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Epithelial ovarian cancer consists of 5 major histotypes: high-grade serous carcinoma (HGSC), endometrioid carcinoma (EC), clear cell carcinoma (CCC), mucinous carcinoma (MC) and low-grade serous (LGSC). Each can have a broad spectrum of morphological appearances, and one histotype can closely mimic histopathological features more typical of another. Historically, there has been a relatively high frequency of mixed, defined by 2 or more distinct histotypes present based on routine histopathological assessment, histotype carcinoma diagnoses (3–11%), however recent immunohistochemical studies identifying histotype specific markers and allowing more refined histotype diagnoses suggests a much lower incidence. We reviewed hematoxylin and eosin stained slides from 871 cases of epithelial ovarian cancer and found the frequency of mixed carcinomas to be 1.7% when modern diagnostic criteria are applied. Through international collaboration, we established a cohort totaling 22 mixed epithelial ovarian cancers, consisting of 9 EC/CCC, 4 EC/LGSC, 3 HGSC/CCC, 2 CCC/MC and 4 other combinations. We interrogated the molecular differences between the different components of each case using immunohistochemistry, gene expression and hotspot sequencing analyses. Immunohistochemical data alone suggested 9 of the 22 cases were not mixed tumors as they presented a uniform immuno-phenotype throughout, and these cases most probably represent morphological mimicry and variation within tumors of a single histotype. Synthesis of molecular data further reduces the incidence of mixed carcinomas. Based on these results, true mixed carcinomas with both morphological and molecular support for the presence of more than one histotype within a given tumor represent less than 1% of epithelial ovarian cancers.

Background: Ovarian cancer is a series of distinct diseases typically identified by their histopathological appearance as high-grade serous (HGSC; 70% of cases), low-grade serous (LGSC; 5%), endometrioid (ENOCa, 8%), clear cell (CCC, 12%), and mucinous (MC; 5%) carcinomas. Each type has defining molecular events, gene/protein expression patterns, genetic risk factors, sites of origin, and responses to treatment. Gold standard treatment is surgery followed by platinum-taxane chemotherapy despite mounting evidence suggesting CCCs, MCs, and LGSCs are largely platinum-taxane resistant. If outcomes are to be improved, it is critical to adopt a type specific strategy. Retrospective review studies have suggested histotype may be misdiagnosed or omitted in up to 30% of cases. However, pathological diagnosis of histotypes has been greatly refined in recent years and the use of biomarkers as aides is becoming more widespread. Nonetheless a rapid and fully objective classifier of histotypes will undoubtedly improve diagnostic accuracy, especially in the case of pre-surgical biopsies where small amounts of material present a challenge. Methods: Over 1000 ovarian carcinoma samples underwent expert gynecopathological review to establish a gold standard diagnosis for the 5 major carcinoma types. RNA was extracted from FFPE tissues and levels of a pre-selected set of >100 genes were quantified using the NanoString GX system. Cohort was split with ~1/3 set aside for independent validation. Several statistical models were tested to generate a prediction algorithm for histological type including PAM, Random Forest, Lasso, Recursive Partitioning, and Discriminant Analysis. Feature selection methods and prediction error were examined using cross-validation in the train /test series prior to validation in the independent set. Results: Preliminary analysis suggests classification of the 5 major histotypes is possible using NanoString derived RNA expression levels. Accuracy appears to be equivalent to interobserver variation amongst expert gynecopathologist. Conclusions: The NanoString GX platform provides a stable and reproducible platform on which a robust single sample histological type classifier can be established. Our algorithm combined with the NanoString platform provides a rapid, and cost-effective option that does not require modification to current pathology lab tissue processing protocols. Diagnostic prediction require little material and is applicable to pre- and post- surgical specimens where an objective measure is desired to confirm diagnosis or aide in especially challenging cases. Citation Format: Michael S. Anglesio, Aline Talhouk, Steve E. Kalloger, Gholamreza Haffari, Robertson Mackenzie, Martin Cheung, Janine Senz, Christine Chow, Sherman Lau, Maria Intermaggio, Susan J. Ramus, Andreas du Bois, Jacobus Pfisterer, Jessica N. McAlpine, Friedrich Kommoss, Blake Gilks, Stefan Kommoss, David G. Huntsman. Rapid RNA-based histotyping of ovarian carcinomas. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research: From Concept to Clinic; Sep 18-21, 2013; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2013;19(19 Suppl):Abstract nr B14.