Citations

Abstract Introduction: CLL pts who qualify for participation in clinical trials are often thought of as a highly selected population. Therefore whether safety and efficacy data from these trials can be generalized to all CLL pts treated in the community setting is unknown. Prospective, observational studies can provide significant insight into pt and disease characteristics as well as practice patterns in different clinical settings. Here we report on baseline demographics and clinical characteristics for 1495 pts who participated in the Connect CLL observational study and were followed prospectively. Methods: Connect®CLL is a non study-specific intervention treatment registry which collects data on the management and outcomes of CLL pts in clinical, academic and community practices in the US. Clinical information was prospectively collected for pts treated in community (n=1312), academic (n=155), or government (n=28) centers in the US between March 2010 – Jan 2014. Pts eligible for treatment or on treatment not more than 2 months were eligible for enrollment. Here we report on the baseline demographics and clinical characteristics of a large CLL cohort stratified by AG, LOT and type of treatment center. Results: In total,1,495 pts were accrued with a median age at enrollment of 69 (range 22–99). Pts were Caucasian (92.2%), African American (6.9%), and male (63.7%). Median duration from diagnosis until initial therapy was 3.7 years (range 0-32). Age of enrolled pts was as follows; 527 pts age <65 years (AG1; 35%); 511 age 65–75 (AG2; 34%); and 457 age ≥75 (AG3; 31%) at the time of enrollment. Eight hundred and ninety-four pts (60%) were treated with front-line therapy (LOT1); 259 pts (17%) treated with second-line therapy (LOT2); and 342 pts (23%) treated beyond second-line therapy (LOT≥3). Patient age: Pt age was associated with significant variation in several clinical variables: Charlson comorbidity index (CCI) score ≥3 (AG 1-3 35/46/52%; p<0.0001), ECOG PS = 0 (AG1-3 60, 48, 33%, p<0.0001) and mean creatinine clearance (CrCl) decreased (AG 1-3 101/74/53 mL/min; p<0.0001). Line of therapy: Differences in baseline characteristics were observed between pts enrolled at LOT1 (previously untreated) and LOT2/≥3 (relapsed). Median age of pts accrued at LOT1 was 68 vs. 70 years for pts enrolled at LOT2/≥3 (p=0.007). ECOG PS = 0 was more frequent at LOT1 (50%) vs. LOT2/≥3 (42%) (p=0.0057). CCI ≥3 at LOT1 was 41% vs 48% at LOT2/≥3 (p=0.0072). When adjusted for differences in age, logistic regression also demonstrated significant differences in performance status (PS=0; p=0.02) and CCI (p=0.03) stratified by LOT. Treating institution: Baseline characteristics also differed by type of treating institution. Median age was 63 vs 70 at academic vs non-academic (p<0.0001). Pts treated at academic sites also had lower CCI (CCI≤2; 72.9% vs 54.2%, academic vs non-academic) and higher median CrCl (86.4 vs 71.4) (p=0.0017). In this registry, only 16% of patients under age 75 had CCI <= 2, CrCl >= 70 mL/min, ECOG 0-1; 18% of patients on LOT1 and 12% of pts on LOT=2 would satisfy these criteria. They represent 36% of eligible pts from academic treating institutions and 13% from community/government centers. Only 20% of patients on LOT1 would satisfy enrollment criteria for CLL8 (FCR vs. FC; Hallek; Lancet 2010) CLL 10 (BR vs FCR; NCT00769522). which did not have enrollment restrictions for age. Conclusions:Baseline characteristics of pts with CLL vary by age, LOT, and type of treatment center. In this large prospective observational study, our data confirms that older pts typically have a worse ECOG PS, more medical comorbidities, more advanced disease when initiating therapy and a lower probability of being treated at an academic medical center. Based on inclusion criteria used for entry in either CLL10 or CLL8, fewer than 20% of pts satisfy age, renal clearance, and fitness profile to be considered suitable for aggressive chemoimmunotherapy in the front line setting. This observation supports the notion that current clinical trial data might not always be applicable to the general CLL population seen in the community and argues for designing clinical trials that are more generalizable to the broader pt population. Disclosures Sharman: Gilead: Research Funding; Calistoga: Honoraria; Phamacyclics: Honoraria, Research Funding; Celgene: Consultancy, Research Funding; TG therapeutics: Research Funding; Roche: Research Funding. Mato:Millennium: Speakers Bureau; Celgene: Speakers Bureau; Genentech: Speakers Bureau. Kay:Celgene: Research Funding. Kipps:Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Lamanna:Genentech-Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Infinity: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Weiss:Celgene: Consultancy; Phamacyclics: Consultancy; Gilead: Consultancy. Nabhan:Celgene: Honoraria, Research Funding. Flinn:Celgene: Research Funding. Grinblatt:Celgene: Honoraria, Speakers Bureau. Kozloff:Celgene: Consultancy. Farber:Alexion: Equity Ownership, Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Gilead: Speakers Bureau; Janssen/Pharmacyclics: Speakers Bureau; Seattle Genetics: Speakers Bureau; Genentech: Membership on an entity's Board of Directors or advisory committees. Sullivan:Celgene: Employment, Equity Ownership. Street:Celgene: Employment, Equity Ownership. Swern:Celgene: Employment, Equity Ownership. Flowers:TG Therapeutics: Research Funding; Phamacyclics: Research Funding; Acerta: Research Funding; Abbvie: Research Funding; Infinity: Research Funding; Lymphoma Research Foundation: Membership on an entity's Board of Directors or advisory committees; Janssen: Research Funding; Spectrum: Research Funding; Gilead: Research Funding; Genentech: Unpaid consultancy Other; Millennium: Research Funding, Unpaid consultancy, Unpaid consultancy Other; Seattle Genetics: Consultancy; Prescription Solutions: Consultancy; Celgene: Unpaid consultancy Other.

Abstract Introduction: Rituximab (R) improves survival in CLL when added to intensive chemotherapy (such as fludarabine and cyclophosphamide [FCR]), and has been shown to improve progression-free survival when given as maintenance therapy following first- or second-line chemoimmunotherapy. However, early phase 2 studies demonstrated only modest activity of R monotherapy (R mono), particularly at standard dose, and its use as a single agent for pts with CLL has remained controversial. Here we describe the characteristics and outcomes of pts with CLL receiving R mono in a real-world setting. Methods: The Connect CLL registry (NCT01081015), a multicenter, prospective observational cohort study, enrolled 1,494 pts with CLL between 2010-2014 from 179 community, 17 academic, and 3 government sites throughout the USA. Pts were ≥ 18 years and were enrolled ≤ 2 months after initiating any line of therapy (LOT). Pts were treated according to individual physician/patient decision making, and adherence to IWCLL standards cannot be determined. Bone marrow biopsy and CT scans were at the physician's discretion; responses were physician assessed and not centrally reviewed. For this analysis, pts were stratified by first (LOT1) or subsequent LOT (LOT≥2), and by treatment with R mono or other treatments (Tx). Kaplan-Meier methods were used to estimate event-free survival (EFS; event defined as death, progression/relapse, or transformation). A log-rank test was used to evaluate differences in EFS. A multivariate analysis of EFS in LOT1 pts was performed using the Cox regression model. Results: Of the pts enrolled, 105 (11.8%) pts in LOT1 and 81 (13.4%) pts in LOT≥2 received R mono. Of the pts receiving other Tx, 620/784 pts (79%) in LOT1 and 333/524 pts (64%) in LOT≥2 received R in combination. In LOT1, pts receiving R mono were older than pts receiving other Tx (74 vs 67 years; P < 0.0001); in LOT≥2 median ages were comparable (71 vs 70 years). Pts receiving R mono were more likely to have Rai stage 0-1 CLL than pts receiving other Tx, both in LOT1 (63.9% vs 51.8%; P = 0.05) and LOT≥2 (70.4% vs 49.9%; P = 0.005). The proportion of pts receiving R mono vs other Tx with a CCI score of ≥ 4 was similar in LOT1 (25.7% vs 22.8%) and LOT≥2 (32.1% vs 28.4%). Median absolute lymphocyte count at enrollment was lower in pts on R mono vs other Tx in LOT1 (27.7 vs 46.1 x 109/L) but similar in LOT≥2 (35.6 vs 30.2 x 109/L). A lower proportion of pts receiving R mono had FISH/cytogenetics performed at enrollment than pts receiving other Tx in LOT1 (48.6% vs 67.0%; P = 0.0002) and LOT≥2 (30.9% vs 49.6%; P = 0.002). The most common Tx regimens in LOT1 were FCR (25.9%), bendamustine plus R ([BR] 21.0%), R mono (11.8%), fludarabine plus R ([FR] 6.3%), and chlorambucil (4.6%); and in LOT≥2, BR (27.6%), R mono (13.4%), FCR (8.6%), bendamustine (7.4%), FR (4.1%), and ofatumumab (4.0%). The most common reason to initiate treatment in any LOT was bone marrow failure (LOT1, 44.8% vs 39.3%; LOT≥2, 37.0% vs 31.5%; R mono vs other Tx). Duration of therapy was shorter for pts receiving R mono vs other Tx in LOT1 (1.4 vs 4.1 months) and LOT≥2 (1.6 vs 3.3 months). In LOT1, responses were lower in pts on R mono vs other Tx: overall response rate (ORR) was 38.1% vs 64.2% (complete response [CR], 16.2% vs 41.2%; P < 0.0001). In LOT≥2, ORR was 25.9% vs 24.6% for pts on R mono vs other Tx (CR, 9.9% vs 11.5%; P = 0.68). Partial response did not differ significantly between pts receiving R mono vs other Tx in LOT1 and LOT≥2. Pts receiving R mono in LOT1 had inferior median EFS vs other Tx (34 vs 50 months; log-rank P = 0.04); however, after adjusting for factors such as ECOG status and del(17p) status, there was no difference in EFS (HR 0.932; P = 0.79). In pts in LOT≥2, median EFS was similar for R mono and other Tx (15 months for both groups; P = 0.93). Conclusions: Pts receiving R mono as frontline CLL therapy were older than pts receiving other Tx. They also had lower stage disease and lower lymphocyte counts, a shorter duration of treatment, and inferior response rates. Pts receiving R mono in relapsed CLL, more closely approximated pts receiving other Tx in terms of age. Despite the shorter duration of therapy for pts receiving R mono in LOT1 and LOT≥2, both groups had similar ORR and EFS in LOT≥2. These data reinforce the idea that R mono is inadequate as frontline therapy in progressive CLL, and demonstrate the opportunity for improvement in relapsed/refractory disease with novel agents relative to traditional chemoimmunotherapy approaches. Disclosures Sharman: Celgene: Research Funding; Gilead: Research Funding; Pharmacyclics: Research Funding; Acerta: Research Funding; TG Therapeutics: Research Funding; Seattle Genetics: Research Funding. Davids:Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Membership on an entity's Board of Directors or advisory committees, Research Funding; Infinity: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie, Janssen, Gilead: Consultancy; Celgene Corporation: Consultancy. Farber:Seattle Genetics: Research Funding. Grinblatt:Celgene Corporation: Consultancy, Speakers Bureau. Lamanna:Gilead: Research Funding; AbbVie: Research Funding; Genentech: Research Funding; Infinity: Research Funding; Pronai: Research Funding; Celgene Corporation: Research Funding. Mato:TG Therapeutics: Consultancy; Abbvie: Research Funding; Acerta Pharma: Research Funding; Gilead Sciences: Research Funding; ProNAi: Research Funding; TG Therapeutics: Research Funding; Theradex: Research Funding; Pharmacyclics: Consultancy; Gilead Sciences: Consultancy; Abbvie: Consultancy. Nabhan:Astellas: Research Funding; Seattle Genetics: Research Funding; Cardinal Health: Consultancy; Infinity: Consultancy; Abbvie: Consultancy; Genentech: Consultancy, Research Funding; Celgene Corporation: Consultancy, Research Funding. Sullivan:Celgene Corporation: Employment, Equity Ownership. Flick:Celgene Corporation: Employment, Equity Ownership. Kiselev:Celgene Corporation: Employment, Equity Ownership. Bhushan:Celgene Corporation: Employment, Equity Ownership. Swern:Celgene: Employment, Equity Ownership. Flowers:Seattle Genetics: Research Funding; Optum Rx, Seattle Genetics, Genentech/Roche: Consultancy; Celgene Corporation: Consultancy, Honoraria; Spectrum, Janssen, Infinity, AbbVie, Acerta, Pharmacyclics, TG Therapeutics: Research Funding; Millennium: Consultancy, Research Funding; Gilead: Consultancy, Research Funding.

Abstract Background Myelofibrosis (MF) is a myeloproliferative neoplasm (MPN) characterised by the frequent presence of driver mutations in genes causing activation of JAK2 signalling pathways (JAK2, CALR and MPL). Additional mutations affecting epigenetic regulators and splicing machinery are common. Anaemia with RBC-transfusion-dependence is common in patients with advanced myelofibrosis and represents a major unmet need. The RESUME study assessed the rates of RBC-transfusion independence (TI) after therapy with Pomalidomide (POM) vs placebo in persons with MPN-associated myelofibrosis and RBC-transfusion dependence. 16% of patients in both the POM and placebo arms became TI. Aims The genetic landscape of strictly confirmed transfusion dependent MF is not fully characterised. Our aim was to analyse the genetics of transfusion-dependent myelofibrosis patients in the RESUME trial and correlate with clinical characteristics, outcome and therapy response. Methods DNA samples were available from 207 of 252 patients and analysed by targeted re-sequencing using a Fluidigm Access Array gene panel followed by next generation sequencing. The panel included JAK2, CALR, MPL, TET2, ASXL1, EZH2, DNMT3A, IDH1/2, CBL, IKZF1, U2AF1, CHEK2, TP53, SF3B1, SRSF2, SH2B3, BARD1, DAP3, HRAS, IRF4, KRAS, KIT, Mir662, NFE2, POLG, SCRIB, SETBP1, TCF12 and VPS45. Results 97% (95-99%) of subjects had a mutation in ≥1 targeted gene. 2 mutations were detected in 41% (34-48%) and ≥3 in 27% (21-33%). 7 had no detectable mutation. Mutations in JAK2V617F, CALR and MPL were identified in 66% (59-72%), 14% (8-19%) and 7% (4-11%) of subjects (Figure 1), with no driver mutation in 27 patients (13%; 9-18%) (triple-negative). 68% (61-74%) had additional non-driver mutations. 42% (35-48%) (N=86) had spliceosome mutations (U2AF1 [21%]; SF3B1 [11%]; SRSF2 [8%]; ZRSR2; [6%]). More spliceosome mutations were detected in men than women (47% [39-55%] vs 27% [15-40%]; p=0.009). Spliceosome mutations were mutually exclusive in 83 subjects and were less common in subjects with prior polycythaemia vera (17% [5-37%]) compared with prior essential thrombocythaemia (39% [22-58%]) and primary MF (46% [38-54%]; p=0.024). Mutations in epigenetic regulators (ASXL1, 28%; TET2, 8%; DNMT3A 5%; EZH2 4%) were detected at similar rates to those previously reported. High molecular risk (HMR) mutations (ASXL1, EZH2, IDH1/2, SRSF2) were detected in 36% [29-43%] of subjects. Only 10 of 105 subjects with an epigenetic regulator gene mutation had ≥1 related mutation. Subjects with JAK2V617F were significantly more likely than subjects with a CALR mutation to have: (1) ≥1 additional mutation (72% [64-79%] vs. 35% [18-54%], p=0.0001); (2) a spliceosome mutation (44% [36-53%] vs. 17% [6-36%], p=0.07), in particular a U2AF1 mutation (24% [17-32%] vs. 0%; p=0.004) and (3) a HMR mutation (38% [30-47%] vs. 21% [8-40%]; p=0.07). Survival at 1.5 years was 62% (55-67%) and was not significantly associated with the presence or number of mutations in this uniformly high-risk cohort. Survival in subjects without an SF3B1 mutation was better than those SF3B1-mutated (80% [56-91%]) vs. 59% [52-65%]; p=0.07). Driver mutation status did not influence the probability of achieving red blood cell (RBC) TI, regardless of therapy. Additional non-driver mutations were more often detected in those failing to achieve RBC-TI than those achieving RBC-TI (70% [63-77%] vs 56% [40-71%], p=0.07). Furthermore, those with additional non-driver mutations were less likely to achieve ≥50% reduction in RBC transfusions (24% [17-32%] vs. 39% [27-51%]; p=0.03). A significant correlation persisted in subjects receiving POM but not in those receiving placebo. There was also a significant correlation between U2AF1 mutations and RBC-TI in POM treated subjects compared with controls; U2AF1-mutated subjects were less likely to achieve RBC-TI (3% [1, 17%]) than U2AF1-unmutated subjects (25% [17, 34%], p=0.008). No other mutations were significantly correlated with response. Conclusion We found a high incidence of spliceosome mutations in persons with MPN-associated MF and RBC-transfusion-dependence. Mutation of U2AF1 correlated with response in subjects receiving POM but not in those receiving placebo. Incorporation of mutation profiling into clinical trial design may help to inform subgroups of patients that may benefit from the intervention. Disclosures Devos: Novartis: Consultancy; Celgene: Consultancy; Takeda: Consultancy. Gisslinger:Shire: Consultancy, Honoraria; Novartis: Honoraria, Research Funding; AOP Orphan Pharmaceuticals AG: Consultancy, Honoraria, Research Funding; Janssen Cilag: Consultancy, Honoraria. Kiladjian:Celgene: Membership on an entity's Board of Directors or advisory committees; AOP Orphan: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding. Mesa:Celgene: Research Funding; UT Health San Antonio - Mays Cancer Center: Employment; Pfizer: Research Funding; Gilead: Research Funding; NS Pharma: Research Funding; CTI Biopharma: Research Funding; Incyte Corporation: Research Funding; Novartis: Consultancy; Promedior: Research Funding; Genentech: Research Funding. Ribrag:argenX: Research Funding; BMS: Consultancy, Honoraria, Other: travel; NanoString Technologies: Consultancy, Honoraria; pharmamar: Other: travel; Infinity: Consultancy, Honoraria; MSD: Honoraria; Amgen: Research Funding; Gilead: Consultancy, Honoraria; Servier: Consultancy, Honoraria; Roche: Honoraria, Other: travel; epizyme: Consultancy, Honoraria; Incyte Corporation: Consultancy. Schiller:Celator/Jazz Pharmaceuticals: Research Funding; Pharmacyclics: Research Funding. Vannucchi:Celgene: Membership on an entity's Board of Directors or advisory committees; ITALFARMACO: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. Reiser:Celgene: Employment. Zhong:Celgene: Employment. Mead:Novartis: Consultancy, Honoraria, Research Funding, Speakers Bureau; ARIAD: Consultancy; Bristol-Myers Squibb: Consultancy; Cell Therapeutics: Consultancy; Celgene: Research Funding; Elstar: Research Funding; Evotek: Research Funding.

Abstract Abstract 3150 Introduction. Affordability of health care in the United States (US), and the impact of patient insurance coverage on it have been shown to be associated with the delivery and timeliness of access to care. With increasing costs of novel therapies in hematology and oncology, this concern has been raised in hematologic cancers. This analysis evaluates whether the health-related quality of life (HRQOL) of patients with active, symptomatic multiple myeloma (MM) patients in the US varies by their insurance coverage as they initiate treatment. Methods. Baseline data were collected in Connect MM®, a prospective observational registry initiated in September 2009 involving centers in the US. Data on patient demographics and clinical characteristics were provided by clinicians. HRQOL was reported by patients in the clinic at enrollment, within two months of diagnosis. Patients completed 3 psychometrically validated instruments: the Brief Pain Inventory (BPI), EQ-5D, and Functional Assessment of Cancer Therapy-Multiple Myeloma (FACT-MM). Standard analyses were conducted of each instrument given clinical characteristics at that time. Patients were characterized as to the main source of their insurance coverage: Medicare, Medicaid or Commercial. The Medicare cohort included those with supplemental commercial coverage, and the Medicaid cohort included those with dual Medicare-Medicaid coverage. Reported mean BPI, EQ-5D and FACT-MM scores were analyzed by patient insurance coverage. Statistical significance of score differences among sub-cohorts was ascertained by ANOVA using SAS 9.1. Results. HRQOL data and insurance status were reported by 863 patients, enrolled from 189 centers. Patients were predominantly male (57%) and white (82%) with mean age at 67.0 (standard deviation [SD] 11.3) yrs. HRQOL scores by insurance status are presented: Overall FACT-MM results indicate that Medicaid insurance status is associated with greater decrement in baseline HRQOL compared to the other groups. Statistically significant differences are noted in the FACT-G general cancer HRQOL score, and its physical, social/family and emotional component scores. BPI data (on a scale of 0 [no pain] to 10 [worst pain] indicate that average pain may be worse among Medicaid patients compared to either Medicare or commercially insured patients. There are no statistically significant differences on EQ-5D scores (on a scale of 1 [no problem] to 2 [some problems] to 3 [incapacity] between cohorts based on insurance status. Conclusions. Initial results from the Connect MM® Registry indicate that HRQOL at baseline prior to initiation of treatment is worse among Medicaid patients compared to those with either Medicare or commercial coverage. This should be investigated to determine whether Medicaid patients are accessing care later in disease progression than others covered by Medicare or commercial insurance. Furthermore, future analyses should be conducted of patients by insurance coverage to determine whether status may be associated with access to medical care, subsequent clinical outcomes, and HRQOL over time. Results reported here should serve as a baseline reference. Disclosures: Pashos: Celgene: Membership on an entity's Board of Directors or advisory committees. Durie:Celgene: Membership on an entity's Board of Directors or advisory committees. Rifkin:Celgene: Membership on an entity's Board of Directors or advisory committees. Terebelo:Celgene: Membership on an entity's Board of Directors or advisory committees. Gasparetto:Celgene: Membership on an entity's Board of Directors or advisory committees. Mehta:Celgene: Membership on an entity's Board of Directors or advisory committees. Toomey:Celgene: Membership on an entity's Board of Directors or advisory committees. Abonour:Celgene: Membership on an entity's Board of Directors or advisory committees. Fonseca:Celgene: Membership on an entity's Board of Directors or advisory committees. Narang:Celgene: Membership on an entity's Board of Directors or advisory committees. Shah:Celgene: Membership on an entity's Board of Directors or advisory committees. Swern:Celgene Corporation: Employment, Equity Ownership. Sullivan:Celgene: Employment, Equity Ownership. Street:Celgene: Employment, Equity Ownership. Khan:Celgene: Employment, Equity Ownership.

Abstract Abstract 5070 Introduction. Studies in the United States (US) have identified variation in incidence and survival of multiple myeloma (MM) patients of different races, and noted that MM is the most common hematologic cancer among African Americans. This analysis was conducted to evaluate whether the health-related quality of life (HRQOL) of patients in the US as they initiate treatment with active, symptomatic MM varies by race. Methods. Data were collected in Connect MM®, a prospective observational registry begun in September 2009 involving centers in the US. Data on patient demographics and clinical characteristics were provided by clinicians. HRQOL was reported by patients in the clinic at enrollment, within two months of diagnosis. Patients completed 3 psychometrically validated instruments: the Brief Pain Inventory (BPI), EQ-5D, and Functional Assessment of Cancer Therapy-Multiple Myeloma (FACT-MM). Standard analyses were conducted of each instrument. Reported mean BPI, EQ-5D and FACT-MM scores were analyzed by patient race. Statistical significance of score differences was ascertained by ANOVA using SAS 9.1 in two ways: (1) among three different racial categories (White, Black and Other), and (2) between Blacks and non-Blacks. Hispanic/Latino patients and Asian patients comprise most of the Other cohort. Results. HRQOL data at baseline were reported by 916 pts, enrolled from 189 centers. Of these patients, 82% were White, 12% were Black, and 5% were of another race (Other). Among evaluable patients, the different race groups did not differ statistically by ECOG status or multiple myeloma stage (measured by either the International Staging System or the Durie and Salmon system). The three racial groups differed by age, with the mean (SD) age of Blacks (63 (11)) and Others (63 (13)) being less than that of Whites (68 (11), p<0.0001). Gender was trending, but not statistically significantly different, between races, with males comprising 58% of Whites, 52% of Blacks, and 46% of Others (p=0.1803). HRQOL scores by race are presented here: Overall FACT-MM results indicate that baseline HRQOL does not vary between races, as the main summary scores do not differ statistically, although the FACT-MM Total scores of Blacks and Whites are trending higher than those of Others. Only the emotional and social/family domain scores of the FACT-MM vary statistically, as Blacks show a worse mean score in the social/family domain and a better score in the emotional domain. BPI data (on a scale of 0 [no pain] to 10 [worst pain] do not show a statistically significant difference between races, and the same is true of the five EQ-5D domains (measured on a scale of 1 [no problem] to 2 [some problems] to 3 [incapacity]. Conclusions. Initial results from the Connect MM® Registry indicate that HRQOL at baseline prior to initiation of treatment does not vary by patient race. As such, these results may serve as a baseline reference for future analyses. In particular, as patients proceed through therapy, analyses should be conducted of patients by race, among other factors, to determine whether it may be associated with subsequent clinical outcomes and HRQOL over time. Disclosures: Pashos: Celgene: Membership on an entity's Board of Directors or advisory committees. Durie:Celgene: Membership on an entity's Board of Directors or advisory committees. Rifkin:Celgene: Membership on an entity's Board of Directors or advisory committees. Terebelo:Celgene: Membership on an entity's Board of Directors or advisory committees. Gasparetto:Celgene: Membership on an entity's Board of Directors or advisory committees. Mehta:Celgene: Membership on an entity's Board of Directors or advisory committees. Toomey:Celgene: Membership on an entity's Board of Directors or advisory committees. Abonour:Celgene: Membership on an entity's Board of Directors or advisory committees. Fonseca:Celgene: Membership on an entity's Board of Directors or advisory committees. Narang:Celgene: Membership on an entity's Board of Directors or advisory committees. Shah:Celgene: Membership on an entity's Board of Directors or advisory committees. Swern:Celgene Corporation: Employment, Equity Ownership. Sullivan:Celgene: Employment, Equity Ownership. Street:Celgene: Employment, Equity Ownership. Khan:Celgene: Employment, Equity Ownership.

Abstract Abstract 2864 Introduction: Clinical trials have illuminated a number of unique treatment strategies for patients with CLL. The impact of these strategies on routine practice remains unknown as trial participants may not reflect the same population encountered outside of a clinical trial setting. Many questions remain regarding the sequencing of therapies based on age and performance status. By characterizing current patterns of care; patients, treating physicians, and regulatory agencies will be able to understand the current landscape of CLL treatment. The Connect® CLL registry was designed to report the natural history and real world management of patients receiving therapy for CLL. In this first report, we characterize the therapeutic approaches used for the treatment of patients with CLL of different age groups (i.e. < 65 years, 65–75 years, and ≥ 75 years) and with an ECOG PS status score of 0 compared to 1 or greater. Methods: Connect® CLL is a prospective, longitudinal, observational, multi-center registry conducted in community and academic research centers in the United States. At present, 237 sites are actively participating with a projected study enrollment of 1500 patients. Eligible patients are to be enrolled within 2 months of being initiated on any line of therapy; whether initial therapy or salvage therapy. Each patient will be followed for up to 60 months. Clinical data, physician choices, patient-reported health-related quality of life, response and survival are to be collected approximately every 3 months during participation. Results: A total of 607 patients have been enrolled (4% from academic sites) with a median age of 70 years. 198 were < 65 years old (age group 1), 187 were between 65–75 years old (age group 2), and 222 were ≥ 75 years old (age group 3). ECOG status varied across the three age groups, with an ECOG status score of ≥ 1 for 39%, 52%, and 70% of patients respectively. Treatment patterns varied across the age groups and by ECOG status in the 496 patients reporting therapies. The most commonly recorded first-line regimens independent of age included fludarabine (F) cyclophosphamide (C) and rituximab (R) (33%), bendamustine (B) +/− R (19%), F +/− R (15%), or investigational therapy (15%). For second-line regimens and beyond, the most frequently recorded regimen was B +/− R (30%), FCR (23%), other F-based regimens (13%), or investigational therapy (8%). The use of FCR for first-line treatment decreased significantly with increasing age group, (45%, 32%, 20%, for age group 1, 2, 3 respectively, p=0.04, spearman correlation) while use of F +/− R remained level across the age groups (14%, 15%, 15%, respectively). Compared to age group 1, first-line therapy with B +/− R in age groups 2 and 3 (15%, 22%, 21%, respectively) was higher but did not achieve statistical significance. B +/− R represented the most common treatment for all age groups (37%, 26%, 29%, respectively) as second line therapy but did not vary by age (P=0.35). The use of chlorambucil was infrequent in all age groups, but was more common in age group 3 patients compared to the others (P=0.01), in both first-line (2%, 4%, 12%, respectively) and subsequent lines of therapy (0%, 1%, 8%, respectively). Treatment assignments did not vary by ECOG PS score for patients in age group 1. First-line therapy for patients with an ECOG PS score of 0 in age groups 2 and 3 consisted of FCR (32% and 15%, respectively), F +/− R (19% and 15%, respectively), B +/− R (16% and 15%, respectively), and alkylating agents (3% and 23%, respectively). Patients in age groups 2 and 3 with ECOG PS score ≥ 1 received B +/− R regimen (33% and 22%, respectively), FCR (23% and 21%, respectively), F +/− R (14% and 10%, respectively) and alkylating agents (7% and 9%, respectively) as first-line therapy. Further description and clarification on the various treatment regimens based on the three age groups and by ECOG PS score will be presented at the meeting. Conclusion: The Connect® CLL Registry is the largest prospective, multicenter registry in the United States evaluating management for patients with CLL. With the currently available data, we characterize the extent to which age and performance status are associated with treatment selection in both first-line and subsequent lines of therapy in routine practice. As enrollment increases and additional follow-up is completed, the data will provide more extensive and real world overview of the current treatment strategies used in CLL patients. Disclosures: Sharman: Celgene - consulting: Consultancy; Pharmacyclics: Honoraria; Calistoga: Honoraria; Portola pharmaceuticals: Consultancy. Flowers:Consultancy: Celgene, Prescription Solutions, Spectrum (future), Seattle Genetics, Millennium (future), Genentech (unpaid): Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Weiss:Celgene: Consultancy. Grinblatt:Celgene: Honoraria, Speakers Bureau. Kay:Genenetech, Celgene, Hospira,: Research Funding. Kipps:Igenica: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Research Funding; Abbot Industries: Research Funding; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Genentech: Research Funding; GSK: Research Funding; Gilead Sciences: Research Funding; Amgen: Research Funding. Lamanna:Celgene Corporation: Advisory board. Pashos:United BioSource Corporation: Research Funding. Flinn:Celgene: Research Funding. Kozloff:Celgene: Consultancy. Lerner:Celgene Connect: Membership on an entity's Board of Directors or advisory committees. Swern:Celgene: Employment. Sullivan:Celgene: Employment. Street:Celgene: Employment. Keating:Celgene Connect: Membership on an entity's Board of Directors or advisory committees.

Abstract Abstract 2085 Introduction. Although advanced patient age is commonly used as a factor in selecting therapy for patients with chronic lymphocytic leukemia (CLL), based on presumed associations with functional status, limited data exist regarding the relationships between age and physical, emotional, social, and functional well being. We examined the relationships between age and these domains of health-related quality of life (HRQOL) for CLL patients treated in US community practices. Methods. Baseline data were collected as part of Connect CLL®, a prospective observational registry initiated in March 2010 involving centers in the US. Data on patient demographics and clinical characteristics were provided by clinicians. HRQOL was self-reported by patients in the clinic at enrollment. Patients completed 3 psychometrically validated instruments: the Brief Fatigue Inventory (BFI), EQ-5D, and Functional Assessment of Cancer Therapy-Leukemia (FACT-Leu). Standard analyses were conducted of each instrument given clinical characteristics at that time. Reported mean BFI, EQ-5D and FACT-Leu scores were analyzed by age group (<65, 65–74, >74). Statistical significance of score differences among sub-cohorts was ascertained by ANOVA using SAS 9.1. Results. Baseline HRQOL data were reported by 604 patients, enrolled from 161 centers. Patients were predominantly male (62%) and white (90%) with mean age at 69.9 (standard deviation [SD] 11.2) yrs. HRQOL scores by age group are presented: There were no significant differences between the age groups in fatigue as measured by the BFI, or differences in overall HRQOL as measured by the EQ-5D Visual Analogue Scale (VAS) or the FACT-G. Anxiety/depression and self care are EQ-5D domains that also did not vary by age. Although mobility was most impaired in the oldest age group compared to the two younger groups, usual activities and pain/discomfort were worse in both the younger and older cohorts compared to those 65–74 years of age. FACT-Leu results indicated that the social/family domain scores did not vary by age, but that physical, emotional, and functional domains did vary statistically with the oldest typically doing better than the 65–74 year olds, but not necessarily better than those <65. Conclusions. Initial results from the Connect CLL® Registry indicate that HRQOL does not worsen monotonically with older age. In this cohort, both the youngest and oldest age groups had worse HRQOL in certain domains, presenting an inverted v-shaped relationship. Future analyses should be conducted on: (1) how HRQOL may be affected over time with changes in disease; and, (2) how HRQOL may be influenced by alternative therapies. Results reported here should serve as a useful baseline reference. Disclosures: Pashos: Celgene: Membership on an entity's Board of Directors or advisory committees. Flowers:Genentech/Roche (unpaid): Consultancy; Celgene: Consultancy; Millennium/Takeda: Research Funding; Wyeth: Research Funding; Novartis: Research Funding. Weiss:Celgene: Membership on an entity's Board of Directors or advisory committees. Lamanna:Celgene: Membership on an entity's Board of Directors or advisory committees. Farber:Celgene: Membership on an entity's Board of Directors or advisory committees. Kipps:Igenica: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Research Funding; Abbot Industries: Research Funding; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Genentech: Research Funding; GSK: Research Funding; Gilead Sciences: Consultancy, Research Funding; Amgen: Research Funding. Lerner:Celgene: Membership on an entity's Board of Directors or advisory committees. Kay:Celgene: Membership on an entity's Board of Directors or advisory committees. Sharman:Celgene: Membership on an entity's Board of Directors or advisory committees. Grinblatt:Celgene: Membership on an entity's Board of Directors or advisory committees. Flinn:Celgene: Membership on an entity's Board of Directors or advisory committees. Kozloff:Celgene: Membership on an entity's Board of Directors or advisory committees. Swern:Celgene Corporation: Employment, Equity Ownership. Kahn:Celgene Corporation: Employment, Equity Ownership. Street:Celgene: Employment, Equity Ownership. Sullivan:Celgene: Employment, Equity Ownership. Keating:Celgene: Membership on an entity's Board of Directors or advisory committees.

Abstract Btz (VELCADE®) is approved for patients (pts) with relapsed MM. This study characterized PK/PD of btz at 2 doses (1.0, 1.3mg/m2) after single- and multiple-dose administration, and evaluated their relationship. 24 pts with relapsed MM and creatinine clearance ≥50mL/min were randomized to btz 1.0 (n=12) or 1.3mg/m2 (n=12) on D 1, 4, 8 and 11 of a 21-D cycle (C). Pts could continue therapy for ≥8 Cs if beneficial. PK/PD samples were drawn at multiple time points on D1 and D11 of C1 and C3 over 48h. Plasma concentrations of btz were similar following administration of 1.0 and 1.3mg/m2; differences in mean plasma Cmax and AUC values were within the degree of intersubject variability (Table). There was a decrease in clearance and an increase in terminal half-life of btz on D11 vs D1 of C1.The volume of distribution of btz suggests extensive peripheral tissue distribution. After single or repeat administration, mean maximum percent inhibition of 20S proteasome activity (vs baseline) in whole blood was 70–84% and 73–83% for the 1.0 and 1.3mg/m2 doses, respectively. The percentage of pts who had >90% inhibition was greater in subsequent doses vs first dose. Reversibility of 20S proteasome inhibition in whole blood was demonstrated. PK/PD modeling revealed an exposure-response relationship, with Emax and EC50 values consistent across study days. These results confirm btz is a potent inhibitor of the 20S proteasome. The efficacy of single-agent btz has been explored and validated in the pivotal phase 2 SUMMIT and phase 3 APEX trials at 1.3mg/m2, and explored in the phase 2 CREST trial at 1.0mg/m2. Complete safety and efficacy data by dose will be presented. Mean±SD PK parameters of btz in plasma 1.0mg/m2 1.3mg/m2 1.0mg/m2 1.3mg/m2 D1/C1 (n=11) D11/C1 (n=12) an=5;bn=11;cn=10;dn=7;en=11;fn=9 Tmax (h) 0.13±0.13 0.11±0.06 0.07±0.03 0.20±0.29 Cmax (ng/mL) 56.7±36.3 112±122 106.2±46.7 88.6±47.6 AUC48h(ng·h/mL) 26.5±12.4 34.6±19.8 82.8±35.9b 82.4±28.6b AUClast (ng·h/mL) 22.6±12.6 31.5±18.6 137±106 122.2±67.4 CL (L/h) 102.1±48.1 111.6±73.6 23.2±17.8 28.0±19.8 Vβ (L) 1976±2498a 2015±2974d 1659±752c 2415±1711 t1/2λz(h) 30.7±44.8a 11.5±12.7d 78.9±50.9c 75.6±49.9 D1/C3 (n=12) D11/C3 (n=12) Tmax (h) 0.12±0.12 0.10±0.05 0.16±0.17 0.10±0.05 Cmax (ng/mL) 66.5±42.6 120.3±70.7 83.9±69.3 114.9±98.3 AUC48h(ng·h/mL) 66.4±24.1 79.4±24.5b 101.6±58.2b 85.2±18.7f AUClast (ng·h/mL) 74.8±35.8 86.0±27.6 227±181 160.7±67.1 CL (L/h) 32.2±19.0 32.1±15.4 15.1±13.9 18.2±9.2 Vβ (L) 1852±951d 2059±1231 3294±2993e 2505±1641 t1/2λz (h) 39.9±14.4d 49.1±34.6 193±169e 108.6±64.8

Abstract Abstract 4591 Introduction: Clinicians and investigators appreciate the value of measuring HRQOL for monitoring CLL and the impact of treatments, and commonly use ECOG performance status (PS) and clinician-reported patient fatigue as surrogates for HRQOL in clinical practice. However, limited data exist on the relationships between PS, fatigue, and HRQOL in CLL patients (pts) undergoing treatment in clinical practices. We examined the associations between these measures and 3 psychometrically validated, patient-reported, HRQOL instruments: the Brief Fatigue Inventory (BFI), EQ-5D, and Functional Assessment of Cancer Therapy-Leukemia (FACT-Leu). Methods: Data were collected as part of Connect CLL®, a prospective observational registry initiated in March 2010 involving US practices. Data on pt demographics and clinical characteristics were provided by clinicians. HRQOL was self-reported by pts at enrollment using the BFI, EQ-5D, and FACT-Leu. Mean BFI, EQ-5D and FACT-Leu scores were analyzed by ECOG PS and clinician-reported fatigue. Differences in HRQOL scores between sub-cohorts were assessed by ANOVA. Results: HRQOL data were reported by 604 pts enrolled from 10 academic, 148 community, and 3 government centers. Pts were predominantly male (62%) and white (90%); mean age was 70 (standard deviation 11) years. BFI data (scale: 0 [no fatigue] - 10 [worst fatigue]) indicated that on average pts report that global fatigue, fatigue severity and fatigue-related interference worsen by ECOG severity (Table 1) and are statistically associated with clinician-reported fatigue (Table 2). Mean EQ-5D overall HRQOL as measured by a Visual Analogue Scale (VAS) from 0 (worst) to 100 (best) worsens by ECOG severity and is significantly worse in pts with fatigue. Mean EQ-5D domain scores (scale: 1 [no problem], 2 [some problems], 3 [incapacity]) indicated that pain/discomfort, mobility and usual activities increase in severity as ECOG worsens and in pts with fatigue. FACT-Leu domains except social/family were statistically worse with worse ECOG PS and in pts with fatigue. Conclusions: Initial results from Connect CLL® indicate that HRQOL worsens with worsening ECOG PS, especially in physical / functioning domains, pain/discomfort, and mobility, and worsens across multiple domains among pts whose physicians reported fatigue. Future analyses should be conducted on how HRQOL, PS and fatigue may change over time with changes in CLL, and how they are influenced by therapies. These results may serve as baseline reference. Disclosures: Pashos: Celgene: Membership on an entity's Board of Directors or advisory committees. Flowers:Genentech/Roche (unpaid): Consultancy; Celgene: Consultancy; Millennium/Takeda: Research Funding; Wyeth: Research Funding; Novartis: Research Funding. Weiss:Celgene: Membership on an entity's Board of Directors or advisory committees. Lamanna:Celgene: Membership on an entity's Board of Directors or advisory committees. Farber:Celgene: Membership on an entity's Board of Directors or advisory committees. Kipps:Igenica: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Research Funding; Abbot Industries: Research Funding; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Genentech: Research Funding; GSK: Research Funding; Gilead Sciences: Consultancy, Research Funding; Amgen: Research Funding. Lerner:Celgene: Membership on an entity's Board of Directors or advisory committees. Kay:Celgene: Membership on an entity's Board of Directors or advisory committees. Sharman:Celgene: Membership on an entity's Board of Directors or advisory committees. Grinblatt:Celgene: Membership on an entity's Board of Directors or advisory committees. Flinn:Celgene: Membership on an entity's Board of Directors or advisory committees. Kozloff:Celgene: Membership on an entity's Board of Directors or advisory committees. Swern:Celgene Corporation: Employment, Equity Ownership. Kahn:Celgene Corporation: Employment, Equity Ownership. Street:Celgene: Employment, Equity Ownership. Sullivan:Celgene: Employment, Equity Ownership. Keating:Celgene: Membership on an entity's Board of Directors or advisory committees.

Abstract Introduction The natural history of patients (pts) with CLL varies considerably. Some pts may have asymptomatic, indolent disease, while others may require therapy soon after diagnosis. Clinical staging systems cannot identify early-stage pts at risk for inferior survival. Genetic and molecular markers can provide additional information regarding prognosis and response prediction, however their utilization is limited in routine practice (Mato, ASCO 2015) and results can be conflicting. There is a need to identify characteristics of early-stage CLL pts at highest risk of early progression and/or death. Such characteristics, if readily available and reproducible, may identify such pts as candidates for early-intervention studies; particularly in the era of kinase inhibitor therapy. The Connect CLL registry is the largest prospective study describing real-world management of a diverse cohort of 1494 CLL pts. In this analysis, we evaluate key factors that are associated with early CLL-associated mortality in clinical practice. Methods Connect CLL is a multicenter, observational cohort study aimed at understanding patterns of CLL management without a study-specific intervention. Eligible pts were adults with a clinical diagnosis of CLL, enrolled between 2010-2014 at 179 community (n=1311), 17 academic (n=155), and 3 government (n=28) sites within ≤ 2 months of initiating either a first line of therapy (LOT1), or a second-line or subsequent LOT (LOT≥2). The goal of this analysis was to assess potential predictors of death occurring within 18 months following initiation of frontline therapy (LOT1). Because CLL usually presents as an indolent disease associated with a prolonged clinical course, we chose to define early mortality as any death occurring ≤ 18 months following enrollment. Univariate logistic regression was performed against potential predictor variables including several clinical characteristics and comorbid conditions, race, treatment (Tx) site, insurance type, geographic region, presence of prior malignancies, genetic prognostic factors (FISH and cytogenetics) at enrollment, cell surface markers (Zap70, CD38), reasons for Tx initiation and Tx choice for LOT1 (BR or FCR). IgVH mutation status and genetic mutations (P53, Notch1, etc.) were not included as these tests were not readily performed in community practice. Predictors that were found to be significant at the 0.15 significance level were included in the multivariable logistic regression using a stepwise variable selection process, to identify factors associated with early mortality. Results 1344 pts completed 18 months of follow-up (n=1111) or died (n=233) within the first 18 months of study enrollment. Of these, 801 pts were enrolled in LOT1 (median time from diagnosis to LOT1 was 1.3 yrs; IQR, 0.1-3.7 yrs). 82 of 801 pts (10.2%) died within 18 months of LOT1 initiation; 41% of pts with available clinical information who died, were defined as early-stage (Rai stage 0-1). Of 26 potential predictors evaluated, 12 were associated with early mortality among LOT1 pts at the 0.15 significance level in univariate analyses including site, race, age>75, CD-38, del(17p), ECOG PS, CCI group, RAI stage, creatinine clearance, insurance status, reasons for Tx initiation, anemia and lymphocytosis. In multivariable logistic regression analyses we identified 4 independent predictors of mortality within 18 months of LOT1 initiation (Table): pt age, CD38 expression, baseline decreased hemoglobin (as a reason for Tx initiation), and race. Conclusions In CLL pts with Rai stage 0-1, we have identified 4 factors which are associated with increased risk of death within 18 months of initiation of frontline therapy. Interestingly, neither choice of FCR vs BR chemoimmunotherapy nor the presence of del(17p)/del(11q) were predictors of early mortality. Using these results, we are currently designing a model to predict early mortality in early-stage pts. Once validated, these results may provide a framework for trials targeting the "early stage, symptomatic" CLL pts for novel interventions. Table. Multivariable Analysis: Predictors of Early Mortality Odds ratio estimate 95% CI P -value LOT1, n=781 Age ≥75 years, yes vs no 2.2 1.4-3.5 0.0012 CD38 expression, positive vs negative/not specified 1.8 1.1-2.9 0.0182 Anemia as reason for treatment, yes vs no 2.1 1.3-3.4 0.0040 Race, other vs Caucasian 1.9 1.0-3.8 0.0501 Disclosures Farber: Gilead: Speakers Bureau; Genentech: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene Corporation: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Honoraria, Speakers Bureau; Pharmacyclics: Honoraria, Speakers Bureau. Mato:TG Therapeutics: Research Funding; Genentech: Consultancy; AbbVie: Consultancy, Research Funding; Pronai Pharmaceuticals: Research Funding; Gilead: Consultancy, Research Funding; Pharmacyclics: Consultancy, Research Funding; Celgene Corporation: Consultancy, Research Funding; Janssen: Consultancy. Nabhan:Celgene Corporation: Honoraria, Research Funding. Kipps:Pharmacyclics Abbvie Celgene Genentech Astra Zeneca Gilead Sciences: Other: Advisor. Flowers:Spectrum: Research Funding; Pharmacyclics: Research Funding; Infinity Pharmaceuticals: Research Funding; OptumRx: Consultancy; Gilead Sciences: Research Funding; Spectrum: Research Funding; Genentech: Research Funding; Onyx Pharmaceuticals: Research Funding; Acerta: Research Funding; OptumRx: Consultancy; Gilead Sciences: Research Funding; AbbVie: Research Funding; Pharmacyclics: Research Funding; Seattle Genetics: Consultancy; Onyx Pharmaceuticals: Research Funding; Genentech: Research Funding; Millennium/Takeda: Research Funding; Celegene: Other: Unpaid consultant, Research Funding; Janssen: Research Funding; Infinity Pharmaceuticals: Research Funding; Acerta: Research Funding; AbbVie: Research Funding; Seattle Genetics: Consultancy; Celegene: Other: Unpaid consultant, Research Funding; Janssen: Research Funding; Millennium/Takeda: Research Funding. Kay:Pharmacyclics: Research Funding; Hospira: Research Funding; Genentech: Research Funding; Gilead: Membership on an entity's Board of Directors or advisory committees, Research Funding; Tolero Pharma: Research Funding; Celgene Corporation: Membership on an entity's Board of Directors or advisory committees, Research Funding. Lamanna:Genentech-Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene Corporation: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Infinity: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Grinblatt:Celgene Corporation: Consultancy, Honoraria, Speakers Bureau. Kozloff:Celgene: Consultancy, Speakers Bureau; Genentech: Consultancy, Speakers Bureau; Roche: Consultancy, Speakers Bureau; AbbVie: Consultancy. Sullivan:Celgene Corporation: Employment, Equity Ownership. Flick:Celgene Corporation: Employment, Equity Ownership. Kiselev:Celgene Corporation: Consultancy. Bhushan:Celgene Corporation: Employment, Equity Ownership. Swern:Celgene Corporation: Employment, Equity Ownership. Sharman:TG Therapeutics, Inc.: Research Funding; Celgene Corporation: Consultancy, Research Funding; Gilead: Consultancy, Honoraria, Research Funding, Speakers Bureau; Janssen: Research Funding; Calistoga: Honoraria; Roche: Research Funding; Pharmacyclics: Consultancy, Honoraria, Research Funding.