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Introduction Waldenstrom macroglobulinemia (WM) is a rare, incurable, indolent non-Hodgkin lymphoma that is preceded by an IgM monoclonal gammopathy of undetermined significance (IgM MGUS). In recent years, better understanding of the pathobiology of WM and IgM MGUS has strengthened the hypothesis that they may represent different stages of the same disease. Studying the relationship between these two conditions is the essential first step to understanding the mechanisms driving the malignant transformation from IgM MGUS to WM. Recently long non-coding RNAs (lncRNAs) have garnered significant recognition for their roles in the regulation of gene expression and cellular function. lncRNA can regulate gene expression of neighboring protein-coding genes through multiple mechanisms such as by recruiting chromatin modifiers and increasing accessibility of genes to transcription proteins, or by directly binding to enhancers or promoters. Here we present the first analysis of the lncRNA expression profile in patients with WM compared to IgM MGUS. Methods Twenty-one patients with untreated WM (n=14) and IgM MGUS (n=7) seen at Mayo Clinic were included in this analysis. Malignant cells were isolated from bone marrow samples using CD19 and CD38 positive selection. Total RNA was extracted and sequencing was performed using the Illumina HiSeq4000 NGS platform. Differentially expressed lncRNAs in WM compared to IgM MGUS were defined as log2 fold change (FC) > 1.0 (upregulated) or < -1.0 (downregulated) and a false discovery rate (FDR) < 0.05. The nearest gene to each lncRNA was also identified and their gene expression was detected. Using the Ingenuity Pathway Analysis software and taking into account the transcriptional expression and known function of these genes, a correlation between cell function and disease analysis was performed. Results A total of 119 lncRNAs were differently expressed between WM and IgM MGUS (17 transcripts were upregulated in WM, while 102 were downregulated in WM). Sixteen unique genes were identified as the nearest gene to 17 upregulated lncRNAs, while 18 genes were identified as the nearest genes to the top 18 downregulated lncRNAs in WM compared to IgM MGUS. Several of the upregulated lncRNA in WM were in proximity to known oncogenes. The well-known oncogenic lncRNA PVT1 was found to be upregulated in WM compared to IgM MGUS (log2 FC 1.35, FRD 0.04). PVT1 is overexpressed in several malignancies and associated with inhibition of apoptosis and increased cell proliferation. PVT1 is in close proximity to MYC and co-expression of PVT1-MYC has been described in multiple cancers. PVT1 also potentiate MYC amplification by increasing MYC protein stability, therefore promoting malignant cell growth. The lncRNA TCB1D27, also upregulated in WM compared to IgM MGUS, is located in close proximity to the oncogene TNFRSF13B (TACI), which was found to be overexpressed in WM patients (log2 FC 1.61, FDR <0.001). TACI is a receptor for BLyS/BAFF, which modulates the development of normal B-cells and plays a role in various B-cell malignancies. In fact, BLyS/BAFF level is known to be increased in WM and to promote survival and proliferation of WM B cells. The upregulated lncRNA RP11-25K21.1 in WM is located in close proximity to the oncogene FCGR2B (CD32B), which was also upregulated in WM compared to IgM MGUS (log2 FC 1.59, FDR 0.001). Overexpression of CD32B was previously shown to be associated with inferior outcomes in DLBCL and FL patients, presumably secondary to an increased internalization and clearance of rituximab due to increased CD32B expression on malignant cells. Several other upregulated lncRNAs in WM compared to IgM MGUS are also in proximity of other oncogenes such as PCED1B, POLD3, CLLU1, and SMAD7. Finally, we considered the expression of the nearest genes and their reported cell function and association with diseases. Several genes that are known to have a role in the cell proliferation and/or neoplasm development were noted to be upregulated in WM (figure 1). Conclusion We present the first report of lncRNAs expression in WM and IgM MGUS in addition to a concurrent analysis of the gene expression of the nearest protein-coding genes. Several known oncogenes are in proximity of overexpressed lncRNAs in WM compared to IgM MGUS that could indicate potential implications in the pathobiology of these diseases and the mechanism driving the malignant transformation to WM. Disclosures Paludo: Celgene: Research Funding; Celgene: Research Funding; Verily Life Sciences: Research Funding; Verily Life Sciences: Research Funding. Dasari:The Binding Site: Patents & Royalties: US Patent Rights on Mass Spectroscopy Licensing agreement with The Binding Site, Research Funding. Kapoor:Celgene: Honoraria; Cellectar: Consultancy; Janssen: Research Funding; Sanofi: Consultancy, Research Funding; Amgen: Research Funding; Takeda: Honoraria, Research Funding; Glaxo Smith Kline: Research Funding. Ailawadhi:Amgen: Consultancy, Research Funding; Pharmacyclics: Research Funding; Takeda: Consultancy; Janssen: Consultancy, Research Funding; Cellectar: Research Funding; Celgene: Consultancy. Gertz:Amyloidosis Foundation: Research Funding; i3Health: Other: Development of educational programs and materials; Medscape: Consultancy, Speakers Bureau; Ionis/Akcea: Consultancy; Celgene: Consultancy; Appellis: Consultancy; Amgen: Consultancy; Teva: Speakers Bureau; Alnylam: Consultancy; Prothena Biosciences Inc: Consultancy; Janssen: Consultancy; Spectrum: Consultancy, Research Funding; Annexon: Consultancy; Physicians Education Resource: Consultancy; Abbvie: Other: personal fees for Data Safety Monitoring board; Research to Practice: Consultancy; Johnson and Johnson: Speakers Bureau; DAVA oncology: Speakers Bureau; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Proclara: Membership on an entity's Board of Directors or advisory committees; Springer Publishing: Patents & Royalties; International Waldenstrom Foundation: Research Funding. Novak:Celgene Coorperation: Research Funding. Ansell:Affimed: Research Funding; Regeneron: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding.

Background: Ibrutinib (ibr) is a small-molecular inhibitor of Bruton's Tyrosine Kinase (BTK), active in the treatment of various B-cell malignancies. B-cell receptor signaling blockade by BTK inhibition using ibr down regulates IRF4 (survival transcription factor) which is down regulated by lenalidomide (R) as well, suggesting possible synergistic effect on cell death. Higher doses of ibr (560-840 mg daily) have been used in combination regimens for MM with no significant dose-limiting toxicities (DLTs) but the combination of ibr and len has not been previously tested. We present safety and preliminary efficacy of the combination ibr, R, and dexamethasone (d) in RRMM patients. Methods: This is a phase I dose escalation study (NCT03015792) of 28-day cycles of ibr (420 mg daily, 560 mg daily, 700 mg daily or 840 mg daily) days 1-28 in combination with R 25 mg PO days 1-21, and d 40 mg PO weekly utilizing a 3+3 dose-escalation design. Eligible RRMM patients had progression after ≥2 prior lines of treatment, measurable disease as per International Myeloma Working Group criteria, ECOG performance status ≤2, adequate bone marrow (BM) (absolute neutrophil count ≥1.0 x 109, platelets ≥50,000 cells/mm3 for patients with BM plasmacytosis <50% or ≥30,000 cells/mm3 for patients with BM plasmacytosis ≥50%), kidney (creatinine clearance ≥30 mL/min), and liver function [total bilirubin ≤1.5 x upper limit of normal (ULN), aspartate aminotransferase and alanine aminotransferase ≤3 x ULN], and PT/INR ≤1.5 X ULN. Treatment was to be continued till progressive disease (PD) or any unacceptable toxicity. The primary objective was to determine the maximum tolerated dose (MTD) of ibr+Rd in RRMM and secondary objective was to evaluate safety profile of this regimen. Results: As of July 26 2019, 18 patients had been enrolled in the trial. Three patients had to be replaced (2 at 700 mg cohort, 1 each due to withdrawal and ineligibility, and 1 at 840 mg due to withdrawal). Median age of all patients was 67 years (range 49-79) with 9 of the 15 evaluable patients being females. Evaluable patients as per ibr dose level included 3 at 420 mg, 3 at 560 mg, 3 at 700 mg, and 6 at 840 mg. Four out of 15 patients had high-risk cytogenetics. Median prior lines of treatment were 4 (range 2-13) and prior treatments included bortezomib in 87% (n=13), carfilzomib in 47% (n=7), ixazomib in 47% (n=7), lenalidomide in 87% (n=13), pomalidomide in 40% (n=6), thalidomide in 40% (n=6), daratumumab in 60% (n=9), and stem cell transplant in 53% (n=8). High risk cytogenetics [del 17p, t(4;14), t(14;16), t(14;20)] were noted in 4 of the 15 evaluable patients (27%). Median follow up for alive patients was 8.6 months (range 1.1-25.1 months) and the median number of treatment cycles was 2 (range 1-5). Most common reason for treatment discontinuation was PD (40%) followed by adverse events (AEs) (26.7%). Only 1 DLT possibly related to ibr was a grade 3 rash at the 840 mg dose. Grade 3/4 AEs at least possibly related to study treatment included anemia (n=3), thrombocytopenia (n=3), neutropenia (n=3), leucopenia (n=3), lymphopenia (n=2), febrile neutropenia (n=1), and rash (n=2). Overall, the most common all grade AEs included anemia (n=12), thrombocytopenia (n=10), fatigue (n=10), neutropenia (n=8), leucopenia (n=5), and diarrhea (n=5). (Figure 1) No treatment-related deaths were noted. Overall response rate (ORR) was 7% with partial response (PR) noted in 1 patient. Additionally, 1 patient achieved a minor response (MR) and 10 patients had stable disease (SD) for a clinical benefit rate (CBR) of 80%. (Figure 2) PD was noted in 1 patient and 2 patients did not get response assessment. Ibr 840 mg (daily) with R 25 mg (days 1-21) and d 40 mg weekly was considered the MTD of this regimen. The median progression-free survival (PFS) for the 15 evaluable patients was 4.5 months (95% CI: 1.8-not reached). Conclusions: We report the first phase 1 trial of combining a BTK inhibitor with Rd in RRMM patients. MTD of ibr was determined as 840 mg (daily) in combination with R 25 mg (days 1-21) and d 40 mg weekly. This dose of ibr is consistent with some other trials showing the benefit of a higher dose of ibr in various regimens for treatment of B-cell malignancies. We noted this regimen to be well-tolerated without much high-grade AEs. Disease stabilization was noted in majority of patients. These data lay the basis for a larger trial in a more uniform cohort of patients to better define the efficacy of this regimen. Disclosures Ailawadhi: Celgene: Consultancy; Amgen: Consultancy, Research Funding; Pharmacyclics: Research Funding; Cellectar: Research Funding; Janssen: Consultancy, Research Funding; Takeda: Consultancy. 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. OffLabel Disclosure: Ibrutinib is not FDA-approved for the treatment of multiple myeloma. The regimen of ibrutinib with lenalidomide and dexamethasone is not FDA-approved for the treatment of multiple myeloma.

Background: With the introduction of the Oncology Care Model, which aims to provide higher quality, more coordinated oncology care at the same or lower cost to Medicare, physician practices are increasingly being held accountable for performance and financial measures.1 Urgent care (emergency room [ER] visits and hospitalizations) is known to contribute a significant proportion of healthcare costs, and while there has been a focus on drug costs, little is known about the utilization or economic impact of urgent care among multiple myeloma (MM) patients. Objective: To identify the primary reasons and economic burden of ER visits and hospitalizations among newly diagnosed MM (NDMM) patients during first line of therapy (LOT1). Methods: Patients from the Optum Clinformatics Extended DataMart - Date of Death database (Optum DOD) with ≥1 medical or pharmacy claim for an NCCN listed MM treatment from 7/1/11 to 3/31/2018 (index date) were included if they had diagnosis code for MM (ICD9/10 code 203/C90) and ≥6-months continuous enrollment without MM treatment prior to index date. Patients were followed until death, end of eligibility, or end of study (9/30/2018), whichever occurred first. The top 20 most common primary diagnoses (PDx) for ER visits and for hospitalizations were identified separately for patients who received stem cell transplant (SCT) or not (nSCT) during LOT1. Total costs and per-patient-per-month (PPPM) costs of ER visits and hospitalizations for each PDx were reported. Results: Total 3,099 patients met inclusion criteria, including 2,761 (89.1%) nSCT and 338 (10.9%) SCT patients. nSCT patients were older than SCT patients (median nSCT=72 years vs SCT=62 years, p < 0.0001). The most common treatment regimens among nSCT were bortezomib in combination with dexamethasone (dex) (34%) followed by lenalidomide in combination with dex (27.4%). The most common treatment regimen among SCT were bortezomib in combination with lenalidomide and dex (46%) followed by bortezomib in combination with chemotherapy (18.3%). Median duration of LOT1 was 5.1 months among all patients, with median 11.2 months among SCT vs 4.5 months among nSCT, p < 0.0001). Among all patients, (Table 1) there was a total of 1,645 ER visits and 3,385 hospitalizations with a total cost of $3,729,208 and $56,751,758, respectively, and a PPPM cost of $146 and $2,220 for ER visits and hospitalizations, respectively. Among nSCT, there were 1,339 (81% of the total) ER visits and 2,776 (82% of the total) hospitalizations with a total cost of $3,063,727 and $39,763,095, respectively, and a PPPM cost of $151 and $1,955 for ER visits and hospitalizations, respectively. Among SCT, there were 306 (19% of the total) ER visits and 609 (18% of the total) hospitalizations, with a total cost of $665,481 and $16,988,663, respectively, and a PPPM cost of $127 and $3,253 for ER visits and hospitalizations, respectively. The most common PDx for hospitalizations among the entire cohort was 'MM, without mention of having achieved remission' with 769 hospitalizations for a total cost of $19,299,593 and a PPPM cost of $7,112. This same PDx was the most common among nSCT with 449 (58%) hospitalizations for a total cost of $7,551,282 and a PPPM cost of $9,292; as well as for SCT with 320 (42%) hospitalizations for a total cost of $11,748,760 and a PPPM cost of $4,435. This was also the most common PDx for ER visits among nSCT and SCT. For hospitalizations, the second most common PDx among the entire cohort and among nSCT was 'shortness of breath' while for SCT it was 'administration of antineoplastic chemotherapy'. The second most common PDx overall and for nSCT for ER visits was 'chest pain' and for SCT patients was 'musculoskeletal dysfunction (Segmental and somatic dysfunction of cervical region)'. Conclusions: In this analysis, NDMM patients experienced frequent ER visits and hospitalizations over a median of ~5 months for a total cost of more than $60 million, with most of the cost among nSCT patients. The most common PDx was related to MM. Even though some hospitalizations for the SCT patients may have been for the SCT procedure itself, the economic burden of urgent care for MM patients remains very high. Additional research to assess the impact of newer treatments on urgent care visits among NDMM, identify patients at high risk of urgent care visits may provide insight into the potential for developing cost-saving interventions to reduce the utilization of urgent care. Disclosures Maiese: Janssen: Employment, Equity Ownership. Zhu:Janssen Scientific Affairs, LLC: Other: contractor for Janssen. Wu:Janssen: Employment, Equity Ownership. Chen:Janssen: Employment, Equity Ownership. Ailawadhi:Janssen: Consultancy, Research Funding; Takeda: Consultancy; Celgene: Consultancy; Cellectar: Research Funding; Amgen: Consultancy, Research Funding; Pharmacyclics: Research Funding.

Background: Chimeric antigen receptor T-cell (CAR-T) therapy has revolutionized the treatment of relapsed/refractory B-cell lymphoid malignancies. Axicabtagene ciloleucel (axi-cel), an anti-CD19 CAR-T not only targets the malignant B-cell, but can potentially also target and eliminate normal B-cells. This can interfere with the normal B-cell repertoire, compromising host humoral immunity such as decreased titers of common vaccines (DTaP, MMR). To assess if this was a clinically significant problem, we evaluated all recipients of axi-cel at our center between 6/2018 through 7/2019. Patients and methods: For patients who received commercial axi-cel, humoral immunity was evaluated both quantitatively [absolute lymphocyte count (ALC)] as well as qualitatively by serology titers [IgG antibodies (Abs)] for diphtheria, tetanus, pertussis, measles, mumps, and rubella, and total Immunoglobulin G (IgG) levels. Data was collected within 30 days prior to CAR-T infusion, then at day +30 and between days +60 and +100 after CAR-T infusion. Results: We identified 10 patients (males = 5, 50%), with a median age of 49.9 years (range 30-65) who received commercial axi-cel during the study period. Patient characteristics and indications for CAR-T therapy are shown in Table 1; the cohort represented a heavily pre-treated aggressive B-cell lymphoma patient population. Baseline information on antibody (Ab) titers was available in 8 patients. At baseline, all patients had positive tetanus IgG Abs (≥0.01 IU/mL), 7 had positive diphtheria IgG Abs (≥0.01 IU/mL), 6 had positive measles IgG Abs (≥ 1.1 AI), 5 had positive rubella IgG Abs (≥ 1.0 AI), 3 had positive mumps IgG Abs (≥ 1.1 AI). None of the patients had a positive pertussis IgG Abs (≥100 IU/mL). At follow-up, all patients with positive Ab at baseline maintained titers in the positive range at day +30 and between days +60 and +100 (Figure 1). None of the patients demonstrated a clinically meaningful decrease in Abs titers, despite a drop in ALC and IgG levels (table 2). Conclusions: Albeit a small sample size, IgG Ab titers for diphtheria, tetanus, measles, mumps, and rubella did not appear to be affected by axi-cel at a short interval follow-up after infusion (up to day +100). We plan to extend this analysis in a larger cohort with a longer-term prospective follow-up to validate our findings, especially in light of dropping absolute lymphocyte counts and IgG levels. Disclosures Ailawadhi: Celgene: Consultancy; Amgen: Consultancy, Research Funding; Cellectar: Research Funding; Janssen: Consultancy, Research Funding; Takeda: Consultancy; Pharmacyclics: Research Funding. Foran:Agios: Honoraria, Research Funding. Tun:Celgene: Research Funding; Curis: Research Funding; TG Therapeutics: Research Funding; Mundi-pharma: Research Funding; DTRM Biopharma: Research Funding; BMS: Research Funding. Chanan-Khan:Pharmacyclics: Research Funding; Merck: Research Funding; Jansen: Research Funding; Mayo Clinic: Employment; Ascentage: Research Funding; Millennium: Research Funding; AbbVie: Research Funding; Xencor: Research Funding. Kharfan-Dabaja:Daiichi Sankyo: Consultancy; Pharmacyclics: Consultancy.

AbstractBackgroundThe Surveillance, Epidemiology, and End Results (SEER) database and National Cancer Database (NCDB) show improved overall survival (OS) in patients with multiple myeloma (MM) over the last 15 years. This analysis evaluated the validity of the largely community‐based Connect MM Registry as a national reference for MM.MethodsBaseline disease characteristics and survival in US newly diagnosed MM patients were examined using the Connect MM Registry as well as SEER and NCDB databases. Baseline characteristics predictive of longer survival in Connect MM were also identified.ResultsAs of February 2017, 3011 patients were enrolled in the Connect MM Registry; 2912 were treated. Median age at time of MM diagnosis and age range were numerically similar from 2010 to 2015 across all 3 registries; SEER had a higher representation of nonwhite racial groups than that in the other 2 registries. OS rates suggest proportionate improvement with year of diagnosis among the 3 registries. A Cox proportional hazards model suggests that younger age (<65 years) is associated with longer survival (vs ≥75; HR, 0.39; 95% confidence interval, 0.34‐0.46) in the Connect MM Registry. However, sex (HR, 0.91; P = .15) and race (black vs white; HR, 0.88; P = .21) were not associated with longer OS.ConclusionsData from the Connect MM Registry appear to be largely representative of national trends, comprehensive, and reliable representations of the national MM population. Baseline characteristics were comparable, and survival similarly improved over time among the 3 registries.ClinicalTrials.gov, identifierNCT01081028.

Aim To compare healthcare resource use and costs between newly diagnosed multiple myeloma (NDMM) patients with and without skeletal-related events (SREs). Methods Adults newly diagnosed with MM (1 January 2006 and 30 June 2017) with at least 12 months continuous health coverage prior to diagnosis were identified using the IBM MarketScan administrative claims. To control for baseline differences, NDMM patients with SREs were propensity score matched to NDMM patients without SREs. Outcomes included annual HRU and costs during follow-up along with number and type of SREs (SRE cohort only). Patients with SREs were stratified by number of SREs, and annual SRE-related costs were reported. Student's t test and Chi-squared test were used to compare outcomes. Results Before matching, the 6648 patients in the SRE cohort had more comorbidities, were more likely to have MM treatment, and had higher pre-index healthcare costs than the 7458 patients in the non-SRE cohort. After matching, cohorts of 3432 patients were well balanced on baseline characteristics. Patients with SREs (vs. without SREs) had significantly higher inpatient, outpatient, and pharmacy HRU. Patients with SREs had significantly higher mean annual all-cause healthcare costs ($213,361 vs. $94,896, p < 0.001) with hospitalization being the leading driver of increased costs (38.7% of total). Among 6648 patients with SREs, the mean annual SRE-related healthcare costs were $39,603, $45,463, and $50,111 for patients with one, two, and three or more events, respectively. Conclusions NDMM patients with SREs have more than twice the all-cause healthcare costs than matched patients without SREs. Costs increase with the number of SRE events.

Abstract The objective of the study was to assess racial disparities in the treatment and outcomes among white, African American, and Hispanic patients with multiple myeloma (MM). Patients with an MM diagnosis from the Surveillance Epidemiology and End Results (SEER)–Medicare (2007-2013) database were included. Continuous Medicare enrollment for 6 months before (baseline) and after MM diagnosis was required unless death occurred. Time from MM diagnosis to novel therapy initiation and autologous stem cell transplant (ASCT), overall survival (OS), and MM-specific survival (MSS) was evaluated. Unadjusted and multivariable regressions compared African Americans and Hispanics vs whites. Trends of novel therapy and ASCT use across MM diagnosis years were assessed using linear regression models. The study included 3504 whites, 858 African Americans, and 468 Hispanics. African Americans and Hispanics had a longer time from MM diagnosis to novel therapy initiation vs whites (median: 5.2 and 4.6 vs 2.7 months, respectively). All cohorts had an increasing trend of novel therapy initiation within 6 months of MM diagnosis, particularly whites (all P &lt; .05). Median MSS was significantly longer for African Americans (5.4 years) than whites (4.5 years; P &lt; .05), and was comparable for Hispanics and whites. Median OS was similar overall (2.6-2.8 years). ASCT rate within 1 year of MM diagnosis rose among whites and African Americans (P &lt; .05), but not Hispanics, who were less likely to receive ASCT vs whites. Significant variations in novel therapy and ASCT use were observed among different racial/ethnic groups with MM. Although OS was similar, both African Americans and Hispanics may not be fully benefitting from the introduction of novel therapies, as they receive them later than whites.

Abstract Introduction Waldenstrom macroglobulinemia (WM) is a rare disease accounting for only 1-2% of all hematologic malignancies and with an overall annual, age-adjusted incidence of approximately 3.5 to 5.5 cases per million-person years. With approximately 13,000 active hematologist/oncologists in the US, most of who are in the community oncology setting, it's estimated that a hematologist/oncologist will only diagnose a WM case approximately every 8 years. Therefore, the clinical experience of a general hematologist/oncologist in the management of WM is likely very limited compared to more prevalent malignances. Previous studies in the setting of rare cancers suggest a correlation between higher volume of care and improved outcomes. Therefore, we explored the volume-outcome relationship in WM using the National Cancer Data Base (NCDB). Methods Patient-level data from the NCDB, a nationwide, facility-based, database covering approximately 70% of all newly diagnosed cancer cases in the US, was queried for all new WM cases diagnosed between 2004 and 2014. Only patients requiring treatment were included. Treatment facilities were divided into quartiles based on the average annual volume of newly diagnosed cases of WM seen. Cox regression was used to analyze the association between facility WM volume and survival, adjusted by demographics (sex, age, race), socioeconomic status (income, education, insurance type), geography (area of residence, treatment facility type, travel distance), comorbidity factors (Charlson-Deyo score), and year of diagnosis. Time-to-event analysis was calculated from frontline therapy initiation date using the Kaplan-Meier method and the log-rank test. Results A total of 3,732 patients with WM treated in 831 facilities were included. The median age at diagnosis was 70 years and 75% of the patients were treated within 20 miles from their residency zip code. Patient characteristics per treatment facility volume quartile are shown in table 1. The median annual facility volume was 1 new WM patient/year (range 0.1 to 21). The median follow-up from frontline treatment was 5 years. The unadjusted median OS by facility volume was: Q1: 6.5 years (95% CI: 5.7-7.4), Q2: 7 years (95% CI: 6.3-8.2), Q3: 8.2 years (95% CI: 7.1-8.9), and Q4: NR (95% CI: 8.5-NR), p<0.0001 (figure 1). The estimated 5-year OS by facility volume was: Q1: 56%, Q2: 61%, Q3: 64%, Q4: 71%, p<0.0001. Multivariate analysis including all variables showed in Table 1 demonstrated that facility volume was independently associated with all-cause mortality. Compared to patients treated at Q4 facilities, patients treated at lower-quartiles facilities had a higher incremental risk of death (Q3 hazard ratio [HR], 1.07 [95% CI: 0.88 to 1.29] p=0.46; Q2 HR, 1.34 [95% CI: 1.11 to 1.63] p=0.002; Q1 HR, 1.52 [95% CI: 1.23 to 1.88] p=<0.0001). Conclusion Our results suggest that a volume-outcome relationship exits in WM as patients treated initially at higher-volume facilities had a lower risk of mortality. Although differences in the underlying disease biology, referral patterns after initial therapy, or cumulative treating-physician experience could not be assessed, these potential biases would only underestimate the magnitude of the volume-outcome relationship reported. Disclosures Ansell: Celldex: Research Funding; Trillium: Research Funding; Takeda: Research Funding; Regeneron: Research Funding; Pfizer: Research Funding; Affimed: Research Funding; Merck & Co: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding. Gertz:Medscape: Consultancy; janssen: Consultancy; Alnylam: Honoraria; Ionis: Honoraria; Physicians Education Resource: Consultancy; Prothena: Honoraria; Teva: Consultancy; Research to Practice: Consultancy; spectrum: Consultancy, Honoraria; celgene: Consultancy; Amgen: Consultancy; Abbvie: Consultancy; annexon: Consultancy; Apellis: Consultancy. Kapoor:Takeda: Research Funding; Celgene: Research Funding. Ailawadhi:Amgen: Consultancy; Janssen: Consultancy; Pharmacyclics: Research Funding; Celgene: Consultancy; Takeda: Consultancy. Reeder:Affimed: Research Funding. Witzig:Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Dispenzieri:Celgene, Takeda, Prothena, Jannsen, Pfizer, Alnylam, GSK: Research Funding. Lacy:Celgene: Research Funding. Kumar:Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding.

Abstract Background: Multiple myeloma (MM) is characterized by the invasion of malignant plasma cells into the bone marrow. While first line treatment options result in significant clinical benefit to patients, spatiotemporal clonal evolution results in disease relapse and mortality. Advances in genomics have armed clinicians with unprecedented insight into the molecular architecture of MM cells, however, the clinical benefit derived by genomics-guided intervention has been limited. We present a novel computational biology modelling (CBM) tool, which takes into account the combined effect of individual mutations, gene copy number abnormalities and large scale chromosomal changes in order to predict the salient molecular pathways utilized by the MM cell for survival. By reverse-engineering MM cell architecture in silico, the CBM tool is able to predict drug response and resistance mechanisms. Thus, our aim was to determine the accuracy of the CBM tool in predicting treatment response of relapsed/refractory MM patients for future management of their disease, in a more individualized manner. Methods: Cytogenetics and somatic mutations (by targeted NGS) for 15 MM patients were input into the CBM model to predict responses to different therapeutic combinations. All patients were relapsed to prior treatment. CBM uses PubMed and other online resources to generate patient-specific protein network maps of activated and inactivated disease pathways. We simulated the specific combinations of the drugs per patient and measured the quantitative drug effect on a composite MM disease inhibition score (i.e., cell proliferation, viability, apoptosis and paraproteins). The actual clinical outcome of the treatments was compared with predicted outcomes. Results: Fifteen patients were analysed using CBM for prediction of treatment response after NGS was performed. 13/15 were clinically evaluable, of which 1 was a responder and 12 were non-responder. 6/13 patients were treated on clinical trial and 7/13 were on drug combinations per physician decision. CBM correctly predicted 1 responder and 11 non-responder with a PPV of 50%, NPV 100%, specificity 91.67%, sensitivity 100%. The accuracy of CBM prediction was 92.30%. CBM also predicted the response of prior drug therapies for its non-response at relapse. For prior drug treatment options, 14 patients were evaluable. All the 14 patients were clinically non-responders and CBM correctly predicted for 13 patients with NPV 100%, Specificity 92.85% and overall accuracy of 92.85%. The majority of patients did not respond to therapies recommended at relapse. As an example, the operative molecular pathways from 2 patients who did not respond to combination treatment, either pre-NGS or post-NGS profiling, are shown in Fig. 1 and Table 1. CBM identified amplification (AMP) of chromosome (chr) 1 (WNT3A, IL6R, CKS1B, MCL1, PIK3C2B, USF1), chr 3 (HES1, PIK3CA, CTNNB1, WNT7A, FANCD2), chr 5 (IL6ST, IRF1, GLRX, SKP2), chr 7 (CDK5, EZH2, IL6, CAV1, ABCB1), chr 9 (NOTCH1, HSPA5, FANCC, FANCG), chr 15 (DLL4, FANCI, ALDH1A2), chr 19 (ERCC1, ERCC2, USF2); deletion(DEL) of chr 13 (CUL4A) , chr 16 (AXIN1, CDH1) and TP53 mutation in different combinations, which confer resistance to therapies at relapse. Conclusions: The CBM technology represents a potential means to identify therapeutic options for MM patients based on the patients individual tumor-genome profile and which can also be deployed for uncovering drug resistance mechanisms. This tool may aid clinicians in decision making for recommending the most appropriate therapy based on standard of care agents or clinical trials; thus improving patient outcomes and reducing unnecessary costs or drug-related toxicities. Disclosures Singh: Cellworks Research India Private Limited: Employment. Sauban:Cellworks Research India Private Limited: Employment. Husain:Cellworks Research India Private Limited: Employment. Kumar:Cellworks Research India Private Limited: Employment. Kumari:Cellworks Research India Private Limited: Employment. Tyagi:Cellworks Research India Private Limited: Employment. Abbasi:Cell Works Group Inc.: Employment. Vali:Cell Works Group Inc.: Employment. Ailawadhi:Pharmacyclics: Research Funding; Takeda: Consultancy; Celgene: Consultancy; Amgen: Consultancy; Janssen: Consultancy.