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Extramedullary multiple myeloma is defined by the presence of plasma cell infiltration outside of the bone marrow. It is associated with a poor prognosis and resistance to therapy and is often associated with high-risk cytogenetics. Aggressive relapsed and refractory extramedullary multiple myeloma is often treated with salvage infusional chemotherapy to achieve rapid disease control. Commonly used regimens include DCEP, CVAD, and VTD-PACE. While VTD-PACE contains bortezomib and thalidomide which have potent antimyeloma activity, the advent of novel agent therapy with proteasome inhibitors and immunomodulatory agents being used in the first-line setting has resulted in many patients being refractory to bortezomib by the time they are treated with VTD-PACE. Herein, we discuss two cases of aggressive relapsed, high-risk, bortezomib-refractory extramedullary multiple myeloma treated with KRD-PACE and review the available clinical data on salvage chemotherapy regimens used in relapsed refractory myeloma.

Background: HIT has the potential to improve the health of individuals and the performance of providers, yielding improved quality, cost savings, and greater engagement by patients. Minorities face a variety of financial and nonfinancial barriers to obtaining appropriate and timely health care. Studying the behavior of minority patients will help understand the specific challenges to HIT adoption and engagement in patients and their caregivers. We undertook a patient-reported survey to explore these factors and understand any underlying disparities. Methods: A 21-question paper-based anonymous questionnaire addressing the responder's attitudes, understanding and needs of the electronic medical record (EMR) was used. This was administered to adult patients with lymphoid malignancies and their caregivers at two campuses of Mayo Clinic in Florida (San Pablo and inner-city; Riverside). Categorical and continuous variables between white and minority responders as well as minority responders from the two campuses were compared using the Chi-square test and U Mann Whitney with a significance level of 0.05. Results: The survey was completed by 1004 patients and caregivers. Average time taken to complete the survey was approximately 11 minutes. Of the responders, 716 (71%) were whites and 271 (27%) were minorities, with race-ethnicity not reported by 17 (2%). Among minorities, 30% (n=80) were seen at San Pablo and 64% (n=173) at the Riverside campus, with 6% at a multiple myeloma patient-caregiver symposium. Whites were older (median age 66 vs. 62 years, p<0.001) and had higher education (undergraduate or higher 60% vs. 52%, p=0.023) as compared to minorities. Minority survey responders had lesser access to a computer (84% vs. 92%, p<0.001) or a smartphone (85% vs. 89%, p=0.08). (Figure 1a) Exploring HIT awareness showed that only 51% of minorities were registered to use the EMR as compared to 72% whites (p<0.001) and a lesser number of minorities even knew that they could access their medical records electronically (81% vs. 92%, p<0.001). even among those registered to use the EMR, 13% minorities reported never using the EMR as compared to 4% whites (p<0.001). Encouragingly, a higher willingness to learn about and use the EMR was noted among minorities who were more interested in knowing how to access the EMR (43% vs. 36%, p=0.04), would increase the use of EMR if someone explained how to use it (17% vs. 10%, p=0.002), and if someone explained about test results (14% vs. 6%, p<0.001). (Figure 1b) Comparing minorities among the two campuses, those from the San Pablo campus were older (67.5 yrs. vs. 57 yrs., P<0.001), had higher level of education (undergraduate or higher in 66% vs. 47%, p=0.005) and a higher number had access to a computer (94% vs. 78%, P=0.001) as compared to those from the inner-city Riverside. (Figure 2a) Minorities from the Riverside campus were less likely to be aware of the current HIT EMR (75% vs. 93%, p<0.001), were significantly less likely to have registered for the patient EMR portal (38% vs. 76%, p<0.001) and never used the EMR even if registered for it (22% vs. 7%, p=0.01) as against those from the San Pablo campus. Minorities at the Riverside campus had a higher interest in knowing how to access the EMR (47% vs. 33%, p=0.03). Linguistic barriers were more striking in the inner-city Riverside campus where a higher proportion of minority survey responders noted that it would help if the EMR was in their primary language (66% vs. 48%, p=0.006). (Figure 2b) Conclusions: Minorities continue to struggle with suboptimal utilization of healthcare system including that of HIT. There are barriers related to access, awareness, language and understanding of HIT that prevent the integration and participation of racial-ethnic minorities in the healthcare system to a full extent. It is important to qualify the minorities better, since there exist HIT utilization disparities even within the minorities from different areas representing socioeconomic and demographic differences. Efforts need to be made at several levels to help the minorities overcome educational, access and linguistic barriers so that they can benefit from advancements in the healthcare system. Disclosures Ailawadhi: Celgene: Consultancy; Amgen: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Pharmacyclics: Research Funding; Cellectar: Research Funding; Takeda: Consultancy. Manochakian:Novocure: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Guardant Health: Membership on an entity's Board of Directors or advisory committees. Chanan-Khan:AbbVie: Research Funding; Xencor: Research Funding; Pharmacyclics: Research Funding; Merck: Research Funding; Jansen: Research Funding; Mayo Clinic: Employment; Ascentage: Research Funding; Millennium: Research Funding.

Background: Survival of patients with MM has improved significantly over recent years due to therapeutic advancements. Population-level data has shown that a substantial proportion of MM patients may die early after their diagnosis and real-world evidence regarding early mortality (≤6 months after MM diagnosis) has been conflicting. We evaluated the trends, causes and predictors of early mortality among newly diagnosed MM (NDMM) patients in the real world. Methods: We used Surveillance Epidemiology and End Results (SEER) database, to identify all adult patients (age ≥ 18 years) who were diagnosed of MM from 1975 to 2015. Data on age, gender, geographical regions, year of diagnosis, survival time, vital status at 6 months after diagnosis and cause of death (COD) were extracted. Ages were categorized as quartiles while period of diagnosis for multivariate analysis were categorized by running a joinpoint analysis separately for the all-cause and myeloma-specific mortality. To look at trends in early mortality, we estimated proportion of deaths for each year and plotted it as a scatterplot against the year of diagnosis and fitted it in the join point regression model by applying the NCI's Join point Regression Program, Version 4.5.0.1. By this method we identified the year(s) when a trend change was produced, calculated the annual percentage change (APC) in rates between trend-change points, and the average annual percentage change (AAPC) in the whole period studied. To correct for known errors with COD attribution, we applied a special COD variable which has been recently developed by the SEER program to indicate if the death was due to the primary cancer diagnosis or other causes. A multivariate analysis was conducted to analyze the predictors of all-cause and myeloma-specific mortality within 6 months by fitting into logistic regression model. Results: Of 90,975 NDMM patients, early mortality due to any cause was noted in 18,810 (20.7%). For the whole cohort, median age was 68 years, majority of the patients were males (53.1%), of non-Hispanic white (NHW) race/ethnicity (65.5%) and were diagnosed in SEER registries in the western region (53.9%). An overall survival (OS) of ≤6 months was seen for approximately 22% of NHW but 18% of non-Hispanic blacks (NHB) and Hispanics. OS of ≤6 months was seen in 10% of patients in the first quartile of age (<60 years) as compared to 36% of patients in the 4th quartile (≥78 years). By geographical regions, 23.3% of MM patients in the Midwest had ≤6-month OS as compared to only 18.4% in the Northeast. The most common early mortality COD was MM in 74.7% of the patients, followed by cardiovascular (11.9%), other (8.8%) and infectious causes (3.1%). The overall pattern of COD at 6 months was similar to COD at any time in the whole cohort. Joinpoint regression analysis of trends data resulted in 1 joinpoint for all-cause 6-month mortality while 2 joinpoints were noticed for myeloma-specific mortality (Figure 1). A steep decline in mortality was noticed in the more recent time periods for both, all-cause (2006-2015) and myeloma-specific (2003-2015) mortality rates with an AAPC of 3.93 (95%CI p<0.001) for all-cause and 3.95 (95%CI p<0.001) for myeloma-specific mortality. Prior to 2006, the decrease in all-cause mortality was only modest whereas decrease in myeloma-specific mortality witnessed a sharp decline from 1975-1987 as well. On MVA, advanced age (p<0.001), NHW race/ethnicity and Midwest geographic region (p<0.001) were associated with higher likelihood of mortality while female gender (p<0.001), more recent period of diagnosis (p<0.001), and "other" races (not NHW, NHB or Hispanics) (p<0.001) were associated with lower risk of mortality. Conclusion: Our results show that early mortality due to MM remains high despite therapeutic improvements and there are significant gender, racial and geographic disparities for this phenomenon. However, significant declines in all-cause and MM-specific early mortality in the more recent times are promising. The risk of early mortality is higher among men, older patients, NHW and Midwest region. A focus on interventions to reduce early mortality, including early diagnosis, use of efficacious anti-MM therapy and aggressive supportive care should be directed at groups with high-risk for early mortality from MM. Disclosures Chanan-Khan: Millennium: Research Funding; Ascentage: Research Funding; AbbVie: Research Funding; Xencor: Research Funding; Pharmacyclics: Research Funding; Merck: Research Funding; Jansen: Research Funding; Mayo Clinic: Employment. Ailawadhi:Celgene: Consultancy; Pharmacyclics: Research Funding; Amgen: Consultancy, Research Funding; Takeda: Consultancy; Cellectar: Research Funding; Janssen: Consultancy, Research Funding.

Despite the evolution of the therapeutic arsenal for the treatment of multiple myeloma (MM) over the past decade, autologous stem-cell transplantation (ASCT) remains an integral part of the treatment of patients with both newly diagnosed and relapsed MM. The advent of novel therapies, such as immunomodulatory agents, proteasome inhibitors, and monoclonal antibodies, has led to unprecedented levels of deep hematologic responses. Nonetheless, studies show that ASCT has an additive effect leading to additional deepening of responses. As the therapeutic agents for MM continue to evolve, the timing, duration, and sequence of their use in combination with ASCT will be crucial to understand to obtain the deepest response and survival benefit for patients with MM. This review aims to discuss the role of ASCT for the management of MM, with a particular focus on the role of ASCT in the context of novel therapies and minimal residual disease.

Multiple myeloma (MM) treatment has advanced significantly over the last 2 decades. In most patients, the disease course has been altered from early fatality to chronic morbidity with multiple lines of treatment. The MM treatment paradigm has shifted toward treating patients before end-organ damage occurs. Thus, timeliness of treatment initiation in this era might improve patient outcomes. This is the first report to our knowledge analyzing disparities and trends in treatment timeliness of patients with MM using the National Cancer Database. Multiple factors affected the timing of treatment initiation in MM and disparities were found. We noted that initiation of treatment was delayed in women (odds ratio [OR], 1.15; 95% CI, 1.1 to 1.2) and blacks (OR, 1.21; 95% CI, 1.14 to 1.28; reference, whites) and in patients diagnosed in more recent years (2012-2015; OR, 1.15; 95% CI, 1.1 to 1.22; reference, 2004-2007). Patients were likely to start treatment earlier if they were age ≥ 80 years (OR, 0.83; 95% CI, 0.76 to 0.9; reference, age < 60 years), were uninsured (OR, 0.81; 95% CI, 0.72 to 0.91; reference, private insurance), had Medicaid (OR, 0.87; 95% CI, 0.79 to 0.95; reference, private insurance), were treated in a comprehensive community cancer program (OR, 0.7; 95% CI, 0.65 to 0.77; reference, community cancer program), lived in a location other than the US Northeast, or had a higher Charlson comorbidity score. Patient education and income levels did not affect time to treatment initiation. Particular aspects of these disparities could be explained by our current health care system and insurance rules, whereas others need to be investigated more deeply.

Management of febrile neutropenia (FN) is an integral part of supportive care for patients undergoing cancer treatment. The NCCN Guidelines for Hematopoietic Growth Factors provide suggestions for appropriate evaluation, risk determination, prophylaxis, and management of FN. These NCCN Guidelines are intended to guide clinicians in the appropriate use of growth factors for select patients undergoing treatment of nonmyeloid malignancies. These NCCN Guidelines Insights highlight important updates to the NCCN Guidelines regarding the incorporation of newly FDA-approved granulocyte-colony stimulating factor biosimilars for the prevention and treatment of FN.

Abstract Background: Disparities in MM survival among different racial groups persist despite improvements in overall survival (OS). We report a comprehensive review of the SEER Medicare database to elucidate the causes of differences in disease presentation, management practices, cost of care and their relationships to outcomes among the MM patients (pts) from different racial groups. Methods: Pts diagnosed with MM between 1991-2010 with continuous Medicare coverage (1 year prior to diagnosis-date of death/end of 2012) were included. MM complications (MM-C); hypercalcemia (C), renal dysfunction (R), anemia (An), bone fractures (B), dialysis (D) occurring prior to/within 30 days of MM diagnosis (at diagnosis; AT-D), or any time later (after diagnosis; AF-D) were summarized. Demographics, survival (PEDSAF files) and disease characteristics, drug/stem cell transplant (SCT) utilization (NCH, OUTSAF, PDESAF files) were obtained. Trends in incidence of MM-C, treatment type over time (proportional odds) and their association with race (logistic regressions) were compared. Associations between race, MM-C AT-D and MM related death using Fine and Gray's competing risk methods were analyzed. Medicare claims adjusted for inflation (2013) within the first 6 months (mth)/total MM care were summed by drug and total charges. Median cost of care by race and year of diagnosis were evaluated by Kruskal-Wallis (univariate) and proportional odds models (multivariate). Results: A total of 35842 pts (52.1% males) with median age of 76 yr (range 18-104) were included. Mutually exclusive racial subgroups were: White (W, 72%), Hispanic (H: 7.3%), African-American (AA, 16.2%), Asian (A, 4.1%), other (0.3%). A significantly increasing trend in the prevalence of all MM-C AT-D was seen over time (all p<0.001). For AF-D, all MM-C initially increased but decreased from 2006-2010 except D that showed continued increase (Fig 1A). For race, MM-C were significantly different, with AA having the most C, R, An and D while W having the most B (Fig 1B). Inferior OS was seen with male gender (p=0.006), increasing age, earlier years of diagnosis, belonging to W or H race, C, R and B (all p<0.001), but not with An (p=0.298) or D (p=0.378). Trends in OS over time showed a significant interaction with race in 1991-1995 (p<0.001), 1996-2000 (p=0.004) and 2001-2005 (p<0.001) but not in 2006-2010 (p=0.182), possibly due to shorter follow up. Medicare claims analysis showed that pts receiving any treatment increased from 12.8% in 1991-1995 to 59.3% in 2006-2010 (p<0.001). Specific therapy utilization, including SCT and all drug classes/agents evaluated increased except for cytotoxic chemotherapy utilization (melphalan/cyclophosphamide), which decreased (Fig 2A). Any therapy utilization was significantly different by race (p<0.001) with highest for H (36.5%) and lowest for AA (31.6%). Specific treatments differed significantly by race (Fig 2B). Cost of care analysis showed significant increase over years for median drug claims (0.23k to 3.63k) and total claims (5.11k to 27.8k) during first 6 mth as well as for any time after MM diagnosis (drug: 1.3k to 9.9k, total: 21.9k to 84k) (p<0.001 for all). Median drug claims in first 6 mth were highest among AA (1.3k) and lowest among A (0.63k) (p<0.001). Same was true for total claims in first 6 mth (p<0.001). For any time after diagnosis, H had the highest drug (p<0.001) and total (p=0.081) claims. There were significant interactions between year of diagnosis, race and cost of care with H seeing the highest increase in drug claims in first 6 mth (p<0.001) and any time (p=0.002). Conclusion: MM-C have generally decreased AF-D due to improved therapeutics although AA continue to have the highest rates of MM-C. Our data shows that utilization and cost of MM care have increased tremendously with resulting improved outcomes, but significant racial disparities persist. These disparities may be in part due to impaired access to new agents from lack of supplemental insurance for certain racial groups. Drug utilization and cost of care disparities may also be due to differences in co-morbidities, MM-C or access leading to differential healthcare utilization and should be explored in order to ameliorate disparities in outcomes. Disclosures Ailawadhi: Pharmacyclics: Consultancy; Novartis: Consultancy; Amgen Inc: Consultancy; Takeda Oncology: Consultancy.

BACKGROUND: Axi-cel is an autologous anti-CD19 chimeric antigen receptor (CAR) T-cell therapy that is approved for treatment of relapsed/refractory (R/R) large B-cell lymphoma and is associated with high response rates and durable remissions. Recent data show that axi-cel is effective across various adverse prognostic features, namely cell of origin, disease bulkiness, and extranodal disease, among others. Hypoalbuminemia is a known adverse prognostic factor in lymphomas. It is unknown if axi-cel overcomes the adverse prognostic feature of hypoalbuminemia in R/R large B-cell or transformed follicular lymphoma. METHODS: We conducted a retrospective analysis of patients treated with axi-cel across three Mayo Clinic campuses (Rochester, Jacksonville, and Phoenix) from 06/01/2018 until 04/01/2019. The primary objective of this analysis was to assess the impact of hypoalbuminemia (defined at day 0, prior to infusion) on outcome after axi-cel therapy. RESULTS: A total of 50 (male=37, 74%) patients (pts), median age of 53 (26-67) years received axi-cel. The median number of prior lines of therapy was 3 (2-8) (Table 1). Two pts had no available serum albumin levels at time of axi-cel infusion. Seven (15%) of 48 pts had serum albumin levels lower than 3.5 g/dL (median= 3.3 g/dL (range 2.6-3.4)) and the median follow up of survivors was 7.6 (1.9-14.3) months. The best overall response rate (ORR) and complete remission (CR) rates in these pts were 57% and 57%, respectively. One (14%) patient had stable disease and 2 (29%) had disease progression. The median overall survival (OS) for pts with hypoalbuminemia was not reached. On the other hand, 41 (85%) pts had a normal serum albumin level (median=4.0 (range 3.5-5.1) g/dL) and the median follow up for survivors was 6.3 months. The best objective response rate (ORR) and complete remission (CR) rates in these pts were 82% and 44%, respectively. The median OS for pts with normal serum albumin was 14 (95%CI=6.3-29.6) months. There was no significant difference at 6-months and 1-year OS between pts with hypoalbuminemia vs. those with normal baseline serum albumin levels [6-month=100% vs. 79%(95%CI=64-93%); 1-year (100% vs. 54% (95%CI=26-82%), p=0.17] (Figure 1). All grades cytokine release syndrome (CRS) was diagnosed in all 7 pts with hypoalbuminemia (100%) and in 38 of 41 (92%) pts without hypoalbuminemia. There was no difference in the median duration of CRS between pts with or without hypoalbuminemia [6 (range 1-11) days vs 5 (range 1-19) days, p=0.89]. Neurotoxicity (all grades) was observed in 5 (71%) pts with hypoalbuminemia compared 26 (63%) with normal albumin levels. There was no statistically significant difference in median duration of neurotoxicity between pts with hypoalbuminemia and those with normal baseline albumin levels [9 (range 1-10) days vs. 3 (range 0-25) days, p= 0.72]. CONCLUSIONS: Hypoalbuminemia does not have a significant impact on the outcomes of axi-cel therapy, including the incidence of CRS or neurotoxicity. These results need to be validated in a large collaborative multicenter study. Further investigation is needed to assess the prognostic impact of severe hypoalbuminemia (<3g/dL) on axi-cel therapy. Disclosures Ansell: Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Trillium: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; LAM Therapeutics: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Affimed: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Affimed: Research Funding. Bennani:Seattle Genetics: Other: Advisory board; Kite Pharma: Other: Advisory board; Seattle Genetics: Other: Advisory board; Adicet Bio: Other: Advisory board; Seattle Genetics: Other: Advisory board; Purdue Pharma: Other: Advisory board; Adicet Bio: Other: Advisory board; Purdue Pharma: Other: Advisory board; Purdue Pharma: Other: Advisory board; Adicet Bio: Other: Advisory board; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Kite Pharma: Other: Advisory board; Bristol-Myers Squibb: Research Funding; Kite Pharma: Other: Advisory board. Paludo:Verily Life Sciences: Research Funding; Celgene: Research Funding; Verily Life Sciences: Research Funding; Celgene: Research Funding. Tun:DTRM Biopharma: Research Funding; Mundi-pharma: Research Funding; BMS: Research Funding; Celgene: Research Funding; Curis: Research Funding; TG Therapeutics: Research Funding. Foran:Agios: Honoraria, Research Funding. Kharfan-Dabaja:Daiichi Sankyo: Consultancy; Pharmacyclics: Consultancy.

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.

Introduction Novel high-risk groups have been identified in adult ALL, including secondary (sALL) and Philadelphia-like ALL (Ph-like, based on CRLF2, IgH, ABL2, JAK2 and other tyrosine kinase translocations), and those with minimal residual disease >0.1% (MRD+) after induction therapy. Novel targeted therapies are now routinely incorporated into 1st line regimens, including tyrosine kinase inhibitors (BCR-ABL1-pos), rituximab (CD20+) and blinatumomab (Blina) for MRD+. The impact of these novel high risk groups and therapies after alloHCT is unknown; therefore, we evaluated their impact on overall survival (OS), relapse rate (REL), non-relapse mortality (NRM) and acute and chronic GVHD. Methods We evaluated pts receiving 1st Allo-HCT for ALL at Mayo Clinic (Rochester, Phoenix, and Jacksonville) from 2008-2018 for outcomes of interest, specifically the impact of novel therapies and risk groups. Associations of patient factors with outcomes were examined using univariable (UVA) and multivariable (MVA) Cox proportional hazards regression models, where the cause-specific hazard of the given outcome was modeled to account for the competing risk of death. Results We identified 261 consecutive AlloHCT recipients during the study period. Median age at transplant was 48 years (18-72) and 147 (56.3%) were male. The median comorbidity (HCT-CI) score was 2 (0-8). 213 pts (81.6%) had B-lineage ALL, of which 85 (32.6%) were BCR-ABL-pos, 17 (6.5%) Ph-like (identified by FISH), 16 (6.1%) hypoploidy/near triploidy (Hy/Tri), and 67 (25.7%) pre-B ALL NOS. The remaining 48 (18.4%) had T-ALL. 30 pts (11.5%) had sALL (i.e. prior chemo/radiotherapy for another malignancy). HyperCVAD was the most common 1st line regimen (68.2%). 243 (93.1%) pts achieved Complete Remission (CR1) after induction therapy, and 203 (77.8%) were in CR1 at the time of alloHCT. Blina was administered for MRD+ in 14 pts (5.4%), and for relapsed/refractory ALL (R/R) in 13 (27% of R/R pts), 7 of whom received Blina as initial therapy for R/R. Donors were matched unrelated in 149 (57.1%), matched related in 98 (37.5%), and haploidentical in 14 (5.4%). Peripheral blood (PB) grafts were used in 233 (89.3%). 103 (54.5%) were donor:recipient (D:R) sex-matched, and 86 D:R mismatched [47 (24.9%) M:F; and 39 (20.6%) F:M]. Myeloablative conditioning was used for the majority (78.5%) mostly with Cy/TBI (60.5%). Standard GVHD prophylaxis regimens were used. Outcomes Median follow-up after transplant was 22.4 months (0.5-135), and 51 (19.5%) had REL. The 1, 2 and 5-year survival rates were 71.9%, 64.9%, and 54.1%, respectively (Figure 1). Acute GVHD developed in 144 (55.2%) and chronic GVHD in 100 (38.3%). Ph-like ALL, Blina for MRD+, Blina for R/R, sALL and CD20-pos had no independent impact on OS. In contrast, age>60, Hy/Tri, and >CR1 at alloHCT were associated with worse OS in UVA, however, in MVA only pre-B ALL NOS was associated with better OS. Female:male D:R status was associated with inferior OS. Blina for R/R disease was associated with increased risk of REL in UVA [HR 5.26 95% CI (1.33, 20.00), p=0.017], whereas other novel high risk groups had no impact on REL. In contrast, T-ALL, Hy/Tri and >CR1 at AlloHCT were associated with increased REL in UVA, but only T-ALL and Hy/Tri continued to predict for increased REL in MVA. Secondary ALL was associated with increased NRM in UVA [HR 1.96 95% CI (1.07, 3.57), p=0.028], whereas other novel high risk groups had no impact on NRM. In contrast, age>60, >CR1 at AlloHCT and D:R sex mismatch were associated with higher NRM in UVA, but only sex mismatch and >CR1 at AlloHCT were associated with higher NRM in MVA. TBI use was associated with higher risk of acute GvHD (p=0.008) and ATG use with lower risk chronic GVHD (p<0.001). Similarly non-PB grafts were associated with a lower risk of chronic GVHD (p=0.005). Results for OS, REL, NRM, acute and chronic GVHD analysis are shown in Table 1. Conclusion Novel high risk groups (CD20+, Ph-like and sALL) do not appear to adversely impact OS after alloHCT, although sALL was associated with increased risk of NRM. Interestingly, pre-B-ALL NOS appear to be associated with favorable OS. Novel targeted therapies also do not independently predict outcome, with the exception of Blina for R/R ALL which may be associated with REL after subsequent alloHCT (a subgroup for whom novel maintenance strategies should be explored). Our analysis highlights the importance of allo-HCT for novel high risk ALL subgroups. Disclosures Patnaik: Stem Line Pharmaceuticals.: Membership on an entity's Board of Directors or advisory committees. Kharfan-Dabaja:Daiichi Sankyo: Consultancy; Pharmacyclics: Consultancy. Foran:Agios: Honoraria, Research Funding.