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.