Single Cell Profiling Reveals Unique CXCL13 Positive T Cell Subsets in the Tumor Microenvironment of Lymphocyte Rich Classic Hodgkin Lymphoma

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 32-33
Author(s):  
Tomohiro Aoki ◽  
Lauren C. Chong ◽  
Katsuyoshi Takata ◽  
Katy Milne ◽  
Elizabeth Chavez ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
B Cells ◽  
Single Cell ◽  
Immune Cells ◽  
Research Funding ◽  
Immune Cell ◽  
Expression Patterns ◽  
The Other ◽  
Tfh Cells

Introduction: Classic Hodgkin lymphoma (CHL) features a unique crosstalk between malignant cells and different types of normal immune cells in the tumor-microenvironment (TME). On the basis of histomorphologic and immunophenotypic features of the malignant Hodgkin and Reed-Sternberg (HRS) cells and infiltrating immune cells, four histological subtypes of CHL are recognized: Nodular sclerosing (NS), Mixed cellularity, Lymphocyte-rich (LR) and Lymphocyte-depleted CHL. Recently, our group described the high abundance of various types of immunosuppressive CD4+ T cells including LAG3+ and/or CTLA4+ cells in the TME of CHL using single cell RNA sequencing (scRNAseq). However, the TME of LR-CHL has not been well characterized due to the rarity of the disease. In this study, we aimed at characterizing the immune cell profile of LR-CHL at single cell resolution. METHODS: We performed scRNAseq on cell suspensions collected from lymph nodes of 28 primary CHL patients, including 11 NS, 9 MC and 8 LR samples, with 5 reactive lymph nodes (RLN) serving as normal controls. We merged the expression data from all cells (CHL and RLN) and performed batch correction and normalization. We also performed single- and multi-color immunohistochemistry (IHC) on tissue microarray (TMA) slides from the same patients. In addition, an independent validation cohort of 31 pre-treatment LR-CHL samples assembled on a TMA, were also evaluated by IHC. Results: A total of 23 phenotypic cell clusters were identified using unsupervised clustering (PhenoGraph). We assigned each cluster to a cell type based on the expression of genes described in published transcriptome data of sorted immune cells and known canonical markers. While most immune cell phenotypes were present in all pathological subtypes, we observed a lower abundance of regulatory T cells (Tregs) in LR-CHL in comparison to the other CHL subtypes. Conversely, we found that B cells were enriched in LR-CHL when compared to the other subtypes and specifically, all four naïve B-cell clusters were quantitatively dominated by cells derived from the LR-CHL samples. T follicular helper (TFH) cells support antibody response and differentiation of B cells. Our data show the preferential enrichment of TFH in LR-CHL as compared to other CHL subtypes, but TFH cells were still less frequent compared to RLN. Of note, Chemokine C-X-C motif ligand 13 (CXCL13) was identified as the most up-regulated gene in LR compared to RLN. CXCL13, which is a ligand of C-X-C motif receptor 5 (CXCR5) is well known as a B-cell attractant via the CXCR5-CXCL13 axis. Analyzing co-expression patterns on the single cell level revealed that the majority of CXCL13+ T cells co-expressed PD-1 and ICOS, which is known as a universal TFH marker, but co-expression of CXCR5, another common TFH marker, was variable. Notably, classical TFH cells co-expressing CXCR5 and PD-1 were significantly enriched in RLN, whereas PD-1+ CXCL13+ CXCR5- CD4+ T cells were significantly enriched in LR-CHL. These co-expression patterns were validated using flow cytometry. Moreover, the expression of CXCR5 on naïve B cells in the TME was increased in LR-CHL compared to the other CHL subtypes We next sought to understand the spatial relationship between CXCL13+ T cells and malignant HRS cells. IHC of all cases revealed that CXCL13+ T cells were significantly enriched in the LR-CHL TME compared to other subtypes of CHL, and 46% of the LR-CHL cases showed CXCL13+ T cell rosettes closely surrounding HRS cells. Since PD-1+ T cell rosettes are known as a specific feature of LR-CHL, we confirmed co-expression of PD-1 in the rosetting cells by IHC in these cases. Conclusions: Our results reveal a unique TME composition in LR-CHL. LR-CHL seems to be distinctly characterized among the CHL subtypes by enrichment of CXCR5+ naïve B cells and CD4+ CXCL13+ PD-1+ T cells, indicating the importance of the CXCR5-CXCL13 axis in the pathogenesis of LR-CHL. Figure Disclosures Savage: BeiGene: Other: Steering Committee; Merck, BMS, Seattle Genetics, Gilead, AstraZeneca, AbbVie: Honoraria; Roche (institutional): Research Funding; Merck, BMS, Seattle Genetics, Gilead, AstraZeneca, AbbVie, Servier: Consultancy. Scott:Janssen: Consultancy, Research Funding; Celgene: Consultancy; NanoString: Patents & Royalties: Named inventor on a patent licensed to NanoString, Research Funding; NIH: Consultancy, Other: Co-inventor on a patent related to the MCL35 assay filed at the National Institutes of Health, United States of America.; Roche/Genentech: Research Funding; Abbvie: Consultancy; AstraZeneca: Consultancy. Steidl:AbbVie: Consultancy; Roche: Consultancy; Curis Inc: Consultancy; Juno Therapeutics: Consultancy; Bayer: Consultancy; Seattle Genetics: Consultancy; Bristol-Myers Squibb: Research Funding.

scholarly journals Global immune characterization of HBV/HCV-related hepatocellular carcinoma identifies macrophage and T-cell subsets associated with disease progression

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Guohe Song ◽  
Yang Shi ◽  
Meiying Zhang ◽  
Shyamal Goswami ◽  
Saifullah Afridi ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
T Cells ◽  
T Cell ◽  
Single Cell ◽  
Immune Cells ◽  
Immune Cell ◽  
T Cell Subsets ◽  
M2 Macrophage ◽  
Advanced Hcc ◽  
Immune Cell Subsets

AbstractDiverse immune cells in the tumor microenvironment form a complex ecosystem, but our knowledge of their heterogeneity and dynamics within hepatocellular carcinoma (HCC) still remains limited. To assess the plasticity and phenotypes of immune cells within HBV/HCV-related HCC microenvironment at single-cell level, we performed single-cell RNA sequencing on 41,698 immune cells from seven pairs of HBV/HCV-related HCC tumors and non-tumor liver tissues. We combined bio-informatic analyses, flow cytometry, and multiplex immunohistochemistry to assess the heterogeneity of different immune cell subsets in functional characteristics, transcriptional regulation, phenotypic switching, and interactions. We identified 29 immune cell subsets of myeloid cells, NK cells, and lymphocytes with unique transcriptomic profiles in HCC. A highly complex immunological network was shaped by diverse immune cell subsets that can transit among different states and mutually interact. Notably, we identified a subset of M2 macrophage with high expression of CCL18 and transcription factor CREM that was enriched in advanced HCC patients, and potentially participated in tumor progression. We also detected a new subset of activated CD8+ T cells highly expressing XCL1 that correlated with better patient survival rates. Meanwhile, distinct transcriptomic signatures, cytotoxic phenotypes, and evolution trajectory of effector CD8+ T cells from early-stage to advanced HCC were also identified. Our study provides insight into the immune microenvironment in HBV/HCV-related HCC and highlights novel macrophage and T-cell subsets that could be further exploited in future immunotherapy.

scholarly journals Single-Cell Analysis of the Classical Hodgkin Lymphoma Immune Environment Reveals a Clonally-Expanded CD8+ T Cell Population with a Cytotoxic Phenotype

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 40-41
Author(s):  
Jovian Yu ◽  
Xiufen Chen ◽  
James Godfrey ◽  
Girish Venkataraman ◽  
Sonali M. Smith ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Single Cell ◽  
Board Of Directors ◽  
Cd8 T Cells ◽  
Research Funding ◽  
Immune Cell ◽  
Clonal Expansion ◽  
Advisory Committees ◽  
Cell Clusters

Introduction: Classical Hodgkin lymphoma (cHL) is characterized by a robust and complex immune cell infiltrate and the rare presence of malignant Hodgkin-Reed-Sternberg (HRS) cells. At the genetic level, HRS cells recurrently acquire alterations that lead to defective antigen presentation (β2 microglobulin mutations) and mediate T cell dysfunction (PD-L1 copy gains/amplifications) in order to subvert host immune surveillance. The clinical relevance of PD-L1 protein over-expression in cHL is clear, as response rates to PD-1 blockade therapy are extremely high among patients with relapsed/refractory (r/r) disease. Despite its remarkable efficacy, the cells that mediate response to anti-PD-1 therapy in cHL remain undefined. Recent analyses have highlighted a possible role for CD4+ T cells in mediating the clinical activity of anti-PD-1 therapy in cHL. CD4+ T cells significantly outnumber CD8+ T cells in cHL lesions and are more frequently juxtaposed to HRS cells in situ. Furthermore, HLA class II expression on HRS cells predicted higher complete response rates to PD-1 blockade therapy in r/r cHL patients. However, a candidate T cell population capable of specific reactivity to antigens expressed by HRS cells has yet to be identified. This information is critical as such T cells might be functionally reinvigorated to mediate HRS cell elimination following PD-1 blockade therapy. In order to address this key knowledge gap, we analyzed data at single cell (sc) resolution using paired RNA and T cell receptor (TCR) sequencing in 9 diagnostic cHL and 5 reactive lymph node (RLN) specimens. Methods: Sequencing was performed using the 10x Genomics Chromium Single Cell 5' Gene Expression and V(D)J workflows. B-cell depletion of each sample was achieved using CD19 microbeads and negative selection to enrich T cell populations. Reads were analyzed and aligned with CellRanger (v3.1.0) and Seurat (v3.2.0) was used to conduct clustering by a shared nearest neighbor (SNN) graph on scRNA data. TCR sequencing data was integrated using scRepertoire (v1.0.0). Results: A detailed map of the immune cell states in cHL was created using scRNA-seq (10X) data on 79,085 cells from 9 cHL (52,602 cells) and 5 RLN samples (26,484 cells) expressing a total of 21,421 genes (mean 5649 cells/sample; mean 2849 mRNA reads/cell). Dimensionality reduction and unsupervised graph-based clustering revealed 21 distinct cell type and activation state clusters, including T cells, NK cells, macrophages, and dendritic cells (Fig 1A-B). A cluster identifying HRS cells was not observed, consistent with a recently published report. Ten T cell clusters were identified (47,573 cells), including naive- and memory-like T cells, effector/cytotoxic CD8+ T cells, regulatory T cells, and T follicular helper cells. Unexpectedly, a putative exhausted T cell cluster was not clearly observed. The relative contributions of cHL and RLNs cases to these clusters are shown in Fig 1C. Paired TCR sequencing was available for 23,943 cells. Overall TCR diversity was lower among cHL samples compared to RLN specimens (Fig 1D). In cHL samples, modest clonal expansion within regulatory T cell and memory CD4+ T cell clusters was observed, but the most striking clonal expansion occurred among cells assigned to effector/cytotoxic CD8+ T cell clusters - a finding not observed in most RLN specimens (Fig 1E). Clonally-expanded effector/cytotoxic CD8+ T cells displayed high expression of granzymes (GZMA, GZMH, GZMK), cytokines (TNF, IFNG) and chemokines (CCL4/CCL5), and modest expression of exhaustion markers (PDCD1, ENTPD1, HAVCR2, ITGAE), contrasting with data from single-cell analyses of solid tumors. Clonal expansion of effector/cytotoxic CD8+ T cells was particularly robust in EBV-positive cHLs, likely due to recognition of viral-derived epitopes displayed on HRS cells (Fig 1F). Phenotypic and functional validation of key immune cell clusters in cHL specimens using spectral cytometry is underway and will be reported at the meeting. Conclusions: For the first time, our data have unveiled the nature of the T cell repertoire in cHL at single cell resolution. Our results reveal a recurrent pattern of clonal expansion within effector CD8+ cells, which may be the HRS antigen-specific T cells that mediate response to PD-1 blockade. This hypothesis requires confirmation through similar analyses of pre- and on-treatment biopsies of cHL patients receiving anti-PD-1 therapy. Disclosures Godfrey: Gilead: Research Funding; Merck: Research Funding; Verastem: Research Funding. Venkataraman:EUSA Pharma: Speakers Bureau. Smith:Janssen: Consultancy; BMS: Consultancy; TG Therapeutics: Consultancy, Research Funding; Genentech/Roche: Consultancy, Other: Support of parent study and funding of editorial support, Research Funding; Karyopharm: Consultancy, Research Funding; FortySeven: Research Funding; Pharmacyclics: Research Funding; Acerta: Research Funding; Celgene: Consultancy, Research Funding. Kline:Kite/Gilead: Speakers Bureau; Seattle Genetics: Membership on an entity's Board of Directors or advisory committees; Merck: Research Funding; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Verastem: Membership on an entity's Board of Directors or advisory committees, Research Funding.

Examining the Heterogeneity of Follicular Lymphoma By Multi-Parameter Flow Cyotmetry in Previously Untreated Patients

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 2947-2947
Author(s):  
Debra K Czerwinski ◽  
Steven R Long ◽  
Michael Khodadoust ◽  
Matthew J. Frank ◽  
Adel Kardosh ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
B Cells ◽  
Follicular Lymphoma ◽  
B Cell ◽  
Tumor Cells ◽  
Immune Cells ◽  
Research Funding ◽  
T Cell Exhaustion ◽  
Cell Exhaustion

Abstract BACKGROUND: Follicular lymphoma (FL) is an indolent form of Non-Hodgkin B cell lymphoma that remains incurable with present therapies. Derived from germinal center B cells, FL B cells experience ongoing hypermutation of the immunoglobulin variable region gene. In addition, Michael Green, et al (PNAS; 2015), reported the presence of numerous somatic mutations to include those of the chromatin-modifying genes. These mutations accumulate over the course of the disease and play an important role in regulating gene transcription, B cell development and immune interactions. Furthermore, FL tumors maintain a resemblance to primary lymphoid follicles, and as such, present with a number of infiltrating immune cells, especially T cells, the numbers of which vary from patient to patient. The close association and interaction of these immune cells with the tumor B cells play an important part in determining the disease biology (Dave SS, et al. N Engl J Med; 2004). For instance, tumor B cells, through cell-cell contact with these immune cells and/or through secretion of inhibitory cytokines such as TGF-b and IL-10, induce T cell exhaustion and apoptosis as well as suppressive T cell phenotypes (FoxP3+ T Regulatory cells) thus evading immune eradication (Yang Z-Z, et al. Blood 2007 and Ai WZ, et al. IntJ Cancer; 2009). They also promote their own survival and proliferation through their interaction with resident T follicular helper cells via CD40L/CD40 interactions (Ame'-Thomas P, et al. Blood; 2005). As a corollary to an ongoing clinical trial, we received fine needle aspirates (FNAs) of easily accessible tumors from 14 patients with FL prior to any treatment. 6 of these patients had samples taken from a second site simultaneously. All samples were processed within 24 hours into a single-cell suspension; red blood cells were lysed. Cells were then stained with antibodies to delineate T, B, NK, dendritic, and myeloid cells, as well as their subsets. Antibodies against activation antigens, T cell exhaustion, inhibition and function were also used to characterize these cells. Finally, the cells were run on a 17-parameter LSRII (Becton Dickinson) and data analyzed via Cytobank, a web-based data storage and analysis tool. PURPOSE: To better understand the biology of FL as represented by protein expression by the tumor cells and the immune cells that make up the microenvironment. We will especially look to evaluate the heterogeneity inherent in FL by flow cytometry across patients as well as within any one individual. RESULTS: Each sample is stained with 4 panels of antibodies, 13 antibodies each, allowing us to measure over 100 cell subsets. A quick preview of all data shows that there is a high variability between patients in the percentage of T cells within the microenvironment (37.7% + 16.6% of all cells collected from all samples). This variability is represented by the differences in the CD4 T cell compartment (27.6 + 12.9%) and to a lesser degree in the CD8 compartment (7.7 + 3.7%). To note, this variability in T cells does not correlate with time from diagnosis to sample collection which ranged from 3.4 years to approximately 5 months. Also, this is in contrast to the similar percentage of CD4 and CD8 T cells expressing PD-1 (55.5 + 8.8% and 46.0 + 8.9%, respectively) across patients. Notably, there is much less variability from site to site within each patient then between patients as demonstrated by Figure 1 where Site A and Site B are 2 separate lesions within each patient listed, sampled at the same time. Since FL presumably begins in a single site in the body and then becomes disseminated, the fact that a characteristic relationship exists between tumor cells and immune cells wherever the disease is found implies a mutual interdependence of the tumor cells in each case and their immune host component. CONCLUSION: Follicular lymphoma is a very heterogeneous disease as would be expected by the diversity of mutations seen at the genomic level. This heterogeneity is also apparent in the microenvironment from one patient to another. Conversely, different tumor sites within each patient have a characteristic and fixed relationship to their immune microenvironment. The emergence of novel therapies for FL, including checkpoint antibodies such as anti-PD-1 and anti-PD-L1 and small molecules such as Ibrutinib, will be informed by understanding the differences as well as the similarities in each case of FL. Disclosures Levy: Kite Pharma: Consultancy; Five Prime Therapeutics: Consultancy; Innate Pharma: Consultancy; Beigene: Consultancy; Corvus: Consultancy; Dynavax: Research Funding; Pharmacyclics: Research Funding.

Abstract 095: CD74 Deficient Mice are Resistant to Toll-Like Receptor-Induced Preeclampsia

Hypertension ◽  
2014 ◽  
Vol 64 (suppl_1) ◽  
Author(s):  
Mohamad Hatahet ◽  
Olga Y Gasheva ◽  
Valorie L Chiasson ◽  
Piyali Chatterjee ◽  
Kelsey R Bounds ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Endothelial Dysfunction ◽  
Mhc Class Ii ◽  
Immune Cells ◽  
Immune Cell ◽  
Class Ii ◽  
Poly I:C ◽  
Hypertensive Disorder ◽  
Toll Like Receptor

Preeclampsia (PE) is a pregnancy-specific hypertensive disorder characterized by vascular endothelial dysfunction and excessive immunity and inflammation. Activation of the dsRNA receptor Toll-like receptor 3 (TLR3) or the ssRNA receptor TLR7 elicits a pregnancy-dependent PE-like syndrome in mice by inducing a pro-inflammatory immune response. CD74 (MHC Class II invariant chain) acts as a chaperone for MHC Class II surface expression on immune cells during antigen presentation and is cleaved into Class II-Associated Invariant Peptide (CLIP) following polyclonal activation of immune cell TLRs. The presence of CLIP in the groove of MHC Class II prevents T cell-dependent death leading to persistent immune cell activation. We hypothesized that genetic deletion of CD74 and subsequent depletion of CLIP on immune cells prevents TLR-induced immune responses and the development of PE in mice. Pregnant WT and CD74 KO mice were given i.p. injections of normal saline (P), poly I:C (TLR3 agonist; P-PIC), or R837 (TLR7 agonist; P-R837) on gestational days 13, 15, and 17 and euthanized on day 18. P-PIC and P-R837 WT mice had significantly increased splenic levels of pro-inflammatory CD3+/gd T cells and plasma levels of the gd T cell-derived cytokines IFNg, TNFa, and IL-17 compared to P WT mice whereas P-PIC and P-R837 CD74 KO mice had significantly increased anti-inflammatory CD3+/gd T cells and no significant increases in plasma IFNg, TNFa, and IL-17 levels. P-PIC and P-R837 CD74 KO mice did not develop the hypertension (gd17 SBP in mmHg: P WT=102±3, P CD74 KO=100±3, P-PIC WT=147±4*, P-PIC CD74 KO=95±3, P-R837 WT=133±2*, P-R837 CD74 KO=97±1; *p<0.05 vs. P WT), endothelial dysfunction, proteinuria, or placental necrosis seen in P-PIC and P-R837 WT mice. In conclusion, CD74 is crucial for the development of TLR-induced PE-like symptoms in mice and CD74/CLIP depletion may be a promising therapeutic target for women with PE.

scholarly journals Single Cell Transcriptome Analysis Reveals Disease-Defining T Cell Subsets in the Tumor Microenvironment of Classic Hodgkin Lymphoma

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 547-547
Author(s):  
Tomohiro Aoki ◽  
Lauren C. Chong ◽  
Katsuyoshi Takata ◽  
Katy Milne ◽  
Monirath Hav ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Single Cell ◽  
Research Funding ◽  
Cell Clusters ◽  
Mhc Ii ◽  
Spatial Profiling ◽  
Classic Hodgkin Lymphoma ◽  
Cellular Components

INTRODUCTION: Classic Hodgkin lymphoma (cHL) is uniquely characterized by an extensively dominant microenvironment composed primarily of different types of non-cancerous immune cells with a rare population (~1%) of tumor cells. Detailed characterization of these cellular components and their spatial relationship is crucial to understand crosstalk and therapeutic targeting in the cellular ecosystem of the tumor microenvironment (TME). METHODS: In this study, we performed high dimensional and spatial profiling of immune cells in the TME of cHL. Single cell RNA sequencing (scRNA-seq) was performed with the 10x Genomics platform on cell suspensions collected from lymph nodes of 22 cHL patients, including 12 of nodular sclerosis subtype, 9 of mixed cellularity subtype and 1 of lymphocyte-rich subtype, with 5 reactive lymph nodes (RLNs) serving as normal controls. Illumina sequencing (HiSeq 2500) was performed to yield single-cell expression profiles for 127,786 cells. We also performed multicolor IHC and imaging mass cytometry (IMC) on TMA slides from the same patients. RESULTS: Unsupervised clustering using PhenoGraph identified 22 cell clusters including 12 T cell clusters, 7 B cell clusters and 1 macrophage cluster. While most immune cell populations were common between cHL and RLN, we observed an enrichment of cells from cHL in all 3 regulatory T cell (Treg) clusters. The most cHL-enriched cluster was characterized by high expression of LAG3, in addition to common Treg markers such as IL2RA (CD25) and TNFRSF18 (GITR), but lacked expression of FOXP3, consistent with a type 1 regulatory (Tr1) T cell population. LAG3+ T cells in cHL had high expression of immune-suppressive cytokines IL-10 and TGF-b . In vitro exposure of T cells to cHL cell line supernatant induced significantly higher levels of LAG3 in naïve T cells compared to co-culture with other lymphoma cell line supernatant or medium only. CD4+ LAG3+ T cells isolated by FACS also suppressed the proliferation of responder CD4+ T cells when co-cultured in vitro. Additionally, Luminex analysis revealed that cHL cell lines secrete substantial amounts of cytokines and chemokines that can promote Tr1 cell differentiation (e.g. IL-6). Our scRNA-seq analysis revealed that LAG3 expression was significantly higher in cHL cases with loss of major histocompatibility class II (MHC-II) expression on HRS cells as compared to MHC-II positive cases (P = 0.019), but was not correlated with EBV status or histological subtype. Strikingly, LAG3 was identified as the most up-regulated gene in cells from MHC-II negative cases compared to MHC-II positive cases. Topological analysis using multicolor IHC and IMC revealed that in MHC-II negative cases, HRS cells were surrounded by LAG3+ T cells. In these cases, the density of LAG3+ T cells in HRS cell-rich regions was significantly increased, and the average distance between an HRS cell and its closest LAG3+ T cell neighbor was significantly shorter. These associations were confirmed in an independent cohort of 166 cHL patients. Finally, we observed a trend towards an inferior disease-specific survival (DSS; P = 0.072) and overall survival (OS; P = 0.12) in cases with an increased number of LAG3+ T cells. A high proportion of LAG3+ T cells (&gt; 20%) was identified as an independent prognostic factor for DSS by multivariate Cox regression. CONCLUSIONS: Our results reveal a diverse TME composition with inflammatory and immunosuppressive cellular components that are linked to MHC class II expression status on HRS cells (Figure). Unprecedented transcriptional and spatial profiling at the single cell level has established the pathogenic importance of HRS cell-induced CD4+ LAG3+ T cells as a mediator of immunosuppression in cHL, with potential implications for novel therapeutic approaches. Figure Disclosures Savage: Seattle Genetics, Inc.: Consultancy, Honoraria, Research Funding; BMS, Merck, Novartis, Verastem, Abbvie, Servier, and Seattle Genetics: Consultancy, Honoraria. Scott:Roche/Genentech: Research Funding; Celgene: Consultancy; Janssen: Consultancy, Research Funding; NanoString: Patents & Royalties: Named inventor on a patent licensed to NanoSting [Institution], Research Funding. Steidl:Bristol-Myers Squibb: Research Funding; Nanostring: Patents & Royalties: Filed patent on behalf of BC Cancer; Roche: Consultancy; Seattle Genetics: Consultancy; Bayer: Consultancy; Juno Therapeutics: Consultancy; Tioma: Research Funding.

scholarly journals Pre-Existing T Cell Subsets Determine Anti-PD1 Blockade Response in Richter's Transformation

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 42-43
Author(s):  
Prajish Iyer ◽  
Lu Yang ◽  
Zhi-Zhang Yang ◽  
Charla R. Secreto ◽  
Sutapa Sinha ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Single Cell ◽  
Board Of Directors ◽  
Peripheral Blood ◽  
Research Funding ◽  
Gene Signature ◽  
T Cell Subsets ◽  
Rna Seq ◽  
Advisory Committees

Despite recent developments in the therapy of chronic lymphocytic leukemia (CLL), Richter's transformation (RT), an aggressive lymphoma, remains a clinical challenge. Immune checkpoint inhibitor (ICI) therapy has shown promise in selective lymphoma types, however, only 30-40% RT patients respond to anti-PD1 pembrolizumab; while the underlying CLL failed to respond and 10% CLL patients progress rapidly within 2 months of treatment. Studies indicate pre-existing T cells in tumor biopsies are associated with a greater anti-PD1 response, hence we hypothesized that pre-existing T cell subset characteristics and regulation in anti-PD1 responders differed from those who progressed in CLL. We used mass cytometry (CyTOF) to analyze T cell subsets isolated from peripheral blood mononuclear cells (PBMCs) from 19 patients with who received pembrolizumab as a single agent. PBMCs were obtained baseline(pre-therapy) and within 3 months of therapy initiation. Among this cohort, 3 patients had complete or partial response (responders), 2 patients had rapid disease progression (progressors) (Fig. A), and 14 had stable disease (non-responders) within the first 3 months of therapy. CyTOF analysis revealed that Treg subsets in responders as compared with progressors or non-responders (MFI -55 vs.30, p=0.001) at both baseline and post-therapy were increased (Fig. B). This quantitative analysis indicated an existing difference in Tregs and distinct molecular dynamic changes in response to pembrolizumab between responders and progressors. To delineate the T cell characteristics in progressors and responders, we performed single-cell RNA-seq (SC-RNA-seq; 10X Genomics platform) using T (CD3+) cells enriched from PBMCs derived from three patients (1 responder: RS2; 2 progressors: CLL14, CLL17) before and after treatment. A total of ~10000 cells were captured and an average of 1215 genes was detected per cell. Using a clustering approach (Seurat V3.1.5), we identified 7 T cell clusters based on transcriptional signature (Fig.C). Responders had a larger fraction of Tregs (Cluster 5) as compared with progressors (p=0.03, Fig. D), and these Tregs showed an IFN-related gene signature (Fig. E). To determine any changes in the cellular circuitry in Tregs between responders and progressors, we used FOXP3, CD25, and CD127 as markers for Tregs in our SC-RNA-seq data. We saw a greater expression of FOXP3, CD25, CD127, in RS2 in comparison to CLL17 and CLL14. Gene set enrichment analysis (GSEA) revealed the upregulation of genes involved in lymphocyte activation and FOXP3-regulated Treg development-related pathways in the responder's Tregs (Fig.F). Together, the greater expression of genes involved in Treg activation may reduce the suppressive functions of Tregs, which led to the response to anti-PD1 treatment seen in RS2 consistent with Tregs in melanoma. To delineate any state changes in T cells between progressors and responder, we performed trajectory analysis using Monocle (R package tool) and identified enrichment of MYC/TNF/IFNG gene signature in state 1 and an effector T signature in state 3 For RS2 after treatment (p=0.003), indicating pembrolizumab induced proliferative and functional T cell signatures in the responder only. Further, our single-cell results were supported by the T cell receptor (TCR beta) repertoire analysis (Adaptive Biotechnology). As an inverse measure of TCR diversity, productive TCR clonality in CLL14 and CLL17 samples was 0.638 and 0.408 at baseline, respectively. Fifty percent of all peripheral blood T cells were represented by one large TCR clone in CLL14(progressor) suggesting tumor related T-cell clone expansion. In contrast, RS2(responder) contained a profile of diverse T cell clones with a clonality of 0.027 (Fig. H). Pembrolizumab therapy did not change the clonality of the three patients during the treatment course (data not shown). In summary, we identified enriched Treg signatures delineating responders from progressors on pembrolizumab treatment, paradoxical to the current understanding of T cell subsets in solid tumors. However, these data are consistent with the recent observation that the presence of Tregs suggests a better prognosis in Hodgkin lymphoma, Follicular lymphoma, and other hematological malignancies. Figure 1 Disclosures Kay: Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncotracker: Membership on an entity's Board of Directors or advisory committees; Rigel: Membership on an entity's Board of Directors or advisory committees; Juno Theraputics: Membership on an entity's Board of Directors or advisory committees; Agios Pharma: Membership on an entity's Board of Directors or advisory committees; Cytomx: Membership on an entity's Board of Directors or advisory committees; Astra Zeneca: Membership on an entity's Board of Directors or advisory committees; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol Meyer Squib: Membership on an entity's Board of Directors or advisory committees, Research Funding; Acerta Pharma: Research Funding; Sunesis: Research Funding; Dava Oncology: Membership on an entity's Board of Directors or advisory committees; Abbvie: Research Funding; MEI Pharma: Research Funding. Ansell:AI Therapeutics: Research Funding; Takeda: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Bristol Myers Squibb: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; ADC Therapeutics: Research Funding. Ding:Astra Zeneca: Research Funding; Abbvie: Research Funding; Octapharma: Membership on an entity's Board of Directors or advisory committees; MEI Pharma: Membership on an entity's Board of Directors or advisory committees; alexion: Membership on an entity's Board of Directors or advisory committees; Beigene: Membership on an entity's Board of Directors or advisory committees; DTRM: Research Funding; Merck: Membership on an entity's Board of Directors or advisory committees, Research Funding. OffLabel Disclosure: pembrolizumab

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

Blood ◽  
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 &lt; 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 &lt; 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.

scholarly journals Dissecting the Immune Cell Progeny of CAR-Expressing Hematopoietic Stem Cells in a Humanized Mouse Model

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 4640-4640
Author(s):  
Heng-Yi Liu ◽  
Nezia Rahman ◽  
Tzu-Ting Chiou ◽  
Satiro N. De Oliveira
Keyword(s):  
Stem Cells ◽  
T Cells ◽  
T Cell ◽  
Hematopoietic Stem Cells ◽  
Cell Lines ◽  
Immune Cells ◽  
Research Funding ◽  
Humanized Mice ◽  
Tumor Challenge ◽  
Hematopoietic Stem

Background: Chemotherapy-refractory or recurrent B-lineage leukemias and lymphomas yield less than 50% of chance of cure. Therapy with autologous T-cells expressing chimeric antigen receptors (CAR) have led to complete remissions, but the effector cells may not persist, limiting clinical efficacy. Our hypothesis is the modification of hematopoietic stem cells (HSC) with anti-CD19 CAR will lead to persistent generation of multilineage target-specific immune cells, enhancing graft-versus-cancer activity and leading to development of immunological memory. Design/Methods: We generated second-generation CD28- and 4-1BB-costimulated CD19-specific CAR constructs using third-generation lentiviral vectors for modification of human HSC for assessment in vivo in NSG mice engrafted neonatally with human CD34-positive cells. Cells were harvested from bone marrows, spleens, thymus and peripheral blood at different time points for evaluation by flow cytometry and ddPCR for vector copy numbers. Cohorts of mice received tumor challenge with subcutaneous injection of lymphoma cell lines. Results: Gene modification of HSC with CD19-specific CAR did not impair differentiation or proliferation in humanized mice, leading to CAR-expressing cell progeny in myeloid, NK and T-cells. Humanized NSG engrafted with CAR-modified HSC presented similar humanization rates to non-modified HSC, with multilineage CAR-expressing cells present in all tissues with stable levels up to 44 weeks post-transplant. No animals engrafted with CAR-modified HSC presented autoimmunity or inflammation. T-cell populations were identified at higher rates in humanized mice with CAR-modified HSC in comparison to mice engrafted with non-modified HSC. CAR-modified HSC led to development of T-cell effector memory and T-cell central memory phenotypes, confirming the development of long-lasting phenotypes due to directed antigen specificity. Mice engrafted with CAR-modified HSC successfully presented tumor growth inhibition and survival advantage at tumor challenge with lymphoma cell lines, with no difference between both constructs (62.5% survival for CD28-costimulated CAR and 66.6% for 41BB-costimulated CAR). In mice sacrificed due to tumor development, survival post-tumor injection was directly correlated with tumor infiltration by CAR T-cells. Conclusions: CAR modification of human HSC for cancer immunotherapy is feasible and continuously generates CAR-bearing cells in multiple lineages of immune cells. Targeting of different malignancies can be achieved by adjusting target specificity, and this approach can augment the anti-lymphoma activity in autologous HSC recipients. It bears decreased morbidity and mortality and offers alternative therapeutic approach for patients with no available sources for allogeneic transplantation, benefiting ethnic minorities. Disclosures De Oliveira: National Institute for Health Research Biomedical Research Centre at Great Ormond Street Hospital for Children NHS Foundation Trust and University College London: Research Funding; NIAID, NHI: Research Funding; Medical Research Council: Research Funding; CIRM: Research Funding; National Gene Vector Repository: Research Funding.

Recovery of Paired T Cell Receptors from Single-cell Seq-Well Libraries

2019 ◽  
Author(s):  
Ang A. Tu ◽  
Todd M. Gierahn ◽  
Brinda Monian ◽  
Duncan M. Morgan ◽  
Naveen K. Mehta ◽  
...  
Keyword(s):  
T Cells ◽  
Food Allergy ◽  
T Cell ◽  
Single Cell ◽  
T Cell Receptors ◽  
Immune Cell ◽  
Cost Effective ◽  
Antigen Specificity ◽  
Activated T Cells ◽  
Cell Receptors

Abstract High-throughput 3’ single-cell RNA-Sequencing (scRNA-Seq) allows for cost-effective, detailed characterization of thousands of individual immune cells from healthy and diseased tissues. Current techniques, however, are limited in their ability to elucidate essential immune cell features, including the variable sequences of T cell receptors (TCRs) that confer antigen specificity in T cells. Here, we present an enrichment strategy that enables simultaneous analysis of TCR variable sequences and corresponding full transcriptomes from 3’ barcoded scRNA-Seq samples. This approach is compatible with common 3’ scRNA-Seq methods, and adaptable to processed samples post hoc. We applied the technique to resolve clonotype-to-phenotype relationships among antigen-activated T cells from immunized mice and from patients with food allergy. We observed diverse but preferential cellular phenotypes manifest among subsets of expanded clonotypes, including functional Th2 states associated with food allergy. These results demonstrate the utility of our method when studying complex diseases in which clonotype-driven immune responses are critical to understanding the underlying biology.

Immunomodulatory Effects and Adaptive Immune Response to Daratumumab in Multiple Myeloma

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 3037-3037 ◽  
Author(s):  
Jakub Krejcik ◽  
Tineke Casneuf ◽  
Inger Nijhof ◽  
Bie Verbist ◽  
Jaime Bald ◽  
...  
Keyword(s):  
Immune Response ◽  
T Cells ◽  
T Cell ◽  
Research Funding ◽  
Immune Cell ◽  
Adaptive Immune Response ◽  
Advisory Committees ◽  
Adaptive Immune ◽  
T Cell Clonality ◽  
Cell Clonality

Abstract Introduction: Daratumumab (DARA) is a novel human monoclonal antibody that targets CD38, a protein that is highly expressed on multiple myeloma (MM) cells. DARA acts through multiple immune effector-mediated mechanisms, including complement-dependent cytotoxicity, antibody-dependent cell-mediated cytotoxicity, and antibody-dependent cellular phagocytosis. In two clinical studies (NCT00574288 [GEN501] and NCT01985126 [Sirius]) of DARA monotherapy in patients with relapsed and refractory MM, overall response rates were 36% and 29%, respectively. CD38 is highly expressed in myeloma cells but also expressed in lymphocytes and other immune cell populations. Therefore, the effects of DARA on immune cell populations and adaptive immune response pathways were investigated. Methods: The patient population investigated included treated subjects with MM that were relapsed after or were refractory to ≥2 prior therapies (GEN501) or had received ≥3 prior therapies, including a proteasome inhibitor (PI) and an immunomodulatory drug (IMiD), or were refractory to both a PI and an IMiD (Sirius). Patients assessed in this analysis were treated with 16 mg/kg DARA. When both studies were combined, median age (range) was 64 (31-84) years and median time from diagnosis was 5.12 (0.77-23.77) years. Seventy-six percent of patients had received >3 prior therapies and 91% were refractory to their last treatment. Clinical response was evaluated using IMWG consensus recommendations. Peripheral blood (PB) samples and bone marrow (BM) biopsies/aspirates were taken at prespecified time points and immunophenotyped by flow cytometry to enumerate various T-cell sub-types. T-cell clonality was measured by TCR sequencing. Antiviral T-cell response and regulatory T-cell (Treg) activity were analysed by functional in vitro assays. T-cell subpopulation counts were modelled over time with linear mixed modelling. Two group comparisons were performed using non-parametric Wilcoxon rank sum tests. Results: Data from 148 patients receiving 16 mg/kg DARA in GEN501 (n = 42) and Sirius (n = 106) were analyzed for changes in immune response. In PB, robust mean increases in CD3+ (44%), CD4+ (32%) and CD8+ (62%) T-cell counts per 100 days were seen with DARA treatment. However, responding evaluable patients (n = 45) showed significantly greater increases from baseline than nonresponders (n = 93) in CD3+ (P = 0.00012), CD4+ (P = 0.00031), and CD8+ (P = 0.00018) T cells. In BM aspirates the number of CD3+, CD4+, and CD8+ T-cells increased during treatment compared to baseline (the median percent increases were 19.95%, 5.66%, and 26.99% [n = 58]). Additionally, CD8+: CD4+ T-cell ratios significantly increased compared to baseline in both PB (P = 0.00017), and BM (P = 0.00016). T cell clonality, assessed by TCR sequencing, increased after DARA treatment compared with pretreatment (P = 0.049), with greater sums of absolute expansion in the repertoire (P = 0.037), as well as greater maximum expansion of a single clone (P = 0.048) in responders compared to nonresponders. Increased antiviral T-cell responses were observed post-DARA treatment, particularly in responders. Interestingly, a novel subpopulation of regulatory T cells was identified that expressed high levels of CD38. These cells comprised ~10% of all Tregs and were depleted by one DARA infusion. In ex vivo analyses, CD38+ Tregs appeared to be highly immune suppressive compared to CD38-Tregs. Conclusions: Robust T cell increases, increased CD8+: CD4+ ratios, increased antiviral responses, and increased T cell clonality were all observed after DARA treatment in a heavily pretreated, relapsed, and refractory patient population not expected to have strong immune responses. Improved clinical responses were associated with changes in these parameters. In addition, a sub-population of regulatory T cells expressing high CD38 levels was determined to be extremely immune suppressive and sensitive to DARA treatment. These data suggest a previously unknown immune modulatory role of DARA that may contribute to its efficacy, and a potential role for CD38 immune targeted therapies. We postulate that there are several distinct and complementary mechanisms that contribute to DARA's efficacy including increased antigen presentation through phagocytosis, targeting of immune suppressive Tregs, and increased adaptive immune responses. JK and TC contributed equally to this work. Disclosures Casneuf: Janssen: Employment. Verbist:Janssen: Employment. Bald:Janssen: Employment. Plesner:Genmab: Membership on an entity's Board of Directors or advisory committees; Roche and Novartis: Research Funding; Janssen and Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding. Liu:Janssen: Employment. van de Donk:Janssen Pharmaceuticals: Research Funding; Amgen: Research Funding; Celgene: Research Funding. Weiss:Janssen and Onclave: Research Funding; Janssen and Millennium: Consultancy. Ahmadi:Janssen: Employment. Lokhorst:Genmab: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Amgen: Honoraria. Mutis:Janssen: Research Funding; Genmab: Research Funding.

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