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.

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.

Figure 1. Representative Immunostains

Paired positive and negative examples for each biomarker.

Figure 2. Biomarker Expression Rates in the Entire Cohort by Stage

*Significant differences between categories (Fisher's exact test).

Figure 3. Biomarker Expression Rates in High-Grade Serous Subtype by Stage

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.

Figure 5. Distribution of Ki-67 Labelling Index across Subtypes

Figure 6. Prognostic Associations of WT1

Kaplan-Meier survival analysis of DSS. (A) Entire cohort grouped by WT1 positive versus negative cases (p = 0.0017, univariable COX regression). (B) high-grade serous subtype grouped by WT1 positive versus negative cases (p = 0.0086, univariable COX regression).