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BACKGROUND: Angioimmunoblastic T cell lymphoma (AITL) has a unique histological phenotype with a relatively small number of malignant T-cells and an extensive infiltrate of normal cells including T-cells, B-cells, NK cells and FDCs. The malignant cell has a T follicular helper (Tfh) cell phenotype with increased expression of immune checkpoint molecules including PD-1 and ICOS. With the routine use of antibodies targeting immune checkpoint molecules, clinical results in limited numbers of AITL patients have thus far been mixed with some responding patients but also some who rapidly progress. Because both malignant T-cells and normal T-cells in AITL may be PD-1 positive, we conducted a study to compare the phenotype of normal T-cells and malignant T-cells within the tumor microenvironment. We also evaluated whether soluble immune checkpoint molecules in the peripheral blood differed between AITLs, controls, and other PTCL subgroups. METHODS: We designed a novel CyTOF antibody (Ab) panel comprised of 37 markers to identify and characterize cells of T, B, NK, macrophage and FDC lineages within a cohort of biopsy specimens from 6 AITL patients compared to 6 normal controls (2 LN, 4 tonsil). Soluble immune receptors and ligands were then measured by multiplex ELISA on peripheral blood from 8 AITLs, 5 controls, and 32 additional samples from PTCL subtypes including PTCL-NOS, ALCL and CTCL. RESULTS: Malignant T-cells in AITL specimens could clearly be identified by viSNE mapping based on CyTOF expression of CD4, CD5, BCL6, CD10 (variable), CXCR5 (variable), PD-1 and ICOS. Aberrant loss of CD3 and CD7 on malignant T-cells was commonly seen. Expression of other immune checkpoint molecules was assessed. Malignant cells expressed high levels of TIGIT and moderate levels of CD27, but malignant T-cell populations were negative for expression of TIM3, LAG3, and CTLA-4. A substantial proportion of normal T-cells also expressed immune checkpoint molecules including PD-1 and CD27, and a subset of these cells also expressed TIGIT and ICOS. A small minority of normal T-cells expressed TIM3, LAG3, and CTLA-4. As others have described, the normal Tfh cell population had an immunophenotype that was otherwise relatively similar to the malignant population, but distinguishable via viSNE mapping with our CyTOF Ab panel. Expression of PD-L1 could be detected on NK-cells, macrophages, and a few select populations of normal T cells, but not on B-cells or FDCs. Malignant T-cells did not express PD-L1. Due to the potential importance of immune checkpoint molecules in this disease, we then measured soluble checkpoint receptors and ligands in the serum. Soluble PD-1 was significantly increased in the peripheral blood of AITL when compared to normal controls (average 2637 pg/mL (SE = 512.1) in AITL vs 57.87 (SE = 16.88) in controls). Similarly, soluble PD-L1 was significantly increased in AITL compared to normal controls (13.8 (SE = 10.5) in AITL vs 0.94 (SE = 0.25) in controls). CONCLUSIONS: Malignant T-cells in AITL have a unique phenotype which was able to be reliably identified through use of viSNE mapping with our CyTOF Ab panel. Furthermore, malignant T-cells were found to consistently express additional immune regulatory molecules beyond PD-1 and ICOS, including TIGIT and CD27. However, a substantial subset of normal T-cells have a very similar phenotype suggesting that activation of T-cells in this disease will likely stimulate both normal and malignant T-cells. This may explain the rapid disease progression seen in a subset of AITL patients treated with PD-1 blockade. Furthermore, soluble PD-1 is significantly increased in the serum of AITL patients and may explain the overall lack of clinical efficacy of anti-PD-1 antibodies in treating AITL. Disclosures Cerhan: NanoString: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding. Ansell:LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Trillium: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Affimed: Research Funding; Mayo Clinic Rochester: Employment; LAM Therapeutics: Research Funding; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; LAM Therapeutics: Research Funding.

Cancer cells have altered energy demand due to their increased proliferative capacity. In Waldenstrom Macroglobulinemia (WM), an indolent yet incurable B-cell lymphoma, the homeostasis of the bone marrow (BM) environment is disturbed due to the infiltration of the lymphoplamacytic cells that continuously produce monoclonal IgM. An alteration in energy demand could skew the balance of key proteins and metabolites towards a permissive niche for WM tumor cells growth, and unfavorably effect on the immune response against tumor cells. Therefore, the aim of our study was to identify how changes in certain metabolites and/or proteins could contribute to the pathobiology of WM and whether the cytokine and chemokine composition of the BM microenvironment play a role in such changes. WM patient's samples including BM plasma, peripheral blood serum and BM cells (n=101) as well as equivalent normal counterparts (n=86) were collected and used for metabolomics analysis. Comprehensive targeted metabolomics analysis was performed using Capillary Electrophoresis Time-of-Flight Mass spectrometry (CE-TOFMS), CE-triple quadrupole mass spectrometry (CE-qQqMS) and Liquid Chromatography (LC-TOFMS). Normal and WM peripheral blood serums samples were also used for untargeted proteomics analysis using a fully automated proteomic technology platform that includes an Agilent 1200 Series Quaternary HPLC system connected to a Q Exactive Plus mass spectrometer. Real-time PCR analysis was performed to detect the gene expression of the relevant metabolite transporters located on the cell membrane. BM cells from control and WM patients' samples were used for flow cytometry analysis. IHC was used to detect the proteins on the BM tissues. Principal Component Analysis (PCA) and Hierarchal Clustering Analysis (HCA) on both metabolomics and proteomics data identified two distinct clusters for disease and normal samples, indicating that there are differentially expressed proteins and metabolites in WM versus normal samples. Furthermore, pathway analysis showed that the majority of the altered metabolites were the members of the glutathione (GSH) metabolism pathway. This finding was further validated not only by data obtained from metabolomics analysis of BM cells and BM plasma, but also by proteomic data WM patients serum, implying that GSH metabolism is key to the biology of WM. Moreover, stimulation of WM cell lines by IL-6 and IL-21, cytokines involved in inducing WM cell proliferation and IgM secretion, resulted in increased gene expression of the transporters mediating uptake of metabolites required for GSH synthesis, including SLC7A11, 4F2HC and LAT1, indicating that cytokines in the WM BM could modulate GSH metabolism. In addition, IHC staining of the BM tissues as well as flow cytometry analysis of patients' lymphoplasmacytic cells identified glutathione peroxidase as one of the major proteins modulating GSH metabolism in WM. In summary, our data highlight a central role for GSH metabolism in WM disease biology and indicate that intervening with the metabolic processes could be a potential therapeutic strategy for patients with WM. Disclosures Ansell: AI Therapeutics: Other: research funding for clinical trials; Seattle Genetics: Other: research funding for clinical trials; Regeneron: Other: research funding for clinical trials; Affimed: Other: research funding for clinical trials; Bristol Myers Squibb: Other: research funding for clinical trials; Pfizer: Other: research funding for clinical trials; Merck: Other: research funding for clinical trials; Takeda: Other: research funding for clinical trials.

Abstract Effector CD8+ T cells play a crucial role in anti-tumor immunity. Two major subsets of effector CD8+ T cells have been identified based on expression of KLRG1 and CD127: KLRG1+CD127- short-lived effector cells (SLECs) and KLRG1-CD127+ memory precursor effector cells (MPECs). In B-cell non-Hodgkin lymphoma (NHL), the frequency and function of SLECs and MPECs are unknown. Furthermore, the underlying mechanism by which these two CD8+ subsets were regulated and differentiate in B-cell NHL remains poorly understood. The goal of the present study is therefore to phenotypically and functionally characterize SLECs and MPECs in B-cell NHL. Using patient biopsy specimens, we observed that the median percentage of SLECs was greater than MPECs and was 39.9% (range: 22.7%~ 54.7%) and 18% (range: 7.66%~31.6%), respectively. In controls, the median percentage of SLECs was lower than that of MPECs and was 13.71% (range: 5.26%~29.2%) and 51.63% (range: 22.5%~70.1%) in PBMC and 12.14% (range: 4.01%~26%) and 51.62% (range: 29.2%~70.3%) in tonsil, respectively. Using mass cytometry (CyTOF), we observed that SLECs have higher expression levels of TIGIT and PD-1 and lower expression of CCR7, CD26 and CD27 when compared to MPECs. SLECs were functionally superior to MPECs as the numbers of cytokine (IFN-γ and TNF-α) - and granule (granzyme B and perforin)-producing cells were higher in SLECs than MPECs. However, SLECs had a lower proliferative capacity and a higher apoptosis rate when compared to MPECs. We observed that a reciprocal differentiation pathway exists between SLECs and MPECs. Activation and cytokine stimulation (IL-2 and IL-15) promoted the development of SLECs and decreased the number of MPECs. This effect was reversed when cells were treated with neutralizing antibodies to block IL-2 or IL-15 signaling. We also found that these two subsets had a distinct transcription factor profile as SLECs had higher expression of Eomes and T-bet, and lower expression of Tcf-1than MPECs. IL-2 and IL-15 enhanced the expression of T-bet and decreased the expression of Tcf-1. Taken together, our results show that SLECs are more prevalent than MPECs in B-cell NHL when compared to normal control tissue. These two effector cell subsets have distinct phenotypical and function profiles and their development is controlled by cytokines that may be dysregulated in lymphoma. Despite the fact that SLECs produce more granzyme B and IFN-γ, they are less proliferative and more susceptible to apoptosis. This may compromise an effective anti-tumor immune response in B-cell NHL. Disclosures Ansell: Celldex: Research Funding; Takeda: Research Funding; Merck & Co: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Pfizer: Research Funding; Seattle Genetics: Research Funding.

Abstract T cell Ig and ITIM domain (TIGIT) is an immune checkpoint molecule that is expressed on a variety of cell types including NK cells, effector and memory T cells, and Treg cells. Upon ligation with CD155, TIGIT delivers an inhibitory signal and negatively regulates anti-tumor responses. While important in normal T-cell biology, the pathological significance of TIGIT expression and function in the tumor microenvironment of the patients with follicular lymphoma (FL) is largely unknown. The present study sought to phenotypically and functionally characterize TIGIT+ T cell subsets in FL. While its expression is not detected on resting T cells in peripheral blood, we found that TIGIT is highly expressed on intratumoral T cells from FL. Treatment with cytokines such as IL-4 and TGF-β downregulated TIGIT expression on T cells. We found that TIGIT is predominantly expressed on effector memory T cells (TEM) with an activation/exhausted phenotype, and TIGIT+ T cells have higher expression levels of CD69 and PD-1 when compared to TIGIT- T cells. Functionally, TIGIT+ T cells displayed reduced capacity of cell proliferation and cytokine production (IFN-γ and IL-2). Using mass cytometry (CyTOF), we observed that TIGIT is abundantly expressed on some intratumoral Treg (CD4+CD25+Foxp3+) cells, while other Treg cells lack TIGIT expression in FL, forming two subsets of Treg cells: CD4+CD25+TIGIT+ and CD4+CD25+TIGIT-. These two subsets are phenotypically distinct in that CD25+TIGIT+ cells exhibited increased expression of activation/costimulatory markers such as CD69, CD27 and CD28 compared to CD25+TIGIT-, suggesting an activated Treg subset of CD25+TIGIT+ cells. This CD25+TIGIT+ subset had increased suppressive function and could more effectively inhibit activation and proliferation of CD8+ T cells than CD25+TIGIT- cells. Furthermore, we found that lymphoma B cells promoted the development of TIGIT-expressing T cells as TGF-β-mediated downregulation of TIGIT on T cells was inhibited when CD19+ lymphoma cells were present. Using a cohort of 31 FL patients, we found that intratumoral TIGIT-expressing T cells were associated with a favorable prognosis. Patients with TIGIT+ T cell numbers greater than 50% had better overall survival than patients with TIGIT+ T cell numbers less than 50%. Taken together, our results reveal a role of TIGIT in defining Treg cell subsets with different immune function and TIGIT expression may be useful in predicting patient outcome in FL. Disclosures Ansell: Takeda: Research Funding; Bristol-Myers Squibb: Research Funding; Pfizer: Research Funding; Seattle Genetics: Research Funding; Celldex: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Merck & Co: Research Funding.

Abstract Background: The initial therapies for multiple myeloma have changed significantly in the recent years with routine incorporation of combinations containing IMiDs and proteasome inhibitors. However, patients with MM invariably relapse after their initial treatment and undergo successive therapies for continued control of their disease. The increasing access to new therapies for treatment of relapsed disease has led to improved survival of patients with MM. Many recent phase 3 trials have studied the impact of new agents on outcome of patients with MM who are relapsing after the initial lines of therapy for relapsed disease. However, the natural history of relapsed myeloma earlier in the course of relapsed disease has not been studied in detail. Patients and methods: We designed a global, multicenter, retrospective study to examine the current treatment approaches for initial relapses in MM and patient outcomes following application of successive treatment regimens. Patients with multiple myeloma who experience a first relapse between January 1, 2007 and December 31, 2011 were identified at participating sites. Clinical and laboratory data pertaining to the time of diagnosis and from the time of individual relapses up to and including the third relapse will be obtained from the clinical records. The first date of treatment with an anti-myeloma regimen for first disease relapse was termed time zero (T0). Results: Three hundred and fifteen patients were enrolled; median age at diagnosis was 59 years (range: 35 - 86), 59% were male. Patients were enrolled from North America (33%), Europe (35%) or Asia (32%). The patients were diagnosed between 1997 and 2011 and the median time to T0 from diagnosis was 23.9 months (range: 0.8 - 131.9 months). An IMiD (thalidomide or lenalidomide), bortezomib or alkylator (melphalan or cyclophosphamide) was part of the initial therapy in 63%, 36% and 51% of patients, respectively; 93 (33%) patients had stem cell transplantation as part of initial treatment. The median follow up from the study entry was 56 months; 117 were alive at the time of data extraction. A second line, third line and fourth line of treatment were recorded in 307, 163 and 84 patients respectively. An IMiD (thalidomide, lenalidomide, or pomalidomide), proteasome inhibitor (bortezomib or carfilzomib) or alkylator (melphalan or cyclophosphamide) was part of the therapy for first relapse in 50%, 59% and 33% of patients, respectively. The responses to the regimens after T0, the TTNT, PFS and OS to each line of therapy are provided in the Table. Conclusion: The current study provides an assessment of the current treatment approaches used for initial treatment of MM and the salvage treatment options utilized. The study again demonstrates the progressively shorter duration of disease control with each successive treatment regimen, reflecting ongoing development of drug resistance. The overall survival of over 3 years from the time of therapy for first relapse highlights the impact of the newer therapies that have been introduced for this disease. Table. Treatment outcomes after initial relapse Endpoint 2nd line therapyN=307 3rd line therapyN=163 4th line therapyN=84 Partial response or better Median in Months (95% Confidence Interval) Overall Survival 36.1 (30.2, 42.4) 18.4 (14.5, 26.8) 12.6 (10.4, 19.0) Progression-Free Survival 13.4 (11.8, 15.8) 8.3 (6.5, 11.2) 6.4 (5.0, 8.1) Time to Next Treatment 15 (12,17) 9.8 (7.4, 12.0) 6.6 (5.8, 8.7) Figure 1. Figure 1. Disclosures Kumar: Onyx: Consultancy, Research Funding; Skyline: Consultancy, Honoraria; BMS: Consultancy; Sanofi: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Novartis: Research Funding; Takeda: Consultancy, Research Funding; Celgene: Consultancy, Research Funding. Dimopoulos:Novartis: Honoraria; Amgen: Honoraria; Janssen-Cilag: Honoraria; Genesis: Honoraria; Janssen: Honoraria; Onyx: Honoraria; Celgene: Honoraria. Leleu:Janssen: Honoraria; Celgene: Honoraria; Takeda: Honoraria; Amgen: Honoraria; TEVA: Honoraria; Novartis: Honoraria; BMS: Honoraria; Pierre Fabre: Honoraria; LeoPharma: Honoraria; Chugai: Honoraria. Moreau:Celgene, Janssen, Takeda, Novartis, Amgen: Membership on an entity's Board of Directors or advisory committees. Lahuerta:Janssen Cilag, Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees. Vij:Takeda, Onyx: Research Funding; Celgene, Onyx, Takeda, Novartis, BMS, Sanofi, Janssen, Merck: Consultancy.

Abstract Background: Treatment of multiple myeloma has evolved considerably in the past few years with availability of several news drugs as well as increasing use of multidrug combinations. These changes have no doubt led to the improved survival seen among patients with MM. We have previously shown that outcomes of patients intolerant or refractory to one of the IMiDs and bortezomib had a poor outcome. Since that time, other drugs of the same class as well as new classes of drugs have been introduced for the treatment of MM. We designed this retrospective study to estimate the outcomes in patients with relapsed myeloma, who have become refractory to the current generation IMiDs and proteasome inhibitors. Patients and Methods: Patients with relapsed multiple myeloma who have received at least 3 prior lines of therapy, is refractory to both an IMiD (lenalidomide or pomalidomide) AND a proteasome inhibitor (bortezomib or carfilzomib), and has been exposed to an alkylating agent were identified from multiple centers. The time patients met the above criteria was defined as T0, and details of all treatment regimens before and after T0 were collected using electronic CRFs. The study was approved by the IRB at the respective centers. Results: 543 patients were enrolled in this study; median age was 62 years (31-87) and 61% were males. Patients were enrolled from centers in North America (n=181), Europe (n=318), and Asia Pacific (n=44). Patients were diagnosed between 2006 and 2014, the median duration between diagnosis of myeloma and study entry (T0) was 3.1 years (0.3 to 9). The median (95% CI) estimated follow up from diagnosis and from T0 were 61 (57, 66) months and 13 (11, 15) months respectively. The median number of lines of therapy prior to T0 was 4 (3-13), 48% had a prior transplant. The median OS from T0 for the entire cohort was 13 (11, 15) months. For these 462 patients, the median number of recorded regimens was 2 (1-9). The overall response and the depth of response to each line of treatment following T0 are as shown in the table. The median (95% CI) PFS and OS from T0 was 5 (4, 6), and 15.2 (13, 17), respectively. The overall survival for the 81 patients with no treatment post T0 was only 2.1 months. In a multivariate analysis, duration from diagnosis to T0, ISS stage III and number of lines of therapy were all associated with inferior PFS, as well as OS, and in addition, serum creatinine>2 mg/dL at T0 also predicted inferior OS. Conclusions: The study provides the expected outcome following development of myeloma that is refractory to a PI and an IMiD. The outcomes of these patients appear to be better than we had seen historically in patients refractory/ intolerant to bortezomib and IMiDs, highlighting the increased treatment options available for these patients. However, there is decreasing response rate to sequential regimens highlighting the development of drug resistance. The data provides a bench mark for comparison of new therapies that are being evaluated in this disease. Table Table. Disclosures Dimopoulos: Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Genesis: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees. Kastritis:Takeda: Consultancy, Honoraria; Genesis: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Amgen: Consultancy, Honoraria. Terpos:BMS: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Other: Travel expenses, Research Funding; Celgene: Honoraria; Takeda: Consultancy, Honoraria; Genesis: Consultancy, Honoraria, Other: Travel expenses; Amgen: Consultancy, Honoraria, Other: Travel expenses, Research Funding; Novartis: Honoraria. Hillengass:Sanofi: Research Funding; Novartis: Research Funding; Amgen: Consultancy, Honoraria; BMS: Honoraria; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria. Leleu:TEVA: Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria; LeoPharma: Honoraria; Amgen: Honoraria; Bristol-Myers Squibb: Honoraria; Pierre Fabre: Honoraria; Celgene: Honoraria; Novartis: Honoraria; Takeda: Honoraria. Oriol:Janssen: Honoraria, Other: Expert board committee; Amgen: Honoraria, Other: Expert board committee. Cavo:Celgene: Consultancy, Honoraria; Millennium: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; Janssen-Cilag: Consultancy, Honoraria; Amgen: Consultancy, Honoraria. Mateos:Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees. Vij:Shire: Consultancy; Takeda: Consultancy, Research Funding; Jazz: Consultancy; Karyopharma: Consultancy; Janssen: Consultancy; Novartis: Consultancy; Celgene: Consultancy; Bristol-Myers Squibb: Consultancy; Amgen: Consultancy, Research Funding. Lokhorst:Genmab: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding. van de Donk:Amgen: Research Funding; Janssen: Research Funding; BMS: Research Funding; Celgene: Research Funding. Mark:Onyx: Research Funding, Speakers Bureau; Millenium: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. Ludwig:Amgen: Research Funding, Speakers Bureau; Takeda: Research Funding, Speakers Bureau; BMS: Speakers Bureau; Janssen: Speakers Bureau. Jagannath:Novartis: Consultancy; Janssen: Consultancy; Bristol-Myers Squibb: Consultancy; Celgene: Consultancy; Merck: Consultancy. Usmani:Array: Research Funding; Britsol-Myers Squibb: Consultancy, Research Funding; Skyline: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; BioPharma: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Onyx: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmacyclics: Research Funding; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Sanofi: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Amgen: Consultancy, Research Funding, Speakers Bureau; Millenium: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novartis: Speakers Bureau. Dytfeld:Janssen Poland: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Research Funding; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees. Moreau:Novartis: Honoraria; Amgen: Honoraria; Celgene: Honoraria; Bristol-Myers Squibb: Honoraria; Takeda: Honoraria; Janssen: Honoraria, Speakers Bureau. Lee:Amgen: Membership on an entity's Board of Directors or advisory committees. Shustik:Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; Millenium: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees. de la Rubia:Celgene: Consultancy; Bristol Myers: Consultancy; Amgen,: Consultancy; Janssen: Consultancy. Durie:Takeda: Consultancy; Amgen: Consultancy; Janssen: Consultancy.

Abstract BACKGROUND Monocytes and macrophages (mo/mΦ) are a key part of the composition of peripheral blood (PB) and tissues and increased numbers of mo/mΦ have been associated with patient outcome in non-Hodgkin lymphoma (NHL). Because CD14 is abundantly expressed on the surface of human mo/mΦ, it is often used to identify or isolate human mo/mΦ, and immunosuppressive CD14+HLA-DRlow monocytes have been shown to be increased in the peripheral blood of NHL patients. However, we have previously shown that CD14 expression on mo/mΦ is substantially lower than CD68 expression suggesting that many CD68+ mo/mΦ are CD14 negative, especially in spleen and lymph node tissues. To characterize both CD14+ and CD14- mo/mΦ in PB and tissues, we isolated all mo/mΦ from B-cell NHL specimens and normal controls and assessed their phenotype and function. METHODS Human mo/mΦ were isolated by negative selection from PB and tissue biopsy specimens (B-cell NHL and normal tissues) using the immunomagnetic isolation (monocyte enrichment kit). Morphological and immunophenotypic characteristics of isolated mo/mΦ were determined by Giemsa stain and flow cytometry. Phagocytosis and migration assays were used to determine the function of isolated mo/mΦ. T cells were co-cultured with mo/mΦ and T cell proliferation was evaluated by CFSE staining and detected by flow cytometry. RESULTS Using a monocyte enrichment kit to isolate all mo/mΦ, the purity of isolated mo/mϕ was 85~99%, which was defined by the percentage of lineage-negative cells (i.e. cells without expression of CD3,CD19, CD20, and CD56). We found that these isolated mo/mΦ constituted 2 populations: a more frequent population of larger cells and a less common population of smaller cells. In contrast to PB, CD14 positive mo/mΦ constituted less than 40% of the tissue mo/mΦ from the isolated population. Furthermore, we found that the cell size from CD14+ mo/mΦ were larger than CD14- mo/mΦ. Using CD14 and SIRP-α, we could identify 3 populations of mo/mΦ: CD14+SIRP-αhigh, CD14-SIRP-αdim and CD14-SIRP-α- cells. CD14+SIRP-αhigh cells and CD14-SIRP-αdim cells typically constituted the population of larger cells, while CD14-SIRP-α- cells constituted the population of smaller cells. CD14-SIRP-α- cells lacked the typical phenotypic markers and had decreased phagocytic and migratory ability compared to CD14+SIRP-αhigh and CD14-SIRP-αdim cells. Furthermore, we found that these 3 populations of mo/mΦ had a differential effect on activated T-cells and that the CD14-SIRP-α- cells appeared increased number in biopsy specimens from NHL when compared to normal tissues. CONCLUSIONS We have identified a unique population of small CD68+ mo/mΦ that lack expression of CD14, SIRP-α, and other FcγR markers. This subset of mo/mΦ is more prevalent in NHL tissues and has limited phagocytic and migratory functions. This CD14-SIRP-α- mo/mΦ subpopulation may play an inhibitory role in anti-cancer and inflammatory responses. Disclosures No relevant conflicts of interest to declare.

Objective The hypertrophic cardiomyopathy (HCM) risk-sudden cardiac death (SCD) calculator endorsed by the 2014 European Society of Cardiology has not been independently validated in the Asians. We aimed to investigate whether the HCM Risk-SCD calculator effectively predicts SCD in Korean HCM population. Methods An observational, longitudinal cohort study was performed in 730 patients with HCM from 2007 to 2017. The primary endpoint was a composite of SCD and appropriate implantable cardioverter-defibrillator (ICD) therapy. Results During a follow-up period of 4288 person-years, 16 (2.2%) patients reached the primary endpoint. This validation study revealed a calibration slope of 0.892 and C-statistics of 0.718. The primary endpoint occurred in 1.1% (7/615), 4.6% (3/65) and 12.0% (6/50) of low-risk, intermediate-risk and high-risk groups, respectively. Although most patients (85.2%) without the primary endpoint were classified into the low-risk group, 7 of 11 SCD (63.6%) occurred in the low-risk group. In univariable and multivariable analysis, sex (woman) was significantly associated with the primary endpoint and emerged as independent predictor. The addition of sex to the HCM Risk-SCD calculator significantly improved the predictive value of the primary endpoint (net reclassification improvement 0.557, p=0.015). Conclusions In the Korean HCM population, the HCM Risk-SCD calculator had a high negative predictive value and accuracy for predicting SCD or appropriate ICD therapy, but misclassified a few patients experiencing the primary endpoint as low-risk or intermediate-risk groups.

Background and Purpose— Hypertrophic cardiomyopathy patients with atrial fibrillation are at increased risk of stroke, and anticoagulation is strongly recommended. However, limited data are available regarding the safety and effectiveness of non–vitamin K antagonist oral anticoagulants (NOACs) for primary prevention of stroke. Methods— Using the Korean Health Insurance Review and Assessment Service database, we identified 2397 patients with hypertrophic cardiomyopathy and nonvalvular atrial fibrillation on oral anticoagulation from 2013 to 2016 without history of ischemic stroke, intracranial hemorrhage (ICH), or gastrointestinal bleeding (992 on warfarin and 1405 on NOACs). Inverse probability of treatment weighting with propensity scores was used to balance covariates between treatment groups. Risk for ischemic stroke, ICH, gastrointestinal bleeding, death, and their composite outcome associated with NOAC use was assessed with warfarin use as the reference. Results— During a mean follow-up of 1.6 years, the incidence rates of ischemic stroke, ICH, gastrointestinal bleeding, death, and composite outcome were all significantly lower in the NOAC than in the warfarin group (stroke, 2.8 versus 5.0; ICH, 0.5 versus 1.3; gastrointestinal bleeding, 2.3 versus 3.0; death, 3.0 versus 5.1; composite, 7.5 versus 12.5 events per 100 person-years). NOACs were associated with significantly lower risks of ischemic stroke (hazard ratio [HR], 0.47; 95% CI, 0.32–0.68), ICH (HR, 0.31; 95% CI, 0.14–0.69), gastrointestinal bleeding (HR, 0.62; 95% CI, 0.40–0.96), death (HR, 0.45; 95% CI, 0.31–0.65), and the composite outcome (HR, 0.48; 95% CI, 0.38–0.61) than warfarin. The same trend was observed regardless of the NOAC dose and across various high-risk subgroups. In analysis of individual NOACs, all NOACs were associated with lower risks of ischemic stroke and composite outcome. Conclusions— NOACs showed superior effectiveness and safety versus warfarin in the primary prevention of stroke versus warfarin in real-world Asian hypertrophic cardiomyopathy with atrial fibrillation.