PLoS MEDICINE
`Ovarian Carcinoma Subtypes Are Different
`Diseases: Implications for Biomarker Studies
`
`Martin Ko¨ bel1,2, Steve E. Kalloger1, Niki Boyd1, Steven McKinney1, Erika Mehl1, Chana Palmer3, Samuel Leung1,
`Nathan J. Bowen4, Diana N. Ionescu1, Ashish Rajput1, Leah M. Prentice1, Dianne Miller5, Jennifer Santos6,
`Kenneth Swenerton6, C. Blake Gilks1, David Huntsman1*
`
`1 Genetic Pathology Evaluation Centre of the Prostate Research Centre, Department of Pathology, Vancouver General Hospital and British Columbia Cancer Agency,
`Vancouver, British Columbia, Canada, 2 Institute of Pathology, Charite´ Hospital, Berlin, Germany, 3 Canary Foundation, San Jose, California, United States of America,
`4 School of Biology, Georgia Institute of Technology, and Ovarian Cancer Institute, Atlanta, Georgia, United States of America, 5 Department of Gynecology, Vancouver
`General Hospital and British Columbia Cancer Agency, Vancouver, British Columbia, Canada, 6 Cheryl Brown Ovarian Cancer Outcomes Unit, British Columbia Cancer Agency,
`Vancouver, British Columbia, Canada
`
`A B S T R A C T
`
`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.
`
`Methods and Findings
`
`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.
`
`The Editors’ Summary of this article follows the references.
`
`Funding: This work was supported
`by the Canary Foundation. MK
`received fellowship support from Eli
`Lilly Canada. LMP is a Canadian
`Institute for Health Research (CIHR)
`Canadian Graduate Scholar and a
`Michael Smith Foundation for Health
`Research (MSFHR) Senior Trainee.
`DGH is a MSFHR Senior Scholar. CBG
`and SL were supported by an
`unrestricted educational grant from
`sanofi aventis Canada. Construction
`of the tissue microarray was
`supported by an operating grant to
`CBG from the National Cancer
`Institute of Canada (number 017051)
`and a Michael Smith Foundation for
`Health Research Unit Grant (number
`INRUA006045). None of the study
`sponsors were involved in study
`design; collection, analysis, and
`interpretation of data; writing of the
`paper; and decision to submit it for
`publication.
`
`Competing Interests: The authors
`have declared that no competing
`interests exist.
`
`Academic Editor: Steven Narod,
`Centre for Research in Women’s
`Health, Canada
`
`Citation: Ko¨ bel M, Kalloger SE, Boyd
`N, McKinney S, Mehl E, et al. (2008)
`Ovarian carcinoma subtypes are
`different diseases: Implications for
`biomarker studies. PLoS Med 5(12):
`e232. doi:10.1371/journal.pmed.
`0050232
`
`Received: April 28, 2008
`Accepted: October 20, 2008
`Published: December 2, 2008
`
`Copyright: Ó 2008 Ko¨ bel et al. This
`is an open-access article distributed
`under the terms of the Creative
`Commons Attribution License, which
`permits unrestricted use,
`distribution, and reproduction in any
`medium, provided the original
`author and source are credited.
`
`Abbreviations: BCCA, British
`Columbia Cancer Agency; CI,
`confidence interval; DSS, disease-
`specific survival; RR, risk ratio; TMA,
`tissue microarray
`
`* To whom correspondence should
`be addressed. E-mail: dhuntsma@
`bccancer.bc.ca
`
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`Table 1. Study Population
`
`Clinical Variable
`
`Numerical Display
`
`All
`
`High-Grade Serous
`
`Clear Cell
`
`Endometrioid Mucinous
`
`Low-Grade Serous
`
`Ovarian Carcinoma Subtypes Are Different
`
`Number of cases
`Proportion
`Age in years
`Follow-up time in years
`Death
`Death of disease
`10 YSR DSS
`Stage I
`Stage II
`Stage III
`Grade 1
`Grade 2
`Grade 3
`
`n
`%
`Mean 6 SE
`Mean 6 SE
`n (%)
`n (%)
`% 6 SE
`n (%)
`n (%)
`n (%)
`n (%)
`n (%)
`n (%)
`
`500
`100
`58.1 6 0.6
`5.9 6 0.2
`233 (46.6)
`164 (32.8)
`57.8 6 2.9
`205 (41.0)
`211 (42.2)
`84 (16.8)
`105 (21.0)
`109 (21.8)
`286 (57.2)
`
`200
`40.0
`60.9 6 0.8
`5.4 6 0.2
`124 (62.0)
`92 (46.0)
`38.9 6 4.7
`49 (24.5)
`86 (43.0)
`65 (32.5)
`0
`56 (28.0)
`144 (72.0)
`
`YSR DSS, year disease-specific survival rate; SE, standard error of the mean.
`doi:10.1371/journal.pmed.0050232.t001
`
`Introduction
`
`Ovarian carcinoma is a heterogeneous disease. On the basis
`of histopathological examination, pathologists classify ovar-
`ian carcinoma into serous, clear cell, endometrioid, and
`mucinous subtypes. Each of theses subtypes is associated with
`different genetic risk factors and molecular events during
`oncogenesis [1,2], and characterized by distinct mRNA
`expression profiles [3,4]. These subtypes differ dramatically
`in frequency, when early stage carcinomas (where the
`majority are nonserous carcinomas [5]) and advanced stage
`carcinomas (which are predominantly of serous subtype [6])
`are compared.
`Oncologists have noted that subtypes respond differently
`to chemotherapy. The dismal response rate of clear cell
`carcinomas (15%) contrasts sharply with that of high-grade
`serous (80%), resulting in a lower 5-y survival for clear cell
`compared with high-grade serous carcinoma in patients with
`advanced stage tumors (20% versus 30%) [7,8]. Therefore, the
`National Cancer Institute (NCI) State of Science meeting
`recently singled out clear cell carcinoma as a candidate for
`clinical trials to identify more active therapy than what is
`currently available [9]. Although these data suggest substan-
`tial differences between subtypes, ovarian carcinoma is
`typically approached as a monolithic entity by researchers
`and clinicians. This practice impedes progress in under-
`standing the biology or improving the management of the less
`common ovarian carcinoma subtypes.
`We hypothesized that correlations between biomarker
`expression and stage at diagnosis or prognosis would reflect
`subtype variation in biomarker expression. To test this
`hypothesis we correlated protein expression rates of a panel
`of 21 candidate biomarkers with stage at diagnosis and
`disease-specific survival (DSS) in a large cohort of ovarian
`carcinomas and also analyzed these associations within
`ovarian carcinoma subtypes.
`
`Methods
`
`Study Population
`The Cheryl Brown Ovarian Cancer Outcomes Unit is an
`ovarian cancer registry serving a population of approxi-
`
`132
`26.4
`56.2 6 1.1
`6.3 6 0.4
`52 (39.4)
`40 (30.3)
`63.7 6 5.2
`68 (51.5)
`56 (42.4)
`8 (6.1)
`0
`0
`132 (100)
`
`125
`25.0
`56.0 6 1.2
`6.4 6 0.3
`39 (31.2)
`19 (15.2)
`83.9 6 4.2
`69 (55.2)
`50 (40.0)
`6 (4.8)
`82 (65.6)
`35 (28.0)
`8 (6.4)
`
`31
`6.2
`55.4 6 2.4
`5.4 6 0.7
`11 (35.5)
`8 (25.8)
`72.0 6 10.0
`18 (58.1)
`12 (38.7)
`1 (3.2)
`11 (35.5)
`18 (58.1)
`2 (6.5)
`
`12
`2.4
`60.2 6 4.1
`5.8 6 1.1
`7 (58.3)
`5 (41.7)
`48.0 6 19.1
`1 (8.3)
`7 (58.3)
`3 (33.3)
`12 (100)
`0
`0
`
`mately four million people in British Columbia. For the
`period 1984–2000, 2,555 patients with ovarian carcinoma
`were recorded in the registry. From these 834 patients were
`selected based on the criterion being free of macroscopic
`apparent residual disease after primary surgery and all
`histological slides underwent gynecopathological review.
`Subtypes were assigned according to refined World Health
`Organization (WHO) criteria [10] as recently described [5]. A
`further 91 patients diagnosed in stage 1a or 1b, grade 1 were
`excluded from the study because of excellent prognosis; only
`3% of women in this group died of disease during the follow-
`up period. From the remaining patients 541 tissue blocks
`were available and used for tissue microarray (TMA)
`construction. A representative area of each tumor was
`selected and duplicate 0.6-mm tissue cores were punched to
`construct a TMA (Beecher Instruments). Review after TMA
`construction revealed that 23 cases were not adequately
`sampled. Of these 23 cases, 20 mixed carcinomas (.10% of
`tumor showing a second histological cell type) were excluded
`because their highest grade component was not sampled on
`the TMA; 18 cases were either of rare histological types
`(including seven undifferentiated, six transitional, and one
`squamous carcinoma) or could not be specified (five cases).
`This approach resulted in a study population of exactly 500
`cases belonging to one of the four major cell types (serous,
`endometrioid, clear cell, and mucinous) (Table 1). The serous
`subtype was further subdivided into low- and high-grade [11].
`Two cases of endometrioid carcinomas containing minor
`mucinous or low-grade serous components (.10%) are
`included in the study.
`
`Adjuvant Therapy and Follow-up
`All patients received standardized treatment according to
`the provincial treatment guidelines of the British Columbia
`Cancer Agency (BCCA)
`[12,13]; however, 3% of patients
`refused the advised adjuvant chemotherapy and were excluded
`from survival analysis. For 3% adjuvant therapy was not
`advised, hence 94% received platinum-based chemotherapy
`(with or without abdomino-pelvic radiotherapy) adjuvant
`treatments. Outcomes were tracked via the Cheryl Brown
`Ovarian Cancer Outcomes Unit at the BCCA and were
`available for all patients. Follow-up information was obtained
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`Ovarian Carcinoma Subtypes Are Different
`
`Table 2. Antibodies
`
`Number
`
`Biomarker
`
`Supplier
`
`Clone
`
`Dilution
`
`Full Name/Description
`
`1
`2
`
`3
`4
`5
`6
`
`7
`8
`9
`10
`11
`
`12
`13
`14
`15
`16
`17
`
`18
`19
`20
`21
`
`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
`
`Cellmarque
`Santa Cruz
`
`R&D Systems
`Labvision
`US Biological
`Signet
`
`Abcam
`Abcam
`Labvision
`Santa Cruz
`Bethyl
`
`Novocastra
`R&D Systems
`Chemicon
`Labvision
`DAKO
`Donationa
`
`Labvision
`Hycult
`R&D Systems
`DAKO
`
`OC125
`Polyclonal
`
`158206
`SP1
`Polyclonal
`Polyclonal
`
`Polyclonal
`2B6
`SP6
`Polyclonal
`Polyclonal
`
`5B2
`Polyclonal
`141–7B2
`DCS-60.2
`DO-7
`Polyclonal
`
`SP2
`31
`Polyclonal
`6F-H2
`
`1:100
`1:25
`
`1:25
`1:200
`1:50
`1:25
`
`1:100
`1:50
`1:200
`1:400
`1:25
`
`1:50
`1:2500
`1:200
`1:40
`1:400
`1:500
`
`1:400
`1:100
`1:25
`1:100
`
`Cancer antigen 125, cell surface glycoprotein
`Cellular retinoic acid-binding protein II, transcriptional regulator of lipid
`metabolism
`Epithelial cell adhesion molecule, cell-cell adhesion
`Estrogen receptor
`Neuronal development
`Human epididymis protein 4 is a member of 4-disulfide core protein with
`unknown function
`Insulin-like growth factor 2
`Cell-cell adhesion protein
`MKI, proliferation-associated antigen detected by Ki67
`Kisspeptins, ligands of G-protein coupled receptor 54
`Type II transmembrane trypsin-like serine protease, degradation of
`extracellular matrix
`Cell surface glycoprotein
`Macophage inhibitory factor, modulator of chronic inflammation
`Matrix metalloproteinase 7, degradation of extracellular matrix
`Cyclin-dependent kinase inhibitor 1A (Cip1)
`Tumor protein p53
`Thyroid specific transcription factor, Pax8/PPARgamma fusion gene in 50%
`of follicular thyroid carcinomas
`Progesteron receptor
`Secretory leukocyte protease inhibitor
`Tumor-associated calcium signal transducer 2
`Wilms tumor suppressor 1, zinc finger transcription factor
`
`aThe a-mPax8-bIII antibody was kindly provided by Roberto Di Lauro, Stazione Zoologica, Naples, Italy.
`doi:10.1371/journal.pmed.0050232.t002
`
`through the electronic patient record of the BCCA or the
`patient’s paper chart. Examples of documentation used to
`ascertain vital status include BCCA progress notes, death
`certificates, and correspondence indicating status from other
`care providers. Ovarian carcinoma specific death was defined
`where ovarian cancer was the primary or underlying cause of
`death. Death from concurrent disease (i.e., second malignancy)
`was coded as ‘‘died of other cause.’’ Death resulting from
`toxicities relating to treatments for ovarian carcinoma was
`coded as ‘‘died of toxicities.’’ Abstracted data were reviewed by
`an experienced medical oncologist (K.S.). Median follow-up
`time was 5.1 y. Approval for the study was obtained from the
`Research Ethics Board of the University of British Columbia.
`
`Marker Selection and Immunohistochemistry
`The goal of our marker selection was to use proteins that
`are consistently expressed in ovarian carcinomas and have
`been reported as prognosticators (p53, p21, Ki-67, PR, WT1)
`[14–19] or being developed as early detection markers in
`ovarian carcinomas [20]. This approach biased our results
`towards selection of markers mostly derived from and
`expressed in high-grade serous subtype. Serial 4-lm sections
`were cut for immunohistochemical (IHC) analysis and run
`through an automated protocol
`including heat antigen
`retrieval (Ventana System). The antibodies and suppliers are
`listed in Table 2. Specificity was determined by using
`appropriate positive controls, with omission of primary
`antibody as a negative control.
`
`Evaluation of Immunohistochemistry
`One or more pathologists (MK, DNI, or AR) scored these
`biomarkers after scanning with a BLISS scanner (Bacus
`
`Laboratories/Olympus America). Except KISS1 [21] and p53
`[22] where recently published cut-off points were used, all
`markers were dichotomized into negative and positive cases
`(cut-off values for positive versus negative for all markers
`except Ki-67 are shown in Table S1). Ki-67 was assessed as a
`continuous variable as a percentage of positive tumor cells
`using automated image analysis software [23]. Prior to
`analysis a pathologist (MK) manually selected regions of
`interest so as to avoid noncancerous cellular areas. The
`median was used to dichotomize into low- and high-
`expressing groups for Ki-67.
`
`Statistical Analysis
`Contingency analysis and Pearson’s Chi2 statistic were used
`to test the change in the distribution of biomarker expression
`across stage and subtypes. The Kruskal-Wallis test was used to
`determine if Ki-67 was differentially expressed across stage
`and subtypes. Univariable DSS was illustrated by the
`generation of Kaplan-Meier curves and subgroup differences
`tested with a univariable Cox model. Multivariable DSS was
`tested using the Cox proportional hazards model. The Cox
`proportional hazards model was used to determine risk ratios
`(RRs) and p-values for all univariable and multivariable DSS
`analyses. Additionally, to assess significance in the presence of
`some small subgroups, permutation tests were performed and
`permutation p-values reported. Under the null hypothesis of
`no association of biomarker status with survival (for survival
`analyses) or stage/histology (for contingency table analyses),
`the biomarker outcomes are exchangeable across cases. For
`the survival analyses, permutations of biomarker outcomes
`were performed within stage/subtype subgroups, to preserve
`the observed distribution of biomarker frequencies within
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`subgroups. Permutation was performed by exchanging each
`case’s entire biomarker panel at random without replacement
`among cases, to preserve correlation structure within case. A
`total of 10,000 permutation replications were performed. p-
`Values were obtained by finding the number of permutation
`sample estimates (Cox model parameter estimate for survival
`analyses, Pearson Chi2 statistic for contingency table analyses)
`as extreme or more extreme than the observed value. p , 0.05
`was considered statistically significant. Hence, any prognostic
`correlations for a single biomarker have to be interpreted
`with caution. Statistical analyses were performed using SPSS
`software (version 15.0; SPSS) and R (version 2.5.1; R
`Foundation for Statistical Computing).
`
`Results
`Biomarker Expression Profile Reflects Subtype
`This cohort of 500 ovarian carcinomas was mainly selected
`based on the criterion of not having apparent residual tumor
`after primary surgery. Since successful surgery is typically
`achieved in lower stage, this case selection strategy can be
`anticipated to include more cases of tumors of histological
`subtypes that are commonly diagnosed at low stage, such as
`clear cell carcinoma (26.4%), endometrioid (25.0%), and
`mucinous (6.2%) carcinomas, although serous carcinomas
`were still the most common subtype (40.0% high-grade and
`2.4% low-grade) in this cohort (Table 1).
`Interpretable results of immunostains for the 21 candidate
`biomarkers (Figure 1) ranged from 363 to 493 (median 488,
`Table S2). The larger numbers of missing data for three
`biomarkers were caused by exhaustion of tumor material in
`the core. All immunostains with annotated clinical informa-
`tion are available online at http://www.gpecimage.ubc.ca
`(username: BCCA-VGH; password: OVCARE). The rate of
`positive cases for each biomarker ranged from 9% (KISS1) to
`83% (EpCam) (detailed expression rates are listed in Table
`S2). Comparing biomarker expression in the entire cohort for
`tumors diagnosed at different stages revealed that ten
`biomarkers (CRABP-II, ER, F-Spondin, K-Cadherin, Ki-67,
`Matriptase, Mesothelin, p21, p53, and WT1) had significantly
`different expression levels between stages, suggesting differ-
`ences between ‘‘early’’ and ‘‘late’’ stage disease (Figure 2,
`Table S2). However, comparing biomarker expression within
`one subtype across FIGO stages, no biomarker remained
`significantly differently expressed by stage (results for high-
`grade serous subtype are shown in Figure 3). This result was
`true for all four major subtypes (unpublished data for
`endometrioid, clear cell, and mucinous). In contrast, 20 of
`21 biomarkers were significantly differentially expressed
`between the subtypes (Figure 4). Only, EpCam (p ¼ 0.23)
`showed a consistent expression frequency across all subtypes.
`Additionally, p-values for biomarker expression rates in the
`entire cohort across subtypes were generally smaller than
`across stages (Table S2), indicating a stronger association with
`subtype than stage.
`High-grade serous carcinoma showed positive staining in
`.75% of cases for WT1, Mesothelin, ER, and CA125 (Table
`S2). The biomarker expression pattern of low-grade serous
`carcinomas was similar to that of their high-grade counter-
`parts. Three markers (PR, p53, K-Cadherin) showed a trend
`towards differential expression in low-grade versus high-
`grade serous subtypes. Only the median Ki-67 labelling index
`
`Ovarian Carcinoma Subtypes Are Different
`
`differed significantly between those groups, with median Ki-
`67 labelling index of 2.5% (95% confidence interval [CI]
`0.5%–20.4%)
`in low-grade serous versus 22.4% (95% CI
`3.6%–69.9%)
`in high-grade serous subtype (Figure 5).
`Endometrioid carcinomas coexpress high rates of hormone
`receptors ER and PR as well as CA125. Endometrioid and
`clear cell subtypes infrequently (,10%) expressed WT1 and
`p53. The median Ki-67 labelling index for endometrioid and
`clear cell carcinomas was similar (endometrioid 8.2%, 95% CI
`0.8%–49.0%; clear cell 7.6%, 95% CI 0.5%–45.0%). Immu-
`nophenotypic characteristics of clear cell carcinomas in-
`cluded low levels of hormone receptors ER (10%) and PR
`(3%). The mucinous subtype displayed an intermediate
`proliferative capacity compared with the other subtypes
`(median Ki-67 labelling index 12.9%, 95% CI 2.1%–60.9%)
`and frequent expression of Matriptase (86%). Many of the
`markers expressed in other subtypes were either infrequently
`(,10%) expressed (p53, ER, PAX8, SLPI, K-Cadherin, and
`CA125), or completely absent (CRABP2, WT-1, and Meso-
`thelin). Of note, EpCam was highly expressed across all
`subtypes included in this study.
`
`Survival Analyses Can Be Confounded by Subtype
`Differences
`To assess the biological importance of a biomarker, its
`expression is usually correlated with outcome. Survival
`analysis was restricted to the three major subtypes (high-
`grade serous, clear cell, and endometrioid) because of
`insufficient numbers of cases of mucinous or low-grade
`serous subtypes. The primary endpoint was defined as DSS
`and the rates after 10 y are shown for subtypes in Table 1. A
`multivariable Cox regression model including age, stage, and
`histological subtype showed significant differences across
`stage (p , 0.0001) and subtype (p ¼ 0.015). Survival by stage
`showed little difference between stages I and II, with stage III
`showing poorer DSS (RR 3.0, 95% CI 1.87%–4.66% relative to
`stage I). Survival by subtype showed poorer DSS for clear cell
`(RR 2.31, 95% CI 1.29%–4.15%) and high-grade serous (RR
`2.74, 95% CI 1.56%–4.81%) relative to endometrioid subtype.
`Age was not predictive in the model (p ¼ 0.211) (Table S3).
`Univariable Cox regression analysis for each biomarker was
`applied on the entire cohort as well as within the three largest
`subtypes (Figure S1, Table 3). RRs and p-values are presented
`in Table 3. Nine of 21 biomarkers show prognostic
`significance in the entire cohort. Of the nine biomarkers
`showing a significant association with DSS in the entire
`cohort, three remain prognostic indicators in the high-grade
`serous and one in the endometrioid subtype. As an extreme
`example, WT1 is an unfavourable prognostic biomarker in
`the entire cohort (p ¼ 0.0017, Figure 6A) but is a favourable
`prognostic biomarker for high-grade serous carcinomas (p ¼
`0.0086, Figure 6B). As WT1 is expressed in 80% of high-grade
`serous carcinomas but rarely in other subtypes, this negative
`prognostic significance in the entire cohort reflects subtype
`differences in expression, with WT1 most commonly ex-
`pressed in the aggressive high-grade serous subtype. Four
`other biomarkers (KISS1, K-Cadherin, Mesothelin, Ki-67) that
`were significant in the entire cohort did not show significance
`in any subtype.
`Ki-67 serves as an additional example, which is prognostic
`in the whole cohort but not when corrected for subtype. The
`median for Ki-67 labelling index in the entire cohort was
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`Ovarian Carcinoma Subtypes Are Different
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`Figure 1. Representative Immunostains
`Paired positive and negative examples for each biomarker.
`doi:10.1371/journal.pmed.0050232.g001
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`Ovarian Carcinoma Subtypes Are Different
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`Figure 2. Biomarker Expression Rates in the Entire Cohort by Stage
`*Significant differences between categories (Fisher’s exact test).
`doi:10.1371/journal.pmed.0050232.g002
`
`Figure 3. Biomarker Expression Rates in High-Grade Serous Subtype by Stage
`doi:10.1371/journal.pmed.0050232.g003
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`
`

`

`Ovarian Carcinoma Subtypes Are Different
`
`Figure 4. Biomarker Expression Rates in the Entire Cohort by Subtype
`*Significant differences between categories (Fisher’s exact test). Note that the order in which biomarkers are presented is based on percentage of
`positivity and that therefore the order is different in Figures 2–4.
`doi:10.1371/journal.pmed.0050232.g004
`
`13.0% and using this as a cut-off for high versus low Ki-67
`labelling index effectively separates high-grade serous carci-
`nomas from low-grade serous, endometrioid, and clear cell
`carcinomas (Figure 5). Mucinous carcinomas showed an
`intermediate Ki-67 labelling index. Associated with high-
`grade serous subtype, it is not surprising that Ki-67 has
`prognostic relevance in the whole cohort (p ¼ 0.0062). When
`using the subtype specific median for separate analysis of
`each subtype however, Ki-67 labelling index was not of
`
`Figure 5. Distribution of Ki-67 Labelling Index across Subtypes
`doi:10.1371/journal.pmed.0050232.g005
`
`prognostic significance in any of the subtypes but Ki-67
`labelling index was different between subtypes.
`
`Discussion
`
`Ovarian carcinomas subtypes are associated with distinct
`genetic risk factors, underlying molecular events during
`oncogenesis, stage at diagnosis, and responses to chemo-
`therapy. With slight modification of the WHO criteria for
`histopathological assignment for subtype we have recently
`shown that classification of ovarian carcinomas into five
`subtypes (high-grade serous,
`low-grade serous, clear cell,
`endometrioid, and mucinous) is reproducible and is sup-
`ported by biomarker expression data [5].
`By demonstrating that biomarker correlations with stage or
`prognosis can be explained by variations in expression rates
`between subtypes, our study offers persuasive evidence
`supporting the view that ovarian carcinoma subtypes are
`different diseases. Biomarker expression is stable across stage
`within a given subtype. Furthermore, differences in the
`expression profile between subtypes confound survival
`analysis for biomarkers, when multiple ovarian carcinoma
`subtypes are considered together. Collectively, these data
`have implications for ovarian carcinoma research and treat-
`ment.
`Cancer treatment in general is beginning to move towards
`therapies tailored for specific cancer subtypes (e.g., breast
`carcinoma and lymphoma [24,25]), and this subtype specific
`approach to treatment has implications for the design of
`clinical trials for women with ovarian carcinomas. It has been
`recognized for some time that certain ovarian carcinoma
`subtypes are less sensitive to platinum-based chemotherapy
`
`PLoS Medicine | www.plosmedicine.org
`
`1755
`
`December 2008 | Volume 5 | Issue 12 | e232
`
`7 of 12
`
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`

`Ovarian Carcinoma Subtypes Are Different
`
`Failed
`0.76
`0.84
`0.35
`0.21
`Failed
`0.62
`0.054
`0.82
`0.96
`0.58
`Failed
`0.067
`0.83
`0.074
`0.075
`Failed
`0.61
`Failed
`0.30
`0.92
`
`0.13
`0.37
`0.96
`0.88
`0.15
`0.77
`0.50
`0.019
`0.80
`0.88
`0.66
`0.44
`0.13
`0.99
`0.13
`0.12
`0.042
`0.28
`0.78
`0.80
`0.89
`
`3.19(0.72–14.14)
`1.62(0.56–4.66)
`0.98(0.35–2.68)
`0.92(0.29–2.88)
`2.07(0.77–5.56)
`1.24(0.28–5.52)
`1.40(0.52–3.77)
`3.37(1.22–9.31)
`1.15(0.41–3.22)
`0.92(0.30–2.86)
`0.77(0.41–4.08)
`
`23.2(0.008–71419)
`
`3.01(0.97–9.32)
`1.00(0.32–3.15)
`0.44(0.15–1.27)
`2.29(0.80–6.52)
`3.14(1.04–9.47)
`1.79(0.62–5.17)
`1.19(0.30–4.30)
`0.85(0.24–2.99)
`0.92(0.26–3.22)
`
`Failed
`0.58
`0.31
`Failed
`0.95
`Failed
`0.77
`0.44
`0.20
`0.86
`0.28
`0.067
`0.75
`0.37
`0.64
`0.94
`0.81
`Failed
`Failed
`0.77
`0.63
`
`0.48
`0.79
`0.14
`0.081
`0.96
`0.95
`0.91
`0.35
`0.25
`0.99
`0.32
`0.078
`0.89
`0.13
`0.59
`0.67
`0.98
`0.48
`0.011
`0.83
`0.56
`
`2.04(0.28–14.96)
`1.09(0.57–2.10)
`1.65(0.84–3.25)
`0.28(0.07–1.17)
`1.02(0.48–2.15)
`0.96(0.23–4.01)
`0.96(0.47–1.98)
`1.49(0.65–3.40)
`1.48(0.76–2.87)
`1.00(0.51–1.96)
`1.58(0.64–3.88)
`2.19(0.92–5.13)
`1.10(0.58–2.11)
`1.68(0.85–3.30)
`1.20(0.61–2.35)
`1.26(0.44–3.620
`1.01(0.46–2.22)
`1.53(0.47–4.98)
`3.17(1.31–7.17)
`1.08(0.54–2.15)
`1.25(0.60–2.60)
`
`0.024
`0.66
`0.90
`0.079
`0.75
`0.45
`0.66
`0.0032
`0.77
`0.40
`0.037
`Failed
`0.834
`0.35
`0.86
`0.75
`0.70
`0.41
`0.34
`0.78
`0.89
`
`0.0086
`0.66
`0.93
`0.049
`0.48
`0.41
`0.72
`0.011
`0.57
`0.69
`0.041
`0.40
`0.92
`0.47
`0.83
`0.78
`0.95
`0.42
`0.55
`0.85
`0.63
`
`0.52(0.32–0.85)
`1.12(0.67–1.87)
`0.98(0.61–1.56)
`1.61(1.00–2.59)
`0.84(0.52–1.36)
`0.84(0.55–1.28)
`0.92(0.59–1.44)
`1.92(1.16–3.17)
`0.88(0.57–1.36)
`1.11(0.65–1.92)
`1.66(1.02–2.72)
`
`20.81(0.02–23357)
`
`1.05(0.68–1.61)
`0.85(0.45–1.32)
`1.06(0.63–1.76)
`1.07(0.66–1.74)
`0.98(0.60–1.60)
`1.23(0.75–2.01)
`1.17(0.69–1.99)
`1.04(0.68–1.59)
`0.86(0.51–1.62)
`
`0.25
`0.48
`0.76
`0.32
`0.94
`0.48
`0.95
`0.0006
`0.50
`0.31
`0.047
`0.0078
`0.39
`0.72
`0.98
`0.45
`0.981
`0.48
`0.082
`0.84
`0.51
`
`0.53
`0.0030
`0.0089
`0.0033
`0.00062
`0.018
`0.28
`0.54
`0.020
`0.70
`0.12
`0.071
`0.32
`
`1.66(1.21–2.29)
`1.08(0.77–1.52)
`1.02(0.72–1.42)
`1.59(1.10–2.28)
`1.38(0.97–1.94)
`1.41(0.99–2.00)
`1.30(0.94–1.79)
`2.44(1.68–3.56),0.0001
`1.11(0.80–1.54)
`1.65(1.19–2.29)
`1.63(1.13–2.36)
`3.42(1.51–7.77)
`1.74(1.25–2.43)
`1.47(1.07–2.04)
`1.22(0.85–1.74)
`1.12(0.78–1.60)
`1.54(1.07–2.20)
`1.07(0.77–1.47)
`1.38(0.92–2.09)
`1.35(0.97–1.88)
`1.23(0.82–1.85)
`
`0.0017
`0.65
`0.92
`0.013
`0.071
`0.051
`0.11
`
`doi:10.1371/journal.pmed.0050232.t003
`bPermutationtestp-values.
`aRawunadjustedasymptoticp-values.
`
`Positive
`Negative
`Negative
`Negative
`Positive
`High
`Negative
`Negative
`Positive
`Positive
`Negative
`Low
`High
`Positive
`Positive
`Negative
`Positive
`Negative
`Negative
`Positive
`Positive
`
`WT1
`TROP2
`SLPI
`PR
`PAX8
`p53
`p21
`MMP7
`MIF
`Mesothelin
`Matriptase
`KISS1
`Ki-67
`K-Cadherin
`IGF2
`HE4
`F-Spondin
`ER
`EpCam
`CRABP-II
`CA125
`
`21
`20
`19
`18
`17
`16
`15
`14
`13
`12
`11
`10
`
`1
`
`2
`
`3
`
`4
`
`5
`
`6
`
`7
`
`8
`
`9
`
`p-Valueap-Valueb
`
`p-Valueap-ValuebRR(95%CI)
`
`p-Valueap-ValuebRR(95%CI)
`
`p-Valueap-ValuebRR(95%CI)
`
`RR(95%CI)
`
`Endometrioid(n¼125)
`
`ClearCell(n¼132)
`
`High-GradeSerous(n¼200)
`
`NumberBiomarkerRiskFactorEntireCohort(n¼500)
`
`Table3.UnivariableCOXRegressionforDisease-SpecificSurvival
`
`PLoS Medicine | www.plosmedicine.org
`
`1756
`
`December 2008 | Volume 5 | Issue 12 | e232
`
`8 of 12
`
`OnCusp
`Ex. 1028
`
`

`

`Ovarian Carcinoma Subtypes Are Different
`
`Figure 6. Prognostic Associations of WT1
`Kaplan-Meier survival analysis of DSS.
`(A) Entire cohort grouped by WT1 positive versus negative cases (p ¼ 0.0017, univariable COX regression).
`(B) high-grade serous subtype grouped by WT1 positive versus negative cases (p ¼ 0.0086, univariable COX regression).
`doi:10.1371/journal.pmed.0050232.g006
`
`than the most common high-grade serous carcinomas. The
`clear cell and mucinous subtypes, in particular, are candi-
`dates for clinical trials to identify more active therapy than
`what is currently used [9]. Given the dramatic differences in
`biomarker expression between ovarian carcinoma subtypes,
`our analysis suggests that advancing our understanding of
`these poorly understood subtypes—including identification
`of potential therapeutic targets—will only come through
`studies focusing on these specific subtypes rather than studies
`of unselected series of patients.
`The biomarker expression profile within a given subtype is
`consistent across stage. Hence, early and advanced stage
`ovarian carcinomas differ primarily based on subtype, while
`within a subtype there is no difference between early and
`advanced stage tumors. This distinction has implications for
`the research on biomarkers for ovarian carcinoma screening,
`where the goal is detection of early stage disease, which has a
`much greater likelihood of cure. If subtypes are neglected, a
`screening marker identified in advanced stage tumors (i.e.,
`high-grade serous carcinomas), may not be expressed in most
`nonserous early stage ovarian carcinomas, and vice versa. For
`example, CA125 is expressed in most high-grade serous
`carcinoma, but only in 60% of mucinous and clear cell
`subtypes, a finding that is consistent with previous studies
`[26]. A related observation is that serum CA125 levels are
`elevated in 80% of patients with advanced stage epithelial
`ovarian carcinoma but are increased in only 60% of patients
`with early stage disease [27,28]. It is likely that a panel of
`tumor markers will be required to detect all subtypes. As the
`biomarker expression was consistent between stages within
`the subtypes, these data support the use of late stage cancers
`to identify biomarkers for the early detection of cancers of
`the same subtype.
`Biomarker correlation with prognosis can be