Single-Cell and Spatial Analyses Characterize Distinct Subsets of Malignant T Cells in Angioimmunoblastic T Cell Lymphoma

Abstract Introduction Angioimmunoblastic T-cell lymphoma (AITL) is the most frequent of nodal peripheral T-cell lymphomas. AITL results from the transformation of T follicular helper (T FH) cells and is characterized by chemo-resistance and poor survival (5-year OS around 30%). Recent data from prospective clinical trials suggest that disease outcome may be impacted by factors other than genomic features, such as the tumor microenvironment (TME) and overall intra-tumoral heterogeneity. Our understanding of AITL intra-tumoral genetic, transcriptional and functional heterogeneity is limited because most molecular data generated so far have come from bulk analyses. Single-cell RNA sequencing (scRNA-seq) enables fine characterization of cell types and functional cell states. When focused on T or B cells, 5'-end scRNA-seq also yields the TCR or BCR sequences that allow tracking clonally related cells. Here we studied the intra-tumor heterogeneity of AITL tumors using integrative scRNA-seq. Methods We analyzed lymph node live cell suspensions from AITL patients (n=10) using droplet-based 10x Genomics 5'-end scRNA-seq. Malignant T cells from 4 AITL samples were also analyzed by FACS index sorting and plate-based 5'-end scRNA-seq to link cell surface phenotype and gene expression profile. We identified malignant T cell clones by intersecting the gene expression and TCR sequencing data, and performed separate focused analyses of TME subsets and malignant T cells. We compared subsets of malignant T cells from all patients using marker gene-based metaclustering to identify AITL T cell states conserved across patients. We explored the genetic heterogeneity of malignant T cells by mapping RHOA G17V mutations and inferring copy number variation (CNV) subclones from scRNA-seq data. In select cases, we performed in situ analysis by immunohistochemistry (IHC) or spatial transcriptomics to characterize the spatial distribution of malignant T cell subsets identified by scRNA-seq. Results Based on gene expression, malignant T cells grouped in patient-specific clusters, while non-malignant T, B and myeloid TME cells from all patients clustered by cell type or cell state. Among TME cells, we identified 7 subsets of B cells (including activated B cells, plasma cells, and one patient-specific monoclonal B cell proliferation), 6 subsets of myeloid cells (including macrophages, conventional and plasmacytoid dendritic cells), and 8 subsets of non-malignant T cells (including activated cytotoxic T lymphocytes (CTL) with clonal expansions). Patient-specific malignant T cells were heterogeneous and divided into several gene-expression based clusters. Metaclustering of malignant T cell subsets identified T central memory (T CM)-like and T FH-like states in 10/10 samples. We also identified in 3/10 samples clusters of CTL-like malignant T cells expressing characteristic marker genes (including NKG7, GNLY, GZMK, PRF1). We observed an intra-sample continuum of gene expression states from quiescent T CM-like to proliferating T FH-like states. T FH-like cells were larger in size and expressed higher levels of surface PD1 and ICOS than T CM-like and CTL-like subsets. We detected the RHOA G17V mutation in malignant T cells of 4/4 mutated cases, with no evidence of subclonal heterogeneity for that mutation. We detected clonal and subclonal CNV in most AITL malignant T cells. CTL-like states were enriched in specific CNV subclones, but the T CM-like to T FH-like continuum was observed in all CNV subclones, suggesting that functional plasticity and subclonal genetic evolution may occur independently. In situ staining of markers for T FH-like (PD1, ICOS, CD200) and CTL-like (GZMK, GZMA) cells showed that T FH-like and CTL-like cells occupied distinct tissue niches within the tumor. In spatial transcriptomics analysis, T FH-like cells mapped to follicular dendritic cell (FDC)-rich areas, while T CM-like cells were associated with T-zone reticular cells. Conclusions Our analyses recapitulate known characteristics of AITL TME, and uncover previously unrecognized heterogeneity among malignant T cells across multiple patients. The distinct gene expression programs, phenotypes, genetics, and locations of T FH-like, T CM-like and CTL-like states suggest that AITL malignant T cells undergo significant functional plasticity and genetic divergence, which could influence response to therapy and overall clinical course. Figure 1 Figure 1. Disclosures Lemonnier: Institut Roche: Research Funding; Gilead: Other: travel grant. Gaulard: Gilead: Consultancy; Innate Pharma: Research Funding; Sanofi: Research Funding; Alderaan: Research Funding; Takeda: Consultancy, Honoraria. Milpied: Institut Roche: Research Funding; Innate Pharma: Research Funding; Bristol Myers Squibb: Research Funding.

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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.

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Paired Single Cell RNA and TCR Sequencing Analysis Following PD-L1 Blockade in Cutaneous T-Cell Lymphoma

Blood ◽

10.1182/blood-2020-139992 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 21-22

Author(s):

Christiane Querfeld ◽

Xiwei Wu ◽

Hanjun Qin ◽

Chingyu Su ◽

Zhen Han ◽

...

Keyword(s):

Gene Expression ◽

T Cells ◽

T Cell ◽

Single Cell ◽

Differential Expression ◽

Cell Population ◽

Immune Cells ◽

Cd4 T Cells ◽

T Cell Subsets ◽

Malignant T Cells

Introduction: T cell exhaustion is a hallmark of CTCL and alterations in mRNA profiles correlate with immune checkpoint expression, with potential clinical relevance (Querfeld et al. 2018). There is no immunophenotypic marker that can distinguish malignant CD4+ T cells from benign CD4+ T cells in the infiltrate and intratumoral heterogeneity poses a major challenge to treatments and long-term remissions. The microenvironment in CTCL harbors multiple immune cells that may contribute to the development of resistance to drug treatments; however, the genomic and molecular determinants of response to therapeutic agents remain incompletely understood. The aim of our study was to distinguish malignant from non-malignant T cells based on TCR α/β repertoires and to understand the transcriptional landscapes of malignant and non-malignant cells in the TME while on anti-PD-L1 therapy. Methods: Migrated cells from skin explants were harvested and subsequently analyzed by our paired single-cell RNA and T cell receptor (TCR; alpha/beta) sequencing on ~3000-4000 cells from skin lesions of 6 patients with mycosis fungoides at baseline and cycle 1 day 15 with anti-PD-L1 + lenalidomide. Results: We identified 14 gene clusters. Differential expression (DE) of genes in each of the unique clusters were identified by comparing gene expression from cells in each cluster to that of all other cells in the dataset, using a cut-off of P < 0.05 and further requiring expression of the gene in >25% of cells in the cluster. Thus, DE-identified genes are expressed either uniquely or by a large proportion of cells within each cluster compared to all other clusters. TCR clones in these cells were also characterized. Through this combined analysis, we demonstrated differences in the diversity, clonal expansion and T cell phenotypes that differentiated expanded malignant T cell populations (cluster 0-3) from non-malignant T cells including tumor infiltrating lymphocytes (TILs), regulatory T cells (Tregs), NK/T cells, and from immune cells such as B cells, antigen presenting cells (dendritic cells, macrophages) and other cells (stromal, epithelial cells) (cluster 4-13). Comparing baseline to C1D15 we were able to identify microenvironmental changes that occurred during treatment, specifically characterized the expression and significance of PD1, LAG3, CTLA4, TIM3 and ICOS in malignant and non-malignant T cell clusters, which demonstrated differential expression of these targets in malignant T cells (clusters 0-4). Non-malignant T cell phenotyping revealed an enriched tumor-infiltrating CD8+ T cell population at baseline with upregulation of LAG3 gene expression, and FOXP3+ CD4+ regulatory T cell population with high expression of CTLA4 and ICOS consistent with inducible Tregs (iTregs) in all, but one baseline sample that did not resolve during treatment (C1D15). Conclusions: Paired scRNA and TCRseq revealed distinctive functional composition of T cells and other immune cells. Combined scRNA expression and scTCR analysis identified malignant from non-malignant T cell subsets. Malignant T cell clones diminished in responders during treatment, while shifted or emerged in non-responders. Clonal enrichment of iTregs and exhausted CD4 and CD8 T cells were identified that did not resolve during treatment. suggesting that potential targeting of ICOS, CTLA4 and/or LAG3 will reverse T cell dysfunction in TILS and iTregs, respectively and increase clinical benefit of anti-PD-L1 blockade. Disclosures Querfeld: Stemline: Consultancy; MiRagen: Consultancy; Kyowa Kirin: Consultancy; Bioniz: Consultancy; Helsinn: Consultancy; Trillium: Consultancy; Celgene: Research Funding. Rosen:Novartis: Consultancy; Pebromene: Consultancy; Aileron Therapeutics: Consultancy; Celgene: Speakers Bureau; paradigm Medical Communications: Speakers Bureau; Abbvie: Speakers Bureau; Seattle Genetics: Consultancy; NeoGenomics: Consultancy.

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HHV-6 in the Lymphoma Microenvironment: Both Chicken and Egg?

Blood ◽

10.1182/blood-2021-146336 ◽

2021 ◽

Vol 138 (Supplement 1) ◽

pp. 1377-1377

Author(s):

Genevieve M Crane ◽

Dmitry Tychinin ◽

Anton Karelin ◽

Aleksandr Cherdintsev ◽

Olga Kudryashova ◽

...

Keyword(s):

Gene Expression ◽

T Cell ◽

Stock Options ◽

Research Funding ◽

Cell Lymphoma ◽

Viral Gene Expression ◽

T Cell Subsets ◽

Viral Gene ◽

Provision Of Services ◽

Privately Held

Abstract While Epstein-Barr virus (EBV) and the Kaposi sarcoma herpesvirus (KSHV)/human herpesvirus (HHV) 8 have shown a definite association with lymphoproliferative disease, a role for the HHV-6 has been less clear. Similar to other herpesviruses, HHV-6 predominantly remains latent following initial infection, but can be reactivated during stress or immune suppression, and is the cause of roseola in young children. Existing as two distinct species, HHV-6B is more common, infecting ~90% of adults. HHV-6B, a T-lymphotropic virus, enters cells via CD134, a TNF receptor superfamily member, expressed on both naïve and CD4 +CD25 + T cells, leading to CD4 + lymphocyte depletion and impaired T cell activation. HHV-6 has been variably detected in classic Hodgkin (CHL) and T-cell lymphomas (TCL) by immunohistochemistry (IHC) and PCR with more recent data suggesting infection may be confined to tumor-associated lymphocytes. The specificity of these IHC antibodies is not well documented. The question remains whether HHV-6 in the tumor microenvironment of advanced disease is a consequence of immune dysfunction, or may play a more direct role in tumor initiation and progression by altering the tumor microenvironment. To address these questions, we evaluated HHV-6B viral gene expression patterns in lymphoma patient samples by RNA sequencing techniques. Following IRB approval, CHL, TCL, B-cell, and post-transplant lymphoproliferative disease (PTLD) cases were screened for potential HHV-6-association by IHC with an antibody against HHV-6 gp60/110 envelope glycoprotein (Millipore Sigma, MAB8537). Positive cases with available frozen tissue and adequate RNA (5) or sorted T-cell subsets from Hodgkin lymphoma (11) underwent bulk RNA-seq (rRNA depletion (Illumina), 50M reads/sample). Viral transcripts were identified by performing the Burrows-Wheeler Alignment by reference host alignment (to filter host and bad quality reads) followed by viral reference host alignment. Previous TCL databases with available RNAseq data were similarly evaluated. IHC analysis revealed 5/25 CHL, 34/52 TCL, 5/13 PTLD, 4/81 diffuse large B-cell lymphoma (DLBCL) and 2/28 follicular lymphoma (FL) with rare gp60/110-positive cells. This included 11 CHL cases with sorted T-cell subsets, of which one showed membranous and Golgi gp60/110 staining in background T-cells (25-year-old female, nodular sclerosis subtype, EBV-negative). Of these 11 CHL cases, RNAseq of T-cell subsets revealed a pattern of HHV-6B transcripts in only this case. Frozen tumor blocks were available from 5 additional cases with positive gp60/110 staining (2 CHL, 1 DLBCL, 1 FL and 1 PTLD), but RNAseq analysis did not identify any HHV-6B transcripts. Notably, these cases had dim cytoplasmic but not Golgi gp60/110 staining. RNA sequencing data derived from two independent TCL cohorts were analyzed for HHV-6B transcripts. Although no HHV-6B transcripts were detected via RNAseq in 20 angioimmunoblastic T-cell lymphoma samples from one TCL cohort, many had EBV-gene expression. HHV-6B transcripts were detected in two cases of anaplastic large cell lymphoma (ALCL) in a second TCL cohort (2/79 cases). High expression of the U67, U68, U79 and U90 genes was found, revealing overlap of the HHV-6B transcript expression between ALCL and CHL samples (Fig 1). Additionally, detection of two genes that could be driving tumor growth (U51, which encodes a G-protein receptor and U24, which inhibits proper T cell activation, reducing secretion of cytokines at infection site) demonstrates a specific viral gene expression pattern within the intratumor T-cell population. The potential presence of HHV-6B infection in the lymphoma microenvironment is controversial. To our knowledge, this is the first report conclusively demonstrating HHV-6B expression in CHL using RNAseq. Notably, the viral gene expression pattern seen in CHL overlaps with that found in two cases of ALCL, highlighting viral proteins of potential particular significance. These data may aid in development of a more reliable means of HHV-6B detection. For example, the immediate early gene U90, a transcriptional activator that may induce expression of both viral and cellular genes that affect the tumor microenvironment, was consistently expressed and may be a reliable marker of HHV-6B infection. Funding: HHV-6 Foundation Figure 1 Figure 1. Disclosures Tychinin: BostonGene Inc.: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties. Karelin: BostonGene Inc.: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties. Cherdintsev: BostonGene Inc.: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties. Kudryashova: BostonGene: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties: BostonGene. Egorov: BostonGene: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties. Degryse: BostonGene Inc.: Current Employment, Current holder of stock options in a privately-held company. Kotlov: BostonGene Corp: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties. Bagaev: BostonGene Corp.: Current Employment, Current holder of stock options in a privately-held company, Patents & Royalties: BostonGene. Roth: Merck: Consultancy; Janssen: Consultancy. Roshal: Celgene: Other: Provision of services; Physicians' Education Resource: Other: Provision of services; Auron Therapeutics: Other: Ownership / Equity interests; Provision of services. Rabadan: Genotwin: Other: Raul Rabadan is founder of Genotwin; AimedBio: Membership on an entity's Board of Directors or advisory committees. Elemento: Owkin: Consultancy, Other: Current equity holder; Freenome: Consultancy, Other: Current equity holder in a privately-held company; Volastra Therapeutics: Consultancy, Other: Current equity holder, Research Funding; One Three Biotech: Consultancy, Other: Current equity holder; Janssen: Research Funding; Eli Lilly: Research Funding; Champions Oncology: Consultancy; AstraZeneca: Research Funding; Johnson and Johnson: Research Funding.

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

Blood ◽

10.1182/blood-2020-134929 ◽

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.

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Susceptibility of human T cells, T-cell subsets, and B cells to cryopreservation

Cryobiology ◽

10.1016/0011-2240(86)90045-3 ◽

1986 ◽

Vol 23 (3) ◽

pp. 199-208 ◽

Cited By ~ 11

Author(s):

M. Venkataraman ◽

M.P. Westerman

Keyword(s):

T Cells ◽

T Cell ◽

B Cells ◽

T Cell Subsets ◽

Human T Cells

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644 Tumor-mediated suppressive signaling and hypoxia supports altered chromatin landscapes that limit the transcriptional and functional potential of terminal T cell exhaustion

Journal for ImmunoTherapy of Cancer ◽

10.1136/jitc-2021-sitc2021.644 ◽

2021 ◽

Vol 9 (Suppl 3) ◽

pp. A673-A673

Author(s):

Rhodes Ford ◽

Natalie Rittenhouse ◽

Nicole Scharping ◽

Paolo Vignali ◽

Greg Delgoffe ◽

...

Keyword(s):

Gene Expression ◽

T Cells ◽

T Cell ◽

Tumor Microenvironment ◽

Histone Modifications ◽

Cd8 T Cells ◽

Tumor Hypoxia ◽

T Cell Subsets ◽

T Cell Exhaustion ◽

Cell Exhaustion

BackgroundCD8+ T cells are a fundamental component of the anti-tumor response; however, tumor-infiltrating CD8+ T cells (TIL) are rendered dysfunctional by the tumor microenvironment. CD8+ TIL display an exhausted phenotype with decreased cytokine expression and increased expression of co-inhibitory receptors (IRs), such as PD-1 and Tim-3. The acquisition of IRs mark the progression of dysfunctional TIL from progenitors (PD-1Low) to terminally exhausted (PD-1+Tim-3+). How the chromatin landscape changes during this progression has not been described.MethodsUsing a low-input ChIP-based assay called Cleavage Under Targets and Release Using Nuclease (CUT&RUN), we have profiled the histone modifications at the chromatin of tumor-infiltrating CD8+ T cell subsets to better understand the relationship between the epigenome and the transcriptome as TIL progress towards terminal exhaustion.ResultsWe have identified two epigenetic characteristics unique to terminally exhausted cells. First, we have identified a unique set of genes, characterized by active histone modifications that do not have correlated gene expression. These regions are enriched for AP-1 transcription factor motifs, yet most AP-1 family factors are actively downregulated in terminally exhausted cells, suggesting signals that promote downregulation of AP-1 expression negatively impacts gene expression. We have shown that inducing expression of AP-1 factors with a 41BB agonist correlates with increased expression of these anticorrelated genes. We have also found a substantial increase in the number of genes that exhibit bivalent chromatin marks, defined by the presence of both active (H3K4me3) and repressive (H3K27me3) chromatin modifications that inhibit gene expression. These bivalent genes in terminally exhausted T cells are not associated with plasticity and represent aberrant hypermethylation in response to tumor hypoxia, which is necessary and sufficient to promote downregulation of bivalent genes.ConclusionsOur study defines for the first time the roles of costimulation and the tumor microenvironment in driving epigenetic features of terminally exhausted tumor-infiltrating T cells. These results suggest that terminally exhausted T cells have genes that are primed for expression, given the right signals and are the basis for future work that will elucidate that factors that drive progression towards terminal T cell exhaustion at the epigenetic level and identify novel therapeutic targets to restore effector function of tumor T cells and mediate tumor clearance.

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Ectopic expression of a novel CD22 splice-variant regulates survival and proliferation in malignant T cells from cutaneous T cell lymphoma (CTCL) patients

Oncotarget ◽

10.18632/oncotarget.3720 ◽

2015 ◽

Vol 6 (16) ◽

pp. 14374-14384 ◽

Cited By ~ 2

Author(s):

Ieva Bagdonaite ◽

Hans H. Wandall ◽

Ivan V. Litvinov ◽

Claudia Nastasi ◽

Jürgen C. Becker ◽

...

Keyword(s):

T Cells ◽

T Cell ◽

Splice Variant ◽

Cell Lymphoma ◽

Ectopic Expression ◽

T Cell Lymphoma ◽

Cutaneous T Cell Lymphoma ◽

Malignant T Cells

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Phase 2 Study of Combination of Cytarabine, Idarubicin, and Nivolumab for Initial Therapy of Patients with Newly Diagnosed Acute Myeloid Leukemia

Blood ◽

10.1182/blood.v130.suppl_1.815.815 ◽

2017 ◽

Vol 130 (Suppl_1) ◽

pp. 815-815

Author(s):

Farhad Ravandi ◽

Naval Daver ◽

Guillermo Garcia-Manero ◽

Christopher B Benton ◽

Philip A Thompson ◽

...

Keyword(s):

T Cells ◽

T Cell ◽

Cd8 T Cells ◽

Myeloid Leukemia ◽

Research Funding ◽

T Cell Subsets ◽

Newly Diagnosed ◽

Effector Cells ◽

T Effector Cells ◽

Grade 3

Abstract Background: Blocking PD-1/PD-L1 pathways enhances anti-leukemia responses by enabling T-cells in murine models of AML (Zhang et al, Blood 2009). PD-1 positive CD8 T-cells are increased in bone marrow (BM) of pts with AML (Daver et al, AACR 2016). PD1 inhibition has shown activity in AML (Berger et al, Clin Cancer Res 2008). We hypothesized that addition of nivolumab to an induction regimen of ara-C and idarubicin may prolong relapse-free survival (RFS) and overall survival (OS); this study was designed to determine the feasibility of this combination. Methods: Pts with newly diagnosed acute myeloid leukemia (by WHO criteria; ≥20% blasts) and high risk MDS (≥10% blasts) were eligible to participate if they were 18-65 yrs of age and had adequate performance status (ECOG ≤3) and organ function (LVEF ≥ 50%; creatinine ≤ 1.5 g mg/dL, bilirubin ≤ 1.5 mg/dL and transaminases ≤ 2.5 times upper limit of normal). Treatment included 1 or 2 induction cycles of ara-C 1.5 g/m2 over 24 hours (days 1-4) and Idarubicin 12 mg/m2 (days 1-3). Nivolumab 3 mg/kg was started on day 24 ± 2 days and was continued every 2 weeks for up to a year. For pts achieving complete response (CR) or CR with incomplete count recovery (CRi) up to 5 consolidation cycles of attenuated dose ara-C and idarubicin was administered at approximately monthly intervals. Eligible pts received an allogeneic stem cell transplant (alloSCT) at any time during the consolidation or thereafter. Results: 3 pts with relapsed AML were treated at a run-in phase with a dose of nivolumab 1 mg/kg without specific drug-related toxicity. Subsequently, 32 pts (median age 53 yrs; range, 26-65) were treated as above including 30 with AML (24 de novo AML, 2 therapy-related AML, 3 secondary AML and 1 therapy-related secondary AML) and 2 high risk MDS. Pre-treatment genetic risk by ELN criteria was 11 adverse, 16 intermediate, and 5 favorable, including 2 FLT3 -ITD mutated, 5 NPM1 mutated, and 7 TP53 mutated. All 32 pts were evaluable for response and 23 (72%) achieved CR/CRi (19 CR, 4 CRi). The 4-week and 8 week mortality was 6% and 6%. The median number of doses of nivolumab received was 6 (range, 0-13); one pt did not receive nivolumab due to insurance issues. 9 pts underwent an alloSCT. After a median follow-up of 8.3 mths (range, 1.5-17.0) the median RFS among the responding pts has not been reached (range, 0.1 - 15.8 mths) and the median OS has not been reached (range 0.5-17.0 mths). Grade 3/4 immune mediated toxicities have been observed in 5 pts and include rash, pancreatitis, and colitis. Other grade 3/4 toxicities thought to be potentially related to nivolumab include cholecystitis in one pt. 9 pts proceeded to an alloSCT. Donor source was matched related in 2, matched unrelated in 6 and haplo-identical in 1 pt. Conditioning regimen was Fludarabine plus busulfan-based in 8, and fludarabine plus melphalan in 1 pt. 4 pts developed graft versus host disease (GVHD)(grade I/II in 3, grade III/IV in 1), which responded to treatment in 3. Multicolor flow-cytometry studies are conducted by the Immunotherapy Platform on baseline (prior to first dose of nivolumab) and on-treatment BM aspirate and peripheral blood to assess the T-cell repertoire and expression of co-stimulatory receptors and ligands on T-cell subsets and leukemic blasts, respectively. The baseline BM was evaluated on 23 of the 32 evaluable pts, including 18 responders and 5 non-responders. Pts who achieved a CR/CRi had a trend of higher frequency of live CD3+ total T cell infiltrate as compared to non-responders in the baseline BM aspirates (Fig 1A). We evaluated expression of immune markers on T cell subsets: CD4 T effector cells [Teff]: CD3+CD4+CD127lo/+Foxp3-, CD4 T regulatory cells [Treg]: CD3+CD4+CD127-Foxp3+, and CD8 T cells. At baseline, BM of non-responders had significantly higher percentage of CD4 T effector cells co-expressing the inhibitory markers PD1 and TIM3 (p<0.05) and a trend towards higher percentage of CD4 T effector cells co-expressing PD1 and LAG3 compared to responders (Fig 1B). Co-expression of TIM3 or LAG3 on PD1+ T cells have been shown to be associated with an exhausted immune phenotype in AML (Zhou et al., Blood 2011). Conclusion: Addition of nivolumab to ara-C and anthracycline induction chemotherapy is feasible and safe in younger pts with AML. Among the pts proceeding to alloSCT the risk of GVHD is not significantly increased. Figure 1 Figure 1. Disclosures Daver: Pfizer Inc.: Consultancy, Research Funding; Otsuka America Pharmaceutical, Inc.: Consultancy; Sunesis Pharmaceuticals, Inc.: Consultancy, Research Funding; Novartis Pharmaceuticals Corporation: Consultancy; Bristol-Myers Squibb Company: Consultancy, Research Funding; Kiromic: Research Funding; Karyopharm: Consultancy, Research Funding; Jazz: Consultancy; Immunogen: Research Funding; Daiichi-Sankyo: Research Funding; Incyte Corporation: Honoraria, Research Funding. Thompson: Pharmacyclics: Honoraria, Membership on an entity's Board of Directors or advisory committees. Jabbour: Bristol-Myers Squibb: Consultancy. Takahashi: Symbio Pharmaceuticals: Consultancy. DiNardo: Novartis: Honoraria, Research Funding; Daiichi-Sankyo: Honoraria, Research Funding; AbbVie: Honoraria, Research Funding; Agios: Honoraria, Research Funding; Celgene: Honoraria, Research Funding. Sharma: Jounce: Consultancy, Other: stock, Patents & Royalties: Patent licensed to Jounce; Astellas: Consultancy; EMD Serono: Consultancy; Amgen: Consultancy; Astra Zeneca: Consultancy; GSK: Consultancy; Consetellation: Other: stock; Evelo: Consultancy, Other: stock; Neon: Consultancy, Other: stock; Kite Pharma: Consultancy, Other: stock; BMS: Consultancy. Cortes: BMS: Consultancy, Research Funding; Sun Pharma: Research Funding; Novartis Pharmaceuticals Corporation: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Teva: Research Funding; ImmunoGen: Consultancy, Research Funding; ARIAD: Consultancy, Research Funding. Kantarjian: Delta-Fly Pharma: Research Funding; Amgen: Research Funding; ARIAD: Research Funding; Novartis: Research Funding; Bristol-Meyers Squibb: Research Funding; Pfizer: Research Funding.

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

Blood ◽

10.1182/blood-2020-138601 ◽

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

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Immune cell topography predicts response to PD-1 blockade in cutaneous T cell lymphoma

10.1101/2020.12.06.20244913 ◽

2020 ◽

Author(s):

Darci Phillips ◽

Magdalena Matusiak ◽

Belén Rivero Gutierrez ◽

Salil S. Bhate ◽

Graham L. Barlow ◽

...

Keyword(s):

T Cells ◽

T Cell ◽

Clinical Response ◽

Tumor Cells ◽

Spatial Organization ◽

Cell Lymphoma ◽

Post Treatment ◽

T Cell Lymphoma ◽

T Cell Subsets ◽

Cutaneous T Cell Lymphoma

Anti-PD-1 immunotherapies have transformed cancer treatment, yet the determinants of clinical response are largely unknown. We performed CODEX multiplexed tissue imaging and RNA sequencing on 70 tumor regions from 14 advanced cutaneous T cell lymphoma (CTCL) patients enrolled in a clinical trial of pembrolizumab therapy. Clinical response was not associated with the frequency of tumor-infiltrating T cell subsets, but rather with striking differences in the spatial organization and functional immune state of the tumor microenvironment (TME). After treatment, pembrolizumab responders had a localized enrichment of tumor and CD4+ T cells, which coincided with immune activation and cytotoxic PD-1+ CD4+ T cells. In contrast, non-responders had a localized enrichment of Tregs pre- and post-treatment, consistent with a persistently immunosuppressed TME and exhausted PD-1+ CD4+ T cells. Integrating these findings by computing the physical distances between PD-1+ CD4+ T cells, tumor cells, and Tregs revealed a spatial biomarker predictive of pembrolizumab response. Finally, the chemokine CXCL13 was upregulated in tumor cells in responders post-treatment, suggesting that chemoattraction of PD-1+ CD4+ T cells towards tumor cells facilitates a positive outcome. Together, these data show that T cell topography reflects the balance of effector and suppressive activity within the TME and predicts clinical response to PD-1 blockade in CTCL.

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