Clinical Applications of Molecular Biomarkers in Prostate Cancer

From molecular mechanisms of prostate cancer to translational applications: based on multi-omics fusion analysis and intelligent medicine

Prostate cancer is the most common cancer in men worldwide and has a high mortality rate. The complex and heterogeneous development of prostate cancer has become a core obstacle in the treatment of prostate cancer. Simultaneously, the issues of overtreatment in early-stage diagnosis, oligometastasis and dormant tumor recognition, as well as personalized drug utilization, are also specific concerns that require attention in the clinical management of prostate cancer. Some typical genetic mutations have been proved to be associated with prostate cancer's initiation and progression. However, single-omic studies usually are not able to explain the causal relationship between molecular alterations and clinical phenotypes. Exploration from a systems genetics perspective is also lacking in this field, that is, the impact of gene network, the environmental factors, and even lifestyle behaviors on disease progression. At the meantime, current trend emphasizes the utilization of artificial intelligence (AI) and machine learning techniques to process extensive multidimensional data, including multi-omics. These technologies unveil the potential patterns, correlations, and insights related to diseases, thereby aiding the interpretable clinical decision making and applications, namely intelligent medicine. Therefore, there is a pressing need to integrate multidimensional data for identification of molecular subtypes, prediction of cancer progression and aggressiveness, along with perosonalized treatment performing. In this review, we systematically elaborated the landscape from molecular mechanism discovery of prostate cancer to clinical translational applications. We discussed the molecular profiles and clinical manifestations of prostate cancer heterogeneity, the identification of different states of prostate cancer, as well as corresponding precision medicine practices. Taking multi-omics fusion, systems genetics, and intelligence medicine as the main perspectives, the current research results and knowledge-driven research path of prostate cancer were summarized.

An Extracellular Matrix Overlay Model for Bioluminescence Microscopy to Measure Single-Cell Heterogeneous Responses to Antiandrogens in Prostate Cancer Cells

Prostate cancer (PCa) displays diverse intra-tumoral traits, impacting its progression and treatment outcomes. This study aimed to refine PCa cell culture conditions for dynamic monitoring of androgen receptor (AR) activity at the single-cell level. We introduced an extracellular matrix-Matrigel (ECM-M) culture model, enhancing cellular tracking during bioluminescence single-cell imaging while improving cell viability. ECM-M notably tripled the traceability of poorly adherent PCa cells, facilitating robust single-cell tracking, without impeding substrate permeability or AR response. This model effectively monitored AR modulation by antiandrogens across various PCa cell lines. Single-cell imaging unveiled heterogeneous antiandrogen responses within populations, correlating non-responsive cell proportions with drug IC50 values. Integrating ECM-M culture with the PSEBC-TSTA biosensor enabled precise characterization of ARi responsiveness within diverse cell populations. Our ECM-M model stands as a promising tool to assess heterogeneous single-cell treatment responses in cancer, offering insights to link drug responses to intracellular signaling dynamics. This approach enhances our comprehension of the nuanced and dynamic nature of PCa treatment responses.

Clinically Translatable Hyperpolarized 13C Bicarbonate pH Imaging Method for Use in Prostate Cancer

Solid tumors such as prostate cancer (PCa) commonly develop an acidic microenvironment with pH 6.5-7.2, owing to heterogeneous perfusion, high metabolic activity, and rapid cell proliferation. In preclinical prostate cancer models, disease progression is associated with a decrease in tumor extracellular pH, suggesting that pH imaging may reflect an imaging biomarker to detect aggressive and high-risk disease. Therefore, we developed a hyperpolarized carbon-13 MRI method to image the tumor extracellular pH (pHe) and prepared it for clinical translation for detection and risk stratification of PCa. This method relies on the rapid breakdown of hyperpolarized (HP) 1,2-glycerol carbonate (carbonyl-13C) via base-catalyzed hydrolysis to produce HP 13CO32-, which is neutralized and converted to HP H13CO3-. After injection, HP H13CO3- equilibrates with HP 13CO2 in vivo and enables the imaging of pHe. Using insights gleaned from mechanistic studies performed in the hyperpolarized state, we solved issues of polarization loss during preparation in a clinical polarizer system. We successfully customized a reaction apparatus suitable for clinical application, developed clinical standard operating procedures, and validated the radiofrequency pulse sequence and imaging data acquisition with a wide range of animal models. The results demonstrated that we can routinely produce a highly polarized and safe HP H13CO3- contrast agent suitable for human injection. Preclinical imaging studies validated the reliability and accuracy of measuring acidification in healthy kidney and prostate tumor tissue. These methods were used to support an Investigational New Drug application to the U.S. Food and Drug Administration. This methodology is now ready to be implemented in human trials, with the ultimate goal of improving the management of PCa.

Analysis of genetic biomarkers, polymorphisms in ADME-related genes and their impact on pharmacotherapy for prostate cancer

Prostate cancer (PCa) is a non-cutaneous malignancy in males with wide variation in incidence rates across the globe. It is the second most reported cause of cancer death. Its etiology may have been linked to genetic polymorphisms, which are not only dominating cause of malignancy casualties but also exerts significant effects on pharmacotherapy outcomes. Although many therapeutic options are available, but suitable candidates identified by useful biomarkers can exhibit maximum therapeutic efficacy. The single-nucleotide polymorphisms (SNPs) reported in androgen receptor signaling genes influence the effectiveness of androgen receptor pathway inhibitors and androgen deprivation therapy. Furthermore, SNPs located in genes involved in transport, drug metabolism, and efflux pumps also influence the efficacy of pharmacotherapy. Hence, SNPs biomarkers provide the basis for individualized pharmacotherapy. The pharmacotherapeutic options for PCa include hormonal therapy, chemotherapy (Docetaxel, Mitoxantrone, Cabazitaxel, and Estramustine, etc.), and radiotherapy. Here, we overview the impact of SNPs reported in various genes on the pharmacotherapy for PCa and evaluate current genetic biomarkers with an emphasis on early diagnosis and individualized treatment strategy in PCa.

Advances in high-risk localized prostate cancer: Staging and management

Nearly 15% of individuals with localized prostate cancer are identified as high risk for recurrence and progression of the disease, which is why the correct staging is vital for the definition of correct treatment-also developing novel therapeutic strategies to find a balance between getting better outcomes without sacrificing the quality of life (QoL). In this narrative review, we introduced the current standards of staging and primary treatment of high-risk localized prostate cancer (PCa), based on international guidelines and arguments in the debate, under the light of the most recent literature. It brings essential tools such as PSMA PET/CT and different nomograms (Briganti. MSKCC, Gandaglia) for accurate staging and selecting wisely the definitive therapy. Even though there is a broad discussion over the best local treatment in curative-intent treatment, it looks more important to define which patient profile would adapt correctly to every different treatment, highlighting the benefits and superior outcomes with multimodal treatment.

P-score in preoperative biopsies accurately predicts P-score in final pathology at radical prostatectomy in patients with localized prostate cancer

BACKGROUND

Prostate cancer (PCa) is a highly heterogeneous, multifocal disease, and identification of clinically significant lesions is challenging, which complicates the choice of adequate treatment. The Prostatype® score (P-score) is intended to guide treatment decisions for newly diagnosed PCa patients based on a three-gene signature (IGFBP3, F3, and VGLL3) and clinicopathological information obtained at diagnosis. This study evaluated association of the P-score measured in preoperative magnetic resonance imaging/transrectal ultrasound fusion-guided core needle biopsies (CNBs) and the P-score measured in radical prostatectomy (RP) specimens of PCa patients. We also evaluated the P-score association with the pathology of RP specimens. Furthermore, concordance of the P-score in paired CNB and RP specimens, as well as in index versus concomitant nonindex tumor foci from the same RP was investigated.

METHODS

The study included 100 patients with localized PCa. All patients were diagnosed by CNB and underwent RP between 2015 and 2018. Gene expression was assessed with the Prostatype® real-time quantitative polymerase chain reaction kit and the P-score was calculated. Patients were categorized into three P-score risk groups according to previously defined cutoffs.

RESULTS

For 71 patients, sufficient CNB tumor material was available for comparison with the RP specimens. The CNB-based P-score was associated with the pathological T-stage in RP specimens (p = 0.02). Moreover, the CNB-based P-score groups were in substantial agreement with the RP-based P-score groups (weighted κ score: 0.76 [95% confidence interval, 95% CI: 0.60-0.92]; Spearman's rank correlation coefficient r = 0.83 [95% CI: 0.74-0.89]; p < 0.0001). Similarly, the P-score groups based on paired index tumor and concomitant nonindex tumor foci (n = 64) were also in substantial agreement (weighted κ score: 0.74 [95% CI: 0.57-0.91]; r = 0.83 [95% CI: 0.73-0.89], p < 0.0001).

CONCLUSIONS

Our findings suggest that the P-score based on preoperative CNB accurately reflects the pathology of the whole tumor, highlighting its value as a decision support tool for newly diagnosed PCa patients.

Prostate cancer in omics era

Recent advances in omics technology have prompted extraordinary attempts to define the molecular changes underlying the onset and progression of a variety of complex human diseases, including cancer. Since the advent of sequencing technology, cancer biology has become increasingly reliant on the generation and integration of data generated at these levels. The availability of multi-omic data has transformed medicine and biology by enabling integrated systems-level approaches. Multivariate signatures are expected to play a role in cancer detection, screening, patient classification, assessment of treatment response, and biomarker identification. This review reports current findings and highlights a number of studies that are both novel and groundbreaking in their application of multi Omics to prostate cancer.

Biomembrane lipids: When physics and chemistry join to shape biological activity

Biomembranes constitute the first lines of defense of cells. While small molecules can often permeate cell walls in bacteria and plants, they are generally unable to penetrate the barrier constituted by the double layer of phospholipids, unless specific receptors or channels are present. Antimicrobial or cell-penetrating peptides are in fact highly specialized molecules able to bypass this barrier and even discriminate among different cell types. This capacity is made possible by the intrinsic properties of its phospholipids, their distribution between the internal and external leaflet, and their ability to mutually interact, modulating the membrane fluidity and the exposition of key headgroups. Although common phospholipids can be found in the membranes of most organisms, some are characteristic of specific cell types. Here, we review the properties of the most common lipids and describe how they interact with each other in biomembrane. We then discuss how their assembly in bilayers determines some key physical-chemical properties such as permeability, potential and phase status. Finally, we describe how the exposition of specific phospholipids determines the recognition of cell types by membrane-targeting molecules.

High Expression of Folate Receptor Alpha (FOLR1) is Associated With Aggressive Tumor Behavior, Poor Response to Chemoradiotherapy, and Worse Survival in Rectal Cancer

Objectives: Recently, molecular medicine targeting Folate Receptor Alpha (FOLR1), which mediates intracellular folate uptake and tumor cell proliferation, has been identified in several malignancies. However, the association between FOLR1 expression and rectal cancer remains unclear. Methods: Immunostaining of FOLR1 was performed on biopsy specimens from 172 rectal cancer patients undergoing preoperative chemoradiotherapy (CRT). FOLR1 expression was measured and divided into low (0+-2+) or high (3+-4+) level. Correlations between FOLR1 status and clinicopathologic features, tumor regression grade, disease-specific survival (DSS), local recurrence-free survival, and metastasis-free survival (MeFS) were analyzed, retrospectively. Results: High FOLR1 expression was significantly associated with advanced post-treatment tumor and nodal status (T3-4; N1-2, P = .001), vascular invasion (P = .042), perineural invasion (P = .012), and poor regression change after CRT (P = .001). In uni- and multi-variable survival analysis, FOLR1 overexpression remained a significant predictor of lower DSS (hazard ratio [HR], 2.328; 95% confidence interval [CI], 1.014-5.344; P = .046) and MeFS (HR, 2.177; 95% CI, 1.000-1.1286; P = .050). Conclusion: These results indicate that high FOLR1 status is associated with aggressive tumor behavior, poor response to CRT, and worse survival. Therefore, FOLR1 expression at initial biopsy may be useful in predicting outcomes and also be a target for the exploration of FOLR1-based therapeutic agents.

On the Road to Accurate Protein Biomarkers in Prostate Cancer Diagnosis and Prognosis: Current Status and Future Advances

Prostate cancer (PC) is a leading cause of morbidity and mortality among men worldwide. Molecular biomarkers work in conjunction with existing clinicopathologic tools to help physicians decide who to biopsy, re-biopsy, treat, or re-treat. The past decade has witnessed the commercialization of multiple PC protein biomarkers with improved performance, remarkable progress in proteomic technologies for global discovery and targeted validation of novel protein biomarkers from clinical specimens, and the emergence of novel, promising PC protein biomarkers. In this review, we summarize these advances and discuss the challenges and potential solutions for identifying and validating clinically useful protein biomarkers in PC diagnosis and prognosis. The identification of multi-protein biomarkers with high sensitivity and specificity, as well as their integration with clinicopathologic parameters, imaging, and other molecular biomarkers, bodes well for optimal personalized management of PC patients.

Predicting Cancer-Specific Survival Among Patients With Prostate Cancer After Radical Prostatectomy Based on the Competing Risk Model: Population-Based Study

Introduction: We aimed to develop an easy-to-use individual survival prognostication tool based on competing risk analyses to predict the risk of 5-year cancer-specific death after radical prostatectomy for patients with prostate cancer (PCa).

Methods: We obtained the data from the Surveillance, Epidemiology, and End Results (SEER) database (2004–2016). The main variables obtained included age at diagnosis, marital status, race, pathological extension, regional lymphonode status, prostate specific antigen level, pathological Gleason Score. In order to reveal the independent prognostic factors. The cumulative incidence function was used as the univariable competing risk analyses and The Fine and Gray's proportional subdistribution hazard approach was used as the multivariable competing risk analyses. With these factors, a nomogram and risk stratification based on the nomogram was established. Concordance index (C-index) and calibration curves were used for validation.

Results: A total of 95,812 patients were included and divided into training cohort (n = 67,072) and validation cohort (n = 28,740). Seven independent prognostic factors including age, race, marital status, pathological extension, regional lymphonode status, PSA level, and pathological GS were used to construct the nomogram. In the training cohort, the C-index was 0.828 (%95CI, 0.812–0.844), and the C-index was 0.838 (%95CI, 0.813–0.863) in the validation cohort. The results of the cumulative incidence function showed that the discrimination of risk stratification based on nomogram is better than that of the risk stratification system based on D'Amico risk stratification.

Conclusions: We successfully developed the first competing risk nomogram to predict the risk of cancer-specific death after surgery for patients with PCa. It has the potential to help clinicians improve post-operative management of patients.

Alternative splicing of NF-YA promotes prostate cancer aggressiveness and represents a new molecular marker for clinical stratification of patients

BACKGROUND

Approaches based on expression signatures of prostate cancer (PCa) have been proposed to predict patient outcomes and response to treatments. The transcription factor NF-Y participates to the progression from benign epithelium to both localized and metastatic PCa and is associated with aggressive transcriptional profile. The gene encoding for NF-YA, the DNA-binding subunit of NF-Y, produces two alternatively spliced transcripts, NF-YAs and NF-YAl. Bioinformatic analyses pointed at NF-YA splicing as a key transcriptional signature to discriminate between different tumor molecular subtypes. In this study, we aimed to determine the pathophysiological role of NF-YA splice variants in PCa and their association with aggressive subtypes.

METHODS

Data on the expression of NF-YA isoforms were extracted from the TCGA (The Cancer Genome Atlas) database of tumor prostate tissues and validated in prostate cell lines. Lentiviral transduction and CRISPR-Cas9 technology allowed the modulation of the expression of NF-YA splice variants in PCa cells. We characterized 3D cell cultures through in vitro assays and RNA-seq profilings. We used the rank-rank hypergeometric overlap approach to identify concordant/discordant gene expression signatures of NF-YAs/NF-YAl-overexpressing cells and human PCa patients. We performed in vivo studies in SHO-SCID mice to determine pathological and molecular phenotypes of NF-YAs/NF-YAl xenograft tumors.

RESULTS

NF-YA depletion affects the tumorigenic potential of PCa cells in vitro and in vivo. Elevated NF-YAs levels are associated to aggressive PCa specimens, defined by Gleason Score and TNM classification. NF-YAl overexpression increases cell motility, while NF-YAs enhances cell proliferation in PCa 3D spheroids and xenograft tumors. The transcriptome of NF-YAs-spheroids has an extensive overlap with localized and metastatic human PCa signatures. According to PCa PAM50 classification, NF-YAs transcript levels are higher in LumB, characterized by poor prognosis compared to LumA and basal subtypes. A significant decrease in NF-YAs/NF-YAl ratio distinguishes PCa circulating tumor cells from cancer cells in metastatic sites, consistently with pro-migratory function of NF-YAl. Stratification of patients based on NF-YAs expression is predictive of clinical outcome.

CONCLUSIONS

Altogether, our results indicate that the modulation of NF-YA isoforms affects prostate pathophysiological processes and contributes to cancer-relevant phenotype, in vitro and in vivo. Evaluation of NF-YA splicing may represent a new molecular strategy for risk assessment of PCa patients.

Subpathologies and genomic classifier for treatment individualization of post-prostatectomy radiotherapy

PURPOSE/OBJECTIVE

Risk-stratification for post-prostatectomy radiotherapy (PORT) using conventional clinicopathologic indexes leads to substantial over- and under-treatment. Better patient selection could spare unnecessary toxicities and improve outcomes. We investigated the prognostic utility of unfavorable subpathologies intraductal carcinoma and cribriform architecture (IDC/CA), and a 22-gene Decipher genomic classifier (GC) in prostate cancer (PCa) patients receiving PORT.

MATERIAL/METHODS

A cohort of 302 men who received PORT at 2 academic institutions was pooled. PORT was predominately delivered as salvage (62% of cases); 20% received HT+PORT. Specimens were centrally reviewed for IDC/CA presence. In 104 cases, GC scores were determined. Endpoints were biochemical relapse-free (bRFR) and metastasis-free (mFR) rates.

RESULTS

After a median follow-up of 6.49-years, 135 (45%) and 40 (13%) men experienced biochemical relapse and metastasis, respectively. IDC/CA were identified in 160 (53%) of cases. Men harboring IDC/CA experienced inferior bRFR (HR 2.6, 95%CI 1.8-3.2, P<0.001) and mFR (HR 3.1, 95%CI 1.5-6.4, P = 0.0014). Patients with GC scores, 22 (21%) were stratified low-, 30 (29%) intermediate-, and 52 (50%) high-risk. GC low-risk was associated with superior bRFR (HR 0.25, 95%CI 0.1-0.5, P<0.001) and mFR (HR 0.15, 95%CI 0.03-0.8, P = 0.025). On multivariable analyses, IDC/CA and GC independently predicted for bRFR, corresponding to improved discrimination (C-index = 0.737 (95%CI 0.662-0.813)).

CONCLUSIONS

IDC/CA subpathologies and GC predict for biochemical relapse and metastasis beyond conventional clinicopathologic indexes in the PORT setting. Patients harboring IDC/CA are at higher risk of relapse after maximal local therapies, thus warranting consideration for treatment intensification strategies. Conversely, for men with absence of IDC/CA and low GC scores, de-intensification strategies could be explored.

Salvage local treatment for localized radio-recurrent prostate cancer: a narrative review and future perspectives

Although dose escalation protocols have improved biochemical control in prostate cancer radiotherapy, 10-45% of patients will experience disease recurrence. The prostate and seminal vesicles are the most frequent site of the first relapse. Traditionally, these patients have been managed with hormonal therapy, which is not curative. Recent improvements in diagnostic tests (e.g., multiparametric magnetic resonance and molecular imaging, including PET/CT scan with choline or Ga-PSMA) and new treatment techniques (e.g., stereotactic body radiation therapy or other minimally invasive alternatives like high-intensity focus ultrasound, cryoablation or high-dose-rate brachytherapy) offer new therapeutic strategies with the potential to cure some patients with limited adverse effects. In this narrative review, the authors present the most recent evidence to help identify the most suitable candidates for salvage treatment.

Diagnostic Strategies for Treatment Selection in Advanced Prostate Cancer

Prostate Cancer (PCa) is a leading cause of morbidity and mortality among men worldwide. For most men with PCa, their disease will follow an indolent course. However, advanced PCa is associated with poor outcomes. There has been an advent of new therapeutic options with proven efficacy for advanced PCa in the last decade which has improved survival outcomes for men with this disease. Despite this, advanced PCa continues to be associated with a high rate of death. There is a lack of strong evidence guiding the timing and sequence of these novel treatment strategies. This paper focuses on a review of the strategies for diagnostic and the current evidence available for treatment selection in advanced PCa.

Pathogenesis and Diagnosis of Genitourinary Cancer

Genitourinary (GU) cancers are among the most common malignant diseases in men [...].

Immunotherapy as a Precision Medicine Tool for the Treatment of Prostate Cancer

Prostate cancer (PCa) is the most frequently diagnosed type of cancer among Caucasian males over the age of 60 and is characterized by remarkable heterogeneity and clinical behavior, ranging from decades of indolence to highly lethal disease. Despite the significant progress in PCa systemic therapy, therapeutic response is usually transient, and invasive disease is associated with high mortality rates. Immunotherapy has emerged as an efficacious and non-toxic treatment alternative that perfectly fits the rationale of precision medicine, as it aims to treat patients on the basis of patient-specific, immune-targeted molecular traits, so as to achieve the maximum clinical benefit. Antibodies acting as immune checkpoint inhibitors and vaccines entailing tumor-specific antigens seem to be the most promising immunotherapeutic strategies in offering a significant survival advantage. Even though patients with localized disease and favorable prognostic characteristics seem to be the ones that markedly benefit from such interventions, there is substantial evidence to suggest that the survival benefit may also be extended to patients with more advanced disease. The identification of biomarkers that can be immunologically targeted in patients with disease progression is potentially amenable in this process and in achieving significant advances in the decision for precision treatment of PCa.

Targeting DNA Damage Response in Prostate and Breast Cancer

Steroid hormone signaling induces vast gene expression programs which necessitate the local formation of transcription factories at regulatory regions and large-scale alterations of the genome architecture to allow communication among distantly related cis-acting regions. This involves major stress at the genomic DNA level. Transcriptionally active regions are generally instable and prone to breakage due to the torsional stress and local depletion of nucleosomes that make DNA more accessible to damaging agents. A dedicated DNA damage response (DDR) is therefore essential to maintain genome integrity at these exposed regions. The DDR is a complex network involving DNA damage sensor proteins, such as the poly(ADP-ribose) polymerase 1 (PARP-1), the DNA-dependent protein kinase catalytic subunit (DNA-PKcs), the ataxia-telangiectasia-mutated (ATM) kinase and the ATM and Rad3-related (ATR) kinase, as central regulators. The tight interplay between the DDR and steroid hormone receptors has been unraveled recently. Several DNA repair factors interact with the androgen and estrogen receptors and support their transcriptional functions. Conversely, both receptors directly control the expression of agents involved in the DDR. Impaired DDR is also exploited by tumors to acquire advantageous mutations. Cancer cells often harbor germline or somatic alterations in DDR genes, and their association with disease outcome and treatment response led to intensive efforts towards identifying selective inhibitors targeting the major players in this process. The PARP-1 inhibitors are now approved for ovarian, breast, and prostate cancer with specific genomic alterations. Additional DDR-targeting agents are being evaluated in clinical studies either as single agents or in combination with treatments eliciting DNA damage (e.g., radiation therapy, including targeted radiotherapy, and chemotherapy) or addressing targets involved in maintenance of genome integrity. Recent preclinical and clinical findings made in addressing DNA repair dysfunction in hormone-dependent and -independent prostate and breast tumors are presented. Importantly, the combination of anti-hormonal therapy with DDR inhibition or with radiation has the potential to enhance efficacy but still needs further investigation.