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding. Twumasi-Ankrah: Takeda: Current Employment. Kumar: Takeda: Current Employment, Current holder of stock options in a privately-held company. Dash: Takeda: Current Employment, Current equity holder in publicly-traded company. Vorog: Takeda: Current Employment. Zhang: Takeda: Current Employment. Suryanarayan: Takeda: Current Employment. Labotka: Takeda: Current Employment. Dimopoulos: Janssen: Honoraria; Takeda: Honoraria; Beigene: Honoraria; BMS: Honoraria; Amgen: Honoraria. OffLabel Disclosure: Use of the oral proteasome inhibitor ixazomib for the initial treatment of multiple myeloma and as maintenance treatment following stem cell transplantation or induction therapy in newly diagnosed patients
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Introduction Despite efficacious modern induction combination therapies a subset of myeloma patients have high-risk disease which manifests as either primary refractory disease or early relapse following initial response. The presence of known molecular high-risk lesions explain the majority of these cases but understanding the factors influencing the poor phenotypic outcome for the remainder will help us improve outcomes further. This exploratory analysis of the Myeloma XI+ trial aimed to understand the population of patients with phenotypic high-disease in the context of carfilzomib and lenalidomide induction therapy. Methods The UK NCRI Myeloma XI trial is a phase III randomised controlled trial that recruited 2568 newly diagnosed transplant eligible patients, of which 526 were randomised to receive the induction combination KRdc comprising carfilzomib (K, 36mg/m2 IV d1-2, 8-9,15-16 (20mg/m2 #1d1-2)), lenalidomide (R, 25mg PO d1-21), dexamethasone (d, 40mg PO d1-4,8-9,15-16) and cyclophosphamide (c, 500mg PO d1,8) as part of an adaptive trial design. Induction therapy was planned for a minimum of 4 cycles or to maximum response prior to autologous stem cell transplant (ASCT). There was a subsequent randomisation to lenalidomide maintenance or observation at 3 months post ASCT. Primary refractory disease was defined as not achieving at least a minimal response (MR) at maximum response after at least 4 cycles of induction therapy or progression at any time during induction regardless of initial response. Early relapse (ER) after ASCT was defined as progression within 12 months of ASCT. Molecular risk was defined as the presence of one (high risk) or more than one (ultra-high risk) of the following lesions: del(17p), gain(1q), t(4,14), t(14;16) or t(14;20). Results The incidence of primary refractory disease with the KRdc combination was very low. Only 8/526 (1.5%) patients were primary refractory, all having progression during induction therapy with a median progression free survival of only 126 days. The number of patients is too small to draw any firm conclusions regarding the characteristics associated with primary refractory disease. 401/526 (76%) of patients underwent high dose melphalan and ASCT on trial after KRdc induction. Those that did not proceed to ASCT on trial were either ineligible, mostly due to not completing the minimum required induction therapy, or were deemed not fit to undergo the procedure based on patient/clinician decision. Of those patients who underwent ASCT, 36/401 (9.0%) relapsed within 12 months (ER). These ER patients had both a shorter PFS2 and second PFS suggesting a continued association with adverse outcome beyond first line therapy. Patients in the ER group were compared with those patients not relapsing until beyond 12 months after ASCT (nER). There was no difference in the sex, age or paraprotein or light chain sub-type of patients. There was evidence of a greater impact of bone marrow disease burden in the ER group with a lower haemoglobin (median 99 g/L vs 115, p = 0.0216), lymphocyte count (1.3 x10^9/L vs 1.8, p = 0.0012) and platelet count (187 x10^9/L vs 252, p = 0.0049) at baseline. Median bone marrow aspirate plasma cell infiltration was ER 33% vs nER 23%. There were no significant differences in ISS stage (ER ISS I 19%, II 50%, III 22%, nER ISS I 35%, II 36%, III 22%, p = 0.1434), lactate dehydrogenase, albumin or beta-2 microglobulin. Patients in the ER group were less likely to have received lenalidomide maintenance (ER 12/36 [33%] vs nER 222/365 [61%]). For some (4/36) not receiving lenalidomide was due to relapse occurring prior to reaching the maintenance randomisation point (100 days post-ASCT). For those with available data 75% of patients in the ER group had molecular high (50%) or ultra-high (25%) risk disease, whilst 25% had standard risk using the trial definition. The individual lesions accounting for high-risk status were: gain(1q) in ER 42% vs nER 35%; t(4;14) ER 50% vs nER 10%; del(17p) ER 8% vs nER 7%. Discussion The combination of a second-generation immunomodulatory agent and proteasome inhibitor in the KRdc induction regimen is associated with deep responses and only a very small proportion of patients have primary refractory disease. Early relapse after KRdc and ASCT occurred in 9% of patients and was associated with high bone marrow disease burden and molecular high-risk features. Disclosures: Pawlyn: Amgen: Honoraria; Janssen: 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; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees. Davies: Amgen: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Roche: Consultancy, Honoraria. Menzies: Celgene / BMS: Research Funding; Amgen: Research Funding; Merck Sharpe and Dohme: Research Funding. Henderson: BMS / Celgene: Research Funding; Merck Sharpe and Dohme: Research Funding; Amgen: Research Funding; Takeda: Research Funding. Cook: Roche: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Karyopharm: Consultancy, Honoraria; Oncopeptides: Consultancy, Honoraria; Sanofi: Consultancy, Honoraria; BMS: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding. Jenner: BMS/Celgene: Consultancy, Honoraria, Speakers Bureau; Janssen: Consultancy, Honoraria, Speakers Bureau; Pfizer: Consultancy; Takeda: Consultancy. Jones: Janssen: Honoraria; BMS/Celgene: Other: Conference fees. Kaiser: AbbVie: Consultancy; Takeda: Consultancy, Other: Educational support; Seattle Genetics: Consultancy; Amgen: Honoraria; Pfizer: Consultancy; Karyopharm: Consultancy, Research Funding; GSK: Consultancy; 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. Cairns: Merck Sharpe and Dohme: Research Funding; Amgen: Research Funding; Takeda: Research Funding; Celgene / BMS: Other: travel support, Research Funding. Morgan: BMS: Membership on an entity's Board of Directors or advisory committees; Jansen: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; GSK: Membership on an entity's Board of Directors or advisory committees. 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: The KRdc combination is off label
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Background The immune system is altered in multiple myeloma (MM) and contributes to therapy resistance. The availability of novel immunotherapies necessitates understanding the influence of the immune microenvironment on disease progression which may inform sensitivity to therapy. The objective of this study is to fully characterize the immune microenvironment in MM precursor diseases and MM and identify any immune contribution to progression. To accomplish this we used high-dimensional mass cytometry (CyTOF) to investigate immune alterations associated with progression in pre-malignant and malignant stages of MM. Methods Cryopreserved bone marrow mononuclear cells (BMMCs) from healthy donors (HD, n=13), MGUS (n=21), SMM (n=19), newly diagnosed MM (NDMM, n=17), and ~3 months post- first autologous stem cell transplant (ASCT, n=21) were assessed using a panel of 35 cell surface and 3 intracellular antibodies that includes cell lineage markers for identification of immune populations and functional markers indicative of positive or negative immune regulation. BMMCs were thawed, stained with antibodies, and analyzed on a Helios mass cytometer. Data were normalized using bead normalization, transformed using the inverse hyperbolic sine function with a cofactor of 5 and gated for 45+ live, intact, singlets for global analysis by gating in FCS express and clustering by viSNE for visualization. Differences in population abundance were identified in an unbiased manner by FlowSOM and in marker intensity by CITRUS. Marker intensity analysis was performed using the multiple testing permutation procedure (SAM), with an FDR of 1% and minimum population size of 0.5%. Results To identify changes in the immune microenvironment associated with progression we compared immune population abundance and marker intensity indicative of immune status including activation, exhaustion, or senescence. MGUS was distinguished from HD by increased abundance of CD4 central memory (CM, p<0.001), effector memory (EM, p<0.001) and plasmacytoid and monocyte-derived dendritic cells (DC, p< 0.01). In MGUS, TIM3 and CD57 were elevated on NK cells and NKT cells, respectively, compared to HD suggesting reduced activity. In SMM increased abundance of B regulatory cells (3.0 vs 5.9 %, p<0.01) but reduced inhibitory markers on T cells including PD1, CTLA4 CD55, FOXP3 and TIGIT was observed compared to MGUS. NDMM was distinguished from SMM by reduced abundance of CD4 EM (p<0.01), CD8 early EM (p< 0.001), and B regulatory cells (p<0.01) and increased abundance of active Tregs (CD38+, P<0.01) and total NK cells (p<0.01) which had increased CD55, a complement inhibitory protein. Post-ASCT changes in immune abundance include increased total CD8 and CD8 terminal effectors (CD57 ⁺, p< 0.0001), B regulatory cells (p<0.0001), and reduced total CD4 and CD4 CM (p<0.0001), compared to NDMM. CD4 T cells post-ASCT were characterized by reduced CD127 and CCR7 and increased CD28, CTLA4, FOXP3 and TIGIT and CD8 T cells had reduced CD28, CD127 and CCR7 and increased CD57 and TIGIT compared to NDMM. Interestingly, significant difference in NK cells were not observed but post-ASCT NK cells may be active as suggested by reduced CD59 and TIM3 compared to NDMM. To determine whether the immune microenvironment had normalized by 3 months post-ASCT we compared population abundance to HD, MGUS, and SMM cases. Immune abundance post-ASCT revealed a significantly lower percentage of CD4 CM, 4 ^-8 ^- T cells, normal PCs, and post-switch B cells (25+) and elevated CD8 terminal effector (57+) and B regulatory cells than all 3 other groups. Overall major differences in abundance of total T and B cells and their subsets were observed with differences in NK cells between stages primarily reflected in marker expression (e.g. CD161+ subset) rather than abundance. Conclusions Early changes in the immune microenvironment observed in MGUS/SMM lead to immune suppression and eventually immune evasion allowing MM to emerge. In this study the immune ME did not appear to normalize 3 months post-therapy indicated by an increase in B regulatory cells and markers of inactive effector cells. Profiling of the immune microenvironment throughout MM treatment may allow us to identify novel therapeutic targets and optimal timing of administration of novel immunotherapies and patients that would most benefit from these therapies. Disclosures: Walker: Sanofi: Speakers Bureau; Bristol Myers Squibb: Research Funding. Morgan: BMS: Membership on an entity's Board of Directors or advisory committees; Jansen: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; GSK: Membership on an entity's Board of Directors or advisory committees.
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Introduction: The incidence of multiple myeloma (MM) varies by ethnicity with Black patients approximately twice as likely to develop MM compared to White or Asian (Black: White males 2.9:1, females 2.2:1). The National Cancer Registration and Analysis Service (NCRAS) in 2015 reported the incidence of MM by ethnicity in England over 10 years to be 85.5% White; 5.4% Black; 3.6% Asian and 1.9% Other. Ethnic minorities have been reported to be under-represented in clinical trials partly because of socio-economic factors; however, it is unknown if these disparities exist in state funded health care systems where access to healthcare is free and should be equitable.
Method(s): Ethnicity, baseline demographics, progression-free survival (PFS) and overall survival (OS) were collected from patients enrolled into 1 ^st line UK academic transplant eligible (TE) and transplant non-eligible (TNE) - Myeloma IX, XI and XIV trials, and at 1 ^st relapse - Myeloma X and XII clinical trials. These trials enrolled from 2003 to 2021. The Myeloma XII and XIV (FiTNEss) trials are currently enrolling, all other trials have closed. Ethnicity was coded by White, Black, Asian and Other in line with Office for National Statistics (ONS) categories. Patients were enrolled across 120 centres covering a wide geographical distribution in the UK. These studies were designed to have permissive eligibility criteria to enrol as close to real world patients as possible. Baseline characteristics were summarised descriptively and comparisons made using the chi-squared test. Comparisons with population-level data used one-sample chi-squared tests. Survivor functions were estimated using the Kaplan-Meier method and were compared using the logrank test. Cox proportional hazards models with suitable interaction terms were used to test for heterogeneity. All tests were called significant at the 5% level.
Result(s): 7,291 patients were enrolled across 5 randomised controlled trials over 18 years. Overall, the ethnic distribution was White 93.8%, Black 2.2%, Asian 1.8%, Other 0.6% and unknown 1.6%. The skew to enrolment of White patients was more apparent in the TNE studies (Myeloma IX non-intensive: White 97.4%, Black 1.3%, Asian 0.4%; Myeloma XI non-intensive: White 94.5%, Black 1.8%, Asian 1.6%, Myeloma XIV: White 94.2%, Black 0%, Asian 3.2%). This was different to the incidence of myeloma cases across the UK with the difference most apparent in TNE studies (TE trials (observed vs NCRAS, P < 0.0001); TNE trials (observed vs NCRAS, P < 0.0001); 1 ^st relapse trials (observed vs NCRAS, P = 0.035)). Enrolment distribution by ethnicity was consistent over the 18 years, with no change in diversity over time despite there being an increase in UK non-white populations. In the Myeloma IX trial, there was no significant difference in age at enrolment; however, the performance status in Black patients was worse than non-Black (P = 0.045), there was fewer cytogenetic high risk Black patients (P = 0.007) and less ISS 1 Black patients vs non-Black (P = 0.0416). There were no demographic differences by ethnicity in the Myeloma XI trial. The outcomes of patients by PFS or OS by ethnic group was similar within each trial (figure 1). An overall improvement in OS for was demonstrated over time from Myeloma IX to the Myeloma XI trial with the incorporation of novel agents (median OS MRC-Myeloma IX: 48 months vs. median OS NCRI Myeloma-XI: 70 months, P < 0.0001). There was no evidence of heterogeneity of effect with respect to ethnicity (P = 0.456) suggesting all ethnic sub-groups benefited from this improvement in OS.
Conclusion(s): Enrolment of ethnic minorities into academic clinical trials in the UK was below that expected despite enrolling from >100 geographically spread sites and intended equitable access to healthcare. All ethnic groups derived an OS benefit from novel agents within trials that were not otherwise routinely available; however, a substantial proportion of ethnic minorities were not enrolled particularly TNE patients, thereby limiting their survival gains. Understanding causes of inequality and addressing these is a priority for the UK-MRA to ensure that all groups can potentially benefit, and trial results are representative of the UK population. [Formula presented] Disclosures: Popat: Abbvie, Takeda, Janssen, and Celgene: Consultancy; AbbVie, BMS, Janssen, Oncopeptides, and Amgen: Honoraria; Takeda: Honoraria, Other: TRAVEL, ACCOMMODATIONS, EXPENSES; GlaxoSmithKline: Consultancy, Honoraria, Research Funding; Janssen and BMS: Other: travel expenses. Craig: Celgene: Research Funding; Merck Sharpe & Dohme: Research Funding; Amgen: Research Funding; Takeda: Research Funding. Davies: Takeda: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Roche: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria. Cairns: Amgen: Research Funding; Merck Sharpe and Dohme: Research Funding; Takeda: Research Funding; Celgene / BMS: Other: travel support, Research Funding. Olivier: Merck Sharpe and Dohme: Research Funding; Takeda: Research Funding; Amgen: Research Funding; Celgene / BMS: Research Funding. Morgan: BMS: Membership on an entity's Board of Directors or advisory committees; Jansen: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; GSK: Membership on an entity's Board of Directors or advisory committees. Cook: BMS/Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Takeda: Consultancy, Research Funding; Sanofi: Consultancy; Karyopharm: Consultancy; Amgen: Consultancy. OffLabel Disclosure: Revlimid and carfilzomib combinations are used off label
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