Evidence map›Paper›PMID 19053170›Full record

ArticlePLoS medicine2008

Ovarian carcinoma subtypes are different diseases: implications for biomarker studies.

Martin Köbel, Steve E Kalloger, Niki Boyd, Steven McKinney, Erika Mehl, Chana Palmer, Samuel Leung, Nathan J Bowen, Diana N Ionescu, Ashish Rajput and 6 more

Open access · goldAbstract read
In one paragraph

Article in PLoS medicine, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 381 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
381citing papers in PubMed, 3 pooled it
56.2field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

381 citing papers in PubMed, 3 syntheses or guidelines pooled it, 815 citations in OpenAlex.

  1. Morphological and molecular characterization of epithelial ovarian cancer: SEOM-SEAP consensus guidelines for biomarker integration in clinical practice.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
    Guideline
  2. Guideline
  3. Pooled it
  4. Using Polygenic Risk Scores to Refine Risk and Outcomes in High-grade Serous Ovarian Cancer in Australia.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2026
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  11. The Effects ofCells · 2026
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  16. BeyondmedRxiv : the preprint server for health sciences · 2025
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321 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors at 4 institutions in 3 countries.

Martin KöbelDepartment of Pathology, Genetic Pathology Evaluation Centre of the Prostate Research Centre, Vancouver General Hospital and British Columbia Cancer Agency, Vancouver, British Columbia, Canada.
Steve E Kalloger
Niki Boyd
Steven McKinney
Erika Mehl
Chana Palmer
Samuel Leung
Nathan J Bowen
Diana N Ionescu
Ashish Rajput
Leah M Prentice
Dianne Miller
Jennifer Santos
Kenneth Swenerton
C Blake Gilks
David Huntsman
BC Cancer Agency · CAVancouver General Hospital · CACanary Foundation · USGeorgia Institute of Technology · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAlthough 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. METHODS AND

findingsIn 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%).

conclusionsThe 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.

Indexed as

Biomarkers, TumorCarcinomaCohort StudiesConfounding Factors, EpidemiologicDiagnosis, DifferentialFemaleFollow-Up StudiesHumansMiddle AgedNeoplasm StagingOvarian DiseasesOvarian NeoplasmsPrognosisRetrospective StudiesSurvival AnalysisTissue Array AnalysisBiomarkers, Tumor

Identifiers

PMID19053170
PMCPMC2592352
OpenAlexW2129355295

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.