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    Distinct transcriptional programs stratify ovarian cancer cell lines into the five major histological subtypes

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    Authors
    Barnes, Bethany M
    Nelson, Louisa
    Tighe, Anthony
    Burghel, G. J.
    Lin, I. H.
    Desai, Sudha
    McGrail, Joanne C
    Morgan, Robert David
    Taylor, Stephen S
    Affiliation
    Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Cancer Research Centre, Oglesby Cancer Research Building, 555 Wilmslow Road, Manchester, M20 4GJ, UK
    Issue Date
    2021
    
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    Abstract
    Background: Epithelial 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. Methods: Non-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. Results: Application 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. Conclusions: This 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.
    Citation
    Barnes BM, Nelson L, Tighe A, Burghel GJ, Lin I-H, Desai S, et al. Distinct transcriptional programs stratify ovarian cancer cell lines into the five major histological subtypes. Genome Med. 2021 Sep 1;13(1). 
    Journal
    Genome Medicine
    URI
    http://hdl.handle.net/10541/624571
    DOI
    10.1186/s13073-021-00952-5
    PubMed ID
    34470661
    Additional Links
    https://dx.doi.org/10.1186/s13073-021-00952-5
    Type
    Article
    Language
    en
    ae974a485f413a2113503eed53cd6c53
    10.1186/s13073-021-00952-5
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