Single-cell landscape of peripheral and tumor-infiltrating immune cells in HPV-negative HNSCC

Rômulo Gonçalves Agostinho Galvani; Adolfo Alexis Rojas Hidalgo; Carlos Alberto Biagi-Junior; Bruno Fernandes Matuck; Jelte Martinus Maria Krol; Brittany Rupp; Nikhil Kumar; Khoa Huynh; Jinze Liu; Siddharth Sheth; Vinicius Maracaja-Coutinho; Kevin Matthew Byrd; Patricia Severino

doi:10.1080/2162402X.2025.2605741

. 2025 Dec 30;15(1):2605741. doi: 10.1080/2162402X.2025.2605741

Single-cell landscape of peripheral and tumor-infiltrating immune cells in HPV-negative HNSCC

Rômulo Gonçalves Agostinho Galvani ^a,^1,², Adolfo Alexis Rojas Hidalgo ^b,², Carlos Alberto Biagi-Junior ^c, Bruno Fernandes Matuck ^d, Jelte Martinus Maria Krol ^a, Brittany Rupp ^d, Nikhil Kumar ^e, Khoa Huynh ^f, Jinze Liu ^f, Siddharth Sheth ^g, Vinicius Maracaja-Coutinho ^b,^h,ⁱ, Kevin Matthew Byrd ^d,^*, Patricia Severino ^a,^j,^*

^aAlbert Einstein Research and Education Institute, Hospital Israelita Albert Einstein, Sao Paulo, SP, Brazil

^bAdvanced Center for Chronic Diseases, Facultad de Ciencias Químicas y Farmacéuticas, Universidad de Chile, Santiago, Chile

^cDepartment of Pediatric Oncology, Dana-Farber Boston Children's Cancer and Blood Disorders Center, Boston, MA, USA

^dDepartment of Oral and Craniofacial Molecular Biology, Philips Institute for Oral Health Research, Virginia Commonwealth University, Richmond, VA, USA

^eDepartment of Innovation and Technology Research, American Dental Association Science and Research Institute, Gaithersburg, MD, USA

^fDepartment of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA

^gDepartment of Medicine, Division of Medical Oncology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

^hInstituto Gonçalo Moniz, Fundaçao Oswaldo Cruz, Salvador, BA, Brazil

ⁱInstituto Nacional de Ciência e Tecnologia em Saúde Digital, Salvador, BA, Brazil

^jMedical Institute of Bioregulation, Kyushu University, Fukuoka, Japan

CONTACT Kevin Matthew Byrd kevinmbyrd@gmail.com Department of Oral and Craniofacial Molecular Biology, Philips Institute for Oral Health Research, Virginia Commonwealth University, Richmond, VA, USA; Patricia Severino patricia.severino@einstein.br Albert Einstein Research and Education Institute, Hospital Israelita Albert Einstein, Sao Paulo, SP, Brazil

Current affiliation: Bioinformatics Laboratory, National Laboratory of Scientific Computing, Petropolis, RJ, Brazil.

Equally contributed.

Roles

Rômulo Gonçalves Agostinho Galvani: Formal analysis, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing

Adolfo Alexis Rojas Hidalgo: Data curation, Formal analysis, Investigation, Methodology, Resources, Validation, Writing – original draft, Writing – review & editing

Carlos Alberto Biagi-Junior: Data curation, Formal analysis, Investigation, Methodology, Validation, Writing – original draft

Bruno Fernandes Matuck: Formal analysis, Investigation, Methodology, Writing – review & editing

Jelte Martinus Maria Krol: Formal analysis, Investigation, Methodology, Writing – original draft

Brittany Rupp: Formal analysis, Methodology, Writing – review & editing

Nikhil Kumar: Formal analysis, Investigation, Methodology

Khoa Huynh: Formal analysis, Methodology

Jinze Liu: Formal analysis, Methodology

Siddharth Sheth: Conceptualization, Funding acquisition, Investigation, Project administration, Resources, Supervision

Vinicius Maracaja-Coutinho: Methodology, Resources, Supervision, Writing – review & editing

Kevin Matthew Byrd: Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing

Patricia Severino: Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing

PMCID: PMC12758321 PMID: 41467967

ABSTRACT

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide. HPV-negative HNSCC, arising in diverse upper airway mucosal niches, is particularly aggressive, with poor 5-y survival and a limited response to immune checkpoint inhibitors. A deeper understanding of the tumor-localized immune landscape is essential to uncover actionable immunotherapeutic targets. Here, we integrated two single-cell RNA sequencing (scRNA-seq) datasets from 29 samples totaling nearly 300,000 immune cells to dissect immune rewiring during tumor progression and lymph node metastasis in HPV-negative HNSCC. We identified distinct shifts in adaptive immune cell populations across 14 peripheral blood mononuclear cell (PBMC) and 21 tumor-infiltrating immune cell (TIC) states. Notably, TICs exhibited enriched interferon response and immunomodulatory gene signatures, in contrast to PBMCs, indicating tumor-specific immune imprinting. Ligand–receptor analysis revealed that immunosuppressive crosstalk between macrophages and cytotoxic cells was associated with advanced disease. To spatially validate these transcriptional states, we conducted multiplexed immunofluorescence profiling on nine locally invasive HPV-negative HNSCCs, all from the ventrolateral tongue mucosa. Spatial proteomics confirmed peritumoral enrichment of activated (CD107a⁺, ICOS⁺) NK and CD8⁺ T cells and intratumoral accumulation of exhausted (PD-1⁺, PD-L1⁺) phenotypes, mirroring pseudotime trajectories inferred from scRNA-seq. These findings highlight spatially localized cytotoxic cell exhaustion as a key immune evasion mechanism in HPV-negative HNSCC and underscore the value of integrating spatial and single-cell data to reveal therapeutic vulnerabilities.

KEYWORDS: HPV-negative HNSCC, tumor-infiltrating immune cells, single-cell RNA sequencing (scRNA-seq), spatial proteomics, tumor immune microenvironment, immune exhaustion

Introduction

Head and neck squamous cell carcinoma (HNSCC) is the most prevalent malignancy in the head and neck region and is driven by a complex interplay of genetic and environmental factors such as tobacco use, alcohol consumption, environmental pollutants, and human papillomavirus (HPV) infection.^1-3 Based on HPV status, HNSCC is classified into two main categories: HPV-associated (HPV-positive; HPV⁺) and HPV-unassociated (HPV-negative; HPV⁻) disease. HPV⁺ HNSCC generally has a more favorable prognosis, while patients with HPV⁻ HNSCC experience significantly worse outcomes and unique clinical challenges.⁴

HPV⁻ HNSCC is characterized by frequent mutations in key regulatory genes such as TP53, CDKN2A (p16INK4a), and CCND1.5 In contrast, HPV⁺ cancers rely on E6 and E7 viral proteins to inactivate the tumor suppressors TP53 and retinoblastoma protein (RB), respectively.^5-7 Molecular differences between HPV⁺ and HPV⁻ HNSCC extend to the tumor microenvironment (TME) and immune landscape. HPV⁻ HNSCC exhibits profound immune dysfunction, including impaired activity of CD8⁺ T cells, which are critical for antitumor immunity.^8-10 Lymph node metastasis is common in HNSCC and involves complex morphological adaptations, such as epithelial-to-mesenchymal transition (EMT), immune evasion, and altered cancer cell metabolism. These processes facilitate tissue invasion, tumor spread, and resistance to immune surveillance.^11-13

Standard treatments for advanced HNSCC, such as surgical excision followed by chemotherapy or chemoradiotherapy, offer limited efficacy for HPV⁻ cases.^14-16 Despite advances in immunotherapies such as immune checkpoint inhibitors (ICIs) targeting the PD-1/PD-L1 pathway, therapeutic resistance remains a significant barrier. For example, pembrolizumab has shown improved survival when combined with chemotherapy compared to cetuximab-based regimens; however, response rates remain low in metastatic HPV⁻ HNSCC.^17-20

These clinical limitations highlight the need for mechanistic insights into how immune cell states are shaped by the tumor microenvironment. In this study, we integrate two previously published single-cell RNA sequencing datasets²¹^,²² to analyze tumor-infiltrating immune cells (TICs) and peripheral blood mononuclear cells (PBMCs). By comparing PBMCs and TICs across nodal involvement and tumor stages, we aimed to define tumor-specific immune rewiring and uncover signaling pathways associated with immune suppression and therapeutic resistance in this aggressive cancer subtype.

Methods

Data acquisition and patient characteristics

We integrated two publicly available single-cell RNA sequencing datasets, GSE139324 and GSE164690.²¹^,²² Clinical metadata and sequencing data were downloaded from the NCBI Gene Expression Omnibus repository.²³ Only HPV-negative (HPV⁻) samples were selected for analysis. Disease staging was carried out previously in the original publications, following the tumor, node, metastasis (TNM) staging system for HNSCC.²⁴ For the GSE139324 dataset, 17 patients were included and grouped based on TNM: T1 (n = 1), T2 (n = 2), T3 (n = 8), and T4 (n = 6). Patients with at least one site of lymph node (LN) metastasis were classified as N+ (n = 10), whereas patients without LN metastasis were classified as N0 (n = 7). For this dataset, a total of 34 samples were included for investigation (17 PBMC samples and 17 tissue samples for TIC analysis). For the dataset GSE164690, we included 12 patients who were also grouped based on TNM: T1 (n = 1), T2 (n = 2), T3 (n = 7), and T4 (n = 2); N0 (n = 7) and N+ (n = 5), resulting in a total of 24 samples (12 PBMC samples and 12 tissue samples). Patient information was recovered according to the original publications (Table S1).²¹^,²²

Initial data processing and quality control

The raw FASTQ files were obtained from the NCBI GEO database.²¹^,²² The initial data were processed using CellRanger v7.1.0 (10x Genomics),²⁵ 83, and the sequences were aligned with the human reference genome GRCh38. The output gene‒count matrix files were analyzed using the Seurat R package v5.0.3.²⁶^,²⁷ within R v4.3.3. Each step used randomized algorithms, and a seed was set at 123 with the set.seed() function. Ambient RNA was corrected using SoupX package v1.6.2.²⁸ Doublets were identified using the scDblFinder package v1.16.0.86,87 and removed from the dataset using Seurat with the command “subset()”.²⁹ Cells with fewer than 200 genes, UMIs above 5 MADs from the median of their sequencing batch, and with more than 10% of mitochondrial RNA genes were excluded.

Data normalization, dimensionality reduction and clustering

Data were normalized using Seurat's NormalizeData function with default settings, which produced log-transformed transcripts per 10,000 reads. The resulting gene/barcode matrix was used for dimensionality reduction. Highly variable genes were selected to account for variance in principal component analysis (PCA). To ensure that only high-quality cells were included, sources of sample- and cell-specific variation were regressed out. MALAT1, NEAT1, MTRNR, hemoglobin, ribosomal, and mitochondrial genes were excluded from the highly variable gene list. PCA was run on the centered and scaled expression of the remaining variable genes using RunPCA in Seurat. For low-dimensional visualization, UMAP was applied using the UMAP-learn Python implementation with “correlation” as the metric. To correct batch effects from merged studies, we used Harmony v1.2.0 to adjust the PCA results, generating a new UMAP with corrected PCs. For FindNeighbors, the nearest neighbor parameter k was set to 10. Both the RunUMAP and FindNeighbors functions used the previously defined number of corrected PCs for the TICs and PBMCs. Clustering was performed using Seurat's FindClusters function with varying resolution values, and a final resolution of 2 was selected for comprehensive clustering, which was visualized on the UMAP plot.

Cell type identification and annotation

To infer cell types, we used CellTypist v1.6.2.³⁰ Reference cell types were obtained from the CellTypist model Immune_All_Low version 2. Assuming that cells in the same cluster (generated by FindClusters) have similar expression patterns, the most probable cell labels from the reference model were matched with the cell clusters (generated by FindClusters). As cell types and states are defined by specific markers, we utilized the “Curated markers” and “Top Model Markers” definitions from CellTypist as a guide to classify each of the cell types and their corresponding possible states, which was followed by the manual selection of the representative markers identified with wilcoxauc() function from presto package version 1.0.0 for each cell type and its corresponding lineage from PBMCs and TICs. After the integration of both datasets, the cell types were annotated using canonical immune markers. Following this annotation, the dataset was split into two subsets: tumor-infiltrating immune cells (TICs) and peripheral blood mononuclear cells (PBMCs) according to their tissue of origin (tumor tissue vs. peripheral blood).

Differential gene expression and gene set enrichment analysis (GSEA)

To identify differentially expressed genes (DEGs) between groups, we analyzed each dataset (GSE139324 and GSE164690) using Seurat's FindMarkers function. Significant differences were determined using the nonparametric Wilcoxon rank sum test. In brief, the gene expression values were ranked, and the distribution of gene ranks in the N0 group was compared to the N⁺ group. The top genes from each N0 cell type were compared to those from the same N⁺ cell type. To include only significant DEGs in the plots, we set an adjusted p-value threshold of <0.05. Genes with low Log2FC values were excluded using Seurat's default cutoff of 0.25. All DEGs were visualized in a jitterplot, highlighting those with known immunological functions among the top 20 Log2FC in N⁺ vs. N0. To explore potential correlations between gene function and biological processes in specific groups or clusters, we conducted gene set enrichment analysis (GSEA). This computational method identifies whether predefined gene sets show significant, coordinated differences between conditions. We used the fgsea R package (v1.28) and the h.all.v2023.2.Hs.symbols reference file from MSigDB.³¹^,³² All genes were ranked by the product of Log2FC and p-value from the differential expression analysis to assess their condition correlation. A threshold of adjusted e-value <0.05 was applied to select significantly enriched gene sets.

Cell‒cell communication

To identify intercellular interactions and receptor‒ligand signaling networks, we used the CellChat R package, a computational tool that infers and visualizes intercellular communication networks from single-cell transcriptomic data.³³ To compare the N0- and N⁺ groups, we used expression data from TICs/PBMCs datasets and performed analyses separately for each group per dataset. We first created a CellChat object from Seurat objects, set “CellChatDB.human” as the interaction database, and limited it to “Secreted Signaling” interactions. To reduce computational costs, we applied the subsetData() function to retain only signaling genes from the CellChat database in the expression matrix. Overexpressed genes and interactions were identified and projected onto a human protein‒protein interaction network. Finally, CellChat computed communication probabilities at the signaling pathway level using computeCommunProbPathway(), and calculated the aggregated cell‒cell communication network across all cell groups with aggregateNet().

Gene trajectory

To investigate the pseudotime trajectory ordering of single cells and corresponding genes in TICs, we use Monocle 2 v2.30.0, a robust tool for single-cell RNA-seq analysis.³⁴^,³⁵ It applies a reversed graph embedding (RGE) algorithm to order cells along a pseudotime axis, reflecting their biological progression. We first converted data from Seurat objects, extracting normalized expression data and cell/gene metadata. Genes expressed in at least one cell at a minimum level of 0.1 (default) were selected to construct the trajectory. We used the DDRTree method (an RGE algorithm) for dimensionality reduction and the orderCells() function to estimate pseudotime values. Although Monocle 3 is available, it replaces traditional pseudotime with a UMAP-based representation, which is faster and more resource-efficient. However, this newer approach is less interpretable for our data type, justifying our choice of Monocle 2.

Bulk RNA-seq deconvolution

To investigate whether findings from scRNA-seq could be replicated in bulk RNA-seq, we deconvoluted selected samples from TCGA HNSC project, which were HPV⁻ from white individuals with oral cavity tumors that matched the sites analyzed in our single-cell dataset using CibersortX (53 samples).⁵^,³⁶ The signature matrix was generated using the gene expression matrix of our scRNA-seq samples with 100 representative cells per cell annotation. The cell fractions were imputed using the generated signature matrix and the gene expression matrix from the TCGA HNSC project. Batch correction was enabled in S mode using the gene expression matrix of 100 representative cells with 50 permutations without quantile normalization.

Multiplex protein immunofluorescence

Samples were selected from the NCT03529422 trial at the University of North Carolina School of Medicine, with input from the oral pathologist BFM (Institutional Review Board approval 17-3078). FFPE blocks were sectioned into sequential 5-μm slices and mounted on SuperFrost Plus slides (Thermo Fisher) for Phenocycler-Fusion 2.0, H&E, and additional staining. All slides were prepared using RNA-free water and RNAse-free protocols on the Leica autostainer. For multiplex immunofluorescence (Multi-IF), we used the Akoya PhenoCycler Fusion 2.0. The samples were deparaffinized through a 100%–30% ethanol gradient. Antigen retrieval used AR9 (EDTA) buffer (Akoya) in a low-pressure cooker for 15 min, followed by 1 h of cooling. The samples were rehydrated in ethanol (2 min) and incubated in staining buffer (20 min). The antibody cocktail, prepared per Phenocycler guidelines, included four blockers and nuclease-free water. Primary antibodies (1:200) were diluted in this mixture and incubated overnight at 4 °C in a Sigma-Aldrich humidity chamber. Post-incubation, slides were rinsed in a staining buffer (2 min), fixed in 10% PFA in a staining buffer (10 min), and washed three times in 1X PBS (2 min each). The slides were immersed in ice-cold methanol (5 min), incubated in the final fixative solution (FFS, per Phenocycler protocol) for 20 min at room temperature, rinsed with PBS, and mounted into the FCAD machine. The flow cells were affixed under high pressure (30 s) and then placed in a PCF buffer (10 min) before scanning. The reporters were prepared with a reporter stock, 5490 µL of nuclease-free water, 675 µL of 10X PCF buffer, 450 µL of PCF assay reagent, and 27 µL of concentrated DAPI at a 1:1000 dilution per cycle. Two slides were processed per run at a 1:50 reporter dilution. The reporters (250 µL) were loaded into a 96-well plate sealed with Akoya aluminum foil. Manual area mapping preceded PhenoImager scanning (brightfield). Low/high DMSO solutions were prepared per Index B of the Phenocycler Fusion 2.0 manual.

AstroSuite

AstroSuite includes spatial biology tools such as TACIT and Astrograph. The threshold-based assignment of cell types from multiplexed imaging data (TACIT) is an unsupervised method for annotating cell types using spatial omics data. It processes a CELLxFEATURE matrix from Cellpose 3.0 segmentation and a TYPExMARKER matrix based on expert-informed marker relevance. The method operates in two stages: first, cells are grouped into homogeneous microclusters (MCs) via the Louvain algorithm. Then, cell type relevance (CTR) scores are computed by correlating marker intensity with cell type signatures—higher scores indicate stronger associations. Segmental regression clusters CTR scores to define a threshold that minimizes inconsistent labeling; cells above this threshold are labeled positive for a given type. If multiple labels are assigned, TACIT applies k-nearest neighbors (k-NN) deconvolution to resolve ambiguity.

Ethical considerations

This study was conducted using publicly available and deidentified data. According to Resolution No. 510/2016 of the Brazilian National Health Council (CNS), research that involves the use of public domain or publicly accessible information that does not allow the identification of individuals is exempt from review by a Research Ethics Committee. In conformity, this study was considered exempt from review by the Hospital Israelita Albert Einstein Research Ethics Committee. For multiplex protein immunofluorescence, samples were selected from the NCT03529422 trial at the University of North Carolina School of Medicine with Institutional Review Board approval 17-3078, and all participants signed a written informed consent.

Results

Generating an integrated HNSCC immune cell atlas

Owing to patient diversity and cellular heterogeneity, HNSCC is complex and difficult to study. We aimed to establish a foundation for an integrated HNSCC single-cell and spatial multiomic atlas using existing scRNA-seq datasets.²¹^,²² Although several HNSCC single-cell datasets have been published³⁷^,³⁸ and reviewed elsewhere,³⁹ we selected two datasets that uniquely included both tumor-infiltrating immune cells (TICs) and peripheral blood mononuclear cells (PBMCs), allowing direct comparisons between tumor-localized and systemic immune states. Future versions will incorporate additional healthy and diseased oral and craniofacial datasets to support panoral immune reference mapping.³⁰^,^40-42

We focused on HPV⁻ HNSCC samples enriched with CD45⁺ immune cells from immunotherapy-naïve patients. The cohort (62% male, 58.6% Caucasian; others unreported) ranged from 30 to 90 y old, with nearly half aged 61–69 y (Table S1). Most reported tobacco (69%) and/or alcohol use (51.7%). The tumors—mainly T3/N0—were from various upper airway sites, especially the oral cavity. This clinical diversity enabled the analysis of immune heterogeneity across lesion-associated and systemic compartments stratified by nodal and tumor stage.

Before integration, we annotated and evaluated immune cell diversity by pathological node status (N0/N⁺). Using CellTypist,⁴³ we identified 14 PBMC and 21 TIC immune populations based on gene expression (Figures 1a,h and S1). UMAPs and bar plots confirmed consistent cell type annotations and frequencies between datasets (Figures S2, S3). Both studies showed comparable immune profiles in PBMC and TIC samples. Alveolar macrophages with high TREM2 expression were reclassified as TREM2⁺ macrophages, in line with recent findings in healthy and diseased oral mucosa.⁴⁰^,⁴¹ Postintegration, 29 samples were merged into an immune atlas of 111,276 PBMCs and 167,565 TICs (Figure 1).

Cell signatures and UMAPs were generated by cell type, nodal status (N0/N⁺), and stage (T1–T4) (for PBMCs Figure 1a–e and for TICs Figure 1h–l). N⁺ PBMCs had more Tem/Temra cytotoxic T cells, while T3/T4 cases showed fewer CD16⁺ NK cells (Figure 1f–g), Figure S4A-B). In TICs, N⁺ samples had fewer Tem/Trm cytotoxic T cells, Tregs, and γδ T cells than N0 (Figure 1m–o), Figure S4C). No differences in the TIC population were observed across tumor stages (Figure S4D). These datasets highlight distinct immune shifts by nodal status or stage, with PBMC and TIC profiles diverging, suggesting that TIC changes reflect tumor-specific or microenvironmental effects.

Single-cell signatures of nodal metastasis and tumor stage

Although numerous studies have elucidated key processes involved in the metastatic cascade, such as epithelial‒mesenchymal transition (EMT), platelet interactions, and tissue remodeling, the precise contribution of specific immune cell subsets in HPV⁻ HNSCC metastasis remains incompletely understood.⁴⁴ While our analyses observed some differences in immune cell enrichment, we next wanted to explore distinct molecular signatures of nodal involvement and pathologic stage (Figures 2, S5). Broadly, major differences in gene expression signatures were observed between the N⁺ and N0 samples and T3/T4 versus T1/T2 stages, as visualized using jitter plots (Figure 2, Table S2). Analyzing the TICs, the main DEGs upregulated in N⁺ when compared to N0 are genes related to the response to interferon, the most frequent being IFIT3, IFIT2, IFIT1 and IFITM1 expressed mainly by cells of the myeloid lineage such as Intermediate Macrophages and Classic Monocytes.⁴⁵^,⁴⁶ In contrast, the genes mostly upregulated in the lymphoid population such as, but not limited to, Cycling NK, CD16-NK cells, Gamma-Delta T cells, Tem/Trm Cytotoxic T cells and Tregs are genes associated with immunomodulation such as IL7R, KLRG1, KLRB1 and IL12RB2.^47-51 In addition to genes associated with the response to interferon and immunomodulation, genes related to angiogenesis such as VEGFA and FN1, in DC2 and TREM2⁺ macrophages, were found (Figure 2a).⁴⁵^,⁴⁶^,^52-54

Figure 2. — Differentially expressed genes at the single-cell level in TICs. Jitter plots indicating differentially expressed genes between N status (a) and pathological stages (b). The size of each dot represents the percentage of cells expressing the gene. The cell types are highlighted by color. Genes were ranked based on the average Log2FC. Highlighted genes are those that showed the highest differential expression with known immunological function among the 20 genes with the highest Log2FC in each cell annotation. The black line represents a Log2FC of zero. The dotted line represents the Log2HR threshold of 0.25. HNSCC—head and neck squamous cell carcinoma; NK—natural killer; pDC—plasmacytoid dendritic cell; Tcm—T central memory; Tem—T effector memory; Trm—T resident memory; Temra—T effector memory expressing CD45RA; DC—dendritic cell; MAIT—mucosal associated invariant T cell.

Regarding the pathological stage, we again found that most of the upregulated DEGs in cells from individuals with tumors at a higher stage of progression (T3 or T4) are related to the response to interferon, with the most frequent being IFIT1, IFI44, IFI44L and IFIT3, however not mainly in cells of myeloid origin, but in lymphoid cells such as CD16⁻ NK cells, CD16⁺ NK cells, Cycling T cells, Tregs, Helper T cells, Memory B cells and Plasma cells.⁴⁵^,⁴⁶ In antigen-presenting cells such as Intermediate Macrophages, Naive B cells, pDC and TREM2⁺ macrophages, overexpression of HLA-DQA2 was found, a gene that encodes the low polymorphic alpha allele of HLA-DQ incapable of dimerizing with HLA-DQB1 (Figure 2b).⁵⁵^,⁵⁶ When comparing PBMCs by nodal status or tumor stage, interferon response genes were not differentially expressed in most cell populations, except for N⁺ non-classical monocytes, which overexpressed IFIT3, IFI44, and IFI44L (Figure S5). No consistent molecular signature was shared between PBMCs and TICs, indicating that immune remodeling and interferon gene induction are mainly confined to the tumor microenvironment.

Gene set enrichment analysis of immune cells in HPV-negative HNSCC

To assess whether genes differentially expressed between N⁺ and N0 groups influence biological pathways, we conducted gene set enrichment analysis (GSEA) using the MSigDB Hallmark library,⁴⁷^,⁴⁸ which includes 50 pathway collections. As expected, GSEA differences were observed between N⁺ and N0 samples and T3/T4 versus T1/T2 stages, as visualized in bubble plots (Table S3, Figure 3). In the TIC atlas, 32 pathways were enriched; 24 were positively enriched in N⁺ cells, with DC2 cells showing the most (19), followed by migratory DCs (10). We observed enrichment of interferon/STING signaling pathways in DCs, a known mechanism that enhances cross-priming between APCs and cytotoxic T cells.⁵⁷

Figure 3. — Gene Set Enrichment Analysis (GSEA). The terms were determined using the MSigDB_Hallmark library. (a) GSEA analysis was conducted per cell type, comparing N⁺/N0 cells (A) or T3 + T4/T1 + T2 cells (b) and considering all DEGs. The graphical representation illustrates the enrichment of cell types present in TICs from the integrated datasets GSE139324 and GSE164690. The size of the points in the figure represents −Log padj value of each enrichment in that specific cell type, and the color represents the normalized enrichment score (NES) from lowest (blue) to highest (red). NK—natural killer; pDC—plasmacytoid dendritic cell; Tcm—T central memory; Tem—T effector memory; Trm—T resident memory; Temra—T effector memory expressing CD45RA; DC—dendritic cell; MAIT—mucosal associated invariant T cell.

Conversely, 26 pathways were negatively enriched in N⁺, primarily in memory B cells (10) and Tem/Effector Helper T cells (8). In the PBMC atlas, only 15 pathways were enriched. Six genes were upregulated in N⁺ cells, including three in memory B cells, while 11 genes were downregulated, with five in MAIT cells. These findings suggest that tumor-resident immune cells are more heavily modulated by tumors than by PBMCs, emphasizing the role of local immunity, especially in the oral mucosa (Figure 3a).⁴⁰^,⁴¹^,⁵⁸

Three gene sets showed broad enrichment across populations. TNFα signaling via NF-κB was enriched in 18 populations: 11 in TICs and seven in PBMCs. Interferon gamma and alpha responses (IFNγ and IFNα) were also prevalent: IFNγ in 11 TIC and two PBMC populations; IFNα in 14 total—13 in TICs and once in PBMC regulatory T cells—highlights the importance of the STING/interferon pathway in HPV⁻ TME.

Comparing T3/T4 to T1/T2 tumors, we found 21 positive enriched pathways in TICs, mainly in migratory DCs (18) and cycling NK cells (7). Conversely, 17 genes were downregulated, especially in CD16⁺ NK cells (10). PBMCs from T3/T4 patients showed minimal changes: 4 upregulated pathways (3 in pDCs) and 19 downregulated pathways, including 17 in Tem/Trm CD8⁺ T cells and 13 in Tem/Temra subsets (Figure 3b). These results reveal that compartmentalized immune activation, with interferon-driven signaling concentrated in TICs, likely reflects STING-mediated remodeling by DCs and macrophages in the TME.

Receptor‒ligand networks in TICs

To investigate communication dynamics in the TME, we focused on TICs and analyzed receptor‒ligand signaling and predicted interaction networks using CellChat.³³ In N0, tumor-associated macrophages, especially intermediate and TREM2⁺ macrophages, had the most outgoing signals (Figure 4a). The same pattern was observed for N⁺, with these two macrophage subsets again dominating outgoing interactions (Figure 4b). For incoming signals, TREM2⁺ and intermediate macrophages were also top-ranked in both groups. However, Temra/Tem Cytotoxic T cells showed the greatest increase in incoming signals (over 70% in N⁺) while Cycling NK cells decreased nearly 30% (Figure 4b). These results highlight TAM subsets as central immune signal hubs in the HPV⁻ TME, both sending and receiving high levels of modulatory cues. Their dual role suggests their involvement in reshaping immune dynamics, particularly during lymph node metastasis, when T and NK cell communication shifts.⁵⁹ Given the rise in macrophage outgoing signals and increased input to Temra/Tem cytotoxic T cells, we next identified pathways showing the greatest changes.

Figure 4. — Cell‒cell communication in TIC. Circular plots illustrating cellular communication among major cell types, representing the numbers of interactions between these cells in TIC from N0 (a) of N⁺ (b). Bar plots representing the Log2 fold change (N⁺ over N0) in outgoing interactions from intermediate macrophages and alveolar/TREM2⁺ macrophages (c) and incoming interactions from Trm/Tem and Temra/Tem cytotoxic T cells (d). HNSCC—head and neck squamous cell carcinoma; NK—natural killer; pDC—plasmacytoid dendritic cell; Tcm—T central memory; Tem—T effector memory; Trm—T resident memory; Temra—T effector memory expressing CD45RA; DC—dendritic cell; MAIT—mucosal associated invariant T cell; UMAP—uniform manifold approximation and projection; TIC—tumor infiltrating cells.

When focusing on outgoing signals from TREM2⁺ and intermediate macrophages and incoming signals to Temra/Tem and Trm/Tem cytotoxic T cells, pathway analysis (Table S4) revealed that TGF-β and IL-10 had the greatest increase in macrophage signaling in N⁺ vs. N0 (Figure 4c). For cytotoxic T cells, the IL-2 and CD70 pathways, which are linked to T cell activation, were elevated in N⁺ cells (Figure 4d). These results indicate increased immunosuppressive interactions from macrophages and increased activation signaling in cytotoxic T cells.

Together, these findings reveal a dual signaling axis in the metastatic TME: macrophages amplify suppression via TGF-β and IL-10, while cytotoxic T cells simultaneously upregulate activation pathways, suggesting a paradoxical immune state of concurrent stimulation and suppression.

Single-cell trajectories of cytotoxic immune cells

To investigate the roles of cytotoxic innate and adaptive immune cells in tumor progression, we analyzed key genes regulating NK cells and cytotoxic T lymphocytes across nodal and pathological stages using pseudotime and gene trajectory analysis. For NK cells (CD16⁺, CD16⁺, and cycling), we evaluated genes related to activation or inhibition: AREG, KLRG1, TCF7, GZMA, and KIR2DL4. NK cells from N0 individuals were evenly distributed along pseudotime, whereas those from N⁺ individuals clustered at later stages, suggesting a transition from N0 to N⁺ (Figure 5a–e). Notably, the immunomodulatory genes AREG and KLRG1 increased over pseudotime, with higher expression in N⁺ NK cells. Similarly, TCF7, which is essential for NK maintenance, was elevated in N⁺ individuals. In contrast, cytotoxicity-related genes GZMA and KIR2DL4 decreased as pseudotime progressed, with lower levels in N⁺ NK cells. These patterns suggest a shift toward an immunomodulatory NK phenotype in N⁺ individuals, with reduced cytotoxic activity.

Figure 5. — Gene trajectories in TICs. Jitter plots with average line illustrating the relative expression of AREG (a), KLRG1 (b), TCF7 (c), GZMA (d) and KIR2DL4 (e) in NK cells. The blue dots represent cells from N0, and the red dots represent cells from N⁺. PCA representing gene trajectories of CHUK, HLA-DRB5, HLA-DQA1, HLA-DQB1, CD3D, HLA-DRA, NFKBIA, HLA-DRB1, TRAC, and UBE2N in cytotoxic T cells over pseudotime (f), pathological stage (g) and PCDC1 expression (h). PCA—Principal component analysis.

For cytotoxic T lymphocytes (Temra/Tem and Trm/Tem), we assessed genes linked to TCR signaling: CHUK, HLA-DRB5, HLA-DQA1, HLA-DQB1, CD3D, HLA-DRA, NFKBIA, HLA-DRB1, TRAC, and UBE2N. Trajectory analysis revealed a distinct branch later in pseudotime enriched in cells from T4-stage patients (Figure 5f–h). PDCD1 (PD-1), a key exhaustion marker, was expressed mainly along this trajectory, indicating that T cells progressing toward T4 adopted an exhausted phenotype. These findings highlight dual immune dysfunctions in the TME: a shift toward immunosuppressive NK cells in N⁺ individuals and progressive CD8⁺ T cell exhaustion in advanced disease, suggesting erosion of cytotoxic surveillance that may drive immune escape in HPV⁻ HNSCC.

Comparison of results across scRNA-seq and bulk RNA-seq datasets

To validate and assess clinical relevance, we analyzed an external dataset from the TCGA-HNSC project.⁵ To align with our study population, we filtered for HPV⁻, Caucasian individuals with tumors from oral cavity sites matching those in our single-cell analysis (Table S1). To assess whether the differences in cell frequencies found in the scRNA-seq data between groups were also found in the bulk RNA-seq data, we used CibersortX to generate the gene signature matrix for each cell annotation.³⁶ Unsupervised hierarchical clustering analysis was unable to cluster the samples using cell frequencies into different node status (Figure S6A) or pathologic status (Figure S6B). Differences in Tem/Trm cytotoxic T cells, Tregs, and γδ T cells between node statuses (Figure 1) were not found after deconvolution (Figure S6C). To infer whether the differences found in gene expression using pseudotime would also be found in the deconvoluted bulk RNA-seq, we correlated gene expression with the estimated abundance of their cells. NK cells from the N⁺ group showed a positive correlation between their abundance and the expression of AREG, in agreement with the pseudotime results, but also with KIR2DL4 and GZMA, discordant with the pseudotime results (Figure S6D). The correlation between PDCD1 expression and cytotoxic T cells was significant only at T2, which is partially in agreement with the pseudotime results (Figure S6E). We classified individuals with AREG expression above the median as AREG^hi and below the median as AREG^lo (Figure S6F) and individuals with NK cell abundance above the median as NK^hi and below as NK^lo (Figure S6G). Survival analysis of the different combinations of AREG expression and NK abundance demonstrated that the NK^hiAREG^hi group had worse overall survival (data now shown) and disease progression-free survival (Figure S6H). Hazard ratio analysis using the NK^hiAREG^hi group as a reference demonstrated that the other groups have a protective action (Figure S6I). Despite the concordance of the protective role of AREG expression by NK cells between bulk RNA-seq and scRNA-seq, these results underscore a key limitation of bulk RNA-seq: its inability to distinguish signals from specific immune subsets in complex tissues. Unlike single-cell RNA-seq, bulk methods average gene expression across all cells, masking cell type- or state-specific immune changes. This highlights the need for high-resolution approaches, such as single-cell and spatial multiomics, to uncover immune alterations that bulk datasets cannot resolve, yet are essential for understanding and targeting immunopathology in HPV⁻ HNSCC.

Understanding the spatial enrichment of cytotoxic cell states in situ

Building on our scRNA-seq findings of cytotoxic immune cell heterogeneity, specifically NK cell immunosuppression and T cell exhaustion in advanced stages, we hypothesized that these transcriptional states would correlate with immune cell positioning in the TME. Prior studies have shown that immune cell function and clinical relevance often depend on anatomical localization (e.g., peri- vs. intratumoral).⁶⁰ To validate and spatially contextualize TIC states, we performed high-dimensional spatial proteomics. The tissue samples used were from HPV⁻ HNSCC patients in the clinical trial NCT03529422, which was selected for ventrolateral tongue tumors, local invasiveness, and similarity to our scRNA-seq and TCGA cohorts (Table S1).

Our goal was to determine whether exhausted or cytolytic states from scRNA-seq localized to distinct tumor niches. A six-marker panel labeled tumor and immune cells: pan-CK (tumor), CD68 (macrophage), CD56 (NK), CD45 (panimmune), CD3E (T cells), and CD8 (cytotoxic T cells) (Figure 6a).⁴⁰^,⁶¹ Four additional markers (CD107a (degranulation), PD-1, PD-L1, and ICOS) captured activation and cytotoxicity (Table S5), reflecting phenotypes from scRNA-seq, including PDCD1⁺ exhausted CD8⁺ T cells and AREG⁺/KLRG1⁺ NK cells.

Figure 6. — Spatial proteomics of HPV-negative HNSCC. The spatial multi-IF assay at lower resolution presents a representative area from one of the tumors analyzed in the spatial proteomics study. In this image, intratumoral and peritumoral regions are highlighted, with analyses focused on compartmentalizing these two subsets within each tumor. The intratumoral region (left) shows NK cells and CD8⁺ T cells (dashed white circle in the central panel) with lower concentrations of immune markers compared to the peritumoral region (right) (a). Z score heatmap representation of immune marker expression levels in NK and CD8⁺ T cells across different tissue compartments (whole tissue, peritumoral, and intratumoral regions). (b) Spatial distribution of immune markers in tumor regions. The left panel shows CD107a enrichment, with peritumoral areas noted in red and tumor-enriched regions marked in blue. The right panels display PD-1, PD-L1, HLA-A, and ICOS expression maps according to TACIT annotation. For each marker, the peritumoral and intratumoral regions are outlined with dashed lines (c). NK—natural killer.

We assessed both the localization and activation of NK and CD8⁺ T cells. CellPose 3.0 was used for image segmentation,⁶² and regions were classified as whole slide, intra-tumoral, or peri-tumoral by a pathologist. We used two AstroSuite tools: 1) TACIT (Threshold-based Assignment of Cell Types from Multiplexed Imaging DaTa) for immune state identification and 2) Astrograph for spatial visualization.⁶³^,⁶⁴

Our analysis revealed spatial differences in immune cell distribution and activity, which was consistent with the scRNA-seq data. Multi-IF showed intratumoral enrichment of NK and CD8⁺ T cells coexpressing PD-1 and PD-L1 (Z = 0.37, 0.83), which are markers of exhaustion (Figure 6b–c). The peritumoral areas had more CD107a⁺ and ICOS⁺ cytotoxic lymphocytes, indicating greater immune activation. These patterns mirrored pseudotime trajectories where NK cells from N⁺ patients showed immunomodulatory shifts (AREG, KLRG1) and CD8⁺ T cells became exhausted in T4 tumors.

This spatial validation suggests immunosuppressive trends in TICs and points to their association with intratumoral zones, where macrophage-driven IL-10 and TGF-β signaling and PDCD1⁺ T cell interactions are enriched. In contrast, peritumoral CD107a/ICOS⁺ cells suggest localized immune activity. Collectively, these findings reinforce the notion that anatomical location within the TME modulates immune cell phenotypes, highlighting the need to integrate spatial context when interpreting single-cell states and designing immunotherapeutic interventions in HPV⁻ HNSCC.

Discussion

The TME critically influences cancer progression, therapy resistance, and patient outcomes. Diverse cell types, including myeloid and lymphoid immune cells, shape tumor behavior via immune modulation. TICs are linked to better prognosis and immunotherapy response in cancers, including HNSCC.^65-68 Yet, TME heterogeneity, interpatient variation, and differences between primary and metastatic sites challenge treatment efficacy. Tools such as scRNA-seq and spatial multiomics are essential to capture tumor heterogeneity and immune dynamics.⁶⁹

Our study builds on integrated atlases such as those from the Human Cell Atlas/Oral & Craniofacial Bionetwork,³⁰^,⁴⁰^,⁴¹ which map the cellular and molecular features of oral and craniofacial tissues. These efforts highlight the value of single-cell and spatial multiomics in biomedical research. The Bionetwork atlas, which incorporates age, sex, and ancestry, aligns with our aim to dissect immune heterogeneity in HPV⁻ HNSCC. Future atlases should enhance comparisons between healthy and diseased tissues to identify immune dysregulation and therapeutic targets in HNSCC.³⁰^,⁴⁰^,⁴¹

To our knowledge, this is one of the few studies to integrate single-cell and spatial immune profiling specifically in HPV⁻ HNSCC, supporting the Human Cell Atlas mission to map human cells in health and disease. We used transcriptional data from PBMCs and TICs combined with spatial immune phenotyping to demonstrate how HCA-based frameworks support hypothesis generation, validation, and translational research in underrepresented cancers. Our dataset provides a platform for studying immune dynamics during tumor progression and treatment response. scRNA-seq has been widely used to explore TME immune infiltration in HNSCC and other cancers.⁷⁰ Although nearly all tumors analyzed in our study were from the oral cavity, and this represents a potential bias, we believe the findings provide valuable insights into HPV⁻ HNSCC. Nevertheless, caution is warranted when extrapolating these results to HNSCC from other subsites.

Cillo et al. profiled CD45⁺ immune cells in both HPV⁺ and carcinogen-induced (HPV⁻) HNSCC. While CD8⁺ T and CD4⁺ Treg populations were similar, differences in CD4⁺ T, B, and myeloid cells were detected. They proposed that HPV⁺ tumors may enhance innate immune infiltration via viral antigens, driving adaptive responses and affecting tumor behavior. Kürten et al. further explored TME inflammation in both HNSCC subtypes, identifying novel fibroblast subsets in HPV⁺ tumors and highlighting PD-L1⁺ macrophages as key immunosuppressive and predictive markers for immunotherapy.²¹

However, the roles of immune subsets during disease progression remain insufficiently characterized. Our study is the first to integrate scRNA-seq data from PBMCs and TICs from HPV⁻ HNSCC, building an immune atlas and evaluating gene expression as immune cells transition from N0 to N⁺ and from T1 to T4.

GSEA revealed enrichment of the IFN pathway in immune cells from N⁺ patients. No TIC population exhibited high IFN expression (data not shown), implying TME-specific modulation. This finding suggests that IFN signaling, likely from tumors, promotes IFI44 expression and immunosuppression, which is correlated with poor progression-free survival in HNSCC.⁷¹ We observed increased cell–cell interactions driven mainly by intermediate and TREM2⁺ macrophages, which activate the IL-10 and TGF-β pathways. IL-10 affects DC2 and intermediate macrophages, key APCs for T cell activation, but can suppress APC function, promoting tumor escape.⁷²^,⁷³ TGF-β pathway enrichment via elevated TGFB1/2/3 expression likely impacts Tem/Temra CD8⁺ T cells through TGFBR1/TGFBR2. This enrichment is correlated with low CD8⁺ immunoscore and high inflammation, which are potentially IFN-mediated.⁷⁴

Cytotoxic T lymphocytes are the main receptors for IL-2 and CD70, both of which are linked to exhaustion phenotypes in N⁺ patients. Previous studies have shown that CD70 induces TIC exhaustion in renal carcinoma and TGF-β drives CD70 overexpression via IL-2 in non-Hodgkin lymphoma, producing PD-1⁺ TICs.⁷⁵^,⁷⁶

These results indicate that N⁺ tumors have immune-suppressed environments. We analyzed genes related to NK and cytotoxic T cell activity using pseudotime analysis. We found higher expression of immunomodulatory and lower expression of effector molecules in tumors, which is consistent with immune suppression. NK cells in N⁺ tumors expressed more KLRG1, a coinhibitory receptor, and AREG, which impairs cytotoxicity and limits granzyme expression.⁴⁷^,^77-81 Recent studies have characterized AREG as a key epithelial driver of tumor aggressiveness in HPV⁻ HNSCC. Zhou et al. showed that AREG marks malignant states with increased EGFR signaling, EMT activation, and increased invasive capacity, while a spatially resolved single-cell atlas identified AREG-expressing epithelial niches at invasive fronts enriched for dedifferentiation and migratory programs. ⁸²^,⁸³ Although these studies focused on tumor-intrinsic epithelial biology, our findings extend the relevance of AREG to the immune compartment. We observed a progressive increase in AREG along the NK-cell pseudotime trajectory in N⁺ tumors, coupled with increased KLRG1 and reduced cytotoxic effector signatures. Importantly, patients in the NK^hiAREG^hi group exhibited the poorest clinical outcomes, whereas lower AREG expression or reduced NK abundance was associated with improved survival. Together, these results suggest that while AREG contributes to epithelial invasion and tumor progression, it also marks dysfunctional NK-cell states associated with worse prognosis, underscoring AREG as a cross-compartment indicator of aggressive disease biology in HPV⁻ HNSCC. Additionally, GZMA was reduced in N⁺ NK cells, as was KIR2DL4, a receptor associated with favorable melanoma outcomes.⁸⁴ Jointly, these findings show reduced NK cytotoxicity and a shift toward immunosuppressive states.

The immune context is crucial in HNSCC, where anti-PD1 therapies such as nivolumab are approved for recurrent/metastatic disease. Nivolumab improves survival in patients with PD-L1 expression >1%.⁸⁵ Pembrolizumab has also shown better survival than chemotherapy with cetuximab in R/M-HNSCC; however, response rates remain relatively low (~18%), especially when compared to the higher efficacy of immune checkpoint inhibitors in melanoma.¹⁹^,²⁰ Our findings support this, as T4 tumors showed higher PDCD1 (PD-1) in cytotoxic T cells. However, progressive PDCD1 expression with advanced stages does not necessarily predict PD-1 blockade efficacy. PD-1 expression is heterogeneous: high PD-1 can mark terminally exhausted cells poorly rescued by inhibition, while only progenitor-like exhausted cells expand after therapy.⁸⁶ The immunosuppressive microenvironment (macrophage-derived IL-10/TGF-β, myeloid populations, metabolic constraints, alternative checkpoints) can limit reinvigoration despite PD-1 blockade.⁸⁷ Anti-PD-1 efficacy depends on tumor-intrinsic and microenvironmental features (neoantigen load, PD-L1 expression, lymphoid niches), contributing to variable benefits in HPV⁻ HNSCC.^88-90 Therefore, PD-1 expression requires cautious interpretation, and combinatorial strategies addressing myeloid suppression, TGF-β signaling, or additional checkpoints may be necessary for durable antitumor immunity.

Minimal coexpression of PD-1 and KLRG1 in TICs across tumors has been reported.⁹¹ In murine models of the breast, colon, and melanoma, anti-KLRG1 alone or with anti-PD-1, reduced tumor size and metastasis.⁹¹ Despite this, anti-KLRG1 (ABC008) is not being tested clinically in HNSCC. Given that KLRG1 is upregulated in N⁺ HNSCC, these patients may benefit from anti-KLRG1, especially those resistant to PD-1 blockade despite PD-L1 positivity.

These results demonstrate that there is greater systemic modulation than local modulation. The divergence between PBMC and TIC profiles reflects tumor microenvironment-specific effects. Cells from TME can secrete cytokines that recruit/modulate Tregs, Tems, Trm cells, and γδ T cells, express immune checkpoint ligands (e.g., PD-L1) that promote immunosuppressive phenotypes, and create metabolic constraints (hypoxia, nutrient deprivation, lactate accumulation) that inhibit effector T cell functions, distinguishing systemic from tumor-infiltrating compartments.⁹⁰^,⁹¹ Consequently, the translational potential of PBMC profiling as a liquid biopsy biomarker for nodal involvement or pathological stage appears limited in this context.

We propose two- and three-cell biomarker combinations to refine HNSCC immunotherapy. Identifying interactions among CD8⁺ T cells, NK cells, and myeloid cells may reveal predictive markers or resistance pathways. Integrating pseudotime trajectories, ligand‒receptor inference, and spatial validation enables precise immune profiling beyond histopathology.

Targeting both immune and structural TME components may further increase immunoregulation. Therapies against stromal elements (e.g., fibroblasts and ECM) can reduce suppression and support cytotoxic cell infiltration. For example, TGF-β inhibition may restore immune function and improve outcomes. These strategies may work synergistically to slow tumor growth and enhance response.

Ultimately, multicell biomarker profiling provides critical insight into TME dynamics, supporting novel therapies that harness immune responses while addressing structural challenges. This integrative strategy may significantly advance immunotherapy and improve outcomes in HPV⁻ HNSCC. Our work illustrates how single-cell and spatial technologies enable translational immune atlas development to guide biomarker discovery and therapy design, especially in advanced, treatment-resistant cases.

Supplementary Material

Supplementary material

Figure S3

KONI_A_2605741_SM6242.tiff^{(32.1MB, tiff)}

Supplementary material

Supplementary Table 3

KONI_A_2605741_SM6241.xlsx^{(67.9KB, xlsx)}

Supplementary material

Supplementary Table 5

KONI_A_2605741_SM6240.xlsx^{(15KB, xlsx)}

Supplementary material

Supplementary Table 1

KONI_A_2605741_SM6239.xlsx^{(15.2KB, xlsx)}

Supplementary material

Figure S1

KONI_A_2605741_SM6238.tiff^{(32.1MB, tiff)}

Supplementary material

Figure S6

KONI_A_2605741_SM6237.tiff^{(26.7MB, tiff)}

Supplementary material

Figure S4

KONI_A_2605741_SM6236.jpg^{(1.8MB, jpg)}

Supplementary material

Supplementary Table 2

KONI_A_2605741_SM6235.xlsx^{(2.8MB, xlsx)}

Supplementary material

FigureS5

KONI_A_2605741_SM6234.tiff^{(32.1MB, tiff)}

Supplementary material

Supplementary Table 4

KONI_A_2605741_SM6233.xlsx^{(15.6KB, xlsx)}

Supplementary material

Figure S2

KONI_A_2605741_SM6232.tiff^{(32.1MB, tiff)}

Funding Statement

RGAG received a Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq) fellowship. Vinicius Maracaja-Coutinho was funded by grants from the Chilean National Agency for Research and Development through the programs FONDAP (15130011) and FONDAP (1523A0008). Jinze Liu was funded by Virginia Commonwealth University Wright Regional Center for Clinical & Translational Science (CCTS), Clinical and Translational Science Award (CTSA) UM1TR004360, NIH-NCI Cancer Center Support Grant P30 CA016059. Vinicius Maracaja-Coutinho and Patricia Severino were funded by the Chan Zuckerberg Initiative (Ancestry Networks for the Human Cell Atlas Projects). ADA Foundation. Fondo de Financiamiento de Centros de Investigación en Áreas Prioritarias. Instituto UNIEMP. Virginia Commonwealth University Wright Regional Center for Clinical & Translational Science, Clinical and Translational Science Award. Phillips Institute for Oral Health Research.

Disclosure of potential conflicts of interest

The authors had access to the study data and reviewed and approved the final manuscript. Although the authors view each of these as noncompeting financial interests, BFM, KLAH, BTR, JL, KMB and PS are all active members of the Human Cell Atlas. KMB is a scientific advisor at Arcato Laboratories; KMB and JL are cofounders of Stratica Biosciences, Inc. All the other authors declare no competing interests.

Supplemental material

Supplemental data for this article can be accessed at https://doi.org/10.1080/2162402X.2025.2605741.

Acknowledgments

The results shown here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga. Data services in support of the research project were provided by the VCU Massey Comprehensive Cancer Center Bioinformatics Shared Resource. Massey is supported in part by funding from the NIH-NCI Cancer Center Support Grant P30CA016059. This work was supported by generous start-up funds from the ADA Science & Research Institute (Volpe Research Scholar Award) and Virginia Commonwealth University to KMB. This manuscript is based on a preprint previously posted on https://www.biorxiv.org/.⁹²

Author contributions

KH, SS, JL, KMB and PS conceptualized the project. RGAG, AR, BFM, KH, CAOBJ, JL, VMC and KMB developed methods for data analysis. RGAG, AR, BFM, CAOBJ and JMMK performed the formal analysis. VMC, KMB and PS acquired financial support. BFM, NK, SS and KMB supported sample collection. NK, BFM, KH and KMB performed the experimental analysis. PS and KMB managed and coordinated the research activity planning and execution. RGAG, AR, JMMK, KMB and PS wrote the original draft. RGAG, AR, BTR, BFM, JL, KMB and PS reviewed and edited the final manuscript.

Data availability statement

Links to original raw data from each of the 2 studies analyzed here can be found at GEO: https://www.ncbi.nlm.nih.gov/geo/. The data can also be analyzed at https://cellxgene.cziscience.com/collections/065ad318-59fd-4f8c-b4b1-66caa7665409.

Analysis notebooks and CELLxFEATURE matrices for Phenocycler-Fusion 2.0 data are available at https://github.com/Loci-lab. Additionally, data will be made available upon reasonable request.

References

1.Blot WJ, McLaughlin JK, Winn DM, Austin DF, Greenberg RS, Preston-Martin S, Bernstein L, Schoenberg JB, Stemhagen A, Fraumeni JF. Smoking and drinking in relation to oral and pharyngeal cancer. Cancer Res. 1988;48(11):3282–3287. [PubMed] [Google Scholar]
2.De Martel C, Georges D, Bray F, Ferlay J, Clifford GM. Global burden of cancer attributable to infections in 2018: a worldwide incidence analysis. Lancet Glob Health. 2020;8(2):e180–e190. doi: 10.1016/S2214-109X(19)30488-7. [DOI] [PubMed] [Google Scholar]
3.Puram SV, Rocco JW. Molecular aspects of head and neck cancer therapy. Hematol Oncol Clin North Am. 2015;29(6):971–992. doi: 10.1016/j.hoc.2015.07.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Ang KK, Harris J, Wheeler R, Weber R, Rosenthal DI, Nguyen-Tân PF, Westra WH, Chung CH, Jordan RC, Lu C, et al. Human papillomavirus and survival of patients with oropharyngeal cancer. N Engl J Med. 2010;363(1):24–35. doi: 10.1056/NEJMoa0912217. [DOI] [PMC free article] [PubMed] [Google Scholar]
5.The Cancer Genome Atlas Network . Comprehensive genomic characterization of head and neck squamous cell carcinomas. Nature. 2015;517(7536):576–582. doi: 10.1038/nature14129. [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Zur Hausen H. Papillomaviruses and cancer: from basic studies to clinical application. Nat Rev Cancer. 2002;2(5):342–350. doi: 10.1038/nrc798. [DOI] [PubMed] [Google Scholar]
7.Leemans CR, Braakhuis BJM, Brakenhoff RH. The molecular biology of head and neck cancer. Nat Rev Cancer. 2011;11(1):9–22. doi: 10.1038/nrc2982. [DOI] [PubMed] [Google Scholar]
8.Russell S, Angell T, Lechner M, Liebertz D, Correa A, Sinha U, Kokot N, Epstein A. Immune cell infiltration patterns and survival in head and neck squamous cell carcinoma. Head Neck Oncol. 2013;5(3):24. [PMC free article] [PubMed] [Google Scholar]
9.Sabatini ME, Chiocca S. Human papillomavirus as a driver of head and neck cancers. Br J Cancer. 2020;122(3):306–314. doi: 10.1038/s41416-019-0602-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Wood O, Woo J, Seumois G, Savelyeva N, McCann KJ, Singh D, Jones T, Peel L, Breen MS, Ward M, et al. Gene expression analysis of TIL rich HPV-driven head and neck tumors reveals a distinct B-cell signature when compared to HPV independent tumors. Oncotarget. 2016;7(35):56781–56797. doi: 10.18632/oncotarget.10788. [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Schaller J, Agudo J. Metastatic colonization: escaping immune surveillance. Cancers. 2020;12(11):3385. doi: 10.3390/cancers12113385. [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Stuelten CH, Parent CA, Montell DJ. Cell motility in cancer invasion and metastasis: insights from simple model organisms. Nat Rev Cancer. 2018;18(5):296–312. doi: 10.1038/nrc.2018.15. [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Bergers G, Fendt S-M. The metabolism of cancer cells during metastasis. Nat Rev Cancer. 2021;21(3):162–180. doi: 10.1038/s41568-020-00320-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Bourhis J, Overgaard J, Audry H, Ang KK, Saunders M, Bernier J, Horiot J-C, Le Maître A, Pajak TF, Poulsen MG, et al. Hyperfractionated or accelerated radiotherapy in head and neck cancer: a meta-analysis. Lancet. 2006;368(9538):843–854. doi: 10.1016/S0140-6736(06)69121-6. [DOI] [PubMed] [Google Scholar]
15.Pignon J-P, Maître AL, Maillard E, Bourhis J. Meta-analysis of chemotherapy in head and neck cancer (MACH-NC): an update on 93 randomised trials and 17,346 patients. Radiother Oncol. 2009;92(1):4–14. doi: 10.1016/j.radonc.2009.04.014. [DOI] [PubMed] [Google Scholar]
16.Mendenhall WM, Hinerman RW, Amdur RJ, Malyapa RS, Lansford CD, Werning JW, Villaret DB. Postoperative radiotherapy for squamous cell carcinoma of the head and neck. Clin Med Res. 2006;4(3):200–208. doi: 10.3121/cmr.4.3.200. [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Havel JJ, Chowell D, Chan TA. The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy. Nat Rev Cancer. 2019;19(3):133–150. doi: 10.1038/s41568-019-0116-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Sharma P, Hu-Lieskovan S, Wargo JA, Ribas A. Primary, adaptive, and acquired resistance to cancer immunotherapy. Cell. 2017;168(4):707–723. doi: 10.1016/j.cell.2017.01.017. [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Burtness B, Rischin D, Greil R, Soulières D, Tahara M, De Castro Jr G, Psyrri A, Brana I, Basté N, Neupane P, et al. Pembrolizumab alone or with chemotherapy for recurrent/metastatic head and neck squamous cell carcinoma in KEYNOTE-048: subgroup analysis by programmed death ligand-1 combined positive score. J Clin Oncol. 2022;40(21):2321–2332. doi: 10.1200/JCO.21.02198. [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Harrington KJ, Burtness B, Greil R, Soulières D, Tahara M, De Castro G, Psyrri A, Brana I, Basté N, Neupane P, et al. Pembrolizumab with or without chemotherapy in recurrent or metastatic head and neck squamous cell carcinoma: updated results of the phase III KEYNOTE-048 study. J Clin Oncol. 2023;41(4):790–802. doi: 10.1200/JCO.21.02508. [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Cillo AR, Kürten CHL, Tabib T, Qi Z, Onkar S, Wang T, Liu A, Duvvuri U, Kim S, Soose RJ, et al. Immune landscape of viral- and carcinogen-driven head and neck cancer. Immunity. 2020;52(1):183–199. doi: 10.1016/j.immuni.2019.11.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Kürten CHL, Kulkarni A, Cillo AR, Santos PM, Roble AK, Onkar S, Reeder C, Lang S, Chen X, Duvvuri U, et al. Investigating immune and non-immune cell interactions in head and neck tumors by single-cell RNA sequencing. Nat Commun. 2021;12(1):7338. doi: 10.1038/s41467-021-27619-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, et al. NCBI GEO: archive for functional genomics data sets—update. Nucleic Acids Res. 2012;41(D1):D991–D995. doi: 10.1093/nar/gks1193. [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Amin MB, Greene FL, Edge SB, Compton CC, Gershenwald JE, Brookland RK, Meyer L, Gress DM, Byrd DR, Winchester DP. The eighth edition AJCC cancer staging manual: continuing to build a bridge from a population‐based to a more “personalized” approach to cancer staging. CA Cancer J Clin. 2017;67(2):93–99. doi: 10.3322/caac.21388. [DOI] [PubMed] [Google Scholar]
25.Zheng GXY, Terry JM, Belgrader P, Ryvkin P, Bent ZW, Wilson R, Ziraldo SB, Wheeler TD, McDermott GP, Zhu J, et al. Massively parallel digital transcriptional profiling of single cells. Nat Commun. 2017;8(1):14049. doi: 10.1038/ncomms14049. [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018;36(5):411–420. doi: 10.1038/nbt.4096. [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Satija R, Farrell JA, Gennert D, Schier AF, Regev A. Spatial reconstruction of single-cell gene expression data. Nat Biotechnol. 2015;33(5):495–502. doi: 10.1038/nbt.3192. [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Young MD, Behjati S. SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data. Gigascience. 2020;9(12):giaa151. doi: 10.1093/gigascience/giaa151. [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Germain P-L, Lun A, Garcia Meixide C, Macnair W, Robinson MD. Doublet identification in single-cell sequencing data using scDblFinder. F1000Res. 2022;10:979. doi: 10.12688/f1000research.73600.2. [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Caetano AJ, Human Cell Atlas Oral and Craniofacial Bionetwork, Sequeira I, Byrd KM, Caetano A, Sharpe P, Volponi AA, Yianni V, Bush M, McKay LK, et al. A roadmap for the human oral and craniofacial cell atlas. J Dent Res 2022;101(11):1274–1288. doi: 10.1177/00220345221110768. [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Korotkevich G, Sukhov V, Budin N, Shpak B, Artyomov MN, Sergushichev A. Fast gene set enrichment analysis. bioRxiv. 2021;060012. doi: 10.1101/060012. [DOI] [Google Scholar]
32.Liberzon A, Birger C, Thorvaldsdóttir H, Ghandi M, Mesirov JP, Tamayo P. The molecular signatures database hallmark gene set collection. Cell Syst. 2015;1(6):417–425. doi: 10.1016/j.cels.2015.12.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Jin S, Guerrero-Juarez CF, Zhang L, Chang I, Ramos R, Kuan C-H, Myung P, Plikus MV, Nie Q. Inference and analysis of cell-cell communication using CellChat. Nat Commun. 2021;12(1):1088. doi: 10.1038/s41467-021-21246-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Qiu X, Mao Q, Tang Y, Wang L, Chawla R, Pliner HA, Trapnell C. Reversed graph embedding resolves complex single-cell trajectories. Nat Methods. 2017;14(10):979–982. doi: 10.1038/nmeth.4402. [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Trapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li S, Morse M, Lennon NJ, Livak KJ, Mikkelsen TS, Rinn JL. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. 2014;32(4):381–386. doi: 10.1038/nbt.2859. [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Newman AM, Steen CB, Liu CL, Gentles AJ, Chaudhuri AA, Scherer F, Khodadoust MS, Esfahani MS, Luca BA, Steiner D, et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat Biotechnol. 2019;37(7):773–782. doi: 10.1038/s41587-019-0114-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Luoma AM, Suo S, Wang Y, Gunasti L, Porter CBM, Nabilsi N, Tadros J, Ferretti AP, Liao S, Gurer C, et al. Tissue-resident memory and circulating T cells are early responders to pre-surgical cancer immunotherapy. Cell. 2022;185(16):2918–2935. doi: 10.1016/j.cell.2022.06.018. [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Quah HS, Cao EY, Suteja L, Li CH, Leong HS, Chong FT, Gupta S, Arcinas C, Ouyang JF, Ang V, et al. Single cell analysis in head and neck cancer reveals potential immune evasion mechanisms during early metastasis. Nat Commun. 2023;14(1):1680. doi: 10.1038/s41467-023-37379-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Conde-Lopez C, Marripati D, Elkabets M, Hess J, Kurth I. Unravelling the complexity of HNSCC using single-cell transcriptomics. Cancers. 2024;16(19):3265. doi: 10.3390/cancers16193265. [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Easter QT, Fernandes Matuck B, Beldorati Stark G, Worth CL, Predeus AV, Fremin B, Huynh K, Ranganathan V, Ren Z, Pereira D, et al. Single-cell and spatially resolved interactomics of tooth-associated keratinocytes in periodontitis. Nat Commun. 2024;15(1):5016. doi: 10.1038/s41467-024-49037-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Fernandes Matuck B, Huynh K, Pereira D, Easter QT, Zhang X, Kunz M, Pratapa A, Rupp BT, Kumar N, Ghodke A, et al. The immunoregulatory architecture of the adult oral cavity. bioRxiv. 2025;18:2024.12.01.626279. doi: 10.1101/2024.12.01.626279. [DOI] [Google Scholar]
42.Massoni-Badosa R, Aguilar-Fernández S, Nieto JC, Soler-Vila P, Elosua-Bayes M, Marchese D, Kulis M, Vilas-Zornoza A, Bühler MM, Rashmi S, et al. An atlas of cells in the human tonsil. Immunity. 2024;57(2):379–399. doi: 10.1016/j.immuni.2024.01.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
43.Domínguez Conde C, Xu C, Jarvis LB, Rainbow DB, Wells SB, Gomes T, Howlett SK, Suchanek O, Polanski K, King HW, et al. Cross-tissue immune cell analysis reveals tissue-specific features in humans. Science. 2022;376(6594):eabl5197. doi: 10.1126/science.abl5197. [DOI] [PMC free article] [PubMed] [Google Scholar]
44.De Visser KE, Joyce JA. The evolving tumor microenvironment: from cancer initiation to metastatic outgrowth. Cancer Cell. 2023;41(3):374–403. doi: 10.1016/j.ccell.2023.02.016. [DOI] [PubMed] [Google Scholar]
45.Zhang Q, Cheng S, Wang Y, Wang M, Lu Y, Wen Z, Ge Y, Ma Q, Chen Y, Zhang Y, et al. Interrogation of the microenvironmental landscape in spinal ependymomas reveals dual functions of tumor-associated macrophages. Nat Commun. 2021;12(1):6867. doi: 10.1038/s41467-021-27018-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Gubin MM, Esaulova E, Ward JP, Malkova ON, Runci D, Wong P, Noguchi T, Arthur CD, Meng W, Alspach E, et al. High-dimensional analysis delineates myeloid and lymphoid compartment remodeling during successful immune-checkpoint cancer therapy. Cell. 2018;175(4):1014–1030. doi: 10.1016/j.cell.2018.09.030. [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Müller-Durovic B, Lanna A, Polaco Covre L, Mills RS, Henson SM, Akbar AN. Killer cell lectin-like receptor G1 Inhibits NK cell function through activation of adenosine 5′-monophosphate–activated protein kinase. J Immunol. 2016;197(7):2891–2899. doi: 10.4049/jimmunol.1600590. [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Dybkaer K, Iqbal J, Zhou G, Geng H, Xiao L, Schmitz A, d'Amore F, Chan WC. Genome wide transcriptional analysis of resting and IL2 activated human natural killer cells: gene expression signatures indicative of novel molecular signaling pathways. BMC Genomics. 2007;8(1):230. doi: 10.1186/1471-2164-8-230. [DOI] [PMC free article] [PubMed] [Google Scholar]
49.Konduri V, Oyewole-Said D, Vazquez-Perez J, Weldon SA, Halpert MM, Levitt JM, Decker WK. CD8+CD161+ T-cells: cytotoxic memory cells with high therapeutic potential. Front Immunol. 2021;11:613204. doi: 10.3389/fimmu.2020.613204. [DOI] [PMC free article] [PubMed] [Google Scholar]
50.Belarif L, Mary C, Jacquemont L, Mai HL, Danger R, Hervouet J, Minault D, Thepenier V, Nerrière-Daguin V, Nguyen E, et al. IL-7 receptor blockade blunts antigen-specific memory T cell responses and chronic inflammation in primates. Nat Commun. 2018;9(1):4483. doi: 10.1038/s41467-018-06804-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
51.Zhao Z, Yu S, Fitzgerald DC, Elbehi M, Ciric B, Rostami AM, Zhang G-X. IL-12Rβ2 promotes the development of CD4+CD25+ regulatory T cells. J Immunol. 2008;181(6):3870–3876. doi: 10.4049/jimmunol.181.6.3870. [DOI] [PMC free article] [PubMed] [Google Scholar]
52.Cheng S, Li Z, Gao R, Xing B, Gao Y, Yang Y, Qin S, Zhang L, Ouyang H, Du P, et al. A pan-cancer single-cell transcriptional atlas of tumor infiltrating myeloid cells. Cell. 2021;184(3):792–809. doi: 10.1016/j.cell.2021.01.010. [DOI] [PubMed] [Google Scholar]
53.Guimarães GR, Maklouf GR, Teixeira CE, De Oliveira Santos L, Tessarollo NG, De Toledo NE, Serain AF, De Lanna CA, Pretti MA, Da Cruz JGV, et al. Single-cell resolution characterization of myeloid-derived cell states with implication in cancer outcome. Nat Commun. 2024;15(1):5694. doi: 10.1038/s41467-024-49916-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
54.Zhang L, Zhang C, Xing Z, Lou C, Fang J, Wang Z, Li M, He H, Bai H. Fibronectin 1 derived from tumor-associated macrophages and fibroblasts promotes metastasis through the JUN pathway in hepatocellular carcinoma. Int Immunopharmacol. 2022;113:109420. doi: 10.1016/j.intimp.2022.109420. [DOI] [PubMed] [Google Scholar]
55.Rudy GB, Lew AM. The nonpolymorphic MHC class II isotype, HLA-DQA2, is expressed on the surface of B lymphoblastoid cells. J. Immunol. 1997;158(5):2116–2125. [PubMed] [Google Scholar]
56.Lenormand C, Bausinger H, Gross F, Signorino-Gelo F, Koch S, Peressin M, Fricker D, Cazenave J-P, Bieber T, Hanau D, et al. HLA-DQA2 and HLA-DQB2 genes are specifically expressed in human langerhans cells and encode a new HLA class II molecule. J Immunol. 2012;188(8):3903–3911. doi: 10.4049/jimmunol.1103048. [DOI] [PubMed] [Google Scholar]
57.Mender I, Zhang A, Ren Z, Han C, Deng Y, Siteni S, Li H, Zhu J, Vemula A, Shay JW, et al. Telomere stress potentiates STING-dependent anti-tumor immunity. Cancer Cell. 2020;38(3):400–411. doi: 10.1016/j.ccell.2020.05.020. [DOI] [PMC free article] [PubMed] [Google Scholar]
58.Krausgruber T, Fortelny N, Fife-Gernedl V, Senekowitsch M, Schuster LC, Lercher A, Nemc A, Schmidl C, Rendeiro AF, Bergthaler A, et al. Structural cells are key regulators of organ-specific immune responses. Nature. 2020;583(7815):296–302. doi: 10.1038/s41586-020-2424-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
59.Bied M, Ho WW, Ginhoux F, Blériot C. Roles of macrophages in tumor development: a spatiotemporal perspective. Cell Mol Immunol. 2023;20(9):983–992. doi: 10.1038/s41423-023-01061-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
60.Knebel M, Körner S, Kühn JP, Wemmert S, Brust L, Smola S, Wagner M, Bohle RM, Morris LGT, Pandey A, et al. Prognostic impact of intra- and peritumoral immune cell subpopulations in head and neck squamous cell carcinomas – comprehensive analysis of the TCGA-HNSC cohort and immunohistochemical validation on 101 patients. Front Immunol. 2023;14:1172768. doi: 10.3389/fimmu.2023.1172768. [DOI] [PMC free article] [PubMed] [Google Scholar]
61.Pranzatelli TJ, Perez P, Ku A, Matuck BF, Huynh K, Sakai S, Abed M, Jang S-I, Yamada E, Dominick K, et al. GZMK+CD8+ T cells Target A Specific Acinar Cell Type in Sjögren’s Disease. Ann Rheum Dis. 2025;S0003-4967(25)04313-4. doi: 10.1016/j.ard.2025.08.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
62.Stringer C, Pachitariu M. Cellpose3: one-click image restoration for improved cellular segmentation. Nat Methods. 2025;22(3):592–599. doi: 10.1038/s41592-025-02595-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
63.Salvi V, Vermi W, Gianello V, Lonardi S, Gagliostro V, Naldini A, Sozzani S, Bosisio D. Dendritic cell-derived VEGF-A plays a role in inflammatory angiogenesis of human secondary lymphoid organs and is driven by the coordinated activation of multiple transcription factors. Oncotarget. 2016;7(26):39256–39269. doi: 10.18632/oncotarget.9684. [DOI] [PMC free article] [PubMed] [Google Scholar]
64.Liu J, Huynh K, Tyc K, Matuck BF, Easter Q, Pratapa A, Kumar N, Pérez P, Kulchar R, Pranzatelli T, et al. Spatial Deconvolution of Cell Types and Cell States at Scale Utilizing TACIT. Nat Commun. 2025;16(1):3747. doi: 10.1038/s41467-025-58874-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
65.Balermpas P, Martin D, Wieland U, Rave-Fränk M, Strebhardt K, Rödel C, Fokas E, Rödel F. Human papilloma virus load and PD-1/PD-L1, CD8⁺ and FOXP3 in anal cancer patients treated with chemoradiotherapy: rationale for immunotherapy. Oncoimmunology. 2017;6(3):e1288331. doi: 10.1080/2162402X.2017.1288331. [DOI] [PMC free article] [PubMed] [Google Scholar]
66.Hwang W-T, Adams SF, Tahirovic E, Hagemann IS, Coukos G. Prognostic significance of tumor-infiltrating T cells in ovarian cancer: a meta-analysis. Gynecol Oncol. 2012;124(2):192–198. doi: 10.1016/j.ygyno.2011.09.039. [DOI] [PMC free article] [PubMed] [Google Scholar]
67.Mei Z, Liu Y, Liu C, Cui A, Liang Z, Wang G, Peng H, Cui L, Li C. Tumour-infiltrating inflammation and prognosis in colorectal cancer: systematic review and meta-analysis. Br J Cancer. 2014;110(6):1595–1605. doi: 10.1038/bjc.2014.46. [DOI] [PMC free article] [PubMed] [Google Scholar]
68.Mao Y, Qu Q, Chen X, Huang O, Wu J, Shen K. The prognostic value of tumor-infiltrating lymphocytes in breast cancer: a systematic review and meta-analysis. PLoS One. 2016;11(4):e0152500. doi: 10.1371/journal.pone.0152500. [DOI] [PMC free article] [PubMed] [Google Scholar]
69.Suvà ML, Tirosh I. Single-cell RNA sequencing in cancer: lessons learned and emerging challenges. Mol Cell. 2019;75(1):7–12. doi: 10.1016/j.molcel.2019.05.003. [DOI] [PubMed] [Google Scholar]
70.Ren X, Zhang L, Zhang Y, Li Z, Siemers N, Zhang Z. Insights gained from single-cell analysis of immune cells in the tumor microenvironment. Annu Rev Immunol. 2021;39(1):583–609. doi: 10.1146/annurev-immunol-110519-071134. [DOI] [PubMed] [Google Scholar]
71.Pan H, Wang X, Huang W, Dai Y, Yang M, Liang H, Wu X, Zhang L, Huang W, Yuan L, et al. Interferon-induced protein 44 correlated with immune infiltration serves as a potential prognostic indicator in head and neck squamous cell carcinoma. Front Oncol. 2020;10:557157. doi: 10.3389/fonc.2020.557157. [DOI] [PMC free article] [PubMed] [Google Scholar]
72.Meissner M, Reichert TE, Kunkel M, Gooding W, Whiteside TL, Ferrone S, Seliger B. Defects in the human leukocyte antigen class I antigen processing machinery in head and neck squamous cell carcinoma: association with clinical outcome. Clin Cancer Res. 2005;11(7):2552–2560. doi: 10.1158/1078-0432.CCR-04-2146. [DOI] [PubMed] [Google Scholar]
73.Mittal SK, Roche PA. Suppression of antigen presentation by IL-10. Curr Opin Immunol. 2015;34:22–27. doi: 10.1016/j.coi.2014.12.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
74.Zheng L, Guan Z, Xue M. TGF-β signaling pathway-based model to predict the subtype and prognosis of head and neck squamous cell carcinoma. Front Genet. 2022;13:862860. doi: 10.3389/fgene.2022.862860. [DOI] [PMC free article] [PubMed] [Google Scholar]
75.Wang QJ, Hanada K, Robbins PF, Li YF, Yang JC. Distinctive features of the differentiated phenotype and infiltration of tumor-reactive lymphocytes in clear cell renal cell carcinoma. Cancer Res. 2012;72(23):6119–6129. doi: 10.1158/0008-5472.CAN-12-0588. [DOI] [PMC free article] [PubMed] [Google Scholar]
76.Yang Z-Z, Grote DM, Xiu B, Ziesmer SC, Price-Troska TL, Hodge LS, Yates DM, Novak AJ, Ansell SM. TGF-β upregulates CD70 expression and induces exhaustion of effector memory T cells in B-cell non-Hodgkin's lymphoma. Leukemia. 2014;28(9):1872–1884. doi: 10.1038/leu.2014.84. [DOI] [PMC free article] [PubMed] [Google Scholar]
77.Wang JM, Cheng YQ, Shi L, Ying RS, Wu XY, Li GY, Moorman JP, Yao ZQ. KLRG1 negatively regulates natural killer cell functions through the akt pathway in individuals with chronic hepatitis C virus infection. J Virol. 2013;87(21):11626–11636. doi: 10.1128/JVI.01515-13. [DOI] [PMC free article] [PubMed] [Google Scholar]
78.Astorga-Gamaza A, Perea D, Sanchez-Gaona N, Calvet-Mirabent M, Gallego-Cortés A, Grau-Expósito J, Sanchez-Cerrillo I, Rey J, Castellví J, Curran A, et al. KLRG1 expression on natural killer cells is associated with HIV persistence, and its targeting promotes the reduction of the viral reservoir. Cell Rep. Med. 2023;4(10):101202. doi: 10.1016/j.xcrm.2023.101202. [DOI] [PMC free article] [PubMed] [Google Scholar]
79.Jeevan-Raj B, Gehrig J, Charmoy M, Chennupati V, Grandclément C, Angelino P, Delorenzi M, Held W. The transcription factor Tcf1 contributes to normal NK cell development and function by limiting the expression of granzymes. Cell Rep. 2017;20(3):613–626. doi: 10.1016/j.celrep.2017.06.071. [DOI] [PubMed] [Google Scholar]
80.Kim S-Y, Zo S, Kim DH, Shin SJ, Jhun BW. Single-cell transcriptomics by clinical course of Mycobacterium avium complex pulmonary disease. Sci Rep. 2024;14(1):15663. doi: 10.1038/s41598-024-66523-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
81.Wang Y, Lifshitz L, Silverstein NJ, Mintzer E, Luk K, StLouis P, Brehm MA, Wolfe SA, Deeks SG, Luban J. Transcriptional and chromatin profiling of human blood innate lymphoid cell subsets sheds light on HIV‐1 pathogenesis. EMBO J. 2023;42(16):e114153. doi: 10.15252/embj.2023114153. [DOI] [PMC free article] [PubMed] [Google Scholar]
82.Punovuori K, Bertillot F, Miroshnikova YA, Binner MI, Myllymäki SM, Follain G, Kruse K, Routila J, Huusko T, Pellinen T, et al. Multiparameter imaging reveals clinically relevant cancer cell-stroma interaction dynamics in head and neck cancer. Cell. 2024 Dec 12;187(25):7267–7284.e20. doi: 10.1016/j.cell.2024.09.046. Epub 2024 Oct 28. [DOI] [PubMed] [Google Scholar]
83.Zhou J, He M, Zhao Q, Shi E, Wang H, Ponkshe V, Song J, Wu Z, Ji D, Kranz G, et al. EGFR-mediated local invasiveness and response to Cetuximab in head and neck cancer. Mol Cancer. 2025;24(1):94. doi: 10.1186/s12943-025-02290-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
84.He Z, Chen M, Luo Z. Identification of immune-related genes and integrated analysis of immune-cell infiltration in melanoma. Aging. 2024. doi: 10.18632/aging.205427. [DOI] [PMC free article] [PubMed] [Google Scholar]
85.Ferris RL, Blumenschein G, Fayette J, Guigay J, Colevas AD, Licitra L, Harrington K, Kasper S, Vokes EE, Even C, et al. Nivolumab for recurrent squamous-cell carcinoma of the head and neck. N Engl J Med. 2016;375(19):1856–1867. doi: 10.1056/NEJMoa1602252. [DOI] [PMC free article] [PubMed] [Google Scholar]
86.Blackburn SD, Shin H, Freeman GJ, Wherry EJ. Selective expansion of a subset of exhausted CD8 T cells by αPD-L1 blockade. Proc Natl Acad Sci. 2008;105(39):15016–15021. doi: 10.1073/pnas.0801497105. [DOI] [PMC free article] [PubMed] [Google Scholar]
87.Mirlekar B. Tumor promoting roles of IL-10, TGF-β, IL-4, and IL-35: its implications in cancer immunotherapy. SAGE Open Med. 2022;10:20503121211069012. doi: 10.1177/20503121211069012. [DOI] [PMC free article] [PubMed] [Google Scholar]
88.Riaz N, Morris L, Havel JJ, Makarov V, Desrichard A, Chan TA. The role of neoantigens in response to immune checkpoint blockade. Int Immunol. 2016;28(8):411–419. doi: 10.1093/intimm/dxw019. [DOI] [PMC free article] [PubMed] [Google Scholar]
89.Zhang L, Ren S, Lan T, Marco V, Liu N, Wei B, Chen Y, Wu J, Li Q, Wu F, et al. Mature tertiary lymphoid structures linked to HPV status and anti-PD-1 based chemoimmunotherapy response in head and neck squamous cell carcinoma. Oncoimmunology. 2025;14(1):2528109. doi: 10.1080/2162402X.2025.2528109. [DOI] [PMC free article] [PubMed] [Google Scholar]
90.Williams HL, Frei AL, Koessler T, Berger MD, Dawson H, Michielin O, Zlobec I. The current landscape of spatial biomarkers for prediction of response to immune checkpoint inhibition. Npj Precis. Oncol. 2024;8(1):178. doi: 10.1038/s41698-024-00671-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
91.Greenberg SA, Kong SW, Thompson E, Gulla SV. Co-inhibitory T cell receptor KLRG1: human cancer expression and efficacy of neutralization in murine cancer models. Oncotarget. 2019;10(14):1399–1406. doi: 10.18632/oncotarget.26659. [DOI] [PMC free article] [PubMed] [Google Scholar]
92.Facciabene A, Motz GT, Coukos G. T-Regulatory cells: key players in tumor immune escape and angiogenesis. Cancer Res. 2012;72(9):2162–2171. doi: 10.1158/0008-5472.CAN-11-3687. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary material

Figure S3

KONI_A_2605741_SM6242.tiff^{(32.1MB, tiff)}

Supplementary material

Supplementary Table 3

KONI_A_2605741_SM6241.xlsx^{(67.9KB, xlsx)}

Supplementary material

Supplementary Table 5

KONI_A_2605741_SM6240.xlsx^{(15KB, xlsx)}

Supplementary material

Supplementary Table 1

KONI_A_2605741_SM6239.xlsx^{(15.2KB, xlsx)}

Supplementary material

Figure S1

KONI_A_2605741_SM6238.tiff^{(32.1MB, tiff)}

Supplementary material

Figure S6

KONI_A_2605741_SM6237.tiff^{(26.7MB, tiff)}

Supplementary material

Figure S4

KONI_A_2605741_SM6236.jpg^{(1.8MB, jpg)}

Supplementary material

Supplementary Table 2

KONI_A_2605741_SM6235.xlsx^{(2.8MB, xlsx)}

Supplementary material

FigureS5

KONI_A_2605741_SM6234.tiff^{(32.1MB, tiff)}

Supplementary material

Supplementary Table 4

KONI_A_2605741_SM6233.xlsx^{(15.6KB, xlsx)}

Supplementary material

Figure S2

KONI_A_2605741_SM6232.tiff^{(32.1MB, tiff)}

Data Availability Statement

Analysis notebooks and CELLxFEATURE matrices for Phenocycler-Fusion 2.0 data are available at https://github.com/Loci-lab. Additionally, data will be made available upon reasonable request.

[cit0001] 1.Blot WJ, McLaughlin JK, Winn DM, Austin DF, Greenberg RS, Preston-Martin S, Bernstein L, Schoenberg JB, Stemhagen A, Fraumeni JF. Smoking and drinking in relation to oral and pharyngeal cancer. Cancer Res. 1988;48(11):3282–3287. [PubMed] [Google Scholar]

[cit0002] 2.De Martel C, Georges D, Bray F, Ferlay J, Clifford GM. Global burden of cancer attributable to infections in 2018: a worldwide incidence analysis. Lancet Glob Health. 2020;8(2):e180–e190. doi: 10.1016/S2214-109X(19)30488-7. [DOI] [PubMed] [Google Scholar]

[cit0003] 3.Puram SV, Rocco JW. Molecular aspects of head and neck cancer therapy. Hematol Oncol Clin North Am. 2015;29(6):971–992. doi: 10.1016/j.hoc.2015.07.003. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0004] 4.Ang KK, Harris J, Wheeler R, Weber R, Rosenthal DI, Nguyen-Tân PF, Westra WH, Chung CH, Jordan RC, Lu C, et al. Human papillomavirus and survival of patients with oropharyngeal cancer. N Engl J Med. 2010;363(1):24–35. doi: 10.1056/NEJMoa0912217. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0005] 5.The Cancer Genome Atlas Network . Comprehensive genomic characterization of head and neck squamous cell carcinomas. Nature. 2015;517(7536):576–582. doi: 10.1038/nature14129. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0006] 6.Zur Hausen H. Papillomaviruses and cancer: from basic studies to clinical application. Nat Rev Cancer. 2002;2(5):342–350. doi: 10.1038/nrc798. [DOI] [PubMed] [Google Scholar]

[cit0007] 7.Leemans CR, Braakhuis BJM, Brakenhoff RH. The molecular biology of head and neck cancer. Nat Rev Cancer. 2011;11(1):9–22. doi: 10.1038/nrc2982. [DOI] [PubMed] [Google Scholar]

[cit0008] 8.Russell S, Angell T, Lechner M, Liebertz D, Correa A, Sinha U, Kokot N, Epstein A. Immune cell infiltration patterns and survival in head and neck squamous cell carcinoma. Head Neck Oncol. 2013;5(3):24. [PMC free article] [PubMed] [Google Scholar]

[cit0009] 9.Sabatini ME, Chiocca S. Human papillomavirus as a driver of head and neck cancers. Br J Cancer. 2020;122(3):306–314. doi: 10.1038/s41416-019-0602-7. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0010] 10.Wood O, Woo J, Seumois G, Savelyeva N, McCann KJ, Singh D, Jones T, Peel L, Breen MS, Ward M, et al. Gene expression analysis of TIL rich HPV-driven head and neck tumors reveals a distinct B-cell signature when compared to HPV independent tumors. Oncotarget. 2016;7(35):56781–56797. doi: 10.18632/oncotarget.10788. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0011] 11.Schaller J, Agudo J. Metastatic colonization: escaping immune surveillance. Cancers. 2020;12(11):3385. doi: 10.3390/cancers12113385. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0012] 12.Stuelten CH, Parent CA, Montell DJ. Cell motility in cancer invasion and metastasis: insights from simple model organisms. Nat Rev Cancer. 2018;18(5):296–312. doi: 10.1038/nrc.2018.15. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0013] 13.Bergers G, Fendt S-M. The metabolism of cancer cells during metastasis. Nat Rev Cancer. 2021;21(3):162–180. doi: 10.1038/s41568-020-00320-2. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0014] 14.Bourhis J, Overgaard J, Audry H, Ang KK, Saunders M, Bernier J, Horiot J-C, Le Maître A, Pajak TF, Poulsen MG, et al. Hyperfractionated or accelerated radiotherapy in head and neck cancer: a meta-analysis. Lancet. 2006;368(9538):843–854. doi: 10.1016/S0140-6736(06)69121-6. [DOI] [PubMed] [Google Scholar]

[cit0015] 15.Pignon J-P, Maître AL, Maillard E, Bourhis J. Meta-analysis of chemotherapy in head and neck cancer (MACH-NC): an update on 93 randomised trials and 17,346 patients. Radiother Oncol. 2009;92(1):4–14. doi: 10.1016/j.radonc.2009.04.014. [DOI] [PubMed] [Google Scholar]

[cit0016] 16.Mendenhall WM, Hinerman RW, Amdur RJ, Malyapa RS, Lansford CD, Werning JW, Villaret DB. Postoperative radiotherapy for squamous cell carcinoma of the head and neck. Clin Med Res. 2006;4(3):200–208. doi: 10.3121/cmr.4.3.200. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0017] 17.Havel JJ, Chowell D, Chan TA. The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy. Nat Rev Cancer. 2019;19(3):133–150. doi: 10.1038/s41568-019-0116-x. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0018] 18.Sharma P, Hu-Lieskovan S, Wargo JA, Ribas A. Primary, adaptive, and acquired resistance to cancer immunotherapy. Cell. 2017;168(4):707–723. doi: 10.1016/j.cell.2017.01.017. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0019] 19.Burtness B, Rischin D, Greil R, Soulières D, Tahara M, De Castro Jr G, Psyrri A, Brana I, Basté N, Neupane P, et al. Pembrolizumab alone or with chemotherapy for recurrent/metastatic head and neck squamous cell carcinoma in KEYNOTE-048: subgroup analysis by programmed death ligand-1 combined positive score. J Clin Oncol. 2022;40(21):2321–2332. doi: 10.1200/JCO.21.02198. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0020] 20.Harrington KJ, Burtness B, Greil R, Soulières D, Tahara M, De Castro G, Psyrri A, Brana I, Basté N, Neupane P, et al. Pembrolizumab with or without chemotherapy in recurrent or metastatic head and neck squamous cell carcinoma: updated results of the phase III KEYNOTE-048 study. J Clin Oncol. 2023;41(4):790–802. doi: 10.1200/JCO.21.02508. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0021] 21.Cillo AR, Kürten CHL, Tabib T, Qi Z, Onkar S, Wang T, Liu A, Duvvuri U, Kim S, Soose RJ, et al. Immune landscape of viral- and carcinogen-driven head and neck cancer. Immunity. 2020;52(1):183–199. doi: 10.1016/j.immuni.2019.11.014. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0022] 22.Kürten CHL, Kulkarni A, Cillo AR, Santos PM, Roble AK, Onkar S, Reeder C, Lang S, Chen X, Duvvuri U, et al. Investigating immune and non-immune cell interactions in head and neck tumors by single-cell RNA sequencing. Nat Commun. 2021;12(1):7338. doi: 10.1038/s41467-021-27619-4. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0023] 23.Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, et al. NCBI GEO: archive for functional genomics data sets—update. Nucleic Acids Res. 2012;41(D1):D991–D995. doi: 10.1093/nar/gks1193. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0024] 24.Amin MB, Greene FL, Edge SB, Compton CC, Gershenwald JE, Brookland RK, Meyer L, Gress DM, Byrd DR, Winchester DP. The eighth edition AJCC cancer staging manual: continuing to build a bridge from a population‐based to a more “personalized” approach to cancer staging. CA Cancer J Clin. 2017;67(2):93–99. doi: 10.3322/caac.21388. [DOI] [PubMed] [Google Scholar]

[cit0025] 25.Zheng GXY, Terry JM, Belgrader P, Ryvkin P, Bent ZW, Wilson R, Ziraldo SB, Wheeler TD, McDermott GP, Zhu J, et al. Massively parallel digital transcriptional profiling of single cells. Nat Commun. 2017;8(1):14049. doi: 10.1038/ncomms14049. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0026] 26.Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018;36(5):411–420. doi: 10.1038/nbt.4096. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0027] 27.Satija R, Farrell JA, Gennert D, Schier AF, Regev A. Spatial reconstruction of single-cell gene expression data. Nat Biotechnol. 2015;33(5):495–502. doi: 10.1038/nbt.3192. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0028] 28.Young MD, Behjati S. SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data. Gigascience. 2020;9(12):giaa151. doi: 10.1093/gigascience/giaa151. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0029] 29.Germain P-L, Lun A, Garcia Meixide C, Macnair W, Robinson MD. Doublet identification in single-cell sequencing data using scDblFinder. F1000Res. 2022;10:979. doi: 10.12688/f1000research.73600.2. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0030] 30.Caetano AJ, Human Cell Atlas Oral and Craniofacial Bionetwork, Sequeira I, Byrd KM, Caetano A, Sharpe P, Volponi AA, Yianni V, Bush M, McKay LK, et al. A roadmap for the human oral and craniofacial cell atlas. J Dent Res 2022;101(11):1274–1288. doi: 10.1177/00220345221110768. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0031] 31.Korotkevich G, Sukhov V, Budin N, Shpak B, Artyomov MN, Sergushichev A. Fast gene set enrichment analysis. bioRxiv. 2021;060012. doi: 10.1101/060012. [DOI] [Google Scholar]

[cit0032] 32.Liberzon A, Birger C, Thorvaldsdóttir H, Ghandi M, Mesirov JP, Tamayo P. The molecular signatures database hallmark gene set collection. Cell Syst. 2015;1(6):417–425. doi: 10.1016/j.cels.2015.12.004. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0033] 33.Jin S, Guerrero-Juarez CF, Zhang L, Chang I, Ramos R, Kuan C-H, Myung P, Plikus MV, Nie Q. Inference and analysis of cell-cell communication using CellChat. Nat Commun. 2021;12(1):1088. doi: 10.1038/s41467-021-21246-9. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0034] 34.Qiu X, Mao Q, Tang Y, Wang L, Chawla R, Pliner HA, Trapnell C. Reversed graph embedding resolves complex single-cell trajectories. Nat Methods. 2017;14(10):979–982. doi: 10.1038/nmeth.4402. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0035] 35.Trapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li S, Morse M, Lennon NJ, Livak KJ, Mikkelsen TS, Rinn JL. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. 2014;32(4):381–386. doi: 10.1038/nbt.2859. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0036] 36.Newman AM, Steen CB, Liu CL, Gentles AJ, Chaudhuri AA, Scherer F, Khodadoust MS, Esfahani MS, Luca BA, Steiner D, et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat Biotechnol. 2019;37(7):773–782. doi: 10.1038/s41587-019-0114-2. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0037] 37.Luoma AM, Suo S, Wang Y, Gunasti L, Porter CBM, Nabilsi N, Tadros J, Ferretti AP, Liao S, Gurer C, et al. Tissue-resident memory and circulating T cells are early responders to pre-surgical cancer immunotherapy. Cell. 2022;185(16):2918–2935. doi: 10.1016/j.cell.2022.06.018. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0038] 38.Quah HS, Cao EY, Suteja L, Li CH, Leong HS, Chong FT, Gupta S, Arcinas C, Ouyang JF, Ang V, et al. Single cell analysis in head and neck cancer reveals potential immune evasion mechanisms during early metastasis. Nat Commun. 2023;14(1):1680. doi: 10.1038/s41467-023-37379-y. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0039] 39.Conde-Lopez C, Marripati D, Elkabets M, Hess J, Kurth I. Unravelling the complexity of HNSCC using single-cell transcriptomics. Cancers. 2024;16(19):3265. doi: 10.3390/cancers16193265. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0040] 40.Easter QT, Fernandes Matuck B, Beldorati Stark G, Worth CL, Predeus AV, Fremin B, Huynh K, Ranganathan V, Ren Z, Pereira D, et al. Single-cell and spatially resolved interactomics of tooth-associated keratinocytes in periodontitis. Nat Commun. 2024;15(1):5016. doi: 10.1038/s41467-024-49037-y. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0041] 41.Fernandes Matuck B, Huynh K, Pereira D, Easter QT, Zhang X, Kunz M, Pratapa A, Rupp BT, Kumar N, Ghodke A, et al. The immunoregulatory architecture of the adult oral cavity. bioRxiv. 2025;18:2024.12.01.626279. doi: 10.1101/2024.12.01.626279. [DOI] [Google Scholar]

[cit0042] 42.Massoni-Badosa R, Aguilar-Fernández S, Nieto JC, Soler-Vila P, Elosua-Bayes M, Marchese D, Kulis M, Vilas-Zornoza A, Bühler MM, Rashmi S, et al. An atlas of cells in the human tonsil. Immunity. 2024;57(2):379–399. doi: 10.1016/j.immuni.2024.01.006. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0043] 43.Domínguez Conde C, Xu C, Jarvis LB, Rainbow DB, Wells SB, Gomes T, Howlett SK, Suchanek O, Polanski K, King HW, et al. Cross-tissue immune cell analysis reveals tissue-specific features in humans. Science. 2022;376(6594):eabl5197. doi: 10.1126/science.abl5197. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0044] 44.De Visser KE, Joyce JA. The evolving tumor microenvironment: from cancer initiation to metastatic outgrowth. Cancer Cell. 2023;41(3):374–403. doi: 10.1016/j.ccell.2023.02.016. [DOI] [PubMed] [Google Scholar]

[cit0045] 45.Zhang Q, Cheng S, Wang Y, Wang M, Lu Y, Wen Z, Ge Y, Ma Q, Chen Y, Zhang Y, et al. Interrogation of the microenvironmental landscape in spinal ependymomas reveals dual functions of tumor-associated macrophages. Nat Commun. 2021;12(1):6867. doi: 10.1038/s41467-021-27018-9. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0046] 46.Gubin MM, Esaulova E, Ward JP, Malkova ON, Runci D, Wong P, Noguchi T, Arthur CD, Meng W, Alspach E, et al. High-dimensional analysis delineates myeloid and lymphoid compartment remodeling during successful immune-checkpoint cancer therapy. Cell. 2018;175(4):1014–1030. doi: 10.1016/j.cell.2018.09.030. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0047] 47.Müller-Durovic B, Lanna A, Polaco Covre L, Mills RS, Henson SM, Akbar AN. Killer cell lectin-like receptor G1 Inhibits NK cell function through activation of adenosine 5′-monophosphate–activated protein kinase. J Immunol. 2016;197(7):2891–2899. doi: 10.4049/jimmunol.1600590. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0048] 48.Dybkaer K, Iqbal J, Zhou G, Geng H, Xiao L, Schmitz A, d'Amore F, Chan WC. Genome wide transcriptional analysis of resting and IL2 activated human natural killer cells: gene expression signatures indicative of novel molecular signaling pathways. BMC Genomics. 2007;8(1):230. doi: 10.1186/1471-2164-8-230. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0049] 49.Konduri V, Oyewole-Said D, Vazquez-Perez J, Weldon SA, Halpert MM, Levitt JM, Decker WK. CD8+CD161+ T-cells: cytotoxic memory cells with high therapeutic potential. Front Immunol. 2021;11:613204. doi: 10.3389/fimmu.2020.613204. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0050] 50.Belarif L, Mary C, Jacquemont L, Mai HL, Danger R, Hervouet J, Minault D, Thepenier V, Nerrière-Daguin V, Nguyen E, et al. IL-7 receptor blockade blunts antigen-specific memory T cell responses and chronic inflammation in primates. Nat Commun. 2018;9(1):4483. doi: 10.1038/s41467-018-06804-y. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0051] 51.Zhao Z, Yu S, Fitzgerald DC, Elbehi M, Ciric B, Rostami AM, Zhang G-X. IL-12Rβ2 promotes the development of CD4+CD25+ regulatory T cells. J Immunol. 2008;181(6):3870–3876. doi: 10.4049/jimmunol.181.6.3870. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0052] 52.Cheng S, Li Z, Gao R, Xing B, Gao Y, Yang Y, Qin S, Zhang L, Ouyang H, Du P, et al. A pan-cancer single-cell transcriptional atlas of tumor infiltrating myeloid cells. Cell. 2021;184(3):792–809. doi: 10.1016/j.cell.2021.01.010. [DOI] [PubMed] [Google Scholar]

[cit0053] 53.Guimarães GR, Maklouf GR, Teixeira CE, De Oliveira Santos L, Tessarollo NG, De Toledo NE, Serain AF, De Lanna CA, Pretti MA, Da Cruz JGV, et al. Single-cell resolution characterization of myeloid-derived cell states with implication in cancer outcome. Nat Commun. 2024;15(1):5694. doi: 10.1038/s41467-024-49916-4. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0054] 54.Zhang L, Zhang C, Xing Z, Lou C, Fang J, Wang Z, Li M, He H, Bai H. Fibronectin 1 derived from tumor-associated macrophages and fibroblasts promotes metastasis through the JUN pathway in hepatocellular carcinoma. Int Immunopharmacol. 2022;113:109420. doi: 10.1016/j.intimp.2022.109420. [DOI] [PubMed] [Google Scholar]

[cit0055] 55.Rudy GB, Lew AM. The nonpolymorphic MHC class II isotype, HLA-DQA2, is expressed on the surface of B lymphoblastoid cells. J. Immunol. 1997;158(5):2116–2125. [PubMed] [Google Scholar]

[cit0056] 56.Lenormand C, Bausinger H, Gross F, Signorino-Gelo F, Koch S, Peressin M, Fricker D, Cazenave J-P, Bieber T, Hanau D, et al. HLA-DQA2 and HLA-DQB2 genes are specifically expressed in human langerhans cells and encode a new HLA class II molecule. J Immunol. 2012;188(8):3903–3911. doi: 10.4049/jimmunol.1103048. [DOI] [PubMed] [Google Scholar]

[cit0057] 57.Mender I, Zhang A, Ren Z, Han C, Deng Y, Siteni S, Li H, Zhu J, Vemula A, Shay JW, et al. Telomere stress potentiates STING-dependent anti-tumor immunity. Cancer Cell. 2020;38(3):400–411. doi: 10.1016/j.ccell.2020.05.020. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0058] 58.Krausgruber T, Fortelny N, Fife-Gernedl V, Senekowitsch M, Schuster LC, Lercher A, Nemc A, Schmidl C, Rendeiro AF, Bergthaler A, et al. Structural cells are key regulators of organ-specific immune responses. Nature. 2020;583(7815):296–302. doi: 10.1038/s41586-020-2424-4. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0059] 59.Bied M, Ho WW, Ginhoux F, Blériot C. Roles of macrophages in tumor development: a spatiotemporal perspective. Cell Mol Immunol. 2023;20(9):983–992. doi: 10.1038/s41423-023-01061-6. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0060] 60.Knebel M, Körner S, Kühn JP, Wemmert S, Brust L, Smola S, Wagner M, Bohle RM, Morris LGT, Pandey A, et al. Prognostic impact of intra- and peritumoral immune cell subpopulations in head and neck squamous cell carcinomas – comprehensive analysis of the TCGA-HNSC cohort and immunohistochemical validation on 101 patients. Front Immunol. 2023;14:1172768. doi: 10.3389/fimmu.2023.1172768. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0061] 61.Pranzatelli TJ, Perez P, Ku A, Matuck BF, Huynh K, Sakai S, Abed M, Jang S-I, Yamada E, Dominick K, et al. GZMK+CD8+ T cells Target A Specific Acinar Cell Type in Sjögren’s Disease. Ann Rheum Dis. 2025;S0003-4967(25)04313-4. doi: 10.1016/j.ard.2025.08.014. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0062] 62.Stringer C, Pachitariu M. Cellpose3: one-click image restoration for improved cellular segmentation. Nat Methods. 2025;22(3):592–599. doi: 10.1038/s41592-025-02595-5. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0063] 63.Salvi V, Vermi W, Gianello V, Lonardi S, Gagliostro V, Naldini A, Sozzani S, Bosisio D. Dendritic cell-derived VEGF-A plays a role in inflammatory angiogenesis of human secondary lymphoid organs and is driven by the coordinated activation of multiple transcription factors. Oncotarget. 2016;7(26):39256–39269. doi: 10.18632/oncotarget.9684. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0064] 64.Liu J, Huynh K, Tyc K, Matuck BF, Easter Q, Pratapa A, Kumar N, Pérez P, Kulchar R, Pranzatelli T, et al. Spatial Deconvolution of Cell Types and Cell States at Scale Utilizing TACIT. Nat Commun. 2025;16(1):3747. doi: 10.1038/s41467-025-58874-4. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0065] 65.Balermpas P, Martin D, Wieland U, Rave-Fränk M, Strebhardt K, Rödel C, Fokas E, Rödel F. Human papilloma virus load and PD-1/PD-L1, CD8⁺ and FOXP3 in anal cancer patients treated with chemoradiotherapy: rationale for immunotherapy. Oncoimmunology. 2017;6(3):e1288331. doi: 10.1080/2162402X.2017.1288331. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0066] 66.Hwang W-T, Adams SF, Tahirovic E, Hagemann IS, Coukos G. Prognostic significance of tumor-infiltrating T cells in ovarian cancer: a meta-analysis. Gynecol Oncol. 2012;124(2):192–198. doi: 10.1016/j.ygyno.2011.09.039. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0067] 67.Mei Z, Liu Y, Liu C, Cui A, Liang Z, Wang G, Peng H, Cui L, Li C. Tumour-infiltrating inflammation and prognosis in colorectal cancer: systematic review and meta-analysis. Br J Cancer. 2014;110(6):1595–1605. doi: 10.1038/bjc.2014.46. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0068] 68.Mao Y, Qu Q, Chen X, Huang O, Wu J, Shen K. The prognostic value of tumor-infiltrating lymphocytes in breast cancer: a systematic review and meta-analysis. PLoS One. 2016;11(4):e0152500. doi: 10.1371/journal.pone.0152500. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0069] 69.Suvà ML, Tirosh I. Single-cell RNA sequencing in cancer: lessons learned and emerging challenges. Mol Cell. 2019;75(1):7–12. doi: 10.1016/j.molcel.2019.05.003. [DOI] [PubMed] [Google Scholar]

[cit0070] 70.Ren X, Zhang L, Zhang Y, Li Z, Siemers N, Zhang Z. Insights gained from single-cell analysis of immune cells in the tumor microenvironment. Annu Rev Immunol. 2021;39(1):583–609. doi: 10.1146/annurev-immunol-110519-071134. [DOI] [PubMed] [Google Scholar]

[cit0071] 71.Pan H, Wang X, Huang W, Dai Y, Yang M, Liang H, Wu X, Zhang L, Huang W, Yuan L, et al. Interferon-induced protein 44 correlated with immune infiltration serves as a potential prognostic indicator in head and neck squamous cell carcinoma. Front Oncol. 2020;10:557157. doi: 10.3389/fonc.2020.557157. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0072] 72.Meissner M, Reichert TE, Kunkel M, Gooding W, Whiteside TL, Ferrone S, Seliger B. Defects in the human leukocyte antigen class I antigen processing machinery in head and neck squamous cell carcinoma: association with clinical outcome. Clin Cancer Res. 2005;11(7):2552–2560. doi: 10.1158/1078-0432.CCR-04-2146. [DOI] [PubMed] [Google Scholar]

[cit0073] 73.Mittal SK, Roche PA. Suppression of antigen presentation by IL-10. Curr Opin Immunol. 2015;34:22–27. doi: 10.1016/j.coi.2014.12.009. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0074] 74.Zheng L, Guan Z, Xue M. TGF-β signaling pathway-based model to predict the subtype and prognosis of head and neck squamous cell carcinoma. Front Genet. 2022;13:862860. doi: 10.3389/fgene.2022.862860. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0075] 75.Wang QJ, Hanada K, Robbins PF, Li YF, Yang JC. Distinctive features of the differentiated phenotype and infiltration of tumor-reactive lymphocytes in clear cell renal cell carcinoma. Cancer Res. 2012;72(23):6119–6129. doi: 10.1158/0008-5472.CAN-12-0588. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0076] 76.Yang Z-Z, Grote DM, Xiu B, Ziesmer SC, Price-Troska TL, Hodge LS, Yates DM, Novak AJ, Ansell SM. TGF-β upregulates CD70 expression and induces exhaustion of effector memory T cells in B-cell non-Hodgkin's lymphoma. Leukemia. 2014;28(9):1872–1884. doi: 10.1038/leu.2014.84. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0077] 77.Wang JM, Cheng YQ, Shi L, Ying RS, Wu XY, Li GY, Moorman JP, Yao ZQ. KLRG1 negatively regulates natural killer cell functions through the akt pathway in individuals with chronic hepatitis C virus infection. J Virol. 2013;87(21):11626–11636. doi: 10.1128/JVI.01515-13. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0078] 78.Astorga-Gamaza A, Perea D, Sanchez-Gaona N, Calvet-Mirabent M, Gallego-Cortés A, Grau-Expósito J, Sanchez-Cerrillo I, Rey J, Castellví J, Curran A, et al. KLRG1 expression on natural killer cells is associated with HIV persistence, and its targeting promotes the reduction of the viral reservoir. Cell Rep. Med. 2023;4(10):101202. doi: 10.1016/j.xcrm.2023.101202. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0079] 79.Jeevan-Raj B, Gehrig J, Charmoy M, Chennupati V, Grandclément C, Angelino P, Delorenzi M, Held W. The transcription factor Tcf1 contributes to normal NK cell development and function by limiting the expression of granzymes. Cell Rep. 2017;20(3):613–626. doi: 10.1016/j.celrep.2017.06.071. [DOI] [PubMed] [Google Scholar]

[cit0080] 80.Kim S-Y, Zo S, Kim DH, Shin SJ, Jhun BW. Single-cell transcriptomics by clinical course of Mycobacterium avium complex pulmonary disease. Sci Rep. 2024;14(1):15663. doi: 10.1038/s41598-024-66523-x. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0081] 81.Wang Y, Lifshitz L, Silverstein NJ, Mintzer E, Luk K, StLouis P, Brehm MA, Wolfe SA, Deeks SG, Luban J. Transcriptional and chromatin profiling of human blood innate lymphoid cell subsets sheds light on HIV‐1 pathogenesis. EMBO J. 2023;42(16):e114153. doi: 10.15252/embj.2023114153. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0082] 82.Punovuori K, Bertillot F, Miroshnikova YA, Binner MI, Myllymäki SM, Follain G, Kruse K, Routila J, Huusko T, Pellinen T, et al. Multiparameter imaging reveals clinically relevant cancer cell-stroma interaction dynamics in head and neck cancer. Cell. 2024 Dec 12;187(25):7267–7284.e20. doi: 10.1016/j.cell.2024.09.046. Epub 2024 Oct 28. [DOI] [PubMed] [Google Scholar]

[cit0083] 83.Zhou J, He M, Zhao Q, Shi E, Wang H, Ponkshe V, Song J, Wu Z, Ji D, Kranz G, et al. EGFR-mediated local invasiveness and response to Cetuximab in head and neck cancer. Mol Cancer. 2025;24(1):94. doi: 10.1186/s12943-025-02290-1. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0084] 84.He Z, Chen M, Luo Z. Identification of immune-related genes and integrated analysis of immune-cell infiltration in melanoma. Aging. 2024. doi: 10.18632/aging.205427. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0085] 85.Ferris RL, Blumenschein G, Fayette J, Guigay J, Colevas AD, Licitra L, Harrington K, Kasper S, Vokes EE, Even C, et al. Nivolumab for recurrent squamous-cell carcinoma of the head and neck. N Engl J Med. 2016;375(19):1856–1867. doi: 10.1056/NEJMoa1602252. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0086] 86.Blackburn SD, Shin H, Freeman GJ, Wherry EJ. Selective expansion of a subset of exhausted CD8 T cells by αPD-L1 blockade. Proc Natl Acad Sci. 2008;105(39):15016–15021. doi: 10.1073/pnas.0801497105. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0087] 87.Mirlekar B. Tumor promoting roles of IL-10, TGF-β, IL-4, and IL-35: its implications in cancer immunotherapy. SAGE Open Med. 2022;10:20503121211069012. doi: 10.1177/20503121211069012. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0088] 88.Riaz N, Morris L, Havel JJ, Makarov V, Desrichard A, Chan TA. The role of neoantigens in response to immune checkpoint blockade. Int Immunol. 2016;28(8):411–419. doi: 10.1093/intimm/dxw019. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0089] 89.Zhang L, Ren S, Lan T, Marco V, Liu N, Wei B, Chen Y, Wu J, Li Q, Wu F, et al. Mature tertiary lymphoid structures linked to HPV status and anti-PD-1 based chemoimmunotherapy response in head and neck squamous cell carcinoma. Oncoimmunology. 2025;14(1):2528109. doi: 10.1080/2162402X.2025.2528109. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0090] 90.Williams HL, Frei AL, Koessler T, Berger MD, Dawson H, Michielin O, Zlobec I. The current landscape of spatial biomarkers for prediction of response to immune checkpoint inhibition. Npj Precis. Oncol. 2024;8(1):178. doi: 10.1038/s41698-024-00671-1. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0091] 91.Greenberg SA, Kong SW, Thompson E, Gulla SV. Co-inhibitory T cell receptor KLRG1: human cancer expression and efficacy of neutralization in murine cancer models. Oncotarget. 2019;10(14):1399–1406. doi: 10.18632/oncotarget.26659. [DOI] [PMC free article] [PubMed] [Google Scholar]

[cit0092] 92.Facciabene A, Motz GT, Coukos G. T-Regulatory cells: key players in tumor immune escape and angiogenesis. Cancer Res. 2012;72(9):2162–2171. doi: 10.1158/0008-5472.CAN-11-3687. [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Single-cell landscape of peripheral and tumor-infiltrating immune cells in HPV-negative HNSCC

Rômulo Gonçalves Agostinho Galvani

Adolfo Alexis Rojas Hidalgo

Carlos Alberto Biagi-Junior

Bruno Fernandes Matuck

Jelte Martinus Maria Krol

Brittany Rupp

Nikhil Kumar

Khoa Huynh

Jinze Liu

Siddharth Sheth

Vinicius Maracaja-Coutinho

Kevin Matthew Byrd

Patricia Severino

Roles

ABSTRACT

Introduction

Methods

Data acquisition and patient characteristics

Initial data processing and quality control

Data normalization, dimensionality reduction and clustering

Cell type identification and annotation

Differential gene expression and gene set enrichment analysis (GSEA)

Cell‒cell communication

Gene trajectory

Bulk RNA-seq deconvolution

Multiplex protein immunofluorescence

AstroSuite

Ethical considerations

Results

Generating an integrated HNSCC immune cell atlas

Figure 1.

Single-cell signatures of nodal metastasis and tumor stage

Figure 2.

Gene set enrichment analysis of immune cells in HPV-negative HNSCC

Figure 3.

Receptor‒ligand networks in TICs

Figure 4.

Single-cell trajectories of cytotoxic immune cells

Figure 5.

Comparison of results across scRNA-seq and bulk RNA-seq datasets

Understanding the spatial enrichment of cytotoxic cell states in situ

Figure 6.

Discussion

Supplementary Material

Funding Statement

Disclosure of potential conflicts of interest

Supplemental material

Acknowledgments

Author contributions

Data availability statement

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases