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Background Classical Hodgkin lymphoma (cHL) is characterized by rare, malignant Hodgkin/Reed Sternberg (HRS) cells that shape their microenvironment (TME) to inhibit anti-tumor immune response. Systemic immune dysregulation may influence treatment response and toxicity, but the systemic influence of the TME is less well described. The wide variety of proteins measured in high-parmater flow cytometry make it a powerful tool for immune monitoring, but presents challenges in immuno-monitoring. Combinatorial expression of these proteins defines cell types that may influence disease. TerraFlow is a fully automated data analysis platform that evaluates millions of phenotypes and selects the populations that best predict clinical variables. The analysis can be performed using classical Boolean gates or a non-gating approach that approximates gates without using manual thresholds, allowing immunophenotypes to be comprehensively surveyed for disease associations. The platform was used to find phenotypes that discriminate healthy versus cHL patients (AUC = 1) and pre versus post treatment patient phenotypes(AUC = 0.79). Methods Human Subjects: Informed consent was obtained from cHL patients (N=44) treated at the Perlmutter Cancer Center (PCC) at NYU Langone Health and New York Presbyterian Weil Cornell (NYP) between 2011 and 2016. Blood samples were drawn at multiple time-points, for this study pre-treatment and 3 month post-treatment samples were used. Age-matched, cryopreserved healthy donor PBMC (n=25) were obtained from STEMCELL Technologies (Cambridge, MA).Patient-derived blood was processed for isolation of PBMC, stained analyzed on a Symphony Flow Cytometer (BD Biosciences, San Jose, CA). Analysis: Data was analyzed using an original platform called terraFlow. Many immune cell subsets are defined by the combinations of proteins they express. TerraFlow systematically evaluates millions of cell types by generating every possible combination of 1 to 5 markers. A network-based algorithm then selects the "best" phenotype from each set of inter-related combinations based on statistical power and ease of interpretation. Each phenotype is defined using a minimal gating strategy that can be replicated in a diagnostic panel or cell sorter. Together, phenotypes describe all the major differences between patient groups. A new platform developed by Epistemic AI was used to mine scientific literature and interpret selected phenotypes. Results We observed clear perturbations in the cHL systemic T-cell compartment pre-treatment as shown in Figure 1. These include higher levels of activated (CD278+), exhausted (CD366+, PD1+, CD152+), and suppressive (GITR+) T-cells compared to healthy donors, and diminished levels of T-cells producing effector cytokines (like IFNγ and IL4). Subsets of cytokine-producing cells that co-express markers of exhaustion (i.e., TNF+ CD366+ cells) are also elevated in cHL patients. Finally, T-cells expressing CD127 a receptor for IL7 involved in homeostatic renewal of cells and observed on naive and central memory T-cells are reduced. Taken together, these findings suggest that in cHL the systemic T-cell compartment is shifted toward a more exhausted profile, and away from less differentiated cells, with the potential for self-renewal. Our data also demonstrates a shift from T-helper 1 and T-helper 2 type toward T-helper 17 cells suggesting that T-cell effector function may be reduced. Conclusion Using a novel data analysis platform, TerraFlow we demonstrate dysregulation in systemic T cell function in cHL patients pre-treatment that persists within 3 months of completing therapy. Associations of phenotypes with clinical variables, and post-treatment phenotypes will be described in detail at the meeting. Our results detail new immunotherapy and biomarker research targets, and suggest novel strategies for combination therapies. Figure 1 Figure 1. Disclosures Li: BD Bioscience: Current Employment. Ruan: Kite Pharma: Consultancy; AstraZeneca: Research Funding; BMS: Consultancy, Research Funding; Daiichi Sankyo: Consultancy, Research Funding; Pharmacyclics: Research Funding; Seagen: Consultancy. Diefenbach: Incyte: Research Funding; Trillium: Research Funding; Celgene: Research Funding; IGM Biosciences: Research Funding; Seattle Genetics: Consultancy, Honoraria, Research Funding; Gilead: Current equity holder in publicly-traded company; AbbVie: Research Funding; Perlmutter Cancer Center at NYU Langone Health: Current Employment; MEI: Consultancy, Research Funding; Genentech, Inc./ F. Hoffmann-La Roche Ltd: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; IMab: Research Funding; Morphosys: Consultancy, Honoraria, Research Funding; Merck Sharp & Dohme: Consultancy, Honoraria, Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria, Research Funding.

Background: Classical Hodgkin lymphoma (HL) has a unique histopathology, with rare malignant Hodgkin/Reed Sternberg (HRS) cells surrounded by a strong inflammatory cellular component in the tumor microenvironment (TME). Although extensive studies describe the interdependence of the HRS cells and the TME, the impact of HL on systemic immunity has not been well described. Here, we develop a new approach, employing a recently commercialized single cell cytokine secretion platform (IsoLight) to assess, precisely and comprehensively, the function of peripheral blood mononuclear cells (PBMCs) in HL patients. Methods: Cells were selected from 4 HL patients: 2 newly diagnosed who had a complete response (CR) to therapy standard first line therapy, and 2 relapsed patients who progressed on second line chemotherapy (PD). Cryopreserved PBMCs from a pre-treatment and post-treatment time point for each patient were thawed, rested overnight, stimulated with PMA/ionomycin and loaded into the IsoLight single cell cytokine secretion system. IsoLight captures single cells in microwells; as cytokines are secreted, they are bound by antibodies lining the microwell cover. Bound cytokines are then revealed by fluorescent secondary antibodies and photos are taken at various time points to assess fluorescence intensity, which corresponds to the relative amount of each cytokine secreted. Twenty thousand cells can be assayed per sample simultaneously. Results: The percentage of cytokine-secreting cells varied dramatically by donor (12%-48%), with monofunctional cells making only TNFa, MIP1b, or IL-15 dominating the functional landscape. Polyfunctional cells, capable of making three or more cytokines simultaneously represented only 0.1-7% of the cells in each sample, but there were more of these cells, and each secreted higher levels of cytokines, in individuals who responded to therapy with a CR. Responders also secreted higher levels of IL2, Perforin, IL4, IL12, MIP1a, and TNFb (p values ranging from 0.005 to 0.03), and lower levels of IL9 and IL22 (p=0.0028 and 0.021, respectively), compared to non-responders at diagnosis. Responders lost expression of IL4, IL7, and MIP1a over the course of treatment (pre- vs post-treatment, p=0.01 to 0.05), while non-responders gained cells that expressed IL4, IL5, IL10, IL17, and TNFb from diagnosis to end of treatment (p=0.001 to 0.05). Conclusion: This work represents an important methodological advance in immune monitoring for hematologic malignancies. Single cell cytokine secretion technology measures more cytokines simultaneously than flow cytometry, providing a sample-sparing and comprehensive overview of the functional landscape of immune cells in a patient. Moreover, the technology provides cell-by-cell information about cytokine secretion, unlike Luminex. Our work represents the first application of this technology to HL, which we use to define, for the first time, the particular combinations of 32 cytokines that can be secreted by individual immune cells. We also identify candidate cytokines whose frequency at diagnosis may predict treatment outcome, and reveal changes in cytokine levels over treatment time that may distinguish patients destined to relapse. Immunotherapy may impact PBMC function differently, this may partially explain the high efficacy of this therapy in the relapsed population. The impact of immunotherapy on cytokine levels is currently under investigation by our group in a larger study. Other important questions which are under investigation include the impact of prior chemotherapy on cytokine profiles in relapsed patients, and whether certain cytokines which increase during treatment may be a surrogage for tumor bulk in patients with PD. Cytokines elevated in patients with poor responses to treatment include IL9, IL10, IL17, and IL22, which may present attractive drug targets if validated in our larger ongoing follow-up study. Disclosures Diefenbach: Bristol-Myers Squibb: Consultancy, Research Funding; MEI: Research Funding; Denovo: Research Funding; Genentech: Consultancy, Research Funding; Incyte: Research Funding; LAM Therapeutics: Research Funding; Merck: Consultancy, Research Funding; Seattle Genetics: Consultancy, Research Funding; Trillium: Research Funding; Millenium/Takeda: Research Funding. Hymes:Celgene: Consultancy. Martin:Janssen: Consultancy; Sandoz: Consultancy; Karyopharm: Consultancy; Celgene: Consultancy; Teneobio: Consultancy; I-MAB: Consultancy. Ruan:Celgene: Consultancy, Honoraria, Research Funding; AstraZeneca: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria; Pharmacyclics LLC, an AbbVie company: Research Funding; Juno: Consultancy; Kite: Consultancy. Leonard:Miltenyi: Consultancy; Akcea Therapeutics: Consultancy; Sandoz: Consultancy; AstraZeneca: Consultancy; Bayer Corporation: Consultancy; Epizyme, Inc: Consultancy; BeiGene: Consultancy; Miltenyi: Consultancy; ADC Therapeutics: Consultancy; Akcea Therapeutics: Consultancy; Sandoz: Consultancy; Celgene: Consultancy; Epizyme, Inc: Consultancy; Karyopharm Therapeutics: Consultancy; AstraZeneca: Consultancy; Bayer Corporation: Consultancy; Celgene: Consultancy; Sutro Biopharma: Consultancy; Merck: Consultancy; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy; Gilead: Consultancy; Karyopharm Therapeutics: Consultancy; Sutro Biopharma: Consultancy; Nordic Nanovector: Consultancy; ADC Therapeutics: Consultancy; MorphoSys: Consultancy; Gilead: Consultancy; Nordic Nanovector: Consultancy; BeiGene: Consultancy; Merck: Consultancy; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy; MorphoSys: Consultancy.

Background: CD123, the trans-membrane alpha chain of the interleukin-3 receptor (IL-3RA) is overexpressed in acute myeloid leukemia (AML) and distinguishes leukemia stem cells from their normal counterparts. There are several novel therapeutics under development to target CD123 in AML, including CD123 fused to Diphtheria toxin, a recombinant chimeric anti-CD123 MoAb, CD3/CD123 bi-specific T cell engagers, and engineered T cells that express chimeric antigen receptors (CARs). Thus, accurate detection and quantification of CD123 is critical for newly diagnosed and relapsed patients, and to follow minimal residual disease for patients in remission. Our data suggest that the evaluation of CD123 by flow cytometry varies significantly with different antibody clones. Objective: To identify the most accurate flow cytometry method for evaluation of CD123 expression in patients with AML to evaluate CD123 targeting therapies. Methods: 51 AML patient samples and 7 normal cord blood or bone marrow samples were stained with five different commercially available monoclonal antibodies to detect CD123 (7G3, 6H6, 9F5, AC145 and FAB301P), as well as CD45 and CD5, for evaluation by multiparameter flow cytometry. CD123 gene expression was also compared between these primary AML samples and bone marrow samples from healthy donors. Cell surface expression (by percentage and MFI) was evaluated relative to transcriptional expression and sensitivity to known therapeutics (cytarabine, parthenolide, and HSP90 inhibitors). Results: We observed CD123 surface expression patterns varied between the antibody clones tested. For the 9F5 and 6H6 clones, 93% and 82% of the samples, respectively, showed >60% CD123+ cells whereas for the 7G3, FAB 301P and AC145 clones, 71 to 76% of the samples showed >60% positivity. Also, surface expression of CD123 using 7G3, AC 145 and FAB 301P did not correlate with transcript levels for IL3RA assessed using qPCR, while surface expression of CD123 using 9F5 and 6H6 did correlate with transcript levels of IL3RA, using both mean fluorescence intensity (MFI) and percentage. For example, the correlation between CD123 surface expression as measured by percentage and IL3RA transcripts was most significant using the 9F5 and 6H6 clone (R2=0.1084, p=0.0183, R2=0.1588, p=0.0038 respectively) whereas the correlation for 7G3 (R2=0.0004, p=0.8945), FAB301P (R2=0.0027, p=0.7151) and AC145 (R2=0.0392, p=0.1638) were not significant. Surface expression of CD123 evaluated with 7G3 antibody did not correlate with overall sensitivity to in vitro treatment with cytarabine (R2=0.03767, p= 0.6451). However, using the 9F5 antibody, we found that higher levels of surface CD123 were associated with resistance to cytarabine in vitro (R2= 0.5502, p= 0.0351). Differences were noted for other experimental therapeutics including parthenolide and PU-H71. Most importantly, when we tested the ability of a novel allogeneic anti-CD123 CAR T-cell therapy (UCART123) to eliminate CD123+ AML cells, we found that CD123 positivity as measured by the 7G3 clone was not predictive of sensitivity to UCART123 in vitro or in vivo AML patient derived xenotransplants. Conclusions: Several novel therapeutic modalities targeting CD123 in AML are under development, including allogeneic anti-CD123 CAR T-cell therapy. Accurate, quantitative assessment of CD123 expression is thus of utmost importance for patient selection in clinical trials as well as disease monitoring. We found discrepancies between antibody clones, and such discrepancies may alter patient selection and data interpretation regarding patient response to CD123 based therapies. For therapies targeting CD123, protocol design and antibody selection should be done considering the results in this study. Based on our findings we recommend 9F5 or 6H6 antibody clones as well as the utilization of qPCR along side flow cytometry for adequate detection. Flow cytometry findings should be reported as percent positive cells. If utilizing the 9F5 clone, samples with > 60% CD123+ should be considered positive for CD123. A comparison in a large cohort may be warranted to determine the impact of multiple CD123 measurements on disease outcome. Disclosures Galetto: Cellectis SA: Employment. Gouble:Cellectis: Employment. Smith:Cellectis SA: Employment. Roboz:Agios, Amgen, Amphivena, Astex, AstraZeneca, Boehringer Ingelheim, Celator, Celgene, Genoptix, Janssen, Juno, MEI Pharma, MedImmune, Novartis, Onconova, Pfizer, Roche/Genentech, Sunesis, Teva: Consultancy; Cellectis: Research Funding. Guzman:Cellectis: Research Funding.

Acute myeloid leukemia (AML) is a disease with a high incidence of relapse and mortality. Relapse is attributed to the inability of current chemotherapeutic agents to eliminate leukemia stem cells (LSCs). Thus, to improve leukemia therapy, it is critical to identify agents that effectively target LSCs, e.g. via unique cell surface antigens. A target of major interest is CD123, the transmembrane alpha chain of the interleukin-3 receptor, expressed on blasts, leukemic progenitor and LSCs in the majority of AML patients. We have developed an allogeneic chimeric antigen receptor (CAR) T-cell platform using T-cells from third-party healthy donors to generate engineered T-cells targeting CD123 (UCART123). UCART123 cells no longer express a TCR, having undergone a disruption of the TCRα gene using TALEN¨ gene editing technology followed by elimination of TCRα/β-positive cells, thus minimizing the potential for engineered T-cells to cause graft versus host disease (GvHD). We tested the activity of UCART123 cells in vitro using primary AML samples, normal bone marrow (nBM) and cord blood (CB) cells. Additionally, we established patient derived xenograft (PDX), nBM- or CB- humanized xenografts (HuX) and a competitive nBM/AML xenograft model to evaluate the in vivo potential of UCART123 cells to preferentially eliminate AML over normal BM cells. In vitro studies reveled that UCART123 cells eliminate AML cells and had minimum effect on normal cells at effector:target ratios as low as 0.5:1. Next, we evaluated the in vivo activity of UCART123 against PDX (AML37, TP53 mutant relapsed AML and AML20, FLT3-ITD+ and TP53 mutant AML) and normal-HuX mice (n=3). At 3 weeks post T-cell injection we found that UCART123 treatment eliminated the leukemic cells when using 10M or 3M UCART123 cells per mouse and no significant difference between PBS or TCR-deficient T-cells (TCRkoT; 10M/mouse). Toxicity to normal cells was dose dependent, doses of 2.5M UCART123 cells did not significantly affect hematopoietic cells. T-cells were detected in the BM at day 14 after treatment, without evidence of GvHD. Since we found complete elimination of human AML cells in the BM of the PDX precluding serial transplantation to evaluate LSC activity, we initiated two new sets of PDX-AML mice [AML2 (NPM1+FLT3-ITD+) and AML37 (TP53 mutant)] to evaluate long-term survival, and time to relapse. Animals were treated with PBS, UCART123 (2.5M or 1M), TCRkoT (2.5M), or Ara-C (60mg/kg 5 days). Animal weight and peripheral blood (PB) was monitored. Cytokines changes were evaluated at day 2. We found that the cytokine release and the kinetics of AML targeting by UCART123 were dose dependent. We found a significant overall survival (OS) benefit with UCART123 in both PDX tested. For example, all PDX-AML2 mice treated with UCART123 are alive to date (day 167; updates will be presented). All other cohorts were lost (PBS day124, TCRkoT day126, AraC day144) (Figure 1A). Finally, to determine selectivity of UCART123 cells for AML cells over nBM cells, we generated a competitive model bearing both nBM and AML (NPM1+FLT3-ITD+). With PB monitoring, treatment with 1M UCAR123 cells resulted in selective elimination of AML cells. Untreated and TCRkoT treated mice showed a rapid progression of AML, while treated mice showed normal hematopoiesis (Figure 1B). NPM1 transcripts were also monitored in the mice and confirmed molecular remission in mice. Taken together, our data show that UCART123, an "off-the-shelf" allogeneic engineered CAR-T product targeting CD123 potently eliminates AML cells in vivo, prevents relapse, and improves OS in PDX mice. Also, UCART123 cells preferentially targets AML cells in a competitive BM/AML model. A phase I trial of UCART123 in AML is under development. Disclosures Guzman: Cellectis: Research Funding. Sugita:Cellectis: Research Funding. Galetto:Cellectis SA: Employment. Gouble:Cellectis: Employment. Smith:Cellectis SA: Employment. Roboz:Agios, Amgen, Amphivena, Astex, AstraZeneca, Boehringer Ingelheim, Celator, Celgene, Genoptix, Janssen, Juno, MEI Pharma, MedImmune, Novartis, Onconova, Pfizer, Roche/Genentech, Sunesis, Teva: Consultancy; Cellectis: Research Funding.

Acute myeloid leukemia (AML) carries a dismal prognosis in older patients, with median survival 8-12 months and especially poor outcomes in patients with unfavorable cytogenetics and/or poor performance status. Decitabine (5-aza-2’-deoxycytidine) has single-agent efficacy in older AML patients, with 40-50% CR (Blum et al, Proc Natl Acad Sci,107(16):7473-8,2010; Ritchie et al Leuk Lymphoma, 2013). AML originates from a rare population of leukemia stem cells (LSCs) that are capable of self-renewal, proliferation and differentiation into malignant blasts. LSCs may persist after treatment and contribute to disease relapse. Drugs that release LSCs from their protective microenvironment may leave them more vulnerable to therapy, as they are strongly dependent on the niche for proliferation and survival. Plerixafor blocks SDF1-mediated CXCR4 signaling and significantly decreases the survival of AML cells in vitro. The objective of this study was to investigate the safety and efficacy of decitabine combined with plerixafor in newly diagnosed older patients with AML and to evaluate the clinical and biological effects of plerixafor on LCSs. Patients were treated with repeated monthly cycles of decitabine 20 mg/m2 days 1-10 combined with escalating doses of plerixafor (320-810 μg/kg) days 1-5. Plerixafor was administered every other cycle so patients could serve as their own control when assessing the effects of CXCR4 blockade. Two cohorts of patients were treated at each dose level: cohort A received plerixafor only during even-numbered cycles and cohort B received plerixafor only during odd-numbered cycles. Patients achieving a clinical response received ongoing monthly maintenance cycles with 5 days of decitabine and plerixafor during alternate cycles. Sixty-nine patients were treated. Baseline patient characteristics were 55% male with a median age of 73 (56-87) yrs; ECOG 0 17%, 1 64%, 2 19%; cytogenetics intermediate 57%, adverse 44%; antecedent hematologic disorder 44%; secondary AML 45%; prior hypomethylating agent treatment (HMA) 20%. The overall response rate was 43% (36% CR, 7% CRi), with median time to response 1.9 mos (2 cycles) and median response duration 4.5 mos. Adverse karyotype did not predict overall response (P=0.31) and 53% of patients with adverse cytogenetics responded to treatment (43% CR, 7% CRi). Median overall survival (OS) for responders was significantly longer (18 mos) than for nonresponders (5 mos, P Toxicities were predictable and typical of older AML patients. Cytopenias and infections were the most common adverse events. There was no clinically significant hyperleukocytosis with plerixafor. Fourteen (56%) patients treated with 810 ug/kg plerixafor experienced reversible, treatment-associated insomnia. Immunophenotypically-defined leukemia stem and progenitor cells (LSPCs) were mobilized in a subset of patients as a consequence of CXCR4 blockade. Importantly, increased proliferation of LSPCs was also observed by Ki-67 staining. Decitabine-induced hypomethylation of AML-specific DNA methylation biomarker regions was observed in LSPCs of responders, but not in non-responders. Interestingly, this biochemical response preceded clinical response by at least one month in the subset of 7 patients analyzed to date. The combination of decitabine and plerixafor resulted in a high response rate with a favorable toxicity profile in poor-prognosis elderly AML patients. The MTD of plerixafor was 810 ug/kg and results from ongoing translational studies suggest that measures to optimize the regimen based on LSPC mobilization and DNA methylation may be feasible. Disclosures: Off Label Use: decitabine for AML.