Simultaneous Monitoring of Drug Responses on Distinct Hematopoietic Cell Populations Allow Assessment of Direct and Indirect Cytotoxic Effects of Targeted Therapies

Abstract Introduction Off-target cytotoxicity resulting in severe side effects and compromising patient survival often hampers the development of new cancer therapeutics. Understanding the complete drug response landscape of different cell populations is crucial to identify drugs that selectively eradicate the malignant cell population, but spare healthy cells. Here, we developed a high content, no wash, multi-parametric flow cytometry based assay that enables testing of blood cancer patient samples and simultaneously monitors the effects of several drugs on 11 hematopoietic cell types. The assay can be used to i) dissect malignant from healthy cell responses and predict off-target effects; ii) assess drug effects on immune cell subsets; iii) identify drugs that can potentially be repositioned to new blood cancer indications. Methods Mononuclear cells were prepared from bone marrow aspirates of 7 multiple myeloma (MM) and 3 acute myeloid leukemia (AML) patients plus the peripheral blood from a healthy donor, which were collected following informed consent and in compliance with the Declaration of Helsinki. Optimal cell density, antibody dilutions, incubation time, and wash versus no wash assay conditions for the selected antibody panels were determined. Cells were incubated at a density of 2 million cells/ml in either 96- or 384-well plates for 3 days. The antibodies were tested in two panels to study the effects of 6 drugs in 5 dilutions (1-10000 nM) (clofarabine, bortezomib, dexamethasone, navitoclax, venetoclax and omipalisib) on 11 cell populations, namely hematopoietic stem cells (HSCs) (CD34+CD38-), common progenitor cells (CPCs) (CD34+CD38+), monocytes (CD14+), B cells (CD45+CD19+), cytotoxic T cells (CD45+CD3+CD8+), T helper cells (CD45+CD3+CD4+), NK-T cells (CD45+CD3+CD56+), NK cells (CD45+CD56+CD3-), clonal plasma cells (CD138+CD38+), other plasma cells (CD138+CD38-) and granulocytes (CD45+, SSC++). Annexin-V and 7AAD were used to distinguish live cell populations from apoptotic and dead cells. After 1 h incubation with antibodies, the plates were read with the iQue Screener PLUS instrument (Intellicyt). Counts for each population were used to generate four parameter nonlinear regression fitted dose response curves with GraphPad Prism 7. Three samples were tested in duplicate to assess reproducibility. Results To decrease the complexity of the assay, we tested all antibodies under wash and no wash conditions, and found that results from both conditions were comparable. To minimize the amount of sample needed as well as maximize the number of drugs tested and cell populations that can be detected, we set up the assay in both 96- and 384-well plates. The assay was highly reproducible when samples were tested in replicate and was scalable to a 384-well format without compromising sensitivity to detect rare populations such as plasma cells. Due to the differentiation of immature cells to specialized cell types, the drug responses of specific populations tended to drift. HSCs (CD34+CD38-) were shown to be refractory to the tested drugs compared to CPCs characterized as (CD34+CD38+) and other cell types. Interestingly, the proteasome inhibitor bortezomib was cytotoxic to all cell populations except for CD138+CD38- plasma cells. Clofarabine, a nucleoside analog used to treat ALL, effectively targeted CPC, NK and B cells, while HSCs and plasma cells were resistant. The glucocorticoid and immunosuppressive drug dexamethasone specifically targeted B and NK cells compared to T cell populations (CD8+, CD4+), while NK-T cells were modestly sensitive. The cell population response patterns were similar in samples derived from MM, AML and healthy individuals, highlighting that the drug responses are highly cell type specific. Summary Using a high content, multi-parametric assay, we could rapidly assess the effect of several drugs on specific cell populations in individual patient samples. Our results demonstrate that many drugs preferentially affect different hematological cell lineages. Although heterogeneity was observed between individual patients, the pattern of cytotoxic response exhibited by specific cell types was consistent among samples derived from MM, AML and healthy donors. The assay will be useful to identify drugs with maximal on-target and minimal off-target specificity, and can potentially be used to guide treatment decision and predict patient response Disclosures Heckman: Celgene: Research Funding; Pfizer: Research Funding.

Download Full-text

High Dimensional Tissue-Based Spatial Analysis of the Tumor Microenvironment of Follicular Lymphoma Reveals Unique Immune Niches inside Malignant Follicles

Blood ◽

10.1182/blood-2020-134941 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 17-18

Author(s):

Jose C Villasboas ◽

Patrizia Mondello ◽

Angelo Fama ◽

Melissa C. Larson ◽

Andrew L. Feldman ◽

...

Keyword(s):

T Cells ◽

Spatial Distribution ◽

T Cell ◽

B Cells ◽

Research Funding ◽

Memory T Cells ◽

Cell Types ◽

T Cell Subsets ◽

Cell Contact ◽

Private Company

Background The importance of the immune system in modulating the trajectory of lymphoma outcomes has been increasingly recognized. We recently showed that CD4+ cells are associated with clinical outcomes in a prospective cohort of almost 500 patients with follicular lymphoma (FL). Specifically, we showed that the absence of CD4+ cells inside follicles was independently associated with increased risk of early clinical failure. These data suggest that the composition, as well as the spatial distribution of immune cells within the tumor microenvironment (TME), play an important role in FL. To further define the architecture of the TME in FL we analyzed a FL tumor section using the Co-Detection by Indexing (CODEX) multiplex immunofluorescence system. Methods An 8-micron section from a formalin-fixed paraffin-embedded block containing a lymph node specimen from a patient with FL was stained with a cocktail of 15 CODEX antibodies. Five regions of interest (ROIs) were imaged using a 20X air objective. Images underwent single-cell segmentation using a Unet neural network, trained on manually segmented cells (Fig 1A). Cell type assignment was done after scaling marker expression and clustering using Phenograph. Each ROI was manually masked to indicate areas inside follicles (IF) and outside follicles (OF). Relative and absolute frequencies of cell types were calculated for each region. Cellular contacts were measured as number and types of cell-cell contacts within two cellular diameters. To identify proximity communities, we clustered cells based on number and type of neighboring masks using Phenograph. The number of cell types and cellular communities were calculated inside and outside follicles after adjustment for total IF and OF areas. The significance of cell contact was measured using a random permutation test. Results We identified 13 unique cell subsets (11 immune, 1 endothelial, 1 unclassified) in the TME of our FL section (Fig. 1A). The unique phenotype of each subset was confirmed using a dimensionality reduction tool (t-SNE). The global composition of the TME varied minimally across ROIs and consisted primarily of B cells, T cells, and macrophages subsets - in decreasing order of frequency. Higher spatial heterogeneity across ROIs was observed in the frequency of T cell subsets in comparison to B cells subsets. Inspecting the spatial distribution of T cell subsets (Fig. 1B), we observed that cytotoxic T cells were primarily located in OF areas, whereas CD4+ T cells were found in both IF and OF areas. Notably, the majority of CD4+ T cells inside the follicles expressed CD45RO (memory phenotype), while most of the CD4+ T cells outside the follicles did not. Statistical analysis of the spatial distribution of CD4+ memory T cell subsets confirmed a significant increase in their frequency inside follicles compared to outside (20.4% vs 11.2%, p < 0.001; Fig. 1D). Cell-cell contact analysis (Fig 1C) showed increased homotypic contact for all cell types. We also found a higher frequency of heterotypic contact between Ki-67+CD4+ memory T cells and Ki-67+ B cells. Pairwise analysis showed these findings were statistically significant, indicating these cells are organized in niches rather than randomly distributed across image. Analysis of cellular communities (Fig. 1C) identified 13 niches, named according to the most frequent type of cell-cell contact. All CD4+ memory T cell subsets were found to belong to the same neighborhood (CD4 Memory community). Analysis of the spatial distribution of this community confirmed that these niches were more frequently located inside follicles rather than outside (26.3±4% vs 0.004%, p < 0.001, Fig. 1D). Conclusions Analysis of the TME using CODEX provides insights on the complex composition and unique architecture of this FL case. Cells were organized in a pattern characterized by (1) high degree of homotypic contact and (2) increased heterotypic interaction between activated B cells and activated CD4+ memory T cells. Spatial analysis of both individual cell subsets and cellular neighborhoods demonstrate a statistically significant increase in CD4+ memory T cells inside malignant follicles. This emerging knowledge about the specific immune-architecture of FL adds mechanistic details to our initial observation around the prognostic value of the TME in this disease. These data support future studies using modulation of the TME as a therapeutic target in FL. Figure 1 Disclosures Galkin: BostonGene: Current Employment, Patents & Royalties. Svekolkin:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Postovalova:BostonGene: Current Employment, Current equity holder in private company. Bagaev:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Ovcharov:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Varlamova:BostonGene: Current Employment, Current equity holder in private company, Patents & Royalties. Novak:Celgene/BMS: Research Funding. Witzig:AbbVie: Consultancy; MorphSys: Consultancy; Incyte: Consultancy; Acerta: Research Funding; Karyopharm Therapeutics: Research Funding; Immune Design: Research Funding; Spectrum: Consultancy; Celgene: Consultancy, Research Funding. Nowakowski:Nanostrings: Research Funding; Seattle Genetics: Consultancy; Curis: Consultancy; Ryvu: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other; Kymera: Consultancy; Denovo: Consultancy; Kite: Consultancy; Celgene/BMS: Consultancy, Research Funding; Roche: Consultancy, Research Funding; MorphoSys: Consultancy, Research Funding. Cerhan:BMS/Celgene: Research Funding; NanoString: Research Funding. Ansell:Trillium: Research Funding; Takeda: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Bristol Myers Squibb: Research Funding; AI Therapeutics: Research Funding; ADC Therapeutics: Research Funding.

Download Full-text

Mezagitamab Induces Immunomodulatory Effect in Patients with Relapsed/Refractory Multiple Myeloma (RRMM)

Blood ◽

10.1182/blood-2020-140527 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 9-9

Author(s):

Michael Abadier ◽

Jose Estevam ◽

Deborah Berg ◽

Eric Robert Fedyk

Keyword(s):

Bone Marrow ◽

T Cells ◽

B Cells ◽

Nk Cells ◽

Peripheral Blood ◽

Immune Cell ◽

Cell Populations ◽

Landscape Changes ◽

Ki 67 ◽

Immune Cell Subsets

Background Mezagitamab is a fully human immunoglobulin (Ig) G1 monoclonal antibody with high affinity to CD38 that depletes tumor cells expressing CD38 by antibody- and complement-dependent cytotoxicity. CD38 is a cell surface molecule that is highly expressed on myeloma cells, plasma cells, plasmablasts, and natural killer (NK) cells, and is induced on activated T cells and other suppressor cells including regulatory T (Tregs) and B (Bregs) cells. Data suggest that immune landscape changes in cancer patients and this may correlate with disease stage and clinical outcome. Monitoring specific immune cell subsets could predict treatment responses since certain cell populations either enhance or attenuate the anti-tumor immune response. Method To monitor the immune landscape changes in RRMM patients we developed a mass cytometry panel that measures 39-biomarkers to identify multiple immune cell subsets, including T cells (naïve, memory, effector, regulatory), B cells (naïve, memory, precursors, plasmablasts, regulatory), NK cells, NKT cells, gamma delta T cells, monocytes (classical, non-classical and intermediate), dendritic cells (mDC; myeloid and pDC; plasmacytoid) and basophils. After a robust analytical method validation, we tested cryopreserved peripheral blood and bone marrow mononuclear cells from 19 RRMM patients who received ≥ 3 prior lines of therapy. Patients were administered 300 or 600 mg SC mezagitamab on a QWx8, Q2Wx8 and then Q4Wx until disease progression schedule (NCT03439280). We compared the percent change in immune cell subsets at baseline versus week 4 and week 16. Results CD38 is expressed at different levels on immune cells and sensitivity to depletion by mezagitamab generally correlates positively with the density of expression. CD38 is expressed at high densities on plasmablasts, Bregs, NK-cells, pDC and basophils at baseline and this was associated with reductions in peripheral blood and bone marrow (plasmablasts, 95%, Bregs, 90%, NK-cells, 50%, pDC, 55% and basophils, 40%) at week 4 post treatment. In contrast, no changes occurred in the level of total T-cells and B-cells, which is consistent with low expression of CD38 on most cells of these large populations. Among the insensitive cell types, remaining NK-cells acquired an activated, proliferative and effector phenotype. We observed 60-150% increase in activation (CD69, HLA-DR), 110-200% increase in proliferation (Ki-67), and 40-375% increase in effector (IFN-γ) markers in peripheral blood and bone marrow. Importantly, NK-cells which did not express detectable CD38, also exhibited a similar phenotype possibly by a mechanism independent of CD38. Consistent with these data, the remaining CD4 and CD8 T-cell populations exhibited an activated effector phenotype as observed by 40-200% increase in activation, 60-200% increase in proliferation and 40-90% increase in effector markers in peripheral blood. A potential explanation for this acquisition of activated effector phenotypes could be a reduction in suppressive regulatory lymphocytes. Next, we measured levels of Tregs and Bregs, and observed that Bregs which are CD24hiCD38hi were reduced to 60-90% in peripheral blood and bone marrow. In contrast, total Tregs were reduced by only 5-25% because CD38 expression in Tregs appears as a spectrum where only ~10-20% are CD38+, and thus CD38+ Tregs were reduced more significantly (45-75%), reflecting the selectively of mezagitamab to cells expressing high levels of CD38. CD38+ Tregs are induced in RRMM patients, thus we looked at the phenotype of CD38-, CD38mid, and CD38high -expressing Tregs. We observed higher level of markers that correlate with highly suppressive Tregs such as Granzyme B, Ki-67, CTLA-4 and PD-1 in CD38high Tregs. Accordingly, the total Treg population exhibited a less active phenotype after exposure to mezagitamab, which selectively depleted the highly suppressive CD38+ Tregs. Conclusions Chronic treatment with mezagitamab is immunomodulatory in patients with RRMM, which is associated with reductions in tumor burden, subpopulations of B and T regulatory cells, and characterized by conventional NK and T cells exhibiting an activated, proliferative and effector phenotype. The immune landscape changes observed is consistent with the immunologic concept of converting the tumor microenvironment from cold-to-hot and highlights a key mechanistic effect of mezagitamab. Disclosures Berg: Takeda Pharmaceuticals Inc: Current Employment.

Download Full-text

Multidimensional Flow Cytometric (MFC) Analysis of the Immune System of Multiple Myeloma (MM) Patients Achieving Long Term Disease Control

Blood ◽

10.1182/blood.v118.21.810.810 ◽

2011 ◽

Vol 118 (21) ◽

pp. 810-810

Author(s):

Roberto J. Pessoa Magalhaes ◽

María-Belén Vidriales ◽

Bruno Paiva ◽

Maria-Victoria Mateos ◽

Norma C. Gutierrez ◽

...

Keyword(s):

T Cells ◽

Immune System ◽

B Cells ◽

Disease Control ◽

B Cell ◽

Nk Cells ◽

Cell Populations ◽

High Dose ◽

Newly Diagnosed

Abstract Abstract 810FN2 Increasing evidence shows that a small fraction of MM patients (pts) treated with high-dose therapy followed by autologous stem cell transplantation achieve long-term remission. Interestingly, this is not restricted to pts in complete response (CR), since those that revert to a monoclonal gammopathy of undetermined significance (MGUS) profile may also achieve long-term remission, despite the persistence of residual myeloma plasma cells (PCs). These results suggest that in addition to the anti-myeloma therapy, other factors may play a role in the control of the disease. Herein, we used 8-color MFC for detailed characterization of the structural components of the immune system and hematopoietic precursor cells (HPC) in paired bone marrow (BM) and peripheral blood (PB) samples from 26 MM patients in long-term disease control (LTDC): 9 in continuous CR and 17 who reverted to an MGUS profile and that subsequently showed stable disease without treatment for ≥5 years (median of 9 years; range, 5–19). As controls, paired BM and PB samples from 23 newly-diagnosed MGUS and 16 MM pts, together with 10 healthy adults (HA), were studied in parallel. In all BM and PB samples the distribution of the major T- (CD4, CD8, Tregs and γδ), NK- (CD56dim and CD56bright) and B-cell subsets (Pro-B, Pre-B, naïve and memory), in addition to normal PCs, dendritic cell (DC) subsets (plasmacytoid, myeloid and monocytic), monocytes, and CD34+ HPC (myeloid and lymphoid), were studied. The percentage and absolute count of each cell population was analysed in the BM and PB, respectively. Comparison of the two groups of MM pts with LTDC (9 CR vs. 17 MGUS-like) showed similar (p>.05) cellular profiles in PB and BM, except for an increased number of BM and PB normal PCs in CR patients (P≤.04). Consequently, for all subsequent analyses, LTDC myeloma pts were pooled together. When compared to HA, patients with LTDC had increased numbers of CD8 T-cells and CD56dim NK-cells in both the BM and PB (p≤.03 and p≤.01, respectively). Despite this, the distribution of BM and PB CD4, CD8 and γδ T-cells among LTDC patients was similar (p>.05) to that of both newly-diagnosed MM and MGUS cases; in contrast, BM and PB Tregs were significantly decreased vs newly-diagnosed MM (P=.03) and MGUS (P=.04). Regarding B-cells and normal PCs, LTDC patients showed increased numbers of BM B-cell precursors (both Pro-B and Pre-B cells) and normal PCs vs. newly diagnosed MM (P≤.05), but not MGUS, together with increased numbers of naïve B-cells vs. both MM and MGUS pts (P≤.01); all such cell populations returned to levels similar (p>.05) to those of HA. As expected, this also included the number of CD34+ B-cell HPC which was increased among patients who achieved LTDC vs MM (p=.02), at levels similar (p>.05) to those of MGUS and HA. Regarding DC, LTDC patients showed normal DC numbers in PB (but with higher PB myeloid-DC numbers vs. MM; p=.02), in association with decreased numbers of plasmacytoid DC and increased monocytic-DC in the BM vs. HA (p≤.04). No differences were found for the numbers of BM and PB monocytes. In summary, here we investigated for the first time the immune cell profile of MM patients who achieve long-term disease control. Our results show that, as newly-diagnosed MM, patients that achieve long-term disease control also show increased numbers of cytotoxic T-cells and CD56dim NK-cells; however, in contrast to newly-diagnosed MM, among LTDC patients such increase is associated with lower numbers of T-regs and an almost complete recovery of the normal PC, B-cell precursor and naïve B-cell compartments both in BM and PB. Further investigations on the activation and functional status of these cell populations are warranted.MO (%)/SP (cels./μl)HA N= 10MGUS N= 23MM N= 16LTDC-MM N= 26T cells9.588110.6117313113711926 CD4+4.85004.6624^6*5085463 CD8+3.7∼216∼4.63865.32645.3431 TCR γδ.2426.3230.2428.3421 Treg.4137.4141^.54*38.3432NK cells.7∼87∼1.51982.11721.6212 CD56 dim.65∼79∼1.41922.21681.6202B cells2.81471.8104.97*68*1.9160 Pro B.11—.06—.02*—.07— Pre B.6—.4—.08—.23— Naive SP—80—57^—36*—118 Normal-PCS.18.9.11.7.008.72*.11.84DCs.3449.3653.6848.558 Monocytes2.22472.42853.43023.1315 m-DC SP—11—14—8*—12 MO-DC.11∼29.2036.434.2837 p-DC.2∼4.1.145.112.8.123.8CD34+.9∼1.46.61.1.261.4.431.4 Mie-HPC.8∼—.53—.26—.36— Linfo-HPC.1—.07—.03*—.05—*p≤.05 LTDC vs MM: ^ p≤.05 LTDC vs MGUS; ∼ p≤.05 LTDC vs HA Disclosures: Paiva: Jansen-Cillag: Honoraria; Celgene: Honoraria. Martinez:Janssen: Honoraria; Celgene: Honoraria. Maiolino:Centocor Ortho Biotech Research & Development: Research Funding.

Download Full-text

Chronic Myeloid Leukemia Patients with Deep Molecular Responses to Tyrosine Kinase Inhibitors Have Increased Effector Natural Killer and Cytotoxic T Cell Immune Responses to Leukaemia-Associated Antigens and Concomitant Reduced Immune Suppressors

Blood ◽

10.1182/blood.v126.23.18.18 ◽

2015 ◽

Vol 126 (23) ◽

pp. 18-18 ◽

Cited By ~ 3

Author(s):

Amy Hughes ◽

Carine Tang ◽

Jade Clarson ◽

Ljiljana Vidovic ◽

Timothy P. Hughes ◽

...

Keyword(s):

T Cells ◽

B Cells ◽

Immune Responses ◽

Nk Cells ◽

Research Funding ◽

Kinase Inhibitors ◽

Nk Cell ◽

Molecular Response ◽

Molecular Responses ◽

B Cell Subsets

Abstract We hypothesized that immune responses contribute to deep BCR-ABL molecular responses in chronic phase chronic myeloid leukaemia (CML) patients on tyrosine kinase inhibitors (TKI). We studied 32 CML patients; 16 at diagnosis, patients treated with imatinib (n=20), nilotinib (n=9) or dasatinib (n=3). Methodology: The effector immune responses of Natural Killer (NK) cells were characterized by flowcytometry and functional analysis by CD107a degranulation assay. Cytotoxic T lymphocyte (CTL) responses to leukaemia-associated antigens (LAAs) WT1, BMI-1, PR3 and PRAME were quantified by interferon-gamma ELISPOT using peptide libraries of 15-mer peptides overlapping by 11 amino acids spanning the entire protein, or HLA-A0201 specific peptides in HLA-A0201+ patients. Immune suppressor regulatory T cells (Treg), Myeloid Derived Suppressor Cells (MDSC), Programmed cell death-1 (PD-1) expression on T cells, NK cells, B cells and monocytes, and major B cell subsets were extensively characterized by flowcytometry. Results: Patients in deep molecular response (MR4.5; BCR-ABL <0.0032%) displayed increased antigen-specific CTL responses to LAAs, both in the number of positive LAAs and frequency of responses, compared to patients at diagnosis and major molecular response (MMR; BCR-ABL <0.1%). The most abundant LAA response was to PRAME [51% of patients in MR4.5 compared to 31% in MMR and 0% at diagnosis] and WT1 [31% of patients in MR4.5 compared to 28% in MMR and 0% at diagnosis]. PR3-specific immune responses were the least abundant, with no difference in response between MR4.5and MMR (both 3%) compared to 0% at diagnosis. Immunophenotypic analysis revealed a shift toward a more mature, cytolytic NK cell phenotype (CD57+, CD161+CD62L-) in MMR and MR4.5, consistent with up-regulation of the CD94/NKG2 family of inhibitory/activating receptors (NKG2A, NKG2C and NKG2D), the cytotoxicity triggering receptor NKp46 and a functional increase in NK cell cytotoxicity capacity against K562 target cells. The percentage of CD3-CD56dimCD16bright cytolytic NK cells as a proportion of total lymphocytes was significantly increased in MMR and MR4.5 [33.6% ± 6.6 p=0.0008 and 33.1% ± 4.1 p=0.01, respectively] compared to 7.8% ± 2.8 at diagnosis. The absolute Treg number/µl was significantly lower in patients in MMR and MR4.5 [13.9 ± 1.7 and 10 ± 1.1, respectively] compared to 32.7 ± 4.4 at diagnosis. Similarly, MDSC were significantly reduced in patients in MMR and MR4.5 [3.9 ± 0.9 and 1.9 ± 0.5 MDSC/µl] compared to diagnosis [18.3 ± 3.9]. A predominantly granulocytic (CD66b+CD15+) MDSC phenotype was seen in CML patients at diagnosis. PD-1 expression as a proportion of total lymphocytes was significantly decreased in cytotoxic CD8+ T cells in MR4.5 [5.7% ± 1.2] compared to MMR [12.3% ± 2.0, p=0.008] and patients at diagnosis [21.7% ± 5.2, p=0.0003]. PD-1 expression was decreased in CD4+ helper T cells in MR4.5 [7.5% ± 1.7] compared to MMR [11.4% ± 1.5, p=0.07] and diagnosis [17% ± 2.9, p=0.008]. Overall, PD-1 expression was lower in NK cells in CML patients, albeit significant in MMR and MR4.5 [0.24% ± 0.09, p=0.006 and 0.36 ± 0.07, p=0.02, respectively] compared to [1.42% ± 0.4] at diagnosis. No difference in PD-1 expression was seen in B cells or monocytes. No significant difference was observed in CD3-CD19+ B cells in MMR and MR4.5 or at diagnosis. Analysis of major B cell subsets revealed no difference in the proportion of transitional, naïve or memory B cells, plasma blasts or plasma cells. Conclusion: Enhanced effector immune responses of NK and LAA-specific CTLs are associated with concomitant reduction in immune suppressor activity, and may increase the rate of deep molecular responses to TKIs in CML. Methods to augment these responses may result in greater rate of success in TKI cessation studies. Disclosures Hughes: ARIAD: Honoraria, Research Funding; Bristol-Myers Squibb: Honoraria, Research Funding; Novartis: Honoraria, Research Funding. Yong:Novartis: Honoraria, Research Funding.

Download Full-text

Off-the-Shelf, Multiplexed-Engineered iPSC-Derived NK Cells Mediate Potent Multi-Antigen Targeting of B-Cell Malignancies with Reduced Cytotoxicity Against Healthy B Cells

Blood ◽

10.1182/blood-2021-148654 ◽

2021 ◽

Vol 138 (Supplement 1) ◽

pp. 407-407

Author(s):

Frank Cichocki ◽

Jode P Goodridge ◽

Ryan Bjordahl ◽

Svetlana Gaidarova ◽

Sajid Mahmood ◽

...

Keyword(s):

T Cells ◽

B Cells ◽

Nk Cells ◽

Board Of Directors ◽

Research Funding ◽

Nk Cell ◽

Advisory Committees ◽

Car T Cells ◽

Car T

Abstract Treatments for B-cell malignancies have improved over the past several decades with clinical application of the CD20-specific antibody rituximab and chimeric antigen receptor (CAR) T cells targeting CD19. Despite the success of these therapies, loss of CD20 after rituximab treatment has been reported in leukemia and lymphoma patients. Additionally, up to 50% of all patients receiving anti-CD19 CAR T-cell therapy relapse within the first year with many of those patients exhibiting CD19 loss. Thus, new therapeutic approaches are needed to address tumor antigen escape. Accordingly, we generated triple gene-modified iPSC-derived NK (iNK) cells, termed "iDuo" NK cells, tailored to facilitate multi-antigen targeting. The iPSC line was clonally engineered to express high-affinity, non-cleavable CD16a (hnCD16), an anti-CD19 CAR optimized for NK cell signaling, and a membrane-bound IL-15/IL-15R fusion (IL-15RF) molecule to enhance NK cell persistence (Fig. 1A). To model antigen escape, we generated CD19 knockout AHR77 lymphoma cells alongside wild type AHR77 cells (both CD20 +) as targets in cytotoxicity assays. Activated peripheral blood NK (PBNK) cells, non-transduced iNK cells, and iDuo NK cells were tested as effectors. Unlike PBNK cells or non-transduced iNK cells, iDuo NK cells efficiently eliminated wild type AHR77 cells with or without the addition of rituximab at all tested E:T ratios. Similarly, iDuo NK cells in combination with rituximab were uniquely able to efficiently eliminate CD19 KO AHR77 cells due to enhanced antibody-dependent cellular cytotoxicity (ADCC) driven by hnCD16 (Fig. 1B-E). Cytotoxicity mediated by iDuo NK cells was also evaluated using primary chronic lymphocytic leukemia (CLL) cells. Compared to expanded PBNK cells and non-transduced iNK cells, only iDuo NK cells (in the absence of rituximab) were able to kill primary CLL cells (Fig. 1F). Expression of IL-15RF by iDuo NK cells uniquely supports in vitro expansion without the need for cytokine supplementation. To determine whether IL-15RF supports in vivo persistence of iDuo NK cells, CD19 CAR iNK cells (lacking IL-15RF) and iDuo NK cells were injected into NSG mice without the addition of cytokines or CD19 antigen availability. iDuo NK cell numbers peaked within a week after injection and persisted at measurable levels for ~5 weeks, in marked contrast to CD19 CAR iNK cell numbers that were undetectable throughout (Fig. 1G). To evaluate the in vivo function of iDuo NK cells, NALM6 leukemia cells were engrafted into NSG mice. Groups of mice received tumor alone or were treated with 3 doses of thawed iDuo NK cells. iDuo NK cells alone were highly effective in this model as evidenced by complete survival of mice in the treatment group (Fig. 1H). To assess iDuo NK cells in a more aggressive model, Raji lymphoma cells were engrafted, and groups of mice received rituximab alone, iDuo NK cells alone, or iDuo NK cells plus rituximab. Mice given the combination of iDuo NK cells and rituximab provided extended survival compared to all other arms in the aggressive disseminated Raji lymphoma xenograft model (Fig. 1I). One disadvantage of anti-CD19 CAR T cells is their inability to discriminate between healthy and malignant B cells. Because NK cells express inhibitory receptors that enable "self" versus "non-self" discrimination, we reasoned that iDuo NK cells could have higher cytotoxicity against tumor cells relative to healthy B cells. To address this, we labeled Raji cells, CD19 + B cells from healthy donor peripheral blood mononuclear cells (PBMCs) and CD19 - PBMCs. Labeled populations of cells were co-cultured with iDuo NK cells, and specific killing was analyzed. As expected, iDuo NK cells did not target CD19 - PBMCs. Intriguingly, iDuo NK cells had much higher cytotoxic activity against Raji cells compared to primary CD19 + B cells, suggesting a preferential targeting of malignant B cells compared to healthy B cells. Together, these results demonstrate the potent multi-antigen targeting capability and in vivo antitumor function of iDuo NK cells. Further, these data suggest that iDuo NK cells may have an additional advantage over anti-CD19 CAR T cells by discriminating between healthy and malignant B cells. The first iDuo NK cell, FT596, is currently being tested in a Phase I clinical trial (NCT04245722) for the treatment of B-cell lymphoma. Figure 1 Figure 1. Disclosures Cichocki: Gamida Cell: Research Funding; Fate Therapeutics, Inc: Patents & Royalties, Research Funding. Bjordahl: Fate Therapeutics: Current Employment. Gaidarova: Fate Therapeutics, Inc: Current Employment. Abujarour: Fate Therapeutics, Inc.: Current Employment. Rogers: Fate Therapeutics, Inc: Current Employment. Huffman: Fate Therapeutics, Inc: Current Employment. Lee: Fate Therapeutics, Inc: Current Employment. Szabo: Fate Therapeutics, Inc: Current Employment. Wong: BMS: Current equity holder in publicly-traded company; Fate Therapeutics, Inc: Current Employment. Cooley: Fate Therapeutics, Inc: Current Employment. Valamehr: Fate Therapeutics, Inc.: Current Employment. Miller: Magenta: Membership on an entity's Board of Directors or advisory committees; ONK Therapeutics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Vycellix: Consultancy; GT Biopharma: Consultancy, Patents & Royalties, Research Funding; Fate Therapeutics, Inc: Consultancy, Patents & Royalties, Research Funding; Sanofi: Membership on an entity's Board of Directors or advisory committees; Wugen: Membership on an entity's Board of Directors or advisory committees.

Download Full-text

Identification of Functionally Primitive and Immunophenotypically Distinct Subpopulations in Secondary Acute Myeloid Leukemia By Mass Cytometry

Blood ◽

10.1182/blood.v128.22.4278.4278 ◽

2016 ◽

Vol 128 (22) ◽

pp. 4278-4278

Author(s):

Shovik Bandyopadhyay ◽

Liyang Yu ◽

Daniel A.C. Fisher ◽

Olga Malkova ◽

Stephen T. Oh

Keyword(s):

Board Of Directors ◽

Myeloid Leukemia ◽

Research Funding ◽

Cell Types ◽

Cell Populations ◽

Specific Cell ◽

Mass Cytometry ◽

Advisory Committees ◽

Secondary Acute Myeloid Leukemia ◽

Acute Myeloid

Abstract Introduction: Mass cytometry is a powerful tool for analyzing cellular networks, with the ability to generate massive data sets encompassing > 40 parameters measured simultaneously at the single cell level. Various groups have created a variety of platforms to analyze this high dimensional data in unique and efficient ways. These tools have a range of applications: from using phenotypic similarities to cluster cells, stratifying unique signaling subpopulations based on observed stimulation responses, mapping the developmental trajectory of cell types, among many others. We have previously utilized mass cytometry to characterize NFkB hyperactivation in myeloproliferative neoplasms. Here we applied mass cytometric analysis to a cohort of patients with secondary acute myeloid leukemia (sAML) following a history of chronic MPN. The objective of this work was to identify populations of functionally primitive leukemic cells, relying not only on traditional immunophenotypic designations (which can vary considerably in sAML), but also by inferring functional status based on the presence or absence of cytokine hypersensitivity and constitutively active signaling in specific cell populations. Results: Dimensionality reduction and clustering analysis by viSNE and SPADE identified multiple cell subsets outside the hematopoietic stem/progenitor cell (HSPC) compartment that exhibited overt thrombopoietin (TPO) sensitivity, while healthy controls had highly localized responses largely restricted to the HSPC compartment. Using Phenograph, ten sAML metaclusters were identified containing cells from six sAML patients analyzed. One of these metaclusters represented a distinct subpopulation of CD61+ CD34- CD38- CD45lo cells with variable CD90 and CD11b expression. This subpopulation of CD61+ cells was not identified by manual gating, and exhibited significantly greater STAT3/STAT5 phosphorylation in response to TPO than did lineage-negative CD34+ CD38- cells in five out of six (83%) AML patients examined. In addition, substantially elevated basal STAT3 phosphorylation in this population was hypersensitive to TPO and largely resistant to ex vivo ruxolitnib. The classify function of Phenograph was utilized to determine whether the cytokine hypersensitivity observed in the viSNE and SPADE analysis could be entirely accounted for by the aforementioned CD61+ CD34- CD38- CD45lo population. The signaling responses highly predictive of specific cell types were identified, which were used to assess the functional status of sAML cells compared to healthy Lin- CD34+ CD38- cells. By this approach, sAML cells were found to exhibit significant incongruity between surface cell type designation and functional designation. Furthermore, functionally primitive cells displayed a spectrum of myeloid surface markers, suggesting that restricting analysis to a subset of strictly surface-defined cells would potentially obscure populations of interest. Conclusions: Our analysis revealed a distinct, previously undescribed population of CD61+ CD34- CD38- CD45lo cells in sAML. While the biological relevance of this population requires validation by functional assays, this result demonstrates that immunophenotypic changes in traditional surface-marker-defined populations may conceal important cell populations. These cells, and other functionally primitive but mature-designated cells could be relevant to studying sAML disease pathogenesis, progression, and/or response to therapy. This study further demonstrates the potential for mass cytometry to elucidate rare leukemic subpopulations in highly heterogeneous tumors. Disclosures Oh: Gilead: Membership on an entity's Board of Directors or advisory committees, Research Funding; Incyte Corporation: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Research Funding; CTI: Research Funding.

Download Full-text

Splicing Factor 3B Subunit 1 (SF3B1) Mutations Impact FOXP1 Splicing in Chronic Lymphocytic Leukemia (CLL) Cells but Conserve the Balance Between Newly Described and Classical Splicing Variants of ZAP-70

Blood ◽

10.1182/blood.v126.23.2927.2927 ◽

2015 ◽

Vol 126 (23) ◽

pp. 2927-2927

Author(s):

Herve Roudot ◽

Jennifer Martret ◽

Fanny Baran-Marszak ◽

Jean-Francois Collon ◽

Abdemalek Dahmani ◽

...

Keyword(s):

T Cells ◽

B Cell ◽

Nk Cells ◽

Peripheral Blood ◽

Research Funding ◽

Sanger Sequencing ◽

Normal Subjects ◽

Cell Types ◽

Alternative Transcripts ◽

Real Time Quantitative Pcr

Abstract Zeta-chain Associated Protein of 70 kDa (ZAP-70) is the second member of the Spleen tyrosine Kinase (Syk) family, and plays a key role in T-cell Receptor signalling. An alternative variant of ZAP-70 was described in 2004 by Hirokawa et al. producing a shorter protein named Truncated ZAP-70 Kinase (TZK). ZAP-70 has also been found expressed in some B-cell subsets and in various B-cell malignancies. In CLL, the expression of ZAP-70 retains a prognostic significance; however, its function is not completely understood. Recently, whole genome sequencing identified mutations affecting the spliceosome such as SF3B1 mutations that are associated with poor outcome in CLL. In this work, we aimed at identifying novel alternative transcripts of ZAP-70 in normal lymphocytes and in CLL cells. We also explored the potential link between ZAP70 alternative transcripts and SF3B1 mutations. Peripheral blood mononuclear cells (PBMC) were obtained from normal subjects and CLL patients. T cells, NK cells and monocytes were purified by negative selection from 11 normal subjects. We explored the transcriptional profile of ZAP-70 by standard PCR using 4 combinations of primers spanning over the 14 exons of the transcript. PCR products were revealed by gel electrophoresis, purified and sequenced by Sanger method. The expression of ZAP-70 was studied by real-time quantitative PCR using SYBR Green® technique after designing specific primers for the four different transcripts individualized. The expression of the full length and truncated forms of FOXP1 was studied by real-time quantitative PCR as previously described (EHA2015 abst#4919). SF3B1 mutations were detected by Sanger sequencing of the exons 14, 15, and 16. In Jurkat cell line, we identified two new variants of ZAP-70 corresponding to the alternative splicing of exon 3 and exons 3 and 4. We named them Δ3 and Δ3-4 respectively after confirmation by Sanger sequencing. To detect both Δ3 and Δ3-4 in the same reaction, we designed a PCR encompassing exons 1 to 5, and we were able to detect ZAP-70, Δ3 and Δ3-4 in peripheral blood unselected normal cells. SYBR Green ® technique with new sets of primers allowed us to explore ZAP-70, TZK and the two new variants in selected NK cells and T lymphocytes. As previously described, ZAP-70 was more expressed in NK cells than in T cells, and at lower level in CLL cells. TZK was also detected in T-lymphocytes and NK cells, Δ3 and Δ3-4 were found at very low levels in both cell types and were absent from ZAP-70 negative CLL cells. We studied ZAP-70 transcripts in 13 ZAP-70 positive CLL. All alternative transcripts were present with a pattern of expression different to that of T-cells. We have shown recently that in all cases of CLL with SF3B1 mutation, a high expression of the FOXP1 truncated variant was observed as compared to a very low level in unmutated samples. This variant was highly expressed in the samples carrying five different mutations of SF3B1 (Y623C, R625H, K700E, G740V and G742D). Among our 13 ZAP70 positive patients, 2 cases harboured the SF3B1 Y623C mutation. In these two cases, the balance between the four ZAP-70 transcripts detected showed no obvious difference with wild-type SF3B1 ZAP-70 positive cases. In conclusion, we studied the transcription profile of ZAP-70 in various cells types and identified two novel alternative transcripts of ZAP-70 (Δ3 and Δ3-4). These transcripts were detected at various levels in all ZAP-70-positive cell types. Interestingly, we found that the presence of SF3B1 mutation impacted the splicing of FOXP1 transcription factor but not the balance between ZAP-70 transcripts We are currently expanding the cohort of ZAP-70 cases with other types of SF3B1 mutations. Mutations affecting the spliceosome such as SF3B1 mutations do not seem to impact transcription of ZAP-70. Disclosures Troussard: Roche: Honoraria; Janssen: Honoraria. Cymbalista:Janssen: Honoraria, Research Funding; Gilead: Honoraria; Roche: Honoraria; Karyopharm: Honoraria. Letestu:Alexion: Honoraria, Research Funding; Roche: Honoraria.

Download Full-text

Immune Cell Profiles in Patients Treated with Lenalidomide and Alternate Day Prednisolone Maintenance Post Upfront ASCT for Multiple Myeloma (LEOPARD Trial)

Blood ◽

10.1182/blood-2020-141746 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 34-35

Author(s):

Anna Kalff ◽

Sam Norton ◽

Tiffany Khong ◽

Malarmathy Ramachandran ◽

Mary H. Young ◽

...

Keyword(s):

T Cells ◽

Nk Cells ◽

Cd8 T Cells ◽

Research Funding ◽

Immune Cell ◽

Cell Populations ◽

Publicly Traded ◽

Unsupervised Analysis ◽

Hla Dr ◽

Immune Profiling

The LEOPARD trial evaluated lenalidomide and alternate day prednisolone (RAP) as post ASCT maintenance in newly diagnosed transplant eligible MM patients (TE NDMM). 60 patients were recruited. Estimated median potential follow-up was 44 months (IQR 26m - 52m). Median PFS from time of commencing RAP was 38.3m (95% CI, 25.8 to 54.8); median OS was not reached (71.4% of patients were alive at 36 months). Here we present the findings from correlative immune studies of this trial. Aims: To undertake mass cytometry (CyTOF) based immune profiling in patients with TE NDMM treated with RAP maintenance post ASCT. Methods: The LEOPARD trial was a phase II, multi centre, open label, single arm study of RAP maintenance after a single melphalan conditioned (200mg/m2) ASCT as part of up-front therapy. Patients were restaged at D+42 ASCT, and if eligible, were commenced on RAP maintenance (LEN 10mg daily increasing to 15mg daily after 8 weeks and alternate day prednisolone 50mg) within 8 weeks of D+0 of the ASCT. Therapy continued until toxicity/progression. CyTOF was performed in sequential samples in two selected groups of patients: long runners (LR, n=7), defined as those with PFS > 36 months (median) and early relapsers (ER, n=8), defined as those who progressed/died before reaching the lower quartile of PFS. [All patients had peripheral blood collected at baseline (pre-ASCT), 6w post-ASCT and weeks 4, 8, 12, 20, 28 and 40 of RAP]. Cells were barcoded using the Cell-ID 20-Plex Pd barcoding kit (Fluidigm) followed by staining with sub-set/function defining antibodies (targeting myeloid, B, T and NK cells: CD45, CD3, CD19, CD5, CD1c, CD226. CD8, CD11c, CD16, CD127, CD138, CD123, NKG2A, TIGIT, TIM3, CD45RA. CD274, CD27, CD197, CD28, Ki67, CD66b, CD183, KLRG1, CD43, NKG2D, CD38, CD278/ICOS, CD25, HLA-DR, CD4, CD57, GramB, PD-1, CD14, CD56, CD11b, Tbet, CD33). Samples were acquired on the Helios instrument. Supervised analysis was performed to determine differences in canonical immune cell populations. Unsupervised analysis was then performed: data were clustered in the VORTEX package. Significant differences in cluster frequency were assessed by Mann-Whitney test for statistical significance. Cluster phenotypes were determined and validated via multiple visualisation approaches. Results: Median age was 56yrs for LR versus 63yrs for ER. Median PFS for LR was 46.3m (38.4 - 51.5m) versus 10.2 m (2.1 - 21.3m) for ER. Supervised analysis was performed on all samples, dichotomized into baseline and last time point sampled for each patient. At baseline, Ki67+CD8+ T cells, ICOS+CD8+ T cells, HLA-DR+CD4+ T cells and CD11c+ myeloid cells were enriched in LR compared to ER. At the last timepoint sampled, Ki67+CD8+ T cells and ICOS+CD8+ T cells were again enriched in LR compared to ER. Conversely, B-reg-like cells (CD19+CD5+CD43-) were enriched in ER compared to LR at the last timepoint sampled. Unsupervised analysis was performed on all samples (all timepoints were pooled). Five clusters were significantly enriched in LR compared to ER. Four of these clusters represented activated/cytotoxic NK cells: CD56 dim, CD16-, NKG2A(CD159a)+, NKG2D(CD314)+, Granzyme B+ and CD38+, and additional expression of CD57 on one cluster; one cluster represented a mature myeloid population, with high expression of HLA-DR, CD11b and CD11c and low expression of CD33. One cluster was significantly enriched in ER compared to LR, representing activated neutrophils, with high expression of CD66b, CD11b and CD16. The clusters that were enriched were then assessed longitudinally over all time points. There was no difference in the kinetics of these populations between groups. Conclusions Significant differences in both T-cell and NK cell populations were demonstrable at baseline in LR versus ER patients. Subsequently, durable responses to post-ASCT lenalidomide maintenance were associated with a cytotoxic, controlled immune response whereas early relapse was characterised by a more uncontrolled inflammatory response and the emergence of B-reg-like cells prior to relapse. We conclude that immune profiling at baseline and after initiation of therapy may help to predict a more sustained response to lenalidomide maintenance enabling pre-emptive tailored treatment decisions. Disclosures Kalff: Roche: Honoraria; Janssen: Honoraria; Amgen: Honoraria; CSL: Honoraria; Celgene: Honoraria. Young:Bristol Meyers Squibb: Current Employment, Current equity holder in publicly-traded company. Pierceall:Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Thakurta:Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company; Oxford University: Other: visiting professor. Oppermann:Bristol Meyers Squibb: Research Funding. Guo:Bristol Meyers Squibb: Research Funding. Reynolds:Novartis AG: Current equity holder in publicly-traded company. Spencer:AbbVie, Celgene, Haemalogix, Janssen, Sanofi, SecuraBio, Specialised Therapeutics Australia, Servier and Takeda: Consultancy; AbbVie, Amgen, Celgene, Haemalogix, Janssen, Sanofi, SecuraBio, Specialised Therapeutics Australia, Servier and Takeda: Honoraria; Amgen, Celgene, Haemalogix, Janssen, Servier and Takeda: Research Funding; Celgene, Janssen and Takeda: Speakers Bureau.

Download Full-text

Immune Microenvironment Analysis of Bone Marrow By Mass Cytometry and RNA Sequencing in Multiple Myeloma Patients Treated with Daratumumab and Durvalumab

Blood ◽

10.1182/blood-2018-99-114453 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 3296-3296 ◽

Cited By ~ 1

Author(s):

Frances Seymour ◽

Mary H Young ◽

Mark Tometsko ◽

Jamie Cavenagh ◽

Ethan G. Thompson ◽

...

Keyword(s):

Bone Marrow ◽

Flow Cytometry ◽

T Cells ◽

Nk Cells ◽

Research Funding ◽

Cell Populations ◽

Mass Cytometry ◽

Immune Microenvironment ◽

Employment Equity ◽

Equity Ownership

Abstract 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.

Download Full-text

Contribution of NK, NK T, γδ T, and αβ T Cells to the Gamma Interferon Response Required for Liver Protection against Trypanosoma cruzi

Infection and Immunity ◽

10.1128/iai.74.4.2031-2042.2006 ◽

2006 ◽

Vol 74 (4) ◽

pp. 2031-2042 ◽

Cited By ~ 36

Author(s):

Luiz Roberto Sardinha ◽

Rosa Maria Elias ◽

Tainá Mosca ◽

Karina R. B. Bastos ◽

Cláudio R. F. Marinho ◽

...

Keyword(s):

T Cells ◽

Trypanosoma Cruzi ◽

Nk Cells ◽

Γδ T Cells ◽

Gamma Interferon ◽

Cell Populations ◽

Liver Protection ◽

Nk T Cells ◽

Ifn Γ ◽

Deficient Mice

ABSTRACT In the present work, we show that intracellular Trypanosoma cruzi is rarely found in the livers of acutely infected mice, but inflammation is commonly observed. The presence of numerous intrahepatic amastigotes in infected gamma interferon (IFN-γ)-deficient mice corroborates the notion that the liver is protected by an efficient local immunity. The contribution of different cell populations was suggested by data showing that CD4- and CD8-deficient mice were able to restrain liver parasite growth. Therefore, we have characterized the liver-infiltrating lymphocytes and determined the sources of IFN-γ during acute T. cruzi infection. We observed that natural killer (NK) cells increased by day 7, while T and B cells increased by day 14. Among CD3+ cells, CD4+, CD8+, and CD4− CD8− cell populations were greatly expanded. A large fraction of CD3+ cells were positive for PanNK, a β1 integrin expressed by NK and NK T cells. However, these lymphocytes were not classic NK T cells because they did not express NK1.1 and showed no preferential usage of Vβ8. Otherwise, liver NK T (CD3+ NK1.1+) cells were not increased in acutely infected mice. The majority of PanNK+ CD4+ and PanNK+ CD8+ cells expressed T-cell receptor αβ (TCRαβ), whereas PanNK+ CD4− CD8− cells were positive for TCRγδ. In fact, γδ T cells showed the most remarkable increase (40- to 100-fold) among liver lymphocytes. Most importantly, intracellular analysis revealed high levels of IFN-γ production at day 7 by NK cells and at day 14 by CD4+, CD8+, and CD4− CD8− TCRγδ+ cells. We concluded that NK cells are a precocious source of IFN-γ in the livers of acutely infected mice, and, as the disease progresses, conventional CD4+ and CD8+ T cells and γδ T cells, but not classic NK-T cells, may provide the IFN-γ required for liver protection against T. cruzi.

Download Full-text