Dicke, Betty A.

Abstract Background: Neuropilin 2 (NRP2) is a co-receptor that enhances signaling of growth factor and cytokine receptors and is expressed in tumor and microenvironment (TME) cells in melanoma and other tumors. Neuropilin 2 expression in tumor cells is associated with more aggressive tumors. Expression in tumor associated macrophages enhances efferocytosis and immunologically silent tumor cell death. Methods: Whole excisional lymph node biopsies were obtained from 10 treatment naïve patients with metastatic cutaneous melanoma who subsequently received anti-PD1 treatment. A single 5mm formalin-fixed paraffin embedded tissue section was used to assess a panel of 39 analytes by cyclic multiplex immunofluorescence (MxIF). Fields of view (~1mm2, n=288) were selected from pathologist-annotated regions of the tumor core, tumor-immune interface and adjacent lymphoid tissue. Pixel-based single cell segmentation and a supervised classifier approach was applied to resolve 12 distinct tumor, stromal and immune cell phenotypes and functional states (NRP2 positive or negative) in 1.5 million cells. Results: Tumor recurrence was observed in 6 patients (relapsed), whereas 4 patients were recurrence-free (non-relapsed) during follow up. Among the different cell types, NRP2 expression was more frequent in macrophages (44%), tumor cells (40%), dendritic cells (40%), followed by Tregs (31%) and T helper cells (29%), and less frequent in neutrophils (24%), cytotoxic T cells (23%) and B cells (15%). In colocalization permutation analysis, NRP2 expressing macrophages clustered away from tumor cells in non-relapsed patients while a random spatial distribution of NRP2 expressing macrophages with respect to tumor cells was observed in relapsed patients. In comparing the odds of tumor cell interactions with NRP2-expressing macrophages to those with NRP2 non-expressing macrophages, we found in the tumor:stromal interface (10% tumor cells) that cells from both non-relapsed and relapsed patients were more likely to interact with NRP2 non-expressing macrophages (OR = 0.73, 95% CI = [0.71, 0.75] and OR = 0.56, 95% CI = [0.55, 0.58] for non-relapsed and relapsed patients, respectively). However, in the tumor core (90% tumor cells), tumor cells from relapsed patients had significantly higher odds of interacting with NRP2 expressing macrophages, (OR = 1.15, 95% CI = [1.12, 1.18]), and this was not seen for non-relapsed patients (OR = 0.68, 95% CI = [0.66, 0.71]). In FOVs dominated by tumor cells (75% tumor), tumor cells from relapsed patients had 4.97 times the odds of expressing NRP2 compared to tumor cells from non-relapsed patients (95% CI = [2.23, 11.09], p = 0.0001). Conclusions: These spatial analytics suggest complex interactions among NRP2 expression in tumor and TME of melanoma determining risk for relapse with anti-PD1 treatment. Citation Format: Anastasios Dimou, Raymond M. Moore, Caitlin Ward, Alexey Leontovich, Ruifeng Guo, Chen Wang, Shankar Suman, Betty Dicke, Jill M. Schimke, Noah A. Stueven, Chathu A. Atherton, Wendy Nevala, Svetomir N. Markovic. Spatial analysis of neuropilin 2 expression in the microenvironment of melanoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2905.

Direct interactions between tumor and immune cells mediate the antitumor effect of all modern cancer immunotherapeutic agents. Simultaneously, tumor cells have evolved mechanisms of evasion, including the downregulation of HLA-I, potentially disrupting the mechanism of action employed by many immune checkpoint inhibitors. And yet, the in situ interplay between these cells within the tumor immune microenvironment (TIME) remains elusive. Recent advances in histologic multiplex bioimaging platforms have enabled in-depth molecular characterization of single cells within spatially preserved and clinically archived tumor tissues. Herein, we applied multiplex immunofluorescence to excisional lymph node biopsies from 14 patients with metastatic melanoma who experienced clear objective responses to immunotherapy (7 complete response; 7 progressive disease) to determine distinguishing features of the TIME in the pretreatment setting. Distinct regions of the TIME were evaluated using 35 proteins probing tumor, immune, and vasculature components across 323 fields of view. Single-cell compositional analysis confirmed established prognostic immune cell types including increased prevalence of cytotoxic T cells within the tumor core fields of view of responders. Integrating single-cell quantification with the spatial arrangement of cellular neighborhoods surrounding tumor cells revealed novel, spatial immune signatures capable of stratifying TIME based on clinical response. Our analysis revealed dynamic cellular composition of the tumor-centric cellular neighborhood (TCCN) based on anatomic subregion, functional expression of HLA-I by the index tumor cell and ultimately clinical response to immunotherapy. Overall, this study provides an analytic framework to resolve the cellular complexity of the TIME, increasingly relevant to the outcomes of modern cancer immunotherapy. Significance: Findings from this work propose a novel approach to resolving clinical heterogeneity of the TIME by objectively quantifying the cellular interactions occurring in metastatic melanoma lymph node tissue utilizing multiplex immunofluorescence. This study provides an analytic and biologically derived unit of measure, the TCCN which is customizable for studying critical paracrine interactions within spatially preserved tissue of various cancers and across the spectrum of multiplex imaging modalities.

e15184 Background: Clinical responses to anti-PD1 immunotherapy in patients with metastatic melanoma (MM) remain challenging to predict. This clinical heterogeneity is also reflected in the tumor-immune microenvironment among patients and within a single tumor lesion. With the emergence of multiplex imaging platforms, defining complex phenotypes at single cell resolution has become possible. Here, we sought to objectively quantify paracrine tumor-immune interactions contributing to the variable clinical responses observed in patients receiving anti-PD1 therapy. Methods: Excisional lymph node (LN) biopsies were obtained from treatment-naïve patients with MM who underwent subsequent anti-PD1 therapy. A single 5µm section of LN tissue was used to assess a 42 analyte panel by multiplex immunofluorescence. From 30 LN samples, 418 fields of view (FOVs) were selected resulting in 14,360 high-resolution images of 4 anatomical subregions: tumor core, tumor-immune interface, tumor infiltrate and adjacent immune stroma. Following image processing, we developed an adaptive classification for tumor-centric cellular neighborhoods (TCCN) to identify and quantify critical paracrine interactions within the tumor-immune microenvironment. Results: Stratification based on responsiveness to anti-PD1 therapy resulted in 4 complete responders (CR) and 12 non-responders (NR) at 12-week follow-up. From 126 FOVs, we defined the cellular composition of 197,865 TCCN across patients based on clinical response and LN subregions. Overall, the percentage of TCCN devoid of any T cells, B cells or macrophages was significantly higher in NR compared to CR irrespective of subregion. However, other markers differentiated TCCN based on subregion. Specifically in CR, tumor core regions were enriched for CD8 T cells, while enrichment for B cells and endothelial cells was demonstrated at the tumor-immune interface. Strikingly, tumor infiltrate regions demonstrate robust immune reactivity with enrichment for M1 polarized macrophages, NK cells and B cells in CR compared to NR. Complete data from the 30 patient cohort across 418 FOVs will be presented. Conclusions: Taken together, this data suggests cellular composition of TCCN across subregions of the LN is dynamic within a single metastatic site. In this small cohort, we introduce a formalized stratification to quantify and classify critical paracrine interactions within the immune-tumor microenvironment with the potential to inform clinical responsiveness to therapy.

OBJECTIVE—Measurement of plasma C2 glucose enrichment is cumbersome. Therefore, the plasma C5 glucose–to–2H2O rather than the plasma C5-to-C2 glucose ratio commonly has been used to measure gluconeogenesis and glycogenolysis during hyperinsulinemic-euglycemic clamps. The validity of this approach is unknown. RESEARCH DESIGN AND METHODS—Ten nondiabetic and 10 diabetic subjects ingested 2H2O the evening before study. The following morning, insulin was infused at a rate of 0.6 mU · kg−1 · min−1 and glucose was clamped at ∼5.3 mmol/l for 5 h. Plasma C5 glucose, C2 glucose, and 2H2O enrichments were measured hourly from 2 h onward. RESULTS—Plasma C2 glucose and plasma 2H2O enrichment were equal in both groups before the clamp, resulting in equivalent estimates of gluconeogenesis and glycogenolysis. In contrast, plasma C2 glucose and plasma C5 glucose enrichments fell throughout the clamp, whereas plasma 2H2O enrichment remained unchanged. Since the C5 glucose concentration and, hence, the C5 glucose–to–2H2O ratio is influenced by both gluconeogenesis and glucose clearance, whereas the C5-to-C2 glucose ratio is only influenced by gluconeogenesis, the C5 glucose–to–2H2O ratio overestimated (P < 0.01) gluconeogenesis during the clamp. This resulted in biologically implausible negative (i.e., calculated rates of gluconeogenesis exceeding total endogenous glucose production) rates of glycogenolysis in both the nondiabetic and diabetic subjects. CONCLUSIONS—Plasma C5 glucose–to–2H2O ratio does not provide an accurate assessment of gluconeogenesis in nondiabetic or diabetic subjects during a traditional (i.e., 2–3 h) hyperinsulinemic-euglycemic clamp. The conclusions of studies that have used this approach need to be reevaluated.

OBJECTIVE—To determine mechanisms by which pioglitazone and metformin effect hepatic and extra-hepatic insulin action. RESEARCH DESIGN AND METHODS—Thirty-one subjects with type 2 diabetes were randomly assigned to pioglitazone (45 mg) or metformin (2,000 mg) for 4 months. RESULTS—Glucose was clamped before and after therapy at ∼5 mmol/l, insulin raised to ∼180 pmol/l, C-peptide suppressed with somatostatin, glucagon replaced at ∼75 pg/ml, and glycerol maintained at ∼200 mmol/l to ensure comparable and equal portal concentrations on all occasions. Insulin-induced stimulation of glucose disappearance did not differ before and after treatment with either pioglitazone (23 ± 3 vs. 24 ± 2 μmol · kg−1 · min−1) or metformin (22 ± 2 vs. 24 ± 3 μmol · kg−1 · min−1). In contrast, pioglitazone enhanced (P < 0.01) insulin-induced suppression of both glucose production (6.0 ± 1.0 vs. 0.2 ± 1.6 μmol · kg−1 · min−1) and gluconeogenesis (n = 11; 4.5 ± 0.9 vs. 0.8 ± 1.2 μmol · kg−1 · min−1). Metformin did not alter either suppression of glucose production (5.8 ± 1.0 vs. 5.0 ± 0.8 μmol · kg−1 · min−1) or gluconeogenesis (n = 9; 3.7 ± 0.8 vs. 2.6 ± 0.7 μmol · kg−1 · min−1). Insulin-induced suppression of free fatty acids was greater (P < 0.05) after treatment with pioglitazone (0.14 ± 0.03 vs. 0.06 ± 0.01 mmol/l) but unchanged with metformin (0.12 ± 0.03 vs. 0.15 ± 0.07 mmol/l). CONCLUSIONS—Thus, relative to metformin, pioglitazone improves hepatic insulin action in people with type 2 diabetes, partly by enhancing insulin-induced suppression of gluconeogenesis. On the other hand, both drugs have comparable effects on insulin-induced stimulation of glucose uptake.

To determine whether the hepatic insulin resistance of obesity and type 2 diabetes is due to impaired insulin-induced suppression of glycogenolysis as well as gluconeogenesis, 10 lean nondiabetic, 10 obese nondiabetic, and 11 obese type 2 diabetic subjects were studied after an overnight fast and during a hyperinsulinemic-euglycemic clamp. Gluconeogenesis and glycogenolysis were measured using the deuterated water method. Before the clamp, when glucose and insulin concentrations differed among the three groups, gluconeogenesis was higher in the diabetic than in the obese nondiabetic subjects (P < 0.05) and glycogenolysis was higher in the diabetic than in the lean nondiabetic subjects (P < 0.05). During the clamp, when glucose and insulin concentrations were matched and glucagon concentrations were suppressed, both glycogenolysis and gluconeogenesis were higher (P < 0.01) in the diabetic versus the obese and lean nondiabetic subjects. Furthermore, glycogenolysis and gluconeogenesis were higher (P < 0.01) in the obese than in the lean nondiabetic subjects. Plasma free fatty acid concentrations correlated (P < 0.001) with glucose production and gluconeogenesis both before and during the clamp and with glycogenolysis during the clamp (P < 0.01). We concluded that defects in the regulation of glycogenolysis as well as gluconeogenesis cause hepatic insulin resistance in obese nondiabetic and type 2 diabetic humans.