Spatial and molecular profiling of classic Hodgkin lymphoma reveals an immunosuppressive mononuclear phagocyte network

doi:10.21203/rs.3.rs-1233380/v2

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Spatial and molecular profiling of classic Hodgkin lymphoma reveals an immunosuppressive mononuclear phagocyte network

https://doi.org/10.21203/rs.3.rs-1233380/v2

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posted 14 Mar, 2022

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Although a lymph node infiltrated by classic Hodgkin lymphoma (cHL) is mostly composed of nonneoplastic immune cells, the malignant Hodgkin Reed-Sternberg cells (HRSC) successfully suppress an anti-tumor immune response, creating a cancer-permissive microenvironment. Accordingly, unleashing the dormant immune cells, for example by checkpoint inhibition, has been a central focus of recent therapeutic advances for this disease. Despite the efficacy of PD-1 blockade in relapsed cHL, a significant proportion of patients have suboptimal or non-durable responses, which may reflect HRSC and microenvironmental adaptation.

Here, we profiled the global immune cell composition of normal and diseased lymph nodes by singlecell RNA sequencing, as a basis for interrogating the immediate vicinity of HRSC. We did so regionally and at cellular resolution, using spatial transcriptomics and multiplexed immunofluorescence, on fixed cHL tissue sections. It is established that tumor associated macrophages (TAMs) are associated with inferior outcomes following combination chemotherapy, but the function, interactions, and distribution of TAMs, and other mononuclear phagocytes, have not been fully explored.

Our analyses revealed specific immune cells and functional states associated with HRSC. We discovered a non-random spatial organization of immunoregulatory mononuclear phagocytes (TAMs and classical monocytes) around HRSC, which express the immune checkpoints PD-L1, TIM-3, and the tryptophan-catabolizing protein IDO1. Dendritic cells (DC), key antigen presenting cells, are regionally polarized according to subtype. Specific DCs are spatially associated with the HRSC ‘neighborhood’ (cDC2), but plasmacytoid DCs and ‘activated’ DCs are excluded. These findings provide a basis for rational targeting and activation of the anti-tumor immune response in cHL.

Lymphoid neoplasia

Hodgkin lymphoma

Hodgkin's disease

Classic Hodgkin lymphoma

tumor microenvironment

microenvironment

dendritic cells

mononuclear phagocytes

classical monocytes

macrophages

Hodgkin cells

immunobiology

immunotherapy

Transcriptomics

proteomics

Mononuclear phagocytes are signaling hubs around malignant Reed-Sternberg cells in Classic Hodgkin lymphoma, potentiating immunosuppression.
Dendritic cells are spatially polarized, with cDC2 closely associated with malignant cells, and plasmacytoid DCs and activated DCs excluded.

Classic Hodgkin lymphoma (cHL) accounts for 12-15% of lymphoma diagnoses and occurs most frequently in adolescents and young adults, with another peak of incidence in older adults. Multi-agent cytotoxic chemotherapy (including selective use of radiotherapy) is curative in 80-95% of patients; in adults the potential for cure is largely determined by the disease stage^1–3. However, for patients with relapsed or refractory disease, the prospect of cure or long-term disease control is significantly diminished.

A cHL tumor is characterized by a minority population of neoplastic CD30⁺ Hodgkin Reed-Sternberg Cells (HRSC), comprising 1-5% of cells, derived from germinal center (GC) B cells⁴. HRSC survive within a tumor microenvironment (TME), primarily composed of immune cells, containing abundant T-cells and a network of mononuclear phagocytes (MNP), including macrophages, monocytes, and dendritic cells (DC)⁴.

HRSC establish an immunosuppressive niche through multiple mechanisms, including downregulation of major histocompatibility complex (MHC) proteins, and enhanced expression of programmed cell death ligand-1 (PD-L1), resulting from 9p23-p24 copy number gain or amplification^5,6. PD-L1 diminishes T-cell activation by ligation of its cognate receptor PD-1, which has been targeted by anti-PD-1 immune checkpoint blockade in relapsed/refractory disease, with evidence of therapeutic efficacy in the majority of patients^7–9. Indeed, PD-L1 expression by HRSC and immune cells, including CD68⁺ tumor-associated macrophages (TAM), is almost universal in cHL¹⁰. However, the phenotypes, roles, and intercellular communication networks of MNP within the TME remain largely unknown. We hypothesize that MNPs extend and maintain the immunosuppressive niche through multiple mechanisms. Here, we present a robust characterization of the critical intercellular communication networks in the tumor microenvironment, with the aim of identifying novel therapeutic targets.

Single cell transcriptional analysis

To gain an overview of the cellular ecosystem of cHL, we compiled a census of 243,753 single cell transcriptomes, from lymphoma affected and unaffected lymph nodes. We sourced data from Aoki et al¹¹incorporating droplet encapsulation single cell transcriptomes (10X genomics platform) from reactive and cHL nodes (Fig. 1a). Additionally, we performed scRNAseq (10X genomics platform) with cell suspensions from healthy lymph nodes acquired from deceased organ donors, and two lymphoma lymph nodes, one with nodular sclerosis cHL (NSCHL), and one with nodular lymphocyte predominant Hodgkin lymphoma (NLPHL), a biologically distinct subtype of Hodgkin lymphoma (Fig. 1a). Together, the combined dataset comprises tissue from 13 non-lymphoma affected donors (8 deceased donors and 5 donors with reactive lymph node hyperplasia), plus one NLPHL and 23 cHL lymph nodes (Fig. 1a). After quality control (Supplementary Methods, Supplementary Fig. 1a-b), we performed dataset integration and dimensionality reduction using single-cell variational inference¹² and annotated cell types on the basis of marker genes and external dataset validation (Fig. 1b, Supplementary Fig. 1c-e).

Tissue samples for scRNAseq

Healthy lymph nodes, unaffected by neoplastic disease, were acquired from donation after circulatory death (DCD) adult donors at the time of removal of organs for transplantation by the Cambridge Biorepository for Translational Medicine (CBTM) with ethical approval (reference 15/EE/0152, East of England—Cambridge South Research Ethics Committee) and consent from donor families. Lymph node samples were collected from inguinal, mesenteric, and thoracic regions at the end of the organ donation procedure, within 1-2 hours of cessation of circulation under cold ischaemic conditions. Lymph node specimens were maintained in ice-cold 0.9% saline for transport. Tissue was dissociated and processed as previously described¹³. Lymph node data from (290b, 298c, 302c) are shared with James et al. and were processed with additional flow sorting as described¹³. Patients who donated diseased tissue for scRNAseq were enrolled in the ‘Investigating how childhood tumors and congenital disease develop’ study (NHS National Research Ethics Service reference 16/EE/0394).

Single cell RNA sequencing analysis

Single-cell RNA sequencing data from Hodgkin lymphoma specimens were acquired by direct data transfer from the Stiedl lab (University of British Columbia) as a raw counts matrix¹¹. Mapping and quantification of scRNAseq generated for this study (Wellcome Sanger Institute dataset) was performed using the kallisto-bustools toolkit (0.24.4)¹⁴, against the GRCh38 human reference. The unfiltered count matrix was profiled using the defaultDrops function in the DropletUtils package (1.10.3)¹⁵, in R (4.0.4).

We performed data integration with batch correction (using donor as the batch key) and dimensionality reduction concurrently by training a single-cell variational inference model using the scvi-tools python package (version 0.9.0a0)¹². We completed quality control on the integrated dataset (Supplementary methods, Supplementary Fig. 1a-b). A 15-dimensional latent representation was used as input to nearest neighbor graph construction, Uniform Manifold and Approximation (UMAP), and Leiden clustering in scanpy. Marker genes were calculated using the tf-idf metric and genes were ranked by the tf-idf metric, and p values calculated by a hypergeometric test, corrected for multiple testing (Benjamini-Hochberg method)¹⁶. These marker genes were used as a guide to manual annotation of cell types. Cell types were annotated according to canonical marker expression (Supplementary Fig. 1c), supplemented with labeling using the celltypist python package (0.1.15)¹⁷. Concordance between annotated celltype labels and predicted reference annotations was assessed by calculating the Jaccard distance between cell label strings (Supplementary Fig. 1d-e). Cell type predictions between the King et al. B cell atlas¹⁸ was calculated by training a multilayer perceptron classifier on the reference data and calculating predictions on unseen test data, implemented in the scikit learn package (0.24.2)¹⁹.

Relative abundance analysis was performed using the MiloR package (0.99.1)²⁰. The annotated dataset was represented as a k=30 nearest neighbor graph calculated in scVI latent space. This graph representation was partitioned into 22,573 overlapping neighborhoods, and cell counts per 10X channel were calculated per neighborhood. Differential abundance between disease and health was calculated using the testNhoods function, setting the design as ~center + disease, and norm.method=”TMM”.

Ligand receptor analysis was performed using CellPhoneDB (version 2.1.7)²¹. The count matrix was normalized to 1e4 total counts before input to the python implementation of CellPhoneDB which was run with --iterations=1000, --threshold=0.1, and --result-precision=3.

Microarray data analysis

The GEO accession GSE39133 was downloaded using the GEOquery R package (version 2.60.0)²². We performed differential expression between GC and HRSC samples using limma (version 3.48.1)²³ and derived genesets by retaining genes with an absolute log fold change >3 and adjusted p value <0.01.

Transcription factor regulon and network analysis

Transcription factor regulons data were acquired using the dorothea R package (version 1.3.3)²⁴. Regulons with confidence levels “A”, “B”, and “C” were used to calculate transcription factor activities using the run_viper command.

We derived a network graph of transcription factors and targets using data from the OmnipathR R package (version 3.12)²⁵, plotting edges between transcription factors and targets, and between targets using the ggraph R package (version 2.0.5).

Tissue samples for Nanostring and Multiplexed Immunofluorescence

The study was approved by a UK NHS Health Research Authority (HRA) Research Tissue Bank (Novopath Biobank, Newcastle upon Tyne Hospitals NHS Foundation Trust, reference number: 17/NE0070). The Research Tissue Bank released link-anonymized, formalin-fixed and paraffin embedded (FFPE) tissue, from patients with cHL. All tissue samples were surplus to diagnostic requirements and were released in accordance with the terms of the ethical approval.

FFPE tumor biopsies (all excised lymph nodes) were selected from the pathology archives of Newcastle upon Tyne Hospitals NHS Foundation Trust, UK, by members of the Novopath biobank, with appropriate ethical approval (reference: 17/NE0070). Limited, link-anonymized clinical data for study patients were collected by the biobank. Hematoxylin and eosin-stained (H&E) tissue sections and immunohistochemical tissue sections were reviewed by a hematologist, together with the original pathology reports. Fifty-four cases were selected for the study, including biopsies from patients of all ages and histomorphological subtypes (Table 1). Patient biopsies were selected on the basis of high-quality, whole lymph node resection biopsy tissue. Patients with coexisting malignant diagnoses were excluded from the study, even if these were not apparent in the tissue.

Nanostring spatial transcriptomics

Ten of the FFPE cHL tissue biopsies used in the multiplexed immunofluorescence analysis were selected for in situ transcriptomics (Table 1), using the Nanostring GeoMx platform (Nanostring, Seattle, WA). Biopsies were selected based upon year of fixation (2014 onwards), high quality multiplex staining, and successful RNAscope quality control (QC) tests (Advanced Cell Diagnostics, Inc., Newark, CA). Five test slides were prepared, each arrayed with 2 unique, whole tissue biopsy sections; one slide also included reactive lymph node tissue. For each test slide, 4 consecutive 4-μm-thick sections were taken from each tissue block, prepared according to the manufacturer’s instructions, and numbered 1-4: Slide 1, Hematoxylin & Eosin counterstain; Slide 2, CD45 and PAX5 immunostains (fluorescent IHC); Slide 3, CD274 (PD-L1) and PDCD1 (PD-1) - RNAscope probes, DNA counterstain; Slide 4, Oligonucleotide RNA probe hybridization.

Circular regions of interest (ROIs; uniform 300-μm diameter) were labeled upon the CD274/PDCD1 (PD-L1) RNAscope slides, with reference to H&E and IHC slides for histomorphological context. PD-L1 was used to identify HRSC, rather than CD30, as fluorescent labeling with CD274 probes was more robust in optimization experiments. In all selected tumors, the HRSC-dense areas of tissue were known to be PD-L1+, and HRSC were identified by DAPI signal and nuclear morphology. For each tumor, 3 ROIs were selected in ‘PD-L1^high’ areas and 3 ROIs in ‘PD-L1^low’ areas of tissue, unless otherwise stated. Assessments of PD-L1 intensity were made by visual inspection of normalized CD274 probe fluorescent signal within each tumor. Sclerotic bands, germinal centers, and areas of low cellularity were avoided in cHL tissues, by referring to DNA counterstaining and paired IHC. In the control RN tissue, 3 ROIs were labeled in germinal centers and 3 ROIs in interfollicular areas. RNA oligonucleotides from the ‘Cancer Transcriptome Atlas’ (n = 1812 probes), coupled to photocleavable oligonucleotide tags (‘barcodes’), were hybridized overnight. The following day, hybridized barcodes were released from tissue by UV exposure from each ROI sequentially, before being counted and sequenced on the Illumina HiSeq 2500 platform.

Nanostring data analysis

Quality control steps were performed on exported data to exclude outlier probes and then data were normalized against the 75th percentile of signal from each tumor. Following data QC, 1372 (75.7%) probes were retained for analysis. The nanostring expression matrix (ROI-by-gene) was analyzed by principal component analysis (PCA) using the scanpy python package, before constructing a shared-nearest neighbor graph representation. Briefly, we identified k=10 nearest neighbors in PCA space using the FNN R package, before calculating the Jaccard distance between neighbor vectors after the approach of Levine et al²⁶. The resulting graph was clustered using the Leiden algorithm²⁷ with default settings.

Deconvolution was performed by first generating a matrix of one-hot-encodings of marker genes per cell type, including HRSC. We then used the rlm function in the MASS R package to perform robust linear regression. The resulting coefficients were treated as fractions to derive counts by multiplying by the number of nuclei in each ROI. These cell count estimates were then used to calculate differential abundance estimates for each cluster using DESeq2²⁸. Differential expression analysis between clusters was performed using limma (version 3.48.1)²³. The top 10 differentially expressed genes per cluster were selected for plotting (Supplementary Fig. 3c)

Multiplexed immunofluorescence

Multiplexed immunofluorescence (IF) was performed by sequentially immunostaining 4-µm-thick tissue sections from selected FFPE tumor biopsies, with primary antibodies, secondary reagents, and unique fluorochromes. Each of the two multiplexes comprises 6 primary antibodies, followed by a nuclear counterstain, 4’,6-diamidino-2-phenylindole, as per published protocols^10,29,30. All immunostaining was performed on the automated Ventana Benchmark platform (Roche Diagnostics, Rotkreuz, Switzerland). Slides were then air dried, mounted with Prolong Diamond Anti-fade mounting medium (#P36965; Life Technologies) and stored at 4˚C in a light proof box until image acquisition. The target antigens, antibody clones, and dilutions for markers included in this report are listed in Table 2.

Image acquisition

Test regions for multiplex IF analysis were initially identified for each tumor biopsy by reviewing the H&E and immunofluorescence tissue sections at 4x magnification. Three non-overlapping regions of interest were then acquired at 20x magnification for each multiplex panel. Each of these 3 test regions comprised 9 contiguous fields of view (FOVs), arranged as a 3x3 rectangular grid (measuring 2008µm x 1508µm in total). Therefore, there were a total of 27 FOVs per tumor, for each multiplex.

Regions were selected to exclude tissue artifacts, best represent the overall tissue, and to include CD30+ HRSCs. Regions were imaged and deconvoluted using the Vectra multispectral imaging platform (Vectra 3 and Inform 2.4.8, Akoya Biosciences, Marlborough, MA), using specific spectral libraries.

The cellular ecosystem of cHL encompassed subsets of CD4⁺ and CD8⁺ T-cells, including regulatory T-cells (Treg), T follicular helper cells (Tfh), and exhausted CD4⁺ (ThExh) and CD8⁺ T-cells (CD8 TExh) cells (Fig. 1a). ThExh matched the recently identified LAG3⁺ subset, and expressed checkpoint molecules and exhaustion markers including CD27, TNFRSF18, LAG3, and ICOS^11,31 (Fig. 1b, Supplementary Fig. 1f). The B cell compartment split into two large clusters of memory and naive subsets, in addition to plasmablasts, and germinal center B cells (Fig. 1b, Supplementary Fig. 1c-e).

Within the myeloid compartment, we identified macrophages coexpressing CD14, CD68, and the M2 polarization markers FOLR2 and MRC1 (Fig. 1c-d). These cells were transcriptionally distinct from classical monocytes, which expressed a characteristic signature including S100A9, CD14, VCAN, and FCN1³² (Fig. 1c-d). Amongst DCs, we identified cDC1 (key transcripts: CLEC9A, CADM1, and IDO1) and cDC2 (key transcripts: CD1C, CLEC10A, and FCER1A) (Fig. 1c-d). In addition to cDC1 and cDC2, we also identified a population of LAMP3+ DCs in both healthy and some lymphoma samples, expressing the chemokine receptor CCR7 and the chemokines CCL17 and CCL19, which we termed “activated DCs” (aDC) consistent with the nomenclature of transcriptionally similar cells described in human thymus and spleen^33,34, and in murine lung neoplasms³⁵ (Fig. 1c-d, Supplementary Fig. 1d). We found a population of plasmacytoid DCs (pDC) with a dominant contribution from lymphoma samples (Fig. 1e, Supplementary Fig. 2a), expressing IL3RA (CD123), CLEC4C, and CXCR3 (Fig. 1c-d). The undiseased reference samples contributed a rare population of mast cells, expressing characteristic marker genes including CPA3, KIT, and TPSAB1 (Fig. 1c-d).

A key challenge in comparing the cellular architecture of lymph nodes lies in quantifying the relative enrichment of different cell-types in health and disease. This has recently been addressed through a differential abundance testing method, based on partially overlapping neighborhoods of cells on a k-NN graph (MiloR tool²⁰). Applying MiloR, we represented the integrated manifold as a k-NN graph partitioned into 22,573 overlapping neighborhoods, before statistical testing for differential cell abundance between lymphoma affected and unaffected within each neighborhood (Methods). In lymphoma nodes, the T-cell compartment was polarized towards cytotoxic subsets including NK cells and effector memory CD8+ T-cells (CD8 Tem), in addition to CD4⁺ T-cells subsets expressing checkpoint apparatus including ThExh and Tfh cells (Fig. 1f, Supplementary Fig. 2b). The association of these subsets with disease subtypes diverged between cHL and NLPHL. Classic HL subtypes were enriched for exhausted T-cell subsets, which contrasted with the NLPHL sample, where the cellular landscape was dominated by cytotoxic subsets (NK cells, CD8 Tem, and CD8 Tcm) (Supplementary Fig. 2c).

To fully appreciate the cHL microenvironment, it is crucial to assess how HRSC influence their immune neighbors. The scRNAseq dataset did not contain a population of HRSC, likely due to the rarity, size, and fragility of these cells. To establish the transcriptional program of HRSC, we leveraged a microarray dataset profiling microdissected HRSC and GCs (Methods)³⁶. Differential expression between HRSC and GC established an upregulated HRSC gene signature, including TNFRSF8 (CD30) (Fig. 1g). Scoring of transcription factor regulons demonstrated activation of NF-κB (NFKB1 and its activatory heterodimer partner RELA) in HRSC (Fig. 1h), consistent with previous reports³⁷. We next performed in silico identification of molecular interactions between active transcription factors and potential targets within the HRSC geneset (Methods). This demonstrated an NF-κB-centric network coordinating the upregulation of the chemokines CCL5, CCL17, and CCL22 capable of the positioning and retention of ThExh via CCR5 and CCR4 ligation (Fig. 1i, Supplementary Fig. 1f).

We used this lymph node-wide account of the immune landscape of Hodgkin lymphoma as a reference to interrogate the cellular composition of cHL tissues. We specified regions surrounding HRSCs, using targeted spatial transcriptomic profiling (Nanostring GeoMx Cancer Transcriptome Atlas), defining 300 µm diameter regions of interest (ROI) in both PD-L1^high and PD-L1^low regions of 9 NSCHL and 1 Mixed cell cHL (MCCHL) lymph nodes, and follicular and interfollicular regions of one control reactive lymph node (Supplementary Fig. 3a-b). We represented these transcriptional profiles in a shared-nearest neighbor graph and identified 5 types (clusters) of microenvironment across cHL and control lymph nodes (Fig. 2a-b). We deconvoluted the cell composition of each cluster, using our scRNAseq atlas as reference, followed by cell-type count estimation and differential cell-type abundance analysis (Fig. 2c). The five types of microenvironments encompassed two ‘neoplastic’ HRSC enriched clusters (clusters 3 & 4), two ‘non-neoplastic’ (i.e. devoid of HRSC signatures; clusters 1 & 5), and one intermediate neighborhood (cluster 2). The two non-neoplastic neighborhoods were PD-L1^low and represented the normal lymph node follicular microenvironment (cluster 5) or fibrosing regions of diseased lymph nodes (cluster 1). Notably, established fibrotic regions are an adjunct diagnostic histological feature of NSCHL³⁸. The neoplastic environments were PD-L1^high and exhibited divergent leukocyte enrichment. Cluster 3 was enriched for ThExh, Th cells, and NK cells, whereas cluster 4 exhibited myeloid cell infiltration, with enrichment of classical monocytes, macrophages, and cDC2 (Fig. 2c). Differentially expressed genes in cluster 4 suggested potent inflammatory signaling, with high expression of chemokines associated with Th2 responses and CCR3-dependent eosinophil recruitment (CCL18, CCL13, CCL24, CCL26, CCL23) and granulocyte attracting chemokines CXCL1, and CXCL6 (Supplementary Fig. 3c). Cluster 1 contained almost exclusively PD-L1^low regions and was enriched for stromal (fibroblast and endothelial) cells and classical monocytes (Fig. 2c). Differential expressed genes in this cluster included genes encoding key signaling mediators of fibrosis FGFR4, TGFB2, and PTCH1, in addition to TNN encoding the matrix extracellular component tenascin (Supplementary Fig. 3c). Overall, these findings suggest heterogeneous immune infiltrates within the broader cHL microenvironment, some of which may progress to fibrosis.

Given the diversity of MNP enrichment in neoplastic regions, we sought to establish the spatial relationships of MNP subsets within the HRSC niche at single-cell resolution. Using MNP marker gene co-expression patterns in the scRNAseq data (Supplementary Fig. 3d), we designed multiplexed immunofluorescence (IF) panels to identify MNPs in fixed cHL tissue sections. We identified CADM1⁺/CD11c⁺ cDC1, CD1c⁺/CD11c⁺ cDC2, LAMP3⁺ aDC with highly distinctive dendritic morphology, CD123⁺ pDC, CD11c⁺ monocytes and macrophages (CD11c⁺ ONLY), and CD30⁺HRSC (Fig. 2d). We then phenotyped segmented cells and performed neighborhood analyses, taking a 25 µm-radius neighborhood around each CD30⁺ HRSC and measuring the relative enrichment of MNP subsets in these aggregated neighborhoods, compared to the remaining regions (‘non-neighborhood’) across the tissue section. This analysis revealed enrichment of cDC2 and CD11c⁺ monocytes in the immediate vicinity of HRSC across cHL samples. In contrast, both pDC and aDC were excluded from the HRSC niche and occupy regions with a low density of CD11c⁺cells (Fig. 2e-g, Fig. 3a-h). These findings indicate non-random, active organization of MNP subsets in the immediate HRSC-neighborhood.

We next asked which signals might coordinate the positioning of the MNP subsets and T-cells found in close association with HRSC. To interrogate these ligand-receptor interactions (LRI), we calculated the statistical enrichment of candidate LRI between MNPs and T-cells in our scRNAseq data using the CellPhoneDB tool²¹. This analysis predicted paracrine CCL3 and CCL4 signaling by macrophages and classical monocytes to CCR5- and CCR1-expressing cDC2 and ThExh (Fig. 4a). Furthermore, classical monocyte derived CXCL10 was predicted to signal to cDC1, ThExh, and Tfh via CXCR3. Nominated inhibitory interactions from ThExh included TIGIT signaling via NECTIN2 expressed by cDC2, classical monocytes, and macrophages (Fig. 4a).

Despite the proximity of cDC2, macrophages, and monocytes to HRSC, the anti-tumor immune response remains inadequate in cHL. The failure of cDC2 to prime an effective T cell response may result from aberrant upregulation of inhibitory ligand and receptor molecules, amplifying immunosuppressive signaling in the HRSC microenvironment. To test this hypothesis, we designed a second IF panel to examine the expression of PD-L1, IDO1, and TIM-3 on CD11c⁺ and CD68⁺cells, and their spatial relationships to CD30⁺ HRSC (Table 1). HRSC exhibited extensive PD-L1 expression as expected. In addition, we found an enrichment of PD-L1⁺CD11c⁺/CD68⁺ and CD11c⁺/CD68^- MNPs in CD30⁺ HRSC-centric neighborhoods (Fig. 4b-c). Similarly, we found variable coexpression of the co-inhibitory receptor TIM-3, and IDO1 - an immunomodulating tryptophan catabolizing enzyme - on CD11c⁺/CD68⁺ and CD11c⁺/CD68^-MNPs in close proximity to HRSC (Fig. 4c-d, Fig. 5a-d). The proportion of CD11c⁺/CD68^- and CD11c⁺/CD68⁺MNPs expressing inhibitory molecules increased with age at diagnosis (p <0.05) (Fig. 6a), suggesting immunosuppressive signaling in pediatric and young-adult cHL may differ compared with cHL in older adults. We noted no significant differences in the proportion of MNPs expressing inhibitory molecules according to sex, tumor type, or EBV status (data not shown).

HRSC have the remarkable ability to cohere a tumor mass, mainly composed of potential anti-tumor cells, by establishing an immunosuppressive microenvironment. Therapeutic efforts to reverse this immune suppression have targeted T cells, and specifically the exhaustion marker PD-1, while recent studies provide a rationale for also targeting other immune checkpoints^11,39. Unbiased scRNAseq profiling of cHL tumors has demonstrated enrichment of exhausted LAG3⁺ CD4⁺ T-cells around MHCII^- HRSC¹¹, and elaboration of CXCL13 by Tfh-like cells in the TME of lymphocyte-rich cHL³¹. There is a surfeit of immunosuppressive mechanisms in cHL, which may be co-opted dynamically by HRSC.

The mononuclear phagocyte system provides a diverse repertoire of pro-tumor and tumoricidal potential, this plasticity contingent upon the tumor context and paracrine signaling. The MNPs identified in this study are a conspicuous histomorphological feature of cHL, and while pDC and LAMP3+ aDC are excluded from the HRSC neighborhood, MNPs expressing CD68 and / or CD11c, as well as CD11⁺ CD1c⁺ cDC2, cluster together and around HRSCs. This inflammatory response should remove apoptotic cells, directly engulf tumor cells, and present cancer-identifying neoantigens to T cells, yet these mechanisms are inhibited. Our findings reveal a network of immunosuppressive MNPs closely associated with PD-L1⁺ HRSCs. These MNPs cooperate with HRSC-derived and NF-κB-directed chemokine expression, providing chemoattractant and inhibitory signals that recruit and instruct immunosuppressive T cells. Our analysis identifies classical monocytes in particular as important signaling hubs, controlling retention of cDC2 and ThExh via CCR1-, CCR4-, CCR5-, and CXCR3-dependent signaling (Fig. 4e).

Previous work has highlighted that increased proportions of CD68⁺ TAM by immunohistochemistry, and an enhanced macrophage-associated gene expression signature, correlate with inferior clinical outcomes in patients with advanced cHL^40,41. The positioning of these cells close to HRSC suggests that they have proximal roles in directing and amplifying the tolerogenic niche. Underlining the importance of the HRSC-associated MNP network, recent reports have demonstrated that eradication of HRSC, following first line anti-PD-1 immune checkpoint blockade, is associated with a reduction in PD-L1⁺ TAM, rather than a detectable cytotoxic response⁴²..

The substantial co-expression of inhibitory molecules and chemokines by HRSC-associated MNPs indicates functional redundancy in immunosuppressive signaling. This may partially explain resistance to targeting of individual components (e.g. PD-1), and offer opportunities for tailoring therapies on the basis of molecular profiling. In a murine model, it has been shown that TAMs rapidly transfer anti-PD-1 complexes away from T cells, through a FcɣR-mediated process⁴³. The active recruitment of infiltrating MNPs suggests that plausible strategies for further study could include TAM-depletion, for example by CCL2 inhibition^44,45 or CSF1-CSF1R blockade⁴⁶, although this has proven unsuccessful in solid tumors, perhaps due to rebound infiltration of haematopoietic-origin MNPs after treatment. An alternative is to potentiate monocyte-derived MNPs and macrophages, including by immune checkpoint blockade, CD40 agonism, or CAR-macrophage cell therapy⁴⁷. Theoretically this could simultaneously increase tumor phagocytosis, antigen presentation, and restore effective T-cell priming, thus augmenting both TAM and DC responses.

Acknowledgements

BJS is supported by a Clinical Doctoral Fellowship from the Wellcome Trust (216366/Z/19/Z).

CDC is supported by grant funding from the JGW Patterson Foundation (https://jgwpattersonfoundation.co.uk), Bright Red (https://brightred.org.uk), the Lymphoma Research Trust, a Medical Research Council ‘Confidence in Concepts’ award, and a Wellcome Trust Small project grant award.

We thank Dr Christian Stiedl (The University of British Columbia) for generous sharing of scRNAseq data. We thank the Newcastle University / Newcastle upon Tyne NHS Foundation Trust Molecular diagnostics laboratory (NovoPath; https://www.novopath.co.uk/) for tissue handling and technical assistance.

Author contributions

BJS performed data analysis and wrote the manuscript, MF performed data analysis and contributed to the manuscript, MDY contributed to data curation, CJ, AS, AB, CB, VR, JRF performed scRNAseq experiments, KTM and KSP acquired deceased donor lymph node tissue, MRC and SB supervised the project, CDC performed microscopy experiments, analysed data, wrote the manuscript, and supervised the project.

Conflict of interest

None declared

Code availability

Code used to analyze data in the project is uploaded to Github at https://github.com/bjstewart1/hodgkin_lymphoma_analysis

Data availability

Data is made available to download on this website as an annotated data object (.h5ad file) and interactive cellxgene portal.

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Table 1. Clinical and Pathological Characteristics of Classic Hodgkin Lymphoma Study Cases for Multiplexed Immunofluorescence and Nanostring

Study Tumor Reference	Disease Subtype	EBV Status	GeoMx	Age at Diagnosis (years)	Sex	Site of Biopsy (Excised Node)	Stage	Clinical Response
CHL-1	MCCHL	NEG		80	M	Inguinal	2A	CR
CHL-3	MCCHL	NEG		77	F	Submandibular	NA	CR / relapse
CHL-4	LRCHL	POS		75	F	Neck	2A	CR
CHL-5	NSCHL	NEG		73	F	Supraclavicular	4B	CR
CHL-6	NSCHL	NEG		73	M	Neck	1A	CR
CHL-7	NSCHL	NEG		72	M	Neck	1A	CR
CHL-9	NSCHL	NEG	YES	69	M	Neck	1A	PD / Salvage CR
CHL-10	NSCHL	NEG	YES	69	F	Neck	3B	CR
CHL-11	CHL-NOS	POS		68	F	Neck	3B	CR / relapse
CHL-12	CHL-NOS	POS		68	F	Axilla	1A	CR / relapse
CHL-13	MCCHL	NEG		68	M	Inguinal	2A	CR
CHL-14	CHL-NOS	NEG		64	M	Mediastinum	NA	PD / salvage CR
CHL-15	MCCHL	POS		64	M	Neck	NA	NA
CHL-16	MCCHL	NEG		63	F	Neck	2A	CR
CHL-18	MCCHL	POS		60	F	Supraclavicular	4B	CR
CHL-19	MCCHL	NA		53	M	Inguinal	NA	PD
CHL-20	LRCHL	NA		46	M	Axilla	NA	NA
CHL-21	MCCHL	POS		45	M	Neck	3S	PD
CHL-22	NSCHL	POS		40	M	Neck	2A	CR / relapse
CHL-23	NSCHL	POS		39	M	Neck	4A	PR / salvage CR
CHL-24	NSCHL	NA		38	M	Supraclavicular	4A	PD
CHL-25	NSCHL	NEG		33	F	Neck	3B	CR
CHL-27	NSCHL	POS	YES	32	M	Supraclavicular	2A	PD
CHL-28	MCCHL	POS		32	M	Neck	3B	CR
CHL-29	NSCHL	NA		32	M	Neck	4A	CR / relapse
CHL-30	MCCHL	POS		28	F	Neck	2A	CR
Study Tumor	Disease Subtype	EBV Status	GeoMx	Age at Diagnosis (years)	Sex	Site of Biopsy (Excised Node)	Stage	Clinical Response
CHL-31	NSCHL	NEG		28	M	Neck	4B	CR
CHL-32	MCCHL	POS	YES	26	M	Neck	2A	CR
CHL-33	NSCHL	POS		25	M	Neck	2B	CR
CHL-34	NSCHL	NEG	YES	24	M	Neck	NA	NA
CHL-36	NSCHL	NEG		23	F	Neck	2A	CR
CHL-39	MCCHL	NEG		22	F	Supraclavicular	NA	NA
CHL-40	NSCHL	NEG	YES	20	M	Supraclavicular	3BS	CR
CHL-41	NSCHL	NEG		19	F	Mediastinum	2B	PD / salvage
CHL-42	NSCHL	NEG		18	F	Neck	2B	PD
CHL-43	NSCHL	NEG	YES	18	M	Neck	2A	CR
CHL-44	NSCHL	POS		18	M	Post nasal space	1A	CR
CHL-45	NSCHL	NEG		17	M	Neck	2A	CR / relapsed & salvage
CHL-48	NSCHL	NEG		16	F	Axilla	3A	PD / salvage
CHL-49	MCCHL	POS		16	M	Axilla	1A	CR / relapsed & salvage
CHL-50	NSCHL	NEG		16	F	Neck	2A	CR
CHL-51	NSCHL	POS		16	F	Supraclavicular	3B	CR
CHL-52	NSCHL	NA		15	F	Neck	2B	CR
CHL-53	CHL-NOS	NEG		14	F	Retroperitoneal	3B	CR
CHL-54	NSCHL	NEG	YES	14	M	Supraclavicular	2A	CR
CHL-55	CHL-NOS	NEG		14	F	Supraclavicular	3A	CR
CHL-56	NSCHL	NEG		14	F	Neck	4A	CR
CHL-57	NSCHL	NEG	YES	14	M	Supraclavicular	2A	CR
CHL-59	NSCHL	NEG	YES	13	M	Neck	4A	CR
CHL-60	LR-CHL	POS		12	M	Neck	1A	CR
CHL-61	NSCHL	POS		12	M	Neck	2B	CR
CHL-62	MCCHL	POS		8	M	Hilar	4A	CR / relapsed & salvage
CHL-63	CHL-NOS	POS		7	M	Neck	4B	CR
Study Tumor	Disease Subtype	EBV Status	GeoMx	Age at Diagnosis (years)	Sex	Site of Biopsy (Excised Node)	Stage	Clinical Response
CHL-64	MCCHL	POS		6	F	Neck	3B	CR

NA = Not available

Disease Subtypes (n=54)

NSCHL = Nodular Sclerosis Classical Hodgkin Lymphoma (n=30 / 55.6%)
MCCHL = Mixed Cell Classical Hodgkin Lymphoma (n=15 / 27.8%)
LRCHL = Lymphocyte rich Classical Hodgkin Lymphoma (n=3 / 5.6%)
CHL-NOS = Classical Hodgkin Lymphoma, Not otherwise specified (n=6 / 11.1%)

EBV Status

Epstein-Barr virus, as assessed by Epstein-Barr virus (EBV)-encoded small RNAs (EBERs).

GeoMx

Tumor biopsies selected for Nanostring GeoMx assay.

Stage

Ann Arbor staging classification of lymphoma

Clinical Response

Best response with first-line treatment / any further event and treatment
CR = complete remission or complete metabolic response
PD = progesssive disease

Table 2 - Antibodies and secondary reagents used for multiplex immunofluorescence panels

	Primary Antibody	Primary Antibody Dilution	Clone	Manufacturer	Secondary Antibody (Ventana)	TSA- Conjugated Fluorochrome
1	CD11c	1:200	5D11	Leica	Omnimap	Opal-520
2	CD30	1:25	BerH2	Cell Marque	Ultramap	Opal-540
3	CD68P^✝	1:1500	PGM1	Dako	Omnimap	Opal-570
4	IDO1^✝	1:4000	V1NC3IDO	Invitrogen	Omnimap	Opal-620
5	PD-L1^✝	Pre-dilute	SP263	Ventana	Optiview	Opal-650
6	TIM-3^✝	1:200	D5D5R	Cell Signaling Technology	Omnimap	Opal-690
7	CADM1*	1:10,000	Polyclonal	Sigma-Aldrich	Omnimap	Opal-570
8	CD1C*	1:100	OTI2F4	Abcam	Omnimap	Opal-620
9	LAMP3*	1:100	1010E1	Dendritics	Ultramap	Opal-650
10	CD123*	1:200	BR4MS	Leica	Optiview	Opal-690

TSA = Tyramide signal amplification.

CD11c & CD30 are used in both multiplexes. Other antibodies are annotated, as below:

✝ Multiplex 1 only

* Multiplex 2 only

There is NO Competing Interest.

SupplementaryBlood03.03.2022.docx
BldfigS1.tiff
Supplementary Figure 1 | Annotation of cell types in the integrated scRNAseq atlas of cHL. a. Quality control (QC) plots with cutoffs (Methods) indicated in red. b. Scaled values of QC metrics across an initial set of clusters (“QC filtering clusters”) identified in the dataset. Two clusters were removed corresponding to B-T doublets and poor quality (likely dying) cells with a high % mitochondrial counts and low total counts and total genes. c. Heatmap showing curated and data-derived marker gene expression patterns across annotated cell types in the integrated scRNAseq dataset. d. Heatmap showing Jaccard distances between manual annotations and celltypist annotations, and Aoki et al. annotations. e. Left panel: Heatmap showing Jaccard distances between manual annotations and predicted labels from the King et al. dataset using a multilayer perceptron model. Right panel: Heatmap showing mean continuous prediction probabilities for each cell type from the King et al. B cell dataset dataset using a multilayer perceptron model. f. Heatmap showing curated inhibitory checkpoint molecule and chemokine gene expression patterns across annotated T cell subsets in the integrated scRNAseq dataset.
BldfigS1.tiff
Supplementary Figure 1 | Annotation of cell types in the integrated scRNAseq atlas of cHL. a. Quality control (QC) plots with cutoffs (Methods) indicated in red. b. Scaled values of QC metrics across an initial set of clusters (“QC filtering clusters”) identified in the dataset. Two clusters were removed corresponding to B-T doublets and poor quality (likely dying) cells with a high % mitochondrial counts and low total counts and total genes. c. Heatmap showing curated and data-derived marker gene expression patterns across annotated cell types in the integrated scRNAseq dataset. d. Heatmap showing Jaccard distances between manual annotations and celltypist annotations, and Aoki et al. annotations. e. Left panel: Heatmap showing Jaccard distances between manual annotations and predicted labels from the King et al. dataset using a multilayer perceptron model. Right panel: Heatmap showing mean continuous prediction probabilities for each cell type from the King et al. B cell dataset dataset using a multilayer perceptron model. f. Heatmap showing curated inhibitory checkpoint molecule and chemokine gene expression patterns across annotated T cell subsets in the integrated scRNAseq dataset.
BldfigS3.tiff
Supplementary Figure 3 | Nanostring spatial transcriptomics profiling of PD-L1 high and PD-L1low cHL microenvironments. a. Representative images of cHL lymph node used for nanostring spatial transcriptomics profiling showing H&E-stained section (upper rows), RNAscope fluorescence signal from probes against CD274 (PD-L1) (green), PDCD1 (PD-1) (red), and DAPI signal (nuclei, blue) (lower rows) from PD-L1 low regions (top panel) and PD-L1 high regions (lower panel). b. Representative images (40x magnification) of PD-L1 low (upper panel) and PD-L1 high (lower panel) ROI identified in cHL lymph nodes. The white circles are 300 µm in diameter. Each panel shows H&E (upper) and RNAscope fluorescence signal (lower) from probes against CD274 (PD-L1) (green), PDCD1 (PD-1) (red), and DAPI signal (nuclei, blue). c. Heatmap showing expression patterns of marker genes identified between nanostring spatial transcriptomic profile clusters (row scaled normalised expression values are shown). d. Co-expression patterns of marker genes selected for multiplex immunofluorescence panels (highlighted in bold) amongst mononuclear phagocytes in the combined scRNAseq dataset. Highlighted marker genes and expressed protein products used for multiplex immunofluorescence are: CD1C (CD1c), CD68 (CD68), IL3RA (CD123), ITGAX (CD11c), LAMP3 (LAMP3), CADM1 (CADM1). Point size shows fractional coexpression.

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Spatial and molecular profiling of classic Hodgkin lymphoma reveals an immunosuppressive mononuclear phagocyte network

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