Lacy, Martha Q.

Background: The treatment of Multiple Myeloma (MM) has evolved significantly in the past decade with the introduction of novel agents and drug combinations, thus enhancing treatment efficacy and allowing more patients to achieve complete response (CR). This has created a need to identify surrogates for depth of treatment response. Serum free light chain (sFLC) ratio normalization has been shown to be prognostic for progression free survival as well as overall survival in patients achieving a complete response to therapy. Consequently, it has been incorporated as a defining feature for stringent CR, along with lack of clonal plasma cells by immunohistochemistry (IHC) or low sensitivity flow cytometry. The routine use of multiparametric flow cytometry with higher sensitivity to detect residual disease than IHC or the older 4-color flow cytometry, has raised the question as to whether sFLC ratio is still a valid indicator of response depth. Moreover, in nearly half of the patients with an abnormal sFLC ratio after treatment, the abnormality is secondary to suppression of one or both serum light chains. Therefore, we designed a retrospective study to address these issues. Patients and Methods: This is a retrospective study using the Multiple Myeloma Database at Mayo Clinic, Rochester. We included patients who, after any line of therapy, had negative serum and urine immunofixation and absence of clonal bone marrow plasma cells by flow cytometry (PC-PRO), which has a sensitivity of >10-4. Simultaneous sFLC data was also extracted. Patients were grouped into three categories based on their sFLC ratios: 1) normal ratio (normal), 2) abnormal ratio due to suppression of the uninvolved light chain (LC), involved LC, or both (Abn-suppressed) and 3) abnormal ratio due to elevation of the involved LC (Abn-inv elevated). The primary endpoint was the median time to next treatment (TTNT), defined as the time from sample collection to the time of initiation of the subsequent therapy or time of last follow up if a subsequent line of treatment was not initiated. Results: The cohort consisted of 510 patients. 285 (56%) were males and 225 (44%) females. Median age was 61 years (IQR: 55-67). Median Follow-up was 41 months. The last treatments administered prior to data collection included stem cell transplant (SCT) (with or without maintenance) in 290 (57%) patients, and non-SCT regimens in the others. The sFLC ratio was normal in 337 (66%) and abnormal in 173 (34%) patients. Among the patients with abnormal sFLC ratios, 81 had elevated involved LC, 25 had suppression of the involved LC, 45 had suppression of the uninvolved LC and 22 had suppression of both LCs. We first examined the TTNT for the three groups and found that the TTNT was identical for those with a normal ratio and those with an abnormal ratio due to suppression of one or both light chains (Figure 1). So, we combined these two groups (Normal-Abn suppressed) and compared their outcomes to the patients with abnormal sFLC ratio due to elevated involved LC. The Abn-inv elevated group had a shorter TTNT as shown in Figure 2 (log-rank 0.06, Wilcoxon <0.01). The Abn-inv elevated group also had decreased overall survival compared to the other group (log-rank: 0.05, Wilcoxon: 0.01) (Figure 3). Conclusion: This study provides 2 important observations. First, patients with an abnormal ratio due to suppression of one or both LCs have outcomes similar to those with a normal ratio, suggesting a need to clarify the current definition of stringent CR. Second, the study suggests an important prognostic value for an abnormal sFLC ratio due to elevated involved LC, suggesting this as an important surrogate for depth of response. Disclosures Kapoor: Janssen: Research Funding; Takeda: Honoraria, Research Funding; Cellectar: Consultancy; Celgene: Honoraria; Sanofi: Consultancy, Research Funding; Amgen: Research Funding; Glaxo Smith Kline: Research Funding. Dispenzieri:Akcea: Consultancy; Intellia: Consultancy; Janssen: Consultancy; Pfizer: Research Funding; Takeda: Research Funding; Celgene: Research Funding; Alnylam: Research Funding. Gertz:Ionis: Honoraria; Alnylam: Honoraria; Prothena: Honoraria; Celgene: Honoraria; Janssen: Honoraria; Spectrum: Honoraria, Research Funding. Lacy:Celgene: Research Funding. Dingli:Karyopharm: Research Funding; Rigel: Consultancy; Millenium: Consultancy; Janssen: Consultancy; alexion: Consultancy. Leung:Takeda: Research Funding; Aduro: Membership on an entity's Board of Directors or advisory committees; Prothena: Membership on an entity's Board of Directors or advisory committees; Omeros: Research Funding. Kumar:Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Takeda: Research Funding.

Introduction: High-risk multiple myeloma can be defined by the presence of specific cytogenetic abnormalities (structural) or by characteristic changes in bone marrow and peripheral blood biomarkers (functional). While both entities are characterized by therapeutic resistance, frequent disease relapses, and adverse survival outcomes, the underlying molecular mechanisms remain incompletely understood. Methods: We performed gene expression profiling (GEP) using an Affymetrix GeneChip Human Genome U133 Plus 2.0 microarray on CD138+ bone marrow cells from 137 patients diagnosed with multiple myeloma between 2004 and 2012. All patients underwent Fluorescence In-situ Hybridization (FISH) evaluation, plasma cell labeling, International Staging System (ISS) risk stratification, and GEP prior to initiating treatment with novel agents. The presence of del(17p), t(4;14), t(14;16), and t(14;20) on FISH, a plasma cell labeling index (PCLI) > 2%, and ISS stage III were considered high-risk abnormalities: FISH-HR (n = 15, structural high-risk, at least one high-risk FISH lesion), PCLI-HR (n = 20; functional high-risk, PCLI > 2%), and ISS-HR (n = 12; functional high-risk, ISS stage III). For each HR group we sampled standard risk (SR) controls in a 4:1 ratio. After data quality control and normalization, differential gene expression was estimated using limma. Statistical significance was adjusted for multiple comparisons using a false discovery rate-based approach for genome-wide experiments (q-value). We employed PANTHER pathway analysis for the differentially expressed genes in each HR group. We implemented a simple gene expression score (GES) by calculating the sum of quartiles of the normalized gene expression values for genes differentially expressed in more than one HR group (GES = ΣUP(quartile - 1) + ΣDN(4 - quartile)) and externally validated its prognostic significance (UAMS TT2 / TT3, GSE24080). Survival outcomes were analyzed using the methods described by Kaplan, Meier, and Cox. Computation and visualization were performed in R. Results: Median age at diagnosis was 63 years (32 - 87), 53% of the patients were male. High-risk disease was associated with inferior overall survival, regardless of the used definition (left Kaplan-Meier plots): FISH-HR (HR 4.3, 95% CI 1.9 - 9.8, p < 0.001), PCLI-HR (HR 2.7, 95% CI 1.4 - 5.3, p = 0.004), and ISS-HR (HR 2.8, 95% CI 1.2 - 6.5, p = 0.015). There were 59 (FISH-HR), 424 (ISS-HR), and 507 (PCLI-HR) differentially expressed genes (q < 0.050 for all genes, volcano plots). PCLI-HR and FISH-HR demonstrated a predominance of transcriptional up-regulation while ISS-HR had a balanced gene expression profile with a similar number of genes being up- and down-regulated. The involved cellular pathways were different across the HR groups except for anti-apoptotic signaling (bar graphs). All HR groups had distinct gene expression profiles with no complete overlap between all HR groups. There were 71 genes with overlap between two HR groups (69 up-regulated, 2 down-regulated, Venn diagrams). The median GES was 97 (18 - 206, higher numbers indicating higher expression of up-regulated and lower numbers of down-regulated high-risk genes) in 559 patients treated on UAMS TT2 / TT3 (GSE24080). Tertiles of the GES were associated with event-free survival (HR 1.4, 95% CI 1.2 - 1.6, p < 0.001) and remained independently prognostic after adjusting for age, sex, and ISS stage (HR 1.3, 95% CI 1.1 - 1.5, p < 0.001). Conclusions: High-risk multiple myeloma remains associated with inferior overall survival, regardless of the used definition (structural or functional). The subtypes of high-risk disease have distinct gene expression profiles and involve different cellular pathways, providing important clues to the underlying biology. A 71 gene signature derived from the different high-risk subtypes was of prognostic significance in a clinical trial population after adjusting for known prognostic factors. Figure Disclosures Lacy: Celgene: Research Funding. Dispenzieri:Akcea: Consultancy; Intellia: Consultancy; Janssen: Consultancy; Pfizer: Research Funding; Takeda: Research Funding; Celgene: Research Funding; Alnylam: Research Funding. Stewart:Takeda: Consultancy; Seattle Genetics: Consultancy; Roche: Consultancy; Ono: Consultancy; Celgene: Consultancy, Research Funding; Ionis: Consultancy; Janssen: Consultancy, Research Funding; Oncopeptides: Consultancy; Amgen: Consultancy, Research Funding; Bristol Myers-Squibb: Consultancy. Bergsagel:Celgene: Consultancy; Ionis Pharmaceuticals: Consultancy; Janssen Pharmaceuticals: Consultancy. Kumar:Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Takeda: Research Funding.

Introduction: Several patient and disease biology characteristics that impact overall survival (OS) outcomes of patients with newly diagnosed multiple myeloma (NDMM) have been well described. However, several prior population-based registry studies in the US such as SEER and NCDB databases have demonstrated the negative prognostic impact of socio-economic (SE) factors such as low median household income based on US national census bureau data for zip code of residence as well as absence of marital support on the OS of NDMM patients. Whether such SE factors are independent of established host and disease biology related factors remains unknown due to the absence of detailed patient-level data in population-based cancer registries. Thus, we evaluated whether such SE factors independently affected the OS of NDMM patients in our institution when accounting for patient and disease biology related factors. Methods: We analyzed a retrospective cohort of consecutive NDMM patients seen at the Mayo Clinic from January 2005 to December 2015. Median household income for each patient's area of residence was estimated by matching their home zip code at diagnosis with data available through the US National Census Bureau. Median household income < $63,000 for a zip code were used to classify patients as having low income based on the National Cancer Database classification of the bottom three quartiles. Absence of marital support was considered in patients divorced, single or widowed at the time of diagnosis. Survival estimates were calculated using the Kaplan-Meier method. Patient and disease biology related factors taken into consideration included age, FISH cytogenetics, ISS stage and LDH levels. Survival estimates were compared between groups using the log-rank test. Multivariable adjusted proportional hazards regression models were fit for the association between known prognostic factors and OS. Results: A total of 2,621 MM patients were analyzed from the Mayo Clinic cohort of which 60% were male and the median age was 64 (22 - 96) years with 14% being 75 years of age or older. In this cohort, 541 (33%) patients had ISS 3 disease and 370 (22%) had high risk cytogenetics by FISH. The median OS based on estimated average household income was 65 months for the group of patients with low household income (N = 32%) and 80 months for the remainder (P = 0.001) (Fig 1A). The median OS based on marital support was 59 months for those without marital support at diagnosis (N = 21%) versus 76 months for remainder (P = 0.003) (Fig 1B). However, in a multivariable analysis that included the aforementioned SE factors and patient and disease biology characteristics , only age of 75 years or older (p < 0.001), the presence of high-risk FISH cytogenetics (p < 0.001), presence of ISS 3 disease (p < 0.001) and elevated LDH (p = 0.005) retained independent statistical significance in predicting for worse OS. Lower household income (p = 0.264) and absence of marital support (p = 0.054) were not independent predictors of worse OS. Conclusions: With the caveat of patient selection bias in a tertiary academic center, SE factors such as estimated average household income and marital support at diagnosis do not appear to impact OS of NDMM patients when considered in the context of traditional host and disease biology risk factors. These findings are in contrast to those suggested by population based registry studies; nevertheless, similar patient-level assessments should be considered in patient cohorts from non-tertiary medical centers as these results may likely be different. Disclosures Dispenzieri: Akcea: Consultancy; Intellia: Consultancy; Janssen: Consultancy; Pfizer: Research Funding; Takeda: Research Funding; Celgene: Research Funding; Alnylam: Research Funding. Dingli:alexion: Consultancy; Janssen: Consultancy; Millenium: Consultancy; Rigel: Consultancy; Karyopharm: Research Funding. Lacy:Celgene: Research Funding. Kapoor:Amgen: Research Funding; Glaxo Smith Kline: Research Funding; Sanofi: Consultancy, Research Funding; Celgene: Honoraria; Janssen: Research Funding; Cellectar: Consultancy; Takeda: Honoraria, Research Funding. Leung:Prothena: Membership on an entity's Board of Directors or advisory committees; Aduro: Membership on an entity's Board of Directors or advisory committees; Takeda: Research Funding; Omeros: Research Funding. Russell:Imanis: Equity Ownership. Gertz:Medscape: Consultancy, Speakers Bureau; Research to Practice: Consultancy; Springer Publishing: Patents & Royalties; Ionis/Akcea: Consultancy; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Alnylam: Consultancy; Prothena Biosciences Inc: Consultancy; Celgene: Consultancy; Janssen: Consultancy; Spectrum: Consultancy, Research Funding; Annexon: Consultancy; Appellis: Consultancy; Amgen: Consultancy; Physicians Education Resource: Consultancy; Abbvie: Other: personal fees for Data Safety Monitoring board; Teva: Speakers Bureau; Johnson and Johnson: Speakers Bureau; DAVA oncology: Speakers Bureau; Proclara: Membership on an entity's Board of Directors or advisory committees; i3Health: Other: Development of educational programs and materials; Amyloidosis Foundation: Research Funding; International Waldenstrom Foundation: Research Funding. Kumar:Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Takeda: Research Funding.

Background: Lenalidomide maintenance therapy post-autologous stem cell transplantation (ASCT) is associated with improved progression-free survival (PFS) and possibly overall survival in multiple myeloma (MM). However, almost all patients do relapse as a result of residual multiple myeloma cells that remain after the high-dose chemotherapy. In the myeloma setting it has been found that the hedgehog (Hh) pathway is essential for maintaining a subset of tumor causing stem cells. LDE 225 (Sonidegib) is a potent selective oral bioavailable antagonist of Smoothened (SMO), a component of the Hh signaling pathway. In in vitro experiments, LDE225 treatment of myeloma cell lines resulted in a modest inhibition of cell proliferation at increasing doses. When LDE225 was combined with lenalidomide, a more than additive effect was observed in terms of cell proliferation, an effect that was more pronounced in the context of myeloma cell lines growing in co-culture with marrow derived stromal cells. These findings form the basis of evaluation of LDE 225 as a strategy to enhance the activity of lenalidomide in the post-transplant maintenance setting. The minimal residual state post SCT provides the most optimal situation for evaluation of a drug that is likely to work by inhibiting the tumor cells that escaped high dose therapy. Methods: Multiple myeloma patients without evidence of progression, who were 60 - 120 days after a single autologous stem cell transplant (SCT), performed within 1 year of diagnosis were eligible for the study. Maintenance therapy was started approximately 3 months after SCT. Treatment consisted of lenalidomide 10 mg days 1-21 and LDE225 400 mg days 1-28 in 28-day cycles for a total of 18 cycles. The goal of the study was to assess toxicity of this combination, complete response rate (CR) progression free survival (PFS) at 1 and 2 year and overall survival (OS). CR and PFS were estimated using an exact binomial distribution and Kaplan Meier curves respectively. Results: A total of 28 patients were accrued from Jan 2014 to Aug 2016, 1 patient canceled prior to treatment and 1 patient was deemed ineligible resulting in 26 evaluable pts for CR and PFS. The median age of all pts (n=26) was 60 years (range 43-69) and 50% were males. Seventy-three percent of patients reported one treatment regimen prior to SCT, while 27% reported 2 or more prior regimens. The other characteristics of the patient are summarized in Table 1. Twenty seven pts received at least one cycle of treatment and are evaluable for toxicities (AE). Patients were treated for a median of 12.5 (range 1-18) cycles. While 10 pts (38.5%) completed protocol treatment (18 cycles), the remaining 16 pts went off treatment due to AEs (6, 23%), disease progression (3, 11.5%), refusal of further treatment (3, 11.5%) and other reasons (4, 15.4%). A grade 3 or higher AE at least possibly attributed to either drug was seen in 63%. Grade 3+ hematologic toxicities were noted in 30%, with 7% neutropenia and 4% thrombocytopenia. Notable grade 2+ non-hematologic toxicities with more than 5% incidence were dysgeusia 22%, alopecia 11%, and anorexia 7%. Grade 3+ non-hematologic toxicities were fatigue, myalgia and arthralgia each at 7%. The CR rate in evaluable patients was 46% (5 CRs and 7 sCRs) with a 95% CI of 27% - 66%. CR rate improved from 31% to 46%. VGPR or better improved from 42% to 85%. The 24-month PFS (time from SCT to progression or death due to any cause) was 73% (95% CI: 57.9 - 92.3%) with a median time to censoring of 38 months. Conclusion: Lenalidomide in combination with LDE225 as posttransplant maintenance therapy was associated with some toxicity but manageable. The combination improved the depth of response after autologous stem cell transplant. Long-term follow-up is needed to determine overall survival. Disclosures Lacy: Celgene: Research Funding. Dispenzieri:Celgene: Research Funding; Takeda: Research Funding; Pfizer: Research Funding; Janssen: Consultancy; Intellia: Consultancy; Akcea: Consultancy; Alnylam: Research Funding. Gertz:Ionis: Honoraria; Alnylam: Honoraria; Prothena: Honoraria; Celgene: Honoraria; Spectrum: Honoraria, Research Funding; Janssen: Honoraria. Kapoor:Glaxo Smith Kline: Research Funding; Amgen: Research Funding; Janssen: Research Funding; Cellectar: Consultancy; Takeda: Honoraria, Research Funding; Celgene: Honoraria; Sanofi: Consultancy, Research Funding. Dingli:alexion: Consultancy; Janssen: Consultancy; Millenium: Consultancy; Rigel: Consultancy; Karyopharm: Research Funding. Russell:Imanis: Equity Ownership. Kumar:Janssen: Consultancy, Research Funding; Takeda: Research Funding; Celgene: Consultancy, Research Funding. OffLabel Disclosure: Sonidegib (LDE 225) is a selective oral bioavailable antagonist of Smoothened (SMO), a component of the hedgehog signaling pathway.

AbstractRisk stratification of multiple myeloma (MM) at diagnosis is critical. We examined the ability of hematopoietic indices including mean corpuscular volume (MCV), hemoglobin (Hgb), and platelet (Plt) to predict outcomes. This was a retrospective study of patients treated at Mayo Clinic between January 2004 and April 2018. We incorporated three variables (Hgb < 10 g/dL, Plt < 150 × 109/L, and MCV > 96 fL), assigning a score of 1 to each. We identified 1540 newly diagnosed MM patients, of whom 707 (46%) had a score of 0, 513 (33%) had a score of 1, 260 (17%) had a score of 2, and 60 (4%) had a score of 3. The score risk stratified patients into four groups with differing survivals. The median PFS was 32.3 months for score 0, 24.8 months for score 1, 21.7 months for score 2, and 18.3 months for score 3, for P < .001. The median OS was 80.7 months for score 0, 59.9 months for score 1, 51.7 months for score 2, and 31.3 months for score 3, P < .0001. Predictors of OS on the multivariable analysis were age ≥ 65 (HR, 1.93; P < .0001), R‐ISS stage (1‐2 vs 3) (HR, 0.48; P < .0001), and hematopoietic score (0‐2 vs 3) (HR, 0.51; P = .006). A hematopoietic score can predict survival in newly diagnosed myeloma patients.

Abstract Background: Modern clinical practice places time restrictions on the amount of information and processing that can be done in a typical office visit. Meeting patient needs within the healthcare system is challenging, contributing to patient (pt) and clinician dissatisfaction and potential suboptimal outcomes. We developed an electronic point-of-care case management system for cancer patients. The system's goal is to obtain patient-reported quality of life (PROQOL) outcomes and provide clinicians with information regarding QOL issues to address. We tested this system in a prospective randomized study against usual care (UC). The primary objective was to determine if PROQOL system would improve mean QOL over time. Methods: Eligible pts were adults with multiple myeloma (MM) or light amyloidosis (AL), seen at Mayo Clinic. Pts were randomized 2:1 to PROQOL system or usual care. PROQOL system was offered prior to every visit. Pts select from various categories about their single biggest concern, and receive a printed list of actionable resources based on selected concern. Clinicians also receive the PROQOL results to review with pts. Providers and pts randomized to PROQOL completed a "was it worth it survey" (WIWI). An 8 item Linear Analogue Self-Assessment was used to assess QOL, the primary endpoint. Secondary endpoints included distribution of choices of most important concern and their relationship to demographic and clinical variables. The study was powered to detect a 0.5 standard deviation difference in QOL between groups. Results: There were 233 patients enrolled (171 MM, 62 AL) in this study between July 1, 2016 and April 17, 2018. Median age was 65 years (range 31-87), and 59% were male. Median time from diagnosis to study consent was 37 months (PROQOL 36.3 versus UC 39.6). Median follow up for all patients was 15 months (range: 0.7-22 months), and median follow up for PROQOL pts was 16 months and 14 months for UC pts (p=0.027). There were 25 pts that have died since enrollment and 208 alive. No statistical significant difference in QOL between PROQOL and UC (median QOL 7 vs 7, p=0.75). The most prevalent main concerns selected were Physical Health (37%) and Cancer and Diagnosis (27%), the pattern remained even when pts were given the option to pick a second major concern 26% and 20% respectively. No other concern was listed more than 8% at baseline. All category selections and percentages are shown in the Figure. The average time to complete the instrument was 6 minutes. Pts found the PROQOL system was worth their time (75%), they would choose it again (87%) and recommend it to others (82%). Eighty-two percent of clinicians did not think the PROQOL tool negatively impacted their practice and 75% believed they may see improvement in pt wellbeing. Discussion: The PROQOL system was well received by pts and clinicians but did not improve QOL relative to UC in this study. The PROQOL demonstrates that majority of patients strongly desire more information regarding ongoing therapy, clinical trials, prognosis and toxicity. Further investigation is needed to understand whether communication is limited by time or mechanism of information delivery, such as verbal or written, or whether it should be undertaken in a separate allied health care visit. Figure. Figure. Disclosures Gertz: spectrum: Consultancy, Honoraria; Abbvie: Consultancy; Research to Practice: Consultancy; annexon: Consultancy; Medscape: Consultancy; Physicians Education Resource: Consultancy; Amgen: Consultancy; janssen: Consultancy; Alnylam: Honoraria; Teva: Consultancy; Apellis: Consultancy; Ionis: Honoraria; celgene: Consultancy; Prothena: Honoraria. Kumar:Novartis: 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; Janssen: 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; KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: 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; 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; AbbVie: Membership on an entity's Board of Directors or advisory committees, 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; Millennium Takeda: Research Funding; Alexion Pharmaceuticals, Inc.: Other: Participates in the International PNH Registry (for Mayo Clinic, Rochester) for Alexion Pharmaceuticals, Inc.. Lacy:Celgene: Research Funding. Dispenzieri:Celgene, Takeda, Prothena, Jannsen, Pfizer, Alnylam, GSK: Research Funding.

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 Abstract 448 Background: POEMS syndrome is a rare form of plasma cell dyscrasia that presents with polyneuropathy and various other systemic findings. Patients with 1–3 lesions and bone marrows without clonal plasma cells have been given targeted radiation as the initial and potentially definitive therapy. Outcomes of patients treated thus have not been systematically studied. Patients and Methods: One hundred and forty-six patients with POEMS syndrome were seen at the Mayo Clinic in Rochester, MN between January 1999 and September 2011. Charts of patients with research authorization were reviewed with IRB approval. Patients who had been given targeted radiation as their initial primary therapy were eligible for this study. Thirty-eight patients satisfied criteria and comprise the study population. Three patients had either short or poorly documented follow-up and were excluded from the response and time to next treatment analyses. Patients were categorized by systems that were involved at diagnosis, time of radiation, response after radiation, and the need for further therapy. Statistical analyses were performed using JMP statistical software (SAS, Carey, NC). Results: The median age was 50, with 76% of the population being male. Median time from diagnosis to radiation was 0.7 months, ranging from 0 to 9.4 months. 20, 14, 11 and 2 patients had immunosuppressive therapy with corticosteroids, IVIG, plasmapheresis, or rituximab, respectively, directed at a prior presumptive, but incorrect, diagnosis of CIDP. Five patients had one cycle or less of alkylator, lenalidomide, or bortezomib, prior to receiving what was hoped to be definitive radiation therapy. The median number of bone lesions was 1, range 1–6. In total 55 lesions of the possible 64 were irradiated being mostly found in the pelvic bones including the sacrum, ileum, ischium, and pubis. The median dose of radiation given was 45 Gy range 35 Gy to 54 Gy. Forty-nine percent had clinical improvement, 26% had a mixed clinical picture, 3% merely stabilized, and 23% had a clinical worsening. 29% had a complete hematologic response, 9% had a very good partial hematologic response, 9% had a partial hematologic response, 26% had no hematologic response, and the remaining were either non- or oligo-secretory or undocumented. 11% had a complete FDG-PET response, 11% had a partial FDG-PET response, 11% had no FDG-PET response, and the remaining had no documented FDG-PETs to analyze. 11% had a complete VEGF response, 3% had partial VEGF response, 14% had no VEGF response, and the rest were either not documented or unevaluable. The median follow up of living patients is 30 months, and the 4-year OS is 95%. Forty-eight percent of patients went on to receive additional therapy after their radiation (Figure). The median time to next therapy (TTNT) for these patients was 7.6 months (range 2.2 –73 months). Subsequent therapies included high dose chemotherapy with autologous hematopoietic stem cell transplant (n=10), low dose alkylator (n=2), thalidomide/lenalidomide (n=3), and more radiation (n=3). None of the baseline characteristics predicted for whether a patient would receive (or require) subsequent therapy, including the number of bone lesions at baseline. Conclusion: The results show that radiation is sufficient to produce clinical improvements and hematologic and radiographic responses approximately 50% of patients, even those with as many as 3 lesions. Disclosures: No relevant conflicts of interest to declare.

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.