The etiology, pathogenesis, and progression of several cancers have been linked to pathways that are strongly influenced by estrogen and/or progesterone (Folkerd and Dowsett, 2010). It has been shown that estrogen and progesterone pathways are involved in the pathogenesis and progression of breast cancer and endometrial cancer, and they have been shown to be effective for their therapy (Richardson, 1972; Weiss et al., 1979; Fasching et al., 2011; Kümmel et al., 2011).
Similarly, the pathogenesis of ovarian cancer also appears to be at least partly sex hormone dependent. For example, parous women have a 50% lower risk of ovarian cancer in comparison with nulliparous women. Breastfeeding is associated with a risk reduction of nearly 20%, with an overall reduction of 1% in the risk for each month of breastfeeding (Whittemore et al., 1992). Use of oral contraceptives lowers the risk for ovarian cancer by 40–50% in comparison with that in women who have never used oral contraceptives (Rosenberg et al., 1994; Purdie et al., 1995). All these facts suggest that larger numbers of menstrual cycles are associated with a higher risk of ovarian cancer. Although this has been interpreted and explained with the incessant ovulation hypothesis (Fathalla, 1971), an increased number of menstrual cycles means an increased amount of repeated high-level estrogen exposure as well.
Furthermore, use of unopposed estrogen as hormone replacement therapy (HRT) also raised the risk of ovarian cancer by about 22% over 5 years. The risk is still statistically significantly increased by about 10% with combined estrogen/progesterone administration (Pearce et al., 2009).
After ovarian cancer has been diagnosed, there is some evidence that sex hormones have an influence on the progression of the disease. Patients with advanced disease show a clinical response to tamoxifen in 9–13% of cases and those with stable disease show a clinical response to tamoxifen in 31–38% of cases (Williams, 2001; Perez-Gracia and Carrasco, 2002). Treatment with aromatase inhibitors shows an effect in recurrent ovarian cancer. Response rates to letrozole therapy in different studies have ranged from 0 to 35.7%, with stable disease rates ranging from 20 to 42% (Miller, 1996; Bowman et al., 2002; Gourlev et al., 2006; Tchekmedyian et al., 2006; Kavanagh et al., 2007). For anastrozole, response rates range from 1.9 to 4.3% and stable disease rates from 42 to 61% (Del Carmen et al., 2003; Krasner, 2007).
In patients with breast cancer, it is known that the use of estrogen and combined estrogen/progesterone as HRT before the breast cancer is diagnosed reduces the mortality by about 36 and 63%, respectively (Schuetz et al., 2007; Marshall et al., 2009). It was concluded that HRT before diagnosis leads to a more favorable primary tumor, with a lower incidence of recurrence and a better overall survival rate.
On the basis of these facts, it was hypothesized that for ovarian cancer as well, previous HRT may influence the prognosis in affected patients, and a retrospective single-institution study was carried out to investigate the effect of previous HRT before the diagnosis of ovarian cancer on the prognosis in these patients.
Patients and methods
The Bavarian Ovarian Cancer Cases and Controls is an ongoing case–control and cohort study investigating epidemiological and genetic factors as well as prognostic factors in the risk for ovarian cancer. As a case–control study, it is part of the Ovarian Cancer Association Consortium and has supplied clinical, epidemiological, and genotype data for several analyses (Song et al., 2009; Bolton et al., 2010; Goode et al., 2010; Notaridou et al., 2011). The present investigation reports on the cohort study (cases only) within Bavarian Ovarian Cancer Cases and Controls.
From 1995 to 2008, 547 patients with malignant or borderline ovarian tumors were primarily treated with surgery at the Gynecological Cancer Center at Erlangen University Hospital. Patients were recruited consecutively, and data were collected using a structured questionnaire and patient charts. Patients were selected using the following criteria, in a hierarchical order: they had to be diagnosed with invasive epithelial ovarian cancer (exclusion of 87 patients); they had to be postmenopausal and older than 40 years of age (exclusion of six patients); stage information after surgery had to be available (exclusion of 12 patients); and information about their current or former HRT had to be available (exclusion of 59 patients). Furthermore, women above the age of 75 were excluded because the percentage of women who could reliably state information about HRT use was low and the overall frequency of HRT was low (exclusion of 139 patients). This resulted in a total of 244 patients.
Postmenopausal status was defined as having been amenorrheal for at least 12 months, having an amenorrheal status following surgical removal of both ovaries, or being over 55 years of age with unknown menorrheal status.
Follow-up data were obtained from the clinical cancer registry for crude overall survival and progression-free survival. Data were collected on an annual basis, and the registry of deaths in Bavaria was contacted for any patients who appeared to be lost to follow-up. Overall survival (OS) was defined as the time interval from the date of histologic diagnosis to either the date of death or the date of censoring. Patients who were lost to follow-up within 10 years after diagnosis were censored at the last date they were known to be alive. A patient who was alive 10 years after diagnosis (maximal observation time) was censored at this date. Progression-free survival was defined as the time interval between the date of histologic diagnosis and the first confirmed sign of disease recurrence and progression, respectively, or the date of censoring. Patients who were lost to follow-up or died within the maximal observation time and patients without disease recurrence or progression at the end of the maximal observation time were censored.
The Ethics Committee of the medical faculty at Erlangen University Hospital approved the study, and patients provided written informed consent.
Data collection and patient treatment
Data were entered into a database, which was designed for research purposes in the context of the Ovarian Cancer Association Consortium and for documentation of quality-assured health care in the context of gynecological cancer centers certified with the European Society of Gynecological Oncology (Beckmann et al., 2006). The database thus contains variables for both requirements. Basic patient, tumor, and treatment characteristics were documented from source data in the patients’ health records, and epidemiological data were obtained using a structured questionnaire. Histopathological information was directly obtained from the pathology reports, and all histopathological assessments were carried out at one site. Information about grading and tumor type was centrally reassessed by two independent pathologists; in the case of divergence, the two pathologists had to agree on the tumor type and grading after reviewing the case together. Information about HRT usage was documented as follows: HRT at any time before the diagnosis, yes or no. This information was collected from the patients charts and did not differentiate between therapy with estrogen only and combined estrogen and progesterone therapy. However, from other studies we know that in that population the frequency of use of combined HRT is as high as over 90% (personal information). It has to be kept in mind that in the addressed population women who have undergone hysterectomy are very rare (in our population n=10). Therefore, for them, estrogen-only therapy is contraindicated (Albring et al., 2010).
The characteristics of patients who have undergone HRT and those who have not were compared using appropriate unpaired statistical tests. Student’s t-tests were used for continuous characteristics, χ2-tests for categorical characteristics, and Wilcoxon’s rank-sum tests for ordinal categorical characteristics. Overall and progression-free survival rates for patients with the characteristics listed in Table 1 were estimated using the Kaplan–Meier product limit method.
The prognostic value of HRT status (use vs. nonuse), in addition to the well-known prognostic factors of age at diagnosis (continuous), BMI (continuous), International Federation of Gynecology and Obstetrics (FIGO) stage (ordinal), grading (ordinal), histology (serous vs. nonserous), and resection status (optimally debulked vs. not optimally debulked, according to which patients with no residual tumor after surgery were considered to be optimally debulked), was studied using Cox proportional hazards models. For overall survival and progression-free survival, the following model selection process was carried out.
Initially, a multifactorial Cox proportional hazards model with all prognostic factors except HRT status was fitted. Thereafter, backward stepwise variable selection was carried out to obtain the best model in accordance with the Akaike information criterion (the first final model). Next, another Cox proportional hazards model was fitted containing HRT status, the prognostic factors from the first final model, and interactions between HRT status and these prognostic factors. The variable selection procedure from above was carried out again, but on the condition that the selected prognostic factors from the first final model were retained. The resulting model (the second final model) was compared with the first final model using the likelihood ratio test. A significant test result means that HRT status influences the survival prognosis independently of the prognostic factors considered. Hazard ratios (HR) and their 95% confidence intervals (95% CIs) from the second final model are shown.
The proportional hazards assumptions in the final models were checked using tests correlating scaled Schoenfeld residuals with a suitable time transformation (Grambsch and Therneau, 1994). If the proportional hazards assumptions had not been fulfilled, the analysis would have been repeated separately for survival times up to 5 years and from 5 years on.
All of the tests were two-sided, and a P-value less than 0.05 was regarded as statistically significant. Calculations were carried out using the R system for statistical computing (version 2.13.1; R Development Core Team, Vienna, Austria, 2011).
The final sample consisted of 244 postmenopausal patients with invasive epithelial ovarian cancer. The patients’ average age was 59 years (±9). Most of the patients were diagnosed at stage III (n=112; 46.1%) and with serous histology (n=151; 61.9%). The most common grade was III (n=136; 57.4%). Patient characteristics are listed in Table 1. With regard to HRT, 77 patients stated that they had received HRT at some point in time before the diagnosis (31.6%) and 167 (68.4%) stated that they had never received any HRT before diagnosis.
Univariate association with hormone replacement therapy usage status
With regard to associations between patient and tumor characteristics, there was no association between previous HRT and BMI, grading, histology, or the decision on the use of chemotherapy (Table 1). However, there did appear to be an association with tumor stage, age, and resection status (Table 1). The older the age at which a patient was diagnosed, the less likely it was that she had previously received hormone therapy. Approximately 30% of women under the age of 60 stated that they had received HRT, whereas only about 20% of those over 60 had undergone HRT.
Patients who were diagnosed at higher stages were less likely to have received HRT. Patients in stages III and IV had received HRT in 26.8 and 20.0% of cases, respectively, whereas patients in stages I and II had received HRT in 43.3 and 34.5% of cases, respectively (P<0.01). This corresponded with the resection status. Patients in whom complete removal of the tumor by surgery was possible stated that they had previously received HRT in 39.1% of cases, whereas patients in whom optimal debulking was not possible had received HRT in 16.5% of cases (P<0.0001).
Univariate survival analysis
All of the commonly known prognostic factors were found to be significantly associated with overall survival and progression-free survival. Previous HRT was associated with overall survival and progression-free survival in the univariate analysis. Patients who had received HRT before the time of the diagnosis showed a 5-year overall survival of 75%, in comparison with 43% in nonrecipients (P<0.0001, log-rank test). Similarly, patients with previous HRT had a 5-year progression-free survival of 55%, whereas nonrecipients had a progression-free survival of only 46% (P=0.08, log-rank test). Univariate survival data are summarized in Tables 2 and 3.
Multivariate overall survival analysis
The preliminary multifactorial Cox survival analysis, which did not take HRT into account, identified age, FIGO stage, grading, and resection status as relevant prognostic factors. The prognostic factors of BMI and histology were dropped during the variable selection process – that is their prognostic value appeared to be irrelevant, or it was already explained by the other factors.
The ultimate survival analysis, taking into account the relevant prognostic factors from the preliminary survival analysis and, in addition, HRT and interactions between HRT and these other prognostic factors, showed that the prognosis can be improved overall by including HRT (P<0.01, likelihood ratio test). In particular, the interaction between HRT and resection status turned out to be significant. This means that the HR for HRT (yes vs. no) significantly differed in the subgroups of patients with optimally debulked (HR=0.37; 95% CI, 0.18–0.75) and not optimally debulked resections (HR=1.40; 95% CI, 0.72–2.74). The HR for resection status (optimally debulked vs. not optimally debulked) also differed between HRT recipients (HR=0.14; 95% CI, 0.06–0.35) and HRT nonrecipients (HR=0.53; 95% CI, 0.32–0.88). None of the other interaction terms improved the model.
In summary, HRT significantly improved the prognosis if the tumor was optimally debulked, whereas a prognostic effect could not be detected if the tumor could not be optimally debulked. Optimal debulking improved the prognosis, in any case, to a greater extent for HRT recipients and to a lesser extent for HRT nonrecipients (Figs 1 and 2).
For all patients – that is for HRT recipients as well as nonrecipients – the overall survival prognosis became significantly poorer with increasing age, FIGO stage, and grading (Fig. 1).
Multivariate progression-free survival analysis
The same multivariate survival analysis was also carried out for progression-free survival. The first variable selection process in the multifactorial Cox survival analyses for the preliminary model identified age, FIGO stage, and resection status as relevant prognostic factors. The progression-free survival prognosis became significantly poorer with increasing age and FIGO stage. Optimal debulking significantly improved the prognosis (Fig. 2). During the second variable selection process, both the main effect of HRT status and also all interaction effects were dropped. A prognostic value of HRT status was therefore not shown for progression-free survival.
This retrospective cohort study adds information about previous HRT usage and its effect on the prognosis in ovarian cancer patients with optimal debulking during primary surgery. This finding was seen in relation to overall survival (adjusted for age at diagnosis, BMI, FIGO stage, grading, histology, and resection status) but was not seen in relation to progression-free survival.
As tens of thousands of women have now been analyzed in case–control studies and some evidence has been obtained from cohort studies and randomized studies, it can be assumed that HRT is associated with a modestly increased risk for ovarian cancer (summarized in La Vecchia, 2006). More recent data support these results and suggest that estrogen-only therapy increases the risk to a greater extent than combined estrogen plus progesterone therapy (Pearce et al., 2009). The comprehensive epidemiological data available link HRT with epithelial ovarian cancer, but there have been few studies explaining the causality in greater detail or showing a molecular basis for the link. Estrogens might either induce neoplastic transformation or increase the cell growth rate in pre-existing subclinical disease. Older studies have shown that invasive epithelial ovarian cancers express estrogen receptors in about 40–60% of cases (Rao and Slotman, 1991; Kommoss et al., 1992). Data from human cohort studies have provided some, although weak, evidence that estrogen receptor expression in invasive epithelial ovarian cancer may be associated with a more favorable prognosis (Halon et al., 2011a, 2011b). This would be in accordance with data from the present study, showing that cancers that are driven by estrogen exposure may have a better prognosis. However, no data on estrogen receptor expression are available for the cohort of patients with ovarian cancer studied, so this assumption remains hypothetical.
At the molecular level, specific genes have been identified in a mouse model of estrogen-stimulated ovarian tumors that are potentially regulated by the estrogen pathway and could contribute to both the pathogenesis and behavior of the condition after diagnosis of the disease (Spillman et al., 2010). These data indicate that there appears to be a molecular difference between tumors that are exposed to estrogen and those that are not. In the present study, the association between HRT and its effect was strongest in women who underwent optimal debulking, whereas no effect was seen in women who still had a large tumor burden after the initial surgery. This may be consistent with the finding in the mouse model that hormone exposure when tumor cells are present leads to a more aggressive tumor with greater motility and tumor growth.
The present analysis has several limitations. Although the direction of the influence on overall survival and progression-free survival is the same in the study (i.e. patients with HRT had a more favorable prognosis), the multivariate model failed to show any influence of HRT on progression-free survival. Therefore, other reasons why HRT may influence the overall survival have to be taken into consideration as well. For breast cancer, it has been hypothesized that women who receive HRT generally have a better health status (Marshall et al., 2009). They are likely to visit their physicians more frequently and thus have access to early detection of gynecological cancers. In the present study, the percentage of patients who had undergone HRT at diagnosis was in fact higher for those with stage I disease (43.3%) and decreased to 20% for those with stage IV disease. However, this effect was adjusted for in the multivariate models, and ongoing HRT at the time of the diagnosis had an effect on overall survival that was independent of this correlation. Finally, the study is not able to differentiate between women who ceased to receive HRT after the diagnosis of ovarian cancer, and this might be a major concern in interpreting the results. It is known that sex hormones have an influence on cancer cells and that antihormonal therapy may be helpful in ovarian cancer therapy. The effect in this study might therefore be a mixed effect of predisease exposure to HRT up to the time of diagnosis and postdisease exposure in some patients. It is not common practice to advise patients to stop HRT after ovarian cancer is diagnosed. So far, as is currently known, there is no evidence to show that HRT should not be administered in women treated for ovarian cancer, independent of the tumor stage (Biglia et al., 2006). In addition, our study cannot provide any differential effect, whether estrogen or progesterone is responsible for the described effect, and was restricted to women over the age of 40 and under the age of 75.
This study indicates that HRT might have an influence on the prognosis for patients with ovarian cancer. This effect was seen particularly in the group of patients in whom optimal debulking was possible. It was not observed in the group of patients who had residual tumor masses. The effect was also not evident in relation to progression-free survival. However, the study supports the hypothesis that ovarian cancer, like other types of cancer, is influenced by sex hormone pathways. The results thus suggest that sex hormones may be able to influence the prognosis for patients with this type of tumor. However, the findings will require confirmation through further research, and a functional explanation is also needed.
Conflicts of interest
There are no conflicts of interest.
Albring C, Baum E, Beckermann MJ, Beckmann MW, Blettner M, Bohm B, et al. Hormone therapy in the peri and postmenopausal women – short version of the S3-guideline. Geburtsh Frauenheilk. 2010;70:R64–R76
Beckmann MW, Gitsch G, Emons G, Berg D, Ortmann O, Jonat W, et al. The newly established subspecialty gynecological oncology – a national and an international chance? Geburtsh Frauenheilk. 2006;66:123–127
Biglia N, Mariani L, Marenco D, Robba C, Peano E, Kubatzki F, et al. Hormonal replacement therapy after gynaecological cancer. Gynakol Geburtshilfliche Rundsch. 2006;46:191–196
Bolton KL, Tyrer J, Song H, Ramus SJ, Notaridou M, Jones C, et al. Common variants at 19p13 are associated with susceptibility to ovarian cancer. Nat Genet. 2010;42:880–884
Bowman A, Gabra H, Langdon SP, Lessells A, Stewart M, Young A, et al. CA125 response is associated with estrogen receptor expression in a phase II trial of letrozole in ovarian cancer: identification of an endocrine-sensitive subgroup. Clin Cancer Res. 2002;8:2233–2239
Del Carmen MG, Fuller AF, Matulonis U, Horick NK, Goodman A, Duska LR, et al. Phase II trial of anastrozole in women with asymptomatic mullerian cancer. Gynecol Oncol. 2003;91:596–602
Fasching PA, Ekici AB, Adamietz B, Wachter DL, Hein A, Bayer CM, et al. Breast cancer risk – genes, environment, and clinical practice. Geburtsh Frauenheilk. 2011;71:1056–1066
Fathalla MF. Incessant ovulation – a factor in ovarian neoplasia? Lancet. 1971;2:163
Folkerd EJ, Dowsett M. Influence of sex hormones on cancer progression. J Clin Oncol. 2010;28:4038–4044
Goode EL, Chenevix-Trench G, Song H, Ramus SJ, Notaridou M, Lawrenson K, et al. A genome-wide association study identifies susceptibility loci for ovarian cancer at 2q31 and 8q24. Nat Genet. 2010;42:874–879
Gourlev C, Smyth JF, Mackean M, Stevenson A, Williams A, Rye T, et al. Phase II study of letrozole in estrogen receptor (ER) positive relapsed epithelial ovarian cancer (EOC). J Clin Oncol. 2006;24:261S–261SS
Grambsch PM, Therneau TM. Proportional hazards tests and diagnostics based on weighted residuals. Biometrika. 1994;81(3):515–526
Halon A, Materna V, Drag-Zalesinska M, Nowak-Markwitz E, Gansukh T, Donizy P, et al. Estrogen receptor alpha expression in ovarian cancer predicts longer overall survival. Pathol Oncol Res. 2011a;17:511–518
Halon A, Nowak-Markwitz E, Maciejczyk A, Pudelko M, Gansukh T, Gyorffy B, et al. Loss of estrogen receptor beta expression correlates with shorter overall survival and lack of clinical response to chemotherapy in ovarian cancer patients. Anticancer Res. 2011b;31:711–718
Kavanagh JJ, Hu W, Fu S, Deavers M, Moore C, Coleman RL, et al. Anti-tumor activity of letrozole in patients with recurrent advanced low malignant potential or low-grade serous ovarian tumors. J Clin Oncol. 2007;25:5582S–5582SS
Kommoss F, Pfisterer J, Thome M, Schafer W, Sauerbrei W, Pfleiderer A. Steroid receptors in ovarian carcinoma: immunohistochemical determination may lead to new aspects. Gynecol Oncol. 1992;47:317–322
Krasner C. Aromatase inhibitors in gynecologic cancers. J Steroid Biochem Mol Biol. 2007;106:76–80
Kümmel S, Kolberg HC, Lüftner D, Lux MP, Maass N, Schütz F, et al. Breast cancer 2011 – new aspects. Geburtsh Frauenheilk. 2011;71:939–953
La Vecchia C. Estrogen–progestogen replacement therapy and ovarian cancer: an update. Eur J Cancer Prev. 2006;15:490–492
Marshall SF, Chang E, Clarke CA, Cress R, Deapen D, Horn-Ross PL, et al. Hormone therapy use before diagnosis and breast cancer survival in the California teachers study. Cancer Res. 2009;69:79S–79SS
Miller WR. Aromatase inhibitors – where are we now? Br J Cancer. 1996;73:415–417
Notaridou M, Quaye L, Dafou D, Jones C, Song H, Hogdall E, et al. Common alleles in candidate susceptibility genes associated with risk and development of epithelial ovarian cancer. Int J Cancer. 2011;128:2063–2074
Pearce CL, Chung K, Pike MC, Wu AH. Increased ovarian cancer risk associated with menopausal estrogen therapy is reduced by adding a progestin. Cancer. 2009;115:531–539
Perez-Gracia JL, Carrasco EM. Tamoxifen therapy for ovarian cancer in the adjuvant and advanced settings: systematic review of the literature and implications for future research. Gynecol Oncol. 2002;84:201–209
Purdie D, Green A, Bain C, Siskind V, Ward B, Hacker N, et al. Reproductive and other factors and risk of epithelial ovarian cancer: an Australian case–control study. Survey of Women’s Health Study Group. Int J Cancer. 1995;62:678–684
Rao BR, Slotman BJ. Endocrine factors in common epithelial ovarian cancer. Endocr Rev. 1991;12:14–26
Richardson GS. Endometrial cancer as an estrogen-progesterone target. N Engl J Med. 1972;286:645–647
Rosenberg L, Palmer JR, Zauber AG, Warshauer ME, Lewis JL Jr., Strom BL, et al. A case–control study of oral contraceptive use and invasive epithelial ovarian cancer. Am J Epidemiol. 1994;139:654–661
Schuetz F, Diel IJ, Pueschel M, von Holst T, Solomayer EF, Lange S, et al. Reduced incidence of distant metastases and lower mortality in 1072 patients with breast cancer with a history of hormone replacement therapy. Am J Obstet Gynecol. 2007;196:342 e1–342 e9
Song H, Ramus SJ, Tyrer J, Bolton KL, Gentry-Maharaj A, Wozniak E, et al. A genome-wide association study identifies a new ovarian cancer susceptibility locus on 9p22.2. Nat Genet. 2009;41:996–1000
Spillman MA, Manning NG, Dye WW, Sartorius CA, Post MD, Harrell JC, et al. Tissue-specific pathways for estrogen regulation of ovarian cancer growth and metastasis. Cancer Res. 2010;70:8927–8936
Tchekmedyian NS, Liem AK, Quan ET, Burtzo DM, Ucar K. Aromatase inhibitor therapy for estrogen receptor positive ovarian cancer. J Clin Oncol. 2006;24:15038S–15038SS
Weiss NS, Szekely DR, English DR, Schweid AI. Endometrial cancer in relation to patterns of menopausal estrogen use. JAMA. 1979;242:261–264
Whittemore AS, Harris R, Itnyre J. Characteristics relating to ovarian cancer risk: collaborative analysis of 12 US case–control studies. II. Invasive epithelial ovarian cancers in white women. Collaborative Ovarian Cancer Group. Am J Epidemiol. 1992;136:1184–1203
Williams CJ. Tamoxifen for relapse of ovarian cancer. Cochrane Database Syst Rev. 2010;3