Transcriptomic subtyping of prostate cancer

Abstract

Prostate cancer is the second most commonly diagnosed cancer in men and the fifth leading cause of cancer death in men worldwide. While survival rates in prostate cancer are high, there is a risk of metastatic recurrence following primary treatment. Currently, there is no molecular subtyping system that is universally accepted and commonly implemented in the treatment of prostate cancer. The primary hypothesis of this research is that there are molecular subtypes of primary prostate cancer, each with a relative potential for metastatic progression characterised by distinctive gene expression patterns. This project aims to discover and validate robust prognostic transcriptomic molecular subtypes across multiple cohorts, and to identify stratifying biomarkers and corresponding dysregulated biologies.

Molecular subtype discovery was considered using multiple meta-analytical approaches. Firstly, using a data-driven approach, multiple primary prostate cancer datasets, comprising both gene expression and clinico-pathological data, were analysed using the Clustering Intra and Inter DatasEts (CoINcIDE) meta-clustering framework and an adaptation of the prior knowledge transfer tool Gene Expression Compositional (GECA). Subtypes were analysed in the context of survival. Using a knowledge-driven, dimension reduction approach with multiple primary prostate cancer datasets, six sets of biologically relevant signature scores, estimated using the claraT tool, were used as inputs for consensus clustering. Clusters were evaluated, considering survival, biological characteristics, including differential gene expression, dysregulated pathways, and genomic alterations.

The clustering outputs of the CoINcIDE and GECA approaches failed to demonstrate consistent prognostic relevance. However, in the claraT approach, two subtypes were discovered: the Genome-Unstable subtype defined by relatively higher expression of the Genome-Instability signature scores and the Genome-Stable subtype associated with lower expression of the Genome-Instability signature scores. The Genome-Unstable subtype also had a higher risk of metastatic recurrence, biochemical recurrence and iv was associated with higher Gleason scores. Of the claraT signature set, the published CIN25 signature was identified as the most accurate diagnostic biomarker of the subtypes. Mitotic dysregulation was identified as a mechanism potentially driving the genomic instability phenotype of the Genome-Unstable subtype. Furthermore, the Genome-Unstable subtype was identified as potentially targetable by PARP inhibitors and/or POLQ inhibition due to dysregulation of DNA damage repair mechanisms and the upregulation of POLQ in this subtype.

Thesis is embargoed until 31 December 2027.

Date of Award	Dec 2022
Original language	English
Awarding Institution	Queen's University Belfast
Sponsors	EC/Horizon 2020 Marie Skłodowska-Curie actions
Supervisor	Jaine Blayney (Supervisor), Nuala McCabe (Supervisor) & Richard Kennedy (Supervisor)