Novak, Anne J.

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

Introduction: New Diffuse Large B-cell (DLBCL) treatments remain a clinical need despite the success of rituximab combined with CHOP chemotherapy (RCHOP), which results in durable responses in 60-70% of patients. Those refractory to, or who relapse following, first-line therapy have a very poor outcome, with only 20% surviving beyond 5 years. Rationally-targeted frontline strategies are needed, especially for those with high-risk disease. Significant advances have been made in the genomic classification of DLBCL, but none have impacted the design of phase III trials for untreated DLBCL patients. Building on new genetic profiling studies to personalize clinical treatment, an NCI initiative, could allow clinicians to add targeted therapies to the RCHOP backbone based on individual tumor signatures. The phase II ECOG-ACRIN1412 trial, which compared RCHOP combined with lenalidomide (R2CHOP) versus RCHOP showed significantly superior event free survival (EFS) and overall survival benefits for those treated with R2CHOP. Herein, we report the profile of a high-risk ABC/non-GCB subset of DLBCL driven by genomic alterations in inflammatory genes that are susceptible to front-line R2CHOP, but continue to experience poor outcome with RCHOP alone. Methods: We studied a total of 196 DLBCL patients. 47 were treated with R2CHOP from an investigator-initiated, open-label, single-arm phase II study (NCT00670358). 149 were newly diagnosed DLBCL cases treated with RCHOP, or R-immunochemotherapy (herein called RCHOP), and followed prospectively through the Molecular Epidemiology Resource (MER) of the University of Iowa/Mayo Clinic SPORE that served as a contemporary cohort. Patients from each treatment group were divided based on their event free survival at 24 months (EFS24). DNA alterations within these populations were identified through whole exome sequencing (WES). Variants were analyzed for their presence in EFS24 achievement or failure groups in both RCHOP and R2CHOP. Both tumor and germline sequencing was performed for 47/47 R2CHOP cases and 49/149 RCHOP cases. Gene expression data from the PanCan panel of 730 B-cell-related genes was analyzed to determine gene expression profiles characteristic of the high-risk/R2CHOP-profile on 59 available non-GCB DLBCL cases (45 RCHOP; 14 R2CHOP). A two-sided comparative marker analysis T statistic test was applied to assess what genes displayed differential expression based on achieving EFS24 in both R2CHOP and RCHOP populations. Positive values represented associations with achieving EFS24 and negative values were associated with cases of EFS24 failure. Results: Three genes were enriched in the RCHOP cases that failed EFS24 but achieved EFS24 with R2CHOP among non-GCB patients: PIM1, SPEN, and MYD88 (L265P), herein referred to as Responder Alterations. In R2CHOP cases, patients with a Responder Alteration had a better overall EFS (P = 0.051) compared to wild type patients. In contrast, RCHOP treated patients with a Responder Alteration in their tumor had a significantly worse overall EFS (P = 0.0004) compared to patients without a mutation. Together, PIM1, SPEN, or MYD88 (L265P) mutations were present in 38.0% (30/79) of all non-GCB cases. Collective R2CHOP and RCHOP EFS24 differential gene expression T values were significantly different for 18 previously-defined signatures. The R2CHOP cases that achieved EFS24 were enriched for genes involved in cell cycle, JAK-STAT, cytokine signaling, and NF-κB pathways based on these signatures and ontology analyses. Lastly, cases with WES and PanCan data were analyzed together to observe differential gene expression patterns between cases with and without signature mutations. These data suggest that R2CHOP disrupts tumors reliant on IRF4, NF-κB, and STAT transcription factors, leading to a loss of proliferative feedback systems. Conclusions: Our combined analysis of DNA and RNA across R2CHOP and RCHOP treatment cohorts identifies a high-risk non-GCB phenotype that encompasses approximately 38% of non-GCB patients, is capable of sustaining JAK-STAT and NF-κB signaling, and is sensitive to R2CHOP. Our study lays the groundwork for a precision therapy approach in DLBCL in which DNA or RNA profiles can be used to identify patients early in treatment who may not benefit from the current standard of care, RCHOP, and who would benefit from the addition of lenalidomide or other targeted agents. Disclosures Gandhi: Celgene Corporation: Employment, Equity Ownership, Patents & Royalties. Ansell:LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Affimed: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; LAM Therapeutics: Research Funding. Cerhan:Celgene: Research Funding; NanoString: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees. Nowakowski:Curis: Research Funding; Bayer: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; F. Hoffmann-La Roche Ltd: Research Funding; Genentech, Inc.: Research Funding; MorphoSys: Consultancy, Research Funding; NanoString: Research Funding; Selvita: Membership on an entity's Board of Directors or advisory committees. Novak:Celgene Coorperation: Research Funding.

The current paradigms of known peripheral immune abnormalities in B-Chronic Lymphocytic Leukemia (CLL) are not able to consistently explain patient complications and it is difficult to correct a given CLL patient's immune status. Here, we expand on our initial report demonstrating bone marrow (BM) hematopoietic dysfunction in untreated CLL patients (Manso et al., Leukemia volume 33, pages 638-652, 2019). CLL patient BM had significantly reduced frequencies and short-term functional capacity of hematopoietic stem and progenitor cells (HSPCs). Additionally, the remaining progenitors exhibited increased protein levels of the key hematopoietic transcriptional regulators GATA-2 and PU.1. We further evaluated the frequency and function of myeloid stem cells from controls and untreated CLL patients by limiting dilution analysis in long-term culture-initiating cell (LTC-IC) assays. Over the 5 week duration of the assay, we observed delayed and partial recovery of myelopoiesis from CLL-derived HSPCs (Figure 1A). These data suggest that removal of HSPCs from the CLL leukemic microenvironment partially recovers their ability to sustain myelopoiesis. A known inflammatory mediator and hematopoiesis-modulating cytokine that is constitutively produced by CLL cells, TNF-α, induced increased expression of GATA-2 and PU.1 in specific HSPC subsets and reduced formation of short-term colony forming units in vitro. Addition of TNF-α to LTC-IC assays resulted in a striking ablation of myelopoiesis in a dose-dependent manner, partially reproducing the ex vivo results (Figure 1B). To further assess the direct impact of CLL cells on HSPC biology, isolated HSPCs from controls were exposed in vitro to leukemic CLL cells. The co-culture induced overexpression of GATA-2 and PU.1 in distinct HSPC populations, recapitulating our ex vivo findings (Figure 1C-D). When cell-cell contact was inhibited by use of Transwell inserts, an intermediate increase in GATA-2 and PU.1 was observed, highlighting the contributions of both soluble mediators and cell-cell contact to HSPC alterations. In both direct and Transwell co-culture conditions, overexpression of GATA-2 and PU.1 was reversed when TNF-α was neutralized (Figure 1E-F). Taken together, these findings indicate a significant role for CLL-derived TNF-α in HSPC modulation and expand our previous observations of BM dysfunction in untreated CLL patients. This data offers new molecular insight into the contribution of the leukemic microenvironment to altered hematopoiesis, contributing to immunodeficiency in CLL, and identifies TNF-α as a potential therapeutic target for correction of hematopoiesis in CLL disease. Disclosures Ding: Merck: Research Funding; DTRM Biopharma: Research Funding. Parikh:AstraZeneca: Honoraria, Research Funding; MorphoSys: Research Funding; AbbVie: Honoraria, Research Funding; Acerta Pharma: Research Funding; Pharmacyclics: Honoraria, Research Funding; Janssen: Research Funding; Ascentage Pharma: Research Funding; Genentech: Honoraria. Novak:Celgene Coorperation: Research Funding. Kay:Agios: Other: DSMB; Celgene: Other: Data Safety Monitoring Board; Infinity Pharmaceuticals: Other: DSMB; MorphoSys: Other: Data Safety Monitoring Board.

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

Abstract Background: 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 Introduction Recent advances in the understanding of the WM pathobiology led to the expansion of our therapeutic options in this disease. A number of targeted drugs are either available or under investigation and are poised to change the chemo-immunotherapy paradigm in the treatment of WM. Now more than ever, an individualized treatment approach for patients with WM is possible, where patient characteristics and preferences can be matched by different treatment goals and side effect profiles. While deep responses have been associated with longer disease-free survival in other hematologic malignancies, data on patients with WM are sparse. We report the impact of depth of response in disease-free and overall survival (OS) in WM. Methods WM patients consecutively seen at Mayo Clinic between 01/1998 and 12/2016 were reviewed for response to therapy and disease burden. The best response achieved after the treatment of interest (TOI) was classified using the IWWM-7 Response Criteria. The included TOI were: BR, DRC, BDR and high-dose chemotherapy followed by ASCT. All time-to-event analyses were performed from the TOI initiation date using the Kaplan-Meier method and the log-rank test. A landmark analysis from the date-of-best response was also performed for OS to prevent immortal-time bias. Univariate and multivariate analyses of progression-free survival (PFS) and time-to-next therapy (TTNT) were performed using the Cox regression method. Results A total of 181 patients had disease response assessed after the TOI. Baseline characteristics at time of diagnosis are summarized in table 1. The median follow up from TOI was 3.8 years (95% CI: 3-4), the majority of patients received TOI as salvage therapy [n=112 (63%); median 2nd line (rage 1-11)]. Best response rates with the corresponding 5-year PFS and TTNT are summarized in table 2. The median OS from TOI was longer in patients who achieved at least a PR [median 7.2 years (95% CI: 5-NR) vs 3.7 years (95% CI: 1.6-NR), p=0.01]. A trend towards a longer OS was also seen with deeper responses (PR or better) achieved with frontline therapy [median 5.8 years (95% CI: 5.8-NR) vs 3.4 years (95% CI: 1.6-NR), p=0.24]. Figure 1 shows the correlation between deeper responses with PFS and TTNT. Among patients achieving a PR or VGPR, those with a normal FLC ratio post TOI demonstrated a longer PFS and TTNT [PFS: median NR (95% CI: 3.4-NR) vs 4.9 years (95% CI: 2.8-8.7), p=0.01; TTNT: median NR (95% CI: 3.5-NR) vs 5.2 years (95% CI: 4.2-9.4), p=0.04], figure 2. In a multivariate analysis including normal FLC ratio, normal IgM level and minimal BM involvement (<5%) post TOI, normal FLC ratio remained an independent prognostic factor for longer PFS and TTNT. Similar trends were seen when considering treatment naïve and relapsed/refractory patients independently (data not shown). Discussion Our results not only suggest an association between depth of response with PFS and TTNT, but also with OS in patients with WM. A normal FLC ratio (not part of the IWWM-7 response criteria) seems to predict a longer disease-free interval in patients achieving a PR or VGPR. Disclosures Gertz: Ionis: Honoraria; Apellis: Consultancy; annexon: Consultancy; Prothena: Honoraria; janssen: Consultancy; Physicians Education Resource: Consultancy; Alnylam: Honoraria; Teva: Consultancy; spectrum: Consultancy, Honoraria; Medscape: Consultancy; celgene: Consultancy; Research to Practice: Consultancy; Amgen: Consultancy; Abbvie: Consultancy. Kapoor:Takeda: Research Funding; Celgene: Research Funding. Ailawadhi:Amgen: Consultancy; Janssen: Consultancy; Takeda: Consultancy; Pharmacyclics: Research Funding; Celgene: 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: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; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding. Ansell:Seattle Genetics: Research Funding; Celldex: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Pfizer: Research Funding; Takeda: Research Funding; Merck & Co: Research Funding; Affimed: Research Funding; Regeneron: Research Funding.

Abstract Introduction Immunoglobulin M monoclonal gammopathy of undetermined significance (IgM MGUS) is a relatively common pre-malignant condition that may progress to Waldenstrom macroglobulinemia (WM), an incurable low-grade non-Hodgkin lymphoma. The acquisition of genomic alterations in a multistep process of progression is likely a central mechanism responsible for the progression of IgM MGUS to WM, leading to increased tumor burden and end-organ damage. The exact role of the immune system in this process is still poorly characterized. The modulation of the immune system by malignant cells leading to anti-lymphoma immunity or immune evasion is a well-stablished principle associated with lymphoma survival and progression, respectively. The understanding of the pathobiology of IgM MGUS and WM has grown significantly over the last few years; however, most of the past research has focused on genetics and flow cytometric analysis of malignant cells. The introduction of CyTOF, a novel platform coupling mass spectometry with single-cell flow cytometry using antibody-metal isotope pairs, has allowed a broader single-cell analysis and the study of functional profiles. We here report the use of CyTOF technology to comprehensively profile the immune system of patients with IgM MGUS and WM. Methods Viably-cryopreserved (DMSO 10%) single-cell suspensions containing peripheral blood mononuclear cells from 15 patients (WM n=9 and IgM MGUS n=6) were used in this study. Recovered cells (1-3 x 106 per sample) were stained with a 30-parameter surface protein CyTOF panel designed to characterize multiple cell types and profile the immune system. Nucleated cellular events were identified using a DNA intercalator conjugated to natural abundance iridium (191Ir and 193Ir). Cisplatin (195Pt) was used for dead-live cell discrimination and calibration beads containing natural abundance cerium (140/142Ce), europium (151/153Eu), holmium (165Ho), and lutetium (175/176Lu) were used for normalization of the instrument signal. CyTOF analysis of normalized files was carried out using the Astrolabe Diagnostics platform. Cell clusters were assigned using the FlowSOM package and differential abundance analysis was performed using the edgeR package. Analysis is adjusted for multiple comparisons. Results The median live single-cell count for the entire cohort was 573,300 (range 50,681 to 869,727), representing a median of 81% (range 39% to 87%) of the total event counts [median 820,800 (range 130,452 to 1,000,000]. The cell count and the proportion of event count was similar in patients with WM or IgM MGUS. T cells represented 45.8% of the total events; granulocytes represented 31.3%; NK cells represented 8.6%; monocytes represented 8.1%; B cells represented 2.4% of the total events for the entire cohort. Figure 1 shows a heat map of different immune cell sub-types proportion per patient sample. A difference was noted in the proportion of monocytes (CD14+, CD16+) and B-cells when comparing patients with IgM MGUS and WM (figure 2). Patients with WM had a higher proportion of monocytes compared to IgM MGUS patients (p=0.05). A higher proportion of B-cells was noted in patients with IgM MGUS when compared to WM (4.2% vs 0.6%, p=0.01). When B-cells were sub-classified as naïve, transitional, non-switched memory, switched memory, and plasmablasts, the proportion of non-switched memory B-cells was higher in IgM MGUS compared to WM (p=0.05). While the results of this pilot study suggest important differences between IgM MGUS and WM as regards the immune repertoire, confirmatory studies are in progress in a larger cohort of patients. Conclusion To our knowledge, this is the first study of mass cytometry in patients with WM and IgM MGUS patients. A higher proportion of monocytes and lower proportion of memory B-cells was noted in patients with WM compared to IgM MGUS. CyTOF study of the immune profile in these patients is feasible and can potentially uncover relationships between cell types not typically associated with the disease progression continuum (IgM MGUS to WM). While definitive conclusion cannot be made using this dataset due to the small sample size, CyTOF is a powerful tool in the study of the immune profile of WM patients. Disclosures Gertz: Amgen: Consultancy; Abbvie: Consultancy; Apellis: Consultancy; annexon: Consultancy; spectrum: Consultancy, Honoraria; Teva: Consultancy; Alnylam: Honoraria; Physicians Education Resource: Consultancy; Ionis: Honoraria; janssen: Consultancy; celgene: Consultancy; Prothena: Honoraria; Research to Practice: Consultancy; Medscape: Consultancy. Kapoor:Celgene: Research Funding; Takeda: Research Funding. Ailawadhi:Takeda: Consultancy; Amgen: Consultancy; Janssen: Consultancy; Celgene: Consultancy; Pharmacyclics: Research Funding. Reeder:Affimed: Research Funding. Ansell:Seattle Genetics: Research Funding; Regeneron: Research Funding; Takeda: Research Funding; Pfizer: Research Funding; Merck & Co: Research Funding; Trillium: Research Funding; Affimed: Research Funding; LAM Therapeutics: Research Funding; Celldex: Research Funding; Bristol-Myers Squibb: Research Funding.

Background The ubiquitin proteasome system (UPS) maintains homeostasis in eukaryotic cells by degrading and recycling proteins associated with DNA transcription and cell cycle regulation. In tumor cells with a high protein turnover rate, particularly in immunoglobulin secreting cancers such as Waldenströms Macroglobulinemia (WM), proteasomes are critical for sustained cell proliferation. Proteasome inhibitors (PI) such as bortezomib and carfilzomib target the B5 subunit of the proteasome catalytic core and disrupt its activity leading to accumulation of polyubiquitinated proteins. Although highly effective in WM, persistent use of bortezomib eventually leads to emergence of a bortezomib resistant (BR) subclone. Point mutation in the PSMB5 gene (encoding the B5 subunit) leading to altered binding kinetics is identified as a potential underlying mechanism of PI resistance. Despite these adaptive modifications that support resistance to PI resistance, the UPS as a whole remains a relevant target with vulnerable sites that can be therapeutically exploited. The 19S cap of the proteasome contains deubiquitinating enzymes (DUBS) that function to deubiquitinate proteins prior to their entry into the proteasomal core. Using a novel DUB-inhibitor b-AP15, which blocks the UCHL5 and USP14 DUBS, D’Arcy et al demonstrated that disruption of these DUBS does not alter proteasomal activity yet induces robust tumor cell death in various cancers. Aim Investigation of b-AP15 in preclinical models of BR WM and characterization of associated downstream signaling interactions. Methods For these studies, we developed and used BR variants of the most commonly used WM cell lines (variants termed BCWM.1/BR, RPCI-WM1/BR and MWCL-1/BR). Proteasomal activity was measured in these cells using synthetic fluorogenic peptide substrates and apoptosis was measured by annexin-v/PI staining. For gene expression profiling (GEP), the Nanostring nCounter mRNA quantification assay was utilized. Results We treated all BR WM clones with either single agent bortezomib (10nM), carfilzomib (10nM), b-AP15 (100nM) or all three agents in combination and assessed their effect on chymotrypsin-like, caspase-like and trypsin-like proteasomal activity. b-AP15, either alone or in combination with the other PI, did not alter proteasomal activity, nor did it abrogate the inhibition of chymotrypsin-like activity of other PI. Although proteasomal activity was not disrupted, we observed an increase in high molecular weight ubiquitinated proteins on immunoblot analysis. We hypothesized that despite the BR cells resistance to B5-targeting PI, they would remain sensitive to UPS inhibition if targeted through DUB inhibition. We treated BR cells with b-AP15 at concentrations ranging from 50nM - 1.25uM and observed that b-AP15 induced robust WM cell death in a dose and time dependent manner. Apoptosis was more pronounced in MWCL-1/BR cells and optimal cell death (55%) was observed at 500nM; however a commitment towards cellular demise was evident by PARP-1 and caspase cleavage as early as 3 hrs. Use of a pan-caspase inhibitor resulted in rescuing of the BR WM cells from b-AP15 mediated death and demonstrated a caspase-dependent death process. In order to gain further mechanistic insight into the processes that regulates response to b-AP15, we performed GEP and immunoblot of BR WM cells treated with 50nM of b-AP15. We observed an increase in phospho-p38 protein, which belongs to the MAPK family and regulates IL8 secretion. When BR WM cells were treated with a p38 inhibitor in combination with b-AP15, a significant decrease in cell viability was noted relative to b-AP15 (100nM) alone. Furthermore, GEP and network analysis identified key regulator genes associated with p53, cell cycle and MAPK signaling. Summary We provide first evidence that inhibition of the DUBS, UCHL5 and USP14 with b-AP15 results in caspase-dependent apoptosis of BR WM cells. Our investigation uncovers important molecular interactions that may dictate the BR cells responsiveness to DUB-inhibition by b-AP15. These observations validate the pervasive role of the UPS and confirm alternate therapeutics targets using our unique BR WM models. We are grateful to the Waterfall Waldenström Macroglobulinemia Research Fund, the Leukemia and Lymphoma Society and the International Waldenström Macroglobulinemia Foundation for their continued support. Disclosures: Martin: Teva: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Genentech: Speakers Bureau; Millennium: Research Funding; Seattle Genetics: Consultancy, Speakers Bureau.

Abstract Background Waldenstršms macroglobulinemia (WM) is a rare and incurable indolent Non-Hodgkin's lymphoplasmacytic lymphoma. Rarity and limited availability of authentic preclinical models has been a major constraint in drug development for WM. We recently reported the establishment and characterization of a novel human WM cell line, RPCI-WM1. Thereafter we evaluated the tumorigenic potential of this unique WM cell line in a xenograft mouse model along with its ability to produce human IgM in vivo. Moreover, we assessed its potential as a possible model for enhanced understanding of WM cell biology in vivo and a mechanism to conduct functional studies and preclinical anti-WM drug evaluation. Aim The primary goal of this experiment was to develop and validate an in vivo WM model that can be used to investigate WM cell biology as well as drug evaluation. Methods The RPCI-WM1 cell line (1x106 cells) was injected both subcutaneously and via tail vein into female SCID mice. Engraftment was validated via detection of human IgM (ELISA) and tumor growth (as well as response to therapy) was monitored by weekly assessment of IgM levels and external tumor formation measurements. Results After subcutaneous injection (flank) into mice, the RPCI-WM1 cells developed localized tumors that secreted human IgM detectable in serum as early as 14 days. Progressive increase in IgM was measured in the serum and this corresponded with tumor growth in a time-dependent fashion. On average, tumor tissues grew to a median of 3 cm and IgM levels reached a median of 823 ng/mL by Day 30 post-implantation. We also assessed the practical utility of this xenograft mouse model for evaluation of therapeutics, using a compound X (propriety drug with demonstrable in vitro anti-WM effects). Eleven days post-implantation, the mice were randomized into 3 groups receiving either DMSO (n=6), 100 mg of compound X (n=7) or 200 mg/kg of compound X (n=7) every alternate day via intraperitoneal injection for 26 days. On Day 37, mice were sacrificed and tumor volume was measured. We observed a 48% reduction in tumor volume in the 100mg/kg cohort, with no additional anti-WM benefit noted in the 200mg/kg cohort. Human IgM secreted by the xenografted tumors was measured by ELISA and was significantly lower in mice treated with compound X versus the control group (Figure 1). We also conducted immunohistochemistry (IHC) analysis on tumor tissues obtained from 9 mice treated with compound X (both dose cohorts) or DMSO alone. IHC staining demonstrated changes in the known target proteins of compound X (such as BCL2, p53, Ki-67) between the treated and untreated mice. Summary Thus we demonstrate that the RPCI-WM1 cell line can be dependably used to induce tumor growth through xenotransplantation in SCID mice and this mimics the IgM secreting clinical disease model. This can then be used to serve as a platform for preclinical testing of novel anti-WM therapeutics and allow for subsequent correlative studies for pharmacodynamics purpose. Comprehensive data regarding this model its utility in evaluation of various biological questions such as those relevant to the tumor microenvironment as well therapeutic modeling will be presented. We are grateful to the Leukemia and Lymphoma Society and the International Waldenstršm Macroglobulinemia Foundation for their continued support. Disclosures: No relevant conflicts of interest to declare.

Abstract Background: B-cell non-Hodgkin lymphomas (NHL) are common lymphoid malignancies in which malignant B-cells arrested at various stages of differentiation proliferate within lymph nodes and occasionally other tissues. The microenvironment that supports the growth and survival of NHL B cells consists of a complex network of immune cells and cytokines and its specific composition has been shown to have significant clinical implications. The intratumoral immune cells include effector and regulatory T (Treg) lymphocytes that are more than simple residual elements from the normal lymph node structure. We previously explored whether the extent of T cell infiltration played a role in the clinical outcome of patients with B-cell NHL and found that an increased percentage of CD4+ T cells in the diagnostic biopsy of lymphoma patients significantly correlated with an improved 5-year overall survival. We have also shown that intratumoral Treg cells significantly suppress the anti-tumor response. Based on the fact that intratumoral T-cells are important in B-cell NHL, we evaluated whether genetic variation in genes that regulate the T-cell response may be associated with lymphoma risk. Methods: We genotyped 257 single nucleotide polymorphisms (SNPs) from 50 candidate genes related to T-cell differentiation and function in a clinic-based study of 441 Caucasian NHL cases and 475 frequency matched Caucasian controls seen at the Mayo Clinic from 2002–2005. Tagging and nsSNPs were selected from HapMap. The most prevalent homozygous genotype was used as the reference group and each SNP was modeled individually as having a log-additive effect, expressed as ordinal odds ratios (OR) per variant allele and 95% confidence intervals, in an age- and sex-adjusted logistic regression analysis. We also evaluated the consistency of the findings for the most common types of B-cell NHL - diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), and CLL/SLL. For gene-level analyses, we used principal components (PC) analysis using all SNPs from a gene; those PCs that explained > 90% of the variability were then modeled in a logistic regression analysis and significance was determined using a likelihood ratio test. We also evaluated the association of haplotypes from each gene with risk of NHL using the score test implemented from HAPLO.SCORE. Results. The mean age at diagnosis was 60.1 years for cases and 58% were male; among controls, the mean age at enrollment was 61.7 years and 55% were men. In the PC gene analyses, PRF1 (perforin gene - involved in cytotoxicity; p=0.004), CD276 (B7-H3 gene - involved in co-stimulation; p=0.01), TBX21 (T-bet gene – regulates Th1 cell development; p=0.02), and IL6 (p=0.02) were significant at p<0.05; haplotype analysis showed similar results, with the addition of CARD15 (NOD2 gene – regulates Th1 responses; p=0.02). There were 2 SNPs in PRF1, and both the intronic SNP rs3758562 (OR=1.38; 1.13–1.69) and the synonymous coding SNP rs885821 (OR=0.80; 0.63–1.02) were associated with NHL risk overall and for each subtype. There were 2 SNPs in CD276, and both the intronic SNP rs7176654 (OR=1.29; 1.07–1.56) and the mrna-utr SNP rs3816661 (OR=0.82; 0.68–0.99) were associated with NHL risk overall and with the CLL/SLL and follicular NHL subtypes specifically. There were 2 SNPs in TBX21, and only the mrna-utr SNP rs7502875 (OR=0.73; 0.59–0.92) was associated with NHL risk overall and for each subtype. There were 11 SNPs in IL6, and the intronic SNP rs2069835 (OR=1.88; 1.24–2.84) and the locus-region SNP rs2069824 (OR=1.73; 1.20–2.50) were each associated with NHL risk overall and each subtype. Finally, there were 9 SNPs in CARD15, with only the nonsynonymous coding SNP rs5743291 (OR=1.43; 1.02–1.98) being associated with overall NHL risk, and risk was specific to FL. Conclusions. Genetic variation in several genes that play critical roles in T-cell maturation and function was associated with lymphoma risk. These genes appeared to be associated with the differentiation and function of effector T-cells particularly Th1 and Th17 cells. These results provide potentially important insights into the role of T-cells in lymphomagenesis.