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17 July 2024: Clinical Research  

Prognostic Impact of Tumor-Infiltrating Immune Cells on Efficacy of Neoadjuvant Chemotherapy in Patients with Advanced or Metastatic Ovarian Cancer: A Retrospective Study

Qunxian Rao1BCE, Miaoling Huang1B, Meimei Guan1C, Changhao Liu1D, Lijuan Wang1D, Zhongqiu Lin1A*, Qing Chen1A

DOI: 10.12659/MSM.943170

Med Sci Monit 2024; 30:e943170

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Abstract

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BACKGROUND: Tumor-infiltrating immune cells (TIICs) are implicated in the survival of ovarian cancer (OVCA) patients, but their prognostic significance in advanced or metastatic OVCA patients treated with neoadjuvant chemotherapy (NCAT) has not been well documented, particularly in the Chinese population.

MATERIAL AND METHODS: A total of 31 advanced or metastatic OVCA patients who underwent NACT were included. The density and positive rate of tumor-infiltrating immune cells (TIICs) within cancer cell nests and in cancer stroma were explored. The correlations of pre- or post-NACT TIICs with the efficacy of NACT and the changes in TIIC subpopulation with NACT were examined.

RESULTS: Compared with patients with partial benefit from NACT, significantly decreased pre-NACT intratumoral CD68⁺CD163⁺ cells (P=0.0043) and increased pre-NACT intratumoral CD56⁺ cells (P=0.038) were observed in patients with benefit. The high level of pre-NACT intratumoral CD68⁺CD163⁻ M1 macrophage (P=0.075) and stromal CD3⁺PD-1⁺ cells (P=0.085) predicated improved progression-free survival, respectively. Increased post-NACT stromal CD68⁺CD163⁻ M1 macrophage (P=0.01), stromal CD8⁺ T cells (P=0.073), and stromal CD8⁺PD-1⁺ cells (P=0.072) were associated with benefit from NACT. Moreover, NACT increased intratumoral CD3⁺ (P=0.031), CD8+ (P=0.031), and CD3⁺CD8⁺ cells (P=0.031).

CONCLUSIONS: High intratumoral CD68⁺CD163⁻, intratumoral CD56⁺ cells, and stromal CD3⁺PD-1⁺ cells pre-NACT predicted good prognosis. Intratumoral CD3⁺, CD8⁺, and CD3⁺CD8⁺ cells were increased after NACT. Evaluation of immune profiles may help to identify patients who might benefit from NACT and allow us to further stratify advanced or metastatic OVCA patients treated with NACT for disease management.

Keywords: Ovarian Neoplasms, Prognosis, Neoadjuvant Therapy, tumor microenvironment, Humans, Female, Middle Aged, Retrospective Studies, Lymphocytes, Tumor-Infiltrating, adult, Aged, Antigens, CD, Antigens, Differentiation, Myelomonocytic, Neoplasm Metastasis, Treatment Outcome, China, Receptors, Cell Surface, CD163 Antigen

Introduction

Ovarian cancer (OVCA) is a heterogeneous group of neoplasms that are classified into 2 primary groups: type I and type II, each following distinct tumorigenic pathways [1–3]. Type I epithelial OVCA is generally indolent and genetically stable, often arising from recognizable precursor lesions, such as endometriosis or borderline tumors with low malignant potential. Conversely, type II epithelial OVCA is biologically aggressive tumors from the outset, with a propensity for metastasis from small-volume primary lesions. High-grade serous OVCA, the most common subtype of epithelial OVCA, accounting for approximately 75% of cases, develops according to the type II pathway and is characterized by TP53 and BRCA1/2 mutations.

Detecting OVCA in its early stages is challenging due to its deep pelvic cavity location with nonspecific symptoms [4]. Research is ongoing to identify economical and cost-effective strategies for early detection and prevention of OVCA. However, early screening for OVCAs has not been implemented for the general population [5]. OVCAs can arise inheritably or sporadically. By the time 70% of patients seek medical attention, the cancer is in the advanced stage, and 70% of these patients do not survive beyond 5 years [6,7]. Among advanced-stage OVCA patients, BRCA1/2 germline mutations are the strongest known genetic risk factors for epithelial OVCAs and are found in 6–15% of women. The BRCA1/2 status can inform patient counselling regarding expected survival, as BRCA1/2 carriers with epithelial OVCAs respond better than non-carriers to platinum-based chemotherapies [8]. BRCA1/2-altered OVCA have longer survival, despite often being diagnosed at a later stage and at higher grades [8].

The standard treatment for advanced OVCA is optimal primary debulking surgery (PDS) with complete resection of all macroscopic diseases and platinum/paclitaxel-based adjuvant chemotherapy [9]. However, in patients with advanced disease where complete resection cannot be achieved, neoadjuvant chemotherapy (NACT) followed by interval debulking surgery (IDS) is a suitable alternative, and has been shown to be associated with improved prognosis and reduced mortality rates [7].

The tumor microenvironment (TME) is a coordinated network of interface cell types, including immune cells, fibroblasts, and mesenchymal stem cells through the cytokines, extracellular matrix, chemokines, growth factors, and various metabolites, that influence the development and progression of cancer [10,11]. Increasing evidence suggests that tumor-infiltrating immune cells (TIICs) play a crucial role in host immune response against tumor growth and have prognostic implications in OVCA [12–16]. Specifically, CD8+ T cell infiltration into tumors, serving as a marker of immune recognition, predicts a favorable prognosis for patients with OVCA. CD8+ T cells eradicate tumor cells by secreting TNF, IFNγ, and granzyme B [17]. Additionally, accumulation of CD3+ cells is associated with a favorable prognosis, while an increase in regulatory T cells is linked to a poor outcome [18,19]. Several studies have examined the impact of NACT on the TME, but most studies have focused on Western populations [18–21]. Currently, there is limited understanding of the prognostic roles of immune subpopulations in Chinese OVCA patients receiving NACT.

In this study, we aimed to explore the impact of NACT on tumor-infiltrating immune cells by characterizing changes in immune subpopulations. Furthermore, we investigated the prognostic significance of these immune subpopulations in Chinese patients with advanced OVCA.

Material and Methods

PATIENTS:

Between December 2017 and June 2021, OVCA patients who met following criteria were included: (1) underwent NACT followed by IDS at Sun Yat-sen Memorial Hospital; (2) response to NACT that had been evaluated; (3) had sufficient tissue samples for tumor microenvironment analysis; (4) with clinical characteristics and survival recorded, including age, sex, histological subtype, stage, family history of cancer, history of other cancers, treatment, and disease-free survival. Patients with unrecorded NACT regimen were excluded. Tumor staging was evaluated using the FIGO staging system [22,23]. This study was approved by the Ethics Committee of Sun Yat-sen Memorial Hospital. Informed consent was obtained from each patient for the use of their tumor tissue samples.

Tumor load before and after NACT was evaluated as a laparoscopic value (predictive index value [PIV]) by summing the scores relative to all clinicopathological characteristics. A cutoff value of 8 is a limit for optimal cytoreduction. PIV ≥8 indicated a tumor with a high tumor load unsuitable for PDS [24,25]. All included women were ineligible for PDS due to their significant tumor burden on imaging. Benefit from NACT was defined as at least a 50% reduction in PIV from a pre-NACT sample or PIV decreased to 0 after NACT. Partial benefit from NACT was defined as a 0–50% reduction in PIV. Patients with increased or unchanged PIV were considered to have no benefit from NACT.

MULTIPLEX IMMUNOFLUORESCENCE ASSAYS:

Multiplex immunofluorescence (mIF) assays were performed for serial formalin-fixed paraffin-embedded (FFPE) slides to visualize TIICs using PANO 7-plex kit (cat. 0004100100, Panovue, Beijing, China). Primary antibodies against cell differentiation CD3, CD8, CD56, CD68, CD163, programmed cell death ligand-1 (PD-L1), and programmed cell death-1 (PD-1) were sequentially applied, followed by horseradish peroxidase-conjugated secondary antibody incubation and tyramide signal amplification. Two FFPE sections from the same tissue sample were performed for mIF assays. One section was stained with PD-1 (green), PD-L1 (yellow), CD3 (indigo), CD8 (red), panCK (purple), and DAPI (blue). Another section was stained with CD163 (red), CD68 (green), CD56 (yellow), pan-cytokeratin (CK) (purple), and DAPI (blue) [26–28]. CD3, CD4, and CD8 are the markers of T cells, helper T cells, and cytotoxic T cells, respectively. CD56 is the marker of natural killer cells. CD68 and CD163 are markers of macrophages.

We used the epithelial cell marker cytokeratin (CK+) to differentiate the tumor parenchyma from the stroma. CK+ area is considered as tumor nests. CK− areas in contact with tumor cells are regarded as stromal areas. Stromal TIICs were defined as immune cells in stroma, while intra-epithelial TIICs were defined as immune cells in tumor nests [20,29].

IMAGE ANALYSIS AND QUANTIFICATION OF TIICS:

The slides stained with mIF assays were scanned using a Mantra System (PerkinElmer, Waltham, MA). For each slide, Mantra automatically captured the fluorescence spectra from 420 to 720 nm at 20 nm wavelength intervals with the same exposure time [30]. Next, the scans were combined with the captured images to build a single-stack image that preserved the spectral signature of all IF markers. We first obtained a low-magnification scan (×4, ×10). Then, we randomly chose 10 regions of interest (ROI) of stomal and tumor nest areas using the OlyVIA viewer, and each ROI covered an area of 0.69×0.92 mm. Subsequently, those ROIs were scanned and acquired at a higher resolution (×20) and processed using InForm image analysis software (version 2.4, PerkinElmer). The software extracted positive cells and negative cells and counted them automatically. The density and positive rate of various cell phenotypes were calculated by the average of data in 10 ROIs. The positive rate was determined by the proportion of positive cells to total cells, and the density was determined by the proportion of positive cells to tissue area (mm2) in the tumor or stroma compartment.

STATISTICS ANALYSIS:

Progression-free survival (PFS) was defined as the interval from the date of the last NACT to the date of recurrence or death. Patients without progressive disease were censored at the time of the last follow-up visit. The median density of a certain TIIC was used as the cutoff to divide patients into 2 groups. Kaplan-Meier curves and log-rank tests were used to describe the correlations of TIICs with survival outcomes. Differences between the 2 groups were assessed by Wilcoxon signed-rank test for continuous variables. P value <0.05 was considered statistically significant. Univariate and multivariate analysis were performed to explore the correlations of pre-NACT TIICs with clinical outcome using Cox proportional-hazards regression model. Hazard ratios (HR) with corresponding 95% confidence intervals (CI) were calculated using multivariate Cox proportional-hazards regression model. P1 represents the unadjusted P value with univariate analysis, and P2 represents the P value after adjusting for combination with other tumors with multivariate analysis. All statistical analyses were performed using GraphPad Prism v.8.0 and R 4.0.0 (https://www.r-project.org/).

Results

CLINICOPATHOLOGICAL CHARACTERISTICS:

A total of 31 OVCA patients were included, with a median age of 58 years (range: 52–64 years). Two (6.5%, 2/31) patients were diagnosed at stage IIIB, 21 (67.7%, 21/31) at stage IIIC, and 8 (25.8%, 8/31) at stage IV (Table 1). One patient had low-grade serous OVCA, and the remaining 30 patients had high-grade serous OVCA. The immunohistochemistry staining images of the low-grade patient and a representative high-grade patient are summarized in Supplementary Figure 1. Five patients (16.1%, 5/31) had a family history of cancer. Of 9 patients who had a history of other tumors (29.0%, 9/31), 6 patients had uterine fibroids, 1 patient had fibroadenoma of the breast, 1 patient with breast cancer had disease-specific survival of more than 13 years after receiving radical surgery, and 1 patient had breast cancer ever. All patients received docetaxel/albumin-bound paclitaxel/paclitaxel (T) combined with cisplatin/carboplatin (weekly) as NACT regimens, including 3 treated with T plus cisplatin, 14 with T plus carboplatin, and 14 patients with T plus cisplatin following T plus carboplatin. All patients received ≥3 weeks of NACT (considered as a treatment cycle) before IDS, except for 1 patient who underwent progressive disease after receiving 1 weekly TP following resection. Among those patients with ≥3 weeks of NACT (n=30), 24 (77.4%, 24/31) and 6 patients (19.4%, 6/31) obtained benefit and partial benefit from NACT treatment, respectively. Eleven patients received granulocyte-colony stimulating factor (G-CSF) agents during the courses of NACT. The clinicopathological characteristics of OVCA patients are summarized in Table 1. In addition, a total of 13 patients had recurrence. The median PFS was 18.4 months (Supplementary Table 1).

TUMOR IMMUNE MICROENVIRONMENT BEFORE NACT:

Among 31 OVCA patients, 29 pre-NACT samples were obtained at laparoscopic diagnosis and 8 post-NACT samples were obtained at IDS for further analysis. mIF analysis was performed to visualize TIICs in archived pre-NACT FFPE tissues from 29 patients. Representative mIF images of the pre-NACT sample in patients with benefit (Figure 1A) and partial benefit (Figure 1B) are described. In this study, intratumoral CD3+ T cells, stromal CD3+ T cells, intratumoral CD8+ T cells, stromal CD8+ T cells, intratumoral CD68+CD163+ cells (M2 macrophages), and stromal M2 macrophages were detected in all tumor samples at diagnosis. The associations of pre-NACT TIICs with response to NACT were investigated. We found that intratumoral CD56+ cells showed a higher density (P=0.038, Figure 2A) and positive rate (P=0.036, Figure 2A) in patients with benefit. Moreover, patients with benefit from NACT displayed significantly lower densities of intratumoral CD68+CD163+ cells (M2-like macrophage, P=0.0043, Figure 2B) and stromal CD68+CD163+ cells (P=0.0019, Figure 2C). In addition, a lower positive rate of intratumoral CD68+CD163+ cells (P=0.006, Figure 2B) and stromal CD68+CD163+ cells (P=0.002, Figure 2C) were also identified in patients with benefit.

Next, the correlations of pre-NACT TIICs with clinical outcome were investigated using univariate and multivariate Cox regression analyses. Patients were divided into high and low groups according to the median density/positive rate of a certain type of pre-NACT TIICs. Univariate Cox regression analysis showed that patients with a history of other cancer had a significantly shorter PFS than those without it (median PFS 10.4 vs 21.0 months, P=0.028, Figure 3). A higher density of intratumoral CD68+CD163− cells (P1=0.038, Figure 4A), stromal PD-1+ cells (P1=0.035, Figure 4B), stromal CD3+PD-1+ cells (P1=0.035, Figure 4C), and a higher positive rate of stromal CD3+PD-1+ cells (P1=0.035, Figure 4D) was significantly associated with a longer PFS, respectively. Moreover, age, family history of cancer, and the use of G-CSF were not associated with PFS. Upon multivariate analysis, after adjusting for combination with other tumor, a higher density of intratumoral CD68+CD163− cells (HR=0.221, confidential interval [CI]: 0.042–1.164, P2=0.075, 25.9 vs 17.7 months, Figure 4A), stromal CD3+PD-1+ cells, stromal PD-1+ cells, and a higher positive rate of stromal CD3+PD-1+ cells (all for HR=0.288, CI: 0.070–1.188, P2=0.085, 22.8 vs 14.5 months, Figure 4B–4D) was associated with a marginally significantly longer PFS, respectively. Collectively, a high level of CD68+CD163− predicted a better prognosis.

TUMOR IMMUNE MICROENVIRONMENT AFTER NACT:

Eight patients provided tumor tissues at IDS, including 6 patients having a benefit from NACT, 1 patient having a partial benefit, and 1 patient having no benefit from NACT. Due to the limited number of cases, the patients with no benefit and with partial benefit were grouped together as partial/no benefit for preliminarily exploring the association of post-NACT TIICs with response to NACT. Representative mIF images of the post-NACT sample in patients with benefit (Figure 1A) and partial benefit are described in Figure 1B. We found that post-NACT iTIICs was not associated with the response to NACT. Post-NACT CD68+CD163− sTIICs (P=0.01, Figure 5A) showed a significantly higher density in patients with benefit than those with partial/no benefit. Moreover, a higher density of post-NACT stromal CD8+ cells (P=0.073, Figure 5B) and stromal CD8+PD-1+ cells (P=0.072, Figure 5C), and a higher positive rate of intratumoral CD3+PD-1+ cells (P=0.061, Figure 5D) displayed a marginally significant association with benefit from NACT. These findings indicated that more stomal CD68+CD163− cells and stromal CD8+ cells were recruited in patients with benefit after NACT.

CHANGES IN TIIC SUBSETS WITH NACT:

In this work, 6 patients had available matched tumor tissues obtained at diagnosis and IDS. Of whom, 1 patient derived partial benefit from NACT and 5 patients derived benefit. Changes in TIIC subsets with NACT in these 6 patients were preliminary explored. Overall, intratumoral CD3+ cells (P=0.031 for Figure 6A), and intratumoral CD3+CD8+ cells (P=0.031 for both Figure 6C) increased significantly after NACT in density and positive rate. Moreover, higher density (P=0.031, Figure 6B) and a trend of higher positive rate of intratumoral CD8+ cells (P=0.063, Figure 6B) in patients with benefit. There was a trend of higher density of intratumoral CD3+PD-1+ cells (P=0.093, Figure 6D) in patients with benefit, while its positive rate was similar between the 2 groups (P=0.16, Figure 6D). These findings suggest that NACT in advanced OVCA leads to immune activation. Further studies are warranted to explore the prognostic roles of intratumoral CD8+ cells, intratumoral CD3+ cells, and intratumoral CD3+CD8+ cells for NACT in advanced OVCA.

Discussion

Given that an increasing number of patients with advanced or metastatic OVCA are treated with NACT, several studies have evaluated the change in immune profile with NACT based on Western populations [18,20,21], but the prognostic roles of immune subpopulations in Chinese OVCA patients who received NACT have not been documented. To address this, the associations of TIIC subpopulations at initial diagnosis with the efficacy of NACT were investigated. In this study, the high level of pre-NACT intratumoral CD68+CD163− cells, intratumoral CD56+ cells, and intratumoral CD3+ PD-1+ cells predicated improved outcome. In addition, NACT increased intratumoral CD3+ cells, intratumoral CD8+ cells, and intratumoral CD3+CD8+ cells.

We showed that neither intratumoral CD3+ T cells nor intratumoral CD8+ T cells at diagnosis or post-NACT were correlated with the efficacy of NACT/PFS. These results were consistent with a recent study indicating that CD3+, CD8+, and CD4+ expression at diagnosis or after NACT are not correlated with outcome [20], but the results disagree with those studies reporting that the density of pre-treatment CD3+ and CD8+ T cells is strongly associated with a better prognosis [15,16]. These inconsistent findings may be attributed to several factors. First, methods for assessing TIICs were different across studies, including IHC and mIF, which might result in interobserver variabilities and variation among different antibody sensitivities and specificities. Second, different end-points of survival were used across studies, including PFS, overall survival, and disease-specific survival.

Macrophages are essential members of the innate immune response, which are significant components among TME, due to their diversity of functions that influence the immune response against tumor cells [31,32]. M1-like macrophages inhibit tumor cell proliferation and promote immune cell infiltration into TME through the secretion of pro-inflammatory cytokines, including IL-1β, IFN-γ, and TNF-β [33,34]. In contrast, M2-like macrophages dampen the immune response and exhibit powerful tumor-promoting functions, including degradation of the destruction of the basement membrane, tumor extracellular matrix, recruitment of immunosuppressor cells, and promotion of angiogenesis, by secreting anti-inflammatory factors TGF-β, IL-10, and IL-1RA [33,34]. In this work, patients with partial benefit had more infiltration with intratumoral CD68+CD163+ macrophages and stromal CD68+CD163+ macrophages at diagnosis, and less infiltration with stromal CD68+CD163− macrophages (M1-like macrophage) after NACT than those with benefit from NACT, and high level of intratumoral CD68+CD163− cells at diagnosis predicted a better prognosis. These results were consistent with prior studies indicating that a high level of CD163+ in epithelial OVCA samples at diagnosis predicts poor prognosis [28,35].

NK cells are innate cytotoxic lymphocytes involved in the surveillance and elimination of cancer [36]. CD56, a marker for NK cells, was used to evaluate NK cells. In this work, increased intratumoral CD56+ cells at diagnosis were observed in patients with benefit from NACT and associated with a favorable DFS. A previous study has revealed that increased NK cell infiltration after NACT was detected in high-grade serous OVCA [37], while a comparable level of NK cells at diagnosis and after NACT was detected in this study.

In this work, we found that high levels of stromal PD-1+ cells, stromal CD3+ PD-1+ cells, and stromal CD8+ PD-1+ cells at diagnosis predicted a favorable outcome in patients. Similar results have been documented in a previous study indicating the positive prognostic implication of PD-1+ tumor-infiltrating lymphocytes in OVCA [38]. These findings are in line with the role of PD-1 as an indicator of T cell activation that PD-1 is expressed on the surface membrane of activated T cells and involved in immunomodulation to prevent autoimmune reactions. To a certain degree, a high level of PD-1+ tumor-infiltrating lymphocytes suggest a strong immune response to OVCA cells. However, unfavorable prognostic implications of PD-1+ tumor-infiltrating lymphocytes in human carcinomas have been reported [39,40]. The conflicting findings might be attributed to the complex interaction of immune effector cells within the TME that impacts the biological significance of particular immune markers.

Significant increases were shown in intratumoral CD3+ cells, intratumoral CD8+ cells, and intratumoral CD3+CD8+ cells after NACT. This is in line with prior studies indicating increased CD3+, CD8+, and CD68+ cells after NACT [20,41]. In addition, increased intratumoral CD3+PD-1+ T cells after NACT were observed. PD-1 (also known as CD279) is a co-inhibitory receptor that is inducibly expressed on T cells upon activation. Increased intratumoral CD3+PD-1+ cells indicate the augment of activation of T cells. These findings suggest that NACT can induce immune activation to destroy OVCA. From a clinical point of view, these results suggest that immune checkpoint inhibitors (ICI) combined with chemotherapy may have synergistic effects to improve the survival outcome in OVCA. Clinical trials on the combination of ICI treatment with platinum-based chemotherapy in NACT are currently ongoing [42].

Although chemotherapy is the main treatment for NACT, targeted agents are being developed or in are clinical testing, such as PI3K/AKT/mTOR inhibitors. The PI3K pathway is frequently upregulated in epithelial OVCA and plays an important role in chemoresistance and preservation of genomic stability, as it is implicated in cell proliferation, growth, cell size, metabolism, motility [43]. PI3K/AKT/mTOR pathway inhibitors are currently under development or already in clinical testing, such as ipatasertib (NCT04561817), TOS-358 (NCT05683418), and BKM120 (NCT01833169). It is reported that BRCA mutated or homologous recombination (HR) deficient OVCAs harbor higher levels of neoantigens, producing more effective anti-tumor immune response [44]. Preclinical data demonstrate that poly(ADP-ribose) polymerase (PARP) inhibitors have synergistic activity when combined with immune checkpoint inhibitors (ICIs) [44–46]. The efficacy of PARPi combined with ICIs in BRCA1/2-altered OC is being assessed in clinical testing.

There are some limitations associated with our study. A small sample size may lead to insufficient statistical power and limit the generalizability of findings to populations. Increasing the sample size can improve the reliability and stability of research results, reduce random errors, and provide a more accurate representation of the characteristics of the entire target population. In future studies, it is advisable to consider expanding the sample size to enhance the credibility and practicality of the research. First, due to the small post-NACT sample size, the correlation between post-NACT TIICs/change in TIICs and survival was not investigated. In addition, the same P value was obtained between different compared groups due to the same subsets of patients among different compared groups. Briefly, patients with a high density of pre-NACT stromal PD-1+ cells also have a high density of pre-NACT stromal CD3+PD-l+ cells and a high positive rate of pre-NACT stromal CD3+ PD-1+ cells. A large-cohort study is warranted to explore the prognostic value of pre-NACT TIICs/post-NACT TIICs/changes in TIICs in advanced OVCA patients. Second, due to the retrospective nature of this study, the status of alterations in BRCA1/2 was unavailable in most patients and different NACT regimens were administered for patients. The impact of BRAC1/2 alterations on TIICs and the prognostic significance of BRCA1/2 combined with pre- or post-NACT TIICs in OVCA should be further explored. In addition, the variability of the NACT regimens might have biased our conclusions. Third, further studies are warranted to investigate the association between anti-tumor (such as CD3+ T cells, CD8+ T cells, M1-like macrophage) versus pro-tumor immune cells (such as FOXP3+ T cells, M2-like macrophage) with survival outcome in patients with advanced OVCA treated with NACT. Fourth, the changes of immune cells in different cycles were not investigated, since it is challenging to obtain sufficient tissue samples from advanced OVCA patients after each cycle of NACT treatment. In addition, previous studies demonstrate that a large number of regulatory T (Treg) cells infiltrate into OVCA, and decreased ratios of tumor-infiltrating CD8+ T cells to FOXP3+ Treg cells correlate with poor prognosis in OVCA patients [16,47]. However, the distribution of FOXP3+ Treg cells and their correlation with prognosis were not explored in this work. Further work is needed to investigate the prognostic value of the ratio of CD8+ T cells to Treg cells due to FOXP3+ Treg cells being a crucial determinant of prognosis.

Conclusions

Evaluation of immune profiles may help to identify patients who can benefit from NACT and allow us to further stratify advanced or metastatic OVCA patients treated with NACT for disease management. Furthermore, NACT could induce immune activation to destroy OVCA, suggesting that it is crucial to establishing the synergistic effect of platinum-based chemotherapy and immunotherapy in advanced or metastatic OVCA treatment.

Figures

Representative mIF images of pre- and post-NACT samples in patients with benefit or partial benefit.(A) Representative mIF images in patients with benefit; (B) representative mIF images in patients with part benefit. Magnification: ×200. Panoramic (PANO)-1 images show the staining of PD-1 (green), PD-L1 (yellow), CD3 (indigo), CD8 (red), panCK (purple), and DAPI (blue). PANO-2 images show the staining of CD163 (red), CD68 (green), and CD56 (yellow), panCK (purple), and DAPI (blue). mIF – multiplex immunofluorescence; NACT – neoadjuvant chemotherapy. Figure 1 was created by Microsoft PowerPoint software (www.microsoft.com).Figure 1. Representative mIF images of pre- and post-NACT samples in patients with benefit or partial benefit.(A) Representative mIF images in patients with benefit; (B) representative mIF images in patients with part benefit. Magnification: ×200. Panoramic (PANO)-1 images show the staining of PD-1 (green), PD-L1 (yellow), CD3 (indigo), CD8 (red), panCK (purple), and DAPI (blue). PANO-2 images show the staining of CD163 (red), CD68 (green), and CD56 (yellow), panCK (purple), and DAPI (blue). mIF – multiplex immunofluorescence; NACT – neoadjuvant chemotherapy. Figure 1 was created by Microsoft PowerPoint software (www.microsoft.com). The associations of TIL subsets at diagnosis with the efficacy of NACT in advanced OVCA patients.(A) The association of density or positive rate of intratumoral CD56+ cells at diagnosis with the efficacy of NACT; (B) The association of density or positive rate of intratumoral M2-like macrophages (CD68+ CD168+) at diagnosis with the efficacy of NACT; (C) The association of density or positive rate of stromal M2-like macrophages (CD68+ CD168+) at diagnosis with the efficacy of NACT. NACT – neoadjuvant chemotherapy. Figure 2 was plotted by R version 4.0.0 (https://www.r-project.org/).Figure 2. The associations of TIL subsets at diagnosis with the efficacy of NACT in advanced OVCA patients.(A) The association of density or positive rate of intratumoral CD56+ cells at diagnosis with the efficacy of NACT; (B) The association of density or positive rate of intratumoral M2-like macrophages (CD68+ CD168+) at diagnosis with the efficacy of NACT; (C) The association of density or positive rate of stromal M2-like macrophages (CD68+ CD168+) at diagnosis with the efficacy of NACT. NACT – neoadjuvant chemotherapy. Figure 2 was plotted by R version 4.0.0 (https://www.r-project.org/). Kaplan-Meier analysis of PFS by the history of other cancer.PFS, progression-free survival. Figure 3 was plotted by GraphPad Prims version 8 (https://www.graphpad.com/).Figure 3. Kaplan-Meier analysis of PFS by the history of other cancer.PFS, progression-free survival. Figure 3 was plotted by GraphPad Prims version 8 (https://www.graphpad.com/). Kaplan-Meier analysis of PFS by the density of intratumoral CD68+CD163− cells (A), stromal PD-1+ cells (B), stromal CD3+PD-1+ cells (C), and the positive rate of stromal CD3+PD-1+ cells (D). NACT – neoadjuvant chemotherapy; PFS – progression-free survival. Figure 4 was plotted by GraphPad Prims version 8 (https://www.graphpad.com/).Figure 4. Kaplan-Meier analysis of PFS by the density of intratumoral CD68+CD163− cells (A), stromal PD-1+ cells (B), stromal CD3+PD-1+ cells (C), and the positive rate of stromal CD3+PD-1+ cells (D). NACT – neoadjuvant chemotherapy; PFS – progression-free survival. Figure 4 was plotted by GraphPad Prims version 8 (https://www.graphpad.com/). The difference of TIIC subsets post-NACT between patients with benefit and those with no/part benefit(A) The difference of stromal CD68+CD163+ cells post-NACT; (B) The difference of stromal CD8+ cells; (C) The difference of stromal CD8+PD-1+ cells; (D) The difference of intratumoral CD3+PD-1+ cells. TIIC – tumor-infiltration immune cells; NACT – neoadjuvant chemotherapy. Figure 5 was plotted by R version 4.0.0 (https://www.r-project.org/).Figure 5. The difference of TIIC subsets post-NACT between patients with benefit and those with no/part benefit(A) The difference of stromal CD68+CD163+ cells post-NACT; (B) The difference of stromal CD8+ cells; (C) The difference of stromal CD8+PD-1+ cells; (D) The difference of intratumoral CD3+PD-1+ cells. TIIC – tumor-infiltration immune cells; NACT – neoadjuvant chemotherapy. Figure 5 was plotted by R version 4.0.0 (https://www.r-project.org/). Changes in TIIC subsets with NACT.Changes in the density and positive rate of intratumoral CD3+ cells (A), intratumoral CD8+ cells (B), intratumoral CD3+CD8+ cells (C), intratumoral CD3+PD-1+ cells (D). TIIC – tumor-infiltration immune cells; NACT – neoadjuvant chemotherapy. Figure 6 was plotted by R version 4.0.0 (https://www.r-project.org/)Figure 6. Changes in TIIC subsets with NACT.Changes in the density and positive rate of intratumoral CD3+ cells (A), intratumoral CD8+ cells (B), intratumoral CD3+CD8+ cells (C), intratumoral CD3+PD-1+ cells (D). TIIC – tumor-infiltration immune cells; NACT – neoadjuvant chemotherapy. Figure 6 was plotted by R version 4.0.0 (https://www.r-project.org/)

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Figures

Figure 1. Representative mIF images of pre- and post-NACT samples in patients with benefit or partial benefit.(A) Representative mIF images in patients with benefit; (B) representative mIF images in patients with part benefit. Magnification: ×200. Panoramic (PANO)-1 images show the staining of PD-1 (green), PD-L1 (yellow), CD3 (indigo), CD8 (red), panCK (purple), and DAPI (blue). PANO-2 images show the staining of CD163 (red), CD68 (green), and CD56 (yellow), panCK (purple), and DAPI (blue). mIF – multiplex immunofluorescence; NACT – neoadjuvant chemotherapy. Figure 1 was created by Microsoft PowerPoint software (www.microsoft.com).Figure 2. The associations of TIL subsets at diagnosis with the efficacy of NACT in advanced OVCA patients.(A) The association of density or positive rate of intratumoral CD56+ cells at diagnosis with the efficacy of NACT; (B) The association of density or positive rate of intratumoral M2-like macrophages (CD68+ CD168+) at diagnosis with the efficacy of NACT; (C) The association of density or positive rate of stromal M2-like macrophages (CD68+ CD168+) at diagnosis with the efficacy of NACT. NACT – neoadjuvant chemotherapy. Figure 2 was plotted by R version 4.0.0 (https://www.r-project.org/).Figure 3. Kaplan-Meier analysis of PFS by the history of other cancer.PFS, progression-free survival. Figure 3 was plotted by GraphPad Prims version 8 (https://www.graphpad.com/).Figure 4. Kaplan-Meier analysis of PFS by the density of intratumoral CD68+CD163− cells (A), stromal PD-1+ cells (B), stromal CD3+PD-1+ cells (C), and the positive rate of stromal CD3+PD-1+ cells (D). NACT – neoadjuvant chemotherapy; PFS – progression-free survival. Figure 4 was plotted by GraphPad Prims version 8 (https://www.graphpad.com/).Figure 5. The difference of TIIC subsets post-NACT between patients with benefit and those with no/part benefit(A) The difference of stromal CD68+CD163+ cells post-NACT; (B) The difference of stromal CD8+ cells; (C) The difference of stromal CD8+PD-1+ cells; (D) The difference of intratumoral CD3+PD-1+ cells. TIIC – tumor-infiltration immune cells; NACT – neoadjuvant chemotherapy. Figure 5 was plotted by R version 4.0.0 (https://www.r-project.org/).Figure 6. Changes in TIIC subsets with NACT.Changes in the density and positive rate of intratumoral CD3+ cells (A), intratumoral CD8+ cells (B), intratumoral CD3+CD8+ cells (C), intratumoral CD3+PD-1+ cells (D). TIIC – tumor-infiltration immune cells; NACT – neoadjuvant chemotherapy. Figure 6 was plotted by R version 4.0.0 (https://www.r-project.org/)

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