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Abstract
Background Cervical cancer has the second-highest mortality rate among malignant tumors of the female reproductive system. Immune checkpoint inhibitors such as programmed cell death protein 1 (PD-1) blockade are promising therapeutic agents, but their efficacy when combined with neoadjuvant chemotherapy (NACT) has not been fully tested, and how they alter the tumor microenvironment has not been comprehensively elucidated.
Methods In this study, we conducted single-cell RNA sequencing using 46,950 cells from nine human cervical cancer tissues representing sequential different stages of NACT and PD-1 blockade combination therapy. We delineated the trajectory of cervical epithelial cells and identified the crucial factors involved in combination therapy. Cell–cell communication analysis was performed between tumor and immune cells. In addition, THP-1-derived and primary monocyte-derived macrophages were cocultured with cervical cancer cells and phagocytosis was detected by flow cytometry. The antitumor activity of blocking CD74 was validated in vivo using a CD74 humanized subcutaneous tumor model.
Results Pathway enrichment analysis indicated that NACT activated cytokine and complement-related immune responses. Cell–cell communication analysis revealed that after NACT therapy, interaction strength between T cells and cancer cells decreased, but intensified between macrophages and cancer cells. We verified that macrophages were necessary for the PD-1 blockade to exert antitumor effects in vitro. Additionally, CD74-positive macrophages frequently interacted with the most immunoreactive epithelial subgroup 3 (Epi3) cancer subgroup during combination NACT. We found that CD74 upregulation limited phagocytosis and stimulated M2 polarization, whereas CD74 blockade enhanced macrophage phagocytosis, decreasing cervical cancer cell viability in vitro and in vivo.
Conclusions Our study reveals the dynamic cell–cell interaction network in the cervical cancer microenvironment influenced by combining NACT and PD-1 blockade. Furthermore, blocking tumor-associated macrophage-derived CD74 could augment neoadjuvant therapeutic efficacy.
- Cervical Cancer
- Tumor Microenvironment
- Macrophage
- Neoadjuvant
- Immunotherapy
Data availability statement
Data are available in a public, open access repository. The raw single-cell RNA sequencing data reported in this paper have been deposited in the Genome Sequence Archive in the National Genomics Data Center, China National Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Sciences (GSA-Human: HRA005959) under project PRJCA021022. The source data is publicly available as of the date of publication.
This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See http://creativecommons.org/licenses/by-nc/4.0/.
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WHAT IS ALREADY KNOWN ON THIS TOPIC
Almost 9.8%–30.6% of patients with locally advanced cervical cancer do not respond to chemotherapy. Neoadjuvant programmed cell death protein 1 (PD-1) blockade plus chemotherapy for locally advanced cervical cancer treatment is still in clinical trials. The studies focusing on molecular mechanisms and the tumor microenvironment (TME) may provide evidence for combination therapy in cervical cancer.
WHAT THIS STUDY ADDS
In this study, we have characterized transcriptomic evolution throughout the sequential stages of combination therapy and compared alterations to tumor microenvironment. Through in vitro and in vivo experiments, we have identified CD74 as an unfavorable factor in macrophage-cancer cell communication during combination therapy. Anti-CD74 antibody combined with cisplatin exerts stronger antitumor effects than traditional neoadjuvant chemotherapy (NACT).
HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY
Our findings provide a comprehensive, single-cell-resolution picture of cervical TME under combined NACT and PD-1 blockade. Targeting CD74 is a potential strategy to improve NACT and PD-1 blockade combination therapy.
Background
Cervical cancer imposes a significant burden on global health, necessitating the exploration of innovative treatment approaches to improve patient outcomes.1 2 Despite advancements in conventional chemotherapy, treatment for cervical cancer still lacks effective and targeted interventions.3 In response to this problem, the immune checkpoint blockade has emerged as an encouraging treatment strategy. Immune checkpoint inhibitors for cervical cancer have been approved with clinical trials testing immunotherapies using different targets such as cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), TNF receptor superfamily member 9 (TNFRSF9), T cell immunoglobulin and mucin domain 3 (TIM-3), and programmed cell death protein 1 (PD-1)/programmed death-ligand 1 (PD-L1).4 5 PD-1 is the most specific target for immunotherapy of cervical cancer. Drugs inhibiting PD-1 include pembrolizumab, nivolumab, balstilimab, cemiplimab, and cadonilimab, all currently second-line treatments for recurrent/metastatic cervical cancer.6 However, over 50% of patients do not respond to PD-1 inhibitors.7 Therefore, new therapies or targets are urgently needed to improve outcomes.
Neoadjuvant chemotherapy (NACT) improves operability and subsequent treatment outcomes.8 Platinum-based NACT has a response rate of ∼60% in patients with locally advanced cervical cancer.9 The NACT drug cisplatin (CDDP) is the most widely used chemotherapeutic agent for gynecology malignancies and acts through cross-linking to interfere with DNA replication. An increasing body of evidence shows that combining chemotherapy and immunotherapy synergistically enhances anticancer effects in multiple solid tumors, including gastric and ovarian cancers. The mechanism is likely to involve increased lymphocyte infiltration and PD-L1 expression with the combination of chemotherapeutics and the enhanced recognition and elimination of tumors by the immune system.10 11 The efficacy of PD-L1 blockade combined with chemotherapy was tested in several clinical trials. For instance, a phase 3 trial of patients with recurrent cervical cancer revealed that survival was significantly longer under PD-1 antibody (cemiplimab) treatment than under single-agent chemotherapy.12 Therefore, National Comprehensive Cancer Network guidelines recommend PD-1 monoclonal antibody pembrolizumab plus chemotherapy as the first-line therapy for PD-L1-positive advanced cervical cancer. However, the effectiveness and necessity of the combination of PD-1 blockade therapy before surgical treatment has not been adequately demonstrated and is not widely used in cervical cancer. More evidence from preclinical studies and clinical trials is needed to support the use of PD-1 in neoadjuvant combination therapies.
Although this combination therapy is promising, the underlying molecular alterations remain largely unknown. One candidate for research on immune regulation of cancer is CD74, a non-polymorphic type II transmembrane glycoprotein responsible for antigen presentation, endocytic maturation, and cell migration.13 14 Furthermore, CD74 is a receptor for macrophage migration inhibitory factors (MIF) that induce changes in macrophage function and polarization,15 and its activation helps trigger the M2 shift in macrophages.16 Several recent studies have provided evidence of CD74 as a cancer prognostic factor and therapeutic target.1 17–19 However, its precise role in cervical cancer progression remains unclear.
In this study, we collected biopsy and surgical specimens from patients with locally advanced cervical cancer receiving neoadjuvant PD-1 blockade plus chemotherapy and who participated in a multicenter, prospective phase II trial (NCT04516616).20 Single-cell RNA sequencing (scRNA-seq) analysis was performed using specimens from patients before chemotherapy, after NACT, and after receiving combination therapy (NACT plus PD-1 blockade). We aimed to describe the transcriptomic evolution throughout the sequential stages of combination therapy and to compare tumor microenvironment (TME) at different stages. Through in vitro and in vivo experiments, we also intended to clarify the role of CD74 in NACT combination immunotherapy for cervical cancer. Our findings should provide a comprehensive, single-cell-resolution picture of cervical TME under combined NACT and PD-1 blockade.
Methods
Patients and sample collection
Nine specimens were collected from stage IB3 and IIA2 (FIGO 2018)21 female patients with locally advanced cervical cancer, who were treated at the Department of Gynecology and Obstetrics, Qilu Hospital, Shandong University. The patients had not undergone prior treatment before receiving platinum-based NACT combined with PD-1 blockade therapy and radical hysterectomy. The pretreatment (T1, T2, T3) and post-NACT (T1a, T2a, T3a) specimens were collected by puncture biopsy. The post-NACT combined with PD-1 blockade specimens (T2b, T2c, T3b) were collected during surgical resection. Pathologic type of tumor tissue determined to be moderately or poorly differentiated squamous cell carcinoma of the cervix. Patient T1 was HPV 16 (+), P16 (+), and Ki-67 proliferation index 80%. Patient T2 was HPV 16 (+), P16 (+), D2-40 (+), and CD31 (+). Patient T3 was P16 (+), P63 (+), P40 (+), CK7 (partially +), and Ki-67 proliferation index 50%. All patients provided written informed consent.
Tissue dissociation and preparation
The newly collected tissue was preserved on ice in sCelLiVE Tissue Preservation Solution (Singleron). The specimens were washed using Hanks’ Balanced Salt Solution and finely fragmented into small pieces. Singleron PythoN Tissue Dissociation System performed tissue digestion with sCelLiVE Tissue Dissociation Solution (Singleron). The cell suspension was filtered through a 40 µm sterile strainer. Mix two volumes of GEXSCOPE erythrocyte lysate (Singleron) and incubate for 5–8 min at room temperature. The mixture was then centrifuged and suspended in PBS (HyClone) for downstream library construction.
Single-cell library construction
Single-cell library construction was performed by Singleron Matrix single cell processing system according to the instructions of the GEXSCOPE Single Cell RNA Library Kits. Single-cell suspensions with a concentration of 2×105 cells/mL were loaded onto a microwell chip. Barcoding beads from the microwell chip were collected and used for reverse transcription and polymerase chain reaction (PCR) amplification. The complementary DNA (cDNA) was fragmented and ligated with sequencing adapters. Libraries were sequenced on the Illumina NovaSeq 6000 platform (PE150).
Primary analysis of the sequencing data
Gene expression profiles were generated from the raw reads utilizing CeleSCOPE (V.1.5.2, Singleron) with default parameters. The process involved extracting barcodes and unique molecular identifiers (UMIs) from R1 reads, followed by their correction. Adapter sequences and poly-A tails were trimmed from R2 reads, and the clean R2 reads were aligned to the GRCh38 (hg38) transcriptome using STAR (V.2.6.1a).22 Uniquely mapped reads were assigned to genes using FeatureCounts (V.2.0.1).23 Reads with the same cell barcode, UMI, and gene were grouped to generate the gene expression matrix for subsequent analysis.
Quality control, dimension-reduction, and clustering
Seurat (V.4.3.0) was used for quality control, dimensionality reduction, and clustering under R (V.4.3.1). For each sample dataset, we filtered the expression matrix by the following criteria: (1) cells with a gene count less than 200 or with a top 2.5% gene count were excluded; (2) cells with a top 2.5% UMI count were excluded; (3) cells with over 20% of UMIs derived from the mitochondrial genome were excluded; (4) genes expressed in less than five cells were excluded. After filtering, 46,950 cells were retained for the downstream analyses. SCTransfrom function in the Seurat R package was used to minimize the batch effects and normalization. 3000 highly variable genes were identified in each sample based on a variance-stabilizing transformation to generate an integrated expression matrix. Principal component analysis was performed on the scaled variable gene matrix, and the top 15 principal components were used for clustering and dimensional reduction. Cells were separated into 24 clusters using the Louvain algorithm, setting the resolution parameter at 0.8. Cell clusters were visualized by using Uniform Manifold Approximation and Projection (UMAP).
Cell type annotation and subtyping of epithelium and macrophages
The differential expressed genes of each cell subcluster were identified by the FindAllMarkers function of Seurat (V.4.3.0) with the parameters logfc.threshold = 0.25, test.use = “wilcox”, min.pct = 0.1, only.pos = T. Cell types were annotated by SingleR with manual correction and identification. Canonical cell type markers for single-cell sequencing data were achieved from CellMakerDB, PanglaoDB, and recently published studies. To obtain a high-resolution map of epithelial cancer cells and macrophages, cells from the specific cluster were extracted and reclustered for more detailed analysis following the same procedures described above and by setting the clustering resolution as 0.3. Cell double detection was performed using a cluster-level approach.24 Cells expressed both monocyte signature genes (CD14, FCGR3A) and cytotoxic signature genes (GZMA, CD3G, CD8B) were identified as potential monocyte-natural killer cell doublets.
Cell–cell interaction analysis
Cell–cell interaction analysis was performed with CellChat (V.1.5.0).25 Based on the previous study, we added 42 extra receptor-ligand pairs of immune checkpoint pathways into the CellChatDB.26 The gene expression matrix was projected to proteome–proteome interaction networks to reduce the dropout effect of signaling genes. Due to the completeness of the series of samples and the stability of the data quality, the T3 sample was selected for preliminary analysis and other samples for validation. The number and strength of the inferred interactions were analyzed between seven main cell types. The differential interaction strength was compared between the pretreatment and NACT groups, and between the NACT and the NACT+anti-PD-1 Ab groups. The relative interaction strength and information flow of the detailed receptor-ligand signal pathways were analyzed. Cell communication was also analyzed between six epithelial subgroups and seven macrophage subgroups. The epithelial and macrophage subgroups were merged to create a CellChat object and analyzed following the same process.
Pseudotime trajectory analysis
The differentiation trajectory of epithelial cancer cells and macrophage subtypes was reconstructed with Monocle 2 (V.2.24.1) and Monocle 3 (V.1.2.9).27 28 For constructing the trajectory, highly variable genes (q value<0.01) were selected by the differentialGeneTest function, and dimension-reduction was performed by DDRTree. The trajectory visualization was conducted using the plot_cell_trajectory function in Monocle 2. Monocle 3 was also used in pseudotime trajectory analysis. UMAP projection was imported from Seurat Object. The trajectory was created with the learn_graph function. The subpopulation of cells in which the pretreatment group predominates was chosen as the starting point for the pseudotime trajectory. The trajectory corresponding to UMAP projections was visualized by the plot_cells function in Monocle 3.
Functional annotation and enrichment analysis
Pathway enrichment in each epithelial cancer cell subgroup was performed with the irGSEA package (V.1.1.3) (https://github.com/chuiqin/irGSEA). The predefined sets of genes in the MSigDB database were used for analysis. The robust rank aggregation algorithm (RRA) integrated the enrichment scores from AUCell, UCell, singscore, and single-sample gene set enrichment analysis (ssGSEA) and was used for visualization.29 Pathway enrichment in each macrophage subgroup was performed with the clusterProfiler package (V.4.4.4).30 The top 100 marker genes in each subgroup were identified with the COSG package (V.0.9.0).31 Significant Reactome pathways (p value<0.05) were visualized with the dot plot function. Gene signatures of M1-like and M2-like macrophages used for ssGSEA enrichment were achieved from the previous study.32 UMAP projection of pathway enrichment and relative expression was visualized by Nebulosa.33
In vivo studies
C57bl-6j and BALB/cJGpt-Cd74em1Cin(hCD74)/Gpt female mice (6–8 weeks old) were purchased from GemPharmatech. A total of 2×105 cells were injected into the right flanks of mice. Anti-mouse PD-1 (CD279)-InVivo (10 mg/kg, every other day, Selleck) and CDDP (7 mg/kg, every 4 days, MedChemExpress) were injected into the abdominal cavity of mice to build an NACT combined with PD-1 therapy model. Milatuzumab (anti-CD74 Ab) (15 mg/kg, every other day, MedChemExpress) was used to block the expression of CD74. Mice in the control group were intraperitoneally injected with InVivoMAb polyclonal Armenian hamster IgG (10 mg/kg, every other day, Bio X Cell) and PBS. Then, after 12 days, the mice were euthanized, the tumors were dissected, and tumor weights were measured. Tumor sizes were measured using a Vernier caliper, and the tumor volume was calculated using the following formula: volume = 1/2 × length × width.2
Immunofluorescence staining
Multiple Immunofluorescence kits (ImmunoWay) are used for multicolor immunofluorescence staining. Deparaffinization, antigen retrieval, endogenous peroxidase removal, and blocking for non-specific binding were performed on tissue sections according to the instructions. The histological section and cells were incubated with primary antibody (online supplemental table 1). Subsequently, the samples were treated with an horseradish peroxidase (HRP) polymer secondary antibody (Anti-Rabbit/Mouse) and fluorescent chromogenic solution. Fluorescence images were taken using fluorescence microscopy and confocal microscopy. At least three fields of view per slide were quantified using ImageJ, and the average intensity density was compared with another group in the clinical specimen validation.
Supplemental material
Cell culture and establishment of a coculture system
CaSki and THP-1 cells were cultured in 1640 medium (Gibco), and HeLa, SiHa, and TC-1 cells were cultured in Dulbecco's Modified Eagle Medium (DMEM) (Gibco). The medium contains 10% fetal bovine serum (Gibco) and 1% Penicillin-Streptomycin (100×) (Solarbio), and cells were cultured at 37°C with a 5% CO2 atmosphere. SiHa was isolated from the primary uterine tissue of a patient with HPV-16-positive squamous cell carcinoma. CaSki is an epithelial cell isolated from the cervix of an HPV-16 and HPV-18 positive patient. HeLa was isolated from an HPV-18 positive patient with cervical cancer. TC-1 cells are mouse lung epithelial cells stably expressing HPV-16 E6/E7 and are commonly used to construct animal models of HPV-positive cancers.34
THP-1-derived macrophages were obtained after phorbol ester (Sigma) (50 ng/mL) treatment for 48 hours and validated CD11b and CD14 expression. Peripheral blood from patients with cervical cancer was collected, and human mononuclear cells were selected using gradient density centrifugation using Percoll gradients (Solarbio). EasySep Human CD14 Positive Selection Kit (STEMCELL) was used to select CD14+ monocytes. CD14+ monocytes were cultured in Iscove's Modified Dulbecco's Medium (IMDM) containing 50 ng/mL M-CSF for 7 days.
For the experiments in which anti-CD74 Ab (Milatuzumab, anti-CD74 Ab, 5 µg/mL, 12 hours, MedChemExpress) was combined with CDDP, adherent monolayers of 3×104 macrophages were directly overlaid with 1×105 cervical cancer cells. For the experiments in which anti-PD-1 antibody was combined with CDDP, both direct and indirect cocultures were performed. Using Transwell inserts (0.4 µm, Corning), 1×104 cervical cancer cells were cultured in the lower part and 5×103 macrophages in the upper part of each well. CDDP (30 µM, 4 hours, MedChemExpress) and pembrolizumab (anti-PD-1 Ab) (20 µg/mL, 24 hours, MedChemExpress) were used to act on a coculture system to build an NACT combined with PD-1 therapy model.
Flow cytometry
THP-1-derived macrophages, primary monocyte-derived macrophages, and single-cell suspension from the mouse xenograft tumor model were collected and evaluated by flow cytometry. Single-cell suspension from tumors or spleens was stained with fluorochrome-labeled antibodies. In the CD45-positive cell population, the macrophages expressing F4/80 and CD11b were gated to detect PD-1 and CD74 expression. THP-1-derived macrophages were stained with fluorochrome-labeled antibodies detecting CD206, CD163, CD74, and CD279. Detailed antibody information is presented in online supplemental table 1.
The apoptotic cells were detected using the Annexin V-FITC Apoptosis Detection Kit (Beyotime). Cells were harvested and resuspended in the Annexin V-FITC and PI binding buffer. The total percentages of the groups containing early and late apoptosis were compared.
The stained samples were detected using CytoFLEX, and the data were analyzed using CytExpert software. All staining was performed according to the manufacturer’s protocols. The control group without antibodies and staining was used for gating, and single-color stain controls were used to enable correct compensation.
Small interfering RNA and transfection
Small interfering RNA (siRNA) targeting CD74 or scramble sequences were purchased from Research Cloud Biology. siRNA and plasmids were transfected into macrophages derived from THP-1 with Lipofectamine 3000 (Invitrogen) according to the manufacturer’s instructions. 80 pmol of siRNA or 2 µg of plasmid was transfected in each well of a 6-well plate. The siRNA sequences are presented in online supplemental table 2.
RNA extraction and real-time quantitative reverse transcription PCR (qRT-PCR)
TRIzol (LIFE Ambion) was used to extract the total RNA of tissues and cells. Total RNA was reverse transcribed into cDNA using the HiScript III RT SuperMix for qPCR (Vazyme, R223-01). Then, we amplified the aimed gene fragment and detected the relative expression with the SYBR Green qPCR kit (TOYOBO). Quantitative real-time PCR was performed for 40 cycles. All experiments were conducted at least three times. Relative gene expression levels were calculated using the 2–∆∆Ct method. Primer sequences are presented in online supplemental table 2.
Phagocytosis assay
Macrophages derived from THP-1 and CaSki cells were digested using Trypsin (Macgene) and stained using the Cell Plasma Membrane Staining Kit with Dil (Red Fluorescence) or DiO (Green Fluorescence) (Beyotime), respectively. The macrophages were plated at a density of 5×104 cells per well in a 24-well tissue-culture plate, and 2×105 cancer cells were used for staining per experiment. After 3 hours coculturation in serum-free medium, the cocultured cells were analyzed by flow cytometer and imaged by a fluorescence microscope. The control group without antibodies and staining was used for gating, and single-color stain controls were used to enable correct compensation.
Cell migration and proliferation assays
Standard transwell inserts (0.8 µm, Corning) were used to detect cell migration. Cells were plated into the upper compartment with 500 µL of serum-free medium, and the lower compartment was filled with medium containing 10% fetal bovine serum. After incubation for 24–48 hours at 37°C, cotton swabs were used to remove the non-invaded cells on the filter’s upper surface, and the cells were fixed for 2 min in 100% methanol. Invaded cells on the lower side of the filter were stained with 0.5% crystal violet for 20 min. Cells were counted at ×100 magnification from three random microscopic fields for each sample in three independent experiments. The counting process was performed in an observer-blind manner.
The CCK-8 (Cell Counting Kit-8) assay kit (Beyotime) was used to evaluate the level of cell proliferation. Briefly, the 10 µL Cell Counting Kit solution was added to the culture medium and incubated for 3 hours. The absorbance was determined at a wavelength of 450 nm.
Results
Cellular dynamics and TME heterogeneity in cervical cancer receiving NACT and PD-1 blockade therapy
Using puncture biopsy or surgery, we collected nine tissue samples from three patients with cervical cancer. T1, T2, and T3 patients underwent neoadjuvant therapy before surgery. We collected samples before (T1, T2, T3) and after (T1a, T2a, T3a) NACT treatment for all three patients, as well as samples after receiving PD-1 blockade therapy (T2b, T2c, T3b) from the patients T2 and T3 (figure 1A). We normalized transcriptome expression profiles from 46,950 cells. After annotation, 24 cell groups were identified and combined into seven clusters: cancer cells, mesenchymal stem cells, fibroblast, endothelial cells, T cells, myeloid cells, and B cells (figure 1B and online supplemental figure S1). Their relative cell ratios and UMAP projection of cells from the nine samples revealed that the proportion of cancer cells decreased after combined NACT and PD-1 antibody treatment (figure 1C and D). Additionally, immune cell proportions were heterogeneous and dynamic in the TME (figure 1E and F). Finally, B cell proportions increased significantly when looking across different treatment stages. Heatmap visualization showed that the top marker genes of each cell type were clustered together (figure 1G). These included EPCAM, CDH1, and KRT8 for epithelial cells,35 as well as HSPB6, SERPINI1, and THY1 for mesenchymal stem cells.36 We also observed specific expression of marker genes for fibroblast (DCN), endothelial cells (VWF), T cells (CD3D), myeloid cells (CD163 and CD68), and B cells (CD79A) (figure 1H–J).
Supplemental material
Study design and single-cell transcriptomic analysis identifying diverse cell types in cervical cancer under neoadjuvant chemotherapy (NACT) and programmed cell death protein 1 (PD-1) antibody treatment. (A) Overall flowchart of study (created with BioRender.com): sample collection, single-cell RNA sequencing library construction, bioinformatics analysis, and experimental validation. (B) The Uniform Manifold Approximation and Projection (UMAP) plot illustrates 7 cell types in cervical cancer tissues (n=9). (C) UMAP projection with each sample colored. (D) Stack bar plot summarizing proportions of assigned cell types per sample. (E) UMAP projection with colors delineating different stages of NACT combined with anti-PD-1 Ab (n=3 per group). (F) Stack bar plot summarizes cell type proportion in samples from different stages of combination therapy (n=3 per group). (G) Heatmap showing marker gene expression in each cell type. (H) Dot plot depicting average expression and expression percentage of marker genes for 7 cell types. (I) Violin plots display representative marker expression across the cell types identified in cervical cancer. The p value was obtained by ordinary one-way analysis of variance. (J) UMAP plots of marker gene expression for cell type identification. The legend shows a color gradient of the normalized read count. Ab, antibody; CDDP, cisplatin; cDNA, complementary DNA; MSC, mesenchymal stem cell; PBS, phosphate buffered saline.
Transcriptional evolution of cervical cancer cells during combination therapy
Epithelial cells were grouped into six subclusters to assess the impact after combination therapy (figure 2A). In post-NACT groups, particularly T3 patients, epithelial subgroup (Epi) 1 proportion decreased while Epi3 proportion increased (figure 2B and online supplemental figure S2A). Evaluation of activated pathways using the RRA algorithm revealed that Epi4 was enriched in the mitotic spindle, E2F targets, and adipogenesis pathways. The immunoreactive Epi3 exhibited upregulation of interferon (IFN)-gamma, IFN-alpha, and complement pathways. Lastly, Epi2 was associated with hypoxia, angiogenesis, apoptosis, and inflammatory response (figure 2C). Pathway enrichment results were consistent with the UMAP distribution of corresponding subclusters (figure 2D and online supplemental figure S2B).
Pseudotime analysis of cervical cancer cells receiving combination therapy. (A) UMAP projection of 6 subgroups generated from clustering epithelial cancer cells. (B) Stack bar plot summarizing proportions of epithelial subgroups in each sample. (C) Pathway enrichment using the RRA algorithm showing the top 100 genes specific to each epithelial subgroup. The statistical test used in pathway enrichment analysis is the hypergeometric test. (D) UMAP projection of mitotic spindle and IFN-alpha response-pathway density. (E) Monocle analysis of unsupervised transcriptional trajectory in epithelial cells, color-coded based on pseudotime. (F) Monocle inference of epithelial cell pseudotime. Colors reflect different stages of combination therapy (n=3 per group). The arrows indicate the direction of the pseudotime. (G) Heatmap showing differential gene expression in each state along the pseudotime of combination therapy, clustered into four groups according to expression pattern. Marker gene-associated pathways are labeled on the right side with font colors consistent with (F). (H) Unsupervised transcriptional trajectory in epithelial cells, color-coded based on pseudotime stages. (I) IFN-alpha response pathway enrichment score of three pseudotime stages. The p value was obtained using a two-tailed unpaired Student’s t-test, and the results are presented in a boxplot (10th–90th percentile). ***p<0.001. Ab, antibody; Epi, epithelial subgroup; IFN, interferon; NACT, neoadjuvant chemotherapy; PD-1, programmed cell death protein 1; RRA, robust rank aggregation; UMAP, Uniform Manifold Approximation and Projection.
Pseudotime analysis revealed the timing of combination therapy correlated with pseudotime, with pretreatment dominant at the start, while NACT and anti-PD-1 Ab groups were dominant at the end (figure 2E–F). Mitosis-related gene expression (CDKN3, HMGB2, TOP2A, and CCNB1) decreased over the pseudotime, while immune-related genes (CD274, CD36, TNFSF10, CD69, TGFBI, and IFI27) were upregulated at later stages of the pseudotime time series. Likewise, ESR1, AGR3, APX8, VIM, MMP7, IGFBP8, XBP1, CD55, and MUC16 were upregulated at the end (figure 2G). Epithelial cells were divided into three states based on pseudotime, and the IFN-alpha response pathway was enriched in post-nonadjacent therapy-specific state 2 (figure 2H–I and online supplemental figure S2C). Monocle 3 analysis was also used and showed the evolutionary processes start from Epi4, which was related to the mitotic spindle; the final destination of cell fate is the immunoreactive Epi3 (online supplemental figure S2D–E). Overall, NACT and anti-PD-1 Ab increased immune response in epithelial cells.
Tumor cell-immune cell communication dynamically responds to combination therapy
Cell communication analysis of seven cell clusters demonstrated that the total number and strength of inferred interactions between tumor and immune cells increased after NACT treatment, suggesting that NACT activates the immune response. However, PD-1 blockade combination treatment decreased these interactions (figure 3A–D). Cell type level communication analysis revealed cancer cell interactions with T cells were weakened after NACT. However, the interaction strength between cancer and myeloid cells increased (figure 3E–G and online supplemental figure S3A). Anti-PD-1 antibody combination treatment then silenced cancer cell-myeloid cell communication induced by NACT (figure 3H).
Cell communication analysis of tumor microenvironment after receiving NACT and PD-1 antibody treatment. (A) Number of significant ligand-receptor pairs between any two cell populations. Edge width is proportional to the indicated interaction number of ligand-receptor pairs. (B, C) Number and strength of inferred interactions among seven cell types in the pretreatment, NACT, combination therapy (NACT+anti-PD-1 Ab) group. (D) Quantification results of the summed intensity of interactions for each cell type in the pretreatment, NACT, and combination therapy (NACT+anti-PD-1 Ab) groups. The p value was obtained using multiple comparisons in an ordinary one-way ANOVA with cancer cells. The solid and dashed lines in the violin plot represent the median and IQR. (E, F) Quantification results of the strength of interaction between cancer (F) or myeloid cells (E) and other cells in different stages of combination therapy. The p value was obtained using a two-tailed unpaired Mann-Whitney test, the results are presented as the mean±SEM. (G, H) Differential interaction strength between any two cell populations before and after NACT only (G) and combination therapy (H). Edge width and heatmap shade are proportional to normalized interaction strength. (I) Heatmap shows the relative interaction strength of 38 significant ligand-receptor signaling pathways belonging to each cell type. Left, pretreatment group. Right, NACT group. (J) Significant signaling pathways were ranked based on differences in overall information flow within inferred networks between NACT and combination therapy groups. (K) Strength of myeloid cell interactions with other cells during different stages of combination therapy within the immune checkpoint pathway. The p value was obtained by a two-tailed paired Wilcoxon test. The bounds of the box were the upper and lower quartile, with the median value in the center. The whiskers indicated the minima and maxima. *p<0.05, **p<0.01, ***p<0.001. Ab, antibody; ANOVA, analysis of variance; MSC, mesenchymal stem cell; NACT, neoadjuvant chemotherapy; PD-1, programmed cell death protein 1.
We subsequently determined the pathways and gene families responsible for these global communication changes. First, we found that major histocompatibility complex (MHC)-I and immune-checkpoint signaling pathways for myeloid cell communication were significantly upregulated (figure 3I). However, immune-checkpoint interaction strength diminished after anti-PD-1 antibody combination therapy (figure 3J–K). As immune checkpoint regulators, both PD-1 and PD-L1 bind ligands to participate in immune escape of cervical cancer. Although PD-1 was not detected in scRNA-seq data, we found PD-1 ligand PD-L1 (CD274) expression was upregulated in immunoreactive Epi2 and Epi3 cancer subgroups after NACT treatment (online supplemental figure S3B–E). Pseudotime analysis confirmed that PD-L1 was expressed during the mid-to-late temporal stage (online supplemental figure S3F). In summary, NACT treatment activated the immune checkpoint pathway between tumor cells and immune cells.
The therapeutic effect of anti-PD-1 antibody in combination with platinum-based agent in cervical cancer partly depends on macrophages
We verified the combined effect of the anti-PD-1 antibody and platinum-based agent on macrophage-tumor cell communication in vitro. We first treated cervical cancer cells (SiHa and HeLa) alone with IgG, platinum-based agent CDDP, anti-PD-1 antibody, or combined therapy (CDDP plus anti-PD-1 antibody) in the absence of macrophages. The percentage of apoptotic cells included late (PI+/Annexin-V+) and early (PI−/Annexin-V+) apoptotic cells. CDDP treatment increased the percentage of apoptotic cells (online supplemental figure S4A–B), and cell viability and migration ability decreased (online supplemental figure S4C–E). However, anti-PD-1 antibody did not directly affect cervical cancer cells, and the combined treatment did not show any difference.
When cervical cancer cells were directly or indirectly cocultured with THP-1-derived and primary monocyte-derived macrophages, the combination therapy increased the number of apoptotic cells compared with CDDP alone (figure 4A–B, online supplemental figure S5A–B, online supplemental figure S5G–H), while also decreasing cell viability and migration (figure 4C–E, online supplemental figure S5C–E). Treatment of cocultured THP-1-derived or primary monocyte-derived macrophages with cervical cancer cells indicated that CDDP inhibited macrophage phagocytosis (figure 4F–G and online supplemental figure S5F). We also constructed a subcutaneous xenograft model with mice TC-1 cells in immunocompetent mice. The results showed that anti-PD-1 antibody increased the inhibitory effect of CDDP on tumor growth (figure 4H–I). Besides, both in vitro and in vivo data demonstrated that CDDP did not increase PD-1 expression, whereas anti-PD-1 Ab treatment decreased PD-1 expression (figure 4J, online supplemental figure S5I–K).
The therapeutic effect of combining anti-PD-1 antibody and CDDP in cervical cancer is dependent on macrophages. (A, C) Percentage of apoptotic cells (A) and cell viability (C) of direct coculture of HeLa cells and primary monocyte-derived macrophages treated with anti-PD-1 Ab and/or CDDP. The upper right and lower right quadrants indicate the percentage of late and early apoptotic cells in the measured cell population. Apoptotic cells percentage included both early and late apoptotic cells. (B, D) Percentage of apoptotic cells (B) and cell viability (D) of indirect coculture of SiHa cells and THP-1-derived macrophages treated with anti-PD-1 Ab and/or CDDP. (E) Transwell migration assay of cervical cancer cells cocultured with THP-1-derived macrophages. The number of purple-stained cells indicates migration capacity. (F) Phagocytosis assay on macrophages derived from THP-1 cocultured with SiHa. Phagocytosis efficiency was quantified as the percentage of Dil and DiO double fluorophore-positive THP-1-derived macrophages. (G) Phagocytosis assay on primary monocyte-derived macrophages cocultured with CaSki cells. (H) Representative image of the subcutaneous tumors. TC-1 cells were subcutaneously inoculated into C57BL/6J mice. Each group (n=5) was treated with CDDP and/or anti-PD-1 Ab separately. (I) Growth curve of subcutaneous tumors in mice (n=5). (J) The PD-1 expression of macrophages isolated from the subcutaneous tumor microenvironment of mice was detected by flow cytometry (n=5). All functional experiments were performed with n=3 biological replicates unless otherwise stated. The p value was obtained using a two-tailed unpaired Student’s t-test (A–G, I–J), and the results are presented as the mean±SD. *p<0.05, **p<0.01, ***p<0.001. Ab, antibody; CDDP, cisplatin; PD-1, programmed cell death protein 1.
Distinct subgroups of myeloid cells and their communication with epithelial cancer cell subgroups in cervical cancer
Myeloid cell-cancer cell communication changed across the duration of our treatments. We determined how combination therapy affected the seven myeloid cell subgroups (M1–M7) (figure 5A). Based on specific marker genes, seven myeloid cell subgroups were categorized as macrophages (M1, M2, M4, M5), monocytes (M3), DC cells (M4), and a potentially doublet subgroup (M7) (figure 5B and online supplemental figure S6A). The proportions of M3 and M5 subgroups increased slightly after NACT treatment (figure 5C). M1 was associated with complement-associated immune responses, neutrophil degranulation, and M2 with interleukin signaling, whereas M5 was related to mitosis (online supplemental figure S6B).
Myeloid subgroups and cell communication with epithelial subgroups. (A) UMAP projection of seven subgroups generated from subclustering macrophages. (B) Dot plot of average expression and expression percentage of marker genes for seven myeloid subgroups. (C) Stack bar plot summarizes the proportions of myeloid subgroups per sample. (D) The strength of inferred interactions among epithelial cancer cell and myeloid subgroups before and after NACT treatment and combination therapy. (E) Strength of significant ligand-receptor pair interactions between any two subgroups of cancer and myeloid cells in the NACT group. Edge width is proportional to the interaction strength of ligand-receptor pairs. (F) The sum of interaction strengths was quantified for each subgroup of myeloid and cancer cells in the NACT group. The p value was obtained by multiple comparisons in an ordinary one-way ANOVA with the Epi3 subgroup. The solid and dashed lines in the violin plot represent the median and IQR. (G, H) Circular network plot (G) and heatmap (H) of the differential interaction strength between any two subgroups of cancer cells and myeloid cells before and after NACT treatment. (I) Relative interaction-strength heatmap of 34 significant ligand-receptor signaling pathways belonging to each epithelial cancer cell and myeloid subgroup. Left, pretreatment group. Right, NACT treatment group. (J) Strength of Epi3 cancer cell interactions with myeloid cells in the pretreatment and NACT group within the immune checkpoint pathway. The p value was obtained using a two-tailed paired Wilcoxon test. The bounds of the box were the upper and lower quartile, with the median value in the center. The whiskers indicated the minima and maxima. (K) Grouping method design for myeloid subgroups to screen for differential marker genes. Seven myeloid subgroups were further divided into strong (M1 and M2), moderate (M3, M4, and M5), and weak (M6 and M7) groups. Positive (M1–M5) and negative (M6 and M7) groups were divided based on differential interaction strength after NACT treatment. (L) UMAP projection of strong (red), moderate (green), and weak (blue) subgroups. (M) Pathway enrichment of common genes between strong and M1 subgroups. (N) Heatmap showing marker gene expression in strong, moderate, and weak subgroups. The statistical test used in pathway enrichment analysis is the hypergeometric test. *p<0.05, **p<0.01, ***p<0.001. Ab, antibody; ANOVA, analysis of variance; Epi3, epithelial subgroup 3; MHC, major histocompatibility complex; NACT, neoadjuvant chemotherapy; PD-1, programmed cell death protein 1; UMAP, Uniform Manifold Approximation and Projection.
Cell communication analysis between the six epithelial and seven myeloid cell subgroups revealed a rise in overall interaction strength after NACT treatment and then decreased after anti-PD-1 treatment (figure 5D–F and online supplemental figure S6C). Inside myeloid subgroups, the increase in communication intensity between Epi3 and the M1 and M2 macrophage subgroups was more pronounced in the NACT group compared with the pretreatment group (figure 5G–H). Detailed analysis showed that ligand-receptor pairs with increased interaction strength included the immune checkpoint family, CD226, CD39, and SN (figure 5I–J). To identify marker genes whose expression changes are correlated with the decreasing trend of interaction strengths, we formed two and three categories from seven subgroups (figure 5K). The bicategorical groups include the positive (M1, M2, M3, M4 and M5) and negative (M6 and M7) group. The strong (M1 and M2), moderate (M3, M4 and M5), and weak (M6 and M7) interaction strength groups correspond to UMAP clustering with clear demarcation (figure 5L). The strong group was related to phagocytosis, antigen processing and presentation, neutrophil degranulation, and PD-1 signaling (figure 5M and online supplemental figure S6D–E). Multiple marker gene identifications revealed CD74, GRN, NPC2, CYBA, SLC40A1, and RAB42 were significantly upregulated in the strong interaction strength group (figure 5N). Moreover, phagocytosis-related C1QC+ macrophages24 in the strong group were decreased after combination therapy (online supplemental figure S6F–G). In addition, the strong group also communicated frequently with B cells (online supplemental figure S6H–I). Collectively, macrophage subgroups that interacted with cancer cells during NACT showed a phagocytic signature related to immune checkpoints.
Combination therapy elevates CD74 expression in macrophage subgroups with strong communication strength
To identify the genes responsible for communication strength between cancer cells and macrophages, we investigated those that were commonly expressed across the M1_Macro, strong, and positive subgroups and this yielded 18 candidate genes (figure 6A). CD74, GRN, CYBA, CTSH, NPC2, and LAPTM5 expression increased along the pseudotime of myeloid subgroups and was highest in the strong subgroup (figure 6B–C and online supplemental figure S7A–E). Tumor-specific expression of CD74, GRN, CYBA, and LAPTM5 was also cross-checked in The Cancer Genome Atlas and MoMac-VERSE databases (figure 6D and online supplemental figure S7F). We compared the expression specificity of the candidates across our patient groups and observed that CD74 was consistently upregulated after NACT and positively correlated with interaction strength (figure 6E–G and online supplemental figure S7G–H).
CD74 expression positively correlated with the strength of myeloid cancer cell communication and increased after NACT treatment. (A) Venn diagram of 18 candidate genes common across M1_Macro, strong, and positive subgroups. (B) Violin plot of CD74, GRN, CYBA, and CTSH expression in strong, moderate, and weak subgroups. The p value was obtained by ordinary one-way ANOVA. (C) Changes in CD74 and GRN expression following pseudotime; colors indicate strong, moderate, and weak subgroups. (D) The CD74, GRN, CYBA, CTSH, NPC2, and LAPTM5 expression in cervical cancer tissues in the TCGA database (n=309) compared with normal tissues in the GTEx database (n=10). The results were shown in boxplot (10th–90th percentile). (E) UMAP projection of CD74 expression in strong, moderate, and weak macrophage subgroups. (F) Spearman correlation analysis of CD74 expression with differential interaction strengths induced by NACT. (G) Normalized CD74 expression after pretreatment, NACT, and combination therapy in patients T1, T2, and T3. Patient T2 received two cycles of combination therapy. (H) mRNA expression validation of CD74, GRN, NPC2, CYBA, CTSH, and LAPTM5 in pretreatment and post-NACT samples. The p value was obtained by a two-tailed paired Student’s t-test. (I) Immunofluorescence assay showed CD74 (red) and CD68 (green) expression in tissues of patient T3 receiving combination therapy. Green and white boxes represent CD74-negative and CD74-positive macrophages, respectively. (J) CD74 mRNA levels in HeLa or CaSki cells cocultured with macrophages derived from THP-1 cells after combination therapy (n=3 independent repetitions, PBS+IgG as reference sample). (L) Flow cytometry measuring the percentage of CD74-positive macrophages cocultured with CaSki cells (n=3 independent repetitions). (L) Immunofluorescence assay depicting CD74 (red) and CD68 (green) expression of subcutaneous tumors treated with CDDP and/or anti-PD-1 Ab. (M) Flow cytometry revealed the number of CD74-positive macrophages in single-cell suspensions isolated from mouse subcutaneous tumors (n=4 independent repetitions). The p value was obtained using a two-tailed unpaired Student’s t-test (D, G, J, K, M) unless otherwise stated, and the results are presented as the mean±SD. *p<0.05, **p<0.01, ***p<0.001. Ab, antibody; ANOVA, analysis of variance; CDDP, cisplatin; GTEx, The Genotype Tissue Expression; mRNA, messenger RNA; NACT, neoadjuvant chemotherapy; PD-1, programmed cell death protein 1; TCGA, The Cancer Genome Atlas; UMAP, Uniform Manifold Approximation and Projection.
We first validated the increased messenger RNA (mRNA) expression of CD74, GRN, CYBA, CTSH, NPC2, and LAPTM5 in 10 pairs of samples before and after NACT treatment (figure 6H). Subsequently, we verified CD74 expression in patient tissues and noted an increase in the CD74+ ratio among CD68+ macrophages, along with upregulated NACT-induced CD74 expression in T1, T2, and T3 patients (figure 6I and online supplemental figure S8A–D). We also validated our findings in an additional eight pairs of tissue sections, using immunofluorescence to confirm elevated CD74 and GRN expression (online supplemental figure S8E). Following CDDP in vitro, macrophages cocultured with CaSki or HeLa cells showed elevated CD74 mRNA and protein levels. Although anti-PD-1 Ab alone did not upregulate CD74, combination therapy increased CD74 mRNA and protein expression approximately two-fold from levels under CDDP alone (figure 6J–K and online supplemental figure S8F–H).
In the subcutaneous graft tumor model, immunofluorescence assays indicated that NACT (combined and alone) groups had more CD74+/CD68+ cells than control or anti-PD-1 Ab groups (figure 6L and online supplemental figure S8I). We then isolated single cells from solid tumors for flow cytometry, which demonstrated an increased CD74+ macrophage ratio in the NACT group (figure 6M and online supplemental figure S8J). Overall, combination therapy upregulates CD74 and influences macrophage-cancer cell communication.
CD74 inhibits phagocytosis and induces M2 polarization
To investigate the impact of NACT-induced CD74 upregulation, siCD74 and CD74 overexpression plasmids were transfected into THP-1-derived macrophages and their efficacy was validated at both mRNA and protein levels (online supplemental figure S9A–D). Subsequently, DiO-stained macrophages were directly co-incubated with Dil-stained cervical cancer SiHa cells for phagocytosis assay. Phagocytic efficiency increased after CD74 knockdown and decreased with CD74 overexpression (figure 7A–B and online supplemental figure S9E–F). Blocking CD74 with an anti-CD74 Ab resulted in greater phagocytic efficiency (figure 7C and online supplemental figure S9G). Additionally, CD74 mRNA levels were unaffected, while protein expression decreased slightly (online supplemental figure S9H–I).
CD74 inhibits phagocytosis and promotes NACT-induced M2 polarization. (A–C) Flow cytometry of phagocytosis ability. SiHa cells were cocultured with CD74-knockdown (A), CD74-overexpression (B), or anti-CD74-treated (C) macrophages derived from THP-1 cells. Phagocytosis efficiency was quantified as the percentage of double fluorophore-positive macrophages (upper right quadrant). (D) Density plot of M1-like and M2-like ssGSEA scores in the strong, moderate, and weak subgroups. (E) M1/M2-like ssGSEA score ratio in pretreatment, NACT, and combination therapy groups. The results were presented in a boxplot (10th–90th percentile). (F) UMAP projection showing the normalized density of CD74 expression, M1-like and M2-like score in macrophage subgroups. (G–J) CD86 (G, I) and CD206 (H, J) expression of THP-1-derived macrophages after CD74 overexpression (G, H) or knockdown (I, J). (K) CD86 and CD206 expression of primary monocyte-derived macrophages after CD74 overexpression or knockdown. (L) Phagocytosis efficiency of THP-1-derived macrophages cocultured with SiHa cells. Knocking down CD74 rescued CDDP-induced loss of phagocytic function in macrophages. (M) Flow cytometry detected differences in phagocytic function between control, CDDP, and combination therapy groups. THP-1-derived macrophages were cocultured with SiHa cells. (N) Representative image of the subcutaneous xenograft model. TC-1 cells were subcutaneously inoculated into immunocompetent CD74 humanized BALB/c-hCD74 mice. Each group consisted of n=6 mice receiving CDDP alone or combined with intraperitoneally injected anti-CD74 Ab. Tumors in four subcutaneous xenograft mice in the combination group (anti-CD74 Ab+CDDP) became invisible on day 4 post-administration. (O) Growth curve of subcutaneous tumors in mice (n=6). (P) Phagocytosis assay of macrophages isolated from mouse spleen cocultured with TC-1 cells. Phagocytosis efficiency was quantified as the percentage of Dil and DiO double fluorophore-positive THP-1-derived macrophages. All functional experiments were performed with n=3 biological repeats unless otherwise stated. The p value was obtained using a two-tailed unpaired Student’s t-test, (A–C, E, G–M, O–P) and the results are presented as the mean±SD. *p<0.05, **p<0.01, ***p<0.001. Ab, antibody; CDDP, cisplatin; NACT, neoadjuvant chemotherapy; ssGSEA, single-sample Gene Set Enrichment Analysis; UMAP, Uniform Manifold Approximation and Projection.
We speculated that CD74-induced changes in phagocytosis were associated with macrophage M1/M2 polarization. To test this hypothesis, relative expressions of M1-like and M2-like macrophage gene signature32 were used for ssGSEA enrichment calculation. M2-like ssGSEA score of the strong subgroup was highest among the strong, moderate, and weak subgroups (figure 7D). After each stage of combination therapy, M1/M2-like scores ratio decreased, reflecting M1 to M2 polarization (figure 7E). CD74 signal intensity was distributed similarly to M2-like score distribution and opposite of M1-like score distribution in UMAP projection (figure 7F and online supplemental figure S9J). Flow cytometry results indicated that CD74 overexpression in THP-1-derived and primary macrophages led to CD86 downregulation and CD206 upregulation, while CD74 knockdown had the opposite effect (figure 7G–K and online supplemental figure S9K). Furthermore, CD74 knockdown reversed CDDP-induced loss of phagocytic ability in vitro (figure 7L and online supplemental figure S9L–M). Therefore, we used an anti-CD74 antibody to improve the therapeutic effect of CDDP. Treating SiHa or CaSki cells cocultured with macrophages demonstrated that anti-CD74 antibody restored CDDP-induced phagocytic impairment (figure 7M and online supplemental figure S9N–O). Subsequently, CD74 humanized subcutaneous xenograft mice were divided into the control group, CDDP-only group, and combination group (anti-CD74 Ab+CDDP) (n=6 for each group). The anti-CD74 antibody combination group showed higher antitumor capacity than the CDDP-only group. Four subcutaneous tumors in the combination group became invisible on day 4 post-administration (figure 7N–O). Spleen-derived macrophages were isolated from the humanized mice and cocultured with TC-1 cells. The phagocytic efficiency of the combined treatment yielded higher phagocytic efficiency than using CDDP alone (figure 7P). Moreover, the expression of the CD74 ligand MIF did not differ across cancer cell subpopulations or samples (online supplemental figure S9P–S). In addition, CD74 expression in macrophages did not influence PD-1 expression (online supplemental figure S9T–U). In summary, anti-CD74 antibody combined with CDDP exerts a stronger antitumor effect than traditional NACT.
Discussion
Platinum-based NACT is widely used in cervical cancer presurgical treatment. It has the potential to eradicate micrometastases and reduce toxicity.37 However, almost 9.8%–30.6% of patients with locally advanced cervical cancer do not respond to chemotherapy.38 At present, neoadjuvant PD-1 blockade plus chemotherapy for locally advanced cervical cancer treatment is still in clinical trials (eg, NCT04516616, NCT04799639, NCT04238988).20 39 It has been shown in preliminary clinical trials that PD-1 blockade improved the efficacy of NACT, but the molecular mechanisms are not clear. In a previous study, the tumor microenvironment between cervical cancer and adjacent normal tissues was compared and the tumor-derived endothelial cells were explored.40 In this study, we performed scRNA-seq analysis on tissues from patients with cervical cancer both before and after receiving NACT combined with anti-PD-1 antibody treatment. We comprehensively investigated dynamic expression profiles of macrophage and epithelial cell subpopulations during various stages of therapy. Our data provide a valuable resource for clarifying the underlying mechanism and regulatory network of combination therapy, facilitating the discovery of new targets to improve therapeutic efficacy and precision.
Our scRNA-seq study elucidated the immune microenvironmental basis of PD-1 blockade in enhancing NACT efficacy. After the first cycle of NACT, cervical cancer cells decreased and the percentage of immune cells, such as B cells, increased, indicating a higher degree of immune infiltration. In response, cancer cells activated their immune response pathways, notably by elevating CD274 expression and sending a “don’t eat me” signal to immune cells. As a critical component of the tumor microenvironment, tumor-associated macrophage (TAM) recruitment is associated with malignant progression, resistance to therapy, and ferroptosis in cervical cancer.41 42 We found that an increased immune checkpoint signal existed between myeloid and cancer cells, which improved the efficacy of combination therapy. The macrophage subgroup receiving the strongest signal was related to chemotaxis, antigen processing and presentation, and phagocytosis. Targeting this subgroup will facilitate immune recognition and activation. Moreover, this macrophage subgroup exhibits M2 polarization, which sensitizes cells to anti-PD-1 treatment.43 44 These results suggest that immunotherapy with anti-PD-1 Ab improves outcomes of NACT in cervical cancer.
Our data also pointed to CD74 as an unfavorable factor in macrophage-cancer cell communication during combination therapy. CD74 was initially thought to participate in antigen presentation in dendritic cells and induce the priming of T cells.45 However, recent studies have identified novel functions of CD74, such as interfering with the antitumor activity of regulatory T cells.46 CD74 signaling was also found to impede microglial M1 polarization and to facilitate the tumorigenesis of gliomas.16 47 In our study, we confirmed that CD74 contributed to macrophage M2 polarization and attenuation of phagocytosis ability in cervical cancer. CD74 exhibited progressive upregulation in macrophages and was correlated with communication strength, partly explaining their diminished phagocytosis ability after PD-1 blockade combination therapy. Anti-CD74 combination therapy reverses macrophages from M2 to M1 morphology, increasing phagocytosis antitumor activity.48 We used siCD74 and anti-CD74 Ab to counteract M2-like macrophages separately in vitro and in vivo and increased the antitumor immune response rate. Targeting CD74 is a potential strategy to improve NACT and PD-1 blockade combination therapy.
Considering that NACT is not suitable for all patients with cervical cancer, the efficacy of NACT for the clinical treatment of cervical cancer needs improvement. Based on our scRNA-seq data, T cell-cancer cell communication decreased after NACT, suggesting T-cell dysfunction. Similarly, studies have reported that platinum-based combination therapy attenuated T cells self-renewal and impaired overall CD8 T-cell effector functions.49 In addition, although myeloid cancer cell communication increased, upregulated ligand-receptor interactions were predominantly immune checkpoints, the MHC-1 family, and T cell immunoreceptor with immunoglobulin and ITIM domain (TIGHT). Except for PD-1, these pathways release defense signals to the immune system and lower macrophage phagocytic ability. Therefore, the molecular outcome of NACT appeared to limit further enhancement of combination therapy, whereas targeting macrophages is promising in improving efficacy.
Several other potential targets that may be involved in macrophage communication in NACT combination therapy include GRN, NPC2, CYBA, and LAPTM5. GRN promotes cancer metastasis and immunotherapy resistance by promoting macrophage M2 polarization through binding to TNFR2.50 We also identified candidate markers that are upregulated in the epithelial cancer cell population with activation of the immune response, including ESR1, AGR3, PAX8, VIM, PAM, MMP7, AGR2, CD55, MUC16, SOX4, S100A7, CD36, TNFSF10, CD69, TGFBI, and IFI27. Their roles in combination therapy warrant further investigation.
Although the study found potential targets for improving neoadjuvant therapy efficacy, our study has several limitations that should be addressed with further research. First, our sample size was small, a larger sample size would be studied in the future to validate our findings. Second, our experiments focused primarily on macrophages. As the TME is complex and dynamic, additional research on T and B cell subclusters is necessary to fully understand how combination therapy of NACT and anti-PD-1 antibody affects the immune system. Third, the molecular profiles of cervical cancer cells in combination therapy require additional validation.
In conclusion, our study provides a comprehensive interaction network in the TME, specifically between macrophages and epithelial cancer cells, during combination therapy of NACT and anti-PD-1 antibody. We validated the efficacy of combination therapy in vivo and in vitro and demonstrated that anti-PD-1 antibody improved the antitumor effect of NACT. Moreover, we provided evidence of CD74 upregulation in macrophages as a molecular signature of combination therapy. Collectively, our findings support targeting CD74 as a new means of advanced cervical cancer treatments.
Data availability statement
Data are available in a public, open access repository. The raw single-cell RNA sequencing data reported in this paper have been deposited in the Genome Sequence Archive in the National Genomics Data Center, China National Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Sciences (GSA-Human: HRA005959) under project PRJCA021022. The source data is publicly available as of the date of publication.
Ethics statements
Patient consent for publication
Ethics approval
The clinical trial and experimental research were approved by the Ethics Committee of Qilu Hospital of Shandong University (KYLL-2020B-029, KYLL-202111-153). Participants gave informed consent to participate in the study before taking part. Animal studies were performed according to institutional guidelines, and all experiments in this study were approved by the Ethics Committee of the Qilu Hospital of Shandong University (DWLL-2023–030).
Acknowledgments
We thank Professor Kezhen Li’s team of Huazhong University of Science and Technology for generously providing detailed information about the clinical trial. We thank Professor Kong Beihua of Shandong University for sponsoring this study. Mutangala Muloye Guy conducted some of the experiments during the article revision process. We thank the Translational Medicine Core Facility of Shandong University for the consultation and instrument availability that supported this work. We also thank the Model Animal Research Centre of Shandong University for mouse housing and care.
References
Supplementary materials
Supplementary Data
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Footnotes
ZW and BW contributed equally.
Contributors BC, ZW, and BW were responsible for the overall conception and design of the study and performed most of the experiments. YF, MX, and JY performed part of the experiments and the data analysis. ZM participated in the drawing of the figure. TZ, WZ, and XJ revised the manuscript. YZ and QZ collaborated on the project. BC was the guarantor of the study. All authors reviewed, edited, and approved the manuscript.
Funding This work was supported by the National Key Research and Development Program of China (grant numbers: 2021YFC2701203, 2022YFC2704401), the National Natural Science Foundation of China (grant numbers: 82172940, 82103425, 82303055), and the Natural Science Foundation of Shandong Province (grant numbers: ZR2023QH312, ZR2021QH187).
Competing interests No, there are no competing interests.
Provenance and peer review Not commissioned; externally peer reviewed.
Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.