title: Ovarian carcinoma subtypes are different diseases: implications for biomarker studies.
abstract: BACKGROUND
  Although it has long been appreciated that ovarian carcinoma subtypes (serous, clear cell, endometrioid, and mucinous) are associated with different natural histories, most ovarian carcinoma biomarker studies and current treatment protocols for women with this disease are not subtype specific. With the emergence of high-throughput molecular techniques, distinct pathogenetic pathways have been identified in these subtypes. We examined variation in biomarker expression rates between subtypes, and how this influences correlations between biomarker expression and stage at diagnosis or prognosis.
  
  
  RESULTS
  In this retrospective study we assessed the protein expression of 21 candidate tissue-based biomarkers (CA125, CRABP-II, EpCam, ER, F-Spondin, HE4, IGF2, K-Cadherin, Ki-67, KISS1, Matriptase, Mesothelin, MIF, MMP7, p21, p53, PAX8, PR, SLPI, TROP2, WT1) in a population-based cohort of 500 ovarian carcinomas that was collected over the period from 1984 to 2000. The expression of 20 of the 21 biomarkers differs significantly between subtypes, but does not vary across stage within each subtype. Survival analyses show that nine of the 21 biomarkers are prognostic indicators in the entire cohort but when analyzed by subtype only three remain prognostic indicators in the high-grade serous and none in the clear cell subtype. For example, tumor proliferation, as assessed by Ki-67 staining, varies markedly between different subtypes and is an unfavourable prognostic marker in the entire cohort (risk ratio [RR] 1.7, 95% confidence interval [CI] 1.2%-2.4%) but is not of prognostic significance within any subtype. Prognostic associations can even show an inverse correlation within the entire cohort, when compared to a specific subtype. For example, WT1 is more frequently expressed in high-grade serous carcinomas, an aggressive subtype, and is an unfavourable prognostic marker within the entire cohort of ovarian carcinomas (RR 1.7, 95% CI 1.2%-2.3%), but is a favourable prognostic marker within the high-grade serous subtype (RR 0.5, 95% CI 0.3%-0.8%).
  
  
  CONCLUSIONS
  The association of biomarker expression with survival varies substantially between subtypes, and can easily be overlooked in whole cohort analyses. To avoid this effect, each subtype within a cohort should be analyzed discretely. Ovarian carcinoma subtypes are different diseases, and these differences should be reflected in clinical research study design and ultimately in the management of ovarian carcinoma.
authors.affiliation: Department of Pathology, Genetic Pathology Evaluation Centre of the Prostate Research Centre, Vancouver General Hospital and British Columbia Cancer Agency, Vancouver, British Columbia, Canada.
authors.forename: Martin
authors.initials: M
authors.lastname: Köbel
authors.forename: Steve E
authors.initials: SE
authors.lastname: Kalloger
authors.forename: Niki
authors.initials: N
authors.lastname: Boyd
authors.forename: Steven
authors.initials: S
authors.lastname: McKinney
authors.forename: Erika
authors.initials: E
authors.lastname: Mehl
authors.forename: Chana
authors.initials: C
authors.lastname: Palmer
authors.forename: Samuel
authors.initials: S
authors.lastname: Leung
authors.forename: Nathan J
authors.initials: NJ
authors.lastname: Bowen
authors.forename: Diana N
authors.initials: DN
authors.lastname: Ionescu
authors.forename: Ashish
authors.initials: A
authors.lastname: Rajput
authors.forename: Leah M
authors.initials: LM
authors.lastname: Prentice
authors.forename: Dianne
authors.initials: D
authors.lastname: Miller
authors.forename: Jennifer
authors.initials: J
authors.lastname: Santos
authors.forename: Kenneth
authors.initials: K
authors.lastname: Swenerton
authors.forename: C Blake
authors.initials: CB
authors.lastname: Gilks
authors.forename: David
authors.initials: D
authors.lastname: Huntsman
mesh_terms: D014408:Biomarkers, Tumor / Q000378:metabolism / Q000502:physiology*; D002277:Carcinoma / Q000145:classification* / Q000175:diagnosis / Q000401:mortality / Q000473:pathology; D015331:Cohort Studies; D015986:Confounding Factors, Epidemiologic; D003937:Diagnosis, Differential; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008875:Middle Aged; D009367:Neoplasm Staging; D010049:Ovarian Diseases / Q000145:classification / Q000175:diagnosis* / Q000378:metabolism; D010051:Ovarian Neoplasms / Q000145:classification* / Q000175:diagnosis / Q000401:mortality / Q000473:pathology; D011379:Prognosis; D012189:Retrospective Studies; D016019:Survival Analysis; D046888:Tissue Array Analysis
chemical_list: D014408:Biomarkers, Tumor