Dhalla, Mazaher

Background: Multiple myeloma (MM) is typically diagnosed in patients (pts) aged 65-74 y (SEER: Myeloma. 2019); therefore, many pts present with concomitant metabolic disorders such as diabetes (18%-22% prevalence of type 2 diabetes [T2D; BMC Cancer. 2020;20:489]). Obesity (body mass index [BMI], ≥ 30 kg/m2) is associated with increased prevalence of T2D (Int J Clin Pract. 2007;61:737), and both are suggested risk factors for MM (Blood. 2012;119:4845; Bone Marrow Transpl. 2014;49:1009). Results from single-center, retrospective studies have shown poorer clinical features and survival outcomes in diabetic pts with MM vs their nondiabetic counterparts (Eur J Haematol. 2012;89:320, Br J Cancer. 2014;111:628). The Connect® MM Registry (NCT01081028) is a large, US, multicenter, prospective observational cohort study of largely community-based pts with NDMM. Reported here is a descriptive analysis assessing differences in baseline (BL) characteristics, treatment patterns, and survival outcomes in diabetic vs nondiabetic pts enrolled in the Connect MM Registry. Methods: Pts with NDMM (N = 3011) were enrolled at 250 community, academic, and government sites. Eligible pts were aged ≥ 18 y with symptomatic MM diagnosed ≤ 2 months (mo) pre-enrollment per International Myeloma Working Group criteria (Leukemia. 2009;23:3). Diabetic pts were defined as those with a history of diabetes and also receiving antidiabetic treatment at BL; those without a history of diabetes and not receiving antidiabetic treatment at BL were defined as nondiabetic pts. Descriptive statistics were used for BL characteristics and treatment regimens. Survival outcomes (progression-free survival [PFS] and overall survival [OS]) were analyzed using Cox regression and adjusted for covariates. Results: Of the 2845 pts included in this analysis, 467 (16%) were diabetic, and 2378 (84%) were nondiabetic. The median age was 69 y for diabetic pts and 66 y for nondiabetic pts, with fewer Black pts (12.2% vs 19.5%) in the nondiabetic group. The proportion of men was higher in the diabetic vs the nondiabetic group (61.9% vs 56.6%). Similar percentages of pts had ECOG PS of 0-1 in both groups (53.3% vs 55.3%). Obesity was more prevalent in diabetic vs nondiabetic pts (55.9% vs 30.4%). More diabetic pts had a history of hypertension requiring treatment vs nondiabetic pts (84.2% vs 52.5%). The prevalence of chromosomal abnormalities was comparable between the groups (within the limitation that data were missing in a substantial number of pts). Among MM-defining criteria, diabetic pts had a higher incidence of hypercalcemia (11.8% vs 8.5%), anemia (50.3% vs 46.5%), and increased creatinine (23.8% vs 19%), whereas measured kidney function was similar. A lower proportion of diabetic than nondiabetic pts received stem cell transplantation (SCT; 28.9% vs 41.5%) and triplet regimens during first induction (48.2% vs 52.8%%). The use of ≥ 2 novel agents during first line (1L) was lower in diabetic than nondiabetic pts (27.8% vs 34.6%), but use of a single novel agent (63.4% vs 60%) and alkylator in 1L (20.6% vs 22.4%) was comparable. The top five 1L regimens were similar for both groups, with lenalidomide + bortezomib + dexamethasone being the most common 1L regimen, and lenalidomide the most common maintenance therapy. Diabetic pts had significantly shorter adjusted median PFS (24.4 vs 31.5 mo; hazard ratio [HR], 1.24; P = 0.001; Fig. 1A) and median OS (60 vs 72.6 mo; HR, 1.29; P = 0.001; Fig. 1B) than nondiabetic pts. In a subgroup analysis by BMI, diabetic pts had significantly shorter OS than nondiabetic pts (for BMI < 30 [50.8 vs 73.1 mo; HR, 1.51; P = 0.0001] and BMI ≥ 30 [56.4 vs 78.6 mo; HR, 1.51; P < 0.0001]). Conclusions: Diabetic pts with NDMM had poorer BL features and were less likely to receive SCT and triplet regimens and/or aggressive upfront treatment. Regardless of BMI, diabetic pts had shorter median OS than nondiabetic pts, suggesting that diabetes might be a stronger factor for survival than obesity. These results support a previous analysis of the Connect MM Registry that identified history of diabetes as a predictive prognostic factor for poor OS (Br J Haematol. 2019;187:602). Limitations of this analysis include limited detail on the care/management of diabetes and steroid use/dosage (which can be challenging for glycemic control). These findings highlight the need to provide better supportive care for diabetes management in MM pts to improve survival. Disclosures La: Celgene BMS: Consultancy. Jagannath:BMS, Janssen, Karyopharm, Legend Biotech, Sanofi, Takeda: Consultancy. Ailawadhi:Amgen: Research Funding; Celgene: Honoraria; Takeda: Honoraria; Janssen: Research Funding; Pharmacyclics: Research Funding; Cellectar: Research Funding; BMS: Research Funding; Medimmune: Research Funding; Phosplatin: Research Funding. Durie:Amgen, Celgene, Johnson & Johnson, and Takeda: Consultancy. Gasparetto:Adaptive Biotechnologies: Consultancy, Honoraria; Sanofi: Consultancy, Honoraria, Other: TRAVEL, ACCOMMODATIONS, EXPENSES (paid by any for-profit health care company), Speakers Bureau; Karyopharm: Consultancy, Honoraria, Other: TRAVEL, ACCOMODATIONS, EXPENSES (paid by any for-profit health care company), Speakers Bureau; Janssen: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; GSK: Consultancy, Honoraria, Other: TRAVEL, ACCOMMODATIONS, EXPENSES (paid by any for-profit health care company); BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; AbbVie: Consultancy, Honoraria. Hardin:Celgene Pharmaceutical Company: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: TRAVEL, ACCOMMODATIONS, EXPENSES. Lee:Sanofi: Consultancy; Janssen: Consultancy, Research Funding; GlaxoSmithKline: Consultancy, Research Funding; Genentech: Consultancy; Takeda: Consultancy, Research Funding; Regeneron: Research Funding; Genentech: Consultancy; Amgen: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Daiichi Sankyo: Research Funding. Narang:Maryland Oncology Hematology: Current Employment; Beigene: Consultancy, Honoraria; BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; J&J: Consultancy, Honoraria. Omel:Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: TRAVEL, ACCOMMODATIONS, EXPENSES; BMS/Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees. Rifkin:McKesson: Current equity holder in publicly-traded company, Ended employment in the past 24 months, Other: Stock ownership; Takeda, Amgen, Celgene, BMS, Mylan, Coherus BioSciences, Fresenius: Consultancy; AbbVie: Other: Investigator in AbbVie sponsored clinical trials; Takeda, Amgen, BMS (Celgene): Membership on an entity's Board of Directors or advisory committees. Terebelo:Newland Medical Associates: Current Employment; BMS: Membership on an entity's Board of Directors or advisory committees. Toomey:Celgene/BMS: Membership on an entity's Board of Directors or advisory committees. Wagner:Celgene Inc.: Membership on an entity's Board of Directors or advisory committees; Connect Multiple Myeloma Registry: Membership on an entity's Board of Directors or advisory committees. Dhalla:BMS: Current Employment, Current equity holder in publicly-traded company. Liu:TechData Inc: Consultancy. Yang:BMS: Current Employment, Current equity holder in publicly-traded company. Joshi:Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company; Eisai Inc.: Ended employment in the past 24 months. Abonour:Takeda: Consultancy; Janssen: Honoraria, Research Funding; Celgene: Consultancy; BMS: Consultancy, Research Funding.

8518 Background: Patients (pts) with newly diagnosed multiple myeloma (NDMM) and renal impairment (RI) are often excluded from clinical trials. Data are limited on the effects of induction treatment in these pts, who may also be ineligible for autologous stem cell transplant (SCT) due to severity of RI. This analysis investigated the impact of RVd induction on renal function in transplant eligible (TE) and noneligible (TNE) pts from the Connect MM Registry, a US, multicenter, prospective, observational study. Methods: Eligible pts were ≥ 18 y and had symptomatic MM diagnosed ≤ 2 mos prior to enrollment, as defined by the International Myeloma Working Group criteria. For this analysis, pts that received front-line RVd for ≥ 3 cycles were grouped per transplant eligibility and renal function at baseline (BL; creatinine clearance [CrCl] < 30, 30-50, > 50-80, and > 80). Pts with progressive disease at BL were excluded. Renal function at 3 mos was measured. Median unadjusted progression-free survival (PFS) was calculated from start of regimen in TE and TNE populations, with pts grouped by CrCl (≤ 60 or > 60) at BL. Results: As of 7/23/19, 421 TE and 212 TNE pts received RVd for ≥ 3 cycles. TE and TNE pts were grouped by BL CrCl of < 30 (20 and 16 pts), 30-50 (36 and 50 pts), > 50-80 (117 and 63 pts), and > 80 (248 and 83 pts). Renal function improvement was observed in all pts receiving RVd, including those with moderate (30-50 CrCl) and severe (< 30 CrCl) RI at BL (Table). In pts with > 60 CrCl and ≤ 60 CrCl at BL, median PFS in TE pts was 48.6 mos and 43.2 mos, respectively. In TNE pts, median PFS was 36.4 mos and 30.6 mos, respectively. Conclusions: The results from the Connect MM Registry indicate that pts with NDMM and RI (including moderate and severe) who receive front-line RVd for ≥ 3 cycles may see improvement in renal function at 3 mos, regardless of transplant eligibility. RVd therefore can potentially be used in pts with RI. This analysis provides real-world data that support further investigation of RVd treatment in pts with moderate or severe RI. Clinical trial information: NCT01081028 . [Table: see text]

8032 Background: The impact of t(11;14) (16%–24% of MM pts) on prognosis is not fully understood. Consensus is lacking on the effects of induction treatment (tx) on outcomes with t(11;14). The Connect MM Registry (NCT01081028) is a large, US, multicenter, prospective observational cohort study of pts with NDMM designed to examine real-world diagnostic patterns, treatment (tx) patterns, clinical outcomes, and HRQoL pt-reported outcomes in pts with NDMM. The impact of t(11;14) on tx outcomes are reported. Methods: Analysis included data from pts from 250 community, academic, and government sites in cohort (C) 1 (9/2009–12/2011) and C2 (12/2012–4/2016), who completed first-line (1L; induction) tx and were tested for t(11;14) by FISH or cytogenetics. Primary end points (progression-free survival [PFS] and overall survival [OS]) were measured from start of 1L tx to earliest event (PFS, death or progression; OS, death), loss to follow-up, or data cutoff, adjusted for baseline (BL) risk factors. A sensitivity analysis excluding pts with concomitant cytogenetic abnormalities [del 17p, t(4;14), t(14;16), 1q+] was also performed. Results: By 1/2018, 3011 pts were enrolled; 2938 were treated. Of 1574 enrolled pts tested for t(11;14), 378 were t(11;14)+ and 1196 were t(11;14)−. More pts in C2 vs C1 were t(11;14)+ (60% vs 40%). BL characteristics were similar between groups. t(11;14) status did not affect PFS ( P= NS) or OS ( P= NS; Table). Pts in C1 and C2 received similar 1L txs (IMiD agent + proteasome inhibitor [PI], 30% vs 42%; PI only, 42% vs 43%; IMiD agent only, 17% vs 11%). Results were similar when pts with concomitant abnormalities were excluded. Conclusions: Results of this analysis suggest that t(11;14) does not affect PFS and OS outcomes in NDMM pts. Clinical trial information: NCT01081028. [Table: see text]

Background: Multiple myeloma (MM) is common among the elderly, with 35% of patients (pts) diagnosed being aged ≥75 years (y). With increasing overall life expectancy, the incidence and prevalence of newly diagnosed and previously treated MM patients ≥80 y is expected to increase over time. Because elderly pts are often excluded from clinical trials, data focused on their treatment patterns and clinical outcomes are lacking. The Connect® MM Registry (NCT01081028) is a large, US, multicenter, prospective observational cohort study of pts with newly diagnosed MM (NDMM) designed to examine real-world diagnostic patterns, treatment patterns, clinical outcomes, and health-related quality of life patient-reported outcomes. This analysis reviews treatment patterns and outcomes in elderly pts from the Connect MM Registry. Methods: Pts enrolled in the Connect MM registry at 250 community, academic, and government sites were included in this analysis. Eligible pts were adults aged ≥18 y with symptomatic MM diagnosed ≤2 months before enrollment, as defined by International Myeloma Working Group criteria; no exclusion criteria were applied. For this analysis, pts were categorized into 4 age groups: <65, 65 to 74, 75 to 84, and ≥85 y. Pts were followed from time of enrollment to the earliest of disease progression (or death), loss to follow-up, or data cutoff date of February 7, 2019. Descriptive statistics were used for baseline characteristics and treatment regimens. Survival outcomes were analyzed using Cox regression. Time to progression (TTP) analysis excluded causes of death not related to MM. Results: Of 3011 pts enrolled (median age 67 y), 132 (4%) were aged ≥85 y, and 615 (20%) were aged 75-84 y at baseline. More pts aged ≥85 y had poor prognostic factors such as ISS stage III disease and reduced hemoglobin (<10 g/dL or >2 g/dL <LLN) compared with other age groups, although no notable differences between creatinine and calcium levels were observed across age groups (Table). A lower proportion of elderly pts (75-84 and ≥85 y) received triplet regimens as frontline therapy. More elderly pts received a single novel agent, whereas use of 2 novel agents was more common in younger pts (Table). The most common frontline regimens among elderly pts were bortezomib (V) + dexamethasone (D), followed by lenalidomide (R) + D, whereas those among younger pts included RVD, followed by VD and CyBorD (Table). No pt aged ≥85 y, and 4% of pts aged 75-84 y received high-dose chemotherapy and autologous stem cell transplant (vs 61% in the <65 y and 37% in the 65-74 y age group). The most common maintenance therapy was RD in pts ≥85 y (although the use was low) and R alone in other age groups (Table). In the ≥85 y group, 27%, 10%, and 4% of pts entered 2L, 3L, and 4L treatments respectively, vs 43%, 23%, and 13% in the <65 y group. Progression-free survival was significantly shorter in the ≥85 y age group vs the 75-84 y age group (P=0.003), 65-74 y age group (P<0.001), and <65 y age group (P<0.001; Fig.1). TTP was significantly shorter in the ≥85 y group vs the <65 y group (P=0.020); however, TTP was similar among the 65-74 y, 75-84 y, and ≥85 y cohorts (Fig. 2). Overall survival was significantly shorter in the ≥85 y group vs the 75-84 y, 65-74 y, and <65 y groups (all P<0.001; Fig. 3). The mortality rate was lowest (46%) during first-line treatment (1L) in pts aged ≥85 y (mainly attributed to MM progression) and increased in 2L and 3L (47% and 54%, respectively); a similar trend was observed in the younger age groups. The main cause of death was MM progression (29% in the ≥85 y vs 16% in the <65 y group). Other notable causes of death in the ≥85 y group included cardiac failure (5% vs 2% in <65 y group) and pneumonia (5% vs 1% in <65 y group). Conclusions: In this analysis, elderly pts received similar types of frontline and maintenance regimens as younger pts, although proportions varied with decreased use of triplet regimens with age. Considering similarities in TTP across the 65-74 y, 75-84 y, and ≥85 y cohorts, these real-world data support active treatment and aggressive supportive care of elderly symptomatic pts, including with novel agents. Additionally, further clinical studies specific to elderly patients with MM should be explored. Disclosures Lee: Amgen: Consultancy, Research Funding; GlaxoSmithKline plc: Research Funding; Sanofi: Consultancy; Daiichi Sankyo: Research Funding; Celgene: Consultancy, Research Funding; Takeda: Consultancy, Research Funding; Janssen: Consultancy, Research Funding. Ailawadhi:Janssen: Consultancy, Research Funding; Takeda: Consultancy; Pharmacyclics: Research Funding; Amgen: Consultancy, Research Funding; Celgene: Consultancy; Cellectar: Research Funding. Gasparetto:Celgene: Consultancy, Honoraria, Other: Travel, accommodations, or other expenses paid or reimbursed ; Janssen: Consultancy, Honoraria, Other: Travel, accommodations, or other expenses paid or reimbursed ; BMS: Consultancy, Honoraria, Other: Travel, accommodations, or other expenses paid or reimbursed . Jagannath:AbbVie: Consultancy; Merck & Co.: Consultancy; Bristol-Myers Squibb: Consultancy; Karyopharm Therapeutics: Consultancy; Celgene Corporation: Consultancy; Janssen Pharmaceuticals: Consultancy. Rifkin:Celgene: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees. Durie:Amgen, Celgene, Johnson & Johnson, and Takeda: Consultancy. Narang:Celgene: Speakers Bureau. Terebelo:Celgene: Honoraria; Jannsen: Speakers Bureau; Newland Medical Asociates: Employment. Toomey:Celgene: Consultancy. Hardin:Celgene: Membership on an entity's Board of Directors or advisory committees. Wagner:Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; American Cancer Society: Other: Section editor, Cancer journal. Omel:Celgene, Takeda, Janssen: Other: Patient Advisory Committees. Srinivasan:Celgene: Employment, Equity Ownership. Liu:TechData: Consultancy. Dhalla:Celgene: Employment. Agarwal:Celgene Corporation: Employment, Equity Ownership. Abonour:BMS: Consultancy; Celgene: Consultancy, Research Funding; Takeda: Consultancy, Research Funding; Janssen: Consultancy, Research Funding.

Background: MM clinical trials often have stringent eligibility criteria that limit the enrollment of patients (pts) reflective of the general patient population (Shah, Clin Lymphoma Myeloma Leuk 2017). The Connect® MM Registry, a prospective observational study of pts with newly diagnosed MM (NDMM) in the US, provided the opportunity to examine differences between pts with MM who were identified as RCT-eligible vs -ineligible, to observe the generalizability of clinical trial results to real-world clinical settings. Differences in baseline characteristics, treatment, clinical outcomes, and HRQoL were examined in RCT-eligible and -ineligible pts from this registry, which represents an unselected, broad real-world population. Methods: Pts with NDMM were enrolled at 250 community, academic, and government sites. Eligible pts were ≥18 y of age with symptomatic MM diagnosed ≤2 months preenrollment per the International Myeloma Working Group (IMWG) criteria. For this analysis, pts were considered ineligible for RCT per the following key exclusion criteria previously described: absolute neutrophil count ≤1.5 × 109/L, platelet count ≤75 × 109/L, creatinine >2.5 mg/dL, AST/ALT >3 times the ULN, peripheral neuropathy grade >2; ECOG PS 3/4; history of myelodysplastic syndromes/other hematologic malignancies, or solid tumors (Shah 2017). Sites administered pt HRQoL questionnaires and provided clinical data at baseline and quarterly during follow-up until death or study discontinuation. Differences in HRQoL at baseline, changes from baseline to 1 and 2 years, and survival outcomes, adjusted for covariates via propensity score, were compared between RCT-eligible/-ineligible pts. Results: Of the 3011 pts enrolled, 2873 pts met the CRAB (hyperCalcemia, Renal failure, Anemia, and Bone disease) criteria per IMWG at enrollment. Of these, 1622 (56.5%) were identified as RCT-eligible and 1251 (43.5%) as RCT-ineligible (data cut-off February 7, 2019). Both groups were similar in median age, sex, race, and ethnicity distribution, although the RCT-ineligible group had more pts aged ≥75 y (26.3% vs 23.2%) and more pts with ECOG PS 3/4 (6.2% vs none). Overall, 34.5% of RCT-ineligible pts received a transplant vs 42.8% of RCT-eligible pts. More RCT-ineligible pts had ISS stage III disease (39.4% vs 19.8%), elevated calcium (≥11.5 mg/dL; 12.7% vs 6.8%), elevated creatinine (>2.0 mg/dL; 40.3% vs 6.5%), and low hemoglobin (<10 g/dL or >2 g/dL <LLN; 60.8% vs 40.3%). Use of triplet treatment (54.2% vs 56.2%) and alkylator (24.9% vs 20.1%) was similar between the RCT-ineligible/-eligible groups. Lenalidomide + bortezomib + dexamethasone (RVd) was the most common initial therapy, and R was the most common maintenance therapy in both groups, but fewer RCT-ineligible pts received RVd as initial therapy (23.9% vs 31.0%) and R as first maintenance regimen (18.1% vs 22.7%). Absolute baseline HRQoL scores were significantly higher, indicating better HRQoL, in RCT-eligible (115.9) vs -ineligible (111.5) pts on the Functional Assessment of Cancer Therapy-Multiple Myeloma (FACT-MM) Total score (P=0.007), FACT-MM Trial Outcome Index (74.3 vs 70.3, P=0.002), and FACT-MM Subscale (37.3 vs 35.5, P=0.003). Scores were similar for EuroQol-5D Overall Index (0.73 vs 0.72, P=0.5) and Brief Pain Inventory (3.42 vs 3.43, P=0.9). Both RCT-eligible and -ineligible pts showed significant improvement in HRQoL from baseline at years 1 and 2, although the magnitude of improvement was similar between both groups (Fig. 1a and b). RCT-ineligible pts showed significantly shorter median PFS and OS vs RCT-eligible pts (Fig. 2 & 3, respectively). Conclusions: The CONNECT MM registry provided the opportunity to examine key differences between RCT-eligible and -ineligible pts. By applying common RCT eligibility criteria, a substantial proportion of MM pts (43.5%) in this real-world population may have been ineligible to participate in a RCT. RCT-ineligible pts had advanced disease, poor performance status, and poor laboratory parameters at baseline vs RCT-eligible pts, which could affect PFS and OS. These findings enhance our understanding of HRQoL among MM pts, which at present, is largely based on RCT participants. A similar magnitude of HRQoL improvement in both groups at 1 and 2 years suggests the need to evaluate RCT eligibility criteria in MM to help broaden enrollment and maximize external validity. Disclosures Wagner: Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; American Cancer Society: Other: Section editor, Cancer journal. Toomey:Celgene: Consultancy. Ailawadhi:Takeda: Consultancy; Amgen: Consultancy, Research Funding; Celgene: Consultancy; Cellectar: Research Funding; Janssen: Consultancy, Research Funding; Pharmacyclics: Research Funding. Jagannath:Merck & Co.: Consultancy; Bristol-Myers Squibb: Consultancy; AbbVie: Consultancy; Celgene Corporation: Consultancy; Janssen Pharmaceuticals: Consultancy; Karyopharm Therapeutics: Consultancy. Gasparetto:Janssen: Consultancy, Honoraria, Other: Travel, accommodations, or other expenses paid or reimbursed ; BMS: Consultancy, Honoraria, Other: Travel, accommodations, or other expenses paid or reimbursed ; Celgene: Consultancy, Honoraria, Other: Travel, accommodations, or other expenses paid or reimbursed . Durie:Amgen, Celgene, Johnson & Johnson, and Takeda: Consultancy. Narang:Celgene: Speakers Bureau. Rifkin:Amgen: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees. Terebelo:Celgene: Honoraria; Jannsen: Speakers Bureau; Newland Medical Asociates: Employment. Omel:Celgene, Takeda, Janssen: Other: Patient Advisory Committees. Hardin:Celgene: Membership on an entity's Board of Directors or advisory committees. Lee:Janssen: Consultancy, Research Funding; Takeda: Consultancy, Research Funding; Daiichi Sankyo: Research Funding; Celgene: Consultancy, Research Funding; Sanofi: Consultancy; GlaxoSmithKline plc: Research Funding; Amgen: Consultancy, Research Funding. Srinivasan:Celgene: Employment, Equity Ownership. Liu:TechData: Consultancy. Dhalla:Celgene: Employment. Catamero:Celgene: Employment, Equity Ownership. Abonour:BMS: Consultancy; Takeda: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Celgene: Consultancy, Research Funding.