Purpose:

Adenocarcinoma of the uterine cervix is the second most common type of cervical cancer after squamous cell carcinoma (SCC). Although both subtypes are treated similarly, patients with adenocarcinoma have a worse prognosis. In this study, immunologic features of the tumor microenvironment in these two subsets were pursued with potential therapeutic implications.

Experimental Design:

The immune microenvironment of primary tumors and nonmetastatic tumor-draining lymph nodes (TDLN) was compared between patients with cervical adenocarcinoma (n = 16) and SCC (n = 20) by polychromatic flow cytometry and by transcriptional profiling of the primary tumors (n = 299) using publicly available data from The Cancer Genome Atlas (TCGA).

Results:

Flow cytometric analyses revealed intact T-cell differentiation in TDLNs, but hampered effector T-cell trafficking to the primary tumors in adenocarcinoma, as compared with SCC. TCGA analysis demonstrated higher expression of chemokines involved in effector T-cell homing (CXCL9/10/11) in SCC primary tumors as compared with adenocarcinoma primary tumors, which was highly correlated to a transcriptional signature for type I conventional dendritic cells (cDC1). This was consistent with elevated frequencies of CD141/BDCA3+cDC1 in primary tumor SCC samples relative to adenocarcinoma and correspondingly elevated levels of CXCL9 and CXCL10 in 24-hour ex vivo cultures. Hampered cDC1 recruitment in adenocarcinoma was in turn related to lower transcript levels of cDC1-recruiting chemokines and an elevated β-catenin activation score and was associated with poor overall survival.

Conclusions:

Our data have identified an opportunity for the investigation of potentially novel therapeutic interventions in adenocarcinoma of the cervix, that is, β-catenin inhibition and cDC1 mobilization.

Translational Relevance

Immune checkpoint blockade (ICB) has recently been approved for cancer of the uterine cervix, although response rates are very low. Patients with adenocarcinoma and squamous cell carcinoma (SCC) undergo the same standard treatment, even though these histologic subtypes differ substantially in terms of HPV and mutation status, immune infiltrate, response to therapy, and patient outcome. In this study, we comprehensively studied immunologic differences in tumors and draining lymph nodes between the two histologic subtypes in an effort to uncover specific potential treatment options. Our findings show that adenocarcinoma tumors are less T-cell inflamed than SCC tumors and that this is linked to β-catenin activation, a defective chemokine response, and low frequencies of conventional type I dendritic cells. Importantly, The Cancer Genome Atlas analyses show an association between high transcript levels of these factors and overall survival benefit. Our findings point to specific therapeutic intervention options for cervical adenocarcinoma that may facilitate effective ICB.

With an estimated 570,000 new cases in 2018 worldwide and over 311,000 deaths, cervical cancer is the fourth most common cancer in women globally (1). The most prevalent histologic subtypes are squamous cell carcinoma (SCC) and adenocarcinoma. SCC accounts for approximately 70% of all cases, and is mostly associated with an infection with human papillomavirus (HPV) type 16, whereas adenocarcinoma accounts for approximately 20% of all cases, and is more often associated with HPV18 (2, 3). As a result of the implementation of population-based screening programs for the early detection of cervical neoplasia, the incidence of cervical cancer has decreased over the last 50 years and is expected to decrease further due to recently introduced prophylactic HPV vaccination (4). However, the incidence of cervical adenocarcinoma has shown a relative and absolute increase compared with SCC over the past decades, especially in younger women (5–7). Several retrospective studies showed that patients with adenocarcinoma have a higher risk of developing metastases, resulting in a poorer prognosis (8–10). Cervical adenocarcinoma also differs from SCC in terms of prognostic factors, biological behavior, mutational profiles and sensitivity to chemo- and/or radiotherapy (2, 11–15). However, patients with adenocarcinoma and SCC are still subjected to the same conventional treatment modalities (ie, surgery and/or chemoradiotherapy), often leading to infertility and reduced quality of life (16). Therefore, there is an urgent need to elucidate the differences between these histologic types, which could lead to more effective and tumor-specific treatment strategies.

Cervical carcinogenesis is fueled by an active infection with high-risk types of HPV, and the immune system has been shown to be suppressed to prevent clearance of the tumor and maintain disease progression (17, 18). Immunotherapy has proven its efficacy in several tumor types (19–21), and the use of pembrolizumab in patients with advanced PD-L1+ cervical cancer has recently been approved by the FDA based on an overall response rate of 12.2% as reported by Chung and colleagues (22). Only five of 98 patients in this trial had cervical adenocarcinoma, but interestingly, all were PD-L1 positive. In a smaller cohort of patients (n = 24), an overall response rate of 17% was reported, but only one patient with adenocarcinoma was included (23). In these studies, no subgroup analyses were performed for adenocarcinoma versus SCC. Furthermore, immune checkpoint blockade (ICB) with anti-CTLA-4 showed disappointing results in patients with recurrent and metastatic cervical cancer, and the only partial responder had SCC, while stable disease was observed in patients with both adenocarcinoma and SCC subtypes (24). Several (combination-) immunotherapy trials are currently ongoing (18), but again, no distinction is being made for histologic subtypes. Considering the rising incidence rates of cervical adenocarcinoma and its poor prognosis, it is of great interest to study the immunologic behavior of the histologic subtypes. Because distinct therapeutic strategies for adenocarcinoma and SCC have not yet emerged, this knowledge can contribute to the development of different immunotherapeutic combinatorial approaches to improve response and tumor control.

In this study, comprehensive flow cytometry analyses, functional assays, and publicly available mRNA sequencing data of The Cancer Genome Atlas (TCGA) were used to determine immune cell subset frequencies, activation status, and chemokine release in adenocarcinoma versus SCC. By mapping out the immune landscape of cervical adenocarcinoma and SCC, we aimed to uncover leads for novel immunotherapeutic options tailored for each histologic subtype.

Patients

Patients (n = 36) were included at the Antoni van Leeuwenhoek Hospital (AVL) and the Amsterdam UMC, location AMC [Center for Gynecologic Oncology Amsterdam (CGOA)]. Clinical samples for immunomonitoring were processed at the Amsterdam UMC, location VUmc - Cancer Center Amsterdam (CCA). All included patients were diagnosed with cervical SCC or adenocarcinoma and were scheduled for a (radical) hysterectomy, and/or pelvic lymphadenectomy. The study was approved by the local Institutional Review Board (no. NL25610.058.08) and executed in accordance with the ethical guidelines of the 1975 Declaration of Helsinki. Patients gave written informed consent prior to participation. The patients' clinicopathologic characteristics are shown in Table 1. We recently published data on the SCC cohort (25). In this study, we have undertaken comparative analyses between immune profiling data from all available SCC primary tumor and metastasis-free lymph node (LN-) samples and data from all the available adenocarcinoma primary tumor and LN- samples. Although they were not a priori matched, no obvious differences in clinical characteristics were apparent between these two small SCC and adenocarcinoma patient cohorts (see Table 1). The clinical characteristics per patient from both adenocarcinoma and SCC cohorts and the collected tissue samples per patient (primary tumor, lymph node, or both) are shown in Supplementary Tables S1 and S2). Of note, primary tumors and corresponding lymph nodes were collected for only eight patients (SCC, n = 5; adenocarcinoma, n = 3). Thus, the performed analyses were not paired.

Table 1.

Clinical and histopathologic characteristics of the study population.

Clinical characteristicsTDLN ACa (n = 11)TDLN SCC (n = 13)PPT AC (n = 11)PT SCC (n = 10)P
Age, mean ± SD 47.2 ± 6.5 43.8 ± 9.4 0.34 43.45 ± 8.9 41.9 ± 12.1 0.48 
FIGO stageb   0.26   0.54 
 IB1 9 (82) 6 (46)  7 (64) 7 (70)  
 IB2 1 (9) 3 (23)  3 (27) 2 (20)  
 IIA1 1 (9)  1 (9)  
 IIA2 1 (8)  1 (10)  
 IIB 2 (15)   
 IIIB 1 (8)   
Vaginal involvementc   0.15   >0.99 
 Yes 1 (9)  1 (10)  
 No 10 (91) 10 (77)  10 (100) 9 (90)  
 Unknown 3 (23)   
Parametrium invasionc   0.13   >0.99 
 Yes 1 (9) 5 (38)  1 (9)  
 No 10 (91) 7 (54)  10 (91) 10 (100)  
 Unknown 1 (8)   
HPV type     
 16 4 (37) 7 (54)  1 (9) 5 (50)  
 18 1 (9)  2 (18)  
 31 1 (8)   
 33  1 (10)  
 59  1 (10)  
 Unknown 1 (9) 4 (30)  1 (9) 2 (20)  
 P16+d 3 (27) 1 (8)  4 (37) 1 (10)  
 Negative 1 (9)   
 hrHPV+e 1 (9)  3 (27)  
Clinical characteristicsTDLN ACa (n = 11)TDLN SCC (n = 13)PPT AC (n = 11)PT SCC (n = 10)P
Age, mean ± SD 47.2 ± 6.5 43.8 ± 9.4 0.34 43.45 ± 8.9 41.9 ± 12.1 0.48 
FIGO stageb   0.26   0.54 
 IB1 9 (82) 6 (46)  7 (64) 7 (70)  
 IB2 1 (9) 3 (23)  3 (27) 2 (20)  
 IIA1 1 (9)  1 (9)  
 IIA2 1 (8)  1 (10)  
 IIB 2 (15)   
 IIIB 1 (8)   
Vaginal involvementc   0.15   >0.99 
 Yes 1 (9)  1 (10)  
 No 10 (91) 10 (77)  10 (100) 9 (90)  
 Unknown 3 (23)   
Parametrium invasionc   0.13   >0.99 
 Yes 1 (9) 5 (38)  1 (9)  
 No 10 (91) 7 (54)  10 (91) 10 (100)  
 Unknown 1 (8)   
HPV type     
 16 4 (37) 7 (54)  1 (9) 5 (50)  
 18 1 (9)  2 (18)  
 31 1 (8)   
 33  1 (10)  
 59  1 (10)  
 Unknown 1 (9) 4 (30)  1 (9) 2 (20)  
 P16+d 3 (27) 1 (8)  4 (37) 1 (10)  
 Negative 1 (9)   
 hrHPV+e 1 (9)  3 (27)  

Note: Values in the table expressed as n (%).

Abbreviations: AC, adenocarcinoma; FIGO, International Federation of Gynecology and Obstetrics; HPV, human papillomavirus; PT, primary tumor; SCC, squamous cell carcinoma.

aTwo lymph nodes were collected from the same patient (sentinel and nonsentinel).

bFIGO-staging was based on clinical characteristics.

cVaginal and parametrium invasion were based on pathology reports and in case this information was missing, based on clinical findings.

dIn case no HPV typing was performed, results of the p16 staining are shown.

ehrHPV-positive, but exact type unknown.

Fresh tissue collection and processing

Cells were scraped from a halved lymph node with a surgical blade, as described previously (25). After collection of samples for this study, the patient material was used for routine pathologic diagnostics. In case of a primary tumor, the specimen was first cut into small fragments. All samples were collected in dissociation medium, consisting of RPMI1640 without HEPES (Lonza), 0.1% DNAse I (Roche), 0.14% collagenase A (Roche), 5% FCS (Hyclone), and a penicillin–streptomycin–glutamine (PSG, Gibco) solution. Next, the cell suspension was transferred to a sterile dissociation flask followed by incubation in a water bath at 37°C on a magnet stirrer. Incubation time differed depending on the sample type, ranging from 30 minutes for lymph node samples to three times 45 minutes (by refreshing dissociation medium each time) for a primary tumor biopsy. After incubation, the cell suspension was filtered through a 100-μm cell strainer (BD Falcon), in Iscove's modified Dulbecco medium (IMDM; Lonza) containing 10% FCS, gentamicin/amphotericin B (Gibco), and PSG. Erythrocytes were lysed and viable cells were counted. Cells were directly used or cryopreserved until further use.

Multicolor flow cytometry

Per sample, 1.5 × 105–2 × 105 cells were stained with the following surface antibodies: BDCA-3 (1:50, Miltenyi Biotec), BDCA-2 (1:20, Miltenyi Biotec; labeled with FITC) CD3 (1:25, BD Biosciences), CD14 (1:20, BD Biosciences; both labeled with PerCP-Cy5.5); CD4 (1:200, BD Biosciences), CD45 (1:200, BioLegend; both labeled with AF700); CD8 (1:75, BD Biosciences; labeled with V500); CD25 (1:75, BD Biosciences), CD11c (1:100, BD Biosciences; both labeled with APC); CD45RA (1:100, BD Biosciences; labeled with APC-Cy7/H7); CD27 (1:100, BD Biosciences; labeled with PE-Cy7); CD127 (1:50, BD Biosciences), Tim-3 (1:20, BioLegend), EpCAM (1:50, BD Biosciences; all labeled with BV421); Lag-3 (1:20, eBioscience; labeled with PE-Cy7); CD123 (1:30, BD Biosciences; labeled with BV650), PD-1 (1:20, BD Biosciences; labeled with BV786), CD1a (1:50, BD Biosciences; labeled with PE), diluted in BD Horizon Brilliant Stain Buffer (BD Biosciences). After membrane staining, T cells were stained with intracellular antibodies against FoxP3 (1:40, eBioscience; labeled with PE); Ki-67 (1:50, BD Biosciences; labeled with FITC); and CTLA-4 (1:100, BD Biosciences; labeled with PE-CF594) using a FoxP3 staining kit according to the manufacturer's instructions (eBioscience). Frequencies of CD4+ T-cell populations were based on expression of FoxP3 and CD45RA as previously proposed by Miyara and colleagues (26). We recently published examples of CD4+ subpopulation T-cell gating strategies within cervical cancer (25). The gating strategy of CD1a+ migratory cDCs, CD14+antigen-presenting cells (APC), the type 1 conventional dendritic cells (cDC1), and plasmacytoid DCs (pDC) are shown in Supplementary Fig. S1. Specifications of the antibodies used are listed in Supplementary Table S3. Multicolor flow cytometry was performed using the LSR Fortessa X-20 flow cytometer (BD Biosciences). Data were analyzed using the Kaluza flow cytometry analysis software version 1.3 (Beckman Coulter).

mRNAseq TCGA patient cohort

Differential gene expression was examined using a publically available dataset. The R2 genomics analysis and visualization platform (https://r2.amc.nl) has the TCGA dataset on gene expression in cervical cancer available (27). For our analyses, RSEM-normalized mRNA expression data from patients with adenocarcinoma and SCC were used (n = 299). For the generation of gene expression signatures, normalized expression values were log2 transformed and the mean value of the various signature defining transcripts was calculated. The following gene signatures were used, based on previously published data: CD8+ effector T-cell score (CD8A, CD8B, IFNG, PRF1, GZMB), CXCL-score (CXCL9, CXCL10, CXCL11; ref. 28), cDC1-score (BATF3, XCR1, CLEC9A, CLNK; refs. 29, 30), cDC1 chemoattractant score (CCL5, XCL1, XCL2; ref. 29), and a β-catenin signaling activation score (ALCAM, AQP4, AQP5, BMP4, CDH16, CHGA, CXCL8, EPCAM, FOXA2, HHIP, IGF2BP1, LGR5, POU3F2, SHH, SIX1, SOX11, TH, TMSB15A, TSPAN8, WIF1; ref. 31). Recurrence-free and overall survival analyses were performed using Kaplan–Meier Plotter (kmplot.com), which was previously used for studies involving several tumor types (32–35).Thresholds for follow-up were set at 60 months.

Chemokine release with TLR7/8 ligands and CBA read-out

Per well, 1 × 105 disaggregated cells from primary tumors (n = 8 for SCC, n = 4 for adenocarcinoma) were plated into a U-bottom 96-well plate in 200-μL IMDM medium with 10% FCS and PSG. Cells were cultured without stimulation, or with the toll-like receptor 7/8 (TLR7/8) ligand resiquimod (R848) (10 μg/mL, InvivoGen). After an incubation of 24 hours at 37°C, supernatant was collected and stored at -20°C until further analysis. A Cytometric Bead Array (CBA) was performed with the human chemokine flex kit (BD Biosciences) to measure CXCL9 (MIG), CXCL10 (IP-10), CXCL11 (I-TAC), CXCL8 (IL8), CCL2 (MCP-1), and CCL5 (RANTES) production in the collected supernatants. For specifications of the used beads, see Supplementary Table S3. Per sample, 15-μL supernatant was used and the samples were analyzed on the BD LSR Fortessa flow cytometer. The quantity of each chemokine was calculated using FCAP array software version 3.0 (BD Biosciences), reported in pg/mL, and values below the detection limit were set to zero.

Statistical analysis

To compare clinicopathologic characteristics of patients in the adenocarcinoma versus SCC cohort, a Fisher exact test (2 × 2 tables) or a χ2 test was used to assess significant differences. Normal distribution was assessed with the D'Agostino–Pearson omnibus test or Shapiro–Wilk test. An unpaired Student t test (in case of normally distributed data) or the Mann–Whitney test were applied to assess statistically significant differences between groups. Correlations were determined by the Pearson r test. All statistical analyses were performed using GraphPad Prism 8.0 (GraphPad Software Inc.). Differences were considered statistically significant when P < 0.05.

Lower level of T-cell infiltration in adenocarcinoma tumors

First, we compared the immune infiltration rates in the primary tumor of patients with cervical SCC with those of patients with adenocarcinoma. On the basis of expression of the marker CD45 within the total cell population, significantly higher percentages of CD45+ immune cells were found in SCC (Fig. 1A, left). Next, CD45+ infiltration was related to the number of tumor cells, based on the expression of the tumor marker EpCAM (Fig. 1A, middle). Indeed, higher ratios of infiltrating immune cells to EpCAM+ tumor cells were found in SCC. Also, CD3+ T cells were observed at higher frequencies within the SCC tumor microenvironment (TME; Fig. 1A, right).

Figure 1.

T-cell frequencies and subsets in primary tumor (PT) samples of patients with cervical adenocarcinoma (AC) and SCC. A, Lower percentages of CD45+ leukocytes (left), lower CD45+:EpCAM ratio (middle) and percentages of tumor-infiltrating CD3+ lymphocytes in adenocarcinoma primary tumor as compared with SCC primary tumor. B, No significant differences in CD4+, CD8+, double negative (DN; CD4CD8) T-cell frequencies, and double positive (DP; CD4+CD8+) T-cell frequencies in adenocarcinoma primary tumor compared with SCC primary tumor. C, Higher frequencies of CD4+ non-Tregs in adenocarcinoma primary tumor, and more CD4+ total Tregs in SCC primary tumors (left). Lower frequencies of naïve (nCD4+, FoxP3CD45RA+) and FCD4+ (FoxP3CD45RA) in SCC primary tumor, same frequencies of F+CD4+ (FoxP3intCD45RA) conventional CD4+ T cells, and resting regulatory T cells (rCD4+Tregs, FoxP3intCD45RA+), but higher percentages of activated Tregs (aCD4+Tregs, FoxP3hiCD45RA) in SCC primary tumor (right). Error bars, SEM (*, P ≤ 0.05; **, P ≤ 0.01).

Figure 1.

T-cell frequencies and subsets in primary tumor (PT) samples of patients with cervical adenocarcinoma (AC) and SCC. A, Lower percentages of CD45+ leukocytes (left), lower CD45+:EpCAM ratio (middle) and percentages of tumor-infiltrating CD3+ lymphocytes in adenocarcinoma primary tumor as compared with SCC primary tumor. B, No significant differences in CD4+, CD8+, double negative (DN; CD4CD8) T-cell frequencies, and double positive (DP; CD4+CD8+) T-cell frequencies in adenocarcinoma primary tumor compared with SCC primary tumor. C, Higher frequencies of CD4+ non-Tregs in adenocarcinoma primary tumor, and more CD4+ total Tregs in SCC primary tumors (left). Lower frequencies of naïve (nCD4+, FoxP3CD45RA+) and FCD4+ (FoxP3CD45RA) in SCC primary tumor, same frequencies of F+CD4+ (FoxP3intCD45RA) conventional CD4+ T cells, and resting regulatory T cells (rCD4+Tregs, FoxP3intCD45RA+), but higher percentages of activated Tregs (aCD4+Tregs, FoxP3hiCD45RA) in SCC primary tumor (right). Error bars, SEM (*, P ≤ 0.05; **, P ≤ 0.01).

Close modal

Subsequently, the frequencies of CD4+ T cells, CD8+ T cells, double-negative (DN; CD4CD8), and double-positive cells (DP; CD4+CD8+) were determined (Fig. 1B). No differences were observed between SCC and adenocarcinoma primary tumors. However, within the CD4+ T-cell population, the distribution between regulatory T cells (Treg) and non-Tregs was skewed in favor of Tregs in SCC primary tumor (Fig. 1C, left), with relatively more naïve T cells (nCD4+; FoxP3CD45RA+), albeit at very low frequencies, and memory T cells (FCD4+; FoxP3CD45RA) present in the adenocarcinoma primary tumor (Fig. 1C, right). Specifically, the frequencies of activated, that is, functionally suppressive (26), Tregs (aTregs; FoxP3+CD45RA) were significantly higher in SCC primary tumor (Fig. 1C, right). Taken together, these data indicate that compared with adenocarcinoma, the TME of SCC is more T-cell infiltrated, but also more immune suppressed, as judged by Treg activation.

Immune checkpoint expression and differentiation state of CD8+ T cells in primary tumors and TDLNs of patients with adenocarcinoma

We next analyzed immune checkpoint expression on effector T cells in primary tumor and TDLN samples. Overall, higher expression levels of the various immune checkpoints were observed on CD8+ T cells in primary tumors of patients with SCC as compared with adenocarcinoma (Fig. 2A). Specifically, the combination of more frequent CD8+ T cells with high PD-1 expression levels and higher rates of CD8+ T cells (co-) expressing TIM-3, LAG-3, and/or PD-1, pointed to a generally more “exhausted” state of CD8+ T cells in SCC. Also in the TDLNs of patients with SCC, more CD8+ T cells were TIM-3+, PD-1+TIM-3+, and LAG-3+ (Fig. 2B). Similarly, higher immune checkpoint expression levels were found on conventional CD4+ Th cells in SCC, both in tumors and TDLNs (Supplementary Fig. S2).

Figure 2.

Effector–memory differentiation of CD8+ T cells within TDLN and primary tumor (PT) of patients with adenocarcinoma (AC) versus SCC. A, In adenocarcinoma primary tumor, significantly more cells had intermediate PD-1 expression as compared with SCC primary tumor, where more cells had high expression of PD-1. (Co) expression of PD-1 and TIM-3 was significantly higher in SCC primary tumor. B, Immune checkpoint expression in TDLN of adenocarcinoma versus SCC patients. Significantly higher (co) expression of PD-1, LAG-3 and TIM-3 was observed in SCC TDLN. Within the tumor microenvironment, frequencies of CD8+ central memory (Tcm, CD27+CD45RA), effector memory (Tem, CD27CD45RA), effector memory RA (Temra, CD27CD45RA+) CD8+ T cells and naïve CD8+ T cells (Tn, CD27+CD45RA+; C). No significant differences in percentages of CD8+ T-cell subsets were observed. D, Only in TDLN differences were observed in percentages of Tcm, Tem, and Tn for adenocarcinoma versus SCC, with more effector memory differentiation of CD8+ T cells within the adenocarcinoma TDLN compared with SCC. Error bars, SEM (*, P ≤ 0.05; **, P ≤ 0.01; ***, P ≤ 0.001).

Figure 2.

Effector–memory differentiation of CD8+ T cells within TDLN and primary tumor (PT) of patients with adenocarcinoma (AC) versus SCC. A, In adenocarcinoma primary tumor, significantly more cells had intermediate PD-1 expression as compared with SCC primary tumor, where more cells had high expression of PD-1. (Co) expression of PD-1 and TIM-3 was significantly higher in SCC primary tumor. B, Immune checkpoint expression in TDLN of adenocarcinoma versus SCC patients. Significantly higher (co) expression of PD-1, LAG-3 and TIM-3 was observed in SCC TDLN. Within the tumor microenvironment, frequencies of CD8+ central memory (Tcm, CD27+CD45RA), effector memory (Tem, CD27CD45RA), effector memory RA (Temra, CD27CD45RA+) CD8+ T cells and naïve CD8+ T cells (Tn, CD27+CD45RA+; C). No significant differences in percentages of CD8+ T-cell subsets were observed. D, Only in TDLN differences were observed in percentages of Tcm, Tem, and Tn for adenocarcinoma versus SCC, with more effector memory differentiation of CD8+ T cells within the adenocarcinoma TDLN compared with SCC. Error bars, SEM (*, P ≤ 0.05; **, P ≤ 0.01; ***, P ≤ 0.001).

Close modal

While the distribution between CD8+ T-cell differentiation states within the primary tumors of adenocarcinoma versus SCC was the same (based on relative percentages within the whole CD8+ T-cell population, see Fig. 2C), surprisingly, in the TDLNs of patients with adenocarcinoma, we observed more CD8+ memory T-cell differentiation. Here, increased rates of central memory CD8+ T cells (Tcm; CD27+CD45RA) and effector memory CD8+ T cells (Tem; CD27CD45RA) were found and lower frequencies of naïve CD8+ T cells (Tn; CD27+CD45RA+) when compared with SCC TDLNs (Fig. 2D).

Lower CD8+ effector T-cell rates and chemokine release levels in adenocarcinoma are related to cDC1 content

The combination of apparently intact CD8+ T-cell differentiation in TDLN, but low levels of T-cell recruitment to the primary tumors of patients with adenocarcinoma led us to hypothesize that the patients' primary tumors might be impaired in the production of effector T-cell–attracting chemokines, such as CXCL9, CXCL10, and CXCL11, which bind to the CXCR3 chemokine receptor on effector T cells. We tested this hypothesis by analyzing publicly available cervical cancer mRNAseq data of the TCGA database. We generated a CXCL9/10/11 score and compared the expression for adenocarcinoma versus SCC. As hypothesized, significantly higher expression of these chemokines was observed in SCC (Fig. 3A, left). We further assessed whether this chemokine score was correlated with the presence of CD8+ effector T cells, by creating a CD8+ T-cell effector score comprising the following genes: CD8A, CD8B, IFNG, PRF1, and GZMB. Indeed, a strong correlation was observed between the chemokine score and the effector CD8+ T-cell score, with adenocarcinoma tumors generally scoring low on both (Fig. 3A, right), thus supporting our hypothesis of hampered trafficking of effector T cells due to low-level chemokine release. Recent studies have identified BDCA3/CD141+ cDC1 as an essential source of T-cell attracting chemokines in the TME (28, 36). We therefore assessed the presence of cDC1 in the primary tumor TME of the two histologic subtypes. Indeed, a higher cDC1 transcriptional signature (which included CLEC9, XCR1, CLNK, and BATF3) was observed in SCC primary tumor (Fig. 3B; left). Furthermore, this signature showed a positive correlation with CXCL9/10/11 transcript levels (Fig. 3B; right). To validate the observation of lower expression levels of T-cell–attracting chemokines in adenocarcinoma, we performed overnight cultures with single-cell suspensions of adenocarcinoma and SCC primary tumors, with and without R848, a ligand for TLR8, which is known to be expressed by cDC1 (37). Subsequently, release of the chemokines CXCL8, CCL2, and -5, and CXCL9, -10, and -11 was assessed by CBA (Fig. 3C). Both in steady state and upon R848-mediated activation, generally higher levels of these chemokines were observed in SCC, except for CXCL11, which remained below detection levels. Upon stimulation, significant increases in the release of CXCL8, CCL5, and CXCL10 were found, but only in SCC, thus confirming an intrinsically hampered ability for effector chemokine release in adenocarcinoma. To assess whether this could be related to cDC1 content, the frequencies of different APC subsets in primary tumor samples from patients with adenocarcinoma versus SCC were determined by flow cytometry. Consistent with the TCGA analysis, the flow cytometry analysis revealed that BDCA3+ cDC1 (CD45+CD11c+CD14CD1aBDCA3+) were significantly more frequent in SCC (both as a ratio per EpCAM+ as well as a percentage of all CD45+CD11c+ cells), whereas no differences for CD1a+ DCs (CD45+CD11c+CD14CD1a+), CD14+ APCs (CD45+CD11c+CD14+), or plasmacytoid DCs (pDC, CD45+BDCA2+CD123+) were observed between SCC and adenocarcinoma (Fig. 3D). In keeping with the notion that cDC1 might be a major source of effector T-cell-attracting chemokines in the TME, low CXCL9- or -10 producing tumors (ie, lower than mean) had indeed lower frequencies of cDC1, whereas high chemokine-producing tumors had correspondingly higher cDC1 frequencies (P < 0.01 for CXCL9, Fig. 3E). A strong positive correlation between the cDC1 gene signature and the CD8+ effector T-cell score lent further support for a role of the cDC1 in attracting CD8+ effector T cells to the TME in cervical cancer (Fig. 3F). This correlation further showed the adenocarcinoma TME to be relatively low on both cDC1 and CD8+ effector T cells.

Figure 3.

Chemokine transcript expression and protein release in adenocarcinoma (AC) versus SCC primary tumor (PT) is related to cDC1 content. A, Mean of CXCL9/10/11 was used to compare the transcriptional signature of the chemokines in adenocarcinoma versus SCC primary tumor (left). The CXCL9/10/11 signature score was plotted against the CD8+ effector T-cell score (CD8A, CD8B, IFNG, PRF1, GZMB, right) showing a significant correlation (right). B, TCGA analyses of mRNA expression data shows that the cDC1 score (CLEC9A, XCR, CLNK, BATF3) is higher within the SCC primary tumor samples compared with adenocarcinoma primary tumor (left). A significant correlation (right) exists for the CXCL9/10/11 score versus cDC1 score. C, The production of the chemokines CXCL8, CXCL9, CXCL10, CCL5, CCL2, and CXCL11 in single cells suspensions of adenocarcinoma (n = 4) and SCC (n = 8) primary tumor in medium and R848 condition was assessed by using a CBA assay. Wilcoxon matched-pairs signed-rank test was used to compare differences in chemokine production upon R848 stimulation. Significantly higher chemokine production of CXCL8, CXCL10, and CCL5 was observed in SCC PT samples upon R848 stimulation compared to no stimulation. D, Flow cytometry analyses reveal a lower ratio per EpCAM+ cell of BDCA3+CD11c+CD45+ DCs, double-negative (DN) for CD14 and CD1a (DN BDCA3+) and lower percentages of DN BDCA3+ as a percentage of all CD11c+CD45+ cells, in adenocarcinoma compared with SCC primary tumor. No differences in CD14+ and CD1a+ myeloid cells and pDCs were observed for adenocarcinoma versus SCC primary tumor. E, Percentages of BDCA3+ DN cells versus chemokine production of CXCL9 (left) and CXCL10 (right), based on mean release in unstimulated condition. F, A significant correlation for mRNA expression data of the cDC1 versus CD8+ T-cell signatures. Error bars, SEM. r, Pearson correlation coefficient (**, P ≤ 0.01; ****, P ≤ 0.0001).

Figure 3.

Chemokine transcript expression and protein release in adenocarcinoma (AC) versus SCC primary tumor (PT) is related to cDC1 content. A, Mean of CXCL9/10/11 was used to compare the transcriptional signature of the chemokines in adenocarcinoma versus SCC primary tumor (left). The CXCL9/10/11 signature score was plotted against the CD8+ effector T-cell score (CD8A, CD8B, IFNG, PRF1, GZMB, right) showing a significant correlation (right). B, TCGA analyses of mRNA expression data shows that the cDC1 score (CLEC9A, XCR, CLNK, BATF3) is higher within the SCC primary tumor samples compared with adenocarcinoma primary tumor (left). A significant correlation (right) exists for the CXCL9/10/11 score versus cDC1 score. C, The production of the chemokines CXCL8, CXCL9, CXCL10, CCL5, CCL2, and CXCL11 in single cells suspensions of adenocarcinoma (n = 4) and SCC (n = 8) primary tumor in medium and R848 condition was assessed by using a CBA assay. Wilcoxon matched-pairs signed-rank test was used to compare differences in chemokine production upon R848 stimulation. Significantly higher chemokine production of CXCL8, CXCL10, and CCL5 was observed in SCC PT samples upon R848 stimulation compared to no stimulation. D, Flow cytometry analyses reveal a lower ratio per EpCAM+ cell of BDCA3+CD11c+CD45+ DCs, double-negative (DN) for CD14 and CD1a (DN BDCA3+) and lower percentages of DN BDCA3+ as a percentage of all CD11c+CD45+ cells, in adenocarcinoma compared with SCC primary tumor. No differences in CD14+ and CD1a+ myeloid cells and pDCs were observed for adenocarcinoma versus SCC primary tumor. E, Percentages of BDCA3+ DN cells versus chemokine production of CXCL9 (left) and CXCL10 (right), based on mean release in unstimulated condition. F, A significant correlation for mRNA expression data of the cDC1 versus CD8+ T-cell signatures. Error bars, SEM. r, Pearson correlation coefficient (**, P ≤ 0.01; ****, P ≤ 0.0001).

Close modal

β-Catenin activation and a lack of cDC1-attracting chemokines as underlying cause for low cDC1 content in adenocarcinoma

Next, factors involved in the attraction of the cDC1 to the TME were assessed for SCC and adenocarcinoma based on TCGA gene expression scores, and all were correlated to the cDC1 score (Fig. 4, top). Expression of CCL5, XCL1, and XCL2 (ie, the cDC1 chemoattractant score; ref. 29) was significantly higher in the SCC samples when compared with adenocarcinoma. No differences were found for the expression of fms-like tyrosine kinase 3 ligand (FLT3L), a growth factor for cDC1, between SCC and adenocarcinoma. Transcript levels of CCL4, a chemokine recently shown in melanoma to attract cDC1 to the TME and to be produced in the absence of active Wnt/β-catenin signaling (38), were also increased in SCC. Interestingly, and consistent with observations made by Spranger and colleagues, transcripts of known response genes of the Wnt/β-catenin pathway as described by Luke and colleagues (31) were found to be increased in adenocarcinoma samples. Strong significant correlations with the cDC1 score were observed for the cDC1 chemoattractant score, CCL4, and the β-catenin response gene score (see Fig. 4, bottom), thus confirming β-catenin activation and a lack of CCL4-mediated cDC1 recruitment to likely be at the root of hampered T-cell recruitment to the primary tumor in adenocarcinoma.

Figure 4.

Factors responsible for attraction and maintenance of cDC1 in TME. Higher transcript levels of the cDC1 chemoattractant signature score (composed of XCL1, XCL2, and CCL5) and CCL4 and lower transcript levels of the β-catenin response signature (activation) score (ALCAM, AQP4, AQP5, BMP4, CDH16, CHGA, CXCL8, EPCAM, FOXA2, HHIP, IGF2BP1, LGR5, POU3F2, SHH, SIX1, SOX11, TH, TMSB15A, TSPAN8, WIF1) in SCC samples compared with adenocarcinoma (AC). Significant positive correlations were found for the cDC1 chemoattractant score and CCL4 versus the cDC1 score. A significant negative correlation was found for the β-catenin signature score versus the cDC1 score. No significant difference for FLT3L and only a weak correlation with the cDC1 score was found between adenocarcinoma and SCC. r, Pearson correlation coefficient (***, P ≤ 0.001; ****, P ≤ 0.0001).

Figure 4.

Factors responsible for attraction and maintenance of cDC1 in TME. Higher transcript levels of the cDC1 chemoattractant signature score (composed of XCL1, XCL2, and CCL5) and CCL4 and lower transcript levels of the β-catenin response signature (activation) score (ALCAM, AQP4, AQP5, BMP4, CDH16, CHGA, CXCL8, EPCAM, FOXA2, HHIP, IGF2BP1, LGR5, POU3F2, SHH, SIX1, SOX11, TH, TMSB15A, TSPAN8, WIF1) in SCC samples compared with adenocarcinoma (AC). Significant positive correlations were found for the cDC1 chemoattractant score and CCL4 versus the cDC1 score. A significant negative correlation was found for the β-catenin signature score versus the cDC1 score. No significant difference for FLT3L and only a weak correlation with the cDC1 score was found between adenocarcinoma and SCC. r, Pearson correlation coefficient (***, P ≤ 0.001; ****, P ≤ 0.0001).

Close modal

Expression of gene signatures related to effector CD8+ T cell and cDC1 recruitment correlate with improved patient survival

Finally, we determined whether the transcriptional signatures that we identified to be connected to cDC1 and/or effector CD8+T-cell recruitment, were related to overall survival (OS) and recurrence-free survival (RFS). Indeed, higher expression of CD8+ effector T-cell and effector T-cell-attracting chemokine (CXCL9, -10, -11) signature genes was significantly associated with improved OS and RFS, respectively (Fig. 5; Supplementary Fig. S3). Also, a high cDC1 signature score was significantly associated with a better OS (Fig. 5). Finally, expression of genes involved in the attraction to or maintenance of cDC1 within the TME (as determined by the cDC1 chemoattractant score, β-catenin activation score, and FLT3L transcript levels) were also significantly associated with OS and/or RFS (Fig. 5; Supplementary Fig. 3). These data show that the prognosis of patients with cervical cancer is related to processes involved in cDC1 and CD8+ effector T-cell recruitment to the TME, underlining the importance of these immune-cell subsets in orchestrating effective antitumor immunity. Of note, adenocarcinoma numbers with available follow-up data were too low to perform separate survival analyses based on histological subtype. Nevertheless, the obvious difference in these signatures between adenocarcinoma and SCC may offer an underlying cause for the difference in prognosis associated with these two histological subtypes as well as provide new leads for the effective treatment of patients with adenocarcinoma.

Figure 5.

TCGA derived mRNAseq gene signatures and their correlation with overall survival. Prognostic value of the CD8+ effector T-cell score, CXCL9/10/11 score, cDC1 score, cDC1 chemoattractant score, and β-catenin activation score comparing top and bottom quartiles for overall survival within the cervical cancer TCGA dataset, obtained via kmplot.com.

Figure 5.

TCGA derived mRNAseq gene signatures and their correlation with overall survival. Prognostic value of the CD8+ effector T-cell score, CXCL9/10/11 score, cDC1 score, cDC1 chemoattractant score, and β-catenin activation score comparing top and bottom quartiles for overall survival within the cervical cancer TCGA dataset, obtained via kmplot.com.

Close modal

In this study, we explored immunological differences between cervical adenocarcinoma and SCC that would provide novel therapeutic targets for the treatment of adenocarcinoma. There is a clear unmet need in this regard, because patients with adenocarcinoma currently receive the same treatment regimen as patients with SCC, despite their clearly worse response to therapy. In freshly collected primary tumor and TDLN tissues, we observed that the immune microenvironment in adenocarcinoma of the cervix is less immune infiltrated compared with SCC in terms of frequencies of CD45+ cells and CD3+ T cells. Also, more evidence of immune suppression was observed in SCC with elevated levels of immune checkpoints (such as PD-1 and TIM-3) and increased rates of Tregs. cDC1, that play an important role in the orchestration of an effective immune response by both priming and attracting effector T cells, were found at lower frequencies in adenocarcinoma. Our findings indicate that different immunotherapy strategies for patients with these different histological subtypes of cervical cancer should be explored.

This is the first study to undertake an in-depth analysis of the differences in infiltrating T-cell content between the two main histologic subtypes of cervical cancer. In adenocarcinoma, lower frequencies of T cells were found. This is of importance, because the presence of CD8+ T cells in the TME has been shown to be prognostically favorable in multiple cancer types including cervical cancer and, importantly, it has also been recognized as a possible predictive biomarker for response to ICB (39–41). Interestingly, this increased immune infiltration in SCC may have actually triggered the suppressive features that we observed in this histologic subtype, that is, increased Treg rates and higher immune checkpoint expression levels on CD8+ T cells. So they may have facilitated immune escape from an ongoing antitumor response in the TME. Indeed, previous IHC-based studies showed that Tregs were more frequently present in SCC than in adenocarcinoma, which in SCC correlated with a poor survival (42). In contrast, the presence of Tregs correlated positively with survival in adenocarcinoma, presumably because it was also indicative of a higher effector T-cell infiltration rate (43). By the same token, the upregulation of HLA-DR and HLA-E on tumor cells that we previously reported in cervical adenocarcinoma, may have been induced by IFNγ, derived from any infiltrating T cells present. Consequently, these parameters may have served as surrogate markers for an ongoing effector response, which could explain their association with improved survival despite their obvious roles in immune suppression (44, 45). SCC were found to express other inhibitory receptors at higher levels as well, such as the C-type lectin MGL and PD-L1 on tumor cells and tumor-associated macrophages, consistent with the more urgent need for immune escape in order for the tumor to grow and spread. Importantly, in lung cancer and esophageal cancer, differences in the immune landscape were also reported among the histologic subtypes, with evidence for different immune escape mechanisms and hence a need for different immune interventions (15, 46, 47).

An obvious explanation for the lower T-cell content of adenocarcinoma tumors might have been a lower intrinsic immunogenicity of adenocarcinoma. However, this notion was refuted by the higher frequencies of CD8+ effector memory and central memory T cells found in the tumor-free TDLNs of patients adenocarcinoma compared with SCC TDLNs, a clear sign of T-cell priming and differentiation. Rather, we interpreted this as being indicative of impaired trafficking of differentiated T cells to the tumor. Expression of the effector T-cell–attracting chemokines CXCL9, CXCL10, and CXCL11 has been associated with increased numbers of T cells in the TME and favorable outcome in patients with melanoma or colorectal cancer (28, 48–51).Similarly, we found that in cervical cancer chemokine gene transcripts showed a positive correlation with a CD8+ effector T-cell signature for both SCC and adenocarcinoma, with higher levels significantly associating with improved OS. Noteworthy, patients with melanoma with high pretreatment CXCL9 levels had an improved response to treatment with anti-PD-L1 (52), underlining the importance of the presence of these chemokines for an effective antitumor immune response. In keeping with the lower T-cell content of adenocarcinoma tumors, they also expressed significantly lower levels of CXCL9/-10/-11 transcripts.

One of the crucial CXCL9- and CXCL10-producing immune-cell subsets in the TME are cDC1 (28). The presence of cDC1has been associated with patient survival and response to ICB across different cancer types (53–55). Indeed, using the cervical cancer TCGA mRNAseq dataset, we found a strong positive correlation between CXCL9/-10/-11 transcripts and the gene expression signature for cDC1. Furthermore, we observed higher numbers of cDC1 within SCC tumors with accordingly higher CXCL9 and CXCL10 production levels. Altogether, these data point to the low frequencies of cDC1as responsible for lower CXCL9 and CXCL10 expression levels and correspondingly lower T-cell content in adenocarcinoma. This also fits with the observation that increased CXCL9 and CXCL10 production was fueled by 24-hour R848 stimulation of SCC but not of adenocarcinoma single-cell suspensions.

As described by Spranger and colleagues (38), activation of the oncogenic WNT/β-catenin signaling pathway might prevent the production of CCL4 by tumor cells, and through that effect defective cDC1 recruitment to the tumor. In this study, gene expression analyses showed that there was a positive correlation between CCL4 and the cDC1 signature and indeed a negative correlation between the β-catenin activation score and the cDC1 transcript levels. In seeming contradiction, Luke and colleagues (31) recently reported a relatively weak association between β-catenin activation and immune exclusion in cervical cancer, but in that study no distinction was made between histologic subtypes. In our study, we found that the β-catenin response score was significantly higher in adenocarcinoma. In keeping with this observation, Noordhuis and colleagues previously reported on higher levels of membrane expressed (ie, inactive) β-catenin in SCC primary tumor (56). Vice versa, we found that CCL4 transcript levels were significantly lower in adenocarcinoma as compared with SCC. This clearly links active β-catenin signaling in adenocarcinoma with defective cDC1 and CD8+ effector T-cell recruitment and points the way to specific therapeutic intervention options for adenocarcinoma of the uterine cervix.

Response rates to ICB in patients with cervical cancer remain low. By enhancing cDC1 abundance and function, we might engender a more T-cell–inflamed TME in adenocarcinoma tumors (ie, “cold” to “hot” conversion) and possibly improve outcome of ICB with anti-PD-(L)1. This might be achieved by targeting the Wnt/β-catenin pathway, eg, by Wnt-inhibitors or by (oncolytic) viruses encoding constitutively active GSK3β (57), intratumoral injection of NK cells (29), or by the application of DC mobilizing and activating compounds like FLT3-L, CpG, or GM-CSF; refs. 58, 59). Importantly, as well-differentiated CD8+ T cells seem to be abundant in the adenocarcinoma TDLNs, targeting these TDLNs by intratumoral therapies, to leverage these T cells, would be an interesting option.

As there is a wide range in β-catenin response scores among T-cell noninflamed adenocarcinoma tumors, other oncogenic signaling pathways might as well be involved in the apparent immune exclusion and could be considered for therapeutic targeting. For instance, the JAK-2/Stat3 and PI3K signaling pathways have also been described to affect T-cell migration and exclusion (60, 61). However, PI3K mutations have been described to occur more often in SCC of the cervix than in adenocarcinoma (15).

Other chemokines responsible for the attraction of cDC1 into tumors are CCL5, XCL1 and XCL2 which are produced by NK cells and by the tumor itself (36). In this study, the transcript levels of CCL5, XCL1 and XCL2, were associated with the cDC1-specific gene signature and found to be significantly lower in adenocarcinoma. Böttcher and colleagues, described that in COX-deficient tumors, PGE2-mediated DC suppression was lifted and cDC1 were attracted to the tumor by the chemokines XCL1 and CCL5 (36). Indeed, in adenocarcinoma, higher COX-2 expression has been observed when compared with SCC (62). Therefore, COX-2 inhibition should also be considered in the treatment of cervical adenocarcinoma and could be combined with other agents acting to recruit cDC1 and/or effector T cells to the TME.

As for SCC tumors, direct activation of the tumor-associated cDC1 by (intratumorally delivered) TLR3 and/or (as applied ex vivo in this study) TLR7/8 ligands seems sufficient to trigger CXCL9 and CXCL10 secretion. Combining this effector T-cell recruitment strategy with anti-PD-L1 with anti-TIM-3 or Treg-depleting agents such as anti-CTLA-4, seems an attractive immunotherapeutic approach for the treatment of SCC.

In conclusion, we have uncovered a link between oncogenic β-catenin signaling and impaired cDC1 and CD8+ effector T-cell recruitment that offers novel and specific treatment options for adenocarcinoma of the uterine cervix. An important future direction for biomarker research would be to study β-catenin protein expression in relation to T-cell markers by IHC, as well as to study the possible role of β-catenin as a predictive biomarker upon anti-PD-1 therapy in patients with cervical cancer. Besides, it would be interesting to explore whether similar observations would hold true for the same histologic subtype across different anatomically determined cancer types.

No potential conflicts of interest were disclosed.

Conception and design: J. Rotman, A.M. Heeren, E.S. Jordanova, T.D. de Gruijl

Development of methodology: J. Rotman, A.M. Heeren, A.G.M. Stam, E.S. Jordanova, T.D. de Gruijl

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): J. Rotman, A.M. Heeren, A.A. Gassama, N. Pocorni, A.G.M. Stam, M.C.G. Bleeker, H.J.M.A.A. Zijlmans, C.H. Mom

Analysis and interpretation of data (eg, statistical analysis, biostatistics, computational analysis): J. Rotman, A.M. Heeren, A.A. Gassama, A.G.M. Stam, E.S. Jordanova, T.D. de Gruijl

Writing, review, and/or revision of the manuscript: J. Rotman, M.C.G. Bleeker, H.J.M.A.A. Zijlmans, C.H. Mom, G.G. Kenter, E.S. Jordanova, T.D. de Gruijl

Administrative, technical, or material support (ie, reporting or organizing data, constructing databases): J. Rotman, A.M. Heeren, S.M. Lougheed

Study supervision: G.G. Kenter, E.S. Jordanova, T.D. de Gruijl

The authors wish to thank Dr. Sanne Samuels for patient inclusion, all the patients who participated in this study and the staff of the CGOA, and pathology departments of AMC and AvL (both in Amsterdam, the Netherlands) for providing lymph node and primary tumor samples. This work was supported by research grants from the Dutch Cancer Society (KWF VU 2013-6015), Stichting VUmc-CCA (CCA2015-306) and the Louise Vehmeijer Foundation. Dr. Ekaterina S. Jordanova was directly supported by these grants.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

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