Revealing the Evolution of the Tumor Immune Microenvironment in Follicular Lymphoma Patients Progressing Within 24 Months Using Single-cell Imaging Mass Cytometry

doi:10.21203/rs.3.rs-1524378/v1

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Revealing the Evolution of the Tumor Immune Microenvironment in Follicular Lymphoma Patients Progressing Within 24 Months Using Single-cell Imaging Mass Cytometry

https://doi.org/10.21203/rs.3.rs-1524378/v1

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Background: The tumor immune microenvironment (TIME) is crucial for follicular lymphoma (FL) pathogenesis and progression. However, TIME evolution during progression of disease within 24 months (POD24) is unknown.

Methods: Spatially-resolved and single-cell image mass cytometry with a panel of 36 meta-tagged antibodies was quantitatively used to dissect the TIME structure in 13 paired FLs at diagnosis and POD24.

Results: Increased CD163⁻macrophages with PD-1 ligand upregulation and decreased CD8⁺T cells with upregulated LAG3 expression were observed in the follicles during POD24. Spatial interactions demonstrated that FL cells interacted more intimately with macrophages rather than Tregs and less with cytotoxic cells during POD24. Additionally, more CD8⁺T cells near FL cells interacted with macrophages but not Tregs during POD24. Notably, macrophages also cooperated with Tregs more frequently to simultaneously hijack the cytotoxic T-cells and protect FL cells, subsequently generating a more immunosuppressive environment in the follicles during POD24.

Conclusions: FL cells reside in a more immune-compromised microenvironment, evading immune-cell attacks during POD24. Novel immunotherapeutic approaches harnessing LAG3, macrophages, and Tregs will be promising to overcome the poor outcomes of patients with FL POD24.

TIME

POD

Immune signature

T-cell

Follicular lymphoma (FL) is the most common indolent B-cell lymphoma and is generally characterized by the malignant transformation of follicular center B cells harboring the t(14;18) translocation involving the IgH/bcl-2 genes[1]. Although emerging therapeutic approaches can improve the overall survival (OS), which might even extend beyond 18 years, FL remains an incurable disease with a prolonged, relapsing-remitting course[2]. Notably, patients experiencing disease progression within 24 months of diagnosis (POD24) display an aggressive course and markedly poorer prognosis with an OS of less than 50% after 5 years[3]. However, POD24 biology remains unclear, contributing to a dilemma in developing novel therapeutic approaches to improve clinical outcomes.

FL is considered an immunologically functional tumor and therefore prompts the application of immune-modulating regimens such as lenalidomide or immune checkpoint inhibitors combined with rituximab[1, 4, 5]. Moreover, abundant immune cells in the tumor immune microenvironment (TIME), such as macrophages (Mφs) and regulatory T (Treg), CD8⁺T, and CD4⁺T cells, have reportedly been associated with the progression-free survival of FL patients in the rituximab and pre-rituximab era. Recently, Joshua et al. demonstrated that PD-L2 expression could reflect immune infiltration in the TIME of FL. Patients with high immune infiltration mainly have higher levels of programmed cell death 1 ligands (PD-L1⁺) macrophages and clonal T cells[6, 7]. Importantly, POD24 in FL has been associated with reduced intra-tumoral immune infiltration at diagnosis in both discovery and validation cohorts. Consequently, these data indicate that the TIME might play a key role during POD24 in FL. Although the causal association between the TIME at diagnosis and the incidence of POD24 has been well established, the precise features of the TIME at POD24 and the evolution of TIME during POD24 are unknown.

The TIME of FL is composed of various cells, including immune cells such as tumor-infiltrating CD8⁺T cells, Tregs, Mφs, follicular helper T cells (Tfhs), conventional CD4⁺T cells (Tcons), follicular dendritic cells (FDCs), and structural cells, such as fibrotic reticular cells (FRCs) and endothelial cells[4, 8]. A comprehensive exploration of TIME evolution during POD24 would not only cover dynamic alterations in the number of cell types but would also include the cell states and cell–cell interactions, which is ultimately essential for designing strategies to prevent POD24 in FL. Therefore, applying technologies that enable a comprehensive spatial analysis at single-cell resolution with high-throughput phenotypic information is critical for fully revealing the TIME. Recently, highly multiplexed tissue imaging technologies based on cyclic immunofluorescence or epitope-targeted mass spectrometry and RNA detection have emerged and could be useful to comprehensively review marker distribution and tissue composition[9, 10].

Imaging mass cytometry (IMC) uses laser ablation on tissues at a resolution of 1 µm to generate plumes of particles that are carried to and analyzed by mass cytometry. IMC enables the simultaneous evaluation of up to 50 biomarkers from tissue sections stained with metal-tagged antibodies[11–15]. Using IMC data, a pathognomonic map of TIME heterogeneity was established, revealing the evolution of cell compositions and interactions between FL and immune cells, and providing a fundamental basis for novel therapeutic strategies to overcome the poor outcomes of patients with FL POD24.

Patient characteristics and sample preparation

In total, 39 paraffin-embedded lymph node samples from FL patients, including 26 samples at diagnosis and 13 paired samples at POD24 from the First Affiliated Hospital of Xiamen University and Sun Yat-sen University Cancer Center from September 2001 to September 2018, were enrolled in this study. The patient characteristics are summarized (supplementary table 1). Briefly, the median patient age was 57 years. Half of the patients were diagnosed with grades 1–2, and 42% (11/26) and 8% (2/26) were diagnosed with grades 3A and 3B disease, respectively. Most patients (24/26) received rituximab-based immunochemotherapy, except for two patients who underwent chemotherapy. After frontline therapy, 73% of patients achieved complete remission (CR), whereas 23% (6/26) achieved partial remission, and one patient remained stable. For patients with POD24, the median time to progression was 562 days. In total, 38% (5/13) failed to achieve CR after first-line therapy.

All tissue sections were produced to a thickness of 5 μm. Regions of interest (ROIs) covering the follicular and interfollicular areas were selected according to the locations of consecutive sections stained with hematoxylin-eosin. For patients with disease that transformed into diffuse large B-cell lymphoma, ROIs covering malignant follicles were selected according to CD21 and Bcl-2 expression in the germinal center. All samples were obtained after the patients provided informed consent. IRB approval for this study was granted by the Institutional Human Ethics Review Committee of the First Affiliated Hospital of Xiamen University and Sun Yat-sen University Cancer Center, in accordance with the Declaration of Helsinki.

Antibody conjugation and staining

For antibodies without available conjugated metal in Fluidigm, conjugated metal and primary antibodies were constructed as described previously[16]. Following conjugation, the antibodies were diluted in 0.5% BSA and stored at 4°C for staining. Slide-tissue sections were baked at 67 °C for 30 min. Tissue sections were deparaffinized with two washes of 100% fresh-xylene and then rehydrated with successive washes with ethanol 100% (2×), 95% (2×), 85% (1×), 70% (1×), and distilled water. The sections were then immersed in antigen retrieval buffer (tris-EDTA buffer, pH 9.0; Abcam, Cambridge, UK), incubated at 97 °C for 30 min, and cooled to room temperature at room temperature. Slides were washed with distilled water (1×) and Dulbecco’s phosphate-buffered saline (1×) for 10 min each and then washed with distilled water for 8 min on an orbital shaker. Next, the tissue was blocked with blocking buffer for 30 min at 25–30°C (Superblock Blocking Buffer in PBS; Thermo Fisher Scientific, Waltham, MA, USA) in a humid chamber. After blocking, the antibody mix was then applied with the dilutions indicated in supplementary table 2 and incubated overnight at 4 °C in a humid chamber. After overnight incubation, the slides were washed on an orbital shaker for 5 min in wash buffer. Thereafter, tissue sections were successively washed with 0.2% triton X-100 (2×) and D-PBS (2×) on an orbital shaker. Subsequently, slides were incubated with intercalator-Ir diluted 1:400 in D-PBS for 30 min at room temperature. After incubation, the sections were washed with distilled water for 5 min. Finally, the slides were dried under airflow for 5 min and stored at 4 °C until ablation.

IMC acquisition

The Hyperion mass cytometry system (Fluidigm Corporation, South San Francisco, CA, USA) was autotuned using a 3-element tuning slide according to the tuning protocol provided by the manufacturer. As an extra threshold for successful tuning, a detection of at least 1,500 mean duals of 175 Lu was used. ROIs were selected based on consecutive tissue sections stained with hematoxylin and eosin. ROIs of 1,000 × 1,000 µm were ablated and acquired at 200 Hz for approximately 2 h. Data were exported as MCD ﬁles and visualized using the Fluidigm MCD^TMviewer. To better separate the antibody signal and noise, each marker was visually inspected, and a minimum signal threshold of one or two dual counts was set in the Fluidigm MCD^TMviewer.

Cell segmentation

For cell segmentation of non-immune cells, we employed DNA-Iridium staining as nuclear and cell membrane markers to define primary and secondary objects. CellProfiler was subsequently used to define the cell masks and quantify marker expression. To define cell borders, nuclei were first identified as primary objects based on ilastik probability maps and expanded through the cytoplasm compartment until either a neighboring cell or the background compartment was reached. Next, cell masks were generated to identify single cells and used to extract single-cell information (marker abundance, spatial, and neighborhood data) from the original images. Finally, the results were exported as .tiff for analysis.

Image visualization and phenotypic analysis

Each sample and mask were imported into HistoCAT version 1.76[17], and the intra- and inter-follicle regions were gated according to CD19 and SMA expression. After gating, each lymph node sample that was noted to morphologically span both the outer and inner stripes was exported from HistoCAT as a csv file, including the cell ID, signal strength of markers, and coordinates of each cell. Data were analyzed and graphed using R and HistoCAT software. the R implementation of FlowSOM (https://github.com/SofieVG/FlowSOM) was used to define the cell type. In particular, an R implementation of tSNE (https://cran.r-project.org/web/packages/tsne) was used to generate tSNE maps. Log₁₀ transformation was applied as indicated in the figures. Unless otherwise stated, the raw IMC counts were 99th-percentile-normalized and scaled from 0 to 1 (scaled counts)[18].

Neighborhood and cell interaction analysis

Neighborhood analysis was performed using R and HistoCAT software. Custom scripts were used to import the cell type into HistoCAT. An expansion of four pixels from the center of cells was used to detect the cell neighbors[17]. Interaction pattern analysis for Tregs, Mφs, CD8⁺T cells, and FL cells was performed as follows. A unique ID was assigned to each identified cell. The IDs of all cells surrounding a cell could be extracted in a csv file via HistoCAT. Based on the cellular phenotypes, cells were classified into different cell types including Tregs, Mφs, CD8+T cells, and FL cells. We analyzed the interactions of each cell type with the other three cell types and further categorized the cells into eight groups according to the interaction patterns of four general interaction patterns as follows: simultaneous interaction with other three cell types, interaction with any two of three other cell types, interaction with only one of three other cell types, and no interactions with any other cell types (as shown in Figure 5A, 6A, 7A). The number of cells in each pattern could be calculated according to the cell IDs. All other parameters and methods were chosen as suggested in the original publication[17].

Statistical analysis

Statistical tests were performed using R. The statistical parameters are reported in the figure legends. For pairwise group comparisons, we used a non-parametric statistical test (an unpaired two-tailed Mann–Whitney U test). Overall, the p-value significance threshold was set at 0.05.

Image data acquisition and processing of FL tissues using IMC

In total, 39 lymph node samples from FL patients, including 26 FL samples at diagnosis and 13 paired samples at POD24, were subjected to IMC analysis after staining with 36 meta-tagged antibodies (Fig. 1A). As shown in Figure 1, the overlay of selected markers enabled the visualization of immune components, such as FL cell staining for CD19 and Bcl-2 in follicles (normal germinal center B cells do not express Bcl-2; Fig. 1B–D), CD4⁺ and CD8⁺ T cells (Fig. 1E, F), macrophages (CD68, Fig.1G), and structural cells such as FDCs (CD21, Fig. 1I) and FRCs (SMA and vimentin, Fig. 1I). In addition, the interfollicular region and follicles could be precisely gated using SMA, vimentin, and CD21 (Fig. 1I). After cell segmentation (Fig. 1J), spatial and phenotypic data were extracted and subjected to analysis of cell categories (Fig. 1K), marker expression (Fig. 1L), neighborhood relationships, and cell–cell interactions (Fig. 1M).

Landscape of the TIME in FL as revealed by IMC

To further illustrate the landscape of the TIME, the cell components were classified by their canonical marker expression. In total, 12 clusters in the tumor environment were displayed, including four FL subsets (clusters 1, 8, 10, and 11); two subsets of CD4⁺helper T cells (Th) distinguished by inducible T-cell costimulator (ICOS) expression including cluster 2 (ICOS^-Th) and cluster 12 (ICOS⁺Th); two types of tumor-associated Mφs (TAMs), including cluster 4 (CD163⁺Mφs) and cluster 6 (CD163⁻Mφs); CD8⁺ T cells (cluster 5); normal B cells (cluster 9); and Tregs (cluster 7) in the TIME (Fig. 2A). We further quantified clusters in the follicular and interfollicular regions. As a result, TAMs, ICOS^-Th (cluster 2) and CD8⁺T cells as well as FRCs were significantly increased in the interfollicular regions, whereas four FL subsets, normal B cells and ICOS⁺Th (cluster 12) were more abundant in follicles (Fig. 2B, C). Consequently, cellular compositions in immune microenvironments of FL showed heterogeneities in spatial distribution that could be comprehensively revealed by IMC.

Evolution of immune and tumor compositions in the follicular and interfollicular area during POD24 in FL

Although immune compositions in the TIME at diagnosis were reported to be associated with POD24 in FL, the evolution of immune cells and its mechanisms in the TIME during POD24 are unknown. As cellular compositions were highly heterogeneous in the intra- and inter-follicle regions, we separately evaluated the dynamic alterations in inter- and intra-follicular region immune components during POD24 in FL. In the follicles, we found that the frequency of CD163⁻Mφs (cluster 6) was significantly higher at POD24 (Fig. 3A, C). However, unlike that in the follicular regions, the percentage of CD163⁺Mφs, rather than CD163⁻Mφs, in the interfollicular regions was markedly elevated at POD24 (Fig. 3B). In addition to that of CD163⁺Mφs, the frequency of interfollicular ICOS^-Th was significantly decreased after POD24 (Fig. 3B). Additionally, regarding FL cells, only the percentages of cluster 8 (CD27⁺CD45RO⁻HLA-DR⁺) in the interfollicular regions were significantly decreased at POD24, whereas FL subsets in the follicles were not significantly altered (Fig. 3B). Further phenotypic analysis showed higher expression of programmed cell death protein ligand 1(PD-L1) rather than PD-L2 in FL cells (cluster 8) in the interfollicular area after POD24 (Fig. 3D). Thus, TAMs evolved distinctly in the follicular and interfollicular areas along with a few other subsets, including Tcons and cluster 8.

Imbalanced evolution in TAMs and CD8⁺T cells surrounding FL cells results in a more immunosuppressive TIME in follicles after POD24 in FL

Although immune and most tumor cells reside in the follicles, they do not accurately represent the neighbors of FL-cells owing to the existence of other components in the follicles, such as normal B cells and FRCs or FDCs. Therefore, we evaluated the evolution of the immune cells neighboring FL-cells at POD24 according to their location information. Consistent with the total follicle data, the percentage of CD163⁻Mφs in the vicinity of FL-cells was significantly increased after POD24, whereas CD8⁺T cells around FL cells showed a significant decrease (Fig. 4A). Further phenotypic analysis showed that CD163⁻Mφs around FL cells displayed higher expression of PD-L1 and PD-L2 after POD24 (Fig. 4C). Regarding CD8⁺T cells neighboring FL cells, higher expression of lymphocyte-activation gene 3 (LAG3), but not programmed cell death protein 1(PD-1), was observed in the POD24 group (Fig. 4B). These data suggest that immune cells in follicles are involved in the POD24 of FL; notably, an increase of CD163⁻Mφs with upregulation of PD-1 ligands, and a decrease in cytotoxic CD8⁺T cells, but with upregulated expression of immune checkpoints, might facilitate the formation of an immunosuppressive microenvironment during POD24 in FL.

Dynamic changes in interactions between tumor and immune cells during POD24 in FL

Microenvironments presented at POD24 in FL were found to have evolved to be more immunosuppressive, as mentioned previously herein. Cytotoxic T cells were considered the main effector cells that kill FL cells, whereas Treg and TAMs were the main immunosuppressive cells restricting CD8⁺T cell functions in the TIME. Tumor cells that interact with different immune cells could represent different immune response statuses. We classified FL cells according to the co-localization patterns with Mφs, Tregs, and CD8⁺T cells, as shown in Fig. 5A. Eight interaction patterns between FL cells and the three types of immune cells were described, and the percentages of FL cells with different interaction patterns accounting for total FL cells were calculated (Fig. 5A). As a result, we found that the proportion of FL cells interacting with CD8⁺T cells alone (pattern 2) was significantly reduced in follicles after POD24 (Fig. 5B). However, FL cells interacting with Mφs alone (patterns 4) and simultaneously with and Tregs (Pattern 7) were robustly increased in follicles after POD24 (Fig. 5C, D). Similarly, a significant decrease in FL cells interacting with CD8⁺T cells alone (pattern 2) and an increase in FL cells interacting with Mφs alone (pattern 4) were observed in the interfollicular regions after POD24 (Fig. 5E, F). These data suggest that more FL cells are protected from immune attack via spatial distancing from cytotoxic T cells and interacting immunosuppressive cells, and especially Mφs.

More frequent cooperation between TAMs and Tregs in hijacking cytotoxic T cells contributes to TIME evolution into enhanced immune-escaping potential during POD24 in FL

Although CD8⁺T cells contributed to the predominant cytotoxic effects on tumor cells, their cytotoxic function was generally suppressed by regulatory cells such as Tregs and TAMs. However, whether Tregs and TAMs suppressed CD8⁺T cells separately or cooperatively in the TIME of FL and how the interaction pattern evolves during POD24 remained unknown. To explore the immunosuppressive pattern of TAMs and Tregs, we analyzed the fractions of Tregs around TAMs, accounting for total Tregs (Fig. 6A). As a result, a larger fraction of Tregs around Mφs was observed after POD24 (Fig. 6B). An analysis of the interaction patterns among Tregs revealed that more Tregs cooperated with Mφs after POD24 in a manner similar to that of Patterns 4 and 7 (Fig. 6C, D), but the frequency of Tregs inhibiting CD8⁺T cells near FL cells alone (Pattern 5) was significantly decreased (Fig. 6E), indicating that Tregs evolved to serve their regulatory function by cooperating more frequently with Mφs, rather than by themselves, after POD24. CD8⁺T cells are the main targets of Tregs and Mφs. Therefore, we similarly classified CD8⁺T cells according to their interaction patterns, as mentioned previously herein (Fig. 7A). Consistently, more CD8⁺T cells were hijacked simultaneously by Tregs and Mφs (Pattern 6) after POD24 (Fig. 7B, C). However, more CD8⁺T cells near FL cells were inhibited by Mφs but not Tregs (Pattern 7, 5 Fig. 7C,D). Taken together, these data suggest that Mφs not only serve an immunosuppressive function by themselves but also cooperate with Tregs more frequently to simultaneously inhibit cytotoxic T cells, to rationally yield enhanced immune-evading potentials of the TIME after POD24.

In the current study, we used highly multiplexed IMC imaging of FL lymph node sections and constructed a spatially-resolved immunophenotypic map in FL with POD24 at a single-cell resolution level. The immune compositions in follicles and the interfollicular region significantly differed. FL cells with different phenotypes showed discrepancies in the fractions of neighboring immune cells. In addition, in the neighbors of FL cells in follicles, increased CD163⁻Mφs with the upregulation of PD-1 ligands and decreased CD8⁺T cells but with upregulated LAG3 were specifically found only at POD24. Further spatial interactions in tumor and immune cells demonstrated that FL cells interacted more frequently with immunosuppressive cells, especially Mφs, but less frequently with cytotoxic cells alone during POD24. Intriguingly, Tregs and Mφs evolved to collectively inhibit cytotoxic T cells in the follicles during POD24. Therefore, F -cells evolved to reside in a more immune-compromised microenvironment, escaping from immune attacks during POD24.

The current understanding of POD24 biology in FL is mainly based on alterations acquired at the onset of FL. These studies mostly focused on predicting POD24 at diagnosis, such as the gene-mutation-based prognostic model POD24-PI[3] and the TIME-based model BioFLIPI[19]. However, the lack of studies over the course of POD24 has hampered an understanding of its biology and therefore delayed new clinical trial designs. In a previous study, increased immune infiltration, including more Mφs and T cells at diagnosis, was negatively linked to POD24 incidence[7]; however, Mφ subsets varied significantly in the follicular and interfollicular regions. Canioni et al. showed that spatial distribution led to a different cut-off value to predict PFS and that the negative effect of Mφs on PFS was circumvented by rituximab in cohorts of the GELA-GOELAMS FL-2000 trial[20]. However, when further identifying the Mφ subtype, CD163⁺Mφs in the TIME showed a positive association with PFS owing to their capacity to mediate antibody-dependent cellular phagocytosis in rituximab-based immunochemotherapy[21]. However, these studies did not involve spatial information. Since most patients in our study received immunochemotherapy, both spatial distribution and subtypes were considered. Unexpectedly, the altered Mφ subset differed in the intra- and extra-follicular regions; CD163⁻Mφs in follicles and CD163⁺Mφs in the interfollicular regions significantly increased during POD24. These data suggested that CD163⁺Mφs could be used predict the anti-lymphoma response to rituximab and that CD163⁻Mφs in follicles also play a vital role in POD24. However, the underlying biological mechanisms need to be explored in the future.

IMC data on spatial location and cell phenotypes allowed us to accurately dissect the evolution of immune cells proximal to FL cells. Although classifying TAMs into M1- and M2-like types using CD163 and CD68 has been widely applied in many studies, accumulating evidence shows that the M1–M2 paradigm of TAMs cannot accurately reflect pro-tumor or anti-tumor functions[22]. Similarly, our study showed that the follicular CD163⁻ TAMs and interfollicular CD163⁺ TAMs surrounding FL cells significantly increased and displayed upregulated expression of PD-1 ligands during POD24. Interactions along the PD-L1/PD-1 axis could significantly impair T cell functions, indicating a negative effect of PD-L1⁺Mφs on FL patients[23, 24]. Consistently, Blaker et al. showed that high degrees of intrafollicular PD-L1⁺Mφs were associated with a shorter time to transformation in FL patients[25]. Further studies demonstrated that the IL-27/Stat3 axis induces the expression of PD-L1/2 on infiltrating Mφs in lymphoma, including FL[26]. However, anti-PD-1 monotherapy showed inferior efficacy, with only a 4% objective response rate in relapsed or refractory FL in the CheckMate 140 trial, which enrolled more than 45% POD24 patients[27]. Further biomarker analysis showed that more tumor-infiltrating CD3⁺PD-1⁺T cells and CD68⁺PD-L1⁺Mφs were observed in responders than in non-responders, but these differences were not statistically significant[27]. Nevertheless, it was remarkable that more TAMs but fewer CD8⁺T cells around FL cells were observed in the TIME during POD24. Moreover, CD8⁺T cells displayed the robust upregulation of LAG3. Yang et al. demonstrated that most LAG3 originates almost exclusively from the PD-1⁺T-cell population, which was linked to T cell exhaustion[28]. The numbers of CD3⁺LAG-3⁺ or TIM-3⁺LAG-3⁺ cells were found to be correlated with poorer survival in FL patients[28]. These data could partially explain the poor efficacy of nivolumab. Therefore, the TIME evolved to be more suitable for the survival of FL cells under immune attacks, which was characterized by more TAMs with the upregulation of PD-1 ligands and fewer CD8⁺T cells, but with an exhausted phenotype around FL cells.

Although a more immunosuppressive microenvironment was revealed by the frequency and phenotype of TAMs and CD8⁺T cells neighboring tumor cells, interaction patterns in the TIME could provide more information to interpret its evolution during POD24. We found that more FL cells preferentially interacted with TAMs, rather than being directly exposed to cytotoxic T cells alone, after POD24. Despite the involvement of TAMs and Tregs in FL, the number of FL cells interacting with Tregs alone was not significantly altered, indicating the critical role of TAMs during POD24. In addition to immune checkpoint engagement via the expression of PD-L1/2 as shown previously herein, TAMs can also directly inhibit CD8⁺T responses by secreting inhibitory cytokines (such as IL-10), as well as the depletion of metabolites such as L-arginine and the production of reactive oxygen species[29]. Thus, the central role of TAMs might partially contribute to the disappointing response to anti-PD-1 therapy. However, as shown in other studies, rituximab could abrogate the negative effect of TAMs on the prognosis of FL in the chemotherapy era by activating TAMs and mediating antibody-dependent cellular phagocytosis effects[20]. Therefore, a regimen combining rituximab and anti-PD-1 therapy could show synergy in anti-FL effects. Consistently, anti-PD-L1 led to an unexpected ORR of 65% and a CR rate of 50% in patients with relapsed/refractory FL[30].

Notably, TAMs could also function indirectly by recruiting Tregs via CCL17/CCL22 and CCL20 or by inhibiting stimulatory populations such as dendritic cells[29]. We found that the dual protection of FL cells from immune attacks owing to Tregs and TAMs became more common during POD24. Moreover, more Tregs were co-localized with TAMs during POD24. Because FL cells did not secrete higher levels of Treg-specific chemokines, including CCL17 and CCL22, than normal B-cells[31], co-localization of Tregs and TAMs might be due to the recruitment of Tregs by TAMs. Essentially, the cooperation of Tregs and TAMs simultaneously led to the hijacking of more cytotoxic T cells in the follicles after POD24. Such cooperation between TAMs and Tregs might compromise the efficacy of combination rituximab and anti-PD-1 therapy. Therefore, combinatorial strategies involving drugs targeting Tregs, such as anti-CLTA-4 therapy[32], could be promising to improve clinical outcomes. However, biomarker-driven clinical trial studies are essential to determine the mechanisms underlying the co-localization of Mφs and Tregs during POD24.

In summary, IMC was used to develop a spatially preserved single-cell-resolution immunophenotypic atlas of FL and understand the evolution of the TIME during POD24, providing a novel and significant insight into TIME evolution during POD24. A more immunosuppressive TIME evolved during POD24, which was discovered and characterized based on the increased cooperation between TAMs and Tregs and the fewer CD8⁺T cells but with higher expression of LAG3 around FL cells. Consequently, new therapeutic approaches harnessing LAG3, TAMs, and Tregs could improve clinical outcomes for patients with POD24.

CR	Complete remission
FDCs	Follicular dendritic cells
FL	Follicular lymphoma
FRCs	Fibrotic reticular cells
ICOS	Inducible T-cell costimulator
IMC	Imaging mass cytometry
Mφs	macrophages
PD-1	Programmed cell death protein 1
PD-L1	Programmed cell death protein ligand 1
PD-L2	Programmed cell death protein ligand 2
POD24	Progression of disease with 24 months
ROIs	Regions of interest
SMA	Smooth muscle alpha actin
TAMs	Tumor associated macrophages
Tcons	Conventional CD4+T cells
Tfhs	Follicular helper T cells
TIM-3	T cell immunoglobulin and mucin domain 3
TIME	Tumor immune microenvironment
Tregs	Regulatory T cells

Ethics approval and consent to participate：

All samples were obtained after the patients provided informed consent. IRB approval for this study was granted by the Institutional Human Ethics Review Committee of the First Affiliated Hospital of Xiamen University and Sun Yat-sen University Cancer Center, in accordance with the Declaration of Helsinki

Availability of data and materials

The original contributions presented in the study are included in the article/supplementary materials. The IMC data will be uploaded in the public repository “Zenodo” once accepted. Further inquiries for data sharing can be directed to the corresponding authors during the review period.

Conflict of Interest

All authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding: This work was financially supported by the National Natural Science Foundation of China (82170180 granted to Bing Xu, 81800163 granted to Jie Zha, 81800196 and 81770161 granted to Zhiming Li, 81900164 granted to Long Liu, 82100204 granted to Xingxing Yu), the Natural Science Foundation of Fujian Province (2020J011246 granted to Bing Xu), the Xiamen Municipal Bureau of Science and Technology (3502Z20209003 granted to Bing Xu, 3502Z20209008 granted for Zhifeng Li), and the Lymphoma Research Fund of the Chinese Anti-Cancer Association (CORP-117 granted to Bing Xu).

Author Contributions: L.L and XX. Y performed the experiments and data analysis. ZF. L, XH.H, J.Z, ZJ. L, Y.H, and ZM. L collected lymph node samples and clinical data; HJ. Z, Q.L, and X.J contributed to antibody tests and meta conjugating; L.L, XX.Y, KY. D, G.F, HF. Y, HY. Y, ZM. L, KH. Y, and B.X aided in interpreting the results and worked on the manuscript; L.L and B.X conceived and planned the experiments. All authors have given approval to the final version of the manuscript.

Acknowledgments

The authors thank the department of pathology of the first affiliated hospital of Xiamen university for their assistance in choosing ROIs.

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No competing interests reported.

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Revealing the Evolution of the Tumor Immune Microenvironment in Follicular Lymphoma Patients Progressing Within 24 Months Using Single-cell Imaging Mass Cytometry

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