Next Article in Journal
Wheat Antipodal Cells with Polytene Chromosomes in the Embryo Sac Are Key to Understanding the Formation of Grain in Cereals
Next Article in Special Issue
Application of Multigene Panels Testing for Hereditary Cancer Syndromes
Previous Article in Journal
Drug Discovery Strategies for Inherited Retinal Degenerations
Previous Article in Special Issue
The Influence of Sexual Behavior and Demographic Characteristics in the Expression of HPV-Related Biomarkers in a Colposcopy Population of Reproductive Age Greek Women
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Biology
Volume 11
Issue 9
10.3390/biology11091339
biology-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

Intraoperative Flow Cytometry for the Characterization of Gynecological Malignancies

by
Zoi Anastasiadi
1,†,
Stefania Mantziou
2,†,
Christos Akrivis
1,
Minas Paschopoulos
3,
Eufemia Balasi
4,
Georgios D. Lianos
5,
George A. Alexiou
6,7,
Michail Mitsis
5,
George Vartholomatos
2,7,* and
Georgios S. Markopoulos
2,7,*
1
Department of Obstetrics and Gynecology, ‘G. Chatzikosta’ General Hospital, 45001 Ioannina, Greece
2
Haematology Laboratory-Unit of Molecular Biology, University Hospital of Ioannina, 45500 Ioannina, Greece
3
Department of Obstetrics and Gynecology, University Hospital of Ioannina, 45500 Ioannina, Greece
4
Pathology Department, ‘G. Chatzikosta’ General Hospital, 45001 Ioannina, Greece
5
Department of Surgery, University Hospital of Ioannina, 45500 Ioannina, Greece
6
Department of Neurosurgery, University Hospital of Ioannina, 45500 Ioannina, Greece
7
Neurosurgical Institute, Faculty of Medicine, University of Ioannina, 45110 Ioannina, Greece
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Biology 2022, 11(9), 1339; https://doi.org/10.3390/biology11091339
Submission received: 1 August 2022 / Revised: 6 September 2022 / Accepted: 8 September 2022 / Published: 11 September 2022
(This article belongs to the Special Issue Prevention and Diagnosis of Gynaecological Cancers)
Browse Figures
Versions Notes

Abstract

:

Simple Summary

Aneuploidy and high proliferative potential are distinct features of neoplastic cells. Based on the established role of intraoperative flow cytometry in various types of cancer, the aim of the present study was to investigate its role in cancer cell identification during surgery for gynecological malignancies. The analysis time was 5–6 min per sample. A large percentage of tumors were characterized as aneuploid, while all tumor samples had a significantly high proliferation. Flow cytometry was performed in accordance with pathological evaluation, and the method had high sensitivity and specificity. Our results verify the value of intraoperative flow cytometry in gynecological malignancies, and warrant further investigation in multicenter studies.

Abstract

Cell-cycle analysis has shown the presence of aneuploidy to be associated with poor prognosis. We developed an innovative rapid cell-cycle analysis protocol (the Ioannina protocol) that permitted the intraoperative identification of neoplastic cells in a plethora of malignancies. Herein, we aimed to investigate the potential role of cell-cycle analysis in the intraoperative characterization of gynecological malignancies. Women who underwent surgery for gynecological malignancies in our institution over a three-year period were included in this study. Permanent section pathology evaluation was used as the gold standard for malignancy evaluation. Total accordance was observed between flow cytometry and pathology evaluation. In total, 21 aneuploid cancers were detected following DNA index calculation. Of these, 20 were hyperploid and 1 was hypoploid. In addition, tumor samples were characterized by a significantly lower percentage of cells in G0/G1, as well as an induced tumor index. The response time for flow cytometry to obtain results was 5–6 min per sample. It seems that flow cytometry analyses for intraoperative tumor evaluation can be safely expanded to gynecological malignancies. This is a novel practical approach that has been proven valuable in several tumor types to date, and also seems to be reliable for gynecological malignancies. Intraoperative flow cytometry is expected to be crucial in decisions of lymph node dissection in endometrial cancers, due to its rapid response regarding the tumor invasion of part or all of the myometrial thickness. In this way, the surgeon can quickly modify the plane of dissection. Our results warrant the further investigation of applying iFC in larger, multicenter studies.

1. Introduction

Cancer is the second leading cause of human mortality worldwide, recording 10 million cancer deaths in the last year, while future estimations remain dismal; cancer-related incidence and mortality is projected to exceed 28 million new cases and, ominously, 16 million deaths by 2040 [1]. From this perspective, gynecological malignancies represent a major health issue, affecting many organs across the female reproductive tract—mainly the endometrium, cervix, ovaries, and vulva [2,3]—accounting for 1,335,453 new cases and approximately 646,453 deaths in the year 2020 [4]. One distinctive feature of cancer is the constant creation of abnormal cells that defy contact inhibition, invade adjoining tissues, and cause metastasis—the primary cause of death from cancer.
Endometrial cancer is the second most common cause of cancer in women, after breast cancer [5], and can be divided into two main subtypes according to Bokhman’s dualistic model [6]: the most common (type I) tumors are adenocarcinomas (with or without squamous morphological features), often well-differentiated [7], and are reported to be correlated with high serum estrogen levels. The remaining sporadic endometrial tumors—nonendometrioid type II—account for the majority of deaths and recurrences, and are histologically distinguished as uterine papillary serous carcinoma (UPSC) and clear-cell carcinoma [8]. Cervical cancers present as endocervical adenocarcinoma or squamous-cell carcinomas. Vulvar and vaginal cancers, typically diagnosed as squamous-cell carcinomas, account for approximately 4% of malignancies. Uterine sarcomas comprise less than 1% of gynecological malignancies and 2–5% of all uterine malignancies, and include sarcomas (arising in the endometrial stroma), carcinosarcomas (mixed mesodermal sarcomas or Mullerian tumors), and leiomyosarcomas (in the myometrial muscle) [9,10].
Ovarian cancer is reported as one of the most prevalent pathologies in gynecological oncology. This malignancy is characterized as epithelial or non-epithelial [11], and can be categorized into further subgroups: low-grade and high-grade serous cancer, [3] endometrioid, clear-cell, and mucinous ovarian cancer [12]. Many genes have been implicated in pathogenesis, with the most prevalent being the p53, KRAS, PTEN, and MEK and their related signalling pathways. Due to the lack of specific symptoms, and as they exhibit sporadic distribution (only 5–10% of cases are correlated with family history), 70% of ovarian cancer cases are detected in the advanced stages [13], thus having the lowest survival rate among all gynecological cancers [12].
Cancer development and progression are manifested by an interplay between environmental and genetic factors, and each type exhibits distinct morphological and molecular characteristics. Classification of cancer subtype and stage is mainly based on several systems, including clinical, histological, endocrinological, and genetic modifications. The latter involves DNA content measurement, profiling of differentially expressed genes, and identification of a different set of cell surface markers (CDs), which can be used as biomarkers [14,15,16]. This procedure is of critical importance, as it can contribute to the optimization of pharmaceutical approaches and treatment strategies, predict chemoresistance, and assess patients’ prognosis [17]. Previous studies showed that independent prognostic factors such as high mitotic rate, large tumor size, and advanced age were associated with shorter survival [18].
In the fight against cancer, computed tomography (CT) scans and, even more, magnetic resonance imaging (MRI), provide crucial information in determining tumor location and size, and can help delineate tumor boundaries. However, some cancer types—such as cervical tumors—can be indistinguishable from adjacent tissue or appear as a non-specific enlargement due to being isodense [19]. These limitations pose a gap between diagnosis and treatment, as accurate definition of a tumor’s healthy margins has a high prognostic value, and is of paramount importance for assessing the risk of recurrence. Successful surgical resection of a tumor offers complete cancer removal, increases the five-year relative survival rate, and is considered to be of major importance in treatment.
Flow cytometry is among the most powerful cell-specific analytical tools, with several applications in the study of cancer, including cancer cell immunophenotyping, characterization of hematological malignancies, revealing minimal residual disease and metastatic progression, and measuring DNA content and ploidy to study cell cycle progression [20]. Intraoperative flow cytometry (iFC) is a breakthrough technique enabling the cytometric analysis of DNA content/ploidy and cell-cycle distribution of cells acquired during surgical resection of tumors to characterize cancer cells and to evaluate the limits of resection, bridging the aforementioned gap between evaluation and surgery. This procedure has been applied by our team during central nervous tumor surgeries, and its significance has since been confirmed in several additional cancer types, including head-and-neck malignancies, breast cancer, hepatocellular cancer, pancreatic cancer, and colorectal cancer [21,22,23,24,25,26,27,28,29,30]. Intraoperative flow cytometry offers good diagnostic potential with high sensitivity and specificity, while a prognostic role is also suggested. With an analysis time of less than 10 min, iFC is considered to be an accurate next-generation cancer-cell evaluation technique that can be applied intraoperatively [24,27,28,31,32]. The diagnostic and prognostic potential of iFC has been independently verified for central nervous system malignancies by a research team in Tokyo, Japan [33,34,35,36,37].
In the present study, we present a novel approach in defining gynecological tumors’ biology during surgery by applying iFC. Our results indicate the impact of iFC during surgery, and support the utilization of iFC in further multicenter studies.

2. Materials and Methods

This study included patients hospitalized in the Department of Obstetrics and Gynecology at the General Hospital of Hatzikosta, over a 3-year period (2018–2021), who underwent surgery for gynecological malignancies. Fifty women (aged 35 to 83 years, with a median age of 64.13 years) were recruited to the study: 36 women with endometrial cancer, 8 women with ovarian cancer, 1 woman with cervical cancer, 2 women with uterine sarcoma, and 3 women with complex endometrial hyperplasia with atypia. During each surgery, a tumor sample with a volume of ~5 mm2 was removed with a scalpel from tumor tissue, along with another sample from macroscopic healthy tissue. The sample was divided into two equal pieces; half was sent for flow cytometry analysis and the other half for frozen-section pathology evaluation. Both the pathology and flow cytometry analysis evaluations were blinded. The pathology examination of samples on permanent tissue sections was considered the gold standard, and was performed by an expert pathologist. Informed consent was obtained from every patient. The study was approved by our institutional review board, and was in accordance with the principles of the Declaration of Helsinki.

2.1. DNA Content Analysis (Ioannina Protocol)

DNA analysis by the Ioannina protocol was performed immediately after tumor excision [21]. Briefly, tumor samples were thoroughly minced (Medimachine System, BD Bioscience, Franklin Lakes, NJ, USA) for 1 min in phosphate-buffered saline (Ca2+- and Mg2+-free, with 0.5 mg/mL RNase), and a homogeneous cell suspension was obtained. The suspension was filtered to remove cell aggregates and to obtain single-cell suspensions. Cells were counted using an automated hematology analyzer to a final concentration of 106 cells/mL. Cells were then processed immediately for staining by adding propidium iodide (125 μg/mL) and, after 3 min, flow cytometric analysis was performed. All of the samples were analyzed using a FACSCalibur flow cytometer (BD Bioscience) equipped with two lasers (488 nm, 635 nm) and six parameters (FSC, SSC, FL1–FL4), using CellQuest V3.1 software (BD Bioscience). Normal peripheral blood mononuclear cells obtained from healthy donors using a Ficoll gradient (Ficoll-Paque separation, GE Healthcare, Little Chalfont, Buckinghamshire, UK) were used as the standard for flow cytometer calibration, and to define the diploid G0/G1 peak in the DNA histograms. An average of 5000 events (cell nuclei) counted in each sample were evaluated. A gating strategy based on area/width analysis of propidium iodide fluorescence was utilized to rule out cell doublets from DNA content quantification, as previously described [28]. The total analysis time per sample was 5–6 min.
Following the analysis, two indices were calculated: First, the DNA index, which is the ratio of the geometric mean of the G0/G1 peak of cancer cells to that of normal cells. A cancer with a DNA index of over 1.1 was considered aneuploid/hyperploid, and if the DNA index was below 0.9 it was considered aneuploid/hypoploid. If the DNA index was calculated as 1, the cancer was considered diploid. The tumor index, indicative of cancer cell proliferation, was also calculated as the total sum of the percentage of cells in the S and G2/M phases.

2.2. Statistical Analysis

We used the Mann–Whitney U test to compare the G0/G1, S-phase, and mitotic fractions of tumors versus normal cells. Continuous data are expressed as the mean ± standard deviation. Receiver operating characteristic (ROC) curve analysis was used to determine the optimal cutoff values and calculate the sensitivity and specificity of the method. The level of significance was defined as a probability value less than 0.05. Statistical analyses were performed using SPSS V.26 software (IBM) and viewed in GraphPad Prism V 8.4.2 (GraphPad Software, LLC).

3. Results

Our study initially included 50 female patients aged 35–81 years (median age: 63 years) who underwent surgery for gynecological malignancies (see Materials and Methods for more details). Cancer cell characterization as well as cancer-to-healthy-tissue margin evaluation is of particular importance in the success of every cancer surgery and patient follow-up, as mentioned elsewhere [38]. We used real-time iFC to determine the proliferative potential of specimen cells in order to designate the status of cancer cells, as a first step towards margin evaluation. To this end, cancer tissue cells from each patient, as well as peripheral mononuclear blood cells (PMBCs) from healthy donors, were used for cytometric analysis.
Following intraoperative flow cytometry screening, samples from 8/50 patients were characterized as inappropriate for iFC analysis, since they were devoid of cells. The other 42 samples were from 31 women with endometrial cancer, 6 women with ovarian cancer, 1 woman with cervical cancer, 2 women with uterine sarcoma, and 2 women with complex endometrial hyperplasia with atypia, and were included in the final analysis. Individual patients’ characteristics—including age, results of flow cytometry analysis, and the final pathology report—are presented in Table S1. A representative analysis is depicted in Figure 1.
First, we calculated the DNA index. In 22 women, the samples were aneuploid. Of these, 20 were hyperploid, with a DNA index from 1.1 to 1.7 and a median 1.24, while 1 was hypoploid with a DNA index of 0.9. The other 20 samples were diploid. The results following DNA index calculation are presented in Table S1. The results of ploidy assessment are presented in Figure 2 and Table S2.
Next, we analyzed the distribution of cells in the various phases of the cell cycle. The mean G0/G1 fractions as values for normal and cancer tissue samples were 95.5 ± 1.13% and 83.8 ± 9.5%, respectively (Figure 3 and Table S2). The G0/G1 cell-cycle phase in normal cells was characterized as significantly higher (p < 0.001) than that in cancer cells (Table S3).
On the other hand, the tumor index was significantly increased from 4.5 ± 1.13% to 16.31% ± 9.60%. The highest tumor index was calculated at 55%, while the median was 15% (Table S2). Collectively, the proliferative potential of cancer cells is mirrored in the increase in the tumor index, as can be seen in Figure 4.
Receiver operating characteristic (ROC) curve analysis was used to evaluate both the sensitivity and specificity of the technique, as well as to estimate the ideal cutoff value for the discrimination of normal tissue from cancer tissue (Figure 5). The optimal cutoff value to delineate the status of tumor tissue is a tumor index of more than 6.5% (or a G0/G1 percentage of less than 94.5%), resulting in 100% sensitivity and 90.5% specificity (Table S4), thus elevating the accuracy of our assay to 95.25%.

4. Discussion

Flow cytometry is a powerful tool for detailed and rapid analysis of heterogeneous cell populations, increasing sensitivity to the single-cell level. This method offers numerous advantages, and can be successfully used for tumor diagnosis, as clonal expansion of cancer cells can be monitored as subpopulations with distinct genetic characteristics. It contributes towards precise intraoperative identification of tumor margins, offering the potential of complete removal, and is currently applied in brain [21,22], head-and-neck [23,24], breast [25,26,27], liver [28], pancreatic [29], and colorectal neoplasms [30]. To the best of our knowledge, the present study is the first application of iFC in the detection and characterization of cancer cells in gynecological malignancies.
The hallmarks of cancer include genomic instability and induction of mutations, as well as evasion of growth suppressors and induced and sustained proliferative signaling [39,40]. Chromosomal instability is a prevalent feature of the majority of human cancers, since cancerous cells are frequently characterized by an increased rate of changes in chromosome quantity and structure [41]. Thus, genomic instability is associated with chromosomal abnormalities [42,43], To this end, aneuploidic phenomena can be monitored intraoperatively as changes in DNA content with the aid of iFC, measuring the relative PI fluorescence intensity—as mirrored by DNA index alterations—of cancer cells compared to their normal counterparts (Figure 2).
Furthermore, cancer cells can avoid apoptosis cascade by retaining proliferative capacity and evading growth suppression signaling [40]. This proliferative signaling [44], linked with evasion of growth suppressors [45], can aid in the detection of cancer, since a larger percentage of cells will cycle in the S and G2/M phases. Consequently, the disturbance of cell-cycle distribution in different phases of the cell cycle explains the significant accumulation of cells in the S/G2 phases that can be directly extracted by the tumor index variable, which is associated with an increased proliferative potential (Figure 4). In parallel, the fact that the percentage of G0/G1 cells is significantly lower in all cancer samples when compared to controls is independent proof of increased proliferative potential—a hallmark of malignancy (Figure 3).
Regarding endometrial cancer, intraoperative flow cytometry analyses are of crucial importance since, during surgery, they can quickly (i.e., within a few minutes) provide information regarding the depth of myometrial invasion, as well as whether to proceed with lymph node dissection. Therefore, surgeons can obtain immediate answers as to whether the section removed corresponds to healthy or malignant tissue, enabling them to convert their surgical plans for the benefit of the oncological patient. Along with patients’ age, histological features, and FIGO classification, the percentage of myometrial invasion can be an additional parameter in predicting the risk of recurrence or remission and the probability of disease-free survival [46]. The aforementioned information is of key importance in chemotherapy or radiation planning. Proper tumor excision can minimize the probability of cancer cells’ dissemination and the risk of future secondary malignancies.
The present manuscript represents a single-center study. This constitutes a limitation, since our results should be verified in larger multicenter studies, in which the potential of iFC to characterize tumor cells could be verified with a larger number of samples from different types of gynecological malignancies. Another limitation is the fact that flow cytometric DNA investigation is a single-parameter analysis. Even though we have proven the accuracy of iFC method based on DNA content quantification in several types of cancer, a more accurate cancer cell characterization could be acquired by immunophenotypic flow cytometric analysis, as we have previously have seen in glioma [47,48]. The development of such analysis, which includes immunophenotyping beyond DNA analysis, may offer novel insights into the diagnostic, prognostic, and therapeutic value of iFC.
It is a fact that, when addressing imaging studies, MRI provides the highest level of accuracy in the assessment of tumor size and location (approximately 93 and 91%, respectively, in cervical tumors). The ability to estimate tumor boundaries and local tumor extension is elevated using flow cytometry as, according to the ROC curve, the best sensitivity and specificity cutoff values were established (100% sensitivity and 90.5% specificity, elevating the accuracy of our method to 95.25%, which is above that provided by MRI). The present study, including 42 women, showed total accordance between flow cytometry and pathology evaluation [49].
In conclusion, based on the results so far, DNA content and cell-cycle analysis provide reliable information for the identification of neoplastic lesions and for the assessment of tumor aggressiveness. Thus, this method could be applied as an adjunct to the standard histopathological evaluation of tumor samples in the field of gynecological malignancies, so as to provide a rapid and accurate characterization of malignancy during surgery. The high sensitivity and specificity found in our first, single-center study warrants the further investigation of our methodology in larger, multicenter studies. Based on our results, we suggest that the application of iFC has the potential to represent a major achievement in the field of surgical oncology.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/biology11091339/s1, Table S1: Patients characteristics. Table S2: Descriptive statistics for G0/G1, Tumor index and DNA-index, cell percentage in control vs cancer cell populations. Table S3: Results of Mann-Whitney test between normal and cancer samples for G0/G1. Table S4: ROC Analysis results for discrimination between normal and cancer cells, based on G0/G1 values and determination of optimal cutoff value.

Author Contributions

Conceptualization, G.V.; methodology, G.V. and G.S.M.; validation, G.S.M., G.A.A., Z.A. and S.M.; formal analysis, Z.A., S.M., E.B. and G.D.L.; investigation, Z.A., S.M. and E.B.; resources, C.A., M.P. and M.M.; data curation, Z.A, S.M. and G.A.A.; writing—original draft preparation, Z.A., S.M., G.V. and G.S.M.; writing—review and editing, all authors; visualization, E.B.; supervision, G.V. and G.S.M.; project administration, G.V. and G.S.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of the General Hospital of Hatzikosta (protocol approval Code: 12/24 November 2018; approval date: 24 November 2018).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available upon reasonable request to the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Lu, H.K.; Broaddus, R.R. Endometrial Cancer. N. Engl. J. Med. 2020, 383, 2053–2064. [Google Scholar] [CrossRef] [PubMed]
  2. Stepanian, M.; Cohn, D.E. Gynecologic malignancies in adolescents. Adolesc. Med. Clin. 2004, 15, 549. [Google Scholar] [CrossRef] [PubMed]
  3. Lõhmussaar, K.; Boretto, M.; Clevers, H. Human-derived model systems in gynecological cancer research. Trends Cancer 2020, 6, 1031–1043. [Google Scholar] [CrossRef] [PubMed]
  4. Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin. 2021, 71, 209–249. [Google Scholar] [CrossRef] [PubMed]
  5. Makker, V.; Colombo, N.; Herráez, A.C.; Santin, A.D.; Colomba, E.; Miller, D.S.; Fujiwara, K.; Pignata, S.; Baron-Hay, S.; Ray-Coquard, I.; et al. Lenvatinib plus Pembrolizumab for Advanced Endometrial Cancer. N. Engl. J. Med. 2022, 386, 437–448. [Google Scholar] [CrossRef] [PubMed]
  6. Giannone, G.; Attademo, L.; Scotto, G.; Genta, S.; Ghisoni, E.; Tuninetti, V.; Aglietta, M.; Pignata, S.; Valabrega, G. Endometrial Cancer Stem Cells: Role, Characterization and Therapeutic Implications. Cancers 2019, 11, 1820. [Google Scholar] [CrossRef]
  7. Ryan, A.J.; Susil, B.; Jobling, T.W.; Oehler, M.K. Endometrial cancer. Cell Tissue Res. 2005, 322, 53–61. [Google Scholar] [CrossRef]
  8. Felix, A.S.; Weissfeld, J.L.; Stone, R.A.; Bowser, R.; Chivukula, M.; Edwards, R.P.; Linkov, F. Factors associated with Type I and Type II endometrial cancer. Cancer Causes Control. 2010, 21, 1851–1856. [Google Scholar] [CrossRef]
  9. Mayr, D.; Horn, L.C.; Hiller, G.G.; Höhn, A.K.; Schmoeckel, E. Endometrial and other rare uterine sarcomas: Diagnostic aspects in the context of the 2020 WHO classification. Pathologe 2022, 43, 183–195. [Google Scholar] [CrossRef]
  10. Chantharasamee, J.; Wong, K.; Potivongsajarn, P.; Qorbani, A.; Motamed, N.; Brackert, S.; Cohen, J.; Chmielowski, B.; Kalbasi, A.; Rao, J.; et al. Retrospective analysis of adjuvant treatment for localized, operable uterine leiomyosarcoma. Cancer Med. 2022, 11, 2906–2912. [Google Scholar] [CrossRef]
  11. Ellwanger, B.; Schüler-Toprak, S.; Jochem, C.; Leitzmann, M.F.; Baurecht, H. Anthropometric factors and the risk of ovarian cancer: A systematic review and meta-analysis. Cancer Rep. 2022, e1618. [Google Scholar] [CrossRef] [PubMed]
  12. Schoutrop, E.; Moyano-Galceran, L.; Lheureux, S.; Mattsson, J.; Lehti, K.; Dahlstrand, H.; Magalhaes, I. Molecular, cellular and systemic aspects of epithelial ovarian cancer and its tumor microenvironment. Semin. Cancer Biol. 2022; Online ahead of print. [Google Scholar] [CrossRef] [PubMed]
  13. Burges, A.; Schmalfeldt, B. Ovarian cancer: Diagnosis and treatment. Dtsch. Arztebl. Int. 2011, 108, 635–641. [Google Scholar] [PubMed]
  14. Greve, B.; Kelsch, R.; Spaniol, K.; Eich, H.T.; Götte, M. Flow cytometry in cancer stem cell analysis and separation. Cytom. Part A 2012, 81, 284–293. [Google Scholar] [CrossRef] [PubMed]
  15. Zhang, H.; Zhang, J.; Wang, Z.; Lu, D.; Feng, J.; Yang, D.; Chen, X.; Yan, X. CD146 is a potential marker for the diagnosis of malignancy in cervical and endometrial cancer. Oncol. Lett. 2013, 5, 1189–1194. [Google Scholar] [CrossRef]
  16. Corrado, G.; Laquintana, V.; Loria, R.; Carosi, M.; De Salvo, L.; Sperduti, I.; Zampa, A.; Cicchillitti, L.; Piaggio, G.; Cutillo, G.; et al. Endometrial cancer prognosis correlates with the expression of L1CAM and miR34a biomarkers. J. Exp. Clin. Cancer Res. 2018, 37, 139. [Google Scholar] [CrossRef]
  17. Liu, L.; Lin, J.; He, H. Identification of Potential Crucial Genes Associated With the Pathogenesis and Prognosis of Endometrial Cancer. Front. Genet. 2019, 10, 373. [Google Scholar] [CrossRef]
  18. D’Angelo, E.; Espinosa, I.; Ali, R.; Gilks, C.B.; van de Rijn, M.; Lee, C.-H.; Prat, J. Uterine leiomyosarcomas: Tumor size, mitotic index, and biomarkers Ki67, and Bcl-2 identify two groups with different prognosis. Gynecol. Oncol. 2011, 121, 328–333. [Google Scholar] [CrossRef]
  19. Salvo, G.; Odetto, D.; Perrotta, M.C.S.; Noll, F.; Perrotta, M.; Pareja, R.; Wernicke, A.; Ramirez, P.T. Measurement of tumor size in early cervical cancer: An ever-evolving paradigm. Int. J. Gynecol. Cancer 2020, 30, 1215–1223. [Google Scholar] [CrossRef]
  20. Shapiro, H.M. Practical Flow Cytometry; John Wiley & Sons: Hoboken, NJ, USA, 2005. [Google Scholar]
  21. Alexiou, G.A.; Vartholomatos, G.; Goussia, A.; Batistatou, A.; Tsamis, K.; Voulgaris, S.; Kyritsis, A.P. Fast cell cycle analysis for intraoperative characterization of brain tumor margins and malignancy. J. Clin. Neurosci. 2015, 22, 129–132. [Google Scholar] [CrossRef]
  22. Vartholomatos, E.; Vartholomatos, G.; Alexiou, G.; Markopoulos, G. The Past, Present and Future of Flow Cytometry in Central Nervous System Malignancies. Methods Protoc. 2021, 4, 11. [Google Scholar] [CrossRef] [PubMed]
  23. Vartholomatos, G.; Basiari, L.; Exarchakos, G.; Kastanioudakis, I.; Komnos, I.; Michali, M.; Markopoulos, G.S.; Batistatou, A.; Papoudou-Bai, A.; Alexiou, G.A. Intraoperative flow cytometry for head and neck lesions. Assessment of malignancy and tumour-free resection margins. Oral Oncol. 2019, 99, 104344. [Google Scholar] [CrossRef] [PubMed]
  24. Vartholomatos, G.; Basiari, L.; Kastanioudakis, I.; Psichogios, G.; Alexiou, G.A. The Role of Intraoperative Flow Cytometry in Surgical Margins of Head and Neck Malignancies. Ear Nose Throat J. 2020, 100, 989S–990S. [Google Scholar] [CrossRef]
  25. Vartholomatos, G.; Harissis, H.; Andreou, M.; Tatsi, V.; Pappa, L.; Kamina, S.; Batistatou, A.; Markopoulos, G.S.; Alexiou, G.A. Rapid assessment of resection margins during breast conserving surgery using intraoperative flow cytometry. Clin. Breast Cancer 2021, 21, e602–e610. [Google Scholar] [CrossRef] [PubMed]
  26. Markopoulos, G.S.; Harissis, H.; Andreou, M.; Alexiou, G.A.; Vartholomatos, G. Intraoperative flow cytometry for invasive breast cancer conserving surgery: A new alternative or adjunct to cavity shaving technique? Surg. Oncol. 2022, 42, 101712. [Google Scholar] [CrossRef]
  27. Vartholomatos, G.; Alexiou, G.A.; Tatsi, V.; Harissis, H.; Markopoulos, G.S. Next-generation margin evaluation techniques in breast conserving surgery: A memorandum on intraoperative flow cytometry. Eur. J. Surg. Oncol. 2022; S0748-7983 (22) 00386-9. Online ahead of print. [Google Scholar]
  28. Markopoulos, G.S.; Glantzounis, G.K.; Goussia, A.C.; Lianos, G.D.; Karampa, A.; Alexiou, G.A.; Vartholomatos, G. Touch imprint intraoperative flow cytometry as a complementary tool for detailed assessment of resection margins and tumor biology in liver surgery for primary and metastatic liver neoplasms. Methods Protoc. 2021, 4, 66. [Google Scholar] [CrossRef]
  29. Markopoulos, G.S.; Goussia, A.; Bali, C.D.; Messinis, T.; Alexiou, G.; Vartholomatos, G. Resection Margins Assessment by Intraoperative Flow Cytometry in Pancreatic Cancer. Ann. Surg. Oncol. 2022, 29, 4643–4645. [Google Scholar] [CrossRef]
  30. Georvasili, V.K.; Markopoulos, G.S.; Batistatou, A.; Mitsis, M.; Messinis, T.; Lianos, G.D.; Alexiou, G.; Vartholomatos, G.; Bali, C.D. Detection of cancer cells and tumor margins during colorectal cancer surgery by intraoperative flow cytometry. Int. J. Surg. 2022, 104, 106717. [Google Scholar] [CrossRef]
  31. Alexiou, G.A.; Vartholomatos, G.; Stefanaki, K.; Markopoulos, G.S.; Kyritsis, A.P. Intraoperative flow cytometry for diagnosis of central nervous system lesions. J. Cytol. 2019, 36, 134–135. [Google Scholar] [CrossRef]
  32. Andreou, M.; Vartholomatos, E.; Harissis, H.; Markopoulos, G.S.; Alexiou, G.A. Past, Present and Future of Flow Cytometry in Breast Cancer—A Systematic Review. EJIFCC 2019, 30, 423–437. [Google Scholar] [PubMed]
  33. Shioyama, T.; Muragaki, Y.; Maruyama, T.; Komori, T.; Iseki, H. Intraoperative flow cytometry analysis of glioma tissue for rapid determination of tumor presence and its histopathological grade. J. Neurosurg. 2013, 118, 1232–1238. [Google Scholar] [CrossRef] [PubMed]
  34. Koriyama, S.; Nitta, M.; Shioyama, T.; Komori, T.; Maruyama, T.; Kawamata, T.; Muragaki, Y. Intraoperative Flow Cytometry Enables the Differentiation of Primary Central Nervous System Lymphoma from Glioblastoma. World Neurosurg. 2018, 112, e261–e268. [Google Scholar] [CrossRef] [PubMed]
  35. Saito, T.; Muragaki, Y.; Shioyama, T.; Komori, T.; Maruyama, T.; Nitta, M.; Yasuda, T.; Hosono, J.; Okamoto, S.; Kawamata, T. Malignancy Index Using Intraoperative Flow Cytometry is a Valuable Prognostic Factor for Glioblastoma Treated With Radiotherapy and Concomitant Temozolomide. Neurosurgery 2018, 84, 662–672. [Google Scholar] [CrossRef] [PubMed]
  36. Matsuoka, G.; Eguchi, S.; Anami, H.; Ishikawa, T.; Yamaguchi, K.; Nitta, M.; Muragaki, Y.; Kawamata, T. Ultrarapid Evaluation of Meningioma Malignancy by Intraoperative Flow Cytometry. World Neurosurg. 2018, 120, 320–327. [Google Scholar] [CrossRef] [PubMed]
  37. A Alexiou, G.; Vartholomatos, G.; Kobayashi, T.; Voulgaris, S.; Kyritsis, A.P. The emerging role of intraoperative flow cytometry in intracranial tumor surgery. Clin. Neurol. Neurosurg. 2020, 192, 105742. [Google Scholar] [CrossRef] [PubMed]
  38. Mishra, A.; Sharma, V. Surgical Oncology: An Overview. In Cancer Diagnostics and Therapeutics; Basu, S.K., Panda, C.K., Goswami, S., Eds.; Springer: Singapore, 2022; pp. 261–270. Available online: https://link.springer.com/chapter/10.1007/978-981-16-4752-9_11#citeas (accessed on 22 August 2022).
  39. Hanahan, D.; Weinberg, R.A. Hallmarks of cancer: The next generation. Cell 2011, 144, 646–674. [Google Scholar] [CrossRef]
  40. Hanahan, D. Hallmarks of cancer: New dimensions. Cancer Discov. 2022, 12, 31–46. [Google Scholar] [CrossRef]
  41. Negrini, S.; Gorgoulis, V.G.; Halazonetis, T.D. Genomic instability—An evolving hallmark of cancer. Nat. Rev. Mol. Cell. Biol. 2010, 11, 220–228. [Google Scholar] [CrossRef]
  42. Fröhling, S.; Döhner, H. Chromosomal abnormalities in cancer. N. Engl. J. Med. 2008, 359, 722–734. [Google Scholar] [CrossRef]
  43. Danielsen, H.E.; Pradhan, M.; Novelli, M. Revisiting tumour aneuploidy—The place of ploidy assessment in the molecular era. Nat. Rev. Clin. Oncol. 2016, 13, 291–304. [Google Scholar] [CrossRef] [PubMed]
  44. DeBerardinis, R.J.; Lum, J.J.; Hatzivassiliou, G.; Thompson, C.B. The biology of cancer: Metabolic reprogramming fuels cell growth and proliferation. Cell Metab. 2008, 7, 11–20. [Google Scholar] [CrossRef] [PubMed]
  45. AAmin, A.R.; Karpowicz, P.A.; Carey, T.E.; Arbiser, J.; Nahta, R.; Chen, Z.G.; Dong, J.-T.; Kucuk, O.; Khan, G.N.; Huang, G.S.; et al. Evasion of anti-growth signaling: A key step in tumorigenesis and potential target for treatment and prophylaxis by natural compounds. Semin. Cancer Biol. 2015, 35, S55–S77. [Google Scholar] [CrossRef]
  46. Geels, Y.; Pijnenborg, J.; Erp, S.V.D.B.-V.; Snijders, M.; Bulten, J.; Massuger, L. Absolute depth of myometrial invasion in endometrial cancer is superior to the currently used cut-off value of 50%. Gynecol. Oncol. 2013, 129, 285–291. [Google Scholar] [CrossRef]
  47. Vartholomatos, E.; Mantziou, S.; Alexiou, G.A.; Lazari, D.; Sioka, C.; Kyritsis, A.; Markopoulos, G.S. An NF-kappaB- and Therapy-Related Regulatory Network in Glioma: A Potential Mechanism of Action for Natural Antiglioma Agents. Biomedicines 2022, 10, 935. [Google Scholar] [CrossRef] [PubMed]
  48. Vartholomatos, E.; Alexiou, G.A.; Markopoulos, G.S.; Lazari, D.; Tsiftsoglou, O.; Chousidis, I.; Leonardos, I.; Kyritsis, A.P. Deglucohellebrin: A Potent Agent for Glioblastoma Treatment. Anti-Cancer Agents Med. Chem. 2020, 20, 103–110. [Google Scholar] [CrossRef] [PubMed]
  49. Demirel, D.; Akyürek, N.; Ramzy, I. Diagnostic and prognostic significance of image cytometric DNA ploidy measurement in cytological samples of cervical squamous intraepithelial lesions. Cytopathology 2013, 24, 105–112. [Google Scholar] [CrossRef] [PubMed]
Biology 11 01339 g001 550
Figure 1. Analysis by iFC and pathology assessment of a representative case of endometrial cancer: (A) DNA content and cell-cycle distribution analysis using iFC: Cancer cells acquired during surgery (presented in red histograms) and normal (control) PBMCs (presented in green histograms) were stained with propidium iodide, and the quantity of DNA was analyzed using flow cytometry. Markers M1, M2, and M3 and M4 represent the G1, S and G2/M cell-cycle phases, respectively. Marker M4 represents cells in subG1 (indicative of cell death). The percentage of cells that correspond to each phase is shown in the upper right part of the histogram. The G0/G1 peak from the PBMC sample was used as a reference for DNA index calculation. The presented case is hyperploid, with a DNA index of 1.2 (the G0/G1 peak of cancer cells in red is discernible from that of normal cells in green in the overlaid histogram). Tumor index (i.e., percentage of proliferative cells) was calculated at ~17%. (B). Pathology evaluation: Ovarian cancer cells stained with hematoxylin/eosin are presented at 40× (I) and 200× (II) magnification. A moderately differentiated tumor (grade II) is recognized.
Figure 1. Analysis by iFC and pathology assessment of a representative case of endometrial cancer: (A) DNA content and cell-cycle distribution analysis using iFC: Cancer cells acquired during surgery (presented in red histograms) and normal (control) PBMCs (presented in green histograms) were stained with propidium iodide, and the quantity of DNA was analyzed using flow cytometry. Markers M1, M2, and M3 and M4 represent the G1, S and G2/M cell-cycle phases, respectively. Marker M4 represents cells in subG1 (indicative of cell death). The percentage of cells that correspond to each phase is shown in the upper right part of the histogram. The G0/G1 peak from the PBMC sample was used as a reference for DNA index calculation. The presented case is hyperploid, with a DNA index of 1.2 (the G0/G1 peak of cancer cells in red is discernible from that of normal cells in green in the overlaid histogram). Tumor index (i.e., percentage of proliferative cells) was calculated at ~17%. (B). Pathology evaluation: Ovarian cancer cells stained with hematoxylin/eosin are presented at 40× (I) and 200× (II) magnification. A moderately differentiated tumor (grade II) is recognized.
Biology 11 01339 g001
Biology 11 01339 g002 550
Figure 2. DNA index as an indicator of aneuploidy in normal control samples versus tumor cells: DNA index values were quantified following iFC analysis, as a quotient of the geometric mean of G0/G1 in normal (based on analysis of PBMCs) vs. cancer cells in individual patient samples. DNA index scores of individual samples are presented as blue or red dots, for normal and cancer tissue, respectively. A DNA index of ≠1 is a distinct hallmark of cancer cells. The median DNA index is shown as a horizontal line in each group.
Figure 2. DNA index as an indicator of aneuploidy in normal control samples versus tumor cells: DNA index values were quantified following iFC analysis, as a quotient of the geometric mean of G0/G1 in normal (based on analysis of PBMCs) vs. cancer cells in individual patient samples. DNA index scores of individual samples are presented as blue or red dots, for normal and cancer tissue, respectively. A DNA index of ≠1 is a distinct hallmark of cancer cells. The median DNA index is shown as a horizontal line in each group.
Biology 11 01339 g002
Biology 11 01339 g003 550
Figure 3. The percentage of cells in G0/G1 in normal control samples versus tumor cells, as quantified by iFC: Percentages of cells in G0/G1 are presented as blue or red dots—for normal and cancer tissue, respectively—in individual patient samples. Median G0/G1 percentages are shown as horizontal lines in each group. There is a significant difference between the two distributions, with cancers cells exhibiting a significantly lower percentage in the G0/G1 cell-cycle phase.
Figure 3. The percentage of cells in G0/G1 in normal control samples versus tumor cells, as quantified by iFC: Percentages of cells in G0/G1 are presented as blue or red dots—for normal and cancer tissue, respectively—in individual patient samples. Median G0/G1 percentages are shown as horizontal lines in each group. There is a significant difference between the two distributions, with cancers cells exhibiting a significantly lower percentage in the G0/G1 cell-cycle phase.
Biology 11 01339 g003
Biology 11 01339 g004 550
Figure 4. Quantification of tumor index in normal control samples versus tumor cells, as quantified by iFC: Tumor index scores (the sum of the % of cells in the S and G2/M cell-cycle phases) of individual patient samples are presented as blue or red dots, for normal and cancer tissue, respectively. Median percentages are shown as horizontal lines in each group. There is a significant difference between the two distributions, with cancer cells exhibiting a higher tumor index, indicative of malignancy.
Figure 4. Quantification of tumor index in normal control samples versus tumor cells, as quantified by iFC: Tumor index scores (the sum of the % of cells in the S and G2/M cell-cycle phases) of individual patient samples are presented as blue or red dots, for normal and cancer tissue, respectively. Median percentages are shown as horizontal lines in each group. There is a significant difference between the two distributions, with cancer cells exhibiting a higher tumor index, indicative of malignancy.
Biology 11 01339 g004
Biology 11 01339 g005 550
Figure 5. iFC evaluation of gynecological neoplasia is highly sensitive and specific. The results of ROC curve analysis are presented. The horizontal axis represents 1-specificity, and the vertical axis represents sensitivity. The blue line represents the accuracy values, based on both values.
Figure 5. iFC evaluation of gynecological neoplasia is highly sensitive and specific. The results of ROC curve analysis are presented. The horizontal axis represents 1-specificity, and the vertical axis represents sensitivity. The blue line represents the accuracy values, based on both values.
Biology 11 01339 g005
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Anastasiadi, Z.; Mantziou, S.; Akrivis, C.; Paschopoulos, M.; Balasi, E.; Lianos, G.D.; Alexiou, G.A.; Mitsis, M.; Vartholomatos, G.; Markopoulos, G.S. Intraoperative Flow Cytometry for the Characterization of Gynecological Malignancies. Biology 2022, 11, 1339. https://doi.org/10.3390/biology11091339

AMA Style

Anastasiadi Z, Mantziou S, Akrivis C, Paschopoulos M, Balasi E, Lianos GD, Alexiou GA, Mitsis M, Vartholomatos G, Markopoulos GS. Intraoperative Flow Cytometry for the Characterization of Gynecological Malignancies. Biology. 2022; 11(9):1339. https://doi.org/10.3390/biology11091339

Chicago/Turabian Style

Anastasiadi, Zoi, Stefania Mantziou, Christos Akrivis, Minas Paschopoulos, Eufemia Balasi, Georgios D. Lianos, George A. Alexiou, Michail Mitsis, George Vartholomatos, and Georgios S. Markopoulos. 2022. "Intraoperative Flow Cytometry for the Characterization of Gynecological Malignancies" Biology 11, no. 9: 1339. https://doi.org/10.3390/biology11091339

APA Style

Anastasiadi, Z., Mantziou, S., Akrivis, C., Paschopoulos, M., Balasi, E., Lianos, G. D., Alexiou, G. A., Mitsis, M., Vartholomatos, G., & Markopoulos, G. S. (2022). Intraoperative Flow Cytometry for the Characterization of Gynecological Malignancies. Biology, 11(9), 1339. https://doi.org/10.3390/biology11091339

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