Faculty Bibliography

BACKGROUND:Dickkopf-related protein 1 (DKK1) is a Wingless-related integrate site (Wnt) signaling modulator that is upregulated in prostate cancers (PCa) with low androgen receptor expression. DKN-01, an IgG4 that neutralizes DKK1, delays PCa growth in pre-clinical DKK1-expressing models. These data provided the rationale for a clinical trial testing DKN-01 in patients with metastatic castration-resistant PCa (mCRPC). METHODS:(combination) for men with mCRPC who progressed on ≥1 AR signaling inhibitors. DKK1 status was determined by RNA in-situ expression. The primary endpoint of the phase 1 dose escalation cohorts was the determination of the recommended phase 2 dose (RP2D). The primary endpoint of the phase 2 expansion cohorts was objective response rate by iRECIST criteria in patients treated with the combination. RESULTS:18 pts were enrolled into the study-10 patients in the monotherapy cohorts and 8 patients in the combination cohorts. No DLTs were observed and DKN-01 600 mg was determined as the RP2D. A best overall response of stable disease occurred in two out of seven (29%) evaluable patients in the monotherapy cohort. In the combination cohort, five out of seven (71%) evaluable patients had a partial response (PR). A median rPFS of 5.7 months was observed in the combination cohort. In the combination cohort, the median tumoral DKK1 expression H-score was 0.75 and the rPFS observed was similar between patients with DKK1 H-score ≥1 versus H-score = 0. CONCLUSION/CONCLUSIONS:DKN-01 600 mg was well tolerated. DKK1 blockade has modest anti-tumor activity as a monotherapy for mCRPC. Anti-tumor activity was observed in the combination cohorts, but the response duration was limited. DKK1 expression in the majority of mCRPC is low and did not clearly correlate with anti-tumor activity of DKN-01 plus docetaxel.

Hepato-pancreatico-biliary (HPB) malignancies are difficult-to-treat and continue to to have a high mortality and significant therapeutic resistance to standard therapies. Immune oncology (IO) therapies have demonstrated efficacy in several solid malignancies when combined with chemotherapy, whereas response rates in pancreatic ductal adenocarcinoma (PDA) are poor. While promising in hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA), there remains an unmet need to fully leverage IO therapies to treat HPB tumors. We therefore defined T cell subsets in the tumor microenvironment of HPB patients utilizing a novel, multiparameter flow cytometry and bioinformatics analysis. Our findings quantify the T cell phenotypic states in relation to checkpoint receptor expression. We demonstrate the presence of CD103⁺ tissue resident memory T cells (T_RM), CCR7⁺ central memory T cells, and CD57⁺ terminally differentiated effector cells across all HPB cancers, while the anti-tumor function was dampened by expression of multiple co-inhibitory checkpoint receptors. Terminally exhausted T cells lacking co-stimulatory receptors were more prevalent in PDA, whereas partially exhausted T cells expressing both co-inhibitory and co-stimulatory receptors were most prevalent in HCC, especially in early stage. HCC patients had significantly higher T_RM with a phenotype that could confer restored activation in response to immune checkpoint therapies. Further, we found a lack of robust alteration in T cell activation state or checkpoint expression in response to chemotherapy in PDA patients. These results support that HCC patients might benefit most from combined checkpoint therapies, whereas efforts other than cytotoxic chemotherapy will likely be necessary to increase overall T cell activation in CCA and PDA for future clinical development.

[This corrects the article DOI: 10.3389/fimmu.2023.1067352.].

T-cell activation is a key step in the amplification of an immune response. Over the course of an immune response, cells may be chronically stimulated, with some proportion becoming exhausted; an enormous number of molecules are involved in this process. There remain a number of questions about the process, namely: (1) what degree of heterogeneity and plasticity do T-cells exhibit during stimulation? (2) how many unique cell states define chronic stimulation? and (3) what markers discriminate activated from exhausted cells? We addressed these questions by performing single-cell multiomic analysis to simultaneously measure expression of 38 proteins and 399 genes in human T cells expanded in vitro. This approach allowed us to study -with unprecedented depth-how T cells change over the course of chronic stimulation. Comprehensive immunophenotypic and transcriptomic analysis at day 0 enabled a refined characterization of T-cell maturational states and the identification of a donor-specific subset of terminally differentiated T-cells that would have been otherwise overlooked using canonical cell classification schema. As expected, activation downregulated naïve-cell markers and upregulated effector molecules, proliferation regulators, co-inhibitory and co-stimulatory receptors. Our deep kinetic analysis further revealed clusters of proteins and genes identifying unique states of activation, defined by markers temporarily expressed upon 3 days of stimulation (PD-1, CD69, LTA), markers constitutively expressed throughout chronic activation (CD25, GITR, LGALS1), and markers uniquely up-regulated upon 14 days of stimulation (CD39, ENTPD1, TNFDF10); expression of these markers could be associated with the emergence of short-lived cell types. Notably, different ratios of cells expressing activation or exhaustion markers were measured at each time point. These data reveal the high heterogeneity and plasticity of chronically stimulated T cells. Our study demonstrates the power of a single-cell multiomic approach to comprehensively characterize T-cells and to precisely monitor changes in differentiation, activation, and exhaustion signatures during cell stimulation.

Background Classical Hodgkin lymphoma (cHL) is characterized by rare, malignant Hodgkin/Reed Sternberg (HRS) cells that shape their microenvironment (TME) to inhibit anti-tumor immune response. Systemic immune dysregulation may influence treatment response and toxicity, but the systemic influence of the TME is less well described. The wide variety of proteins measured in high-parmater flow cytometry make it a powerful tool for immune monitoring, but presents challenges in immuno-monitoring. Combinatorial expression of these proteins defines cell types that may influence disease. TerraFlow is a fully automated data analysis platform that evaluates millions of phenotypes and selects the populations that best predict clinical variables. The analysis can be performed using classical Boolean gates or a non-gating approach that approximates gates without using manual thresholds, allowing immunophenotypes to be comprehensively surveyed for disease associations. The platform was used to find phenotypes that discriminate healthy versus cHL patients (AUC = 1) and pre versus post treatment patient phenotypes(AUC = 0.79). Methods Human Subjects: Informed consent was obtained from cHL patients (N=44) treated at the Perlmutter Cancer Center (PCC) at NYU Langone Health and New York Presbyterian Weil Cornell (NYP) between 2011 and 2016. Blood samples were drawn at multiple time-points, for this study pre-treatment and 3 month post-treatment samples were used. Age-matched, cryopreserved healthy donor PBMC (n=25) were obtained from STEMCELL Technologies (Cambridge, MA).Patient-derived blood was processed for isolation of PBMC, stained analyzed on a Symphony Flow Cytometer (BD Biosciences, San Jose, CA). Analysis: Data was analyzed using an original platform called terraFlow. Many immune cell subsets are defined by the combinations of proteins they express. TerraFlow systematically evaluates millions of cell types by generating every possible combination of 1 to 5 markers. A network-based algorithm then selects the "best" phenotype from each set of inter-related combinations based on statistical power and ease of interpretation. Each phenotype is defined using a minimal gating strategy that can be replicated in a diagnostic panel or cell sorter. Together, phenotypes describe all the major differences between patient groups. A new platform developed by Epistemic AI was used to mine scientific literature and interpret selected phenotypes. Results We observed clear perturbations in the cHL systemic T-cell compartment pre-treatment as shown in Figure 1. These include higher levels of activated (CD278+), exhausted (CD366+, PD1+, CD152+), and suppressive (GITR+) T-cells compared to healthy donors, and diminished levels of T-cells producing effector cytokines (like IFNgamma and IL4). Subsets of cytokine-producing cells that co-express markers of exhaustion (i.e., TNF+ CD366+ cells) are also elevated in cHL patients. Finally, T-cells expressing CD127 a receptor for IL7 involved in homeostatic renewal of cells and observed on naive and central memory T-cells are reduced. Taken together, these findings suggest that in cHL the systemic T-cell compartment is shifted toward a more exhausted profile, and away from less differentiated cells, with the potential for self-renewal. Our data also demonstrates a shift from T-helper 1 and T-helper 2 type toward T-helper 17 cells suggesting that T-cell effector function may be reduced. Conclusion Using a novel data analysis platform, TerraFlow we demonstrate dysregulation in systemic T cell function in cHL patients pre-treatment that persists within 3 months of completing therapy. Associations of phenotypes with clinical variables, and post-treatment phenotypes will be described in detail at the meeting. Our results detail new immunotherapy and biomarker research targets, and suggest novel strategies for combination therapies. [Formula presented] Disclosures: Li: BD Bioscience: Current Employment. Ruan: Kite Pharma: Consultancy; AstraZeneca: Research Funding; BMS: Consultancy, Research Funding; Daiichi Sankyo: Consultancy, Research Funding; Pharmacyclics: Research Funding; Seagen: Consultancy. Diefenbach: Incyte: Research Funding; Trillium: Research Funding; Celgene: Research Funding; IGM Biosciences: Research Funding; Seattle Genetics: Consultancy, Honoraria, Research Funding; Gilead: Current equity holder in publicly-traded company; AbbVie: Research Funding; Perlmutter Cancer Center at NYU Langone Health: Current Employment; MEI: Consultancy, Research Funding; Genentech, Inc./ F. Hoffmann-La Roche Ltd: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; IMab: Research Funding; Morphosys: Consultancy, Honoraria, Research Funding; Merck Sharp & Dohme: Consultancy, Honoraria, Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria, Research Funding.
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INTRODUCTION Lymphoma cells are dependent on the tumor microenvironment (TME) for their survival and proliferation. Dissection of TME composition provides insight into lymphomagenesis, better prognostication, and enhancement of therapeutic options. Flow cytometry provides a robust approach for single-cell analysis. Nonetheless lack of well-defined comparator groups and/or a robust, reproducible approaches for analyzing the multidimensional data often limited such studies. In the current study, we analyzed the T cell background on a large reference set of follicular hyperplasia (FH) lymph node samples utilizing a robust standardized high dimensionality flow cytometry and a novel reproducible analytical pipeline. We then compared this baseline reference set with tumor infiltrating T cell subsets in follicular lymphoma (FL; in treatment naive FL-NA and relapsed/refractory FL-RR sets). METHODS On behalf of the imCORE Network we analyzed 44 FH and 81 FL (35 FL-NA and 44 FL-RR) with 2 standardized flow cytometry panels (23 antigen/18-color) (Table 1). We evaluated the T cell subset distribution as well as immune checkpoint expression (TIGIT, TIM3, PD-1, CD96, LAG3, CTLA4, CD73) within analytically defined T cell clusters. Analysis was performed using semiautomated multi-step analytical pipeline as outlined in Figure 1. Using standardized instrument settings and an R-based algorithms (gaussNorm) to minimize technical variations in sample acquisition we analyzed the T-cells subpopulations using a dimensionality reduction technique (UMAP), combined with an unsupervised clustering algorithm (FlowSOM). Statistical analysis was performed using non-parametric Wilcoxon test. RESULTS In the 3 cohorts, several marked differences in the composition T cells subsets were observed. Compared to FH the FL lymph nodes were depleted for CD4 & CD8 naive subsets (Table 2) and were characterized by an immune suppressive microenvironment enriched in specific subsets of activated T regulatory cells and exhausted memory effector cells. The pool of CD8 naive cells was restored in FL-RR cases (Table 2). FL-NA nodes showed enrichment of T follicular regulatory cells (Tfr; Table 2) (0.9% vs 2.7%, p<0.0001) expressing FoxP3, dim to negative CD25, CD45RO, TIGIT, PD1, CTLA and CXCR5 (Table 2) and activated regulatory T cells (T-Reg) characterized by a highly suppressive phenotype CD25+, CTLA4+, TIGIT+ and PD1+ (Fig. 2B). Moreover, Tfr compartment was further expanded in relapsed/refractory FL cases (2.7% vs 4.5%, p=0.02). In association with the increase of Tfr cells, the concentration of CTLA4+, TIGIT+, PD1bright T follicular helper cells (Tfh) was reduced in FL-RR compared to FL-NA (Fig. 2C). The Tfr/Treg expansion was also associated with a marked increase in exhausted memory T cells in both CD4 and CD8 compartments (CD4 EM TP and CD8 EM DP, Table 2). In FL isolates the CD4 compartment was characterized by the expression of triple positive (CTLA4, TIGIT, PD1) phenotype in EM cells while the memory CD8 cells overexpressed TIGIT and PD1 (Fig. 2D). A smaller subpopulation of memory CD8 cells, almost undetectable in FH samples, characterized by the expression of CTLA4, PD1, TIGIT and TIM3 is expanded in FL isolates (Fig. 2D). CONCLUSION Our data suggest that change in balance between TFH and Tfr may lead to more aggressive therapy resistant disease in FL. The interplay between TFH and Tfr, has been postulated to shape the immune response in FL with TFH promoting germinal center formation and Tfr inhibiting TFH and follicular effector T cells. While both TFH and Tfr compartments are expanded in FL, in the relapsed/ refractory FL cases, the Tfr compartment is further expanded at the expense of TFH leading to more immunosuppressive background. Furthermore our study suggests a rational way of designing immune checkpoint inhibitor studies in FL. Effector memory T-cells in FL isolates show an exhausted phenotype characterized by the expression of the inhibitory receptors CTLA4, TIGIT, PD1 in the CD4 compartment, and TIGIT, PD1 in the CD8. In addition, the noteworthy expansion of TIM3+ memory CD8 cells in FL. Targeting these most highly expressed checkpoints in FL alone or in combination may provide an avenue for rational trial design. [Formula presented] Disclosures: Roshal: Celgene: Other: Provision of services; Auron Therapeutics: Other: Ownership / Equity interests; Provision of services; Physicians' Education Resource: Other: Provision of services. Dogan: Physicians' Education Resource: Honoraria; Peer View: Honoraria; Roche: Consultancy, Research Funding; Takeda: Consultancy, Research Funding; Seattle Genetics: Consultancy; EUSA Pharma: Consultancy.
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Maintenance of immune homeostasis involves a synergistic relationship between the host and the microbiome. Canonical interferon (IFN) signaling controls responses to acute microbial infection, through engagement of the STAT1 transcription factor. However, the contribution of tonic levels of IFN to immune homeostasis in the absence of acute infection remains largely unexplored. We report that STAT1 KO mice spontaneously developed an inflammatory disease marked by myeloid hyperplasia and splenic accumulation of hematopoietic stem cells. Moreover, these animals developed inflammatory bowel disease. Profiling gut bacteria revealed a profound dysbiosis in the absence of tonic IFN signaling, which triggered expansion of T_H17 cells and loss of splenic T_reg cells. Reduction of bacterial load by antibiotic treatment averted the T_H17 bias and blocking IL17 signaling prevented myeloid expansion and splenic stem cell accumulation. Thus, tonic IFNs regulate gut microbial ecology, which is crucial for maintaining physiologic immune homeostasis and preventing inflammation.

Cytometry is playing a crucial role in addressing the COVID-19 pandemic. In this commentary - written by a variety of stakeholders in the cytometry, immunology, and infectious disease communities - we review cytometry's role in the COVID-19 response, and discuss workflow issues critical to planning and executing effective research in this emerging field. We discuss sample procurement and processing, biosafety, technology options, data sharing, and the translation of research findings into clinical environments. This article is protected by copyright. All rights reserved.

Over a remarkably short period of time, a great deal of knowledge about severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) infection has been acquired, through the focused and cooperative effort of the international scientific community. Much has become known about how the immune response is coordinated to fight infection, and how it becomes dysregulated in severe disease. In this review, we take an in-depth look at the many immune features associated with the host response to SARS-CoV2, as well as those that appear to mark severe disease.

BACKGROUND:The reshaping of the immune landscape by nivolumab (NIVO) and ipilimumab (IPI) and its relation to patient outcomes is not well-described. METHODS:We used high-parameter flow cytometry and a novel computational platform, CytoBrute, to define immunophenotypes of up to 15 markers to assess peripheral blood samples from metastatic melanoma patients receiving sequential NIVO>IPI or IPI>NIVO (CheckMate-064). RESULTS:The two treatments were associated with distinct immunophenotypic changes and had differing profiles associated with response. Only two immunophenotypes were shared but had opposing relationships to response/survival. To understand the impact of sequential treatment on response/survival, phenotypes that changed after the initial treatment and differentiated response in the other cohort were identified. Immunophenotypic changes occurring post-NIVO were predominately associated with response to IPI>NIVO, but changes occurring post-IPI were predominately associated with progression after NIVO>IPI. Among these changes, CD4+CD38+CD39+CD127-GARP- T-cell subsets were increased after IPI treatment and were negatively associated with response/survival for the NIVO>IPI cohort. CONCLUSION/CONCLUSIONS:Collectively, these data suggest that the impact of IPI and NIVO on the immunophenotypic landscape of patients is distinct and that the impact of IPI may be associated with resistance to subsequent NIVO therapy, consistent with poor outcomes in the IPI>NIVO cohort of Checkmate-064.