Molecular Biomarkers in Localized Prostate Cancer: ASCO Guideline

Morin promotes autophagy in human PC3 prostate cancer cells by modulating AMPK/mTOR/ULK1 signaling pathway

AMP-activated protein kinase (AMPK) suppresses tumorigenesis by modulating autophagy and apoptosis. This study evaluated the impact of Morin on PC3 prostate cancerous cells by examining the AMPK/ mechanistic target of rapamycin (mTOR)/ ULK1 (UNC-51-like kinase 1) pathway and autophagy process. The PC3 cells were treated with Morin (50 µg/ml) and AICAR (an AMPK activator). Cell viability, apoptosis, autophagy, and level of phosphorylated and non-phosphorylated ULK1, AMPK, and mTOR, as well as LC3B/LC3A, have been investigated. Through DAPI staining, measurement of Bax/Bcl-2 ratio, Caspase activity, and Annexin V/PI method, it has been revealed that Morin induces apoptosis and reduces the growth of PC3 cells. Morin enhanced the protein level of phosphorylated AMPK (p-AMPK) and ULK1 (p-ULK1) and decreased the expression of phosphorylated mTOR (p-mTOR) in the PC3 cells. Morin could also increase the LC3B/LC3A ratio, Acridine Orange-positive cells, expression of Beclin-1 and ATG5 genes, and decrease the p62 protein level indicating autophagy-inducing. AICAR (an AMPK activator) enhanced the impact of Morin on apoptosis, cell growth, and expression of LC3B, p-AMPK, p-ULK1, and p-mTOR proteins in the PC3 cells. These findings suggest that Morin induces apoptotic and autophagic cell death by activating AMPK and ULK1 and suppressing mTOR pathways.

Personalized treatment using predictive biomarkers in solid organ malignancies: A review

In recent years, the influence of specific biomarkers in the diagnosis and prognosis of solid organ malignancies has been increasingly prominent. The relevance of the use of predictive biomarkers, which predict cancer response to specific forms of treatment provided, is playing a more significant role than ever before, as it affects diagnosis and initiation of treatment, monitoring for efficacy and side effects of treatment, and adjustment in treatment regimen in the long term. In the current review, we explored the use of predictive biomarkers in the treatment of solid organ malignancies, including common cancers such as colorectal cancer, breast cancer, lung cancer, prostate cancer, and cancers associated with high mortalities, such as pancreatic cancer, liver cancer, kidney cancer and cancers of the central nervous system. We additionally analyzed the goals and types of personalized treatment using predictive biomarkers, and the management of various types of solid organ malignancies using predictive biomarkers and their relative efficacies so far in the clinical settings.

Deep learning for automated scoring of immunohistochemically stained tumour tissue sections - Validation across tumour types based on patient outcomes

We aimed to develop deep learning (DL) models to detect protein expression in immunohistochemically (IHC) stained tissue-sections, and to compare their accuracy and performance with manually scored clinically relevant proteins in common cancer types. Five cancer patient cohorts (colon, two prostate, breast, and endometrial) were included. We developed separate DL models for scoring IHC-stained tissue-sections with nuclear, cytoplasmic, and membranous staining patterns. For training, we used images with annotations of cells with positive and negative staining from the colon cohort stained for Ki-67 and PMS2 (nuclear model), the prostate cohort 1 stained for PTEN (cytoplasmic model) and β-catenin (membranous model). The nuclear DL model was validated for MSH6 in the colon, MSH6 and PMS2 in the endometrium, Ki-67 and CyclinB1 in prostate, and oestrogen and progesterone receptors in the breast cancer cohorts. The cytoplasmic DL model was validated for PTEN and Mapre2, and the membranous DL model for CD44 and Flotillin1, all in prostate cohorts. When comparing the results of manual and DL scores in the validation sets, using manual scores as the ground truth, we observed an average correct classification rate of 91.5 % (76.9-98.5 %) for the nuclear model, 85.6 % (73.3-96.6 %) for the cytoplasmic model, and 78.4 % (75.5-84.3 %) for the membranous model. In survival analyses, manual and DL scores showed similar prognostic impact, with similar hazard ratios and p-values for all DL models. Our findings demonstrate that DL models offer a promising alternative to manual IHC scoring, providing efficiency and reproducibility across various data sources and markers.

Association of Genomic Prostate Score at positive margin with recurrence after radical prostatectomy

OBJECTIVES

To evaluate the utility of the 17-gene Genomic Prostate Score® (GPS; MDxHealth, Irvine, CA, USA) performed on prostate cancer at the positive margin of the radical prostatectomy (RP) for its association with risk of subsequent biochemical recurrence (BCR).

PATIENTS AND METHODS

We designed a case-cohort for the outcome of BCR, selecting 223 from a cohort of 813 RP patients treated at Johns Hopkins from 2008 to 2017 with positive margins and available clinical data; of these, 213 had available tissue and clinical data. RNA was isolated from formalin-fixed paraffin-embedded tumour tissue adjacent to the positive surgical margin and the GPS was evaluable in 203 of these patients with a score ranging from 0 to 100, with higher scores indicating higher risk. All patients underwent RP with or without adjuvant radiation therapy (ART). The statistical analysis employed Cox proportional hazards regression models for outcome of BCR weighted for case-cohort design.

RESULTS

In univariable analysis, every 20-unit increase in the GPS was associated with a nearly threefold increase in risk of BCR (hazard ratio [HR] per 20 units 2.82, P < 0.001). In a multivariable Cox model adjusted for age, race, Cancer of the Prostate Risk Assessment Postsurgical score, Grade Group at the positive margin, and ART, the GPS was significantly associated with BCR (HR 1.56 per 20 units; 95% confidence interval 1.11-2.19; P = 0.011). The study is limited by its retrospective and single institution design.

CONCLUSIONS

The GPS at the positive surgical margin could help stratify prognosis and inform clinical decision-making regarding adjuvant therapy after RP.

Prostatic adenocarcinoma: molecular underpinnings and treatment-related options

Prostate cancer is heterogeneous with varied pathologic features and presents with a wide spectrum of clinical manifestations from indolent to advanced cancer. Interrogation of the molecular landscape of prostate cancer has unveiled the complex genomic alterations in these tumors, which significantly impacts tumor biology. The documented array of chromosomal alterations, gene fusions, and epigenetic changes not only play a crucial role in oncogenesis and disease progression, but also impacts response and resistance to various therapeutic modalities. Various gene expression assays have been developed and are currently recommended in aiding clinical decision making in these clinically and molecularly heterogeneous cancer. In this review, we provide an overview of the molecular underpinnings of prostate cancer, and briefly review the current status of molecular testing and therapeutic options in the management of these tumors.

Identification of immune-related genes and small-molecule drugs in hypertension-induced left ventricular hypertrophy based on machine learning algorithms and molecular docking

Background

Left ventricular hypertrophy (LVH) is a common consequence of hypertension and can lead to heart failure. The immune response plays an important role in hypertensive LVH; however, there is no comprehensive method to investigate the mechanistic relationships between immune response and hypertensive LVH or to find novel therapeutic targets. This study aimed to screen hub immune-related genes involved in hypertensive LVH as well as to explore immune target-based therapeutic drugs.

Materials and methods

RNA-sequencing data from a mouse model generated by angiotensin II infusion were subjected to weighted gene co-expression network analysis (WGCNA) to identify core expression modules. Machine learning algorithms were applied to screen immune-related LVH characteristic genes. Heart structures were evaluated by echocardiography and cardiac magnetic resonance imaging (CMRI). Validation of hub genes was conducted by RT-qPCR and western blot. Using the Connectivity Map database and molecular docking, potential small-molecule drugs were explored.

Results

A total of 1215 differentially expressed genes were obtained, most of which were significantly enriched in immunoregulation and collagen synthesis. WGCNA and multiple machine learning strategies uncovered six hub immune-related genes (Ankrd1, Birc5, Nuf2, C1qtnf6, Fcgr3, and Cdca3) that may accurately predict hypertensive LVH diagnosis. Immune analysis revealed that fibroblasts and macrophages were closely correlated with hypertensive LVH, and hub gene expression was significantly associated with these immune cells. A regulatory network of transcription factor-mRNA and a ceRNA network of miRNA-lncRNA was established. Notably, six hub immune-related genes were significantly increased in the hypertensive LVH model, which were positively linked to left ventricle wall thickness. Finally, 12 small-molecule compounds with the potential to reverse the high expression of hub genes were ruled out as potential therapeutic agents for hypertensive LVH.

Conclusion

This study identified and validated six hub immune-related genes that may play essential roles in hypertensive LVH, providing new insights into the potential pathogenesis of cardiac remodeling and novel targets for medical interventions.

Upregulation of shelterin and CST genes and longer telomeres are associated with unfavorable prognostic characteristics in prostate cancer

INTRODUCTION

Search for new clinical biomarkers targets in prostate cancer (PC) is urgent. Telomeres might be one of these targets. Telomeres are the extremities of linear chromosomes, essential for genome stability and control of cell divisions. Telomere homeostasis relies on the proper functioning of shelterin and CST complexes. Telomeric dysfunction and abnormal expression of its components are reported in most cancers and are associated with PC. Despite this, there are only a few studies about the expression of the main telomere complexes and their relationship with PC progression. We aimed to evaluate the role of shelterin (POT1, TRF2, TPP1, TIN2, and RAP1) and CST (CTC1, STN1, and TEN1) genes and telomere length in the progression of PC.

METHODS

We evaluated genetic alterations of shelterin and CST by bioinformatics in samples of localized (n = 499) and metastatic castration-resistant PC (n = 444). We also analyzed the expression of the genes using TCGA (localized PC n = 497 and control n = 152) and experimental approaches, with surgical specimens (localized PC n = 81 and BPH n = 10) and metastatic cell lines (LNCaP, DU145, PC3 and PNT2 as control) by real-time PCR. Real-time PCR also determined the telomere length in the same experimental samples. All acquired data were associated with clinical parameters.

RESULTS

Genetic alterations are uncommon in PC, but POT1, TIN2, and TEN1 showed significantly more amplifications in the metastatic cancer. Except for CTC1 and TEN1, which are differentially expressed in localized PC samples, we did not detect an expression pattern relative to control and cell lines. Nevertheless, except for TEN1, the upregulation of all genes is associated with a worse prognosis in localized PC. We also found that increased telomere length is associated with disease aggressiveness in localized PC.

CONCLUSION

The upregulation of shelterin and CST genes creates an environment that favors telomere elongation, giving selective advantages for localized PC cells to progress to more aggressive stages of the disease.

RARPKB: a knowledge-guide decision support platform for personalized robot-assisted surgery in prostate cancer

BACKGROUND

Robot-assisted radical prostatectomy (RARP) has emerged as a pivotal surgical intervention for the treatment of prostate cancer (PCa). However, the complexity of clinical cases, heterogeneity of PCa, and limitations in physician expertise pose challenges to rational decision-making in RARP. To address these challenges, the authors aimed to organize the knowledge of previously complex cohorts and establish an online platform named the RARP knowledge base (RARPKB) to provide reference evidence for personalized treatment plans.

MATERIALS AND METHODS

PubMed searches over the past two decades were conducted to identify publications describing RARP. The authors collected, classified, and structured surgical details, patient information, surgical data, and various statistical results from the literature. A knowledge-guided decision-support tool was established using MySQL, DataTable, ECharts, and JavaScript. ChatGPT-4 and two assessment scales were used to validate and compare the platform.

RESULTS

The platform comprised 583 studies, 1589 cohorts, 1 911 968 patients, and 11 986 records, resulting in 54 834 data entries. The knowledge-guided decision support tool provide personalized surgical plan recommendations and potential complications on the basis of patients' baseline and surgical information. Compared with ChatGPT-4, RARPKB outperformed in authenticity (100% vs. 73%), matching (100% vs. 53%), personalized recommendations (100% vs. 20%), matching of patients (100% vs. 0%), and personalized recommendations for complications (100% vs. 20%). Postuse, the average System Usability Scale score was 88.88±15.03, and the Net Promoter Score of RARPKB was 85. The knowledge base is available at: http://rarpkb.bioinf.org.cn .

CONCLUSIONS

The authors introduced the pioneering RARPKB, the first knowledge base for robot-assisted surgery, with an emphasis on PCa. RARPKB can assist in personalized and complex surgical planning for PCa to improve its efficacy. RARPKB provides a reference for the future applications of artificial intelligence in clinical practice.

Clinical Biomarkers of Tumour Radiosensitivity and Predicting Benefit from Radiotherapy: A Systematic Review

Modern advanced radiotherapy techniques have improved the precision and accuracy of radiotherapy delivery, with resulting plans being highly personalised based on individual anatomy. Adaptation for individual tumour biology remains elusive. There is an unmet need for biomarkers of intrinsic radiosensitivity that can predict tumour response to radiation to facilitate individualised decision-making, dosing and treatment planning. Over the last few decades, the use of high throughput molecular biology technologies has led to an explosion of newly discovered cancer biomarkers. Gene expression signatures are now used routinely in clinic to aid decision-making regarding adjuvant systemic therapy. They have great potential as radiotherapy biomarkers. A previous systematic review published in 2015 reported only five studies of signatures evaluated for their ability to predict radiotherapy benefits in clinical cohorts. This updated systematic review encompasses the expanded number of studies reported in the last decade. An additional 27 studies were identified. In total, 22 distinct signatures were recognised (5 pre-2015, 17 post-2015). Seventeen signatures were 'radiosensitivity' signatures and five were breast cancer prognostic signatures aiming to identify patients at an increased risk of local recurrence and therefore were more likely to benefit from adjuvant radiation. Most signatures (15/22) had not progressed beyond the discovery phase of development, with no suitable validated clinical-grade assay for application. Very few signatures (4/17 'radiosensitivity' signatures) had undergone any laboratory-based biological validation of their ability to predict tumour radiosensitivity. No signatures have been assessed prospectively in a phase III biomarker-led trial to date and none are recommended for routine use in clinical guidelines. A phase III prospective evaluation is ongoing for two breast cancer prognostic signatures. The most promising radiosensitivity signature remains the radiosensitivity index (RSI), which is used to calculate a genomic adjusted radiation dose (GARD). There is an ongoing phase II prospective biomarker-led study of RSI/GARD in triple negative breast cancer. The results of these trials are eagerly anticipated over the coming years. Future work in this area should focus on (1) robust biological validation; (2) building biobanks alongside large radiotherapy randomised controlled trials with dose variance (to demonstrate an interaction between radiosensitivity signature and dose); (3) a validation of clinical-grade cost-effective assays that are deliverable within current healthcare infrastructure; and (4) an integration with biomarkers of other determinants of radiation response.

The Association between Urine N-Glycome and Prognosis after Initial Therapy for Primary Prostate Cancer

Next to prostate-specific antigen, no biochemical biomarkers have been implemented to guide patient follow-up after primary therapy for localized prostate cancer (PCa). We evaluated the prognostic potential of urine N-glycome in terms of event-free survival (EFS) in patients undergoing primary therapy for PCa. The prognostic features of the urine N-glycosylation profile at diagnosis, assessed in 77 PCa patients, were determined in terms of EFS next to standard clinical parameters. The majority of patients were diagnosed with International Society of Urological Pathology grade ≤ 3 (82%) T1-2 tumors (79%) and without pelvic lymph node invasion (96%). The patients underwent active surveillance (14%), robot-assisted laparoscopic prostatectomy (48%), or external beam radiotherapy (37%). Decreased ratios of biantennary core-fucosylation were noted in patients who developed an event, which was linked to a shorter EFS in both the intention-to-treat cohort and all subcohort analyses. Combining the urine N-glycan biomarker with the D'Amico Risk Classification for PCa resulted in an improved nomogram for patient classification after primary therapy. The rate of urine N-glycan biantennary core-fucosylation, typically linked to more aggressive disease status, is lower in patients who eventually developed an event following primary therapy and subsequently in patients with a worse EFS. The combination of urine N-glycan biomarkers together with clinical parameters could, therefore, improve the post-therapy follow-up of patients with PCa.

Multiplexed quantitative proteomics in prostate cancer biomarker development

Prostate cancer (PCa) is the most common non-skin cancer among men in the United States. However, the widely used protein biomarker in PCa, prostate-specific antigen (PSA), while useful for initial detection, its use alone cannot detect aggressive PCa and can lead to overtreatment. This chapter provides an overview of PCa protein biomarker development. It reviews the state-of-the-art liquid chromatography-mass spectrometry-based proteomics technologies for PCa biomarker development, such as enhancing the detection sensitivity of low-abundance proteins through antibody-based or antibody-independent protein/peptide enrichment, enriching post-translational modifications such as glycosylation as well as information-rich extracellular vesicles, and increasing accuracy and throughput using advanced data acquisition methodologies. This chapter also summarizes recent PCa biomarker validation studies that applied those techniques in diverse specimen types, including cell lines, tissues, proximal fluids, urine, and blood, developing novel protein biomarkers for various clinical applications, including early detection and diagnosis, prognosis, and therapeutic intervention of PCa.

Liquid Biomarkers in Prostate Cancer Diagnosis: Current Status and Emerging Prospects

Prostate cancer (PCa) is a major health concern that necessitates appropriate diagnostic approaches for timely intervention. This review critically evaluates the role of liquid biopsy techniques, focusing on blood- and urine-based biomarkers, in overcoming the limitations of conventional diagnostic methods. The 4Kscore test and Prostate Health Index have demonstrated efficacy in distinguishing PCa from benign conditions. Urinary biomarker tests such as PCa antigen 3, MyProstateScore, SelectMDx, and ExoDx Prostate IntelliScore test have revolutionized risk stratification and minimized unnecessary biopsies. Emerging biomarkers, including non-coding RNAs, circulating tumor DNA, and prostate-specific antigen (PSA) glycosylation, offer valuable insights into PCa biology, enabling personalized treatment strategies. Advancements in non-invasive liquid biomarkers for PCa diagnosis may facilitate the stratification of patients and avoid unnecessary biopsies, particularly when PSA is in the gray area of 4 to 10 ng/mL.

Biomarkers for Pre-Treatment Risk Stratification of Prostate Cancer Patients: A Systematic Review

BACKGROUND

Prostate cancer (PCa) is one of the most frequently occurring malignancies. Although most cases are not life-threatening, approximately 20% endure an unfavorable outcome. PSA-based screening reduced mortality but at the cost of an increased overdiagnosis/overtreatment of low-risk (lrPCa) and favorable intermediate-risk (firPCa) PCa. PCa risk-groups are usually identified based on serum Prostate-Specific Antigen (PSA), the Gleason score, and clinical T stage, which have consistent although variable specificity or subjectivity. Thus, more effective and specific tools for risk assessment are needed, ideally making use of minimally invasive methods such as liquid biopsies. In this systematic review we assessed the clinical potential and analytical performance of liquid biopsy-based biomarkers for pre-treatment risk stratification of PCa patients.

METHODS

Studies that assessed PCa pre-treatment risk were retrieved from PubMed, Scopus, and MedLine. PCa risk biomarkers were analyzed, and the studies' quality was assessed using the QUADAS-2 tool.

RESULTS

The final analysis comprised 24 full-text articles, in which case-control studies predominated, mostly reporting urine-based biomarkers (54.2%) and biomarker quantification by qPCR (41.7%). Categorization into risk groups was heterogeneous, predominantly making use of the Gleason score.

CONCLUSION

This systematic review unveils the substantial clinical promise of using circulating biomarkers in assessing the risk for prostate cancer patients. However, the standardization of groups, categories, and biomarker validation are mandatory before this technique can be implemented. Circulating biomarkers might represent a viable alternative to currently available tools, obviating the need for tissue biopsies, and allowing for faster and more cost-effective testing, with superior analytical performance, specificity, and reproducibility.

The association between patient and disease characteristics, and the risk of disease progression in patients with prostate cancer on active surveillance

PURPOSE

The objective of this study was to identify and assess patient and disease characteristics associated with an increased risk of disease progression in men with prostate cancer on active surveillance.

METHODS

We studied patients with low-risk (ISUP GG1) or favorable intermediate-risk (ISUP GG2) PCa. All patients had at least one repeat biopsy. Disease progression was the primary outcome of this study, based on pathological upgrading. Univariate and multivariate Cox proportional hazard analyses were used to evaluate the association between covariates and disease progression.

RESULTS

In total, 240 men were included, of whom 198 (82.5%) were diagnosed with low-risk PCa and 42 (17.5%) with favorable intermediate-risk PCa. Disease progression was observed in 42.9% (103/240) of men. Index lesion > 10 mm (HR = 2.85; 95% CI 1.74-4.68; p < 0.001), MRI (m)T-stage 2b/2c (HR = 2.52; 95% CI 1.16-5.50; p = 0.02), highest PI-RADS score of 5 (HR 3.05; 95% CI 1.48-6.28; p = 0.002) and a higher PSA level (HR 1.06; 95% CI 1.01-1.11; p = 0.014) at baseline were associated with disease progression on univariate analysis. Multivariate analysis showed no significant baseline predictors of disease progression.

CONCLUSION

In AS patients with low-risk or favorable intermediate-risk PCa, diameter of index lesion, MRI (m)T-stage, height of the PI-RADS score and the PSA level at baseline are significant predictors of disease progression to first repeat biopsy.

International Society of Urological Pathology (ISUP) Gleason Grade Groups stratify outcomes in the CHHiP Phase 3 prostate radiotherapy trial

OBJECTIVES

To compare the results of Gleason Grade Group (GGG) classification following central pathology review with previous local pathology assessment, and to examine the difference between using overall and worst GGG in a large patient cohort treated with radiotherapy and short-course hormone therapy.

PATIENTS AND METHODS

Patients with low- to high-risk localized prostate cancer were randomized into the multicentre CHHiP fractionation trial between 2002 and 2011. Patients received short-course hormone therapy (≤6 month) and radical intensity-modulated radiotherapy (IMRT). Of 2749 consented patients, 1875 had adequate diagnostic biopsy tissue for blinded central pathology review. The median follow-up was 9.3 years. Agreement between local pathology and central pathology-derived GGG and between central pathology-derived overall and worst GGG was assessed using kappa (κ) statistics. Multivariate Cox regression and Kaplan-Meier methods were used to compare the biochemical/clinical failure (BCF) and distant metastases (DM) outcomes of patients with GGG 1-5.

RESULTS

There was poor agreement between local pathology- and central pathology-derived GGG (κ = 0.19) but good agreement between overall and worst GGG on central pathology review (κ = 0.89). Central pathology-derived GGG stratified BCF and DM outcomes better than local pathology, while overall and worst GGG on central pathology review performed similarly. GGG 3 segregated with GGG 4 for BCF, with BCF-free rates of 90%, 82%, 74%, 71% and 58% for GGGs 1-5, respectively, at 8 years when assessed using overall GGG. There was a progressive decrease in DM-free rates from 98%, 96%, 92%, 88% and 83% for GGGs 1-5, respectively, at 8 years with overall GGG. Patients (n = 57) who were upgraded from GGG 2-3 using worst GS had BCF-free and DM-free rates of 74% and 92% at 8 years. CHHiP eligibility criteria limit the interpretation of these results.

CONCLUSION

Contemporary review of International Society of Urological Pathology GGG successfully stratified patients treated with short-course hormone therapy and IMRT with regard to both BCF-free and DM-free outcomes. Patients upgraded from GGG 2 to GGG 3 using worst biopsy GS segregate with GGG 3 on long-term follow-up. We recommend that both overall and worst GS be used to derive GGG.

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.

High expression of Trop2 is associated with aggressive localized prostate cancer and is a candidate urinary biomarker

Distinguishing indolent from clinically significant localized prostate cancer is a major clinical challenge and influences clinical decision-making between treatment and active surveillance. The development of novel predictive biomarkers will help with risk stratification, and clinical decision-making, leading to a decrease in over or under-treatment of patients with prostate cancer. Here, we report that Trop2 is a prognostic tissue biomarker for clinically significant prostate cancer by utilizing the Canary Prostate Cancer Tissue Microarray (CPCTA) cohort composed of over 1100 patients from a multi-institutional study. We demonstrate that elevated Trop2 expression is correlated with worse clinical features including Gleason score, age, and pre-operative PSA levels. More importantly, we demonstrate that elevated Trop2 expression at radical prostatectomy predicts worse overall survival in men undergoing radical prostatectomy. Additionally, we detect shed Trop2 in urine from men with clinically significant prostate cancer. Our study identifies Trop2 as a novel tissue prognostic biomarker and a candidate non-invasive marker for prostate cancer.

Novel hormone therapy and coordination of care in high-risk biochemically recurrent prostate cancer

Biochemical recurrence (BCR) occurs in 20-50% of patients with prostate cancer (PCa) undergoing primary definitive treatment. Patients with high-risk BCR have an increased risk of metastatic progression and subsequent PCa-specific mortality, and thus could benefit from treatment intensification. Given the increasing complexity of diagnostic and therapeutic modalities, multidisciplinary care (MDC) can play a crucial role in the individualized management of this patient population. This review explores the role for MDC when evaluating the clinical evidence for the evolving definition of high-risk BCR and the emerging therapeutic strategies, especially with novel hormone therapies (NHTs), for patients with either high-risk BCR or oligometastatic PCa. Clinical studies have used different characteristics to define high-risk BCR and there is no consensus regarding the definition of high-risk BCR nor for management strategies. Next-generation imaging and multigene panels offer potential enhanced patient identification and precision-based decision-making, respectively. Treatment intensification with NHTs, either alone or combined with radiotherapy or metastasis-directed therapy, has been promising in clinical trials in patients with high-risk BCR or oligometastases. As novel risk-stratification and treatment options as well as evidence-based literature evolve, it is important to involve a multidisciplinary team to identify patients with high-risk features at an earlier stage, and make informed decisions on the treatments that could optimize their care and long-term outcomes. Nevertheless, MDC data are scarce in the BCR or oligometastatic setting. Efforts to integrate MDC into the standard management of this patient population are needed, and will likely improve outcomes across this heterogeneous PCa patient population.

Lymphovascular Invasion at the Time of Radical Prostatectomy Adversely Impacts Oncological Outcomes

Lymphovascular invasion, whereby tumour cells or cell clusters are identified in the lumen of lymphatic or blood vessels, is thought to be an essential step in disease dissemination. It has been established as an independent negative prognostic indicator in a range of cancers. We therefore aimed to assess the impact of lymphovascular invasion at the time of prostatectomy on oncological outcomes. We performed a multicentre, retrospective cohort study of 3495 men who underwent radical prostatectomy for localised prostate cancer. Only men with negative preoperative staging were included. We assessed the relationship between lymphovascular invasion and adverse pathological features using multivariable logistic regression models. Kaplan-Meier curves and Cox proportional hazard models were created to evaluate the impact of lymphovascular invasion on oncological outcomes. Lymphovascular invasion was identified in 19% (n = 653) of men undergoing prostatectomy. There was an increased incidence of lymphovascular invasion-positive disease in men with high International Society of Urological Pathology (ISUP) grade and non-organ-confined disease (p < 0.01). The presence of lymphovascular invasion significantly increased the likelihood of pathological node-positive disease on multivariable logistic regression analysis (OR 15, 95%CI 9.7-23.6). The presence of lymphovascular invasion at radical prostatectomy significantly increased the risk of biochemical recurrence (HR 2.0, 95%CI 1.6-2.4). Furthermore, lymphovascular invasion significantly increased the risk of metastasis in the whole cohort (HR 2.2, 95%CI 1.6-3.0). The same relationship was seen across D'Amico risk groups. The presence of lymphovascular invasion at the time of radical prostatectomy is associated with aggressive prostate cancer disease features and is an indicator of poor oncological prognosis.

An integrative analysis of GEO data to identify possible therapeutic biomarkers of prostate cancer and targeting potential protein through Zea mays phytochemicals by virtual screening approaches

Prostate cancer (PC) is a prevalent type of cancer among men. Delaying the treatment of patients with upgraded or upstaged cancer may lead to unmanageable circumstances. The aim of this study is to contribute to the finding of biomarkers that are specific to PC and identify drug candidates derived from plants. The information about cancer is critical for clinicians to make decisions about patient treatment in the era of precision medicine. Advances in genomics technology have opened up new possibilities for identifying genes that are associated with cancer, including PC. This study identifies novel differentially expressed genes for PC. The seven PC microarray datasets were selected from the National Center for Biotechnology Information (NCBI)/Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) were found based on a fold change of |logFC| ≥ 1 and an adjusted p-value of <0.05. The DEGs were further studied using several bioinformatics tools, including STRING, CytoHubba, SRplot, Coremine Medical database, FunRich and GeneMANIA, cBioPortal. The six new potential biomarkers, GAGE2A, GAGE12G, GAGE2E, GAGE13, GAGE12F and CSAG1 were identified. These biomarkers are associated with biological processes (BPs) such as cell division, and gene expression regulation, so these genes may have a crucial role in PC progression and may serve as potential biomarkers for PC. A total of 497 phytochemicals from corn plants have been screened against the target protein and found LTS0176591 as the best lead molecule with docking score of -6.31 kcal/mol. Further, molecular mechanics-generalized born surface area (MM-GBSA), molecular dynamics simulation, principal component analysis (PCA), free energy landscape (FEL) and molecular mechanics-Poisson-Boltzmann surface area (MM-PBSA) were carried out to validate the findings.Communicated by Ramaswamy H. Sarma.

Development, Validation, and Clinical Utility of a Six-gene Signature to Predict Aggressive Prostate Cancer

BACKGROUND

Molecular signatures in prostate cancer (PCa) tissue can provide useful prognostic information to improve the understanding of a patient's risk of harbouring aggressive disease.

OBJECTIVE

To develop and validate a gene signature that adds independent prognostic information to clinical parameters for better treatment decisions and patient management.

DESIGN, SETTING, AND PARTICIPANTS

Expression of 14 genes was evaluated in radical prostatectomy (RP) tissue from an Irish cohort of PCa patients (n = 426). A six-gene molecular risk score (MRS) was identified with strong prognostic performance to predict adverse pathology (AP) at RP or biochemical recurrence (BCR). The MRS was combined with the Cancer of the Prostate Risk Assessment (CAPRA) score, to create a molecular and clinical risk score (MCRS), and validated in a Swedish cohort (n = 203).

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The primary AP outcome was assessed by the likelihood ratio statistics and area under the receiver operating characteristics curves (AUC) from logistic regression models. The secondary time to BCR outcome was assessed by likelihood ratio statistics and C-indexes from Cox proportional hazard regression models.

RESULTS AND LIMITATIONS

The six-gene signature was significantly (p < 0.0001) prognostic and added significant prognostic value to clinicopathological features for AP and BCR outcomes. For both outcomes, both the MRS and the MCRS increased the AUC/C-index when added to European Association of Urology (EAU) and CAPRA scores. Limitations include the retrospective nature of this study.

CONCLUSIONS

The six-gene signature has strong performance for the prediction of AP and BCR in an independent clinical validation study. MCRS improves prognostic evaluation and can optimise patient management after RP.

PATIENT SUMMARY

We found that the expression panel of six genes can help predict whether a patient is likely to have a disease recurrence after radical prostatectomy surgery.

Renal Function Parameters in Distinctive Molecular Subtypes of Prostate Cancer

Prostate cancer is a prevalent malignancy in male patients, having diverse clinical outcomes. The follow-up of patients diagnosed with prostate cancer involves the evaluation of renal function, because its impairment reduces patient survival rates and adds complexity to their treatment and clinical care. This study aimed to investigate the relationship between renal function parameters and distinctive molecular subtypes of prostate adenocarcinomas, defined by the immunoexpression of the SPINK1, ERG, HOXB13, and TFF3 markers. The study group comprised 72 patients with prostate cancer and associated chronic kidney disease (CKD) who underwent radical prostatectomy. Histopathological, molecular, and renal parameters were analyzed. Patients were categorized based on ERG/SPINK1 and HOXB13/TFF3 status, and correlations with renal function and prognostic grade groups were assessed. The ERG+/SPINK1+ subgroup exhibited significantly higher postoperative CKD stages and serum creatinine levels compared to the ERG+/SPINK1- subgroup. This suggests an intricate relationship between SPINK1 overexpression and renal function dynamics. The HOXB13-/TFF3+ subgroup displayed higher preoperative serum creatinine levels and CKD stages than the HOXB13-/TFF3- subgroup, aligning with TFF3's potential role in renal function. Furthermore, the study revealed associations between CKD stages and prognostic grade groups in different molecular subtypes, pointing out an intricate interplay between renal function and tumor behavior. Although the molecular classification of prostate acinar ADK is not yet implemented, this research underscores the variability of renal function parameters in different molecular subtypes, offering potential insights into patient prognosis.

ProsTAV, a novel blood-based test for biopsy decision management in significant prostate cancer

BACKGROUND

Current pathways in early diagnosis of prostate cancer (PCa) can lead to unnecessary biopsy procedures. Here, we used telomere analysis to develop and evaluate ProsTAV®, a risk model for significant PCa (Gleason score >6), with the objective of improving the PCa diagnosis pathway.

METHODS

This retrospective, multicentric study analyzed telomeres from patients with serum PSA 3-10 ng/mL. High-throughput quantitative fluorescence in-situ hybridization was used to evaluate telomere-associated variables (TAVs) in peripheral blood mononucleated cells. ProsTAV® was developed by multivariate logistics regression based on three clinical variables and six TAVs. The predictive capacity and accuracy of ProsTAV® were summarized by receiver operating characteristic (ROC) curves and its clinical benefit with decision curves analysis.

RESULTS

Telomeres from 1043 patients were analyzed. The median age of the patients was 63 years, with a median PSA of 5.2 ng/mL and a percentage of significant PCa of 23.9%. A total of 874 patients were selected for model training and 169 patients for model validation. The area under the ROC curve of ProsTAV® was 0.71 (95% confidence interval [CI], 0.62-0.79), with a sensitivity of 0.90 (95% CI, 0.88-1.0) and specificity of 0.33 (95% CI, 0.24-0.40). The positive predictive value was 0.29 (95% CI, 0.21-0.37) and the negative predictive value was 0.91 (95% CI, 0.83-0.99). ProsTAV® would make it possible to avoid 33% of biopsies.

CONCLUSIONS

ProsTAV®, a predictive model based on telomere analysis through TAV, could be used to increase the prediction capacity of significant PCa in patients with PSA between 3 and 10 ng/mL.

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.

Can We Predict Prostate Cancer Metastasis Based on Biomarkers? Where Are We Now?

The incidence of prostate cancer (PC) has risen annually. PC mortality is explained by the metastatic disease (mPC). There is an intermediate scenario in which patients have non-mPC but have initiated a metastatic cascade through epithelial-mesenchymal transition. There is indeed a need for more and better tools to predict which patients will progress in the future to non-localized clinical disease or already have micrometastatic disease and, therefore, will clinically progress after primary treatment. Biomarkers for the prediction of mPC are still under development; there are few studies and not much evidence of their usefulness. This review is focused on tissue-based genomic biomarkers (TBGB) for the prediction of metastatic disease. We develop four main research questions that we attempt to answer according to the current evidence. Why is it important to predict metastatic disease? Which tests are available to predict metastatic disease? What impact should there be on clinical guidelines and clinical practice in predicting metastatic disease? What are the current prostate cancer treatments? The importance of predicting metastasis is fundamental given that, once metastasis is diagnosed, quality of life (QoL) and survival drop dramatically. There is still a need and space for more cost-effective TBGB tests that predict mPC disease.

A Blood-Based Immune Gene Signature with Prognostic Significance in Localized Prostate Cancer

Prostate cancer (PCa) is one of the most common male cancers worldwide and one of the deadliest if unsuccessfully treated. Τhe need for reliable, easily accessible immune-related molecular biomarkers that could be combined with clinically defined criteria, including PSA and Gleason score, to accurately predict PCa patients' clinical outcomes is emerging. Herein, we describe for the first time a blood-identified immune-related gene signature comprising eight upregulated multi-functional genes associated with poor prognosis. Next-generation sequencing (NGS) analysis of PCa patients' peripheral blood samples revealed a more than three-fold upregulation of each of the eight genes as compared to samples originating from healthy donors. The construction of gene and protein interaction networks revealed different extents of the functional implications of these genes in the regulation of cell proliferation and immune responses. Analysis of the available data from The Cancer Genome Atlas (TCGA) regarding gene expression and survival of prostate adenocarcinoma (PRAD) and pan-cancer (PANCAN) patients revealed that intra-tumoral upregulation of this eight-gene signature (8-GS) was associated with poor 5-year progression-free intervals in PCa patients, even in those with high Gleason scores, and also with an unfavorable prognosis for cancer patients irrespective of the cancer type and even in the early stages. These observations suggest that further investigation of the 8-GS prospectively in randomized clinical trials, in which clinical benefit in terms of evaluating time to disease progression can be assessed, is warranted.

Critical Evaluation of Transcripts and Long Noncoding RNA Expression Levels in Prostate Cancer Following Radical Prostatectomy

INTRODUCTION

The clinical course of prostate cancer (PCa) is highly variable, ranging from indolent behavior to rapid metastatic progression. The Gleason score is widely accepted as the primary histologic assessment tool with significant prognostic value. However, additional biomarkers are required to better stratify patients, particularly those at intermediate risk.

METHODS

In this study, we analyzed the expression of 86 cancer hallmark genes in 171 patients with PCa who underwent radical prostatectomy and focused on the outcome of the 137 patients with postoperative R0-PSA0 status.

RESULTS

Low expression of the IGF1 and SRD52A, and high expression of TIMP2, PLAUR, S100A2, and CANX genes were associated with biochemical recurrence (BR), defined as an increase of prostate-specific antigen above 0.2 ng/mL. Furthermore, the analysis of the expression of 462 noncoding RNAs (ncRNA) in a sub-cohort of 39 patients with Gleason score 7 tumors revealed that high levels of expression of the ncRNAs LINC00624, LINC00593, LINC00482, and cd27-AS1 were significantly associated with BR. Our findings provide further evidence for tumor-promoting roles of ncRNAs in PCa patients at intermediate risk. The strong correlation between expression of LINC00624 and KRT8 gene, encoding a well-known cell surface protein present in PCa, further supports a potential contribution of this ncRNA to PCa progression.

CONCLUSION

While larger and further studies are needed to define the role of these genes/ncRNA in PCa, our findings pave the way toward the identification of a subgroup of patients at intermediate risk who may benefit from adjuvant treatments and new therapeutic agents.

Clinical and histological predictive factors of reclassification of prostate cancer patients on active surveillance

INTRODUCTION AND OBJECTIVE

Active surveillance (AS) has been established as a therapeutic strategy in patients with low-risk prostate cancer. Demographic and anatomopathological factors that increase the probability of reclassifying patients have been identified.

MATERIALS AND METHODS

Laboratory and histopathological data were collected from 116 patients included on AS since 2014. Univariate analysis was performed with Chi-square, t-student and Kendall's Tau, multivariate analysis according to logistic regression and Kaplan-Meier curves were calculated.

RESULTS

Of the 116 patients in AS, the median age at diagnosis was 66 years and the median follow-up was 13 months (2-72). Of these, 61 (52.6%) are still on surveillance, while 55 (47.4%) have left the program, mostly due to histological progression (52 patients (45.2%)); radical prostatectomy was performed in 27 (49.1%). Prostate volume (PV)≤60cc and the number of positive cylinders >1 in diagnostic biopsy (P=.05) were associated with higher reclassification rate in univariate analysis (P<.05). Multivariate analysis showed that these two variables significantly correlated with higher reclassification rate (PV 60 cc: OR 4.39, P=.04; >1 positive cylinder at diagnostic biopsy: OR 2.48, P=.03).

CONCLUSIONS

It has been shown that initial ultrasound volume and the number of positive cylinders in the diagnostic biopsy are independent risk factors for reclassification. Initial PSA, laterality of the affected cylinders and PSA density were not predictive factors of progression in our series.

The roles of proteases in prostate cancer

Since the proposition of the pro-invasive activity of proteolytic enzymes over 70 years ago, several roles for proteases in cancer progression have been established. About half of the 473 active human proteases are expressed in the prostate and many of the most well-characterized members of this enzyme family are regulated by androgens, hormones essential for development of prostate cancer. Most notably, several kallikrein-related peptidases, including KLK3 (prostate-specific antigen, PSA), the most well-known prostate cancer marker, and type II transmembrane serine proteases, such as TMPRSS2 and matriptase, have been extensively studied and found to promote prostate cancer progression. Recent findings also suggest a critical role for proteases in the development of advanced and aggressive castration-resistant prostate cancer (CRPC). Perhaps the most intriguing evidence for this role comes from studies showing that the protease-activated transmembrane proteins, Notch and CDCP1, are associated with the development of CRPC. Here, we review the roles of proteases in prostate cancer, with a special focus on their regulation by androgens.

Accurate prognosis for localized prostate cancer through coherent voting networks with multi-omic and clinical data

Localized prostate cancer is a very heterogeneous disease, from both a clinical and a biological/biochemical point of view, which makes the task of producing stratifications of patients into risk classes remarkably challenging. In particular, it is important an early detection and discrimination of the indolent forms of the disease, from the aggressive ones, requiring post-surgery closer surveillance and timely treatment decisions. This work extends a recently developed supervised machine learning (ML) technique, called coherent voting networks (CVN) by incorporating a novel model-selection technique to counter the danger of model overfitting. For the challenging problem of discriminating between indolent and aggressive types of localized prostate cancer, accurate prognostic prediction of post-surgery progression-free survival with a granularity within a year is attained, improving accuracy with respect to the current state of the art. The development of novel ML techniques tailored to the problem of combining multi-omics and clinical prognostic biomarkers is a promising new line of attack for sharpening the capability to diversify and personalize cancer patient treatments. The proposed approach allows a finer post-surgery stratification of patients within the clinical high-risk category, with a potential impact on the surveillance regime and the timing of treatment decisions, complementing existing prognostic methods.

Risk stratification for early biochemical recurrence of prostate cancer in the era of multiparametric magnetic resonance imagining-targeted biopsy

BACKGROUND

Multiparametric magnetic resonance imaging (MRI) and MRI-targeted biopsy are nowadays recommended in the prostate cancer (PCa) diagnostic pathway. Ploussard and Mazzone have integrated these tools into novel risk classification systems predicting the risk of early biochemical recurrence (eBCR) in PCa patients who underwent radical prostatectomy (RP). We aimed to assess available risk classification systems and to define the best-performing.

METHODS

Data on 1371 patients diagnosed by MRI-targeted biopsy and treated by RP between 2014 and 2022 at eight European tertiary referral centers were analyzed. Risk classifications systems included were the European Association of Urology (EAU) and National Comprehensive Cancer Network (NCCN) risk groups, the Cancer of the Prostate Risk Assessment (CAPRA) score, the International Staging Collaboration for Cancer of the Prostate (STAR-CAP) classification, the Ploussard and Mazzone models, and ISUP grade group. Kaplan-Meier analyses were used to compare eBCR among risk classification systems. Performance was assessed in terms of discrimination quantified using Harrell's c-index, calibration, and decision curve analysis (DCA).

RESULTS

Overall, 152 (11%) patients had eBCR at a median follow-up of 31 months (interquartile range: 19-45). The 3-year eBCR-free survival rate was 91% (95% confidence interval [CI]: 89-93). For each risk classification system, a significant difference among survival probabilities was observed (log-rank test p < 0.05) except for NCCN classification (p = 0.06). The highest discrimination was obtained with the STAR-CAP classification (c-index 66%) compared to CAPRA score (63% vs. 66%, p = 0.2), ISUP grade group (62% vs. 66, p = 0.07), Ploussard (61% vs. 66%, p = 0.003) and Mazzone models (59% vs. 66%, p = 0.02), and EAU (57% vs. 66%, p < 0.001) and NCCN (57% vs. 66%, p < 0.001) risk groups. Risk classification systems demonstrated good calibration characteristics. At DCA, the CAPRA score showed the highest net benefit at a probability threshold of 9%-15%.

CONCLUSIONS

The performance of risk classification systems using MRI and MRI-targeted information was less optimistic when tested in a contemporary set of patients. CAPRA score and STAR-CAP classification were the best-performing and should be preferred for treatment decision-making.

Impact of the 12-gene recurrence score in influencing adjuvant chemotherapy prescription in mismatch repair proficient stage II/III colonic carcinoma-a systematic review and meta-analysis

INTRODUCTION

The 12-gene recurrence score (RS) is a clinically validated assay which predicts recurrence risk in patients with stage II/III colon cancer. Decisions regarding adjuvant chemotherapy may be guided using this assay or based on the judgement of tumour board.

AIMS

To assess the concordance between the RS and MDT decisions regarding adjuvant chemotherapy in colon cancer.

METHODS

A systematic review was performed in accordance with PRISMA guidelines. Meta-analyses were performed using the Mantel-Haenszel method using the Review Manager version 5.4 software.

RESULTS

Four studies including 855 patients with a mean age of 68 years (range: 25-90 years) met inclusion criteria. Overall, 79.2% had stage II disease (677/855) and 20.8% had stage III disease (178/855). For the entire cohort, concordant results between the 12-gene assay and MDT were more likely than discordant (odds ratio (OR): 0.38, 95% confidence interval (CI): 0.25-0.56, P < 0.001). Patients were more likely to have chemotherapy omitted than escalated when using the RS (OR: 9.76, 95% CI: 6.72-14.18, P < 0.001). For those with stage II disease, concordant results between the 12-gene assay and MDT were more likely than discordant (OR: 0.30, 95% CI: 0.17-0.53, P < 0.001). In stage II disease, patients were more likely to have chemotherapy omitted than escalated when using the RS (OR: 7.39, 95% CI: 4.85-11.26, P < 0.001).

CONCLUSIONS

The use of the 12-gene signature refutes the decision of tumour board in 25% of cases, with 75% of discordant decisions resulting in omission of adjuvant chemotherapy. Therefore, it is possible that a proportion of such patients are being overtreated when relying on tumour board decisions alone.

Leveraging transcriptomics for precision diagnosis: Lessons learned from cancer and sepsis

Diagnostics require precision and predictive ability to be clinically useful. Integration of multi-omic with clinical data is crucial to our understanding of disease pathogenesis and diagnosis. However, interpretation of overwhelming amounts of information at the individual level requires sophisticated computational tools for extraction of clinically meaningful outputs. Moreover, evolution of technical and analytical methods often outpaces standardisation strategies. RNA is the most dynamic component of all -omics technologies carrying an abundance of regulatory information that is least harnessed for use in clinical diagnostics. Gene expression-based tests capture genetic and non-genetic heterogeneity and have been implemented in certain diseases. For example patients with early breast cancer are spared toxic unnecessary treatments with scores based on the expression of a set of genes (e.g., Oncotype DX). The ability of transcriptomics to portray the transcriptional status at a moment in time has also been used in diagnosis of dynamic diseases such as sepsis. Gene expression profiles identify endotypes in sepsis patients with prognostic value and a potential to discriminate between viral and bacterial infection. The application of transcriptomics for patient stratification in clinical environments and clinical trials thus holds promise. In this review, we discuss the current clinical application in the fields of cancer and infection. We use these paradigms to highlight the impediments in identifying useful diagnostic and prognostic biomarkers and propose approaches to overcome them and aid efforts towards clinical implementation.

Utilizing clinical, pathological and radiological information to guide postoperative radiotherapy in prostate cancer

INTRODUCTION

A detectable and rising PSA following radical prostatectomy is indicative of recurrent prostate cancer. Salvage radiotherapy (SRT) with/without androgen deprivation therapy represents the main treatment option for these patients and has been historically associated with a biochemical control rate of ~70%. To determine the optimal timing, diagnostic workup, radiotherapy dosefractionation, treatment volume, and use of systemic therapy, several informative studies have been conducted in the last decade.

AREAS COVERED

This review examines the recent evidence to guide radiotherapy decision making in the SRT setting. Key topics include adjuvant vs salvage RT, utilization of molecular imaging and genomic classifiers, length of androgen deprivation therapy, inclusion of elective pelvic volume, and emerging role for hypofractionation.

EXPERT OPINION

Recently reported trials, conducted in an era prior to the routine use of molecular imaging and genomic classifiers, have been pivotal in establishing the current standard of care for SRT in prostate cancer. However, decisions about radiation treatment and systemic therapy may be tailored based on available prognostic and predictive biomarkers. Data from contemporary clinical trials are awaited to define and establish individualized, biomarker-driven approaches for SRT.

Genomic Prostate Score: A New Tool to Assess Prognosis and Optimize Radiation Therapy Volumes and ADT in Intermediate-Risk Prostate Cancer

Genomic classifiers such as the Genomic Prostate Score (GPS) could help to personalize treatment for men with intermediate-risk prostate cancer (I-PCa). In this study, we aimed to evaluate the ability of the GPS to change therapeutic decision making in I-PCa. Only patients in the intermediate NCCN risk group with Gleason score 3 + 4 were considered. The primary objective was to assess the impact of the GPS on risk stratification: NCCN clinical and genomic risk versus NCCN clinical risk stratification alone. We also analyzed the predictive role of the GPS for locally advanced disease (≥pT3+) and the potential change in treatment strategy. Thirty patients were tested for their GPS between November 2018 and March 2020, with the median age being 70 (45-79). Twenty-three patients had a clinical T1 stage. Eighteen patients were classified as favorable intermediate risk (FIR) based on the NCCN criteria. The median GPS score was 39 (17-70). Among the 23 patients who underwent a radical prostatectomy, Gleason score 3 + 4 was found in 18 patients. There was a significant correlation between the GPS and the percentage of a Gleason grade 4 or higher pattern in the surgical sample: correlation coefficient r = 0.56; 95% CI = 0.2-0.8; p = 0.005. In this study, the GPS combined with NCCN clinical risk factors resulted in significant changes in risk group.

Biomarkers: Promising and valuable tools towards diagnosis, prognosis and treatment of Covid-19 and other diseases

The use of biomarkers as early warning systems in the evaluation of disease risk has increased markedly in the last decade. Biomarkers are indicators of typical biological processes, pathogenic processes, or pharmacological reactions to therapy. The application and identification of biomarkers in the medical and clinical fields have an enormous impact on society. In this review, we discuss the history, various definitions, classifications, characteristics, and discovery of biomarkers. Furthermore, the potential application of biomarkers in the diagnosis, prognosis, and treatment of various diseases over the last decade are reviewed. The present review aims to inspire readers to explore new avenues in biomarker research and development.

Novel Insights into the Role of the Antioxidants in Prostate Pathology

To date, it is known that antioxidants protect cells from damage caused by oxidative stress and associated with pathological conditions. Several studies have established that inflammation is a state that anticipates the neoplastic transformation of the prostate. Although many experimental and clinical data have indicated the efficacy of antioxidants in preventing this form of cancer, the discrepant results, especially from recent large-scale randomized clinical trials, make it difficult to establish a real role for antioxidants in prostate tumor. Despite these concerns, clinical efficacy and safety data show that some antioxidants still hold promise for prostate cancer chemoprevention. Although more studies are needed, in this review, we briefly describe the most common antioxidants that have shown benefits in preclinical and clinical settings, focusing our attention on synthesizing the advances made so far in prostate cancer chemoprevention using antioxidants as interesting molecules for the challenges of future therapies.

Prostate cancer mortality rates in low- and favorable intermediate-risk active surveillance patients: a population-based competing risks analysis

PURPOSE

To determine prostate cancer (PCa) and other-cause mortality rates in low- and favorable intermediate-risk (FIR) active surveillance (AS) patients.

METHODS

The SEER Prostate with Watchful Waiting database was used to identify men diagnosed with NCCN low or FIR PCa, between 2010 and 2015, managed with AS. FIR patients were subdivided into three subgroups, based on their intermediate risk factor: grade group two (GG2), PSA 10-20 ng/ml or cT2b-c disease. Cumulative incidence function curves with other-cause mortality as the competing risk were utilized. Predictors of PCa mortality were assessed using multivariable regression analysis with semi-parametric proportional hazards modeling.

RESULTS

Among 70,871 patients, 48,127 (67.9%) had low and 22,744 (32.1%) had FIR disease. Median patient age was 64.0 years, and median PSA was 5.70 ng/ml. Median follow-up was 49.0 months. There were 166 (0.2%) PCa and 3,176 (4.48%) other-cause mortalities. The 5-year mortality rates in the low and FIR cohorts overall were 0.29% and 0.28%, respectively (p = 0.64). Within the FIR cohort, the corresponding rates were highest in the PSA 10-20 ng/ml subgroup at 0.73%, followed by 0.32% for GG2 FIR and 0.052% for cT2b-c FIR disease (p < 0.001). Older age at diagnosis (sHR 2.38, p = 0.006), Medicaid insurance (sHR: 2.58, p < 0.001), low socioeconomic (sHR 1.39, p = 0.032), and non-married statuses (sHR: 2.58, p < 0.001) were associated with increased PCa mortality.

CONCLUSION

Intermediate-term PCa mortality rates in FIR PCa patients are non-significantly different to those with low-risk PCa. However, there is significant within-group heterogeneity, with PCa mortality rates significantly higher in the PSA 10-20 subgroup.

Genomic Classifiers in Personalized Prostate Cancer Radiation Therapy Approaches: A Systematic Review and Future Perspectives Based on International Consensus

Current risk-stratification systems for prostate cancer (PCa) do not sufficiently reflect the disease heterogeneity. Genomic classifiers (GC) enable improved risk stratification after surgery, but less data exist for patients treated with definitive radiation therapy (RT) or RT in oligo-/metastatic disease stages. To guide future perspectives of GCs for RT, we conducted (1) a systematic review on the evidence of GCs for patients treated with RT and (2) a survey of experts using the Delphi method, addressing the role of GCs in personalized treatments to identify relevant fields of future clinical and translational research. We performed a systematic review and screened ongoing clinical trials on ClinicalTrials.gov. Based on these results, a multidisciplinary international team of experts received an adapted Delphi method survey. Thirty-one and 30 experts answered round 1 and round 2, respectively. Questions with ≥75% agreement were considered relevant and included in the qualitative synthesis. Evidence for GCs as predictive biomarkers is mainly available to the postoperative RT setting. Validation of GCs as prognostic markers in the definitive RT setting is emerging. Experts used GCs in patients with PCa with extensive metastases (30%), in postoperative settings (27%), and in newly diagnosed PCa (23%). Forty-seven percent of experts do not currently use GCs in clinical practice. Expert consensus demonstrates that GCs are promising tools to improve risk-stratification in primary and oligo-/metastatic patients in addition to existing classifications. Experts were convinced that GCs might guide treatment decisions in terms of RT-field definition and intensification/deintensification in various disease stages. This work confirms the value of GCs and the promising evidence of GC utility in the setting of RT. Additional studies of GCs as prognostic biomarkers are anticipated and form the basis for future studies addressing predictive capabilities of GCs to optimize RT and systemic therapy. The expert consensus points out future directions for GC research in the management of PCa.

SGOL2 promotes prostate cancer progression by inhibiting RAB1A ubiquitination

Prostate cancer is the most prevalent genitourinary malignant cancer in men worldwide. Patients with prostate cancer who progress to castration-resistant prostate cancer (CRPC) or metastatic CRPC have significantly poorer survival. Advanced prostate cancer is a clinical challenge due to the lack of effective treatment strategies. In the field of oncology, SGOL2 was an emerging and differentially expressed molecule, which enhanced the proliferation of cell populations in vitro in our studies. Mass spectrum and Co-IP validated the interaction of SGOL2 and RAB1A in a protein-protein manner. We further investigated the role of SGOL2 in the regulatory mechanism of RAB1A in prostate cancer cell lines. Furthermore, SGOL2 regulated RAB1A expression by inhibiting its ubiquitination. Rescue Experiments demonstrated that SGOL2 promoted prostate cancer cell proliferation and migration by upregulating RAB1A expression. Finally, we found that SGOL2 and RAB1A may regulate the tumor microenvironment (TME) in prostate cancer. In conclusion, our findings concluded that SGOL2 stabilized RAB1A expression to promote prostate cancer development. Both of them were of great importance in TME modulation.

68Ga-PSMA-11 PET/MRI in Patients with Newly Diagnosed Intermediate- or High-Risk Prostate Adenocarcinoma: PET Findings Correlate with Outcomes After Definitive Treatment

Prostate-specific membrane antigen (PSMA) PET offers an accuracy superior to other imaging modalities in initial staging of prostate cancer and is more likely to affect management. We examined the prognostic value of ⁶⁸Ga-PSMA-11 uptake in the primary lesion and presence of metastatic disease on PET in newly diagnosed prostate cancer patients before initial therapy. Methods: In a prospective study from April 2016 to December 2020, ⁶⁸Ga-PSMA-11 PET/MRI was performed in men with a new diagnosis of intermediate- or high-grade prostate cancer who were candidates for prostatectomy. Patients were followed up after initial therapy for up to 5 y. We examined the Kendall correlation between PET (intense uptake in the primary lesion and presence of metastatic disease) and clinical and pathologic findings (grade group, extraprostatic extension, nodal involvement) relevant for risk stratification, and examined the relationship between PET findings and outcome using Kaplan-Meier analysis. Results: Seventy-three men (age, 64.0 ± 6.3 y) were imaged. Seventy-two had focal uptake in the prostate, and in 20 (27%) PSMA-avid metastatic disease was identified. Uptake correlated with grade group and prostate-specific antigen (PSA). Presence of PSMA metastasis correlated with grade group and pathologic nodal stage. PSMA PET had higher per-patient positivity than nodal dissection in patients with only 5-15 nodes removed (8/41 vs. 3/41) but lower positivity if more than 15 nodes were removed (13/21 vs. 10/21). High uptake in the primary lesion (SUV_max > 12.5, P = 0.008) and presence of PSMA metastasis (P = 0.013) were associated with biochemical failure, and corresponding hazard ratios for recurrence within 2 y (4.93 and 3.95, respectively) were similar to or higher than other clinicopathologic prognostic factors. Conclusion: ⁶⁸Ga-PSMA-11 PET can risk-stratify patients with intermediate- or high-grade prostate cancer before prostatectomy based on degree of uptake in the prostate and presence of metastatic disease.

Molecular Genetics of Prostate Cancer and Role of Genomic Testing

Prostate cancer (PCa) is characterized by profound genomic heterogeneity. Recent advances in personalized treatment entail an increasing need of genomic profiling. For localized PCa, gene expression assays can support clinical decisions regarding active surveillance and adjuvant treatment. In metastatic PCa, homologous recombination deficiency, microsatellite instability-high (MSI-H), and CDK12 deficiency constitute main actionable alterations. Alterations in DNA repair genes confer variable sensitivities to poly(ADP-ribose)polymerase inhibitors, and the use of genomic instability assays as predictive biomarker is still incipient. MSI can be assessed by immunohistochemistry To date there is a lack of consensus as to testing standards.

[Importance of genetic testing in prostate cancer]

Due to the increasing accessibility, molecular diagnostics are becoming more important in urology. There are validated predictive and prognostic biomarkers available for prostate cancer. This has clinical implications for screening diagnosis and personalized treatment options. The best known examples are mutations in the DNA damage repair genes breast cancer 1 (BRCA1) and BRCA2 for which the poly (ADP-ribose) polymerase (PARP) inhibitor olaparib has been approved in 2020. This continuous progress enables a more tumor-specific and personalized treatment for prostate cancer. In order to provide this precision medicine, it is necessary to know the most important basic principles, have a close-knit interdisciplinary cooperation and access to a molecular tumor board due to the increased complexity.

Complexities of Prostate Cancer

Prostate cancer has a long disease history and a wide variety and uncertainty in individual patients' clinical progress. In recent years, we have seen a revolutionary advance in both prostate cancer patient care and in the research field. The power of deep sequencing has provided cistromic and transcriptomic knowledge of prostate cancer that has not discovered before. Our understanding of prostate cancer biology, from bedside and molecular imaging techniques, has also been greatly advanced. It is important that our current theragnostic schemes, including our diagnostic modalities, therapeutic responses, and the drugs available to target non-AR signaling should be improved. This review article discusses the current progress in the understanding of prostate cancer biology and the recent advances in diagnostic and therapeutic strategies.

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.

The 17-gene Genomic Prostate Score assay as a predictor of biochemical recurrence in men with intermediate and high-risk prostate cancer

The validated 17-gene Oncotype DX Genomic Prostate Score® (GPS™) assay risk-stratifies prostate-cancer patients with localized disease. The assay has primarily been utilized in lower risk patients deciding between active surveillance versus definitive therapy. In this retrospective cohort study, we analyze the association of the GPS result with time to biochemical recurrence post-prostatectomy in patients with National Comprehensive Cancer Network® (NCCN) intermediate and higher risk prostate cancer. The 141 patients included in the study were from the NorthShore University HealthSystem diagnosed 2014–2019 with NCCN intermediate (n = 109) or higher risk (n = 32) prostate cancer, treated with radical prostatectomy 2015–2019. The association of GPS result with time to biochemical recurrence was evaluated using univariable and multivariable Cox proportional hazards models in 120 patients with unfavorable intermediate or higher risk. Median (interquartile range) follow-up time was 28 (20 to 38) months. The GPS result was significantly associated with time to biochemical recurrence as both a continuous and dichotomous variable in univariable (hazard ratio [HR] per 20 GPS units 2.36, 95% CI 1.45–3.80, p < 0.001; HR for GPS result 41–100 vs 0–40 3.28, 95% CI 1.61–7.19, p < 0.001) and in multivariable models accounting for NCCN risk group (HR per 20 GPS units 2.14, 95% CI 1.31–3.46, p = 0.003; HR for GPS result 41–100 vs 0–40 3.00, 95% CI 1.43–6.72, p = 0.003) or biopsy Gleason Score and diagnostic PSA or PSA density. These results indicate that the GPS assay was a strong predictor of biochemical recurrence after radical prostatectomy in this unfavorable intermediate and higher risk prostate cancer patient population.

Active Surveillance in Intermediate-Risk Prostate Cancer: A Review of the Current Data

Simple Summary

AS is an option for the initial management of selected patients with intermediate-risk PC. The proper way to predict which men will have an aggressive clinical course or indolent PC who would benefit from AS has not been unveiled. Genetics and MRI can help in the decision-making, but it remains unclear which men would benefit from which tests. In addition, there are several differences between AS protocols in inclusion criteria, monitoring follow-up, and triggers for active treatment. Large series and a few RCTs are under investigation, and more research is needed to establish an optimal therapeutic strategy for patients with intermediate-risk PC. This study summarizes the current data on patients with intermediate-risk PC under AS, recent findings, and discusses future directions.

Abstract

Active surveillance (AS) is a monitoring strategy to avoid or defer curative treatment, minimizing the side effects of radiotherapy and prostatectomy without compromising survival. AS in intermediate-risk prostate cancer (PC) has increasingly become used. There is heterogeneity in intermediate-risk PC patients. Some of them have an aggressive clinical course and require active treatment, while others have indolent disease and may benefit from AS. However, intermediate-risk patients have an increased risk of metastasis, and the proper way to select the best candidates for AS is unknown. In addition, there are several differences between AS protocols in inclusion criteria, monitoring follow-up, and triggers for active treatment. A few large series and randomized trials are under investigation. Therefore, more research is needed to establish an optimal therapeutic strategy for patients with intermediate-risk disease. This study summarizes the current data on patients with intermediate-risk PC under AS, recent findings, and discusses future directions.

Grade group 1 prostate cancer on biopsy: are we still missing aggressive disease in the era of image-directed therapy?

PURPOSE

Recently, Eggener et al. reignited a debate consisting to redefine Gleason Grade Group (GGG) 1 prostate cancer (PCa) as a precancerous lesion to reduce overdiagnosis and overtreatment. However, historical cohorts showed that some GGG1-labeled disease at biopsy may be underestimated by the standard PCa diagnostic workup. The aim was to assess whether the risk of adverse features at radical prostatectomy (RP) in selected GGG1 patients still exists in the era of pre-biopsy mpMRI and image-guided biopsies.

METHODS

We retrospectively reviewed our data from a European RP dataset to assess in contemporary patients with GGG1 at mpMRI-targeted biopsy the rate of adverse features at final pathology, defined as ≥ pT3a and/or pN+ and/or GGG ≥ 3.

RESULTS

A total of 419 patients with cT1-T2 cN0 GGG1-PCa were included. At final pathology, 143 (34.1%) patients had adverse features. In multivariate analysis, only unfavorable intermediate-risk/high-risk disease (defined on PSA or stage) was predictive of adverse features (OR 2.45, 95% CI 1.11-5.39, p = 0.02). A significant difference was observed in the 3-year biochemical recurrence-free survival between patients with and without adverse features (93.4 vs 87.8%, p = 0.026). In sensitivity analysis restricted low- and favorable intermediate-risk PCa, 122/383 patients (31.8%) had adverse features and no preoperative factors were statistically associated with this risk.

CONCLUSION

In this European study, we showed that there is still a risk of underestimating GGG1 disease at biopsy despite the routine use of image-guided biopsies. Future studies are warranted to improve the detection of aggressive disease in GGG1-labeled patients by incorporating the latest tools such as genomic testing or radiomics.

Future directions for precision oncology in prostate cancer

Clinical genomic testing is becoming routine in prostate cancer, as biomarker-driven therapies such as poly-ADP ribose polymerase (PARP) inhibitors and anti-PD1 immunotherapy are now approved for select men with castration-resistant prostate cancer harboring alterations in DNA repair genes. Challenges for precision medicine in prostate cancer include an overall low prevalence of actionable genomic alterations and a still limited understanding of the impact of tumor heterogeneity and co-occurring alterations on treatment response and outcomes across diverse patient populations. Expanded tissue-based technologies such as whole-genome sequencing, transcriptome analysis, epigenetic analysis, and single-cell RNA sequencing have not yet entered the clinical realm and could potentially improve upon our understanding of how molecular features of tumors, intratumoral heterogeneity, and the tumor microenvironment impact therapy response and resistance. Blood-based technologies including cell-free DNA, circulating tumor cells (CTCs), and extracellular vesicles (EVs) are less invasive molecular profiling resources that could also help capture intraindividual tumor heterogeneity and track dynamic changes that occur in the context of specific therapies. Furthermore, molecular imaging is an important biomarker tool within the framework of prostate cancer precision medicine with a capability to detect heterogeneity across metastases and potential therapeutic targets less invasively. Here, we review recent technological advances that may help promote the future implementation and value of precision oncology testing for patients with advanced prostate cancer.