# CD206+CD14- Skin-Resident Macrophages and DC-T Cell Clusters Are Spatial Features Characterizing Nonrelapsing Cutaneous Squamous Cell Carcinoma

Roxane Elaldi, Aïda Meghraoui, Axel Elaldi, Luciana Petti, Alizé Gouleau, Patrice Hemon, Wassila Khatir, Jonas Meziane, Xavier Descombes, Henri Montaudié, et al.

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## ► To cite this version:

Roxane Elaldi, Aïda Meghraoui, Axel Elaldi, Luciana Petti, Alizé Gouleau, et al. CD206+CD14- Skin-Resident Macrophages and DC-T Cell Clusters Are Spatial Features Characterizing Nonrelapsing Cutaneous Squamous Cell Carcinoma. Cancer Immunology Research, 2025, Online ahead of print. 10.1158/2326-6066.CIR-25-0182. [hal-05367600](https://hal.science/hal-05367600v1)

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CD206<sup>+</sup>CD14<sup>-</sup> Skin-resident Macrophages and DC-T Cell clusters are spatial features characterizing non-Relapsing Cutaneous Squamous Cell Carcinoma

Roxane Elaldi<sup>1,2</sup>, Aida Meghraoui-Kheddar<sup>1,3</sup>, Axel Elaldi<sup>4</sup>, Luciana Petti<sup>1</sup>, Alizé Gouleau<sup>1</sup>, Patrice Hemon<sup>5</sup>, Wassila Khatir<sup>6</sup>, Jonas Meziane<sup>1</sup>, Xavier Descombes<sup>6</sup>, Henri Montaudié<sup>7</sup>, Julien Boyer<sup>8</sup>, Alexandre Bozec<sup>2</sup>, Gilles Poissonnet<sup>2</sup>, Jacques-Olivier Pers<sup>5</sup>, Anne Sudaka<sup>1,8</sup>, Véronique M Braud<sup>1</sup> and Fabienne Anjuère<sup>1</sup>.

<sup>1</sup>Université Côte d'Azur, CNRS UMR7275, INSERM U1323, Institut de Pharmacologie Moléculaire et Cellulaire, Valbonne, France

<sup>2</sup> Institut Universitaire de la Face et du Cou, Nice, France

<sup>3</sup> Present address : AMK Biotech, BIOPARC, Biot, France

<sup>4</sup> VIDA Center, Computer Science and Engineering, New York University, New York, USA

<sup>5</sup> LBAI, UMR1227, University of Brest, INSERM, Brest, France

<sup>6</sup> Université Côte d'Azur, INRIA, CNRS, INSERM, Valbonne, France

<sup>7</sup> Université Côte d'Azur, INSERM U1065, Centre Méditerranéen de Médecine Moléculaire, Nice, France

<sup>8</sup> Centre Antoine Lacassagne, Pathology Laboratory and Human Biobank, Nice, France

**Corresponding author:** Fabienne Anjuère, Université Côte d'Azur, CNRS UMR7275, INSERM U1323, Institut de Pharmacologie Moléculaire et Cellulaire, Valbonne, France; Phone: +33493957771; E-mail: anjuere@ipmc.cnrs.fr

**Running title:** Skin-resident macrophages define non-relapsing cSCC

**Funding:** This study was supported by the following funding agencies: Centre National de la Recherche Scientifique; Institut National de la Santé Et de la Recherche Médicale; Université Côte d'Azur; Région Provence-Alpes-Côte d'Azur; French National Agency ANR-21-CE34-
0016 and ANR through the "Investments for the Future" programs LABEX SIGNALIFE ANR-11-028 and IDEX UCAJedi ANR-15-IDEX-01; INCa/LNCC ECMpact (AAP2017.LNCC); Cancéropole PACA, Institut National du Cancer and Région Sud; Fondation ARC pour la recherche sur le Cancer; Ligue Nationale contre le Cancer; Fondation d'entreprise SILAB Jean PAUFIQUE, and with the support of Fondation Bristol-Myers Squibb (BMS) for Oncoimmunology research; European FEDER grant program Progos RU 000950 to Brest Hyperion platform.

**Authors' disclosures:** The authors declare no potential conflicts of interest.

**Key words:** Cutaneous squamous cell carcinoma, tumor immune microenvironment, prognostic biomarkers, head and neck tumors, imaging mass cytometry, spatial biology
ABSTRACT

Current histopathological classifications do not reliably distinguish patients with primary cutaneous squamous cell carcinoma (cSCC) at risk of relapse from those with non-relapsing tumors. This underscores the need for molecular signatures capable of stratifying patients during primary tumor resection. Here, we used high-dimensional imaging mass cytometry (IMC) and a 39-antibody panel to define the immune landscape of twenty primary cSCC with distinct clinical outcomes, four relapsing cSCC and their perilesional skins. Computational analysis of spatially-resolved single-cell data from forty-seven IMC images identified twelve immune-cell subsets that discriminated primary cSCC from perilesional skin. Regulatory T cells, cytotoxic CD8⁺ T lymphocytes, tumor-associated macrophages and neutrophils characterized tumors while Langerhans cells and skin-resident macrophages defined perilesional skin. Skin-resident macrophages were characterized by the expression of CD206, CD11c and HLA-DR and the absence of CD14. These cells infiltrated tumors from non-relapsing patients more efficiently. We found a higher density of proliferating, mature and cytotoxic cells within this macrophage subset, consistent with the absence of relapse. Spectral flow cytometry analysis on fresh tumor biopsies revealed the skin-resident macrophages had phagocytic properties, suggesting a role in tumor antigen processing. Additionally, neighborhood profiling revealed DC-LAMP⁺ dendritic cells (DC) were in close proximity with helper and cytotoxic T lymphocytes in primary cSCC from patients without relapse, indicative of active adaptive immunity. Our findings identify phagocytic skin-resident macrophages and DC-T cell clusters as features differentiating non-relapsing cSCC from primary cSCC at risk of relapse. These data have the potential to guide the identification of prognostic biomarkers for cSCC.

Synopsis

This study maps the immune landscape of cutaneous squamous cell carcinoma, identifying
spatial features associated with relapse risk. The presence of skin-resident macrophages and
specific DC–T cell clusters characterize non-relapsing tumors, offering insights for improved
prognosis.
**Introduction**

Squamous cell carcinomas (cSCC) are the 2nd most deadly skin cancers (1). They are treated by surgery but can reach a stage of advanced disease associated with local relapse. There is currently no consensus on the prognostic factors that are able to discriminate at diagnosis primary cSCC at “high risk” of relapse from those that will not relapse, even if some tumor-associated markers have been proposed (2-4). It is therefore crucial to improve the prediction of cSCC relapses.

Immunocompromised patients have a significantly higher risk of developing high-risk cSCC (5) and immunotherapies targeting immune checkpoints have provided promising results in cSCC patients with approximately 50% of patients having a response (6, 7), highlighting the role of the immune system in controlling these tumors. Recent studies have provided evidence that immunosuppression is associated with aggressive cSCC (8, 9). Previously, we characterized the gene expression profile and functional behavior of innate lymphoid cells (NK/ILC) and subsets of myeloid cells, including dendritic cells (DC), neutrophils and macrophages, in both the 7,12-dimethylbenzene[a]anthracene/phorbol 12-myristate (DMBA/PMA) mouse model mimicking the human pathology and fresh human cSCC biopsies (8). We identified in relapsing tumors innate lymphoid cells type 1 (ILC1) displaying an inflammatory phenotype, together with dysfunctional conventional NK cells that no longer exert antitumoral functions at late stages of tumor development. We found a massive tumor infiltration of neutrophils expressing PD-L1, which contributes to cSCC growth by limiting effector CD8+ T-cell responses (9). We also characterized the heterogeneity of DC subsets and monocyte-derived and resident macrophages infiltrating mouse and human cSCC (10). Nonetheless, these studies involved tumor dissociation and did not provide a comprehensive atlas of the tumor immune microenvironment (TiME).
Here, we report a spatial and high-dimensional analysis of the myeloid and lymphoid compartments in human primary cSCC, with a focus on phagocyte subsets and their interactions with other cell types. This study used samples from a well-curated cohort of patients with distinct clinical outcomes and was designed to identify primary cSCC cases at high risk of local relapse (11). Clinically annotated formalin-fixed paraffin-embedded (FFPE) archived cSCC tissues were carefully selected from matched immunocompetent patients with primary cSCC, distinguishing patients who did not relapse from those who relapsed within two years following surgery. We used an integrative approach based on Imaging Mass Cytometry (IMC) technology and a 39-marker antibody panel (12), designed to provide a comprehensive characterization of the TiME in cSCC and perilesional nontumoral (NT) skin. This analysis aimed to identify immune features in primary cSCC that could discriminate patients who do not relapse from those who did relapse. Our findings identified a subset of skin-resident macrophages and cell clusters of mature DCs and helper and cytotoxic T lymphocytes that defined non-relapsing primary cSCC. This will guide the validation of future prognostic biomarkers for the identification of cSCC with high risk of relapse.

# Methods

## Patient eligibility, tumor selection and data collection

Tissue samples (FFPE surgical blocks) were selected from a cohort of 160 patients who underwent surgery for primary head and neck cSCC at the Centre Antoine Lacassagne in Nice (11). From this database, inclusion criteria were absence of immunosuppression, follow-up of at least 2 years for patients without recurrence, and absence of invasive surgical margins and bone invasion. Patients with Human Immunodeficiency Virus, myeloproliferative or myelodysplastic diseases, cancer treated with chemotherapy, inflammatory disease treated with immunosuppressive or immunomodulatory therapies and organ-transplanted patients were also not included. Among the patients who met these inclusion criteria, we first selected
those with local or lymph node recurrence. We then matched them with patients without
recurrence, in a 1:1 ratio to the recurrent cases based on histo-clinical criteria predicting poor
prognosis (Table 1 and reference 13), in order to eliminate the effect of these criteria in the
comparative analysis. Patients with and without lymph node involvement at diagnosis were
included to ensure that the cohort was representative of different clinical situations, allowing
for the most comprehensive descriptive analysis of the TiME. Accordingly, four groups of
patients were identified for inclusion in the present discovery cohort: Patients without nodal
invasion at diagnosis (N0), who did not experience local or nodal recurrence within two years
after the surgery of their primary cSCC (group 1; n=8), patients who experienced local or
nodal recurrence within two years, and who were matched with patients of group 1 based on
their clinical and histological profiles (group 2; n=9), patients with nodal invasion (N+) at the
initial diagnosis (group 3; n=3), and locally recurrent tumors from patients of group 2 who
underwent a second surgery (group 4; n=4) (Fig.1). Patient data, including sex, age, tumor
characteristics, and treatment history were collected from clinical records (Supplementary
Table S1). This study received approval from the institutional ethics committee of the Centre
Antoine Lacassagne and was conducted in compliance with MR-004 methodology
(Deliberation No. 2018-155) and the Declaration of Helsinki. It was registered with the
Health Data Hub under the number 16399441. Patient samples and clinical data were
anonymized and patients were informed of the use of their data in accordance with the
General Data Protection Regulation and gave written informed consent.

**Selection of regions of interest**

FFPE blocks archived at the Tumor biobank of Centre Antoine Lacassagne (Nice)
were used. Two consecutive slides of each FFPE block, 4 µm thick, were used. The first one
was stained with haematoxylin, eosin and saffron (HES) on the Sakura Prisma automated
slide stainer (Sakura Finetek) and used for the selection of the ROI. Briefly, Tissue sections were first deparaffinized in xylene and rehydrated through a graded alcohol series, then stained with Mayer's hemalum solution (Sigma Aldrich) to highlight cell nuclei, followed by Erythrosine B solution (RAL diagnostics) to stain cytoplasm and extracellular matrix. Finally, saffron solution (VWR) was applied to stain collagen fibers. Slides were mounted, observed using an Olympus BX45 upright light microscope and scanned at high resolution (0.26 µm/pixel) using the Leica Aperio GT450 pathology slide scanner for further analysis (Supplementary Fig. S1). The second tissue section was used for tissue staining and acquisition by IMC. To best recapitulate the features of the TiME, ROIs corresponding to tumor areas were consistently selected at the tumor-stroma interface by an experienced pathologist. This expert reviewed all the slides to identify the most representative ROI in terms of immune infiltration, tumor differentiation, presence of perineural invasion and mode of tumor invasion (Supplementary Fig. S1). Large tumor ROIs, with an average size of 2.4 ± 0.6 mm², were acquired to capture the complexity of the TiME for each sample. The ROIs corresponding to perilesional NT skin were chosen at least at 50 µm from the tumor edge, did not include any appendages and had an average size of 0.4 ± 0.1 mm² (Supplementary Fig. S1 and Supplementary Table S2).

## Staining and IMC acquisition

Tissue sections were stained with a 39 metal-tagged antibody panel previously designed for the characterization of the TiME of epithelial tumors (Supplementary Table S3) (12). The selected ROIs from the patients of the discovery cohort (47 ROI in total) were then acquired on the Hyperion mass cytometry system (Standard BioTools). Prior to acquisition, the imaging mass cytometer was autotuned using a 3-element tuning slide according to the provider's recommendation. As an extra-threshold for successful tuning, a
detection of at least 700 mean duals of 175Lu was used. The ROIs were ablated and acquired
at 200 Hz. IMC Raw data were exported as MCD files (Supplementary Fig. S1).

Normalization and cell and tumor segmentation

Each MCD file (n=47) contained 39 images corresponding to the staining of the tissue
section with the 39-antibody panel and 2 additional images corresponding to nuclei labelling
with an iridium-conjugated DNA intercalator. MCD files were converted into multi-TIFF files
(41 files per ROI) using the pipeline described by Bodenmiller's laboratory (14) and
preprocessed using the FIJI platform (15). The preprocessing of TIFF files included a
denoising step using a 3x3 median filter and a contrast adjustment on 16 bits scale. The
preprocessed 41 TIFF files per ROI were then stacked on FIJI (RRID:SCR_002285) and
implemented in QuPath-0.5.1 software (RRID:SCR_018257) to perform cell segmentation
using STARDIST, which led to spatially resolved single cells from each ROI (mean cell
number per NT skin image: 1072 ± 403, n=23 and mean cell number per cSCC image: 13690
± 4795, n=24) (Supplementary Fig. S1). For each ROI, a CSV table containing the average
intensity of labelling of each cell by each antibody and its coordinates was then exported from
QuPath-0.5.1 software for computational analysis. Tumor masks and epidermis masks were
generated using the pan-cytokeratin marker for the identification of tumor islets within cSCC
images and epidermis within NT skin images. TIFF files corresponding to pan-cytokeratin
staining were binarized on FIJI using a threshold adapted to each sample and processed using
the "fillPolly" python modules from the "open cv" library, and "binary_fill_holes" from the
"ndimage" python library with a 3x3-dimension window (Supplementary Fig. S1).

Cell clustering and quantification
The analyses of single-cell data were carried out using OMIQ™ software (https://www.omiq.ai) on arc-sinh transformed values. The data from the 47 images were split into CD45⁻ and CD45⁺ immune cells (CD45⁺) using manual gating. Gating thresholds were set on negative cells within concatenated samples to differentiate the positive signal for each marker from noise. Immune cells were separated into neutrophils (CD15⁺), B lymphocytes (CD20⁺), monocyte/macrophage/DC population (CD45⁺CD3⁻CD15⁻CD20⁻ cells) and T lymphocytes (CD45⁺CD3⁺ cells). Monocyte/macrophage/DC populations and T lymphocytes were then sub-clustered separately using FlowSOM, applying a consensus metaclustering with 12 k-values for the monocyte/macrophage/DC population and 6 k-values for the T-cell population. K-values were chosen to limit overclustering. The markers used for monocyte/macrophage/DC subclustering included CD11c, CD14, CD16, CD68, CD163, CD204, CD206, DC-SIGN, DC-LAMP, HLA-DR and langerin and those chosen for T-cell subclustering included CD4, CD8, Foxp3 and granzyme B. The metaclusters (MC) were represented on a heatmap of median intensity for each selected marker normalized per row (**Supplementary Fig. S2**). The identity of each MC was inferred based on the expression of the above-mentioned markers. Some MC sharing parents within two sub-branch points and/or sharing specific markers were grouped. Dimensional reduction of the data was performed by t-distributed stochastic neighbor embedding (t-SNE) using the markers selected to visualize the different immune subsets. PD-L1, TIM-3 and TIGIT markers were not included in the initial hierarchical clustering. The expression of these three markers was assessed on annotated immune populations using a supervised gating strategy. The proportions of each cell type were calculated among their parent population or among CD45⁺ immune cells. For each cell type, the cell density (counts per mm²) was calculated based on the cell counts obtained over the entire ROI or, when indicated, as the area corresponding to the tumor islet masks and NT epidermis masks.
## Spatial analysis

For tumor or epidermis infiltration analysis, the counts of each annotated subset inside tumor masks or epidermis masks were calculated per mm² of tumor islet or epidermis. For cell-cell neighborhood analysis, a permutation test was used to identify geographic cellular groupings (or cell clusters). Cell subtypes categorized into distinct phenotypes were assessed for the statistical analysis of their spatial relationships through a permutation test, employing Monte Carlo sampling (16). This process seeks to identify statistically significant attractions or avoidances between different phenotypes. The first step was to compute each phenotype/phenotype neighborhood distribution. We defined cell neighborhoods per ROI as cells co-localizing within a 30 µm radius by mapping all the image. Then, we calculated the neighborhood phenotype distribution for each cell phenotype, representing the distribution of phenotypes among its neighboring cells. Finally, we averaged the neighborhood distributions of cells with the same phenotype to obtain each phenotype/phenotype neighborhood distribution for the entire image. The second step was to compute the random phenotype/phenotype neighborhood distribution. 5,000 simulations were conducted, each involving a random permutation of cell phenotype labels, resulting in 5,000 random phenotype/phenotype neighborhood distributions. The third step was to compute the *p*-value of each phenotype/phenotype distribution against the simulated distribution. The *p*-value corresponds to the average number of simulated permutations for which the observed phenotype/phenotype distribution is higher than the averaged simulated distribution.

## Spectral flow cytometry

Fresh cSCC biopsies and perilesional skin samples (n=6) were collected in physiological saline from patients undergoing surgery at the surgery department, Centre Antoine
Lacassagne (Nice, France), between 2019 and 2021, in accordance with institutional ethical guidelines. All samples were anonymized and written informed consent was obtained from each patient, in compliance with the Declaration of Helsinki and with approval from CAL institutional review board. After mechanical dilaceration, each fresh sample was digested with collagenase IV from Clostridium histolyticum (1.25 mg/mL) (Sigma-Aldrich) and DNase II-S from bovine pancreas (0.13 mg/mL) (Sigma-Aldrich) at 37°C for 40 minutes under shaking. Cell suspensions were pre-incubated with mouse serum (1/10) (Biowest) and human serum IgG (100 µg/mL) (Sigma-Aldrich) for 30 minutes at 4°C to prevent non-specific binding and then with a mix of fluorescent antibodies for phenotyping macrophage subsets as detailed (Supplementary Table S4) for 30 minutes at 4°C. Before acquisition, cell suspensions were incubated with 7-AAD dye according to the manufacturer's instructions. Controls included unstained cells, single-color stained cells, and Fluorescence Minus One (FMO) stained cells. Stained cells were analyzed on a SP6800 spectral cytometer (SONY Biotechnology) and the data were processed using FlowJo V10 software (RRID:SCR_008520).

## Statistical analysis

Comparison of histo-clinical data between groups 1 and 2 was performed using the non-parametric Mann-Whitney-Wilcoxon test or the Chi2 test modified by Fisher's exact test, depending on the nature of the data being compared. Multiple comparisons of subset frequencies were done using the non-parametric one-way ANOVA test (Dunn's test). Comparison of the different subset frequencies or cell densities between groups (tumor vs. perilesional NT skin and tumor group 1 vs. group 2) were performed using two-tailed non-parametric paired Wilcoxon test and Mann-Whitney test, respectively. All p-values were 2-sided with type I error (α < 0.05) considered to be statistically significant. Statistical analyses were carried out with Prism software, version 6.0 (GraphPad Prism, San Diego, CA).
**Data availability statement**

HES scans and exemplar IMC images are available in the article and in supplementary files and other data available from the corresponding author upon reasonable request.
# RESULTS

## A longitudinal cohort of cSCC patients with or without subsequent relapse

The goal of this study was to explore whether spatial features of the TiME can distinguish patients with primary cSCC who relapsed within two years post-surgery from those who did not, in order to establish ways to identify patients with “high risk” primary tumors. To achieve this goal, we mapped the phenotype and spatial distribution of tumor, non-immune stromal and immune cells within 24 FFPE patient tissues using IMC technology (Fig. 1 and Table 1).

The cohort included Non Tumoral perilesional skin (NT skin; n=23), cSCC (n=20) from 20 patients with a mean age of 79.7 years (SD = 10.1) at surgery and 4 local relapses of these tumors (Fig. 1 and Table 1). After surgery, the median follow up of these patients was 20 months (IQR = 14), with a minimum of 24 months for patients without relapse. Eight primary cSCC (N0) without relapse were included in group 1, 9 primary cSCC (N0) with local or nodal relapse in group 2, 3 primary cSCC with nodal invasion at diagnosis (N+) in group 3, and 4 local relapses of patients from group 2 in group 4 (Fig. 1). Tumors in groups 1 and 2 were matched in terms of patient age at surgery, completion of adjuvant radiotherapy, and criteria categorizing "high-risk" tumors (17), including ear/temple/lip location, size, depth, differentiation and presence of perineural invasion (PNI). Such matching eliminates the effect of these criteria in the comparative analysis (Supplementary Table S1).

## A single-cell atlas of immune cell types in the cSCC TiME

To characterize the TiME of cSCC, successive FFPE tissue sections from primary tumors and relapsing tumors were used for HES staining and high-dimensional IMC analysis. ROIs corresponding to tumor-stroma interface and distant NT skin were manually outlined by a pathologist on each HES tissue. The IMC staining was then performed on each adjacent
tissue section using a 39-antibody panel, and IMC acquisition and computational analysis
done on selected ROIs (Fig. 2). This panel, which was designed for the characterization of the
TiME of epithelial tumors, includes markers for tumor cells, non-immune stromal cells and
different immune cell types as well as functional markers (Supplementary Table S3 and
reference (12)).

We first focused on the identification of the immune cells present in cSCC and NT
skin. To achieve this goal, we used a stepwise semi-supervised gating strategy performed on
the complete single-cell dataset. Using a manual gating strategy, we first separated the CD45-
non-immune cells from the CD45+ cells. We then identified the main immune cell types
including CD3+ T lymphocytes among CD45+ immune cells, CD15+ neutrophils among CD3-
CD45+ cells, CD20+ B lymphocytes among CD15-CD3-CD45+ cells and cells from the
mononuclear phagocytic system (monocytes/macrophages/DCs) among CD20-CD15-CD3-
CD45+ immune cells (Fig. 3A). Using FlowSOM, we then sub-clustered the CD3+ T cells in
six MC using CD4, CD8, Foxp3 and Granzyme B markers (Supplementary Fig. S2A). In
addition, we identified twelve myeloid MC by FlowSOM sub-clustering on CD20-CD15-
CD3-CD45+ immune cells using Langerin/CD207, DC-LAMP/CD208, CD16, CD14, CD206,
DC-SIGN/CD209, CD163, CD204, CD68, CD11c, HLA-DR markers (Supplementary Fig.
S2C). After grouping some related MC, this stepwise semi-supervised clustering identified
twelve immune cell types including neutrophils, B lymphocytes as well as four subsets of T
lymphocytes and six subsets of the mononuclear phagocytic immune system
(monocytes/macrophages/DCs) (Fig. 3 and Supplementary Fig. S2B and S2D). Among
CD3+ T lymphocytes, the annotation led to the characterization of four subsets: MC01
corresponding to Foxp3-CD4+ T lymphocytes, MC02 and MC03 identified GzB-CD8+ T
cells, MC04 characterized cytotoxic GzB+CD8+ T cells, and MC05 and MC06 identified
Foxp3+CD4+ regulatory T cells (Treg) (Supplementary Fig. S2A-B and Fig. 3B). Their
localization and the expression of CD3, CD4, CD8, Foxp3 and GzB markers in the four
annotated T-cell clusters were visualized on cSCC IMC images, as illustrated on one
representative image (Fig. 3C). Regarding monocyte/macrophage/DC subsets, we
characterized six different subsets associated with cSCC after annotation of the twelve MC
identified automatically (Supplementary Fig. S2C-D and Fig. 3D). We found a subset of
CD14<sup>+</sup>CD68<sup>+</sup> immature monocyte-derived cells named monocytes (MC01). Based on the
expression of langerin, we identified two subsets of Langerhans cells (LC), one subset named
LC1 that could correspond to epidermal long-lived LC (MC02) and another one named LC2-
expressing DC-LAMP, HLA-DR corresponding to mature LC and expressing some CD14
(MC03), suggesting that they derive from adult hematopoiesis in inflammatory conditions
(18). We also identified one subset of mature DCs expressing HLA-DR, CD11c and DC-
LAMP named DC (MC04). Moreover, the clustering identified a population of CD68<sup>+</sup> cells
corresponding to MC06 named Macro1. Macro1 is a discrete cell subset expressing CD206,
CD11c and HLA-DR but not the CD14 and CD16 monocytic markers or other macrophage
markers, suggesting that it could be related to non-monocytic skin-resident macrophages
described in mouse skin (19) or to DCs based on their expression of CD11c and HLA-DR.
The population named Macro2 corresponds to an abundant and heterogeneous population of
CD68<sup>+</sup>CD14<sup>+</sup>HLA-DR<sup>+</sup> monocyte-derived macrophages with variable expression of CD163,
CD16, CD204, CD206 and DC-SIGN markers grouping MC05 and MC07 to MC12, all of
them belonging to the same branch of the hierarchical clustering (Supplementary Fig. S2C
and Fig. 3D). The heterogeneity of the different monocyte/macrophage/DC subsets can be
visualized on a representative IMC image showing the expression of CD68, HLA-DR, CD14,
CD16, CD11c, CD204, CD206, DC-SIGN, CD163 and langerin by the different myeloid-cell
subsets (Fig. 3E, 3F and 3G).
The abundance of these annotated immune-cell subsets was quantified in the total cSCC TiME including intratumoral and peritumoral immune cells (Fig. 4). T lymphocytes accounted for 63% of immune cells and mononuclear phagocytes represented 28% of immune cells and were the most abundant immune cells in the cSCC TiME, whereas neutrophils and B lymphocytes were more discrete populations (Fig. 4A-B). CD8⁺ T cells and CD4⁺ T cells accounted for 57% and 43% of CD3⁺ T lymphocytes and the CD3⁺ T lymphocytes comprised 16% GzB⁺CD8⁺ T cells and 15% Foxp3⁺ Tregs (Fig. 4C, left panel). Among the phagocytic cells, monocytes and monocyte-derived macrophages (Macro2) were the most abundant, accounting for 33% and 57% of the monocyte/macrophage/DC subsets, respectively, whereas DC and LC subsets, as well as CD14⁻CD206⁺ macrophages (Macro1), represented 11% of monocyte/macrophage/DC subsets altogether (Fig. 4C, right panel).

Identification of the spatial immune profile that characterizes cSCC

To identify the immune profile that best defines cSCC, we first compared the abundance of the immune phenotypes infiltrating cSCC (n=24) and NT skin (n=23). The hierarchical clustering of the immune-cell subsets with significantly different abundances in cSCC and NT skins revealed immune signatures that distinguished these two immune microenvironments (Fig. 5A). Phagocytic cells were observed to preferentially populate NT skin while B/T lymphocytes, mature DC and neutrophils were more characteristic of the cSCC TiME (Fig. 5A and **Supplementary Fig. S3A**). This was consistent with the dominant lymphoid infiltration observed in cSCC (Fig. 4B). LC1 and Macro1 cell types were more abundant in NT skin than in cSCC, consistent with their skin residency (Fig. 5A and **Supplementary Fig. S3A**). We then quantified the density of the twelve MCs identified in **Fig. 3** in NT skin and cSCC TiME. We confirmed that the cSCC TiME was defined by an increased density of lymphocytes including GzB⁺CD8⁺ T cells, Foxp3⁻CD4⁺ T cells, Treg, B
cells as well as neutrophils, and CD14-derived cells including Macro2, monocytes, DC and LC2 compared to NT skin (Fig. 5B). Although these tumors were infiltrated with antigen-presenting cells and cytotoxic T lymphocytes, the increased density of TIGIT⁺ T-cell subsets and PD-L1⁺ myeloid-cell subsets in cSCC compared to NT skin suggests an inhibition of the antitumor immunity (Fig. 5B and Supplementary Fig. S3B-C). In contrast, LC1 remained the cell type that best characterized NT skin, whereas the Macro1 subset infiltrated both NT skin and cSCC with a broad range of densities between individual samples (Fig. 5B, last boxplot).

Our recent data identified autofluorescent skin-resident macrophages as phagocytes expressing the CD206 endocytic receptor but no other macrophage markers, endowed with exceptional phagocytic properties, and expressing TNF-α upon stimulation in mice and human skin (10). We investigated whether the Macro1 subset identified in NT skin and cSCC by IMC corresponded to skin autofluorescent macrophages. To evaluate this, we compared the autofluorescence profiles of HLA-DR⁺ macrophages expressing or not expressing the CD14 and CD206 markers using six fresh biopsies of primary human cSCC and their corresponding NT skin by spectral flow cytometry (Fig. 5C). We found that CD206⁺CD14⁻ HLA-DR⁺ macrophages, displaying the phenotype closest to that of Macro1, were more abundant in perilesional NT skin compared to cSCC, as observed by IMC (Fig. 5A-C). Among autofluorescent macrophages, a greater proportion were CD206⁺CD14⁻HLA-DR⁺ macrophages in cSCC than in NT skin, indicative of enhanced phagocytic properties in cSCC (Fig. 5C, lower right panel, and 10).

To further characterize the interactions between immune cells and tumoral cells, we then quantified the density of immune-cell subsets within tumor islets compared to epidermis (Fig. 5D, Supplementary Fig. S1 and Supplementary Fig. S4). Tumor islets were significantly more infiltrated by CD45⁺ immune cells compared to NT epidermis, highlighting
the inflammatory profile of primary cSCC (Supplementary Fig. S4A). The heterogeneous subset of monocyte-derived macrophages (Macro2) was the population with the highest density in tumor islets together with Foxp3<sup>-</sup>CD4<sup>+</sup> T and GzB<sup>-</sup>CD8<sup>+</sup> T lymphocytes (Supplementary Fig. S4A, right panel and Supplementary Tables S5 and S6). In contrast, LC1 was the cell type with the highest density in the epidermis, consistent with its skin residency (Supplementary Fig. S4A, middle panel and Supplementary Tables S5 and S6).

To precisely identify the immune cells preferentially distributed within tumor islets, we then determined the ratio of the density of each cell type in tumor islets to the density of the same cell type in total cSCC TIME (Fig. 5D and Supplementary Table S7). It confirmed that LC1 and LC2 were mainly located in tumor islets, consistent with their skin residency. Moreover, it revealed that Macro1, with a ratio near 1, was the cell subset that interacted the most with tumor cells, followed by neutrophils. In contrast, monocytes and GzB<sup>+</sup>CD8<sup>+</sup> T cells, with ratios of 0.2 and 0.3, respectively, were the most abundant cell types localized in cSCC outside tumor islets.

To identify immune cell-cell neighborhoods in tumors and NT skin, we then compared immune cell-cell groupings in these two microenvironments using a permutation test (Supplementary Fig. S5). No significant cell-cell interaction was observed in NT skin except proximity between different lymphoid-cell subsets. Cell groupings of mature phagocytic cells including LC2, DC and Macro1 and of different T-cell subsets were observed in cSCC as well as cell clusters involving DC and preferentially CD4<sup>+</sup> T cells, consistent with an active immune response.

## Resident-skin macrophages and DC-T cell clusters define non-relapsing cSCC

To identify the immune features that distinguished primary cSCC from patients who relapsed from those who did not within two years post-surgery, we compared the abundance
and density of the previously described immune-cell subsets (Fig. 3). By comparing non-relapsing primary cSCC (group 1, n=8) with primary cSCC from patients who relapsed within two years post-surgery (group 2, n=9), we found a higher proportion of Foxp3⁺ Treg among total lymphocytes in primary cSCC from cases with recurrence. However, we observed no significant differences in the proportion of Treg among total CD4⁺ T lymphocytes and in their overall density between the two groups and Treg significantly infiltrated more into tumors than perilesional NT skins in both groups (Fig. 6A). Conversely, the increased abundance and density of Macrol macrophages significantly distinguished non-relapsing primary cSCC (group 1) from primary cSCC who relapsed (group 2). In contrast, no significant difference in Macrol abundance and density was found in NT skins between the two groups, suggesting that Macrol macrophages have a greater capacity to infiltrate cSCC from non-relapsing patients than cSCC from cases with recurrence (Fig. 6B). A more detailed analysis of Macrol phenotypes using the maturation marker PD-L1, the proliferation marker Ki67, and the cytotoxicity-associated marker myeloperoxidase (MPO) revealed higher densities of mature (PD-L1⁺), proliferating (Ki67⁺), and cytotoxic (MPO⁺) cells in group 1 compared to group 2. Similar phenotypic patterns were observed in perilesional NT skin samples (Fig. 6C), supporting the stronger association of Macrol with primary cSCC that did not relapse. In these two groups, no significant differences in density were observed among the other immune phenotypes (Supplementary Fig. S6).

The densities of the various immune cell types from group 3 and group 4 were also shown in total ROI (Supplementary Fig. S6) and in tumor masks (Supplementary Fig. S7). Comparison of primary cSCC from different clinical situations (group 1/group 2/group 3) revealed that primary cSCC with nodal invasion (group 3) tended to be more infiltrated by Gzb⁻CD8⁺ T cells, Foxp3⁻CD4⁺ T cells, Treg, B cells and Macrol macrophages, all of which are potentially pro-tumoral immune populations (Supplementary Fig. S7A). In contrast,
relapsing cSCC (group 4) showed only a reduced density in Gzb⁻CD8⁺ cells compared to primary cSCC (group1). Moreover, the comparison of paired samples from group 2 (primary cSCC with relapse) and group 4 (relapse) revealed no significant differences in the densities of the analyzed immune cell types (Supplementary Fig. S8).

The distribution of Macrol within cSCC from group 1 compared to group 2 was also visualized on representative images (Fig. 6D). These images illustrated that Macrol macrophages accumulated at the tumor-stroma interface. When analyzing the density of the different immune cell types in tumor masks (generated based on the expression of pancytokeratin), we found that Macro 1 had more contact with tumor cells in group 1 than in group 2, suggesting increased phagocytosis and tumor antigen uptake (Fig. 6D and 20).

We then compared the immune cell-cell interactions that discriminated non-relapsing primary cSCC (group 1) from primary tumors that relapse (group 2) using a permutation test (Fig. 6E). We identified several types of cell-cell clusters. Primary cSCC from patients who did not relapse have mature DC and LC2 interacting with GzB⁺CD8⁺ T cells and DC interacting with CD4⁺ T lymphocytes, whereas such cell-cell clusters were avoided or absent in cSCC from patients who relapsed, consistent with an improved antitumor response in non-relapsing tumors (Fig. 6E, red crossing). In addition, we detected a DC-B cell proximity in cSCC in group 2 whereas there was an avoidance of such cell cluster interactions in cSCC from group 1 (Fig. 6E, blue crossing).

Discussion

This study reports the spatial characterization of the myeloid and lymphoid compartments of matched human primary cSCC that ultimately had different clinical outcomes using high dimensional imaging (12). Our primary goal was to comprehensively characterize the TiME of cSCC and adjacent NT perilesional skin and to identify the immune
features distinguishing primary cSCC from patients relapse-free within two years post-surgery
from those who relapsed. Two studies on the spatial characterization of cSCC preceded the
present work, but they used differently conceived cohorts and did not focus on phagocyte
subsets and their interactions with other immune cell types (21, 22).

This study revealed that primary cSCC are mainly infiltrated by T-cell subsets
including Treg and cytotoxic CD8+ T lymphocytes, confirming previous observations
showing a major infiltration of lymphocytes in cSCC (21, 23, 24). Among the four subsets of
lymphocytes identified in the present study, Foxp3+ Treg are associated with relapse,
consistent with a study showing that a high infiltration in Treg correlated with a poor
prognosis in cSCC (25). Contrary to our findings, a recent study reported an association
between Treg and the absence of relapse in primary cSCC among immunocompetent patients
(21). This difference may be attributed to several factors, including differences in the nature
and size of the ROIs analyzed, variations in single-cell data processing, and/or differences in
cohort design. These authors included exclusively patients at high risk of relapse according to
the 7th AJCC histo-clinical criteria, which was not the case in our cohort. We also identified an
infiltration of neutrophils in cSCC compared to NT perilesional skin, confirming our previous
work in a mouse model of chemical carcinogenesis showing an infiltration of neutrophils
associated with tumor progression (9). This phenotype is also consistent with a meta-analysis
of several types of human solid cancers in which polymorphonuclear neutrophils were
associated with tumor progression (26).

We uncovered spatial features involving CD4+ and CD8+ T cells interacting with
mature DC and LC2 and foci of mature phagocytes that distinguish the TiME of cSCC from
the NT skin. Previous studies in ovarian cancer and melanoma identified correlations between
DC-LAMP+ DC and CD8+ T-lymphocyte infiltration associated with a favorable clinical
outcome and antitumoral immunity (27, 28). Our study extends these findings as we identified
a privileged interaction of DC-LAMP⁺ DC with helper and cytotoxic T lymphocytes, consistent with active immune response in non-relapsing cSCC. In addition, cell clusters of mature DC and LC and skin-resident macrophages organized in foci also defined cSCC, which is consistent with a study showing that mature DC cell clusters in LN from breast cancer patients were associated with a better prognosis (29). More detailed characterization of such antigen-presenting cell clusters and their interactions with other cell types in cSCC would be helpful to determine their function. As these different cell types are known to be involved in the processing of antigens or their phagocytosis, it is tempting to propose that they contribute to the local stimulation of CD4⁺ and CD8⁺ T lymphocytes, but this remains to be investigated.

This study also characterized a discrete population of macrophages expressing the CD206 mannose receptor but not the CD14 and CD16 monocytic markers in human skin and cSCC, suggesting that this cell subset could be skin-resident macrophages derived from embryonic hematopoiesis as recently shown in mouse skin and in other tissues (10, 30). Some of these macrophages were autofluorescent, suggesting that they are highly phagocytic and that they could have antigen-presenting capacity in the TIME. Tissue-resident macrophages expressing FOLR2 marker were recently identified in mammary glands and breast cancer. These macrophages were associated with a favorable prognostic and CD8⁺ T-lymphocyte stimulation (20). We found that skin-resident macrophages defined non-relapsing cSCC, suggesting they could also play a role in CD8⁺ T-cell antitumoral immunity.

The use of a cohort with a small number of patients per clinical group or TMA can limit the identification of biomarkers as recently highlighted for cSCC (21). Nevertheless, the present study revealed that 8 or 9 samples per group were sufficient to identify immune phenotypes and spatial features discriminating clinical groups with distinct outcomes. The limitation due to patient number was compensated with the use of a well-conceived cohort
design based exclusively on immunocompetent patients also matched according to several
other criteria including age, tumor location, size, depth, differentiation and the presence of
PNI, thus excluding patients with intrinsic risks to develop cSCC like in the case of organ
transplant recipient immunosuppressed patients.

In summary, mapping spatially the myeloid and lymphoid immune compartments of
NT perilesional skin and primary cSCC with different clinical outcomes provides a
comprehensive immune-cell atlas of cSCC, which will be helpful to identify new therapeutic
targets. Moreover, defining immune phenotypes and spatial immune features that distinguish
cSCC from patients who do not relapse from those who relapse offers the opportunity to
evaluate prognostic biomarkers. The present study identified a subset of skin-resident
macrophages and cell clusters of DCs with helper and cytotoxic T lymphocytes defining non-
relapsing primary cSCC, which will guide the validation of prognostic biomarkers for
identifying “high risk” cSCC.

Contributions

**Authors' contributions:** **R. Elaldi:** Conceptualization, data curation, wet-lab and computational analysis, investigation, writing original manuscript, review of original manuscript and editing. **A. Meghraoui-Kheddar:** Conceptualization, data curation, wet-lab and computational analysis, review of original manuscript and editing. **A. Elaldi:** Computational analysis, review of original manuscript and editing. **L. Petti:** Conceptualization, wet-lab analysis, review of original manuscript and editing. **A. Gouleau:** Computational analysis, review of original manuscript and editing. **J. Meziane:** Computational analysis, review of original manuscript and editing. **P. Hemon:** Conceptualization, IMC data acquisition, review of original manuscript and editing. **W. Khatir:** Computational analysis, review of original manuscript and editing. **X. Descombes:**
Computational analysis, review of original manuscript and editing. **H. Montaudié:** Conceptualization, review of original manuscript and editing. **J. Boyer:** Resources, review of original manuscript and editing. **A. Bozec:** Resources, review of original manuscript and editing. **J.O. Pers:** Supervision of IMC data acquisition, review of original manuscript and editing. **G. Poissonnet:** Conceptualization, resources, review of original manuscript and editing. **A. Sudaka:** Resources, data curation, formal analysis, validation, review of original manuscript and editing. **V.M. Braud:** Conceptualization, funding acquisition, investigation, project administration, review of original manuscript and editing. **F. Anjuère:** Conceptualization, study supervision, funding acquisition, validation, investigation, writing original manuscript, project administration, review of original manuscript and editing.

## Acknowledgments

We acknowledge Dr J. Haudebourg as well as all the technical staff of the pathology laboratory and the Human Biobank of Nice Cancer Center (Centre Antoine Lacassagne, CAL, Nice, France) for the extemporaneous preparation of human biopsies and histological analyses, respectively. We thank the IPMC's Imaging/Flow cytometry core facilities for providing assistance, the cytometry flow core facility Hyperion (Brest, France) as well as the European FEDER grant program Progos RU 000950 members of the Scientific Interest Group (GIS) Biogenouest for technical assistance.
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# Tables

<table><caption>**Table 1.** Comparison of patients from group 1 and 2</caption><thead><tr><th>Patient characteristics (n = 17)</th><th>Group 1 (n=8)</th><th>Group 2 (n=9)</th><th>P-value</th></tr></thead><tbody><tr><td><strong>Age at surgery</strong></td><td></td><td></td><td></td></tr><tr><td>Mean [SD]</td><td>79,6 [7,1]</td><td>81,3 [9,6]</td><td>p = 0.7722<sup>a</sup></td></tr><tr><td><strong>Sex, No (%)</strong></td><td></td><td></td><td></td></tr><tr><td>Female</td><td>0 (0)</td><td>2 (22)</td><td></td></tr><tr><td>Male</td><td>8 (100)</td><td>7 (78)</td><td>p = 0.4706<sup>b</sup></td></tr><tr><td><strong>Tumor characteristics (n = 17)</strong></td><td></td><td></td><td></td></tr><tr><td><strong>Tumor, No (%)</strong></td><td></td><td></td><td></td></tr><tr><td>Primary</td><td>8 (100)</td><td>7 (100)</td><td></td></tr><tr><td><strong>Location, No (%)</strong></td><td></td><td></td><td></td></tr><tr><td>Ear or lip or temple</td><td>5 (62,5)</td><td>3 (33)</td><td>p = 0.3469<sup>b</sup></td></tr><tr><td><strong>Diameter, No (%)</strong></td><td></td><td></td><td></td></tr><tr><td>≥ 2 cm</td><td>5 (62,5)</td><td>6 (67)</td><td>p = 1<sup>b</sup></td></tr><tr><td><strong>Differentiation, No (%)</strong></td><td></td><td></td><td></td></tr><tr><td>Poorly differentiated</td><td>1 (12,5)</td><td>1 (11)</td><td>p = 1<sup>b</sup></td></tr><tr><td><strong>Perineural invasion, No (%)</strong></td><td></td><td></td><td></td></tr><tr><td>- Yes and ≥ 0,1 mm</td><td>2 (25)</td><td>4 (44)</td><td>p = 0.6199<sup>b</sup></td></tr><tr><td><strong>Tumor depth, No (%)</strong></td><td></td><td></td><td></td></tr><tr><td>- Beyond subcutaneous fat</td><td>3 (37,5)</td><td>5 (55)</td><td>p = 0.6371<sup>b</sup></td></tr><tr><td><strong>Treatment, No (%)</strong></td><td></td><td></td><td></td></tr><tr><td>Surgery + adjuvant radiotherapy</td><td>0 (0)</td><td>2 (22)</td><td>p = 0.4706<sup>b</sup></td></tr></tbody></table>

No: number

SD: standard deviation

IQR: Interquartile range

<footnote><sup>a</sup> P calculated from the Mann-Whitney-Wilcoxon test</footnote>

<footnote><sup>b</sup> P calculated from Chi2 test modified by Fisher's exact test</footnote>
**Figure Legends**

**Figure 1.** A cohort of cSCC patients with or without relapse. Group 1 included primary cSCC from patients without nodal invasion at diagnosis (N0) and no history of relapse within two years post-surgery. Group 2 was primary tumors from N0 patients but with local or nodal relapse within 2 years post-surgery. Group 3 was primary tumors with nodal invasion at diagnosis (N+). Group 4 was the relapsing local tumors from the primary tumors in group 2.

**Figure 2.** Overview of experimental and analytical workflow. (A) Tumors from patients described in Fig. 1. Two successive tissue sections from each FFPE cSCC surgical block, one stained with hematoxylin-eosin-saffron (HES) to select two regions of interest (ROI) chosen with a pathologist, one "tumor" ROI and one Non Tumoral (NT) skin ROI. Staining of the second slide with a 39-Antibody panel and IMC acquisition of the selected ROI enabling the characterization of the cSCC tumor immune microenvironment and NT skin. Processing of the 47 IMC images to generate spatially-resolved single cells (cell segmentation) and tumor and epidermis masks (tissue segmentation). These steps are illustrated with representative images from cSCC003 sample. (B) Identification of the various immune cells of the TiME using semi-supervised analysis, quantification of their abundance and density and spatial analysis of the different actors of the TiME.

**Figure 3.** Identification of the immune cell types infiltrating cSCC. (A) Supervised identification of immune cells (CD45+, red), T cell subsets (CD45+CD3+, blue), neutrophils (CD45+CD3-CD15+, pink), B lymphocytes (CD45+CD3-CD15-CD20+, cyan) and monocytes/macrophages/dendritic cells subsets (CD45+CD3-CD15-CD20-, orange). (B) Hierarchical clustering of CD3 T lymphocytes based on the expression of CD4, CD8, Foxp3 and granzyme B (GzB) identifying four subsets. (C) Visualization of the T cell subsets identified in (B) on a representative IMC image from cSCC002 sample. (D) Hierarchical clustering of CD45+CD3-CD15-CD20- mononuclear phagocytic cells identifying six subsets among which monocytes, tissue-resident Langerhans cells (LC1) and inflammatory Langerhans cells (LC2), dendritic cells expressing DC-LAMP (DC), CD14-CD206+CD68+ skin-resident macrophages (Macro 1), and CD14+ monocyte-derived macrophages (Macro 2), based on the expression of langerin, DC-LAMP, CD68, CD14, CD16, CD163, CD206, CD204, DC-SIGN, HLA-DR and CD11c. (E) Visualization of Mono/Mac/DC subsets on a representative IMC image from cSCC002 sample. (F-G) Highlight of markers expressed (F)
by Macro 1 and Macro 2 subsets or (G) by LC1, LC2 and DC subsets in the indicated tumor zones from E (dotted squares).

**Figure 4.** Characterization of cSCC TiME. (A) Total counts of immune cells, lymphocytes, myeloid cells, T cells, B cells, neutrophils and Mono/Mac/DC subsets in the 24 cSCC IMC images. (B) Boxplots showing the median, the interquartile range and the range values of the abundance of lymphoid and myeloid cell subsets among immune cells infiltrating cSCC (n=24). (C) Pie charts of the mean proportions of T lymphocyte subsets and Mono/Mac/DC subsets in cSCC and boxplots showing the median, the interquartile range and the range values of the abundance of each indicated subset per cSCC. Individual data values are plotted as dots.

**Figure 5.** Identification of the immune profile that specifies cSCC. (A) Hierarchical clustering showing the abundance of the immune cell subsets among CD45+ immune cells in NT skin (green, n=23) and cSCC (red, n=23) with a significant difference, mean-centered values (z-score). (B) Boxplots showing the median, the interquartile range, the range and individual values of the density (counts per mm²) of the indicated subsets in NT skin (green) and cSCC (red). (C) Spectral flow cytometry analysis of macrophage subsets in NT skin and cSCC fresh biopsies. Flow cytometry gating strategy of the supervised analysis of macrophage subsets in the cell suspension from a representative cSCC (middle panels). Boxplots showing the median, the interquartile range, the range and individual values of the abundance of the indicated subsets in NT skin (green) and cSCC (red). (A-C) Two-tailed non-parametric paired Wilcoxon test, *p*-values: \* *p*<0.05, \*\* *p*<0.01, \*\*\* *p*<0.001, \*\*\*\* *p*<0.0001. (D) Boxplots showing the median, the interquartile range, the range and individual values of the immune cell density ratio of the indicated subsets in tumor islets on total cSCC TiME, significant differences as detailed in supplementary Table 7, on one-way ANOVA, Dunn's multiple comparisons test, *p*-values: \* *p*<0.05, \*\* *p*<0.01, \*\*\* *p*<0.001, \*\*\*\* *p*<0.0001.

**Figure 6.** Resident-skin macrophages and dendritic cell-cytotoxic T cell clusters define non-relapsing cSCC. (A-B) Boxplots showing the median, the interquartile range, the range and individual values of the abundance and density (counts/mm²) of Treg (A) and total Macrol (B) in primary cSCC who did not relapse (Group 1, n=8) or who relapsed within two years following surgery (Group 2, n=9) and in their perilesional Non Tumoral (NT) skins. (C) Boxplots showing the median, the interquartile range, the range and individual values of the
density of the indicated Macro1 subsets in cSCC and NT skins of patients from group 1 and group 2. (D) Representative images of three cSCC from group 1 and group 2 illustrating the distribution of Macro1 subset around the pancytokeratin staining. For each ROI, the pancytokeratin staining was used to define the epithelial/tumor masks as shown in Supplementary Fig.1. The boxplot shows the median, the interquartile range, the range and individual values of the Macro1 density (counts/mm²) within epithelial/tumor masks in ROI from group 1 and group 2. (A-D) Mann-Whitney test for comparisons of cSCC from different patients and wilcoxon test for comparisons of paired skins and cSCC; \* p<0.05, \*\* p<0.01. (F) Neighborhood analysis showing all the cell-cell interactions in cSCC from group 1 and group 2 patients in heatmaps in which the indicated cell type in a row is significantly neighbors (red) or avoided (blue) by the cell type in the column. Blue-white-red scale showing the cell forming clusters together (red) and the cell subsets avoiding each other (blue) within 30 µm radius, compared to a random null distribution (white representing an interaction prevalence of less than 10%). Plotted are mean significant images determined by a permutation test over all images. Crossing of colored boxes showing selected differences in neighborhood between group 1 and group 2.
<figure><img src="image_3.png" /></figure>
A Cohort
constitution

ROI
selection

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

Staining and IMC acquisition
---

<figure><img src="image_6.png" /></figure>

B Immune cell phenotyping

<figure><img src="image_7.png" /></figure>

Abundance and density analysis

<figure><img src="image_8.png" /></figure>

Neighborhood
analysis

<figure><img src="image_9.png" /></figure>

Spatial analysis

<figure><img src="image_10.png" /></figure>

Tumor islet
infiltration analysis
A

<figure><img src="image_11.png" /></figure>

B

<figure><img src="image_12.png" /></figure>

D

<figure><img src="image_13.png" /></figure>

C

<figure><img src="image_14.png" /></figure>

E

<figure><img src="image_15.png" /></figure>

F

<figure><img src="image_16.png" /></figure>

G

<figure><img src="image_17.png" /></figure>
<figure><img src="image_18.png" /><figcaption>B</figcaption></figure>

<figure><img src="image_19.png" /><figcaption>C</figcaption></figure>

<figure><img src="image_20.png" /><figcaption>T cell subsets</figcaption></figure>

<figure><img src="image_21.png" /></figure>

<figure><img src="image_22.png" /><figcaption>Mono/Mac/DC subsets</figcaption></figure>

<figure><img src="image_23.png" /></figure>
<figure><img src="image_24.png" /><figcaption>B</figcaption></figure>

<figure><img src="image_25.png" /><figcaption>C</figcaption></figure>

<figure><img src="image_26.png" /><figcaption>D</figcaption></figure>

<figure><img src="image_27.png" /></figure>

<figure><img src="image_28.png" /></figure>
<figure><img src="image_29.png" /><figcaption>Treg</figcaption></figure>

<figure><img src="image_30.png" /></figure>

<figure><img src="image_31.png" /><figcaption>B</figcaption></figure>

<figure><img src="image_32.png" /></figure>

<figure><img src="image_33.png" /><figcaption>C</figcaption></figure>

<figure><img src="image_34.png" /><figcaption>Ki67+ Macro1</figcaption></figure>

<figure><img src="image_35.png" /><figcaption>MPO+ Macro1</figcaption></figure>

<figure><img src="image_36.png" /><figcaption>D</figcaption></figure>

<figure><img src="image_37.png" /><figcaption>Macro1</figcaption></figure>

<figure><img src="image_38.png" /><figcaption>E</figcaption></figure>

<figure><img src="image_39.png" /></figure>