Ailawadhi, Sikander

Abstract Background: As of 2013, approximately 558,340 people are living with non-Hodgkin lymphoma (NHL). Outcome disparities have been reported in lymphoid malignancies but a comprehensive analysis, especially with expanding ethnic minorities is necessary. We undertook a large Surveillance Epidemiology and End Results (SEER) based analysis to describe outcome disparities in different subgroups of T-cell and B-cell NHL patients (pts), with a focus on various ethnicities. Methods: The SEER 18 Registry data (1973-2011) was utilized for pts with a confirmed diagnosis of T-cell or B-cell NHL. Cases that received a diagnosis at death certificate or autopsy, no follow-up records, no documented age at diagnosis, sex, or race/ethnicity were excluded. Cox proportional hazards models, adjusted for gender, age, race, year (yr) of diagnosis, and stratified by SEER registries were used to evaluate association between pt characteristics and survival. All statistical tests were two-sided and utilized the SAS software (v9.4) with a two-sided significance level of 0.05. Results: The final analysis included 7,662 pts with T-cell NHL and 84,910 pts with B-cell NHL. The pt demographics are shown in Table 1. Pts were stratified based on yr of diagnosis to study the impact of therapeutic developments on pt outcomes (Table 1). Pts were also divided by T-cell NHL subtype: mature T-cell (9702/3) 51%, angioimmunoblastic T-cell (9705/3) 16%, anaplastic large cell (ALCL) (9714/3) 32%, and hepatosplenic gamma-delta cell (HSϒd) (9716/3) 0.8%. Median age at diagnosis was youngest for AA pts (54 yrs) and oldest for Asian pts (65 yrs) (Figure 1 A). Survival analysis revealed that females had a better median OS than males (3.3 yrs vs. 2.3 yrs, HR 0.876; 95% CI 0.824, 0.932; p<0.001). Increasing age was an independent predictor of worse overall survival, with pts age 18-44 having a median OS 9.4 yrs (HR 0.345; 95% CI 0.312, 0.382) and pts age ≥75 having a median OS of 1.0 yr (p<0.001). There was no statistically significant difference seen in median OS for pts with respect to yr of diagnosis (p=0.081). Among the different T-cell NHL subtypes, pts with ALCL had the best (3.8 yrs, HR 0.766; 95% CI 0.714, 0.822) and pts with HSϒd had the worst median OS (0.7 yrs HR 1.959; 95% CI 1.424, 2.695) (p<0.001). AA pts (1.7 yrs, HR 1.292; 95% CI 1.176, 1.419) and Hispanic pts (1.9 yrs, HR 1.215; 95% CI 1.087, 1.357) had significantly worse OS compared to White pts who had the longest median OS (3.1 yrs) (p<0.001) (Figure 1 B). To compare and contrast with aggressive B-cell NHL, similar analyses were performed using diffuse large B-cell (DLBCL) NHL (9680/3). DLBCL showed similar trends regarding sex, age and race. Ethnic minorities had significantly worse median OS including Hispanic (2.8 yrs, HR 1.154; 95% CI 1.111, 1.198) and AA pts (3.3 yrs, HR 1.261; 95% CI 1.212, 1.312) compared to White pts with a median OS of 5.8 yrs (p<0.001). B-cell NHL pts had a significant improvement in median OS over time with pts diagnosed in 2007-2011 having more than 3 times the median OS of pts diagnosed in 1973-1991 (7 yrs vs. 2.4 yrs; p<0.001). Conclusions: Studies of outcome disparities are important for evaluating disease characteristics and their relationship to specific pt populations over time. We have performed the largest population-based analysis for T-cell and B-cell NHL including various ethnicities in the novel therapeutic agent era. We observed that males, older pts and ethnic minorities had worse median OS for both T-cell and B-cell NHL. Despite novel therapeutics targeting T cells there has been no statistically significant improvement in OS over time. In contrast, significant improvement in B-cell NHL OS has been noted, presumably due to the advent of monoclonal antibodies. These results underline the urgency for the development of novel therapeutics as well as aggressive studies of disease biology and healthcare utilization for better triage of healthcare resources, especially for ethnic minorities. Table 1. Table 1. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.

Abstract Background: Race/ethnicity is associated with disparate incidence and outcomes in MM, as seen in population-based studies. Their effect on outcomes has not been reported in prospective studies. Obesity is the most consistently associated modifiable risk factor for MM and is influenced by race/ethnicity but its association with outcomes has not been explored. We analyzed patient-level data from published ECOG-ACRIN/SWOG clinical trials in newly diagnosed MM (NDMM) patients (pts) to explore the interplay of race/ethnicity, obesity and outcomes. Methods: Pooled data from 9 ECOG-ACRIN/SWOG clinical trials (E1A06, E4A03, E1A00, E2A02, E5A93, E9486, S0204, S0232, S9321) in NDMM (1988-2011) were analyzed. Pts were divided by race: White (W), African-American (AA), other (O); race/ethnicity: Hispanic (H), non-Hispanic White (NHW), non-Hispanic AA (NHAA), non-Hispanic other (NHO); and body mass index (BMI): <25, 25-29.9, ≥30. Associations between these and baseline pt/disease characteristics were assessed using chi-square (categorical) and Wilcoxon rank sum (continuous) tests. Survival distributions were estimated with Kaplan-Meier method and compared between groups using log-rank test. Cox proportional hazards regression was used to estimate hazard ratios in univariate and multivariate models. Time to event data was censored at 6 yrs. Results: Pt distribution by race, race/ethnicity and BMI is shown in Table 1. Race: AA were younger than W (median 59 vs 62 yr), had significantly more females (48% vs 40%; p=0.001) and had a smaller proportion of pts ≥70 yr (18% vs 26%; p<0.001). Median BMI was highest in AA (27.2) and lowest in O (24) (p=0.001) but not significantly different categorically between W and AA only (p=0.908). AA had significantly more pts with hemoglobin (Hb) ≤10 g/dL than W (55% vs 36%; p<0.001). Median creatinine (Cr) was 1.3 mg/dL in O and 1.1 mg/dL in W (p=0.03). There were no differences by race for ECOG performance status (PS), MM stage, lytic lesions, paraprotein subtype, serum calcium (Ca) or β2-microglobulin (β2M). Enrollment to clinical trials over years was significantly different by race (p=0.009). The only study design showing different enrollment by race was with/out stem cell transplant (SCT) (p=0.03; 37% of AA vs 31% of W got SCT trials). Race/Ethnicity: NHO were the youngest (median 58 yr) and NHW the oldest (median 62 yr) subgroup (p<0.001). NHAA had the highest proportion of pts with BMI ≥30 (16%) and NHO the lowest (7%) (p=0.018). H had the smallest proportion of females (33%) while NHO had the largest (54%) (p=0.002). H had the highest number of pts ≥70 yr (28%) and NHAA the smallest (18%) (p=0.003). Anemia rates were significantly different (p<0.001) with Hb ≤10 g/dL in 36% NHW vs 56% in NHAA. No significant differences were seen for PS, MM stage, lytic lesions, paraprotein subtype or other lab parameters. H and NHAA were enrolled in larger numbers in 1992-2011 rather than prior or later years (p<0.001). Among the subgroups, H had largest proportion of patients (75%) on trials without novel agent vs 61.5% for NHW (p=0.047). NHAA had the highest proportion of patients on trials with SCT (38%) while NHO had the smallest (27.3%) (p=0.034). BMI: By gender, 30% of males and 39% of females having BMI <25 (p<0.001). BMI <25 had the highest proportion of pts with ISS stage III (31%) vs 21.5% for ≥30 (p=0.001). A similar trend was seen for DS stage (p=0.005). 64% of ≥30 pts had Hb >10 while 51% of <25 did (p=0.003). There was significant difference in continuous Cr distribution by BMI (p=0.004). Efficacy analysis did not show any significant difference in response rate by race (p=0.132), race/ethnicity (p=0.286) or BMI (p=0.896). In univariate/multivariate analysis, significantly worse overall survival (OS) was seen in males, age ≥70 yr, ECOG PS >0, ISS and DS stage III, presence of lytic lesions, Cr ≥2 mg/dL, Ca >12 mg/dL, Hb ≤10 g/dL. No significant difference was seen in OS or progression free survival by race, race/ethnicity or BMI (Figure 1). Conclusions: Significant differences exist in demographic and clinical characteristics of NDMM pts by race/ethnicity. Median OS was significantly associated with these characteristics but not with race/ethnicity itself. Observed racial/ethnic disparity in outcomes for MM pts may be overcome by access to comparable therapeutic options as seen in clinical trials. Obesity, while associated with MM incidence, does not seem to influence outcomes. Figure 1 Figure 1. Disclosures Ailawadhi: Pharmacyclics: Consultancy; Novartis: Consultancy; Amgen Inc: Consultancy; Takeda Oncology: Consultancy. Stewart:Janssen Pharmaceuticals: Consultancy; Takeda Oncology: Consultancy; Celgene: Consultancy; Bristol Myers Squibb: Consultancy. Hussein:Celgene: Employment. Zonder:Takeda: Consultancy, Honoraria; Bristol Myers Squibb: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria; Pharmacyclics: Other: DSMC membership; Prothena: Consultancy, Honoraria. Barlogie:Mount Sinai Hospital: Employment.

Abstract Background: There is a significant variation in disease incidence and outcomes among Multiple Myeloma (MM) patients (pts) from different ethnic backgrounds. Currently, there is a paucity of data regarding the influence of demographics and ethnicity on behavioral patterns of pts, their attitudes, beliefs, and knowledge about MM and its treatment, which could in turn affect outcomes. We conducted a questionnaire-based study to examine behavioral patterns of MM pts, especially focusing on ethnic minorities and how this may impact patient awareness and symptom complex related to the disease and its treatment. Methods: Apaper-basedquestionnaire consisting of 61 questions encompassing four broad categories was provided to participating pts at the University of Southern California Medical Centers. These included demographics, disease/treatment related quality of life (QoL), satisfaction regarding treatment, and satisfaction regarding treating physician/healthcare staff. Information on clinical characteristics and MM-related treatment was collected through an IRB-approved protocol. Pts were stratified by age, gender, and ethnicity (Hispanics vs. Non-Hispanics). Any missing patient responses were withdrawn from the final analysis of that question. Categorical and continuous variables were compared among groups using Chi-square and Kruskal-Wallis tests, respectively. All statistical analysis utilized SAS software (v9.3) with a two-sided significance level of 0.05. Results: A total of 89 pts (males: 40, females: 49) from March 2012 to October 2013 responded to the questionnaire of the 100 pts that were consented (81%). Median age was 59 (range: 23-81) years and 43 (48%) pts were Hispanic while 46 (52%) were Non-Hispanic. Baseline clinical characteristics are shown in Table 1A. As compared to Non-Hispanics, Hispanic pts were less likely to have completed college (70% vs. 30%, p<0.001), attend myeloma support groups (90% vs. 10%, p=0.026), feel they had sufficient disease/treatment-related information (72% vs. 22.5%, p<0.001). Hispanic pts were more likely to report being unemployed/without any income (57.5% vs. 9%, p<0.001), have 3-4 people living in the house (57.5% vs. 26 %, p=0.009), feeling ill (43% vs. 21%, p=0.029), have concern about deterioration in clinical condition (66% vs. 41%, p=0.022), and inability to work due to symptoms from MM or its treatment (38.6% vs. 11.9%, p=0.005) when compared to Non-Hispanics. With respect to MM treatment, Hispanic pts were less likely to report that side effects were as expected (34% vs. 57%, p=0.036) and believe that treating staff worked towards the same goal (88% vs. 100%, p=0.020) as them. Hispanic pts also reported a lower level of satisfaction with their doctor /treatment staff, as compared to Non-Hispanic pts and this difference was statistically significant as shown in Table 1B. Using gender as the stratification factor, we noted that female pts were more likely to report lack of energy (p=0.037), pain (p=0.010), satisfaction with discussion on emotional health (p=0.012) as compared to males. With age as the stratification factor, patients < 60 years of age were less likely to report expected effectiveness from treatment (p=0.031), satisfaction regarding appropriateness of treatment (p=0.008) or explanation of test results by their doctor (p=0.036), and were more likely to worry about deterioration in their condition (p=0.014). We did not find a statistically significant association between age, gender, or ethnicity and response to initial myeloma treatment. Conclusions: We found a significant association between MM patient ethnicity and the pts responses to questions pertaining to their QoL, satisfaction with treatment and treating physician. This difference in disease/treatment related knowledge and attitudes may be related to socio-cultural and behavioral patterns and may contribute to factors such as treatment compliance, effectiveness and symptom complex burden amongst others, that can impact outcomes among ethnic subgroups of MM pts. Efforts to mitigate these differences should focus on improving methods of patient education, spread more awareness among ethnic minorities, integration of psychosocial care into cancer therapy and promoting health psychology research. Disclosures No relevant conflicts of interest to declare.

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 is associated with significant morbidity even at the time of presentation and there is ample data to support that MM treatment improves overall survival (OS) as well as quality of life (QoL) due to effective symptom control. Patient refusal to recommended cancer treatment is not common in clinical practice but has been reported in certain cancers. There is no data on characteristics of MM patients who refuse treatment despite physician recommendation and outcomes of such patients. Methods: We used the National Cancer Database (NCDB) from 2004-2015 to extract data on adult MM patients who refused any systemic therapy as part of their initial care despite physician recommendation. Information on patient age, gender, race/ethnicity, education, year of diagnosis, income, geographical location, comorbidities, systemic treatment, survival time and vital status was obtained. Average annual percent change (AAPC) and average percent change (APC) were calculated to analyze the trends, and periods with similar APC were compared for statistically significant changes by applying join point regression. Multivariate models were employed to identify factors predicting refusal to therapy and to assess its impact on OS. Results: Of 75,734 eligible patients who were recommended treatment for MM (chemotherapy and or immunotherapy), 2138 (2.8%) patients did not receive any treatment due to refusal. Significantly higher number of females, patients in older age groups (age groups 60-79 and ³ 80 years), Medicare recipients and with ≥2 comorbidities refused treatment recommendation. On multivariate analysis, advanced age groups 60-79 years (OR = 1.47; 95% CI 1.27-1.31 p&lt;0.001) and ³80 years (OR = 5.02; 95% CI 4.23-5.96, p&lt;0.001), female gender (OR =1.16 ; 95% CI 1.05-1.27, p&lt;0.001), patients with certain primary payor types including Medicaid (OR = 1.59; 95% CI 1.28-1.98, p&lt;0.001), Medicare (OR = 1.63; 95% CI 1.43-1.87, p&lt;0.001) and uninsured (OR = 1.81; 95% CI 1.38-2.38, p&lt;0.001), and presence of ³2 comorbidities (OR = 1.39; 95% CI 1.2-1.61, p&lt;0.001) were the independent factors associated with higher likelihood of refusing treatment. Whereas being seen at academic research programs (OR = 0.7; 95% CI 0.6-0.8, p&lt;0.001), higher education (OR =0.81 ; 95% CI 0.67-0.98, p=0.03), geographical location (Midwest OR =0.87 ; 95% CI 0.76-0.99, p=0.04), longer distance to facility being at 3rd quartile (10.1-26.2 miles: OR =0.78 ; 95% CI 0.68-0.89, p&lt;0.001) or 4th quartile (&gt;26.2 miles: OR = 0.59; 95% CI 0.51-0.68, p&lt;0.001) were associated with lower likelihood of refusing treatment. The overall rate of refusing treatment by MM patients was quite stable during 2004-2015 with an AAPC of 1.1 (95% CI -1.1-3.3 p=0.3). However, on fitting scatter plot into Joinpoint regression model, a concerning trend in the more recent years was noted. While the rate of refusing recommended treatment decreased during 2004-2008, a rising trend was observed during the period 2008-2015 with an APC of 3.8 (95% CI 1.3-6.4, p≤0.001) (Fig 1a). Patients who were recommended treatment but refused had significantly worse survival than those who received treatment (median survival 17.7 vs 52.9 months, Cox hazard ratio 1.21; CI 1.17-1.24, p&lt;0.001) (Fig 1b). Conclusions: The percentage of MM patients refusing any systemic treatment as a part of their initial care despite physician recommendation appears to be increasing in recent years and this needs to be systematically addressed. Disparities in refusal to recommended treatment are independently associated with several variables including age, gender, comorbidities, geographical location, facility and insurance type. These need to be addressed urgently since there are several effective and well-tolerated anti-MM treatments which can potentially benefit patients in case the initial socioeconomic, clinical and psychosocial barriers are overcome. Disclosures Chanan-Khan: Xencor: Research Funding; Pharmacyclics: Research Funding; Merck: Research Funding; Jansen: Research Funding; Mayo Clinic: Employment; Ascentage: Research Funding; Millennium: Research Funding; AbbVie: Research Funding. Ailawadhi:Pharmacyclics: Research Funding; Celgene: Consultancy; Amgen: Consultancy, Research Funding; Cellectar: Research Funding; Janssen: Consultancy, Research Funding; Takeda: Consultancy.

Background: Median age at diagnosis for MM is 69 years with approximately 33% of the newly diagnosed MM (NDMM) patients aged ≥80 years (elderly). Yet, outcomes data and clinical trial participation in elderly MM patients are limited due to frailty and frequent comorbidities. Moreover, elderly patients are not candidates for stem cell transplant (SCT) and age is an independent poor prognostic marker, leading to disparate outcomes. The present study evaluated trends in outcomes and predictors of survival among elderly NDMM patients in the United States (U.S.). Methods: Using Surveillance Epidemiology and End Results (SEER) database, we extracted data on age, gender, geographical regions, year of diagnosis, and vital status for elderly patients (age ≥ 80 years) with MM from 1975 to 2016. Relative survival (RS) was estimated as the ratio of observed survival to the survival expected in the general population in the absence of malignancy. This adjusted for the competing causes of mortality prevalent in elderly patients. Mortality rates were age-adjusted. To estimate trends in mortality, we estimated annual percentage change (APC) and average annual percentage change (AAPC) by fitting the data in the join point regression model using the NCI's Join point Regression Program, Version 4.5.0.1. A multivariate analysis was conducted to analyze the predictors of overall and myeloma-specific mortality among elderly patients fitting into Cox proportion hazard regression model. Results: A total of 17,265 elderly MM patients were eligible for the study. Majority were females (n=9,487, 55%), were non-Hispanic whites (NHW) (n=12,895, 74.7%) and were diagnosed in the Western region of the U.S. (n=8,541, 49.5%). The median age was 84 years (IQR 81-87). The median RS for the whole period (1975-2016) was 19.8 months with 1- and 5- year RS of 60.1% (95%CI: 58.8-61.3) and 20.9% (95%CI: 19.5-22.3) respectively. The median RS increased from 8.45 months in 1975 to 29.86 months in 2013. One and 5- year RS also increased from 42.7% (95%CI: 32.4-52.5) and 12.9% (95%CI: 5.8-22.7) in 1975 to a 1- and 5-year RS of 72.3% (95%CI: 65.4-78.1) and 31.1% (95%CI: 23.4-39.1) in 2013, respectively (Fig.1). However, the age standardized mortality rate for the whole period showed an increasing trend with an AAPC of 2.3 (0.5-4, P<0.001). On fitting joinpoint regression, 2 distinct joinpoints were identified, calendar years 1977 and 1989 (Fig 2). Most of the increase in mortality rate was noted in the period 1975-1989. Since 1989, the mortality rates have shown a decreasing trend which is marginal but statistically significant, with an APC of -0.41 (95%CI: -0.7- -0.1, p<0.001). On multivariate analysis, increasing age (p<0.001) and non-northeast location (p<0.01) were associated with higher overall and MM-specific mortality. Female gender was associated with lower overall mortality (p<0.001) but not myeloma-specific mortality. While "other" race/ethnicity (not NHW, non-Hispanic black; NHB or Hispanic) was associated with lower risk of overall and myeloma-specific mortality, NHB were associated with a lower MM-specific (p<0.001) but not overall mortality. Conclusions: Our results suggest that although RS has improved significantly in the elderly patients, the mortality rates have not changed drastically over the years. Besides, factors like advancement in diagnosis and treatment which have improved RS, early diagnosis (leading to lead time bias) may also be contributing. Furthermore, there are several gender-specific, geographic and racial/ethnic disparities in outcomes of elderly MM patients, which need to be assessed prospectively. Although, a small decline in mortality rates in the more recent time is promising, further studies are warranted on interventions including aggressive supportive care directed at elderly patients with MM, so that MM survivorship can be improved. There should be strong consideration to increaseenrollment of the elderly in prospective clinical trials so that evidence-based guidelines can be developed. Disclosures Manochakian: Takeda: Membership on an entity's Board of Directors or advisory committees; Guardant Health: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Novocure: Membership on an entity's Board of Directors or advisory committees. Chanan-Khan:Merck: Research Funding; Ascentage: Research Funding; Millennium: Research Funding; Jansen: Research Funding; Mayo Clinic: Employment; Pharmacyclics: Research Funding; AbbVie: Research Funding; Xencor: Research Funding. Ailawadhi:Janssen: Consultancy, Research Funding; Takeda: Consultancy; Celgene: Consultancy; Amgen: Consultancy, Research Funding; Cellectar: Research Funding; Pharmacyclics: Research Funding. Off Label Use: Do not remove- this information is relevant to Abstract ID 125629: TAK-169 is a dimeric fusion protein of an anti-CD38 antibody single chain variable fragment fused to a modified Shiga-like toxin-A subunit..

Multiple myeloma (MM) is a neoplasm thought to arise from a damaged germinal center B-cell that progresses to a plasma cell clone arising in bone marrow. MM comprises 20% of all hematologic cancer deaths. Persons of African ancestry (AA) have a 1.5 to 2-fold higher risk compared to individuals of European ancestry (EA). Genetically driven differences in hematopoiesis may lead to variation in the levels white blood cell (WBC) subsets which could, in turn, be associated with MM etiology. There are differences in genetic determinants of WBC traits between EA and AA populations, with possible implications for the racial disparity in risk. We tested the above hypothesis using Mendelian randomization (MR), an approach that leverages genetic determinants of specific traits (i.e. WBC counts) to estimate their effects on the risk of an outcome; and a transcriptome-wide association study (TWAS), which utilizes genetic predictors of gene expression to identify susceptibility genes. These analytic approaches were applied to data from the African American Multiple Myeloma Study (AAMMS) consisting of 1813 cases and 8871 AA cancer-free controls to examine how differences in heritable WBC gene expression profiles influence MM risk. Genetic determinants of variation in WBC subsets in AA were obtained from the literature and supplemented with new genome-wide association findings in AA subjects from the UK Biobank cohort (n=6108). Odds ratios (OR) for MM per 1 standard deviation (SD) increase in each WBC phenotype were estimated using independent (linkage disequilibrium (LD) r2<0.10) variants with P<10-6 as genetic instruments. Analyses based on variants associated with WBC traits in AA populations did not identify any statistically-significant associations between MM risk and WBC overall (p=0.81, using 15 SNPs) or subsets (lymphocytes, monocytes, eosinophils, neutrophils and basophils, p>0.05 for each). However, when we applied genetic determinants of WBC identified in 330,000 cancer-free EA UK Biobank participants (P<10-8, replication P<0.05, LD r2<0.05), a statistically significant inverse relationship emerged between increasing lymphocyte counts and MM risk (OR=0.80, 95% CI: 0.66-0.97, p=0.02, using 385 SNPs), as well as increasing basophil counts (OR=0.63, 95% CI: 0.41-0.96, p=0.03, using 140 SNPs). Next, we examined the association between WBC gene expression profiles and MM risk in AAMMS data. We applied published and validated ancestry-specific models developed using the PrediXcan approach, which leverage germline genetic and transcriptomic data from the Multi-Ethnic Study of Atherosclerosis (MESA) (Mogil et al. PMID: 30096133). The primary TWAS used gene expression models trained in AA subjects (n=233), with sensitivity analyses using models developed in AA and Hispanic subjects (n=585). The TWAS significance threshold was based on the number of genes with significant germline prediction models (p<0.05 and R2 ≥0.05) in AA, corresponding to P < 0.05/2700 = 1.85×10-5. The expression of two genes was significantly associated with MM risk: KANK1at 9p24.3 (P = 1.01×10-5) and DNAJC27at 2p23.3 (P = 1.56×10-5). KANK1is a candidate tumor suppressor gene for renal cell carcinoma and has recently been associated with MM risk in AA [Du, Blood 2017 130:3058]. Here we provide additional evidence for its role in MM etiology via gene expression-mediated mechanisms. DNAJC27 (previously known as RBJ) is a novel MM risk gene linked to constitutive activation of ERK in solid tumors. We also identified two suggestively associated genes: PRR14 (P = 1.34×10-4; combined AA-Hispanic sample: P = 1.56×10-6), which has been linked to MM risk in EA populations, and PARP16 (P = 9.46×10-5). To our knowledge this is the first study to comprehensively examine variation in WBC traits and gene expression profiles with respect to MM risk in AA. Our TWAS analysis leveraged data from the largest collection of genetic and gene expression data in AA, enabling ancestry-matched inference and identification of two novel risk genes. Although the limited availability of genetic instruments for WBC limited the power of MR analysis, findings using variants identified in European populations may offer some insight into trans-ethnic etiologic pathways and contribute to risk stratification strategies using genetic and blood cell count biomarkers. Future studies, particularly with MGUS-free controls, are needed to validate these results. Disclosures Song: Millennium Pharmaceuticals Inc: Employment. Rand:Ancestry.com: Employment. Ailawadhi:Cellectar: Research Funding; Janssen: Consultancy, Research Funding; Celgene: Consultancy; Pharmacyclics: Research Funding; Amgen: Consultancy, Research Funding; Takeda: Consultancy. Nooka:Takeda: Honoraria, Other: advisory board participation; Janssen: Honoraria, Other: advisory board participation; GSK: Honoraria, Other: advisory board participation; Spectrum pharmaceuticals: Honoraria, Other: advisory board participation; Adaptive technologies: Honoraria, Other: advisory board participation; Amgen: Honoraria, Other: advisory board participation; Celgene: Honoraria, Other: advisory board participation; BMS: Honoraria, Other: advisory board participation. Singhal:Bureau of Millennium/Takeda, Celgene, Janssen, Celgene, Bristol-Myers Squibb and Bluebird: Speakers Bureau. van Rhee:Takeda: Consultancy; Sanofi Genzyme: Consultancy; Castleman Disease Collaborative Network: Consultancy; EUSA: Consultancy; Adicet Bio: Consultancy; Kite Pharma: Consultancy; Karyopharm Therapeutics: Consultancy. Mehta:Millennium/Takeda, Celgene; stock in Celgene, Bristol-Myers Squibb and Bluebird: Speakers Bureau. Wolf:Takeda: Consultancy; Celgene: Consultancy; Novartis: Consultancy; Janssen: Consultancy; Amgen: Consultancy. Martin:Roche and Juno: Consultancy; Amgen, Sanofi, Seattle Genetics: Research Funding. Fiala:Incyte: Research Funding. Terebelo:Jannsen: Speakers Bureau; Celgene: Honoraria; Newland Medical Asociates: Employment. Anderson:Bristol-Myers Squibb: Other: Scientific Founder; Oncopep: Other: Scientific Founder; Amgen: Consultancy, Speakers Bureau; Janssen: Consultancy, Speakers Bureau; Takeda: Consultancy, Speakers Bureau; Celgene: Consultancy, Speakers Bureau; Sanofi-Aventis: Other: Advisory Board. Vij:Bristol-Myers Squibb: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Genentech: Honoraria; Janssen: Honoraria; Karyopharm: Honoraria; Sanofi: Honoraria; Takeda: Honoraria, Research Funding. Bernal-Mizrachi:TAKEDA: Research Funding; Kodikas Therapeutic Solutions, Inc: Equity Ownership; Winship Cancer Institute: Employment, Patents & Royalties. Morgan:Amgen, Roche, Abbvie, Takeda, Celgene, Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Other: research grant, Research Funding. Zonder:Celgene Corporation: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Intellia: Consultancy, Membership on an entity's Board of Directors or advisory committees; Caelum: Consultancy, Membership on an entity's Board of Directors or advisory committees; Alnylam: Consultancy, Membership on an entity's Board of Directors or advisory committees; BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Consultancy, Membership on an entity's Board of Directors or advisory committees. Huff:Member of Safety Monitoring Board for Johnson and Johnson: Membership on an entity's Board of Directors or advisory committees; Karyopharm, Sanofi, MiDiagnostics: Consultancy. Lonial:Karyopharm: Consultancy; Takeda: Consultancy, Research Funding; Amgen: Consultancy; BMS: Consultancy; Janssen: Consultancy, Research Funding; GSK: Consultancy; Celgene Corporation: Consultancy, Research Funding; Genentech: Consultancy. Orlowski:Poseida Therapeutics, Inc.: Research Funding.

Background Multiple myeloma (MM), a malignant neoplasm of plasma cells in the bone marrow, accounts for up to 1.8% of all cancers in the U.S., most frequently affecting people 65-74 years old. A variety of therapies are available to manage MM, including stem cell transplantation (SCT), immunomodulatory drugs (IMiD), proteasome inhibitors (PI), monoclonal antibodies (mAB), and alkylating agents (alk). Given the heterogeneity of MM and the rapidly evolving therapeutic landscape, MM contemporaneous real-world treatment patterns are not well described. We examined the patient characteristics and first-line (LT1) treatment patterns in NDMM patients. Methods MM patients (≥18 years), diagnosed in April 30, 2015 - April 29, 2017 (early cohort) or in April 30, 2017 - April 30, 2019 (recent cohort), were followed retrospectively from MM diagnosis to last patient activity in the Flatiron Health database - a geographically-diverse, longitudinal electronic health record spanning over 280 community and academic cancer clinics in the U.S. LT1 regimens were described as: 1) containing an IMiD (thalidomide, lenalidomide [R], or pomalidomide), PI (bortezomib [V], carfilzomib, or ixazomib), alk (melphalan, cyclophosphamide [C], bendamustine), mAB (daratumumab, elotuzumab), or combinations of these; and 2) doublet/monotherapy (doublets-) vs. triplet or greater agent (triplets+) combinations. Treatment patterns were examined by SCT status and by cytogenetic risk (high: del17p, t(4;14) and/or t(14;16); standard: ≥1 cytogenetic tests without high cytogenetic risk) and age groups (<65, 65-74, ≥75). Duration of therapy (DOT) and time to next therapy (TTNT) were estimated using Kaplan-Meier methods in the early cohort with longer follow-up. Results Of 4,070 NDMM patients, 3,433 were non-SCT (nSCT: early cohort: n=1,736; recent cohort: n=1,697) and 637 had SCT (early cohort: n=407; recent cohort: n=230). In nSCT patients, mean age at diagnosis was 70 years (SD: 10); 46% were female; 36% had stage III (699/1916, among non-missing), and 15% (392/2574, among non-missing) had high risk MM (25% had unknown cytogenetics). SCT patients were younger at diagnosis (mean [SD]: 61 years [9]); 44% were female; 25% (117/470, in non-missing) had stage III, and 19% (102/547, in non-missing) had high risk MM (14% had unknown cytogenetics). Overall, proportions with known cytogenetic risk were similar within SCT status cohorts over time but were lower in the SCT group (nSCT early vs. recent cohort: 26% vs. 24% had unknown cytogenetics; and in SCT: 15% vs. 13%, respectively). In nSCT and SCT patients, respectively, most common regimens were VRd (d: dexamethasone; 44% and 58%), Rd (16% and 7%), Vd (13% and 1%), and VCd (12% and 4%). In nSCT patients, the use of VRd increased over time (37% [early cohort] to 51% [recent cohort]), while frontline therapy with Rd/Vd doublets (19% to 14%/16% to 9%) and with VCd (13% to 11%) decreased. In the nSCT recent cohort, VRd (51%) frontline therapy dominated, with a slightly higher proportion of patients in the high-risk group vs. standard and unknown risk receiving VRd (56% vs. 53% and 46%); use of doublet therapy with Rd/Vd was lower in the high risk (12%/5%) vs. standard risk group (14%/9%). Irrespective of age, VRd was the most common frontline regimen in the nSCT recent cohort, albeit its use was lower among patients 75+ years of age (43%) vs. younger patients (54% [<65 years] and 59% [65-74 years]); 75+ year old patients had a higher use of Rd/Vd doublets (19%/15%) vs. <65 (10%/5%) or 65-74 (10%/6%) years of age. Triplets+ were more commonly used than doublets- across all cohorts: 59% vs. 41% (nSCT early cohort); 74% vs. 26% (nSCT recent cohort); and 89% vs. 11% (SCT early cohort); 95% vs. 5% (SCT recent cohort). mAB use in the recent cohort was low: 1.4% nSCT and 2.2% SCT patients. In the nSCT early cohort, the median (95% CI) LT1 DOT was 10 months (9-11) and for TTNT was 14 months (13-16). Conclusions PI/IMiD treatment combinations were most commonly observed in both nSCT and SCT patients, with an increase in use from early (40%) to recent (56%) cohort in nSCT patients. Use of triplets, generally, is on the rise from early (60%) to recent cohorts (74%). LT1 TTNT was lower than has been shown in clinical trials. These findings indicate a notable change in treatment patterns over time in nSCT NDMM patients, highlighting the changing landscape of MM management. Disclosures Ailawadhi: Celgene: Consultancy; Takeda: Consultancy; Cellectar: Research Funding; Amgen: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Pharmacyclics: Research Funding. Romanus:Takeda: Employment. Cherepanov:Takeda: Employment. Yin:Takeda: Employment. Cheng:Takeda: Employment. Hari:Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria, Research Funding; BMS: Consultancy, Research Funding; Janssen: Consultancy, Honoraria; Kite: Consultancy, Honoraria; Amgen: Research Funding; Spectrum: Consultancy, Research Funding; Sanofi: Honoraria, Research Funding; Cell Vault: Equity Ownership; AbbVie: Consultancy, Honoraria.

Background: The development of novel treatment strategies has extended the median survival of MM to nearly a decade but the disease remains incurable and relapse is inevitable. The Bcl-2 pathway is highly relevant to MM cell survival and can be mitigated therapeutically. AT-101 is a novel, orally available pan Bcl-2 inhibitor (Bcl-2, Bcl-xl, Mcl-1, and Bcl-w). Preclinical in vitro and in vivo studies showed that AT-101 enhanced cytotoxicity of lenalidomide-dexamethasone (Rd). We conducted a phase I study in RRMM patients to establish the effective dose of AT-101 with Ld as well as record safety and preliminary efficacy of this combination (NCT02697344). Methods: Key eligibility criteria included: RRMM with measurable disease (serum monoclonal protein ≥1.0 g/dL or urine monoclonal protein >200mg/24 hour or serum immunoglobulin free light chain >10mg/dL AND abnormal serum free light chain ratio). Patients must have received 1-3 prior treatment regimens and have an absolute neutrophil count ≥1.0 x 109, platelets 75 x 109, creatinine clearance ≥50 mL/min, and ECOG performance status ≤2. AT-101 dosing was designed to reach a maximum daily target of 20mg (Cohort 1; 10 mg PO QD, Cohort 2; 20 mg PO QD) utilizing a standard 3 +3 dose escalation design in combination with standard doses of Rd. Treatment was given as outpatient for a maximum of 12, 28-day cycles. For pharmacodynamic studies, AT-101 alone was given in cycle 1, with R (25 mg on days 1-21) and d (40 mg weekly) added cycle 2 onwards. Results: Enrolled patients (n=10) included 60% males with median age 68.5 years (range 55-75) and median time since MM diagnosis 4.5 years (range 0.6-8.3). MM ISS stage was II/III in 7 patients and 8/10 had high-risk cytogenetics with 4 each having del17p and 1q+. Only 1 patient had t(11;14).Patients had received median 2 prior lines of therapy (range 1-3), with 7 having had prior autologous stem cell transplant (ASCT) and the initial induction regimen being bortezomib (V), R and dexamethasone (d) (VRd) in 8, Rd in 1 and cyclophosphamide (C) with Vd (VCd) in 1 patient. At the time of study entry, 3 patients were R refractory while 2 were refractory to both, V and daratumumab (Dara). Median duration of treatment was 7.5 cycles (range 2-12) and 3 patients completed all planned 12 cycles of treatment. Among the evaluable patients, dose limiting toxicities (DLTs) at 20 mg daily dose of AT-101 with 25 mg of R and 40 mg weekly included one patient with grade 4 febrile neutropenia and grade 4 neutropenia lasting 9 days and one patient with grade 4 thrombocytopenia lasting 8 days. G3/4 adverse events (AEs) included atrial flutter (n=1), white blood cell count decrease (n=3), neutropenia (n=5), febrile neutropenia (n=1) and thrombocytopenia (n=2), and back pain (n=1) . No G3/4 non-hematological AEs were noted. Any grade non-hematologic AEs seen in at least 20% (n=2) patients included fatigue (n=9), neuropathy (n=6), nausea (n=3), diarrhea (n=5), constipation (n=3), and creatinine increased (n=2). No patients experienced tumor lysis syndrome. Overall response rate (ORR) was 44% (2 each with very good partial response, VGPR and PR) and clinical benefit rate (CBR) was 89% with 2 additional patients showing minor response (MR) and 2 experiencing stable disease (SD) (Fig 1). Patients with high-risk disease had an ORR of 43% and a CBR of 100%. Median progression-free survival (PFS) for all patients was 8.1 months. Correlative analysis from patients who showed an objective response to treatment revealed a significant increase in bone marrow Th-effector cells, NK cells and cytotoxic CD8+ T-cells along with a significant decrease in immunosuppressive T-regulatory and B-regulatory cells was noted after 1 complete cycle of the combination therapy (p<0.05, Fig 2). Conclusions: This is the first reported clinical trial combining a Bcl-2 inhibitor with immunomodulatory drugs (IMiDs) in MM. AT-101-Rd is a clinically active regimen with an ORR of 40% in predominantly high-risk RRMM patients with an acceptable toxicity profile. Additional patients with MM experienced clinical benefit despite refractory status to prior therapy in this early phase clinical trial. These early findings support the further investigation of AT-101 specifically, and Bcl-2 inhibitors in general, with IMiDs in patients with MM. Disclosures Ailawadhi: Celgene: Consultancy; Amgen: Consultancy, Research Funding; Pharmacyclics: Research Funding; Cellectar: Research Funding; Janssen: Consultancy, Research Funding; Takeda: Consultancy. Chanan-Khan:Xencor: Research Funding; Pharmacyclics: Research Funding; Merck: Research Funding; Jansen: Research Funding; Mayo Clinic: Employment; Ascentage: Research Funding; Millennium: Research Funding; AbbVie: Research Funding. OffLabel Disclosure: AT-101 is not currently FDA-approved for treatment of any condition.

Background: The 2018 iwCLL guidelines recommend close observation of early stage CLL pts who do not meet indications for therapy ("watch and wait" strategy). Prior attempts at early therapeutic intervention in unselected early-stage CLL pts using alkylating agents such as chlorambucil and chemoimmunotherapy failed to show a significant benefit; and these therapies were associated with substantial toxicities. The introduction of Bruton tyrosine kinase (BTK) inhibitors has revolutionized the treatment landscape of CLL. The German CLL Study group recently presented results of the CLL12 study which demonstrated a significant improvement in event-free survival (EFS) among untreated early stage high risk CLL pts who received ibrutinib compared to placebo (median EFS not reached vs. 47.8 months, respectively; Langerbeins, EHA, 2019). Acalabrutinib is a more selective second-generation BTK inhibitor that has fewer adverse events in pts with CLL, and has shown equally impressive clinical activity for CLL pts. We are conducting an investigator initiated randomized phase 2 study to compare acalabrutinib with or without obinutuzumab (a glycoengineered anti-CD20 monoclonal antibody approved in the treatment of CLL) among high risk early stage CLL pts. Study Design: All pts with newly diagnosed (<2 years from registration) previously untreated early stage CLL who do not meet the 2018 iwCLL guidelines for initiation of therapy are eligible for enrollment in this study. Pts undergo risk stratification according to the CLL-International prognostic index (CLL-IPI) which consists of the following 5 variables: age >65 years (1 point), Rai stage I-IV (1 point), del17p and/or TP53 mutation (4 points), unmutated immunoglobulin heavy chain (IGHV) genes (2 points), and serum β2-microglobulin >3.5 g/dL (2 points). Pts with high (4-6) and very high (7-10) risk CLL-IPI are randomly (1:1) assigned to acalabrutinib 100 mg orally twice daily (Arm A) or acalabrutinib with obinutuzumab at a standard approved schedule (Arm B). Treatment with acalabrutinib is continued for a minimum of 2 years unless the patient has unacceptable side effects or withdraws consent. Pts with low (0-1) and intermediate (2-3) risk CLL-IPI score are assigned to an observation arm with follow-up once every 6 months for 2 years, and then according to routine clinical practice (Arm C). Endpoints: The primary endpoint of the intervention arms (Arms A and B) is the achievement of minimal residual disease (MRD)-negative complete remission in the bone marrow after 2 years of therapy. MRD is assessed using an 8-color flow cytometry with a detection limit of 0.01%. The primary endpoint of the observation arm (Arm C) is time to first therapy. Secondary endpoints for all arms are: progression-free survival, overall survival, and safety. Statistical design: A randomized trial comparing the experimental arm (acalabrutinib plus obinutuzumab) against the control arm (acalabrutinib alone) will be conducted as described by Rubinstein. A sample size of 36 pts per arm provides 80% power to detect an improvement in MRD-negative complete response rate from 10% to 30%, using a one-sided test at a significance level of 0.10 (EAST 6.4). We anticipate accruing an additional 8 high/very high risk pts (4 in each arm) to account for ineligibility, cancellation, or major treatment violation. The total enrollment for Arms A and B will be 80 pts. We will enroll 40 pts to Arm C; for a total enrollment of 120 pts. Key correlatives: Comprehensive profiling to assess the innate and adaptive immune system in paired peripheral blood and bone marrow will be conducted in all pts registered to Arms A and B at baseline, 12 and 24 months after enrollment. In addition, bone marrow hematopoietic function will be assessed in all pts at these time points. We will employ a CLL focused custom 61-gene panel to estimate tumor mutational burden as well as mutational profiles for each patient at baseline, 12, and 24 month time points, as well at the time of disease progression. Preliminary results: The trial is registered at clinicaltrials.gov NCT03516617. The trial opened to accrual at all three Mayo Clinic sites (Rochester, MN; Jacksonville, FL; and Scottsdale, AZ) in September 2018. Twenty-eight pts have been registered; the baseline characteristics are shown in Table 1. The trial is expected to complete enrollment in September 2020; and the primary analysis will be available in September 2022. Disclosures Parikh: Janssen: Research Funding; AstraZeneca: Honoraria, Research Funding; Pharmacyclics: Honoraria, Research Funding; MorphoSys: Research Funding; AbbVie: Honoraria, Research Funding; Acerta Pharma: Research Funding; Ascentage Pharma: Research Funding; Genentech: Honoraria. Ding:DTRM Biopharma: Research Funding; Merck: Research Funding. Ailawadhi:Pharmacyclics: Research Funding; Cellectar: Research Funding; Takeda: Consultancy; Amgen: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Celgene: Consultancy. Kenderian:Novartis: Patents & Royalties, Research Funding; Tolero: Research Funding; Humanigen: Other: Scientific advisory board , Patents & Royalties, Research Funding; Lentigen: Research Funding; Morphosys: Research Funding; Kite/Gilead: Research Funding. Chanan-Khan:Mayo Clinic: Employment; Ascentage: Research Funding; Millennium: Research Funding; Jansen: Research Funding; AbbVie: Research Funding; Xencor: Research Funding; Pharmacyclics: Research Funding; Merck: Research Funding. Kay:Infinity Pharmaceuticals: Other: DSMB; Celgene: Other: Data Safety Monitoring Board; Agios: Other: DSMB; MorphoSys: Other: Data Safety Monitoring Board. OffLabel Disclosure: Drug: Acalabrutinib Purpose: treatment of CLL