Tissue-Based Biomarkers for the Risk Stratification of Men With Clinically Localized Prostate Cancer

Genomics in active surveillance and post-prostatectomy patients: A review of when and how to use effectively

PURPOSE OF REVIEW

Prostate cancer (PCa) represents a significant health burden globally, ranking as the most diagnosed cancer among men and a leading cause of cancer-related mortality. Conventional treatment methods such as radiation therapy or radical prostatectomy have significant side effects which often impact quality of life. As our understanding of the natural history and progression of PCa has evolved, so has the evolution of management options.

RECENT FINDINGS

Active surveillance (AS) has become an increasingly favored approach to the management of very low, low, and properly selected favorable intermediate risk PCa. AS permits ongoing observation and postpones intervention until definitive treatment is required. There are, however, challenges with selecting patients for AS, which further emphasizes the need for more precise tools to better risk stratify patients and choose candidates more accurately. Tissue-based biomarkers, such as ProMark, Prolaris, GPS (formerly Oncotype DX), and Decipher, are valuable because they improve the accuracy of patient selection for AS and offer important information on the prognosis and severity of disease. By enabling patients to be categorized according to their risk profiles, these biomarkers help physicians and patients make better informed treatment choices and lower the possibility of overtreatment. Even with their potential, further standardization and validation of these biomarkers is required to guarantee their broad clinical utility. Active surveillance has emerged as a preferred strategy for managing low-risk prostate cancer, and tissue-based biomarkers play a crucial role in refining patient selection and risk stratification. Standardization and validation of these biomarkers are essential to ensure their widespread clinical use and optimize patient outcomes.

Predicting prostate adenocarcinoma patients' survival and immune signature: a novel risk model based on telomere-related genes

Alterations in telomeres constitute some of the earliest occurrences in the tumourigenesis of prostate adenocarcinoma (PRAD) and persist throughout the progression of the tumour. While the activity of telomerase and the length of telomeres have been demonstrated to correlate with the prognosis of PRAD, the prognostic potential of telomere-related genes (TRGs) in this disease remains unexplored. Utilising mRNA expression data from the Cancer Genome Atlas (TCGA), we devised a risk model and a nomogram to predict the survival outcomes of patients with PRAD. Subsequently, our investigations extended to the relationship between the risk model and immune cell infiltration, sensitivity to chemotherapeutic drugs, and specific signalling pathways. The risk model we developed is predicated on seven key TRGs, and immunohistochemistry results revealed significant differential expression of three TRGs in tumours and paracancerous tissues. Based on the risk scores, PRAD patients were stratified into high-risk and low-risk cohorts. The Receiver operating characteristics (ROC) and Kaplan-Meier survival analyses corroborated the exceptional predictive performance of our novel risk model. Multivariate Cox regression analysis indicated that the risk score was an independent risk factor associated with Overall Survival (OS) and was significantly associated with T and N stages of PRAD patients. Notably, the high-risk group exhibited a greater response to chemotherapy and immunosuppression compared to the low-risk group, offering potential guidance for treatment strategies for high-risk patients. In conclusion, our new risk model, based on TRGs, serves as a reliable prognostic indicator for PRAD. The model holds significant value in guiding the selection of immunotherapy and chemotherapy in the clinical management of PRAD patients.

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.

The glycosylation landscape of prostate cancer tissues and biofluids

An overview of the role of glycosylation in prostate cancer (PCa) development and progression is presented, focusing on recent advancements in defining the N-glycome through glycomic profiling and glycoproteomic methodologies. Glycosylation is a common post-translational modification typified by oligosaccharides attached N-linked to asparagine or O-linked to serine or threonine on carrier proteins. These attached sugars have crucial roles in protein folding and cellular recognition processes, such that altered glycosylation is a hallmark of cancer pathogenesis and progression. In the past decade, advancements in N-glycan profiling workflows using Matrix Assisted Laser Desorption/Ionization Mass Spectrometry Imaging (MALDI-MSI) technology have been applied to define the spatial distribution of glycans in PCa tissues. Multiple studies applying N-glycan MALDI-MSI to pathology-defined PCa tissues have identified significant alterations in N-glycan profiles associated with PCa progression. N-glycan compositions progressively increase in number, and structural complexity due to increased fucosylation and sialylation. Additionally, significant progress has been made in defining the glycan and glycopeptide compositions of prostatic-derived glycoproteins like prostate-specific antigen in tissues and biofluids. The glycosyltransferases involved in these changes are potential drug targets for PCa, and new approaches in this area are summarized. These advancements will be discussed in the context of the further development of clinical diagnostics and therapeutics targeting glycans and glycoproteins associated with PCa progression. Integration of large scale spatial glycomic data for PCa with other spatial-omic methodologies is now feasible at the tissue and single-cell levels.

The Impact of Positron Emission Tomography Imaging and Tumor Molecular Profiling on Risk Stratification, Treatment Choice, and Oncological Outcomes of Patients with Primary or Relapsed Prostate Cancer: An International Collaborative Review of the Existing Literature

CONTEXT

The clinical introduction of next-generation imaging methods and molecular biomarkers ("radiogenomics") has revolutionized the field of prostate cancer (PCa). While the clinical validity of these tests has thoroughly been vetted, their clinical utility remains a matter of investigation.

OBJECTIVE

To systematically review the evidence to date on the impact of positron emission tomography (PET) imaging and tissue-based prognostic biomarkers, including Decipher, Prolaris, and Oncotype Dx, on the risk stratification, treatment choice, and oncological outcomes of men with newly diagnosed PCa or those with biochemical failure (BCF).

EVIDENCE ACQUISITION

We performed a quantitative systematic review of the literature using the MEDLINE, EMBASE, and Web of Science databases (2010-2022) following the Preferred Reporting Items for Systematic Reviews and Meta-analyses statement guidelines. The validated Quality Assessment of Diagnostic Accuracy Studies 2 scoring system was used to assess the risk of bias.

EVIDENCE SYNTHESIS

A total of 148 studies (130 on PET and 18 on biomarkers) were included. In the primary PCa setting, prostate-specific membrane antigen (PSMA) PET imaging was not useful in improving T staging, moderately useful in improving N staging, but consistently useful in improving M staging in patients with National Comprehensive Cancer Network (NCCN) unfavorable intermediate- to very-high-risk PCa. Its use led to a management change in 20-30% of patients. However, the effect of these treatment changes on survival outcomes was not clear. Similarly, biomarkers in the pretherapy primary PCa setting increased and decreased the risk, respectively, in 7-30% and 32-36% of NCCN low-risk and 31-65% and 4-15% of NCCN favorable intermediate-risk patients being considered for active surveillance. A change in management was noted in up to 65% of patients, with the change being in line with the molecular risk-based reclassification, but again, the impact of these changes on survival outcomes remained unclear. Notably, in the postsurgical primary PCa setting, biomarker-guided adjuvant radiation therapy (RT) was associated with improved oncological control: Δ↓ 2-yr BCF by 22% (level 2b). In the BCF setting, the data were more mature. PSMA PET was consistently useful in improving disease localization-Δ↑ detection for T, N, and M staging was 13-32%, 19-58%, and 9-29%, respectively. Between 29% and 73% of patients had a change in management. Most importantly, these management changes were associated with improved survival outcomes in three trials: Δ↑ 4-yr disease-free survival by 24.3%, Δ↑ 6-mo metastasis-free survival (MFS) by 46.7%, and Δ↑ androgen deprivation therapy-free survival by 8 mo in patients who received PET-concordant RT (level 1b-2b). Biomarker testing in these patients also appeared to be helpful in risk stratifying and guiding the use of early salvage RT (sRT) and concomitant hormonal therapy. Patients with high-genomic-risk scores benefitted from treatment intensification: Δ↑ 8-yr MFS by 20% with the use of early sRT and Δ↑ 12-yr MFS by 11.2% with the use of hormonal therapy alongside early sRT, while low-genomic-risk score patients did equally well with initial conservative management (level 3).

CONCLUSIONS

Both PSMA PET imaging and tumor molecular profiling provide actionable information in the management of men with primary PCa and those with BCF. Emerging data suggest that radiogenomics-guided treatments translate into direct survival benefits for patients, however, additional prospective data are awaited.

PATIENT SUMMARY

In this review, we evaluated the utility of prostate-specific membrane antigen positron emission tomography and tumor molecular profiling in guiding the care of men with prostate cancer (PCa). We found that these tests augmented risk stratification, altered management, and improved cancer control in men with a new diagnosis of PCa or for those experiencing a relapse.

Identification of a 24-gene panel and a novel marker of PODXL2 essential for the pathological diagnosis of early prostate cancer

Precise diagnosis of early prostate cancer (PCa) is critical for preventing tumor progression. However, the diagnostic outcomes of currently used markers are far from satisfactory due to the low sensitivity or specificity. Here, we identified a diagnostic subpopulation in PCa tissue with the integrating analysis of single-cell and bulk RNA-seq. The representative markers of this subpopulation were extracted to perform intersection analysis with early-PCa-related gene module generated from weighted correlation network analysis (WGCNA). A total of 24 overlapping genes were obtained, the diagnostic roles of which were validated by distinguishing normal and tumorous prostate samples from the public dataset. A least absolute shrinkage and selection operator (LASSO) model was constructed based on these genes and the obtained 24-gene panel showed high sensitivity and specificity for PCa diagnosis, with better identifying capability of PCa than the commercially used gene panel of Oncotype DX. The top two risk factors, TRPM4 and PODXL2, were verified to be highly expressed in early PCa tissues by multiplex immunostaining, and PODXL2 was more sensitive and specific compared to TRPM4 and the pathologically used marker AMACR for early PCa diagnosis, suggesting a novel and promising pathology marker.

Risk of Short-Term Prostate-Specific Antigen Recurrence and Failure in Patients With Prostate Cancer: A Secondary Analysis of a Randomized Clinical Trial

Importance

A shorter time interval to prostate-specific antigen (PSA) failure is associated with worse clinical outcomes; however, specific factors defining this state remain unknown.

Objective

To evaluate the factors of a short time interval to PSA failure in order to identify patients for treatment escalation randomized clinical trials.

Design, Setting, and Participants

This secondary analysis of a randomized clinical trial was a secondary analysis of the Dana-Farber Cancer Institute 05-043 trial and included 350 patients with nonmetastatic unfavorable risk prostate cancer (PC).

Interventions

Patients were randomized 1:1 to receive androgen deprivation therapy (ADT) and radiation therapy (RT) plus docetaxel vs ADT and RT.

Main Outcomes and Measures

Cumulative incidence rates curves of PSA failure, defined as PSA nadir plus 2 ng/mL or initiation of salvage therapies, and the Fine and Gray competing risks regression was used to assess the prognostic association between these factors and time to PSA failure.

Results

The study included 350 males who primarily had a good performance status (330 [94.3%] with Eastern Cooperative Oncology Group score of 0), median (range) age of 66 (43-86) years, with 167 (46.6%) having Gleason scores of 8 to 10, and 195 (55.2%) presenting with a baseline PSA of more than 10 ng/mL. After a median (IQR) follow-up of 10.2 (8.0-11.4) years, having a PSA level of 10 ng/mL to 20 ng/mL (subdistribution hazard ratio [sHR], 1.98; 95% CI, 1.28-3.07; P = .002) and a Gleason score of 8 to 10 (sHR, 2.55; 95% CI, 1.63-3.99; P < .001) were associated with a shorter time to PSA failure, and older age (sHR, 0.82; 95% CI, 0.72-0.93; P = .002) was associated with reduced risk for PSA failure after adjusting for other baseline clinical factors. The high-risk category, defined by these 3 factors, was associated with a shorter time to PSA failure (sHR, 2.69; 95% CI, 1.84-3.93; P < .001).

Conclusions and Relevance

In this secondary analysis of a randomized clinical trial of males with unfavorable risk PC, young age, PSA of 10 ng/mL or more, and a Gleason score of 8 to 10 estimated a shorter time to PSA failure. A subgroup of males at very high-risk for early PSA failure, as defined by our study, may benefit from treatment escalation with androgen receptor signaling inhibitors or cytotoxic chemotherapy and should be the subject of a prospective randomized clinical trial.

Trial Registration

NCT00116142.

Artificial Intelligence-Based PTEN Loss Assessment as an Early Predictor of Prostate Cancer Metastasis After Surgery: A Multicenter Retrospective Study

Phosphatase and tensin homolog (PTEN) loss is associated with adverse outcomes in prostate cancer and can be measured via immunohistochemistry. The purpose of the study was to establish the clinical application of an in-house developed artificial intelligence (AI) image analysis workflow for automated detection of PTEN loss on digital images for identifying patients at risk of early recurrence and metastasis. Postsurgical tissue microarray sections from the Canary Foundation (n = 1264) stained with anti-PTEN antibody were evaluated independently by pathologist conventional visual scoring (cPTEN) and an automated AI-based image analysis pipeline (AI-PTEN). The relationship of PTEN evaluation methods with cancer recurrence and metastasis was analyzed using multivariable Cox proportional hazard and decision curve models. Both cPTEN scoring by the pathologist and quantification of PTEN loss by AI (high-risk AI-qPTEN) were significantly associated with shorter metastasis-free survival (MFS) in univariable analysis (cPTEN hazard ratio [HR], 1.54; CI, 1.07-2.21; P = .019; AI-qPTEN HR, 2.55; CI, 1.83-3.56; P < .001). In multivariable analyses, AI-qPTEN showed a statistically significant association with shorter MFS (HR, 2.17; CI, 1.49-3.17; P < .001) and recurrence-free survival (HR, 1.36; CI, 1.06-1.75; P = .016) when adjusting for relevant postsurgical clinical nomogram (Cancer of the Prostate Risk Assessment [CAPRA] postsurgical score [CAPRA-S]), whereas cPTEN does not show a statistically significant association (HR, 1.33; CI, 0.89-2; P = .2 and HR, 1.26; CI, 0.99-1.62; P = .063, respectively) when adjusting for CAPRA-S risk stratification. More importantly, AI-qPTEN was associated with shorter MFS in patients with favorable pathological stage and negative surgical margins (HR, 2.72; CI, 1.46-5.06; P = .002). Workflow also demonstrated enhanced clinical utility in decision curve analysis, more accurately identifying men who might benefit from adjuvant therapy postsurgery. This study demonstrates the clinical value of an affordable and fully automated AI-powered PTEN assessment for evaluating the risk of developing metastasis or disease recurrence after radical prostatectomy. Adding the AI-qPTEN assessment workflow to clinical variables may affect postoperative surveillance or management options, particularly in low-risk patients.

Cribriform pattern and intraductal carcinoma of the prostate can have a clinicopathological impact, regardless of their percentage and/or number of cores

Cribriform pattern and intraductal carcinoma of the prostate (IDC-P) are widely accepted as poor prognostic factors in prostate cancer. However, it remains unclear to what extent the presence of these morphological features in prostate biopsy specimens, as diagnosed by hematoxylin-eosin-stained specimens only, affects the clinicopathological impact. In this study, we summarized the characteristics of the cribriform pattern and IDC-P in 850 prostate biopsy cases. The results showed a statistically significant increase in the incidence of cribriform pattern and IDC-P as grade group (GG) increased (especially in cases ≥ GG4, Chi-square test P < 0.001). The independent risk factors for cribriform pattern and IDC-P in biopsy specimens in the multivariate logistic regression analysis were the former GG, presence of IDC-P, lesion length of the highest GG core, latter GG, presence of the cribriform pattern, number of biopsies obtained, and number of highest GG core. Overall, 125 cases in which radical prostatectomy was conducted after biopsy were selected for further analysis. Multivariate logistic regression analysis using biopsy and surgical specimens confirmed that the presence of the cribriform pattern and IDC-P in biopsy specimens were independent risk factors for lymph node metastasis (odds ratios [95% confidence interval] were 6.54 [1.15-37.05] for the cribriform pattern and 23.71 [1.74-322.42] for IDC-P). The presence of the cribriform pattern and/or IDC-P in a biopsy specimen was a significant factor, even if only partially present, indicating lymph node metastasis. However, further validation is required to predict poor prognostic factors more accurately.

Somatic mutations reveal complex metastatic seeding from multifocal primary prostate cancer

Primary prostate cancer shows a striking intraorgan molecular heterogeneity, with multiple spatially separated malignant foci in the majority of patients. Metastatic prostate cancer, however, typically reveals more homogenous molecular profiles, suggesting a monoclonal origin of the metastatic lesions. Longitudinal mutational spectra, comparing multiple primary lesions with metastases from the same patients remain poorly defined. We have here analyzed somatic mutations in multisampled, spatio-temporal biobanked lesions (38 samples from primary foci and 1 sample from each of 8 metastases from seven prostate cancer patients) applying a custom-designed panel targeting 68 prostate cancer relevant genes. The metastatic samples were taken at time of primary surgery and up to 7 years later, and sampling included circulating tumor DNA in plasma or solid metastatic tissue samples. A total of 282 somatic mutations were detected, with a range of 0 to 25 mutations per sample. Although seven samples had solely private mutations, the remaining 39 samples had both private and shared mutations. Seventy-four percent of mutations in metastases were not found in any primary samples, and vice versa, 96% of mutations in primary cancers were not found in any metastatic samples. However, for three patients, shared mutations were found suggesting the focus of origin, including mutations in AKT1, FOXA1, HOXB13, RB1 and TP53. In conclusion, the spatio-temporal heterogeneous nature of multifocal disease is emphasized in our study, and underlines the importance of testing a recent sample in genomics-based precision medicine for metastatic prostate cancer.

Aquaporins as Prognostic Biomarker in Prostate Cancer

Prostate cancer is a complex heterogeneous disease that affects millions of males worldwide. Despite rapid advances in molecular biology and innovation in technology, few biomarkers have been forthcoming in prostate cancer. The currently available biomarkers for the prognosis of prostate cancer are inadequate and face challenges, thus having limited clinical utility. To date, there are a number of prognostic and predictive biomarkers identified for prostate cancer but lack specificity and sensitivity to guide clinical decision making. There is still tremendous scope for specific biomarkers to understand the natural history and complex biology of this heterogeneous disease, and to identify early treatment responses. Accumulative studies indicate that aquaporins (AQPs) a family of membrane water channels may serve as a prognostic biomarker for prostate cancer in monitoring disease advancement. In the present review, we discuss the existing prostate cancer biomarkers, their limitations, and aquaporins as a prospective biomarker of prognostic significance in prostate cancer.

The prostate tissue‐based telomere biomarker as a prognostic tool for metastasis and death from prostate cancer after prostatectomy

Current biomarkers are inadequate prognostic predictors in localized prostate cancer making treatment decision‐making challenging. Previously, we observed that the combination of more variable telomere length among prostate cancer cells and shorter telomere length in prostate cancer‐associated stromal cells – the telomere biomarker – is strongly associated with progression to metastasis and prostate cancer death after prostatectomy independent of currently used pathologic indicators. Here, we optimized our method allowing for semi‐automated telomere length determination in single cells in fixed tissue, and tested the telomere biomarker in five cohort studies of men surgically treated for clinically localized disease (N = 2,255). We estimated the relative risk (RR) of progression to metastasis (N = 311) and prostate cancer death (N = 85) using models appropriate to each study's design adjusting for age, prostatectomy stage, and tumor grade, which then we meta‐analyzed using inverse variance weights. Compared with men who had less variable telomere length among prostate cancer cells and longer telomere length in prostate cancer‐associated stromal cells, men with the combination of more variable and shorter telomere length had 3.76 times the risk of prostate cancer death (95% confidence interval [CI] 1.37–10.3, p = 0.01) and had 2.23 times the risk of progression to metastasis (95% CI 0.99–5.02, p = 0.05). The telomere biomarker was associated with prostate cancer death in men with intermediate risk disease (grade groups 2/3: RR = 9.18, 95% CI 1.14–74.0, p = 0.037) and with PTEN protein intact tumors (RR = 6.74, 95% CI 1.46–37.6, p = 0.015). In summary, the telomere biomarker is robust and associated with poor outcome independent of current pathologic indicators in surgically treated men.

Prostate cancer histopathology using label-free multispectral deep-UV microscopy quantifies phenotypes of tumor aggressiveness and enables multiple diagnostic virtual stains

Identifying prostate cancer patients that are harboring aggressive forms of prostate cancer remains a significant clinical challenge. Here we develop an approach based on multispectral deep-ultraviolet (UV) microscopy that provides novel quantitative insight into the aggressiveness and grade of this disease, thus providing a new tool to help address this important challenge. We find that UV spectral signatures from endogenous molecules give rise to a phenotypical continuum that provides unique structural insight (i.e., molecular maps or “optical stains") of thin tissue sections with subcellular (nanoscale) resolution. We show that this phenotypical continuum can also be applied as a surrogate biomarker of prostate cancer malignancy, where patients with the most aggressive tumors show a ubiquitous glandular phenotypical shift. In addition to providing several novel “optical stains” with contrast for disease, we also adapt a two-part Cycle-consistent Generative Adversarial Network to translate the label-free deep-UV images into virtual hematoxylin and eosin (H&E) stained images, thus providing multiple stains (including the gold-standard H&E) from the same unlabeled specimen. Agreement between the virtual H&E images and the H&E-stained tissue sections is evaluated by a panel of pathologists who find that the two modalities are in excellent agreement. This work has significant implications towards improving our ability to objectively quantify prostate cancer grade and aggressiveness, thus improving the management and clinical outcomes of prostate cancer patients. This same approach can also be applied broadly in other tumor types to achieve low-cost, stain-free, quantitative histopathological analysis.

Molecular Characterization of Prostate Cancers in the Precision Medicine Era

Prostate cancer (PCa) therapy has been recently revolutionized by the approval of new therapeutic agents in the metastatic setting. However, the optimal therapeutic strategy in such patients should be individualized in the light of prognostic and predictive molecular factors, which have been recently studied: androgen receptor (AR) alterations, PTEN-PI3K-AKT pathway deregulation, homologous recombination deficiency (HRD), mismatch repair deficiency (MMRd), and tumor microenvironment (TME) modifications. In this review, we highlighted the clinical impact of prognostic and predictive molecular factors in PCa patients' outcomes, identifying biologically distinct subtypes. We further analyzed the relevant methods to detect these factors, both on tissue, i.e., immunohistochemistry (IHC) and molecular tests, and blood, i.e., analysis of circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA). Moreover, we discussed the main pros and cons of such techniques, depicting their present and future roles in PCa management, throughout the precision medicine era.