Go, Ronald S.

Abstract Background: Waldenström macroglobulinemia (WM) is an indolent B-cell lymphoma with a heterogeneous disease course. For nearly a decade, the International Prognostic Scoring system (IPSS), requiring 5 variables (platelet count, hemoglobin, age, Beta 2 microglobulin and immunoglobulin M size) has been used to prognosticate patients with active WM. In this study, we attempt to propose a simplified prognostic model for patients with active/symptomatic WM. Methods: All patients with active WM diagnosed between 01/01/1996 and 12/31/2017 and evaluated consecutively at Mayo Clinic, Rochester were included in the study after Institutional Review Board approval. Active WM was defined as ≥10% marrow infiltration with lymphoplasmacytic cells plus monoclonal IgM of any size requiring systemic therapy. The follow up and overall survival (OS) were calculated from the time of active WM until date of last follow up or death. Baseline parameters at active disease were used to assess prognostic factors for OS using a univariate followed by a multivariate (MV) Cox regression analysis. Independent variables on the MV analysis were given a score proportional to their risk ratios (RR). All time-to-event analyses were performed using the Kaplan-Meier method and the survival curves were compared using log-rank test. Discriminatory ability of the IPSS and the currently proposed prognostic system was assessed using Harrell's concordance index wherein a higher c-statistic is associated with a better discriminatory ability. Results: We identified 889 patients with active WM. The median follow up was 8.2 years (95% CI: 7.5-9 years). The baseline characteristics of the patients are shown in Table 1. At the time of diagnosis of WM, 16% of patients had previously documented MGUS and 16% patients had smoldering (asymptomatic) WM. The course of WM was complicated by systemic amyloidosis in 8% (n=69), transformation to high-grade lymphoma in 4% (n=38), symptomatic hyperviscosity in 13% (n=118), myelodysplastic syndrome in 2% (n=17) and Bing-Neel syndrome in 1.4% (n=12) of patients. Of the patients tested, cryoglobulinemia was present in 17% (n=48), cold agglutinin disease in 34% (n=43) and Coombs positive hemolytic anemia in 9% (n=34) patients. The median OS was 10.6 years (95% CI: 9.7-11.4 years). The factors affecting OS are shown in Table 2. While the patients' MYD88L265P mutation status was not found to be prognostic for OS, age >65 years [RR 1.8 (95% CI: 1.2-2.8); p=0.01], LDH > upper limit of normal [RR 2.5 (95% CI: 1.3-4.6); p=0.003] and serum albumin <3.5 g/dL [RR 1.6 (95% CI: 1.1-2.5); p=0.02] were independently prognostic. Owing to comparable RRs, each of these were assigned a score of 1 point and a prognostic model with a minimum score of 0 and maximum score of 3 was constructed. A score of 0 was considered as low risk WM and 1 as intermediate risk WM. The patients with scores of 2 and 3 were analyzed together to form the high risk cohort due to comparable OS for these two groups. The patient outcomes based on this prognostic model are depicted in Figure 1. Using the new prognostic model (n-=341), the median OS for the low risk group (n=71, 21%) was 14.6 years (95% CI: 9 years- not reached) compared to 11 years (95% CI: 8.1-14.7 years) for intermediate risk (n=140, 41% ) and 7.2 years (95 % CI: 5.4-10 years) for the high risk group (n=130, 38%), p<0.0001. The data for the IPSS-WM were available in 319 patients in the entire cohort. The OS for low risk IPSS (n=56) was 14.6 years (95% CI: 12.1 years-NR), 15.6 years (95% CI: 8.3-18.5 years) for intermediate risk IPSS (n=131) and 9.1 years (95% CI: 6.8-11.2 yeas) for the high risk IPSS group (n=132; p=0.009). Among the 220 patients having data for IPSS-WM as well as our proposed model, the current three variable model held its prognostic significance (p=0.0004). The Harrell's c-index was 0.647 (95% CI 0.592-0.702) for our prognostic model and 0.58 (95%CI: 0.521-0.638) for IPSS-WM. Common frontline treatment comprised of rituximab monotherapy (n=277, 33%), rituximab with alkylator or purine analog combination (n=255, 30.5%) and other therapies, including alkylators/purine analog monotherapy, ibrutinib, proteasome inhibitors, etc., in 37% patients. Conclusion: A simpler, clinically useful, risk stratification model based on age, serum albumin and serum LDH with a potentially higher discriminatory value than IPSS-WM may be used to prognosticate patients with WM. This model requires external validation. Disclosures Ansell: Pfizer: Research Funding; Affimed: Research Funding; Celldex: Research Funding; Bristol-Myers Squibb: Research Funding; Takeda: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Merck & Co: Research Funding; Regeneron: Research Funding. Dispenzieri:Celgene, Takeda, Prothena, Jannsen, Pfizer, Alnylam, GSK: Research Funding. Gertz:Abbvie: Consultancy; Prothena: Honoraria; Medscape: Consultancy; annexon: Consultancy; Amgen: Consultancy; Alnylam: Honoraria; Teva: Consultancy; janssen: Consultancy; Physicians Education Resource: Consultancy; Ionis: Honoraria; spectrum: Consultancy, Honoraria; celgene: Consultancy; Research to Practice: Consultancy; Apellis: Consultancy. Witzig:Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Lacy:Celgene: Research Funding. Dingli:Millennium Takeda: Research Funding; Alexion Pharmaceuticals, Inc.: Other: Participates in the International PNH Registry (for Mayo Clinic, Rochester) for Alexion Pharmaceuticals, Inc.; Alexion Pharmaceuticals, Inc.: Other: Participates in the International PNH Registry (for Mayo Clinic, Rochester) for Alexion Pharmaceuticals, Inc.; Millennium Takeda: Research Funding. Kumar:Takeda: Membership on an entity's Board of Directors or advisory committees; Merck: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: 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; 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; Novartis: Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; Roche: Research Funding; KITE: 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. Kapoor:Takeda: Research Funding; Celgene: Research Funding.

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 Introduction Del(17p) detected by fluorescence in situ hybridisation (FISH) of bone marrow plasma cells (BMPCs) is an adverse prognostic factor in patients with multiple myeloma (MM). While the natural history and prognostic factors in patients with del (17p) detected at diagnosis have been studied, the outcomes of patients who acquire del (17p) later in their course are not known. We aimed to define the outcomes of MM patients who acquire del(17p). Methods We reviewed the Dysproteinemia database at Mayo Clinic, MN to identify 80 patients with MM who had their first FISH test negative for del (17p), but acquired del(17p) later in the course of the disease. Sixty (75%) patients had their first FISH within 6 months (mo) of diagnosis; 20 (25%) patients had their first FISH more than 6 mo after diagnosis of MM. Del(17p) included del(17p13.1) and/or monosomy 17. We identified 2 control patients for each case, who were diagnosed during the same time period, but did not demonstrate del (17p) at any time during follow-up. Results Characteristics of patients at acquisition of del(17p) are shown in Table 1. Of these, 76 patients had relapsed and/or refractory MM; in 4 patients, del (17p) was detected after having attained a response to prior therapy. Seventy (87.5%) patients had del (17p13.1); 5 (6.3%) patients had monosomy 17; 1 (1.2%) patient had both del (17p13.1) and monosomy 17. Four (5.0%) patients had relative loss of 17p. High-risk translocations (HRT) [t(4;14), t(14;16) and t(14;20)] were present in 14 (17.5%) patients. The percentage of PCs with del(17p) were available in 75 patients. The median percentage of PCs with del(17p) was 76 (range, 9-100). Sixty two (77.5%) patients received exclusively chemotherapy as subsequent treatment; stem cell transplant (SCT) was part of the next treatment in 16 (20%) patients (allogeneic SCT in 1). Among 71 evaluable patients, ≥very good partial response and ≥partial response were attained in 24 (33.8%) and 35 (49.2%) patients respectively. The median progression free survival (PFS) from detection of del (17p) was 5.7 mo (95% CI, 3.5-7.8); overall survival (OS) was 18.2 mo (CI, 13.1-27.5). From a comparable time point, the OS for 160 controls was 56.2 mo (CI, 37.1-75.3) (P<0.001). The results were similar when patients were stratified based on variables at diagnosis such as age, presence of HRT, international staging system (ISS) stage, serum lactate dehydrogenase (LDH) and PC proliferative rate, and proteasome inhibitor (PI)-containing induction and SCT within a year of diagnosis and before acquisition of del(17p). Evaluating outcomes from initial diagnosis, PFS in the acquired del (17p) and control groups were 23.0 (CI, 20.2-25.8) and 30.2 (CI, 26.7-33.9) mo (P=0.028) and OS were 68.2 (CI, 50.8-74.8) and 111.6 (CI, 103.4-127.8) mo respectively (P<0.001). High LDH at baseline [OR- 4.31 (CI, 1.34-13.86)] predicted acquisition of del(17p) while presence of HRT showed borderline significance [OR- 2.02 (0.90-4.54)]. Age ≥65 yrs, ISS III vs. I/II stage, t(11;14), any trisomy, monosomy 13, BMPC%, initial therapy and SCT in first year and before acquiring del (17p) were not predictive. We performed univariable analysis with variables at detection of del(17p) such as age ≥65 vs. <65 years, serum creatinine >2 vs. ≤2 mg/dL, BMPC% ≥50 vs. <50%, ISS III vs. I/II stage, elevated vs. normal LDH, presence vs. absence of a HRT, presence vs. absence of monosomy 13, presence vs. absence of any trisomy, high vs. low PC proliferation rate, PI and immunomodulatory drug (IMiD)-refractoriness and percentage of PCs with del(17p) as independent variables to determine their association with PFS and OS. ISS stage III, high PC proliferative rate and PI and IMiD-refractoriness were selected after univariable analysis and included in the multivariable Cox proportional hazards model. A higher PC proliferative rate alone predicted shorter OS. None of the above factors were associated with shorter PFS. The median OS in those with high PC-proliferative rate from detection of del(17p) was 10.9 mo (CI, 6.0-17.1) vs. 37.2 mo (CI, 18.2-47.5) in those with low PC proliferative rate (P=0.005). Conclusion The prognosis of patients who acquire del (17p) is poor with OS of 6.5 years against 9 years from diagnosis in patients who do not acquire del (17p). This was valid across prognostic sub-groups. High PC proliferative rate was the only factor which impacted OS once patients acquired del(17p). Disclosures Kapoor: Celgene: Research Funding; Takeda: Research Funding. Gertz:Research to Practice: Consultancy; Teva: Consultancy; Ionis: Honoraria; annexon: Consultancy; Physicians Education Resource: Consultancy; Abbvie: Consultancy; spectrum: Consultancy, Honoraria; Prothena: Honoraria; Alnylam: Honoraria; Apellis: Consultancy; Medscape: Consultancy; Amgen: Consultancy; janssen: Consultancy; celgene: Consultancy. Lacy:Celgene: Research Funding. Dingli:Alexion Pharmaceuticals, Inc.: Other: Participates in the International PNH Registry (for Mayo Clinic, Rochester) for Alexion Pharmaceuticals, Inc.; Alexion Pharmaceuticals, Inc.: Other: Participates in the International PNH Registry (for Mayo Clinic, Rochester) for Alexion Pharmaceuticals, Inc.; Millennium Takeda: Research Funding; Millennium Takeda: Research Funding. Dispenzieri:Celgene, Takeda, Prothena, Jannsen, Pfizer, Alnylam, GSK: Research Funding. Russell:Vyriad: Equity Ownership. Kumar:AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: 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; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; KITE: 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; Takeda: Membership on an entity's Board of Directors or advisory committees; AbbVie: 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; Novartis: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding.

Abstract The plasma cell proliferative index (PCPI) provides an insight into plasma cell biology in plasma cell disorders. It recognizes cells that are actively synthesizing DNA and gives an indication of the proliferative rate of the malignant plasma cells.The PCPI has been shown to be a prognostic tool in patients with multiple myeloma and amyloidosis. We conducted a retrospective review analyzing the prognostic impact of PCPI in 306 patients with smoldering multiple myeloma (SMM). The median age was 66 (56-73) years and 61% (n=186) were males. The median follow-up for the entire cohort was 10.2 years (95% confidence interval: 9.0-10.9). One-hundred sixty-nine (55%) patients were alive at the time of study analysis while 118 (39%) had progressive disease. The median time to progression was 5.9 years (95% confidence interval: 4.8-8.2). Symptomatic events at the time of progression included anemia in 41% (n=48), bone complications in 37% (n=44), renal complications in 9% (n=11), hypercalcemia in 2% (n=2) and development of amyloidosis in 2% (n=2). Seventeen patients (14%) were categorized as having progression due to rapid progressive elevation in the serum free light chains (sFLC) and/or progressive increase in the size of their M-spike. The median time between the diagnosis and PCPI date was 0 months (interquartile range 0-1). An elevated PCPI was defined as a level >0.5%. Seventy-nine (26%) patients had an elevated PCPI. Patients with an elevated PCPI were significantly older (median age: 69 years for elevated PCPI vs 64 years for low PCPI (p= 0.008) and predictably had more proliferative disease with a higher rate of patients with bone marrow plasma cells >20% (48% for elevated PCPI vs 34% for low PCPI, p=0.03). An elevated PCPI predicted a shorter time to progression (TTP); median 3.0 years versus 7.1 years for those with a low PCPI (p= 0.0004). Within 24 months, the progression rate was significantly higher for patients with an elevated PCPI; 49% versus 20% (p<0.0001). We constructed two multivariable models using the conventional and recently proposed Mayo risk stratification tools. In both models, an elevated PCPI was an independent predictor of to multiple myeloma. PCPI is a valuable tool in risk stratifying patients with SMM and identifies patients with earlier progression who may benefit from closer follow up and consideration of early intervention trials. Disclosures Dispenzieri: Celgene, Takeda, Prothena, Jannsen, Pfizer, Alnylam, GSK: Research Funding. Gertz:Research to Practice: Consultancy; spectrum: Consultancy, Honoraria; Abbvie: Consultancy; Amgen: Consultancy; janssen: Consultancy; Physicians Education Resource: Consultancy; Teva: Consultancy; celgene: Consultancy; annexon: Consultancy; Alnylam: Honoraria; Prothena: Honoraria; Medscape: Consultancy; Apellis: Consultancy; Ionis: Honoraria. Lacy:Celgene: Research Funding. Dingli:Alexion Pharmaceuticals, Inc.: Other: Participates in the International PNH Registry (for Mayo Clinic, Rochester) for Alexion Pharmaceuticals, Inc.; Millennium Takeda: Research Funding; Alexion Pharmaceuticals, Inc.: Other: Participates in the International PNH Registry (for Mayo Clinic, Rochester) for Alexion Pharmaceuticals, Inc.; Millennium Takeda: Research Funding. Kapoor:Celgene: Research Funding; Takeda: Research Funding. Kumar:Oncopeptides: Membership on an entity's Board of Directors or advisory committees; 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; Celgene: 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; Takeda: Membership on an entity's Board of Directors or advisory committees; Merck: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding; 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; Roche: Research Funding; KITE: 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 The survival of patients with multiple myeloma (MM) has improved significantly over the past two decades with the introduction of novel treatment agents. However, MM is still largely considered an incurable malignancy with a relapsing-remitting course. A follow-up of at least 10 years from active disease is required to determine whether a plateau in progression-free survival has been attained. Prior literature has used 10 years as a cutoff for "long-term survivorship". In this study, we have assessed the biological disease characteristics and outcomes of long-term survivors with MM (≥10 years from active disease). Methods All patients with active MM evaluated at the Mayo Clinic, Rochester between January 1, 1999 and July 1, 2008 were included in the study after approval of the Institutional Review Board. Patients with smoldering multiple myeloma were excluded. The overall survival (OS) was calculated from the time of symptomatic disease requiring treatment. Patients were then divided into two cohorts: (1) long-term survivors, which included patients who had an overall survival of at least 10 years; and (2) short-term survivors, which included patients who had an overall survival of less than 5 years from the diagnosis of active MM. The baseline characteristics between these two groups were compared using Wilcoxon, chi-square, and Fisher's exact test as applicable. All time-to-event analyses were performed using the Kaplan-Meier method and the survival curves were compared using Log-Rank test. Results During the time frame of the study, 2,125 patients were identified who fulfilled the diagnostic criteria for active MM. The median follow-up for the entire cohort was 12.6 years (95% CI: 12.5-13.4).The median OS for the entire cohort was 4.4 years (95% CI: 4.2-4.7 years). Three-hundred and ninety nine (18.7%) patients survived at least 10 years whereas 872 patients (41%) survived less than 5 years from the date of initial diagnosis. The median OS was 14.1 years for the long-term survivors (95% CI: 13.9-14.6 years) and 2.1 years for the short-term survivors (95% CI: 1.8-2.2 years). The clinical features at diagnosis comparing long-term survivors and short-term survivors are shown in Table 1. Among long-term survivors (n=399), based on the available data regarding remission and ongoing treatment status, 331 patients were categorized into 6 cohorts (Table 2). The MM specific survival data of these 6 cohorts is depicted in Table 2.Figure 1 shows survival outcomes from the 10-year landmark. Of the 6 cohorts, 38 patients in Cohort 1 and 19 patients in Cohort 2 (total 57; 17% of long term survivors, ~3% of the entire cohort ) have been off therapy for at least 5 years and remain in remission, representing a distinct group of patients with 100% 15-year survival. Conclusion In our large database with prolonged follow-up, long-term survivors appear to have distinct baseline characteristics, but also constitute a heterogenous group of patients with disparate outcomes. A small subset (17% of long-term survivors) was identified that may represent patients closest to being considered as 'operationally cured'. Disclosures Lacy: Celgene: Research Funding. Dispenzieri:Celgene, Takeda, Prothena, Jannsen, Pfizer, Alnylam, GSK: Research Funding. Gertz:Teva: Consultancy; spectrum: Consultancy, Honoraria; Medscape: Consultancy; Physicians Education Resource: Consultancy; Amgen: Consultancy; Apellis: Consultancy; Alnylam: Honoraria; Abbvie: Consultancy; janssen: Consultancy; celgene: Consultancy; Prothena: Honoraria; annexon: Consultancy; Ionis: Honoraria; Research to Practice: Consultancy. Dingli:Alexion Pharmaceuticals, Inc.: Other: Participates in the International PNH Registry (for Mayo Clinic, Rochester) for Alexion Pharmaceuticals, Inc.; Millennium Takeda: Research Funding; Alexion Pharmaceuticals, Inc.: Other: Participates in the International PNH Registry (for Mayo Clinic, Rochester) for Alexion Pharmaceuticals, Inc.; Millennium Takeda: Research Funding. Kumar:KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; Merck: Membership on an entity's Board of Directors or advisory committees, Research Funding; 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; Roche: Research Funding; KITE: 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; Celgene: 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; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding. Kapoor:Celgene: Research Funding; Takeda: Research Funding.

Abstract Background Hodgkin lymphoma is a rare hematological malignancy, with 8,500 new cases per year in the United States. Prior studies in oncology have shown that higher patient volume, whether at the treatment facility or provider level, is associated with reduced mortality and improved overall survival (OS). We used a national oncology data set to test our hypothesis that a volume-outcome relationship may also exist in classical Hodgkin lymphoma. Methods Using the National Cancer Data Base (NCDB), we identified patients diagnosed with classical Hodgkin lymphoma between 2003 and 2014. The NCDB is a nationwide oncology outcomes database for ~70% of newly diagnosed cases from more than 1,500 accredited cancer programs in the United States and Puerto Rico. We classified the facilities by quartiles (Q; mean patients with HL treated per year): Q1: < 3.19, Q2: 3.2 to 5.0, Q3: 5.01 to 9.19, and Q4: ≥9.2. Our primary outcome of interest was all-cause mortality. We used random intercepts to account for clustering of patients within facilities and a Cox regression model to determine the volume-outcome relationship, adjusting for demographic (sex, age, race, ethnicity), socioeconomic (income, education, insurance type), geographic (area of residence, treatment facility location, travel distance), and comorbid (Charlson-Deyo score) factors, as well as year of diagnosis. Kaplan-Meier analysis was performed to examine the impact of patient volume (by quartile) on overall survival. Results There were 47,633 patients with classical Hodgkin lymphoma treated at 1,310 facilities. The median age at diagnosis was 40 years, and 54.2% were men. The median annual facility volume was 2.7 patients per year (range, 1 to 42.8). The distribution of facilities according to patient volume was: Q1: 58.4%, Q2: 22.4%, Q3: 13.3%, and Q4: 5.9%. After a median follow-up of 58 months, the highest volume quartile (Q4) had significantly higher 5- and 12-year overall survival rates compared to Q1-Q3 (Table 1). Overall survival by quartile is depicted as a Kaplan-Meier curve (Figure 1). Compared to the patients treated at Q4 facilities, those treated at lower-quartile facilities had a higher risk of death (Q3 hazard ratio [HR], 1.18 [95% CI, 1.10 to 1.27]; Q2 HR, 1.21 [95% CI, 1.13 to 1.30]; Q1 HR, 1.19 [95% CI, 1.11 to 1.28]) after adjustment for the sociodemographic factors discussed in our methods. Multivariable analysis showed that facility volume as a continuous variable was independently associated with all-cause mortality (Figure 2) (P<0.0001). Compared with facilities treating 10 patients per year, facilities treating 20, 30, and 40 patients per year had approximately 10%, 17%, and 23% lower overall mortality rates. Conclusions Patients treated for classical Hodgkin lymphoma at higher-volume facilities had a lower risk of mortality compared with those treated at lower-volume facilities. This adds to the growing body of evidence that a volume-outcomes relationship exists in some of the less common hematologic malignancies. Further research is needed to ascertain whether such differences in survival are related to provider-level or facility-level attributes, and whether patients with Hodgkin lymphoma will benefit from referral to high-volume centers. Disclosures No relevant conflicts of interest to declare.

Abstract Introduction: MGUS is generally an incidental finding in the diagnostic work-up for clinical signs and symptoms suggestive of lymphoplasmacytic malignancies (multiple myeloma, light chain amyloidosis, and Waldenström macroglobulinemia), which are relatively rare (<35,000 annual new cases in the US). While much is known about the natural history of MGUS, information regarding the pre- and post-diagnostic part of MGUS patient care is lacking. Our study objectives were to determine the following: 1) indications for monoclonal protein testing; 2) subsequent diagnoses found for those indications; 3) specialty of ordering clinicians; 4) follow-up patterns after MGUS diagnosis. Methods: We identified MGUS patients residing in southeastern Minnesota who were diagnosed from 2011-2014 and followed at the Mayo Clinic. Medical records were reviewed to confirm the diagnosis and obtain relevant clinical data. Laboratory tests and visits were identified using Current Procedural Terminology-4th edition (CPT-4) codes from billing data. We defined a follow-up visit as: 1) any face-to-face encounter linked to MGUS diagnosis 30 days after the date of MGUS diagnosis regardless of whether ancillary test was performed or not; and 2) MGUS-specific tests or laboratory tests linked to an MGUS diagnosis claim performed without a face-to-face encounter. Based on the Mayo Clinic MGUS risk stratification model, we classified our cohort into either low-risk or non-low risk. Criteria for low-risk used were: serum monoclonal protein <1.5 g/dL, IgG subtype, and normal serum free light chain ratio. Follow-up patterns were analyzed according to year of diagnosis, demographics, and the specialty of clinicians performing the follow-up. Results: 330 MGUS patients were included in the study. The median age at diagnosis was 73 years (range, 21-98) and most were males (59.7%). The common indications for monoclonal protein studies were neuropathy (19.6%), kidney disease (13.6%), anemia (12.7%), bone symptoms/signs (12.7%), cutaneous disorders (5.8%), congestive heart failure (4.8%), and hypercalcemia (2.7%). The most common subsequent diagnoses for these indications were neuropathy not otherwise specified (NOS;100%), chronic kidney disease NOS (35.5%), anemia of chronic kidney disease (19%), osteopenia/osteoporosis (45.2%), congestive heart failure NOS (57.1%), and dermatitis NOS (100%), respectively. The practice specialties that most commonly diagnosed MGUS were internal medicine (31.3%), neurology (13.7%), nephrology (10.3%), family medicine (6.1%), and hematology (5.8%). Low risk MGUS comprised 44.8% of the cohort. After a median follow-up of 53.5 months (range, 13.0-77.4; IQR, 40.8-77.4), the total number of follow-up visits was 937. Majority (85.5%) of the visits were a combination of office visit with laboratory testing, while the rest were either office visit (11.2%) or laboratory tests (3.3%) only. The distribution of patients by mean interval between visits was: every <6 months (7.9%); every 6-12 months (19.4%); every 13-24 months (15.2%), and every >24 months or no follow-up at all (57.6%). The follow-up patterns did not change significantly (Kruskal Wallis; P=0.6759) over time (Figure 1) and were similar when age groups were compared (Figure 2; P=0.1328). However, males were followed more frequently than females (P=0.0365). Among patients 80 years and older, 32.1% continued to be followed at least once every 2 years (Figure 2). Hematologists were more likely than non-hematologists to follow MGUS patients regardless of the risk category (Figures 3-4). Among low risk patients, 31.1%, 22.2%, 20.7%, and 19.1% had at least one follow-up during years 2, 3, 4, and 5, after MGUS diagnosis (Figure 3). Conclusions: Approximately 1/3 of MGUS diagnoses were made during the evaluation of signs and symptoms not related to lymphoplasmacytic malignancies. The subsequent diagnoses found were a wide variety of common diseases. Most MGUS diagnoses were made by general internists, neurologists, and nephrologists. Follow-up practices varied between hematologists and non-hematologists. Nearly 1/3 of the oldest old patients continued to have follow-up, despite limited life expectancy. Disclosures No relevant conflicts of interest to declare.

Abstract Background: Limited data exist regarding histological transformation in patients with Waldenström macroglobulinemia (WM). In this study, we present the disease characteristics, outcomes and risk factors for histological transformation in WM. Methods: Patients with WM seen at Mayo Clinic, Rochester, MN between January 1996 and December 2017 were included. Patients with aggressive non-Hodgkin lymphoma in the setting of WM were considered to have transformation. Univariate analysis for comparing baseline characteristics of transformed versus non-transformed WM was performed using Wilcoxon, Fisher's exact and Chi square tests, as appropriate. Multivariate analysis was performed using logistic regression analysis. Cox proportional hazard method was used to assess the impact on time-to-transformation. All time-to-event analyses were calculated by the Kaplan-Meier method. Results: Of 1014 patients with WM, 42 patients (4.1%) developed histological transformation. The median follow-up for the entire cohort was 9.5 years (95% CI: 8.8-10.5 years). The cumulative probability of transformation was 2.3% at 5 years, 5.3% at 10 years and 8.5% at 15 years from diagnosis of WM, respectively. There was no difference in the 5-year cumulative probability of transformation in patients with WM diagnosed between 1996-2000 (2.1%), 2001-2005 (2.9%), 2006-2010 (2.7%) and 2011-2015 (3.8%), p=0.61.The disease characteristics, laboratory parameters and histology at transformation are outlined in Table 1. In patients with DLBCL histology (n=39), Revised-International Prognostic Index was calculable in 28 patients, with 50% (n=14), 46% (n=13) and 4% (n=1) of DLBCL belonging to poor, good and very good risk groups, respectively. For DLBCL histology, 18 patients were classifiable using Hans algorithm into germinal center B-cell (GCB) type (n=3, 17%) and non-GCB type histology (n=15, 83%). The median lines of therapy received prior to transformation for the entire cohort was 2 (range 0-9), with 5 (11%) patients not having received any therapy for WM before transformation. Number of lines of alkylator-based therapy used prior to transformation [median 1 line (range 0-4)] was comparable to that used in patients in whom WM did not occur [median 1, (range 0-6); p=0.78]. The median time-to-transformation from diagnosis of WM was 4.8 years (95% CI: 2.6- 6.8 years). Overall survival (OS) from transformed disease was 3.2 years (95% CI 1.1-3.9 years). The 10-year OS of patients with WM who transformed was lower (46%) compared to patients without transformation (60%) with a trend towards statistical significance (p=0.08), Figure 1. The MYD88L265P mutation status was available in 333 patients in the entire cohort (22/42 pts in the transformed cohort and 311/972 in the non-transformed cohort. Of the 22 patients in the transformed WM cohort, 13 (59%) exhibited MYD88WT genotype and 9 (41%) exhibited MYD88L265P genotype. The risk of transformation was higher in patients with MYD88WT status [Odds ratio 6.3 (95% CI: 2.6-15.5); p<0.0001]. Additionally, serum lactate dehydrogenase (LDH) was higher at diagnosis of WM in patients who transformed, Table 2. On multivariate analysis, MYD88WT status remained an independent predictor of disease transformation [p=0.003; odds ratio 10 (95% CI: 2.1-48)]. On univariate Cox proportional hazard analysis for identifying predictors of time-to-transformation, only MYD88WT mutational status was associated with shorter time-to-transformation with a 5-year transformation rate of 15.5 % for MYD88WT versus 2.6 % with MYDL265P mutated patients [risk ratio 6.2 (95% CI: 2.5-16.4); p<0.0001]. Treatment-related details in the transformed cases were available for 37 (90%) patients; R-CHOP was the most common (41%) frontline therapy for transformation; 33% patients underwent autologous stem cell transplantation for transformed disease during their disease course. Conclusion: Histological transformation is an uncommon event in WM but confers poorer survival. MYD88WT genotype is independently associated with histological transformation a shorter time-to- transformation in patients with WM. Disclosures Ansell: Trillium: Research Funding; Pfizer: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Takeda: Research Funding; Celldex: Research Funding; Regeneron: Research Funding; Merck & Co: Research Funding; Bristol-Myers Squibb: Research Funding. Gertz:Apellis: Consultancy; Alnylam: Honoraria; spectrum: Consultancy, Honoraria; janssen: Consultancy; annexon: Consultancy; Prothena: Honoraria; Abbvie: Consultancy; Amgen: Consultancy; celgene: Consultancy; Teva: Consultancy; Medscape: Consultancy; Ionis: Honoraria; Research to Practice: Consultancy; Physicians Education Resource: Consultancy. Dingli:Millennium Takeda: Research Funding; Millennium Takeda: Research Funding; Alexion Pharmaceuticals, Inc.: Other: Participates in the International PNH Registry (for Mayo Clinic, Rochester) for Alexion Pharmaceuticals, Inc.; Alexion Pharmaceuticals, Inc.: Other: Participates in the International PNH Registry (for Mayo Clinic, Rochester) for Alexion Pharmaceuticals, Inc.. 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:Merck: 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; AbbVie: 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; KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: 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; Roche: Research Funding; Celgene: 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; Novartis: Research Funding; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding. Kapoor:Takeda: Research Funding; Celgene: Research Funding.

Abstract Background: The incidence of multiple myeloma (MM) in the US is increasing among non-Hispanic (NH) White males and females as well as NH Black males (Costa LJ, Blood Advances 2017). However, more specific incidence studies accounting for geographic and age subgroups are limited. There is also a growing concern that incidence of many cancers is increasing in the younger population. Utilizing the Surveillance, Epidemiology, and End Results Program (SEER), we investigated the trends in MM incidence in the US according to demographic and geographic subgroups with a focus on the young adults. Methods: Incidence rates (case/100,0000) were calculated using SEER*Stat and 2000-2015 data from SEER-18 registries age-adjusted to the U.S. 2000 standard population. We calculated the annual percentage change (APC) of incidence rates according to age groups and stratified by sex, race, and registry. Age at diagnosis was analyzed in 10-year intervals starting at 20 years. All analyses used ICD-O-3 codes 9732/3 with microscopically confirmed MM. Results: There were 69,613 MM patients included in the analysis. The mean age at diagnosis was 68 years and decreased over time from 68.5 years in 2000 to 67.6 years in 2015. Men comprised 55.5% of the population. Patients <50 years constituted 7.9% of the population with 6.6% between 40-49 years. The incidence significantly (all p values <0.01) increased from 2000-2015 for the entire cohort (incidence 6.9-9.5; APC 1.1) and for males (8.6-9.5; 1.1) and females (5.7-6.3; 0.8). The highest increase in incidence among the age groups (Figure) was in 40-49 years (1.9-2.7; 2.1). When analyzing the trends for age groups stratified by sex, women ages 40-49 years had the highest increase in incidence (1.6-2.6; 2.4). The APCs of SEER-18 registries stratified by age and sex are reported in the Table. The highest APCs (>3.0) were observed in the registries of Atlanta, Greater Georgia, Hawaii, and New Jersey. When the SEER-18 registries were further stratified by race, white men ages 40-49 years in Seattle and New Jersey had APCs of 5.5 and 2.9 respectively, while Black women ages 40-49 years in Greater Georgia had an APC of 3.3. Only 3 groups of individuals had a significant decline in the incidence: Asian women ≥70 years in Los Angeles (APC -6.4), White women ≥70 years in Los Angeles (APC -1.9), and men 50-59 years in San Jose (APC -1.5). Conclusions: The overall incidence of MM is increasing in the US with the highest increment among young adults between the age groups 40-49 years. We observed geographic and demographic differences in the incidence rates, which may reflect genetic and environmental influences. Interestingly, a few geo-demographic subgroups had a decline in the incidence. Further research is needed to determine the reasons behind the observed trends. Disclosures Kapoor: Celgene: Research Funding; Takeda: Research Funding.

Abstract Introduction Histiocytic neoplasms are rare hematological malignancies that have protean clinical manifestations and can pose significant management challenges. Recently, vemurafenib was approved by the US-FDA for treatment of BRAF-V600-mutant Erdheim-Chester disease (ECD). However, there is a lack of FDA-approved therapies for other histiocytic neoplasms such as Langerhans cell histiocytosis (LCH) and Rosai-Dorfman disease (RDD). Over the last 5 years, immune checkpoint inhibitors such as programmed death-1 (PD-1) and programmed death-ligand 1 (PD-L1) inhibitors have shown significant improvement in outcomes among patients with several hematological and solid organ malignancies. In order to identify appropriate treatment candidates for these therapies, predictive biomarkers have been developed in various cancers. Evidence from solid tumors has suggested a favorable response to checkpoint inhibitor therapy with higher tumor mutational burden (TMB), defined as the number of mutations within a tumor genome. Next generation sequencing (NGS) of various tumors has shown an association between TMB of < 5 mutations/megabase (mut/Mb) to be associated with an absence of benefit from checkpoint inhibitors. In addition, high levels of PD-1/PD-L1 expression and microsatellite instability (MSI) are also correlated with response to therapy. The latter may be a result of somatic or germline alterations in DNA mismatch repair genes. In this study, we report the results for these biomarkers using NGS in patients with histiocytic neoplasms. Methods We utilized TempusTM NGS platform to analyze the tissue specimen of patients with histiocytic neoplasm. The Tempus xO Assay (Tempus Labs; Chicago, IL) combines a 1,711 gene targeted somatic and germline DNA sequencing panel with RNA-sequencing to detect both germline and somatic single nucleotide polymorphisms, indels, copy number variants, and gene rearrangements causing chimeric mRNA transcript expression in a wide array of solid tumor types. The assay utilizes formalin-fixed paraffin-embedded tumor samples and matched blood samples. TMB was calculated and reported as somatic mutations in tumor tissue per million base-pairs or mut/Mb. RNA sequencing was utilized to assess for PD-L1 and PD-1 gene expressions as compared to matched tumor and normal reference sets. Both PD-L1 and PD-1 gene expressions were reported as percentiles. DNA mismatch repair status was predicted by analysis of alterations in five common mismatch repair genes in somatic and germline DNA (MSH2, MSH6, MLH1, PMS2, and EPCAM). If there were no alterations identified in these genes, the mismatch repair status was predicted as microsatellite stable (MSS). Results A total of 13 patients with histiocytic neoplasms were included in the study. The distribution of individual histiocytic neoplasms was as follows: RDD (n=9), ECD (n=3), and LCH (n=1). The median TMB for RDD and ECD patients was 0.17 mut/Mb. For the one patient with LCH, the TMB level was 0.51 mut/Mb. Individual TMB levels are shown in figure 1. PD-L1 and PD-1 expression levels are depicted in table 1. Compared to normal reference sets, the PD-L1 expression was elevated in one patient each with RDD and ECD, and PD-1 expression was elevated in two patients each with RDD and ECD. For both ECD patients with higher PD-1 expression, NGS also showed presence of BRAF-V600E in the tumor tissue. The LCH patient had a low level of PD-L1 and PD-1 expression. For patients where evaluation of DNA mismatch repair was feasible on the tissue specimen (n=4), none showed related somatic or germline alterations. Conclusions In our series, the histiocytic neoplasms RDD, ECD, and LCH demonstrated low levels of TMB. None of the RDD and ECD patients were found to have alterations in DNA mismatch repair genes. Other markers of immunotherapy such as PD-L1/PD-1 expression appeared to be higher in ECD patients with BRAF-V600E, but in only a small subset of RDD patients. The low TMB seen in our study suggests that these histiocytic neoplasms may be less likely to respond to immune checkpoint inhibitors such as anti-PD-1 and anti-PD-L1 agents as compared to the tumors with high TMB. Disclosures No relevant conflicts of interest to declare.