Yang, Zhi-Zhang

Abstract T cell Ig and ITIM domain (TIGIT) is an immune checkpoint molecule and its ligation delivers an inhibitory signal to T cells that negatively regulates anti-tumor responses. However, the expression and biological relevance of TIGIT in B-cell non-Hodgkin lymphoma (NHL) is completely unknown. To phenotypically characterize TIGIT+ T cell subsets, we profiled TIGIT-expressing CD4+ and CD8+ T cells from biopsy specimens of B-cell NHL. TIGIT expression was not detectable on T cells from peripheral blood, however, TIGIT is constitutively expressed on CD4+ T cells from biopsy specimens of B-cell NHL with a median of 44.8% (range: 25.9–62.2, n=8) of CD4+ T cells expressing TIGIT. By profiling the CD4+TIGIT+ T cells, we observed that CD25 was highly expressed on intratumoral CD4+TIGIT+ T cells. Intracellular staining revealed that Foxp3 was expressed by intratumoral CD4+TIGIT+ T cells. These intratumoral CD4+TIGIT+ T cells expressed high level of chemokine receptor CCR4 and were absent of IL-7 receptor-α (CD127). Functionally, CD4+TIGIT+ T cells displayed reduced cytokine production, as the number of IFN-γ- and TNF-α-producing cells was lower in the TIGIT+ population than in TIGIT− T cells. While TIGIT expression can be induced by TCR activation, treatment of T cells with IL-2 and TGF-β inhibited TCR-mediated TIGIT induction. Furthermore, lymphoma B cells were involved in TIGIT induction as in vitro depletion of lymphoma B cells altered upregulation of TIGIT on CD4+ T cells in B-cell NHL. Taken together, these results indicated that constitutive expression of TIGIT defines a CD4+ population with similar phenotype to regulatory T cells. Inhibition of TIGIT signaling may be an additional mechanism to prevent T-cell suppression in B-cell NHL

Abstract Although extensively studied, the exact mechanism by which TGF-β mediates T-cell inhibition is not entirely clear. When we examined the effect of TGF-β on the phenotype and function of T cells, we noted that CD70 was significantly up-regulated by TGF-β. This surprising finding led us to explore the underlying mechanism by which TGF-β upregulates CD70 expression on T cells and to define the function of CD70+ cells in B-cell NHL. Interestingly, we observed that TGF-β preferentially induced CD70 expression on effector memory T cells while it predominantly up-regulated Foxp3 on naïve T cells. CD70 induction is Smad3-dependent and involves Stat5 signaling. CD70+ T cells were highly susceptible to TGF-β-mediated apoptosis and that transfection of Jurkat cells with the CD70 gene led to a significantly reduced survival. TGF-β-induced CD70+ T cells had a diminished response to immune stimulation associated with downregulation of co-stimulatory molecules CD27 and CD28. CD70-expressing T cells from biopsy specimens of patients with B-cell NHL failed to produce cytokines and displayed no signal transduction when stimulated. CD70-expressing T cells were highly represented in follicular NHL biopsy specimens and increased numbers of CD70+ cells correlated with an inferior patient outcome. These novel findings not only revealed a biological role for a TGF-β/CD70 axis in mediating immune inhibition, but also may lead to therapeutic approaches in patients with B-cell NHL.

Abstract Purpose: IgM monoclonal gammopathy of undetermined significance (MGUS) and Waldenström macroglobulinemia (WM) represent a disease spectrum with highly varied therapeutic management, ranging from observation to chemoimmunotherapy. The current classification relies solely on clinical features and does not explain the heterogeneity that exists within each of these conditions. Further investigation is warranted to shed light on the biology that may account for the clinical differences. Experimental Design: We used bone marrow (BM) clonal CD19+ and/or CD138+ sorted cells, matched BM supernatant, and peripheral blood serum from 32 patients (7 MGUS, 25 WM) to perform the first multi-omics approach including whole-exome sequencing, RNA sequencing, proteomics, metabolomics, and mass cytometry. Results: We identified three clusters with distinct pathway activation, immune content, metabolomic, and clinical features. Cluster 1 included only patients with WM and was characterized by transcriptional silencing of genes involved in cell cycle and immune response, enrichment of mitochondrial metabolism, infiltration of senescent T effector memory cells, and aggressive clinical behavior. Genetic/structural alterations of TNFAIP3 were distinct events of this cluster. Cluster 2 comprised both MGUS and WM patients with upregulation of inflammatory response, senescence and glycolysis signatures, increased activated T follicular helper and T regulatory cells, and indolent clinical behavior. Cluster 3 also included both MGUS and WM patients and exhibited intermediate features, including proliferative and inflammatory signaling, as well as glycolysis and mitochondrial metabolism. Conclusions: We have identified three distinct molecular clusters, suggesting a potential biologic classification that may have therapeutic implications.

Abstract The role and immunological constitution of the tumor microenvironment (TME) in splenic marginal zone lymphoma (sMZL) is largely unknown and studies on the phenotype and subset composition of intratumoral T cells are lacking. In the present study, we evaluated 36 sMZL spleen specimens by single cell analysis to gain a better understanding of the TME in sMZL. Using mass cytometry (CyTOF), we observed that the TME in sMZL is distinct from that of control non-malignant reactive spleen (rSP). We found that the number of TFH cells varied greatly in sMZL, ICOS+ TFH cells were more abundant in sMZL than rSP, and TFH cells positively correlated with increased numbers of memory B cells. Treg cell analysis revealed that TIGIT+ Treg cells are enriched in sMZL and correlate with suppression of TH17 and TH22 cells. Intratumoral CD8+ T cells were comprised of subsets of short-lived, exhausted and late-stage differentiated cells, thereby functionally impaired. We observed that T-cell exhaustion was present in sMZL and TIM-3 expression on PD-1low cells identified cells with severe immune dysfunction. Gene expression profiling by CITE-seq analysis validated this finding. Taken together, our data sugget that the TME as a whole, and T-cell population specifically, are heterogenous in sMZL and immune exhaustion is one of the major factors impairing T-cell function.

Abstract Purpose: Regulatory T-cells (Treg) are essential to Tregs homeostasis and modulate the antitumor immune response in patients with lymphoma. However, the biology and prognostic impact of Tregs in splenic marginal zone lymphoma (SMZL) have not been studied. Experimental Design: Biopsy specimens from 24 patients with SMZL and 12 reactive spleens (rSP) from individuals without lymphoma were analyzed by using CITE-seq (cellular indexing of transcriptomes and epitopes by sequencing), CyTOF (mass cytometry) analysis, and flow cytometry to explore the phenotype, transcriptomic profile, and clinical significance of intratumoral Tregs and their subsets. The biological characteristics and cell signaling pathways of intratumoral Treg subsets were confirmed by in vitro functional assays. Results: We found that Tregs are more abundant in SMZL patients' spleens than rSP, and Tregs from patients with SMZL and rSP can be separated into CD161+Treg and CD26+Treg subsets. CD161+Tregs are increased in SMZL but have dysregulated immune function. We found that CD161+Treg and CD26+Tregs have unique gene expression and phenotypic profiles and are differentially correlated with patient outcomes. Specifically, increased CD161+Tregs are significantly associated with a favorable prognosis in patients with SMZL, whereas CD26+Tregs are associated with a poor prognosis. Furthermore, activation of the IL2/STAT5 pathway contributes to the induction of CD26+Tregs and can be reversed by STAT5 inhibition. Conclusions: IL2/STAT5-mediated expansion of CD26+Tregs contributes to a poor clinical outcome in SMZL and may represent a therapeutic opportunity in this disease.

Abstract Previous studies have found that the prevalence of subtypes of T cells may be associated with patient outcomes, but a comprehensive analysis of the immune profile in the TM of FL has not been done. In the present study, we identified groups of FL patients with discretely unique TMEs and determined whether the T-cell phenotypes in the TME differed among groups. Using a cohort of 82 FL patients with biopsy specimens collected before treatment, we defined the type of TME based on the content of major lineages (T, B, monocytes/macrophages and NK cells) determined by CyTOF analysis. Hierarchical clustering of this cohort stratified patients into 4 groups with different TME: group 1 (G1) included patients with a high percentage of monocyte/macrophages/NK cells; patients from G2 and G3 were enriched for intratumoral T and B cells, respectively. Patients with intermediate numbers of T and B cells were included in G4. CITRUS analysis revealed that T-cell clusters with phenotypes expressing KLRG1, CD57, PD-1dim and rich in TEMRA cells were significantly more abundant in G1 when compared to G2, G3 or G4. In contrast, T-cell clusters with phenotypes containing CD127, CD45RA, CCR7 or PD-1high cells were significantly less abundant in G1 when compared to other groups. When compared to G3 or G4, 2 classes of T cell clusters, all from CD4+ T cells, were significantly more (CD127+KLRG1-) or less (CD57+PD-1high) abundant in G2, respectively. Clusters with a phenotype rich in short-lived effector cells (SLEC) (from CD8+) were upregulated in G3 when compared to G4. These results suggest that patient groups with distinct TME exhibited variable T-cell phenotypes. To determine the role of T-cell differentiation in predicting patient outcome in FL, we identified T-cell subsets using tSNE plots based on the expression of T-cell maturation markers. We identified 18 subsets (S1-S18) of T cells (11 from CD4, 6 from CD8 and 1 from CD4-CD8-) in each sample. Four subsets - S4 (CD4+TN), S5 (CD4+TCM), S7 (CD8+TN) and S10 (CD8+MEPCs) - were considered to be naïve cells or cells in the early stages of differentiation. Four additional subsets - S8 (CD57+TFH), S9 (CD4+TEXH), S13 (CD8+SLECs) and S17 (CD4+PD-1+Treg) - were memory cells with expression of surface markers indicting late-stage differentiation. We found that the 4 subsets (S4: p=0.08, S5: p=0.01, S7: p=0.04 and S10: p<0.01) with an early-stage phenotype were associated with a favorable clinical outcome. In contrast, subsets (S8: p=0.02, S9: p=0.04, S13: p=0.07 and S17: p=0.06) with a late-stage differentiation phenotype had an unfavorable survival. Supporting this finding, we observed that increased numbers of CD45RA+ T cells correlated with a favorable survival. These results indicate that the differentiation stage may determine the role of T cells in predicting patient outcome in FL. Citation Format: Zhi-Zhang Yang, Hyo Jin Kim, Hongyan Wu, Xinyi Tang, Jordan Krull, Patrizia Mondello, Jose Villasboas, Anne Novak, Stephen Ansell. T-cell phenotype and differentiation vary in the tumor microenvironment of follicular lymphoma and are associated with patient outcome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2529.

Abstract The tumor microenvironment plays a crucial role in mediating the tumor immune response, thereby affecting patient outcomes in follicular lymphoma (FL). Using CyTOF, we analyzed single cell suspensions created from pre-treatment biopsies in a cohort of 82 FL patients and retrospectively extracted patient clinical parameters. Hierarchical analysis of the tumor microenvironment composition stratified these patients into 4 groups: group 1 represents patients with high percentage of monocyte/macrophages/NK cells; patients from group 2 and 3 are rich of intratumoral T and B cells, respectively. Patients with intermediate number of T and B cells are classed into group 4. Intratumoral T cells from these 4 groups exhibited distinct phenotypical characteristics. Group 1 was enriched with KLRG1 +CD8 + T cells, while group 4 showed increased number of CXCR3 - T EM and CD4 + T N cells. Patients from group 2 and 3 featured with increased number of ICOS + T reg cells and CD57 + T FH cells, respectively. We observed that higher numbers of T cells significantly correlated with early disease stage, lack of B-symptoms, event-free survival at 24 months (EFS24) and a favorable overall and EFS survival in patients with histological grade 1 and 2. However, this association was not seen in patients with histological grade 3a and b, suggesting that T cell-mediated anti-tumor immunity is preserved in low grade patients, but overridden in higher grade patients. CITRUS analysis revealed that a cluster that is CD57 + and PD-1 high was significantly more abundant in patients who had disease progression than patients who had a complete response to therapy, suggesting a role of CD57 + T FH cells in promoting malignant cell growth in FL. Consistent with this finding, we observed that higher number of CD57 + T FH cells correlated with an inferior survival in FL. Using CITE-seq technology, we found that CD57 + T FH cells exhibited a substantially different transcriptome when compared to CD57 - T FH cells. Genes that were differentially upregulated in CD57 + T FH cells when compared to CD57 - T FH cells included genes involved in cell survival, compromised inflammatory response, and metabolism activation including GZMK, CCL4, CST7, DUSP2, LGALS3, CYTOR, CHI3L2, SYNE2, CXCL13, CD27 and FABP5. Taken together, our results indicate different tumor microenvironments among FL patient groups that is associated with variable T-cell phenotype. We found that CD57 + T FH cells play an important role in predicting disease progression and patient prognosis in FL. Figure 1 Figure 1. Disclosures Novak: Celgene/BMS: Research Funding. Ansell: Bristol Myers Squibb, ADC Therapeutics, Seattle Genetics, Regeneron, Affimed, AI Therapeutics, Pfizer, Trillium and Takeda: Research Funding.

Abstract Background: Follicular lymphoma (FL) exhibits significant clinical, cellular, molecular, and genetic heterogeneity. Our understanding of FL biology and molecular classifications of FL, to date, has largely been driven by pathologic classification (Grade 1-3b), genetic classification (m7-FLIPI), or gene expression profiling (IR-1/2; Huet-23), along with limited studies on small cohorts or targeted panels. In order to further understand the biological underpinnings and complexity of FL, large-scale and integrated whole exome sequencing (WES) and RNA sequencing (RNAseq) studies are needed. Using a highly-annotated cohort of 93 FL tumors with matched RNAseq, WES, and CyTOF data, we have explored the transcriptomic signature of purified FL B cells and identified unique molecular subsets that are defined by distinct pathway activation, immune content, and genomic signatures. Methods: Frozen cell suspensions from 93 untreated FL (Grade 1-3b) patients' tumor biopsies, enrolled in the University of Iowa/Mayo Clinic Lymphoma SPORE, were used for the study. DNA was isolated from whole tumor cell suspensions, and RNA was isolated from both whole tumor and B cell-enriched cell suspensions. RNAseq and WES were performed in the Mayo Clinic Genome Analysis Core. RNAseq and WES data were processed using a standard pipeline and novel driver genes were identified using Chasm+ driver analysis. Copy number variants were identified from WES data using GISTIC 2.0. NMF clustering and single sample gene set testing for B cell lineage and tumor microenvironment (TME) signatures were performed in R using the NMF and singscore packages. Results: Unsupervised clustering of RNAseq data identified three distinct expression programs which separated patient B cell samples into 3 groups: Group 1 (G1, n=20), Group 2 (G2, n=24), Group 3 (G3, n=43). While no clinical attributes were defined by any single group, G1 consisted of cases that had less aggressive characteristics (63% Stage I-II, 79% FLIPI 0-1). To identify unique transcriptional pathways driving the three expression programs, we scored gene level contributions to NMF expression programs and employed gene set enrichment analysis. This revealed significant pathway association with type-I IFN signaling (G1), DNA repair and stress response (G2), and epigenetic modulation (G3) as differentiating programs between the 3 groups (FDR q<0.001). VIPER master regulator activity inferencing revealed that these pathways were likely being controlled by differential activity in NF-kB, IRFs, STAT1, BCL6, and FOXO1. Each program significantly enriched for, but were not defined by, portions of specific germinal center programs, such as pre-memory (G1), light-zone-to-dark-zone transition (G2), and a pre-light-zone intermediate (G3). We next assessed the connection between B cell programs and the tumor microenvironment (TME) using available paired CyTOF data on 67 cases, which revealed an active TME in G1, with an abundance of CD8 T cell and NK cell populations, a wide variety of immune content in G2 that consisted mostly of Tfh and myeloid cells, and a poorly populated immune compartment in G3 compared to G1 and G2. Finally, somatic driver mutations and copy number alterations from WES were identified and associated with the three clusters. The three groups distinguished themselves by significant enrichment of copy number alterations (TNFAIP3-loss , 1q23-gain, 1q32-gain) in G2, while 10q-loss and mutations in BCL2 and chromatin modifiers (KMT2D and CREBBP) enriched in G3. G1, overall, had lower alteration burden and had weak associations with any specific alterations, suggesting an alternative mechanism for driving the G1 program. Conclusion: In this study, we have identified three unique FL tumor B cell groups, defined by specific transcriptional programs. Pathways such as inflammation, DNA damage response, and chromatin modification contribute most to the differences between B cell samples and group membership. Additionally, each program associated with specific genetic events as well as TME composition, highlighting potential drivers of these B cell states. This study improves the understanding of the mechanisms driving FL tumors and motivates further investigation into transcriptional consequences of genetic events as well as potential tumor intrinsic factors that may influence the TME. Figure 1 Figure 1. Disclosures Maurer: BMS: Research Funding; Genentech: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees, Research Funding; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Nanostring: Research Funding. Rimsza: NanoString Technologies: Other: Fee-for-service contract. Link: MEI: Consultancy; Genentech/Roche: Consultancy, Research Funding; Novartis, Jannsen: Research Funding. Habermann: Tess Therapeutics: Other: Data Monitoring Committee; Seagen: Other: Data Monitoring Committee; Incyte: Other: Scientific Advisory Board; Morphosys: Other: Scientific Advisory Board; Loxo Oncology: Other: Scientific Advisory Board; Eli Lilly & Co.,: Other: Scientific Advisor. Ansell: Bristol Myers Squibb, ADC Therapeutics, Seattle Genetics, Regeneron, Affimed, AI Therapeutics, Pfizer, Trillium and Takeda: Research Funding. King: Celgene/BMS: Research Funding. Cerhan: Genentech: Research Funding; Regeneron Genetics Center: Other: Research Collaboration; Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; NanoString: Research Funding. Novak: Celgene/BMS: Research Funding.