Villasboas, José C.

Background: Axicabtagene ciloleucel (axi-cel) is an autologous anti-CD19 chimeric antigen receptor (CAR) T cell therapy approved for the treatment of adult patients who have relapsed/refractory large B cell lymphoma (LBCL) and have had ≥ 2 prior systemic therapies. In ZUMA-1, the registrational study of axi-cel in patients with refractory LBCL, the objective response rate was 83% (complete response rate, 58%), with ongoing responses in 39% after a median follow-up of 27.1 months (Locke FL, et al. Lancet Oncol. 2019). Despite the success of axi-cel, approximately 60% of patients have no response or relapse after treatment, indicating that additional strategies are needed for patients with relapsed/refractory LBCL. Preclinical murine studies have shown that rituximab augmented the tumor-suppressing effects of anti-CD19 CAR T cells, and the combination led to higher rates of tumor reduction (Mihara K, et al. Br J Haematol. 2010; Rufener GA, et al. Cancer Immunol Res. 2016). The IMiD® immunomodulatory agent lenalidomide has shown activity in patients with relapsed/refractory diffuse large B cell lymphoma and has also been shown to enhance the antitumor functions of anti-CD19 and anti-CD20 CAR T cells in mice (Otahal P, et al. Oncoimmunology. 2016). In ZUMA-14, the aim is to investigate the efficacy and safety of axi-cel in combination with rituximab or lenalidomide in adult patients with refractory LBCL. Methods: This Phase 2 study (NCT04002401) has a planned enrollment of approximately 60 patients aged ≥ 18 years with refractory LBCL, defined as a response of either progressive disease or stable disease to previous chemotherapy or progressive disease or relapse ≤ 12 months after an autologous stem cell transplant. Patients with prior IMiD® treatment, including lenalidomide, prior CAR T cell therapy, and/or prior CD19-targeted therapy are excluded. After leukapheresis, patients will be assigned 1:1 to receive axi-cel with either rituximab (Cohort 1) or lenalidomide (Cohort 2). Patients will receive lymphodepleting chemotherapy of fludarabine (30 mg/m2) and cyclophosphamide (500 mg/m2) on days -5 to -3 before axi-cel infusion (2 × 106 cells/kg) on Day 0. Cohort 1 will receive rituximab (375 mg/m2) every 28 days starting on Day -5 for a total of 6 doses. Cohort 2 will receive lenalidomide (10 mg) daily starting 7 days after leukapheresis through Day 3 and for 5 additional cycles (20 mg, first 21 days of each 28-day cycle) beginning after axi-cel infusion starting on Day 21. Patients may not receive any therapy other than conditioning therapy and rituximab or lenalidomide, as specified by cohort, between leukapheresis and axi-cel infusion. The primary endpoint is investigator-assessed complete response rate per the Lugano classification (Cheson BD, et al. J Clin Oncol. 2014). Key secondary endpoints include safety, objective response rate, duration of response, progression-free survival, overall survival, and pharmacokinetics (levels of blood CAR T cells over time). Exploratory endpoints for both cohorts include pharmacodynamic assessment of cytokine profiles and the rate of CD19-negative relapses. An additional exploratory endpoint for Cohort 2 is to investigate immunomodulation of the tumor microenvironment, including the number and activation of T cells and natural killer cells. Enrollment is expected to start in September 2019. Disclosures Neelapu: Cell Medica: Consultancy; Pfizer: Consultancy; BMS: Research Funding; Kite, a Gilead Company: Consultancy, Research Funding; Novartis: Consultancy; Precision Biosciences: Consultancy; Acerta: Research Funding; Karus: Research Funding; Incyte: Consultancy; Merck: Consultancy, Research Funding; Unum Therapeutics: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Cellectis: Research Funding; Allogene: Consultancy; Poseida: Research Funding. Kharfan-Dabaja:Daiichi Sankyo: Consultancy; Pharmacyclics: Consultancy. Oluwole:Pfizer: Consultancy; Spectrum: Consultancy; Gilead Sciences: Consultancy; Bayer: Consultancy. Krish:Celgene: Consultancy, Research Funding, Speakers Bureau; Genetech: Consultancy, Speakers Bureau; Pharmacyclics: Consultancy, Research Funding, Speakers Bureau; Takeda: Research Funding; Curis: Research Funding; MEI Pharma: Research Funding; Bristol Meyers Squibb: Research Funding; Xencor: Research Funding; Roche: Research Funding; AstraZeneca: Consultancy, Research Funding, Speakers Bureau; Sunesis: Consultancy, Research Funding. Reshef:BMS: Consultancy; Shire: Research Funding; Incyte: Consultancy, Research Funding; Kite, a Gilead Company: Consultancy, Honoraria, Research Funding; Atara: Consultancy, Research Funding; Magenta: Consultancy; Pfizer: Consultancy; Pharmacyclics: Consultancy, Research Funding; Celgene: Research Funding. Riedell:Bayer: Honoraria, Speakers Bureau; Kite/Gilead: Honoraria, Research Funding, Speakers Bureau; Verastem: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding. Stiff:Amgen: Research Funding; Gamida-Cell: Research Funding; Incyte: Research Funding; Cellectar: Research Funding; Unum: Research Funding; Gilead/Kite Pharma: Consultancy, Honoraria, Research Funding. Goyal:Kite, a Gilead Company: Employment. Kawashima:Kite, a Gilead Company: Employment, Equity Ownership. Milletti:Roche: Employment, Equity Ownership, Other: Travel Expenses, Patents & Royalties; Gilead: Employment, Equity Ownership, Other: Travel Expenses, Patents & Royalties; Kite, a Gilead Company: Employment. Oliva:Kite, a Gilead Company: Employment. Sun:Kite, A Gilead Company: Employment. Munoz:Pharmacyclics /Janssen: Consultancy, Research Funding, Speakers Bureau; Bayer: Consultancy, Speakers Bureau; Merck: Consultancy; Kyowa: Consultancy, Honoraria, Speakers Bureau; Seattle Genetics: Consultancy, Honoraria, Research Funding, Speakers Bureau; Celgene/Juno: Consultancy, Research Funding; Genentech: Consultancy, Research Funding, Speakers Bureau; Kite/Gilead: Consultancy, Research Funding, Speakers Bureau; Bristol-Myers Squibb: Consultancy; Alexion: Consultancy; Pfizer: Consultancy; Fosunkite: Speakers Bureau; AstraZeneca: Speakers Bureau; Portola: Research Funding; Incyte: Research Funding. OffLabel Disclosure: ZUMA-14 is a clinical trial evaluating the investigational combination of axicabtagene ciloleucel with either rituximab or lenalidomide in refractory large B cell lymphoma

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 Tumor surface matrix metalloproteases ADAM10 and ADAM17 are associated with poor outcomes in multiple malignancies. We previously showed that these proteases shed PD-L1 from lymphoma cell line Karpas-299 to produce soluble PD-L1 (sPD-L1) that induces CD8+ T cell apoptosis (1). While higher levels of soluble PD-L1 are associated with worse prognosis in patients with non-Hodgkin lymphoma treated with standard chemoimmunotherapy (cutoff of 1.52 ng/ml, 3-year OS of 76% versus 89%) (2), it is unknown whether the loss of PD-L1 on the surface of B cell lymphoma cells affects clinical outcomes. We hypothesized that tumors expressing low PD-L1 levels despite high PD-L1 mRNA levels would (1) produce higher ADAM10 or ADAM17 transcript levels and (2) predict poorer prognosis in diffuse large B cell lymphomas. Methods We queried the cancer genome atlas (TCGA) public database for cases of diffuse large B cell lymphoma (DLBCL) for which PD-L1 (CD274) protein levels are reported by reverse phase protein array (RPPA). We then queried TCGA for RNA-Seq data for CD274, ADAM10, and ADAM17 expressed as transcripts per million (FPKM). We re-normalized RPPA data and calculated a PD-L1 protein-to-mRNA ratio for each tumor sample. We then compared groups of high and low PD-L1 protein-to-mRNA ratios for ADAM10 and ADAM17 mRNA expression. We further compared groups of high and low PD-L1 protein-to-mRNA ratios for survival (adjusted for age at diagnosis). All statistical analyses were performed using R Statistical Software (R Foundation). Unpaired student's t-test assessed statistical differences in experimental groups except where otherwise indicated. Figures comprise box plots showing quartile values and individual data points. P<0.05 was considered statistically significant. In figures, p values are denoted <0.05 with *, <0.01 with **, and <0.001 with ***. Results Data were available for a total of 33 diffuse large B cell (DLBC) lymphomas. 18 of those samples were categorized as having low PD-L1 protein-to-mRNA ratios (cutoff 6.38e-6) with the remaining categorized as high ratio. DLBCL tumors demonstrating low PD-L1 protein-to-mRNA ratios expressed significantly higher ADAM10 (p=0.0237) and ADAM17 (p<0.0001) transcripts than tumors expressing low PD-L1 protein-to-mRNA ratios (Figure 1). Furthermore, patients with tumors demonstrating low PD-L1 protein-to-mRNA ratios experienced significantly poorer survival according to likelihood ratio (6.6, p=0.01) and log rank (4.67, p=0.03) tests (Figure 2). Conclusions Our findings may explain a crucial phenomenon seen in diffuse large B cell lymphoma, namely that many purportedly PD-L1-positive tumors appear to express PD-L1 but do not respond to PD-(L)1 inhibitor therapy (3,4). This may be caused by the activity of ADAM10 and ADAM17 to cleave PD-L1, which we previously showed in lymphoma cell line Karpas-299 (1). This posits pre-treatment ADAM10/ADAM17 inhibition to "boost" PD-(L)1 inhibitor therapy in lymphoma. Our results contrast with previous findings that PD-L1-positive DLBCL progression free survival is inferior to PD-L1-negative DLBCL (5). This may be due to different methods of protein detection (immunohistochemistry versus RPPA) and the lack of localization of PD-L1 expression in tumor cells versus the microenvironment. Notably, this study is only correlative with a limited case number. Larger prospective studies will be needed to further elucidate this relationship. Acknowledgments The results shown here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga. References 1. Orme et al. AACR Adv Malig Lymphoma 2018. Pres 423 2. Rossille et al. Leukemia 2014. 28:2367-75. 3. Moskowitz et al. Blood 2014 124:290. 4. Armand et al. J Clin Oncol 2013. 31:4199-206. 5. Kiyasu et al. 2015.126:2193-201. Figure Disclosures No relevant conflicts of interest to declare.

Background: Two-year follow-up from ZUMA-1 trial for axicabtagene ciloleucel (axi-cel) CD19 chimeric antigen receptor T-cell (CART) in aggressive non-Hodgkin lymphoma (NHL) demonstrated that patients (pts) achieving complete remission (CR) as their best response have the longest progression free survival (PFS), while PFS remained poor for others. Majority of the best response are achieved by month 3 post-infusion. Moreover, a subset of pts who do not achieve CR by month 1 may still achieve CR while others will progress within months. Having a clinically available test to predict for durable response can assist clinicians in selecting pts who can be observed for continued response vs. those who may need intervention earlier. Peak CAR-T expansion has been correlated with best clinical response to axi-cel. However there is no clinically available test to quantify CART cells post treatment. Given the prolonged lymphotoxic effect of the lymphodepletion (LD) chemotherapy administered before CART infusion, we hypothesize that the peak expansion of absolute lymphocyte count (ALC, ALCpeak) after infusion likely corresponds to CART expansion. We examined for correlation between ALCpeak and clinical response at month 3 post-infusion for pts who received axi-cel. Methods: Data was collected from pts who received axi-cel between June 2016 and March 2019 at Mayo Clinic Rochester (NCT02348216, NCT03153462, and standard of care). All infused pts were included in the intention to treat analysis for response and event-free survival (EFS). Lugano 2014 criteria were used for clinical response. EFS was defined as the date of infusion until progression or death due to any cause and evaluated using Kaplan Meier curves with log-rank test. Comparison between subgroups was investigated using Fisher's exact test and Wilcoxon test for categorical and continuous variables, respectively. Statistical analysis performed using JMP Pro 14.1. Results: Of the thirty-six NHL pts treated with axi-cel, the median age was 56 yrs (range 26-65); 78% (28) were male; 44% (16) had DLBCL; the median number of prior therapies was 3 (2-6), 47% (17) had stem cell transplant. Nineteen (53%) pts received bridging therapy after leukapheresis. All pts received fludarabine and cyclophosphamide for LD chemotherapy and achieved ALC nadir by the day of CART infusion (median 0.03 x 10^9/L, range 0.01-0.11). As CART expansion were found to peak within the first two weeks in ZUMA-1 trial, we examined ALC trend over 15 days post infusion and found that ALCpeak generally happened within the same timeframe (median ALCpeak day: 9, range 6-15). At 3 months post infusion, 33.3% (12/36) of pts achieved CR, 8.3% (3/36) pts were in partial remission (PR). No correlation was found between CR response and clinical characteristics: age >60 years, stage III/IV, B symptoms, elevated LDH, IPI score 3-4, number of previous treatments, use of bridging therapy, peak CRP and ferritin. However, the ALCpeak was significantly different between CR and non-CR pts (median, range: 1.09, 0.35-2.55; 0.67, 0.15-1.03; p=0.04). Using receiver operator curve (ROC)-derived area under the curve (AUC) estimates, ALCpeak 1.08 x 10^9/L was identified as the best threshold to discriminate patients at different likelihood to achieve CR at month 3. Pts achieving ALCpeak ≥ 1.08 showed a strong increased likelihood of achieving CR at month 3 (OR 2.8; 95% CI, 1.12-6.99; P=0.02). There was no statistical difference between the ALCpeak high and low groups in terms of use of bridging therapy, occurrence of any grade or higher grade cytokine release syndrome or neurotoxicity, or the use of tocilizumab or steroid. Interestingly, for pts who had changes in clinical response from month 1 to month 3, all 3 pts who converted to CR by month 3 had ALCpeak≥1.08, while the 13 of the 15 pts who had progressed by month 3 had ALCpeak<1.08. In addition, EFS was significantly different between pts who had ALCpeak above and below 1.08 (6 months EFS rate 75% vs. 25%, respectively, Figure 1). Conclusions: We found that the ALC expansion occurred in the first 15 days post axi-cel infusion, consistent with the known time frame for CART expansion. Higher ALCpeak level in this timeframe correlated with higher likelihood of achieving CR, and is associated with conversion to CR. This study is limited by the small sample size and limited follow-up, but the results are promising for further prospective investigation in a larger cohort of patients. Disclosures Paludo: Verily Life Sciences: Research Funding; Verily Life Sciences: Research Funding; Celgene: Research Funding; Celgene: Research Funding. Ansell:Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Affimed: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Regeneron: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; LAM Therapeutics: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding. Bennani:Purdue Pharma: Other: Advisory board; Purdue Pharma: Other: Advisory board; Seattle Genetics: Other: Advisory board; Adicet Bio: Other: Advisory board; Bristol-Myers Squibb: Research Funding; Kite Pharma: Other: Advisory board; Kite Pharma: Other: Advisory board; Adicet Bio: Other: Advisory board; Seattle Genetics: Other: Advisory board; Adicet Bio: Other: Advisory board; Bristol-Myers Squibb: Research Funding; Kite Pharma: Other: Advisory board; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Other: Advisory board; Purdue Pharma: Other: Advisory board. Dingli:alexion: Consultancy; Janssen: Consultancy; Millenium: Consultancy; Karyopharm: Research Funding; Rigel: Consultancy. Kapoor:Celgene: Honoraria; Janssen: Research Funding; Cellectar: Consultancy; Sanofi: Consultancy, Research Funding; Amgen: Research Funding; Takeda: Honoraria, Research Funding; Glaxo Smith Kline: Research Funding. Kenderian:Novartis: Patents & Royalties, Research Funding; Tolero: Research Funding; Humanigen: Other: Scientific advisory board , Patents & Royalties, Research Funding; Lentigen: Research Funding; Morphosys: Research Funding; Kite/Gilead: Research Funding. Kumar:Takeda: Research Funding; Janssen: Consultancy, Research Funding; Celgene: Consultancy, Research Funding.

Abstract Introduction: The role of surveillance imaging in diffuse large B-cell lymphoma (DLBCL) after treatment with curative intent is debated. In 2013, the American Society of Hematology (ASH) Choosing Wisely® campaign (Hicks LK, et al. Blood 2013) released a recommendation to "limit surveillance CT scans in asymptomatic patients following curative-intent treatment for aggressive lymphoma." This was based on previous studies demonstrating a lack of survival benefit as well as a significant impact on the patient in terms of cost, anxiety, incidental findings and radiation exposure. Similar findings have been described regarding the use of PET imaging, especially in low risk patients. We aimed to study the changes in practice patterns of surveillance imaging for DLBCL survivors treated with curative intent before and after the publication of the ASH Choosing Wisely® campaign. Methods: Administrative claims data from a large U.S commercial insurance database (OptumLabs Data Warehouse) were used to retrospectively identify adult patients with an incident diagnosis of DLBCL between 2008 and 2016 and at least one year of follow-up. We excluded those who did not receive treatment with curative intent: RCHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) x 6 cycles or RCHOP x 3-5 cycles plus radiation. Patients were also excluded if they had a co-existing or second cancer, or if they received any chemotherapy/radiation during their surveillance period, as to exclude anyone with a recurrence of their lymphoma. The surveillance period started 90 days after the last chemotherapy or radiation and continued for the duration of follow-up. Surveillance imaging was defined as any PET, PET-CT, CT chest, CT abdomen, and/or CT pelvis. To examine trends over time, three time periods were defined: 2008-2010, 2011-2013 and 2014-2016. The Cochran Armitage test was used to test the trend hypothesis. Results: A total of 1,472 patients were included. Cohort characteristics by time period are described in Table 1. Surveillance imaging frequency decreased from 84% in Year 1 of surveillance to 43% in Year 5 (Figure 1). Overall, surveillance imaging during the first 3 surveillance years significantly decreased over the three time periods (Figure 2) (P<0.01 for Years 1 and 2, P=0.04 for Year 3). Imaging surveillance for the first two years of surveillance did not significantly decrease from 2008-2010 to 2011-2013 (P>0.05), but significantly decreased from 2011-2013 to 2014-2016 (P<0.01). Of the groups that received imaging surveillance during year 1, the median number of scans during the first year was 2 (inter-quartile range, 1-2) and did not significantly change over time. In addition, PET/PET-CT surveillance decreased from 49% in Year 1 to <10% in Year 5. PET/PET-CT also decreased over the three time periods, from 62% (2008-2010) to 48% (2011-2013) to 43% (2014-2016) during the first year of surveillance and 40% to 31% to 25% during the second year. Younger age (<65 years versus ≥65 years) was associated with higher imaging utilization (86% versus 81%, respectively), and specifically PET/PET-CT utilization (55% versus 43%, respectively), during the first year of surveillance. However, race (white versus non-white) did not significantly affect imaging (85% versus 83%) or PET utilization (50% versus 47%) during the first year of surveillance. Conclusions: Our study demonstrates that the rate of surveillance CT and PET imaging in DLBCL patients treated with curative intent decreased significantly in the years after the publication of the ASH Choosing Wisely® campaign. However, over half of the DLBCL patients diagnosed in 2014-2016 continued to undergo surveillance imaging, indicating either incomplete guideline dissemination or ongoing debate regarding optimal surveillance practice. Future studies are necessary to determine whether such a decline in surveillance imaging is sustained and to examine factors (patient, provider, and payer) associated with surveillance imaging in DLBCL. Disclosures No relevant conflicts of interest to declare.

Abstract Introduction: Chronic myelomonocytic leukemia (CMML) is a chronic myeloid malignancy associated with monocytosis, autoimmunity (~30%) & an inherent risk for leukemic transformation. Bone marrow (BM) dendritic cell (DC) populations occur in ~30% of patients, with a poorly defined biological & prognostic role. The malignant immune microenvironment is regulated by indoleamine 2,3-dioxygenase-1 (IDO-1) expressing DCs, which modulate regulatory T (Treg) cells & block their conversion into proinflammatory T helper (Th17)-like cells. IDO-1 is a known immune checkpoint & functions by catabolizing tryptophan, an amino acid essential for T cell function. We hypothesized that distinct IDO-1 expressing DC populations in CMML modulate Tregs & contribute towards immune tolerance & aggressive disease biology. Methods: Primary diagnostic CMML peripheral blood mononuclear cells (PBMC) & BM biopsy specimens were obtained after Mayo Clinic IRB approval. A DC population was defined on H&E stained biopsy sections as focal collections (>10) of cells with characteristic elongated nuclei & cytoplasmic extensions. Transcriptomic & protein expression studies assessing IDO-1 expression were done by previously described methods. In addition, IHC expression of PD-1, PD-L1 & CTLA-4 was also done. IDO-1 promoter methylation studies with DIP-seq were performed. The impact on immune tolerance was assessed using mass cytometry (CyTOF). Results: Cohort: Twenty eight patients with CMML were included in the study, median age 70 (range: 51-80) years; 71% males. Eleven (39%) patients had coexisting autoimmune conditions. Of these, 8 (73%) had detectable DC populations either at diagnosis, or during the course of their disease. At a median follow-up of 46 (95% CI 27, 84) months, there were 14 (50%) deaths & 9 (32%) leukemic transformations.IHC results: Nine (32%) patients were identified to have a DC population at CMML diagnosis. CD123 & TCL1 staining was performed in 5 (56%) patients, with 3 being positive for both, & 2 positive for CD123 only (additional IHC studies ongoing). IDO1 expression by IHC was documented in all 9 (100%) cases (Fig 1A & 1B), while rare populations of PD-1, PD-L1 & CTLA-4 lymphocytes were also seen in all cases. Due to the low DC burdens (median cellularity ≤ 5%) & uniform staining intensity, IHC-based grading was not done. Samples at serial time-points, post-HMA therapy & at the time of blast transformation, were available in 5 & 3 patients respectively. Among the patients who did not have DC populations at diagnosis, 5 (42%) developed them post-HMA therapy, while 3 (50%) developed them at the time of LT. The development of DC populations was associated with loss of response to HMA (50%) & disease progression (50%).Transcriptomic analysis: RNA expression data was available on 7 (25%) patients, of whom only 1 (14%) had DC populations at diagnosis. The IDO-1 RPKM value in the former was higher than the mean pooled value in the latter group (330 versus 74, p=0.05).Methylation studies: DIP-seq was performed on 12 (43%) cases from the primary IHC cohort. Qualitative analysis of IDO-1 promoter hypomethylation was conducted & confirmed in all 9 (100%) cases with 5-mC & 5-hmC marks compared to input as displayed in figure 1C.Immune profiling: CyTOF was performed on 4 CMML samples (3 with IDO-1 expressing DC populations at diagnosis) from the primary IHC cohort & compared to a normal PBMC control. Results confirmed an increase in DC populations (fig 1D& 1E), & reduced % of Th17-like T cells in CMML samples compared to control (1.1 versus 5.07, p=0.05, fig 1F).Clinical correlates & survival analysis: With the exception that CMML patients with DC populations had a higher frequency of NRAS (P=0.007) mutations, the two groups were comparable for cytogenetic & molecular abnormalities. The median OS for the cohort was 45 (95% CI 29, 84) months. CMML patients with IDO-1 expressing DC populations at diagnosis had a shorter median OS, in comparison to those without (median OS 30 vs 45, p=0.03, Kaplan-Meier analysis in fig 1G). Conclusions: In conclusion, we demonstrate that DC populations are seen in ~30% of patients with CMML with a uniform expression of IDO-1 & limited expression of PD-1, PD-L1 & CTLA-4. CMML patients with BM DC populations have a higher frequency of NRAS mutations & DC IDO-1 expression is associated with tumor induced immune tolerance. Additional IHC, genomic & preclinical studies with IDO-1 inhibitors are ongoing. Figure 1. Figure 1. Disclosures Al-Kali: Novartis: Research Funding.

Abstract INTRODUCTION: Traditional cytometry methods have been unable to capture the immense heterogeneity of the tumor immune microenvironment in various malignancies including multiple myeloma (MM). Cytometry by time of flight (CyTOF) can, to some extent, overcome this limitation. However, the computational challenges that come with analyzing these complex datasets in a reproducible manner remain. In this study, using a large cohort of patients, we compare the bone marrow immunomes from patients with MGUS, multiple myeloma (MM), smoldering MM (SMM) and light chain amyloidosis (AL) at diagnosis, after induction therapy with lenalidomide and dexamethasone and after autologous transplant (ASCT). METHODS: We studied a total of 118 cryopreserved samples as follows: 14 healthy donors, 43 AL (27 newly diagnosed-ND, of which 13 with <10% bone marrow plasma cells, 16 matched samples post ASCT), 12 with ND MGUS, 11 with SMM (of which 6 were ND), 14 with ND MM, 13 paired MM samples post induction therapy and 11 paired MM samples post ASCT. Our CyTOF surface staining panel included the following 33 markers: CD45, HLA-DR, CD19, CD3, CD4, CD8, CD14, CCR6, CD11a, Cd123, CCR5, CD7, ICOS, CD25, CD57, CD45RA, CD163, PD-1, PDL-1, CXCR3, CCR4, CCR7, CD28, CTLA4, CD11c, CD56, CD45RO, CD44, CD27, CD138, CD38, CD-127 and CD16. Data processing and analysis was performed using Cytobank. Live cells were identified based on Pt195 and Ir193 staining. Myeloma cells and CD45- cells were excluded and only CD45+ cells were used for subsequent analyses. Single-cell data were downsampled using VisNE, a permutation of t-Distributed Stochastic Neighbor Embedding (tSNE) and clustered using CITRUS (using 10,000 events per sample with a minimum cluster size of 1%). A Significance analysis of microarrays (SAM) analysis was performed to ascertain differences between groups. Significance was inferred for a false discovery rate <1% . All CITRUS analyses were repeated at least 3 times and only clusters found to differ consistently across runs were considered. RESULTS: The proportions of immune subsets identified to vary by CITRUS before and after induction therapy and ASCT for MM and AL are shown in the table. No differences were identified between MGUS, SMM and NDMM. The proportion of a subset (369850) of CD14+/C16- monocytes, a group of cells shown to correlate positively with survival and response to therapy in solid malignancies, increased after induction therapy in MM. Naïve B cells increased dramatically post ASCT in both AL (429918) and MM (369948), consistent with expected immune reconstitution patterns, although a CCR6+ B Cell subset (429940) shown to mediate effective antibody responses, decreased (in grey). A subset (369980) of functionally exhausted (PD1/CTLA4+) central memory (CM) CD4 T cells decreased after induction therapy in MM but recovered early post ASCT whereas a CM CD4+ subset (369986) lacking major activation markers (CD28, CD25) decreased gradually with therapy and post ASCT. A subset (369953) of naïve CD8 T cells shown to be actively recruited in tumor sites (CXCR3+) decreased after ASCT. CD57+ senescent effector memory (EM) CD8 T cells (369962) decreased with induction therapy but recovered post ASCT. EM CD8 T cells associated with long term immune memory (CCR5+, CD127+, cluster 369959), also decreased after ASCT. LIMITATIONS: Include a) No barcoding b) batch effects were difficult to avoid when processing large number of samples c) use of cryopreserved samples d) need for downsampling to cope with the computational burden. The latter could have been one of the reasons we did not identify any differences between MGUS, SMM and MM. At the time of the meeting we will present confirmatory analyses using conceptually different methods of clustering and of performing across group comparisons as well as analyses that do not include downsampling. CONCLUSIONS: Mass cytometry can provide a more granular view of the bone marrow immunome in plasma cell dyscrasias. Novel agent induction therapy can create an immunologically favorable anti-tumor microenvironment although, in some cases, these favorable immunomic shifts are temporarily reversed by ASCT. Confirmatory analyses, baseline immune profiles, comparisons with healthy donors and correlation with other clinically relevant patient characteristics and outcomes will be reported at the meeting. Disclosures Dispenzieri: Celgene, Takeda, Prothena, Jannsen, Pfizer, Alnylam, GSK: Research Funding. Kumar:Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding.

Abstract Background: Limited data exist regarding histological transformation in patients with Waldenström macroglobulinemia (WM). In this study, we present the disease characteristics, outcomes and risk factors for histological transformation in WM. Methods: Patients with WM seen at Mayo Clinic, Rochester, MN between January 1996 and December 2017 were included. Patients with aggressive non-Hodgkin lymphoma in the setting of WM were considered to have transformation. Univariate analysis for comparing baseline characteristics of transformed versus non-transformed WM was performed using Wilcoxon, Fisher's exact and Chi square tests, as appropriate. Multivariate analysis was performed using logistic regression analysis. Cox proportional hazard method was used to assess the impact on time-to-transformation. All time-to-event analyses were calculated by the Kaplan-Meier method. Results: Of 1014 patients with WM, 42 patients (4.1%) developed histological transformation. The median follow-up for the entire cohort was 9.5 years (95% CI: 8.8-10.5 years). The cumulative probability of transformation was 2.3% at 5 years, 5.3% at 10 years and 8.5% at 15 years from diagnosis of WM, respectively. There was no difference in the 5-year cumulative probability of transformation in patients with WM diagnosed between 1996-2000 (2.1%), 2001-2005 (2.9%), 2006-2010 (2.7%) and 2011-2015 (3.8%), p=0.61.The disease characteristics, laboratory parameters and histology at transformation are outlined in Table 1. In patients with DLBCL histology (n=39), Revised-International Prognostic Index was calculable in 28 patients, with 50% (n=14), 46% (n=13) and 4% (n=1) of DLBCL belonging to poor, good and very good risk groups, respectively. For DLBCL histology, 18 patients were classifiable using Hans algorithm into germinal center B-cell (GCB) type (n=3, 17%) and non-GCB type histology (n=15, 83%). The median lines of therapy received prior to transformation for the entire cohort was 2 (range 0-9), with 5 (11%) patients not having received any therapy for WM before transformation. Number of lines of alkylator-based therapy used prior to transformation [median 1 line (range 0-4)] was comparable to that used in patients in whom WM did not occur [median 1, (range 0-6); p=0.78]. The median time-to-transformation from diagnosis of WM was 4.8 years (95% CI: 2.6- 6.8 years). Overall survival (OS) from transformed disease was 3.2 years (95% CI 1.1-3.9 years). The 10-year OS of patients with WM who transformed was lower (46%) compared to patients without transformation (60%) with a trend towards statistical significance (p=0.08), Figure 1. The MYD88L265P mutation status was available in 333 patients in the entire cohort (22/42 pts in the transformed cohort and 311/972 in the non-transformed cohort. Of the 22 patients in the transformed WM cohort, 13 (59%) exhibited MYD88WT genotype and 9 (41%) exhibited MYD88L265P genotype. The risk of transformation was higher in patients with MYD88WT status [Odds ratio 6.3 (95% CI: 2.6-15.5); p<0.0001]. Additionally, serum lactate dehydrogenase (LDH) was higher at diagnosis of WM in patients who transformed, Table 2. On multivariate analysis, MYD88WT status remained an independent predictor of disease transformation [p=0.003; odds ratio 10 (95% CI: 2.1-48)]. On univariate Cox proportional hazard analysis for identifying predictors of time-to-transformation, only MYD88WT mutational status was associated with shorter time-to-transformation with a 5-year transformation rate of 15.5 % for MYD88WT versus 2.6 % with MYDL265P mutated patients [risk ratio 6.2 (95% CI: 2.5-16.4); p<0.0001]. Treatment-related details in the transformed cases were available for 37 (90%) patients; R-CHOP was the most common (41%) frontline therapy for transformation; 33% patients underwent autologous stem cell transplantation for transformed disease during their disease course. Conclusion: Histological transformation is an uncommon event in WM but confers poorer survival. MYD88WT genotype is independently associated with histological transformation a shorter time-to- transformation in patients with WM. Disclosures Ansell: Trillium: Research Funding; Pfizer: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Takeda: Research Funding; Celldex: Research Funding; Regeneron: Research Funding; Merck & Co: Research Funding; Bristol-Myers Squibb: Research Funding. Gertz:Apellis: Consultancy; Alnylam: Honoraria; spectrum: Consultancy, Honoraria; janssen: Consultancy; annexon: Consultancy; Prothena: Honoraria; Abbvie: Consultancy; Amgen: Consultancy; celgene: Consultancy; Teva: Consultancy; Medscape: Consultancy; Ionis: Honoraria; Research to Practice: Consultancy; Physicians Education Resource: Consultancy. Dingli:Millennium Takeda: Research Funding; Millennium Takeda: Research Funding; Alexion Pharmaceuticals, Inc.: Other: Participates in the International PNH Registry (for Mayo Clinic, Rochester) for Alexion Pharmaceuticals, Inc.; Alexion Pharmaceuticals, Inc.: Other: Participates in the International PNH Registry (for Mayo Clinic, Rochester) for Alexion Pharmaceuticals, Inc.. Witzig:Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Dispenzieri:Celgene, Takeda, Prothena, Jannsen, Pfizer, Alnylam, GSK: Research Funding. Lacy:Celgene: Research Funding. Kumar:Merck: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees; Novartis: Research Funding; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding. Kapoor:Takeda: Research Funding; Celgene: Research Funding.

Abstract Introduction: Our group (Porrata et al, BBMT 2016; 22: 1017-1023) previously published that the infusion of autograft natural killer (NK) cells correlates with survival post-autologous stem cell transplantation (ASCT) in non-Hodgkin's lymphoma (NHL) patients. A limitation of our previous study was the lack of killer-immunoglobulin-like receptor (KIR) analysis. Thus, we set up to investigate if the infusion of autograft activating and inhibitory KIR affects survival in NHL. Methods: From December 2007 to October 2010, 122 NHL patients participated in our Phase III clinical trial. A total of 111 patients finished the trail and samples were available for analysis. We analyzed the content of the autograft for inhibitory and activating KIR using flow cytometry analysis of each apheresis collection. Overall survival (OS) and progression-free survival (PFS) were analyzed from the date of transplant. Results: With a median follow-up of 82.8 months (range: 2.1-122.3 months), patients infused with an activating NKp30 (CD158b-CD337+) KIR ≥ 0.09 x 109 cells/kg experienced superior OS and PFS vs < 0.09 x 109 cells/kg: [OS: median was not reached vs 39.8 months, 5 years OS rates of 95% (95%CI, 56%-98%) vs 43% (95%CI, 22%-48%), p < 0.0001, respectively; and PFS: median was not reached vs 12.7 months, 5-years PFS rates of 77% (95%CI, 65%-86%) vs 13% (95%CI, 6%-24%), p < 0.0001, respectively]. Patients infused with the inhibitory KIR2DL2 (CD158b+CD337-) KIR < 0.066 x 109 cells/kg experienced superior OS and PFS vs ≥ 0.066 x 109 cells/kg : [OS: median was not reached vs 48.2 months, 5 years OS rates of 86% (95%CI, 75%-93%) vs 44% (95%CI, 31%-88%), p < 0.0001, respectively; and PFS: median was not reached vs 25.6 months, 5-years PFS rates of 62% (95%CI, 49%-74%) vs 31% (95%CI, 20%-44%), p < 0.0002, respectively]. We combined NKp30 and KIR2DL2 into NKp30/KIR2DL2 ratio. Patients infused with an NKp30/KIR2DL2 ratio ≥ 1.8 experienced superior OS (Figure 1) and PFS (Figure 2) vs < 1.8 : [OS: median was not reached vs 33.4 months, 5 years OS rates of 95% (95%CI, 86%-98%) vs 33% (95%CI, 21%-47%), p < 0.0001, respectively; and PFS: median was not reached vs 14.9 months, 5-years PFS rates of 76% (95%CI, 63%-85%) vs 14% (95%CI, 7%-27%), p < 0.0001, respectively]. Multivariate analysis revealed that the NKp30/KIR2DL2 ratio was an independent predictor for OS [HR = 0.03, 95%CI 0f 0.01-0.07, p < 0.0001] and PFS [HR = 0.17, 95%CI 0f 0.09-0.31, p < 0.0001]. Conclusion: The NKp30/KIR2DL2 ratio provides a platform to develop an immunocompetent autograft to improve clinical outcomes in NHL patients undergoing ASCT. Figure 1. Figure 1. Disclosures Ansell: LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Celldex: Research Funding; Takeda: Research Funding; Merck & Co: Research Funding; Pfizer: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding.