Incorporation of tissue-based genomic biomarkers into localized prostate cancer clinics

Prostate cancer biomarkers: from early diagnosis to precision treatment

Prostate cancer (PCa) is the second most prevalent cancer in men. In 2020, approximately 1,414,259 new cases were reported that accounted for 3,75,324 deaths (Sung et al. in CA 71:209-249, 2021). PCa is often asymptomatic at early stages; hence, routine screening and monitoring based on reliable biomarkers is crucial for early detection and assessment of cancer progression. Early diagnosis of disease is key step in reducing PCa-induced mortality. Biomarkers such as PSA have played vital role in reducing recent PCa deaths. Recent research has identified many other biomarkers and also refined PSA-based tests for non-invasive diagnosis of PCa in patients. Despite progress in screening methods, an important issue that influences treatment is heterogeneity of the cancer in different individuals, necessitating personalized treatment. Currently, focus is to identify biomarkers that can accurately diagnose PCa at early stage, indicate the stage of the disease, metastatic nature and chances of survival based on individual patient profile (Fig. 1). Fig. 1 Graphical abstract.

Lymphatic Dissemination in Prostate Cancer: Features of the Transcriptomic Profile and Prognostic Models

Radical prostatectomy is the gold standard treatment for prostate cancer (PCa); however, it does not always completely cure PCa, and patients often experience a recurrence of the disease. In addition, the clinical and pathological parameters used to assess the prognosis and choose further tactics for treating a patient are insufficiently informative and need to be supplemented with new markers. In this study, we performed RNA-Seq of PCa tissue samples, aimed at identifying potential prognostic markers at the level of gene expression and miRNAs associated with one of the key signs of cancer aggressiveness-lymphatic dissemination. The relative expression of candidate markers was validated by quantitative PCR, including an independent sample of patients based on archival material. Statistically significant results, derived from an independent set of samples, were confirmed for miR-148a-3p and miR-615-3p, as well as for the CST2, OCLN, and PCAT4 genes. Considering the obtained validation data, we also analyzed the predictive value of models based on various combinations of identified markers using algorithms based on machine learning. The highest predictive potential was shown for the "CST2 + OCLN + pT" model (AUC = 0.863) based on the CatBoost Classifier algorithm.

ALDH3A2, ODF2, QSOX2, and MicroRNA-503-5p Expression to Forecast Recurrence in TMPRSS2-ERG-Positive Prostate Cancer

Following radical surgery, patients may suffer a relapse. It is important to identify such patients so that therapy tactics can be modified appropriately. Existing stratification schemes do not display the probability of recurrence with enough precision since locally advanced prostate cancer (PCa) is classified as high-risk but is not ranked in greater detail. Between 40 and 50% of PCa cases belong to the TMPRSS2-ERG subtype that is a sufficiently homogeneous group for high-precision prognostic marker search to be possible. This study includes two independent cohorts and is based on high throughput sequencing and qPCR data. As a result, we have been able to suggest a perspective-trained model involving a deep neural network based on both qPCR data for mRNA and miRNA and clinicopathological criteria that can be used for recurrence risk forecasts in patients with TMPRSS2-ERG-positive, locally advanced PCa (the model uses ALDH3A2 + ODF2 + QSOX2 + hsa-miR-503-5p + ISUP + pT, with an AUC = 0.944). In addition to the prognostic model’s use of identified differentially expressed genes and miRNAs, miRNA–target pairs were found that correlate with the prognosis and can be presented as an interactome network.

Molecular biomarkers in the context of focal therapy for prostate cancer: recommendations of a Delphi Consensus from the Focal Therapy Society

BACKGROUND

Focal therapy (FT) for prostate cancer (PCa) is promising. However, long-term oncological results are awaited and there is no consensus on follow-up strategies. Molecular biomarkers (MB) may be useful in selecting, treating and following up men undergoing FT, though there is limited evidence in this field to guide practice. We aimed to conduct a consensus meeting, endorsed by the Focal Therapy Society, amongst a large group of experts, to understand the potential utility of MB in FT for localized PCa.

METHODS

A 38-item questionnaire was built following a literature search. The authors then performed three rounds of a Delphi Consensus using DelphiManager, using the GRADE grid scoring system, followed by a face-to-face expert meeting. Three areas of interest were identified and covered concerning MB for FT, 1) the current/present role; 2) the potential/future role; 3) the recommended features for future studies. Consensus was defined using a 70% agreement threshold.

RESULTS

Of 95 invited experts, 42 (44.2%) completed the three Delphi rounds. Twenty-four items reached a consensus and they were then approved at the meeting involving (N.=15) experts. Fourteen items reached a consensus on uncertainty, or they did not reach a consensus. They were re-discussed, resulting in a consensus (N.=3), a consensus on a partial agreement (N.=1), and a consensus on uncertainty (N.=10). A final list of statements were derived from the approved and discussed items, with the addition of three generated statements, to provide guidance regarding MB in the context of FT for localized PCa. Research efforts in this field should be considered a priority.

CONCLUSIONS

The present study detailed an initial consensus on the use of MB in FT for PCa. This is until evidence becomes available on the subject.

Protein signatures to distinguish aggressive from indolent prostate cancer

BACKGROUND

Distinguishing men with aggressive from indolent prostate cancer is critical to decisions in the management of clinically localized prostate cancer. Molecular signatures of aggressive disease could help men overcome this major clinical challenge by reducing unnecessary treatment and allowing more appropriate treatment of aggressive disease.

METHODS

We performed a mass spectrometry-based proteomic analysis of normal and malignant prostate tissues from 22 men who underwent surgery for prostate cancer. Prostate cancer samples included Grade Groups (3-5), with 8 patients experiencing recurrence and 14 without evidence of recurrence with a mean of 6.8 years of follow-up. To better understand the biological pathways underlying prostate cancer aggressiveness, we performed a systems biology analysis and gene enrichment analysis. Proteins that distinguished recurrent from nonrecurrent cancer were chosen for validation by immunohistochemical analysis on tissue microarrays containing samples from a larger cohort of patients with recurrent and nonrecurrent prostate cancer.

RESULTS

In all, 24,037 unique peptides (false discovery rate < 1%) corresponding to 3,313 distinct proteins were identified with absolute abundance ranges spanning seven orders of magnitude. Of these proteins, 115 showed significantly (p < 0.01) different levels in tissues from recurrent versus nonrecurrent cancers. Analysis of all differentially expressed proteins in recurrent and nonrecurrent cases identified several protein networks, most prominently one in which approximately 24% of the proteins in the network were regulated by the YY1 transcription factor (adjusted p < 0.001). Strong immunohistochemical staining levels of three differentially expressed proteins, POSTN, CALR, and CTSD, on a tissue microarray validated their association with shorter patient survival.

CONCLUSIONS

The protein signatures identified could improve understanding of the molecular drivers of aggressive prostate cancer and be used as candidate prognostic biomarkers.

Upregulation of miR-886 indicates poor prognosis and promotes tumour progression of prostate cancer

Prostate cancer is a heterogeneous disease with high incidence and mortality. The functional role of miR-886 has been reported in various cancers and its dysregulation in prostate cancer was also found. Whether miR-886 was involved in the development of prostate cancer remains unclear, which was explored. miR-886 was evaluated in prostate cancer by RT-qPCR, and its clinical value was also assessed. Additionally, the role of miR-886 in prostate cancer cells was assessed by MTT and transwell assay. miR-886 was upregulated and was associated with the Gleason score and TNM stage of prostate cancer patients. miR-886 could predict the poor survival of patients. Moreover, miR-886 was a tumour promoter, of which the upregulation significantly promoted major cellular processes of prostate cancer. miR-886 mediated the disease development and predicted the clinical outcomes of patients. The knockdown of miR-886 inhibits cellular processes of prostate cancer, which provides a novel therapeutic target.

Integration of Urinary EN2 Protein & Cell-Free RNA Data in the Development of a Multivariable Risk Model for the Detection of Prostate Cancer Prior to Biopsy

Simple Summary

Prostate cancer is a disease responsible for a large proportion of all male cancer deaths but there is a high chance that a patient will die with the disease rather than from. Therefore, there is a desperate need for improvements in diagnosing and predicting outcomes for prostate cancer patients to minimise overdiagnosis and overtreatment whilst appropriately treating men with aggressive disease, especially if this can be done without taking an invasive biopsy. In this work we develop a test that predicts whether a patient has prostate cancer and how aggressive the disease is from a urine sample. This model combines the measurement of a protein-marker called EN2 and the levels of 10 genes measured in urine and proves that integration of information from multiple, non-invasive biomarker sources has the potential to greatly improve how patients with a clinical suspicion of prostate cancer are risk-assessed prior to an invasive biopsy.

Abstract

The objective is to develop a multivariable risk model for the non-invasive detection of prostate cancer prior to biopsy by integrating information from clinically available parameters, Engrailed-2 (EN2) whole-urine protein levels and data from urinary cell-free RNA. Post-digital-rectal examination urine samples collected as part of the Movember Global Action Plan 1 study which has been analysed for both cell-free-RNA and EN2 protein levels were chosen to be integrated with clinical parameters (n = 207). A previously described robust feature selection framework incorporating bootstrap resampling and permutation was applied to the data to generate an optimal feature set for use in Random Forest models for prediction. The fully integrated model was named ExoGrail, and the out-of-bag predictions were used to evaluate the diagnostic potential of the risk model. ExoGrail risk (range 0–1) was able to determine the outcome of an initial trans-rectal ultrasound guided (TRUS) biopsy more accurately than clinical standards of care, predicting the presence of any cancer with an area under the receiver operator curve (AUC) = 0.89 (95% confidence interval(CI): 0.85–0.94), and discriminating more aggressive Gleason ≥ 3 + 4 disease returning an AUC = 0.84 (95% CI: 0.78–0.89). The likelihood of more aggressive disease being detected significantly increased as ExoGrail risk score increased (Odds Ratio (OR) = 2.21 per 0.1 ExoGrail increase, 95% CI: 1.91–2.59). Decision curve analysis of the net benefit of ExoGrail showed the potential to reduce the numbers of unnecessary biopsies by 35% when compared to current standards of care. Integration of information from multiple, non-invasive biomarker sources has the potential to greatly improve how patients with a clinical suspicion of prostate cancer are risk-assessed prior to an invasive biopsy.

Clinical proteomics for prostate cancer: understanding prostate cancer pathology and protein biomarkers for improved disease management

Following the introduction of routine Prostate Specific Antigen (PSA) screening in the early 1990′s, Prostate Cancer (PCa) is often detected at an early stage. There are also a growing number of treatment options available and so the associated mortality rate is generally low. However, PCa is an extremely complex and heterogenous disease and many patients suffer disease recurrence following initial therapy. Disease recurrence commonly results in metastasis and metastatic PCa has an average survival rate of just 3–5 years. A significant problem in the clinical management of PCa is being able to differentiate between patients who will respond to standard therapies and those who may benefit from more aggressive intervention at an earlier stage. It is also acknowledged that for many men the disease is not life threatenting. Hence, there is a growing desire to identify patients who can be spared the significant side effects associated with PCa treatment until such time (if ever) their disease progresses to the point where treatment is required. To these important clinical needs, current biomarkers and clinical methods for patient stratification and personlised treatment are insufficient. This review provides a comprehensive overview of the complexities of PCa pathology and disease management. In this context it is possible to review current biomarkers and proteomic technologies that will support development of biomarker-driven decision tools to meet current important clinical needs. With such an in-depth understanding of disease pathology, the development of novel clinical biomarkers can proceed in an efficient and effective manner, such that they have a better chance of improving patient outcomes.

miRNAs expression signature potentially associated with lymphatic dissemination in locally advanced prostate cancer

Background

Prostate cancer is one of the most common and socially significant cancers among men. The aim of our study was to reveal changes in miRNA expression profiles associated with lymphatic dissemination in prostate cancer and to identify the most prominent miRNAs as potential prognostic markers for future studies.

Methods

High-throughput miRNA sequencing was performed for 44 prostate cancer specimens taken from Russian patients, with and without lymphatic dissemination (N1 – 20 samples; N0 – 24 samples).

Results

We found at least 18 microRNAs with differential expression between N0 and N1 sample groups: miR-182-5p, miR-183-5p, miR-96-5p, miR-25-3p, miR-93-5p, miR-7-5p, miR-615-3p, miR-10b, miR-1248 (N1-miRs; elevated expression in N1 cohort; p < 0.05); miR-1271-5p, miR-184, miR-222-3p, miR-221-5p, miR-221-3p, miR-455-3p, miR-143-5p, miR-181c-3p and miR-455-5p (N0-miRs; elevated expression in N0; p < 0.05). The expression levels of N1-miRs were highly correlated between each other (the same is applied for N0-miRs) and the expression levels of N0-miRs and N1-miRs were anti-correlated. The tumor samples can be divided into two groups depending on the expression ratio between N0-miRs and N1-miRs.

Conclusions

We found the miRNA expression signature associated with lymphatic dissemination, in particular on the Russian patient cohort. Many of these miRNAs are well-known players in either oncogenic transformation or tumor suppression. Further experimental studies with extended sampling are required to validate these results.

Active monitoring, radical prostatectomy and radical radiotherapy in PSA-detected clinically localised prostate cancer: the ProtecT three-arm RCT

BACKGROUND

Prostate cancer is the most common cancer among men in the UK. Prostate-specific antigen testing followed by biopsy leads to overdetection, overtreatment as well as undertreatment of the disease. Evidence of treatment effectiveness has lacked because of the paucity of randomised controlled trials comparing conventional treatments.

OBJECTIVES

To evaluate the effectiveness of conventional treatments for localised prostate cancer (active monitoring, radical prostatectomy and radical radiotherapy) in men aged 50-69 years.

DESIGN

A prospective, multicentre prostate-specific antigen testing programme followed by a randomised trial of treatment, with a comprehensive cohort follow-up.

SETTING

Prostate-specific antigen testing in primary care and treatment in nine urology departments in the UK.

PARTICIPANTS

Between 2001 and 2009, 228,966 men aged 50-69 years received an invitation to attend an appointment for information about the Prostate testing for cancer and Treatment (ProtecT) study and a prostate-specific antigen test; 82,429 men were tested, 2664 were diagnosed with localised prostate cancer, 1643 agreed to randomisation to active monitoring (n = 545), radical prostatectomy (n = 553) or radical radiotherapy (n = 545) and 997 chose a treatment.

INTERVENTIONS

The interventions were active monitoring, radical prostatectomy and radical radiotherapy.

TRIAL PRIMARY OUTCOME MEASURE

Definite or probable disease-specific mortality at the 10-year median follow-up in randomised participants.

SECONDARY OUTCOME MEASURES

Overall mortality, metastases, disease progression, treatment complications, resource utilisation and patient-reported outcomes.

RESULTS

There were no statistically significant differences between the groups for 17 prostate cancer-specific (p = 0.48) and 169 all-cause (p = 0.87) deaths. Eight men died of prostate cancer in the active monitoring group (1.5 per 1000 person-years, 95% confidence interval 0.7 to 3.0); five died of prostate cancer in the radical prostatectomy group (0.9 per 1000 person-years, 95% confidence interval 0.4 to 2.2 per 1000 person years) and four died of prostate cancer in the radical radiotherapy group (0.7 per 1000 person-years, 95% confidence interval 0.3 to 2.0 per 1000 person years). More men developed metastases in the active monitoring group than in the radical prostatectomy and radical radiotherapy groups: active monitoring, n = 33 (6.3 per 1000 person-years, 95% confidence interval 4.5 to 8.8); radical prostatectomy, n = 13 (2.4 per 1000 person-years, 95% confidence interval 1.4 to 4.2 per 1000 person years); and radical radiotherapy, n = 16 (3.0 per 1000 person-years, 95% confidence interval 1.9 to 4.9 per 1000 person-years; p = 0.004). There were higher rates of disease progression in the active monitoring group than in the radical prostatectomy and radical radiotherapy groups: active monitoring (n = 112; 22.9 per 1000 person-years, 95% confidence interval 19.0 to 27.5 per 1000 person years); radical prostatectomy (n = 46; 8.9 per 1000 person-years, 95% confidence interval 6.7 to 11.9 per 1000 person-years); and radical radiotherapy (n = 46; 9.0 per 1000 person-years, 95% confidence interval 6.7 to 12.0 per 1000 person years; p < 0.001). Radical prostatectomy had the greatest impact on sexual function/urinary continence and remained worse than radical radiotherapy and active monitoring. Radical radiotherapy's impact on sexual function was greatest at 6 months, but recovered somewhat in the majority of participants. Sexual and urinary function gradually declined in the active monitoring group. Bowel function was worse with radical radiotherapy at 6 months, but it recovered with the exception of bloody stools. Urinary voiding and nocturia worsened in the radical radiotherapy group at 6 months but recovered. Condition-specific quality-of-life effects mirrored functional changes. No differences in anxiety/depression or generic or cancer-related quality of life were found. At the National Institute for Health and Care Excellence threshold of £20,000 per quality-adjusted life-year, the probabilities that each arm was the most cost-effective option were 58% (radical radiotherapy), 32% (active monitoring) and 10% (radical prostatectomy).

LIMITATIONS

A single prostate-specific antigen test and transrectal ultrasound biopsies were used. There were very few non-white men in the trial. The majority of men had low- and intermediate-risk disease. Longer follow-up is needed.

CONCLUSIONS

At a median follow-up point of 10 years, prostate cancer-specific mortality was low, irrespective of the assigned treatment. Radical prostatectomy and radical radiotherapy reduced disease progression and metastases, but with side effects. Further work is needed to follow up participants at a median of 15 years.

TRIAL REGISTRATION

Current Controlled Trials ISRCTN20141297.

FUNDING

This project was funded by the National Institute for Health Research Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 24, No. 37. See the National Institute for Health Research Journals Library website for further project information.

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.

Tumor Immune Microenvironment Clusters in Localized Prostate Adenocarcinoma: Prognostic Impact of Macrophage Enriched/Plasma Cell Non-Enriched Subtypes

Background: Prostate cancer (PCa) is characterized by significant heterogeneity in its molecular, genomic, and immunologic characteristics. Methods: Whole transcriptome RNAseq data from The Cancer Genome Atlas of prostate adenocarcinomas (n = 492) was utilized. The immune microenvironment was characterized using the CIBERSORTX tool to identify immune cell type composition. Unsupervised hierarchical clustering was performed based on immune cell type content. Analyses of progression-free survival (PFS), distant metastases, and overall survival (OS) were performed using Kaplan–Meier estimates and Cox regression multivariable analyses. Results: Four immune clusters were identified, largely defined by plasma cell, CD4⁺ Memory Resting T Cells (CD4 MR), and M0 and M2 macrophage content (CD4 MR^HighPlasma Cell^HighM0^LowM2^Mid, CD4 MR^LowPlasma Cell^HighM0^LowM2^Low, CD4 MR^HighPlasma Cell^LowM0^HighM2^Low, and CD4 MR^HighPlasma Cell^LowM0^LowM2^High). The two macrophage-enriched/plasma cell non-enriched clusters (3 and 4) demonstrated worse PFS (HR 2.24, 95% CI 1.46–3.45, p = 0.0002) than the clusters 1 and 2. No metastatic events occurred in the plasma cell enriched, non-macrophage-enriched clusters. Comparing clusters 3 vs. 4, in patients treated by surgery alone, cluster 3 had zero progression events (p < 0.0001). However, cluster 3 patients had worse outcomes after post-operative radiotherapy (p = 0.018). Conclusion: Distinct tumor immune clusters with a macrophage-enriched, plasma cell non-enriched phenotype and reduced plasma cell enrichment independently characterize an aggressive phenotype in localized prostate cancer that may differentially respond to treatment.

Combining molecular and imaging metrics in cancer: radiogenomics

Background

Radiogenomics is the extension of radiomics through the combination of genetic and radiomic data. Because genetic testing remains expensive, invasive, and time-consuming, and thus unavailable for all patients, radiogenomics may play an important role in providing accurate imaging surrogates which are correlated with genetic expression, thereby serving as a substitute for genetic testing.

Main body

In this article, we define the meaning of radiogenomics and the difference between radiomics and radiogenomics. We provide an up-to-date review of the radiomics and radiogenomics literature in oncology, focusing on breast, brain, gynecological, liver, kidney, prostate and lung malignancies. We also discuss the current challenges to radiogenomics analysis.

Conclusion

Radiomics and radiogenomics are promising to increase precision in diagnosis, assessment of prognosis, and prediction of treatment response, providing valuable information for patient care throughout the course of the disease, given that this information is easily obtainable with imaging. Larger prospective studies and standardization will be needed to define relevant imaging biomarkers before they can be implemented into the clinical workflow.

Molecular profiling of radical prostatectomy tissue from patients with no sign of progression identifies ERG as the strongest independent predictor of recurrence

BACKGROUND

As a major cause of morbidity and mortality among men, prostate cancer is a heterogenous disease, with a vast heterogeneity in the biology of the disease and in clinical outcome. While it often runs an indolent course, local progression or metastasis may eventually develop, even among patients considered "low risk" at diagnosis. Therefore, biomarkers that can discriminate aggressive from indolent disease at an early stage would greatly benefit patients. We hypothesized that tissue specimens from early stage prostate cancers may harbor predictive signatures for disease progression.

METHODS

We used a cohort of radical prostatectomy patients with longitudinal follow-up, who had tumors with low grade and stage that revealed no signs of future disease progression at surgery. During the follow-up period, some patients either remained indolent (non-BCR) or progressed to biochemical recurrence (BCR). Total RNA was extracted from tumor, and adjacent normal epithelium of formalin-fixed-paraffin-embedded (FFPE) specimens. Differential gene expression in tumors, and in tumor versus normal tissues between BCR and non-BCR patients were analyzed by NanoString using a customized CodeSet of 151 probes.

RESULTS

After controlling for false discovery rates, we identified a panel of eight genes (ERG, GGT1, HDAC1, KLK2, MYO6, PLA2G7, BICD1 and CACNAID) that distinguished BCR from non-BCR patients. We found a clear association of ERG expression with non-BCR, which was further corroborated by quantitative RT-PCR and immunohistochemistry assays.

CONCLUSIONS

Our results identified ERG as the strongest predictor for BCR and showed that potential prognostic prostate cancer biomarkers can be identified from FFPE tumor specimens.

Extracapsular extension on MRI indicates a more aggressive cell cycle progression genotype of prostate cancer

PURPOSE

To explore associations between magnetic resonance imaging (MRI) features of prostate cancer and expression levels of cell cycle genes, as assessed by the Prolaris^® test.

MATERIALS AND METHODS

Retrospective analysis of 118 PCa patients with genetic testing of biopsy specimen and prostate MRI from 08/2013 to 11/2015. Associations between the cell cycle risk (CCR) score and MRI features [i.e., PI-RADSv2 score, extracapsular extension (ECE), quantitative metrics] were analyzed with Fisher's exact test, nonparametric tests, and Spearman's correlation coefficient. In 41 patients (34.7%), test results were compared to unfavorable features on prostatectomy specimen (i.e., Gleason group ≥ 3, ECE, lymph node metastases).

RESULTS

Fifty-four (45.8%), 60 (50.8%), and 4 (3.4%) patients had low-, intermediate-, and high-risk cancers according to American Urological Association scoring system. Patients with ECE on MRI had significantly higher mean CCR scores (reader 1: 3.9 vs. 3.2, p = 0.015; reader 2: 3.6 vs. 3.2, p = 0.045). PI-RADSv2 scores and quantitative MRI features were not associated with CCR scores. In the prostatectomy subset, ECE on MRI (p = < 0.001-0.001) and CCR scores (p = 0.049) were significantly associated with unfavorable histopathologic features.

CONCLUSION

The phenotypic trait of ECE on MRI indicates a more aggressive genotype of prostate cancer.

A TMEFF2-regulated cell cycle derived gene signature is prognostic of recurrence risk in prostate cancer

Background

The clinical behavior of prostate cancer (PCa) is variable, and while the majority of cases remain indolent, 10% of patients progress to deadly forms of the disease. Current clinical predictors used at the time of diagnosis have limitations to accurately establish progression risk. Here we describe the development of a tumor suppressor regulated, cell-cycle gene expression based prognostic signature for PCa, and validate its independent contribution to risk stratification in several radical prostatectomy (RP) patient cohorts.

Methods

We used RNA interference experiments in PCa cell lines to identify a gene expression based gene signature associated with Tmeff2, an androgen regulated, tumor suppressor gene whose expression shows remarkable heterogeneity in PCa. Gene expression was confirmed by qRT-PCR. Correlation of the signature with disease outcome (time to recurrence) was retrospectively evaluated in four geographically different cohorts of patients that underwent RP (834 samples), using multivariate logistical regression analysis. Multivariate analyses were adjusted for standard clinicopathological variables. Performance of the signature was compared to previously described gene expression based signatures using the SigCheck software.

Results

Low levels of TMEFF2 mRNA significantly (p < 0.0001) correlated with reduced disease-free survival (DFS) in patients from the Memorial Sloan Kettering Cancer Center (MSKCC) dataset. We identified a panel of 11 TMEFF2 regulated cell cycle related genes (TMCC11), with strong prognostic value. TMCC11 expression was significantly associated with time to recurrence after prostatectomy in four geographically different patient cohorts (2.9 ≤ HR ≥ 4.1; p ≤ 0.002), served as an independent indicator of poor prognosis in the four RP cohorts (1.96 ≤ HR ≥ 4.28; p ≤ 0.032) and improved the prognostic value of standard clinicopathological markers. The prognostic ability of TMCC11 panel exceeded previously published oncogenic gene signatures (p = 0.00017).

Conclusions

This study provides evidence that the TMCC11 gene signature is a robust independent prognostic marker for PCa, reveals the value of using highly heterogeneously expressed genes, like Tmeff2, as guides to discover prognostic indicators, and suggests the possibility that low Tmeff2 expression marks a distinct subclass of PCa.

Electronic supplementary material

The online version of this article (10.1186/s12885-019-5592-6) contains supplementary material, which is available to authorized users.

N-Linked Glycosylation and Near-Infrared Spectroscopy in the Diagnosis of Prostate Cancer

Background: Performing a prostate biopsy is the most robust and reliable way to diagnose prostate cancer (PCa), and to determine the disease grading. As little to no biochemical markers for prostate tissue exist, we explored the possibilities of tissue N-glycosylation and near-infrared spectroscopy (NIR) in PCa diagnosis. Methods: Tissue specimens from 100 patients (benign prostate hyperplasia (BPH), n = 50; and PCa, n = 50) were obtained. The fresh-frozen tissue was dispersed and a tissue N-glycosylation profile was determined. Consequently, the formalin-fixed paraffin-embedded slides were analyzed using NIR spectroscopy. A comparison was made between the benign and malignant tissue, and between the various Gleason scores. Results: A difference was observed for the tissue of N-glycosylation between the benign and malignant tissue. These differences were located in the fycosylation ratios and the total amount of bi- and tetra-antennary structures (all p < 0.0001). These differences were also present between various Gleason scores. In addition, the NIR spectra revealed changes between the benign and malignant tissue in several regions. Moreover, spectral ranges of 1055–1065 nm and 1450–1460 nm were significantly different between the Gleason scores (p = 0.0042 and p = 0.0195). Conclusions: We have demonstrated biochemical changes in the N-glycan profile of prostate tissue, which allows for the distinction between malignant and benign tissue, as well as between various Gleason scores. These changes can be correlated to the changes observed in the NIR spectra. This could possibly further improve the histological assessment of PCa diagnosis, although further method validation is needed.

Correlation between MRI phenotypes and a genomic classifier of prostate cancer: preliminary findings

OBJECTIVES

We sought to evaluate the correlation between MRI phenotypes of prostate cancer as defined by PI-RADS v2 and the Decipher Genomic Classifier (used to estimate the risk of early metastases).

METHODS

This single-center, retrospective study included 72 nonconsecutive men with prostate cancer who underwent MRI before radical prostatectomy performed between April 2014 and August 2017 and whose MRI registered lesions were microdissected from radical prostatectomy specimens and then profiled using Decipher (89 lesions; 23 MRI invisible [PI-RADS v2 scores ≤ 2] and 66 MRI visible [PI-RADS v2 scores ≥ 3]). Linear regression analysis was used to assess clinicopathologic and MRI predictors of Decipher results; correlation coefficients (r) were used to quantify these associations. AUC was used to determine whether PI-RADS v2 could accurately distinguish between low-risk (Decipher score < 0.45) and intermediate-/high-risk (Decipher score ≥ 0.45) lesions.

RESULTS

MRI-visible lesions had higher Decipher scores than MRI-invisible lesions (mean difference 0.22; 95% CI 0.13, 0.32; p < 0.0001); most MRI-invisible lesions (82.6%) were low risk. PI-RADS v2 had moderate correlation with Decipher (r = 0.54) and had higher accuracy (AUC 0.863) than prostate cancer grade groups (AUC 0.780) in peripheral zone lesions (95% CI for difference 0.01, 0.15; p = 0.018).

CONCLUSIONS

MRI phenotypes of prostate cancer are positively correlated with Decipher risk groups. Although PI-RADS v2 can accurately distinguish between lesions classified by Decipher as low or intermediate/high risk, some lesions classified as intermediate/high risk by Decipher are invisible on MRI.

KEY POINTS

• MRI phenotypes of prostate cancer as defined by PI-RADS v2 positively correlated with a genomic classifier that estimates the risk of early metastases. • Most but not all MRI-invisible lesions had a low risk for early metastases according to the genomic classifier. • MRI could be used in conjunction with genomic assays to identify lesions that may carry biological potential for early metastases.

Updates on Grading and Staging of Prostate Cancer

Since its development between 1966 and 1977, the Gleason grading system has remained one of the most important prognostic indicators in prostatic acinar adenocarcinoma. The grading system was first majorly revised in 2005 and again in 2014. With the publication of the 8th edition of the American Joint Committee on Cancer TNM staging manual in 2018, the classification of prostate cancer and its reporting have further evolved and are now included as part of staging criteria. This article reflects the aspects that are most influential on daily practice. A brief summary of 3 ancillary commercially available genomic tests is also provided.

Diffusion weighted MRI as an early predictor of tumor response to hypofractionated stereotactic boost for prostate cancer

We evaluated the feasibility of using the kinetic of diffusion-weighted MRI (DWI) and the normalized apparent coefficient diffusion (ADC) map value as an early biomarker in patients treated by external beam radiotherapy (EBRT). Twelve patients were included within the frame of a multicenter phase II trial and treated for intermediate risk prostate cancer (PCa). Multiparametric MRI was performed before treatment (M0) and every 6 months until M24. Association between nADC and PSA or PSA kinetic was evaluated using the test of nullity of the Spearman correlation coefficient. The median rates of PSA at the time of diagnosis, two years and four years after EBRT were 9.29 ng/ml (range from 5.26 to 17.67), 0.68 ng/ml (0.07–2.7), 0.47 ng/ml (0.09–1.39), respectively. Median nADC increased from 1.14 × 10⁻³ mm²/s to 1.59 × 10⁻³ mm²/s between M0 and M24. Only one patient presented a decrease of nADC (1.35 × 10⁻³ mm²/s and 1.11 × 10⁻³ mm²/s at M0 and M12 respectively). The increase in nADC at M6 was correlated with PSA decrease at M18, M24 and M30 (p < 0.05). The increase in nADc at M12 was correlated with PSA decrease at M36 (p = 0.019). Early nADC variation were correlated with late PSA decrease for patients with PCa treated by EBRT.

A five-CpG DNA methylation score to predict metastatic-lethal outcomes in men treated with radical prostatectomy for localized prostate cancer

BACKGROUND

Prognostic biomarkers for localized prostate cancer (PCa) could improve personalized medicine. Our group previously identified a panel of differentially methylated CpGs in primary tumor tissue that predict disease aggressiveness, and here we further validate these biomarkers.

METHODS

Pyrosequencing was used to assess CpG methylation of eight biomarkers previously identified using the HumanMethylation450 array; CpGs with strongly correlated (r >0.70) results were considered technically validated. Logistic regression incorporating the validated CpGs and Gleason sum was used to define and lock a final model to stratify men with metastatic-lethal versus non-recurrent PCa in a training dataset. Coefficients from the final model were then used to construct a DNA methylation score, which was evaluated by logistic regression and Receiver Operating Characteristic (ROC) curve analyses in an independent testing dataset.

RESULTS

Five CpGs were technically validated and all were retained (P < 0.05) in the final model. The 5-CpG and Gleason sum coefficients were used to calculate a methylation score, which was higher in men with metastatic-lethal progression (P = 6.8 × 10-6 ) in the testing dataset. For each unit increase in the score there was a four-fold increase in risk of metastatic-lethal events (odds ratio, OR = 4.0, 95%CI = 1.8-14.3). At 95% specificity, sensitivity was 74% for the score compared to 53% for Gleason sum alone. The score demonstrated better prediction performance (AUC = 0.91; pAUC = 0.037) compared to Gleason sum alone (AUC = 0.87; pAUC = 0.025).

CONCLUSIONS

The DNA methylation score improved upon Gleason sum for predicting metastatic-lethal progression and holds promise for risk stratification of men with aggressive tumors. This prognostic score warrants further evaluation as a tool for improving patient outcomes.

Emerging proteomics biomarkers and prostate cancer burden in Africa

Various biomarkers have emerged via high throughput omics-based approaches for use in diagnosis, treatment, and monitoring of prostate cancer. Many of these have yet to be demonstrated as having value in routine clinical practice. Moreover, there is a dearth of information on validation of these emerging prostate biomarkers within African cohorts, despite the huge burden and aggressiveness of prostate cancer in men of African descent. This review focusses of the global landmark achievements in prostate cancer proteomics biomarker discovery and the potential for clinical implementation of these biomarkers in Africa. Biomarker validation processes at the preclinical, translational and clinical research level are discussed here, as are the challenges and prospects for the evaluation and use of novel proteomic prostate cancer biomarkers.

Systematic identification of functionally relevant risk alleles to stratify aggressive versus indolent prostate cancer

Novel approaches for classification, including molecular features, are needed to direct therapy for men with low-grade prostate cancer (PCa), especially men on active surveillance. Risk alleles identified from genome-wide association studies (GWAS) could improve prognostication. Those risk alleles that coincided with genes and somatic copy number aberrations associated with progression of PCa were selected as the most relevant for prognostication.

In a systematic literature review, a total of 698 studies were collated. Fifty-three unique SNPs residing in 29 genomic regions, including 8q24, 10q11 and 19q13, were associated with PCa progression. Functional studies implicated 21 of these single nucleotide polymorphisms (SNPs) as modulating the expression of genes in the androgen receptor pathway and several other oncogenes. In particular, 8q24, encompassing MYC, harbours a high density of SNPs conferring unfavourable pathological characteristics in low-grade PCa, while a copy number gain of MYC in low-grade PCa was associated with prostate-specific antigen recurrence after radical prostatectomy.

By combining GWAS data with gene expression and structural rearrangements, risk alleles were identified that could provide a new basis for developing a prognostication tool to guide therapy for men with early prostate cancer.

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.

Elevated preoperative neutrophil-lymphocyte ratio predicts upgrading at radical prostatectomy

BACKGROUND

Neutrophil-lymphocyte ratio (NLR) is a widely used, representative marker of systemic inflammatory response within the body. NLR can be calculated from simple, inexpensive peripheral blood samples. High NLR is a negative prognostic factor in a variety of malignancies including urological tumors. In this study, we aim to assess the prognostic value of preoperative neutrophil- lymphocyte ratio (NLR) in patients treated with radical prostatectomy (RP) for localized prostate cancer (PCa).

MATERIALS AND METHODS

Records of 7426 patients were retrospectively analyzed from prospectively collected datasets. A cut-off point of 3 was taken for NLR based on ROC analyses and previous literature.

RESULTS

23% (n = 1707) of patients had an NLR of ≥3. Patients with NLR ≥3 were more likely to harbor unfavorable pathological features such as higher biopsy Gleason score (GS), higher RP GS, higher rates of extra capsular extension, nodal involvement (all p < 0.001) and positive surgical margins (p = 0.002). On multivariable analyses, NLR ≥ 3 was associated with higher RP GS (OR 2.32; p < 0.001), seminal vesicle invasion (OR 1.60; p < 0.001) and nodal involvement (OR 1.43; p < 0.001). On multivariable analyses, NLR ≥ 3 was significantly associated with GS upgrading at RP (OR 1.39 p < 0.001). During a median follow up of 45 months, NLR ≥ 3 was associated with higher risk of BCR (p = 0.001). However, on multivariable Cox regression analysis such association was not shown (HR 0.86; p = 0.4).

CONCLUSION

Preoperative NLR ≥ 3 was associated with aggressive PCa, such as upgrading at RP. Even though its effect on clinical-decision making seems to be limited when all clinical and pathological confounders are taken into account, preoperative NLR may still be useful in selected patients to identify aggressive PCa helping patient selection for active surveillance protocols. Conversely, it does not predict BCR when adjusted for the effect of pathological features.

Whom to Treat: Postdiagnostic Risk Assessment with Gleason Score, Risk Models, and Genomic Classifier

Management of prostate cancer presents unique challenges because of the disease's variable natural history. Accurate risk stratification at the time of diagnosis in clinically localized disease is crucial in providing optimal counseling about management options. To accurately distinguish pathologically indolent tumors from aggressive disease, risk groups are no longer sufficient. Rather, multivariable prognostic models reflecting the complete information known at time of diagnosis offer improved accuracy and interpretability. After diagnosis, further testing with genomic assays or other biomarkers improves risk classification. These postdiagnostic risk assessment tools should not supplant shared decision making, but rather facilitate risk classification and enable more individualized care.

Background, current role, and potential applications of radiogenomics

With the genomic revolution in the early 1990s, medical research has been driven to study the basis of human disease on a genomic level and to devise precise cancer therapies tailored to the specific genetic makeup of a tumor. To match novel therapeutic concepts conceived in the era of precision medicine, diagnostic tests must be equally sufficient, multilayered, and complex to identify the relevant genetic alterations that render cancers susceptible to treatment. With significant advances in training and medical imaging techniques, image analysis and the development of high-throughput methods to extract and correlate multiple imaging parameters with genomic data, a new direction in medical research has emerged. This novel approach has been termed radiogenomics. Radiogenomics aims to correlate imaging characteristics (ie, the imaging phenotype) with gene expression patterns, gene mutations, and other genome-related characteristics and is designed to facilitate a deeper understanding of tumor biology and capture the intrinsic tumor heterogeneity. Ultimately, the goal of radiogenomics is to develop imaging biomarkers for outcome that incorporate both phenotypic and genotypic metrics. Due to the noninvasive nature of medical imaging and its ubiquitous use in clinical practice, the field of radiogenomics is rapidly evolving and initial results are encouraging. In this article, we briefly discuss the background and then summarize the current role and the potential of radiogenomics in brain, liver, prostate, gynecological, and breast tumors.

LEVEL OF EVIDENCE

5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017;47:604-620.

Current Management Strategy for Active Surveillance in Prostate Cancer

PURPOSE OF REVIEW

Active surveillance has been increasingly utilized as a strategy for the management of favorable-risk, localized prostate cancer. In this review, we describe contemporary management strategies of active surveillance, with a focus on traditional stratification schemes, new prognostic tools, and patient outcomes.

RECENT FINDINGS

Patient selection, follow-up strategy, and indication for delayed intervention for active surveillance remain centered around PSA, digital rectal exam, and biopsy findings. Novel tools which include imaging, biomarkers, and genetic assays have been investigated as potential prognostic adjuncts; however, their role in active surveillance remains institutionally dependent. Although 30-50% of patients on active surveillance ultimately undergo delayed treatment, the vast majority will remain free of metastasis with a low risk of dying from prostate cancer. The optimal method for patient selection into active surveillance is unknown; however, cancer-specific mortality rates remain excellent. New prognostication tools are promising, and long-term prospective, randomized data regarding their use in active surveillance will be beneficial.

Providing guidance for genomics-based cancer treatment decisions: insights from stakeholder engagement for post-prostatectomy radiation therapy

BACKGROUND

Despite the emergence of genomics-based risk prediction tools in oncology, there is not yet an established framework for communication of test results to cancer patients to support shared decision-making. We report findings from a stakeholder engagement program that aimed to develop a framework for using Markov models with individualized model inputs, including genomics-based estimates of cancer recurrence probability, to generate personalized decision aids for prostate cancer patients faced with radiation therapy treatment decisions after prostatectomy.

METHODS

We engaged a total of 22 stakeholders, including: prostate cancer patients, urological surgeons, radiation oncologists, genomic testing industry representatives, and biomedical informatics faculty. Slides were at each meeting to provide background information regarding the analytical framework. Participants were invited to provide feedback during the meeting, including revising the overall project aims. Stakeholder meeting content was reviewed and summarized by stakeholder group and by theme.

RESULTS

The majority of stakeholder suggestions focused on aspects of decision aid design and formatting. Stakeholders were enthusiastic about the potential value of using decision analysis modeling with personalized model inputs for cancer recurrence risk, as well as competing risks from age and comorbidities, to generate a patient-centered tool to assist decision-making. Stakeholders did not view privacy considerations as a major barrier to the proposed decision aid program. A common theme was that decision aids should be portable across multiple platforms (electronic and paper), should allow for interaction by the user to adjust model inputs iteratively, and available to patients both before and during consult appointments. Emphasis was placed on the challenge of explaining the model's composite result of quality-adjusted life years.

CONCLUSIONS

A range of stakeholders provided valuable insights regarding the design of a personalized decision aid program, based upon Markov modeling with individualized model inputs, to provide a patient-centered framework to support for genomic-based treatment decisions for cancer patients. The guidance provided by our stakeholders may be broadly applicable to the communication of genomic test results to patients in a patient-centered fashion that supports effective shared decision-making that represents a spectrum of personal factors such as age, medical comorbidities, and individual priorities and values.

Low-risk Prostate Cancer: Identification, Management, and Outcomes

CONTEXT

The incidence of low-risk prostate cancer (PCa) has increased as a consequence of prostate-specific antigen testing.

OBJECTIVE

In this collaborative review article, we examine recent literature regarding low-risk PCa and the available prognostic and therapeutic options.

EVIDENCE ACQUISITION

We performed a literature review of the Medline, Embase, and Web of Science databases. The search strategy included the terms: prostate cancer, low risk, active surveillance, focal therapy, radical prostatectomy, watchful waiting, biomarker, magnetic resonance imaging, alone or in combination.

EVIDENCE SYNTHESIS

Prospective randomized trials have failed to show an impact of radical treatments on cancer-specific survival in low-risk PCa patients. Several series have reported the risk of adverse pathologic outcomes at radical prostatectomy. However, it is not clear if these patients are at higher risk of death from PCa. Long-term follow-up indicates the feasibility of active surveillance in low-risk PCa patients, although approximately 30% of men starting active surveillance undergo treatment within 5 yr. Considering focal therapies, robust data investigating its impact on long-term survival outcomes are still required and therefore should be considered experimental. Magnetic resonance imaging and tissue biomarkers may help to predict clinically significant PCa in men initially diagnosed with low-risk disease.

CONCLUSIONS

The incidence of low-risk PCa has increased in recent years. Only a small proportion of men with low-risk PCa progress to clinical symptoms, metastases, or death and prospective trials have not shown a benefit for immediate radical treatments. Tissue biomarkers, magnetic resonance imaging, and ongoing surveillance may help to identify those men with low-risk PCa who harbor more clinically significant disease.

PATIENT SUMMARY

Low-risk prostate cancer is very common. Active surveillance has excellent long-term results, while randomized trials have failed to show a beneficial impact of immediate radical treatments on survival. Biomarkers and magnetic resonance imaging may help to identify which men may benefit from early treatment.

Bioorthogonal Labeling of Human Prostate Cancer Tissue Slice Cultures for Glycoproteomics

Sialylated glycans are found at elevated levels in many types of cancer and have been implicated in disease progression. However, the specific glycoproteins that contribute to the cancer cell-surface sialylation are not well characterized, specifically in bona fide human disease tissue. Metabolic and bioorthogonal labeling methods have previously enabled the enrichment and identification of sialoglycoproteins from cultured cells and model organisms. Herein, we report the first application of this glycoproteomic platform to human tissues cultured ex vivo. Both normal and cancerous prostate tissues were sliced and cultured in the presence of the azide-functionalized sialic acid biosynthetic precursor Ac₄ ManNAz. The compound was metabolized to the azidosialic acid and incorporated into cell surface and secreted sialoglycoproteins. Chemical biotinylation followed by enrichment and mass spectrometry led to the identification of glycoproteins that were found at elevated levels or uniquely in cancerous prostate tissue. This work therefore extends the use of bioorthogonal labeling strategies to problems of clinical relevance.

Improving accuracy of RNA-based diagnosis and prognosis of oral cancer by using noninvasive methods

RNA-based diagnosis and prognosis of squamous cell carcinoma has been slow to come to the clinic. Improvements in RNA measurement, statistical evaluation, and sample preservation, along with increased sample numbers, have not made these methods reproducible enough to be used clinically. We propose that, in the case of squamous cell carcinoma of the oral cavity, a chief source of variability is sample dissection, which leads to variable amounts of stroma mixed in with tumor epithelium. This heterogeneity of the samples, which requires great care to avoid, makes it difficult to see changes in RNA levels specific to tumor cells. An evaluation of the data suggests that, paradoxically, brush biopsy samples of oral lesions may provide a more reproducible method than surgical acquisition of samples for miRNA measurement. The evidence also indicates that body fluid samples can show similar changes in miRNAs with oral squamous cell carcinoma (OSCC) as those seen in tumor brush biopsy samples - suggesting much of the miRNA in these samples is coming from the same source: tumor epithelium. We conclude that brush biopsy or body fluid samples may be superior to surgical samples in allowing miRNA-based diagnosis and prognosis of OSCC in that they feature a rapid method to obtain homogeneous tumor cells and/or RNA.

Correlation between Chromosome 9p21 Locus Deletion and Prognosis in Clinically Localized Prostate Cancer

Background

Some studies have reported that deletions at chromosome arm 9p occur frequently and represent a critical step in carcinogenesis of some neoplasms. Our aim was to evaluate the deletion of locus 9p21 and chromosomes 3, 7 and 17 in localized prostate cancer (PC) and correlate these alterations with prognostic factors and biochemical recurrence after surgery.

Methods

We retrospectively evaluated surgical specimens from 111 patients with localized PC who underwent radical prostatectomy. Biochemical recurrence was defined as a prostate-specific antigen (PSA) >0.2 ng/mL and the mean postoperative follow-up was 123 months. The deletions were evaluated using fluorescence in situ hybridization with centromeric and locus-specific probes in a tissue microarray containing 2 samples from each patient. We correlated the occurrence of any deletion with pathological stage, Gleason score, ISUP grade group, PSA and biochemical recurrence.

Results

We observed a loss of any probe in only 8 patients (7.2%). The most common deletion was the loss of locus 9p21, which occurred in 6.4% of cases. Deletions of chromosomes 3, 7 and 17 were observed in 2.3%, 1.2% and 1.8% patients, respectively. There was no correlation between chromosome loss and Gleason score, ISUP, PSA or stage. Biochemical recurrence occurred in 83% cases involving 9p21 deletions. Loss of 9p21 locus was significantly associated with time to recurrence (p = 0.038).

Conclusions

We found low rates of deletion in chromosomes 3, 7 and 17 and 9p21 locus. We observed that 9p21 locus deletion was associated with worse prognosis in localized PC treated by radical prostatectomy.

Individual Patient-Level Meta-Analysis of the Performance of the Decipher Genomic Classifier in High-Risk Men After Prostatectomy to Predict Development of Metastatic Disease

Purpose To perform the first meta-analysis of the performance of the genomic classifier test, Decipher, in men with prostate cancer postprostatectomy. Methods MEDLINE, EMBASE, and the Decipher genomic resource information database were searched for published reports between 2011 and 2016 of men treated by prostatectomy that assessed the benefit of the Decipher test. Multivariable Cox proportional hazards models fit to individual patient data were performed; meta-analyses were conducted by pooling the study-specific hazard ratios (HRs) using random-effects modeling. Extent of heterogeneity between studies was determined with the I² test. Results Five studies (975 total patients, and 855 patients with individual patient-level data) were eligible for analysis, with a median follow-up of 8 years. Of the total cohort, 60.9%, 22.6%, and 16.5% of patients were classified by Decipher as low, intermediate, and high risk, respectively. The 10-year cumulative incidence metastases rates were 5.5%, 15.0%, and 26.7% ( P < .001), respectively, for the three risk classifications. Pooling the study-specific Decipher HRs across the five studies resulted in an HR of 1.52 (95% CI, 1.39 to 1.67; I² = 0%) per 0.1 unit. In multivariable analysis of individual patient data, adjusting for clinicopathologic variables, Decipher remained a statistically significant predictor of metastasis (HR, 1.30; 95% CI, 1.14 to 1.47; P < .001) per 0.1 unit. The C-index for 10-year distant metastasis of the clinical model alone was 0.76; this increased to 0.81 with inclusion of Decipher. Conclusion The genomic classifier test, Decipher, can independently improve prognostication of patients postprostatectomy, as well as within nearly all clinicopathologic, demographic, and treatment subgroups. Future study of how to best incorporate genomic testing in clinical decision-making and subsequent treatment recommendations is warranted.

Exosomes and Exosomal MicroRNAs in Prostate Cancer Radiation Therapy

Despite current risk stratification systems using traditional clinicopathologic factors, many localized and locally advanced prostate cancers fail radical treatment (ie, radical prostatectomy, radiation therapy with or without androgen deprivation therapy). Therefore, a pressing need exists for enhanced methods of disease stratification through novel prognostic and predictive tools that can reliably be applied in clinical practice. Exosomes are 50- to 150-nm small vesicles released by cancer cells that reflect the genetic and nongenetic materials of parent cancer cells. Cancer cells can contain distinct sets of microRNA profiles, the expression of which can change owing to stress such as radiation therapy. These alterations or distinctions in contents allow exosomes to be used as prognostic and/or predictive biomarkers and to monitor the treatment response. Additionally, microRNAs have been shown to influence multiple processes in prostate tumorigenesis, including cell proliferation, induction of apoptosis, migration, oncogene inhibition, and radioresistance. Thus, comparative exosomal microRNA profiling at different levels could help portray tumor aggressiveness and response to radiation therapy. Although technical challenges persist in exosome isolation and characterization, recent improvements in microRNA profiling have evolved toward in-depth analyses of the exosomal cargo and its functions. We have reviewed the role of exosomes and exosomal microRNAs in biologic processes of prostate cancer progression and radiation therapy response, with a particular focus on the development of clinical assays for treatment personalization.

Epigenomic Regulation of Androgen Receptor Signaling: Potential Role in Prostate Cancer Therapy

Androgen receptor (AR) signaling remains the major oncogenic pathway in prostate cancer (PCa). Androgen-deprivation therapy (ADT) is the principle treatment for locally advanced and metastatic disease. However, a significant number of patients acquire treatment resistance leading to castration resistant prostate cancer (CRPC). Epigenetics, the study of heritable and reversible changes in gene expression without alterations in DNA sequences, is a crucial regulatory step in AR signaling. We and others, recently described the technological advance Chem-seq, a method to identify the interaction between a drug and the genome. This has permitted better understanding of the underlying regulatory mechanisms of AR during carcinogenesis and revealed the importance of epigenetic modifiers. In screening for new epigenomic modifiying drugs, we identified SD-70, and found that this demethylase inhibitor is effective in CRPC cells in combination with current therapies. The aim of this review is to explore the role of epigenetic modifications as biomarkers for detection, prognosis, and risk evaluation of PCa. Furthermore, we also provide an update of the recent findings on the epigenetic key processes (DNA methylation, chromatin modifications and alterations in noncoding RNA profiles) involved in AR expression and their possible role as therapeutic targets.

Novel concepts for risk stratification in prostate cancer

Since Partin introduced the analysis of prostate-specific antigen, clinical T-stage and Gleason scores to estimate the risk of progression in men with localised prostate cancer, our understanding of factors that modify this risk has changed drastically. There are now multiple risk stratification tools available, including look-up tables, risk stratification/classification analyses, regression-tree analyses, nomograms and artificial neural networks. Concurrently, descriptions of novel biopsy strategies, imaging modalities and biomarkers are frequently published with the aim of improving risk stratification. With an abundance of new information available, incorporating advances into clinical practice can be confusing. This article aims to outline the major novel concepts in prostate cancer risk stratification for men with biopsy confirmed prostate cancer. We will detail which of these novel techniques and tools are likely to be adopted to aid treatment decisions and enable more accurate post-diagnosis, pretreatment risk stratification.

Active Surveillance for Intermediate Risk Prostate Cancer: Survival Outcomes in the Sunnybrook Experience

PURPOSE

To assess the applicability of active surveillance in patients with intermediate risk prostate cancer, we compared the survival outcomes of patients with low risk and intermediate risk disease.

MATERIALS AND METHODS

Active surveillance was offered to all patients with low risk (cT1-T2b and Gleason score 6 and prostate specific antigen 10 ng/ml or less) and select intermediate risk disease (age greater than 70 years with cT2c or prostate specific antigen 15 ng/ml or less, or Gleason score 3+4 or less). Data from November 1995 to May 2013 were extracted from a prospectively collected database. The primary outcome was metastasis-free survival, and secondary outcomes were overall survival, cause specific survival and treatment-free survival.

RESULTS

A total of 213 intermediate risk and 732 low risk cases were identified. Median age was 72 years (IQR 67.3, 76.8) in the intermediate risk cohort and 67 years (IQR 60.6, 71.9) in the low risk group. Median followup was comparable (6.7 years for intermediate risk vs 6.5 years for low risk). Gleason 7 disease comprised 60% of the intermediate risk cohort. The 15-year metastasis-free, overall, cause specific and treatment-free survival rates were inferior in the intermediate risk group (metastasis-free survival HR 3.14, 95% CI 1.51-6.53, p=0.001, 82% for intermediate risk vs 95% for low risk). On further evaluation the estimated 15-year metastasis-free survival for cases of Gleason 6 or less with prostate specific antigen less than 10 ng/ml was 94%, Gleason 6 or less with prostate specific antigen 10 to 20 ng/ml was 94%, Gleason 3+4 with prostate specific antigen 20 ng/ml or less was 84% and Gleason 4+3 with prostate specific antigen 20 ng/ml or less was 63%.

CONCLUSIONS

These data support the use of active surveillance in low risk and intermediate risk cases of Gleason 6 but not Gleason 7 prostate cancer. Multiparametric magnetic resonance imaging and novel biomarkers might be vital in detecting favorable Gleason 7 disease.

The Role of Proteomics in Biomarker Development for Improved Patient Diagnosis and Clinical Decision Making in Prostate Cancer

Prostate Cancer (PCa) is the second most commonly diagnosed cancer in men worldwide. Although increased expression of prostate-specific antigen (PSA) is an effective indicator for the recurrence of PCa, its intended use as a screening marker for PCa is of considerable controversy. Recent research efforts in the field of PCa biomarkers have focused on the identification of tissue and fluid-based biomarkers that would be better able to stratify those individuals diagnosed with PCa who (i) might best receive no treatment (active surveillance of the disease); (ii) would benefit from existing treatments; or (iii) those who are likely to succumb to disease recurrence and/or have aggressive disease. The growing demand for better prostate cancer biomarkers has coincided with the development of improved discovery and evaluation technologies for multiplexed measurement of proteins in bio-fluids and tissues. This review aims to (i) provide an overview of these technologies as well as describe some of the candidate PCa protein biomarkers that have been discovered using them; (ii) address some of the general limitations in the clinical evaluation and validation of protein biomarkers; and (iii) make recommendations for strategies that could be adopted to improve the successful development of protein biomarkers to deliver improvements in personalized PCa patient decision making.