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

Genome Medicine

I-Hsuan Lin¹  Sudha Desai²  Stephen S. Taylor³  Bethany M. Barnes³  Louisa Nelson³  Robert D. Morgan³  Anthony Tighe³  Joanne C. McGrail³  George J. Burghel⁴ 

[1]Bioinformatics Core Facility, Faculty of Biology, Medicine and Health, University of Manchester

[2]Department of Histopathology, The Christie NHS Foundation Trust

[3]Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Cancer Research Centre

[4]Manchester Centre for Genomic Medicine, St Mary’s Hospital, Manchester University NHS Foundation Trust

关键词: Ovarian cancer;    Non-negative matrix factorization;    RNA sequencing;    Subtype classification;    Machine learning;    Transcriptomics;   

DOI  :  10.1186/s13073-021-00952-5

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown