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Exploring survival-associated transcriptomic subtypes in ovarian cancer using RNAseq from FFPE tissues in a clinical
Maj K Kjeldsen1, Frederik Otzen Bagger2, Henrik Roed3
1Department of Oncology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark; Center for Genomic Medicine, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark; Nordic Society of Gynecological Oncology-Clinical Trial Unit, NSGO-CTU.
Objective:
Transcriptomic subtyping is not yet standardized for prognostic use in epithelial ovarian cancer (EOC). This study aims to validate RNA sequencing (RNAseq) from formalin-fixed, paraffin-embedded (FFPE) tissues and to evaluate survival-associated transcriptomic subtypes and differentially expressed genes (DEGs) in a clinical trial cohort.
Methods:
An exploratory post hoc analysis was conducted on FFPE samples from patients enrolled in the ENGOT-ov24/NSGO-AVANOVA1&2 trial. RNA was extracted and sequenced, and gene expression analysis was performed to classify subtypes using established, microarray-based, algorithms. Differentially expressed genes (DEGs) were identified based on survival groups, and survival outcomes were analyzed using Kaplan-Meier curves.
Results:
Of 96 eligible samples, 82 were included in the final analysis. Subtype classifications showed moderate agreement across RNAseq data formats. However, gene expression variability showed inconsistent concordance with clinical metadata and molecular subtypes. Eighteen genes were differentially expressed between long- and short-term survivors. Notably, DPEP3 and SLC14A1, were significantly upregulated in long-term survivors. Despite distinct expression patterns, no significant survival differences were observed between subtypes.
Conclusions:
This study demonstrates the feasibility of using RNAseq on FFPE tissue in EOC, while also highlighting challenges of applying microarray-based transcriptomic subtypes to RNAseq data. Transcriptomic analysis identified potential prognostic gene candidates but also highlighted the need to refine classification tools. Further research is essential to improve the molecular classification of EOC, thereby enhancing prognostic accuracy and guiding future therapeutic strategies.
