scholarly journals Selective Cell State in the Clonally Expanded T-Cell Compartment of Vκ*MYC Mice Responding to Treatment with Checkpoint Inhibitors

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 1581-1581
Author(s):  
Danielle C Croucher ◽  
Laura M Richards ◽  
Zhihua Li ◽  
Ellen nong Wei ◽  
Xian Fang Huang ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
T Cell ◽  
Mouse Model ◽  
Single Cell ◽  
Research Funding ◽  
Genetically Engineered ◽  
Cell State ◽  
Genetically Engineered Mouse Model ◽  
Tcr Repertoire

Abstract Introduction: Immune checkpoint receptor (ICR) blockade has emerged as an effective anti-tumour modality, but only in a subset of cancer patients. Moreover, in Multiple myeloma (MM), single-agent activity has not been observed, highlighting the need to better understand the mechanism of action of this class of drugs. We recently showed that combinatorial ICR blockade using αLAG3 and αPD-1 delays disease progression and improves survival in the transplantable Vκ*MYC model of MM (Croucher et al. ASH 2018). However, despite this being a controlled study with genetically-homogeneous tumours, anti-tumour immune responses were heterogeneous, with only a subset of mice demonstrating a delay in tumour progression (17/29 mice, response rate = 58.6%). Thus, using this model, we set out to define mechanisms underlying variability in response to ICR blockade. Methods: We established a cohort of mice by engrafting 5-week-old C57BL/6 mice with Vκ12598 cells via tail vein injection. Treatment with αLAG3/αPD-1 or Ig-control was initiated 1-week post-engraftment and bone marrow (BM) samples were collected 3 weeks after the start of treatment. Following FACS-enrichment of T cells and plasma cells (PCs), single cell suspensions were subjected to matched single-cell gene expression (5' scRNA-seq) and T cell receptor (TCR)/B cell receptor (BCR) profiling (10x Genomics). Results: Samples were selected for profiling based on response to treatment, with responders (n=4) defined by significantly lower disease burden compared to non-responders (n=3) and control-treated mice (n=5), as measured by serum M-protein and %PCs in BM/spleen at sacrifice. Unsupervised clustering of scRNA-seq data from PCs (n=3,318 cells) identified no gene expression or BCR repertoire differences between control and treated, or between responder and non-responder samples, supporting that variability in response was not related to malignant Vκ12598 cells themselves. Across all samples, a statistically significant difference was not detected between the total number of unique TCR sequences (clonotypes) comparing control-treated (351-2369), non-responders (1185-2327) and responders (1378-1698), with no overlapping TCR sequences between top clonotypes. Evaluation of TCR repertoire diversity revealed that αLAG3/αPD-1 treatment induces clonal T cell expansion in control versus treated mice, but this was not significantly different between responders and non-responders. Analysis of paired scRNA-seq data (n=21,520 cells) revealed that expanded T cells from αLAG3/αPD-1-treated mice occupy a different cell state in responder vs. non-responder mice. We speculate that underlying differences in the TCR repertoire may dictate the downstream phenotype of expanded, anti-tumour T cells in mice treated with combinatorial αLAG3/αPD-1. Tumour control following treatment was associated with clonal expansion of T cells expressing genes related to cytoxicity and activation (Ccl5, Ifng, Fasl, Gzmb), whereas tumour progression was associated with clonal expansion of proliferative T cells (Cdkn3, Birc5, Ccna2, Aurka, Mki67). Although T cell proliferation is typically a phenotype ascribed to effector T cells, recent studies have similarly observed this proliferative cell state in dysfunctional T cells within melanoma tumours. Moreover, emerging evidence supports suppression of T cell proliferation by CDK4/6 inhibitors as a means to augment anti-tumour activity of ICR-based therapy. Thus, studies exploring whether reversal of the observed proliferative T cell state can restore response to αLAG3/αPD-1 treatment in non-responding Vκ12598 mice are ongoing and will be reported. Conclusions: ICR inhibitors demonstrate significant activity in some cancers, however many patients fail to respond and a similarly promising level of efficacy has not been achieved in MM. Studies aimed at unraveling the mechanisms of response and resistance to ICR inhibitors are therefore needed to improve the utility of this class of drugs for all patients. Our approach of using paired single-cell gene expression and TCR repertoire profiling has enabled identification of molecular cell states specifically in expanded T cells of responder vs. non-responder mice. In turn, our work nominates novel mechanisms that may be used as potential biomarkers for anti-tumour immune responses as well as potential targets to augment responses to ICR blockade therapy. Disclosures Chesi: Abcuro: Patents & Royalties: Genetically engineered mouse model of myeloma; Novartis: Consultancy, Patents & Royalties: human CRBN transgenic mouse; Pfizer: Consultancy; Pi Therapeutics: Patents & Royalties: Genetically engineered mouse model of myeloma; Palleon Pharmaceuticals: Patents & Royalties: Genetically engineered mouse model of myeloma. Bergsagel: GSK: Consultancy, Honoraria; Genetech: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Oncopeptides: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Patents & Royalties: human CRBN mouse; Pfizer: Consultancy, Honoraria; Celgene: Consultancy, Honoraria. Sebag: Janssen: Research Funding; Bristol Myers-Squibb: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Novartis: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Sanofi: Consultancy, Honoraria; Karyopharm Therapeutics: Consultancy, Honoraria. Trudel: BMS/Celgene: Consultancy, Honoraria, Research Funding; Amgen: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; GlaxoSmithKline: Consultancy, Honoraria, Research Funding; Roche: Consultancy; Sanofi: Honoraria; Pfizer: Honoraria, Research Funding; Genentech: Research Funding.

scholarly journals Single Cell RNA Sequencing and V(D)J Profiling of T-Cell Large Granular Lymphocytosis

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 2404-2404
Author(s):  
Shouguo Gao ◽  
Zhijie Wu ◽  
Carrie Diamond ◽  
Bradley Arnold ◽  
Valentina Giudice ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cell ◽  
Single Cell ◽  
Research Funding ◽  
Clonal Expansion ◽  
Rna Seq ◽  
Healthy Donors ◽  
Tcr Repertoire ◽  
Before And After ◽  
Repertoire Diversity

Abstract Introduction . T-cell large granular lymphocytosis (T-LGL) is a low grade lymphoproliferative disorder, often clinically manifest as bone marrow failure. Treatment with immunosuppressive therapies is effective, but the dominant clone may persist even in responding patients. The pathogenesis of T-LGL has not been fully elucidated. In this study, we performed single cell RNA sequencing (sc-RNA seq) and V(D)J profiling to discern clonotypes and gene expression patterns of T lymphocytes from T-LGL patients who were sampled before and after treatment. Methods. Blood was obtained from patients participating in a phase 2 protocol of alemtuzumab as second line therapy (NCT00345345; Dumitriu B et al, Lancet Haematol 2016). Leukapheresis was performed in 13 patients (M/F 7/6; median age 51 years, range 26-85) before and after 3-6 months alemtuzumab administration and in 7 age-matched healthy donors. Cryopreserved blood was enriched for T cells with the EasySep Human T cell Isolation Kit (Stem cell). sc-RNA seq was performed on the 10XGenomics Chromium Single Cell V(D)J + 5' Gene Expression platform, and sequencing obtained on the HiSeq3000 Platform. Barcode assignment, alignment, unique molecular index counting and T cell receptor sequence assembly were performed using Cell Ranger 2.1.1. Results. Four hundred fifty thousand cells from 13 patients and 107,000 cells from 7 healthy donors were profiled. We measured productive TCR chains (which fully span the V and J regions, with a recognizable start codon in the V region and lacking a stop codon in the V-J region, thus potentially generating a protein). We detected at least one productive TCR α-chain in 50%, one productive TCR β-chain in 69% and paired productive αβ-chains in 47% of all cells. There was loss of TCR repertoire diversity in patients which was quantified by Simpson's diversity index; most patients showed oligoclonal or, less frequently, monoclonal expansion of the TCR repertoire (Fig. A). Regardless of clinical response, alemtuzumab treatment did not correct the low TCR repertoire diversity. TCR repertoires can be classified as "public", when they express identical TCR sequences across multiple individuals, or "private", when each individual displays distinct TCR clonotypes. No TCRA or TCRB CDR3 homology among patients was observed: most TCR clonotypes appeared to be private. Our data suggests that T-LGL is etiologically heterogenous disease, consistent with T cell expansion in response to a variety antigens, in diverse HLA contexts, or randomly. Despite differences of TCR among patients and healthy donors, and the presence of large clones in patients, distribution of TCR diversity followed the power law distribution in healthy donors and patients (Fig. B, showing the negative linear relationship between logarithmic expression of clone frequency and clone size). The observed distribution is consistent with a somatic evolution model, in which cell fitness depends on cellular receptor response to specific antigens and stimulation of cells by cytokine and other signals from the environment; fitted clones have higher birth-death ratios and thus expand (Desponds J et al, PNAS 2016). CD4 and CD8 T cells can be virtually separated by imputation from their transcriptomes (Fig. C). Comparison of gene expression between patients and healthy donors showed dysregulation of genes involved in pathways related to the immune response and cell apoptosis, consistent with a pathophysiology of T cell clonal expansion. We used diffusion mapping, which localizes datapoints to their eigen components in low-dimesional space, to characterize sources contributing to the gene expression phenotype: the first component was mainly from T cell activation and the second was associated with TCR expression. In LGL the T cell transcriptome appeared to be shaped by both lineage development and TCR rearrangement. Conclusion. We describe at the single cell level T clonal expansion profiles in T-LGL, pre- and post-treatment. Single cell analysis allows accurate recovery of paired α and β chains in the same cell and demonstrates a continuum of cell lineage differentiation. We found a range of differences in transcriptome and TCR repertoires across patients. Transcriptome data, coupled with detailed TCR-based lineage information, provides a rich resource for understanding of the pathology of T-LGL and has implications for prognosis, treatment, and monitoring in the clinic. Figure. Figure. Disclosures Young: GlaxoSmithKline: Research Funding; CRADA with Novartis: Research Funding; National Institute of Health: Research Funding.

scholarly journals Single-Cell Profiling Reveals Contribution of Tumor Extrinsic and Intrinsic Factors to BCMA-Targeted CAR-T Cell Efficacy in Multiple Myeloma

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 326-326
Author(s):  
David T. Melnekoff ◽  
Yogita Ghodke-Puranik ◽  
Oliver Van Oekelen ◽  
Adolfo Aleman ◽  
Bhaskar Upadhyaya ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
T Cell ◽  
Single Cell ◽  
Cd8 T Cells ◽  
Research Funding ◽  
Mononuclear Cells ◽  
Poor Responders ◽  
Car T Cell ◽  
Car T

Abstract Background: BCMA CAR-T cell therapy has shown great promise in relapsed/refractory multiple myeloma (RRMM) patients, even though there is unpredictable variability in the duration and depth of response. The mechanisms behind these divergent outcomes and relapse are not well understood and heterogeneity of MM patients at the level of both tumor genomics and tumor microenvironment (TME) likely contributes to this important knowledge gap. To explore this question, we performed a longitudinal high resolution single cell genomic and proteomic analysis of bone marrow (BM) and peripheral blood (PB) samples in MM patients treated with BCMA CAR-T. Methods: Longitudinal comprehensive immune phenotyping of 3.5 million peripheral blood mononuclear cells (PBMC, CD45+CD66b-) from 11 BCMA CAR-T (idecabtagene vicleucel, ide-cel) patients was achieved via mass cytometry (CyTOF) with a panel of 39 markers. In addition, a total of 45,161 bone marrow mononuclear cells (BMMC) were analyzed from 6 patients before initiation of ide-cel therapy and at relapse by unbiased mRNA profiling via single-cell RNA-seq (scRNA-seq) using the GemCode system (10x Genomics). Downstream analysis was performed using the CATALYST and Seurat R packages, respectively. Immune cell populations are reported as % of PBMC and CD138- BMMC respectively, unless noted otherwise. Reported p values correspond to non-parametric tests or paired t test where applicable. Results: We compared baseline immune cell populations in the PB and the TME (BM) with regards to depth of CAR-T response. In PB, good responders (≥VGPR) had a higher proportion of CD8+ T cells (37% in good vs 11% in poor responders (<VGPR), p=0.08) and a lower proportion of CD14+ monocytes (30% vs 61%, p=0.28) and NK cells (2% vs 6%, p=0.08). In the TME, a similar trend was confirmed for CD8+ T cells and CD14+ monocytes. (Fig. 1A) Longitudinal analysis of PBMCs revealed phenotypic changes coinciding with CAR-T expansion; CD14+ monocytes declined from week 0 to week 4 after CAR-T infusion (40% vs 13%, p=0.04), while (non-CAR) CD8+ T cells expanded from week 0 to week 4 (32% vs 43%, p=0.15). The non-CAR CD8+ T cell expansion is characterized by differentiation towards a CD8+ effector-memory phenotype (EM, CCR7-CD45RA-) (73% vs 92% of CD8+ T cells, p=0.005). (Fig. 1B) BM samples at CAR-T relapse showed reversal of this shift: CD14+ monocyte levels remain constant or are slightly elevated, while non-CAR CD8+ T cells decrease at relapse. scRNA-seq of BMMC revealed significant gene expression changes between screening and relapse tumor samples, suggesting tumor-intrinsic factors of CAR-T response. For example, when comparing the pre and post tumor samples of a patient with durable response (PFS 652 days), we observed a significant upregulation of gene expression of pro-inflammatory chemokines (CCL3, CCL4), anti-apoptotic genes (MCL-1, FOSB, JUND), and NF-kB signaling genes (NFKBIA) in post tumor. Gene Set Enrichment Analysis (GSEA) of differentially expressed genes showed significant enrichment for TNFA signaling via NF-kB Hallmark Pathway (p.adj = 0.04). We observed similar statistically significant findings between other screening and relapse samples within our cohort, as well as upon comparison of baseline samples of poor vs good responders. (Fig. 1C, D) Thus, our data suggest that anti-apoptotic gene expression could be one of the tumor intrinsic mechanisms of CAR-T therapy resistance. Notably, we did not observe loss of BCMA expression in any tumor samples. Conclusion: Single cell immune profiling and transcriptomic sequencing highlights changes in the PB, TME and within the tumor, which in concert may influence CAR-T efficacy. Our integrated data analysis indicates general immune activation after CAR-T cell infusion that returns to baseline levels at relapse. Specifically, the expansion of non-CAR cytotoxic CD8+ EM T cells provides a rationale for co-administration of IMiDs to enhance CAR-T efficacy. Significant up-regulation of anti-apoptotic genes at baseline in poor responders, and at relapse in good responders, suggest a novel tumor-mediated escape mechanism. Targeting the MCL-1/BCL-2 axis may augment CAR-T efficacy by sensitizing tumor cells and enhancing the effect of CAR-T killing. We will confirm these findings in a longitudinal cohort of BMMC/PBMC CITE-seq patients (n=23) and will present results at the conference. Figure 1 Figure 1. Disclosures Sebra: Sema4: Current Employment. Parekh: Foundation Medicine Inc: Consultancy; Amgen: Research Funding; PFIZER: Research Funding; CELGENE: Research Funding; Karyopharm Inv: Research Funding.

scholarly journals Targeted reconstruction of T cell receptor sequence from single cell RNA-sequencing links CDR3 length to T cell differentiation state

2016 ◽  
Author(s):  
Shaked Afik ◽  
Kathleen B. Yates ◽  
Kevin Bi ◽  
Samuel Darko ◽  
Jernej Godec ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Response ◽  
T Cell Response ◽  
Effector Memory ◽  
Cell State ◽  
Tcr Repertoire ◽  
Single Cell Rna Sequencing

ABSTRACTThe T cell compartment must contain diversity in both TCR repertoire and cell state to provide effective immunity against pathogens1,2. However, it remains unclear how differences in the TCR contribute to heterogeneity in T cell state at the single cell level because most analysis of the TCR repertoire has, to date, aggregated information from populations of cells. Single cell RNA-sequencing (scRNA-seq) can allow simultaneous measurement of TCR sequence and global transcriptional profile from single cells. However, current protocols to directly sequence the TCR require the use of long sequencing reads, increasing the cost and decreasing the number of cells that can be feasibly analyzed. Here we present a tool that can efficiently extract TCR sequence information from standard, short-read scRNA-seq libraries of T cells: TCR Reconstruction Algorithm for Paired-End Single cell (TRAPeS). We apply it to investigate heterogeneity in the CD8+T cell response in humans and mice, and show that it is accurate and more sensitive than previous approaches3,4. We applied TRAPeS to single cell RNA-seq of CD8+T cells specific for a single epitope from Yellow Fever Virus5. We show that the recently-described "naive-like" memory population of YFV-specific CD8+T cells have significantly longer CDR3 regions and greater divergence from germline sequence than do effector-memory phenotype CD8+T cells specific for YFV. This suggests that TCR usage contributes to heterogeneity in the differentiation state of the CD8+T cell response to YFV. TRAPeS is publicly available, and can be readily used to investigate the relationship between the TCR repertoire and cellular phenotype.

scholarly journals Differences in TCR repertoire and T cell activation underlie the divergent outcomes of antitumor immune responses in tumor-eradicating versus tumor-progressing hosts

2021 ◽  
Vol 9 (1) ◽  
pp. e001615
Author(s):  
Rachel A Woolaver ◽  
Xiaoguang Wang ◽  
Alexandra L Krinsky ◽  
Brittany C Waschke ◽  
Samantha M Y Chen ◽  
...  
Keyword(s):  
T Cell ◽  
Mouse Model ◽  
Immune Responses ◽  
Single Cell ◽  
T Cell Activation ◽  
Antitumor Immunity ◽  
Cell Activation ◽  
Tcr Repertoire ◽  
Underlying Mechanisms ◽  
Personalized Cancer

BackgroundAntitumor immunity is highly heterogeneous between individuals; however, underlying mechanisms remain elusive, despite their potential to improve personalized cancer immunotherapy. Head and neck squamous cell carcinomas (HNSCCs) vary significantly in immune infiltration and therapeutic responses between patients, demanding a mouse model with appropriate heterogeneity to investigate mechanistic differences.MethodsWe developed a unique HNSCC mouse model to investigate underlying mechanisms of heterogeneous antitumor immunity. This model system may provide a better control for tumor-intrinsic and host-genetic variables, thereby uncovering the contribution of the adaptive immunity to tumor eradication. We employed single-cell T-cell receptor (TCR) sequencing coupled with single-cell RNA sequencing to identify the difference in TCR repertoire of CD8 tumor-infiltrating lymphocytes (TILs) and the unique activation states linked with different TCR clonotypes.ResultsWe discovered that genetically identical wild-type recipient mice responded heterogeneously to the same squamous cell carcinoma tumors orthotopically transplanted into the buccal mucosa. While tumors initially grew in 100% of recipients and most developed aggressive tumors, ~25% of recipients reproducibly eradicated tumors without intervention. Heterogeneous antitumor responses were dependent on CD8 T cells. Consistently, CD8 TILs in regressing tumors were significantly increased and more activated. Single-cell TCR-sequencing revealed that CD8 TILs from both growing and regressing tumors displayed evidence of clonal expansion compared with splenic controls. However, top TCR clonotypes and TCR specificity groups appear to be mutually exclusive between regressing and growing TILs. Furthermore, many TCRα/TCRβ sequences only occur in one recipient. By coupling single-cell transcriptomic analysis with unique TCR clonotypes, we found that top TCR clonotypes clustered in distinct activation states in regressing versus growing TILs. Intriguingly, the few TCR clonotypes shared between regressors and progressors differed greatly in their activation states, suggesting a more dominant influence from tumor microenvironment than TCR itself on T cell activation status.ConclusionsWe reveal that intrinsic differences in the TCR repertoire of TILs and their different transcriptional trajectories may underlie the heterogeneous antitumor immune responses in different hosts. We suggest that antitumor immune responses are highly individualized and different hosts employ different TCR specificities against the same tumors, which may have important implications for developing personalized cancer immunotherapy.

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 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 (> 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 The MEK Inhibitor Trametinib Enhances Diverse T Cell Reconstitution with Suppressing Xenogeneic Graft-Versus-Host-Disease

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 1929-1929
Author(s):  
Hidekazu Itamura ◽  
Hiroyuki Muranushi ◽  
Takero Shindo ◽  
Kazutaka Kitaura ◽  
Seiji Okada ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Research Funding ◽  
Mek Inhibitor ◽  
Effector Memory ◽  
Graft Versus Host ◽  
T Cell Reconstitution ◽  
Tcr Repertoire ◽  
Repertoire Diversity ◽  
Equity Ownership

Introduction: Early immune reconstitution without severe graft-versus-host disease (GVHD) is required for the success of allogeneic hematopoietic stem cell transplantation (allo-HSCT). We showed that MEK inhibitors suppress GVHD but retain antiviral immunity and graft-versus-tumor (GVT) effects (Shindo, Blood2013; Itamura, Shindo, JCI Insight2016). Furthermore, we have shown that they attenuate graft rejection but spare thymic function following rat lung transplantation (Takahagi, Shindo, Am J Respir Cell Mol Biol2019). Here we analyzed their effects on human polyclonal T cell reconstitution in xenogeneic transplant by evaluating T-cell receptor (TCR) repertoire diversity. Methods: As a xenogeneic GVHD model, human PBMCs were infused to NOD/Scid/JAK3null mice, immunodeficient mice lacking T/B/NK cells, after total body irradiation. Vehicle, tacrolimus, or the MEK inhibitor trametinib was administered from day 0 through 28 or day 15 through 28. Human TCR repertoire diversity was evaluated by an adapter ligation PCR method with next generation sequencing (Shindo, Oncoimmunol2018) in the liver, lung, and spleen. The assignment and frequencies of TCRαV/J clones were determined at the single-cell level. Their diversity and clonality were evaluated by Inv. Simpson's index 1/λ. Results: Trametinib prolonged their survival compared with vehicle (median survival: 88 vs 46 days, p<0.05). It enhanced engraftment of human leukocytes in peripheral blood (human CD45+cells: 11.0 vs 2.5%), but prevented their infiltration into the lung (human CD45+cells on day 60: 1.5 vs 6.5%). Treatment with vehicle resulted in skewed TCR repertoire with limited clones in the spleen, liver and lung. Interestingly, expansion of one specific clone (TRAV20/J10) was commonly observed, which might reflect the GVHD-inducing pathological clone (Fig. 1: 3D graphs show the frequencies of TCRαV/J clones). However, trametinib enabled diverse and polyclonal T cell engraftment without the TRAV20/J10 clone. While CD4+and CD8+T cells within injected human PBMCs mainly consisted of naïve (CD45RA+CD27+) and central memory (CD45RA-CD27+) T cells, infiltrating T cells in each organ showed effector memory (CD45RA-CD27-) T cell phenotype. Of note, CD8+T cells in the bone marrow, spleen, and lung of trametinib-treated recipients showed reduced effector memory T cells (CD45RA-CD27-) compared with vehicle-treated mice at day 28 (bone marrow 21.7 vs 74.7%, p<0.01; spleen 66.3 vs 88.7%, p<0.05; lung 33.0 vs 72.5%, p<0.05), which indicating that MEK inhibition suppresses functional differentiation of human T cells in vivo. Furthermore, trametinib treatment from day 14 to 28 still ameliorated clinical GVHD score, and maintained polyclonal T cell repertoire. Conclusions:GVHD can be characterized with skewed TCR repertoire diversity and expansion of pathological T cell clones in the target tissues. Trametinib suppresses GVHD but maintains polyclonal T cell reconstitution, even in established GVHD. These results explain the facts that MEK inhibitors separate GVHD from GVT effects/antimicrobial immunity. Furthermore, MEK inhibition enhances immune reconstitution after allo-HSCT, which would avoid post-transplant complications. Disclosures Shindo: Novartis: Research Funding. Kitaura:Repertoire Genesis Inc.: Employment. Okada:Bristol-Myers Squibb: Research Funding; Japan Agency for Medical Research and Development: Research Funding. Shin-I:BITS Co., Ltd: Equity Ownership. Suzuki:Repertoire Genesis Inc.: Equity Ownership. Takaori-Kondo:Celgene: Honoraria, Research Funding; Novartis: Honoraria; Bristol-Myers Squibb: Honoraria, Research Funding; Ono: Research Funding; Takeda: Research Funding; Kyowa Kirin: Research Funding; Chugai: Research Funding; Janssen: Honoraria; Pfizer: Honoraria. Kimura:Ohara Pharmaceutical Co.: Research Funding; Novartis: Honoraria, Research Funding.

scholarly journals Single-Cell Multi-Omic Analysis Uncovers Comprised Immune Function and Primary Resistance Mechanism in Acute Myeloid Leukemia

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 378-378
Author(s):  
Jianbiao Zhou ◽  
Jonathan Adam Scolnick ◽  
Stacy Xu ◽  
Melissa Ooi ◽  
Priscella Shirley Chia ◽  
...  
Keyword(s):  
Bone Marrow ◽  
T Cells ◽  
T Cell ◽  
Nk Cells ◽  
Single Cell ◽  
Research Funding ◽  
Immune Escape ◽  
Nk Cell ◽  
Primary Resistance ◽  
Bm Niche

Abstract Background: Approximately 20% of AML patients do not respond to induction chemotherapy (primary resistance) and 40-60% of patients develop secondary resistance, eventually leading to relapse followed by refractory disease (RR-AML). Diversified molecular mechanisms have been proposed for drug resistance and RR phenotype. However, we still cannot predict when relapse will occur, nor which patients will become resistant to therapy. Single-cell multi-omic (ScMo) profiling may provide new insights into our understanding of hematopoietic stem cell (HSC) differentiation trajectories, tumor heterogeneity and clonal evolution. Here we applied ScMo to profile bone marrow (BM) from AML patients and healthy controls. Methods: AML samples were collected at diagnosis with institutional IRB approval. Cells were stained with a panel of 62 DNA barcoded antibodies and 10x Genomics Single Cell 3' Library Kit v3 was used to generate ScMo data. After normalization, clusters were identified using Uniform Manifold Approximation and Projection (UMAP) and annotated using MapCell (Koh and Hoon, 2019). We analyzed 23,933 cells from 4 adult AML BM samples, and 39,522 cells from 2 healthy adults and 3 sorted CD34+ normal BM samples. Gene set enrichment analysis (GSEA) and Enrichr program were used to examine underlying pathways among differentially expressed genes between healthy and AML samples. Results: We identified 16 cell types between the AML and normal samples (Fig 1a) amongst 45 clusters in the UMAP projection (Fig 1b). Comparative analysis of the T cell clusters in AML samples with healthy BM cells identified an "AML T-cell signature" with over-expression of genes such as granzymes, NK/T cell markers, chemokine and cytokine, proteinase and proteinase inhibitor (Fig 2a). Among them, IL32 is known to be involved in activation-induced cell death in T cells and has immunosuppressive role, while CD8+ GZMB+ and CD8+ GZMK+ cells are considered as dysfunctional or pre-dysfunctional T cells. Indeed, Enrichr analysis showed the top rank of phenotype term - "decreased cytotoxic T cell cytolysis". We next examined whether NK cells, are similarly dysfunctional in the AML ecosystem. The "AML NK cell signature" includes Fc Fragment family, IFN-stimulated genes (ISGs), the effector protein-encoding genes and other genes when compared to normal NK cells (Fig 2b). GSEA analysis revealed "PD-1 signalling" among the top 5 ranked pathways in AML-NK cells, though no increase in PD-1 protein nor PDCD1 gene were identified in these cells. Inhibitory receptor CD160 was expressed higher in AML samples along with exhaustion (dysfunction) associated genes TIGIT, PRF1 and GZMB (Fig 2c). Enrichr analysis uncovered enrichment of "abnormal NK cell physiology and "impaired natural killer cell mediated cytotoxicity". Similarly, the "AML monocyte signature" was significantly enriched with genes in "Tumor Infiltrating Macrophages in Cancer Progression and Immune Escape" and "Myeloid Derived Suppressor Cells in Cancer Immune Escape". We also analyzed HSPC component in one pair of cytogenetically matched, untreated complete remission (CR) /RR AML pair (Fig 2d). Notably, half of the 10 genes overexpressed in RR-AML, CXCR4, LGALS1, S100A8, S100A9, SRGN (Serglycin), regulate cell-matrix interaction and play pivotal roles in leukemic cells homing bone marrow niche. The first 4 of these genes have been demonstrated as prognostic indicators of poor survival and associated with chemo-resistance and anti-apoptotic function. Furthermore, single-cell trajectory analysis of this CR/RR pair illustrated a change in differentiation pattern of HSPCs in CR-AML to monocytes in RR-AML. We are currently analyzing more AML samples to validate these findings. Conclusions: Our ScMo analysis demonstrates that the immune cells are systematically reprogrammed and functionally comprised in the AML ecosystem. Upregulation of BM niche factors could be the underlying mechanism for RR-AML. Thus, reversing the inhibited immune system is an important strategy for AML therapy and targeting leukemic cell-BM niche interaction should be considered for cases with high expression of these molecules on AML HSPCs. Note: J.Z. and J.A.S. share co-first authorship. Figure 1 Figure 1. Disclosures Scolnick: Proteona Pte Ltd: Current holder of individual stocks in a privately-held company. Xu: Proteona Pte Ltd: Current Employment. Ooi: Jansen: Honoraria; Teva Pharmaceuticals: Honoraria; GSK: Honoraria; Abbvie: Honoraria; Amgen: Honoraria. Lovci: Proteona Pte Ltd: Current Employment. Chng: Aslan: Research Funding; Takeda: Honoraria; Johnson & Johnson: Honoraria, Research Funding; BMS/Celgene: Honoraria, Research Funding; Amgen: Honoraria; Novartis: Honoraria, Research Funding; Antengene: Honoraria; Pfizer: Honoraria; Sanofi: Honoraria; AbbVie: Honoraria.

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