Integrative transcriptomic profiling reveals subtype-specific therapeutic vulnerabilities and resistance mechanisms in prostate cancer.

OBJECTIVE

Advanced prostate cancer (PCa) remains therapeutically challenging due to heterogeneous mechanisms of resistance to androgen receptor (AR)-targeting agents. While AR signaling persists in castration-resistant PCa (CRPC), emerging evidence suggests AR-independent survival pathways may contribute to therapeutic escape. This study integrates transcriptomic data and clinical profiling to dissect AR dependency and resistance mechanisms in PCa, aiming to identify subtype-specific vulnerabilities and therapeutic targets.

METHODS

We performed CRISPR-Cas9 screens in AR-dependent (VCaP, LNCaP, 22Rv1) and AR-independent (DU145, PC-3, WPE1-NA22, P4E6, Shmac5) cell lines to identify core essential genes. RNA sequencing data from TCGA-PRAD, Changhai, and DKFZ cohorts were integrated to define molecular subtypes using consensus clustering. Spatial transcriptomics (ST) and single-cell RNA sequencing (scRNA-seq) were employed to validate gene expression patterns in primary tumors and metastatic samples. Temporal expression dynamics were analyzed using fuzzy clustering to identify resistance mediators, with a focus on MCL1. Drug sensitivity analysis revealed that AR-dependent cells were more sensitive to MCL1 inhibitor UMI-77, and MCL1 expression was higher in Enzalutamide-resistant cell lines. Functional validation via MCL1 knockdown confirmed its role in supporting the proliferation and inhibiting apoptosis of resistant cells.

RESULTS

CRISPR screening identified 952 shared essential genes in prostate cancer, with 157 AR-high essential signature and 130 AR-low essential signature genes. AR-high essential signature genes enriched in cell cycle/polycomb pathways, while AR-low essential signature genes correlated with oxidative phosphorylation/mTOR signaling. Consensus clustering of TCGA-PRAD data revealed three molecular subtypes (Clusters 1-3); Cluster 3 showed worst prognosis (shorter PFI/OS) and advanced clinical features (higher T/N stage, Gleason grade). External validation confirmed Cluster 3's aggressive phenotype and independent prognostic value (meta-cohort HR = 1.98, 95% CI: 1.19-3.27). Cluster 3 signature genes were upregulated in metastatic/CRPC tissues and spatially enriched in CRPC epithelium. Notably, Cluster 3 shared essential gene expression decreased after Enzalutamide treatment, whereas AR-high essential signature genes remained stable. MCL1 emerged as a key resistance driver, demonstrating persistent upregulation in Enzalutamide-resistant cells and CRPC models.

CONCLUSIONS

This study elucidates distinct AR dependency landscapes in PCa, revealing AR-independent survival pathways and a clinically actionable molecular subtype (Cluster 3) linked to therapy resistance. MCL1 emerges as a critical mediator of adaptive resistance, highlighting the need for combination therapies targeting both AR-driven and AR-independent programs to improve outcomes in advanced PCa.