Yang, Zhi-Zhang

Background: Follicular lymphoma (FL) is the most frequent indolent lymphoma. While immunochemotherapy (IC) treated FL patients who achieve event-free status at 24 months after diagnosis (EFS24) have the subsequent life expectancy of the general population, those who fail to achieve EFS24 have aggressive disease with poor outcomes. Similarly, in FL patients initially observed or treated with rituximab monotherapy, EFS at 12 months (EFS12) is a strong predictor of subsequent outcome. Thus, an unmet patient need is to predict at diagnosis those at greatest risk of early failure in order to identify better treatments. The lymphoma microenvironment may be a key determinant of early failure. Therefore, we aimed to improve risk stratification of newly diagnosed FL patients by using a discovery and validation study design to identify microenvironment determinants of early failure and then integrate them into the Follicular Lymphoma International Prognostic Index (FLIPI). Patients and Methods: We evaluated 496 patients with FL grade 1-3A who were prospectively enrolled into the Molecular Epidemiology Resource (MER) cohort from 2002-2012. In the discovery set of patients (N=166), we stained tissue microarrays for CD4, CD8, FOXP3, CD32b, CD14, CD68, CD70, SIRPa, TIM3, PD-1 and PDL1. Immunohistochemical staining was determined both within and between malignant follicles. Stains were scored to the nearest decile as the percentages of cells positive. Scores for each stain were dichotomized as 0% vs >0% except for CD32b (<20% vs ≥20%), CD70 (<10% vs ≥10%), and SIRPa (<30% vs ≥30%). EFS was defined as time from diagnosis to progression, relapse, retreatment, or death. Early failure was defined as failing to achieve EFS24 for patients treated with IC at diagnosis and failing to achieve EFS12 for patients who were observed or received other treatments. Risk of early failure was estimated using odds ratios (ORs) and 95% confidence intervals (CI) from logistic regression models. Markers at P≤0.15 were brought forward for validation in a separate set of patients from the MER cohort (N=330), with P<0.005 being declared statistically significant after a Bonferroni correction (0.05/10 tests). In the combined dataset, we also used Cox regression to assess associations with continuous EFS and overall survival (OS). Mass cytometry (CyTOF) was then performed on a subset of patients (N=51) to comprehensively characterize the phenotype of intratumoral immune cells associated with outcome. Results: In the discovery set, CD4, CD8, FOXP3, PD-1, and SIRPα were associated with risk of early failure at P≤0.15 and were subsequently evaluated in the validation set. Only CD4 expression inside the follicle validated. Specifically, lack of CD4 expression inside the follicle was associated with higher risk of early failure in the discovery (OR=1.77, P=0.15) and validation (OR=2.59, P=0.001) sets, with a pooled OR=2.29 (95% CI 1.47-3.58; P<0.001). CyTOF analysis characterized the favorable CD4+ T-cell population associated with achieving EFS12/24 as activated effector memory T cells lacking exhaustion markers (Figure 1A). Adjustment of CD4 expression for FLIPI in the combined dataset did not weaken this association (OR=2.38, 95% CI 1.51-3.79), and lack of CD4 expression was prognostic within each FLIPI group 0-1 (OR=2.65; p=0.036), 2 (OR=2.07; P=0.064) and 3-5 (OR=2.51; P=0.015). We next established a bio-clinical risk model (termed BioFLIPI) that combined CD4 intrafollicular expression and FLIPI into a 1-4 scale, where any expression of intrafollicular CD4 moved a patient up one FLIPI risk group, adding a fourth (high) risk group for FLIPI 3-5 and any CD4 inside expression. The BioFLIPI score was strongly associated with risk of early failure: compared to a BioFLIPI score of 1 (24% of patients), patients with a score of 2 (35% of patients; OR=2.17, 95% CI 1.08-4.69), 3 (31% of patients; OR=3.53, 95% CI 1.78-7.54), and 4 (10% of patients; OR=8.92, 95% CI 4.00-21.1) had increasing risk of early failure. The c-statistic for BioFLIPI was 0.665, which was higher than that for FLIPI alone (0.636). BioFLIPI was also strongly predictive of continuous EFS [Figure 1B], OS [Figure 1C], and for EFS and EFS24 when restricted to IC-treated patients [Figure 1D]. Conclusion: Integration of CD4 expression inside the follicle at diagnosis with FLIPI improves identification of FL patients at risk for early failure. Disclosures Novak: Celgene Coorperation: Research Funding. Nowakowski:Curis: Research Funding; Bayer: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; F. Hoffmann-La Roche Ltd: Research Funding; Selvita: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding; Genentech, Inc.: Research Funding; MorphoSys: Consultancy, Research Funding. Cerhan:NanoString: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding. Ansell:LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment.

Abstract Background - The phenotype and biological role of monocytes in B-cell non-Hodgkin lymphoma (NHL) is not fully understood, however, an increased absolute monocyte count in the peripheral blood of lymphoma patients is associated with a poor prognosis. We have previously reported that monocytes from patients with relapsed B-cell NHL displayed an immunosuppressive CD14+HLA-DRlow/- phenotype that correlated with a poor prognosis. However, the underlying mechanism by which CD14+HLA-DRlow/- monocytes develop in patients with B-cell NHL is unknown. The goal of this study was to determine whether cytokines are responsible for the increased number and phenotype of CD14+HLA-DRlow/-monocytes present in lymphoma patients. Methods – Whole blood from patients with newly diagnosed, untreated B-cell NHL (n=20) and healthy donors (n=20) was stained with a panel of antibodies and analyzed by 10-color flow cytometry. Cytokine levels in serum and culture supernatants were measured by Luminex and ELISA, respectively. Polarization of monocytes from healthy donors was performed by incubating CD14+ cells with specific cytokines or supernatants from B-cell NHL cell lines for 24 hours. Activation and proliferation of CD4+T cells cocultured with IL-10-pretreated monocytes were measured by flow cytometry. Results – By flow cytometry, we observed that the absolute number of peripheral monocytes was increased in newly-diagnosed lymphoma patients compared to healthy donors. A significant proportion of these monocytes displayed an immunosuppressive CD14+HLA-DRlow/- phenotype and the numbers of CD14+HLA-DRlow/- cells were significantly higher in lymphoma patients than in healthy donors. To identify the underlying mechanism by which CD14+HLA-DRlow/- monocytes develop thereby leading to increased numbers of CD14+HLA-DRlow/- monocytes in B-cell NHL, we tested a panel of cytokines and found that IL-10 played a key role in the process. Firstly, treatment of monocytes with IL-10 in vitro resulted in the generation of CD14+HLA-DRlow/- cells. Second, monocytes co-cultured with IL-10 producing lymphoma B-cells, or treated with supernatants from lymphoma cell cultures, developed a CD14+HLA-DRlow/- phenotype. Thirdly, IL-10 levels were increased in the serum of DLBCL (p<0.0001) and FL patients (p=0.010) compared to healthy controls, and serum IL-10 levels correlated with increased numbers of peripheral monocytes (p=0.02). Finally, IL-10-pretreated CD14+HLA-DRlow/- monocytes were significantly immunosuppressive and inhibited the proliferation and activation of CD4+T cells in co-culture assays. Conclusions- Taken together, our results suggest that IL-10 signaling contributes to the increased numbers of CD14+HLA-DRlow/- monocytes in B-cell NHL. Strategies to inhibit IL-10 production may therefore have therapeutic potential in B-cell NHL. Disclosures No relevant conflicts of interest to declare.

Abstract Regulatory T-cells (TREG) are the gateway to immune function and typically regulate immune cell activation. Cytokines, including interleukin-2 (IL-2), induce T-cell differentiation and promote a regulatory phenotype. Once activated via the IL-2 receptor (IL-2R), a cascade of events in T-cells initiate signal transducer and activator of transcription 5 (STAT5) and Forkhead box P3 (FOXP3) activation which appear to function as important regulators of this immunologic pathway and promote the development and function of TREG cells. In non-Hodgkin lymphoma (NHL), we have found that intratumoral TREG cells are increased in number and suppress immune function. In previous work, we have found that TREG cells inhibit T-cell proliferation, suppress cytokine production and limit effector cell cytotoxicity. We have also shown that increased serum levels of soluble sIL-2Rα is a prognostic factor in NHL and that sIL-2Rα can bind to IL-2 and promote its signaling thereby increasing TREG cell numbers. In this study, we developed a strategy to inhibit the binding of IL-2 to sIL-2Rα with the goal of suppressing the induction of FOXP3 and decreasing TREG cell numbers. To do this, we developed peptides designed to disrupt the interaction between IL2 and sILRα. In collaboration with CMDBioscienceSM, we developed and analyzed 22 peptide compounds derived by structure-based computational design. Initially, we screened each peptide at increasing concentrations using an ELISA assay to test the inhibition of IL-2/IL-2Rα binding by the solubilized peptide. Candidate peptides were then further tested using upregulation of pSTAT5 and FOXP3 in T-cells measured by flow cytometry as a measure of inhibition of IL-2 signaling. The peptides were developed according to different design hypotheses and grouped into different families; the screening ELISA results indicated 4 promising peptides that inhibited IL2/IL2Rα binding (up to 100% inhibition; max peptide concentration of 100uM). These peptides were then used to determine their effect on STAT5 and FOXP3 expression. A lead candidate peptide consistently reduced the expression of FOXP3 and STAT5 expression compared to cells not exposed to peptide. Use of the peptide to disrupt IL-2 signaling inhibited the development of cells with a TREG phenotype. We conclude that structure-based peptide design can be used to identify novel peptide inhibitors that block IL-2/IL-2Rα signaling and inhibit STAT5 and FOXP3 upregulation. These peptides could be used as new therapeutic agents to limit immune suppression by TREG cells and promote a more effective anti-tumor immune response in NHL. Disclosures No relevant conflicts of interest to declare.

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.

Myeloid derived suppressor cells (MDSC) are a heterogeneous population of undifferentiated myeloid cells that are expanded and activated in pathological conditions and have the ability to potently suppress T-cell function and thereby contribute to immunosuppression and tumor progression. While there have been studies showing a role for MDSC in a variety of hematological malignancies, no data is available indicating that MDSCs contribute to the tumor progression in Waldenstrom's Macroglobulinemia (WM), an indolent lymphoma characterized by bone marrow (BM) infiltration of lymphoplasmacytic (LPL) cells and increased secretion of monoclonal IgM. In previous work, we have found increased GM-CSF and reduced arginine and cysteine in the BM microenvironment in WM. We hypothesized that this was due to the presence and activity of MDSCs in WM. BM aspirates from WM patients (n=17) were therefore processed to isolate LPL (CD19+/CD138+) cells from the rest of the BM cells (CD19-/CD138-). Sorted (CD19-/CD138-) cells from BM of patients with WM were studied with flow cytometry. Using a sequential gating strategy (lack of lineage markers, low levels of HLADR, CD33+, CD11b+) we identified a population of MDSCs that were then subdivided using CD14 and CD15 expression into total-, monocytic-, or granulocytic- MDSCs (m-MDSC, g-MDSC). We also analyzed unsorted BM cells using cytometry by time-of-flight (CyTOF) in order to further identify and phenotypically characterize the BM MDSC population in a group of WM patients with smoldering (asymptomatic) disease, symptomatic disease, or in remission post-treatment. BM samples from normal subjects were used as a control. Flow cytometry data showed significant higher numbers of MDSC subsets expressing PD-L1 and Arginase1 in WM patients when compared to the normal samples. BM cells from WM patients (n=18) then were compared to controls (n=11), and the absolute number of the total MDSC (p=0.05), m-MDSC (p=0.002), g-MDSC (p=0.02) was increased in WM specimens. When MDSCs from WM or normal monocytes from healthy controls were co-cultured with activated T-cells, the proliferation of activated T-cells in the presence of MDSCs from WM patients was impaired compared to controls, confirming the suppressive role of MDSCs. We then performed high dimensional analysis of the total BM MDSC cells using t-SNEand identified phenotypically distinct MDSC cell populations in the BM that were differentially present when healthy controls were compared to patients with smoldering WM or those with WM needing treatment. Specifically, WM patients needing treatment had increased numbers of a distinct MDSC population that was highly positive for CD163, and CD138. Moreover, conventional markers denoting m-MDSC and g-MDSC, such as CD14 and CD15, were highly expressed in all populations and their pattern of expression did not specifically define the MDSC subtypes, indicating that high dimensional phenotyping further details the MDSC sub-compartments beyond the conventional categorization of MDSC using conventional cytometry. In summary, we find that MDSCs are increased in the BM of WM patients compared to controls. MDSCs expressing CD163 and CD138 increase when WM patients become symptomatic and require therapy. Furthermore, MDSCs in the BM of WM patients suppress T-cell function and likely contribute to progression of the disease. MDSCs in the BM therefore present a therapeutic target that should be explored in WM patients. Disclosures Ansell: Bristol Myers Squibb: Other: research funding for clinical trials; Merck: Other: research funding for clinical trials; AI Therapeutics: Other: research funding for clinical trials; Affimed: Other: research funding for clinical trials; Takeda: Other: research funding for clinical trials; Pfizer: Other: research funding for clinical trials; Regeneron: Other: research funding for clinical trials; Seattle Genetics: Other: research funding for clinical trials.

The outcomes of lymphoma patients treated with PD1 inhibitors (PD1i) vary significantly across different histologies with objective responses rates that are around 70% in classical Hodgkin lymphoma, but significantly lower, around 10%, in indolent non-Hodgkin lymphomas (iNHL). This may be due in part to the inability of PD1i to restore the function of exhausted T-cells (Tex) in the tumor microenvironment (TME). PD1 is expressed on various T-cell subsets, including effector cells and Tex. In this study we sought to identify markers of exhaustion beyond PD1 that define T-cell subsets with high reactivation potential in marginal zone lymphoma (MZL). To define different stages of exhaustion in MZL, we developed and validated a mass cytometry (CyTOF) panel which integrates T-cell markers of lineage, differentiation, activation, suppression, cytokine production and transcription factors associated with exhaustion. We analyzed 35 MZL spleens obtained at diagnosis and 6 reactive spleens. We identified a population of CD8 cells in MZL that express TIM3 without PD1, compose 1.5% (range 0.14-4.88%) of total CD8 cells and expand with activation through the TCR (6.2% of CD8, range 0.6-14.2%). Prolonged cytokine exposure has been shown to induce exhaustion in healthy T-cells, and so to determine whether PD1-TIM3+ cells are Tex, we treated healthy donor T-cells with activating beads and IL12 and analyzed them by flow cytometry. We showed that PD1-TIM3+ cells expand with repetitive activation and IL12 treatment suggesting that they are Tex. Since Tex have been shown to have a distinct transcription profile compared to functional effector T-cells, we analyzed the expression of several transcription factors (TFs) that are upregulated in Tex by CyTOF and compared them to other subsets of T-cells. PD1-TIM3+ cells expressed higher levels of Eomes, Tbet, TOX and Helios compared to activated effector cells, confirming that they are Tex, but lower levels levels of the same TFs compared to PD1+TIM3+ cells, consistent with a less severely exhausted phenotype. To characterize the function of PD1-TIM3+ cells, we manually gated on subsets of CD8+ non-naïve cells that were stained with the CyTOF panel and compared the different subsets. We showed that PD1-TIM3+ cells arise from effector cells, maintain their polyfunctionality by producing cytokines and chemokines and have a unique cytokine production profile, including perforin, granzyme B and proinflammatory cytokines, such as IL17A. Importantly, PD1-TIM3+ cells produced higher levels of most cytokines compared to more severely Tex expressing both PD1 and TIM3 (Fig 1). We then performed tSNE analysis where similar objects are modeled by nearby points. We determined that PD1-TIM3+ cells cluster together, suggesting that they are phenotypically homogeneous cells in terms of function and expression of activation markers and are significantly different from PD1+TIM3+ cells (Fig 2). We finally blocked PD1 or TIM3 and analyzed the effect of each antibody blockade on function. We showed that TIM3 blockade was able to restore the production of most cytokines to higher degrees than PD1 blockade. In addition, TIM3 blockade led to upregulation of CD28, which is critical for the rescue of Tex through costimulation, while PD1 blockade did not. We then analyzed the clinical outcomes of all 34 MZL patients. The frequency of PD1-TIM3+ cells was lower among observed patients who failed to achieve an event-free survival (EFS) of 12 months (EFS12) (0.8% vs 1.7%, p=0.02) and among treated patients that failed an EFS of 24 months (EFS24) (0.9 vs 1.7%, p=0.03), when compared to patients that reached these endpoints. Higher levels of PD1-TIM3+ cells were also associated with a trend towards improved overall survival (OS). Five-year OS was 88% vs. 69% in patients with PD1-TIM3+ levels >2% and <2% of total CD8 cells respectively (p=0.1) (Fig 3). Our study provides insights into the mechanisms of failure of PD1i in MZL and identifies a novel population of Tex with high reactivation potential which is clinically relevant and could serve as a target for future immunotherapies in iNHL. Disclosures Anagnostou: American Society of Hematology, Mayo Clinic/Iowa Lymphoma SPORE, Mayo Clinic Immune Monitoring Core, Mayo Clinic Hematology Small Grant: Research Funding. Novak:Celgene Coorperation: Research Funding. Ansell:Mayo Clinic Rochester: Employment; Trillium: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Seattle Genetics: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Regeneron: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: 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.

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.