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Distinct Patterns of Stromal and Tumor Expression of ROR1 and ROR2 in Histological Subtypes of Epithelial Ovarian Cancer.

ABSTRACT: The ROR1 and ROR2 receptor tyrosine kinases have both been implicated in ovarian cancer progression and have been shown to drive migration and invasion. There is an increasing importance of the role of stroma in ovarian cancer metastasis; however, neither ROR1 nor ROR2 expression in tumor or stromal cells has been analyzed in the same clinical cohort.To determine ROR1 and ROR2 expression in ovarian cancer and surrounding microenvironment and examine associations with clinicopathological characteristics.Immunohistochemistry for ROR1 and ROR2 was used to assess receptor expression in a cohort of epithelial ovarian cancer patients (n=178). Results were analyzed in relation to clinical and histopathological characteristics and survival. Matched patient sample case studies of normal, primary, and metastatic lesions were used to examine ROR expression in relation to ovarian cancer progression.ROR1 and ROR2 are abnormally expressed in malignant ovarian epithelium and stroma. Higher ROR2 tumor expression was found in early-stage, low-grade endometrioid carcinomas. ROR2 stromal expression was highest in the serous subtype. In matched patient case studies, metastatic samples had higher expression of ROR2 in the stroma, and a recurrent sample had the highest expression of ROR2 in both tumor and stroma.ROR1 and ROR2 are expressed in tumor-associated stroma in all histological subtypes of ovarian cancer and hold potential as therapeutic targets which may disrupt tumor and stroma interactions.

SUBMITTER: Henry CE

PROVIDER: S-EPMC5367847 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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Distinct Patterns of Stromal and Tumor Expression of ROR1 and ROR2 in Histological Subtypes of Epithelial Ovarian Cancer.

Henry C E CE Emmanuel C C Lambie N N Loo C C Kan B B Kennedy C J CJ de Fazio A A Hacker N F NF Ford C E CE

Translational oncology 20170323 3

<h4>Objective</h4>The ROR1 and ROR2 receptor tyrosine kinases have both been implicated in ovarian cancer progression and have been shown to drive migration and invasion. There is an increasing importance of the role of stroma in ovarian cancer metastasis; however, neither ROR1 nor ROR2 expression in tumor or stromal cells has been analyzed in the same clinical cohort.<h4>Aim</h4>To determine ROR1 and ROR2 expression in ovarian cancer and surrounding microenvironment and examine associations wit ...[more]

PMID: 28342318

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Project description:BackgroundEpithelial ovarian cancer (OC) is a heterogenous disease consisting of five major histologically distinct subtypes: high-grade serous (HGSOC), low-grade serous (LGSOC), endometrioid (ENOC), clear cell (CCOC) and mucinous (MOC). Although HGSOC is the most prevalent subtype, representing 70-80% of cases, a 2013 landmark study by Domcke et al. found that the most frequently used OC cell lines are not molecularly representative of this subtype. This raises the question, if not HGSOC, from which subtype do these cell lines derive? Indeed, non-HGSOC subtypes often respond poorly to chemotherapy; therefore, representative models are imperative for developing new targeted therapeutics.MethodsNon-negative matrix factorisation (NMF) was applied to transcriptomic data from 44 OC cell lines in the Cancer Cell Line Encyclopedia, assessing the quality of clustering into 2-10 groups. Epithelial OC subtypes were assigned to cell lines optimally clustered into five transcriptionally distinct classes, confirmed by integration with subtype-specific mutations. A transcriptional subtype classifier was then developed by trialling three machine learning algorithms using subtype-specific metagenes defined by NMF. The ability of classifiers to predict subtype was tested using RNA sequencing of a living biobank of patient-derived OC models.ResultsApplication of NMF optimally clustered the 44 cell lines into five transcriptionally distinct groups. Close inspection of orthogonal datasets revealed this five-cluster delineation corresponds to the five major OC subtypes. This NMF-based classification validates the Domcke et al. analysis, in identifying lines most representative of HGSOC, and additionally identifies models representing the four other subtypes. However, NMF of the cell lines into two clusters did not align with the dualistic model of OC and suggests this classification is an oversimplification. Subtype designation of patient-derived models by a random forest transcriptional classifier aligned with prior diagnosis in 76% of unambiguous cases. In cases where there was disagreement, this often indicated potential alternative diagnosis, supported by a review of histological, molecular and clinical features.ConclusionsThis robust classification informs the selection of the most appropriate models for all five histotypes. Following further refinement on larger training cohorts, the transcriptional classification may represent a useful tool to support the classification of new model systems of OC subtypes.

Dataset Information

Distinct Patterns of Stromal and Tumor Expression of ROR1 and ROR2 in Histological Subtypes of Epithelial Ovarian Cancer.

Publications

Distinct Patterns of Stromal and Tumor Expression of ROR1 and ROR2 in Histological Subtypes of Epithelial Ovarian Cancer.

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