1. Introduction
Prostate cancer (PCa) is one of the most commonly diagnosed malignancies in men worldwide, with over 1.4 million new cases and more than 375,000 deaths annually [
1]. North America, Europe, and Australia report particularly high incidence rates, reflecting genetic factors, lifestyle, and screening practices [
2]. While localized or locally advanced disease can often be managed with surgery or radiation therapy, a notable percentage of patients present with metastatic disease or progress to metastasis, notably to the bones, lymph nodes, lungs, or liver [
1,
2]. Once PCa metastasizes, the five-year survival rate remains poor, especially in metastatic castration-resistant PCa (mCRPC) [
3].
Over the past two decades, next-generation androgen receptor (AR) pathway inhibitors, taxane-based chemotherapy, bone-targeted therapies, and prostate-specific membrane antigen (PSMA)-directed radioligand therapies have resulted in increased survival. Nevertheless, treatment resistance frequently arises, driven by AR splice variants, lineage plasticity, and upregulation of survival pathways [
4]. The emergence of precision oncology is driven by high-throughput DNA/RNA sequencing, single-cell omics, and other advanced technologies [
5]. Landmark projects (TCGA, SU2C/PCF Dream Teams) have identified alterations in DNA damage r repair (DDR) genes (BRCA2, ATM, and CHEK2), the PI3K/AKT/mTOR pathway (often via PTEN loss), and AR amplifications/mutations [
6]. The clinical relevance is evident in the success of poly-ADP ribose polymerase (PARP) inhibitors (olaparib, rucaparib) in mCRPC patients harboring DDR mutations [
7,
8,
9].
AR splice variant detection, particularly of AR-V7, has emerged as a biomarker of resistance to AR-targeted therapies [
10]. PSMA PET-CT enables the earlier detection of micrometastatic disease and guides PSMA-targeted radioligand therapy [
11].
However, metastatic PCa remains highly heterogeneous [
12,
13]. Clonal evolution and an immunosuppressive tumor microenvironment (TME) impede durable responses, emphasizing the need for TME reprogramming [
14,
15,
16,
17,
18,
19].
Multidisciplinary strategies integrating genomic data, advanced imaging, adaptive trials, and real-world evidence aim to refine patient stratification, identify actionable targets, and optimize therapy sequences [
20,
21,
22,
23,
24].
As PCa management becomes increasingly complex, owing to the rapid expansion of precision oncology tools, novel therapeutic agents, and dynamic insights into the TME, an up-to-date synthesis of these advancements is vital. A timely review provides clinicians and researchers with the necessary context to integrate emerging data into evidence-based practice, help overcome resistance mechanisms, and realize the promise of truly personalized, potentially transformative care.
This review discusses the molecular underpinnings of disease progression, key diagnostic and stratification methods (e.g., next-generation sequencing (NGS) liquid biopsies), current and emerging therapeutics (AR inhibitors, PARP inhibitors, immunotherapies, radioligand therapies), and future directions for TME modulation and artificial intelligence (AI)-driven approaches. Ultimately, this comprehensive synthesis aimed to improve outcomes and explore the potential for long-term disease control—even cure—in carefully defined molecular subsets.
5. Conclusion and Future Perspectives
In this review, we summarize a broad range of findings that underscore the molecular and clinical complexities driving metastatic prostate cancer. At the molecular level, key themes include the centrality of AR signaling, the importance of DDR pathways (particularly BRCA1/2 and related genes), and the convergence of PI3K/AKT/mTOR and WNT signaling. Studies of the TME have further highlighted the immunosuppressive barriers encountered in advanced disease. In clinical settings, newer AR inhibitors, PARP inhibitors, radioligand therapies, and emerging immuno-oncological approaches have all contributed to improved outcomes; however, resistance remains pervasive. Resistance is exacerbated by the heterogeneous and evolving genetic and epigenetic landscapes of metastatic lesions, underscoring the need for refined molecular stratification and real-time monitoring. Although genomic and imaging technologies have expanded substantially, obstacles such as cost, access, and lack of standardized interpretation limit their full-scale adoption in diverse clinical settings.
Conflicting evidence, particularly regarding biomarkers such as TMPRSS2-ERG fusions and AR splice variants, stems from methodological inconsistencies and highlights the necessity for harmonized protocols. By consolidating recent mechanistic discoveries and linking them to evolving clinical strategies, this review offers an integrated framework that spans the molecular, immunological, and therapeutic dimensions of mPCa. Clinically, the insights discussed—mapping core pathways, identifying known resistance mechanisms, and emphasizing the role of molecular diagnostics—provide strategic advantages for improving patient selection and optimizing therapeutic sequences.
Several opportunities and future agendas have emerged. First, synergistic clinical trials that systematically assess combination regimens (for instance, AR inhibition plus DDR-targeted or immunotherapeutic agents) could clarify not only the survival benefits but also the molecular trajectories of resistance. Secondly, longitudinal biomarker tracking, particularly through liquid biopsies, could reveal early resistance pathways and enable adaptive therapy switching before overt clinical progression. Third, future trial designs could benefit from multi-omic, prospective cohorts that unify genomic, transcriptomic, and imaging data along with standardized clinical endpoints, potentially reconciling conflicting biomarker outcomes. The incorporation of advanced computational tools, including AI-driven analytics, can further integrate these data streams, while CRISPR-based functional assays could unearth new molecular targets within the AR signaling and DDR pathways. In parallel, more refined cellular therapies, such as CAR-T and natural killer cell platforms, may eventually overcome immunosuppressive obstacles in PCa if issues of specificity and toxicity can be resolved. Finally, global collaborative efforts coordinated by academic institutions, industry partners, and regulatory bodies are essential for validating assay standards, sharing biobanks, and rapidly translating bench discoveries into bedside interventions. Therefore, the field of mPCa is at a moment of convergence, and the integration of multi-omic insights, expanding therapeutic approaches, and computational advances has laid the groundwork for more precise and durable treatment strategies. Addressing the current evidence gaps, refining trial methodologies, and fostering cross-sector partnerships will be pivotal to ensure that these innovations yield tangible life-extending benefits for patients in real-world practice.
Figure 1.
Mechanisms of AR Reactivation and Resistance. This schematic illustrates multiple pathways by which AR signaling can be reactivated in prostate cancer, ultimately driving therapy resistance. (A) Aberrant AR Activation: Growth factors such as EGF, IGF-1, or IL-6 can transactivate the AR via tyrosine-kinase receptors, enabling AR signaling even under low-androgen conditions. (B) Increased Steroidogenic Signaling Pathways: Adrenal androgens and their precursors (e.g., DHEA) are converted intratumorally to DHT through 5α-reductase activity. Elevated levels of SHBG and other ligands also contribute to AR activation. (C) AR Splicing Variants: Certain variants (e.g., AR-V7) lack the ligand-binding domain, allowing constitutive AR target gene activation independently of androgen binding. (D) Alterations in AR Co-Regulators: Decreased expression of co-repressors (e.g., NCoR and SMRT) or increased expression of coactivators (e.g., SRC1, SRC2, SRC3, and ARA70) can further enhance AR-mediated transcription. (E) AR Overexpression: AR gene amplification, epigenetic modifications, and miRNA dysregulation can lead to AR overexpression, thus amplifying AR signaling and promoting tumor growth. Together, these mechanisms converge to maintain or upregulate AR signaling despite therapeutic interventions, resulting in increased cancer cell survival and disease progression. Abbreviations: ARA70, Androgen Receptor Coactivator 70; AR, Androgen Receptor; DHEA: Dehydroepiandrosterone; DHT, Dihydrotestosterone; EGF, Epidermal Growth Factor; IGF-1, Insulin-Like Growth Factor-1; IL-6, Interleukin-6; miRNA, MicroRNA; NCoR, Nuclear Receptor Co-Repressor; SHBG, Sex Hormone-Binding Globulin; SMRT, Silencing Mediator for Retinoid or Thyroid Hormone Receptor; SRC, Steroid Receptor Coactivator. Created with BioRender.com.
Figure 1.
Mechanisms of AR Reactivation and Resistance. This schematic illustrates multiple pathways by which AR signaling can be reactivated in prostate cancer, ultimately driving therapy resistance. (A) Aberrant AR Activation: Growth factors such as EGF, IGF-1, or IL-6 can transactivate the AR via tyrosine-kinase receptors, enabling AR signaling even under low-androgen conditions. (B) Increased Steroidogenic Signaling Pathways: Adrenal androgens and their precursors (e.g., DHEA) are converted intratumorally to DHT through 5α-reductase activity. Elevated levels of SHBG and other ligands also contribute to AR activation. (C) AR Splicing Variants: Certain variants (e.g., AR-V7) lack the ligand-binding domain, allowing constitutive AR target gene activation independently of androgen binding. (D) Alterations in AR Co-Regulators: Decreased expression of co-repressors (e.g., NCoR and SMRT) or increased expression of coactivators (e.g., SRC1, SRC2, SRC3, and ARA70) can further enhance AR-mediated transcription. (E) AR Overexpression: AR gene amplification, epigenetic modifications, and miRNA dysregulation can lead to AR overexpression, thus amplifying AR signaling and promoting tumor growth. Together, these mechanisms converge to maintain or upregulate AR signaling despite therapeutic interventions, resulting in increased cancer cell survival and disease progression. Abbreviations: ARA70, Androgen Receptor Coactivator 70; AR, Androgen Receptor; DHEA: Dehydroepiandrosterone; DHT, Dihydrotestosterone; EGF, Epidermal Growth Factor; IGF-1, Insulin-Like Growth Factor-1; IL-6, Interleukin-6; miRNA, MicroRNA; NCoR, Nuclear Receptor Co-Repressor; SHBG, Sex Hormone-Binding Globulin; SMRT, Silencing Mediator for Retinoid or Thyroid Hormone Receptor; SRC, Steroid Receptor Coactivator. Created with BioRender.com.

Figure 2.
PARP Inhibitors and the Synthetic Lethality Paradigm. Initially, it was believed that PARP inhibitors worked by blocking PARylation and causing cytotoxicity. However, it was later discovered that the primary reason for tumor cell death was the trapping of the PARP1 enzyme at DNA lesions. When DNA damage leads to SSBs, PARP1 is responsible for their accurate repair. However, when PARP1 becomes trapped, it poses a threat to replication forks during the S phase of the cell cycle. This ultimately results in the collapse of the replication fork and the creation of double-strand breaks. In cells with functional BRCA genes, homologous recombination (HR) can repair these breaks without errors. On the other hand, cells lacking BRCA1/2 are deficient in HR and rely on error-prone DNA EJ pathways, such as classical non-homologous EJ or alternative EJ, to fix the double-strand breaks caused by the collapse of replication forks. This triggers the accumulation of chromosomal abnormalities and cell death through mitotic catastrophe. Abbreviations: BRCA1 / BRCA2, Breast Cancer Susceptibility Gene 1 / 2; DSB, Double-Strand Break; EJ, End-Joining; HR, Homologous Recombination; PARP, Poly(ADP-ribose) Polymerase; PARPi, PARP Inhibitor (Poly(ADP-ribose) Polymerase Inhibitor); SSB, Single-Strand Break. Created with BioRender.com.
Figure 2.
PARP Inhibitors and the Synthetic Lethality Paradigm. Initially, it was believed that PARP inhibitors worked by blocking PARylation and causing cytotoxicity. However, it was later discovered that the primary reason for tumor cell death was the trapping of the PARP1 enzyme at DNA lesions. When DNA damage leads to SSBs, PARP1 is responsible for their accurate repair. However, when PARP1 becomes trapped, it poses a threat to replication forks during the S phase of the cell cycle. This ultimately results in the collapse of the replication fork and the creation of double-strand breaks. In cells with functional BRCA genes, homologous recombination (HR) can repair these breaks without errors. On the other hand, cells lacking BRCA1/2 are deficient in HR and rely on error-prone DNA EJ pathways, such as classical non-homologous EJ or alternative EJ, to fix the double-strand breaks caused by the collapse of replication forks. This triggers the accumulation of chromosomal abnormalities and cell death through mitotic catastrophe. Abbreviations: BRCA1 / BRCA2, Breast Cancer Susceptibility Gene 1 / 2; DSB, Double-Strand Break; EJ, End-Joining; HR, Homologous Recombination; PARP, Poly(ADP-ribose) Polymerase; PARPi, PARP Inhibitor (Poly(ADP-ribose) Polymerase Inhibitor); SSB, Single-Strand Break. Created with BioRender.com.

Figure 3.
TMPRSS2-ERG Gene Fusion and AR-Driven Oncogenic Transcription. Loss of PTEN and concomitant activation of AKT could act in partnership with the TMPRSS2-ERG fusion protein to promote progression to prostate cancer through downstream pathways that increase the selective advantage of premalignant prostatic intraepithelial neoplasia (PIN) cells. Abbreviations: AKT, Protein Kinase B; AREs, Androgen Response Elements; ERG, ETS-Related Gene; ETS, E26 Transformation-Specific; PIN, Prostatic Intraepithelial Neoplasia; PTEN, Phosphatase and Tensin Homolog; TA, Transactivation Domain; TF, Transcription Factor; TMPRSS2, ransmembrane Protease, Serine 2. Created with BioRender.com.
Figure 3.
TMPRSS2-ERG Gene Fusion and AR-Driven Oncogenic Transcription. Loss of PTEN and concomitant activation of AKT could act in partnership with the TMPRSS2-ERG fusion protein to promote progression to prostate cancer through downstream pathways that increase the selective advantage of premalignant prostatic intraepithelial neoplasia (PIN) cells. Abbreviations: AKT, Protein Kinase B; AREs, Androgen Response Elements; ERG, ETS-Related Gene; ETS, E26 Transformation-Specific; PIN, Prostatic Intraepithelial Neoplasia; PTEN, Phosphatase and Tensin Homolog; TA, Transactivation Domain; TF, Transcription Factor; TMPRSS2, ransmembrane Protease, Serine 2. Created with BioRender.com.
Figure 6.
Immune Landscape and TME in Prostate Cancer. This schematic highlights the complex interactions among PCa cells, immune cells, and stromal components within the TME. NFs help maintain tissue homeostasis, whereas CAFs secrete factors such as NRG-1, SPP1, and IL-6 that remodel the ECM and support tumor progression. TAMs originate from M0 precursors and can polarize into M1 or M2 phenotypes: M1-TAMs secrete pro-inflammatory cytokines (e.g., IFN-γ, TNF-α, IL-1) that facilitate antitumor immunity, whereas M2-TAMs produce immunosuppressive mediators (e.g., IL-10, Arg-1, TGF-β), enhancing tumor growth and immune evasion. Tregs further suppress antitumor responses by inhibiting CD8+ T-cell activity through mechanisms involving PD-L1, ROS, and other immunosuppressive factors. Collectively, these dynamic cellular and molecular interactions shape the immune milieu in prostate cancer, driving disease progression and influencing therapeutic responses. Abbreviation: APC, Antigen-Presenting Cell; Arg-1, Arginase-1; CAF, Cancer-Associated Fibroblast; D8+ T, CD8+ T Cell; ECM, Extracellular Matrix; IFN-γ, Interferon-gamma; IL-1, Interleukin-1; IL-10, Interleukin-10; M0-TAM, M0 Tumor-Associated Macrophage; M1-TAM, M1 Tumor-Associated Macrophage; M2-TAM, M2 Tumor-Associated Macrophage; Mφ, Macrophage; NF, Normal Fibroblast; NRG-1, Neuregulin-1; Pca, Prostate Cancer; PD-L1, Programmed Death-Ligand 1; RNS, Reactive Nitrogen Species; ROS, Reactive Oxygen Species; SPP1, Secreted Phosphoprotein 1; TGF-β, Transforming Growth Factor-beta; TNF-α, Tumor Necrosis Factor-alpha; Treg, Regulatory T Cell. Created with BioRender.com.
Figure 6.
Immune Landscape and TME in Prostate Cancer. This schematic highlights the complex interactions among PCa cells, immune cells, and stromal components within the TME. NFs help maintain tissue homeostasis, whereas CAFs secrete factors such as NRG-1, SPP1, and IL-6 that remodel the ECM and support tumor progression. TAMs originate from M0 precursors and can polarize into M1 or M2 phenotypes: M1-TAMs secrete pro-inflammatory cytokines (e.g., IFN-γ, TNF-α, IL-1) that facilitate antitumor immunity, whereas M2-TAMs produce immunosuppressive mediators (e.g., IL-10, Arg-1, TGF-β), enhancing tumor growth and immune evasion. Tregs further suppress antitumor responses by inhibiting CD8+ T-cell activity through mechanisms involving PD-L1, ROS, and other immunosuppressive factors. Collectively, these dynamic cellular and molecular interactions shape the immune milieu in prostate cancer, driving disease progression and influencing therapeutic responses. Abbreviation: APC, Antigen-Presenting Cell; Arg-1, Arginase-1; CAF, Cancer-Associated Fibroblast; D8+ T, CD8+ T Cell; ECM, Extracellular Matrix; IFN-γ, Interferon-gamma; IL-1, Interleukin-1; IL-10, Interleukin-10; M0-TAM, M0 Tumor-Associated Macrophage; M1-TAM, M1 Tumor-Associated Macrophage; M2-TAM, M2 Tumor-Associated Macrophage; Mφ, Macrophage; NF, Normal Fibroblast; NRG-1, Neuregulin-1; Pca, Prostate Cancer; PD-L1, Programmed Death-Ligand 1; RNS, Reactive Nitrogen Species; ROS, Reactive Oxygen Species; SPP1, Secreted Phosphoprotein 1; TGF-β, Transforming Growth Factor-beta; TNF-α, Tumor Necrosis Factor-alpha; Treg, Regulatory T Cell. Created with BioRender.com.

Figure 7.
PSMA-Targeted Radioligand Therapy. This schematic illustrates the principle of PSMA-based imaging and therapy in prostate cancer. Radioligands (e.g., PSMA-11 for imaging and PSMA-617 for therapy) bind to the extracellular domain of PSMA on tumor cells. After binding, the complex undergoes internalization, delivering radioactive payloads into the cell. For imaging (PSMA-PET), positron-emitting isotopes enable visualization of tumor lesions, whereas therapeutic radionuclides (PSMA-RLT) emit alpha or beta particles that induce DNA damage and cancer cell death. This targeted approach exploits high PSMA expression on prostate cancer cells, offering both diagnostic and therapeutic benefits. Abbreviation: APC, Antigen-Presenting Cell; Arg-1, Arginase-1; CAF, Cancer-Associated Fibroblast; CD8+ T, CD8+ T Cell; ECM, Extracellular Matrix; IFN-γ, Interferon-gamma; IL-1, Interleukin-1; IL-10, Interleukin-10; M0-TAM, M0 Tumor-Associated Macrophage; M1-TAM, M1 Tumor-Associated Macrophage; M2-TAM, M2 Tumor-Associated Macrophage; Mφ, Macrophage; NF, Normal Fibroblast; NRG-1, Neuregulin-1; PCa, Prostate Cancer; PD-L1, Programmed Death-Ligand 1; PSMA, prostate-specific membrane antigen; RNS, Reactive Nitrogen Species; ROS, Reactive Oxygen Species; SPP1, Secreted Phosphoprotein 1; TGF-β, Transforming Growth Factor-beta; TNF-α, Tumor Necrosis Factor-alpha; Treg, Regulatory T Cell. Created with BioRender.com.
Figure 7.
PSMA-Targeted Radioligand Therapy. This schematic illustrates the principle of PSMA-based imaging and therapy in prostate cancer. Radioligands (e.g., PSMA-11 for imaging and PSMA-617 for therapy) bind to the extracellular domain of PSMA on tumor cells. After binding, the complex undergoes internalization, delivering radioactive payloads into the cell. For imaging (PSMA-PET), positron-emitting isotopes enable visualization of tumor lesions, whereas therapeutic radionuclides (PSMA-RLT) emit alpha or beta particles that induce DNA damage and cancer cell death. This targeted approach exploits high PSMA expression on prostate cancer cells, offering both diagnostic and therapeutic benefits. Abbreviation: APC, Antigen-Presenting Cell; Arg-1, Arginase-1; CAF, Cancer-Associated Fibroblast; CD8+ T, CD8+ T Cell; ECM, Extracellular Matrix; IFN-γ, Interferon-gamma; IL-1, Interleukin-1; IL-10, Interleukin-10; M0-TAM, M0 Tumor-Associated Macrophage; M1-TAM, M1 Tumor-Associated Macrophage; M2-TAM, M2 Tumor-Associated Macrophage; Mφ, Macrophage; NF, Normal Fibroblast; NRG-1, Neuregulin-1; PCa, Prostate Cancer; PD-L1, Programmed Death-Ligand 1; PSMA, prostate-specific membrane antigen; RNS, Reactive Nitrogen Species; ROS, Reactive Oxygen Species; SPP1, Secreted Phosphoprotein 1; TGF-β, Transforming Growth Factor-beta; TNF-α, Tumor Necrosis Factor-alpha; Treg, Regulatory T Cell. Created with BioRender.com.

Table 1.
Major Molecular Alterations in Metastatic Prostate Cancer and Their Clinical Implications.
Table 1.
Major Molecular Alterations in Metastatic Prostate Cancer and Their Clinical Implications.
Molecular Target or Genetic Alteration |
Key Mechanism/Function |
Clinical Features |
Clinical Utility |
AR Amplification / AR Splice Variants (e.g., AR-V7) |
Sustained AR signaling under low-androgen conditions; ligand-independent activation |
Poor response or resistance to AR-targeted therapies; commonly seen in mCRPC |
Predicts resistance to enzalutamide or abiraterone; potential biomarker for treatment selection |
PTEN Loss |
Hyperactivation of the PI3K/AKT/mTOR pathway; cross-talk with AR signaling |
Associated with high-grade tumors and aggressive clinical course |
May guide PI3K/AKT/mTOR inhibitor-based combination trials; potential prognostic indicator |
DDR Defects (e.g., BRCA2, ATM) |
Impaired DNA repair and increased genomic instability; vulnerability to PARP inhibition |
More aggressive behavior if untreated; better response to PARP inhibitors |
Companion diagnostics for PARP inhibitors; synthetic lethality-based therapy targeting |
TMPRSS2-ERG Fusion |
ETS transcription factor (ERG) overexpression; promotes invasion, EMT, and genomic instability |
High prevalence in localized prostate cancer; variable association with outcomes in mPCa |
Potential prognostic marker in combination with other alterations (e.g., PTEN) |
PI3K/AKT/mTOR Mutations |
Aberrant cell proliferation and survival; metabolic reprogramming |
Often co-occurs with AR pathway alterations; contributes to therapeutic resistance |
Under investigation in clinical trials targeting AKT and mTOR; potential combination strategy with AR inhibitors |
TP53 / RB1 Co-mutations |
Disruption of cell-cycle checkpoints; may facilitate lineage plasticity or neuroendocrine differentiation |
Common in advanced mPCa; associated with poor prognosis |
Emerging biomarker for early switch to chemotherapy or combination therapies |
Table 3.
Key Molecular Diagnostic Panels and Recommended Biomarkers in Prostate Cancer.
Table 3.
Key Molecular Diagnostic Panels and Recommended Biomarkers in Prostate Cancer.
Diagnostic Panel/Biomarker |
Testing Method |
Clinical Significance |
Limitations/Considerations |
DDR-Focused Panel (BRCA1/2, ATM, etc.) |
- Targeted NGS or expanded gene panels - Germline vs. somatic testing |
- Identifies candidates for PARP inhibitors and platinum-based therapies - May inform familial genetic risk |
- Cost and limited access in some regions - May miss epigenetic alterations |
AR Splice Variants (e.g., AR-V7) |
- RT-PCR or ddPCR on CTCs - Tissue-based RNA assays |
- Predicts resistance to enzalutamide or abiraterone - Can guide switch to chemotherapy or other targeted agents |
- Variable sensitivity depending on assay - Not yet universally available or standardized |
PTEN / PI3K / AKT |
- IHC, FISH - Targeted sequencing for hotspot mutations |
- Potential biomarker for AKT/mTOR inhibitors - May correlate with disease aggressiveness |
- Limited predictive validation in some trials - Reimbursement issues in certain regions |
TP53 / RB1 |
- Targeted NGS or WES/WGS - IHC for protein loss |
- Associated with poor prognosis - May indicate early progression toward neuroendocrine differentiation |
- Rarely used in routine practice - Data interpretation can be complex (co-occurring events) |
TMPRSS2-ERG Fusion |
- FISH, RT-PCR, or RNA-seq |
- Possible prognostic marker when combined with other aberrations (e.g., PTEN) |
- Prognostic impact remains debated - May not be actionable with current therapies |
Table 4.
Liquid Biopsy Modalities in Metastatic Prostate Cancer: Key Features and Clinical Applications.
Table 4.
Liquid Biopsy Modalities in Metastatic Prostate Cancer: Key Features and Clinical Applications.
Modality |
Specimen Characteristics |
Analytical Techniques |
Clinical Applications |
Advantages |
Limitations |
ctDNA |
- Cell-free DNA fragments shed by tumor cells - Detected in plasma or serum |
- Targeted/Whole-Exome NGS - ddPCR |
- Real-time monitoring of tumor burden - Detection of actionable mutations (e.g., BRCA2) |
- Minimally invasive - Repeat sampling feasible - Reflects genomic heterogeneity |
- Low abundance in early disease - Sensitivity depends on tumor fraction - Assay costs and standardization issues |
CTCs |
- Intact, viable tumor cells in the bloodstream - May be enriched via immunomagnetic or size-based separation methods |
- Immunophenotyping - Single-cell genomics/transcriptomics |
- Prognostic biomarker (CTC count) - AR-V7 status for therapy guidance - Potential ex vivo drug testing |
- Allows morphological and molecular analyses - Provides insight into specific cell populations |
- Rare cells, labor-intensive- Limited sensitivity in low-volume disease - Heterogeneity among different CTC populations |
Exosomes and Extracellular Vesicles |
- Nano-scale vesicles containing proteins, RNA, and DNA - Released by tumor and stromal cells into bodily fluids |
- RNA-seq, proteomics - Nanoparticle tracking - Advanced mass spectrometry |
- May reveal early resistance signatures - Potential biomarkers for immune- and stromal interactions |
- Reflects active secretory pathways - Can capture tumor–stromal communication |
- Isolation protocols not standardized - Complexity of vesicle subtypes - Data interpretation is challenging |
Table 5.
Major Clinical Trials of Targeted and Immunotherapeutic Approaches in Prostate Cancer.
Table 5.
Major Clinical Trials of Targeted and Immunotherapeutic Approaches in Prostate Cancer.
Treatment or Combination |
Primary Target/Mechanism |
Trial Phase |
Patient Population |
Key Outcomes |
Current Status |
Reference |
Olaparib vs. Abiraterone/Enzalutamide (PROfound) |
PARP inhibition (DDR deficiency) |
Phase III |
mCRPC with HRR gene alterations (e.g., BRCA1/2) |
Improved radiographic PFS and OS in biomarker-selected patients |
Approved for HRR-mutated mCRPC |
[171] |
Ipatasertib + Abiraterone (IPATential150) |
AKT inhibitor + AR axis blockade |
Phase III |
mCRPC, particularly with PTEN loss |
Prolonged PFS in the PTEN-loss subgroup |
Ongoing or completed; subset analyses continuing |
[148] |
177Lu-PSMA-617 + Standard of Care (VISION) |
PSMA-targeted radioligand therapy |
Phase III |
Heavily pretreated mCRPC |
Improved OS and PFS vs. standard care |
Approved in multiple regions |
[172] |
Nivolumab + Ipilimumab (CheckMate 650) |
Dual immune checkpoint blockade (PD-1, CTLA-4) |
Phase II |
mCRPC, previously treated |
Moderate objective response; significant immune-related toxicity |
Further refinement of combination strategies needed |
[173] |
Pembrolizumab (KEYNOTE-199) |
PD-1 immune checkpoint blockade |
Phase II |
mCRPC with prior treatments |
Modest response rates; better outcomes in MSI-H or DNA repair defects |
Investigational in selected biomarker-defined subgroups |
[174] |
Apalutamide (SPARTAN) |
Next-generation AR antagonist |
Phase III |
nmCRPC (non-metastatic CRPC) |
Significantly improved metastasis-free survival (MFS) |
Approved for nmCRPC |
[175] |