Integrated analysis of single-cell and bulk transcriptomes uncovers clinically relevant molecular subtypes in human prostate cancer.

OBJECTIVE: Prostate cancer (PCa) is a complex disease characterized by diverse cellular ecosystems within the tumor microenvironment (TME) and high tumor heterogeneity, which challenges clinically stratified management and reinforces the need for novel strategies to fight against castration-resistant PCa (CRPC). METHODS: We performed single-cell RNA sequencing (scRNA-seq) on 10 untreated primary PCa tissues and integrated public scRNA-seq resources from three normal prostate tissues, two untreated primary PCa tissues, and six CRPC tumors to portray a comprehensive cellular and molecular interaction atlas of PCa. We further integrated the single-cell and bulk transcriptomes of PCa to establish a molecular classification system. RESULTS: scRNA-seq profiles revealed substantial inter- and intra-tumoral heterogeneity across different cell subpopulations in untreated PCa and CRPC tumors. In the malignant epithelial reservoir, cells evolved along decoupled paths in treatment-naive PCa and CRPC tumors, and distinct transcriptional reprogramming processes were activated, highlighting anti-androgen therapy-induced lineage plasticity. Based on the specifically expressed markers of the epithelial subpopulations, we conducted unsupervised clustering analysis in The Cancer Genome Atlas prostate adenocarcinoma (TCGA-PRAD) cohort and identified three molecularly and clinically distinct subtypes. The C1 subtype, characterized by high enrichment of CRPC-enriched epithelial cells, had a high risk of rapid development of anti-androgen resistance and might require active surveillance and additional promising intervention treatments, such as integrin A3 (ITGA3) + integrin B1 (ITGB1) inhibition. The C2 subtype resembled the immune-modulated subtype that was most likely to benefit from anti-LAG3 immunotherapy. The C3 subtype had a favorable prognosis. CONCLUSIONS: Our study provides a comprehensive and high-resolution landscape of the intricate architecture of the PCa TME, and our trichotomic molecular taxonomy could help facilitate precision oncology.