New developments in prostate cancer biomarkers

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.

Development of a nomogram combining multiparametric magnetic resonance imaging and PSA-related parameters to enhance the detection of clinically significant cancer across different region

OBJECTIVE

Prostate cancer (PCa) is the most prevalent cancer among males. This study attempted to develop a clinically significant prostate cancer (csPCa) risk nomogram including Prostate Imaging-Reporting and Data System (PI-RADS) score and other clinical indexes for initial prostate biopsy in light of the different prostate regions, and internal validation was further conducted.

PATIENTS AND METHODS

A retrospective study was performed including 688 patients who underwent ultrasound-guided transperineal magnetic resonance imaging fusion prostate biopsy from December 2016 to July 2019. We constructed nomograms combining PI-RADS score and clinical variables (prostate-specific antigen [PSA], prostate volume (PV), age, free/total PSA, and PSA density) through univariate and multivariate logistic regression to identify patients eligible for biopsy. The performance of the predictive model was evaluated by bootstrap resampling. The area under the curve (AUC) of the receiver-operating characteristic (ROC) analysis was appointed to quantify the accuracy of the primary nomogram model for csPCa. Calibration curves were used to assess the agreement between the biopsy specimen and the predicted probability of the new nomogram. The χ² test was also applied to evaluate the heterogeneity between fusion biopsy and systematic biopsy based on different PI-RADS scores and prostate regions.

RESULTS

A total of 320 of 688 included patients were diagnosed with csPCa. csPCa was defined as Gleason score ≥7. The ROC and concordance-index both presented good performance. The nomogram reached an AUC of 0.867 for predicting csPCa at the peripheral zone; meanwhile, AUC for transitional and apex zones were 0.889 and 0.757, respectively. Statistical significance was detected between fusion biopsy and systematic biopsy for PI-RADS score >3 lesions and lesions at the peripheral and transitional zones.

CONCLUSION

We produced a novel nomogram predicting csPCa in patients with suspected imaging according to different locations. Our results indicated that PI-RADS score combined with other clinical parameters showed a robust predictive capacity for csPCa before prostate biopsy. The new nomogram, which incorporates prebiopsy data including PSA, PV, age, and PI-RADS score, can be helpful for clinical decision-making to avoid unnecessary biopsy.

Metabolomics of Prostate Cancer Gleason Score in Tumor Tissue and Serum

Gleason score, a measure of prostate tumor differentiation, is the strongest predictor of lethal prostate cancer at the time of diagnosis. Metabolomic profiling of tumor and of patient serum could identify biomarkers of aggressive disease and lead to the development of a less-invasive assay to perform active surveillance monitoring. Metabolomic profiling of prostate tissue and serum samples was performed. Metabolite levels and metabolite sets were compared across Gleason scores. Machine learning algorithms were trained and tuned to predict transformation or differentiation status from metabolite data. A total of 135 metabolites were significantly different (P _adjusted < 0.05) in tumor versus normal tissue, and pathway analysis identified one sugar metabolism pathway (P _adjusted = 0.03). Machine learning identified profiles that predicted tumor versus normal tissue (AUC of 0.82 ± 0.08). In tumor tissue, 25 metabolites were associated with Gleason score (unadjusted P < 0.05), 4 increased in high grade while the remainder were enriched in low grade. While pyroglutamine and 1,5-anhydroglucitol were correlated (0.73 and 0.72, respectively) between tissue and serum from the same patient, no metabolites were consistently associated with Gleason score in serum. Previously reported as well as novel metabolites with differing abundance were identified across tumor tissue. However, a "metabolite signature" for Gleason score was not obtained. This may be due to study design and analytic challenges that future studies should consider. IMPLICATIONS: Metabolic profiling can distinguish benign and neoplastic tissues. A novel unsupervised machine learning method can be utilized to achieve this distinction.

Aberrant SOCS3 Promoter Methylation as a Noninvasive Diagnostic Biomarker for Locally Advanced Prostate Cancer

Objective

The aim of this study was to investigate the promoter methylation status of Rasassociated domain family 1A (RASSF1A), O-6-methylguanine-DNA methyltransferase (MGMT), Phosphatase with tensin homology (PTEN) and Suppressor of cytokine signaling 3 (SOCS3) tumor suppressor genes and evaluate the clinical utility of these genes as noninvasive, blood-based epigenetic biomarkers for the diagnosis of Prostate Cancer (PCa).

Method

A total of 41 consecutive patients and 10 healthy control groups were enrolled in the study. Pyrosequencing was performed to analyze the methylation levels of the promoter regions of the four tumor suppressor genes in patients compared to healthy controls.

Results

The promoter methylation levels of RASSF1A, MGMT, PTEN and SOCS3 did not differ between the patient and control groups. However, SOCS3 promoter methylation level was significantly higher for patients having locally advanced PCa compared to those having localizedPCa (p<0.05).

Conclusion

Our results indicated that SOCS3 could be a useful, noninvasive blood-based epigenetic biomarker for the diagnosis of locally advanced PCa.

A calculator based on prostate imaging reporting and data system version 2 (PI-RADS V2) is a promising prostate cancer predictor

This research is to develop a new tool to improve the performance of predicting prostate cancer (PCa) and reducing unnecessary biopsies. The clinical data of patients who were definitely diagnosed by prostate biopsy were retrospectively analyzed. PCa risks that include age, prostate-specific antigen (PSA), PSA density (PSAD), free-PSA (fPSA), the ratio of fPSA to PSA (%fPSA), prostate volume (PV), digital rectal examination (DRE) and multi-parametric magnetic resonance imaging (MP-MRI) were selected by univariate and multivariate analysis. The satisfactory risks were used to establish predictor (Prostate Biopsy Rating Scale, PBRS). The total score (TS) that was obtained from PBRS was performed to forecast PCa. The areas under the receiver operating characteristic curve (AUC) and the net reclassification index (NRI) were used to compare the predictive ability. A total of 1078 cases were recruited. The mean values of TS in PCa and non-PCa were 15.94 ± 3.26 and 10.49 ± 3.36 points respectively. The AUC of PBRS was higher than PSA, PSAD and MP-MRI (0.87 vs. 0.75, 0.78, 0.80, respectively). PBRS can reduce unnecessary biopsies compared with PSA, PSAD and MP-MRI by up to 63%, 54% and 44%, respectively. In brief, PBRS is a promising predictor of forecasting PCa.

Construction of a set of novel and robust gene expression signatures predicting prostate cancer recurrence

We report here numerous novel genes and multiple new signatures which robustly predict prostate cancer (PC) recurrence. We extracted 696 differentially expressed genes relative to a reported PC signature from the TCGA dataset (n = 492) and built a 15‐gene signature (SigMuc1NW) using Elastic‐net with 10‐fold cross‐validation through analyzing their expressions at 1.5 standard deviation/SD below and 2 SD above a population mean. SigMuc1NW predicts biochemical recurrence (BCR) following surgery with 56.4% sensitivity, 72.6% specificity, and 63.24 median months disease free (MMDF) (P = 1.12e‐12). The prediction accuracy is improved with the use of SigMuc1NW's cutpoint (P = 3e‐15) and is further enhanced (sensitivity 67%, specificity 75.7%, MMDF 45.2, P = 0) when all 15 genes were analyzed through their cutpoints instead of their SDs. These genes individually associate with BCR using either SD or cutpoint as the cutoff points. Eight of 15 genes are individual risk factors after adjusting for age at diagnosis, Gleason score, surgical margin, and tumor stage. Eleven of 15 genes are novel to PC. SigMuc1NW discriminates BCR with time‐dependent AUC (tAUC) values of 76.6% at 11.5 months (76.6%–11.5 m), 73.8%‐22.3 m, 78.5%‐32.1 m, and 76.4%–48.4 m. SigMuc1NW is correlated with adverse features of PC, high Gleason scores (odds ratio/OR 1.48, P < 2e‐16), and advanced tumor stages (OR 1.33, P = 4.37e‐13). SigMuc1NW remains an independent risk factor of BCR (HR 2.44, 95% CI 1.53–3.87, P = 1.62e‐4) after adjusting for age at diagnosis, Gleason score, surgical margin, and tumor stage. In an independent PC (MSKCC) cohort (n = 140), these 15 genes were altered in PC vs normal tissue, metastatic PCs vs primary PCs, and recurrent PCs vs nonrecurrent PCs. Importantly, a 10‐gene subsignature SigMuc1NW1 predicts BCR in MSKCC (P = 3.11e‐15) and TCGA (P = 3.13e‐12); SigMuc1NW1 discriminates BCR at 18.4 m with tAUC as 82.5%. Collectively, our analyses support SigMuc1NW as a novel and robust signature in predicting BCR of PC.

Upregulation of CXCR7 Is Associated with Poor Prognosis of Prostate Cancer

Background

Prostate cancer (PCa) is a prevalent cancer in males. CXCR7 exhibits oncogenic actions in various cancers. The aim of our study was to explore the clinical significance of CXCR7 in patients with PCa.

Material/Methods

QRT-PCR was used to detect the expression level of CXCR7 in PCa tissues. The relationship between CXCR7 expression and clinicopathologic parameters was evaluated by chi-square test. Kaplan-Meier survival curve was used for the survival analysis of patients. Cox regression analyses were performed to assess the potential of CXCR7 as a prognosis biomarker for PCa patients. We performed MTT and Transwell assays to determine the effect of CXCR7 on proliferative and migratory abilities of PCa cells, respectively.

Results

CXCR7 was upregulated in PCa tissues (P<0.05) and was correlated with PSA (P=0.023), differentiation (P=0.022), and lymph node metastasis (P=0.018). The results of MTT and Transwell assays demonstrated that inhibition of CXCR7 suppressed PCa cells growth and migration. Additionally, high CXCR7 level predicted poor overall survival (log rank test, P=0.019). CXCR7 was a valuable prognostic biomarker for PCa patients (HR=2.271, 95%CI=1.093–4.719, P=0.028).

Conclusions

CXCR7 is an oncogene in PCa that can promote aggressive progression of PCa through enhancing proliferation and migration of the tumor cells. CXCR7 is an independent biomarker for the prognosis of PCa.

Germline variants in IL4, MGMT and AKT1 are associated with prostate cancer-specific mortality: An analysis of 12,082 prostate cancer cases

Background

Prostate cancer (PCa) is a leading cause of mortality and genetic factors can influence tumour aggressiveness. Several germline variants have been associated with PCa-specific mortality (PCSM), but further replication evidence is needed.

Methods

Twenty-two previously identified PCSM-associated genetic variants were genotyped in seven PCa cohorts (12,082 patients; 1544 PCa deaths). For each cohort, Cox proportional hazards models were used to calculate hazard ratios and 95% confidence intervals for risk of PCSM associated with each variant. Data were then combined using a meta-analysis approach.

Results

Fifteen SNPs were associated with PCSM in at least one of the seven cohorts. In the meta-analysis, after adjustment for clinicopathological factors, variants in the MGMT (rs2308327; HR 0.90; p-value = 3.5 × 10⁻²) and IL4 (rs2070874; HR 1.22; p-value = 1.1 × 10⁻³) genes were confirmed to be associated with risk of PCSM. In analyses limited to men diagnosed with local or regional stage disease, a variant in AKT1, rs2494750, was also confirmed to be associated with PCSM risk (HR 0.81; p-value = 3.6 × 10⁻²).

Conclusions

This meta-analysis confirms the association of three genetic variants with risk of PCSM, providing further evidence that genetic background plays a role in PCa-specific survival. While these variants alone are not sufficient as prognostic biomarkers, these results may provide insights into the biological pathways modulating tumour aggressiveness.

Overexpression of MUC1 and Genomic Alterations in Its Network Associate with Prostate Cancer Progression

We investigate the association of MUC1 with castration-resistant prostate cancer (CRPC), bone metastasis, and PC recurrence. MUC1 expression was studied in patient-derived bone metastasis and CRPCs produced by prostate-specific PTEN^−/− mice and LNCaP xenografts. Elevations in MUC1 expression occur in CRPC. Among nine patients with hormone-naïve bone metastasis, eight express MUC1 in 61% to 100% of PC cells. Utilizing cBioPortal PC genomic data, we organized a training (n = 300), testing (n = 185), and validation (n = 194) cohort. Using the Cox model, a nine-gene signature was derived, including eight genes from a MUC1-related network (APC, CTNNB1/β-catenin, GALNT10, GRB2, LYN, SIGLEC1, SOS1, and ZAP70) and FAM84B. Genomic alterations in these genes reduce disease-free survival (DFS) in the training (P = .00161), testing (P = .00699), entire (training + testing, P = 5.557e-5), and a validation cohort (P = 3.326e-5). The signature independently predicts PC recurrence [hazard ratio (HR) = 1.731; 95% confidence interval (CI): 1.104-2.712; P = .0167] after adjusting for known clinical factors and stratifies patients with high risk of PC recurrence using the median (HR 2.072; 95% CI: 1.245-3.450, P = .0051) and quartile 3 (HR 3.707, 95% CI: 1.949-7.052, P = 6.51e-5) scores. Several novel β-catenin mutants are identified in PCs leading to a rapid onset of death and recurrence. Genomic alterations in APC and CTNNB1/β-catenin reduce DFS in two independent PC cohorts (n = 485, P = .0369; n = 84, P = .0437). The nine-gene signature also associates with reductions in overall survival (P = .0458) and DFS (P = .0163) in melanoma patients (n = 367). MUC1 upregulation is associated with CRPC and bone metastasis. A nine-gene signature derived from a MUC1 network predicts PC recurrence.

Zinc as an Imaging Biomarker of Prostate Cancer

Zinc has long been the focus of many biological investigations because of its essential role in biology including a catalytic role in many enzymes, a structural role in the many zinc finger proteins, and a physiological role in many secretory cell processes. Divalent zinc is known to be highly abundant in healthy prostate tissues and lower in prostate cancer (PCa). Given the need for newer diagnostic methods for detection of prostate cancer, zinc-responsive probes of various types have been considered as imaging tools for detecting tissue levels of zinc. Among them, recent zinc-responsive MRI probes show great promise for non-invasive detection of zinc ion secretion from the prostate and other tissues in vivo. In this review, we summarize the need for new diagnostic tools and demonstrate how responsive zinc probes and MRI could satisfy this unmet need.

The context of prostate cancer genomics in personalized medicine

Prostate cancer is one of the most common types of cancer in males. Heterogeneous genomic aberrations may lead to prostate cancer onset, progression and metastasis. This heterogeneity also contributes to the variety in cancer risk and outcomes, different drug responses and progression, observed between individual patients. Classical prognostic factors, including prostate-specific antigen, Gleason Score and clinical tumor staging, are not sufficient to portray the complexity of a clinically relevant cancer diagnosis, risk prognosis, treatment choice and therapy monitoring. There is a requirement for novel genetic biomarkers in order to understand the oncogenic heterogeneity in a patient-personalized clinical setting and to improve the efficacy of risk prognosis and treatment choice. A number of biomarkers and gene panels have been established from patient sample cohort studies. These previous studies have provided distinct information to the investigation of heterogeneous malignancy in prostate cancer, which aids in clinical decision-making. Biomarker-guided therapies may facilitate the effective selection of drugs during early treatment; therefore, are beneficial to the individual patient. A non-invasive approach allows for convenient and repeated sampling to screen for cancer and monitor treatment response without the requirement for invasive tissue biopsies. With the current availability of numerous advanced technologies, reliable detection of the minimal tumor residues present following treatment may become clinical practice and, therefore, inform further in the field of personalized medicine.

Asporin is a stromally expressed marker associated with prostate cancer progression

BACKGROUND

Prostate cancer shows considerable heterogeneity in disease progression and we propose that markers expressed in tumour stroma may be reliable predictors of aggressive tumour subtypes.

METHODS

We have used Kaplan-Meier, univariate and multivariate analysis to correlate the expression of Asporin (ASPN) mRNA and protein with prostate cancer progression in independent cohorts. We used immunohistochemistry and H scoring to document stromal localisation of ASPN in a tissue microarray and mouse prostate cancer model, and correlated expression with reactive stroma, defined using Masson Trichrome staining. We used cell cultures of primary prostate cancer fibroblasts treated with serum-free conditioned media from prostate cancer cell lines to examine regulation of ASPN mRNA in tumour stromal cells.

RESULTS

We observed increased expression of ASPN mRNA in a data set derived from benign vs tumour microdissected tissue, and a correlation with biochemical recurrence using Kaplan-Meier and Cox proportional hazard analysis. ASPN protein localised to tumour stroma and elevated expression of ASPN was correlated with decreased time to biochemical recurrence, in a cohort of 326 patients with a median follow up of 9.6 years. Univariate and multivariate analysis demonstrated that ASPN was correlated with progression, as were Gleason score, and clinical stage. Additionally, ASPN expression correlated with the presence of reactive stroma, suggesting that it may be a stromal marker expressed in response to the presence of tumour cells and particularly with aggressive tumour subtypes. We observed expression of ASPN in the stroma of tumours induced by p53 inhibition in a mouse model of prostate cancer, and correlation with neuroendocrine marker expression. Finally, we demonstrated that ASPN transcript expression in normal and cancer fibroblasts was regulated by conditioned media derived from the PC3, but not LNCaP, prostate cancer cell lines.

CONCLUSIONS

Our results suggest that ASPN is a stromally expressed biomarker that correlates with disease progression, and is observed in reactive stroma. ASPN expression in stroma may be part of a stromal response to aggressive tumour subtypes.