Abstract

   Hodgkin Reed-Sternberg (HRS) cells of classic Hodgkin lymphoma (cHL),
   like many solid tumors, elicit ineffective immune responses. However,
   patients with cHL are highly responsive to PD-1 blockade, which largely
   depends on HRS cell-specific retention of MHC class II and implicates
   CD4^+ T cells and additional MHC class I-independent immune effectors.
   Here, we utilize single-cell RNA sequencing and spatial analysis to
   define shared circulating and microenvironmental features of the immune
   response to PD-1 blockade in cHL. Compared with non-responders,
   responding patients have more circulating CD4^+ naïve and central
   memory T cells and B cells, as well as more diverse CD4^+ T cell and B
   cell receptor repertoires. Importantly, a population of circulating and
   tumor-infiltrating IL1β^+ monocytes/macrophages is detectable in
   patients with cHL but not healthy donors, and a proinflammatory,
   tumor-promoting signature of these circulating IL1β^+ monocytes is
   associated with resistance to PD-1 blockade in cHL. Altogether, our
   findings reveal extensive immune rewiring and complementary roles of
   CD4^+ T cells, B cells and IL1β^+ monocytes in the response to PD-1
   blockade and suggest that these features can be captured with a
   peripheral blood test.

   Subject terms: Hodgkin lymphoma, Tumour immunology, Cancer
   microenvironment, Systems analysis
     __________________________________________________________________

   Patients with classic Hodgkin lymphoma (cHL) respond well to PD-1
   blockade, but the underlying cellular insights are still lacking. Here,
   the authors use single-cell transcriptome and spatial analyses to
   identify distinct circulating and tumor-infiltrating CD4^+ T cell, B
   cell and IL1β^+ monocyte/macrophage features associated with response
   to PD-1 blockade in cHL.

Introduction

   Classic Hodgkin lymphomas (cHLs) are composed of rare malignant Hodgkin
   Reed-Sternberg (HRS) cells embedded within an extensive T cell and
   macrophage-rich inflammatory/immune cell infiltrate^[68]1. HRS cells
   exhibit near-universal copy gains of chromosome 9p24.1/CD274
   (PD-L1)/PDCD1LG2 (PD-L2) and copy number-dependent increased expression
   of these PD-1 ligands^[69]2–[70]4. This genetic basis for enhanced PD-1
   signaling prompted clinical evaluation of PD-1 blockade in patients
   with relapsed/refractory (R/R) cHL.

   Over 70% of patients with R/R cHL obtained clinical responses to
   single-agent PD-1 blockade and a subset of these remissions were
   durable^[71]5,[72]6. In this setting, patients with the highest level
   of chromosome 9p24.1 copy gain and associated PD-L1 expression had the
   most favorable responses to therapy^[73]7. More recently, PD-1 blockade
   has been added to combination regimens at relapse and initial
   diagnosis^[74]8–[75]14. In a randomized trial of nivolumab (anti PD-1
   antibody)/doxorubicin, vinblastine and dacarbazine (AVD) versus
   brentuximab/AVD in patients with newly diagnosed advanced-stage cHL,
   progression-free survival was significantly higher in the PD-1
   blockade-containing treatment arm^[76]15.

   In addition to chromosome 9p24 copy gain, cHLs frequently exhibit
   inactivating mutations of B2M or copy loss of B2M or MHC class I
   genes which limit or preclude MHC class I-mediated antigen presentation
   to CD8^+ T cells^[77]4,[78]7,[79]16,[80]17. However, malignant HRS
   cells often retain expression of MHC class II, likely reflecting their
   derivation from MHC class II^+ germinal center B cells and associated
   ability to present antigens to CD4^+ T cells^[81]7,[82]17. In patients
   with R/R cHL, HRS cell expression of MHC class II, but not MHC class I,
   was associated with a more favorable response to PD-1 blockade^[83]7.

   The cHL tumor immune microenvironment (TIME) is CD4^+ T cell rich with
   concomitant expansion of Th1-polarized regulatory T cells and PD-1^+ T
   effector cells^[84]18. In intact cHLs, the malignant HRS cells are in
   close proximity to PD-1^+CD4^+ T cells and PD-L1^+ macrophages which
   form a localized immunoprotective niche^[85]19.

   Despite the abundance and likely importance of tumor-infiltrating
   macrophages in cHL^[86]19,[87]20, these cells were under-represented
   and largely uncharacterized in recent single-cell analyses of primary
   tumor cell suspensions^[88]18,[89]21. However, circulating
   monocyte/macrophage progenitors are readily detectable and amenable to
   characterization at a single-cell level^[90]22.

   Our previous analysis of the circulating immune signature in patients
   with cHL revealed the importance of recently expanded, clonally diverse
   CD4^+ T cells and incompletely characterized innate effectors,
   including monocyte subsets, in the response to PD-1 blockade^[91]22.

   Herein, we utilize single-cell RNA sequencing (scRNA-seq), T cell
   receptor (TCR) and B cell receptor (BCR) clonotype assessments and
   selected spatial analyses of the intact cHL TIME to define shared
   circulating and microenvironmental features in patients with cHL and
   analyze the interplay between previously unidentified CD4^+ T cell, B
   cell and innate cell subsets in response to PD-1 blockade. These
   studies highlight the value of characterizing the circulating immune
   compartment and perturbed T cell, B cell and proinflammatory innate
   effectors in cHL.

Results

Analysis of the circulating immune signature in patients with cHL and control
healthy donors

   We performed scRNA-seq of CD3^+ and CD3^− peripheral blood mononuclear
   cells (PBMCs) from 20 patients with R/R cHL who were treated with
   single-agent PD-1 blockade (nivolumab)^[92]5 at two timepoints,
   baseline (cycle 1 day 1 [C1D1]) and on therapy (cycle 4 day 1 [C4D1]).
   These samples were annotated for the patients’ best overall response to
   PD-1 blockade: complete response (CR), 9 patients; partial response
   (PR), 5 patients; and progressive disease (PD), 6 patients. PBMCs from
   11 patients with newly diagnosed, previously untreated cHL and 13
   control healthy donors were similarly sequenced (Supplementary
   Data [93]1a).

Characterization of circulating CD3^+CD8^− T cell subsets, diversity and
clonality

   We initially focused on CD4^+ T cells given their demonstrated
   importance in the response to PD-1 blockade in cHL^[94]7,[95]22. First,
   we computationally removed all CD8A- and CD8B-expressing cells from our
   CD3^+ scRNA-seq data and processed the CD4^+ T cell enriched samples
   using the standard Seurat workflow (see Methods). Clusters that were
   present in at least 10% of the specimens at a level equal to or greater
   than 1% of the mean number of sequenced cells per sample were included
   in the analysis (Supplementary Data [96]2). We then visualized the
   CD3^+CD8^−, CD4^+−enriched, T cell space on a Uniform Manifold
   Approximation and Projection (UMAP) (Fig. [97]1a and Supplementary
   Fig. [98]1). The 172,274 cells in 26 clusters (Fig. [99]1a) were
   annotated using a knowledge-guided manual approach based on the
   differential expression of canonical genes, highlighted in heatmaps and
   dot plots (Fig. [100]1b-c and Supplementary Fig. [101]2, [102]3).

Fig. 1. Analysis of circulating CD3^+CD8^- (CD4^+ enriched) immune cell
populations in healthy donors (HD), patients with newly diagnosed (ND) and
relapsed/refractory (R/R) cHL.

   [103]Fig. 1
   [104]Open in a new tab

   a Annotated Uniform Manifold Approximation and Projection (UMAP) of
   CD3^+CD8^- single-cell expression profiles (n = 172,274 cells). Each
   cluster is labeled with a distinct color and unique number. b Feature
   plots of selected cell lineage and differentiation markers. c Dot plot
   with relative expression of selected genes in CD3^+CD8^- clusters
   following bidirectional clustering. Displayed genes were curated from
   the top differentially expressed genes that defined clusters using a
   two-sided Wilcoxon rank sum test, adjusted for multiple comparisons
   (adj p < 0.05, fold change > 1.75) and supplemented with relevant
   markers based on a priori knowledge. The size of the dot indicates the
   percentage of marker-expressing cells in each cluster and the z-score
   reflects mean marker expression across the clusters. d TCR repertoire
   diversity, as determined by Chao1 diversity index, obtained from
   single-cell TCR-seq data (n = 126,159 cells) for each T cell cluster
   and overlaid onto the UMAP. e TCR clonotypes expansion levels overlaid
   onto the UMAP (n = 126,159 cells), “Singleton” 1 clone, “Expanded” 2–5
   clones, “Hyperexpanded” >5 clones. d, e Gamma delta T cell clusters
   (Cluster 7b, 13 and 23), MAIT (Cluster 7a) and NKT cells (Cluster 16)
   were not analyzed and are shown in gray.

   The CD3^+CD8^− space was organized into specific stages of T cell
   differentiation, including naïve/central memory (CM), effector,
   cytotoxic and regulatory T cells (Fig. [105]1a)^[106]23. Clusters 0, 1,
   9, 11, 12, 15, 18, 21 and 24 were defined by the highest expression of
   early differentiation markers including CCR7, SELL and TCF7 and were
   designated naïve/CM cells (Fig. [107]1b–c)^[108]24–[109]26.

   Certain naïve/CM T cells clustered together because of their expression
   of specific TCR variable (V) genes including TRBV30 (Cluster 11) and
   TRAV8-2 or TRAV13-1 (Cluster 12) or their selective expression of SOX4
   (Cluster 18). The SOX4^+ naïve/CM subset is of interest because this
   transcription factor is a downstream target of TGFβ that negatively
   regulates Th2 differentiation in peripheral CD4^+ T cells^[110]27.

   Following the T cell differentiation trajectory, we identified several
   helper and effector T cell populations, including Clusters 2, 3 and 6.
   Cluster 2 cells selectively expressed CXCL13 and shared certain
   features with T follicular helper (TFH) cells^[111]28 but lacked
   additional markers of classic TFH cells (e.g., BCL6), possibly due to
   incomplete polarization in the peripheral blood. In this space, we also
   identified circulating Th2 T cells (Cluster 3, GATA3^+ and CCR4^+) and
   Th17-like T cells (Cluster 6, CCR6^+ and RORC ^+) (Fig. [112]1b,
   c)^[113]24,[114]29.

   Given the relative absence of MHC class I on HRS
   cells^[115]4,[116]7,[117]16,[118]17, potential MHC class I-independent
   mechanisms of tumor cell killing merit particular attention. We
   identified several circulating CD3^+CD8^− T cell populations with
   potential cytotoxic function including CD4^+ cytotoxic T lymphocytes
   (CTLs) (Clusters 5 and 22, GZMB^+ and PRF1^+)^[119]30, NKT cells
   (Cluster 16, GZMB^+, NKG7^+ and CCL5^+)^[120]31, VD2 γ/δ T cells
   (Clusters 7b and 23, GZMB^+, TRDC^+, TRGC2^+ and TRDV2^+)^[121]32 and
   MAITs (Cluster 7a, CD161 [KLRB1]^high and TRAV1-2^+)^[122]33,[123]34
   (Fig. [124]1a–c). Although CD4^+ T cells from Clusters 5 and 22 had
   similar cytotoxic signatures, the latter cluster had transcriptional
   features of prior IFN stimulation (OASL^+, IFIT1^+, IFIT2^+ and
   IFIT3^+)^[125]35,[126]36 (Fig. [127]1c). An additional non-cytotoxic
   CD4^+ T cell subset (Cluster 8) also exhibited prior IFN stimulation
   and activation (CD69^+)^[128]37 (Fig. [129]1a–c).

   We also identified two subsets of regulatory T cells – canonical Tregs
   (Cluster 10, FOXP3 ^+ and CD25 [IL2RA] ^+)^[130]38 and Type 1
   regulatory (Tr1) T cells (Cluster 17, IL10^+ and LAG3^+)^[131]39 – and
   additional proliferating (MKI67^+) circulating CD4^+ T cells that
   expressed the highest levels of CTLA4 and LAG3 in this space (Cluster
   19) (Fig. [132]1a-c). Several additional subsets were classified as
   Other due to insufficient polarization in peripheral blood (Clusters 4,
   14, 20).

   We performed single-cell TCR-sequencing for the same samples and
   assessed TCR diversity, measured by Chao1 scores^[133]40,[134]41, and
   relative levels of clonal TCR expansion in CD3^+CD8^− cells
   (Fig. [135]1d, e, Supplementary Fig. [136]4a and Supplementary
   Data [137]3, [138]4). As expected, the least differentiated naïve/CM
   clusters had the highest Chao1 diversity scores, whereas the more
   mature populations of helper and effector T cells had lower Chao1
   scores (Fig. [139]1d). Conversely, the putative cytotoxic CD4^+ T cell
   clusters (Clusters 5 and 22) exhibited the highest levels of TCR clonal
   expansion (Fig. [140]1e).

CD3^+CD8^− T cell subsets in patients with cHL and healthy donors –
response-related TCR diversity and naïve/CM T cell abundance

   After identifying the circulating CD3^+CD8^− clusters in the full
   dataset (Fig. [141]1), we assessed quantitative and qualitative
   differences between these cells in the clinically annotated cohorts of
   patients with cHL and control healthy donors (Fig. [142]2). Patients
   with R/R cHL had markedly lower baseline TCR diversity than healthy
   donors across the entire CD4 naïve/CM T cell space (Fig. [143]2a and
   Supplementary Fig. [144]4b). Of note, the identified differences in TCR
   diversity were not age-related (Supplementary Fig. [145]4c and
   Supplementary Data [146]1c).

Fig. 2. Comparative analyses of CD3^+CD8^- (CD4^+ enriched) clusters in
healthy donors (HD), patients with newly diagnosed (ND) and relapsed/
refractory (R/R) cHL.

   [147]Fig. 2
   [148]Open in a new tab

   a TCR repertoire diversity, as determined by Chao1 diversity index, in
   CD4^+ naïve/CM T cell clusters from HD (n = 13) and patients with R/R
   cHL at baseline [C1D1] stratified by the best overall response (BOR) to
   PD-1 blockade. CR Complete response, PR partial response, PD
   progressive disease. Number of patients with Chao1 diversity data per
   cluster: Cluster 0, HD (n = 13), CR (n = 8), PR (n = 5), PD (n = 6);
   Cluster 1, HD (n = 13), CR (n = 9), PR (n = 5), PD (n = 6); Cluster 11,
   HD (n = 13), CR (n = 8), PR (n = 5), PD (n = 6); Cluster 12, HD
   (n = 13), CR (n = 8), PR (n = 4), PD (n = 6); Cluster 15, HD (n = 13),
   CR (n = 8), PR (n = 4), PD (n = 6); Cluster 18, HD (n = 13), CR
   (n = 9), PR (n = 4), PD (n = 4); Cluster 21, HD (n = 13), CR (n = 7),
   PR (n = 2), PD (n = 3). b Relative abundance of CD4^+ naïve/CM T cell
   clusters in HD (n = 13) and patients with R/R cHL on treatment [C4D1]
   (n = 20) stratified by BOR to PD-1 blockade, CR (n = 9), PR (n = 5), PD
   (n = 6). c Dot plot with relative expression of selected genes
   associated with T cell naïveté and activation in all CD4^+ naïve/CM
   clusters (Cluster 0, 1, 9, 11, 12, 15, 18, 21, 24) in HD (n = 13),
   patients with ND cHL (n = 11) and patients with R/R cHL at baseline
   [C1D1] (n = 20) and C4D1 (n = 20), stratified by BOR. The size of the
   dot indicates the percentage of marker-expressing cells and the z-score
   reflects mean marker expression across cohorts. d Relative abundance of
   cHL-specific T cell populations. e Venn diagram illustrating the shared
   clonotypes between CD4^+ CTLs (Cluster 5, n = 992 clonotypes) and
   IFN-responsive CD4^+ CTLs (Cluster 22, n = 231 clonotypes) in patients
   with cHL. f, g Relative abundance of gamma delta VD2 T cells (Cluster
   23) and cycling CTLA4^+ T cells (Cluster 19). d, f, g HD (n = 13), ND
   (n = 11), C1D1 and C4D1: CR (n = 9), PR (n = 5), PD (n = 6). h Dot plot
   illustrating the mean expression (color) and percentage of cells (dot
   size) expressing genes that positively identified cycling CTLA4^+ T
   cells (Cluster 19) in comparison to the additional CD3^+CD8^− clusters.
   i Cell segmentation and phenotype map for a representative multiplex IF
   image of ND cHL (magenta, HRS cells; yellow, CD4^+CTLA4^+Ki67^+ cells;
   blue outline, FOXP3^+ cells; gray-fill, Other cells less than 75 μm
   from HRS cells; gray-outline, Other cells greater than or equal to 75
   μm from HRS cells). The experiment was performed in 9 ND cHL samples. j
   Relative abundance of CD4^+CTLA4^+Ki67^+ and CD4^+CTLA4^+Ki67^+FOXP3^−
   cells in relation to HRS cells (red dots, less than 75 μm from HRS
   cells; blue dots, greater than or equal to 75 μm from the HRS cells) in
   patients with ND cHL (n = 9). The indicated nominal p values were
   calculated by using a two-sided paired T-test and adjusted for multiple
   comparisons using the Benjamini-Hochberg method; p values that remained
   significant are noted (*). a, b, d, f, g Differences between HDs and
   patients with ND cHL were assessed by a two-sided Wilcoxon rank-sum
   test. The one-sided Cuzick trend test was used to compare patients with
   R/R cHL by BOR (CRs, PRs and PDs). Nominal p values that were
   significant (p < 0.05) are listed and those that remained significant
   after Benjamini–Hochberg correction FDR < 0.1 are noted (*). a, b, d,
   f, g, j All box plots generated in R display the 25th and 75th
   percentiles (lower and upper hinges), median values, and whiskers. The
   whiskers extend from the hinges to the largest/smallest values within
   1.5 times the interquartile range (IQR) from the hinge. Data points
   beyond the end of the whiskers are plotted individually. Source data
   are provided as a Source Data file.

   Patients with the most favorable responses to PD-1 blockade (CRs) had
   significantly higher CD4^+ naïve/CM TCR diversity at baseline (C1D1)
   and more abundant naïve/CM subsets on treatment (C4D1) (Fig. [149]2a, b
   and Supplementary Fig. [150]5). Taken together, these data suggest that
   responding patients have an increased and sustained ability to mount
   CD4^+ T cell responses to new tumor
   neoantigens^[151]22,[152]42–[153]46.

   Patients with cHL (newly diagnosed and R/R) and healthy donors had
   additional qualitative differences in their circulating CD4^+ naïve/CM
   subsets. In patients with cHL, CD4^+ naïve/CM cells had relatively
   lower levels of CCR7, SELL, TCF7 and higher levels of S100A4 and CD69
   expression (Fig. [154]2c), potentially reflecting increased antigen
   exposure and activation^[155]37,[156]47. Additionally, in patients with
   cHL, the relative paucity of SELL expression in circulating naïve/CM
   CD4^+ T cells might limit their recruitment into secondary lymphoid
   organs^[157]48,[158]49.

   We further assessed qualitative response-related differences in
   circulating naïve/CM CD4^+ T cells in patients who achieved CRs or
   progressed following PD-1 blockade. Pathway enrichment analyses
   revealed that naïve/CM CD4^+ T cell subsets (Cluster 0, 1, 9, 11, 12,
   15, 18, 21 and 24) from complete responders had increased expression of
   TCF7, LEF1 and LRNN3 and a more naïve cell phenotype, potentially
   reflecting a greater capacity for self renewal (Supplementary
   Fig. [159]6 and Supplementary Data [160]5)^[161]50,[162]51.

More abundant CD3^+CD8^− T cell effector subsets in patients with cHL

   We identified several circulating CD4^+ T cell populations that were
   significantly more abundant in patients with newly diagnosed cHL and
   R/R cHL than in healthy donors: CXCL13 ^+ T cells (Cluster 2);
   IFN-stimulated effectors (Cluster 8) and CTLs (Cluster 22); and cycling
   CTLA4^+ T cells (Cluster 19) (Fig. [163]2d and g). The cHL-specific
   CXCL13 ^+ T cells are noteworthy given the recent identification of
   CXCL13 ^+ TFH-like cells that form rosettes around HRS cells and
   interact with CXCR5 ^+ B cells^[164]52. This CXCL13/CXCR5 interaction
   reportedly attracts circulating B cells and limits their germinal
   center entry, activation and maturation^[165]53,[166]54.

Qualitative and quantitative differences in circulating CD4^+ CTLs in
patients with cHL and healthy donors

   We identified 2 subsets of circulating CD4^+ CTLs, a major population
   in healthy donors and patients with cHL (Cluster 5) and a smaller
   subset primarily in patients with cHL (IFN-responsive CTLs, Cluster 22)
   (Figs. [167]1, [168]2d and Supplementary Fig. [169]5). There were
   response-related differences in Cluster 5 CD4^+ CTLs from patients who
   achieved CRs or had progressive disease (PD) following treatment (C4D1)
   (Supplementary Data [170]5). These included increased expression of the
   inhibitory Killer cell lectin-like receptor G1 (KLRG1) in patients with
   progressive disease (Supplementary Fig. [171]6c and Supplementary
   Data [172]5). In recent preclinical models, KLRG1 expression on CD4^+
   T-effector cells was associated with tumor progression and lack of
   response to PD-1 blockade^[173]55.

   To delineate the relationship between the CD4^+ Cluster 5 CTLs and
   Cluster 22 IFN-stimulated CTLs, we characterized their TCR clonotype
   distributions (Supplementary Data [174]3, [175]4). In patients with
   cHL, Clusters 5 and 22 shared 103 clonotypes, including 10.4% (103/992)
   of the detected clonotypes in Cluster 5 and 44.6% (103/231) of the
   identified clonotypes in Cluster 22 (Fig. [176]2e). These results
   reveal the common origins of Clusters 5 and 22 CTLs and the likely
   additional inflammatory modulation in cHL-predominant Cluster 22 CTLs.
   It is also noteworthy that both CTL subsets in patients with cHL,
   Cluster 5 and 22, express lower levels of cytotoxic effectors such as
   perforin, in comparison to CTLs (Cluster 5) from healthy donors
   (Supplementary Fig. [177]3). These findings and the selective
   identification of IFN-stimulated cytotoxic and non-cytotoxic CD4^+ T
   cells (Clusters 22 and 8) in patients with cHL may also reflect a
   broader cancer-related inflammatory process^[178]56,[179]57. In this
   regard, the IFN-stimulated genes in Cluster 22 and 8 (OASL, IFIT1,
   IFIT2, IFIT3) are known to be induced by Type I IFN (IFNα and β)
   treatment of T cells^[180]58. These findings are of interest given the
   dual role of Type I IFNs in inflammation and feedback
   immunosuppression^[181]57.

Response-related proliferating Tr1-type cells in the periphery and the intact
TIME

   The circulating cycling CTLA4 ^+CD4 ^+ T cells (Cluster 19) were also
   significantly more abundant in patients with cHL than in healthy donors
   (Fig. [182]2g). These proliferating cells (MKI67^+) with enhanced
   survival (BIRC5^+) shared certain features with Tr1 (Cluster 17) cells,
   including high-level expression of CTLA4 and LAG3 and additional IL10,
   and decreased expression of CD25 (IL2RA) and FOXP3 (Fig. [183]2h). Of
   note, patients who did not respond to PD-1 blockade (PD) had
   significantly higher numbers of circulating Cluster 19 cells at
   baseline (C1D1) (Fig. [184]2g). Using a multiplexed immunofluorescence
   panel, we identified a similar population of CD4^+CTLA4^+Ki-67^+FOXP3^−
   cells in the intact TIME of an additional cohort of patients with newly
   diagnosed cHL (Fig. [185]2i and Supplementary Fig. [186]7). These
   cycling CD4^+CTLA4^+ T cells were primarily observed in close proximity
   to HRS cells (less than 75 μm from the tumor cells) (Fig. [187]2j).
   Given their immunosuppressive features and proximity to malignant HRS
   cells, these newly identified Cluster 19-like cycling CD4^+CTLA4^+
   cells may limit the response to PD-1 blockade (Fig. [188]2g-j).

Decreased numbers of VD2 γδ T cells in patients with cHL

   Although patients with cHL had more abundant IFN-stimulated effectors
   (Clusters 8 and 22) and proliferating CTLA4^+LAG3^+ regulatory cells
   (Cluster 19), they had significantly reduced numbers of cytotoxic VD2
   γδ T cells (Clusters 23 and 7b) (Fig. [189]2f and Supplementary
   Fig. [190]5). As γδ T cells recognize antigens in the absence of MHC
   molecules, the paucity of these cells may be an additional mechanism of
   immune evasion in largely MHC class I-negative cHLs^[191]59–[192]61.

Characterization of circulating CD3^− immune cell subsets – NK cells, B cells
and myeloid cells

   We applied the same inclusion criteria to the CD3^− space and
   identified 229,670 cells in 24 clusters (Fig. [193]3a, Supplementary
   Fig. [194]8 and Supplementary Data [195]2). Cell clusters were
   annotated using a knowledge-guided manual approach based on
   differentially expressed genes and prototypical lineage markers
   (Supplementary Fig. [196]9). We identified three broad categories of
   CD3^− cells defined by their classic markers: NK cells (NKG7^+, CD16
   [FCGR3A]^+, GZMB^+ and PRF1^+)^[197]62; B cells (CD19^+, CD20 [MS4A1]^+
   and PAX5^+)^[198]63; and myeloid cells, including monocytes (CD33^+,
   CD68^+ and CD14^+)^[199]64, conventional dendritic cells (cDCs) (CD33^+
   and CLEC10A^+)^[200]65 and plasmacytoid dendritic cells (pDCs) (CD123
   [IL3RA]^+)^[201]65 (Fig. [202]3a, b and Supplementary Fig. [203]10).

Fig. 3. Analysis of circulating CD3^− immune cell populations in healthy
donors (HD), patients with newly diagnosed (ND) and relapsed/refractory (R/R)
cHL and detailed analysis of NK cell subsets.

   [204]Fig. 3
   [205]Open in a new tab

   a Annotated UMAP of CD3^− single-cell expression profiles (n = 229,670
   cells) by cell type. Each cluster is labeled with a distinct color and
   unique number, beginning with the most abundant cluster (Cluster 0). b
   Feature plots of selected cell lineage and differentiation markers. c
   Dot plot with relative expression of selected genes in NK cell
   clusters. Displayed genes were curated from the top differentially
   expressed genes that defined clusters using a two-sided Wilcoxon rank
   sum test, adjusted for multiple comparisons (adj p < 0.05, fold
   change > 1.75) and supplemented with relevant markers based on a priori
   knowledge. The size of the dot indicates the percentage of
   marker-expressing cells in each cluster and the z-score reflects mean
   marker expression across the clusters. d Relative abundance of NK cell
   clusters in HD (n = 13) and patients with ND cHL (n = 11). Differences
   between HD and patients with ND cHL were assessed by a two-sided
   Wilcoxon rank sum test. P values that remained significant after
   Benjamini–Hochberg correction FDR < 0.1 are noted (*). e Lineage
   trajectory analysis of NK cell populations (n = 40,844 cells) by STREAM
   overlaid on UMAP (left panel); Subway map plot (middle panel) showing
   all individual NK cells; Stream plot (right panel) visualization of
   cell density along different trajectories. At a given pseudotime, the
   width of each branch is proportional to the total number of cells. f
   Stream plots visualization of the selected marker genes, representing
   stages of NK cell maturation and/ or function. Source data are provided
   as a Source Data file.

Qualitative and quantitative differences in NK cell subsets in patients with
cHL and healthy donors

   The circulating NK cells included immature CD56 (NCAM1)^bright (Cluster
   11), more mature CD56^dim (Cluster 5) and terminally differentiated NK
   cells (Cluster 8) and an additional less well-defined population that
   includes NKT cells (Cluster 21, CD3E^+)
   (Fig. [206]3a–c)^[207]62,[208]66. We identified an additional subset of
   NK cells with a signature of IFN stimulation (Cluster 17, OASL^+,
   IFIT2^+ and IFIT3^+) (Fig. [209]3a–c and Supplementary Fig. [210]11a–c)
   that was more abundant in patients with cHL (Fig. [211]3d). A similar
   population of IFN-stimulated NK cells, termed Type I IFN-responding
   cells^[212]62, was described in the context of chronic viral
   infection^[213]62,[214]66,[215]67. Of note, Cluster 17 was the only
   circulating NK cell subset that was more abundant in patients with cHL;
   all other NK cell clusters were significantly decreased in comparison
   to healthy donors (Fig. [216]3d and Supplementary Fig. [217]11d).

   To further delineate the relationship between IFN-stimulated NK cells
   (Cluster 17) and the additional NK subsets, we performed STREAM
   analysis (Single-cell Trajectories Reconstruction, Exploration and
   Mapping)^[218]68. As anticipated, STREAM reflected the differentiation
   of CD56^bright (Cluster 11) into CD56^dim (Cluster 5) NK cells and
   their further maturation into terminally differentiated NK cells
   (Cluster 8) (Fig. [219]3e). Although Cluster 17 cells were derived from
   CD56^dim NK cells (Cluster 5), these IFN-stimulated effectors had a
   distinct differentiation trajectory (Fig. [220]3e). Additionally,
   Cluster 17 NK cells expressed lower levels of GZMB and PRF1 and higher
   levels of CCL3 and CCL4 than canonical terminally differentiated NK
   cells (Cluster 8) suggesting perturbed effector function
   (Fig. [221]3f). Therefore, patients with cHL had significantly reduced
   numbers of classically defined circulating NK cells and an expanded
   population of IFN-stimulated NK cells with likely decreased cytotoxic
   potential (Fig. [222]3d, f).

Response-related differences in B cell abundance and BCR diversity

   The circulating B cell clusters reflected maturation stages defined by
   the expression of canonical marker genes and immunoglobulin heavy
   chains (Fig. [223]4a–c). A small population of transitional B cells
   (Cluster 19) expressed IGHD, IGHM, PLD4, CD38, MZB1 and
   IGLL5 ^[224]63,[225]69 and two subsets of mature naïve B cells
   (IGHD ^+, IGHM^+, TCL1A^+ and CD27 ^−)^[226]63,[227]69 differed only in
   their respective light chain expression (kappa^+, Cluster 3 and
   lambda^+, Cluster 6) (Fig. [228]4a–c and Supplementary
   Fig. [229]12a–c). An additional memory B cell subset (CD27 ^+ and
   TNFRSF13B^+)^[230]63,[231]69 included both unswitched (IGHM ^+) and
   class-switched (IGHG^+ and IGHA^+) populations (Cluster 9)
   (Fig. [232]4a–c). Another cluster contained mature naïve (IGHD^+,
   IGHM ^+, TCL1A^+ and CD27 ^−) and memory B cells (IGHM/IGHG ^+ and
   CD27 ^+) that selectively expressed a specific immunoglobulin kappa
   variable gene, IGKV1-39 (Fig. [233]4a–c).

Fig. 4. Analysis of the circulating B cell populations in healthy donors
(HD), patients with newly diagnosed (ND) and relapsed/refractory (R/R) cHL.

   [234]Fig. 4
   [235]Open in a new tab

   a Annotated UMAP of B cell clusters (n = 55,598 cells). Each cluster is
   labeled with a distinct color and unique number. b Feature plots of
   selected cell lineage and differentiation markers. c Dot plot with
   relative expression of selected genes in B cell clusters. Displayed
   genes were curated from the top differentially expressed genes that
   defined clusters using a two-sided Wilcoxon rank sum test, adjusted for
   multiple comparisons (adj p < 0.05, fold change >1.75) and supplemented
   with relevant markers based on a priori knowledge. The size of the dot
   indicates the percentage of marker-expressing cells in each cluster and
   the z-score reflects mean marker expression across the clusters. d
   Relative abundance of B cell clusters in HD (n = 13) and patients with
   ND cHL (n = 11). e Relative abundance of B cell clusters in patients
   with R/R cHL at baseline [C1D1] (n = 20) and C4D1 (n = 20), stratified
   by BOR to PD-1 blockade, CR (n = 9), PR (n = 5), PD (n = 6). f Number
   of total BCRs (left panel), number of unique BCRs (middle panel) and
   BCR repertoire diversity, as determined by Chao1 diversity index (right
   panel) in all B cells from HD (n = 13) and patients with ND cHL
   (n = 10) and R/R cHL at baseline [C1D1] (n = 20) and C4D1 (n = 20)
   stratified by BOR to PD-1 blockade. CR Complete response (n = 9),
   PR=partial response (n = 5), PD progressive disease (n = 6). d–f
   Differences between HD and patients with ND cHL were assessed by the
   two-sided Wilcoxon rank-sum test. The one-sided Cuzick trend test was
   used to compare patients with R/R cHL by response (CRs, PRs and PDs).
   d, e Nominal p values that were significant (p < 0.05) are listed and
   those that remained significant after Benjamini–Hochberg correction
   FDR < 0.1 are noted (*). In f p values are nominal with no
   Benjamini–Hochberg correction. e, f All box plots generated in R
   display the 25th and 75th percentiles (lower and upper hinges), median
   values, and whiskers. The whiskers extend from the hinges to the
   largest/smallest values within 1.5 times the interquartile range (IQR)
   from the hinge. Data points beyond the end of the whiskers are plotted
   individually. Source data are provided as a Source Data file.

   After defining the circulating B cell clusters, we assessed
   quantitative differences in these subsets in patients with cHL and
   healthy donors. Patients with newly diagnosed and R/R cHL had
   significantly lower numbers of almost all of the circulating B cell
   subsets in comparison to healthy donors (Fig. [236]4d and Supplementary
   Fig. [237]12d). Additionally, patients with R/R cHL who did not respond
   to PD-1 blockade (progressive disease, PD) had significantly fewer
   circulating naïve and memory B cells than responding patients
   (Fig. [238]4e). In the subset of patients with current scRNA-seq and
   prior CyTOF analyses, there was an excellent correlation between
   circulating B cell numbers (Supplementary Fig. [239]10c).

   We also evaluated B cell abundance and proximity to HRS cells in
   available diagnostic biopsies from 7 of the 11 patients with newly
   diagnosed cHL and scRNA-seq analyses of circulating B cells
   (Fig. [240]4d and Supplementary Fig. [241]13). As all of the
   circulating B cell subsets were significantly less abundant in patients
   with newly diagnosed cHL than in healthy donors (Fig. [242]4d), we
   utilized a pan B cell marker, PAX5, and PD-L1 to identify small
   PAX5^bright normal infiltrating B cells and PAX5^dim/PD-L1^+ HRS cells
   by dual immunohistochemistry and digital imaging. Normal B cells were
   significantly less abundant in all evaluated newly diagnosed cHL
   biopsies than in control lymphoid tissue (Supplementary Fig. [243]13c).
   Additionally, normal B cells were relatively excluded from the
   immediate HRS cell (PAX5^dim/PD-L1^+) niche, defined as within 25 μm of
   the tumor cells (Supplementary Fig. [244]13d).

   We next utilized the circulating B cell scRNA-seq data to reconstruct
   individual BCR sequences with the TRUST4 algorithm^[245]70 and assess
   circulating BCR diversity (Supplementary Data [246]6). Of interest, the
   numbers of total and unique BCRs, as well as Chao1 diversity, were
   significantly lower in patients with newly diagnosed cHL in comparison
   to healthy donors (Fig. [247]4f). Among patients with R/R cHL, those
   who did not respond to PD-1 blockade (PD) had significantly lower
   numbers of unique and total BCRs and decreased BCR diversity in
   comparison to responding patients (Fig. [248]4f). The identified
   differences in BCR diversity were not age-related (Supplementary
   Fig. [249]12e and Supplementary Data [250]1c). These data highlight the
   likely importance of B cells in the response to PD-1 blockade in cHL
   via antibody production to tumor neoantigens and/or Fc receptor binding
   of innate effectors. Additionally, these findings build upon the
   recently described importance of tumor tertiary germinal centers in the
   response to checkpoint blockade^[251]71–[252]74 and extend these
   observations to circulating B cells.

Characterization of circulating monocyte and dendritic cell subsets

   We also characterized circulating classical, intermediate and
   non-classical monocytes using the canonical markers, CD14 and CD16, and
   additional subtype-specific transcripts: classical, CD14^+, CD16^−
   (Clusters 1 and 2); intermediate, CD14^+, CD16^+ (Cluster 4); and
   non-classical, CD14^−, CD16^+, RHOC^+, C1Q^+ and SIGLEC10^+ (Clusters 7
   and 22) (Fig. [253]5a–c)^[254]64,[255]75. Circulating monocytes with
   features of prior IFN stimulation (Cluster 14), Type-2 conventional
   dendritic cells (cDCs) (Cluster 10, CD33^+, CLEC10A^+, CD1c^+ and MHC
   class II [HLA-DMA, HLA-DPA1 and HLA-DRA]^high) and plasmacytoid
   dendritic cells (pDCs) (Cluster 18, CLEC4C^+, CD123^+, NRP1^+ and CD11c
   [ITGAX] ^−) were also detected^[256]76 (Fig. [257]5a–c, Supplementary
   Fig. [258]14 and [259]15a, b).

Fig. 5. Analysis of the circulating monocyte and dendritic cell populations
in healthy donors (HD), patients with newly diagnosed (ND) and
relapsed/refractory (R/R) cHL.

   [260]Fig. 5
   [261]Open in a new tab

   a Annotated UMAP of myeloid clusters (n = 126,707 cells). Each cluster
   is labeled with a distinct color and unique number. b Relative
   abundance of myeloid clusters between HD (n = 13) and patients with ND
   cHL (n = 11). Differences between HD and patients with ND cHL were
   assessed by the two-sided Wilcoxon rank-sum test. Nominal p values that
   remained significant after Benjamini–Hochberg correction with an
   FDR < 0.1 are noted (*). c Dot plot with relative expression of
   selected genes in monocyte and dendritic clusters. Displayed genes were
   curated from the top differentially expressed genes that defined
   clusters using a two-sided Wilcoxon rank sum test, adjusted for
   multiple comparisons (adj p < 0.05, fold change >1.75) and supplemented
   with relevant markers based on a priori knowledge. The size of the dot
   indicates the percentage of marker-expressing cells in each cluster and
   the z-score reflects mean marker expression across the clusters. d (Top
   panel) Cellular phenotype maps of the representative RNAscope images
   from patients with ND cHL (magenta, HRS cells; blue, Cluster 0-like
   macrophages CXCL2^+ (left), CXCL3^+ (middle) IL1B^+(right); green,
   other macrophages CXCL2^-/CXCL3^-/IL1B^-) (all images). The experiment
   was performed in 4 ND cHL samples. (Bottom panel) Proximity plots for
   the indicated CXCL2^+, CXCL3^+ and IL1^+ Cluster 0-like macrophages and
   HRS cells. Data are presented as mean values +/− SEM. e UMAP showing
   Cluster 0-like (IL1β^+) monocytes/macrophages (our data) (left panel,
   blue dots, n = 5783 cells) after cell label transfer to Mulder et al.
   (Immunity 54, 1883-1900 e1885 (2021)) data set (light grey dots,
   n = 60,691 cells), overlaid on the same UMAP. The right panel
   represents the IL1β^+ monocytes and macrophages cluster from Mulder et
   al. (dark grey dots, n = 7985 cells). Source data are provided as a
   Source Data file.

Identification of an abundant cancer-associated IL1β^+ monocyte/macrophage
population in the periphery and intact TIME

   The most abundant subset of circulating monocytes (Cluster 0) was
   primarily detected in patients with cHL and virtually absent in healthy
   donors (Fig. [262]5b and Supplementary Fig. [263]15c). These inflamed
   IL1β^+ monocytes (Cluster 0) resembled classical monocytes with
   markedly increased expression of multiple likely immunosuppressive and
   tumor-promoting cytokines/chemokines, including IL1Β, CXCL8, CXCL2,
   CXCL3 and CCL2, and more abundant PD-L1 (CD274) and SIRPA
   (Fig. [264]5c).

   We postulated that the circulating inflamed IL1β^+ monocytes (Cluster
   0) in patients with cHL shared features with recently described
   “inflammatory monocytes” including differentiation into
   tissue-infiltrating macrophages^[265]77–[266]80. For this reason, we
   asked whether tumor-associated macrophages (TAMs) in intact biopsies of
   newly diagnosed cHLs expressed Cluster 0 markers using
   RNAscope^[267]81,[268]82. With this 4-plex in situ hybridization
   approach, primary HRS cells were defined by their expression of CD30
   (TNFRSF8) and PD-L1 and CD68 ^+ TAMs were assessed for their expression
   of the individual Cluster 0 markers, CXCL2, CXCL3 and IL1B
   (Fig. [269]5c and Supplementary Fig. [270]16). We identified
   tumor-infiltrating CD68 ^+ monocytes/macrophages with Cluster 0
   features (CXCL2, CXCL3 and IL1B expression) in the intact cHL TIME and
   the immediate proximity of CD30^+PD-L1^+ HRS cells (Fig. [271]5d).
   Proximity analysis revealed that the majority of Cluster 0 – like
   macrophages (83% of CD68^+CXCL2^+, 88% of CD68^+CXCL3^+ and 88% of
   CD68^+IL1B^+ cells) were localized within 75 μm of the CD30^+PD-L1^+
   HRS cells (Fig. [272]5d).

   To further validate our findings, we performed multiplex
   immunofluorescence (mIF) with a 4-plex panel (CD68, IL1β, PAX5 and
   PD-L1) on the same cHL cases and confirmed the presence of IL1β^+
   CD68^+ monocytes/macrophages in the intact TIME at the protein level.
   IL1β^+ monocytes/macrophages were primarily detected in HRS-rich
   regions of the intact TIME whereas monocytes/macrophages in HRS-poor
   regions were largely IL1β^− (Supplementary Fig. [273]17). These mIF
   analyses reinforce our RNAscope results (Fig. [274]5d) and highlight
   the likely significance of tumor-infiltrating IL1β^+
   monocytes/macrophages in cHL.

   After identifying Cluster 0-like IL1β^+ monocytes/macrophages in the
   cHL TIME, we asked whether similar cells were detectable in other
   cancers. We interrogated a single-cell compendium of monocytes and
   macrophages from multiple solid tumors (Fig. [275]5e)^[276]83 and
   performed a cell type label transfer^[277]84 using our CD3^− scRNA-seq
   dataset as a reference. In this publicly available compendium of
   tumor-associated monocytes and macrophages (60,691 total cells), we
   identified 5783 cells with a transcriptional profile similar to our
   Cluster 0 inflamed IL1β^+ monocytes (Fig. [278]5e, left panel, blue
   dots). Of interest, 81% (4682/5783) of the cells with a Cluster 0
   transcriptional signature were also previously identified as “IL1β^+
   monocytes” in the compendium (Fig. [279]5e, right panel, grey dots and
   Supplementary Data [280]7). There was a highly significant enrichment
   of Cluster 0-like cells in the previously designated IL1β^+ cluster
   (4682 Cluster 0-like cells/7985 IL1β cells) compared to the rest of the
   dataset (1102 Cluster 0-like cells/52,706 Other cells, χ² = 25707.926
   p < 0.0001, Chi-squared test with Yates correction). The identification
   of Cluster 0/IL1β^+ tumor-infiltrating monocytes/macrophages in this
   solid tumor compendium indicates that similarly programmed inflammatory
   monocytes/macrophages are detectable in multiple cancers.

Response-related circulating IL1β^+ monocyte transcriptional signature in
patients with cHL

   We next returned to the periphery to assess the clinical significance
   of circulating inflamed IL1β^+ monocytes (Cluster 0) in patients with
   R/R cHL who were treated with PD-1 blockade. As a first step, we
   performed a differential analysis of response-related Cluster 0
   monocyte transcripts in patients who achieved a CR or progressed (PD)
   on PD-1 blockade (Fig. [281]6a and Supplementary Table [282]1). In
   comparison with complete responders, those who progressed on nivolumab
   had significant upregulation of an extended Cluster 0 transcriptional
   signature at C4D1 (Fig. [283]6a). Gene set enrichment analysis (GSEA)
   of the response-related Cluster 0 signature revealed highly significant
   upregulation of pathways associated with response to prostaglandin,
   TNFα, Toll-like receptor (TLR) and IL-1 signaling, inflammasome
   activation and modulation by Triggering Receptor Expressed on Myeloid
   cells 1 (TREM1), an Ig-like immunoregulatory receptor and amplifier of
   innate immune responses (Fig. [284]6b and Supplementary
   Fig. [285]18)^[286]85–[287]90. Together, these findings highlighted the
   likely role of prostaglandin and TNFα stimulation, TLR engagement by
   damage-associated molecular patterns (DAMPs), inflammasome activation
   and IL1β-dependent downstream signaling in Cluster 0
   monocytes/macrophages.

Fig. 6. Identification of IL1β^+ monocyte transcriptional signature
associated with lack of response to PD-1 blockade.

   [288]Fig. 6
   [289]Open in a new tab

   a Differential expression analysis of response-related Cluster 0
   transcripts in patients with relapsed/refractory (R/R) cHL who achieved
   a complete response (CR) or progressed on PD-1 blockade (PD). A
   two-sided permutation test, adjusted for multiple comparisons (adj
   p < 0.1, fold change > 1.5) was used to filter the differentially
   expressed Cluster 0 genes in patients who achieved a CR or progressed
   on therapy at C4D1. The top 50 of 368 differentially expressed genes
   are shown for patients by response at C4D1 and baseline C1D1. b Pathway
   enrichment analysis of response-related differentially expressed genes
   in Cluster 0 using Molecular Signatures Database (MSigDB) gene sets:
   CP:REACTOME: Reactome gene sets; CP:WIKIPATHWAYS: WikiPathways gene
   sets; and C7: Immunologic gene sets. Gene Ratio (x-axis) represents the
   number of genes in the AUCell signature over the total number of genes
   in a given gene set; FDR significant < 0.05; Count represents the
   number of genes in the overlap between the AUCell gene signature and
   given gene set. c AUCell signature score in circulating Cluster 0
   monocytes from healthy donors (HD) and patients with newly diagnosed
   cHL (ND) or R/R cHL at C1D1 and C4D1 at the single-cell (upper panel:
   HD n = 73 cells; ND n = 8266 cells; CR C1D1 n = 3942 cells; PD C1D1
   n = 2390 cells; CR C4D1 n = 5403 cells; PD C4D1 n = 4335 cells) and
   per-patient (lower panel: HD n = 12 samples; ND n = 11 samples; CR C1D1
   n = 9 samples; PD C1D1 n = 6 samples; CR C4D1 n = 9 samples; PD C4D1
   n = 5 samples) levels. d AUCell signature score in all
   circulating monocytes from HDs and patients with cHL at the single-cell
   (upper panel: HD n = 28,001 cells; ND n = 20,633 cells; CR C1D1
   n = 16,851 cells; PD C1D1 n = 7464 cells; CR C4D1 n = 17,343 cells; PD
   C4D1 n = 10,999 cells) and per-patient (lower panel: HD n = 13 samples;
   ND n = 11 samples; CR C1D1 n = 9 samples; PD C1D1 n = 6 samples; CR
   C4D1 n = 9 samples; PD C4D1 n = 5 samples) levels. e AUCell signature
   score in all circulating monocytes from patients with metastatic
   urothelial carcinoma (mUC) (Yuen et al. Nat Med 26, 693-698 (2020)) at
   baseline (C1D1), stratified by the subsequent response to PD-L1
   blockade, at the single-cell (upper panel: Responders n = 3718 cells,
   Non-responders n = 4134 cells) and per-patient (lower panel: Responders
   n = 5 samples, Non-responders n = 5 samples) levels. c–e A one-sided
   Wilcoxon rank-sum test was used to assess the distributions of AUCell
   gene signature scores between HD and ND, as well as between CR and PD
   at C1D1 and C4D1, at both the single-cell and per-patient levels. c–e
   (upper panel) All violin plots generated in R illustrate the data
   distribution with kernel density estimation. Plots display the 25th and
   75th percentiles (lower and upper hinges) and median values. The width
   of the violin at different points reflects the density of the data.
   (upper panels) All box plots display the 25th and 75th percentiles
   (lower and upper hinges), median values, and whiskers. The whiskers
   extend from the hinges to the largest/smallest values within 1.5 times
   the interquartile range (IQR) from the hinge. Data points beyond the
   end of the whiskers are plotted individually. Source data are provided
   as a Source Data file.

   We then used the response-related Cluster 0 genes (Fig. [290]6a and
   Supplementary Table [291]1) to develop a transcriptional (AUCell)
   signature score^[292]91 associated with lack of response to PD-1
   blockade at C4D1 and predictive for unfavorable outcome at baseline
   (C1D1) (Fig. [293]6c and Supplementary Fig. [294]19a, b). On both a
   single-cell and per-patient level, the Cluster 0 AUCell score was
   significantly higher in patients with cHL who did not respond to PD-1
   blockade than in complete responders (Fig. [295]6c, upper and lower
   panels). Response-related differences in the AUCell score were also
   detected when all circulating monocytes, rather than pre-selected
   Cluster 0 cells, were analyzed (Fig. [296]6d).

Response-related circulating IL1β^+ monocyte transcriptional signature in
patients with another solid tumor

   After defining the response-related Cluster 0 AUCell signature in
   patients with R/R cHL (Fig. [297]6c, d), we assessed the performance of
   the signature in an additional series of patients with a solid tumor
   (metastatic urothelial carcinoma [mUC])^[298]92 who were treated with
   PD-L1 blockade and had publicly available PBMC scRNA-seq data
   (Supplementary Fig. [299]19c, e). In this mUC series, circulating
   monocytes from patients who did not respond to PD-L1 blockade had
   significantly higher baseline AUCell signature scores than those who
   responded to this treatment (Fig. [300]6e). Therefore, circulating
   Cluster 0-like monocytes were also detectable and associated with
   inferior responses to blockade of the PD-1 pathway in an additional
   solid tumor.

Discussion

   In patients with cHL, scRNA-seq of the circulating immune signature
   revealed extensive reprogramming and/or modulation of specific CD4^+ T
   cell, B cell and innate effectors in the setting of cancer-associated
   inflammation. In this largely MHC class I-negative tumor, quantitative
   differences in circulating CD4^+ naive/CM T cell abundance and TCR
   diversity were associated with response to PD-1 blockade, highlighting
   the importance of a continued capacity to respond to new tumor
   neoantigens. In comparison to healthy donors, patients with cHL also
   exhibited qualitative differences in these essential CD4^+ naive/CM T
   cells, with decreased expression of TCF7, CCR7 and SELL. Recent studies
   highlight the importance of TCF7 ^+ T cell progenitors in the response
   to PD-1 blockade^[301]93. We now extend these findings to the
   circulating CD4^+ T cell compartment.

   In patients with cHL, there were also qualitative and quantitative
   differences in circulating MHC class I-independent cytotoxic effectors
   including: CD4^+ CTLs with decreased perforin expression and increased
   expression of IFN-stimulated genes; reduced numbers of γ/δ VD2 cells;
   and perturbed NK cell differentiation with fewer mature perforin- and
   granzyme-positive cells and an expanded population of IFN-stimulated
   cells resembling those in chronic viral infections^[302]62,[303]67.

   Additionally, patients with cHL had significantly reduced numbers of
   circulating B cells across all stages of differentiation. Both the
   abundance of peripheral blood B cells and their BCR diversity were
   positively associated with response to PD-1 blockade. These findings
   underscore the importance of a continued capacity to mount B cell, as
   well as T cell, responses to new tumor neoantigens and extend prior
   observations regarding the prognostic significance of
   tumor-infiltrating B cells in cHL and additional solid
   tumors^[304]71–[305]74,[306]94,[307]95.

   Our scRNA-seq analysis also identified a major population of
   circulating IL1β^+ monocytes with a proinflammatory, tumor-promoting
   cytokine/chemokine signature that was primarily detected in patients
   with cHL, but not in healthy donors, and associated with lack of
   response to PD-1/PD-L1 blockade. Monocytes/macrophages with a similar
   cytokine/chemokine signature were localized in proximity to HRS cells
   in intact cHLs and detected in multiple solid tumors. Additionally,
   circulating monocytes with the same transcriptional profile were
   associated with a lack of response to PD-L1 blockade in another solid
   tumor (mUC), underscoring the broad relevance of these findings.

   IL1β^+ TAMs with a similar transcriptional signature were also
   identified recently in pancreatic ductal adenocarcinoma (PDAC)^[308]80.
   In PDAC, these proinflammatory IL1β^+ TAMs were induced by
   prostaglandin E2 (PGE2) and TNF, associated with disease progression
   and modulated by PGE2 or IL1β^+ blockade^[309]80. Circulating monocytes
   with certain shared features were also described recently in biliary
   cancer^[310]96. These recent findings further highlight the importance
   and potential targetability of proinflammatory IL1β^+
   monocytes/macrophages in multiple tumors. Additionally, the data
   suggest that IL1β^+ proinflammatory monocytes/macrophages negatively
   impact outcome in settings beyond PD-1 blockade.

   The coordinated upregulation of cytokine/chemokines, including IL1B,
   CXCL2, CXCL3, CXCL8, and CCL2, and the additional response-related
   factors, IL-6, CXCL1 and CCL3, in Cluster 0 monocytes/macrophages
   identifies candidate targets for combination therapies with PD-1
   blockade and suggests associated treatment
   strategies^[311]77,[312]87,[313]97–[314]106. In previous studies,
   inhibition of IL-1 receptor signaling abrogated the recruitment of
   inflammatory monocytes into the TIME, reduced immune suppression and
   tumor progression, and synergized with PD-1 blockade in murine models
   of renal cell carcinoma (RCCa) and breast
   cancer^[315]77,[316]98,[317]99. Additionally, neutralizing CXCL8 (IL8)
   antibodies and CXCR2 antagonists, which block multiple
   cytokines/chemokines including IL-8, CXCL1, CXCL2 and CXCL3, suppress
   inflammasome activation, myeloid cell recruitment, tumor growth and
   angiogenesis, and enhance the efficacy of PD-1 blockade in certain
   solid tumors^[318]87,[319]100–[320]102,[321]107,[322]108. Furthermore,
   CCL2 blockade decreases macrophage infiltration, tumor proliferation
   and metastasis in inflammatory breast cancer, in part via downstream
   activation of CCL3^[323]103,[324]104. Given the coordinated
   upregulation of multiple proinflammatory tumor-promoting cytokines/
   chemokines in Cluster 0 monocyte/macrophages, it may be more effective
   to target the cells themselves, their polarizing program or their
   shared receptors rather than individual soluble factors.

   Despite their abundance in the intact TIME, inflammatory IL1β^+
   monocyte/macrophages were poorly captured in prior single-cell analyses
   of disaggregated cHL tumor cell suspensions^[325]21,[326]22,
   highlighting the importance and potential practical value of assessing
   the circulating immune compartment. More broadly, cancer-associated
   inflammation and perturbed CD4^+ T cells, B cells and innate effectors,
   including newly identified Cluster 0/proinflammatory IL1β^+ monocytes,
   can be captured and serially monitored with peripheral blood assays
   providing a framework for more targeted treatment.

Methods

Patient samples

   Cryopreserved peripheral blood mononuclear cells (PBMCs) were obtained
   from: normal healthy donors; patients with newly diagnosed, previously
   untreated cHL; and patients with relapsed/refractory cHL who received
   single-agent nivolumab in the CheckMate 205 phase II trial
   (ClinicalTrials.gov identifier: [327]NCT02181738)^[328]5 with
   Dana-Farber Cancer Institute review board (IRB) approval. Healthy donor
   PBMC specimens were utilized under a DFCI IRB-approved Secondary Use
   Protocol with a waiver of informed consent. Demographics including age
   and sex of patient and donor cohorts are included in Supplementary
   Data [329]1a and c. Baseline (cycle 1 day 1) and on-treatment (cycle 4
   day 1) PBMCs were obtained from CheckMate 205 trial patients who
   received nivolumab 3 mg/kg every 2 weeks until disease progression or
   unacceptable toxicity. An independent review committee (IRC) used the
   2007 International Working Group response criteria^[330]109 to assess
   best overall response (BOR) to therapy – complete response (CR),
   partial response (PR) or progressive disease (PD).

   Formalin-fixed, paraffin-embedded (FFPE) tumor biopsies from patients
   with newly diagnosed cHL were obtained from the archives of Brigham &
   Women’s Hospital (BWH) with BWH IRB approval (Supplementary
   Data [331]1b).

Isolation of CD3^+ and CD3^− peripheral blood mononuclear cells

   Unmanipulated CD3^+ and CD3^− cells were purified from cryopreserved
   bulk PBMCs by negative selection with Miltenyi Biotec separation kits.
   CD3^+ cells were isolated using the Pan T cell isolation kit, which
   included CD14, CD15, CD16, CD19, CD34, CD36, CD56, CD123, and CD235a
   biotin-conjugated monoclonal antibodies (Miltenyi #130-096-535). CD3^−
   cells were selected by positively depleting CD3^+ cells with
   CD3-conjugated MicroBeads (Miltenyi #130-050-101).

Single-cell RNA (scRNA-seq) and Single-cell T cell Receptor (scTCR-seq)
sequencing

   Viable CD3^+ and CD3^− cells were resuspended in PBS (Gibco #10010023)
   with 0.04% UltraPure BSA (Invitrogen #AM2616) at a concentration of
   1000 cells/µL and 17,000 cells were loaded onto a 10× Genomics
   Chromium^TM instrument according to the manufacturer’s instructions.
   The scRNA-seq libraries were processed using Chromium^TM single cell 5’
   library & gel bead kit (10× Genomics #PN-1000165). Quality controls for
   amplified cDNA libraries and final sequencing libraries were performed
   using Bioanalyzer High Sensitivity DNA Kit (Agilent #5067-4626). For
   CD3^+ samples, 2 µL of post ctDNA amplification material was also used
   to prepare scTCR-seq libraries. The sequencing libraries for scRNA-seq
   and scTCR-seq were normalized to 4 nM concentration, pooled using a
   volume ratio of 4:1 and sequenced on Illumina NovaSeq S4 300 cycle
   platform.

Upstream scRNA-seq analytical pipeline

   The sequencing data were demultiplexed and raw single-cell RNAseq reads
   were aligned to GRCh38-3.0.0 using Cell Ranger version 3.1.0 pipeline
   (10× Genomics). The count matrix for each sample was read using Seurat
   V3.0^[332]84 and made into a Seurat object. All of the Seurat objects
   were merged and batch corrected with Harmony^[333]110 for CD3^+ cells
   and CD3^− cells. The Seurat objects were processed by the standard
   Seurat workflow. Briefly, cells that expressed fewer than 200 genes, or
   greater than 5000 genes, or had >20% mitochondrial reads were removed.
   The count matrix was log normalized and the top 2000 most variable
   genes were calculated by the vst method in Seurat. The UMAP was
   generated using the first 50 dimensions of the Harmony coordinates. To
   find clusters, the shared nearest neighbor (SNN) graph was built on the
   first 50 dimensions of the Harmony coordinates followed by the Louvain
   community detection algorithm with a resolution of 1.5. Cells
   expressing either CD8A or CD8B were computationally removed from the
   CD3^+ Seurat object to generate an additional CD3^+CD8^− (CD4^+
   enriched) Seurat object.

QC cluster inclusion/exclusion criteria and annotation

   Based on the Seurat clustering at resolution 1.5, the mean number of
   sequenced cells per sample was calculated. Clusters that were present
   in at least 10% of the specimens at a level equal to or greater than 1%
   of the mean number of sequenced cells per sample were included in the
   downstream analyses. BBrowser v3 and BBrowserX (BioTuring) software
   were used to analyze the data and annotate the clusters that met the
   inclusion criteria. Two additional clusters were excluded from the
   downstream analysis because their features (canonical marker genes)
   were those of contaminating red blood cells or platelets.

Derivation and display of cluster marker features

   Marker genes for each cluster were determined using the function
   wilcoxauc in the presto ([334]https://github.com/immunogenomics/presto)
   R package. P adj <=0.05 and fold change >=1.75 (Wilcox rank sum test,
   two side, adjusted for multiple comparisons) were used to filter the
   top marker genes for each cluster and those genes were used to generate
   a heatmap using ComplexHeatmap^[335]111. Dot plot visualization was
   used to show the differences in the expression of selected top marker
   genes across clusters.

Differential expression analysis

   A Bioconductor package called Distinct ^[336]112 was used to perform
   comparative multi-sample, multi-group single-cell RNAseq differential
   expression analyses. The Seurat objects were converted to
   SingleCellExperiment objects for analysis with Distinct.

Gene set enrichment analysis (GSEA)

   Pathway enrichment analyses were carried out using the following
   Molecular Signatures Database (MSigDB) gene sets: CP:REACTOME: Reactome
   gene sets; CP:WIKIPATHWAYS: WikiPathways gene sets; and C7: immunologic
   gene sets^[337]113,[338]114.

Development of the AUCell signature

   AUCell was used to quantify the enrichment of a specific input gene
   signature in the cellular subsets^[339]91. Specifically,
   AUCell_buildRankings were used to rank genes in each cell by expression
   level, and AUCell_calcAUC was used to calculate a gene set enrichment
   score per cell using the most highly expressed 5% of the genes in that
   cell.

Single-cell trajectories reconstruction, exploration and mapping (STREAM)
analysis

   Single-cell trajectories reconstruction was performed using STREAM
   v1.0^[340]68. Epg_alpha = 0.02, epg_mu = 0.05, epg_lambda = 0.05 were
   set in the elastic_principal_graph function and STREAM plots were
   plotted using the plot_stream function.

TCR and B cell receptor (BCR) analyses

   The results from the TCR single-cell V(D)J sequencing were read into
   Immunarch^[341]115 to calculate TCR diversity. TCR diversity for each
   cluster was calculated using the Chao1 function in the tcr R package
   and super-imposed on the corresponding UMAP. TCR clonal expansion
   levels were binned as singleton (n = 1), expanded (2 < n ≤ 5) or
   hyper-expanded (n > 5), and overlaid on the corresponding UMAP.

   The individual BCR sequences were reconstructed by TRUST4^[342]70 from
   the single-cell RNAseq BAM files. The TRUST4 output for the
   reconstructed BCRs was also imported to Immunarch to calculate BCR
   Chao1 diversity.

RNAscope^TM multiplex fluorescent assay

   FFPE tissue sections (4 µm) from patients with newly diagnosed cHL
   (demographics in Supplementary Data [343]1b) were selected for
   hybridization and simultaneous visualization of four RNA targets using
   the RNAscope^TM LS Multiplex Fluorescent Assay (Advanced Cell
   Diagnostics (ACD) #322800) and automated Leica Biosystems’ BOND RX
   System. RNA-specific probes were hybridized to target transcripts (set
   I: CD68-channel 1 (ACD #560598), IL1B-channel 2 (ACD #310368-C2),
   PD-L1-channel 3 (ACD #600868-C3), CD30-channel 4 (ACD #310838-C4); set
   II: CD68-channel 1 (ACD #560598), CXCL2-channel 2 (ACD #425258-C2),
   PD-L1-channel 3 (ACD #600868-C3), CD30-channel 4 (ACD #310838-C4); set
   3: CD68-channel 1 (ACD #560598), CXCL3-channel 2 (ACD #1002158-C2),
   PD-L1-channel 3 (ACD #600868-C3), CD30-channel 4 (ACD #310838-C4))
   followed by staining with nuclear
   counterstain/4′,6-diamidino-2-phenylindole (DAPI). Signals were
   amplified and detected using the Opal^TM dyes (Akoya Biosciences
   #OP-001001, #OP-001003, #OP-001004 and #OP-001006) specific for each
   channel (channel 1 – Opal 520, channel 2- Opal 570, channel 3 – Opal
   620, channel 4 – Opal 690) (Supplementary Table [344]2a). Slides were
   mounted with Prolong Gold Anti-fade reagent (Invitrogen #[345]P36934)
   mounting medium and whole slide tissue scans were imaged and digitized
   using the PhenoImager HT multispectral imaging platform (Akoya
   Biosciences).

   Digitized whole slide scans were analyzed with HALO and HALO AI
   software (Indica Labs HALO Image Analysis Platform version 3.5.3577 and
   HALO AI version 3.5.3577). Tissue scans were first evaluated for RNA
   quality and regions without RNA signals were excluded from the
   analysis. The AI-custom nuclear segmentation algorithm was then trained
   and developed for cell segmentation. Cell phenotyping was subsequently
   performed based on the combination of marker genes expression and HALO
   spatial plots were generated. Proximity analyses were utilized to
   identify specific cell subsets in the immediate vicinity of malignant
   HRS cells (< 75 um from an HRS cell) and illustrated with proximity
   histograms.

Immunohistochemistry

   Double staining of PD-L1 (G.J.F.19 clone 405.9A11) and PAX5 (BD
   Biosciences #610862) was performed using the BOND RX fully automated
   stainer (Leica Biosystems)^[346]116. 4 μm thick FFPE tissue sections
   from patients with newly diagnosed cHL (demographics in Supplementary
   Data [347]1a) were baked for 1 h at 60 °C and subsequently dewaxed and
   rehydrated. Epitope retrieval was performed using Epitope Retrieval
   Solution 2 (pH 8) for 30 min (Leica Biosystems #AR9961). The PD-L1
   primary antibody was incubated for 2 h, twice. Thereafter, a
   post-primary blocking reagent was applied for 8 min, horseradish
   peroxidase-labeled polymer was added for 12 min, and peroxidase
   blocking was performed for 5 min, followed by 15 min of DAB
   development. PAX5 immunostaining was subsequently performed with two
   2-h application of the primary antibody, a 20-min incubation with a
   post-primary AP-blocking reagent, and a 15-min application of an
   AP-labeled polymer followed by 10 min of Red substrate development.
   Slides were subsequently counterstained with hematoxylin for 10 min,
   dehydrated, and cover-slipped (Supplementary Table [348]2b).

   Images were acquired using the PhenoImager HT multispectral imaging
   platform (Akoya Biosciences). Slides underwent whole-slide imaging at
   20× resolution and images were viewed with Phenochart viewing software
   (Akoya Biosciences) to select 3–10 regions of interest (ROIs) under the
   guidance of an expert pathologist (SJR). The images were spectrally
   separated and analyzed using inForm 2.6.0 (Akoya Biosciences). The ROIs
   were segmented, and each marker was quantified using the inForm
   analysis tools. Data was exported from inForm and examined through a
   custom data extraction pipeline to obtain cell population densities and
   perform cell spatial analysis.

   Spatial quantitative analysis was performed by establishing cellular
   labels for each cell being proximal (within 25 µm) or distal (>25 um)
   to a cellular feature of interest, in this case the HRS cells. This was
   accomplished using our analysis software
   ([349]https://github.com/dfci/pythologist) and these features enabled
   the measurement of percent cellularity of cell-types in regions
   proximal-to and distal-from HRS cells. The same software was used to
   generate heat-map visualizations of the local densities of cell-types
   of interest.

Multiplex immunofluorescence

   Multiplex Immunofluorescence staining was performed using the BOND RX
   fully automated stainer (Leica Biosystems)^[350]117. 5 μm thick FFPE
   tissue sections from patients with newly diagnosed cHL (demographics in
   Supplementary Data [351]1b) were initially baked at 60 °C for 3 h, then
   deparaffinated (Leica Biosystems #AR9590) and rehydrated with a series
   of graded ethanol solutions to deionized water. Antigen retrieval was
   conducted at 98 °C using Epitope Retrieval Solution 1 (pH 6) or 2 (pH
   9) (Leica Biosystems #AR9961 and #AR9640). Primary antibodies were
   applied for 40 min. Subsequently, secondary antibodies, including
   anti-mouse and anti-rabbit Opal Polymer Horseradish Peroxidase (Akoya
   Biosciences #ARH1001EA), along with Post Primary Block (Leica
   Biosystems #RE7159) and Polymer (Leica Biosystems #RE7161), were
   applied as detailed in Supplementary Table [352]2c. The antibody
   complex was then incubated for 5 min with the corresponding Opal
   Fluorophore Reagents (Akoya Biosciences #NEL871001KT) to visualize the
   signal. Following the final fluorophore application, samples were
   removed from the BOND autostainer and incubated with NucBlue Fixed Cell
   Stain ReadyProbes reagent (Invitrogen #[353]R37606) for nuclei
   detection. Samples were air-dried and mounted with Prolong Diamond
   Anti-fade mounting medium (Life Technologies #[354]P36965). Details on
   target antigens, antibody clones, marker dilutions, diluents, secondary
   applications, and antigen retrieval are provided in Supplementary
   Table [355]2c.

   Images were acquired using PhenoImager HT multispectral imaging
   platform (Akoya Biosciences). Slides underwent whole-slide imaging at
   20× resolution and images were viewed with Phenochart viewing software
   (Akoya Biosciences) to select 4–6 regions of interest (ROIs) under the
   guidance of an expert pathologist (SJR). The images were spectrally
   separated and analyzed using inForm 2.6.0 (Akoya Biosciences). The ROIs
   were segmented, and each marker was quantified using the inForm
   analysis tools. Data was exported from inForm and examined through a
   custom data extraction pipeline to obtain cell population densities
   (number of cells per mm^2) and perform cell spatial analysis.

   Spatial regions were annotated based on their proximity, <75 um or
   ≥75 um, to HRS cells. In these respective regions, the percent
   cellularity of prioritized cell types (CD4^+CTLA4^+Ki67^+ and
   CD4^+CTLA4^+Ki67^+FOXP3^−) was measured in each sample. Within each
   sample, the cumulative regions across all ROIs were treated as a
   singular aggregated entity for <75 um or ≥75 um from HRS cells. The
   differences in percent cellularity of the indicated cell types <75 um
   or ≥75 um from HRS cells were evaluated using a two-sided paired T-test
   and P-values that were adjusted for multiple comparisons using the
   Benjamini-Hochberg method.

External scRNA-seq datasets

   An external PBMC scRNA-seq dataset from patients with metastatic
   urothelial carcinoma (mUC) (EGAD00001005481, EGAS00001004008 and
   [356]GSE145281) who were treated with PD-L1 blockade^[357]92 was also
   used to evaluate the Cluster 0 monocyte/macrophage AUCell signature.
   The count matrix for this study was obtained from GEO, converted into
   Seurat objects and normalized using NormalizeData in Seurat and batch
   corrected with Harmony. A UMAP was generated using the first 20
   dimensions of the Harmony coordinates. Cells were clustered using an
   SNN graph and Louvain algorithm at a resolution of 0.5 based on the
   batch-corrected reduction. Clusters were manually annotated and
   inspected in the BioTuring browser v2.

   A recently published integrated dataset of mononuclear phagocytes from
   13 tissues across 41 single-cell RNAseq datasets^[358]83
   ([359]GSE178209 and [360]https://gustaveroussy.github.io/FG-Lab/) was
   used to generate a compendium of conserved monocyte/macrophage gene
   programs. We used a cell type label transfer method and implemented
   it in Seurat^[361]84 to assess the presence of the cHL Cluster 0
   monocytes transcriptional program in specific subsets of
   cancer-associated monocytes in other solid tumors. Briefly, cell type
   label transfer was performed by projecting the PCA structure of our
   reference dataset onto the query dataset. First, common anchors were
   identified between our dataset and the “MoMac-VERSE” dataset^[362]83
   using FindTransferAnchors, and then TransferData was used to classify
   the cells in the MoMac-VERSE dataset based on the cell type labels of
   our dataset. Principal component analysis was used as the
   dimensionality reduction method, and the first 50 principal components
   were used for this analysis. Statistical significance was assessed by
   performing a Chi-square test for enrichment of Cluster 0-like cells in
   the IL1ß^+ monocyte cluster versus all other cell types in the Mulder
   et al. dataset.

Statistical analyses

   Two-group comparisons including comparisons between cell abundances,
   TCR or BCR diversity and AUCell gene signature score distributions were
   measured by Wilcoxon rank-sum test. Three-group (CR, PR, PD)
   comparisons were assessed with Cuzick trend test (PMCMRplus package,
   Pohlert T (2014). The Pairwise Multiple Comparison of Mean Ranks
   Package (PMCMR). R package,
   [363]https://CRAN.R-project.org/package=PMCMR). For both types of
   analyses, nominal one-sided p-values were reported (p-values < 0.05)
   and adjusted for multiple comparisons using the Benjamini-Hochberg
   method (FDR < 0.1). Separate Benjamini-Hochberg corrections were
   performed in the following immune cell subsets: CD3^+CD8^− naïve/CM,
   non-naïve and gamma delta T cell clusters; CD3^− NK cell, B cell and
   myeloid clusters. Separate Benjamini–Hochberg corrections were also
   performed to assess TCR diversity in CD3^+CD8^− naïve/CM and non-naïve
   T cells.

Reporting summary

   Further information on research design is available in the [364]Nature
   Portfolio Reporting Summary linked to this article.

Supplementary information

   [365]Supplementary Information^ (103.4MB, pdf)
   [366]41467_2024_54512_MOESM2_ESM.pdf^ (178.6KB, pdf)

   Description of Additional Supplementary Files
   [367]Supplementary Data 1^ (18.3KB, xlsx)
   [368]Supplementary Data 2^ (36.6KB, xlsx)
   [369]Supplementary Data 3^ (13MB, xlsx)
   [370]Supplementary Data 4^ (15.3MB, xlsx)
   [371]Supplementary Data 5^ (121.4MB, xlsx)
   [372]Supplementary Data 6^ (1.8MB, xlsx)
   [373]Supplementary Data 7^ (20.4MB, xlsx)
   [374]Reporting Summary^ (200.1KB, pdf)
   [375]Transparent Peer Review file^ (18.6MB, pdf)

Source data

   [376]Source Data^ (2.6MB, xlsx)

Acknowledgements