Next Article in Journal
The Use of Low-Dose Methadone as Add-On to Ongoing Opioid Treatment in Palliative Cancer Care—An Underrated Treatment?
Next Article in Special Issue
In-Silico Analysis of Deleterious SNPs of FGF4 Gene and Their Impacts on Protein Structure, Function and Bladder Cancer Prognosis
Previous Article in Journal
Herbal Medicines against Hydatid Disease: A Systematic Review (2000–2021)
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Life
Volume 12
Issue 5
10.3390/life12050678
life-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Detection of Specific Immune Cell Subpopulation Changes Associated with Systemic Immune Inflammation–Index Level in Germ Cell Tumors

by
Katarina Kalavska
1,2,†,
Zuzana Sestakova
3,4,†,
Andrea Mlcakova
5,
Paulina Gronesova
6,
Viera Miskovska
7,
Katarina Rejlekova
8,9,
Daniela Svetlovska
1,
Zuzana Sycova-Mila
9,
Jana Obertova
8,9,
Patrik Palacka
8,9,
Jozef Mardiak
8,9,
Miroslav Chovanec
3,
Michal Chovanec
8,9 and
Michal Mego
1,3,8,9,*
1
Translational Research Unit, Faculty of Medicine, Comenius University, 833 10 Bratislava, Slovakia
2
Department of Molecular Oncology, Cancer Research Institute, Biomedical Research Center, Slovak Academy Sciences, 814 39 Bratislava, Slovakia
3
Department of Genetics, Cancer Research Institute, Biomedical Research Center, Slovak Academy Sciences, 814 39 Bratislava, Slovakia
4
Department of Laboratory Medicine, National Institute of Children’s Diseases, 831 01 Bratislava, Slovakia
5
Department of Hematology, National Cancer Institute, 833 10 Bratislava, Slovakia
6
Department of Tumor Immunology, Cancer Research Institute, Biomedical Research Center, Slovak Academy Sciences, 814 39 Bratislava, Slovakia
7
1st Department of Oncology, Faculty of Medicine, St. Elisabeth Cancer Institute, Comenius University, 812 50 Bratislava, Slovakia
8
2nd Department of Oncology, Faculty of Medicine, National Cancer Institute, Comenius University, 833 10 Bratislava, Slovakia
9
Department of Oncology, National Cancer Institute, 833 10 Bratislava, Slovakia
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Life 2022, 12(5), 678; https://doi.org/10.3390/life12050678
Submission received: 1 April 2022 / Revised: 27 April 2022 / Accepted: 29 April 2022 / Published: 2 May 2022
(This article belongs to the Special Issue Tumor Microenvironment of Urogenital Cancers: Looking for Novel Biomarkers and Treatments)
Browse Figure
Versions Notes

Abstract

:
The tumor microenvironment (TME) and the host inflammatory response are closely interconnected. The interplay between systemic inflammation and the local immune response may influence tumor development and progression in various types of cancer. The systemic immune–inflammation index (SII) represents a prognostic marker for germ cell tumors (GCTs). The aim of the present study was to detect specific immune cell subpopulation changes which were associated with the SII level in chemotherapy-naïve GCT patients. In total, 51 GCT patients, prior to cisplatin-based chemotherapy, were included in the present study. Immunophenotyping of peripheral blood leukocyte subpopulations was performed using flow cytometry. The SII level was correlated with the percentage of various leukocyte subpopulations. The obtained results demonstrated that SII levels above the cut-off value of SII ≥ 1003 were associated with higher neutrophil percentages. An inverse correlation was found between the SII and the peripheral lymphocyte percentage that logically reflects the calculations of the SII index. Furthermore, the presented data also showed that in the lymphocyte subpopulation, the association with the SII was driven by T-cell subpopulations. In innate immunity–cell subpopulations, we observed a correlation between SII level and neutrophils as well as associations with eosinophil, basophil, natural killer cell and dendritic cell percentages. We suppose that the described interactions represent a manifestation of cancer-induced immune suppression. The results of the present study contribute to the elucidation of the interrelationship between tumor cells and the innate/adaptive immune system of the host.

1. Introduction

The tumor microenvironment (TME) and the host inflammatory response are closely interconnected. Increasing evidence suggests that the interplay between systemic inflammation and the local immune response may influence tumor development and progression in various types of cancer [1,2,3,4]. Moreover, inflammation is reported to be a mechanism that participates in tumor immunoresistance [2]. The ‘complete blood count’ represents a simple tool for monitoring systemic inflammation via immune–inflammatory elements, including neutrophils, lymphocytes and platelets, which may help characterize the inflammatory TME [5,6]. In addition to these aforementioned single-parameter markers, several inflammatory cell ratios derived from these mediators, such as the neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), C-reactive protein/albumin ratio and systemic immune–inflammation index (SII) have previously been investigated. These markers consider several different types of immune cells characterized by both pro-tumor and anti-tumor activities, and therefore more accurately reflect the host inflammatory response to tumor cells [7].
The SII is assessed using peripheral neutrophil, lymphocyte and platelet counts and was first described by Hu et al. [8]. Therefore, the SII is considered to reflect the interplay between the local immune response and systemic inflammation in patients with cancer [9,10,11]. Neutrophils are able to secrete several inflammatory mediators, including vascular endothelial growth factor, IL-6, IL-10 and IL-22 and are therefore implicated in tumor-promoting activities [12]. Platelets have previously been reported as immune cells that support the formation of metastases and disease spread via the promotion of tumor cell arrest in the endothelium [13]. Platelets have also been reported to protect cancer cells from lysis by natural killer (NK) cells [14]. Moreover, lymphocytes inhibit tumor burden, and therefore their low levels may lead to an inadequate immunological response in patients with cancer [15].
The SII can also be used as a prognostic marker, predicting a patient’s survival in numerous types of cancer [4,16,17,18,19,20,21]. Moreover, recent data indicates that the SII is of higher prognostic value compared with other biomarker ratios, such as the NLR and PLR [16,22,23,24,25].
Numerous reports on germ cell tumors (GCTs) have also demonstrated the prognostic value of SII [7,26,27]. Chovanec et al. reported that the SII is significantly associated with poor-risk clinical features. Low SII levels determined prior to chemotherapy are correlated with longer progression-free survival (PFS) and overall survival (OS) [26]. Similar results were obtained in a study of 146 patients with metastatic GCTs undergoing first-line chemotherapy, whereby a higher SII was independent of the International Germ Cell Cancer Collaborative Group (IGCCCG) risk classification associated with a worse OS [7]. Göger et al. compared the SII levels in testicular cancer patients with a control group: the median SII was demonstrated to be significantly higher in testicular tumors. Furthermore, subgroup analysis revealed that the median SII value was significantly higher in testicular cancer patients with advanced disease stages and the presence of metastases. The SII value was also confirmed as an independent predictor of OS in this cohort of patients [27]. Furthermore, the predictive role of SII was assessed by Cursano et al. in a study involving 62 GCT patients receiving high-dose chemotherapy (HDCT). The results from this study suggested that SII is significantly associated with the overall response to HDCT as well as with patient outcome [28].
Testicular cancer is the most common type of solid tumor in 20–40-year-old males. The incidence of this disease is increasing worldwide. GCTs are traditionally referred to as chemotherapy-sensitive malignancies, with a 5-year survival rate of 98% for localized disease [29,30]. The mammalian testes are characterized as immunologically privileged sites, where a balance between immune privilege and the ability to respond to infections and inflammation plays an important role [31]. However, knowledge of immune cell roles in GCTs is rather limited. Siska et al. reported that advanced stages of testicular tumors are associated with an increased signature of regulatory T cells, neutrophils and mast cells, regardless of the histological subtype. Moreover, elevated levels of macrophage signatures and decreased T-cell and NK-cell signatures are also detected [32].
The aim of the present study was to identify the specific subpopulations of innate and adaptive immune cells that are associated with changes in the SII levels in chemotherapy-naïve GCTs. To achieve this, the percentage of immune cell subpopulations was associated with SII.

2. Materials and Methods

2.1. Patients

In total, 51 chemotherapy-naïve GCT patients, treated between January 2017 and January 2020 at the National Cancer Institute (Bratislava, Slovakia), and with available SII and immunophenotype data, were enrolled in the present study. Patients with evidence of concomitant malignancies, with the exception of nonmelanoma skin cancer, in the previous 5 years were excluded from the study. The following clinical data was collected for each participant: age, tumor histologic subtype, clinical stage of primary disease at diagnosis, type and number of metastatic sites and the date of diagnosis. The clinical stage of the primary disease was determined according to the tumor–node–metastasis staging system 2017 criteria. TNM classification combines the anatomic extent of disease (including primary tumor (pT), regional lymph nodes (pN) and distant metastasis (pM)) with the serum tumor markers alpha-fetoprotein (AFP), human chorionic gonadotropin (hCG) and lactate dehydrogenase (LDH), which are surrogates for anatomic disease extent [33]. The response to the therapy was assessed among the evaluable population according to standard RECIST (Response Evaluation Criteria In Solid Tumors) criteria, version 1.1 [34]. Favorable response to therapy included patients with complete remission or partial remission with negative tumor markers. The Institutional Review Board and Ethical Committee of the National Cancer Institute, Bratislava, Slovakia (protocol no. IZLO1; Chair: M. Mego, from 10 February 2010) approved the study protocol. A written informed consent form was signed by each participant prior to enrollment into the study.

2.2. SII

The SII was determined using counts of peripheral blood platelets (P), neutrophils (N) and lymphocytes (L) per liter, which were retrieved from routine prechemotherapy blood tests. The equation SII = P × N/L was used. A cut-off value of SII ≥ 1003 was chosen in order to dichotomize SII into low (<1003) and high (≥1003) categories according to the previous study by Chovanec et al. [26].

2.3. Determination of Leukocyte Immunophenotypes

In the morning of day −1 or 0 of first-line of chemotherapy, 1 mL atraumatic peripheral blood was collected at the antecubital fossa into an EDTA-treated collection tube. Analyzed samples were processed within 24 h following collection, as previously described by Kalavska et al. [34]. Briefly, leukocytes were stained using fluorochrome-conjugated antibodies from BD Pharmingen and, subsequently, leukocytes with defined immunophenotypes were quantified using flow cytometry (Canto II Cytometer; Becton, Dickinson and Company, Franklin Lakes, NJ, USA). The following antibody combinations were used for the basic panel: CD8 FITC (clone SK1, cat. no.: 345772, BD Biosciences, San Jose, CA 95131, USA), CD56 phycoerythrin (PE (clone MY31, cat. no.: 345810, BD Biosciences, San Jose, CA 95131, USA), CD45 PerCP Cy5.5 (clone SK3, cat. no.: 332772, BD Biosciences, San Jose, CA 95131, USA), CD19 PE-Cy7 (cat. no.: IM3628, Beckman Coulter Immunotech SAS, Marseille, France), CD3 APC (clone SK7, cat. no.: 345767, BD Biosciences, San Jose, CA 95131, USA), CD16 APC-H7 (clone 3G8, cat. no.: 560195, BD Pharmingen, San Diego, CA 92121, USA), CD4 V450 (clone RPA-T4, cat. no.: 560345, BD Biosciences, San Jose, CA 95131, USA) and CD14 HV500 (clone M5E2, cat. no.: 561391, BD Biosciences, San Jose, CA 95131, USA). The regulatory T-cell panel included the following antibodies: CD3 FITC (clone SK7, cat. no.: 345763, BD Biosciences, San Jose, CA 95131, USA), CD127 PE (clone hIL-7R-M21, cat. no.: 557938, BD Pharmingen, San Diego, CA 92121, USA), CD4 PerCP Cy5.5 (clone SK3, cat. no.: 566923, BD Biosciences, San Jose, CA 95131, USA), CD25 PE-Cy7 (clone 2A3, cat. no.: 335824, BD Biosciences, San Jose, CA 95131, USA) and CD45 HV450 antibody (clone HI30, cat. no.: 560367, BD Biosciences, San Jose, CA 95131, USA). For the dendritic-cell (DC) panel, the following antibodies were used: Lineage cocktail 2 FITC (cat. no.: 643397, BD Biosciences, San Jose, CA 95131, USA), CD1c PE (clone F10/21A3, cat. no.: 564900, BD Pharmingen, San Diego, CA 92121, USA), human leukocyte antigen (HLA)-DR PerCP (clone, L243, cat. no.: 347402, BD Biosciences, San Jose, CA 95131, USA), CD123 PE-Cy7 (clone 7G3, cat. no.: 560826, BD Pharmingen San Diego, CA 92121, USA), CD11c APC (clone B-Ly 6, cat. no.: 560895, BD Biosciences, San Jose, CA 95131, USA), CD16 APC-H7 (clone 3G8, cat. no.: 560195, BD Pharmingen, San Diego, CA 92121, USA) and CD45 HV450 (clone HI30, cat. no.: 560367, BD Biosciences, San Jose, CA 95131, USA) Finally, the myeloid-derived suppressor–cell panel used the following antibodies: CD15 FITC (cat. no.: IM1423U, Beckman Coulter Immunotech SAS, Marseille, France), CD11b PE (cat. no.: IM2581U, Beckman Coulter Coulter Immunotech SAS, Marseille, France), HLA-DR PerCP (clone L243, cat. no.: 347402, BD Biosciences, San Jose, CA 95131, USA), CD62L PE-Cy7 (clone DREG-56, cat. no.: 565535, BD Biosciences, San Jose, CA 95131, USA), CD33 APC (clone P67.6, cat. no.: 345800, BD Biosciences, San Jose, CA 95131, USA), CD14 APC-H7 (clone MΦP9, cat. no.: 641394, BD Biosciences, San Jose, CA 95131, USA), CD66b V450 (clone G10F5, cat. no.: 561649, BD Biosciences, San Jose, CA 95131, USA) and CD45 BV510 antibody (clone 30-F11, cat. no.: 103138, BioLegend, San Diego, CA 91121, USA). A cocktail of the aforementioned antibodies was incubated with 300,000–500,000 white blood cells in 200 µL for 20 min at room temperature. Before the fixation of cells using 1X BD FACS Lysing Solution (BD Bioscience, San Jose, CA, USA, cat. no: 349202), lysis of red blood cells was performed. For the assessment with a BD FACSCanto™ II flow cytometer (Becton Dickinson, Franklin Lakes, NJ, USA), a minimum of 100,000 leukocytes were utilized. KALUZA software (Beckman Coulter, Inc., Brea, CA, USA) was used for the analysis of the flow cytometry data. Forward scatter (FSC) and side scatter were used to exclude debris according to size and granularity, while exclusion of doublets was performed using FSC-Height and FSC-Area. The number of gated cells considered as the minimum for evaluation was 100.

2.4. Statistical Analysis

Patient data were tabulated and subsequently assessed using descriptive statistical methods. The median values (ranges) were used as continuous variables, whereas categorical variables were assessed by frequency (percentage). The distribution of the data was evaluated using the Kolmogorov–Smirnov test. Normally distributed data were assessed using one-way ANOVA, whereas non-normally distributed data were statistically analyzed using the nonparametric Mann–Whitney U test or Kruskal–Wallis H test. Pearson’s correlation coefficient or Spearman’s rank-correlation coefficient tests were used according to the normality of the data.
A cut-off value of SII ≥ 1003 was chosen in order to dichotomize SII into low (<1003) and high (≥1003) categories according to the previous study by Chovanec et al. [26]. The used cut-off of ≥1003 represents, as previously reported in this work, the median value of SII obtained from the discovery set of GCT patients, which was subsequently verified in survival analyses performed on an independent validation GCT patients cohort. The dichotomized data were further associated with specific immune cell counts using univariate analysis. Subsequently, a multivariate logistic regression analysis was performed, which included the variables identified as significantly linked to the SII level in the univariate analysis.
Similarly, the data dichotomized as ‘low’ or ‘high’ according to the SII cut-off value were used in survival analyses. The median follow-up period was defined as the median observation time among all patients and among the patients who were alive at the time of their last follow-up. PFS was defined as the period from day 1 of the first cycle of chemotherapy administration to the date of the progression of the disease or last follow-up, whereas OS was defined as the time from day 1 of the first cycle of chemotherapy administration to the date of death or last follow-up. PFS and OS were assessed using the Kaplan–Meier product-limit method and were compared between different groups using the log-rank test. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using logistic regression and Cox proportional hazard analysis, respectively.
Statistical analysis was performed using NCSS 11 Statistical Software (NCSS, LLC., Kaysville, UT, USA, ncss.com/software/ncss, accessed on 4 December 2021). Data are presented as the mean ± SEM. All the presented p-values are two-sided. A value of p ≤ 0.05 was considered to indicate a statistically significant difference.

3. Results

3.1. Patient Characteristics

In total, 51 chemotherapy-naïve GCT patients were enrolled in the present study. Patient baseline characteristics are summarized in Table 1. The median patient age was 34 years (range, 22–59 years). The majority of patients (62.7%) were classified in a good risk group according to the IGCCCG criteria. Primary tumors were predominantly located in the testes (48 patients; 94.1%). Overall, 39 patients (76.5%) presented with nonseminomatous histology, whereas 12 patients (23.5%) had seminomatous GCTs. Most patients (56.9%) had metastatic disease with one to two metastatic sites. Metastases were located mainly in retroperitoneal lymph nodes (74.5%). Platinum-based chemotherapy was administrated to all enrolled patients. All patients also received granulocyte colony–stimulating factor support (filgrastim or pegfilgrastim) following chemotherapy.

3.2. Correlation between the SII Level and Percentage of Different Innate Immune Cells in Chemotherapy-Naïve GCT Patients

Using univariate logistic regression analysis, the results demonstrated that patients with an SII under the cut-off value (≥1003) had a significantly lower mean percentage of neutrophils ± standard error of the mean (SEM) 56.7 ± 1.7%, compared with patients with an SII higher than the cut-off value, 74.5 ± 2.2% (p < 0.00001). Similarly, a lower NK cell percentage correlated with an SII under the cut-off value (10.3 ± 1.6% vs. 18.3 ± 2.1%; p = 0.02264). Moreover, a negative correlation was detected between an SII under the cut-off value and a higher percentage of eosinophils (3.3 ± 0.4% vs. 1.4 ± 0.5%; p = 0.00431) and basophils (0.7 ± 0.06% vs. 0.5 ± 0.07%; p = 0.00852). Moreover, a higher mean percentage of DCs (0.9 ± 0.08% vs. 0.7 ± 0.11%; p = 0.02728), as well as plasmocytoid DCs (0.2 ± 0.01% vs. 0.1 ± 0.02%; p = 0.00310), significantly correlated with an SII level under the cut-off value. However, statistical analysis of the association between the SII and innate immune cell–count using multivariate logistic regression analysis demonstrated that only the percentage of neutrophils was independently associated with the SII level, dichotomized according to the cut-off value (Table 2).

3.3. Association between the SII Level and Selected Adaptive Immune Cell Percentages

It was determined using univariate logistic regression analysis that a higher percentage (±SEM) of lymphocytes was significantly associated with a low SII level (31.2 ± 1.5% vs. 15.0 ± 2.0%; p < 0.00001). A similar association was also demonstrated between T-cell and cytotoxic T-cell percentages and the SII. In patients with a low SII, a significantly higher percentage of T cells was determined compared with the patients with a high SII (76.3 ± 1.7% vs. 68.1 ± 2.2%; p = 0.01410). Furthermore, a higher percentage of cytotoxic T cells was observed in patients with a low SII (27.7 ± 1.0% vs. 23.4 ± 1.3%; p = 0.02383).
Multivariate analysis of all adaptive immune-cell subpopulations that were significantly linked to the SII level in the univariate analysis determined that CD3+ T cells were the only subpopulation of adaptive immune cells that were independently associated with the SII level in GCT patients (p = 0.01385) (Table 3).

3.4. The Prognostic Role of the SII

The determination of PFS and OS according to the SII was estimated using Kaplan–Meier analysis in GCT patients. The cut-off value of SII used in this analysis was ≥1003, according to our previous study [26]. The results demonstrated that patients with a low SII had a significantly longer PFS (HR, 0.13; 95% CI, 0.03–0.67; p = 0.0274; Figure 1A), whereas the prognostic value of SII in determining OS was not statistically significant (HR, 0.19; 95% CI, 0.02–1.44; p = 0.1025; Figure 1B).
For each time interval, the survival probability, expressed as number at risk, was calculated as the number of subjects surviving divided by the number of patients at risk. Subjects who died or dropped out were not counted as “at risk”.

4. Discussion

Growing evidence suggests there is an intensive cross-talk between the host immune system and cancer. Inflammation is implicated in several aspects of cancer biology, including cancer development, progression and prognosis [5,35,36]. The malignant process influences the host immune system not only at the tumor site but also on a global level by forming a systemic inflammatory response [37]. An impaired intratumoral inflammatory response and an elevated systemic response suggests decreased immunological control of tumors at the local level, whereas the formation of a systemic pro-inflammatory environment creates suitable conditions for cancer progression [38,39]. Therefore, deeper insight into both the innate and adaptive immune system responses within the TME may lead to a better understanding of the interactions between cancer and host immune cells.
The aim of the present study was to determine which subpopulations of innate and adaptive immune cells are associated with SII in GCT patients. To the best of our knowledge, this is the first study to assess the link between the peripheral immune cells and the SII prior to chemotherapy. The results demonstrated a positive correlation between the SII level and neutrophil percentage, whereas an inverse association was determined between the SII value and T-cell percentage.
Recently, an increasing number of studies have reported that SII may serve as a prognostic marker in GCTs, whereby high SII levels are significantly associated with worse PFS and OS [7,26,27]. In the present study, the prognostic value of SII was also confirmed in GCT patients. A significant association between PFS and the SII value was reported. The association between SII and OS was not statistically significant, which may be explained by the limited number of patients enrolled in the present study. A strong significant association between SII and poor clinical features, such as primary extragonadal tumors, bulky retroperitoneal disease, nonpulmonary visceral metastases and elevated tumor markers was also observed. This data suggest that the host immune system is implicated in the progression of GCTs. However, the results of the present study did not determine whether systemic inflammation, expressed by the SII, formed a permissive microenvironment which resulted in disease characterized by poor clinical features, or whether the SII simply reflected an aggressive disease [26].
The SII is a value determined by a combination of the following three parameters: (i) neutrophils, (ii) platelets and (iii) lymphocytes. Therefore, a high SII could be attributed to changes in the counts of these cells [40]. Ma et al. reported that high pretreatment levels of SII reflect increased neutrophil and platelet counts or decreased lymphocyte counts [41]. Results obtained in the present study are in accordance with these data. The results demonstrated that a high SII level was independently associated with elevated peripheral neutrophils and decreased lymphocytes simultaneously. Neutrophilia is relatively common in patients with cancer. Neutrophils contribute to the creation of a highly immunosuppressive microenvironment via numerous signaling pathways, and therefore neutrophilia facilitates tumor growth and metastasis [42]. Low lymphocyte counts are traditionally considered a reflection of impaired host immunosurveillance. Lymphocytopenia observed prior to treatment could be regarded as a surrogate marker of cancer-induced immunosuppression and its prognostic role has previously been described in several solid types of cancer, including metastatic breast, renal and colorectal cancer [43,44]. However, the causes of tumor-induced lymphocytopenia are not fully understood. Recent data postulates that low peripheral lymphocyte counts are a consequence of impaired lymphocyte homeostasis and the increased activation of lymphocyte apoptosis [43]. This is a result of the enhanced secretion of several immunosuppressive molecules, including TGF-β, by the tumor and others factors in the TME, which lead to the impairment of cytotoxic and helper lymphocyte activation, whereas the recruitment of suppressive regulatory T cells is promoted [45]. It can also be hypothesized that there are common signaling pathways implicated in the immune escape of malignant cells which simultaneously support lymphocytopenia in the host immune system [43].
Beyond the logical correlation between the neutrophil percentage and SII level, the present study also determined the association of SII with other innate immune cell subpopulations, including eosinophils, basophils, NK cells and DCs. Eosinophils, as well as lymphocytes, neutrophils and macrophages, are important factors in the cross-talk between inflammation and cancer [46]. Depending on the microenvironment and biological interactions [47,48], eosinophils may be implicated in both the pro-inflammatory and anti-inflammatory signaling pathways. Furthermore, the varying impacts of eosinophil count on a patient’s outcome are reported to depend on the type of malignancy. A high eosinophil count is correlated with a better prognosis in hepatocellular carcinoma treated with sorafenib, in melanoma, renal carcinoma, and in colorectal, lung, cervical and pancreatic cancer. However, eosinophilia is an unfavorable prognostic marker in breast cancer and lymphoma [49,50,51,52,53,54,55,56,57,58]. In the present study, the association between a low SII and eosinophilia in chemotherapy-naïve GCT patients was described.
A similar association was observed between the baseline basophil percentage and SII. Recent studies have demonstrated both a protective [59] and pro-tumorigenic role [60,61] of basophils in tumorigenesis. Numerous studies have reported that basophils affect the TME of human [61,62,63] and experimental [60,61] tumors. The connection between the peripheral blood basophils and tumor burden has been reported in certain solid tumors [64]. Basopenia is suggested to be a negative prognostic marker in patients with colorectal cancer [51,65]. Furthermore, basophilia is reported to be associated with a better outcome in patients with melanoma treated with immunotherapy (nivolumab with ipilimumab) [50]. Basophilia as a positive prognostic marker has also been demonstrated in patients with ovarian cancer [63].
An interesting association between NK cells and SII was revealed in the present study. A high NK-cell percentage was significantly correlated with a high SII. NK cells represent a specialized population of innate immune cells which play a critical role in the host immune response against tumor growth [66,67]. The importance of NK cells in tumor immunosurveillance and in the mediation of antimetastatic effects has previously been determined in mouse models and clinical studies [67,68,69,70]. High levels of tumor-infiltrating NK cells are associated with a good prognosis in certain solid tumors, such as breast cancer [71], gastrointestinal stromal tumors [72,73,74], neuroblastoma [75], head and neck cancer [76] and prostate cancer [77]. However, there are malignancies that are refractory to their antitumor function, mainly due to the presence of immunosuppressive microenvironment favoring neoplastic progression [66,67]. The results of the present study are in line with these observations as an immunosuppressive TME (expressed as a high SII) was associated with a high percentage of NK cells herein. A high SII level was confirmed as a negative prognostic marker for PFS. However, understanding the mechanism of how the TME is able to hinder NK-cell function remains to be elucidated.
The last subpopulation of innate immune cells that displayed percentage changes and was significantly associated with SII was represented by DCs. DCs are specialized antigen-presenting cells that are responsible for the initiation of specific T-cell responses and humoral responses which inhibit tumor development [78,79,80,81]. Several reports have demonstrated that the DC percentage in patients with cancer is significantly lower compared with healthy subjects [82,83,84,85]. Furthermore, the spontaneous apoptosis of peripheral DCs in patients with cancer has been described and is hypothesized to be the result of culture conditions or contact with cancer cells [86,87,88,89]. However, the role of pDCs in the TME remains controversial. While tumor-infiltrating pDCs possess immunosuppressive properties, the ability of pDCs to produce type I IFN and TNF-α indicates their antitumorigenic potential [90]. The human pDCs gene signature has been described as a positive prognostic factor in lung carcinoma [91], whereas the presence of pDCs in breast cancer is associated with a poor prognosis [92].
Assessing the changes in adaptive immune cell subpopulations in association with the SII level demonstrated the inverse correlation between cytotoxic T cells and the SII level. Cytotoxic T lymphocytes are considered to be the most powerful immune cells in the anticancer immune response [93]. However, during cancer progression, cytotoxic T cells display dysfunction and exhaustion as a consequence of immune-related tolerance and the presence of immunosuppression within the TME [94].
The present study has certain limitations related to the limited number of recruited patients. Furthermore, the competence or functions of selected leukocyte subpopulations were not analyzed. Therefore, a larger data set is needed in order to determine the characteristics of the selected leukocyte subpopulations associated with SII in GCT patients.
In conclusion, the present study demonstrates that high pretreatment SII levels are associated with higher neutrophil and lower lymphocyte percentages in the analyzed GCT patients. Beyond the association with neutrophil and lymphocyte percentages that are involved in the SII assessment, it was also observed that this association was driven by the T-cell subpopulation. Furthermore, by assessing innate immune cells, we showed that, beyond the correlation between neutrophil percentage and SII level, correlations with eosinophil, basophil, NK cell and DC percentages were also determined.

Author Contributions

K.K., M.M. and Z.S.-M. conceived and designed the study. K.K., Z.S.-M., A.M., P.G., V.M., K.R., D.S., Z.S.-M., P.P., J.O., J.M., M.C. (Michal Chovanec) and M.M. acquired the data. K.K., M.M. and Z.S.-M. analyzed and interpreted the data. K.K. and M.M. performed statistical analysis. K.K. and M.M. wrote the first draft of the manuscript, and Z.S., A.M., M.C. (Michal Chovanec) and M.C. (Miroslav Chovanec) critically revised the manuscript. K.K., M.M. and M.C. (Miroslav Chovanec) obtained funding. D.S. contributed administrative and technical support. M.M. supervised the whole study. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the VEGA Grant Agency of the Slovak Republic under grant numbers 1/0043/18, 1/0327/19, 1/0349/21 and 2/0053/19; the Slovak Research and Development Agency under grant numbers APVV-15-0086, APVV-19-0411 and APVV-20-0158; the Ministry of Health of the Slovak Republic under grant number 2019/57-BMCSAV-1; and The Integrated Infrastructure Operational Program for the Project Systemic Public Research Infrastructure—Biobank for Cancer and Rare Diseases, ITMS: 313011AFG5; and was co-financed by the European Regional Development Fund.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and the protocol used in this study was approved by the Institutional Review and Ethical Committee of the National Cancer Institute, Bratislava, Slovakia (No. IZLO1; Chair: M. Mego, from 10 February 2010).

Informed Consent Statement

All participants provided signed informed consent before study enrollment.

Data Availability Statement

Data are contained within the article.

Acknowledgments

The authors thank James M. Reuben for fruitful discussions and for critically reading the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Fridman, W.H.; Zitvogel, L.; Sautès-Fridman, C.; Kroemer, G. The immune contexture in cancer prognosis and treatment. Nat. Rev. Clin. Oncol. 2017, 14, 717–734. [Google Scholar] [CrossRef] [PubMed]
  2. Sharma, P.; Allison, J.P. The future of immune checkpoint therapy. Science 2015, 348, 56–61. [Google Scholar] [CrossRef] [PubMed]
  3. Padoan, A.; Plebani, M.; Basso, D. Inflammation and Pancreatic Cancer: Focus on Metabolism, Cytokines, and Immunity. Int. J. Mol. Sci. 2019, 20, 676. [Google Scholar] [CrossRef] [Green Version]
  4. Huang, H.; Liu, Q.; Zhu, L.; Zhang, Y.; Lu, X.; Wu, Y.; Liu, L. Prognostic Value of Preoperative Systemic Immune-Inflammation Index in Patients with Cervical Cancer. Sci. Rep. 2019, 9, 3284. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Mantovani, A.; Allavena, P.; Sica, A.; Balkwill, F. Cancer-related inflammation. Nature 2008, 454, 436–444. [Google Scholar] [CrossRef]
  6. Lippitz, B.E. Cytokine patterns in patients with cancer: A systematic review. Lancet Oncol. 2013, 14, e218–e228. [Google Scholar] [CrossRef]
  7. Fankhauser, C.D.; Sander, S.; Roth, L.; Gross, O.; Eberli, D.; Sulser, T.; Seifert, B.; Beyer, J.; Hermanns, T. Systemic inflammatory markers have independent prognostic value in patients with metastatic testicular germ cell tumours undergoing first-line chemotherapy. Br. J. Cancer 2018, 118, 825–830. [Google Scholar] [CrossRef] [Green Version]
  8. Hu, B.; Yang, X.R.; Xu, Y.; Sun, Y.F.; Sun, C.; Guo, W.; Zhang, X.; Wang, W.M.; Qiu, S.J.; Zhou, J.; et al. Systemic immune-inflammation index predicts prognosis of patients after curative resection for hepatocellular carcinoma. Clin. Cancer Res. 2014, 20, 6212–6222. [Google Scholar] [CrossRef] [Green Version]
  9. Murthy, P.; Zenati, M.S.; Al Abbas, A.I.; Rieser, C.J.; Bahary, N.; Lotze, M.T.; Zeh, H.J., 3rd; Zureikat, A.H.; Boone, B.A. Prognostic Value of the Systemic Immune-Inflammation Index (SII) After Neoadjuvant Therapy for Patients with Resected Pancreatic Cancer. Ann. Surg. Oncol. 2020, 27, 898–906. [Google Scholar] [CrossRef]
  10. Wang, C.; Jin, S.; Xu, S.; Cao, S. High Systemic Immune-Inflammation Index (SII) Represents an Unfavorable Prognostic Factor for Small Cell Lung Cancer Treated with Etoposide and Platinum-Based Chemotherapy. Lung 2020, 198, 405–414. [Google Scholar] [CrossRef]
  11. Zhang, K.; Hua, Y.Q.; Wang, D.; Chen, L.Y.; Wu, C.J.; Chen, Z.; Liu, L.M.; Chen, H. Systemic immune-inflammation index predicts prognosis of patients with advanced pancreatic cancer. J. Transl. Med. 2019, 17, 30. [Google Scholar] [CrossRef] [PubMed]
  12. Tan, K.W.; Chong, S.Z.; Wong, F.H.; Evrard, M.; Tan, S.M.; Keeble, J.; Kemeny, D.M.; Ng, L.G.; Abastado, J.P.; Angeli, V. Neutrophils contribute to inflammatory lymphangiogenesis by increasing VEGF-A bioavailability and secreting VEGF-D. Blood 2013, 122, 3666–3677. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Gay, L.J.; Felding-Habermann, B. Contribution of platelets to tumour metastasis. Nat. Rev. Cancer 2011, 11, 123–134. [Google Scholar] [CrossRef] [PubMed]
  14. Nieswandt, B.; Hafner, M.; Echtenacher, B.; Männel, D.N. Lysis of tumor cells by natural killer cells in mice is impeded by platelets. Cancer Res. 1999, 59, 1295–1300. [Google Scholar] [PubMed]
  15. Chen, L.; Zhang, F.; Sheng, X.G.; Zhang, S.Q. Decreased pretreatment lymphocyte/monocyte ratio is associated with poor prognosis in stage Ib1-IIa cervical cancer patients who undergo radical surgery. Onco. Targets Ther. 2015, 8, 1355–1362. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Gao, Y.; Guo, W.; Cai, S.; Zhang, F.; Shao, F.; Zhang, G.; Liu, T.; Tan, F.; Li, N.; Xue, Q.; et al. Systemic immune-inflammation index (SII) is useful to predict survival outcomes in patients with surgically resected esophageal squamous cell carcinoma. J. Cancer 2019, 10, 3188–3196. [Google Scholar] [CrossRef] [Green Version]
  17. Huang, X.; Hu, H.; Zhang, W.; Shao, Z. Prognostic value of prognostic nutritional index and systemic immune-inflammation index in patients with osteosarcoma. J. Cell Physiol. 2019, 234, 18408–18414. [Google Scholar] [CrossRef]
  18. Man, Y.N.; Chen, Y.F. Systemic immune-inflammation index, serum albumin, and fibrinogen impact prognosis in castration-resistant prostate cancer patients treated with first-line docetaxel. Int. Urol. Nephrol. 2019, 51, 2189–2199. [Google Scholar] [CrossRef]
  19. Mirili, C.; Paydas, S.; Kapukaya, T.K.; Yılmaz, A. Systemic immune-inflammation index predicting survival outcome in patients with classical Hodgkin lymphoma. Biomark Med. 2019, 13, 1565–1575. [Google Scholar] [CrossRef]
  20. Nie, D.; Gong, H.; Mao, X.; Li, Z. Systemic immune-inflammation index predicts prognosis in patients with epithelial ovarian cancer: A retrospective study. Gynecol. Oncol. 2019, 152, 259–264. [Google Scholar] [CrossRef]
  21. Wang, D.; Guo, D.; Shi, F.; Zhu, Y.; Li, A.; Kong, L.; Teng, F.; Yu, J. The predictive effect of the systemic immune-inflammation index for patients with small-cell lung cancer. Future Oncol. 2019, 15, 3367–3379. [Google Scholar] [CrossRef] [PubMed]
  22. Merloni, F.; Pistelli, M.; Cantini, L.; Della Mora, A.; Bastianelli, L.; De Lisa, M.; Burattini, M.; Maccaroni, E.; Ballatore, Z.; Savini, A.; et al. Role of inflammation parameters in locally advanced breast cancer: The debate is still open. Ann. Oncol. 2017, 28, 37. [Google Scholar] [CrossRef]
  23. van der Willik, K.D.; Koppelmans, V.; Hauptmann, M.; Compter, A.; Ikram, M.A.; Schagen, S.B. Inflammation markers and cognitive performance in breast cancer survivors 20 years after completion of chemotherapy: A cohort study. Breast Cancer Res. 2018, 20, 135. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Fest, J.; Ruiter, R.; Mulder, M.; Groot Koerkamp, B.; Ikram, M.A.; Stricker, B.H.; van Eijck, C.H.J. The systemic immune-inflammation index is associated with an increased risk of incident cancer-A population-based cohort study. Int. J. Cancer 2020, 146, 692–698. [Google Scholar] [CrossRef] [PubMed]
  25. Miklikova, S.; Minarik, G.; Sedlackova, T.; Plava, J.; Cihova, M.; Jurisova, S.; Kalavska, K.; Karaba, M.; Benca, J.; Smolkova, B.; et al. Inflammation-Based Scores Increase the Prognostic Value of Circulating Tumor Cells in Primary Breast Cancer. Cancers 2020, 12, 1134. [Google Scholar] [CrossRef]
  26. Chovanec, M.; Cierna, Z.; Miskovska, V.; Machalekova, V.; Kalavska, K.; Rejlekova, K.; Svetlovska, D.; Macak, D.; Spanik, S.; Kajo, K.; et al. Systemic immune-inflammation index in germ-cell tumours. Br. J. Cancer 2018, 118, 831–838. [Google Scholar] [CrossRef] [Green Version]
  27. Göger, Y.; Özkent, M.; Karaağaç, M.; Uçmak, H.; Artaç, M. Prognostic Value of Systemic Immune-Inflammation Index in Patients with Testicular Cancer: A Retrospective Case-Control Study. Bull. Urooncology 2021, 20, 252–257. [Google Scholar] [CrossRef]
  28. Cursano, M.C.; Kopf, B.; Scarpi, E.; Menna, C.; Casadei, C.; Schepisi, G.; Lolli, C.; Altavilla, A.; Gallà, V.; Santini, D.; et al. Prognostic Role of Systemic Inflammatory Indexes in Germ Cell Tumors Treated With High-Dose Chemotherapy. Front. Oncol. 2020, 10, 1325. [Google Scholar] [CrossRef]
  29. Chia, V.M.; Quraishi, S.M.; Devesa, S.S.; Purdue, M.P.; Cook, M.B.; McGlynn, K.A. International trends in the incidence of TC, 1973–2002. Cancer Epidemiol. Biomark. Prev. 2010, 19, 1151–1159. [Google Scholar] [CrossRef] [Green Version]
  30. Stang, A.; Jansen, L.; Trabert, B.; Rusner, C.; Eberle, A.; Katalinic, A.; Emrich, K.; Holleczek, B.; Brenner, H. Survival after a diagnosis of testicular germ cell cancers in Germany and the United States, 2002–2006, a high resolution study by histology and age. Cancer Epidemiol. 2013, 37, 492–497. [Google Scholar] [CrossRef] [Green Version]
  31. Bhushan, S.; Theas, M.S.; Guazzone, V.A.; Jacobo, P.; Wang, M.; Fijak, M.; Meinhardt, A.; Lustig, L. Immune Cell Subtypes and Their Function in the Testis. Front. Immunol. 2020, 11, 583304. [Google Scholar] [CrossRef] [PubMed]
  32. Siska, P.J.; Johnpulle, R.A.N.; Zhou, A.; Bordeaux, J.; Kim, J.Y.; Dabbas, B.; Dakappagari, N.; Rathmell, J.C.; Rathmell, W.K.; Morgans, A.K.; et al. Deep exploration of the immune infiltrate and outcome prediction in testicular cancer by quantitative multiplexed immunohistochemistry and gene expression profiling. Oncoimmunology 2017, 6, e1305535. [Google Scholar] [CrossRef] [PubMed]
  33. Testis. In TNM Classification of Malignant Tumours, 8th ed.; Brierley, J.D.; Gospodarowicz, M.K.; Wittekind, C. (Eds.) Wiley-Blackwell: Oxford, UK, 2017. [Google Scholar]
  34. Therasse, P.; Arbuck, S.G.; Eisenhauer, E.A.; Wanders, J.; Kaplan, R.S.; Rubinstein, L.; Verweij, J.; Van Glabbeke, M.; van Oosterom, A.T.; Christian, M.C.; et al. New guidelines to evaluate the response to treatment in solid tumors. European Organization for Research and Treatment of Cancer, National Cancer Institute of the United States, National Cancer Institute of Canada. J. Natl. Cancer Inst. 2000, 92, 205–216. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Greten, F.R.; Grivennikov, S.I. Inflammation and Cancer: Triggers, Mechanisms, and Consequences. Immunity 2019, 51, 27–41. [Google Scholar] [CrossRef]
  36. Ohno, Y. Role of systemic inflammatory response markers in urological malignancy. Int. J. Urol. 2019, 26, 31–47. [Google Scholar] [CrossRef] [Green Version]
  37. Pęksa, R.; Kunc, M.; Popęda, M.; Piątek, M.; Bieńkowski, M.; Żok, J.; Starzyńska, A.; Perdyan, A.; Sowa, M.; Duchnowska, R.; et al. Combined Assessment of Immune Checkpoint Regulator VISTA on Tumor-Associated Immune Cells and Platelet-to-Lymphocyte Ratio Identifies Advanced Germ Cell Tumors with Higher Risk of Unfavorable Outcomes. Cancers 2021, 13, 1750. [Google Scholar] [CrossRef]
  38. Dimitriou, N.; Felekouras, E.; Karavokyros, I.; Alexandrou, A.; Pikoulis, E.; Griniatsos, J. Neutrophils to lymphocytes ratio as a useful prognosticator for stage II colorectal cancer patients. BMC Cancer 2018, 18, 1202. [Google Scholar] [CrossRef]
  39. Pedrazzani, C.; Mantovani, G.; Fernandes, E.; Bagante, F.; Luca Salvagno, G.; Surci, N.; Campagnaro, T.; Ruzzenente, A.; Danese, E.; Lippi, G.; et al. Assessment of neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio and platelet count as predictors of long-term outcome after R0 resection for colorectal cancer. Sci. Rep. 2017, 7, 1494. [Google Scholar] [CrossRef] [Green Version]
  40. Zhang, Y.; Sun, Y.; Zhang, Q. Prognostic value of the systemic immune-inflammation index in patients with breast cancer: A meta-analysis. Cancer Cell Int. 2020, 20, 224. [Google Scholar] [CrossRef]
  41. Ma, M.; Yu, N.; Wu, B. High systemic immune-inflammation index represents an unfavorable prognosis of malignant pleural mesothelioma. Cancer Manag. Res. 2019, 11, 3973–3979. [Google Scholar] [CrossRef] [Green Version]
  42. Cools-Lartigue, J.; Spicer, J.; McDonald, B.; Gowing, S.; Chow, S.; Giannias, B.; Bourdeau, F.; Kubes, P.; Ferri, L. Neutrophil extracellular traps sequester circulating tumor cells and promote metastasis. J. Clin. Investig. 2013, 123, 3446–3458. [Google Scholar] [CrossRef] [PubMed]
  43. Joseph, N.; Dovedi, S.J.; Thompson, C.; Lyons, J.; Kennedy, J.; Elliott, T.; West, C.M.; Choudhury, A. Pre-treatment lymphocytopaenia is an adverse prognostic biomarker in muscle-invasive and advanced bladder cancer. Ann. Oncol. 2016, 27, 294–299. [Google Scholar] [CrossRef] [Green Version]
  44. Ray-Coquard, I.; Cropet, C.; Van Glabbeke, M.; Sebban, C.; Le Cesne, A.; Judson, I.; Tredan, O.; Verweij, J.; Biron, P.; Labidi, I.; et al. Lymphopenia as a prognostic factor for overall survival in advanced carcinomas, sarcomas, and lymphomas. Cancer Res. 2009, 69, 5383–5391. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Gorelik, L.; Flavell, R.A. Transforming growth factor-beta in T-cell biology. Nat. Rev. Immunol. 2002, 2, 46–53. [Google Scholar] [CrossRef] [PubMed]
  46. Varricchi, G.; Galdiero, M.R.; Loffredo, S.; Lucarini, V.; Marone, G.; Mattei, F.; Marone, G.; Schiavoni, G. Eosinophils: The unsung heroes in cancer? Oncoimmunology 2017, 7, e1393134. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. Carretero, R.; Sektioglu, I.M.; Garbi, N.; Salgado, O.C.; Beckhove, P.; Hämmerling, G.J. Eosinophils orchestrate cancer rejection by normalizing tumor vessels and enhancing infiltration of CD8(+) T cells. Nat. Immunol. 2015, 16, 609–617. [Google Scholar] [CrossRef]
  48. Wong, D.; Winter, O.; Hartig, C.; Siebels, S.; Szyska, M.; Tiburzy, B.; Meng, L.; Kulkarni, U.; Fähnrich, A.; Bommert, K.; et al. Eosinophils and megakaryocytes support the early growth of murine MOPC315 myeloma cells in their bone marrow niches. PLoS ONE 2014, 9, e109018. [Google Scholar] [CrossRef] [Green Version]
  49. Zahoor, H.; Barata, P.C.; Jia, X.; Martin, A.; Allman, K.D.; Wood, L.S.; Gilligan, T.D.; Grivas, P.; Ornstein, M.C.; Garcia, J.A.; et al. Patterns, predictors and subsequent outcomes of disease progression in metastatic renal cell carcinoma patients treated with nivolumab. J. Immunother. Cancer 2018, 6, 107. [Google Scholar] [CrossRef] [Green Version]
  50. Rosner, S.; Kwong, E.; Shoushtari, A.N.; Friedman, C.F.; Betof, A.S.; Brady, M.S.; Coit, D.G.; Callahan, M.K.; Wolchok, J.D.; Chapman, P.B.; et al. Peripheral blood clinical laboratory variables associated with outcomes following combination nivolumab and ipilimumab immunotherapy in melanoma. Cancer Med. 2018, 7, 690–697. [Google Scholar] [CrossRef]
  51. Wei, Y.; Zhang, X.; Wang, G.; Zhou, Y.; Luo, M.; Wang, S.; Hong, C. The impacts of pretreatment circulating eosinophils and basophils on prognosis of stage I-III colorectal cancer. Asia-Pac. J. Clin. Oncol. 2018, 14, e243–e251. [Google Scholar] [CrossRef]
  52. Tanizaki, J.; Haratani, K.; Hayashi, H.; Chiba, Y.; Nakamura, Y.; Yonesaka, K.; Kudo, K.; Kaneda, H.; Hasegawa, Y.; Tanaka, K.; et al. Peripheral Blood Biomarkers Associated with Clinical Outcome in Non-Small Cell Lung Cancer Patients Treated with Nivolumab. J. Thorac. Oncol. 2018, 13, 97–105. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Holub, K.; Biete, A. Impact of systemic inflammation biomarkers on the survival outcomes of cervical cancer patients. Clin. Transl. Oncol. 2019, 21, 836–844. [Google Scholar] [CrossRef]
  54. Holub, K.; Conill, C. Unveiling the mechanisms of immune evasion in pancreatic cancer: May it be a systemic inflammation responsible for dismal survival? Clin. Transl. Oncol. 2020, 22, 81–90. [Google Scholar] [CrossRef] [PubMed]
  55. Ownby, H.E.; Roi, L.D.; Isenberg, R.R.; Brennan, M.J. Peripheral lymphocyte and eosinophil counts as indicators of prognosis in primary breast cancer. Cancer 1983, 52, 126–130. [Google Scholar] [CrossRef]
  56. Utsunomiya, A.; Ishida, T.; Inagaki, A.; Ishii, T.; Yano, H.; Komatsu, H.; Iida, S.; Yonekura, K.; Takeuchi, S.; Takatsuka, Y.; et al. Clinical significance of a blood eosinophilia in adult T-cell leukemia/lymphoma: A blood eosinophilia is a significant unfavorable prognostic factor. Leuk. Res. 2007, 31, 915–920. [Google Scholar] [CrossRef] [PubMed]
  57. Hude, I.; Sasse, S.; Bröckelmann, P.J.; von Tresckow, B.; Momotow, J.; Engert, A.; Borchmann, S. Leucocyte and eosinophil counts predict progression-free survival in relapsed or refractory classical Hodgkin Lymphoma patients treated with PD1 inhibition. Br. J. Haematol. 2018, 181, 837–840. [Google Scholar] [CrossRef] [Green Version]
  58. Orsi, G.; Tovoli, F.; Dadduzio, V.; Vivaldi, C.; Brunetti, O.; Ielasi, L.; Conti, F.; Rovesti, G.; Gramantieri, L.; Rizzato, M.D.; et al. Prognostic role of blood eosinophil count in sorafenib-treated hepatocellular carcinoma patients: Time to reconsider the minorities. Target Oncol. 2020, 15, 773–785. [Google Scholar] [CrossRef]
  59. Sektioglu, I.M.; Carretero, R.; Bulbuc, N.; Bald, T.; Tüting, T.; Rudensky, A.Y.; Hämmerling, G.J. Basophils Promote Tumor Rejection via Chemotaxis and Infiltration of CD8+ T Cells. Cancer Res. 2017, 77, 291–302. [Google Scholar] [CrossRef] [Green Version]
  60. Hayes, M.D.; Ward, S.; Crawford, G.; Seoane, R.C.; Jackson, W.D.; Kipling, D.; Voehringer, D.; Dunn-Walters, D.; Strid, J. Inflammation-induced IgE promotes epithelial hyperplasia and tumour growth. eLife 2020, 9, e51862. [Google Scholar] [CrossRef]
  61. De Monte, L.; Wörmann, S.; Brunetto, E.; Heltai, S.; Magliacane, G.; Reni, M.; Paganoni, A.M.; Recalde, H.; Mondino, A.; Falconi, M.; et al. Basophil Recruitment into Tumor-Draining Lymph Nodes Correlates with Th2 Inflammation and Reduced Survival in Pancreatic Cancer Patients. Cancer Res. 2016, 76, 1792–1803. [Google Scholar] [CrossRef] [Green Version]
  62. Lavin, Y.; Kobayashi, S.; Leader, A.; Amir, E.D.; Elefant, N.; Bigenwald, C.; Remark, R.; Sweeney, R.; Becker, C.D.; Levine, J.H.; et al. Innate Immune Landscape in Early Lung Adenocarcinoma by Paired Single-Cell Analyses. Cell 2017, 169, 750–765.e17. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  63. Bax, H.J.; Chauhan, J.; Stavraka, C.; Khiabany, A.; Nakamura, M.; Pellizzari, G.; Ilieva, K.M.; Lombardi, S.; Gould, H.J.; Corrigan, C.J.; et al. Basophils from Cancer Patients Respond to Immune Stimuli and Predict Clinical Outcome. Cells 2020, 9, 1631. [Google Scholar] [CrossRef] [PubMed]
  64. Varricchi, G.; Ameri, P.; Cadeddu, C.; Ghigo, A.; Madonna, R.; Marone, G.; Mercurio, V.; Monte, I.; Novo, G.; Parrella, P. Antineoplastic Drug-Induced Cardiotoxicity: A Redox Perspective. Front. Physiol. 2018, 9, 167. [Google Scholar] [CrossRef] [PubMed]
  65. Liu, Q.; Luo, D.; Cai, S.; Li, Q.; Li, X. Circulating basophil count as a prognostic marker of tumor aggressiveness and survival outcomes in colorectal cancer. Clin. Transl. Med. 2020, 9, 6. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  66. Liu, S.; Galat, V.; Galat, Y.; Lee, Y.K.A.; Wainwright, D.; Wu, J. NK cell-based cancer immunotherapy: From basic biology to clinical development. J. Hematol. Oncol. 2021, 14, 7. [Google Scholar] [CrossRef] [PubMed]
  67. Melaiu, O.; Lucarini, V.; Cifaldi, L.; Fruci, D. Influence of the Tumor Microenvironment on NK Cell Function in Solid Tumors. Front. Immunol. 2020, 10, 3038. [Google Scholar] [CrossRef]
  68. Guerra, N.; Tan, Y.X.; Joncker, N.T.; Choy, A.; Gallardo, F.; Xiong, N.; Knoblaugh, S.; Cado, D.; Greenberg, N.M.; Raulet, D.H. NKG2D-deficient mice are defective in tumor surveillance in models of spontaneous malignancy. Immunity 2008, 28, 571–580, Erratum in Immunity 2008, 28, 723. [Google Scholar] [CrossRef] [Green Version]
  69. Imai, K.; Matsuyama, S.; Miyake, S.; Suga, K.; Nakachi, K. Natural cytotoxic activity of peripheral-blood lymphocytes and cancer incidence: An 11-year follow-up study of a general population. Lancet 2000, 356, 1795–1799. [Google Scholar] [CrossRef]
  70. López-Soto, A.; Gonzalez, S.; Smyth, M.J.; Galluzzi, L. Control of Metastasis by NK Cells. Cancer Cell 2017, 32, 135–154. [Google Scholar] [CrossRef]
  71. Denkert, C.; von Minckwitz, G.; Darb-Esfahani, S.; Lederer, B.; Heppner, B.I.; Weber, K.E.; Budczies, J.; Huober, J.; Klauschen, F.; Furlanetto, J.; et al. Tumour-infiltrating lymphocytes and prognosis in different subtypes of breast cancer: A pooled analysis of 3771 patients treated with neoadjuvant therapy. Lancet Oncol. 2018, 19, 40–50. [Google Scholar] [CrossRef]
  72. Pernot, S.; Terme, M.; Radosevic-Robin, N.; Castan, F.; Badoual, C.; Marcheteau, E.; Penault-Llorca, F.; Bouche, O.; Bennouna, J.; Francois, E.; et al. Infiltrating and peripheral immune cell analysis in advanced gastric cancer according to the Lauren classification and its prognostic significance. Gastric. Cancer 2020, 23, 73–81. [Google Scholar] [CrossRef] [PubMed]
  73. Ménard, C.; Blay, J.Y.; Borg, C.; Michiels, S.; Ghiringhelli, F.; Robert, C.; Nonn, C.; Chaput, N.; Taïeb, J.; Delahaye, N.F.; et al. Natural killer cell IFN-gamma levels predict long-term survival with imatinib mesylate therapy in gastrointestinal stromal tumor-bearing patients. Cancer Res. 2009, 69, 3563–3569. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  74. Delahaye, N.F.; Rusakiewicz, S.; Martins, I.; Ménard, C.; Roux, S.; Lyonnet, L.; Paul, P.; Sarabi, M.; Chaput, N.; Semeraro, M.; et al. Alternatively spliced NKp30 isoforms affect the prognosis of gastrointestinal stromal tumors. Nat. Med. 2011, 17, 700–707. [Google Scholar] [CrossRef] [PubMed]
  75. Semeraro, M.; Rusakiewicz, S.; Minard-Colin, V.; Delahaye, N.F.; Enot, D.; Vély, F.; Marabelle, A.; Papoular, B.; Piperoglou, C.; Ponzoni, M.; et al. Clinical impact of the NKp30/B7-H6 axis in high-risk neuroblastoma patients. Sci. Transl. Med. 2015, 7, 283ra55. [Google Scholar] [CrossRef]
  76. Habif, G.; Crinier, A.; André, P.; Vivier, E.; Narni-Mancinelli, E. Targeting natural killer cells in solid tumors. Cell. Mol. Immunol. 2019, 16, 415–422. [Google Scholar] [CrossRef] [PubMed]
  77. Pasero, C.; Gravis, G.; Guerin, M.; Granjeaud, S.; Thomassin-Piana, J.; Rocchi, P.; Paciencia-Gros, M.; Poizat, F.; Bentobji, M.; Azario-Cheillan, F.; et al. Inherent and Tumor-Driven Immune Tolerance in the Prostate Microenvironment Impairs Natural Killer Cell Antitumor Activity. Cancer Res. 2016, 76, 2153–2165. [Google Scholar] [CrossRef] [Green Version]
  78. Broz, M.L.; Binnewies, M.; Boldajipour, B.; Nelson, A.E.; Pollack, J.L.; Erle, D.J.; Barczak, A.; Rosenblum, M.D.; Daud, A.; Barber, D.L.; et al. Dissecting the tumor myeloid compartment reveals rare activating antigen-presenting cells critical for T cell immunity. Cancer Cell 2014, 26, 638–652. [Google Scholar] [CrossRef] [Green Version]
  79. Sánchez-Paulete, A.R.; Cueto, F.J.; Martínez-López, M.; Labiano, S.; Morales-Kastresana, A.; Rodríguez-Ruiz, M.E.; Jure-Kunkel, M.; Azpilikueta, A.; Aznar, M.A.; Quetglas, J.I.; et al. Cancer Immunotherapy with Immunomodulatory Anti-CD137 and Anti-PD-1 Monoclonal Antibodies Requires BATF3-Dependent Dendritic Cells. Cancer Discov. 2016, 6, 71–79. [Google Scholar] [CrossRef] [Green Version]
  80. Roberts, E.W.; Broz, M.L.; Binnewies, M.; Headley, M.B.; Nelson, A.E.; Wolf, D.M.; Kaisho, T.; Bogunovic, D.; Bhardwaj, N.; Krummel, M.F. Critical Role for CD103(+)/CD141(+) Dendritic Cells Bearing CCR7 for Tumor Antigen Trafficking and Priming of T Cell Immunity in Melanoma. Cancer Cell 2016, 30, 324–336. [Google Scholar] [CrossRef] [Green Version]
  81. Salmon, H.; Idoyaga, J.; Rahman, A.; Leboeuf, M.; Remark, R.; Jordan, S.; Casanova-Acebes, M.; Khudoynazarova, M.; Agudo, J.; Tung, N.; et al. Expansion and Activation of CD103(+) Dendritic Cell Progenitors at the Tumor Site Enhances Tumor Responses to Therapeutic PD-L1 and BRAF Inhibition. Immunity 2016, 44, 924–938. [Google Scholar] [CrossRef] [Green Version]
  82. Wang, J.B.; Huang, X.; Li, F.R. Impaired dendritic cell functions in lung cancer: A review of recent advances and future perspectives. Cancer Commun. 2019, 39, 43. [Google Scholar] [CrossRef] [Green Version]
  83. Adhikaree, J.; Franks, H.A.; Televantos, C.; Vaghela, P.; Kaur, A.P.; Walker, D.; Schmitz, M.; Jackson, A.M.; Patel, P.M. Impaired circulating myeloid CD1c+ dendritic cell function in human glioblastoma is restored by p38 inhibition—implications for the next generation of DC vaccines. Oncoimmunology 2019, 8, 1593803. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  84. Legitimo, A.; Consolini, R.; Failli, A.; Orsini, G.; Spisni, R. Dendritic cell defects in the colorectal cancer. Hum. Vaccin Immunother. 2014, 10, 3224–3235. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  85. Cornwall, S.M.; Wikstrom, M.; Musk, A.W.; Alvarez, J.; Nowak, A.K.; Nelson, D.J. Human mesothelioma induces defects in dendritic cell numbers and antigen-processing function which predict survival outcomes. Oncoimmunology 2015, 5, e1082028. [Google Scholar] [CrossRef] [Green Version]
  86. Pinzon-Charry, A.; Maxwell, T.; McGuckin, M.A.; Schmidt, C.; Furnival, C.; López, J.A. Spontaneous apoptosis of blood dendritic cells in patients with breast cancer. Breast Cancer Res. 2006, 8, R5. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  87. Xiu, W.; Ma, J.; Lei, T.; Zhang, M.; Zhou, S. Immunosuppressive effect of bladder cancer on function of dendritic cells involving of Jak2/STAT3 pathway. Oncotarget 2016, 7, 63204–63214. [Google Scholar] [CrossRef] [Green Version]
  88. Kushwah, R.; Hu, J. Dendritic cell apoptosis: Regulation of tolerance versus immunity. J. Immunol. 2010, 185, 795–802. [Google Scholar] [CrossRef] [Green Version]
  89. Krawczyk, P.; Wojas, K.; Milanowski, J.; Roliński, J. The influence of different culture microenvironments on the generation of dendritic cells from non-small-cell lung cancer patients. Arch. Immunol. Ther. Exp. 2007, 55, 405–415. [Google Scholar] [CrossRef] [Green Version]
  90. Gardner, A.; de Mingo Pulido, Á.; Ruffell, B. Dendritic Cells and Their Role in Immunotherapy. Front. Immunol. 2020, 11, 924. [Google Scholar] [CrossRef]
  91. Zilionis, R.; Engblom, C.; Pfirschke, C.; Savova, V.; Zemmour, D.; Saatcioglu, H.D.; Krishnan, I.; Maroni, G.; Meyerovitz, C.V.; Kerwin, C.M.; et al. Single-Cell Transcriptomics of Human and Mouse Lung Cancers Reveals Conserved Myeloid Populations across Individuals and Species. Immunity 2019, 50, 1317–1334.e10. [Google Scholar] [CrossRef]
  92. Treilleux, I.; Blay, J.Y.; Bendriss-Vermare, N.; Ray-Coquard, I.; Bachelot, T.; Guastalla, J.P.; Bremond, A.; Goddard, S.; Pin, J.J.; Barthelemy-Dubois, C.; et al. Dendritic cell infiltration and prognosis of early stage breast cancer. Clin. Cancer Res. 2004, 10, 7466–7474. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  93. Raskov, H.; Orhan, A.; Christensen, J.P.; Gögenur, I. Cytotoxic CD8+ T cells in cancer and cancer immunotherapy. Br. J. Cancer 2021, 124, 359–367. [Google Scholar] [CrossRef] [PubMed]
  94. Farhood, B.; Najafi, M.; Mortezaee, K. CD8+ cytotoxic T lymphocytes in cancer immunotherapy: A review. J. Cell Physiol. 2019, 234, 8509–8521. [Google Scholar] [CrossRef] [PubMed]
Life 12 00678 g001 550
Figure 1. Kaplan–Meier curves. The probabilities of (A) PFS (p = 0.0274) and (B) OS (p = 0.1025) according to the SII value.
Figure 1. Kaplan–Meier curves. The probabilities of (A) PFS (p = 0.0274) and (B) OS (p = 0.1025) according to the SII value.
Life 12 00678 g001
Table
Table 1. Patient characteristics.
Table 1. Patient characteristics.
VariableN%
All patients51100.0
Histology
Seminoma1223.5
Nonseminoma3976.5
Primary tumor localization
Testicular4894.1
Extragonadal35.9
IGCCCG risk group
Good risk3364.7
Intermediate risk59.8
Poor risk1325.5
Stage IA and IB (adjuvant therapy)917.6
Sites of metastases
Retroperitoneum3874.5
Mediastinum713.7
Lungs1529.4
Liver713.7
Brain23.9
Other12.0
Visceral nonpulmonary metastases917.6
No. of metastatic site(s)
01019.6
1 to 22956.9
>31223.5
Staging (UICC)
IA23.9
IB713.7
IS12.0
IIA47.8
IIB917.6
IIC12.0
IIIA713.7
IIIB611.8
IIIC1427.5
Response to therapy *
Favorable response4894.1
Unfavorable response23.9
Median age (range)34(22–59)
Median follow-up (range)21.1(0.2–39.1)
IGCCCG, International Germ Cell Consensus Classification Group; UICC, Union for International Cancer Control, * in one patient NA.
Table
Table 2. Association between the systemic immune–inflammation index and percentage of innate immune cell subpopulations in chemotherapy-naïve GCT patients.
Table 2. Association between the systemic immune–inflammation index and percentage of innate immune cell subpopulations in chemotherapy-naïve GCT patients.
Total White Blood Cell Population (CD45+ Population)% of Innate Immune Cell Subpopulations
VariableNMeanSEMMedianp-Value UNIp-Value MVA
Total leukocyte subpopulations (percentage)Neutrophil percentage
SII < 10033256.71.758.70.000000.00496
SII ≥ 10031974.52.273.8
Monocyte percentage
SII < 10033210.00.610.20.11455
SII ≥ 1003198.70.77.9
Monocyte subpopulations (percentage)Classical monocyte percentage
SII < 10032384.91.785.70.12215
SII ≥ 10031786.42.090.7
Intermediate monocyte percentage
SII < 1003175.30.75.30.84080
SII ≥ 1003105.10.95.2
Nonclassical monocyte percentage
SII < 1003215.80.94.70.61768
SII ≥ 1003175.21.05.0
Total leukocyte subpopulations (percentage)Polymorphonuclear monocyte (PNMs) percentage
SII < 1003160.20.70.20.72347
SII ≥ 1003141.80.80.2
Eosinophil percentage
SII < 1003323.30.42.90.004310.63565
SII ≥ 1003191.40.51.0
Basophil percentage
SII < 1003320.70.10.70.008520.92744
SII ≥ 1003190.50.10.4
Lymphocyte subpopulations (percentage)NKT-cell percentage
SII < 1003312.50.51.30.98405
SII ≥ 1003192.60.71.8
CD4+ NKT-cell percentage
SII < 1003180.30.10.20.24894
SII ≥ 1003100.60.20.3
CD8+ NKT-cell percentage
SII < 1003192.20.41.10.16867
SII ≥ 1003102.50.62.3
NK-cell percentage
SII < 10033210.31.69.60.022640.06767
SII ≥ 10031918.32.115.8
Total leukocyte subpopulations (percentage)Dendritic cell (cDCs) percentage
SII < 1003220.90.10.90.027280.76790
SII ≥ 1003140.70.10.6
Plasmocytoid dendritic cell (pDCs) percentage
SII < 1003220.20.00.20.003100.84143
SII ≥ 1003140.10.00.1
Subpopulation of DCs (percentage)CD16+ HLADR+ Lin- DC percentage
SII < 10031348.95.346.11.00000
SII ≥ 10031246.95.547.4
CD1c+ within DC percentage
SII < 10031822.51.820.30.02497
SII ≥ 10031516.22.014.7
UNI—univariate logistic regression analysis; MVA—multivariate logistic regression analysis; significant p values are in bold. The variability within the total number of examined patient samples (N) within the evaluated subpopulations was due to the individual technical limitations, including missing antibodies or the poor quality of the examined samples.
Table
Table 3. Association between the systemic immune–inflammation index and the percentages of the different adaptive immune cell subpopulations in patients with germ cell tumors.
Table 3. Association between the systemic immune–inflammation index and the percentages of the different adaptive immune cell subpopulations in patients with germ cell tumors.
Total White Blood Cell Population (CD45+ Population)% of Adaptive Immune Cell Subpopulations
VariableNMeanSEMMedianp-Value UNIp-Value MVA
Total leukocyte subpopulations (percentage)Lymphocyte percentage
SII < 10033231.21.529.50.000000.42563
SII ≥ 10031915.02.013.8
Subpopulations of lymphocytes (percentage)B-cell percentage (CD14+)
SII < 10033211.00.810.70.60565
SII ≥ 10031910.71.08.8
T-cell percentage (CD3+)
SII < 10033276.31.777.30.014100.01385
SII ≥ 10031968.12.272.3
Helper T-cell percentage
SII < 10033145.71.747.20.37378
SII ≥ 10031942.72.143.8
Cytotoxic T-cell percentage
SII < 10033227.71.027.90.023830.12797
SII ≥ 10031923.41.323.4
T-reg percentage
SII < 1003324.10.23.80.53298
SII ≥ 1003193.90.33.9
UNI—univariate logistic regression analysis; MVA—multivariate logistic regression analysis; significant p values are in bold. The variability within the total number of examined patient samples (N) within the evaluated subpopulations was due to the individual technical limitations, including missing antibodies or the poor quality of the examined samples.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Kalavska, K.; Sestakova, Z.; Mlcakova, A.; Gronesova, P.; Miskovska, V.; Rejlekova, K.; Svetlovska, D.; Sycova-Mila, Z.; Obertova, J.; Palacka, P.; et al. Detection of Specific Immune Cell Subpopulation Changes Associated with Systemic Immune Inflammation–Index Level in Germ Cell Tumors. Life 2022, 12, 678. https://doi.org/10.3390/life12050678

AMA Style

Kalavska K, Sestakova Z, Mlcakova A, Gronesova P, Miskovska V, Rejlekova K, Svetlovska D, Sycova-Mila Z, Obertova J, Palacka P, et al. Detection of Specific Immune Cell Subpopulation Changes Associated with Systemic Immune Inflammation–Index Level in Germ Cell Tumors. Life. 2022; 12(5):678. https://doi.org/10.3390/life12050678

Chicago/Turabian Style

Kalavska, Katarina, Zuzana Sestakova, Andrea Mlcakova, Paulina Gronesova, Viera Miskovska, Katarina Rejlekova, Daniela Svetlovska, Zuzana Sycova-Mila, Jana Obertova, Patrik Palacka, and et al. 2022. "Detection of Specific Immune Cell Subpopulation Changes Associated with Systemic Immune Inflammation–Index Level in Germ Cell Tumors" Life 12, no. 5: 678. https://doi.org/10.3390/life12050678

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop