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Variability in CRP, regulatory T cells and effector T cells over time in gynaecological cancer patients: a study of potential oscillatory behaviour and correlations

Mutsa T Madondo1, Sandra Tuyaerts2, Brit B Turnbull3, Anke Vanderstraeten2, Holbrook Kohrt4, Balasubramanian Narasimhan3, Frederic Amant2, Michael Quinn5 and Magdalena Plebanski16*

Author Affiliations

1 Department of Immunology, Vaccine and Infectious Diseases Laboratory, Monash University, Melbourne, Australia

2 Division of Gynaecologic Oncology, Department of Oncology, KU Leuven, Leuven, Belgium

3 Department of Statistics and Department of Health Research and Policy, Stanford University, Stanford, CA, USA

4 Division of Haematology and Oncology, Centre for Clinical Sciences Research, Stanford University, Stanford, CA, USA

5 Womens Cancer Research Centre, Royal Women’s Hospital, Melbourne, Australia

6 Department of Immunology, Vaccine and Infectious Diseases Laboratory, School of Medicine and Health Science, Monash University Alfred Hospital Campus, 99 Commercial Road, Prahran, VIC 3181, Australia

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Journal of Translational Medicine 2014, 12:179  doi:10.1186/1479-5876-12-179

The electronic version of this article is the complete one and can be found online at: http://www.translational-medicine.com/content/12/1/179


Received:29 July 2013
Accepted:16 June 2014
Published:23 June 2014

© 2014 Madondo et al.; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.

Abstract

Background

The inflammatory marker, C reactive protein has been proposed to also be a biomarker for adaptive immune responses in cancer patients with a possible application in time based chemotherapy. Fluxes in serum CRP levels were suggested to be indicative of a cyclical process in which, immune activation is followed by auto-regulating immune suppression. The applicability of CRP as a biomarker for regulatory or effector T cells was therefore investigated in a cohort of patients with gynaecological malignancies.

Methods

Peripheral blood samples were obtained from a cohort of patients at 7 time points over a period of 12 days. Serum and mononuclear cells were isolated and CRP levels in serum were detected using ELISA while regulatory and effector T cell frequencies were assessed using flow cytometry. To test periodicity, periodogram analysis of data was employed while Pearson correlation and the Wilcoxon signed rank test were used to determine correlations.

Results

The statistical analysis used showed no evidence of periodic oscillation in either serum CRP concentrations or Teff and Treg frequencies. Furthermore, there was no apparent correlation between serum CRP concentrations and the corresponding frequencies of Tregs or Teffs. Relative to healthy individuals, the disease state in the patients neither significantly affected the mean frequency of Tregs nor the mean coefficient of variation within the Treg population over time. However, both Teff mean frequency and mean coefficient of variation were significantly reduced in patients.

Conclusion

Using our methods we were unable to detect CRP oscillations that could be used as a consistent serial biomarker for time based chemotherapy.

Keywords:
Inflammation; CRP; Gynaecological malignancies; Treg; Teff

Background

The link between inflammation and tumour development has been widely reported and recently, in an update to their seminal paper on the hallmarks of cancer, Hanahan and Weinberg refer to inflammation as an “enabling characteristic” for tumour development [1]. This link is well illustrated in gynaecological malignancies. The processes of ovulation and menstruation are associated with inflammation of the ovarian surface and endometrial epithelia [2,3]. During ovulation there is an increase in the macrophage population in the perifollicular stroma of pre-ovulatory follicles [4,5]. These macrophages produce pro-inflammatory cytokines Interleukin (IL)-6, IL-1β and Tumour Necrosis Factor (TNF), [2]. Oestrogen in the endometrium also induces the up-regulation of these pro-inflammatory cytokines [6]. Over time, the cyclical process of epithelial damage and repair, involving inflammation, predisposes to neoplastic growth. Not only does inflammation play a role in tumourigenesis, it also facilitates disease progression. The tumour microenvironment is rich in inflammatory mediators produced by tumour cells and infiltrating leukocytes [7-9]. Specifically, IL-6 triggers tumour cells to produce matrix metalloproteinase (MMP)-9 that promotes tumour growth by initiating angiogenesis [10].

C reactive protein (CRP), an acute phase protein produced by hepatocytes is the most widely used indicator of inflammation. The transcription of CRP is induced by IL-6 alone while the presence of either IL-1β or TNF augments its production [11,12]. Gynaecological cancer patients exhibit elevated levels of CRP, however these levels are not static. In a preliminary study of melanoma and ovarian cancer patients, serum CRP levels oscillated about a mean with a periodicity (λ) of 7 days [13]. The dynamic state of inflammation could indicate an underlying persistent but regulated anti-tumour immune response in the cancer patients, which is characterised by a cyclical repeating process of endogenous auto-vaccination, preceding suppression by regulatory cells.

In the past few decades the role of the adaptive immune system in driving anti-tumour immunity has been highlighted. Studies have shown a positive correlation between the number of tumour infiltrating effector T cells and patient survival, as well as a negative correlation with Foxp3+CD25HiCD4+ regulatory T cells [14,15]. Inflammatory cytokines and chemokines such as TNF and CCL22 facilitate the infiltration of T cells into the tumour microenvironment. When activated, effector T cells up-regulate CD25 and transiently express FoxP3 [16]. This enables them to expand and secrete pro-inflammatory cytokines. FoxP3+CD25HiCD4+ regulatory T cells, which express high levels of the CCL22 receptor CCR4, also infiltrate tumour sites where they expand in response to TNF and IL-2 produced by effector T cells, and subsequently inhibit the immune response [17-19].

A lag between the expansion phase of effector T cell and regulatory T cell populations could account for the oscillations in CRP levels observed in cancer patients. If this is the case, oscillations in serum CRP levels could be used to target regulatory T cells for depletion during their expansion phase, by using chemotherapy. Pre-treatment results are reported herein on a prospective phase II trial of cyclophosphamide treatment for gynaecological malignancy, where repeated sampling was taken prior to trial initiation. This allowed the opportunity to assess the hypothesis that CRP levels oscillate, and may be indicative of effector and regulatory T cell proportions and that the latter may also display predictable oscillations in patients with advanced gynaecological malignancies.

Methods

Trial design and patient details

In total, 19 patients with gynaecological tumours were included in this bicentric phase II trial. The patients had end stage treatment-refractory gynaecological tumours, i.e. ovarian cancer (n = 5), papillary serous cystadenocarcinoma (n = 9), adenocarcinoma (n = 1), serous cystadenocarcinoma (n = 1), endometrial carcinoma (n = 1), peritoneal tumour, (n = 1) or malignant mixed müllerian tumours (n = 1) and were recruited from the Royal Women’s Hospital, Melbourne, Australia (n = 12) and the University Hospital Leuven, Belgium (n = 7). Patient age ranged from 43–82 years (Table 1). The relative institution’s research and ethics committees approved the study. From the time of recruitment, written consent was obtained from patients and 7 blood collections were performed over 12 days with daily and second daily bleeds, to assess the presence of a CRP cycle. Blood was collected in the morning on days 1, 3, 5, 6, 8, 10 and 12 with one exception who had blood collected on days 1, 2, 4, 5, 6, 8, 12. Following collection, the blood samples were immediately transported to the laboratory for processing with a maximum of 12 hours between blood collection and freezing of plasma. Based on predicted CRP cycling patterns [13], patients went on to received treatment with low-dose cyclophosphamide, 50 mg administered p.o. twice daily for 3 consecutive days. Blood was also collected from 7 healthy volunteers at the Alfred Hospital, Melbourne, Australia. Ethics was obtained from the institution’s research board and written consent was also obtained from the volunteers. The blood collection protocol for the healthy volunteers was similar to that of the cancer patients.

Table 1. Patient summary

hsCRP plasma level measurements

Plasma was isolated from whole blood collected either in EDTA-coated tubes (Belgian patients) or in serum separation tubes (Australian patients) by centrifugation. After removing cellular and protein debris, plasma was aliquoted and stored at −80°C for later use. Hs CRP levels were determined by ELISA (Human High Sensitivity C-Reactive Protein ELISA kit; Cusabio Biotech Co., LTD, China) according to manufacturer’s instructions. For each treatment cycle, batch analysis was performed on all the necessary samples. To increase experimental precision, all samples were analysed in duplicate.

Peripheral blood mononuclear cells isolation

Mononuclear cells were obtained from peripheral whole blood collected in EDTA-coated tubes via Ficoll (Amersham Pharmacia Biotech, Sweden) density gradient centrifugation. The isolated PBMCs were cryopreserved using a freeze mixture containing 10% DMSO (Sigma-Aldrich, Australia) and either 90% Foetal Calf Serum (Australian samples, JRH Bioscience) or human AB serum (Belgian samples, Sera Laboratories International) and stored in freezing containers (Nalgene) and finally in liquid nitrogen until use. For use, cells were thawed in a 37°C water-bath and quickly re-suspended using AIM-V media (Invitrogen) with 5% human serum (Sigma). PBMC samples were available at all time points from 10 patients in total.

Flow cytometric analysis

To determine the frequency and phenotype of T cell populations in PBMCs of patients with gynaecological malignancies and age matched female, healthy volunteers, multicolour flow cytometric analysis was performed using the following surface antibodies: anti-CD3 Q655 (Invitrogen), anti-CD4 AF700 (BD Pharmingen), anti-CD25 PE (BD Pharmingen), and anti-CD127 Biotin (BD Pharmingen). Following primary staining, a fixable dead cell dye (Invitrogen) was also used to distinguish between dead and live cells. Intracellular levels of FoxP3 were determined following fixation and permeabilization using a fixation/permeabilization buffer kit (eBioscience) then staining with anti-FoxP3 PercpCy5.5 (eBioscience). Flow cytometry data was acquired on a Becton Dickinson LSR II using FACSDiva software, collecting a minimum of 100,000 events per sample. Fluorescence minus one (FMO) and isotype matched antibodies were used as controls with all samples. Data were analysed using Flowjo software (TreeStar).

Serum cytokine analysis

To assess serum IL-6 concentration, frozen sera from all patients were thawed over night at 4°C. BD™ Cytometric bead array was used for IL-6 (BD) detection in 50 μl of undiluted serum following the instructions prescribed by the manufacturer. Samples were then acquired by Flow cytometry on a Becton Dickinson LSR II using FACSDiva software, collecting a maximum of 5,000 events per sample. Data were analysed using Flowjo software (TreeStar).

Statistics

To determine the respective periodicity of serum CRP concentration, and Teff and Treg frequencies periodogram analysis was used. The null hypothesis was that there was no consistent period to the measurements, which implies no peaks in the mean periodogram beyond noise. Individual subjects’ periodograms were calculated and standardized to have sum of squares equal to 1, then averaged pointwise. Under the null hypothesis, the population pointwise mean periodogram is a horizontal line at 1. The pointwise, lower one-sided 95% confidence bound for the mean periodogram was calculated by the bias-corrected bootstrap method. This lower confidence bound was then compared to the null mark at 1. Exceeding 1 at a peak would be necessary to suggest a significant peak.

The Pearson correlation coefficient between Teff first differences and CRP first differences was estimated for each subject, and the Wilcoxon signed rank test was used to test whether the mean correlation coefficient was non-zero. The same procedure was performed to test for a relationship between Treg and CRP, Tregs and Teffs, and CRP and IL-6. The first difference is defined as the change in value from one time point to the next.

Coefficients of variation in the frequencies of Tregs and Teffs among 7 time points over a period of 12 days were determined by initially calculating the standard deviation, which was then expressed as percentages of the mean frequency of the respective population over the 7 time points. Statistical significances between mean values of Treg and Teff frequencies as well as coefficients of variation for both populations were calculated using non-parametric (Wilcoxon Mann Whitney) tests. P values < 0.05 were considered significant. All mean values were presented ± the standard error from the mean (SEM).

Results

CRP levels, and Treg and Teff frequencies did not appear to be oscillatory

The oscillation in serum CRP concentration with a period of 7 days has previously been reported in 15 late-stage advanced melanoma and 4 late-stage advanced ovarian cancer patients [13]. In a cohort of 19 patients with gynaecological malignancies, we investigated the periodicity of serum CRP concentration oscillations over time (raw data provided in Additional file 1: Table S1). In the individual patients, serum CRP concentrations appeared to fluctuate over time (Figure 1A), however when a mean standard periodogram analysis was carried out for the pooled patient cohort at every time point, while small peaks were apparent in the periodograms at 3.6 days and 4.6 days, neither was significant (Figure 1A). In a smaller cohort of 10 patients from whom PBMCs were collected, the periodicity of peripheral blood Treg and Teff frequencies was also investigated. Tregs were defined as expressing high levels of CD25 and FoxP3, which we confirmed expressed low levels of CD127, while Teffs were defined as expressing intermediate levels of CD25 (Additional file 2: Figure S1) as per previous studies [20,21]. Similar to serum CRP concentration, both frequencies appeared to fluctuate over time, in some individual patients (Figure 1B and C). In the pooled cohort, mean standard periodogram analysis revealed small peaks for Treg frequencies at 4.8 days and for Teff frequencies at 3.6 and 4.6 days, but none was significant (Figure 1B and C) statistically at the p = 0.05 level.

Additional file 1: Table S1a. CRP raw data. Table S1b. Treg and Teff frequency raw data.

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thumbnailFigure 1. The periodicity of serum CRP concentration, and Treg and Teff frequency. Patients with gynaecological malignancies had blood collected 7 times over a period of 12 days. Serum was collected from 19 patients. While in 10 of the patients, PBMCs were also isolated. CRP levels in sera were determined along with the frequencies of Tregs and Teff within CD4+ T cells from PBMCs. The values for A, serum CRP concentration (n = 19, solid lines use the left Y axis while dashed lines use the right Y axis) (mg/L), B Treg and C, Teff frequencies (%, n = 10, solid lines use the left Y axis while dashed lines use the right Y axis) over time (days) were plotted in spaghetti plots. The periodicity for A, CRP concentration, B, Treg and C, Teff frequencies were assessed using the null hypothesis, that the population pointwise mean periodogram was a horizontal line at 1 (dotted line). The pointwise lower one-sided 95% confidence bound (dashed line) needed to exceed the null mark (dotted line) to suggest a significant peak.

Additional file 2: Figure S1. Gating strategy for Tregs and Teffs. Peripheral blood mononuclear cells were stained for the following markers CD3, CD4, CD25 and FoxP3. A, Teffs (dashed line) were defined as CD3+CD4+CD25intermediate while Tregs (dotted line) were CD3+CD25HiFoxP3+. B, FoxP3 and CD127 expression on Tregs (solid line) and Teffs (dashed line).

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Serum CRP concentrations were not correlated with Treg or Teff frequencies

Although there was no evidence of a predictable oscillatory pattern of serum CRP concentration over time, we investigated whether CRP could still be used as a surrogate marker for Treg or Teff frequency in peripheral blood. Using the 10 patients from which, both serum and PBMCs were collected, serum CRP concentrations from each time point within each individual patient were then correlated with serum IL-6 concentration as well as the corresponding frequencies of Tregs or Teffs. The correlation coefficients from the patients showed a significant positive correlation between serum CRP and serum IL-6 concentrations (p = 0.03; Table 2). However, there appeared to be no apparent relationship between serum CRP concentration, and Treg or Teff frequencies (Table 3 and Table 4). Tregs and Teffs frequencies on the other hand, correlated significantly with each other (p = 0.01; Table 5).

Table 2. Correlation coefficients of CRP vs IL-6

Table 3. Correlation coefficients of CRP vs Tregs

Table 4. Correlation coefficients of CRP vs Teffs

Table 5. Correlation coefficients of Tregs vs Teffs

Disease state in the cancer patients did not increase the variation of Treg or Teff frequencies within CD4+ T cells over time

There did not appear to be a correlative linear relationship between inflammation as shown by serum CRP concentration and peripheral blood Tregs and Teffs frequencies. We investigated whether the disease state in patients may still influence the magnitude of variation in the frequencies of Tregs and Teffs over time. We initially compared the mean frequencies of Tregs and Teffs within CD4+ T cells between 10 cancer patients and 7, age and sex matched, healthy donors. Although the mean frequency of Teffs only, was significantly lower in cancer patients (8% ± 2%) than healthy donors (11% ± 0.6%, p = 0.005; Figure 2A), the mean ratio of Teffs to Tregs was not significantly different. We then assessed whether cancer patients, who have raised inflammation, evident by higher CRP levels relative to healthy individuals [13], exhibited greater variation in the frequencies of either Tregs or Teffs over time. The variation in Treg and Teff frequencies from 7 blood collections over a 12-day period was compared between the 7 healthy volunteers and 10 cancer patients. Both cancer patients and healthy volunteers had a similar mean coefficient of variation in Treg frequency (26% ± 5% and 29% ± 3% respectively, Figure 2B). The mean coefficient of variation in Teffs frequency was significantly lower in cancer patients (13% ± 1%, p = 0.002) compared to healthy volunteers (22% ± 2%, Figure 2B). While the mean coefficient of variation in the ratio of Teffs to Tregs was lower in cancer patients compared to healthy volunteers (21.7% ± 3.3% and 27.8% ± 2.2%, Figure 2B), this was not significant.

thumbnailFigure 2. Variation in Treg and Teff frequencies. A, The mean frequency from 7 readings of Tregs and Teffs over a period of 12 days was obtained for each of 10 cancer patients and 7 healthy donors. The ratio of Teff to Tregs was also calculated. B, The coefficients of variation in the frequencies of Tregs and Teffs as well as the ratio of Teffs to Tregs were determined by calculating the standard deviation among the 7 time points for each cancer patient and healthy donor and expressing it as a percentage of the mean frequency. Wilcoxon and Mann Whitney tests were used to determine significant differences between mean values, with p < 0.05 indicating significance. Graphs show mean ± standard error from the mean (SEM).

Discussion

The dynamic nature of the adaptive immune responses has been described in two ways. Firstly, there are diurnal fluctuations, regulated by the circadian clock exemplified in CD4 T cells by the rhythmic expression of genes that control cytokine secretion and cell function [22]. Secondly, antigen dependent fluctuations occur during acute infections and the kinetics of the immune response are controlled by a system of positive and negative feedback mechanisms designed to limit the immune response when pathogenic insults are resolved. In chronic diseases such as cancer, antigenic clearance does not occur and the persistent antigen exposure results in a constant state of immune activation. The tumour however, limits immune activation by secreting immune inhibitory cytokines such as IL-10, and tumour growth factor (TGF)-β as well as inducing regulatory cell populations including myeloid derived suppressor cells (MDSCs) and regulatory T cells [18,23-25]. Although intratumoural immune cells are skewed towards immune inhibition, there may still exist a homeostatic balance that needs to be maintained in the periphery of cancer patients in which an oscillating sequence of immune activation is followed by negative feedback immune suppression [13]. The study herein however, showed that CRP concentrations in all the patients did not appear to oscillate periodically nor did the concentration of CRP correlate significantly with Treg or Teff frequencies. Therefore, CRP may not be a practical useful surrogate marker for either cell population. In a study of 12 patients with melanoma, less than 50% showed possible time dependent CRP concentration profiles [26]. The two data sets available therefore suggest that inflammation in cancer patients may not consistently follow an oscillatory, sinusoidal (or other mathematical) pattern with a constant period or amplitude. This may be because any such model oversimplifies the inflammatory process, and correlations are easily disrupted by a number of potential additional in vivo co-parameters or process variations. Multiple factors influence the levels of cytokines that regulate inflammation. One such factor is the tumour growth. In patients with papillary thyroid cancer CD4+ T cell frequencies correlate with tumour size while Treg frequencies correlate with lymph node metastasis [27]. Such variables may hence significantly influence the magnitude of T cell effector and suppressor frequency and function.

In our study, neither the coefficient of variation of both Tregs and Teffs nor that of the ratio of Teff to Tregs, in cancer patients was greater than that of healthy donors suggesting that in the periphery the fluctuation of Treg within the CD4 population was not affected by the presence of the tumour. The coefficient of variation of Teffs was lower in cancer patients compared to healthy donors, which may have been due to immune suppression. Consistent with this suggestion, the frequency of Teffs was significantly lower in the patients than in healthy donors. Although Treg frequencies in the periphery were not significantly increased, other cell subsets such as MDSCs may also facilitate immune suppression. Pro-inflammatory mediators have also been suggested to promote accumulation of MDSC in cancer patients’ peripheral blood [28]. Within the tumour microenvironment, the fluxes between Treg and Teff populations may be more evident.

Although changes in the frequencies of conventional Treg and Teff did not correlate with inflammation, it is still possible that minor subsets within each phenotype (Treg or Teff) or their specific function over time, may correlate. Indeed, effector and regulatory T cells are heterogeneous populations of cells. Further breaking down these populations based on phenotype and function may also show greater variation between patients and healthy donors. For example, Treg populations with enhanced suppressive function due to elevated expression of inhibitory receptors such as glucocorticoid induced tumour necrosis factor receptor (GITR) and cytotoxic T-lymphocyte antigen (CTLA)-4 as well as increased production of suppressive factors such as adenosine and cytokines TGF-β and IL-10, have been reported to be elevated in cancer patients [29-31]. Similarly, effector T cells can be broken down into different functional phenotypes such as IL-17 secreting and type-1 interferon secreting Teffs. Type-1 interferon secreting Teffs promote proliferation of cytotoxic CD8 T cells, which contribute towards an anti-tumour effect [32-35]. It cannot therefore be excluded that the frequencies of some of these functional subsets, as well as CD8 T cells, may be subject to regulation by inflammatory factors, even when the results presented herein show that total Treg and Teff populations are not correlated to inflammatory status as reflected by CRP levels in blood. Mathematical models that aim to predict the balance that exists between immune activation and regulation within accessible blood samples, will therefore benefit from additionally taking into account the following variables: i) the effect of diurnal variation ii) the tumour growth rate iii) the heterogeneity present within both regulatory and effector T cell populations.

In conclusion, in our sample of patients with gynaecological malignancies, CRP concentrations do not oscillate in a consistent predictable manner, and do not correlate either positively or negatively with conventional Treg or Teff subsets. Therefore, there is no evidence to suggest that CRP can reliably be used across cancer patients as a surrogate, time-sensitive and most importantly, predictive marker, to reflect circulating effector or regulatory T cell frequencies, as previously suggested [13]. Time based therapy founded on modelling a consistent cyclical pattern of inflammation using serum CRP concentration as a predictive marker of regulatory T cell expansion may not be possible. However, we cannot exclude that further investigating the kinetics of inflammation in cancer patients, perhaps by taking more frequent blood samples or else by taking into consideration multiple inflammatory and immune-regulatory parameters, the progressive nature of immune suppression, as well as the heterogeneity of effector and regulatory T cell populations could all help in modelling more complex, and potentially predictive, equations.

Abbreviations

Tregs: Regulatory T cells; Teffs: Effector T cells; CRP: C reactive protein.

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

FA, MQ and MP contributed to the design of the study and FA and MQ organised patient recruitment. MM, ST and AV conducted experiments, while MM, MP and ST analysed data and drafted the manuscript. HK, BT and BN carried out statistical analysis of data. All authors proofread, edited and approved the manuscript.

Acknowledgements

The authors would like to acknowledge the nurses who collected blood samples and kept patient records for the study as well as the patients and healthy volunteers who participated in this study. We would like to thank Brad Efron for statistical advice along with Alison Hamlett and Karen Scalzo Inguanti for assisting with experimental setup. We would also like to thank Anticancer Fund for scientific and financial support.

References

  1. Hanahan D, Weinberg RA: Hallmarks of cancer: the next generation.

    Cell 2011, 144:646-674. PubMed Abstract | Publisher Full Text OpenURL

  2. Maccio A, Madeddu C: Inflammation and ovarian cancer.

    Cytokine 2012, 58:133-147. PubMed Abstract | Publisher Full Text OpenURL

  3. Wallace AE, Gibson DA, Saunders PT, Jabbour HN: Inflammatory events in endometrial adenocarcinoma.

    J Endocrinol 2010, 206:141-157. PubMed Abstract | Publisher Full Text OpenURL

  4. Brannstrom M, Pascoe V, Norman RJ, McClure N: Localization of leukocyte subsets in the follicle wall and in the corpus luteum throughout the human menstrual cycle.

    Fertil Steril 1994, 61:488-495. PubMed Abstract OpenURL

  5. Dahm-Kahler P, Ghahremani M, Lind AK, Sundfeldt K, Brannstrom M: Monocyte chemotactic protein-1 (MCP-1), its receptor, and macrophages in the perifollicular stroma during the human ovulatory process.

    Fertil Steril 2009, 91:231-239. PubMed Abstract | Publisher Full Text OpenURL

  6. He YY, Cai B, Yang YX, Liu XL, Wan XP: Estrogenic G protein-coupled receptor 30 signaling is involved in regulation of endometrial carcinoma by promoting proliferation, invasion potential, and interleukin-6 secretion via the MEK/ERK mitogen-activated protein kinase pathway.

    Cancer Sci 2009, 100:1051-1061. PubMed Abstract | Publisher Full Text OpenURL

  7. Charles KA, Kulbe H, Soper R, Escorcio-Correia M, Lawrence T, Schultheis A, Chakravarty P, Thompson RG, Kollias G, Smyth JF, Balkwill FR, Hagemann T: The tumor-promoting actions of TNF-alpha involve TNFR1 and IL-17 in ovarian cancer in mice and humans.

    J Clin Invest 2009, 119:3011-3023. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  8. Kulbe H, Chakravarty P, Leinster DA, Charles KA, Kwong J, Thompson RG, Coward JI, Schioppa T, Robinson SC, Gallagher WM, Galletta L, Salako MA, Smyth JF, Hagemann T, Brennan DJ, Bowtell DD, Balkwill FR, Australian Ovarian Cancer Study Group: A dynamic inflammatory cytokine network in the human ovarian cancer microenvironment.

    Cancer Res 2012, 72:66-75. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  9. Burke F, Relf M, Negus R, Balkwill F: A cytokine profile of normal and malignant ovary.

    Cytokine 1996, 8:578-585. PubMed Abstract | Publisher Full Text OpenURL

  10. Rabinovich A, Medina L, Piura B, Segal S, Huleihel M: Regulation of ovarian carcinoma SKOV-3 cell proliferation and secretion of MMPs by autocrine IL-6.

    Anticancer Res 2007, 27:267-272. PubMed Abstract | Publisher Full Text OpenURL

  11. Young DP, Kushner I, Samols D: Binding of C/EBPbeta to the C-reactive protein (CRP) promoter in Hep3B cells is associated with transcription of CRP mRNA.

    J Immunol 2008, 181:2420-2427. PubMed Abstract | Publisher Full Text OpenURL

  12. Yoshizaki K: Pathogenic role of IL-6 combined with TNF-alpha or IL-1 in the induction of acute phase proteins SAA and CRP in chronic inflammatory diseases.

    Adv Exp Med Biol 2011, 691:141-150. PubMed Abstract | Publisher Full Text OpenURL

  13. Coventry BJ, Ashdown ML, Quinn MA, Markovic SN, Yatomi-Clarke SL, Robinson AP: CRP identifies homeostatic immune oscillations in cancer patients: a potential treatment targeting tool?

    J Transl Med 2009, 7:102. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text OpenURL

  14. Li J, Mo HY, Xiong G, Zhang L, He J, Huang ZF, Liu ZW, Chen QY, Du ZM, Zheng LM, Qian CN, Zeng YX: Tumor microenvironment MIF directs the accumulation of IL-17-producing tumor-infiltrating lymphocytes and predicts favorable survival in nasopharyngeal carcinoma patients.

    J Biol Chem 2012, 287:35484-35495. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  15. Katz SC, Bamboat ZM, Maker AV, Shia J, Pillarisetty VG, Yopp AC, Hedvat CV, Gonen M, Jarnagin WR, Fong Y, D’Angelica MI, DeMatteo RP: Regulatory T Cell infiltration predicts outcome following resection of colorectal cancer liver metastases.

    Ann Surg Oncol 2013, 20:946-955. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  16. Allan SE, Crome SQ, Crellin NK, Passerini L, Steiner TS, Bacchetta R, Roncarolo MG, Levings MK: Activation-induced FOXP3 in human T effector cells does not suppress proliferation or cytokine production.

    Int Immunol 2007, 19:345-354. PubMed Abstract | Publisher Full Text OpenURL

  17. Miller AM, Lundberg K, Ozenci V, Banham AH, Hellstrom M, Egevad L, Pisa P: CD4 + CD25high T cells are enriched in the tumor and peripheral blood of prostate cancer patients.

    J Immunol 2006, 177:7398-7405. PubMed Abstract | Publisher Full Text OpenURL

  18. Curiel TJ, Coukos G, Zou L, Alvarez X, Cheng P, Mottram P, Evdemon-Hogan M, Conejo-Garcia JR, Zhang L, Burow M, Zhu Y, Wei S, Kryczek I, Daniel B, Gordon A, Myers L, Lackner A, Disis ML, Knutson KL, Chen L, Zou W: Specific recruitment of regulatory T cells in ovarian carcinoma fosters immune privilege and predicts reduced survival.

    Nat Med 2004, 10:942-949. PubMed Abstract | Publisher Full Text OpenURL

  19. Hamano R, Huang J, Yoshimura T, Oppenheim JJ, Chen X: TNF optimally activatives regulatory T cells by inducing TNF receptor superfamily members TNFR2, 4-1BB and OX40.

    Eur J Immunol 2011, 41:2010-2020. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  20. Seddiki N, Santner-Nanan B, Martinson J, Zaunders J, Sasson S, Landay A, Solomon M, Selby W, Alexander SI, Nanan R, Kelleher A, Fazekas de St Groth B, Kelleher A, Fazekas-de-St-Groth B: Expression of interleukin (IL)-2 and IL-7 receptors discriminates between human regulatory and activated T cells.

    J Exp Med 2006, 203:1693-1700. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  21. Triplett TA, Curti BD, Bonafede PR, Miller WL, Walker EB, Weinberg AD: Defining a functionally distinct subset of human memory CD4+ T cells that are CD25POS and FOXP3NEG.

    Eur J Immunol 2012, 42:1893-1905. PubMed Abstract | Publisher Full Text OpenURL

  22. Bollinger T, Leutz A, Leliavski A, Skrum L, Kovac J, Bonacina L, Benedict C, Lange T, Westermann J, Oster H, Solbach W: Circadian clocks in mouse and human CD4+ T cells.

    PLoS One 2011, 6:e29801. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  23. Mustea A, Konsgen D, Braicu EI, Pirvulescu C, Sun P, Sofroni D, Lichtenegger W, Sehouli J: Expression of IL-10 in patients with ovarian carcinoma.

    Anticancer Res 2006, 26:1715-1718. PubMed Abstract | Publisher Full Text OpenURL

  24. Hirashima Y, Kobayashi H, Suzuki M, Tanaka Y, Kanayama N, Terao T: Transforming growth factor-beta1 produced by ovarian cancer cell line HRA stimulates attachment and invasion through an up-regulation of plasminogen activator inhibitor type-1 in human peritoneal mesothelial cells.

    J Biol Chem 2003, 278:26793-26802. PubMed Abstract | Publisher Full Text OpenURL

  25. Lechner MG, Megiel C, Russell SM, Bingham B, Arger N, Woo T, Epstein AL: Functional characterization of human Cd33+ and Cd11b + myeloid-derived suppressor cell subsets induced from peripheral blood mononuclear cells co-cultured with a diverse set of human tumor cell lines.

    J Transl Med 2011, 9:90. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text OpenURL

  26. Leontovich AA, Dronca RS, Suman VJ, Ashdown ML, Nevala WK, Thompson MA, Robinson A, Kottschade LA, Kaur JS, McWilliams RR, Ivanov LV, Croghan GA, Markovic SN: Fluctuation of systemic immunity in melanoma and implications for timing of therapy.

    Front Biosci (Elite Ed) 2012, 4:958-975. PubMed Abstract | Publisher Full Text OpenURL

  27. French JD, Weber ZJ, Fretwell DL, Said S, Klopper JP, Haugen BR: Tumor-associated lymphocytes and increased FoxP3+ regulatory T cell frequency correlate with more aggressive papillary thyroid cancer.

    J Clin Endocrinol Metab 2010, 95:2325-2333. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  28. Ostrand-Rosenberg S, Sinha P: Myeloid-derived suppressor cells: linking inflammation and cancer.

    J Immunol 2009, 182:4499-4506. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  29. Park HJ, Kusnadi A, Lee EJ, Kim WW, Cho BC, Lee IJ, Seong J, Ha SJ: Tumor-infiltrating regulatory T cells delineated by upregulation of PD-1 and inhibitory receptors.

    Cell Immunol 2012, 278:76-83. PubMed Abstract | Publisher Full Text OpenURL

  30. Schuler PJ, Schilling B, Harasymczuk M, Hoffmann TK, Johnson J, Lang S, Whiteside TL: Phenotypic and functional characteristics of CD4+ CD39+ FOXP3+ and CD4+ CD39+ FOXP3neg T-cell subsets in cancer patients.

    Eur J Immunol 2012, 42:1876-1885. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  31. Chen ZF, Xu Q, Ding JB, Zhang Y, Du R, Ding Y: CD4 + CD25 + Foxp3+ Treg and TGF-beta play important roles in pathogenesis of Uygur cervical carcinoma.

    Eur J Gynaecol Oncol 2012, 33:502-507. PubMed Abstract OpenURL

  32. Chiba S, Baghdadi M, Akiba H, Yoshiyama H, Kinoshita I, Dosaka-Akita H, Fujioka Y, Ohba Y, Gorman JV, Colgan JD, Hirashima M, Uede T, Takaoka A, Yagita H, Jinushi M: Tumor-infiltrating DCs suppress nucleic acid-mediated innate immune responses through interactions between the receptor TIM-3 and the alarmin HMGB1.

    Nat Immunol 2012, 13:832-842. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  33. Nunez S, Saez JJ, Fernandez D, Flores-Santibanez F, Alvarez K, Tejon G, Ruiz P, Maldonado P, Hidalgo Y, Manriquez V, Bono MR, Rosemblatt M, Sauma D: Th17 cells contribute to anti-tumor immunity and promote the recruitment of th1 cells to the tumor.

    Immunology 2013, 139:61-71. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  34. Gao Y, Whitaker-Dowling P, Griffin JA, Bergman I: Treatment with targeted vesicular stomatitis virus generates therapeutic multifunctional anti-tumor memory CD4 T cells.

    Cancer Gene Ther 2012, 19:282-291. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  35. Quezada SA, Simpson TR, Peggs KS, Merghoub T, Vider J, Fan X, Blasberg R, Yagita H, Muranski P, Antony PA, Restifo NP, Allison JP: Tumor-reactive CD4(+) T cells develop cytotoxic activity and eradicate large established melanoma after transfer into lymphopenic hosts.

    J Exp Med 2010, 207:637-650. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL