ALBERT

Description: Background Loss of immune surveillance is thought to contribute to disease progression and treatment resistance in a range of malignancies including multiple myeloma (MM). Understanding the degree and pattern of immunological abnormality present within the bone marrow microenvironment at the time of MM diagnosis is vital if we are to utilize emerging immunological therapeutic strategies successfully in MM. While immune checkpoint inhibition in the relapsed refractory setting has been disappointing, early intervention before immune subversion mechanisms have become established may enable more effective restoration of immunological disease control. Method Bone marrow samples from 18 patients with newly diagnosed, untreated, myeloma (NDMM) and 9 age matched bone marrow controls were labelled with metal-antibody conjugates and assessed by time of flight cytometry using the CyTOF platform. Expression of 36 protein targets including markers of proliferation, degranulation and cytokine production alongside phenotyping and viability markers were measured at the single cell level. Differences in the abundance and function of distinct cellular populations were assessed using the unsupervised, automated CITRUS algorithm with the goal to identify novel cell populations. Results We observed a decrease in three key populations in NDMM; CD4 T cells with an effector phenotype (CD4EF), CD8 T cells with an IL2 producing effector phenotype (CD8EF), and dendritic cells. The expected elevation in malignant plasma cells was also seen and characterized. Interestingly CD8 T cells with a cytotoxic phenotype were not decreased. The dendritic cell (DC) population contained two distinct subpopulations characterized by their level of CD16 expression. The CD16 positive population (DC16) expressed a range of activating receptors and cytokines while the CD16 negative population (DCTOL) exhibited strong Ki67 expression. Within NDMM samples the DC16 population had stronger expression of PDL1 (p=0.0349) and loss of TIM3 (p=0.0122) and 2B4 (p=0.0148) compared to controls suggesting that this subset is less functionally active in NDMM. The DCTOL population had a similar increase in PDL1 (p=0.0065) and loss of TIM3 (p=0.0165) but also had a shift towards CD107a (p=0.0014) and perforin (p=0.0196) expression, suggesting a tolerogenic role for this subset in myeloma. Within the CD4EF subset NDMM samples exhibited reduction in Ki67 (p=0.0054) compared to controls, suggesting that the decrease in population abundance might be due to loss of proliferation. Furthermore, the NDMM population had increased expression of TGFβ (p=0.0027) and FoxP3 (p=0.0409) suggesting that those cells that are present may have a regulatory role. The CD8EF population also showed reduction of Ki67 (p=0.0494) in NDMM compared to control samples. This was accompanied by a loss of DNAM1 (p=0.0096), suggesting a loss of co-stimulatory capacity, alongside elevations in TGFb (p=0.0022) suggesting a pro-tumor cytokine shift. A broad spread of cellular abundance levels was noted in NDMM compared to controls which led us to investigate whether differences in cell population abundance was associated with survival. This was seen for the CD8EF population, with higher abundance correlating with longer survival (r=0.6643, p=0.0026). Individuals with higher abundance of both the CD8EF population and the DC16 population had a reduction in relapse and death in the first 36 months following diagnosis (p=0.0366). Conclusions This data demonstrates that even at the early time point of myeloma diagnosis there is evidence of both numerical and functional defects in key cell populations involved in antigen presentation and anti-tumor activity. We propose that ineffective antigen presentation by PDL1 expressing DC populations results in poorly proliferative CD8 and CD4 effector populations with pro-tumor cytokine production. The high FoxP3 expressing CD4 population may also have a regulatory role. This data highlights PDL1 as an important therapeutic target in NDMM where it may have a role in restoring immune surveillance at an early disease time point. Harnessing emerging deep profiling technology to identify patterns of immunological change across multiple cellular subsets within one individual may enable us to identify immune signatures which predict outcome and response to treatment. Disclosures Seymour: Celgene: Research Funding. Cavenagh:Celgene: Honoraria, Research Funding, Speakers Bureau; Novartis: Honoraria, Speakers Bureau; Takeda: Research Funding, Speakers Bureau; Janssen: Honoraria, Speakers Bureau; Amgen: Honoraria, Speakers Bureau. Gribben:Kite: Honoraria; Roche: Honoraria; TG Therapeutics: Honoraria; Pharmacyclics: Honoraria; Cancer Research UK: Research Funding; Medical Research Council: Research Funding; Unum: Equity Ownership; Wellcome Trust: Research Funding; Acerta Pharma: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; NIH: Research Funding; Novartis: Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Abbvie: Honoraria.

Description: Background: Diffuse large B cell lymphoma (DLBCL) is the most frequent lymphoma subtype with up to two thirds of patients achieving cure following standard immuno-chemotherapy. However, approximately 10-15% will be primary refractory and a further 20-30% relapse. There is an urgent need for improved biomarkers to identify these poor risk patients at diagnosis who are destined to fail standard therapy so that novel approaches can be considered. The diagnostic peripheral blood monocyte count is known to be predictive of outcome in DLBCL, with higher counts identifying patients with worse outcome but mechanistic understanding is currently lacking. The aim of this study was to investigate the peripheral blood monocyte population driving this phenomenon and we present here a deep phenotypic analysis of the monocyte compartment at diagnosis in patients with extremes of outcome following immuno-chemotherapy. Methods: We identified 112 immuno-chemotherapy treated DLBCL patients with viably cryopreserved peripheral blood mononuclear cells (PBMCs) in our tissue bank. The diagnostic absolute monocyte (AMC) and lymphocyte counts (ALC) were examined as prognostic variables. For phenotypic analysis a subset of 20 patients, 10 with relapse/refractory disease within 12 months of therapy (Poor risk) and 10 with continued complete remission 〉24 months post therapy (Good risk), were selected. PBMCs from these patients were stained with a panel of 29 metal-tagged antibodies designed to identify immune populations and characterize the monocyte compartment and analyzed using cytometry by time-of-flight (CyTOF2TM). Samples were acquired in batches run with the same healthy donor control PBMCs to ensure consistency of staining. Normalized data were subjected to traditional Boolean gating in Cytobank (www.mrc.cytobank.org) to identify CD45+ live single cells and further analyzed using FlowSOM and CITRUS. Results: In the full 112 patient cohort, a higher diagnostic AMC (≥0.6 x 109/L) predicted worse outcome (5-year OS, 59% vs 74%, p=0.047*). The lymphocyte to monocyte ratio (LMR) was more discriminatory, with a low LMR (

Description: Introduction Relapsed and refractory multiple myeloma (RRMM) remains a challenging disease to treat due to its heterogeneity and complexity. There is an urgent need for novel combination strategies, including immunotherapy. The study of the tumour and immune microenvironment before and after treatment with combination therapy is a crucial part of understanding the underpinning of disease response. Methods Longitudinal samples of bone marrow aspirates and whole blood were collected from a phase II clinical trial, MEDI4736-MM-003 (NCT02807454) where daratumumab and durvalumab naïve patients were exposed simultaneously to both these drugs. A combination of mass cytometry (CyTOF), RNAseq and flow cytometry were performed on a subset of samples from these subjects. Specifically, paired bone marrow mononuclear cells (BMMC) samples from nine patients taken at screening and six weeks post-treatment were analysed by mass cytometry (CyTOF) using a 37-marker pan-immune panel that included both lineage and functional intracellular/extracellular markers. In addition, whole blood sample specimens were collected at screening and on treatment (8, 15, 30, and 45 days after treatment) and analysed by flow cytometry. Flow cytometry panels were designed to allow interrogation of the abundance and activation status of immune cell subsets. Finally, RNA from bone marrow aspirates at screening and C2D15 were analysed by RNA sequencing. Expression profiles from the aspirates were used to estimate cell proportions by computational deconvolution. Individual cell types in these microenvironments were estimated using the DCQ algorithm and a gene expression signature matrix based on the published LM22 leukocyte matrix (Newman et al., 2015) augmented with 5 bone marrow- and myeloma-specific cell types. Results In a heavily pre-treated population with RRMM, treatment with durvalumab and daratumumab leads to shifts in a number of key immunological populations when compared to pre-treatment. In the bone marrow, CD8 and CD4 populations rise (by CyTOF and RNAseq), while NK, DC and B cell populations fall (by CyTOF). In the bone marrow within CD8+ T lymphocyte populations, we observed a post-treatment rise in markers of degranulation (granzyme p=0.0195, perforin p=0.0078, Wilcoxon signed-rank test). This is also accompanied by a fall in PD1 expression (p=0.0078) and rise in the co-stimulatory receptor DNAM1 (p=0.0273). These changes are most marked on cells with an effector memory CD45RA+ CD8+ T cell phenotype. In the blood, similar to the bone marrow, CD8+ T cells proliferate over the course of treatment (flow cytometry). A fall in both naïve and active NK cell populations is seen following treatment in bone marrow. NK cells express high levels of CD38 and are therefore depleted by daratumumab. Those NK cells which remain have an active phenotype with increased expression of TNFa (p=0.0039) and IFNg (p=0.0195) following treatment. Across the time points sampled in peripheral blood, NK cells were also decreased and those that remained were proliferating. Dendritic cells with a tolerogenic phenotype can be identified prior to treatment and are seen to fall in abundance following treatment with durvalumab and daratumumab. Conclusions The combination of durvalumab and daratumumab leads to several immune microenvironment changes that biologically portend clinical effect. We see increases in the abundance of cell populations with functional anti-tumour activity, including granzyme B+ CD8 T cells and a reduction in PD1high T cells. Despite the treatment expectedly reducing NK cell numbers, many functionally competent NK cells remain, as evidenced by the presence of anti-tumour cytokines. This combination strategy also reduces immunosuppressive tolerogenic DCs, which suppress CD4 and CD8 T cell activity. Taken together, this suggests that this chemotherapy free, doublet treatment has the potential to up-regulate anti-tumour immunological responses, which may restore immunosurveillance mechanisms critically needed in these highly refractory patients. Disclosures Seymour: Celgene: Research Funding. Young:Celgene Corporation: Employment, Equity Ownership. Tometsko:Celgene Corporation: Employment, Equity Ownership. Cavenagh:Celgene: Honoraria, Research Funding, Speakers Bureau; Janssen: Honoraria, Speakers Bureau; Takeda: Research Funding, Speakers Bureau; Novartis: Honoraria, Speakers Bureau; Amgen: Honoraria, Speakers Bureau. Thompson:Celgene Corporation: Employment, Equity Ownership. Whalen:Celgene Corporation: Employment, Equity Ownership. Danziger:Celgene Corporation: Employment, Equity Ownership. Fitch:Celgene Corporation: Employment, Equity Ownership. Fox:Celgene Corporation: Employment, Equity Ownership. Dervan:Celgene Corporation: Employment, Equity Ownership. Foy:Celgene Corporation: Employment, Equity Ownership. Newhall:Celgene Corporation: Employment, Equity Ownership. Gribben:Acerta Pharma: Honoraria, Research Funding; Cancer Research UK: Research Funding; TG Therapeutics: Honoraria; Roche: Honoraria; NIH: Research Funding; Medical Research Council: Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Abbvie: Honoraria; Kite: Honoraria; Pharmacyclics: Honoraria; Novartis: Honoraria; Janssen: Honoraria, Research Funding; Wellcome Trust: Research Funding; Unum: Equity Ownership.

Description: Diffuse large B-cell Lymphoma (DLBCL) is the most frequent non-Hodgkin's lymphoma with 3 molecularly distinct subtypes based on cell of origin. Genetic alterations in DLBCL, expression of checkpoint molecules and an immunosuppressive microenvironment (ME) all contribute to escape from host anti-lymphoma immunity. The clinical success of monoclonal antibodies that engage the immune system and CAR-T cellular therapy have further highlighted the importance and therapeutic potential of the immune ME in DLBCL. Here we present data from comprehensive phenotyping of cell suspensions from diagnostic DLBCL and reactive lymph node / tonsil (RLNT) biopsies by cytometry by time of flight (CyTOF), with a focus on the T-cell compartment. Cryopreserved samples from 6 DLBCL (5 LN, 1 spleen) at diagnosis and 5 RLNT (3 LN, 2 tonsil) were stained with a panel of metal-tagged antibodies and analysed by CyTOF2. Samples were acquired in 2 batches with the same RLNT (LN) sample with each to ensure staining consistency. Data were normalised, uploaded to Cytobank, gated to CD45+ CD3+ live single cells and exported for further analysis with Cytofkit in R. CD3+ events were gated further into CD4+ and CD8+ subsets, which demonstrated that CD4+ T cells were the predominant phenotype in all samples. However, there was a marked skewing of the CD4:CD8 ratio, with CD4+ T cells lower as a percentage of CD3+ T cells in the DLBCL samples (55.84 v 78.18, p=0.0173*). CD8+ T cells were higher as a percentage in DLBCL (36.22 v 16.75, p=0.03*) with no difference seen in double negative (DN) T cells. CD3+ T cells were then clustered with FlowSOM and visualised according to the tSNE algorithm. A heatmap of median marker expression intensity was generated to facilitate cluster identification. This revealed a number of differences in cluster abundance between the groups, with a significant shift in differentiation away from naïve and towards an effector memory (EM) phenotype in DLBCL. There were fewer cells in the CD27+ CD28+ CCR7+ CD45RA+ CD4+ naïve cluster in the DLBCL samples than the RLNT (p=0.0173*). Although the DLBCL samples showed an overall reduction in CD4+ T cells, the clusters of regulatory T cells (Treg: CD4+ CD25+ FOXP3+ and CD127-/low) consisted of more cells from these cases than the RLNT (p=0.0043**). Within the Treg population, the DLBCL patients had more Th1 polarised (T-bet+) Tregs and more PD-1 expressing Tregs. The Th1 Tregs predominantly secreted the suppressive cytokines IL-2, IL-10 and TGF-β on stimulation and may play a role in inhibiting Th1 responses. Conventional Th1 were not increased in DLBCL resulting in a higher Th1 Treg to Th1 ratio than in RLNT. There was a trend for RLNT samples to contribute more cells to the PD-1 high follicular helper T cell (TFH) cluster and DLBCL to the PD-1+ TIM-3+ DN cluster. The DLBCL ME had relatively more CD8+ T cells and contributed more to the CCR7- CD45RA- CD8+ EM clusters (p=0.0173*) but the CD8+ T cells in the RNLT samples tended to a naïve CCR7+ CD45RA+ PD-1- phenotype (p=0.0519). The CD8+ EM cells enriched in the DLBCL ME expressed the cytotoxic markers granzyme and perforin and responded to stimulation with degranulation (CD107a) and cytokine production (IFNγ, TNFα, TGFβ and IL-10), not suggestive of exhaustion. It is also notable that a cluster of PD-1+ TIM-3+ CD8+ EM with reduced markers of cytotoxicity, low CD107a expression and poor cytokine production after stimulation was predominantly made up of cells from DLBCL suspensions (p=0.002**). CyTOF analysis of the DLBCL ME has demonstrated a shift in the balance of T cell subsets and CD4:CD8 ratio with a relative abundance of immunosuppressive Tregs despite an overall reduction in the CD4+ population and a skew towards differentiation in CD4+ and CD8+ populations. The cytotoxic T cells in DLBCL tended to have an EM phenotype and express immune checkpoint molecules but remained capable of cytokine production. However, the production of IFNγ by these effector T cells may play a role in the development of inhibitory Tregs with a Th1 phenotype, which were enriched in these patients. A cluster of CD8+ EM cells expressing checkpoint molecules and displaying characteristics of exhaustion following stimulation was also seen in these DLBCL patients. These data provide new insights into the immunosuppressive nature of the DLBCL ME and provide a rationale for targeting the ME alongside existing therapeutic approaches, including CAR-T cells to improve outcomes. Disclosures Gribben: Janssen: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy, Honoraria, Research Funding; Acerta/Astra Zeneca: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding.