Prostate cancer (PCa) is a multifocal disease with diverse clinical presentation and outcome. Currently, histopathological scoring of tissue biopsies and assessment of blood prostate-specific antigen levels are used to stratify patients into risk categories. These do not encapsulate the underlying biology and often fail to predict therapeutic response. Numerous attempts to identify clinically relevant molecular subtypes have been hindered by significant inter- and intra-patient heterogeneity.
This project aims to address the imposing clinical challenge of optimal patient treatment through multi-region analysis of the tumour and adjacent ‘normal’ tissue regions, by single-cell RNA-sequencing (scRNA-seq). We are generating a transcriptomic cellular landscape of 25 treatment-naïve prostate cancer samples, collected consecutively from radical prostatectomies. The carefully curated cohort encompasses a broad clinical-risk range, with a focus on intermediate-risk patients, making it highly representative of the natural occurrence of the disease.
Our cryopreservation and tissue processing workflows have been optimised to yield high-viability cell suspensions from sensitive primary clinical tissue, enabling generation of high-fidelity scRNA-seq data. Additionally, we have adapted CITE-seq technology and tailored a PCa-specific antibody panel, enabling us to complement the transcriptomic data with proteomic information.
Preliminary experiments have highlighted remarkable epithelial cell diversity along with non-canonical androgen receptor (AR) activity in the prostatic stroma. Future analyses will explore these cellular subtypes and interactions with the AR-active microenvironment. Another focal aspect will be the interplay between the AR and PSMA (Prostate Specific Membrane Antigen) expression, which is currently revolutionising PCa diagnostic imaging and treatment but remains poorly understood. Our CITE-seq antibody panel includes PSMA to provide insight into the relationship between AR and PSMA.
Findings from this project will be validated and further investigated in large publicly available datasets, generated both from primary and metastatic disease, with known clinical outcomes.
This is the first study to approach this complex disease at a single-cell resolution. We hypothesise it will unravel and define novel cellular subtypes and interactions that govern inherently aggressive PCa phenotypes.