Ovarian cancer risk and survival according to tumor sex hormone receptor expression: An ovarian Cancer association consortium and ovarian tumor tissue analysis consortium pooled analysis

Zhuxuan Fu ¹, Lauren Borho ², Sarah E Taylor ²

¹Department of Epidemiology, University of Pittsburgh School of Public Health, Pittsburgh, PA 15213, USA; St. Luke's Mid America Heart Institute. St Luke's Health System. Kansas City, MO, USA.
²Division of Gynecologic Oncology, Department of Obstetrics, Gynecology and Reproductive Sciences. University of Pittsburgh School of Medicine. Pittsburgh, PA 15213, USA.
³South Carolina Department of Public Health. Columbia, SC, USA.
⁴Centre for Cancer Research. The Westmead Institute for Medical Research. Sydney, New South Wales 2145, Australia; Faculty of Medicine and Health. The University of Sydney. Sydney, New South Wales 2006, Australia.; Department of Gynaecological Oncology. Westmead Hospital. Sydney, New South Wales 2145, Australia; The Daffodil Centre, The University of Sydney. A Joint Venture with Cancer Council NSW. Sydney, New South Wales 2006, Australia.
⁵Population Health Program. QIMR Berghofer. Brisbane, Queensland 4006, Australia; School of Public Health. The University of Queensland. Brisbane 4006, Australia.
⁶Department of Pathology and Laboratory Medicine. University of Calgary, Foothills Medical Center. Calgary, AB T2N 2T9, Canada.
⁷The Daffodil Centre, The University of Sydney. A Joint Venture with Cancer Council NSW. Sydney, New South Wales 2006, Australia; School of Clinical Medicine, UNSW Medicine and Health. University of NSW Sydney. Sydney, New South Wales 2052, Australia.
⁸Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care. University of Cambridge. Cambridge CB1 8RN, UK.
⁹Faculty of Medicine and Health. The University of Sydney. Sydney, New South Wales 2006, Australia.; Department of Gynaecological Oncology. Westmead Hospital. Sydney, New South Wales 2145, Australia.
¹⁰Centre for Cancer Research. The Westmead Institute for Medical Research. Sydney, New South Wales 2145, Australia; Faculty of Medicine and Health. The University of Sydney. Sydney, New South Wales 2006, Australia.; Department of Gynaecological Oncology. Westmead Hospital. Sydney, New South Wales 2145, Australia.
¹¹Department of Computational Biomedicine. Cedars-Sinai Medical Center. West Hollywood, CA 90069, USA.
¹²University of Hawaii Cancer Center. Honolulu, HI, USA.
¹³Department of Quantitative Health Sciences, Division of Computational Biology. Mayo Clinic. Rochester, MN 55905, USA.
¹⁴Center for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge CB1 8RN, UK.
¹⁵Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg. University Hospital Erlangen. Erlangen 91054, Germany.
¹⁶School of Clinical Medicine, UNSW Medicine and Health. University of NSW Sydney. Sydney, New South Wales 2052, Australia; Adult Cancer Program, Lowy Cancer Research Centre. University of NSW Sydney. Sydney, New South Wales 2052, Australia.
¹⁷Peter MacCallum Cancer Centre. Melbourne, Victoria 3000, Australia; Sir Peter MacCallum Department of Oncology. The University of Melbourne. Parkville, Victoria 3010, Australia.
¹⁸Cancer Research UK Cambridge Institute, University of Cambridge. Cambridge CB2 0RE, UK.
¹⁹Department of Obstetrics and Gynecology, John A. Burns School of Medicine. University of Hawaii. Honolulu, HI, USA.
²⁰Department of Gynecological Surgery and Gynecological Oncology of Adults and Adolescents. Pomeranian Medical University. Szczecin 70-111, Poland.
²¹Epidemiology, School of Public Health, University of Colorado. Aurora, CO, USA; Community Health Sciences. University of Calgary. Calgary, AB, Canada.
²²International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland.
²³Division of Cancer Epidemiology. German Cancer Research Center (DKFZ). Heidelberg 69120, Germany; Department of Research, Cancer Registry of Norway. Norwegian Institute of Public Health. Oslo, Norway.
²⁴Genomic Biomarkers and Precision Oncology Laboratory, Cancer Department. Sols-Morreale Biomedical Research Institute (IIBM), CSIC-UAM. Madrid, Spain.
²⁵Department of Quantitative Health Sciences, Division of Epidemiology. Mayo Clinic. Rochester, MN 55905, USA.
²⁶Cancer Prevention and Control Program, Cedars-Sinai Cancer. Cedars-Sinai Medical Center. Los Angeles, CA 90048, USA.
²⁷Department of Genetics and Pathology. Pomeranian Medical University. Szczecin 71-252, Poland.
²⁸Department of Pathology, Herlev Hospital. University of Copenhagen. Copenhagen DK-2100, Denmark.
²⁹Department of Obstetrics and Gynecology. University of British Columbia. Vancouver, BC V5Z 1L3, Canada; Department of Molecular Oncology. BC Cancer Research Centre. Vancouver, BC V5Z 4E6, Canada.
³⁰Department of Virus, Lifestyle and Genes, Danish Cancer Institute, Copenhagen DK-2100, Denmark.
³¹Department of Histopathology. Addenbrooke's Hospital. Cambridge, UK.
³²Department of Cancer Prevention and Control. Roswell Park Comprehensive Cancer Center. Buffalo, NY 14263, USA.
³³David Geffen School of Medicine, Department of Obstetrics and Gynecology. University of California at Los Angeles. Los Angeles, CA 90095, USA.
³⁴Department of Pathology, Faculty of Medicine, University of Rzeszow. 35-559 Rzesow, Poland.
³⁵Department of Virus, Lifestyle and Genes, Danish Cancer Institute, Copenhagen DK-2100, Denmark; Department of Gynaecology, Rigshospitalet. University of Copenhagen. Copenhagen DK-2100, Denmark.
³⁶Department of Gynecology, Gynecology Oncology and Obstetrics. Institute of Medical Sciences, Medical College of Rzeszow University. Rzesz-w: Poland.
³⁷Department of Obstetrics and Gynaecology. University of Calgary. Calgary, AB, Canada.
³⁸Department of Pathology. Stanford University School of Medicine. Stanford, CA 94305, USA.
³⁹Department of Obstetrics and Gynecology, Institute of Clinical Science. Sahlgrenska University hospital, University of Gothenburg. Gothenburg: Sweden.
⁴⁰Department of Epidemiology and Population Health. Stanford University School of Medicine. Stanford, CA 94305, USA; Department of Epidemiology and Biostatistics. University of California San Francisco. San Francisco, CA 94158, USA.
⁴¹School of Population and Public Health. University of British Columbia. Vancouver, BC V6T 1Z4, Canada; Cancer Control Research. BC Cancer Agency. Vancouver, BC V5Z 1L3, Canada.
⁴²Pharmacogenomics and Tumour Biomarkers Laboratory, Cancer Department. Sols-Morreale Biomedical Research Institute (IIBM), CSIC-UAM. Madrid, Spain; Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III. Madrid 28029, Spain.
⁴³Department of Epidemiology. University of Texas MD Anderson Cancer Center. Houston, TX 77030, USA; Department of Population Health Science and Policy. Icahn School of Medicine at Mount Sinai. New York, NY 10029, USA.
⁴⁴Faculty of Medicine and Health. The University of Sydney. Sydney, New South Wales 2006, Australia.; Department of Tissue Pathology and Diagnostic Oncology, ICPMR West Mead Hospital, New South Wales, Australia.
⁴⁵Department of Epidemiology. University of Texas MD Anderson Cancer Center. Houston, TX 77030, USA; Department of Genetics and Genomic Sciences. Icahn School of Medicine at Mount Sinai. New York, NY 10029, USA.
⁴⁶Division of Gynecologic Oncology, Department of Obstetrics and Gynecology. University of Alberta. Edmonton, Alberta T5H 3V9, Canada; Section of Gynecologic Oncology Surgery, North Zone. Alberta Health Services. Edmonton, Alberta, Canada.
⁴⁷Department of Obstetrics and Gynecology, Institute of Clinical Science, Sahlgrenska Center for Cancer Research. University of Gothenburg. Gothenburg, Sweden.
⁴⁸Department of Obstetrics and Gynecology. University of British Columbia. Vancouver, BC V5Z 1L3, Canada; British Columbia's Gynecological Cancer Research Team (OVCARE). University of British Columbia, BC Cancer, and Vancouver General Hospital. Vancouver, BC V5Z 4E6, Canada.
⁴⁹Division of Cancer Epidemiology and Genetics. National Cancer Institute. Bethesda, MD 20892, USA.
⁵⁰Department of Epidemiology and Population Health. Stanford University School of Medicine. Stanford, CA 94305, USA; Department of Biomedical Data Science. Stanford University School of Medicine. Stanford, CA 94305, USA.
⁵¹Department of Epidemiology, University of Pittsburgh School of Public Health, Pittsburgh, PA 15213, USA.
⁵²Department of Biostatistics. University of Pittsburgh Graduate School of Public Health. Pittsburgh, PA, USA.
⁵³Department of Epidemiology, University of Pittsburgh School of Public Health, Pittsburgh, PA 15213, USA; Division of Gynecologic Oncology, Department of Obstetrics, Gynecology and Reproductive Sciences. University of Pittsburgh School of Medicine. Pittsburgh, PA 15213, USA; Women's Cancer Research Center. Magee-Womens Research Institute and Hillman Cancer Center. Pittsburgh 15213, PA, USA; Department of Biomedical Informatics. University of Pittsburgh School of Medicine. Pittsburgh, PA 15213, USA.

⁰Department of Epidemiology, University of Pittsburgh School of Public Health, Pittsburgh, PA 15213, USA; St. Luke's Mid America Heart Institute. St Luke's Health System. Kansas City, MO, USA.

Gynecologic Oncology +

|

June 7, 2025

View abstract on PubMed

Summary

Epithelial ovarian cancer (EOC) risk and survival vary based on tumor hormone receptor expression (androgen receptor, estrogen receptor, progesterone receptor) and histotype. Understanding these associations is crucial for personalized EOC risk assessment and treatment.

Area Of Science

Gynecologic Oncology
Reproductive Endocrinology
Cancer Biomarkers

Background

Epithelial ovarian cancer (EOC) risk factors are linked to sex hormones.
The relationship between these hormonal factors and the expression of androgen receptor (AR), estrogen receptor-α (ER), and progesterone receptor (PR) in EOC tumors remains largely unknown.

Purpose Of The Study

To investigate the association between hormonally-linked factors and the expression of AR, ER, and PR in epithelial ovarian cancer tumors.
To evaluate how AR, ER, and PR expression influences EOC survival, considering tumor histotype.

Main Methods

Utilized data from the Ovarian Cancer Association Consortium (OCAC) and Ovarian Tumor Tissue Analysis (OTTA) consortium, linking epidemiologic, tumor receptor expression, and survival data.
Employed polytomous logistic regression to estimate odds ratios for associations between hormonally-linked factors and AR/ER/PR expression.
Assessed survival outcomes using Kaplan-Meier curves and Cox proportional hazards models stratified by AR/ER/PR expression and histotype.

Main Results

Overweight/obesity correlated with higher risk of ER- EOC. Hysterectomy was linked to increased risk of ER+ EOC, with variations by AR expression.
Postmenopause was associated with a higher risk of PR- EOC, showing differential associations based on AR and ER expression.
Gravidity, oral contraception, and breastfeeding duration exhibited dose-response relationships with AR/ER/PR expression. Survival outcomes were differentially associated with hormone therapy, postmenopause, physical inactivity, and obesity based on receptor expression and histotype.

Conclusions

Epithelial ovarian cancer presents diverse risk and prognostic profiles influenced by both histotype and AR, ER, and PR expression.
Further research into the biological mechanisms connecting hormonally-linked factors to EOC is warranted, specifically examining variations by histotype and receptor expression.

Keywords:

Epithelial ovarian cancer Hormonal factors Hormone receptor Risk Survival androgen receptor estrogen receptor progesterone receptor

Related Concept Videos

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...