biomedicines-10-02694

biomedicines-10-02694

Biomedicines 2022, 10(11), 2694; doi:10.3390/biomedicines10112694

Article

Combined Expression of HGFR with Her2/neu, EGFR, IGF1R, Mucin-1 and Integrin α2β1 Is Associated with Aggressive Epithelial Ovarian Cancer

Bastian Czogalla 1,2,†Orcid, Katharina Dötzer 3,†, Nicole Sigrüner ³, Franz Edler von Koch ⁴, Christine E. Brambs ⁵, Sabine Anthuber ⁶, Sergio Frangini ⁷Orcid, Alexander Burges 1,2, Jens Werner 2,3, Sven Mahner 1,2 and Barbara Mayer 2,3,*

¹

Department of Obstetrics and Gynecology, University Hospital, Ludwig-Maximilians-University Munich, Marchioninistraße 15, 81377 Munich, Germany

²

German Cancer Consortium (DKTK), Partner Site Munich, Pettenkoferstraße 8a, 80336 Munich, Germany

³

Department of General, Visceral and Transplant Surgery, University Hospital, Ludwig-Maximilians-University Munich, Marchioninistraße 15, 81377 Munich, Germany

⁴

Gynecology and Obstetrics Clinic, Klinikum Dritter Orden, Menzinger Straße 44, 80638 Munich, Germany

⁵

Department of Obstetrics and Gynecology, Klinikum Rechts der Isar, Technical University Munich, Ismaninger Straße 22, 81675 Munich, Germany

⁶

Department of Obstetrics and Gynecology, Starnberg Hospital, Oßwaldstraße 1, 82319 Starnberg, Germany

⁷

Department of Obstetrics and Gynecology, Munich Clinic Harlaching, Sanatoriumsplatz 2, 81545 Munich, Germany

*

Correspondence: barbara.mayer@med.uni-muenchen.de

†

These authors contributed equally to this work.

Academic Editors: Francesca Magherini and Tania Gamberi

Received: 30 August 2022 / Accepted: 21 October 2022 / Published: 25 October 2022

Abstract

:

Hepatocyte growth factor receptor (HGFR), also known as c-mesenchymal–epithelial transition factor (c-MET), plays a crucial role in the carcinogenesis of epithelial ovarian cancer (EOC). In contrast, the mechanisms contributing to aberrant expression of HGFR in EOC are not fully understood. In the present study, the expression of HGFR with its prognostic and predictive role was evaluated immunohistochemically in a cohort of 42 primary ovarian cancer patients. Furthermore, we analyzed the dual expression of HGFR and other druggable biomarkers. In the multivariate Cox regression analysis, high HGFR expression was identified as an independent prognostic factor for a shorter progression-free survival (PFS) (hazard ratio (HR) 2.99, 95% confidence interval (CI95%) 1.01–8.91, p = 0.049) and overall survival (OS) (HR 5.77, CI95% 1.56–21.34, p = 0.009). In addition, the combined expression of HGFR, human epidermal growth factor receptor 2 (Her2/neu), epithelial growth factor receptor (EGFR), insulin-like growth factor 1 (IGF1R), Mucin-1 and Integrin α2β1 further significantly impaired PFS, platinum-free interval (PFI) and OS. Protein co-expression analyses were confirmed by transcriptomic data in a large, independent cohort of patients. In conclusion, new biomarker-directed treatment targets were identified to fight poor prognosis of primary EOC.

Keywords:

epithelial ovarian cancer; prognosis; immunohistochemistry; HGFR; Her2/neu; EGFR; IGF1R; Muc-1; α2β1

1. Introduction

Epithelial ovarian cancer (EOC) is one of the most lethal tumor entities [1]. Lack of adequate screening methods and rising resistances towards chemotherapy over the clinical course contribute to a low 5-year survival rate at around 45% [1,2]. The standard of care for advanced EOC is a radical cytoreductive surgery followed by adjuvant platinum-based chemotherapy and maintenance targeted therapy such as anti-angiogenic antibody, bevacizumab or poly-ADP-ribose-polymerase inhibitors [3]. Even though initial response rates are between 60–80%, the majority of patients will develop therapy resistance, leading to subsequent recurrence or progression of disease. Therefore, translational research approaches must elucidate molecular mechanisms in the carcinogenesis of EOC for developing new prognostic and therapeutic strategies. Taking the heterogeneity of EOC into account appears crucial for future personalized cancer therapy.

Hepatocyte growth factor receptor (HGFR), also known as c-mesenchymal–epithelial transition factor (c-MET), is a tyrosine kinase receptor. It regulates important cellular processes, such as differentiation, proliferation, cell cycle, motility and apoptosis, through its sole ligand hepatocyte growth factor (HGF) [4,5]. Despite its physiological functions, prolonged or continuous HGFR signaling activity with subsequent catalytic activation of signal transduction cascades is involved in the carcinogenesis of liver, lung, breast, pancreatic, gastric, head and neck, renal and cervical cancer [6,7,8,9,10]. With growing evidence of important crosstalks between HGFR and other cell surface receptors and proteins attributing to carcinogenesis, the characterization of this molecular mechanism is pivotal to explore new therapeutical approaches [11].

Former studies could demonstrate enhanced HGFR expression in EOC correlating with higher histological grading, distant metastasis and impaired survival rates [12,13,14,15,16,17,18]. HGFR levels in the blood are an independent prognostic biomarker for ovarian cancer [19]. Furthermore, high HGFR expression is associated with TP53 mutations, pathognomonic for high-grade EOC [20]. Nevertheless, the mechanisms contributing to increased expression of HGFR in EOC are not fully understood so far.

Molecular targeting of HGFR and associated cell surface receptors and proteins could be a promising strategy for new therapeutical approaches in EOC, which remains to be explored. In the present study, the expression of HGFR in EOC with its prognostic and predictive role was evaluated. Furthermore, we analyzed the effect of the combined expression of HGFR and the growth factor receptors estrogen receptor alpha (ERα), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2/neu), epithelial growth factor receptor (EGFR) and insulin-like growth factor 1 (IGF1R) as well as the cell adhesion molecules Mucin-1, CD44v6 and Integrin α2β1 on EOC patients’ survival.

2. Materials and Methods

2.1. Study Population

Forty-two patients with a primary, chemonaive ovarian, fallopian tube or peritoneal cancer from the SpheroID-Study were included. Patients with another neoplasia within the last five years were excluded. Informed consent was obtained from all patients in the study. The study was approved by the Ethics Committee of Ludwig Maximilians University, Munich, Germany (approval number 278-04). Between September 2012 and January 2015, the patients were recruited in five clinics: University Hospital, LMU Munich (n = 16); Klinikum Dritter Orden (n = 13); Klinikum rechts der Isar; Technical University Munich (n = 7); Munich Clinic Harlaching (n = 4); and Starnberg Hospital (n = 2). Standardized surgery and pathological analysis were performed by the respective clinics. Relevant clinicopathological data for statistical analyses were selected from routine reports and delivered in a pseudonymized form. Survival analysis was performed after chemotherapy. All patients received 6 cycles of a carboplatin–paclitaxel treatment. Progression-free survival (PFS) was defined as the time from surgery to progression or relapse. Platinum-free interval (PFI) was defined as the time from end of the platinum-based chemotherapy to progression or relapse. Overall survival (OS) was defined as the time from surgery to death. Data from patients without death and progression/relapse were censored at the date of their last visit.

2.2. Immunohistochemistry

After surgical resection, tumor samples were snap-frozen in liquid nitrogen. Serial cryosections (5 µm) were performed. The samples were stained immunohistochemically with the avidin–biotin–peroxidase method as described in detail in [21,22,23]. Briefly, after fixation and blocking of unspecific Fc receptors and endogenous biotin, tissue sections were stained with the primary antibodies for one hour. Secondary biotinylated antibodies and peroxidase-conjugated streptavidin (Dianova, Hamburg, Germany) were incubated for 30 min. The antigen–antibody reaction was visualized by incubation in 3-amino-9-ethylcarbazole pH 4.7 (Sigma-Aldrich, Steinheim, Germany) peroxidase solution for eight minutes. Tissue sections were counterstained in Mayer’s hematoxylin (Merck, Darmstadt, Germany) and embedded with Aquatex® (Merck, Darmstadt, Germany). Details about the used antibodies and working concentrations including positive and negative controls are given in Table 1.

2.3. Evaluation of Biomarker Expression

Sections were evaluated semiquantitatively. The percentage of positively stained cancer cells was calculated for each analyzed antigen [21,22,23]. Her2/neu expression was evaluated according to breast cancer and gastric cancer guidelines [24,25]. Due to missing standardized cutoffs for other biomarkers, cutoffs were determined according to biphasic distribution or group size (Table 1).

2.4. Statistical Analysis

HGFR expression was correlated with clinicopathological factors and other biomarker expressions using the Fisher’s exact two-tailed test. Univariate analysis was evaluated by calculating cumulative survival probabilities with the Kaplan–Meier method and comparing them with the log-rank test. Significant variables identified in the univariate analysis were further considered in the multivariate Cox regression model of survival. Calculations were performed for PFS, PFI and OS. Confirmatory validation studies were performed with the open-access Kaplan–Meier plotter database and the evaluation tools that are available online [26]. p-values < 0.05 were defined to be statistically significant. IBM SPSS Statistics 26 (Armonk, NY, USA) was used for all statistical analyses.

3. Results

3.1. Patient Characteristic

The clinicopathological characteristics of the analyzed ovarian cancer cohort are listed in Table 2.

Forty-two patients were included in this study. The median age at diagnosis was 66 years (range: 24–83 years). High-grade ovarian cancer in an advanced FIGO (Fédération Internationale de Gynécologie et d’Obstétrique) stage was most frequent. Of all patients, 71% had a complete surgical resection of all macroscopic visible tumor; 93% of patients received carboplatin–paclitaxel-based chemotherapy according to guideline recommendations; and 33% suffered from a relapse after chemotherapy within 12 months. Median OS (overall survival) was 42 months, median PFS (progression-free survival) was 22 months and median PFI (platinum-free interval) was 17 months.

3.2. Prognostic Impact of HGFR Protein Expression

Univariate analysis revealed that high (≥50%) HGFR expression was associated with an impaired PFS (p = 0.041), PFI (p = 0.048) and OS (p = 0.012) (Table 3). Multivariate analysis confirmed high HGFR expression as an independent factor for poor prognosis (PFS: HR 2.99, CI95% 1.01–8.91, p = 0.049; OS: HR 5.77, CI95% 1.56–21.34, p = 0.009) (Table 3). In the same cohort, the presence of macroscopic residual tumor after surgery was found as an independent factor for short PFS (HR 2.19, CI95% 1.03–4.68, p = 0.043) and short OS (HR 8.42, CI95% 1.59–44.61, p = 0.012) (Table 3). No significant correlation between HGFR expression and clinicopathological characteristics could be detected (data not shown).

3.3. Correlation of HGFR Expression and Other Protein Biomarkers and Prognostic Impact of Combined Expression Profiles

To reveal clinically important correlations of HGFR with other biomarkers, we analyzed the combinations of HGFR with the growth factor receptors ERα, PR, HER2/neu, EGFR and IGF1R as well as the cell adhesion molecules Mucin-1, CD44v6 and Integrin α2β1. We could not find a correlation between the expression of these biomarkers and HGFR in our cohort (Table 4).

We analyzed the effect of the combined expression of HGFR and the other biomarkers on patients’ survival and detected significant associations with impaired PFS, PFI and OS (Table 5).

Patients with high expression of HGFR and Her-2/neu showed a shorter PFS (p = 0.009) and PFI (p = 0.008) compared to the remaining combinations. High expression of HGFR and EGFR was associated with an impaired PFS (p ≤ 0.001), PFI (p ≤ 0.001) and OS (p = 0.011). Likewise, the combination of high HGFR and IGF1R expression correlated with shorter OS (p = 0.03). In addition, combined HGFR and MUC-1 expression was associated with impaired PFS (p = 0.002), PFI (p = 0.003) and OS (p < 0.001). Furthermore, patients with high expression of HGFR and Integrin α2β1 showed a shorter PFS (p = 0.004) and PFI (p = 0.004).

3.4. High Co-Expression of MET and the Other Biomarker Genes Is Significantly Associated with Impaired Patient Survival in a Large Independent EOC Cohort

Aiming to validate the prognostic impact of MET (HGFR gene) and the other biomarker genes (ESR1—ERα gene, PGR—PR gene, ERBB2—Her-2/neu gene, EGFR—EGFR gene, IGF1R—IGF1R gene, MUC1—MUC-1 gene, CD44—CD44 gene, ITGA2—Integrin α2 gene) on patients’ survival regarding a larger total of EOC patients, the Kaplan–Meier plotter database was used [26]. For all genes, patients were divided into high- and low-expression groups based on gene-specific cutoff values, before performing analyses concerning OS and PFS (Table 6).

The analysis revealed that high MET expression was associated with an impaired PFS (p = 0.018) and OS (p = 0.033). Patients with high expression of MET and ERBB2 showed a shorter PFS (p = 0.036) and OS (p = 0.043) compared to the remaining combinations. High expression of MET and EGFR was associated with an impaired PFS (p = 0.0047). Likewise, the combination of high MET and IGF1R expression correlated with shorter OS (p = 0.048). In addition, combined MET and MUC1 expression was associated with impaired PFS (p = 0.018) and OS (p = 0.043). Furthermore, patients with high expression of MET and ITGA2 showed a shorter PFS (p = 0.003). Corresponding Kaplan–Meier plots are shown in Figure S1.

4. Discussion

In the present study, we analyzed HGFR in our EOC cohort and confirmed it as a prognostic and predictive protein biomarker. High HGFR expression was associated with an impaired PFS, PFI and OS and could be proven as an independent prognostic factor for PFS and OS (Table 3). In addition, we analyzed the effect of the combined expression of HGFR and the growth factor receptors ERα, PR, HER2/neu, EGFR and IGF1R as well as the cell adhesion molecules Mucin-1, CD44v6 and Integrin α2β1 on EOC patients’ survival and found significant associations with shorter PFS, PFI and OS (Table 5). The combined high expression of HGFR and other biomarkers is associated with impaired PFS, PFI and OS compared to a high HGFR expression alone (Table 5). These data could be validated on mRNA expression levels in an independent EOC cohort (Table 6).

The role of HGFR in the carcinogenesis of many tumor types is well established. HGFR overexpression in EOC is associated with higher histological grading, higher FIGO stage, distant metastasis and impaired survival rates [12,16,17,27,28,29]. Thus, our data are in line with previous studies. Despite these observed associations, the particular molecular mechanism is not well understood so far. HGFR has crosstalks with several pathways such as PI3K/Akt, BRAF and RAS-MAPK influencing carcinogenesis [30,31]. Regarding HGFR´s pivotal role in cancer, the inhibition of the HGFR/HGF pathway seems to be an interesting therapeutical approach [32,33,34]. Indeed, several inhibitors of the HGFR/HGF pathway were analyzed in different cancer entities including lung, liver and kidney cancer [35,36,37,38]. Cabozantinib, a tyrosine kinase inhibitor targeting HGFR and vascular endothelial growth factor receptor 2 (VEGFR-2), is used in advanced renal cell carcinoma after a phase-three trial demonstrated a significant PFS and OS benefit compared to mTOR inhibitor everolimus [37]. Tivantinib, a selective HGFR tyrosine kinase inhibitor, showed promising results in advanced hepatocellular carcinoma and non-small-cell lung carcinoma, especially in HGFR-overexpressing subgroups [35,38].

HGFR inhibition has also been considered in the treatment of EOC [39,40,41,42,43,44]. In vitro HGFR overexpression improved ovarian cancer cell survival and caused resistance to the chemotherapeutics cisplatin and paclitaxel. siRNA knockdown of HGFR restored chemosensitivity in this cell culture model [39]. Furthermore, it was shown that the HGFR-specific inhibitor MK8033 increases chemosensitivity to carboplatin and paclitaxel in different ovarian cancer cell lines [40]. Other studies underlined the antiproliferative and chemosensitizing effect of HGFR inhibition [42,43]. In a phase-two randomized discontinuation trial of cabozantinib in EOC, the HGFR inhibitor demonstrated clinical activity with acceptable toxicities. Seventy patients, 50% platinum refractory/resistant and 83% with at least two former systemic therapies, were enrolled in the study. Cabozantinib showed an objective response rate of 21%, with a median PFS of 5.5 months [41]. Considering the high percentage of platinum-refractory/resistant tumors and former therapy lines, the monotherapy with cabozantinib should be discussed with patients with limited therapeutic options. In contrast, a phase-two trial with 13 patients with recurrent clear cell ovarian, primary peritoneal or fallopian tube cancer could not confirm significant therapeutic effects by cabozantinib monotherapy [44].

Our analysis of the combined expression of HGFR with HER2/neu, EGFR, IGF1R, Mucin-1 and Integrin α2β1 underlines the potential efficiency of dual combination therapies in EOC. Patients with combined overexpression of these factors showed worse prognosis and platinum resistance. Our analysis demonstrated that patients with a combined high expression of HGFR and HER2/neu, EGFR, IGF1R, Mucin-1 and Integrin α2β1 show a very aggressive tumor biology with an impaired median survival compared to HGFR high-expressing tumors alone. These data show that the progression of primary ovarian cancer is a complex multifactorial process involving molecular crosstalks between different signaling pathways [22]. Multi-target biomarker-driven treatment may be a strategy to overcome platinum resistance and poor prognosis.

Moreover, studies of dual targeting demonstrated promising results in tumor growth inhibition in vitro and in vivo [45,46,47,48]. MicroRNA-mediated HGFR/EGFR repression caused an ovarian cancer cell proliferation arrest and an inhibition of tumor growth in an EOC mouse model [45]. Combined HGFR/EGFR expression was associated with an impaired survival in patients with advanced ovarian cancer [48]. Furthermore, combined inhibition by a dual EGFR/HER-2/neu inhibitor (canertinib) and a HGFR inhibitor (PHA665752) resulted in a decreased ovarian cancer cell proliferation [46,47]. To our knowledge, there are no studies on the dual inhibition of HGFR/IGF1R, HGFR/MUC-1 or HGFR/Integrin α2β1 in EOC yet. Thus, further research is needed to elucidate the potential effect of these combined inhibitions.

In conclusion, our present study demonstrated HGFR in combination with HER2/neu, EGFR, IGF1R, Mucin-1 and Integrin α2β1 as candidates for new biomarker-directed treatment strategies in EOC. Validation studies in independent cohorts are needed to prove our findings.

Supplementary Materials

The following is available online at https://www.mdpi.com/article/10.3390/biomedicines10112694/s1, Figure S1: Gene co-expression analyses of MET and the other biomarker genes.

Author Contributions

Conceptualization, B.M.; formal analysis, B.C., K.D. and B.M.; funding acquisition, B.M.; investigation, K.D.; methodology, B.C., K.D. and B.M.; project administration, B.M.; resources, N.S., F.E.v.K., C.E.B., S.A., S.F., B.C., A.B. and S.M.; validation, B.C., K.D., N.S. and B.M.; visualization, B.C.; writing—original draft, B.C. and B.M.; writing—review and editing, K.D., N.S., C.E.B., S.F., J.W. and S.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the German Federal Ministry of Education and Research (Leading Edge Cluster m4) to B.M., Grant FKZ 16EX1021N.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and approved on 7 August 2012 by the institutional review board of the Ludwig-Maximilians-Universität, Munich, Germany (No. 278/04).

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 on request from the corresponding author. The data are not publicly available due to protection of detailed patient-related data.

Acknowledgments

The authors appreciate Anton Stolp, Frank Arnold and Michael Pohr for technical support.

Conflicts of Interest

All but one author declare no conflict of interest. Sven Mahner: Research support, advisory board, honoraria and travel expenses from AbbVie, AstraZeneca, Clovis, Eisai, GlaxoSmithKline, Medac, MSD, Novartis, Olympus, PharmaMar, Roche, Sensor Kinesis, Teva and Tesaro. This support was not related to work in the present study. The funder had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

Siegel, R.L.; Miller, K.D.; Jemal, A. Cancer statistics, 2019. CA Cancer J. Clin. 2019, 69, 7–34. [Google Scholar] [CrossRef] [PubMed]

Baldwin, L.A.; Huang, B.; Miller, R.W.; Tucker, T.; Goodrich, S.T.; Podzielinski, I.; DeSimone, C.P.; Ueland, F.R.; van Nagell, J.R.; Seamon, L.G. Ten-Year Relative Survival for Epithelial Ovarian Cancer. Obstet. Gynecol. 2012, 120, 612–618. [Google Scholar] [CrossRef] [PubMed]

Colombo, N.; Sessa, C.; Du Bois, A.; Ledermann, J.; McCluggage, W.G.; McNeish, I.; Morice, P.; Pignata, S.; Ray-Coquard, I.; Vergote, I.; et al. ESMO–ESGO consensus conference recommendations on ovarian cancer: Pathology and molecular biology, early and advanced stages, borderline tumours and recurrent disease. Ann. Oncol. 2019, 30, 672–705. [Google Scholar] [CrossRef] [PubMed]

Zhang, J.; Jiang, X.; Jiang, Y.; Guo, M.; Zhang, S.; Li, J.; He, J.; Liu, J.; Wang, J.; Ouyang, L. Recent advances in the development of dual VEGFR and c-Met small molecule inhibitors as anticancer drugs. Eur. J. Med. Chem. 2016, 108, 495–504. [Google Scholar] [CrossRef] [PubMed]

Maulik, G.; Shrikhande, A.; Kijima, T.; Ma, P.C.; Morrison, P.T.; Salgia, R. Role of the hepatocyte growth factor receptor, c-Met, in oncogenesis and potential for therapeutic inhibition. Cytokine Growth Factor Rev. 2002, 13, 41–59. [Google Scholar] [CrossRef]

Yan, S.; Jiao, X.; Zou, H.; Li, K. Prognostic significance of c-Met in breast cancer: A meta-analysis of 6010 cases. Diagn. Pathol. 2015, 10, 62. [Google Scholar] [CrossRef]

Pyo, J.S.; Kang, G.; Cho, W.J.; Choi, S.B. Clinicopathological significance and concordance analysis of c-MET immunohistochemistry in non-small cell lung cancers: A meta-analysis. Pathol. Res. Pract. 2016, 212, 710–716. [Google Scholar] [CrossRef] [PubMed]

Yu, S.; Yu, Y.; Zhao, N.; Cui, J.; Li, W.; Liu, T. c-Met as a prognostic marker in gastric cancer: A systematic review and meta-analysis. PLoS ONE 2013, 8, e79137. [Google Scholar] [CrossRef] [PubMed]

Kim, J.H.; Kim, H.S.; Kim, B.J.; Jang, H.J.; Lee, J. Prognostic value of c-Met overexpression in hepatocellular carcinoma: A meta-analysis and review. Oncotarget 2017, 8, 90351–90357. [Google Scholar] [CrossRef]

Peng, J.; Qi, S.; Wang, P.; Li, W.; Liu, C.; Li, F. Diagnosis and Prognostic Significance of c-Met in Cervical Cancer: A Meta-Analysis. Dis. Markers 2016, 2016, 6594016. [Google Scholar] [CrossRef] [PubMed]

Zhang, Y.; Du, Z.; Zhang, M. Biomarker development in MET-targeted therapy. Oncotarget 2016, 7, 37370–37389. [Google Scholar] [CrossRef] [PubMed]

Ayhan, A.; Ertunc, D.; Tok, E.C.; Ayhan, A. Expression of the c-Met in advanced epithelial ovarian cancer and its prognostic significance. Int. J. Gynecol. Cancer 2005, 15, 618–623. [Google Scholar] [CrossRef] [PubMed]

Di Renzo, M.F.; Olivero, M.; Katsaros, D.; Crepaldi, T.; Gaglia, P.; Zola, P.; Sismondi, P.; Comoglio, P.M. Overexpression of the MET/HGF receptor in ovarian cancer. Int. J. Cancer 1994, 58, 658–662. [Google Scholar] [CrossRef]

Bu, R.; Uddin, S.; Bavi, P.; Hussain, A.R.; Al-Dayel, F.; Ghourab, S.; Ahmed, M.; Al-Kuraya, K.S. HGF/c-Met pathway has a prominent role in mediating antiapoptotic signals through AKT in epithelial ovarian carcinoma. Lab. Investig. 2011, 91, 124–137. [Google Scholar] [CrossRef] [PubMed]

Huntsman, D.; Resau, J.H.; Klineberg, E.; Auersperg, N. Comparison of c-met expression in ovarian epithelial tumors and normal epithelia of the female reproductive tract by quantitative laser scan microscopy. Am. J. Pathol. 1999, 155, 343–348. [Google Scholar] [CrossRef]

Sawada, K.; Radjabi, A.R.; Shinomiya, N.; Kistner, E.; Kenny, H.; Becker, A.R.; Turkyilmaz, M.A.; Salgia, R.; Yamada, S.D.; Woude, G.F.V.; et al. c-Met overexpression is a prognostic factor in ovarian cancer and an effective target for inhibition of peritoneal dissemination and invasion. Cancer Res. 2007, 67, 1670–1679. [Google Scholar] [CrossRef] [PubMed]

Kim, J.H.; Jang, H.J.; Kim, H.S.; Kim, B.J.; Park, S.H. Prognostic impact of high c-Met expression in ovarian cancer: A meta-analysis. J. Cancer 2018, 9, 3427–3434. [Google Scholar] [CrossRef] [PubMed]

Bååth, M.; Jönsson, J.-M.; Fremer, S.W.; de la Fuente, L.M.; Tran, L.; Malander, S.; Kannisto, P.; Måsbäck, A.; Honeth, G.; Hedenfalk, I. Met expression and cancer stem cell networks impact outcome in high-grade serous ovarian cancer. Genes 2021, 12, 742. [Google Scholar] [CrossRef]

Klotz, D.M.; Link, T.; Goeckenjan, M.; Wimberger, P.; Kuhlmann, J.D. The levels of soluble cMET ectodomain in the blood of patients with ovarian cancer are an independent prognostic biomarker. Mol. Oncol. 2021, 15, 2491–2503. [Google Scholar] [CrossRef] [PubMed]

The Cancer Genome Atlas Research Network. Integrated genomic analyses of ovarian carcinoma. Nature 2011, 474, 609–615. [Google Scholar] [CrossRef] [PubMed]

Mayer, B.; Funke, I.; Johnson, J.P. High expression of a Lewis(x)-related epitope in gastric carcinomas indicates metastatic potential and poor prognosis. Gastroenterology 1996, 111, 1433–1446. [Google Scholar] [CrossRef]

Dötzer, K.; Schlüter, F.; Koch, F.E.; Brambs, C.E.; Anthuber, S.; Frangini, S.; Czogalla, B.; Burges, A.; Werner, J.; Mahner, S.; et al. Integrin α2β1 represents a prognostic and predictive biomarker in primary ovarian cancer. Biomedicines 2021, 9, 289. [Google Scholar] [CrossRef] [PubMed]

Hoffmann, O.I.; Regenauer, M.; Czogalla, B.; Brambs, C.; Burges, A.; Mayer, B. Interpatient Heterogeneity in Drug Response and Protein Biomarker Expression of Recurrent Ovarian Cancer. Cancers 2022, 14, 2279. [Google Scholar] [CrossRef] [PubMed]

Wolff, A.C.; Hammond, M.E.H.; Hicks, D.G.; Dowsett, M.; McShane, L.M.; Allison, K.H.; Allred, D.C.; Bartlett, J.M.S.; Bilous, M.; Fitzgibbons, P.; et al. Recommendations for human epidermal growth factor receptor 2 testing in breast. J. Clin. Oncol. 2013, 31, 3997–4013. [Google Scholar] [CrossRef]

Rüschoff, J.; Dietel, M.; Baretton, G.; Arbogast, S.; Walch, A.; Monges, G.; Chenard, M.-P.; Penault-Llorca, F.; Nagelmeier, I.; Schlake, W.; et al. HER2 diagnostics in gastric cancer-guideline validation and development of standardized immunohistochemical testing. Virchows Arch. 2010, 457, 299–307. [Google Scholar] [CrossRef] [PubMed]

Fekete, J.T.; Ősz, Á.; Pete, I.; Nagy, G.R.; Vereczkey, I.; Győrffy, B. Predictive biomarkers of platinum and taxane resistance using the transcriptomic data of 1816 ovarian cancer patients. Gynecol. Oncol. 2020, 156, 654–661. [Google Scholar] [CrossRef] [PubMed]

Yamamoto, S.; Tsuda, H.; Miyai, K.; Takano, M.; Tamai, S.; Matsubara, O. Gene amplification and protein overexpression of MET are common events in ovarian clear-cell adenocarcinoma: Their roles in tumor progression and prognostication of the patient. Mod. Pathol. 2011, 24, 1146–1155. [Google Scholar] [CrossRef] [PubMed]

Li, H.; Zhang, H.; Zhao, S.; Shi, Y.; Yao, J.; Zhang, Y.; Guo, H.; Liu, X. Overexpression of MACC1 and the association with hepatocyte growth factor/c-Met in epithelial ovarian cancer. Oncol. Lett. 2015, 9, 1989–1996. [Google Scholar] [CrossRef]

Wang, H.; Tan, M.; Zhang, S.; Li, X.; Gao, J.; Zhang, D.; Hao, Y.; Gao, S.; Liu, J.; Lin, B. Expression and significance of CD44, CD47 and c-met in ovarian clear cell carcinoma. Int. J. Mol. Sci. 2015, 16, 3391–3404. [Google Scholar] [CrossRef] [PubMed]

Lorenzato, A.; Olivero, M.; Patanè, S.; Rosso, E.; Oliaro, A.; Comoglio, P.M.; Di Renzo, M.F. Novel somatic mutations of the MET oncogene in human carcinoma metastases activating cell motility and invasion. Cancer Res. 2002, 62, 7025–7030. [Google Scholar] [PubMed]

Gherardi, E.; Birchmeier, W.; Birchmeier, C.; Woude, G.V. Targeting MET in cancer: Rationale and progress. Nat. Rev. Cancer 2012, 12, 89–103. [Google Scholar] [CrossRef] [PubMed]

Blumenschein, G.R.; Mills, G.B.; Gonzalez-Angulo, A.M. Targeting the hepatocyte growth factor-cMET axis in cancer therapy. J. Clin. Oncol. 2012, 30, 3287–3296. [Google Scholar] [CrossRef] [PubMed]

Kim, H.-J.; Yoon, A.; Ryu, J.-Y.; Cho, Y.-J.; Choi, J.-J.; Song, S.Y.; Bang, H.; Lee, J.S.; Cho, W.C.; Choi, C.H.; et al. C-MET as a Potential Therapeutic Target in Ovarian Clear Cell Carcinoma. Sci. Rep. 2016, 6, 38502. [Google Scholar] [CrossRef] [PubMed]

Peters, S.; Adjei, A.A. MET: A promising anticancer therapeutic target. Nat. Rev. Clin. Oncol. 2012, 9, 314–326. [Google Scholar] [CrossRef]

Santoro, A.; Rimassa, L.; Borbath, I.; Daniele, B.; Salvagni, S.; Van Laethem, J.L.; Van Vlierberghe, H.; Trojan, J.; Kolligs, F.T.; Weiss, A.; et al. Tivantinib for second-line treatment of advanced hepatocellular carcinoma. Lancet Oncol. 2013, 14, 55–63. [Google Scholar] [CrossRef]

Xiang, Q.; Chen, W.; Ren, M.; Wang, J.; Zhang, H.; Deng, D.Y.; Zhang, L.; Shang, C.; Chen, Y. Cabozantinib suppresses tumor growth and metastasis in hepatocellular carcinoma by a dual blockade of VEGFR2 and MET. Clin. Cancer Res. 2014, 20, 2959–2970. [Google Scholar] [CrossRef] [PubMed]

Choueiri, T.K.; Escudier, B.; Powles, T.; Mainwaring, P.N.; Rini, B.I.; Donskov, F.; Hammers, H.; Hutson, T.E.; Lee, J.-L.; Peltola, K.; et al. Cabozantinib versus Everolimus in Advanced Renal-Cell Carcinoma. N. Engl. J. Med. 2015, 373, 1814–1823. [Google Scholar] [CrossRef] [PubMed]

Scagliotti, G.; Von Pawel, J.; Novello, S.; Ramlau, R.; Favaretto, A.; Barlesi, F.; Akerley, W.; Orlov, S.; Santoro, A.; Spigel, D.R.; et al. Phase III Multinational, Randomized, Double-Blind, Placebo-Controlled Study of Tivantinib (ARQ 197) Plus Erlotinib Versus Erlotinib Alone in Previously Treated Patients With Locally Advanced or Metastatic Nonsquamous Non–Small-Cell Lung Cancer. J. Clin. Oncol. 2015, 33, 2667–2674. [Google Scholar] [CrossRef]

Tang, M.K.S.; Zhou, H.Y.; Yam, J.W.P.; Wong, A.S.T. c-Met overexpression contributes to the acquired apoptotic resistance of nonadherent ovarian cancer cells through a cross talk mediated by phosphatidylinositol 3-kinase and extracellular signal-regulated kinase 1/2. Neoplasia 2010, 12, 128–138. [Google Scholar] [CrossRef] [PubMed]

Marchion, D.C.; Bicaku, E.; Xiong, Y.; Zgheib, N.B.; Al Sawah, E.; Stickles, X.B.; Judson, P.L.; Lopez, A.S.; Cubitt, C.L.; Gonzalez-Bosquet, J.; et al. A novel c-Met inhibitor, MK8033, synergizes with carboplatin plus paclitaxel to inhibit ovarian cancer cell growth. Oncol. Rep. 2013, 29, 2011–2018. [Google Scholar] [CrossRef] [PubMed]

Vergote, I.B.; Smith, D.C.; Berger, R.; Kurzrock, R.; Vogelzang, N.J.; Sella, A.; Wheler, J.; Lee, Y.; Foster, P.G.; Weitzman, R.; et al. A phase 2 randomised discontinuation trial of cabozantinib in patients with ovarian carcinoma. Eur. J. Cancer 2017, 83, 229–236. [Google Scholar] [CrossRef] [PubMed]

Wang, J.; Cheng, J.X. c-Met inhibition enhances chemosensitivity of human ovarian cancer cells. Clin. Exp. Pharmacol. Physiol. 2017, 44, 79–87. [Google Scholar] [CrossRef] [PubMed]

Wu, C.C.; Weng, C.S.; Hsu, Y.T.; Chang, C.L. Antitumor effects of BMS-777607 on ovarian cancer cells with constitutively activated c-MET. Taiwan J. Obstet. Gynecol. 2019, 58, 145–152. [Google Scholar] [CrossRef] [PubMed]

Konstantinopoulos, P.A.; Brady, W.E.; Farley, J.; Armstrong, A.; Uyar, D.S.; Gershenson, D.M. Phase II study of single-agent cabozantinib in patients with recurrent clear cell ovarian, primary peritoneal or fallopian tube cancer (NRG-GY001). Gynecol. Oncol. 2018, 150, 9–13. [Google Scholar] [CrossRef]

Choi, B.-H.; Young Ryu, D.; Ryoo, I.-G.; Kwak, M.-K. NFE2L2/NRF2 silencing-inducible miR-206 targets c-MET/EGFR and suppresses BCRP/ABCG2 in cancer cells. Oncotarget 2017, 8, 107188. [Google Scholar] [CrossRef]

Hassan, W.; Chitcholtan, K.; Sykes, P.; Garrill, A. Ascitic fluid from advanced ovarian cancer patients compromises the activity of receptor tyrosine kinase inhibitors in 3D cell clusters of ovarian cancer cells. Cancer Lett. 2018, 420, 168–181. [Google Scholar] [CrossRef]

Hassan, W.; Chitcholtan, K.; Sykes, P.; Garrill, A. A Combination of Two Receptor Tyrosine Kinase Inhibitors, Canertinib and PHA665752 Compromises Ovarian Cancer Cell Growth in 3D Cell Models. Oncol. Ther. 2016, 4, 257–274. [Google Scholar] [CrossRef]

Puvanenthiran, S.; Essapen, S.; Haagsma, B.; Bagwan, I.; Green, M.; Khelwatty, S.A.; Seddon, A.; Modjtahedi, H. Co-expression and prognostic significance of the HER family members, EGFRvIII, c-MET, CD44 in patients with ovarian cancer. Oncotarget 2018, 9, 19662–19674. [Google Scholar] [CrossRef]

Table 1. Biomarkers and antibodies.

Table 1. Biomarkers and antibodies.

Antigen | Clone | Species | Fixation | Use of Kit | wc (μg/mL) | Supplier | Cutoff for Positivity
Primary antibodies
HGFR | SP44 | r | Acetone | - | 2.12 | Spring Bioscience, Pleasanton, CA, USA | ≥50%
ERα | 1D5 | m | Formalin | + | 2.50 | Dako, Santa Clara, CA, USA | ≥1%
PR | PgR 636 | m | Formalin | + | 2.50 | Dako, Santa Clara, CA, USA | ≥1%
HER-2/neu | 4B5 | r | Acetone | - | 1.50 | Ventana, Roche, Basel, CH | ≥10% (Intensity 2+/3+)
EGFR | H11 | m | Acetone | - | 2.94 | Dako, Santa Clara, CA, USA | ≥50%
IGF1R | 23-41 | m | Acetone | + | 4.00 | Invitrogen, Carlsbad, CA, USA | ≥80%
MUC-1 | Ma552 | m | Acetone | - | 0.50 | Monosan, Uden, NL | ≥70%
CD44v6 | VFF-18 | m | Acetone | - | 1.00 | Affymetrix eBioscience, Santa Clara, CA, USA | ≥10%
Integrin α2β1 | BHA2.1 | m | Acetone | - | 2.50 | Millipore, Burlington, MA, USA | ≥20%
Positive controls
Epithelial Antigen | Ber-EP4 | m | Acetone | - | 2.50 | Dako, Santa Clara, CA, USA |
Isotype controls
MOPC 21 | MOPC 21 | m |  | - | 5.00 | Sigma-Aldrich, St. Louis, MO, USA |
| MOPC 21 | m |  | + | 4.00 | Sigma-Aldrich, St. Louis, MO, USA |
| DA1E | r |  | - | 2.12 | Cell Signaling, Danvers, MA, USA |
Biotin-conjugated secondary antibodies
| 111-065-114 | g anti r |  |  | 7.00 | Jackson Immunoresearch, West Grove, PA, USA
| 315-065-048 | r anti m |  |  | 0.75 | Jackson Immunoresearch, West Grove, PA, USA

wc: working concentration; m: mouse; r: rabbit; g: goat. All used antibodies’ isotype was IgG1. HGFR: hepatocyte growth factor receptor, Her-2/neu: human epidermal growth factor receptor 2, EGFR: epidermal growth factor receptor, IGF1R: insulin-like growth factor 1, MUC-1: Mucin-1.

Table 2. Patient characteristics.

Table 2. Patient characteristics.

|  | n or Value | %
Age | Mean/median | 61/66 years |
| Range | 24–83 years |
FIGO Stage | I/II | 0 | 0.0
| III | 29 | 69.0
| IV | 13 | 31.0
pT | pT2 | 4 | 9.5
| pT3 | 38 | 90.5
pN | pN0 | 5 | 11.9
| pN1 | 28 | 66.7
| Nx | 9 | 21.4
cM | cM0 | 29 | 69.0
| cM1 | 13 | 31.0
Primary Tumor Site | Ovarian | 35 | 83.3
| Fallopian tube | 5 | 11.9
| Peritoneal | 2 | 4.8
Histological Subtype | Serous | 38 | 90.5
| Other | 4 | 9.5
Grading | Low grade | 1 | 2.0
| High grade | 41 | 98.0
Ascites | Yes | 36 | 85.7
| No | 6 | 14.3
Macroscopic Residual Tumor after Surgery | None | 30 | 71.4
| <1 cm | 7 | 16.7
| >1 cm | 5 | 11.9
Lymphatic Vessel Invasion | Yes | 23 | 54.7
| No | 17 | 40.5
| Missing | 2 | 4.8
Vascular Invasion | Yes | 6 | 14.3
| No | 34 | 80.9
| Missing | 2 | 4.8
First-Line Treatment | C | 3 | 7.2
| C + P | 14 | 33.3
| C + P + B | 25 | 59.5
Relapse after Chemotherapy | <6 months | 2 | 4.8
| 6–12 months | 12 | 28.5
| >12 months | 28 | 66.7

n: number of patients, FIGO: International Federation of Gynecology and Obstetrics, p: pathological, c: clinical, T: extent of primary tumor, N: regional lymph node metastasis, Nx: no evaluation of lymph node status, M: distant metastasis, C: Carboplatin, P: Paclitaxel, B: Bevacizumab.

Table 3. Univariate and multivariate survival analysis of clinicopathological factors and HGFR.

Table 3. Univariate and multivariate survival analysis of clinicopathological factors and HGFR.

Variable |  | PFS | PFI | OS
n | Log-Rank | MV Cox Regression | Log-Rank | MV Cox Regression | Log-Rank | MV Cox Regression
MS | p | HR (CI 95%) | p | MS | p | HR (CI 95%) | p | MS | p | HR (CI 95%) | p
Age ≤ 61 years | 19 | 22 | 0.965 |  |  | 17 | 0.970 |  |  | nr | 0.193 |  |
Age > 61 years | 23 | 22 | 17 | 42
<pT3c | 7 | 27 | 0.665 |  |  | 22 | 0.679 |  |  | 45 | 0.928 |  |
pT3c | 35 | 22 | 17 | 42
pN0 | 5 | 29 | 0.163 |  |  | 17 | 0.145 |  |  | 45 | 0.929 |  |
pN1 | 28 | 22 | 22 | 42
cM0 | 29 | 27 | 0.081 |  |  | 22 | 0.068 |  |  | nr | 0.015 | 2.25 (0.62–8.18) | 0.217
cM1 | 13 | 16 | 11 | 30
G1/G2 | 2 | 14 | 0.579 |  |  | 8 | 0.610 |  |  | 30 | 0.843 |  |
G3 | 40 | 22 | 17 | 42
Ascites absent | 6 | 35 | 0.147 |  |  | 30 | 0.139 |  |  | 42 | 0.408 |  |
Ascites present | 36 | 19 | 15 | 38
MR Tumor absent | 30 | 27 | 0.008 | 2.19 (1.03–4.68) | 0.043 | 22 | 0.010 | 2.10 (0.99–4.51) | 0.057 | 45 | 0.041 | 8.42 (1.59–44.61) | 0.012
MR Tumor present | 12 | 13 | 9 | 26
HGFR low | 19 | 35 | 0.041 | 2.99 (1.01–8.91) | 0.049 | 30 | 0.048 | 2.87 (0.97–8.49) | 0.057 | nr | 0.012 | 5.77 (1.56–21.34) | 0.009
HGFR high | 23 | 18 | 13 | 35

PFS: progression-free survival, PFI: platinum-free interval; OS: overall survival, n: number of patients, MV Cox Regression: multivariate Cox regression, MS: median survival (in months) in Kaplan–Meier estimator, HR: hazard ratio, CI: confidence interval, MR Tumor: macroscopic residual tumor, nr: median survival not reached.

Table 4. Correlation between HGFR expression and other protein biomarkers.

Table 4. Correlation between HGFR expression and other protein biomarkers.

|  |  |  | HGFR
| n | <50% | ≥50% | p #
Growth Factor Receptor | ERα |  | 42 |  |  | 0.468
<1% |  | 6 | 4 |
≥1% |  | 13 | 19 |
PR |  | 42 |  |  | 0.750
<1% |  | 13 | 14 |
≥1% |  | 6 | 9 |
Her-2/neu |  | 42 |  |  | 1
Negative |  | 14 | 17 |
Positive |  | 5 | 6 |
EGFR |  | 42 |  |  | 0.707
<50% |  | 14 | 19 |
≥50% |  | 5 | 4 |
IGF1R |  | 42 |  |  | 1
<80% |  | 4 | 4 |
≥80% |  | 15 | 19 |
Cell Adhesion Molecule | MUC-1 |  | 42 |  |  | 0.757
<70% |  | 10 | 10 |
≥70% |  | 9 | 13 |
CD44v6 |  | 42 |  |  | 1
<10% |  | 14 | 16 |
≥10% |  | 5 | 7 |
Integrin α2β1 |  | 42 |  |  | 0.108
<20% |  | 15 | 12 |
≥20% |  | 4 | 11 |

n: number of patients, # p-value calculated by Fisher’s exact two-tailed test.

Table 5. Univariate survival analysis of dual expression of HGFR and other protein biomarkers.

Table 5. Univariate survival analysis of dual expression of HGFR and other protein biomarkers.

|  | PFS | PFI | OS
| n | MS | p * | MS | p * | MS | p *
HGFR low | 19 | 35 | 0.041 | 30 | 0.048 | nr | 0.012
HGRF high | 23 | 18 | 13 | 35
HGFRhigh/ERαhigh | 19 | 19 | 0.186 | 14 | 0.199 | 38 | 0.051
Remaining combinations # | 23 | 30 | 25 | nr
HGFRhigh/PRhigh | 9 | 19 | 0.481 | 14 | 0.489 | 35 | 0.281
Remaining combinations # | 33 | 27 | 22 | 42
HGFRhigh/Her-2/neuhigh | 6 | 16 | 0.009 | 11 | 0.008 | 22 | 0.42
Remaining combinations # | 36 | 27 | 22 | 42
HGFRhigh/EGFRhigh | 4 | 12 | <0.001 | 8 | <0.001 | 23 | 0.011
Remaining combinations # | 38 | 24 | 19 | 42
HGFRhigh/IGF1Rhigh | 19 | 18 | 0.058 | 13 | 0.069 | 35 | 0.03
Remaining combinations # | 23 | 30 | 25 | nr
HGFRhigh/MUC-1high | 13 | 16 | 0.002 | 11 | 0.003 | 26 | <0.001
Remaining combinations # | 29 | 30 | 25 | nr
HGFRhigh/CD44v6high | 7 | 16 | 0.065 | 11 | 0.081 | 38 | 0.059
Remaining combinations # | 35 | 27 | 22 | 45
HGFRhigh/Integrin α2β1high | 11 | 15 | 0.004 | 10 | 0.004 | 27 | 0.054
Remaining combinations # | 31 | 29 | 25 | 45

n: number of patients, MS: median survival (in months) in Kaplan–Meier estimator, * p-value calculated by log-rank test. # Remaining combinations means tumor samples which were HGFR high/Biomarker X low or HGFR low/Biomarker X high or HGFR low/Biomarker X low. nr: median survival not reached.

Table 6. Univariate survival analysis of dual expression of MET and other biomarker genes.

Table 6. Univariate survival analysis of dual expression of MET and other biomarker genes.

|  | PFS | OS
| n | MS | p * | n | MS | p *
MET low | 192 | 19 | 0.018 | 138 | 49 45 | 0.033
MET high | 94 | 16 | 188
MET high/ESR1 high | 161 | 22 | 0.29 | 378 | 49 49 | 0.22
Remaining combinations # | 406 | 19 | 229
MET high/PGRhigh | 155 | 22 | 0.17 | 313 | 49 49 | 0.15
Remaining combinations # | 412 | 19 | 294
MET high/ERBB2 high | 124 | 16 | 0.036 | 192 | 45 49 | 0.043
Remaining combinations # | 109 | 19 | 145
MET high/EGFR high | 69 | 17 | 0.0047 | 69 | 49 73 | 0.083
Remaining combinations # | 49 | 27 | 51
MET high/IGF1R high | 172 | 18 | 0.076 | 198 | 45 49 | 0.048
Remaining combinations # | 157 | 19 | 139
MET high/MUC1 high | 94 | 16 | 0.018 | 186 | 45 48 | 0.043
Remaining combinations # | 192 | 19 | 140
MET high/CD44 high | 186 | 18 | 0.071 | 192 | 45 49 | 0.057
Remaining combinations # | 143 | 19 | 145
MET high/ITGA2 high | 70 | 17 | 0.003 | 70 | 49 73 | 0.058
Remaining combinations # | 51 | 27 | 53

n: number of patients, MS: median survival (in months) in Kaplan–Meier estimator, * p-value calculated by log-rank test. # Remaining combinations means tumor samples which were MET high/Biomarker X low or MET low/Biomarker X high or MET low/Biomarker X low.

| Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.