Citations

Abstract Introduction: Features of multiple myeloma (MM) include a proliferative clonal plasma cell population, bone resorption, and neovascularization. Cytokines and chemokines represent two families of molecules that are capable of propagating and enhancing these disease features. In this study, we have utilized antibody array technology to assess the contributions of cytokines and chemokines to the progression of disease, and evaluated the contribution of stromal cells (SCs) in their production. Methods: Wild-type and IL-1beta transduced KAS 6/1 myeloma cell lines or bone marrow cells isolated from patients with monoclonal gammopathy of undetermined significance (MGUS), smoldering myeloma (SMM), and MM were cultured for 48 hrs. Culture supernatants were either analyzed directly, or co-cultured with normal SCs for an additional 48 hrs +/− IL-1 inhibitors, after which the supernatants were removed and analyzed using antibody arrays. SCs alone were also cultured with recombinant IL-1beta +/− IL-1 receptor antagonist (IL-1Ra) to define IL-1 dependent effects. IL-6 and IL-8 ELISAs were utilized to quantify IL-6 and IL-8 levels, and validate antibody array findings. Results: Antibody array analysis of IL-1 effects on stromal cell cultures using recombinant IL-1beta and supernatants from an IL-1beta transduced myeloma cell line demonstrated that stimulation of IL-6, MCP-1 and IL-8 were induced in an IL-1 and stromal cell dependent manner. Although levels of TIMP-2 varied in these cultures, they appeared unrelated to an IL-1 effect. Studies utilizing supernatants from patient bone marrow cells co-cultured with SCs resulted in levels of IL-6, MCP-1, and IL-8 higher than those seen with patient supernatants alone or SC cultures alone. More interestingly, the IL-8 levels appeared to correlate with diagnosis; MGUS samples generated low levels and MM samples stimulated high levels. Furthermore, this stimulation was reduced by the addition of IL-1 inhibitors, demonstrating a dependence on IL-1. To confirm the relationship between diagnosis and IL-8 production, the levels of IL-8 produced by the bone marrow supernatants were quantified directly by ELISA. Correlating with the antibody array data, background production of IL-8 from the cultures of patient cells alone was lower than the corresponding co-culture value. Supernatants from MM patients and a subset of SMM patients stimulated high levels of SC IL-8 secretion in contrast to bone marrow cell supernatants from MGUS patients and most SMM patients. This activity was inhibited by IL-1 inhibitors (see Figure). The IL-8 levels closely parallel the IL-1beta induced IL-6 levels in the same samples. Conclusion: These data indicate that the concentration of IL-8 may be relevant to the pathogenesis of MM. IL-8 production is largely dependent on SCs, and production appears to be at least partially dependent on IL-1 function. IL-8 is a chemokine with activities including chemotaxis of neutrophils, increased vascular permeability and angiogenesis. IL-8 expression has been implicated in multiple tumor types and may play an important role in the stimulation of angiogenesis during the progression from MGUS to active MM. Figure Figure

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 Multiple myeloma (MM) is characterized by significant genetic diversity at subclonal levels that likely plays a defining role in the heterogeneity of tumor progression, clinical aggressiveness and drug sensitivity. Such heterogeneity is a driving factor in the evolution of MM, from founder clones through outgrowth of subclonal fractions. DNA Sequencing studies on MM samples have indeed demonstrated such heterogeneity in subclonal architecture at diagnosis based on recurrent mutations in pathologically relevant genes that may ultimately to lead to relapse. However, no study so far has reported a predictive gene expression signature that can identify, distinguish and quantify drug sensitive and drug-resistant subpopulations within a bulk population of myeloma cells. In recent years, our laboratory has successfully developed a gene expression profile (GEP)-based signature that could not only distinguish drug response of MM cell lines, but also was effective in stratifying patient outcomes when applied to GEP profiles from MM clinical trials using proteasome inhibitors (PI) as chemotherapeutic agents. Further, we noted myeloma cell lines that responded to the drug often contained residual sub-population of cells that did not respond, and likely were selectively propagated during drug treatment in vitro, and in patients. In this study, we performed targeted qRT-PCR analysis of single cells using a gene panel that included PI sensitivity genes and gene signatures that could discriminate between low and high-risk myeloma followed by intensive bioinformatics and statistical analysis for the classification and prediction of PI response in individual cells within bulk multiple myeloma tumors. Fluidigm's C1 Single-Cell Auto Prep System was used to perform automated single-cell capture, processing and cDNA synthesis on 576 pre-treatment cells from 12 cell lines representing a wide range of PI-sensitivity and 370 cells from 7 patient samples undergoing PI treatment followed by targeted gene expression profiling of single cells using automated, high-throughput on-chip qRT-PCR analysis using 96.96 Dynamic Array IFCs on the BioMark HD System. Probability of resistance for each individual cell was predicted using a pipeline that employed the machine learning methods Random Forest, Support Vector Machine (radial and sigmoidal), LASSO and kNN (k Nearest Neighbor) for making single-cell GEP data-driven predictions/ decisions. The weighted probabilities from each of the algorithms were used to quantify resistance of each individual cell and plotted using Ensemble forecasting algorithm. Using our drug response GEP signature at the single cell level, we could successfully identify distinct subpopulations of tumor cells that were predicted to be sensitive or resistant to PIs. Subsequently, we developed a R Statistical analysis package (http://cran.r-project.org), SCATTome (Single Cell Analysis of Targeted Transcriptome), that can restructure data obtained from Fluidigm qPCR analysis run, filter missing data, perform scaling of filtered data, build classification models and successfully predict drug response of individual cells and classify each cell's probability of response based on the targeted transcriptome. We will present the program output as graphical displays of single cell response probabilities. This package provides a novel classification method that has the potential to predict subclonal response to a variety of therapeutic agents. Disclosures Kumar: Skyline: Consultancy, Honoraria; BMS: Consultancy; Onyx: Consultancy, Research Funding; Sanofi: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Novartis: Research Funding; Takeda: Consultancy, Research Funding; Celgene: Consultancy, Research Funding.

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: ENESTop, an ongoing, single-arm, phase 2 study (ClinicalTrials.gov, NCT01698905), is the first trial specifically evaluating treatment-free remission (TFR; ie, stopping tyrosine kinase inhibitor [TKI] treatment without a loss of response) in patients with chronic myeloid leukemia in chronic phase (CML-CP) who achieved a sustained deep molecular response after switching from imatinib (IM) to nilotinib (NIL). Of 126 patients in ENESTop who were eligible to stop NIL, 57.9% (95% CI, 48.8%-66.7%) maintained TFR at 48 weeks. Here we present results from a subgroup analysis based on reasons for switching from IM to NIL, categorized as intolerance, resistance, and physician preference. Methods:Eligible patients were adults with CML-CP who received ≥ 3 years of total TKI therapy (> 4 weeks of IM, followed by ≥ 2 years of NIL) and achieved a sustained MR4.5 (BCR-ABL1 ≤ 0.0032% on the International Scale [BCR-ABL1IS]) on NIL therapy; patients with a documented MR4.5 at the time of switch from IM to NIL were not eligible. Enrolled patients continued NIL treatment in a 1-year consolidation phase, and those without confirmed loss of MR4.5 (ie, consecutive BCR-ABL1IS > 0.0032%) were eligible to stop NIL in the TFR phase. Patients with loss of major molecular response (MMR; ie, BCR-ABL1IS > 0.1%) or confirmed loss of MR4 (ie, consecutive BCR-ABL1IS > 0.01%) during the TFR phase reinitiated NIL treatment. The primary endpoint was the proportion of patients who maintained TFR (ie, no loss of MMR, confirmed loss of MR4, or treatment reinitiation) at 48 weeks after stopping NIL. In this post hoc analysis, rates of TFR at 48 weeks after stopping NIL and a Kaplan-Meier (KM) analysis of treatment-free survival (TFS; defined as the time from the start of TFR to the earliest occurrence of any of the following: loss of MMR, confirmed loss of MR4, reinitiation of NIL due to any cause, progression to accelerated phase/blast crisis, death due to any cause) were evaluated in subgroups of patients who switched from IM to NIL due to intolerance, resistance, or physician preference. These categories were determined by grouping the reasons for switching from IM to NIL, as reported by the investigators, based on relatedness to safety (intolerance), loss of response/treatment failure (resistance), and the physician's clinical judgment (physician preference); individual reasons included within each category are presented in the Figure. Results:A total of 125 patients who entered the TFR phase were included in this analysis; 1 patient who was found to have had atypical transcripts was excluded. Among these 125 patients, the reasons for switching to NIL were categorized as intolerance in 51 patients (40.8%), resistance in 30 patients (24.0%), and physician preference in 44 patients (35.2%). The proportion of patients who maintained TFR at 48 weeks after stopping NIL was generally similar across the 3 subgroups: 30 of 51 (58.8%; 95% CI, 44.2%-72.4%) in the intolerance subgroup, 16 of 30 (53.3%; 95% CI, 34.3%-71.7%) in the resistance subgroup, and 27 of 44 (61.4%; 95% CI, 45.5%-75.6%) in the physician preference subgroup. KM analysis of TFS showed that in all 3 subgroups, the majority of TFS events occurred within the first 24 weeks after stopping NIL (Figure). There were no notable differences in the kinetics of TFS events among subgroups. The KM-estimated median duration of TFS was not reached by the data cutoff date in all 3 subgroups. Conclusion: Primary analysis from ENESTop showed that among patients with CML-CP who achieved a sustained MR4.5after switching from IM to NIL, 57.9% of those who stopped NIL maintained TFR at 48 weeks. In the present analysis, TFR was maintained at 48 weeks after stopping NIL by > 50% of patients in the intolerance, resistance, and physician preference subgroups, with generally similar results across subgroups. These findings suggest that the rate of successful TFR following second-line NIL does not differ based on the reasons for switching from IM to NIL. Figure. Figure. Disclosures Hughes: Ariad: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Australasian Leukaemia and Lymphoma Group (ALLG): Other: Chair of the CML/MPN Disease Group. Boquimpani:Novartis: Research Funding, Speakers Bureau; BMS: Speakers Bureau. Takahashi:Novartis: Honoraria, Research Funding; Pfizer: Honoraria, Research Funding; BMS: Honoraria. Shuvaev:Pfizer: Honoraria; BMS: Honoraria; Novartis pharma: Honoraria. Ailawadhi:Pharmacyclics: Consultancy; Novartis: Consultancy; Amgen Inc: Consultancy; Takeda Oncology: Consultancy. Lipton:Pfizer: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Ariad: Consultancy, Research Funding. Turkina:Pfizer: Honoraria; Novartis Pharma: Honoraria; BMS: Honoraria. Moiraghi:BMS: Speakers Bureau; NOVARTIS: Speakers Bureau. Nicolini:Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Honoraria; Ariad pharmaceuticals: Honoraria, Membership on an entity's Board of Directors or advisory committees. Sacha:BMS: Consultancy, Honoraria, Speakers Bureau; Incyte: Consultancy, Honoraria, Speakers Bureau; Pfizer: Consultancy, Honoraria, Speakers Bureau; Novartis: Consultancy, Honoraria, Speakers Bureau; Adamed: Consultancy, Honoraria. Kim:Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Novartis: Consultancy, Honoraria, Research Funding, Speakers Bureau; ILYANG: Consultancy, Honoraria, Research Funding. Fellague-Chebra:Novartis: Employment. Acharya:Novartis Healthcare Pvt. Ltd.: Employment. Krunic:Novartis: Employment, Equity Ownership. Jin:Novartis: Employment, Equity Ownership. Mahon:BMS: Honoraria; PFIZER: Honoraria; NOVARTIS PHARMA: Honoraria, Research Funding; ARIAD: Honoraria.

Abstract Background: ENESTop (NCT01698905) is an ongoing, single-arm, phase 2 study evaluating treatment-free remission (TFR) in patients (pts) with chronic myeloid leukemia in chronic phase (CML-CP) who achieved sustained molecular response 4.5 (MR4.5; BCR-ABL1 ≤ 0.0032% on the International Scale [BCR-ABL1IS]) on second-line nilotinib (NIL). In the primary analysis, 57.9% (95% CI, 48.8%-66.7%) of pts who stopped NIL maintained TFR (no loss of major molecular response [MMR; ie, BCR-ABL1IS > 0.1%], confirmed loss of MR4 [ie, consecutive BCR-ABL1IS > 0.01%], or treatment reinitiation) at 48 weeks. Adverse events (AEs) were reported in 73.8% of pts during the first 48 weeks of TFR vs 77.0% during the year prior to stopping treatment. The incidence of musculoskeletal pain-related AEs was higher during TFR (42.1% vs 14.3%). To assess the potential effect of stopping NIL on quality of life (QOL), pt-reported outcomes were assessed before and during TFR. Methods: ENESTop enrolled adult pts with CML-CP with ≥ 3 years of prior tyrosine kinase inhibitor (TKI) therapy (> 4 weeks of imatinib [IM], then ≥ 2 years of NIL). Pts must have achieved sustained MR4.5 on NIL after switching from IM. Enrolled pts entered a 1-year NIL treatment consolidation phase; those without confirmed loss of MR4.5 (ie, consecutive BCR-ABL1IS > 0.0032%) entered the TFR phase and stopped NIL. Pts with loss of MMR or confirmed loss of MR4 during the TFR phase reinitiated NIL. QOL was assessed via questionnaires completed by pts at specified time points. The MD Anderson Symptom Inventory for CML (MDASI-CML) questionnaire assessed, for a defined set of symptoms, the levels of severity and interference with daily life on a scale of 0 to 10, with 0 being the lowest level. The EQ-5D-5L questionnaire assessed problems experienced by pts (no, slight, moderate, severe, or extreme [ie, interfering with functioning] problems) in 5 dimensions: mobility, self-care, usual activities, anxiety/depression, and pain/discomfort; additionally, overall level of health was assessed on a scale of 0 to 100 using the EQ VAS, with 0 being the lowest level. Results: Of 163 enrolled pts, 126 met the criteria for stopping NIL in the TFR phase; of these 126, 53 had loss of MMR or confirmed loss of MR4, of whom 51 reinitiated NIL. Among pts who completed each questionnaire at week 48 of the consolidation phase, week 12 of the TFR phase, or week 48 of the TFR phase, mean MDASI-CML severity scores were 1.7, 1.5, and 1.2, respectively; mean MDASI-CML interference scores were 1.7, 1.6, and 1.4, respectively; and mean EQ VAS scores were 82.2, 78.8, and 82.3, respectively (Table). Among pts with sustained TFR and scores at both week 48 of the consolidation phase and week 12 or 48 of the TFR phase, minimal changes were observed in MDASI-CML and EQ VAS scores during TFR vs week 48 of the consolidation phase. Among pts who reinitiated NIL, mean MDASI-CML severity and interference scores and mean EQ VAS scores at 24 weeks after the start of retreatment were 1.6, 1.5, and 78.8, respectively; changes in scores between week 24 after treatment reinitiation and week 48 of the consolidation phase were minimal among pts with scores at both time points. Among pts who completed the EQ-5D-5L questionnaire, the proportions who reported problems (any level of severity) in the 5 dimensions were generally similar across time points, although problems with mobility and pain/discomfort were more common during TFR vs week 48 of the consolidation phase, particularly at week 12 of the TFR phase (Table). Conclusion: Changes in QOL after stopping NIL, as assessed using these questionnaires, were minimal. This may be related to pts having a relatively high QOL prior to stopping treatment given that they had tolerated ≥ 3 years of TKI therapy, including ≥ 2 years of NIL, prior to enrollment. Despite the higher incidence of musculoskeletal pain-related AEs observed during TFR (42.1% vs 14.3%), the overall QOL was generally similar between the TFR and consolidation phases. This suggests that these AEs did not have a significant negative impact on QOL. Longer follow-up in ENESTop will be needed to further evaluate trends in QOL after stopping second-line NIL. Disclosures Mahon: NOVARTIS PHARMA: Honoraria, Research Funding; BMS: Honoraria; ARIAD: Honoraria; PFIZER: Honoraria. Boquimpani:BMS: Speakers Bureau; Novartis: Research Funding, Speakers Bureau. Takahashi:Novartis: Honoraria, Research Funding; Pfizer: Honoraria, Research Funding; BMS: Honoraria. Shuvaev:Novartis pharma: Honoraria; Pfizer: Honoraria; BMS: Honoraria. Ailawadhi:Pharmacyclics: Consultancy; Novartis: Consultancy; Amgen Inc: Consultancy; Takeda Oncology: Consultancy. Lipton:Novartis: Consultancy, Research Funding; Ariad: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding. Turkina:Novartis Pharma: Honoraria; Pfizer: Honoraria; BMS: Honoraria. Moiraghi:NOVARTIS: Speakers Bureau; BMS: Speakers Bureau. Nicolini:Ariad: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Honoraria. Sacha:Pfizer: Consultancy, Honoraria, Speakers Bureau; Incyte: Consultancy, Honoraria, Speakers Bureau; BMS: Consultancy, Honoraria, Speakers Bureau; Novartis: Consultancy, Honoraria, Speakers Bureau; Adamed: Consultancy, Honoraria. Kim:BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; ILYANG: Consultancy, Honoraria, Research Funding; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Novartis: Consultancy, Honoraria, Research Funding, Speakers Bureau. Fellague-Chebra:Novartis: Employment. Acharya:Novartis Healthcare Pvt. Ltd.: Employment. Brandt:Novartis: Employment. Gnanasakthy:Novartis: Consultancy, Equity Ownership, Research Funding. Jin:Novartis: Employment, Equity Ownership. Hughes:Ariad: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding.

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.

Abstract Background: In the pivotal FIRST trial, at the pre-specified planned analysis for progression-free survival (PFS), treatment (Tx) with lenalidomide plus low-dose dexamethasone until disease progression (Rd continuous) improved outcomes for transplant-ineligible patients (pts) with newly diagnosed multiple myeloma (NDMM) compared with melphalan, prednisone, and thalidomide (MPT), as well as Rd for 18 cycles (Rd18; Benboubker, NEJM 2014). Rd18 was added as a third arm to investigate whether Rd continuous Tx would control progression of NDMM longer than Rd18. Here, we present the final analysis of the overall survival (OS) data from the trial. Methods: Transplant-ineligible pts with NDMM were randomized 1:1:1 to Rd continuous (with lenalidomide given on days 1-21 of 28-day cycles until disease progression), Rd18 (with lenalidomide given on days 1-21 of eighteen 28-day cycles), or MPT (twelve 42-day cycles); both Rd18 and MPT were 72 weeks in duration. The primary endpoint was PFS and the key secondary endpoint was OS; the primary comparison was Rd continuous vs MPT. Other secondary endpoints included time to second therapy (TTST) and safety. Time from randomization to second progression or death (PFS2) was an exploratory analysis. Response assessment used for PFS and PFS2 analysis was determined by investigators based on International Myeloma Working Group criteria. Results: At the time of cutoff for the final OS analysis (January 21, 2016), 52 of the 535 pts in the Rd continuous arm and none of the pts in the Rd18 (n = 541) and MPT (n = 547) arms continued to receive Tx. The median follow-up for surviving pts was 67.0 months (range, 0-86.8 months). In the pre-specified final OS analysis for the primary comparison, a statistically significant advantage in OS was shown with Rd continuous over MPT (hazard ratio [HR], 0.78; 95% confidence interval [CI], 0.67-0.92; P = .00234; Table). OS for Rd continuous vs Rd18 was evaluated (HR, 1.02; 95% CI, 0.86-1.20). In this updated analysis, a PFS advantage continued to be seen for Rd continuous vs MPT (HR, 0.69; 95% CI, 0.59-0.79; P < .00001) and vs Rd18 (HR, 0.70; 95% CI, 0.60-0.81). Median TTST was longer for Rd continuous vs MPT (HR, 0.63; 95% CI, 0.54-0.73; Table). More pts in the Rd18 and MPT arms started second-line therapy (n = 377 and 381, respectively) compared with the Rd continuous arm (n = 299). Pts received a variety of Tx at progression, with bortezomib-based regimens the most common in the Rd continuous (n = 179; 59.9%), Rd18 (n = 208; 55.2%), and MPT (n = 170; 44.6%) arms. Pts who received bortezomib after Rd continuous or Rd18 had better outcomes than those who received it after MPT. PFS2 for Rd continuous was improved vs MPT (HR, 0.74; 95% CI, 0.64-0.85). Grade 3/4 adverse events (AEs) occurred in 86.3%, 80.2%, and 88.7% of the 532, 540, and 541 pts in the safety populations of the Rd continuous, Rd18, and MPT arms, respectively; the most common grade 3/4 AEs were neutropenia (29.5%, 26.5%, and 44.9%) and infections (31.6%, 21.9%, and 17.2%), and no new safety signals were seen compared with earlier analyses. Study discontinuation was most commonly due to disease progression and was less common in the Rd continuous vs Rd18 and MPT arms (50.7% vs 66.9% and 61.6%, respectively). Discontinuation due to AEs was similar across the Rd continuous, Rd18, and MPT arms (12.0%, 13.1%, and 13.9%). Solid tumor second primary malignancies (SPMs) occurred in 6.0, 6.9%, and 5.9% of pts, and hematologic malignancies occurred in 0.8%, 0.4%, and 2.6% of pts in the Rd continuous, Rd18, and MPT arms, respectively. Conclusions: Rd continuous significantly prolonged PFS and OS, and improved other secondary endpoints compared with MPT in transplant-ineligible pts with NDMM. Rd continuous also showed a PFS benefit compared with Rd18, delaying the time to next Tx. PFS2 outcomes suggest that Rd does not induce resistant relapses. Few hematologic malignancies occurred in the Rd arms, and the incidence of SPMs was similar between Rd continuous and Rd18. Rd continuous remains a standard of care for transplant-ineligible pts with NDMM. Disclosures Facon: Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: travel and expense, Speakers Bureau. Dimopoulos:Genesis: Consultancy, Honoraria; Celgene: 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; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees. Dispenzieri:Celgene: Research Funding. Belch:Takeda: Honoraria; Janssen: Honoraria; Amgen: Honoraria; Celgene: Honoraria. Cavo:Bristol-Myers Squibb: Consultancy, Honoraria; Janssen-Cilag: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Millennium: Consultancy, Honoraria. Weisel:BMS: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Onyx: Consultancy, Research Funding; Takeda: Consultancy, Research Funding; Novartis: Consultancy, Research Funding. Ludwig:Janssen: Speakers Bureau; BMS: Speakers Bureau; Takeda: Research Funding, Speakers Bureau; Amgen: Research Funding, Speakers Bureau. Bahlis:BMS: Honoraria; Amgen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Other: Travel Expenses, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Other: Travel Expenses, Research Funding, Speakers Bureau; Onyx: Consultancy, Honoraria. Delforge:Janssen: Honoraria; Celgene: Honoraria; Amgen: Honoraria. Cavenagh:Amgen: Consultancy, Speakers Bureau; Novartis: Consultancy, Speakers Bureau; Janssen: Consultancy, Speakers Bureau; Celgene: Consultancy, Speakers Bureau. Geraldes:Celgene: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Janssen: Consultancy, Honoraria. Oriol:Janssen: 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:Amgen, Bristol, Celgene, Janssen: Consultancy, Speakers Bureau. White:Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Moreau:Celgene: Honoraria; Amgen: Honoraria; Takeda: Honoraria; Novartis: Honoraria; Janssen: Honoraria, Speakers Bureau; Bristol-Myers Squibb: Honoraria. Attal:janssen: Consultancy, Research Funding; amgen: Consultancy, Research Funding; celgene: Consultancy, Research Funding; sanofi: Consultancy. Ervin-Haynes:Celgene: Employment, Equity Ownership. Chen:Celgene: Employment, Equity Ownership. Houck:Celgene: Employment. Hulin:celgene: Honoraria; Bristol: Honoraria; Janssen: Honoraria; Amgen: Honoraria; takeda: Honoraria. Benboubker:Amgen: 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; 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.

Abstract Introduction: Daratumumab is a human CD38 IgGκ monoclonal antibody that demonstrated significant activity and a manageable safety profile in combination with bortezomib and dexamethasone. In a randomized phase 3 study, daratumumab in combination with bortezomib and dexamethasone (DVd) significantly prolonged progression-free survival (PFS) versus bortezomib and dexamethasone alone (Vd) in a pre-specified interim analysis of patients (pts) with relapsed or refractory multiple myeloma (RRMM; Palumbo A. N Engl J Med 2016; in press). Herein, we examine subgroups from this study to compare the efficacy of DVd vs Vd in bortezomib-naive and bortezomib-experienced pt populations. In addition, the efficacy of DVd vs Vd in pts who were refractory to lenalidomide at last prior line of therapy was also evaluated. Methods: Pts who received ≥1 prior line of therapy were randomized (1:1) to 8 cycles (q3w) of Vd (bortezomib: 1.3 mg/m2 SC on Days 1, 4, 8, 11; dexamethasone: 20 mg PO on Days 1, 2, 4, 5, 8, 9, 11, 12) with or without daratumumab (16 mg/kg IV qw in Cycles 1-3, Day 1 of Cycles 4-8, then q4w until progression). Pts who were refractory to bortezomib were not eligible. The primary endpoint was PFS. Bone marrow aspirate samples that had been prepared with Ficoll were evaluated for minimal residual disease (MRD) using three different thresholds (10-4, 10-5, and 10-6) based on the ClonoSEQ assay (Adaptive Biotechnologies, Seattle, WA, USA). Results: Median follow-up was 7.4 months. Among bortezomib-naive pts (DVd, n=89; Vd, n=83), PFS was significantly improved with DVd vs Vd (median: not reached [NR] vs 7.5 months; HR, 0.25; 95% CI, 0.13-0.47; P&lt;0.0001); estimated 12-month PFS rates were 72% vs 28%, respectively. ORR was 88% with DVd vs 70% with Vd (P=0.0040), with ≥very good partial response (VGPR) rates of 72% vs 42% (P&lt;0.0001), and ≥complete response (CR) rates of 30% vs 20% (P=0.1199), respectively. For pts who previously received a bortezomib-containing regimen (DVd, n=162; Vd, n=164), PFS was also significantly longer with DVd vs Vd (median: 12.3 vs 6.7 months; HR, 0.46; 95% CI, 0.32-0.66; P&lt;0.0001). Estimated 12-month PFS rates were 55% vs 27%, respectively. ORR was significantly higher with DVd vs Vd (80% vs 60%; P=0.0001), along with significantly higher rates of ≥VGPR (52% vs 22%; P&lt;0.0001) and ≥CR (13% vs 3%; P=0.0019). Among pts who were refractory to lenalidomide at the last prior line of therapy (DVd, n=45; Vd, n=60), PFS was significantly longer in DVd vs Vd (median: 10.3 vs 4.4 mo; HR, 0.37; 95% CI, 0.21-0.65; P=0.0004; Figure). Within this subgroup, ORR was significantly higher for DVd vs Vd (81% vs 50%; P=0.0021), and the same trends were observed for rates of ≥VGPR (54% vs 12%; P&lt;0.0001) and ≥CR (20% vs 5%; P=0.0261). Updated efficacy and safety data, including MRD analyses across different sensitivity thresholds (10-4, 10-5, and 10-6), will be presented at the meeting. Conclusions: These analyses confirm that addition of daratumumab to Vd significantly improves outcomes for RRMM pts regardless of prior treatment with bortezomib. Importantly, this treatment benefit of DVd vs Vd was maintained in pts who were refractory to lenalidomide at the last prior line of therapy. These data lend further support to adding daratumumab to a standard-of-care regimen in RRMM. Figure Progression-free Survival in Patients Refractory to Lenalidomide at the Last Prior Line of Therapy Figure. Progression-free Survival in Patients Refractory to Lenalidomide at the Last Prior Line of Therapy Disclosures Lentzsch: BMS: Consultancy; Foundation One: Consultancy; Celgene: Consultancy, Honoraria. Quach:Celgene: Membership on an entity's Board of Directors or advisory committees; Janssen Cilag: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees. Ovilla:Janssen: Consultancy. Qi:Janssen: Employment. Deraedt:Janssen: Employment, Equity Ownership. Schecter:Janssen: Employment, Equity Ownership. Amin:Janssen: Employment. Qin:Janssen: Employment. Casneuf:Johnson & Johnson: Equity Ownership; Janssen R&D, Beerse, Belgium: Employment. Chiu:Janssen: Employment. Sasser:Johnson & Johnson: Equity Ownership; Janssen Pharmaceuticals R&D: Employment. Sonneveld:Karyopharm: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Amgen: Consultancy, Honoraria, Research Funding.

Abstract DNA methylation at cytosine position 5 (5mC) is a genetic modification that regulates gene expression and is critical for cancer initiation and development. The methylcytosine dioxygenase TET2 participates in active DNA demethylation by converting 5mC to 5-hydroxymethylcytosine (5hmC) and its subsequent intermediates thereby regulating gene expression. TET2 gene is frequently mutated in hematological disorders, including 15-25% of myelodysplastic syndrome (MDS) cases. Loss of TET2 function leads to DNA hypermethylation and subsequent dysregulated gene expression in hematopoietic stem cells, and has been considered as an initial step of myeloid malignant transformation including MDS and acute myeloid leukemia (AML). While enzymatic activity of TET2 is well studied, little is known about post-translational modifications (PTMs) that regulate its activity in hematopoietic cells. Lysine acetylation, one of the most crucial PTMs, occurs in a variety of proteins and modulates protein-protein or protein-nucleic acids interactions. Here, we first observed that ectopic expression of histone acetyltransferase EP300 or CREB binding protein (CBP) increased the endogenous TET2 acetylation in 293T cells. Mass spectrometry analysis of TET2 identified several conserved acetylated lysine residues located on the exterior surface of its catalytic domain (CD), which is predicted to be essential for the interaction of TET2 with DNA. Modeling of TET2/DNA interaction using x-ray crystal structure (PDB code: 4NM6) indicates acetylated TET2 interferes with DNA binding thereby impairing the catalytic activity of the enzyme. Like other acetylated proteins, TET2 modification is reversible, since ectopic expression of the protein deacetylase Sirtuin1 (SIRT1) significantly decreases acetylation of TET2. A domain mapping analysis revealed that SIRT1 preferentially interacts with TET2 CD, further supporting the regulatory role of SIRT1 on TET2. To explore the role of SIRT1 mediated deacetylaion of TET2 in more physiological condition, SIRT1 knockdown (KD) MDS-L cells as well as SIRT1 knockout (KO) murine hematopoietic c-kit+ cells were used. In MDS-L cells derived from a MDS patient, SIRT1 KD promoted hyperacetylation of endogenous TET2 which is associated with 50% decrease in 5hmC levels compared to control (p=0.03, n=3). Similarly, SIRT1 KO murine c-Kit+ cells showed TET2 hyperacetylation and 62% decrease in 5hmC compared to wildtype (WT) counterpart (p=0.009, n=5). Phenotypically, the transforming ability of SIRT1 shRNA transduced MDS-L cells was evaluated in vitro through serial replating assay. In contrast to first plating which showed no difference between two groups, the number of colonies derived from SIRT1 KD cells was 2.39 fold higher than that from the control shRNA transduced group after two rounds of replating (p=0.0002, n=3). Importantly, we tested the in-vivo effect of SIRT1 KD on MDS-L cells engrafted in immunodeficient NSGS mice. SIRT1 KD significantly enhanced MDS-L cell engraftment compared to control shRNA (50±5% of human CD45+ cells in SIRT1 KD group vs. 19±4% human CD45+ cells in control group, p=0.003, n=6). Interestingly, increased transformation capacity of SIRT1 KD MDS-L cells was associated with decreased expression of TET2 regulated tumor suppressor genes. For example, expression levels of Mtss1 and Dusp6 were significantly decreased in SIRT1 KD MDS-L cells compared to control shRNA counterpart (Mtss1, 49±5% decrease compared to control, p=0.005; Dusp6, 25±2% decrease compared to control, p=0.01). Similar results were observed in SIRT1 KO cells. The validation of physical binding of TET2 to its targets is ongoing. Moreover, the effect of an SIRT1 allosteric activator-SRT1720 in MDS-L cells is similar to that of Vitamin C, a known TET2 activator. Specifically, SRT1720 significantly increased Mtss1 expression in MDSL cells (p=0.0005, n=3), indicating that SIRT1 agonist may lead to activation of TET2 downstream tumor suppressor genes. In summary, our data demonstrate that TET2 activity can be functionally modified by acetylation and may be enhanced in MDS patients that do not harbor loss-of-function mutations through the SIRT1 mediated deacetylation. These results support further exploration of molecular mechanisms inducing TET2 acetylation, and evaluation of SIRT1 activation as a potential therapeutic approach in MDS. Disclosures No relevant conflicts of interest to declare.